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Lewis, S., A. King, and S. Perkins-Kirkpatrick, 2016: Defining a new normal for
extremes in a warming world. Bull. Amer. Meteor. Soc. doi:10.1175/BAMS-D-160183.1, in press.
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Defining a new normal for extremes in a warming world
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Sophie C. Lewisa,d* , Andrew D. Kingb,d and Sarah E. Perkins-Kirkpatrickc,d
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a
Fenner School of Environment and Society, The Australian National University,
Canberra, ACT, Australia
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b
School of Earth Sciences, The University of Melbourne, Parkville, Victoria,
Australia
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c Climate
Change Research Centre, University of New South Wales, Sydney,
UNSW, Australia
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d
ARC Centre of Excellence for Climate System Science
* Corresponding
author: Tel: +61 2 6125 2623; email: [email protected]
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Abstract
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The term ‘new normal’ has been used in scientific literature and public
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commentary to contextualise contemporary climate events as an indicator of a
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changing climate due to enhanced greenhouse warming. A new normal has been
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used broadly, but tends to be descriptive and ambiguously defined. Here we review
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previous studies conceptualising this idea of a new climatological normal and argue
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that this term should be used cautiously and with explicit definition in order to avoid
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confusion. We provide a formal definition of a new climate normal relative to present
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based around record-breaking contemporary events and explore the timing of when
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such extremes become statistically normal in the future model simulations. Applying
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this method to the record-breaking global average 2015 temperatures as a reference
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event and a suite of model climate models, we determine that 2015 global annual
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average temperatures will be the new normal by 2040 in all emissions scenarios. At
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the regional level, a new normal can be delayed through aggressive greenhouse gas
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emissions reductions. Using this specific case study to investigate a climatological
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‘new normal’, our approach demonstrates the greater value of the concept of a
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climatological new normal for understanding and communicating climate change
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when the term is explicitly defined. This approach moves us one step forward to
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understanding how current extremes will change in the future in a warming world.
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Capsule
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The term ‘new normal’ is defined and applied to 2015 record-breaking
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temperatures. A new normal can be useful for understanding and communicating
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extremes in a changing climate when precisely defined.
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1. Background
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The term ‘a new normal’ has been used to describe various aspects of recently
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observed climate and weather. This term is widely used in mainstream media reports
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to succinctly categorise observed extreme weather and climate events as both
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unusual and influenced, in some regard, by anthropogenic climate change (e.g.
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Lewis and Perkins-Kirkpatrick 2016; Franz Prein 2016). The use of this terminology
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that climatological events represent a new normal is also used in scientific literature
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focused on understanding recent extreme climate events. Trenberth and co-authors
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(2015) argue
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“The climate is changing: we have a new normal. The environment in which all
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weather events occur is not what it used to be. All storms, without exception, are
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different. Even if most of them look just like the ones we used to have, they are
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not the same.”
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Other analyses have attempted to distinguish whether ‘new normal’ climatic
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conditions have emerged in a specific region (University of Regina. Canadian Plains
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Research Center 2010; Wood et al. 2013). These studies implicitly define the
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observational record as the ‘old normal’ and delineate observed contemporary
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climatological characteristics as the ‘new normal’, which provides a diagnostic of an
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unprecedented change in climate due to greenhouse warming. However, such
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terminology tends to be descriptive, and used ambiguously without precise definition
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in both scientific literature and public commentary on climate change. In this study,
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we explore the concept of a new normal for climate and propose a framework for its
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calculation. Specifically, what is meant by a new normal and is this term a useful
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concept for understanding climatic change?
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Although a new normal has been applied to describe a diversity of weather and
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climate phenomena, we focus our specific exploration on a specific set of climate
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events. Directing our analysis of a new normal to extreme temperatures in the first
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instance is motivated by several factors. First, extreme weather and climate events
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occurring on sub-daily to multi-year timescales have significant socio-economic costs
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and impacts on natural systems. Furthermore, there has been an observed increase
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in heat extremes on various temporal scales in the observed record (Perkins et al.
