* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Basic physical mechanisms for monsoon failure in past and future
Numerical weather prediction wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Climate governance wikipedia , lookup
Economics of global warming wikipedia , lookup
Fred Singer wikipedia , lookup
Global warming controversy wikipedia , lookup
Climatic Research Unit documents wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Politics of global warming wikipedia , lookup
Atmospheric model wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Climate change and poverty wikipedia , lookup
Climate sensitivity wikipedia , lookup
Climate change in the United States wikipedia , lookup
Effects of global warming wikipedia , lookup
Solar radiation management wikipedia , lookup
Global warming wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Physical impacts of climate change wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Global warming hiatus wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Years of Living Dangerously wikipedia , lookup
Instrumental temperature record wikipedia , lookup
Climate change feedback wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Institut für Physik und Astronomie Arbeitsgruppe Prof. A. Levermann Basic physical mechanisms for monsoon failure in past and future climate Kumulative Dissertation zur Erlangung des akademischen Grades “doctor rerum naturalium” (Dr. rer. nat.) in der Wissenschaftsdisziplin “Klimaphysik” eingereicht an der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam von Jacob Schewe Potsdam, im Mai 2011 Abstract In this work, I first present simulations with a coupled climate model of intermediate complexity, CLIMBER-3α, that project monsoon rainfall around the world to increase quasi–linearly with global warming in the coming centuries. While this is generally consistent with many other studies, the atmospheric component of CLIMBER-3α is based on a simplified statistical–dynamical approach, and may not sufficiently represent all processes that are relevant for the response of monsoon circulations to rapid and intense climate change. Therefore, this study attempts to identify those physical mechanisms that are of first–order importance for large–scale monsoon dynamics and their response to external changes. I perform a scaling analysis of the heat and moisture budgets of the Earth’s major monsoon systems, based on reanalysis data and theoretical considerations. I find that, during the monsoon season, a self– amplifying feedback involving the advection and release of latent heat is essential for sustaining monsoon rainfall after the surface land–sea thermal contrast has ceased. I frame this moisture–advection feedback in a minimal conceptual model and show that it leads to a threshold behaviour with respect to changes in the system’s energy budget. In particular, when either net radiation over land or specific humidity over the adjacent ocean region fall short of a critical value, no conventional monsoon circulation can exist. I thus define a domain of existence for continental monsoon rainfall, and estimate the threshold values within the restrictions of the conceptual model. I demonstrate the applicability of this concept to abrupt and persistent monsoon shifts observed in paleoclimatic records. To understand monsoon failure occurring on shorter timescales and within the domain of existence, I develop a minimal theory of intraseasonal monsoon dynamics. Supported by observations and by results from a comprehensive global climate model, the core assumption of this theory is that the positive moisture–advection feedback and its interaction with the smaller–scale eddy field render the circulation inherently unstable. I apply this theory to an ensemble of millennial climate simulations and show that both multi–decadal variability and projected future trends in Indian summer monsoon (ISM) rainfall can be reproduced with a simple stochastic model of the inherent instability, modulated by ambient climate conditions only during the onset period. A projected increase in ISM failure in response to a global warming scenario can thus be readily explained by a shift in central Pacific mean spring–time climate that persistently alters the initial conditions for internal ISM dynamics. I thereby propose a novel perspective on monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions. In summary, the results in this thesis offer a simplified framework for the investigation of both long– term (permanent) and short–term (seasonal) monsoon failure, including the basic physical mechanisms that lead to a non–linear response of the monsoon system to external changes. Zusammenfassung In dieser Arbeit stelle ich zunächst Zukunftsprojektionen mit dem Erdsystemmodell mittlerer Komplexität CLIMBER-3α vor, die eine weltweite Zunahme des Monsunniederschlags über die nächsten Jahrhunderte zeigen, welche annähernd proportional zum Anstieg der globalen Mitteltemperatur verläuft. Zwar legen auch viele andere Studien einen solchen Anstieg nahe, jedoch verwendet CLIMBER-3α eine vereinfachte, statistisch–dynamische Atmosphärenkomponente und gibt wahrscheinlich nicht alle Prozesse, die für die Reaktion von Monsunzirkulationen auf rasanten Klimawandel relevant sind, hinreichend wieder. Um die physikalischen Mechanismen zu identifizieren, die für die großskalige Monsundynamik und ihre Reaktion auf äußere Veränderungen von zentraler Bedeutung sind, unterziehe ich die Wärmeund Feuchtebilanzen der wichtigsten Monsunsysteme anhand von Reanalysedaten und theoretischen Überlegungen einer Skalenanalyse. Es zeigt sich, dass der Monsun während der Regenzeit in erster Linie von einem selbstverstärkenden Rückkopplungsmechanismus angetrieben wird, bei dem die Advektion und Freisetzung latenter Wärme den atmosphärischen Temperaturunterschied zwischen Land und Ozean aufrechterhält. Ich stelle diese Feuchte–Advektions–Rückkopplung in einem minimalistischen konzeptionellen Modell dar und zeige, dass sich aus ihr ein nichtlineares Verhalten des Monsunsystems gegenüber Störungen der Energiebilanz ergibt: Wenn etwa die atmosphärische Strahlungsbilanz über dem Kontinent oder die Luftfeuchtigkeit über der benachbarten Ozeanregion einen kritischen Wert unterschreiten, kann sich keine konventionelle Monsunzirkulation entwickeln. Durch diesen kritischen Wert wird entsprechend der Parameterbereich definiert, in dem Monsunniederschlag über Land möglich ist. Ich nehme eine Abschätzung des kritischen Wertes für verschiedene Monsunregionen vor und zeige, dass sich das Konzept auf abrupte Monsunveränderungen anwenden lässt, wie sie anhand von in paläoklimatischen Rekonstruktionen dokumentiert sind. Des Weiteren entwerfe ich eine minimalistische Theorie intrasaisonaler Monsundynamik mit dem Ziel, Monsunausfälle zu verstehen, die auf kürzeren Zeitskalen und innerhalb des durch den kritischen Wert definierten Existenzbereiches auftreten. Die zugrundeliegende Hypothese ist, dass die positive Feuchte– Advektions–Rückkopplung und ihre Wechselwirkung mit turbulenten Störungen auf synoptischer und kleinerer Skala zu einer inhärenten Instabilität führen. Ich entwickle ein einfaches stochastisches Modell, in dem die Monsundynamik von dieser Instabilität bestimmt und lediglich zu Beginn der Monsunsaison von äußeren klimatischen Einflüssen moduliert wird. Dieses Modell vergleiche ich mit einem Ensemble von Langzeitsimulationen eines realistischen Klimamodells und zeige, dass sowohl die multidekadische Variabilität als auch die für die Zukunft projizierten Entwicklungen reproduziert werden können. Eine Häufung von Monsunausfällen unter einem Klimawandelszenario kann ich auf diese Weise mit einer 6 Veränderung des zentralpazifischen Frühjahrsklimas erklären. Die vorgestellte Theorie eröffnet somit eine neue Sichtweise auf die Variabilität des Monsunniederschlags als das Ergebnis einer instabilen internen Dynamik, die zu Beginn der Saison durch das umgebende Klima moduliert wird. Die in dieser Arbeit vorgestellten Ergebnisse bieten einen vereinfachten theoretischen Rahmen für die Untersuchung von Monsunausfällen auf langen (paläoklimatischen) wie auch kurzen (saisonalen) Zeitskalen, sowie der grundlegenden physikalischen Prozesse, die zu einer nichtlinearen Reaktion von Monsunsystemen auf äußere Störungen führen können. Contents 1 Introduction 9 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.2 Scope and contents of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 1.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2 Original manuscripts 17 2.1 Climate change under a scenario near 1.5◦ C of global warming . . . . . . . . . . . . . . . 17 2.2 Basic mechanism for abrupt monsoon transitions . . . . . . . . . . . . . . . . . . . . . . . 31 2.3 A critical humidity threshold for monsoon failure . . . . . . . . . . . . . . . . . . . . . . . 49 2.4 More frequent future monsoon failure due to inherent instability . . . . . . . . . . . . . . 81 3 Discussion and Conclusions 125 References 131 7 1 Introduction 1.1 Motivation Monsoon systems are among the most prominent large–scale phenomena in the Earth’s atmosphere. They govern seasonal and year–to–year climate variability in many tropical and subtropical regions, and the associated rainfall is the dominant natural variable that human economies in those regions are built around. Changes in monsoon rainfall appear to have affected human societies throughout history, as illustrated for example by the fate of ancient civilizations in the Indus Valley (Rashid et al., 2011) or the rise and fall of dynasties and kingdoms in China (Zhang et al., 2008). Also today, agricultural productivity in South Asia, East Asia, and Africa is closely tied to the magnitude and timing of monsoon rainfall (Parthasarathy et al., 1988; Kumar et al., 2004; Tao et al., 2004; Haile, 2005; Gregory et al., 2005; Auffhammer et al., 2006). Present–day monsoon rainfall, as observed over the past century, exhibits significant intraseasonal and interannual variability (e.g. Webster, 1987b), including catastrophic floods and droughts that pose risks to large parts of the population in those regions the affected regions (e.g. Sikka, 2003; Webster et al., 2011). While decadal–scale average monsoon rainfall has been relatively stable during the past century of direct observations, rising trends have been observed in the annual number of extreme rain events in India (Goswami et al., 2006a). The future evolution of the Indian summer monsoon, and other monsoon systems, under a combination of anthropogenic forcing factors is unclear, according to an intercomparison of comprehensive climate models (Meehl et al., 2007). Recent projections indicate that the response to increased greenhouse gas (GHG) concentrations may differ in sign among major monsoon regions, and reveal large uncertainties about the magnitude of the response (Kripalani et al., 2007a,b; Cherchi et al., 2010). The effect of increased aerosol abundance is significant and may be counteracting that of GHGs (Ramanathan et al., 2005; Lau & Kim, 2006), while human-induced vegetation changes feed back on precipitation (Ganopolski et al., 1998; Claussen, 2009). Observations and modelling studies suggest a recent regime shift in Asian monsoon convection (Turner & Hannachi, 2010) and its relation to northern hemisphere thermal gradients (Li et al., 2009; Sun et al., 2010). At the same time, paleoclimatic records show evidence of abrupt and strong monsoon shifts in India, the Bay of Bengal, and East Asia, during the last two glacial cycles (Burns et al., 2003; Wang et al., 2005a, 2008) and the Holocene (Gupta et al., 2003; Hong et al., 2003; Wang et al., 2005b; Rashid et al., 2011). Some of these abrupt changes have been linked to climatic events in the North Atlantic for the last glacial period (Overpeck et al., 1996; Burns et al., 2003) as well as for the Holocene (Gupta et al., 2003; Wang et al., 2005b). A physical mechanism for this teleconnection has been suggested (Goswami et al., 2006b), but relevant climatic signals of the North Atlantic events in Asia (such as temperature 10 1 INTRODUCTION and moisture anomalies) are very small (Zhang & Delworth, 2005). East Asian monsoon shifts during the last two glacial cycles have been roughly in phase with hemispheric insolation changes (Wang et al., 2008), but the latter are far too slow to explain the rapidity of the transitions between different periods of relatively stable monsoon strength. These observations indicate that internal feedbacks in monsoon dynamics may have amplified the weak external forcing. Both the unclear cause of paleoclimatic monsoon shifts and the uncertain future evolution of monsoon rainfall in a changing climate call for an improved understanding of possible non–linearities in large–scale monsoon dynamics. This thesis aims at contributing to such an improvement by identifying the primary driving mechanisms of continental monsoon rainfall and examining their stability properties and their implications for the response of the monsoon circulation to external forcing. It does not attempt to account for all aspects of monsoon dynamics in general, but focuses on the possibility of large–scale monsoon failure and the associated processes. 1.2 Scope and contents of the thesis Monsoons are among the most complex components of the climate system, and they interact with various other components on multiple timescales (Webster et al., 1998; Wang, 2005). Both spatial patterns and temporal evolution of monsoon rainfall are influenced by a number of physical processes, such as the El Niño–Southern Oscillation (ENSO) phenomenon (e.g. Krishnamurthy & Goswami, 2000; Goswami & Xavier, 2005), regional (Clark et al., 2000; Kucharski et al., 2006; Yang et al., 2007) and remote sea surface temperatures (Goswami et al., 2006b), atmospheric aerosol concentrations (Ramanathan et al., 2005), or Eurasian snow cover (Hahn & Shukla, 1976; Dash et al., 2005), as well as by characteristics of vegetation (Meehl, 1994; Claussen, 1997; Robock et al., 2003) and topography (Liu & Yin, 2002). Direct observational records of monsoon rainfall, available for the last century, exhibit a high variability on different timescales. For instance, in India (where data availability is highest among the monsoon regions), seasonal mean rainfall amounts vary from year to year and, though correlated to some degree with ENSO, are difficult to predict in advance (Goswami et al., 2006). Within the monsoon season, rainfall does not occur uniformly, but is concentrated in periods of roughly a few weeks length, called active spells, which are intercepted by drier periods, called break spells (Krishnamurthy & Shukla, 2000; Rajeevan et al., 2010). These active and break spells vary in number and length from year to year, and within them, daily rainfall amounts also vary greatly. In addition, there are large inhomogeneities in the spatial distribution of rainfall over the monsoon regions. Despite this complexity, however, there is one fundamental driving force behind large–scale continental monsoon rainfall: Namely, the atmospheric temperature contrast between land and ocean (e.g. 1.2 Scope and contents of the thesis 11 6 surface column Δ T (K) 4 2 0 −2 −4 2 4 6 8 month 10 12 Figure 1.1: Climatological temperature difference ∆T between land and ocean in the Indian region, derived from NCEP/NCAR reanalysis data (Kistler et al., 2001). At the surface (solid line), ∆T is largest is spring and then decreases due to cooling of the land surface by precipitation. Averaged over the atmospheric column (dashed line), ∆T is maintained throughout the rainy season. Webster, 1987a). This temperature contrast develops in spring when the continent heats up faster than the ocean, owing to the large differences in heat capacity; and it is maintained throughout the rainy season, even when the rains have started to cool the land surface and sensible heating has ceased (Fig. 1.1). It shapes the anomalous pressure system that draws strong, mainly ageostrophic winds towards the continent in the lower troposphere, carrying the moisture that is then released in convection. While this is far from a complete description of monsoon dynamics, it is a central and necessary condition for monsoon rainfall to develop. Furthermore, the magnitude of the land–sea atmospheric temperature difference is generally correlated with seasonal rainfall amounts: The stronger the difference during a given monsoon season, the more rainfall can be expected, irrespective of where and when exactly that rain falls. That also means that long–term changes in the temperature contrast can be expected to affect long–term mean monsoon rainfall. The seasonal development of the land–sea atmospheric temperature contrast, as a driving force of monsoon rainfall, is captured by linear empirical models (e.g. Srinivasan, 2001) and also by coarse– resolution climate models that otherwise may not have the spatial resolution and the degree of realism in atmospheric physics necessary to capture all aspects of monsoon dynamics. Such models can therefore 12 1 INTRODUCTION be used to make meaningful projections of the direct effect of temperature changes on large–scale monsoon characteristics. In the first article of this thesis (Schewe et al., 2011a), I have applied the Earth system model of intermediate complexity, CLIMBER-3α (Montoya et al., 2005), to project the climatic consequences of the Representative Concentration Pathways, a set of recently developed GHG concentration scenarios for use in the forthcoming assessment of the Intergovernmental Panel on Climate Change (IPCC). I find that average monsoon rainfall in South Asia, East Asia and Africa increases approximately linearly with the regional land–ocean surface temperature contrast, which in turn is a direct consequence of global surface warming (due to the increase in GHG abundance.) Depending on the region and the scenario, the projected rainfall increases are substantial; e.g. between about 25% (South Asia) and 50% (East Asia) until the end of the 21st century under the highest scenario. As discussed above, paleoclimatic records reveal large and abrupt shifts in monsoon intensity that obviously cannot be explained as a linear response to external forcing. They therefore require different concepts than a perturbation analysis around the present–day state. In the second and third articles of this thesis, I take the approach of a non–linear empirical model to obtain a first–order understanding of the processes that may have led to such abrupt events. The second article (Levermann et al., 2009) sets up a minimal conceptual model of a monsoon circulation, comprising only the conservation of heat and moisture and knowingly neglecting many other important physical processes, in order to distill the fundamental non–linearity of monsoon dynamics. The model is based on a scaling analysis of the heat and moisture budgets of major monsoon systems around the world, using present–day reanalysis data. I find that sensible heating is important in establishing the atmospheric land–sea temperature contrast prior to the rainy season, but becomes small after the onset of heavy rainfall. During the rainy season, the temperature gradient is maintained by the release of latent heat over the continent. Thus, the advection of moist air from the ocean and subsequent condensation of that moisture sustains the driving force for the monsoon winds, and thereby constitutes a self–amplifying feedback (illustration in Fig. 1.2). I show in the conceptual model that this moisture–advection feedback implies a threshold behaviour with respect to quantities that affect the energy budget: E.g., if net radiative flux to the atmospheric column falls below a critical value, no conventional monsoon circulation can develop. If the system was close to the threshold, a small variation in external parameters could therefore lead to a transition from a “normal” monsoon regime into a regime without continental monsoon rainfall. However, considering the present–day state of the Earth’s monsoon systems, huge changes in net radiation would be necessary to reach the threshold. In the third article (Schewe et al., 2011b), I show that the minimal conceptual model also yields a threshold behaviour with respect to atmospheric humidity over the ocean adjacent to the monsoon region. This quantity is more volatile than net 1.2 Scope and contents of the thesis 13 Figure 1.2: Geometry of the minimal conceptual monsoon model used in the second and third articles of the thesis, with wind W, precipitation P, and net radiative flux R. The tripartite loop illustrates the fundamental moisture–advection feedback; arrows indicate the amplification of one physical process by another. radiation, and the thresholds are relatively closer to the present–day situation. Based on reanalysis data, I estimate the threshold values for four major monsoon regions. I apply this concept to a proxy record of East Asian monsoon rainfall that exhibits a series of abrupt monsoon transitions during the penultimate glacial period. Assuming that average humidity over the ocean was altered by orbital–scale changes in hemispheric solar insolation, I show that the conceptual model can qualitatively explain these transitions. As evaporation from the ocean surface can also be affected by a number of other processes (e.g. wind speed, oceanic upwelling) on different timescales, the model could serve to improve the understanding of other past monsoon events as well. The basic dynamics captured in the conceptual model thus define a domain of existence for continental monsoon rainfall; in the sense that a conventional monsoon can only develop within this domain, e.g. above the humidity threshold. This however does not mean that within the domain of existence, monsoon rainfall will be at full strength at all times. As I show in the fourth article of this thesis (submitted and under review at Nature), even under present–day conditions, seasonal–mean Indian summer monsoon (ISM) rainfall can fall short of its long–term average by 70% and more in individual years, according to a comprehensive atmosphere–ocean general circulation model (AOGCM). Such dry monsoon years have 14 1 INTRODUCTION not been observed in the last century, but an ensemble simulation that covers over 6,000 model years shows a characteristic frequency distribution of seasonal–mean rainfall that continuously extends down to these extremely low values, though with low frequency of occurrence. Understanding such temporary monsoon failure again requires looking at the fundamental driving mechanism of monsoon rainfall. Using AOGCM results and observational data, I show that the moisture– advection feedback generally acts on a short sub–seasonal timescale (on the order of days rather than months) and is frequently interrupted because of continuous perturbation by stochastic fluctuations from the synoptic–scale eddy field. Moreover, during such interruptions, the large-scale monsoon circulation does not simply slow down or come to a hold, but even tends to reverse, in the sense that convection is replaced by subsidence of upper–tropospheric air over large parts of the subcontinent and the adjacent ocean. Since this air is much drier than lower–tropospheric air, the subsidence effectively dries out the monsoon winds and further inhibits a recommencement of the moisture–advection feedback. Thus, another self–amplifying feedback is constituted that counteracts the moisture–advection feedback. In the article, I develop a minimal theory of intraseasonal monsoon dynamics, based on the assumption that the monsoon season is governed by a permanent interplay of those two counteracting feedbacks: Each feedback itself tends to persist, and stochastic fluctuations tend to perturb the currently active feedback and induce a flip into the other one. I frame this assumption in a simple, statistically predictive model of seasonal–mean monsoon rainfall and show that it can reproduce the characteristic frequency distribution found in the AOGCM, including the very dry years, or failures. An important aspect of this simple model is that it is linked to external forcing only during the onset period; for the rest of the monsoon season, only the idealized internal dynamics are at work and produce rainy and dry periods that then add up to a seasonal average. I show that the model can reproduce a large portion of multidecadal monsoon variability when forced by central–Pacific mean sea level pressure (MSLP) anomalies in May, i.e. at the onset time. The central Pacific is known to have a distinct influence on Indian monsoon climate, as is evident in the correlation between ISM rainfall and ENSO. Anomalously low spring-time MSLP in the central Pacific is assumed to induce atmospheric conditions that favor more subsidence over the Indian region and thus lead to more deficient monsoon onsets. I then apply the simple model to global warming simulations using the same AOGCM, where ISM failure is projected to become much more frequent until the end of the 22nd century. At the same time, a shift towards lower spring-time MSLP in the central Pacific is projected. Again forcing the simple model with central–Pacific MSLP anomalies in May, it successfully reproduces the projected trend in average monsoon rainfall. The minimal theory presented in this work thereby offers a novel perspective on monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions. 1.3 Overview 1.3 15 Overview This thesis is organized around four scientific articles which are either published or under review. Each article provides their own introductory and concluding remarks, and references; some also carry supplementary material. Here, a brief overview is given of the titles, contents, and author contributions of the individual articles. The original manuscripts are included in the following section. Article 1: Climate change under a scenario near 1.5◦ C of global warming: monsoon intensification, ocean warming and steric sea level rise Jacob Schewe, Anders Levermann, & Malte Meinshausen The first article, published in Earth System Dynamics, explores the climatic consequences of the latest set of greenhouse gas concentration scenarios for the coming centuries. The response of the most important large–scale oceanic, atmospheric, and coupled processes is investigated in a coupled climate model of intermediate complexity. Among other results, monsoon rainfall is projected to increase approximately linearly with the regional land–sea temperature contrast due to global warming. Jacob Schewe performed the climate model simulations, analyzed the results, and wrote the paper. Malte Meinshausen provided the AOGCM emulations. All three authors participated in the interpretation of the results and the improvement of the manuscript. Article 2: Basic mechanism for abrupt monsoon transitions Anders Levermann, Jacob Schewe, Vladimir Petoukhov, & Hermann Held The second article, published in Proceedings of the National Academy of Sciences of the USA (PNAS), presents a scaling analysis of the heat and moisture budgets of the Earth’s major monsoon systems, and develops a minimal conceptual monsoon model capturing the essential moisture–advection feedback. It shows that this feedback yields a threshold behaviour with respect to changes in net radiation, and estimates the thresholds from reanalysis data. Jacob Schewe analyzed the data; Anders Levermann, Vladimir Petoukhov and Jacob Schewe devised the conceptual model; Hermann Held performed statistical tests of the robustness of the results; Anders Levermann performed the computations and wrote the paper. All four authors participated in the interpretation of the results and the improvement of the manuscript. Anders Levermann initiated the research. 16 1 INTRODUCTION Supporting information is included following the main manuscript. Article 3: A critical humidity threshold for monsoon failure Jacob Schewe, Anders Levermann, & Hai Cheng The third article, published in Climate of the Past Discussions, shows that the minimal conceptual monsoon model implies a threshold behaviour with respect to specific humidity over the ocean, a quantity which is more volatile than net radiation and exhibits threshold values closer to modern climate. The threshold values are estimated from reanalysis data for four major monsoon regions, and the model is applied to a paleoclimatic reconstruction of East Asian summer monsoon rainfall, yielding a series of abrupt transitions in response to gradual insolation changes which is similar to those observed in the proxy record. Jacob Schewe performed the computations, analyzed data, and wrote the paper. Hai Cheng provided proxy data. Jacob Schewe and Anders Levermann interpreted the results and improved the manuscript. Anders Levermann initiated the research. Article 4: More frequent future monsoon failure due to inherent instability Jacob Schewe & Anders Levermann The fourth article (submitted and currently under review) projects Indian summer monsoon failure to become considerably more frequent under a global warming scenario, using a comprehensive coupled climate model. It presents a minimal theory of intraseasonal monsoon dynamics, based on an inherent instability that is modulated by ambient climate merely during the onset period. Forced only by global mean temperature and central–Pacific sea level pressure anomalies in May, this simple model reproduces future trends as well as past multidecadal variability of monsoon rainfall as found in the comprehensive climate model. The study proposes a novel perspective on monsoon variability as the result of internal instabilities modulated by pre-seasonal ambient climate conditions. Jacob Schewe analyzed data and climate model results and wrote the paper. Jacob Schewe and Anders Levermann developed the minimal theory, interpreted the results, and improved the manuscript. R Supplementary Material, and the Matlab code of the simple “day-to-day model”, are included following the main manuscript. 2 Original manuscripts 2.1 Climate change under a scenario near 1.5◦ C of global warming Climate change under a scenario near 1.5◦ C of global warming 2.1 Earth Syst. Dynam., 2, 25–35, 2011 www.earth-syst-dynam.net/2/25/2011/ doi:10.5194/esd-2-25-2011 © Author(s) 2011. CC Attribution 3.0 License. 19 Earth System Dynamics Climate change under a scenario near 1.5 ◦C of global warming: monsoon intensification, ocean warming and steric sea level rise J. Schewe1,2 , A. Levermann1,2 , and M. Meinshausen1 1 Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany Institute, Potsdam University, Potsdam, Germany 2 Physics Received: 30 September 2010 – Published in Earth Syst. Dynam. Discuss.: 14 October 2010 Revised: 8 February 2011 – Accepted: 4 March 2011 – Published: 8 March 2011 Abstract. We present climatic consequences of the Representative Concentration Pathways (RCPs) using the coupled climate model CLIMBER-3α, which contains a statisticaldynamical atmosphere and a three-dimensional ocean model. We compare those with emulations of 19 state-of-the-art atmosphere-ocean general circulation models (AOGCM) using MAGICC6. The RCPs are designed as standard scenarios for the forthcoming IPCC Fifth Assessment Report to span the full range of future greenhouse gas (GHG) concentrations pathways currently discussed. The lowest of the RCP scenarios, RCP3-PD, is projected in CLIMBER-3α to imply a maximal warming by the middle of the 21st century slightly above 1.5 ◦ C and a slow decline of temperatures thereafter, approaching today’s level by 2500. We identify two mechanisms that slow down global cooling after GHG concentrations peak: The known inertia induced by mixingrelated oceanic heat uptake; and a change in oceanic convection that enhances ocean heat loss in high latitudes, reducing the surface cooling rate by almost 50%. Steric sea level rise under the RCP3-PD scenario continues for 200 years after the peak in surface air temperatures, stabilizing around 2250 at 30 cm. This contrasts with around 1.3 m of steric sea level rise by 2250, and 2 m by 2500, under the highest scenario, RCP8.5. Maximum oceanic warming at intermediate depth (300–800 m) is found to exceed that of the sea surface by the second half of the 21st century under RCP3-PD. This intermediate-depth warming persists for centuries even after surface temperatures have returned to present-day values, with potential consequences for marine ecosystems, oceanic methane hydrates, and ice-shelf stability. Due to an enhanced land-ocean temperature contrast, all scenarios yield an intensification of monsoon rainfall under global warming. Correspondence to: J. Schewe ([email protected]) 1 Introduction In December 2010, the international community agreed, under the United Nations Framework Convention on Climate Change, to limit global warming to below 2 ◦ C (Cancún Agreements, see http://unfccc.int/files/meetings/ cop 16/application/pdf/cop16 lca.pdf). At the same time, it was agreed that a review, to be concluded by 2015, should look into a potential tightening of this target to 1.5 ◦ C – in part because climate change impacts associated with 2 ◦ C are considered to exceed tolerable limits for some regions, e.g. Small Island States. So far, research into climate system dynamics under strong mitigation scenarios that keep warming below 2 ◦ C or even 1.5 ◦ C has been sparse. Individual AOGCMs were run for scenarios stabilizing at 2 ◦ C (May, 2008) or below (Washington et al., 2009), or for idealized CO2 rampdown experiments (Wu et al., 2010). Here we investigate climate projections for the full range of Representative Concentration Pathways (RCPs; Moss et al., 2010) but focus in particular on the lowest scenario RCP3-PD, which reflects a scenario that will peak global mean temperatures slightly above, but close to, 1.5 ◦ C above pre-industrial levels in our model. The RCPs were recently developed in order to complement, and in part replace, the Special Report on Emissions Scenarios (SRES; Nakicenovic and Swart, 2000) scenarios, and will be used in the Climate Model Intercomparison Project’s Phase 5 (CMIP5) that is to be assessed in the forthcoming Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). The RCP3-PD scenario is characterized by a peak of atmospheric greenhouse gas (GHG) concentrations in 2040 and a subsequent decline in GHG abundance. After 2070, CO2 emissions turn negative and remain at around −1 Gt CO2 eq yr−1 after 2100 (Meinshausen et al., 2011). Concentrations in the medium-low RCP4.5 and the medium-high RCP6 Published by Copernicus Publications on behalf of the European Geosciences Union. 20 2 J. Schewe et al.: Climate near 1.5 ◦ C warming 26 stabilize by 2150, while concentrations in the high RCP8.5 continue to rise until 2250. In Sect. 2, we describe the models and their experimental setup for this study. Simulation results are presented in Sect. 3, in particular for global mean temperature (Sect. 3.1), and changes in large scale climate components like oceanic meridional overturning circulation (Sect. 3.2), monsoon (Sect. 3.3), global sea level (Sect. 3.4), and deep ocean temperature (Sect. 3.5). In Sect. 4 we provide the physical mechanisms responsible for an asymmetrically slower cooling than warming under RCP3-PD. Section 5 concludes. 2 Models and experiments Our primary model for investigating key large-scale aspects of climate change under the RCP scenarios is the Earth system model of intermediate complexity CLIMBER-3α (Montoya et al., 2005). CLIMBER-3α combines a statisticaldynamical atmosphere model (Petoukhov et al., 2000) with a three-dimensional ocean general circulation model based on the GFDL MOM-3 code (Pacanowski and Griffies, 1999) and a dynamic and thermodynamic sea-ice model (Fichefet and Maqueda, 1997). In this study, CLIMBER-3α is used without a carbon cycle. The atmosphere model has a coarse horizontal resolution of 22.5◦ in longitude and 7.5◦ in latitude, and employs parameterized vertical temperature and humidity profiles. Oceanic wind stress anomalies are computed with respect to the control simulation and added to the climatology of Trenberth et al. (1989). The oceanic horizontal resolution is 3.75◦ × 3.75◦ with 24 variably spaced vertical levels. The model’s sensitivity to vertical diffusivity (Mignot et al., 2006) and wind stress forcing (Schewe and Levermann, 2010) has been investigated as well as the model’s behaviour under glacial boundary conditions (Montoya and Levermann, 2008) and global warming (Levermann et al., 2007). When compared to AOGCMs of the third Coupled Model Intercomparison Project (CMIP3) and previous generations, the model shows qualitatively and quantitatively similar results with respect to large-scale quantities (Gregory et al., 2005; Stouffer et al., 2006b). The model version used here features a low background value of oceanic vertical diffusivity (0.3 × 10−4 m2 s−1 ) and an improved representation of the Indonesian throughflow as compared to the version described by Montoya et al. (2005). We complement our CLIMBER-3α projections of global mean temperature with emulations of 19 AOGCMs used in the IPCC Fourth Assessment Report (AR4). These emulations were performed with MAGICC6, a reduced complexity model with an upwelling-diffusion ocean which has been used in the past three IPCC assessment reports (Wigley and Raper, 2001). MAGICC6 was shown to be able to closely emulate the global and hemispheric mean temperature evolution of AOGCMs (Meinshausen et al., 2008). Our AOGCM emulations use RCPs harmonized emission inputs Earth Syst. Dynam., 2, 25–35, 2011 ORIGINAL MANUSCRIPTS with default efficacies for the individual forcing agents, identical to the model’s setup for creating the default RCP GHG concentration recommendations for CMIP5 (Meinshausen et al., 2011). The only exception is that MAGICC6’s climate model is calibrated and run for the range of 19 individual AOGCMs, rather than a single median set of climate module parameters. Our CLIMBER-3α experiments focus on the four new RCPs, namely RCP3-PD (van Vuuren et al., 2007), RCP4.5 (Clarke et al., 2007; Smith and Wigley, 2006; Wise et al., 2009), RCP6 (Fujino et al., 2006), and RCP8.5 (Riahi et al., 2007). We use the historical, 21st century and long-term (until 2500) RCP forcing trajectories as provided on http:// www.pik-potsdam.de/∼mmalte/rcps/ and described in Meinshausen et al. (2011). These forcings arose from the process of harmonizing RCP emissions, and producing a single default set of GHG concentrations, which are the basis for the CMIP5 intercomparison runs that extend from preindustrial times to 2300 (CMIP5, http://cmip-pcmdi.llnl.gov/ cmip5/forcing.html). The extension beyond 2300 follows the same guiding principle as the extension up to 2300, i.e. a continuation of constant emissions for the RCP3-PD scenario (and correspondingly dropping forcing levels) and a stabilization of GHG concentrations and forcing levels for the upper three RCPs, RCP4.5, RCP6 and RCP8.5. For being used in CLIMBER-3α, we group our forcings on a forcing-equivalence basis, i.e. we aggregate longwave absorbers into a CO2 -equivalence concentration (Fig. 1a and d). The radiative forcing of agents that scatter or absorb shortwave radiation is aggregated and assumed to modulate the incoming solar irradiance, taking into account geometry and albedo (Fig. 1b and e). CLIMBER-3α’s climate sensitivity is about 3.4 ◦ C, which is higher than the average climate sensitivity of the transient AOGCM emulations of 2.9 ◦ C (Meinshausen et al., 2008, Table 4), very close to the average of the slab–ocean GCMs of 3.26 ◦ C and still close to the IPCC AR4 best estimate of 3 ◦ C (Meehl et al., 2007a, Box 10.2). The transient climate response is about 1.9 ◦ C for CLIMBER-3α, compared to about 1.8 ◦ C for the average of IPCC AR4 AOGCMs (Meehl et al., 2007b, Table 8.2). 3 3.1 Results Global mean temperature Global mean surface air temperatures, normalized to the period 1980–1999, are shown in Fig. 1c and f relative to preindustrial (1860–1890) using the median observed temperature increase of 0.52 ◦ C (Brohan et al., 2006). The warming projected by CLIMBER-3α lies well within the emulation of the AOGCMs (Fig. 1c and f). For the highest scenario, RCP8.5, the simulation yields a temperature increase of up to 8.5 ◦ C, while the lowest scenario, RCP3-PD, reaches up to 1.6 ◦ C of global warming compared to pre-industrial and www.earth-syst-dynam.net/2/25/2011/ J. Schewe et al.: Climate near 1.5◦ C warming 2.1 9 Climate change under a scenario near 1.5◦ C of global warming 21 J. Schewe et al.: Climate near 1.5 ◦ C warming 1500 d a 2000 1000 ppmv ppmv 3000 27 1000 500 e 12 10 1365 1360 1355 1350 b c RCP 6 extension f RCP8.5 4 C ° ° C 8 6 W/m2 W/m2 0 1365 1360 1355 1350 RCP6 2 4 RCP4.5 2 0 RCP3−PD 0 2000 2200 year 2400 1950 2000 2050 2100 year Fig. 1. Forcing and global mean temperature response of the CLIMBER-3α climate model under the RCP3-PD (blue), RCP4.5 (yellow), RCP6 andresponse their extensions until 2500. The grey model verticalunder band the marks the RCP(blue), periodRCP4.5 2005 to(yellow), 2100. Fig. 1. (grey) Forcingand andRCP8.5 global (red) meanscenarios temperature of the CLIMBER-3α climate RCP3-PD (a) CO(grey) concentration (in ppmv) longwave absorbers (KyotoThe and grey Montreal protocol gasesperiod as well2005 as tropospheric RCP6 and RCP8.5 (red) scenarios and of their extensions until 2500. vertical band greenhouse marks the RCP to 2100. (a) 2 -equivalence ozone). (b) Incoming solar irradiance (W mof−2longwave ), modified by the radiative of agents active greenhouse in the shortwave (mainly volcanic CO concentration (in ppmv) absorbers (Kyoto forcing and Montreal protocol gasesrange as well as tropospheric 2 -equivalence and anthropogenic aerosols) and changes in2surface albedo to land-use change. (c) Global temperature difference ozone). (b) Incoming solar irradiance (W/m ), modified by due the radiative forcing of agents activesurface in the air shortwave range(SAT) (mainly volcanic et surface al., 2006), for the simulations (solid lines) andair19temperature AOGCM emulations using in ◦anthropogenic C compared toaerosols) pre-industrial (Brohan in and and changes albedo dueCLIMBER-3α to land-use change. (c) Global surface (SAT) difference ◦ (the dashed line denotes the median, darkfor andthe light shading denotes the 50% and 80% range, to (f): Asusing (a) inMAGICC6 C compared to pre–industrial (Brohan et al.,and 2006), CLIMBER-3α simulations (solid lines) andrespectively). 19 AOGCM(d) emulations to (c), but enlarged for the 1950–2100. MAGICC6 (the dashed lineperiod denotes the median, and dark and light shading denotes the 50% and 80% range, respectively). (d) to (f): As (a) to (c), but enlarged for the period 1950-2100. then drops at an average rate of about −0.16 ◦ C per century. This is about ten times slower than the currently observed temperature rise of 0.16 to 0.18 ◦ C per decade (Trenberth et al., 2007, section 3.4). Although the reduction in GHG concentrations in the RCP3-PD is generally slower than the increase before the peak, this explains only part of the warming/cooling asymmetry: The average cooling rate during the first 100 years after the peak is 12% of the warming rate in the 100 years before the peak; over the same period, the GHG reduction rate is 35% of the increase rate prior to the peak. The mechanisms responsible for this asymmetry will be discussed in Sect. 4. 3.2 Spatial warming pattern and oceanic overturning The spatial distribution of temperature change in 2100 reflects the pattern of polar amplification (Winton, 2006), i.e. above-average surface warming in high latitudes (Fig. 2). In the low RCP3-PD scenario (Fig. 2a), warming in the northern North Atlantic region is offset by the cooling effect www.earth-syst-dynam.net/2/25/2011/ of a 20% reduction of the Atlantic meridional overturning circulation (AMOC; Fig. 3a) and the associated reduction in oceanic convection and heat release (compare Sect. 4). As the AMOC recovers over the course of the 22nd and 23rd century, this offsetting effect will disappear. In the RCP8.5 scenario (Fig. 2b), the AMOC reduction is relatively smaller compared to the warming, and has no large offsetting effect. The recovery of the AMOC beyond 2200 is facilitated by the retreat of sea ice cover in the North Atlantic (Levermann et al., 2007), which in the case of RCP3-PD even leaves the AMOC stronger in the long-term than under pre-industrial conditions. The behaviour of the AMOC under global warming in CLIMBER-3α is a robust feature of most CMIP3 AOGCMs (Gregory et al., 2005), and the mechanisms at play are in qualitative agreement across the models (Levermann et al., 2007). Quantitatively, AOGCMs differ significantly in their response. With respect to the pre-industrial overturning strength, CLIMBER-3α is comparable to the IPCC AR4 model average and consistent with Earth Syst. Dynam., 2, 25–35, 2011 J. Schewe et al.: Climate near 1.5◦ C warming 10 J. Schewe et al.: Climate near 1.5◦ C warming 10 22 2 ORIGINAL MANUSCRIPTS J. Schewe et al.: Climate near 1.5 ◦ C warming 28 3 80ºN 80ºN 2 a 1 80ºS 40ºS 0 80ºN 80ºS 10 40ºN 80ºN 0º 8 b 6 40ºN 40ºS b 0º 80ºS 40ºS 100ºW 0º 100ºE a AMOC AMOC 16 2 14 12 1 Sv a 0 30 10 4 8 2 6 0 4 14 12 10 25 b 10 30 b 20 15 25 Sv 0º 40ºS a 18 16 Sv 40ºN 0º 3 Sv 40ºN 18 SPG 20 2 10 2000 2200 2400 Fig. 2. (a) Surface air temperature anomaly for the year 2100, in 15 year SPG ◦ C, 80ºS Fig.RCP3-PD. 2. (a) Surface air temperature anomaly for the◦year 2100, intimes 0 for Average warming south of 60 S is 1.60 ◦ for RCP3-PD. Average warming of 60◦ S is 1.60 times◦ 100ºW 0º south 100ºE higherC,than the global mean. Average warming north of 60 N is higher than the global mean. Average◦ warming north of 60◦ N is only only 0.830.83 times the global mean (1.4 ◦ C), because the cooling ef-Fig. 3. (a) Maximum 10 AMOC strength of the Atlantic Meridtimes the global mean (1.4 C), because the cooling ef2000 (AMOC) in2200 fect of a of reduction in in thetheAtlantic Overturning Circula-ional Overturning Circulation Sv (106 m3 s−1 ), 2400 for fect a reduction Atlantic Meridional Meridional Overturning Circulayear and RCP8.5 (red). tionFig. (AMOC) for RCP8.5 (a)counteracts Surface air polar temperature anomaly forSame the 2100, inRCP3-PD (blue), RCP4.5 (yellow), RCP6 (grey), tion2.(AMOC) counteracts polaramplification. amplification. (b)(b) Same foryear RCP8.5 ◦ ◦ (with a global mean of 4.8 C).warming The polar amplification are times C,a for RCP3-PD. south of 60◦ Sfactors isfactors 1.60 (with global mean ofAverage 4.8 ◦ C). The polar amplification are(b) North Atlantic subpolar◦ gyre strength, in◦ Sv, computed from velocities 55 N between 33.8 W and the of Labrador 1.48 in the south and 1.53 in the north. Fig. 3. (a) atMaximum AMOC strength the Atlantic Meridhigher theand global Average warming north of 60◦ N ismeridional 1.48 in thethan south 1.53mean. in the north. ◦ coast (62 W).3. 6 m3 s−1 Fig. (a) Maximum AMOC strength inof Sv the(10 Atlantic Meridonly 0.83 times the global mean (1.4◦ C), because the cooling efional Overturning Circulation (AMOC) ), for 6 3 −1 ional Overturning Circulation (AMOC) in Sv (10 s ),(red). for fect of a reduction in the Atlantic Meridional Overturning CirculaRCP3-PD (blue), RCP4.5 (yellow), RCP6 (grey), and m RCP8.5 RCP3-PD (blue), RCP4.5 (yellow), RCP6 (grey), andcomputed RCP8.5 (red). tion (AMOC) counteracts polar amplification. (b) Same forAMOC RCP8.5 (b) North Atlantic subpolar gyre strength, in Sv, from observations (cf. Fig. 10.15 in Meehl et al., 2007a). (b) North Atlantic gyre strength, Sv,and computed from (with ain global mean to of global 4.8◦ C). warming The polar in amplification factorsare are meridional velocitiessubpolar at 55◦ N between 33.8in◦ W the Labrador changes response CLIMBER-3α ◦ W). meridional velocities at 55◦ N between 33.8◦ W and the Labrador 1.48 in the south and 1.53 in the north. coast (62 dominated by changes in heat flux, as in most other CMIP3 coast (62◦ W). models, while hydrological changes tend to have a minor, strengthening effect (Gregory et al., 2005). Further possible AMOC reduction due to Greenland ice sheet melting is not accounted for in these simulations. 3.3 Monsoon intensification Directly influenced by atmospheric temperature patterns, large-scale monsoon circulations are arguably among the most societally relevant atmospheric systems. Within the limitations of the statistical-dynamical atmosphere model and its coarse resolution, CLIMBER-3α simulates the principal patterns of monsoon dynamics and precipitation reasonably well (Fig. 4a), and its seasonal rainfall cycle compares favourably with reanalysis data (Fig. 4b) and IPCC AR4 models (cf. Kripalani et al., 2007, Fig. 1). We find that average monsoon rainfall in Asia and Africa intensifies under global warming (Fig. 5), consistent with many studies using more complex models (e.g. Kripalani et al., 2007). Seasonal (June–August, JJA) mean rainfall associated with the South Asian summer monsoon (including India and the Bay of Bengal) strengthens by 10% (RCP3-PD) to 20% (RCP8.5) until the middle of the 21st century and, for RCP8.5, by up Earth Syst. Dynam., 2, 25–35, 2011 to 30% during the 22nd century (Fig. 5a). Similar results are found for the East Asian (including China, Fig. 5b) and West African (Fig. 5c) monsoon, which both increase by up to 50% for RCP8.5. In absolute terms, this means increases in JJA rainfall by up to 3–5 mm day−1 for RCP8.5. The decline of the South Asian monsoon for RCP8.5 after 2150 is due to a shift of the center of maximum precipitation out of the South Asian region towards South China. While the magnitude and timing of this shift must be viewed in the context of our intermediate-complexity model, observations suggest that a displacement of the center of precipitation may be possible under global warming (Wang et al., 2009). In all regions we find a strong quasi-linear correlation of monsoon rainfall with the regional temperature difference between land and ocean (Fig. 5d–f). Note that changes due to direct and indirect aerosol effects are not captured by simulations with CLIMBER-3α and may have significant influence on monsoon rainfall and circulation which is likely to counter-act that of global warming (Lau and Kim, 2006; Rosenfeld et al., 2008). www.earth-syst-dynam.net/2/25/2011/ Climate change under a scenario near 1.5◦ C of global warming 2.1 23 J. Schewe et al.: Climate near 1.5 ◦ C warming 40ºN 6 0º0º 2 2 -2 -2 -6 -6 40ºS 40ºS -10 -10 -14 80ºS 80ºS 100ºW mm/day mm/day b 10 100ºW 0º 100ºE CLIMBER−3α CLIMBER−3α b 10 NCEP 5 10 NCEP 5 5 8 6 b 10 c 2000 0 2 4 6 month 8 10 12 South Asia a South Asia b e East AsiaEast Asia d d 12 12 -14 100ºE 0º 14 mm/day 6 a 14 mm/day 10 40ºN 10 16 mm/day a 14 mm/day 80ºN 14 mm/day a mm/day 80ºN 29 16 e 5 8 6 Africa c f Africa 2200 year 2000 2400 2200 year f 1 2 3 4 ° 2400 C 1 2 3 4 ° C Fig. 5. Average seasonal (JJA) precipitation of (a) South Asian 4 2 6 8 10 12 ◦ E, 15–22.5◦ N), (b) East Asian (90–135◦ E, 22.5– (67.5–112.5 Fig. 5. Average seasonal (JJA) precipitation of (a) South month ◦ W–22.5◦ E, 0–15◦ N) ◦summer ◦ 37.5◦ (67.5-112.5 N), and (c)◦ E, African Fig. 4. (a) Difference between average boreal summer (JJA) Asian 15-22.5(22.5 N), (b) East Asian (90-135 E, 22.5◦ ◦ show the ◦ respective ◦ monsoon (mm/day). Panels (d-f) regional andFig. winter (DJF) precipitation (shading, mm/day), and (JJA) av4. (a) Difference between averageinboreal summer 37.5 N), and (c) African (22.5 W-22.5 E, 0-15 N) precipitation summer monmon- of (a) South Fig. 5. Average seasonal (JJA) −1 soon precipitation versus the difference in JJA regional surface air erage summer (JJA) near-surface winds (vectors) in the control and winter (DJF) precipitation (shading, in mm day ), and avsoon (mm/day). Panels (d-f) show the respective regional monsoon ◦ ◦ Fig. 4. (a) Difference between average boreal summer (JJA)temperature Asianover (67.5-112.5 E,adjacent 15-22.5ocean. N), (b) East Asian (90-135◦ E, 22.5land and the Generally thistemre(pre–industrial) of CLIMBER-3α. (b) Seasonal of erage summerclimate (JJA) near-surface winds (vectors) in thecycle control precipitation versus the difference in JJA regional surface air ◦ ◦ ◦ ◦ andmonthly winteraverage (DJF)precipitation precipitation (shading, in mm/day), and av37.5 N), and (c) African (22.5of Generally W-22.5 E, 0-15 N) summer monlation shows clear linear trend. A ocean. shift precipitation the in the South Asian monsooncycle regionof (pre-industrial) climate of CLIMBER-3α. (b) Seasonal perature over aland and the adjacent thisfrom relation erage summer (JJA)precipitation near-surface winds (vectors) in NCEPthe controlshows south Asian region east Asian region leads to (mm/day). Panels show the respective regional monsoon average in the South Asian region in monthly CLIMBER-3α’s control climate (solid line) andmonsoon in the asoon clearmonsoon linear trend. Atowards shift(d-f) ofthe precipitation from the south deviations for strong and doesAsian not represent a regional qualitative (pre–industrial) climate ofetCLIMBER-3α. Seasonal cycle ofAsian in CLIMBER-3α’s control climate (solid line)(b) and in the the period NCEPNCAR reanalysis (Kistler al., 2001), averaged over precipitation versus difference in JJA monsoon regionwarming towards the the east region leads to devia-surface air temchange in this relation. NCAR reanalysis (Kistler et al., 2001), averaged over the period monthly average precipitation in the South Asian monsoon regiontions 1948-2007 (dashed line). forperature strong warming and does change this relation over land and not therepresent adjacenta qualitative ocean. Generally 1948–2007 (dashed line). in CLIMBER-3α’s control climate (solid line) and in the NCEP-in this relation. shows a clear linear trend. A shift of precipitation from the south 0 NCAR reanalysis (Kistler et al., 2001), averaged over the period Asian monsoon region towards the east Asian region leads to deviasurface warming (Fig. 6, inset; cf. Rahmstorf, 2007). How1948-2007 (dashed line). tions for strongrelation warming and represent a qualitative change ever, the quasi-linear fails as does soon not as global warming 3.4 Steric sea level rise in this relation. starts to decelerate, i.e. around 2100 for RCP8.5, and some Oceanic warming yields a steric sea level rise (SLR) of nearly 0.5 m for RCP8.5 by 2100 compared to the 1980–1999 average (Fig. 6). Thus, thermal oceanic expansion under RCP8.5 in our CLIMBER-3α simulations is about 20% higher than the upper 95% percentile (0.41 m by 2100) for the highest SRES scenario A1FI (see Table 10.7 in Meehl et al., 2007a) – in part because of slightly stronger anthropogenic forcing in RCP8.5. For RCP4.5 and RCP6, steric SLR is about 0.3 m by 2100 and thereby close to the upper 95% percentile provided in IPCC AR4 for the similar SRES B1 scenario. While for the upper three RCPs, steric SLR continues beyond 2500, the declining temperatures in RCP3-PD lead to a deceleration of steric SLR, a peaking at ∼0.3 m and a gradual reversal in the second half of the 23rd century, about 200 years after the peak in global temperatures. Other contributions to total sea level rise, in particular from melting of the Greenland and West Antarctic Ice Sheets, are beyond the scope of this study. During an initial phase, we find a quasi-linear relationship between the rate of steric sea level rise and the global mean www.earth-syst-dynam.net/2/25/2011/ time earlier for the lower scenarios. As suggested by Vermeer and Rahmstorf (2009), validity of semi-empirical projections of sea level change based on this relation might be extended by taking rapid adjustment processes into account. The horizontal distribution of steric SLR, shown in Fig. 7 for RCP3-PD, is qualitatively similar under different scenarios. By 2100 (Fig. 7a), the weakening of the AMOC maximum (cf. Fig. 3a) and of the North Atlantic current produces a southeast-to-northwest SLR gradient in the North Atlantic via geostrophic adjustment (Levermann et al., 2005; Yin et al., 2010). Small shifts in the northern subpolar and subtropical gyre systems induce smaller-scale variations of SLR. The interhemispheric sea level pattern found by Levermann et al. (2005) for an AMOC shutdown is not reflected here because the AMOC change is largely confined to the North Atlantic; Southern Ocean outflow, i.e. the AMOC flux across 30 ◦ S, is only reduced by about 10% (not shown). By 2200, the AMOC has partly recovered, and the most prominent feature in the North Atlantic is a negative SLR anomaly (Fig. 7b) due to a 60% increase in the subpolar gyre (Fig. 3b; Earth Syst. Dynam., 2, 25–35, 2011 24 2 J. Schewe et al.: Climate near 1.5 ◦ C warming 30 2.5 2.5 mm 1.5 1.5 1 1 a=1.66 mm/yr/°C 0 a=1.66 mm/yr/°C 0 0 ° C ° C 3 3 mm/yr mm/yr 6 6 2 2 0 0.5 0.5 0 0 2000 2000 2200 2200 year year 2400 2400 Fig. 6. Globally averaged steric sea level change (in m) relaFig. Globally averaged sea level change (in (yellow), m) relative 6. to 1980–1999, under thesteric RCP3-PD (blue), RCP4.5 Fig.to6.1980-1999, Globally averaged steric sea level (in (yellow), m) relative under the RCP3-PD (blue), RCP4.5 RCP6 (grey) and RCP8.5 (red) scenarios andchange their extensions in tive to(grey) 1980-1999, under the RCP3-PD RCP4.5 (yellow), RCP6 and The RCP8.5 scenarios and their extensions in CLIMBER-3α. inset(red) shows the rate(blue), of steric sea level rise RCP6 RCP8.5 (red) scenarios andsteric their extensions in (in mm(grey) yr−1 , and smoothed a 15-year moving average) between CLIMBER-3α. The insetwith shows the rate of sea level rise CLIMBER-3α. Thea inset shows rate of steric sea level rise 1800 and 2100 as function of the global surface warming above (in mm/yr, smoothed with a 15-year moving average) between ◦ C).a The (in 1980–1999 mm/yr, smoothed 15-year average) the mean (inwith slopemoving ofsurface the quasi-linear part is 1800 and 2100 as a function of global warmingbetween above ◦ C−1 1800 andyr−1 2100 as (black a(infunction of slope globalofsurface warming above ◦ 1.66 mm line; cf. Rahmstorf, 2007). Circles mark the 1980-1999 mean C). The the quasi-linear part is ◦ the timing 1980-1999 (in emissions. C). The slope of the quasi-linear part is ◦ mean −1GHG the of peak 1.66 mm yr−1 −1 C ◦ −1(black line; cf. Rahmstorf, 2007). Circles mark 1.66 mm yr (black line; cf. Rahmstorf, 2007). Circles mark the timing of peakC GHG emissions. the timing of peak GHG emissions. Häkkinen and Rhines, 2004; Levermann and Born, 2007). In the Southern Ocean, SLR patterns in 2200 are similar to those in 2100: A strengthening of the Antarctic Circumpolar Current above the level of no motion by about 4 Sv leads to below-average SLR around Antarctica (Fig. 7). On top of that, strengthening of the Ross and Weddell gyres by 5 Sv and 6 Sv, respectively, induces large horizontal SLR anomalies. Hattermann and Levermann (2010) found that a strengthening of those gyres may significantly enhance basal ice shelf melting around Antarctica. Yin et al. (2010) showed by comparison of simulated and observed present-day dynamic sea level patterns in twelve IPCC AR4 AOGCMs that their ensemble mean performs better than any of the individual models. The SLR pattern found in our analysis is in good qualitative agreement with the ensemble mean projection of those models under the SRES A1B scenario (Yin et al., 2010). 3.5 ORIGINAL MANUSCRIPTS Deep ocean warming In contrast to the sea surface, deep ocean temperatures respond to atmospheric warming on centennial time scales. Due to its peaking characteristic, the RCP3-PD scenario is well suited to study the propagation of the warming signal into the deep ocean. Global average temperatures at 500 m and 1000 m depth exhibit delayed peaks around the years 2200 and 2300, respectively, compared to a surface warming peak in the middle of the 21st century (Fig. 8a). In the year Earth Syst. Dynam., 2, 25–35, 2011 80ºN 80ºN 40ºN 40ºN 0º 0º 40ºS 40ºS 80ºS 80ºS 80ºN 80ºN 40ºN 40ºN 69 61 69 53 61 45 53 45 37 29 37 29 21 21 13 135 5-3 -3 -11 -11 86 a a b b 0º 0º 40ºS 40ºS 80ºS 80ºS 100ºW 100ºW 0º 0º 100ºE 100ºE 86 76 76 66 66 56 56 46 46 36 36 26 26 16 166 6-4 -4 -14 160ºW -14 160ºW Fig. 7. Horizontal pattern of steric sea level change (in cm), relative Fig. 7. Horizontal pattern of steric sea level change (in cm), relative to pre-industrial, RCP3-PD: (a)level Yearchange 2100,(in (b)cm), yearrelative 2200. Fig. 7. Horizontalunder pattern of steric sea to pre–industrial, under RCP3-PD: (a) Year 2100, (b) year 2200. The shading emphasizes anomalies to the (b) global average to pre–industrial, under the RCP3-PD: (a)relative Year 2100, year 2200. The shading emphasizes the anomalies relative to the global average steric SLR (about 29 cm the in 2100 and 36relative cm in 2200). The shading emphasizes anomalies to the global average steric SLR (about 29 cm in 2100 and 36 cm in 2200). steric SLR (about 29 cm in 2100 and 36 cm in 2200). 