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2012; Coumou and Rahmstorf 2012). As a result, understanding the causes, and
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potential future changes in the frequency, intensity and spatial extent of temperature
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anomalies has become an active research direction (National Academies of
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Sciences, Engineering, Medicine 2016). Extreme temperatures are also appropriate
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for exploring the concept of a new normal, as this term is popularly applied following
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extreme or record-breaking temperature events. For example, Australian heatwaves
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(Perkins and Pitman 2014) and anomalously warm months (Lewis and Perkins-
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Kirkpatrick 2016), and record-breaking global average temperatures have been
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referred to as a new normal. In summary, the public and scientific interest in extreme
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observed temperatures, and the common discussion of such events as indicative of
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a “new normal” enforces the need for a clear definition of this term.
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Extreme climate events have increasingly been explored from an attribution
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perspective, now forming the basis for a dedicated annual report investigating the
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contributing factors to observed extremes (e.g. Peterson et al. 2012). Attribution
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studies provide insight into the relative contributions of natural and anthropogenic
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forcings to a specific observed extreme weather or climate event (Stott et al. 2004).
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The results of event attribution studies are described as potentially useful for
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providing information for preparing for future climatic change. For example, the
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likelihood of hot Australia summers, such as the record of 2012/2013 was found to
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have increased fivefold due to anthropogenic forcings, including greenhouse gases
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(Lewis and Karoly 2013). As a corollary, an increase in the frequency of hot
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summers is expected under increased greenhouse warming, although such results
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around future probabilities are not typically within the purview of attribution analyses.
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A review of attribution approaches states that “By determining the causes of
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extreme weather events being observed now, robust information can also be
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provided on the extent to which a specific extreme event is a harbinger of the future,
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and therefore an impact against which a society, which the recent event has shown
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to be vulnerable to, may want to develop further resilience” (Stott et al. 2012).
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However, without a specific focus on providing insight into future projections about
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the characteristics of contemporary extremes, attribution studies alone are limited in
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informing adaptive decision-making.
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A further category of climatological studies has attempted to address questions
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around extremes by posing the idea of ‘time of emergence’ (Mora et al. 2013; King et
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al. 2015). These studies investigate when the signal of climate change will emerge
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distinctly from the background noise of climate variability (Hawkins et al. 2014),
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arguing that the time of emergence (ToE) metric is potentially important for
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adaptation planning. For example, Mora and co-authors (2013) determined the
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projected year in which the mean climate of a given location moves to a state
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continuously outside the bounds of simulated historical variability. Recognising the
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importance of extremes for societal impacts, King and co-authors (2015) proposed
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the alternative time of anthropogenic emergence (TAE) and calculate the emergence
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for various extreme indices in historical and future model simulations relative to a
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quasi-natural state.
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Both attribution and time of emergence approaches have a collective overarching
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aim of informing adaptation planning. However, these approaches only go part way
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to doing so. Attribution studies quantify the influence of a specific forcing (e.g.
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anthropogenic greenhouse gases) on current climate records, but do not specifically
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address questions around the occurrence of such records in the future, which
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depends on the nature of future changes in temperatures, both through the rate of
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warming and higher order changes in the shape of the temperature distributions.
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Similarly, time of emergence studies have largely focussed on determining when
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future mean climates can be considered outside the range of historical variability, but
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typically do not focus on specific events and their future occurrence. Hence, the
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potential of the information these studies provide for informing decision-making
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around future planning for potentially high-impact events is inherently limited.
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2. Time of emergence of new normal
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Previous studies have begun to specifically investigate the incidence of historical
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heat records in future greenhouse gas emissions scenarios (Christidis et al. 2014).
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One such recent study examined the incidence of historical record summers in the
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future, determining that historically hot summers will be the norm for large areas
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globally within the next 20 years (Mueller et al. 2016). This supports previous model-
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based findings centred on exploring changes in heat regimes. For example,
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Diffenbaugh and Scherer (2011) previously determined the point in time at which the
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coolest warm-season of the 21st century became hotter than the hottest warm-
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season of the late 20th century in simulations, indicating a new and permanent
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climate regime shift. Such approaches extend information provided by event
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attribution analyses and explicitly indicate that historical temperature records on
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seasonal timescales occur more frequently under increased greenhouse gas
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warming. While these future heat records approaches demonstrate that
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contemporary extremes occur more frequently in the future, they are also limited in
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providing insight into current extremes broadly. Furthermore, an examination of the
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widely used concept of a new normal more broadly has not yet been made.