2370, about 300 years after the peak in global surface temperatures, major anomalies of up to 2 ◦ C are found in the upper 1000 m of the North Atlantic and Southern Ocean (Fig. 8b). In the North Atlantic, substantial warming is observed even below 2000 m depth. Despite the weakening of the AMOC noted earlier, the northern oceanic warming pattern clearly reflects the structure of the overturning cell. In general, the strong deep oceanic warming signal results from outcropping of isopycnals (black lines in Fig. 8b) at high latitudes, i.e. a lack of density stratification, which is a characteristic and robust feature of the modern ocean circulation. Mixing along these surfaces of constant density is strongly enhanced compared to diapycnal mixing across these surfaces. In combination with the observed polar warming amplification, isopycnal mixing facilitates enhanced heat uptake as also observed in AOGCMs (e.g. Stouffer et al., 2006a) and is the reason for the observed deep ocean warming. These heat anomalies spread at intermediate depths around 500 m, with the effect that peak global-average warming at those depths exceeds that of the ocean surface (Fig. 8a). After surface temperatures have relaxed, oceanic heat uptake is reduced and, after 2300, the ocean eventually becomes a very weak heat source, further damping the decline of surface atmospheric temperatures (compare Fig. 9b). This weak heat exchange between ocean and atmosphere www.earth-syst-dynam.net/2/25/2011/ J. Schewe et al.: Climate near 1.5◦ C warming 13 1.5 a 1 Climate change under a scenario near 1.5◦ C of global warming 25 °C 2.1 0.5 1.5 ◦ C J. Schewe et al.: Climate near warming J. Schewe et al.: Climate near 1.5◦ C warming 2 a b 1.52 a 1.5 2.4 °C 1600 2000 2200 2.0 2ºC RCP3−PD 0.5 1.6 surface 500m 1000m 0 1800 2000 80ºS 40ºS b 400 0.4ºC year 2200 0º 40ºN 2400 80ºN 0.4 2.0 Depth eventually cools deeper oceanic layers, but this cooling is so Fig. 8. Ocean response to the RCP3-PD scenario: (a) Global averslow that the intermediate-depth warming persists for cenage ocean temperature difference relative to pre–industrial levels, at turies evensurface after surface temperatures have reached the ocean (black) and at 500 m (dark blue) and 1000presentm (light day levels of approximately 0.8 ◦ C relative to pre-industrial. blue) depth. Due to polar amplification and outcropping oceanic Conversely, oceanic peak heatwarming anomalies serve atasintermedia longisopycnals atthese high latitudes, is stronger ate depth around 500 m thandischarges at the surface. Zonal average ocean term reservoir that slowly into(b)the atmosphere and warming in thecooling, year 2370, levels (shaddelays surface ascompared discussedtoinpre–industrial the following section. ing, in ◦ C; ocean depth in m). Overlaid are contours of constant density (isopycnals; in kg/m3 ). Slow cooling under RCP3-PD As mentioned in Sect. 3.1, global cooling after the temperature peak in RCP3-PD is much slower, relative to the rate of GHG emissions, than the warming before the peak (Fig. 9a, blue line). We find that two processes are responsible for this asymmetry. Generally oceanic heat uptake by vertical mixing creates thermal inertia that delays any temperature change at the surface (Fig. 9b). In order to identify additional effects, we www.earth-syst-dynam.net/2/25/2011/ 0 2000 0.8 2ºC Fig. 8. Ocean response to the RCP3-PD scenario: (a) gobal average 800 1.6 ocean8.temperature difference to pre-industrial the Fig. Ocean response to therelative RCP3-PD scenario: (a) levels, Globalataverocean surface (black) and at 500 m (dark blue) and 1000 m (light age ocean temperature difference relative to pre–industrial levels, 1200 1.2at blue) depth. Due(black) to polar and outcropping the ocean surface andamplification at 500 m (dark blue) and 1000 oceanic m (light isopycnals at high peak warmingand is stronger at intermedi1600 0.8 blue) depth. Due latitudes, to polar amplification outcropping oceanic ate depth around m than peak at thewarming surface. (b) Zonal average ocean isopycnals at high500 latitudes, is stronger at intermedi2000 0.4 warming in the year to pre-industrial levels (shadate depth around 5002370, m thancompared at 0.4ºC the surface. (b) Zonal average ocean ing, in ◦ C; ocean depth incompared m). Overlaid are contourslevels of constant warming in the year 2370, to pre–industrial (shad2200 0 density in kginm−3 ing, in ◦(isopycnals; C; ocean depth m).). Overlaid contours of constant 80ºS 40ºS 40ºN 80ºN 0º are 3 density (isopycnals; in kg/m ). 4 250 1 1.2 02.4 c 300 b 2 2400 b 400 1 1200 year 2200 0 −2 W mm 2000 11 0.5 0 surface 500m 1000m 0 Depth W m−2 RCP3−PD 0.5 800 °C 1 300 c 2200 year 2400 Fig. 9. Slow-down of global cooling under the RCP3-PD scenario: (a) Global surface air temperature anomaly as in Fig. 1c (blue line), compared to the result of the simple energy-balance equation 250 (1) that only takes into account diffusive oceanic mixing (dashed black line). Thin grey lines represent modified scenarios that are identical to RCP3-PD until 2070, and after that have zero emissions or two, three, four or five times as large negative 2000 2200 2400emissions as RCP3-PD, respectively. All curves are smoothed with an 11-year year running mean to remove short-term variability from solar and volcanic sources. The vertical dashed line marks the year 2110. (b) Fig. 9. Slow-down of flux global cooling under totheocean. RCP3-PD sceGlobally averaged heat from atmosphere Increasing Fig. 9.(a) Slow-down of air global cooling anomaly under theasRCP3-PD scenario: global surface temperature in Fig. 1c (blue GHG concentration results in enhanced oceanic heat uptake which nario: compared (a) Global surface air temperature anomaly as in Fig. 1c (blue line), the in result of the simple energy-balance (1) declines after thetopeak atmospheric warming and vanishesEq. around line), compared to the result diffusive of the simple energy-balance equation that only takes into account oceanic mixing (dashed black the year 2300 after which the ocean becomes a source for atmo(1) that only takes into accountmodified diffusive oceanic mixing (dashed line). Thin grey lines represent that aresimulation, identical spheric warming. The solid line is the scenarios CLIMBER-3α black line). Thin grey lines represent modified scenarios that to RCP3-PD until 2070, and after that have zero emissions or are two, while the 19 AOGCM emulations using MAGICC6 are represented identical toorRCP3-PD until 2070, and after that have zero emisthree, four five times as large negative emissions as RCP3-PD, reby the dashed line (median) and shading (50% and 80% range). The sions or two, three, fourare or smoothed five times as large negative emissions as spectively. All curves with an 11-year running mean onset of convection in theAll southern North Atlanticwith appears here as a RCP3-PD, respectively. curves are smoothed an 11-year to remove short-term variability solar volcanic sources. distinct drop intoocean heat uptakefrom after 2110and (vertical dashed line). running mean remove short-term variability from solar and volThe verticalare dashed line marks the year 2110. (b) Globally All curves smoothed asdashed in (a). (c) Average depth of the averNorth canic sources. The vertical line marks the year 2110. (b) aged heatocean flux from atmosphere to ocean. Increasing south GHG of conAtlantic mixed layer in winter (January-April) the Globally averaged heat flux from atmosphere to ocean. Increasing centration in (40 enhanced ◦ ◦ oceanic◦ heat uptake which declines latitudes ofresults Iceland W-0 , 50-65 N). Starting around the year GHG concentration results in enhanced oceanic heat uptake which after peak dashed in atmospheric warming and vanishes around the 2110 the (vertical line), an abrupt increase in mixed layer depth declines after the peak in atmospheric warming and vanishes around year 2300 after which the ocean becomes a source for atmospheric marks the onset of enhanced convection. the year 2300 after which the ocean becomes a source for atmowarming. The solid line is the CLIMBER-3α simulation, while the spheric warming. The solid line is the CLIMBER-3α simulation, 19 AOGCM emulations using MAGICC6 are represented by the while the 19 AOGCM emulations using MAGICC6 are represented dashed line (median) and shading (50% and 80% range). The onby the dashed line (median) and shading (50% and 80% range). The set of convection in the southern North Atlantic appears here as a onset of convection in the southern North Atlantic appears here as a distinct drop in ocean heat uptake after 2110 (vertical dashed line). distinct drop in ocean heat uptake after 2110 (vertical dashed line). All curves are smoothed as in (a). (c) Average depth of the North All curves are smoothed as in (a). (c) Average depth of the North Atlantic mixed layer layer in in winter (January-April) (January–April)south southofofthe the Atlantic ocean ocean mixed ◦ W–0◦◦ ,winter latitudes 50–65◦◦N). N).Starting Startingaround aroundthe theyear year latitudes of of Iceland Iceland (40 (40◦ W-0 , 50-65 2110 dashed line), line), an an abrupt abruptincrease increaseininmixed mixedlayer layerdepth depth 2110 (vertical (vertical dashed marks onset of of enhanced enhanced convection. convection. marks the the onset m °C 1.5 2 1800a 31 13 0 Earth Syst. Dynam., 2, 25–35, 2011 26 2 J. Schewe et al.: Climate near 1.5 ◦ C warming 32 isolate this ocean mixing effect with an intentionally simple energy-balance equation for global mean surface temperature anomaly T (t), assuming a diffusive ocean (following Allen et al., 2009; Hansen et al., 1985): C C0 Z − a0 T − a2 0 t dT (t 0 ) dt 0 (1) √ dt 0 t − t0 where C(t) is CO2 concentration; C0 = 280 ppm is the initial concentration at t = 0; a1 is the heat capacity of the oceanic mixed layer; a2 is ocean vertical diffusivity; a3 ' 1.3◦ C is climate sensitivity not accounting for any feedbacks; and 1/a0 is the climate feedback factor, such that a3 /a0 is the full climate sensitivity, which is ∼3.4 ◦ C for CLIMBER-3α. This model, with parameters a0−2 calibrated to match CLIMBER-3α, reproduces the global mean temperature simulated by CLIMBER-3α very well until about 2100 (black dashed line in Fig. 9a). However, at the beginning of the 22nd century, the CLIMBER-3α result deviates from the simple diffusive ocean heat uptake relationship: While the latter projects a steady cooling trend all the way until 2500, CLIMBER-3α projects a substantial slow-down of the cooling around the year 2110 (vertical dashed line in Fig. 9). The cooling rate thereafter remains almost 50% lower than suggested by Eq. (1) for about two centuries, consequently arriving at a significantly higher temperature. Plotted versus CO2 -equivalent GHG concentration, this is visible as a clear excursion from the smooth hysteresis projected according to Eq. (1) (Fig. 10). To test the robustness of this behaviour, we have conducted additional simulations using a set of scenarios that are identical to RCP3-PD until 2070. Thereafter, we set CO2 emissions in RCP3-PD equal to zero or two, three, four or five times as large negative emissions as in the original RCP3-PD, respectively. Using these modified RCP3-PD scenarios, we then computed radiative forcings following the same process as in generating the recommended CMIP5 GHG concentrations of the RCPs (for details, see Meinshausen et al., 2011). Under all these modified RCP3-PD scenarios, CLIMBER3α projects a drop in the cooling rate at the same time, near the year 2110, i.e., some decades after global mean temperature started to decline (thin grey lines in Fig. 9a). For zero emissions after 2070 (top grey line), this even leads to a slow global warming until the early 24th century, despite the net decrease in radiative forcing. Again, viewed relative to CO2 -equivalent GHG concentration, Eq. (1) yields essentially the same hysteresis for all the scenarios (Fig. 10, dashed grey lines), while the CLIMBER-3α projections for the modified scenarios depart from that hysteresis soon after the peak (solid grey lines). This result suggests that, on the one hand, the global mean temperature response of the coupled climate model to a peakand-decline scenario such as RCP3-PD is, up until about 70 years after the peak in GHG concentrations, mainly governed by the heat capacity of the oceanic mixed layer and heat exchange with the deep ocean due to mixing. The inertia Earth Syst. Dynam., 2, 25–35, 2011 2200 2300 2050 2100 2025 2400 1 C dT = a3 log2 dt 1.5 ° a1 ORIGINAL MANUSCRIPTS 2500 0.5 2000 1975 0 1925 1900 −0.5 300 400 500 CO2 concentration (ppm) Fig. 10. As Fig. 9a, but plotted versus CO2 -equivalence concentration of longwave absorbers) instead time, and with the Fig. 10. (sum As Fig. 9a, but plotted versus CO2of -equivalence concenfor the modified scenarios as and dashed results(sum of Eq.of(1) tration longwave absorbers) instead shown of time, withgrey the lines. ofThis the scenarios transient “hysteresis” of global results eq. figure (1) forrepresents the modified shown as dashed grey warming in figure RCP3-PD (blue line, 25 years)ofand the lines. This represents the marked transientevery “hysteresis” global modified peak-and-decline scenarios, i.e. how much GHG reducwarming in RCP3-PD (blue line, marked every 25 years) and the tion it takes to cool the surface back to a given temperature that it modified peak-and-decline scenarios, i.e. how much GHG reduchad during the warming phase. The dashed lines show the hysteresis tion it takes to cool the surface back to a given temperature that it expected from the processes represented by Eq. (1), while the solid had during the warming phase. The dashed lines show the hysteresis lines show the hysteresis behaviour observed in CLIMBER-3α. The expected from the processes represented by eq. (1), while the solid convection-related slow-down of the cooling rate (marked by a blue lines show the hysteresis behaviour observed in CLIMBER-3α. The circle for the RCP3-PD scenario) translates into a widening of the convection-related slow-down of the cooling rate (marked by a blue hysteresis. The slow-down occurs at the same time under different circle for the RCP3-PD scenario) translates into a widening of the scenarios (at the beginning of the 21st century, see thin grey lines in hysteresis. The slow-down occurs at the same time under different Fig. 9a), and at different CO2 concentrations. scenarios (at the beginning of the 21st century, see thin grey lines in Fig. 9a), and at different CO2 concentrations. induced by these processes delays the cooling that results from the decline in GHG concentrations (Stouffer, 2004). On the other hand, another mechanism comes into play around the year 2110 that further reduces the cooling rate, over a period of two centuries, by almost 50%. We find that a relatively rapid change in oceanic convection is responsible for this reduction. The depth of the wintertime oceanic mixed layer in the North Atlantic is a direct indicator of the strength of convection associated with the AMOC. This mixed layer depth shrinks during the warming phase in the 21st century, but then extends strongly between the years 2110 and 2150, which coincides with the change in the surface cooling rate (Fig. 9c). Enhanced convection in these latitudes results in enhanced heat loss of the ocean to the atmosphere; thus, globally, net ocean heat uptake is reduced by this effect (Fig. 9b, solid blue line), slowing down atmospheric cooling. www.earth-syst-dynam.net/2/25/2011/ 2.1 Climate change under a scenario near 1.5◦ C of global warming J. Schewe et al.: Climate near 1.5 ◦ C warming 5 Discussion and conclusions We have presented large-scale climatic consequences of the new RCP scenarios, which are designed for the forthcoming IPCC AR5 to span the full range of future pathways of anthropogenic GHG emissions currently discussed in the literature (Moss et al., 2008, page i). CLIMBER-3α atmospheric temperature projections and AOGCM emulations using MAGICC6 are qualitatively and quantitatively similar for the 21st century. CLIMBER-3α temperatures tend to be slightly higher than the median of the AOGCM emulations (cf. Fig. 1), owing to the difference in climate sensitivity. While the CLIMBER-3α simulations are based on the standard settings presented in Montoya et al. (2005), the wider range of possible climate responses is covered by the emulation ensemble with MAGICC6, spanning climate sensitivities from 1.9 ◦ C (emulation of the NCAR PCM model) to 5.7 ◦ C (emulation of the MIROC3.2 high resolution model, see Meinshausen et al., 2008, Table 4). With respect to atmospheric quantities, the coarse resolution of CLIMBER-3α and the limitations of the statistical-dynamical representation must be kept in mind. On the other hand, large-scale oceanic quantities have been shown to be in good agreement with recent AOGCM results. Our evaluation of the peak-and-decline scenario RCP3-PD reveals that global maximal temperatures can be expected close to 1.5 ◦ C warming relative to pre-industrial levels. Owing to negative CO2 emissions, concentrations under this scenario are projected to drop markedly after peaking in 2070, and induce a slow cooling. This finding is consistent with recent studies using other models of varying complexity (e.g. Solomon et al., 2009), which showed that under zero-emission scenarios temperatures are projected not to drop substantially for several centuries. Our work goes beyond those studies by demonstrating that in a physical climate model, cooling is not only delayed by mixing-related heat exchange with the ocean, but that dynamical effects can significantly add to the delay. The abrupt strengthening of convection in the North Atlantic indicates an important role of internal dynamical processes in the oceans, especially because the timing of the convection change seems to be independent of the rate of (negative) GHG emissions, once atmospheric temperatures have started to fall. Although the exact timing will probably differ across models, the onset of strong convection is likely to be a robust feature, because declining atmospheric temperatures lead to stronger cooling of surface waters and thus reduce the stability of the water column. The projections of steric sea level rise presented here are generally consistent with previous simulations. The highest scenario, RCP8.5, being warmer than the highest SRES scenario, yields enhanced steric sea level rise of up to 2 m by 2500. According to our simulations, thermal oceanic expansion can be halted only for emission trajectories corresponding to, or below, RCP3-PD. In this scenario we observe an enhanced oceanic warming of intermediate depth due to www.earth-syst-dynam.net/2/25/2011/ 27 33 polar amplification in combination with the lack of oceanic density stratification in high latitudes. The associated heat content persists for centuries. Thus, these results will allow future studies to quantify the risk of such a mid-ocean warming for marine ecosystems (Sarmiento et al., 2004) and environments. For example, prolonged deep ocean warming could be sufficient to trigger the dissociation of shallow methane hydrates trapped in ocean sediments, and thereby release additional amounts of greenhouse gases into the atmosphere (Reagan and Moridis, 2008; Archer et al., 2009). Furthermore, melting of Antarctic ice shelves (Holland et al., 2008) and the initiation of oceanic anoxic events (Hofmann and Schellnhuber, 2009; Stramma et al., 2009) could be facilitated. Acknowledgements. This work was supported by the Heinrich Böll Foundation, the German National Academic Foundation, and the BMBF PROGRESS project (support code 03IS2191B). MM received support from the UFOPLAN project FKZ 370841103 by the German Federal Environment Agency. NCEP Reanalysis Derived data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. We thank two anonymous referees for their helpful comments. Edited by: K. Keller References Allen, M. R., Frame, D. J., Huntingford, C., Jones, C. D., Lowe, J. A., Meinshausen, M., and Meinshausen, N.: Warming caused by cumulative carbon emissions towards the trillionth tonne, Nature, 458, 1163–1166, doi:10.1038/nature08019, 2009. Archer, D., Buffett, B., and Brovkin, V.: Ocean methane hydrates as a slow tipping point in the global carbon cycle, P. Natl. Acad. Sci. USA, 106, 20596–20601, doi:10.1073/pnas.0800885105, 2009. Brohan, P., Kennedy, J., Harris, I., Tett, S., and Jones, P.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res.-Atmos., 111, D12106, doi:10.1029/2005JD006548, 2006. Clarke, L., Edmonds, J., Jacoby, H., Pitcher, H., Reilly, J., and Richels, R.: Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations, Sub-report 2.1A of Synthesis and Assessment Product 2.1 by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research, US Department of Energy, p.154, 2007. Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res., 102, 12609–12646, 1997. Fujino, J., Nair, R., Kainuma, M., Masui, T., and Matsuoka, Y.: Multi-gas Mitigation Analysis on Stabilization Scenarios Using Aim Global Model, Energy J., Special Issue 3, 343–354, 2006. Gregory, J. M., Dixon, K. W., Stouffer, R. J., Weaver, A. J., Driesschaert, E., Eby, M., Fichefet, T., Hasumi, H., Hu, A., Jungclaus, J. H., Kamenkovich, I. V., Levermann, A., Montoya, M., Murakami, S., Nawrath, S., Oka, A., Sokolov, A. P., and Thorpe, R. B.: A model intercomparison of changes in the Atlantic thermohaline circulation in response to Earth Syst. Dynam., 2, 25–35, 2011 28 34 increasing atmospheric CO2 concentration, Geophys. Res. Lett., 32, L12703, doi:10.1029/2005GL023209, 2005. Häkkinen, S. and Rhines, P. B.: Decline of Subpolar North Atlantic Circulation During the 1990s, Science, 304, 555–559, 2004. Hansen, J., Russell, G., Lacis, A., Fung, I., Rind, D., and Stone, P.: Climate response-times - dependence on climate sensitivity and ocean mixing, Science, 229, 857–859, 1985. Hattermann, T. and Levermann, A.: Response of Southern Ocean circulation to global warming may enhance basal ice shelf melting around Antarctica, Clim. Dynam., 35, 741–756, doi:10.1007/s00382-009-0643-3, 2010. Hofmann, M. and Schellnhuber, H.-J.: Oceanic acidification affects marine carbon pump and triggers extended marine oxygen holes, P. Natl. Acad. Sci. USA, 106, 3017–3022, doi:10.1073/pnas.0813384106, 2009. Holland, P. R., Jenkins, A., and Holland, D. M.: The response of ice shelf basal melting to variations in ocean temperature, J. Climate, 21, 2558–2572, doi:10.1175/2007JCLI1909.1, 2008. Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., van den Dool, H., Jenne, R., and Fiorino, M.: The NCEP/NCAR 50-year reanalysis, B. Am. Meteorol. Soc., 82, 247–267, 2001. Kripalani, R. H., Oh, J. H., and Chaudhari, H. S.: Response of the East Asian summer monsoon to doubled atmospheric CO2 : Coupled climate model simulations and projections under IPCC AR4, Theor. Appl. Climatol., 87, 1–28, doi:10.1007/s00704006-0238-4, 2007. Lau, K. M. and Kim, K. M.: Observational relationships between aerosol and Asian monsoon rainfall, and circulation, Geophys. Res. Lett., 33, L21810, doi:10.1029/2006GL027546, 2006. Levermann, A. and Born, A.: Bistability of the subpolar gyre in a coarse resolution climate model, Geophys. Res. Lett., 34, L24605, doi:10.1029/2007GL031732, 2007. Levermann, A., Griesel, A., Hofmann, M., Montoya, M., and Rahmstorf, S.: Dynamic sea level changes following changes in the thermohaline circulation, Clim. Dynam., 24, 347–354, 2005. Levermann, A., Mignot, J., Nawrath, S., and Rahmstorf, S.: The role of northern sea ice cover for the weakening of the thermohaline circulation under global warming, J. Climate, 20, 4160– 4171, 2007. May, W.: Climatic changes associated with a global “2 degrees C-stabilization” scenario simulated by the ECHAM5/MPIOM coupled climate model, Clim. Dynam., 31, 283–313, doi:10.1007/s00382-007-0352-8, 2008. Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z.C.: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Global Climate Projections, Cambridge University Press, Cambridge, Uk and New York, NY, USA, 2007a. Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., and Zhao, Z.-C.: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to theFourth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Climate Models and their Evaluation, Cambridge University Press, Earth Syst. Dynam., 2, 25–35, 2011 2 ORIGINAL MANUSCRIPTS J. Schewe et al.: Climate near 1.5 ◦ C warming Cambridge, UK and New York, NY, USA, 2007b. Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, doi:10.5194/acp11-1417-2011, 2011. Meinshausen, M., Smith, S., Calvin, K., Daniel, J. S., Kainuma, M., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A. M., Velders, G. J. M., and van Vuuren, D.: The RCP Greenhouse Gas Concentrations and their Extension from 1765 to 2300, Climatic Change, http://www.pik-potsdam.de/∼mmalte/pubs/ 09 GHG Concentrations&Extension 1Sep2010.pdf, last access: 7 March 2011, submitted, 2011. Mignot, J., Levermann, A., and Griesel, A.: A decomposition of the Atlantic Meridional Overturning Circulation into physical components using its sensitivity to vertical diffusivity, J. Phys. Oceanogr., 36, 636–650, 2006. Montoya, M. and Levermann, A.: Surface wind-stress threshold for glacial Atlantic overturning, Geophys. Res. Lett., 35, L03608, doi:10.1029/2007GL032560, 2008. Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski, A., and Rahmstorf, S.: The Earth System Model of Intermediate Complexity CLIMBER-3α, Part I: description and performance for present day conditions, Clim. Dynam., 25, 237–263, 2005. Moss, R., Babiker, M., Brinkman, S., Calvo, E., Carter, T., Edmonds, J., Elgizouli, I., Emori, S., Erda, L., Hibbard, K., Jones, R., Kainuma, M., Kelleher, J., Lamarque, J. F., Manning, M., Matthews, B., Meehl, J., Meyer, L., Mitchell, J., Nakicenovic, N., O’Neill, B., Pichs, R., Riahi, K., Rose, S., Runci, P., Stouffer, R., van Vuuren, D., Weyant, J., Wilbanks, T., van Ypersele, J. P., and Zurek, M.: Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies, http://www.ipcc.ch/pdf/supporting-material/ expert-meeting-report-scenarios.pdf (last access: 7March 2011), 2008. Moss, R., Edmonds, J., Hibbard, K., Manning, M. R., Rose, S., van Vuuren, D., Carter, T., Emori, S., Kainuma, M., Kram, T., Meehl, G., Mitchell, J., Nakicenovic, N., Riahi, K., Smith, S., Stouffer, R., Thomson, A., Weyant, J., and Wilbanks, T.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, doi:10.1038/nature08823, 2010. Nakicenovic, N. and Swart, R.: IPCC Special Report on Emissions Scenarios, Cambridge University Press, Cambridge, 2000. Pacanowski, R. C. and Griffies, S. M.: The MOM-3 manual, Tech. Rep. Tech. Rep. 4, NOAA/Geophyical Fluid Dynamics Laboratory, Princeton, NJ, USA, 1999. Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A., Kubatzki, C., and Rahmstorf, S.: CLIMBER-2: a climate system model of intermediate complexity, Part I: model description and performance for present climate, Clim. Dynam., 16, 1, 2000. Rahmstorf, S.: A Semi-Empirical Approach to Projecting Future Sea-Level Rise, Science, 315, 368–370, doi:10.1126/science.1135456, 2007. Reagan, M. T. and Moridis, G. J.: Dynamic response of oceanic hydrate deposits to ocean temperature change, J. Geophys. Res.Oceans, 113, C12023, doi:10.1029/2008JC004938, 2008. www.earth-syst-dynam.net/2/25/2011/ 2.1 Climate change under a scenario near 1.5◦ C of global warming J. Schewe et al.: Climate near 1.5 ◦ C warming Riahi, K., Gruebler, A., and Nakicenovic, N.: Scenarios of long-term socio-economic and environmental development under climate stabilization, Technol. Forecast. Soc., 74, 887–935, doi:10.1016/j.techfore.2006.05.026, 2007. Rosenfeld, D., Lohmann, U., Raga, G. B., O’Dowd, C. D., Kulmala, M., Fuzzi, S., Reissell, A., and Andreae, M. O.: Flood or drought: How do aerosols affect precipitation?, Science, 321, 1309–1313, doi:10.1126/science.1160606, 2008. Sarmiento, J., Slater, R., Barber, R., Bopp, L., Doney, S., Hirst, A., Kleypas, J., Matear, R., Mikolajewicz, U., Monfray, P., Soldatov, V., Spall, S., and Stouffer, R.: Response of ocean ecosystems to climate warming, Global Biogeochem. Cy., 18, GB3003, doi:10.1029/2003GB002134, 2004. Schewe, J. and Levermann, A.: The role of meridional density differences for a wind-driven overturning circulation, Clim. Dynam., 34, 547–556, doi:10.1007/s00382-009-0572-1, 2010. Smith, S. J. and Wigley, T. M. L.: Multi-gas forcing stabilization with Minicam, Energ. J., Special Issue 3, 373–391, 2006. Solomon, S., Plattner, G.-K., Knutti, R., and Friedlingstein, P.: Irreversible climate change due to carbon dioxide emissions, P. Natl. Acad. Sci. USA, 106, 1704–1709, doi:10.1073/pnas.0812721106, 2009. Stouffer, R.: Time scales of climate response, J. Climate, 17, 209– 217, 2004. Stouffer, R., Broccoli, A., Delworth, T., Dixon, K., Gudgel, R., Held, I., Hemler, R., Knutson, T., Lee, H., Schwarzkopf, M., Soden, B., Spelman, M., Winton, M., and Zeng, F.: GFDL’s CM2 global coupled climate models, Part IV: Idealized climate response, J. Climate, 19, 723–740, 2006a. Stouffer, R. J., Yin, J., Gregory, J. M., Dixon, K. W., Spelman, M. J., Hurlin, W., Weaver, A. J., Eby, M., Flato, G. M., Hasumi, H., Hu, A., Jungclaus, J. H., Kamenkovich, I. V., Levermann, A., Montoya, M., Murakami, S., Nawrath, S., Oka, A., Peltier, W. R., Robitaille, D. Y., Sokolov, A. P., Vettoretti, G., and Weber, S. L.: Investigating the Causes of the Response of the Thermohaline Circulation to Past and Future Climate Changes, J. Climate, 19, 1365–1387, 2006b. Stramma, L., Visbeck, M., Brandt, P., Tanhua, T., and Wallace, D.: Deoxygenation in the oxygen minimum zone of the eastern tropical North Atlantic, Geophys. Res. Lett., 36, L20607, doi:10.1029/2009GL039593, 2009. www.earth-syst-dynam.net/2/25/2011/ 29 35 Trenberth, K., Olson, J., and Large, W.: A Global Ocean Wind Stress Climatology based on ECMWF Analyses, Tech. Rep. NCAR/TN-338+STR, National Center for Atmospheric Research, Boulder, Colorado, USA, 1989. Trenberth, K., Jones, P., Ambenje, P., Bojariu, R., Easterling, D., Tank, A. K., Parker, D., Rahimzadeh, F., Renwick, J., Rusticucci, M., Soden, B., and Zhai, P.: Climate Change 2007: The Physical Science Basis, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, chap. Observations: Surface and Atmospheric Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2007. van Vuuren, D. P., Den Elzen, M. G. J., Lucas, P. L., Eickhout, B., Strengers, B. J., van Ruijven, B., Wonink, S., and van Houdt, R.: Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs, Climatic Change, 81, 119–159, doi:10.1007/s10584-006-9172-9, 2007. Vermeer, M. and Rahmstorf, S.: Global sea level linked to global temperature, P. Natl. Acad. Sci. USA, 106, 21527–21532, doi:10.1073/pnas.0907765106, 2009. Wang, C., Kim, D., Ekman, A. M. L., Barth, M. C., and Rasch, P. J.: Impact of anthropogenic aerosols on Indian summer monsoon, Geophys. Res. Lett., 36, L21704, doi:10.1029/2009GL040114, 2009. Washington, W. M., Knutti, R., Meehl, G. A., Teng, H., Tebaldi, C., Lawrence, D., Buja, L., and Strand, W. G.: How much climate change can be avoided by mitigation?, Geophys. Res. Lett., 36, L08703, doi:10.1029/2008GL037074, 2009. Wigley, T. M. L. and Raper, S. C. B.: Interpretation of high projections for global-mean warming, Science, 293, 451–454, doi:10.1126/science.1061604, 2001. Winton, M.: Amplified Arctic climate change: What does surface albedo feedback have to do with it?, Geophys. Res. Lett., 33, L03701, doi:10.1029/2005GL025244, 2006. Wise, M., Calvin, K., Thomson, A., Clarke, L., Bond-Lamberty, B., Sands, R., Smith, S. J., Janetos, A., and Edmonds, J.: Implications of Limiting CO2 Concentrations for Land Use and Energy, Science, 324, 1183–1186, doi:10.1126/science.1168475, 2009. Wu, P., Wood, R., Ridley, J., and Lowe, J.: Temporary acceleration of the hydrological cycle in response to a CO2 rampdown, Geophys. Res. Lett., 37, L12705, doi:10.1029/2010GL043730, 2010. Yin, J., Griffies, S. M., and Stouffer, R. J.: Spatial Variability of Sea Level Rise in Twenty-First Century Projections, J. Climate, 23, 4585–4607, doi:10.1175/2010JCLI3533.1, 2010. Earth Syst. Dynam., 2, 25–35, 2011 2.2 Basic mechanism for abrupt monsoon transitions Basic mechanism for abrupt monsoon transitions 33 SPECIAL FEATURE 2.2 Basic mechanism for abrupt monsoon transitions Anders Levermanna,b,1 , Jacob Schewea,b , Vladimir Petoukhova , and Hermann Helda a Earth System Analysis, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; and b Institute of Physics, Potsdam University, 14473 Potsdam, Germany Monsoon systems influence the livelihood of hundreds of millions of people. During the Holocene and last glacial period, rainfall in India and China has undergone strong and abrupt changes. Though details of monsoon circulations are complicated, observations reveal a defining moisture-advection feedback that dominates the seasonal heat balance and might act as an internal amplifier, leading to abrupt changes in response to relatively weak external perturbations. Here we present a minimal conceptual model capturing this positive feedback. The basic equations, motivated by observed relations, yield a threshold behavior, robust with respect to addition of other physical processes. Below this threshold in net radiative influx, Rc , no conventional monsoon can develop; above Rc , two stable regimes exist. We identify a nondimensional parameter l that defines the threshold and makes monsoon systems comparable with respect to the character of their abrupt transition. This dynamic similitude may be helpful in understanding past and future variations in monsoon circulation. Within the restrictions of the model, we compute Rc for current monsoon systems in India, China, the Bay of Bengal, West Africa, North America, and Australia, where moisture advection is the main driver of the circulation. Earth system | tipping element | abrupt climate change | atmospheric circulation | nonlinear dynamics M onsoon rainfall shapes regional culture and the livelihoods of hundreds of millions of people (e.g., 1, 2). The future evolution of monsoon rainfall under increasing levels of atmospheric CO2 and aerosol pollution is highly uncertain (3). Although greenhouse gas abundance tends to increase monsoon rainfall strength (4–6), the situation is more complex with changing aerosol distribution (7, 8). Given this large uncertainty in the future forcing of monsoons, it is crucial to understand internal monsoon dynamics, especially with respect to self-amplifying feedbacks, which might result in potentially strong responses to small perturbations. Zickfeld et al. (2005) found two stable states in a simple model of the Indian summer monsoon, which in principle allows for rapid transition between radically different monsoon circulations (9, 10) and thereby identified the Indian monsoon as a potential tipping element of the climate system (11). Evidence for such behavior is found in paleodata that show rapid and strong variations in Indian and East Asian monsoon rainfall (12, 13). These abrupt changes have been linked to climatic events in the North Atlantic for the last glacial period (14, 15) as well as for the Holocene (16, 17). Though a physical mechanism for this teleconnection has been suggested (18), relevant climatic signals of the North Atlantic events in Asia (such as temperature and moisture anomalies) are very small (19) indicating that internal feedbacks in monsoon dynamics may have amplified the weak external forcing. Both spatial patterns and temporal evolution of monsoon rainfall are influenced by a number of physical processes (7, 18, 20–28) as well as characteristics of vegetation (29–31) and topography (32). Though these details are crucial for the specific behavior of different monsoon systems and their significance will vary from region to region, there exist defining processes fundamental to any monsoon dynamics (e.g. 33, 34). These processes are the advection of heat and moisture during monsoon season and the associated rainfall and release of latent heat. In accordance with Zickfeld www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106 et al. (9), we suggest the positive moisture-advection feedback (21) as a candidate for the main cause of abrupt changes in monsoon dynamics. We derive a minimal conceptual model of a monsoon circulation (Fig. 1A), comprising merely conservation of heat and moisture, knowingly neglecting a large number of relevant physical processes in order to distill the fundamental nonlinearity of monsoon circulations. The resulting governing equation exhibits the necessary solution structure to explain qualitatively both strong, persistent changes in monsoon rainfall, as observed in paleorecords, and abrupt variablity within one rainy season. This equation’s dynamic similitude, expressed through a single dimensionless number l, which defines the threshold behavior and makes different monsoon systems comparable with respect to their transition, may serve as a building block for understanding past and future abrupt changes in monsoon dynamics. Results Moisture-Advection Feedback in Monsoon Dynamics. The seasonal evolution of the continental heat budget for different monsoon systems (Fig. 2) shows that sensible heat flux from the land surface increases during spring and heats up the atmospheric column prior to the rainy season. The onset of heavy rainfall (red vertical lines in Fig. 2) is associated with a drop in surface temperature on land, and consequently, sensible heat flux reduces drastically. During the monsoon season, latent heat release dominates the atmospheric heat content, whereas net radiative fluxes are relatively constant throughout the year, reflecting the stabilizing long-wave radiative feedback. In response to the latent heat release, thermal energy is transported out of the region through large-scale advection and synoptic processes. The main dynamical driver of the monsoon is therefore the positive moisture-advection feedback (Fig. 1A): The release of latent heat from precipitation over land adds to the temperature difference between land and ocean, thus driving stronger winds from ocean to land and increasing in this way landward advection of moisture, which leads to enhanced precipitation and associated release of latent heat. In the following, we seek to capture this feedback in a minimal conceptual model. Minimal Conceptual Model for Abrupt Monsoon Transitions. For this purpose, consider the heat-balance equation of the monsoon season (Fig. 2, for example at blue vertical line). L · P − Cp W · ΔT + R = 0, [1] where latent heat release and net radiation into the atmospheric column, R, balance heat divergence, and the relatively weak contribution from sensible heat transport from the land surface to the atmospheric column has been neglected. ΔT is the atmospheric Author contributions: A.L. designed research; A.L. and V.P. performed research; A.L., J.S., and H.H. analyzed data; and A.L. wrote the paper. The authors declare no conflict of interest. This article is a PNAS Direct Submission. 1 To whom correspondence should be addressed. E-mail: [email protected]. This article contains supporting information online at www.pnas.org/cgi/content/full/ 0901414106/DCSupplemental. Early Edition 1 of 6 GEOPHYSICS Edited by Hans Joachim Schellnhuber, Potsdam Institute for Climate Impact Research, Potsdam, Germany and approved August 18, 2009 (received for review February 11, 2009) 34 2 ORIGINAL MANUSCRIPTS some regions, does not alter the model behavior qualitatively. This offset is discussed together with other possibly relevant processes in the SI Appendix. Here we seek to capture only processes relevant to the self-amplification feedback. Neglecting the effect of evaporation over land and associated soil-moisture processes in the continental moisture budget, precipitation has to be balanced by the net landward flow of moisture W · ρ(qO − qL ) − P = 0, [3] where qO and qL are specific humidity over ocean and land, and ρ = 1.3 kg/m3 is mean air density. Note that evaporation is clearly an important process for the moisture budget (e.g. (38)) and is omitted in Eq. 3 only for the sake of clarity. Including evaporation does not change the model behavior qualitatively (see SI Appendix). It does, however, shift the value of the critical threshold, as we will show in the next section when applying our model to data. In the minimalistic spirit of this section, we omit the effect of evaporation here because it is not of first order to the problem. Consistent with reanalysis data (Fig. 4) and theoretical considerations (36, 39), continental rainfall is assumed to be proportional to the mean specific humidity within the atmospheric column P = βqL . [4] The effect of an offset between these quantities does not change the model behavior qualitatively (see SI Appendix). This set of assumptions (Eqs. 1–4) yields the dimensional governing equation of the model β α αβ W3 + W2 − (LqO β + R) · W − 2 · R = 0. [5] ρ Cp ρCp Fig. 1. Basic mechanism of abrupt monsoon transitions. (A) Geometry of conceptual model and fundamental moisture-advection feedback. The same notation as in the text is used for wind W , precipitation P, net radiative influx R, vertical scale H and horizontal scale L. Arrows in the feedback loop indicate the amplification of one physical processes by another. (B) Mechanism of the abrupt transition. Heating by latent heat release and cooling through heat advection compensate each other, and both decrease with decreasing winds (or equivalently, land–ocean temperature difference ΔT ; see Eq. 2). The resultant heating balances the negative net radiative flux as long as it is above a threshold RC , below which no conventional monsoon exists. temperature difference between land and ocean. Latent heat of condensation is L = 2.6 · 106 J/kg and volumetric heat capacity of air at constant pressure Cp = 1, 295 J/m3 /K. P is the mean precipitation over land (in kg/m2 /s). The ratio = H/L between vertical extent H of the lower troposphere and the horizontal scale L of the region of precipitation (Fig. 1) enters because of the balance of the horizontal advective heat transport and the vertical fluxes of net radiative influx R and precipitation P. A length scale for the coastline drops out. Note that no annual cycle is included in the model. Only budgets for the rainy season are considered. Consequently, this model does not capture any interseasonal or any interannual dynamics. Equations are only valid for landward winds, W ≥ 0. Assuming dominance of ageostrophic flow in low latitudes, the landward mean wind W is taken to be proportional to the temperature difference between land and ocean (33, 36, 37): W = α · ΔT. [2] This assumption of a linear relation between the two quantities is supported by National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data (Fig. 3) with correlation coefficients above 50% for all regions. There is significant scatter in some plots, reflecting the fact that other processes may be relevant for the monsoon dynamics in the corresponding regions. A possible offset, as observed in 2 of 6 www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106 Note that through the linear relation of Eq. 2, this equation can equally be understood as an expression for the temperature difference beween land and ocean ΔT, which might be more useful for some applications. Introduction of nondimensional variables w ≡ W ρ/β and p = P/(qO β) results in the nondimensional equation w3 + w2 − (l + r)w − r = 0, [6] which depends on two parameters only: The dimensionless net radiative influx r ≡ R · αρ2 /(Cp β2 ) and a measure for the relative role of latent and advective heat transport l ≡ (αρ2 LqO )/(Cp β) = (LqO β)/(Cp β2 /(αρ2 )). [7] Large l corresponds to a strong influence of moisture advection (scaling as LqO βp) on the continental heat budget compared with heat advection by large-scale and synoptic processes (scaling as Cp β2 w2 /(αρ2 )). The nondimensional precipitation is directly related to the wind through p = w/(1 + w). Solutions w(r) of Eq. 6 are determined entirely by a choice of the only free parameter l, which can be expressed in terms of a critical threshold of net radiative flux rc , below which no physical solution exists (Fig. 5). The critical point (rc , wc ) will vary for different monsoon systems. It is directly linked to the only remaining parameter l, through wc (wc + 1)2 = l/2. [8] and therefore uniquely defines the solution w(r) of the model. The critical radiation can be computed from rc = −w2c (2wc + 1) [9] Thus for large l (as observed in some monsoon systems) the critical threshold is well approximated by rc ≈ −l. Note that l is scaling like qO α/β where α and β have clear-cut physical meaning (39). α is essentially a function of the near-surface cross-isobar angle and thereby a function of surface roughness and static stability of the Levermann et al. Basic mechanism for abrupt monsoon transitions 35 GEOPHYSICS SPECIAL FEATURE 2.2 Fig. 2. Seasonal heat contributions to the atmospheric column over different continental monsoon regions in NCEP/NCAR reanalysis data (35). Radiative heating of the land surface in spring enhances sensible heat flux from the ground (’Sensible’). During the rainy season, latent heat release dominates the heat budget (’Latent’). Radiative heat flux comprises all radiative fluxes in and out of the atmospheric column (’Radiative’). The excess heat is transported out of the continental monsoon region though large-scale advective and synoptic processes (’Convergence’). Error bars give the standard deviation from the 60 years for which data is available (1948–2007). Regions from which values were taken are defined in the (SI Appendix). The red and blue vertical lines emphasize the months of maximum sensible heat flux and latent heat flux, respectively. planetary boundary layer (PBL). β is governed by the characteristic turnover (recycling) time of liquid water in the atmosphere and thereby determined by static stability and vertical velocity in the PBL. Any physical solution for r > rc is characterized by landward winds w > 0 and positive precipitation p > 0. Let us now try to understand the physical mechanism behind the threshold behavior observed in Fig. 5. In the tropics net radiative influx is negative, i.e. radiation cools the atmospheric column. During monsoon season the same is true for the advection of heat by the winds because winds blow predominantly from the colder oceanic surrounding. The release of latent heat compensates for both of these heat-loss processes. If monsoon winds get weaker, condensation and therefore latent heat release through precipitation are reduced (moisture-advection feedback, Fig. 1A). The abruptness of the transition emerges through an additional stabilizing effect of the direct heat advection which is cooling the atmospheric column and is also reduced for reduced monsoon winds. Thus both advection-related processes, precipitative warming and thermal cooling, are simultaneously reduced and partly compensate until a threshold is reached at which condensation/precipitation cannot provide the necessary latent heat to sustain a circulation. As a consequence, land-ocean Levermann et al. temperature difference ΔT and therewith monsoon winds break down (Fig. 1B). Estimate of Critical Threshold for Current Monsoon Systems. In order to estimate the critical threshold of different monsoon systems within the limitation of this very simple model, we use time series of precipitation P, radiation R, temperature difference ΔT, and specific humidity qO from the NCEP/NCAR reanalysis data (35) to compute time series for α(t) = (LP + R)/(Cp ΔT 2 ) and β(t) = ((LP +R)·ρP)/((LP +R)qO ρ−Cp ΔTP), assuming applicability of the model and stationary statistics within the observational period (1948-2007). Via α(t) and β(t), the parameter l(t) is known and the system is estimated for each year. As a simple test for the model, we calculate the remaining quantity that is not used for the computation of α(t) and β(t), the specific humidity over land qL (t) = qO (t) − CP ΔT(t)P(t) . ρ(LP(t) + R(t)) [10] The resulting model estimate of the specific humidity qL compares reasonably well (Fig. S2 in the SI Appendix) with the independently observed qL that was used in Fig. 4 to motivate the relation between specific humidity and precipitation (Eq. 4). Early Edition 3 of 6 36 2 Fig. 3. Landward zonal wind versus temperature difference between land and ocean during monsoon season [NCEP/NCAR reanalysis data (35)]. The lines show best linear regression with correlation r. Via the definition of l, we compute Rc from the time series α(t) and β(t) for each year between 1948-2007. Note that the only quantity that is not constrained by data in this computation is the parameter , which defines the ratio of vertical and horizontal scale. However, the critical threshold RC is independent of , and thus the calculation depends only on relatively robust averaged values of precipitation, net radiation, average temperature difference between land and ocean, specific humidity over ocean, and the natural constants ρ, L, and Cp . We interpret the resulting distribution of the critical threshold Rc (Fig. 6, blue) as a noisy estimate of a stationary critical threshold. Within the limitations of the model, the observed net radiation is higher than the critical threshold in the Bay of Bengal, West Africa, and China. In India, North America, and Australia, the distributions have significant overlap. Incorporating evaporation into the model shifts the distribution toward lower thresholds (Fig. 6, red), while at the same time increasing the precipitation threshold Pc . Standard bootstrapping (see SI Appendix) reveals that the estimates in Fig. 6 are already relative robust distributions, in view of the simplicity of the model approach. Discussion A minimal conceptual model for monsoon circulations that captures the moisture-advection feedback is presented. The model is unlikely to describe details of monsoon circulations quantitatively, nor is it meant to capture all dynamical processes of a monsoon circulation. Following a minimalistic philosophy, the model comprises the necessary processes for a positive feedback and thereby demonstrates the possibility of an abrupt transition of monsoon circulations from a state with strong rainfall to a weak precipitation state. All model equations are backed by relations found in NCEP/NCAR reanalysis data. For India, it has been shown that this data-set properly represents the statistics of precipitation when compared with regional observations with higher spatial resolution (40). 4 of 6 www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106 ORIGINAL MANUSCRIPTS Because the processes represented in our model are fundamental to monsoon systems, we believe that the results strongly suggest the possibility of abrupt monsoon transitions. Because the dominant driving process is captured, it is not impossible that the model can provide a reasonable estimate for the critical threshold, Rc , once all necessary processes are incorporated. The bifurcation structure of the model is robust with respect to incorporation of other physical processes (see SI Appendix) and only changes qualitatively when either of these perturbations dominate the dynamics. Thus, the applicability of our model is based on the assumption that moisture advection is the dominant process in the heat budget of a monsoon system. The possibility of abrupt transition is due to the competition of the main heat transport processes during the rainy season. Although latent heat release through precipitation warms the atmospheric column, direct advection of heat is cooling it. Both processes decrease with decreasing monsoon winds and thereby compensate each other with respect to the net heat injection into the atmospheric column. The threshold of this stabilizing effect is set by the radiative cooling, which is characteristic to low-latitudes and is strongly influenced by aerosol distribution in the region. According to our model, abrupt transitions may occur in two different ways. For net radiation above the critical threshold R > RC , the system is bistable. Because the model only describes the rainy season and does not capture the annual monsoon cycle, abrupt transitions in the bistable regime can only be interpreted intraseasonally, e.g., a month of heavy rain followed by a month of extraordinarily weak precipitation. An example could be the extremely weak rainfall in July and September observed in India in the year 2002, in which the rest of the season exhibited average rainfall (41). Our model does not capture the dynamics of a decline or increase in monsoon strength over several years. Thus, paleodata in which strong variation in monsoon rainfall have been recorded cannot be explained by the bistable regime because these recordings show monsoon changes over several years, decades, or Fig. 4. Precipitation versus specific humidity over land during monsoon season [NCEP/NCAR reanalysis data (35)]. The lines show best linear regression with correlation r. Levermann et al. Basic mechanism for abrupt monsoon transitions 37 Fig. 6. State of current monsoon systems with respect to the critical point computed from the conceptual model. The black distribution reflects observed fluctuations in net radiation in the different monsoon systems. The blue distribution provides the computed critical threshold from the conceptual model. The red distribution includes the effect of evaporation. Lines give a Gaussian function with the same mean and standard deviation as the corresponding discrete distribution. Fig. 5. Solution of the nondimensional governing Eq. 6. (Top) Nondimensional landward wind for two values of the only parameter l = 0 and l = 1. (Middle) Corresponding nondimensional precipitation. (Bottom) Nondimensional precipitation for higher values of l as observed in some monsoon systems. The functional form of the solution is not changed qualitatively. The critical threshold (wc (l), pc (l)) is given as the black curve in each frame. even centuries. Such behavior would correspond to a shift of the system across the critical threshold into the monostable regime R < RC without a conventional monsoon. If persistent, such a shift would be visible in paleorecords. The reduction of the full set of model parameters to a single scaling number l, which determines the system and thereby the critical threshold, testifies to a remarkable dynamic similitude with respect to the atmospheric quantities α, β, and q0 . Different monsoon systems with the same l will have the same transition behavior. As illustrated in Fig. 5, l provides a measure for the position and the sharpness of the transition, i.e., for the point (rc , wc ) in state space. This means, in particular, that a decrease in inflowing humidity q0 associated with, e.g., colder climate conditions (which would decrease the threshold Rc and shift the system closer to a collapse) could be compensated by decreasing β, representing a lower turnover (recycling) time of moisture in the atmosphere, which is influenced by, e.g., aerosols. 1. Auffhammer M, Ramanathan V, Vincent JR (2006) Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proc Natl Acad Sci USA 103:19668–19672. 2. Zhang P, et al. (2008) A test of climate, sun, and culture relationships from an 1810-year chinese cave record. Science 322:940–942. 3. Patra PK, Behera SK, Herman JR, Akimoto S, Yamagata T (2005) The indian summer monsoon rainfall: Interplay of coupled dynamics, radiation and cloud microphysics. Atmos Chem Phys Discuss 5:2879–2895. 4. Meehl GA, Washington WM (1993) South Asian summer monsoon variability in a model with doubled atmospheric carbon dioxide concentration. Science 260:1101– 1104. 5. Zwiers FW, Kharin VV (1998) Changes in the extremes of the climate simulated by CCC GCM2 under CO2 doubling. J Climate 11:2200–2222. 6. May W (2002) Simulated changes of the Indian summer monsoon under enhanced greenhouse gas conditions in a global time-slice experiment. Geophys Res Lett 29:1118. 7. Ramanathan V, et al. (2005) Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proc Natl Acad Sci USA 102:5326–5333. Levermann et al. The parameter q0 in our model can be interpreted in a rather broad sense as a specific humidity of the vicinity influencing a monsoon region. As an example, the years with anomalously high snow cover over the Tibetan Plateau in spring and early summer (20, 26) could be characterized by a decrease in q0 during midsummer, which would shift the threshold value Rc for the Indian monsoon closer to the observed precipitation over the region, thus increasing a possibility of monsoon breakdown in those years. Similarly, a colder climate with generally decreased humidity qO could be closer to the critical threshold, which might be the reason for less-stable monsoon circulations during glacial periods. In the future, net radiation may be reduced through aerosol pollution, which will push the system qualitatively closer to the critical threshold (7). On the Indian subcontinent, in China, and in parts of sub-Saharan Africa, agricultural productivity is closely linked to and limited by monsoon rainfall. Food security in these regions is particularly sensitive to monsoon variability (42–45). Studies with comprehensive models are necessary to confirm or reject the idea of the existence of a threshold as well as its position. ACKNOWLEDGMENTS. We thank B.N. Goswami, R. Krishnan, and J. Srinivasan for helpful hints and discussions; and T. Lenton for useful comments on the manuscript. This work was funded by the Heinrich Böll Foundation, the German National Academic Foundation, and the German Federal Ministry of Education and Research. 8. Lau KM, Kim KM (2006) Observational relationships between aerosol and asian monsoon rainfall, and circulation. Geophys Res Lett 33:L21810. 9. Zickfeld K, Knopf B, Petoukhov V, Schellnhuber HJ (2005) Is the indian summer monsoon stable against global change? Geophys Res Lett 32:L15707. 10. Knopf B, Zickfeld K, Flechsig M, Petoukhov V (2008) Sensitivity of the Indian monsoon to human activities. Adv Atmos Sci 25:932–945. 11. Lenton TM, et al. (2008) Tipping elements in the earth’s climate system. Proc Natl Acad Sci USA 105:1786–1793. 12. Wang P, et al. (2005) Evolution and variability of the Asian monsoon system: State of the art and outstanding issues. Quaternary Sci Rev 24:595–629. 13. Wang Y, et al. (2008) Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years. Nature 451:1090–1093. 14. Overpeck JT, Anderson D, Trumbore S, Prell W (1996) The southwest Indian monsoon over the last 18000 years. Climate Dynamics 12:213–225. 15. Burns SJ, Fleitmann D, Matter A, Kramers J, Al-Subbary AA (2003) Indian ocean climate and an absolute chronology over Dansgaard/Oeschger events 9 to 13. Science 301:1365–1367. Early Edition 5 of 6 GEOPHYSICS SPECIAL FEATURE 2.2 38 2 16. Gupta AK, Anderson DM, Overpeck JT (2003) Abrupt changes in the Asian southwest monsoon during the Holocene and their links to the North Atlantic ocean. Nature 421:354–357. 17. Wang Y, et al. (2005) The Holocene Asian Monsoon: Links to solar changes and North Atlantic climate. Science 308:854–857. 18. Goswami BN, Madhusoodanan MS, Neema CP, Sengupta D (2006) A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophys Res Lett 33:L02706. 19. Zhang R, Delworth TL (2005) Simulated tropical response to a substantial weakening of the Atlantic Thermohaline Circulation. J Climate 18:1853–1860. 20. Hahn DG, Shukla J (1976) An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. J Atmos Sci 33:2461–2462. 21. Webster PJ, et al. (1998) Monsoons: Processes, predictability, and the prospects for prediction. J Geophys Res 103:14451–14510. 22. Krishnamurthy V, Goswami BN (2000) Indian monsoon-ENSO relationship on interdecadal timescale. J Climate 13:579–595. 23. Clark CO, Cole JE, Webster PJ (2000) Indian ocean SST and Indian summer rainfall: Predictive relationships and their decadal variability. J Climate 13:2503–2519. 24. Kucharski F, Molteni F, Yoo JH (2006) SST forcing of decadal Indian monsoon rainfall variability. Geophys Res Lett 33:L03709. 25. Goswami BN, Xavier PK (2005) ENSO control on the south Asian monsoon through the length of the rainy season. Geophys Res Lett 32:L18717. 26. Dash SK, Singh GP, Shekhar MS, Vernekar AD (2005) Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia. Climate Dynamics 24:1–10. 27. Wang B (2005) The Asian Monsoon (Springer, Berlin). 28. Yang J, Liu Q, Xie SP, Liu Z, Wu L (2007) Impact of the Indian ocean SST basin mode of the Asian summer monsoon. Geophys Res Lett 34:L02708. 29. Meehl GA (1994) Influence of the land surface in the Asian summer monsoon: External conditions versus internal feedbacks. J Climate 7:1033–1049. 30. Claussen M (1997) Modeling bio-geophysical feedback in the African and Indian monsoon region. Climate Dynamics 54:247–257. 31. Robock A, Mu M, Vinnikov K, Robinson D (2003) Land surface conditions over Eurasia and Indian summer monsoon rainfall. J Geophys Res 108:4131. 6 of 6 www.pnas.org / cgi / doi / 10.1073 / pnas.0901414106 ORIGINAL MANUSCRIPTS 32. Liu X, Yin Z (2002) Sensitivity of East Asian monsoon climate to the uplift of the Tibetan Plateau. Palaeogeogr Palaeoclimatol Palaeoecol 183:223–245. 33. Webster PJ (1987) The elementary monsoon. In Monsoons, eds Fein JS, Stephens PL (Wiley, New York), pp. 3-32. 34. Webster PJ (1987) The variable and interactive monsoon. In Monsoons, eds Fein JS, Stephens PL (Wiley, New York), pp. 269–330. 35. Kistler R, et al. (2001) The NCEP/NCAR 50-year reanalysis. Bull Amer Meteor Soc 82:247–267. 36. Petoukhov VK (1982) Two mechanisms of temperature oscillations in a thermodynamical model of the troposphere-stratosphere system. Atmos Ocean Phys 18: 126–137. 37. Brovkin V, Claussen M, Petoukhov V, Ganopolski A (1998) On the stability of the atmosphere- vegetation system in the Sahara/Sahel region. J Geophys Res 103:31613– 31624. 38. Eltahir EAB (1998) A soil moisture-rainfall feedback mechanism: 1. theory and observations. Water Resour Research 34:765–776. 39. Petoukhov V, et al. (2000) CLIMBER-2: A climate system model of intermediate complexity. Part I: model description and performance for present climate. Climate Dynamics 16:1. 40. Goswami BN, Ramesh KV (2006) A comparison of interpolated NCEP (I-NCEP) rainfall with high-resolution satellite observations. Geophysl Res Lett 33:L19821. 41. Fasullo J (2005) Atmospheric hydrology of the anomalous 2002 Indian summer monsoon. Monthly Weather Rev 133:2996–3014. 42. Kumar KK, Kumar KR, Ashrit RG, Deshpande NR, Hansen JW (2004) Climate impacts in Indian agriculture. Int J Climatol 24:1375–1393. 43. Gregory PJ, Ingram JSI, Brklacich M (2005) Climate change and food security. Philos Trans R Soc London Ser B 360:2139–2148. 44. Haile M (2005) Weather patterns, food security and humanitarian response in sub-Saharan Africa. Philos Trans R Soc London Ser B 360:2169–2182. 45. Tao F, et al. (2004) Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation. Climate Res 28:23–30. 