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Our study builds on this set of prior studies in examining the concept of a new
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normal by combining aspects of attribution, time of emergence and future heat
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records analytical approaches. We note that from a purely statistical viewpoint, a
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new normal may be viewed as having limited utility. An extreme event is typically
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reported as a new normal when its occurrence is considered to have been influenced
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by anthropogenic warming, and will likely become more frequent under future
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warming. However, by definition the climate system under the influence of
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anthropogenic warming is nonstationary and exhibits a nonconstant mean (i.e. a
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warming trend). Here in a true statistical sense ‘new’ and ‘normal’ are essentially
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oxymoronic; such extremes cannot be considered as categorical evidence of a
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distinct state. However, moving beyond semantics, the concept of a new normal is
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persistent and widely employed for framing and understanding observed weather
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and climate phenomena. Hence, we instead propose the more precise concept of
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the time of emergence of a new normal (ToENN).
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The time of emergence of a new normal is defined here as having occurred when
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more than 50% of future anomalies exceed a reference event in magnitude or
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intensity. This definition can be applied broadly to a diversity of events. We begin by
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applying this general ToENN framework using the record-breaking global average
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2015 temperatures as a reference event. When will years as hot as 2015 become
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the norm? We focus on annual and seasonal-scale events, rather than short
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duration, high impact extremes such as heatwaves, as such large-scale observed
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record-breaking events have been widely discussed in the public domain using a
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‘new normal’ framing. However, our proposed methodology is intended to be applied
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to investigating events across spatial and temporal scales.
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3. Case study: 2015 temperatures
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We investigate the ToENN2015 in Coupled Model Intercomparison Phase 5
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(CMIP5) climate projections (Taylor et al. 2012). Using a key element of the event
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attribution approach, we focus on an observed extreme event, defined as the
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magnitude of the highest global annual-average mean temperature (Tmean)
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anomaly recorded in the observational record (∆T2015). We combine this attribution-
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derived focus with a key element of the time of emergence approach to explore the
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timing of when extreme contemporary annual and seasonal-scale temperatures
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become statistically normal. We formally define and assess the concept of a ‘new
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normal’ relative to present based around extreme temperatures, which pose a
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significant risk of societal impacts (IPCC 2012).
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We demonstrate ToENN by using a single realisation from each of 18 models
participating in the fifth phase of the Coupled Model Intercomparison Project
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(CMIP5) with temperature data (tas) available for standard historical and
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historicalNat experiments, and Representative Concentration Pathway (RCP)
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experiments RCP2.6, RCP4.5, RCP6.0 and RCP8.5 (Taylor et al. 2012) (see Table
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1). Global mean annual land-only temperature anomalies are calculated for land
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surface gridboxes relative to each model’s 1961-1990 climatology. Models are
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regridded onto a uniform 1.5 degree latitude by 1.5 degree longitude horizontal grid.
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In addition to determining ToENN2015, we also explore the time of emergence of
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seasonal and regional-scale temperatures. Regional area-mean temperatures are
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calculated for Australia (50-10°S, 110-155°E), Europe (30-70°N, 10°W-60°E), Asia
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(10-70°N, 60-170°E) and North America (20-70°N, 160°-50W). Seasonal (DJF and
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JJA) data are also analysed for regions. Observations are derived as the mean from
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the GISTEMP (Hansen et al. 2010) and CRUTEM4 (Morice et al. 2012) global
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temperature datasets (Table 2).
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We apply a model evaluation step demonstrated in CMIP5-based attribution
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studies, and compare model variability in historical simulations against observed
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variability (Lewis and Karoly 2013; Lewis et al. 2014). Using a Perkins skill score
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(Perkins et al. 2007), we assess the similarity of probability density functions (PDFs)
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of modelled and observed regional-average temperatures. A skill score is
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determined for each model as a measure of the common area between simulated
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and observed distributions, which ultimately provides a simple measure of the
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similarity of models to observations across the entire PDF. We compare each
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CMIP5 model’s historical realisation to both GISTEMP and CRUTEM4 for annual,
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DJF and JJA global averages. Models with skill score below 0.5 when compared to
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either observational dataset for any of these temporal averages are excluded from
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further analysis. This resulted in 11 of 18 models (Table 2) available for further
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analysis, and a multi-model mean, and 5th and 95th percentile values are calculated
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for each experiment using these 11 models (Fig. 1). Following this evaluation step,
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the historical experiment is analysed for years 1976-2005 and the historicalNat 1900-
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2005. While further CMIP5 models are available than the 11 utilised, we assert that
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greater confidence in establishing a ToENN occurs when models capture observed
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variability over the historical period.