46. Hansen J, et al. (1983) Efficient three-dimensional global models for climate studies: Models I and II. Monthly Weather Rev 111:609–662. Levermann et al. 2.2 Basic mechanism for abrupt monsoon transitions 39 Basic mechanism for abrupt monsoon transitions Anders Levermann ∗ † , Jacob Schewe ∗ ∗ † , Vladimir Petoukhov ∗ , and Hermann Held ∗ Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany, and † Institute of Physics, Potsdam University, Potsdam, Germany Submitted to Proceedings of the National Academy of Sciences of the United States of America Supporting Information Addition of evaporation. To our understanding, the strongest miss- ing process is the effect of evaporation over land. In order to estimate Monsoon regions and definitions the critical threshold of different monsoon systems we generalize the NCEP/NCAR reanalysis data was obtained from http://www.cdc.noaa.gov/model by adding evaporation to the moisture budget (equation [3]). In the reanalysis data evaporation provides a very weak feedback within as 60-year monthly mean time series, starting January 1948. Heat flux the dynamics and is well approximated by P − E ≈ γP − EO , with and precipitation data are averaged over land in each monsoon region. region-specific constants E0 and γ which is close to unity (figure S4). ∆T is the difference between the average temperatures over land and For this purpose we replace equation [S2] by w (1 − p) − (γp − e) ocean. Humidities qL and qO refer to the same land and ocean rewith e ≡ E0 / (βqO ). This equation can also be derived from the apgions, respectively. The near-surface, landward zonal wind velocity proach by Hansen et al. [1] and an additional assumption of constant W is averaged over a third region. All three regions are given in total soil moisture within a rainy season. We obtain table 1 and illustrated in figures S1 and S2, together with the respecw+e tive definitions of the monsoon season that are used for the temporal [ S3 ] p= averages shown in figures 3 and 4. W is averaged vertically between w+γ 850hPa and 1000hPa. qO is averaged vertically between 600hPa and Accordingly, the governing equation transforms to 1000hPa. All other vertical averages are over the entire atmospheric w3 + γw2 − (l + r) w − (el + γr) = 0 [ S4 ] column. Robustness of Rc estimate In order to determine the statistical stability of the estimate of the distribution of RC , we proceeded in two steps. (1) From the time series’ autocorrelation we decided to treat the time series of α and β as containing virtually no memory, i.e. values from different years can be treated as statistically independent. Note that this assumption does not contradict the existence of interannual to decadal variability that is forced externally. (2) Via bootstrapping we generated surrogate time series of length 60. From this time series ensemble we found that the standard deviation of mean and standard deviation of the RC -distribution are one order of magnitude below the standard deviation of the shown distribution of RC -estimates. Hence the red curves in figure 6 are already relatively robust estimates, in view of the simplicity of the model approach. Structural sensitivity of conceptual model In order to analyse the structural robustness of the governing equation [6] to inclusion of further physical processes we start from the non-dimensional forms of the unperturbed equations [1] and [3] lp − w2 + r w (1 − p) − p = = 0 0 [ S1 ] [ S2 ] using the same definitions of parameters l and r and non-dimensional variables w and p as in the main text. Note that the parameter l as computed from observations is of the order 104 . Its qualitative influence on the solution structure can, however, already been seen for l = 1. Since 1 is the only other scale in the non-dimensional governing equation, we will use l = 1 as an example. Similarly we will show the qualitative influence of other paramters by setting them to 0.5 without claiming this to be an observed value. Note that for some cases the critical precipitation reduces and could in principle become zero or negative. This would change the model behaviour qualitatively. However this can only be the case when the corresponding process dominates the dynamics and is not merely a perturbation to the dynamics described in the core model. Non of the processes included eliminates the bifurcation for small parameter values. In this sense the model behaviour is robust. www.pnas.org/cgi/doi/10.1073/pnas.0709640104 Both constant and precipitaton-dependent evaporation shift critical radiation to lower and critical precipitation towards higher values (figure S5). As in the minimal model set-up, the critical threshold can be computed analytically from wc (wc + γ)2 = (γ − e) l/2 [ S5 ] rc = 3wc2 + 2γwc − l By additional use of the evaporation time series E(t) from NCEP/NCAR reanalysis, the parameter e can be computed. e = E(t)/ (βqO (t)) [ S6 ] By assuming γ to be constant (taken from the regression in figure S4), the critical threshold of this generalized model can be computed (figure 6). Addition of cloud-albedo feedback. Assuming that cloud-albedo over land increases with the atmospheric moisture content we add a term −a′ qL to equation [1] where a′ is a constant. Consequently the nondimensional heat equation is transformed into (l − a) p − w2 + r = 0 ` ´ where a ≡ a qO ǫα/ Cp β 2 . The governing equation [ S7 ] ′ w3 + w2 − (l + r − a) w − r = 0 [ S8 ] shows the same functional form with an effective shift of the original l-parameter towards lower values (figure S6). This reduces the significance of the moisture-advection feedback for the monsoon circulation by lowering the threshold precipitation value. On the other hand the threshold is reached at higher net radiation rc . Reserved for Publication Footnotes PNAS Issue Date Volume Issue Number 1–10 40 2 ORIGINAL MANUSCRIPTS Tab. S1: Regional definitions used for data analysis Monsoon system Land region Ocean region Wind region Monsoon season INDIA BAY OF BENGAL CHINA W.AFRICA N.AMERICA AUSTRALIA 70 − 90◦ E 5 − 30◦ N 65 − 78◦ E 5 − 30◦ N 65 − 78◦ E 5 − 30◦ N 80 − 100◦ E 15 − 30◦ N 80 − 100◦ E 10 − 20◦ N 80 − 100◦ E 15 − 30◦ N 100 − 110◦ E 25 − 30◦ N 80 − 100◦ E 10 − 20◦ N 90 − 105◦ E 15 − 25◦ N 15◦ W − 10◦ E 2 − 14◦ N 28◦ W − 10◦ E 5◦ S − 14◦ N 15◦ W − 10◦ E 2 − 9◦ N 110 − 100◦ W 20 − 30◦ N 120 − 110◦ W 20 − 30◦ N 111 − 109◦ W 20 − 30◦ N 120 − 150◦ E 18 − 10◦ S 100 − 130◦ E 10 − 0◦ S 100 − 130◦ E 10◦ S − 0◦ JJA JJA Addition of constant equatorial easterlies. The effect of a constant inflow of moist air leads to an addition of a constant wt to the winds in the heat balance and moisture balance equations lp − w (w + wt ) + r = 0 [ S9 ] (w + wt ) (1 − p) − p = 0 [ S10 ] yielding the governing equation JJA JAS JJA JFM where σ ≡ σ ′ / (Cp β) and tO ≡ TO αǫ/β is the nondimensional atmospheric temperature over the ocean. The resulting governing equation w3 +(σ + 1) w2 −(l + r − σ (σ + 1)) w −(r + σtO ) = 0 [ S13 ] w3 +(2wt + 1) w2 −(l + r − wt (wt + 1)) w−((1 + wt ) r + lwt ) = 0 resembles the corresponding relation with additional trade winds (figure S8). [ S11 ] and a shift of the critical threshold towards lower radiation and precipitation values (figure S7). Addition of threshold for precipitation. Adding a threshold moisAddition of stabilizing radiative feedback. Adding a negative conture value qth to equation [4] above which precipitation is initi′ tribution −σ TL to the heat balance may be used to parameterize a ated does not change the governing equation after redefining p ≡ stabilizing temperature feedback due to changes in long wave radiaP/ ((qO − qth ) β). It however changes the physical quantities. Crittion. This addition transforms the non-dimensional heat balance into ical precipitation then reduces to zero when the threshold value approaches qO . lp − (w + σ) w − σtO + r = 0 [ S12 ] 1. Hansen J, et al. (1983) Efficient three-dimensional global models for climate studies: Models I and II. Monthly Weather Review 111:609–662. 2 www.pnas.org/cgi/doi/10.1073/pnas.0709640104 Footline Author 2.2 Basic mechanism for abrupt monsoon transitions 41 Fig. S1. Difference in precipitation between seasons (JJA-DJF) and the different monsoon regions studied (black boxes). Fig. S2. Different ocean (blue), land (dark gray) and wind (red box) regions for the different monsoon systems as used for computation of the different quantities used to motivate the conceptual model. Flow lines represent summer winds connecting the ocean with the land region. Footline Author PNAS Issue Date Volume Issue Number 3 42 2 ORIGINAL MANUSCRIPTS 11 Bay of Bengal India 8 10 ′ qL (g/kg) 9 7 9 6 8.5 9 9.5 10 9 8 11 N. America 8 ′ qL (g/kg) W. Africa 10.5 7 7 7.5 8 8.5 7.5 11 China 8 8.5 9 Australia 10 10 9 ′ L q (g/kg) 12 8 8 10 11 12 6.5 q (g/kg) L 7 7.5 8 8.5 q (g/kg) L ′ over land as computed by the model from time seFig. S3. Specific humidity qL ries for precipitation, radiation, temperature difference and specific humidity over the ocean versus observed mean specific humidity over land qL . The line gives ′ =q the unit function qL L 4 www.pnas.org/cgi/doi/10.1073/pnas.0709640104 Footline Author P−E (mm/day) P−E (mm/day) P−E (mm/day) 2.2 4 Basic mechanism for abrupt monsoon transitions 7 INDIA r=0.98 3 BAY OF BENGAL r=0.99 6 γ=0.84 2 E =−2.5 γ=0.98 5 E =−3.8 0 1 5 4 6 0 4 8 8 9 10 11 4 N.AMERICA W.AFRICA r=0.99 r=0.98 3 3 γ=0.99 2 γ=0.68 2 E =−4.4 1 E =−1.2 1 43 0 6 8 10 6 0 4 6 8 6 CHINA 4 r=0.99 4 AUSTRALIA r=0.99 2 γ=1.2 2 γ=0.83 0 E =−6.2 0 E =−3.6 0 5 10 P (mm/day) 0 5 10 P (mm/day) Fig. S4. Scaling of precipitation minus evaporation with precipitation in NCEPNCAR reanalysis data. Footline Author PNAS Issue Date Volume Issue Number 5 44 Wind w 1 2 γ=0 γ=0.9 ORIGINAL MANUSCRIPTS e=0.2 e=0 0 Precipitation p −1 0.4 γ=0.9 0.2 γ=0 0 −0.5 e=0 e=0.2 0 Radiation r 0.5 −0.5 0 Radiation r 0.5 Fig. S5. Change in solution structure due to inclusion of evaporation. Left: constant offset e = 0.2 without linear dependence on precipitation γ = 1 (equation [S4]). Right: linearly dependent evaporation γ = 0.9 without constant offset e = 0. 6 www.pnas.org/cgi/doi/10.1073/pnas.0709640104 Footline Author 2.2 Basic mechanism for abrupt monsoon transitions 45 1 Wind w a=0 a=0.5 0 Precipitation p −1 0.4 a=0 a=0.5 0.2 0 −0.5 0 Radiation r 0.5 Fig. S6. Change in solution structure due to inclusion of the cloud-albedo feedback Footline Author PNAS Issue Date Volume Issue Number 7 46 2 ORIGINAL MANUSCRIPTS Wind w 1 wT=0 0 wT=0.5 Precipitation p −1 wT=0.5 w =0 0.4 T 0.2 0 −0.5 0 Radiation r 0.5 Fig. S7. Change in solution structure due to inclusion of an inflow of moisture and heat by constant trade winds 8 www.pnas.org/cgi/doi/10.1073/pnas.0709640104 Footline Author 2.2 Basic mechanism for abrupt monsoon transitions 47 1 Wind w σ=0 0 σ=0.5 Precipitation p −1 0.4 0.2 σ=0 0 −0.5 σ=0.5 0 Radiation r 0.5 Fig. S8. Change in solution structure due to inclusion of a stabilizing long wave radiation feedback Footline Author PNAS Issue Date Volume Issue Number 9 2.3 A critical humidity threshold for monsoon failure 2.3 A critical humidity threshold for monsoon failure 51 Manuscript prepared for Clim. Past Discuss. with version 2.2 of the LATEX class copernicus discussions.cls. Date: 4 May 2011 A critical humidity threshold for monsoon transitions Jacob Schewe1,2 , Anders Levermann1,2 , and Hai Cheng3,4 1 Earth System Analysis, Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany 2 Institute of Physics, University of Potsdam, 14476 Potsdam, Germany 3 Institute of Global Environmental Change, Xi’an Jiaotong University, Xi’an 710049, China 4 Department of Geology and Geophysics, University of Minnesota, Minneapolis 55455, USA Correspondence to: J. Schewe ([email protected]) Abstract Monsoon systems around the world are governed by the so–called moisture–advection feedback. Here we show that, in a minimal conceptual model, this feedback implies a critical threshold with respect to the atmospheric specific humidity qo over the ocean adjacent to the monsoon region. If qo falls short of this critical value qoc , monsoon rainfall over land cannot be sustained. Such a case could occur if evaporation from the ocean was reduced, e. g. due to low sea surface temperatures. Within the restrictions of the conceptual model, we estimate qoc from present–day reanalysis data for four major monsoon systems, and demonstrate how this concept can help understand abrupt variations in monsoon strength on orbital timescales as found in proxy records. 1 52 1 2 ORIGINAL MANUSCRIPTS Introduction Monsoon rainfall is the major prerequisite of agricultural productivity in many tropical and subtropical regions of the world, and its variability has been affecting the livelihoods of a large share of the world’s population from ancient civilizations until today (e.g. Parthasarathy et al., 1988; Kumar et al., 2004; Auffhammer et al., 2006; Zhang et al., 2008; Rashid et al., 2011). Proxy records show evidence of abrupt and strong monsoon shifts during the last two glacial cycles (Burns et al., 2003; Wang et al., 2005a, 2008) and the Holocene (Gupta et al., 2003; Hong et al., 2003; Wang et al., 2005b; Rashid et al., 2011) in India, the Bay of Bengal, and East Asia. In many instances in the past, periods of strong monsoon rainfall thus appear to have alternated with periods of prolonged drought, with comparatively rapid transitions between the two. Both spatial patterns and temporal evolution of continental monsoon rainfall are influenced by a number of physical processes (Hahn and Shukla, 1976; Webster et al., 1998; Krishnamurthy and Goswami, 2000; Clark et al., 2000; Kucharski et al., 2006; Goswami et al., 2006; Goswami and Xavier, 2005; Dash et al., 2005; Ramanathan et al., 2005; Wang, 2005; Yang et al., 2007) as well as characteristics of vegetation (Meehl, 1994; Claussen, 1997; Robock et al., 2003) and topography (Liu and Yin, 2002). Though these details are crucial for the specific behavior of different monsoon systems, and their significance will vary from region to region, there exist defining processes fundamental to any monsoon dynamics (e.g. Webster, 1987a,b). These are the advection of heat and moisture during the monsoon season and the associated rainfall and release of latent heat. While differential heating of land and ocean in spring is important for the initiation of the monsoon season, land surface temperatures drop substantially after the onset of heavy precipitation, diminishing the surface temperature gradient. The monsoon circulation over the continent is thereafter predominantly sustained by the release of latent heat and subsequent warming of the atmospheric column over land (Webster et al., 1998). Using a complex conceptual model, Zickfeld et al. (2005) found that a monsoon circulation that is sustained by the 2 2.3 A critical humidity threshold for monsoon failure 53 moisture-advection feedback can undergo abrupt changes in response to changes in the land surface albedo. Knopf et al. (2006) however showed that the threshold albedo in this model is very far away from modern conditions and is highly uncertain due to the dependence on various model parameters. Here we apply a minimal conceptual model that comprises the heat and moisture budgets of an idealized monsoon circulation. It reflects the dominant role of the self– amplifying moisture–advection feedback during the monsoon season, which is supported by observations Levermann et al. (2009). We find that the model yields a threshold behaviour with respect to the atmospheric humidity over the ocean adjacent to the monsoon region. Below the threshold, the advection and release of latent heat is not sufficient to sustain monsoon rainfall over land. It is important to note that globally, rainfall associated with the intertropical convergence zone will naturally be sustained even without continental monsoon rainfall. Furthermore the seasonal reversal of cross-equatorial winds, driven by the seasonal change in hemispheric insolation, is not affected by our analysis. The question addressed here is which conditions are necessary in order to sustain a rainy season over land which goes beyond the zonal– mean dynamics of the intertropical convergence zone. The basic dynamics captured in our model thus form a necessary condition for continental monsoon rainfall to exist. We describe the conceptual model in section 2 and analyze its implications in section 3. In section 4, the critical threshold is estimated for four major monsoon regions. In section 5, we apply the model to abrupt monsoon changes on orbital timescales. Section 6 concludes. 2 Conceptual model We use the minimal conceptual model presented by Levermann et al. (2009) (see illustration in Fig. 1). It is based on the observation that, during the rainy season, the regional-scale moist static energy balance of the atmospheric column is dominated by latent heating due to precipitation, which is balanced by advective as well as radiative 3 54 2 ORIGINAL MANUSCRIPTS cooling. According to NCEP/NCAR reanalysis data (1948-2007; Kistler et al., 2001), this holds for all major monsoon regions (Levermann et al., 2009). The moist static energy balance can thus be approximated by L · P − Cp W · ∆T + R = 0, (1) W = α · ∆T. (2) W · (qo − qL ) − P = 0, (3) where ∆T is the atmospheric temperature difference between land and ocean, and P is the mean precipitation over land (in kg/m2 s). Latent heat of condensation is L =2.6·106 J/kg and volumetric heat capacity of air Cp =1,295 J/m3 /K. The ratio = H/L between vertical extent H of the lower troposphere and the horizontal scale L of the region of precipitation enters due to the balance of the horizontal advective heat transport and the vertical fluxes of net radiative forcing R and precipitation P . Note that this is a model of the monsoon season only, and includes no annual cycle. The above balance therefore neglects the contribution from sensible heating, which is important at the onset stage but relatively weak once heavy rainfall has started to cool the land surface (Fig. 2). Consequently, this model does not capture any interseasonal or interannual dynamics. Equations are only valid for landward winds, W ≥ 0. That means that situations in which no solution of the model with positive W can be found will be considered as parameter and forcing combinations for which no monsoon rainfall can be sustained. Assuming dominance of ageostrophic flow in low latitudes, the landward mean wind W is taken to be proportional to the temperature difference between land and ocean (Petoukhov, 1982; Webster, 1987a; Brovkin et al., 1998): Reanalysis data confirm that this is a valid first–order approximation (Fig. 3). Neglecting the effect of evaporation over land (which will be discussed below) and associated soil moisture processes, precipitation has to be balanced by the net landward flow of moisture 4 2.3 A critical humidity threshold for monsoon failure 55 where qo and qL are specific humidity over ocean and over land, respectively. Consistent with reanalysis data (Fig. 4) and theoretical considerations (Petoukhov, 1982; Petoukhov et al., 2000), continental rainfall is assumed to be proportional to the mean specific humidity within the atmospheric column: P = βqL . (4) Note that the proportionality constants α and β have explicit physical interpretations. α is essentially a function of the near-surface cross-isobar angle and thereby a function of surface roughness and static stability of the planetary boundary layer (PBL); β is governed by the characteristic turnover (recycling) time of liquid water in the atmosphere and thereby determined by static stability and vertical velocity in the PBL (Petoukhov et al., 2000). While equations (1) to (4) are the basic relations necessary to capture the moisture–advection feedback, eq. (4) can be made more realistic by considering an offsets in qL , which will be discussed further below. 3 Critical qo threshold for monsoon existence From equations (1), (3) and (4) it follows that β β Lβ qo = 1 + Cp W · ∆T − 1 + ·R ρW ρW (5) This equation represents the heat budget of the conceptual monsoon circulation in terms of latent heat. The two terms on the r. h. s. represent the loss of heat from the land region by advection of warm air and by radiation, respectively (note that R < 0). Their sum must be balanced by latent heat as provided by the inflow of humid air from the ocean, namely Lβ qo . The term (1 + β/ρW ) incorporates the fact that the latent heat has to be transported from ocean to land by means of advection; the lower the advective velocity W , the higher the specific humidity qo that is necessary to provide the required amount of latent heat to the atmospheric column over land. 5 56 2 ORIGINAL MANUSCRIPTS Using equation (2) and the relations w ≡ W ρ/β; r ≡ R · αρ/(Cp β 2 ), l ≡ αρ2 Lqo /(Cp β), the non-dimensional form of eq. (5) is obtained: l = 1 + w−1 · w2 − 1 + w−1 · r and (6) where l is proportional to qo . This is the governing equation of the conceptual model. Its solution is determined entirely by the only free parameter r. The physical part (l ≥ 0, w ≥ 0) of the solution of eq. (6) is shown in Fig. 5 for the case r = −0.05, where the thick red line denotes a stable solution and the thin red line an unstable one. The advective (dashed line) and radiative (dotted line) terms are also plotted separately to show how the solution is obtained as the sum of these two contributions (to illustrate this, the figure is organized with the control parameter l on the y-axis). In the case r ≡ 0, only the advective part of the solution remains (i. e. the red line would collapse onto the dashed line). The y-axis in Fig. 5 can be interpreted as the demand in latent heating that results from the loss of heat from the land region due to radiation and advection. It turns out that no physical solution exists below a critical threshold lc (horizontal line in Fig. 5), which corresponds to a critical value of specific humidity over the ocean, qoc . When qo falls short of this value, the supply of moisture is not sufficient to maintain the monsoon circulation driven by the moisture–advection feedback. No conventional monsoon circulation can thus develop in a climate where qo < qoc . Equation (6) can also be expressed in terms of non–dimensional precipitation p ≡ P/(qo β), which is directly related to the wind through (7) p = w/(1 + w). The solution in terms of p has a similar shape as in terms of w (Fig. 6), while dimensional precipitation P scales approximately linearly with l as long as l is sufficiently above the threshold lc (Fig. 7). While we do not expect to find this quasi-linear relation perfectly reflected in observations, NCEP/NCAR reanalysis data show that seasonal mean precipitation and specific humidity over ocean are correlated to some extent (Fig. 8). 6 2.3 4 A critical humidity threshold for monsoon failure 57 Estimation of the critical threshold qoc The critical point [lc , wc ] (or [qoc , wc ]) will vary for different monsoon systems. It is determined by the non–dimensional radiation r via −wc2 (2wc + 1) = r. (8) lc = 2wc (wc + 1)2 , (9) The critical l can then be computed from and the critical humidity threshold qoc via the definition of l. Within the limitations of this minimal conceptual model, we estimate qoc for four different monsoon regions. We use seasonal mean precipitation P , radiation R, land–ocean temperature difference ∆T , and specific humidity over ocean qo from the NCEP/NCAR reanalysis to compute time 2 series for α(t) = (LP + R)/ C p ∆T , β(t) = ((LP + R) · ρP )/((LP + R)qo ρ − Cp ∆T P ), and r(t) = R·α(t)/ Cp β(t)2 , assuming applicability of the model and stationary statistics within the observational period (1948-2007). Because the observational period is subject to significant anthropogenic global warming, we remove a linear trend from all reanalysis data to get a closer approximation of a stationary climate. From α(t), β(t) and r(t), qoc can then be obtained for each year via equations (8), (9) and the definition of l. Note that qoc is independent of , the only quantity that is not constrained by data. The resulting qoc distribution (Fig. 9, blue) is much lower than the observed distribution of qo (black) in the Bay of Bengal, West Africa and China, while in India the distributions are closer. The blue pin marks the qoc estimate that is obtained from the time–mean values α(t), β(t) and R(t). The spread in the distribution of qoc is due to substantial variability in α(t), β(t) and r(t) throughout the reanalysis period. The interannual variability in the dimensional net radiation R is about 15–20% during the reanalysis period, depending on the region. On longer timescales, R can be expected to be rather stable because of the negative long– wave radiation feedback according to the Stefan–Boltzmann law. However, the factors α and β may also vary over time. Moreover, in reality the basic relations of eq. (2) and 7 58 2 ORIGINAL MANUSCRIPTS eq. (4) are blurred by higher–order physical processes that are not represented in our idealized model, limiting our ability to determine α and β (cf. Fig. 3 and 4). Depending on the relative importance of actual variability and observational uncertainty, the distribution of qoc can be interpreted either (i) as a noisy estimate of a stationary critical threshold, or (ii) as a probability distribution of an interannually varying threshold. In order to obtain more realistic estimates of qoc , we extend eq. (4) by an offset qLo that terrestrial humidity qL needs to exceed before precipitation is initiated (as suggested by the correlation in Fig. 4): P = β (qL − qLo ). (10) After replacing qo by (qo − qLo ) in the definitions of l and p, the non–dimensional equations (6)–(9) remain unchanged. As above, we estimate qoc for this refined version of the model, obtaining the parameter qLo from linear regression of the corresponding reanalysis data (Fig. 4). Note that, due to eq. (10), qoc now also depends on . Since we have no direct observation of , we choose such that α(t) matches the α that we observe as the slope of the linear regression between W and ∆T (Fig. 3). The results for qoc are shown in Fig. 9 (red), where again the pin marks the estimate from mean quantities. The consideration of the offset qLo generally yields a distribution of qoc which is narrower and closer to, while still clearly seperate from, the present-day range of qo . Only for the Indian region, the distribution of qoc overlaps with that of the observed qo ; however, when considered pointwise, qoc (t) is still lower than qo (t) for all years. 5 Application to past abrupt monsoon changes Wang et al. (2008) presented a speleothem δ 18 O record from central China that testifies to several large and persistent changes in the strength of the East Asian summer monsoon (EASM) during the penultimate glacial period. These changes are in phase 8 2.3 A critical humidity threshold for monsoon failure 59 with, but much more abrupt than, precession–dominated oscillations in northern hemisphere summer insolation (NHSI): While the latter follow a quasi-sinusoidal cycle, the form of the monsoon changes rather resembles that of a step–function, with variations around either a strong or a weak mean state, followed by a comparatively rapid transition into the other state (cf. Fig. 2b in Wang et al. (2008)). This behaviour is especially apparent before about 160 kyr BP (Fig. 10, grey line) and suggests that non–linear processes inherent to the monsoon system might have amplified changes in external forcing. In particular, the abrupt transitions might have been triggered by the mean insolation crossing a certain threshold that separated two different states of the monsoon circulation. Our conceptual monsoon model offers a simplified but robust mechanism to explain such sort of behaviour. It shows that the moisture–advection feedback implies a threshold qoc that seperates two regimes: One where a conventional monsoon circulation can exist, and one where it cannot. We therefore speculate that orbital–timescale variations in NHSI and the associated surface temperature changes might have affected evaporation at the ocean surface such that average humidity over the ocean persistently crossed the threshold, thus critically altering the moisture supply for the adjacent monsoon region and triggering a transition between the two regimes. In the following we apply our model to demonstrate how such variations in qo could have led to monsoon variations consistent with those observed in the proxy record. In doing so, we assume that the values of α, β, , and qLo estimated for modern climate from reanalysis data also hold for the penultimate glacial period; and that R was also comparable during that period to its modern value. In reality, R might have also varied in phase with NHSI, however this variation would have been damped by the stabilizing long–wave radiation feedback. Moreover, a variation of R along with NHSI would act to exacerbate the threshold effect, moving the threshold towards higher values when insolation, and thus the inferred qo , is low (cf. Fig. 11). Therefore, neglecting variations in R yields a conservative result with respect to the volatility of the system. The solution of the conceptual model depends on the non-dimensional net radiation 9 60 2 ORIGINAL MANUSCRIPTS r. We choose r = r(t) − σ/2 = -106.5, where σ denotes a standard deviation (Fig. 11). This corresponds to a critical threshold qoc = 8.0 g/kg, which is in the upper part of the estimated qoc distribution for the China region (cf. Fig. 9). We further assume qo to vary linearly with NHSI (Fig. 12). The linear relation is chosen such that the maximum qo is close to the range of present–day observations. Finally, we assume that when the threshold qoc is crossed from below (i. e., coming from a no–monsoon regime), it takes an additional increase ∆q to trigger the transition into the monsoon regime (Fig. 11). The EASM precipitation thus resulting from the conceptual model is shown in Fig. 10 (red line). We set P to zero during periods when the model yields no physical solution, to illustrate the idea that no conventional monsoon circulation can exist during those periods, and no rainfall associated with that circulation would occur. However we would expect sources of rainfall other than the large–scale monsoon circulation to play a role, too, so that actual rainfall would not completely cease during such periods. Note that neither the hysteresis width ∆q nor the second degree of freedom in the relation qo ∝ NHSI are constrained by data; instead they are chosen such that the result of the conceptual model matches the transition behaviour found in the proxy record, taking into account dating errors in the latter (grey bars in Fig. 10). 6 Discussion and conclusions We have shown that, in a minimal conceptual model of large–scale monsoon circulation, a critical threshold qoc exists with respect to specific humidity over the ocean region upwind of the continental monsoon region. This threshold follows from the central role of the self–amplifying moisture–advection feedback, which governs the atmospheric MSE balance during the monsoon season. If qo falls short of the threshold qoc , no conventional monsoon circulation can exist over land. The model neglects any processes that are not crucial to the moisture–advection feedback, in order to isolate the consequences of this feedback. The basic dynamics captured in the model therefore form a necessary condition for the existence of continental monsoon rainfall beyond what 10 2.3 A critical humidity threshold for monsoon failure 61 is accounted for by the zonal-mean dynamics of the intertropical convergence zone. Our results complement those of Levermann et al. (2009), who found a threshold with respect to the net radiation R. While qo can generally be expected to be more volatile than R, the model allows for a superposition of changes in both quantities, with the one either damping or amplifying the effect of the other, depending on the direction of change. As the model contains the physical feedback that causes the threshold behaviour, it can be used to produce meaningful first–order estimates of the threshold values. Within the framework of the minimal model, we have estimated the critical threshold qoc for four major monsoon regions, using seasonally averaged reanalyses of regional precipitation, net radiation, specific humidity, and temperature for the past sixty years. The resulting distribution of qoc can be interpreted either as a noisy estimate of a stationary critical threshold, or as a probability distribution of an interannually varying threshold. The degree to which either of these interpretations is valid depends chiefly on the relative importance of actual variability and observational uncertainty in the parameters α and β (see equations (2) and (4)), the assessment of which is beyond the scope of this study. However we have seen that the consideration of an offset in terrestrial specific humidity in eq. (4) leads to a qoc distribution which is significantly narrower than with the basic version of the model, suggesting that at least some of the spread in qoc can be eliminated by making the model more realistic, and thus does not reflect actual year–to–year variability in qoc . Consequently, relevant second–order physical processes would have to be included into the model in order to obtain more robust results for qoc . Probably one of the most important missing processes is evaporation over land (e. g. Eltahir, 1998). Its effect on the heat budget would be mainly to reduce sensible heat flux to the atmosphere, which we have already neglected (eq. (1)) because it is comparatively small during the rainy season; on the other hand, its effect on the moisture budget (eq. (3)) would be to stabilize the monsoon regime by recycling a part of the atmospheric humidity that is lost by precipitation. Therefore considering evaporation would tend to move the critical 11 62 2 ORIGINAL MANUSCRIPTS threshold towards lower values of qo . While the estimation of qoc could profit from a refinement of the model, the aim of this study is to demonstrate how the simple concept that the model is based on can help understanding past monsoon variations. The non–linear solution structure of the model can lead to abrupt changes in the modelled monsoon rainfall in response to smooth changes in the control parameter, qo . Changes in qo could be brought about by various factors acting on different timescales. For instance, as wind speed over the oceans increases due to global warming (Young et al., 2011), evaporation e.g. in the Arabian Sea could be affected both directly and via the amount of upwelling of cold waters at the continental margins, and thereby alter the moisture supply for the Indian summer monsoon. For the East Asian summer monsoon (EASM), we have shown that, assuming variations in qo along orbital–timescale insolation changes, the model yields a series of abrupt monsoon transitions similar to that observed in a proxy record of the penultimate glacial period. While the additional assumption of a hysteresis is not crucial for the transition behaviour, it changes the timing of the individual transitions such that they are all consistent, within dating errors, with those found in the proxy record (physically, a hysteresis might be induced by inert climate components such as e.g. large–scale oceanic circulation or Himalayan glaciation, rather than by atmospheric processes). The idea of a threshold behaviour in monsoon circulations due to the defining mechanism of the moisture–advection feedback may thus be a useful first– order concept for understanding past large–scale monsoon changes. The conceptual model investigated here may also serve as a basic building block that can be made more realistic by the inclusion of other relevant processes and by a more detailed estimation of the model parameters. For a complete understanding of monsoon variations on multiple timescales, of course, more complex models will have to be invoked. 12 2.3 A critical humidity threshold for monsoon failure 63 Appendix A Methods NCEP/NCAR reanalysis data has been obtained as monthly–mean time series (January 1948 – December 2007), and regionally aggregated as indicated in Table 1. W is averaged vertically between 850hPa and 1000hPa; qo between 600hPa and 1000hPa; qL between 400hPa and 1000hPa; and ∆T over the entire atmospheric column, as represented in the reanalysis data. Acknowledgements. This work was funded by the Heinrich Böll Foundation, the German National Academic Foundation, and the BMBF PROGRESS project (support code 03IS2191B). NCEP Reanalysis Derived data was provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. References Auffhammer, M., Ramanathan, V., and Vincent, J. R.: Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India, Proceedings of the National Academy of Sciences, 103, 19 668–19 672, 2006. Brovkin, V., Claussen, M., Petoukhov, V., and Ganopolski, A.: On the stability of the atmosphere- vegetation system in the Sahara/Sahel region, Journal of Geophysical Research, 103, 31 613–31 624, 1998. Burns, S. J., Fleitmann, D., Matter, A., Kramers, J., and Al-Subbary, A. A.: Indian Ocean Climate and an Absolute Chronology over Dansgaard/Oeschger Events 9 to 13, Science, 301, 1365– 1367, 2003. Clark, C. O., Cole, J. E., and Webster, P. J.: Indian Ocean SST and Indian summer rainfall: Predictive relationships and their decadal variability, Journal of Climate, 13, 2503–2519, 2000. Claussen, M.: Modeling bio-geophysical feedback in the African and Indian monsoon region, Climate Dynamics, 54, 247–257, 1997. 13 64 2 ORIGINAL MANUSCRIPTS Dash, S. K., Singh, G. P., Shekhar, M. S., and Vernekar, A. D.: Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia, Climate Dynamics, 24, 1–10, 2005. Eltahir, E. A. B.: A soil moisture-rainfall feedback mechanism: 1. Theory and observations, Water Resources Research, 34, 765–776, 1998. Goswami, B. N. and Xavier, P. K.: ENSO control on the south Asian monsoon through the length of the rainy season, Geophysical Research Letters, 32, L18 717, 2005. Goswami, B. N., Madhusoodanan, M. S., Neema, C. P., and Sengupta, D.: A physical mechanism for North Atlantic SST influence on the Indian summer monsoon, Geophysical Research Letters, 33, L02 706, 2006. Gupta, A. K., Anderson, D. M., and Overpeck, J. T.: Abrupt changes in the Asian southwest monsoon during the Holocene and their links to the North Atlantic Ocean, Nature, 421, 354– 357, 2003. Hahn, D. G. and Shukla, J.: An apparent relationship between Eurasian snow cover and Indian monsoon rainfall, Journal of Atmospheric Sciences, 33, 2461–2462, 1976. Hong, Y., Hong, B., Lin, Q., Zhu, Y., Shibata, Y., Hirota, M., Uchida, M., Leng, X., Jiang, H., Xu, H., Wang, H., and Yi, L.: Correlation between Indian Ocean summer monsoon and North Atlantic climate during the Holocene, Earth and Planetary Science Letters, 211, 371–380, doi:10.1016/S0012-821X(03)00207-3, 2003. Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., den Dool, H. V., Jenne, R., and Fiorino, M.: The NCEP/NCAR 50-year reanalysis, Bull. Amer. Meteor. Soc., 82, 247 – 267, 2001. Knopf, B., Flechsig, M., and Zickfeld, K.: Multi-parameter uncertainty analysis of a bifurcation point, Nonlinear Processes in Geophysics, 13, 531–540, 2006. Krishnamurthy, V. and Goswami, B. N.: Indian monsoon-ENSO relationship on inter-decadal timescale, Journal of Climate, 13, 579–595, 2000. Kucharski, F., Molteni, F., and Yoo, J. H.: SST forcing of decadal Indian monsoon rainfall variability, Geophysical Research Letters, 33, L03 709, 2006. Kumar, K. K., Kumar, K. R., Ashrit, R. G., Deshpande, N. R., and Hansen, J. W.: Climate impacts on Indian agriculture, International Journal of Climatology, 24, 1375–1393, doi:10. 1002/joc.1081, 2004. Levermann, A., Schewe, J., Petoukhov, V., and Held, H.: Basic mechanism for abrupt monsoon transitions, Proceedings of the National Academy of Sciences, 106, 20 572–20 577, 2009. 14 2.3 A critical humidity threshold for monsoon failure 65 Liu, X. and Yin, Z.: Sensitivity of East Asian monsoon climate to the uplift of the Tibetan Plateau, Palaeogeography, Palaeoclimatology, Palaeoecology, 183, 223–245, 2002. Meehl, G. A.: Influence of the Land Surface in the Asian Summer Monsoon: External Conditions versus Internal Feedbacks, Journal of Climate, 7, 1033–1049, 1994. Parthasarathy, B., Munot, A., and Kothawale, D.: Regression model for estimation of Indian foodgrain production from summer monsoon rainfall, Agricultural and Forest Meteorology, 42, 167 – 182, doi:10.1016/0168-1923(88)90075-5, 1988. Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A., Kubatzki, C., and Rahmstorf, S.: CLIMBER-2: a climate system model of intermediate complexity. Part I: model description and performance for present climate, Climate Dynamics, 16, 1, 2000. Petoukhov, V. K.: Two mechanisms of temperature oscillations in a thermodynamical model of the troposphere-stratosphere system, Izvestiya, Atmospheric and Oceanic Physics, 18, 126–137, 1982. Ramanathan, V., Chung, C., Kim, D., Bettge, T., Kiehl, J. T., Washington, W. M., Fu, Q., Sikka, D. R., and Wild, M.: Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle, Proceedings of the National Academy of Sciences, 102, 5326–5333, 2005. Rashid, H., England, E., Thompson, L., and Polyak, L.: Late Glacial to Holocene Indian Summer Monsoon Variability Based upon Sediment Records Taken from the Bay of Bengal, Terrestrial Atmospheric and Oceanic Sciences, 22, 215–228, doi:10.3319/TAO.2010.09.17. 02(TibXS), 2011. Robock, A., Mu, M., Vinnikov, K., and Robinson, D.: Land surface conditions over Eurasia and Indian summer monsoon rainfall, Journal of Geophysical Research, 108, 4131, 2003. Wang, B.: The Asian monsoon, Springer-Verlag, 2005. Wang, P., Clemens, S., Beaufort, L., Braconnot, P., Ganssen, G., Jian, Z., Kershaw, P., and Sarnthein, M.: Evolution and variability of the Asian monsoon system: state of the art and outstanding issues, Quaternary Science Reviews, 24, 595–629, 2005a. Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly, M. J., Dykoski, C. A., and Li, X.: The Holocene Asian Monsoon: Links to Solar Changes and North Atlantic Climate, Science, 308, 854–857, 2005b. Wang, Y., Cheng, H., Edwards, R. L., Kong, X., Shao, X., Chen, S., Wu, J., Jiang, X., Wang, X., and An, Z.: Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years, Nature, 451, 1090–1093, 2008. Webster, P. J.: The Elementary Monsoon, in: Monsoons, edited by Fein, J. S. and Stephens, 15 66 2 ORIGINAL MANUSCRIPTS P. L., pp. 3–32, John Wiley, New York, N.Y., 1987a. Webster, P. J.: The Variable and Interactive Monsoon, in: Monsoons, edited by Fein, J. S. and Stephens, P. L., pp. 269–330, John Wiley, New York, N.Y., 1987b. Webster, P. J., Magaña, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M., and Yasunari, T.: Monsoons: Processes, predictability, and the prospects for prediction, Journal of Geophysical Research, 103, 14,451–14,510, 1998. Yang, J., Liu, Q., Xie, S.-P., Liu, Z., and Wu, L.: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon, Geophysical Research Letters, 34, L02 708, 2007. Young, I. R., Zieger, S., and Babanin, A. V.: Global Trends in Wind Speed and Wave Height, Science, 332, 451–455, doi:10.1126/science.1197219, 2011. Zhang, P., Cheng, H., Edwards, R. L., Chen, F., Wang, Y., Yang, X., Liu, J., Tan, M., Wang, X., Liu, J., An, C., Dai, Z., Zhou, J., Zhang, D., Jia, J., and Johnson, K. R.: A Test of Climate, Sun, and Culture Relationships from an 1810-Year Chinese Cave Record, Science, 322, 940–942, 2008. Zickfeld, K., Knopf, B., Petoukhov, V., and Schellnhuber, H. J.: Is the Indian summer monsoon stable against global change?, Geophysical Research Letters, 32, L15 707, 2005. 16 2.3 A critical humidity threshold for monsoon failure 67 R W P W qo qL Ocean Land Fig. 1. Geometry of the conceptual model, illustrating wind W , precipitation P , net radiative flux R, and atmospheric specific humidity over land (qL ) and ocean (qo ). 17 68 2 300 W/m2 200 300 INDIA Latent Sensible 100 0 0 −100 −100 Radiative 2 300 4 Convergence 6 8 W.AFRICA 10 W/m2 Radiative 2 300 Latent 4 6 Converg. 8 10 12 CHINA Latent 200 Sensible 100 0 −100 Latent Sensible −200 12 200 100 BAY OF BENGAL 200 100 −200 ORIGINAL MANUSCRIPTS Sensible 0 Radiative −200 2 4 −100 Convergence −200 Radiative 6 8 Month 10 12 2 4 Convergence 6 8 Month 10 12 Fig. 2. Seasonal heat flux contributions to the atmospheric column over four major continental monsoon regions in NCEP/NCAR reanalysis data (Kistler et al., 2001). Radiative heating of the land surface in spring enhances sensible heat flux from the ground (’Sensible’). During the rainy season, latent heat release dominates the heat budget (’Latent’). Radiative heat flux comprises all radiative fluxes in and out of the atmospheric column (’Radiative’). The excess heat is transported out of the continental monsoon region through large–scale advective and synoptic processes (’Convergence’). Error bars give the standard deviation from the reanalysis period (1948-2007). See Table 1 for the geographical definitions of the monsoon regions. The red and blue vertical lines emphasize the months of maximum sensible heat flux and latent heat flux, respectively. 18 2.3 A critical humidity threshold for monsoon failure 4.5 W (m/s) 8.5 INDIA BAY OF BENGAL 4 8 r=0.49 3.5 7.5 r=0.57 3 7 1.8 2 α=3.62 α=1.85 2.2 2 2.5 2.4 1.6 1.8 4 6 W.AFRICA W (m/s) 69 r=0.44 4 3 CHINA r=0.63 2 0 0 0.2 0.4 Δ T (K) α=3.00 2 α=9.64 0.6 2.2 0.5 1 Δ T (K) 1.5 Fig. 3. Wind W versus temperature difference between land and ocean region, ∆T , from NCEP/NCAR reanalysis data, for the major monsoon regions of India, the Bay of Bengal, West Africa, and China (East Asia; see Table 1). The correlation coefficient r is indicated, as well as the slope α of a linear fit through the origin (black line). 19 2 P (mm/day) 70 11 8 INDIA B. OF BENGAL L 5 β=0.0265 8 P (mm/day) 10 r=0.65 7 r=0.83 qo=6.27 6 9 8 ORIGINAL MANUSCRIPTS 9 8 β=0.0218 10 10.5 11 11 CHINA 10 r=0.61 qo=4.73 9 L o qL=4.58 β=0.0271 6 o qL=5.52 10 W.AFRICA r=0.63 7 9 7.5 8 q (g/kg) 8 7 8.5 9 L β=0.0205 10 q (g/kg) L Fig. 4. Precipitation P versus specific humidity over land, qL , from NCEP/NCAR reanalysis data. The black line shows the result of a linear regression, the correlation coefficient r is o indicated, as well as the slope β (in kg/m2 s) and the offset in terrestrial humidity, qL (in g/kg). 20 2.3 A critical humidity threshold for monsoon failure 71 1 parameter l ∝ qo 0.8 0.6 advection radiation resulting latent heat demand lc 0.4 0.2 0 0 0.2 0.4 Wind w 0.6 0.8 Fig. 5. Solution structure of the conceptual model as a function of the non–dimensional parameter l, which is proportional to specific humidity over the ocean, qo . For illustrative purposes, l is plotted on the y-axis. The latent heat demand (red line; bold part indicates the stable branch) results from heat loss due to net radiative flux (dotted) and advection of warm air out of the land region (dashed). 21 72 2 ORIGINAL MANUSCRIPTS 0.4 0.35 Precipitation p 0.3 0.25 0.2 0.15 0.1 0.05 0 0 lc 0.2 0.4 0.6 parameter l ∝ qo 0.8 1 Fig. 6. As Fig. 5 (red line), but in terms of non–dimensional precipitation p, and organized with the control parameter l on the x-axis. 22 A critical humidity threshold for monsoon failure 73 P (mm/day) 2.3 lc 0 0.2 0.4 0.6 parameter l ∝ qo 0.8 1 Fig. 7. As Fig. 6, but in terms of dimensional precipitation P . The shape of the solution is different than in terms of p because the relation between p and P depends on l. Units on the y-axis are arbitrary. 23 2 P (mm/day) 74 P (mm/day) 11 8 INDIA BAY OF BENGAL r=0.63 7 10 6 9 5 9 9.5 10 9 W.AFRICA r=0.46 8 7 r=0.47 8 10.5 11 10 10.5 11 CHINA 10 r=0.51 9 8 6 5 8 ORIGINAL MANUSCRIPTS 8.5 9 qO (g/kg) 7 9.5 10 11 qO (g/kg) 12 Fig. 8. Correlation between precipitation and specific humidity over the ocean from NCEP/NCAR reanalysis data. Black lines show the result of a linear regression, the correlation coefficients are indicated. 24 2.3 A critical humidity threshold for monsoon failure 75 50 40 India 30 30 20 20 10 0 10 0 40 0.005 qo (kg/kg) 0 0.01 20 10 10 0.005 qo (kg/kg) 0 0.01 0.005 0.01 0.005 0.01 qo (kg/kg) China 30 20 0 0 40 W.Africa 30 0 Bay of Bengal 40 0 qo (kg/kg) Fig. 9. Estimate of critical specific humidity value over the ocean, qoc , from NCEP/NCAR reanalysis data for the basic minimal model (blue) and including the effect of a minimum terrestrial o humidity qL required for the onset of precipitation (red). The black histogram shows the observed distribution of qo . Pins mark the estimates obtained from time–mean parameter values. 25 76 2 ORIGINAL MANUSCRIPTS 18 0 −9 O ( /00, VPDB) 5 δ P (mm/day) −11 −7 0 160 170 180 190 200 time (kyr BP) 210 220 Fig. 10. Grey: Speleothem δ 18 O record from central China, used as EASM proxy for the penultimate glacial period, where more negative values indicate stronger rainfall (Wang et al., 2008). The record is smoothed with a 5–point running average, and dating errors (± 2σ) are shown for selected dates (grey horizontal bars). Red: Result of the conceptual model for EASM precipitation P in response to qo variations driven by northern hemisphere (65◦ N) summer insolation, assuming a hysteresis of 0.5 g/kg width. For illustration, we set P to zero during periods when no monsoon circulation exists according to the model. 26 2.3 A critical humidity threshold for monsoon failure 77 10 P (mm/day) 8 6 4 r=−106.5 r=−83.8 r=−61.0 2 0 6 8 10 humidity over ocean q (kg/kg) 0 12 −3 x 10 Fig. 11. Physical solution for EASM precipitation P from the conceptual model, for three different values of the non–dimensional parameter r: r(t) (dashed), r(t) + σ/2 (dot–and–dash), and r(t)−σ/2 (solid), where r(t) is the estimate from NCEP/NCAR reanalysis data, and σ denotes a standard deviation. Bold lines indicate the upper, stable branch. The value r(t) − σ/2 = -106.5, which corresponds to a critical threshold qoc = 8.0 g/kg, is used for the comparison of the model with EASM proxy data. Vertical dashed lines mark qoc and qoc + ∆q , where we choose ∆q = 0.5 g/kg as the width of an assumed hysteresis that is thought to appear when the threshold is crossed from either side (illustrated by arrows). 27 78 2 ORIGINAL MANUSCRIPTS −3 10 x 10 9.5 o q (kg/kg) 9 8.5 8 7.5 7 6.5 160 170 180 190 200 time (kyr BP) 210 220 Fig. 12. Time series of qo used for the application of the conceptual model. qo is assumed to vary linearly with northern hemisphere (65◦ N) summer insolation. The horizontal dashed line marks qoc . 28 2.3 A critical humidity threshold for monsoon failure 79 Table 1. Regional definitions used for data analysis. Monthly–mean NCEP/NCAR reanalysis data has been averaged over the indicated regions and seasons; Land and Ocean indicate that only terrestrial or oceanic grid points have been considered, respectively; and ∆T = TL − To . The bottom row lists the values for the dimensionless parameter that have been used in the estimation of the critical threshold (see section 4). Quantity P , R, qL , TL (Land) qo , To (Ocean) W Monsoon season INDIA 70-90◦ E 5-30◦ N 65-78◦ E 5-30◦ N 65-78◦ E 5-30◦ N June–Aug. 4.5·10−3 BAY OF BENGAL 80-100◦ E 15-30◦ N 80-100◦ E 10-20◦ N 80-100◦ E 15-30◦ N June–Aug. 2.3·10−2 29 W.AFRICA 15◦ W-10◦ E 2-14◦ N 35-15◦ W 2-14◦ N 15◦ W-10◦ E 2-9◦ N July–Sep. 2.0·10−1 CHINA (EASM) 100-120◦ E 20-32◦ N 105-115◦ E 15-25◦ N 100-120◦ E 20-32◦ N June–Aug. 6.7·10−2 2.4 More frequent future monsoon failure due to inherent instability 2.4 More frequent future monsoon failure due to inherent instability More frequent future monsoon failure due to inherent instability Jacob Schewe1,2 & Anders Levermann1,2 1 Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany 2 Institute of Physics, Potsdam University, Potsdam, Germany Indian summer monsoon rainfall is vital for a large share of the world’s population. Both reliably projecting India’s future rainfall and unraveling abrupt monsoon shifts found in paleo–records require improved understanding of its stability properties. Here we project monsoon failure to become considerably more frequent due to global warming, and provide a simple dynamical explanation for this trend as well as for multi–decadal rainfall variability. Based on fundamental properties of observed monsoon dynamics and an associated inherent instability that is modulated by ambient climate merely during the onset period, we develop a statistically predictive model of seasonal rainfall. Forced only by global mean temperature and central-Pacific sea level pressure anomalies in May, this simple model reproduces past and future monsoon trends as found in a comprehensive climate model. We thereby propose a novel perspective on monsoon variability as the result of internal instabilities modulated by pre–seasonal ambient climate conditions. Indian summer monsoon (ISM) rainfall is the major prerequisite of agricultural productivity in the region, and its variability severely affects the livelihoods of a large share of the world’s population1, 2 . While average ISM rainfall has been relatively stable during the past century of di1 83 84 2 ORIGINAL MANUSCRIPTS rect observations, rising trends have been observed in the annual number of extreme rain events3 . The future evolution of the ISM, and other monsoon systems, under a combination of anthropogenic forcing factors is unclear4 : Recent projections indicate that the response to increased greenhouse gas (GHG) concentrations may differ in sign among major monsoon regions, and reveal large uncertainties about the magnitude of the response5–7 . The effect of increased aerosol abundance is significant and may be counteracting that of GHGs8, 9 , while human-induced vegetation changes feed back on precipitation10, 11 . Observations and modelling studies suggest a recent regime shift in Asian monsoon convection12 and its relation to northern hemisphere thermal gradients13, 14 . At the same time, paleodata testify to abrupt and strong shifts in both the Indian and the East Asian monsoon during the last two glacial cycles15–17 and the Holocene18, 19 . Fundamental monsoon dynamics: Persistence and self-amplification These uncertainties call for an understanding of the internal monsoon dynamics to project their apparently non-linear response to external perturbations. In order to assess large-scale failure of seasonal rainfall, we focus here on fundamental monsoon dynamics which are universal across the different regions: The onset of monsoon precipitation in spring is initiated by the development of a tropospheric temperature contrast between land and ocean, and associated convergence of moist air over the continent. During the rainy season, the atmospheric heat budget in the monsoon region (cf. Supplementary Fig. S1) is then dominated by the so-called moisture-advection feedback: The release of latent heat by precipitation enhances the sea-level pressure gradient between land and ocean, thereby stabilizing the circulation that brings in more moist air from the ocean, which in 2 2.4 More frequent future monsoon failure due to inherent instability 85 turn maintains precipitation (Fig. 1A and B, top). In conceptual models, this self-amplification yields a threshold behaviour which can lead to a permanent cessation of the monsoon circulation in response to shifts in external forcing20, 21 . Unless the threshold is crossed, however, the moisture-advection feedback stabilizes the monsoon circulation by adding inertia to the system: Rainfall over a certain period within the rainy season releases latent heat, thereby reinforces the circulation and increases the probability for rainfall in subsequent days. This “memory effect”, or persistence, can be seen in direct observations, where it is reflected as a quasi-proportionality between the expectation value of daily rainfall and the amount of latent heating accumulated during some period prior to the specific day. Here we use daily rainfall data for 1951-2003 on a 1◦ grid from India Meteorological Department (IMD)22 , averaged over all available data within 5-30◦ N, 70-90◦ E. For the summer months (MaySeptember), we plot each day’s precipitation against the normalized average rainfall p̃ during a memory period τ prior to that day, such that at day t, hP it−1 t−τ − P− , p̃t ≡ P+ − P− (1) where P is the daily precipitation rate; h...i indicate temporal averaging; and P+ and P− are maximum and minimum precipitation rates, respectively (see Methods section). The memory effect is reflected in the correlation between Pt and p̃t for all days within the season, shown in Fig. 2A for τ = 17 days. The correlation depends on the choice of τ , and a sensitivity test (Supplementary Fig. S2) reveals that the rainfall memory is effective on a timescale of 2-3 weeks; after that time, the information on the rainfall history is lost from the system. In particular, the signal-to-noise ratio of the correlation is greater than 1 for τ ≤ 18 days. We interpret p̃ as a probability for daily 3 86 2 ORIGINAL MANUSCRIPTS precipitation. Although the memory effect can be seen clearly in the IMD data, the observational period is not long enough to probe the full influence of this effect on the statistics of seasonal mean precipitation, especially with respect to extreme events. Instead, we use an ensemble of five millennial climate simulations performed with the comprehensive Earth system model COSMOS developed at MPI-M Hamburg23 . The model comprises the atmospheric general circulation model (GCM) ECHAM5 at T31 (∼ 3.75◦ ) horizontal resolution, as well as an oceanic GCM, MPI-OM, and modules for terrestrial vegetation and ocean biogeochemistry. Comparison with other GCMs and with observational data shows that ECHAM5 reproduces the Indian summer monsoon realistically24, 25 . The five simulations each span the period 800-2005 CE and do not differ in forcing. While they include the past century of anthropogenic GHG emissions, we consider the total of 1206 years sufficiently homogenous to be treated as belonging to the same climate state. Using the same value τ = 17 days as above, and averaging daily rainfall over 5-30◦ N, 70-90◦ E, we find the memory effect well represented in the COSMOS output (Fig. 2B, inset). This confirms not only the presence of the moisture-advection feedback in the model, but also the approximate timescale on which the feedback operates. Additional feedback mechanism for sustained dry–state At the same time, seasonal (June-August) mean rainfall over the total of 6030 model years shows a characteristic frequency distribution (Fig. 2B, gray bars) which extends far towards the lower end 4 2.4 More frequent future monsoon failure due to inherent instability of the spectrum, featuring very weak monsoon years with seasonal mean rainfall below 2 mm/day. Latent–classes analysis suggests that this characteristic distribution represents a superposition of two monsoon modes (Supplementary Fig. S3). While the rainy mode is dominated by the moistureadvection feedback, the dry mode is determined by a second dynamical feedback. In order to identify this second feedback, we investigate year 1158 of the first ensemble member as one of the driest monsoon years within the entire simulation ensemble (due to significant internal variability in this class of climate models, this year cannot be expected to coincide with any specific historical year, and will henceforth be referred to as the ‘dry year’). Average seasonal precipitation over India in this year is ∼ 70% below the long-term mean of 5.7 mm/day (Fig. 3A, circles). The dynamical South Asian monsoon index26 indicates an exceptionally large deviation of the zonal tropospheric wind shear from the long-term mean (Fig. 3A, bars). The dry year’s rainfall cycle (Fig. 3B) shows that large-scale monsoon precipitation is erratic and largely suppressed during the entire summer, caused by a qualitatively different circulation pattern compared to normal monsoon years: The initial lack of latent heating over India leaves the regional-scale sea level pressure anomalously high throughout the summer (Fig. 3C and map in Supplementary Fig. S4). This causes subsidence of upper-tropospheric air both over the subcontinent and over the Arabian sea (Fig. 3D and map in Supplementary Fig. S5). Since specific humidity is lower in the upper troposphere, this large-scale subsidence effectively dries out the monsoon region: Specific humidity in the lower troposphere (i.e. in the lower, landward branch of the ISM circulation, Fig. 1A) substantially decreases over India and the eastern Arabian Sea, and remains exceptionally low through June-August (Fig. 3E and map in Supplementary Fig. S6; 5 87 88 2 ORIGINAL MANUSCRIPTS also note the spatial congruence of subsidence and moisture anomalies in Fig. S6). This acts to further suppress precipitation and sustain the dry state, as illustrated in Fig. 1A (bottom). Note that the dry regime is associated with a qualitatively different circulation pattern compared to the wet regime: Instead of convective upwelling, the monsoon region is characterized by subsidence of dry air from the upper troposphere; also, the flow direction in the upper troposphere changes from generally westward to northward or even eastward, which is consistent with an anomalously low zonal wind shear as reflected by the dynamical monsoon index (Fig. 3A, bars). Once initiated, this self-amplifying feedback sustains the dry state in a similar way as the wet state is sustained by the moisture-advection feedback (Fig. 1B). A model of inherent instability The dry year examined here is an extreme case in the sense that the dry feedback is dominant throughout the season. Most years in the spectrum of the climate model (Fig. 2B, gray bars) are less extreme, i.e. their seasonal precipitation averages somewhere between the dry state and an upper limit of about 9 mm/day. We propose a simple statistically predictive model for seasonal mean monsoon precipitation that explains this characteristic spectrum by an interplay between the wet moisture-advection feedback and the dry subsidence feedback on a quasi-daily timescale. This “day-to-day” model (illustration Fig. 1C) is based only on the two feedbacks and the memory effect. It employs the idealized assumption of two discrete circulation states: One with weak precipitation (we choose P− ≡ 0 mm/day in our example, Supplementary Table S1), corresponding to the dry feedback loop in Fig. 1B, and one with maximum precipitation (P+ , here chosen to be 9 6 2.4 More frequent future monsoon failure due to inherent instability mm/day), corresponding to the wet feedback loop in Fig. 1B. It is further assumed that the system can flip between these two states on a daily timescale, and that the probability pt for ending up in the wet state at time step (day) t depends on the ratio of dry and rainy days during a certain period τ prior to t - this corresponds to the precipitation probability p̃ of equation (1): That is, the more rainy days there were in the period τ before t, the more likely it is to have rainfall at time t. Thus here the memory effect is represented by an exact proportionality between rainfall and rainfall history. It acts to impede a flip between the two circulation states. Still, a flip can always be triggered by stochastic atmospheric fluctuations, e.g. regional and local weather, that perturb the large-scale dynamical flows comprised in the two feedback mechanisms. In the day-to-day model, these perturbations are represented by an unbiased random process that, together with pt , ultimately determines the system state at each time step. This very basic auto-regression model of internal monsoon dynamics needs to be complemented by information on the onset of the rainy season, prior to the dominance of the two positive feedback loops. During the period of length τ at the beginning of the season, a constant initial probability for rainfall, pinit , replaces the dynamical rainfall probability p̃. pinit represents external factors that crucially influence the development of the monsoon circulation at a time when it is most sensitive to external perturbations26 . In the case of India this could for example comprise the role of El Niño/Southern Oscillation (ENSO), Indian Ocean sea surface temperatures, or Eurasian snow cover27 , as well as the current phase of the Madden-Julian Oscillation (MJO)28 . Finally, we introduce a maximum probability pm , such that pt cannot be greater than pm or smaller than (1 − pm ). This prevents locking into either P− or P+ when pt becomes zero or one, which oth7 89 90 2 ORIGINAL MANUSCRIPTS erwise could occur as an artifact of the discrete nature of the model. In this form, the day-to-day model can reproduce the spectrum of the comprehensive climate model (Fig. 2B, blue line). The R Matlab code is provided as Supplementary Information. Monsoon failure under global warming The COSMOS climate model simulations of the past, as used above, have been complemented with IPCC SRES A1B scenario29 simulations until 2100, followed by constant CO2 concentrations until 220030 . This results in an increase in global mean surface air temperature of 4.6◦ C relative to pre–industrial by the end of the 22nd century (Fig. 4A, black line). We find that in the warm climate of 2151-2200 CE, the frequency distribution of seasonal mean ISM rainfall is completely inverted, in the sense that now dry years are much more frequent than wet years (Fig. 2C, gray bars). This inversion occurs gradually over the course of the warming scenario and involves a sign change in the distribution’s skewness at the end of the 21st century (Fig. 4B, red line). The associated change in the expected seasonal mean rainfall, however, is not monotonic: An increase throughout much of the 21st century is followed by a rapid decrease, falling short of the pre–industrial long–term mean roughly by the turn of the 22nd century (Fig. 4C, red line). These changes can be understood in the simple framework of the day-to-day model. A warmer tropical troposphere can hold more water vapor, which tends to enhance rainfall in the absence of counter–acting processes31 . We translate this into a linear shift of the precipitation range [P− , P+ ] towards higher values with increasing global mean temperature. In addition to this 8 2.4 More frequent future monsoon failure due to inherent instability thermodynamical effect, dynamical changes that affect the monsoon development during the onset period can be represented by the day-to-day model via the parameter pinit . In order to understand which process dominates the initiation of monsoon rainfall in the comprehensive climate model, consider the dry year investigated above. Both during April and May, mean sea level pressure (MSLP) was extremely low over the central Pacific Nino3.4 region (170-120◦ W, 5◦ S-5◦ N; Supplementary Fig. S7), which tends to suppress the development of the low pressure system over India, represented by a low pinit value in the day-to-day-model. This relation can be found throughout the 6030 years of historical climate simulations: There is a clear correlation between the two monsoon modes present in the distribution of seasonal mean rainfall and anomalous spring–time (May) Nino3.4 MSLP (Fig. 2B, red bars; see also Supplementary Fig. S8). As a simple approximation, we scale pinit linearly with May Nino3.4 MSLP. Forced with decadal averages of global mean temperature and May Nino3.4 MSLP, the dayto-day model reproduces the long–term trends in mean and skewness of the ISM rainfall frequency distribution observed in COSMOS (Fig. 4B and C, gray lines and shading). The non–trivial peak– and–decline response in ISM rainfall is captured due to the delay in the Pacific MSLP signal compared to the warming signal (Fig. 4A). Moreover, a significant portion of multidecadal rainfall variability is reproduced (Fig. 4C). Similar to the historical period, the model can explain the frequency distribution of the future period 2151-2200 CE (Fig. 2C). Note that the variation of pinit along with decadally–averaged spring–time Nino3.4 MSLP represents the influence of a slowly varying central Pacific mean climate on the ISM onset, rather than a direct forcing of the ISM season by ENSO. 9 91 92 2 ORIGINAL MANUSCRIPTS Discussion and conclusions We conclude that major characteristics of seasonal–mean monsoon rainfall can be explained on the basis of only two fundamental assumptions: (i) An inherent instability of the monsoon circulation on a daily timescale, in the form of a competition between two different circulation regimes, each of which is associated with a self-amplifying feedback. (ii) A memory effect that is induced by those feedbacks, and determines, in a probabilistic manner, the transition between the wet and the dry periods. Both assumptions are backed by observations and comprehensive climate model results. The subsidence feedback not only complements the picture of the atmospheric circulation in the temporary absence of monsoon rainfall, but constitutes an active antagonist of the moistureadvection feedback due to its self-amplifying nature. The memory effect is a logical consequence of the dominant role of these two feedbacks, and is found in observational data as well as in climate model results. Monsoon rainfall exhibits intraseasonal variability on various time scales which influences the seasonal mean. While some of this variability, in particular its slower components, is related to interaction with larger-scale atmospheric phenomena such as the MJO28 , internally generated variability is an equally important factor in shaping the monsoon cycle and determining the seasonal mean rainfall32, 33 . The notion of an internal instability of the large-scale monsoon circulation on a quasi–daily timescale, incorporated here in a slim and fundamental framework, offers a first– order explanation for the driving mechanism of internally generated intraseasonal variability. We have demonstrated that this mechanism alone can explain the non–trivial, long–term frequency 10 2.4 More frequent future monsoon failure due to inherent instability distribution of seasonal mean monsoon rainfall in a complex climate model. Moreover, it can explain ISM changes on decadal to centennial time scales with a minimal amount of external information. We have shown that the “day-to-day model” can reproduce past ISM multidecadal variability as well as projected future changes, including a substantial increase in monsoon failure, when driven by the decadal changes in global mean temperature and tropical Pacific MSLP in May. While global warming is assumed to elevate the baseline of monsoon rainfall due to increased atmospheric moisture content, a shift towards lower spring–time MSLP in the tropical Pacific is assumed to induce atmospheric conditions that favor more subsidence over the Indian region and thus lead to more deficient monsoon onsets. In the day-to-day model, this translates into a lower initial probability pinit . This parameter summarizes the influence of ambient climatic factors that, to some extent, predispose the monsoon system during the onset season34 . It does not determine the rainfall amount of an individual monsoon season, but it has strong influence on the probability distribution of seasonal mean rainfall. While in the comprehensive climate model applied here, pinit is dominated by the influence of central Pacific climate variations, other factors such as changing spring–time Indian Ocean sea surface temperatures might play a significant role, too35 , and could be translated into monsoon statistics using the statistically predictive day-to-day model. 11 93 94 2 ORIGINAL MANUSCRIPTS Methods summary We used a gridded observational data set of daily ISM rainfall to demonstrate the memory effect. Seasonal mean rainfall statistics as well as the feedback mechanism sustaining a dry circulation regime were established using millennial historical simulations with a comprehensive climate model. Based on those results, we developed a simple, statistically predictive model for seasonal mean monsoon rainfall and applied it to the characteristics and trends found in the complex climate model, including future projections under a global warming scenario. 1. Parthasarathy, B., Munot, A. & Kothawale, D. Regression model for estimation of Indian foodgrain production from summer monsoon rainfall. Agricultural and Forest Meteorology 42, 167 – 182 (1988). 2. Auffhammer, M., Ramanathan, V. & Vincent, J. R. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proceedings of the National Academy of Sciences 103, 19668–19672 (2006). 3. Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. & Xavier, P. K. Increasing trend of extreme rain events over India in a warming environment. Science 314, 1442–1445 (2006). 4. Meehl, G. A. et al. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate 12 2.4 More frequent future monsoon failure due to inherent instability Change, chap. Global Climate Projections (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007). 5. Kripalani, R. H., Oh, J. H. & Chaudhari, H. S. Response of the East Asian summer monsoon to doubled atmospheric CO2: Coupled climate model simulations and projections under IPCC AR4. Theor. Appl. Climatol. 87, 1–28 (2007). 6. Kripalani, R. H., Oh, J. H., Kulkarni, A., Sabade, S. S. & Chaudhari, H. S. South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4. Theor. Appl. Climatol. 90, 133–159 (2007). 7. Cherchi, A., Alessandri, A., Masina, S. & Navarra, A. Effects of increased CO2 levels on monsoons. Climate Dynamics (online) (2010). 8. Ramanathan, V. et al. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proceedings of the National Academy of Sciences 102, 5326–5333 (2005). 9. Lau, K. M. & Kim, K. M. Observational relationships between aerosol and asian monsoon rainfall, and circulation. Geophysical Research Letters 33, L21810 (2006). 10. Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V. & Petoukhov, V. The influence of vegetation-atmosphere-ocean interaction on climate during the mid-holocene. Science 280, 1916–1919 (1998). 11. Claussen, M. Late quaternary vegetation-climate feedbacks. Clim. Past 5, 203–216 (2009). 13 95 96 2 ORIGINAL MANUSCRIPTS 12. Turner, A. G. & Hannachi, A. Is there regime behavior in monsoon convection in the late 20th century? Geophysical Research Letters 37, L16706 (2010). 13. Li, J. et al. Summer monsoon moisture variability over China and Mongolia during the past four centuries. Geophysical Research Letters 36, L22705 (2009). 14. Sun, Y., Ding, Y. & Da, A. Changing links between South Asian summer monsoon circulation and tropospheric land-sea thermal contrasts under a warming scenario. Geophysical Research Letters 37, L02704 (2010). 15. Burns, S. J., Fleitmann, D., Matter, A., Kramers, J. & Al-Subbary, A. A. Indian ocean climate and an absolute chronology over Dansgaard/Oeschger events 9 to 13. Science 301, 1365–1367 (2003). 16. Wang, P. et al. Evolution and variability of the Asian monsoon system: state of the art and outstanding issues. Quaternary Science Reviews 24, 595–629 (2005). 17. Wang, Y. et al. Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years. Nature 451, 1090–1093 (2008). 18. Gupta, A. K., Anderson, D. M. & Overpeck, J. T. Abrupt changes in the Asian southwest monsoon during the Holocene and their links to the North Atlantic ocean. Nature 421, 354– 357 (2003). 19. Wang, Y. et al. The Holocene Asian Monsoon: Links to solar changes and North Atlantic climate. Science 308, 854–857 (2005). 14 2.4 More frequent future monsoon failure due to inherent instability 20. Zickfeld, K., Knopf, B., Petoukhov, V. & Schellnhuber, H. J. Is the indian summer monsoon stable against global change? Geophysical Research Letters 32, L15707 (2005). 21. Levermann, A., Schewe, J., Petoukhov, V. & Held, H. Basic mechanism for abrupt monsoon transitions. Proceedings of the National Academy of Sciences 106, 20572–20577 (2009). 22. Rajeevan, M., Bhate, J., Kale, J. A. & Lal, B. High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells. Current Science 91, 296–306 (2006). 23. Haak, H. MPI-M Earth System Modelling Framework: millennium full forcing experiment (ensemble members 1–5) (2008). World Data Center for Climate. CERA-DB ”mil0010”– ”mil0015”. 24. Annamalai, H., Hamilton, K. & Sperber, K. R. The South Asian summer monsoon and its relationship with ENSO in the IPCC AR4 simulations. Journal of Climate 20, 1071–1092 (2007). 25. Li, J. & Zhang, L. Wind onset and withdrawal of Asian summer monsoon and their simulated performance in AMIP models. Climate Dynamics 32, 935–968 (2009). 26. Webster, P. J. & Yang, S. Monsoon and ENSO: Selectively interactive systems. Quarterly Journal of the Royal Meteorological Society 118, 877–926 (1992). 27. Charney, J. G. & Shukla, J. Monsoon Dynamics, chap. Predictability of Monsoons (Cambridge University Press, Cambridge, United Kingdom, 1981). 15 97 98 2 ORIGINAL MANUSCRIPTS 28. Wheeler, M. & Hendon, H. An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review 132, 1917–1932 (2004). 29. Nakicenovic, N. & Swart, R. (eds.) IPCC Special Report on Emissions Scenarios (Cambridge University Press, 2000). 30. Jungclaus, J. MPI-M Earth System Modelling Framework: millennium scenarios (continuation of full forcing experiment ensemble members 1–5) (2009). World Data Center for Climate. CERA-DB ”mil0016”–”mil0020”. 31. Allen, M. & Ingram, W. Constraints on future changes in climate and the hydrologic cycle. Nature 419, 224–232 (2002). 32. Krishnamurthy, V. & Shukla, J. Intraseasonal and interannual variability of rainfall over India. Journal of Climate 13, 4366–4377 (2000). 33. Goswami, B. N., Wu, G. & Yasunari, T. The annual cycle, intraseasonal oscillations, and roadblock to seasonal predictability of the Asian summer monsoon. Journal of Climate 19, 5078–5099 (2006). 34. Palmer, T. N. Chaos and predictability in forecasting the monsoons. Proc. Indian Nat. Sci. Acad. 60, 57–66 (1994). 35. Li, T., Zhang, Y., Chang, C. & Wang, B. On the relationship between Indian Ocean sea surface temperature and Asian summer monsoon. Geophysical Research Letters 28, 2843– 2846 (2001). 16 2.4 More frequent future monsoon failure due to inherent instability 99 36. Roeckner, E. et al. The atmospheric general circulation model ECHAM5 - Part I: Model description. Report No. 349, Max Planck Institute for Meteorology, Hamburg, Germany (2003). 37. Hagemann, S., Arpe, K. & Roeckner, E. Evaluation of the hydrological cycle in the ECHAM5 model. Journal of Climate 19, 3810–3827 (2006). 38. Raddatz, T. J. et al. Will the tropical land biosphere dominate the climate-carbon cycle feedback during the twenty-first century? Climate Dynamics 29, 565–574 (2007). 39. Marsland, S., Haak, H., Jungclaus, J., Latif, M. & Roske, F. The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Modelling 5, 91–127 (2003). 40. Wetzel, P. et al. Effects of ocean biology on the penetrative radiation in a coupled climate model. Journal of Climate 19, 3973–3987 (2006). 41. Crowley, T. J. et al. Volcanism and the Little Ice Age. PAGES Newsletter 16, 22–23 (2008). 42. Krivova, N. A. & Solanki, S. K. Models of Solar Irradiance Variations: Current Status. J. Astrophys. Astr. 29, 151–158 (2008). 43. Pongratz, J., Reick, C., Raddatz, T. & Claussen, M. A reconstruction of global agricultural areas and land cover for the last millennium. Global Biogeochemical Cycles 22 (2008). 44. http://www.mpimet.mpg.de/fileadmin/ozean/drg/web page millennium nov09 model details.pdf. 45. Leisch, F. FlexMix: A general framework for finite mixture models and latent class regression in R. Journal of Statistical Software 11, 1–18 (2004). 17 100 2 ORIGINAL MANUSCRIPTS 46. Schwarz, G. Estimating dimension of a model. Annals of statistics 6, 461–464 (1978). Supplementary Information is linked to the online version of the paper at www.nature.com/nature. Acknowledgements This work was funded by the Heinrich Böll Foundation, the German National Academic Foundation, and the BMBF PROGRESS project (support code 03IS2191B). The climate model simulations were performed in the framework of the MPI-M project MILLENNIUM and have been partly funded by the German Earth System Research Partnership Program ENIGMA. We thank J. Jungclaus for access to the model output; V. Petoukhov, V. Brovkin, and R. Krishnan for helpful comments on the manuscript; and K. Frieler for advice on statistical methods. Author Information The authors declare that they have no competing financial interests. Correspondence and requests for materials should be addressed to J.S. (email: [email protected]). 18 2.4 More frequent future monsoon failure due to inherent instability A B WET 101 C pinit pt WET 1-pt DRY 1-pinit pt DRY 1-pt Figure 1: Illustration of idealized ISM circulation regimes (A), associated positive feedback mechanisms (B), and the simple ‘day-to-day model’ (C). In the wet regime, latent heating due to precipitation (P) creates a regional sea level pressure (SLP) low, which leads to atmospheric upwelling and associated inflow of moist air towards the ISM region. In turn, a lack of latent heating causes a regional positive SLP anomaly, which leads to subsidence of dry upper-troposphere air and lowers the humidity of the monsoon winds, thereby inhibiting precipitation and sustaining a dry regime. In the ‘day-to-day model’, the probability for a wet or dry day depends on the amount of latent heating accumulated during some previous period, except during the onset when it is determined by an initial probability pinit . 2 10 5 years out of 6030 0 0 0.2 0.4 ~ 0.6 p 10 400 5 200 0 years out of 250 ORIGINAL MANUSCRIPTS A 0.5 ~ p 0 1 0.8 P (mm/day) P (mm/day) 102 1 B 0 20 C day−to−day model 10 COSMOS 0 0 2 4 6 mm/day 8 Figure 2: ISM rainfall statistics. (A) IMD observed daily rainfall during May-September versus precipitation probability p̃ (see main text). The thick line connects the mean values for each of 50 bins on the p̃-axis; shading denotes ±1 standard deviation. Days with p̃ < 0.2 stem mainly from before the monsoon onset. (B) Distribution of seasonal (JJA) mean ISM precipitation from the COSMOS ensemble over the period 800–2005 CE (gray bars; 5 simulations, 1206 years each) and from 6030 runs of the stochastic day-to-day model (blue line shows mean value, and error bars show ±1 standard deviation, from 100 realizations of the model). Dark red (light red) bars show the distribution of those COSMOS years where the Nino3.4 mean sea level pressure in May exceeds (falls short of) the long-term mean by more than one standard deviation. Inset: As A, but with P and p̃ diagnosed from one of the COSMOS simulations. (C) Gray bars as in B, but over the period 2151–2200 CE in a global warming scenario (see main text). Blue line and errorbars as in B, but from 250 runs and with a different set of model parameters (see Supplementary Table S1). More frequent future monsoon failure due to inherent instability mm/day, index 2.4 5 0 −5 1140 mm/day 10 1150 year 1160 B precipitation 5 0 mb precipitation monsoon index J F MA MJ J A S ON D 1010 C g/kg 10−2 Pa/s 1005 sea level pressure 6 0 −6 12 D subsidence E 8 4 humidity May Jun Jul Aug Sep Figure 3: The dry year. (A) Seasonal mean precipitation over India (circles, in mm/day; dashed line shows long-term mean) and South Asian monsoon index26 (bars) in a 19-year interval of the simulation; the dry year is marked in black. (B) Annual cycle of average daily precipitation over India during the dry year (thick) and averaged over the previous 17 years (thin; shading denotes the 16% and 84% quantiles). See Supplementary Fig. S9 for a comparison covering the entire simulation period. (C-E) Characteristics of the monsoon circulation over western India and the Arabian Sea during May-September: (C) Mean sea level pressure (MSLP, in hPa); (D) vertical velocity ω (in 10−2 Pa/s) at 500hPa, with positive values indicating subsidence of air; and (E) near-surface (850-1000hPa) specific humidity, in g/kg. In B-E, a 3-day running mean was applied; thick lines are for the dry year; and thin lines give the average over the previous 17 years, where the shading denotes ± 1 standard deviation. 103 104 2 A 1011 Nino3.4 SLP May Δ Tglobal 0 mb 2.5 ° C 5 ORIGINAL MANUSCRIPTS 1009 1 B precip skewness 0 mm/day −1 6 C 5 4 800 D2D model COSMOS precip mean 1000 1200 1400 1600 1800 2000 2200 year Figure 4: Application of the ‘day-to-day model’ to past and future ISM variability. (A) Global mean surface temperature (black, in ◦ C relative to the 1980-1999 mean) and May MSLP over the Nino3.4 region (blue, in mb) over the full period of the millennial COSMOS simulations and their continuations under a global warming scenario. Data have been averaged decadally (801–810, etc.) and over the 5 ensemble members, such that each point represents an average over 50 model years. (B) Skewness of the decadal frequency distribution of seasonal mean ISM rainfall in COSMOS (red). Each point is computed from a set of 50 model years (10 years, 5 ensemble members). The skewness computed from 50 runs each of the day-to-day model, with parameters set according to global mean temperature and May Nino3.4 MSLP as in A, is shown in gray (thin line shows mean value, and shading shows ±1 standard deviation, from 100 realizations of the model). (C) As B, but for the mean of the frequency distribution (in mm/day). 2.4 More frequent future monsoon failure due to inherent instability Methods Memory effect in observations. For demonstrating the memory effect in the IMD observational data set, we choose P+ = 12 mm/day and P− = 0 mm/day. P+ is chosen to approximate the maximum observed 17-day average daily precipitation. We test the sensitivity of the memory effect to the length of the memory period τ by computing the ‘signal-to-noise ratio’ of the correlation for a range of τ values (Fig. S2): A linear regression of the mean values between p̃ = 0.4 and 0.6 is evaluated at p̃ = 0.2 and p̃ = 0.8, and the difference in P is divided by the average standard deviation over the same interval. As the maximum observed average daily precipitation is higher for shorter averaging periods and vice versa, we have varied P+ along with τ – namely, between 14 and 10 mm/day – in producing Fig. S2. This variation however only has a minor effect on the signal-to-noise ratio, which is much more sensitive to the choice of τ . Climate model analysis. The climate model simulations used in this study belong to the “Millennium” simulation ensemble23 . They were performed with the comprehensive MPI-M Earth System Model COSMOS, comprising the atmospheric GCM ECHAM536, 37 Version 5.4.01 at T31L19 resolution; the land surface scheme JSBACH38 at T31 resolution, one soil layer, 13 vegetation types and four vegetation tiles per grid box; and the oceanic GCM MPI-OM39 Version 1.3.0, including the ocean biogeochemistry module HAMOCC40 , at GR3.0L40 resolution. The model is run for the years 800 to 2005 CE with time-dependent external forcing including the effects of volcanic stratospheric sulphate aerosols41 , variation of total solar irradiance42 , land-use change43 , and changes in orbital parameters. Following the historical period, the simulations are continued 23 105 106 2 ORIGINAL MANUSCRIPTS with forcing according to the IPCC SRES A1B scenario29 until 2100, and with CO2 emissions corresponding to constant CO2 concentrations from 2101 until 220030 . Further details can be found on the MPI-M website44 . In analyzing the climate model results, precipitation is averaged over the land area in 530◦ N, 70-90◦ E. The dynamical monsoon index26 is computed as the zonal wind shear difference over 0-20◦ N, 60-100◦ E according to (Ū850hP a − hŪ850hP a i) − (Ū200hP a − hŪ200hP a i), where Ū is the seasonal (May-September) average zonal wind speed, and angle brackets indicate the long term mean over the historical simulation period. Sea level pressure is averaged over 0-25◦ N, 60-80◦ E; vertical velocity ω in pressure coordinates is averaged over 5-30◦ N, 55-80◦ E; and specific humidity over 5-25◦ N, 60-80◦ E. For demonstrating the memory effect in COSMOS, the same procedure is applied as for the IMD observational data, except that we now choose P+ = 9 mm/day, which is closer to the maximum 17-day average daily precipitation observed in the climate model. The latent–classes analysis is performed using the flexmix routine45 , fitting a model of one, two or three superposed Gaussian distributions to the data. According to the Bayesian Information Criterion46 (BIC), the assumption of two Gaussians improves the model substantially (from a BIC value of 21364 to 20034) as compared to only one Gaussian, while a third Gaussian yields no significant improvement, and is not physically motivated. Statistically predictive model. The precipitation probability used in the simple day-to-day 24 2.4 More frequent future monsoon failure due to inherent instability model is defined as pm if p̃t ≥ pm , pt = (1 − pm ) if p̃t ≤ (1 − pm ), p̃t else, 107 (2) where pm is a maximum probability, and p̃t is defined according to equation (1) in the main text; except that t is now the index of model time steps, not days. However, the length of the season is chosen such that a time step corresponds approximately to one day (see Supplementary Table S1). During an initial period of length τ in the beginning of the season, pt is not (and cannot be) determined by equation (2). Instead, we introduce an initial probability pinit which is treated as a model parameter. This day-to-day model is integrated over l time steps, corresponding to the length of the model season, and the seasonal mean precipitation P̄ , P− ≤ P̄ ≤ P+ , is saved. See Supplementary Table S1 for sets of parameters used in the model integration, and the attached R Matlab script for the model code. For reproducing multidecadal trends in the historical as well as the future climate simulations (Fig. 4 in the main paper), we run the day-to-day model for each decade, varying the parameter pinit according to pinit = p0 · (m − m0 ) + p0 , where m is the decadally-averaged May Nino3.4 MSLP in mb; m0 = 1008.9 mb; p0 = 0.39 mb−1 ; and p0 = 0.2. The parameters P+ and P− are varied according to P± = P 0 · ∆T + P0± , where ∆T is the decadally-averaged global mean surface temperature anomaly in ◦ C; P 0 = 0.42 mm day−1 ◦ C−1 ; P0+ = 9 mm day−1 ; and P0− = 0 mm day−1 . 25 2.4 More frequent future monsoon failure due to inherent instability Supplementary Material for More frequent future monsoon failure due to inherent instability Jacob Schewe1,2 , Anders Levermann1,2 1 Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam, Germany 2 Institute of Physics, Potsdam University, Potsdam, Germany 1 109 110 2 ORIGINAL MANUSCRIPTS 300 Latent INDIA NCEP/NCAR 200 Sensible W/m2 100 0 −100 Radiative Convergence −200 2 4 6 Month 8 10 12 Figure S1: Net contributions (climatology) to the annual moist static energy budget of the atmospheric column over India (5-30◦ N, 70-90◦ E), from NCEP/NCAR reanalysis data1 (1948-2007; errorbars denote one standard deviation): Radiative fluxes (black solid line), sensible heat flux from the ground (red), latent heat flux due to precipitation (blue), and convergence of heat due to advection by the large-scale time-mean circulation and eddies (black dashed line). Sensible heat flux is strongest just before the monsoon onset, but during the rainy season, latent heat release dominates the energy budget, balanced by advective processes that transport the excess heat out of the region. This dominance is found in all major monsoon regions2 . 2 2.4 More frequent future monsoon failure due to inherent instability 111 signal−to−noise ratio 3 2.5 2 1.5 1 0.5 0 0 10 20 τ (days) 30 40 Figure S2: Sensitivity of the memory effect to the memory period τ . The ‘signal-to-noise ratio’, i.e. the ratio of the slope of the correlation vs. the standard deviation (cf. Fig. 2A in main paper), is greater than one for τ ≤ 18 days, and becomes very small for τ greater than 25 days. The filled circle marks τ = 17 days, the value used for the analysis in the main paper. 3 112 2 ORIGINAL MANUSCRIPTS 500 number of years 400 300 200 100 0 0 2 4 mm/day 6 8 Figure S3: Gray bars as in Fig. 2B in the main paper. Black dashed lines show the characteristics of the two modes obtained from latent–classes analysis, and the solid line shows their superposition. 4 2.4 More frequent future monsoon failure due to inherent instability 113 June May 1030 30oN 15oN 0o 1020 15oS 30oS A dry B dry 1010 30oN 15oN 1000 0o 15oS 30oS C average o 40 E o D o average o 70 E 100 E 40 E o 70 E 100oE 990 Figure S4: (A) Monthly mean sea level pressure (in hPa) in May of the dry year; (B) same for June; (C) as (A), but averaged over the 17 previous years; (D) same for June. Contour spacing is 4 hPa. Due to the absence of convection and associated latent heat release, the Eurasian surface Low is diminished over India in the dry year, which favors a northward expansion of the Indian Ocean surface High and subsequent subsidence over India and the northwestern Indian ocean region (cf. Fig. S5). 5 114 2 ORIGINAL MANUSCRIPTS June May 0.1 30oN 15oN 0o 15oS 30oS A dry B dry 0 30oN 15oN 0o 15oS 30oS C average 40oE D 70oE 100oE average 40oE 70oE 100oE −0.1 Figure S5: (A) Monthly mean vertical velocity ω (in Pa/s) at 500hPa in May of the dry year; (B) same for June; (C) as (A), but averaged over the 17 previous years; (D) same for June. Positive ω (red shading) indicates downward motion of air. Contour spacing is 0.02 Pa/s. In the dry year, central India and the Arabian Sea are characterized by subsidence instead of upwelling. 6 2.4 More frequent future monsoon failure due to inherent instability 115 June May 0.1 30oN 15oN 0 0o 15oS 30oS ω anomaly A ω anomaly B 30oN −0.1 3 15oN 0 0o 15oS 30oS C q anomaly 40oE D 70oE 100oE q anomaly 40oE −3 70oE 100oE Figure S6: (A) Anomaly of monthly mean vertical velocity ω (in Pa/s, contour spacing 0.02) at 500hPa in May of the dry year, with respect to the average over the 17 previous years; (B) same for June; (C) anomaly of monthly mean lower troposphere (500-1000hPa) specific humidity (g/kg, contour spacing 1 g/kg) in May of the dry year, with respect to the average over the 17 previous years; (D) same for June. Anomalous subsidence of dry air over India and the Northern Indian Ocean leads to moisture depletion in the monsoon region. 7 116 2 1013 ORIGINAL MANUSCRIPTS Nino3.4 MSLP 1009 1005 Feb Mar Apr May Jun Figure S7: Mean sea level pressure (MSLP) over the Nino3.4 region (170-120◦ W, 5◦ S-5◦ N) during spring in the COSMOS simulation, for the dry year (thick line) and averaged over the previous 17 years (thin line; shading denotes ± 1 standard deviation). 8 2.4 More frequent future monsoon failure due to inherent instability 117 1 correlation 0.8 0.6 0.4 0.2 0 previous year O N D J F M A M J J Figure S8: Coefficient of the correlation between seasonal (JJA) mean ISM rainfall and monthlyaveraged mean sea level pressure (MSLP) over the Nino3.4 region (170-120◦ W, 5◦ S-5◦ N) in the COSMOS simulation. The correlation is strongest when MSLP is taken in May just before the monsoon season (indicated by the gray shading). 9 118 2 ORIGINAL MANUSCRIPTS 10 Precipitation mm/day 8 6 long−term mean 4 2 dry years 0 J F M A M J J A S O N D Figure S9: Annual cycle of daily precipitation over India as a long-term mean over the first 1206year COSMOS simulation (thin line; shading denotes the 16% and 84% quantiles), and averaged over all dry years within this simulation (JJA mean precipitation lower than 2.5 mm/day, thick line). 10 2.4 More frequent future monsoon failure due to inherent instability Additional references 1. Kistler, R. et al. The NCEP/NCAR 50-year reanalysis. Bull. Amer. Meteor. Soc. 82, 247 – 267 (2001). 2. Levermann, A., Schewe, J., Petoukhov, V. & Held, H. Basic mechanism for abrupt monsoon transitions. Proceedings of the National Academy of Sciences 106, 20572–20577 (2009). 11 119 120 2 ORIGINAL MANUSCRIPTS Table S1: Parameters of the day-to-day model Parameter Symbol Value used for Value used for Fig. 2B Fig. 2C length of season l 135 time steps length of memory period τ 17 time steps precipitation in wet state P+ 9 mm/day 10.9 mm/day Value used for Fig. 4 varying linearly with global temperature, see Methods text precipitation in dry state P− 0 mm/day 1.9 mm/day varying linearly with global temperature, see Methods text initial probability pinit 0.75 0.2 varying linearly with Nino3.4 MSLP, see Methods text maximum probability pm 0.8 0.82 12 0.82 2.4 More frequent future monsoon failure due to inherent instability 121 122 2 ORIGINAL MANUSCRIPTS 3 Discussion and Conclusions In this thesis, I have offered a novel perspective on the possibility of large–scale monsoon failure, motivated by the observation of two different types of events: The dry regimes, observed in paleoclimatic proxy records, that persist on millennial and longer timescales; and individual years of extremely deficient monsoon rainfall as found in present–day climate simulations with a comprehensive global climate model. Previous studies have linked paleoclimatic monsoon failure to external influences such as remote forcing from the North Atlantic region (Burns et al., 2003; Wang et al., 2005b), or orbital–scale changes in solar insolation (Wang et al., 2008), but no consistent theory has been developed so far that can explain the strong and abrupt response of monsoon rainfall to these either weak or gradual external perturbations. Seasonal monsoon failure under present–day climate conditions, on the other hand, has been investigated here in a realistic climate model, as a possible scenario that goes far beyond the interannual variability found in direct observations. In the first part of the thesis (section 2.1), I have presented simulations with a coupled climate model of intermediate complexity, CLIMBER-3α, that project monsoon rainfall around the world to increase quasi–linearly with global warming in the coming centuries. While this is generally consistent with many other studies (e.g. Kripalani et al., 2007a), the atmospheric component of CLIMBER-3α is based on a simplified statistical–dynamical approach, and may not sufficiently represent all processes that are relevant for the response of monsoon circulations to rapid and intense climate change. Therefore, in the subsequent work I have attempted to identify those physical mechanisms that are of first–order importance for large–scale monsoon dynamics and their response to external changes. The second and third part of my work (sections 2.2 and 2.3) have focused on the conditions that are necessary in order to sustain a rainy season over land. It is important to note that globally, the seasonal reversal of cross-equatorial winds is driven by the seasonal change in hemispheric insolation, and in general the rainfall associated with the intertropical convergence zone will naturally be sustained even without continental monsoon rainfall. However, the excursion of the tropical rain belt towards high latitudes and the intense continental precipitation in monsoon regions goes beyond the zonal-mean dynamics of the intertropical convergence zone. I have identified the so–called moisture–advection feedback as the primary driving force of large–scale continental monsoon rainfall (section 2.2). While differential heating of the land and ocean surfaces is important in establishing the atmospheric land–sea thermal contrast during the onset period, the moisture–advection feedback is crucial in maintaining this contrast after the rainy season has started. From an energetic point of view, the advection of latent heat in the form of moisture from the ocean to the continent is the main source of energy that keeps the monsoon circulation going during the rainy 126 3 DISCUSSION AND CONCLUSIONS season. I have shown in a minimal conceptual model that the self–amplifying nature of the moisture–advection feedback implies a threshold behaviour with respect to changes in the energy budget of the monsoon system. In particular, if specific humidity over the ocean falls below a critical value, no conventional monsoon circulation can develop according to the basic dynamics captured in the minimal model (section 2.3). These basic dynamics thus define a domain of existence for continental monsoon rainfall, which allows for abrupt transitions between wet and dry regimes when the threshold is crossed. Physically, the possibility for abrupt transition arises from the competition among the main heat transport processes during the rainy season. Although latent heat release through precipitation warms the atmospheric column, direct advection of heat is cooling it. Both processes become weaker when monsoon winds decrease, and thereby compensate each other with respect to the net heat injection into the atmospheric column. The threshold of this stabilizing effect is set by the net radiative cooling, which is a characteristic feature of low latitudes. I have demonstrated that this concept is qualitatively consistent with a series of abrupt monsoon transitions found in a proxy record of the East Asian summer monsoon for the penultimate glacial period. In this application, I have assumed a hysteresis that occurs when the threshold is crossed from different directions. While this assumption is not crucial for the transition behaviour, it changes the timing of the individual transitions such that they are all consistent, within dating errors, with those found in the proxy record. Physically, a hysteresis on these paleoclimatic timescales might be induced by inert climate components such as e.g. large-scale oceanic circulation or Himalayan glaciation, rather than by atmospheric processes. While it is thus not inconsistent with the underlying physics, it remains a hypothesis at this stage and should be further investigated in future research. My findings suggest that the moisture–advection feedback could indeed be the main physical mechanism within the dynamics of monsoon circulations that facilitates abrupt and persistent monsoon failure on long timescales. Beyond these qualitative results, I have estimated the threshold humidity values for major monsoon regions, using present–day reanalysis data. The resulting distribution of threshold values can be interpreted either as an uncertain estimate of a stationary critical threshold, or as a probability distribution of an interannually varying threshold. I have shown that the estimates become more robust when further relevant processes are incorporated in the conceptual model. However, it must be kept in mind that the conceptual model is designed in a minimalistic spirit, comprising only those physical processes that are essential for the moisture–advection feedback, and neglecting many other processes that are nevertheless important for a quantitative assessment. Therefore my estimates of the critical threshold need to be considered a first attempt of quantification. For a more accurate estimation, more complex models would be necessary that, for instance, take into account evaporation over land and associated soil moisture processes, and regionally specific features such as orography. An extended 127 analysis of paleoclimatic reconstructions as well as detailed present–day observations could also be useful to further constrain the results. However, the example of the Atlantic thermohaline circulation suggests that it might be generally very difficult to quantify thresholds in the climate system with the accuracy that would be necessary for future projection of a transition (Rahmstorf et al., 2005; Drijfhout et al., 2010). I have also suggested a basic physical mechanism for seasonal monsoon failure under modern climate conditions, which is again based on the moisture–advection feedback (section 2.4). Within the rainy season, rainfall over a certain period tends to reinforce the circulation by adding heat to the continental atmosphere, and thereby increases the probability for rainfall in subsequent days, as I have shown for India in observational data as well as in a comprehensive AOGCM. On the other hand, the AOGCM simulations feature years where average Indian summer monsoon rainfall is up to ∼70% below the long– term mean; this is much lower than any drought year observed within the past century. Examining one such dry year, I have identified a second self–amplifying feedback that counteracts the moisture–advection feedback: Subsidence of dry air from the upper troposphere dries out the monsoon winds and thereby further inhibits the onset of convection, leading to more subsidence, and so on. While this dry feedback is most dominant and visible in those years with very weak rainfall, I suggest that it is a general dynamical feature that, to a lesser extent, is also present in regular monsoon years. I have developed a minimal theory of intraseasonal monsoon dynamics that is based on the idea of a permanent interplay between these two, antagonistic feedbacks, where each of them tends to persist, while synoptic–scale weather events can perturb the currently active feedback and induce a flip into the other one. The timescale of this interplay is on the order of days, and follows from the timescale of synoptic and smaller–scale tropospheric features and the time that the heating anomaly induced by the moisture–advection feedback can persist in the atmosphere. I have framed this minimal theory in a simple, statistically predictive model for seasonal mean monsoon rainfall. The inherently dynamics of the two counteracting feedbacks produces a series of alternating wet and dry periods of varying length and frequency, which finally add up to a total seasonal rainfall amount. While this is a highly idealized representation of internally generated intraseasonal variability, the fact that real monsoon systems also exhibit variability on similar timescales supports the idea that an instability similar to the one captured in the simple model may indeed be at work in reality. This would offer an explanation for some of the observed intraseasonal variability, on the basis of internal monsoon dynamics. Note that these results do not contradict other sources of monsoon variability on various timescales. For example, the Madden–Julian Oscillation (Wheeler & Hendon, 2004) plays an important role for intraseasonal monsoon variability on a monthly timescale. A corresponding external forcing could in principle be added to the statistically predictive model without qualitatively altering its characteristics. On the other hand, it is a robust assumption that a major part of intraseasonal variability 128 3 DISCUSSION AND CONCLUSIONS is generated by internal monsoon dynamics and not by external forcing (Krishnamurthy & Shukla, 2000; Goswami et al., 2006). I have shown that this simple conceptual model explains the characteristic, bimodal frequency distribution of seasonal–mean Indian summer monsoon rainfall found in the AOGCM. Moreover, it can also reproduce a large portion of multidecadal monsoon variability when forced with central–Pacific mean sea level pressure (MSLP) anomalies in May, i.e. at the onset time; no external forcing is applied during the rest of the rainy season. Anomalous spring-time conditions in the central Pacific are assumed to induce atmospheric conditions that favor either convection or subsidence over the Indian region, thus leading to either intense or deficient monsoon onsets. This initial condition is then propagated into the seasonal–mean rainfall in a probabilistic sense; in principle however, even after a deficient onset an overall strong monsoon season can develop according to the basic internal dynamics. We can now delineate a common perspective for both seasonal and long–term monsoon failure. Under present–day climate conditions, the monsoon season is governed by the inherent instability induced by the self–amplifying moisture–advection feedback and the counteracting dry subsidence feedback. The seasonal–mean rainfall is therefore determined mainly by stochastic internal dynamics, in conjunction with an initial condition set by external forcing during the onset period. The resulting statistics of seasonal– mean rainfall include very dry years, or monsoon failure, albeit with a low frequency of occurrence (or low probability, if the frequency distribution is interpreted as a probability distribution). If however overall climatic conditions change substantially, the system can shift into a regime where the moisture– advection feedback cannot be established, and thus continental monsoon rainfall cannot be sustained. Such a regime shift would go along with crossing a threshold that is associated with non–zero values of expected seasonal–mean precipitation; in this sense, the regime shift would be abrupt, i.e. from “rain” to “no rain”. Of course, this is a highly simplified perspective. It cannot, and is not meant to, explain all relevant aspects of monsoon circulations, nor to make quantitatively accurate assessments or predictions of monsoon characteristics. I have taken this simplified approach because for understanding large–scale monsoon failure, whether seasonally or permanently, it might be a useful intermediate step to consider only the most fundamental physical processes that are essential for the non–linearity which is dominant for large temporal and spatial scales. My results are qualitatively consistent with abrupt monsoon transitions found in paleo–records, as well as with monsoon variability under present–day conditions as observed in a realistic climate model; and they offer a simple and readily understood explanation for both. This may be seen as supporting the simplified approach. It is important to note that, besides the quantitative weaknesses that result from the many simplifications, some of my conclusions may also depend on regional specifics. While in the first three articles 129 I have considered several major monsoon regions and set up a conceptual model that can be expected to hold for all those regions to a similar degree, the fourth study has focused especially on India. There is reason to believe that much of the fundamental dynamics I have extracted from the analysis of the Indian summer monsoon are universal across different monsoon regions, but detailed analysis of those other regions is needed to confirm this hypothesis. The final part of this thesis lends further support to my conceptual results, and it also sheds light on a possible risk associated with the response of unstable monsoon dynamics to future anthropogenic climate change. Simulations with a realistic climate model project seasonal failure of Indian summer monsoon rainfall to become much more frequent in response to global warming. The minimal, statistically predictive model closely reproduces this trend, with spring-time MSLP in the central Pacific again determining the initial condition at the monsoon onset. Within this simplified framework, the increase in monsoon failure is thus caused by a strong but delayed response of spring–time central Pacific MSLP to global warming. While in my analysis these central Pacific climate variations seem to be the dominant influence on the initial condition, other factors such as changing springtime Indian Ocean sea surface temperatures might play a significant role, too (Li et al., 2001), and could be translated into monsoon statistics using the statistically predictive model. Note that I have only considered climate simulations with a single AOGCM. Comparison with other comprehensive models is needed to test the robustness of the projected trends in Indian summer monsoon and central Pacific climate. The interrelation between the two needs to be investigated in detail using complex models as well as observations, in order to confirm the link suggested by my results. Considering the potential consequences of frequent large–scale monsoon failure in the near future, such research efforts appear worthwhile. References Auffhammer, M., Ramanathan, V., & Vincent, J. R. 2006. Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. Proceedings of the National Academy of Sciences, 103, 19668–19672. Burns, S. J., Fleitmann, D., Matter, A., Kramers, J., & Al-Subbary, A. A. 2003. Indian Ocean Climate and an Absolute Chronology over Dansgaard/Oeschger Events 9 to 13. Science, 301, 1365–1367. Cherchi, Annalisa, Alessandri, Andrea, Masina, Simona, & Navarra, Antonio. 2010. Effects of increased CO2 levels on monsoons. Climate Dynamics, (online), 1–19. Clark, C. O., Cole, J. E., & Webster, P. J. 2000. Indian Ocean SST and Indian summer rainfall: Predictive relationships and their decadal variability. Journal of Climate, 13(14), 2503–2519. Claussen, M. 1997. Modeling bio-geophysical feedback in the African and Indian monsoon region. Climate Dynamics, 54, 247–257. Claussen, M. 2009. Late Quaternary vegetation-climate feedbacks. Climate of the Past, 5(2), 203–216. Dash, S. K., Singh, G. P., Shekhar, M. S., & Vernekar, A. D. 2005. Response of the Indian summer monsoon circulation and rainfall to seasonal snow depth anomaly over Eurasia. Climate Dynamics, 24, 1–10. Drijfhout, S. S, Weber, S. L, & van der Swaluw, E. 2010. The stability of the MOC as diagnosed from model projections for pre-industrial, present and future climates. Climate Dynamics, 112. Ganopolski, A., Kubatzki, C., Claussen, M., Brovkin, V., & Petoukhov, V. 1998. The Influence of Vegetation-Atmosphere-Ocean Interaction on Climate During the Mid-Holocene. Science, 280, 1916–1919. Goswami, B. N., & Xavier, P. K. 2005. ENSO control on the south Asian monsoon through the length of the rainy season. Geophysical Research Letters, 32, L18717. Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S., & Xavier, P. K. 2006a. Increasing Trend of Extreme Rain Events Over India in a Warming Environment. Science, 314, 1442–1445. Goswami, B. N., Madhusoodanan, M. S., Neema, C. P., & Sengupta, D. 2006b. A physical mechanism for North Atlantic SST influence on the Indian summer monsoon. Geophysical Research Letters, 33, L02706. 132 REFERENCES Goswami, B. N., Wu, Guoxiong, & Yasunari, T. 2006. The annual cycle, intraseasonal oscillations, and roadblock to seasonal predictability of the Asian summer monsoon. Journal of Climate, 19(20), 5078–5099. Gregory, P. J., Ingram, J. S. I., & Brklacich, M. 2005. Climate change and food security. 360, 2139–2148. Gupta, A. K., Anderson, D. M., & Overpeck, J. T. 2003. Abrupt changes in the Asian southwest monsoon during the Holocene and their links to the North Atlantic Ocean. Nature, 421, 354–357. Hahn, D. G., & Shukla, J. 1976. An apparent relationship between Eurasian snow cover and Indian monsoon rainfall. Journal of Atmospheric Sciences, 33(12), 2461–2462. Haile, M. 2005. Weather patterns, food security and humanitarian response in sub-Saharan Africa. 360, 2169–2182. Hong, YT, Hong, B, Lin, QH, Zhu, YX, Shibata, Y, Hirota, M, Uchida, M, Leng, XT, Jiang, HB, Xu, H, Wang, H, & Yi, L. 2003. Correlation between Indian Ocean summer monsoon and North Atlantic climate during the Holocene. Earth and Planetary Science Letters, 211(3–4), 371–380. Kistler, R., Kalnay, E., Saha, S., White, G., Woollen, J., Chelliah, M., Ebisuzaki, W., Kanamitsu, M., Kousky, V., den Dool, H. Van, Jenne, R., & Fiorino, M. 2001. The NCEP/NCAR 50-year reanalysis. Bull. Amer. Meteor. Soc., 82, 247 – 267. Kripalani, R. H., Oh, J. H., & Chaudhari, H. S. 2007a. Response of the East Asian summer monsoon to doubled atmospheric CO2: Coupled climate model simulations and projections under IPCC AR4. Theoretical and Applied Climatology, 87(1-4), 1–28. Kripalani, R. H., Oh, J. H., Kulkarni, A., Sabade, S. S., & Chaudhari, H. S. 2007b. South Asian summer monsoon precipitation variability: Coupled climate model simulations and projections under IPCC AR4. Theoretical and Applied Climatology, 90(3-4), 133–159. Krishnamurthy, V., & Goswami, B. N. 2000. Indian monsoon-ENSO relationship on inter-decadal timescale. Journal of Climate, 13, 579–595. Krishnamurthy, V, & Shukla, J. 2000. Intraseasonal and interannual variability of rainfall over India. Journal of Climate, 13(24), 4366–4377. Kucharski, F., Molteni, F., & Yoo, J. H. 2006. SST forcing of decadal Indian monsoon rainfall variability. Geophysical Research Letters, 33(3), L03709. REFERENCES 133 Kumar, K. Krishna, Kumar, K. Rupa, Ashrit, R. G., Deshpande, N. R., & Hansen, J. W. 2004. Climate impacts on Indian agriculture. International Journal of Climatology, 24, 1375–1393. Lau, K. M., & Kim, K. M. 2006. Observational relationships between aerosol and Asian monsoon rainfall, and circulation. Geophysical Research Letters, 33, L21810. Levermann, A., Schewe, J., Petoukhov, V., & Held, H. 2009. Basic mechanism for abrupt monsoon transitions. Proceedings of the National Academy of Sciences, 106(49), 20572–20577. Li, J., Cook, E. R., Chen, F., Davi, N., D’Arrigo, R., Gou, X., Wright, W. E., Fang, K., Jin, L., Shi, J., & Yang, T. 2009. Summer monsoon moisture variability over China and Mongolia during the past four centuries. Geophysical Research Letters, 36, L22705. Li, T, Zhang, YS, Chang, CP, & Wang, B. 2001. On the relationship between Indian Ocean sea surface temperature and Asian summer monsoon. Geophysical Research Letters, 28(14), 2843–2846. Liu, X., & Yin, Z. 2002. Sensitivity of East Asian monsoon climate to the uplift of the Tibetan Plateau. Palaeogeography, Palaeoclimatology, Palaeoecology, 183, 223–245. Meehl, G. A. 1994. Influence of the Land Surface in the Asian Summer Monsoon: External Conditions versus Internal Feedbacks. Journal of Climate, 7, 1033–1049. Meehl, G. A., Stocker, T. F., Collins, W. D., Friedlingstein, P., Gaye, A. T., Gregory, J. M., Kitoh, A., Knutti, R., Murphy, J. M., Noda, A., Raper, S. C. B., Watterson, I. G., Weaver, A. J., & Zhao, Z.-C. 2007. Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press. Chap. Global Climate Projections. Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski, A., & Rahmstorf, S. 2005. The Earth System Model of Intermediate Complexity CLIMBER-3α. Part I: description and performance for present day conditions. Climate Dynamics, 25, 237–263. Overpeck, J. T., Anderson, D., Trumbore, S., & Prell, W. 1996. The southwest Indian Monsoon over the last 18000 years. Climate Dynamics, 12, 213–225. Parthasarathy, B., Munot, A.A., & Kothawale, D.R. 1988. Regression model for estimation of Indian foodgrain production from summer monsoon rainfall. Agricultural and Forest Meteorology, 42(2-3), 167 – 182. 134 REFERENCES Rahmstorf, S., Crucifix, M., Ganopolski, A., Goosse, H., Kamenkovich, I., Knutti, R., Lohmann, G., Marsh, B., Mysak, L. A., Wang, Z., & Weaver, A. 2005. Thermohaline circulation hysteresis: A model intercomparison. Geophysical Research Letters, 32, L23605. Rajeevan, M, Gadgil, Sulochana, & Bhate, Jyoti. 2010. Active and break spells of the Indian summer monsoon. Journal of Earth System Science, 119(3), 229–247. Ramanathan, V., Chung, C., Kim, D., Bettge, T., Kiehl, J. T., Washington, W. M., Fu, Q., Sikka, D. R., & Wild, M. 2005. Atmospheric brown clouds: Impacts on South Asian climate and hydrological cycle. Proceedings of the National Academy of Sciences, 102(15), 5326–5333. Rashid, Harunur, England, Emily, Thompson, Lonnie, & Polyak, Leonid. 2011. Late Glacial to Holocene Indian Summer Monsoon Variability Based upon Sediment Records Taken from the Bay of Bengal. Terrestrial Atmospheric and Oceanic Sciences, 22(2, Sp. Iss. SI), 215–228. Robock, A., Mu, M., Vinnikov, K., & Robinson, D. 2003. Land surface conditions over Eurasia and Indian summer monsoon rainfall. Journal of Geophysical Research, 108(D4), 4131. Schewe, J., Levermann, A., & Meinshausen, M. 2011a. Climate change under a scenario near 1.5◦ C of global warming: monsoon intensification, ocean warming and steric sea level rise. Earth System Dynamics, 2(1), 25–35. Schewe, J., Levermann, A., & Cheng, H. 2011b. A critical humidity threshold for monsoon transitions. Climate of the Past Discussions, 7. Sikka, D. R. 2003. Evaluation of monitoring and forecasting of summer monsoon over India and a review of monsoon drought of 2002. Proceedings of the Indian National Science Academy Part A, 69(5), 479–504. Srinivasan, J. 2001. A simple thermodynamic model for seasonal variation of monsoon rainfall. Current Science, 80(1), 73–77. Sun, Ying, Ding, Yihui, & Da, Aiguo. 2010. Changing links between South Asian summer monsoon circulation and tropospheric land-sea thermal contrasts under a warming scenario. Geophysical Research Letters, 37, L02704. Tao, F., Yokozawa, M., Zhang, Z., Hayashi, Y., Grassl, H., & Fu, C. 2004. Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation. 28, 23–30. Turner, A. G., & Hannachi, A. 2010. Is there regime behavior in monsoon convection in the late 20th century? Geophysical Research Letters, 37, L16706. REFERENCES 135 Wang, B. 2005. The Asian monsoon. Springer-Verlag. Wang, P., Clemens, S., Beaufort, L., Braconnot, P., Ganssen, G., Jian, Z., Kershaw, P., & Sarnthein, M. 2005a. Evolution and variability of the Asian monsoon system: state of the art and outstanding issues. Quaternary Science Reviews, 24, 595–629. Wang, Y., Cheng, H., Edwards, R. L., He, Y., Kong, X., An, Z., Wu, J., Kelly, M. J., Dykoski, C. A., & Li, X. 2005b. The Holocene Asian Monsoon: Links to Solar Changes and North Atlantic Climate. Science, 308, 854–857. Wang, Y., Cheng, H., Edwards, R. L., Kong, X., Shao, X., Chen, S., Wu, J., Jiang, X., Wang, X., & An, Z. 2008. Millennial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years. Nature, 451, 1090–1093. Webster, P. J. 1987a. The Elementary Monsoon. Pages 3–32 of: Fein, J. S., & Stephens, P. L. (eds), Monsoons. New York, N.Y.: John Wiley. Webster, P. J. 1987b. The Variable and Interactive Monsoon. Pages 269–330 of: Fein, J. S., & Stephens, P. L. (eds), Monsoons. New York, N.Y.: John Wiley. Webster, P. J., Magaña, V. O., Palmer, T. N., Shukla, J., Tomas, R. A., Yanai, M., & Yasunari, T. 1998. Monsoons: Processes, predictability, and the prospects for prediction. Journal of Geophysical Research, 103, 14,451–14,510. Webster, P. J., Toma, V. E., & Kim, H.-M. 2011. Were the 2010 Pakistan floods predictable? Geophysical Research Letters, 38, L04806. Wheeler, MC, & Hendon, HH. 2004. An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Monthly Weather Review, 132(8), 1917–1932. Yang, J., Liu, Q., Xie, S.-P., Liu, Z., & Wu, L. 2007. Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophysical Research Letters, 34, L02708. Zhang, P., Cheng, H., Edwards, R. L., Chen, F., Wang, Y., Yang, X., Liu, J., Tan, M., Wang, X., Liu, J., An, C., Dai, Z., Zhou, J., Zhang, D., Jia, J., & Johnson, K. R. 2008. A Test of Climate, Sun, and Culture Relationships from an 1810-Year Chinese Cave Record. Science, 322, 940–942. Zhang, R., & Delworth, T. L. 2005. Simulated Tropical Response to a Substantial Weakening of the Atlantic Thermohaline Circulation. Journal of Climate, 18, 1853–1860. Danksagung Mein Dank gilt zuallererst Anders Levermann für die hervorragende Betreuung, die für den Erfolg meiner Arbeit und nicht zuletzt die Freude an derselben von unschätzbarem Wert war und mich stets sehr motiviert hat. Herzlich bedanken möchte ich mich bei meinen Kolleginnen und Kollegen am PIK. Malte, Hermann, Vladimir, danke für die gute Zusammenarbeit bei den jeweiligen Studien. Arathy, Alex, Carl, Dana, Daria, Dim, Friederike, Hendrik, Johannes, Katja, Mahé, Maria, Marianne, Matthias, Rica, Tore, Torsten... danke für Rat und Tat, Obst und Kaffee, Wandern und Oper, für schöne drei Jahre! Dank gebührt auch der Heinrich-Böll-Stiftung für die Finanzierung meiner Promotion und für ein Begleitprogramm, das mir Ausblicke und Eindrücke jenseits der Naturwissenschaft ermöglicht hat; und der Studienstiftung des deutschen Volkes für die ideelle Förderung. Schließlich danke ich meiner Familie für alles. “Siehst du! Jetzt fängt alles erst richtig an. Was du bisher erfahren hast, das war doch nur eine Vorahnung vom Anfang und längst noch nicht alles.” Hans Bemmann, Stein und Flöte Diese Arbeit ist bisher an keiner anderen Hochschule eingereicht worden. Sie wurde selbständig und ausschließlich mit den angegebenen Mitteln angefertigt. Jacob Schewe Potsdam, im Mai 2011 Diese Arbeit wurde durchgeführt am Potsdam–Institut für Klimafolgenforschung