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To determine the time that 2015 emerges as the new normal using a multi-model
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ensemble, we start by calculating ToENN2015 for each model realisation. For this
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case study, the ToENN future state is the subsequent 20-year period. In order to
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account adequately for multi-decadal variability (Hawkins et al. 2014), the ToENN in
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each model realisation is the year that for any subsequent 20-year period, 50% of
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anomalies exceed the reference event. We take the multi-model average median
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across the 11-member ensemble (Fig. 2). For global and regional area-mean ToENN
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calculations, we additionally report a very likely ToENN value if an event has
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emerged as a new normal in >90% of model realisations. This provides an
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assessment of the spread of ToENN values in the model realisations. We note that
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the year for differing reference events will also be different in most cases, such that,
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for example the hottest DJF in Australia occurred in 2013 and hence ToENN2013
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would be explored for this event at this location.
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Following Knutti and Sedlácĕk (2012), we apply a measure of robustness (R) to
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ToENN calculations, which combines measures of ranked probability skill score and
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the ratio of model spread to the predicted change. R is defined as:
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R=1-A1/A2,
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where A1 is the integral of the squared area between two cumulative density
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functions (each RCP model realisation and the multi-model mean) and A2 is the
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integral of the squared area between two cumulative density functions (the RCP
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multi-model mean and the historical multi-model mean). A higher robustness scores
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corresponds to a relative model agreement on sign and magnitude, with R=1
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representing perfect model agreement, and stippling in Figure 2 indicates robustness
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of >0.8.
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Return times of heat events in the various regions (Australia, Europe, Asia and
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North America) are also calculated to allow comparison with previous studies.
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Following Christidis et al. (2014), return times are based on distributions of
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temperature anomalies. The probability of exceedance of ∆T2015 is calculated and
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the return time computed as the inverse of the probability. Return times were
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estimated using a bootstrap resampling technique, whereby sub-samples of 50% of
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model data were resampled 10,000 times, with replacement, for each experiment
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suite and a spread of probability values determined with 90th values percentile
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presented in this study as the calculated return time. In several cases, the precise
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return time could not be reliably quantified as such temperatures anomalies were
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very rare or did not occur in model realisations. In this case, return times are
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reported as greater than the number of simulated years (N), >N.
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Finally, a more detailed investigation of ToENN uncertainties is made to
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determine the impact of experimental design on the concept of a new normal for
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extreme seasonal- and annual-mean temperature anomalies. The ToENN and return
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times calculated here are robust to changes in definition and experimental design.
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For example, including the full set of 18 CMIP5 models does not impact the
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emergence patterns of annual and seasonal-scale regional and global temperatures.
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The ToENN for annual average global-mean temperatures was re-examined using
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an ensemble defined using multiple realisations from models (in this case three
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realisations each from MIROC5, CESM-CAM5 and CSIRO-Mk3-6-0). This ensemble
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produced ToENN values similar to the ensemble constitute by single realisations of
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11 models, though a new normal tended to occur slightly earlier.
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3. A new normal for extreme temperatures
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The timeseries of annual average global Tmean shows that the 2015 record
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occurs outside the simulated range in the historicalNat multi-model ensemble and
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near the limit of the historical experiment terminating in 2005 (Fig. 1). However, such
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global anomalies fall well below the multi-model mean for all Representative
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Concentration Pathway scenarios, indicating ToENN will occur. The time of
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emergence of a new normal is explored using individual model realisations for the
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RCP scenarios (Fig. 2), which demonstrates that 2015 emerges as the new normal
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in all RCP scenarios for >90% of models by 2040. The median time of emergence of
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2015 as the new norm occurs between 2020 and 2030 under all emissions
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trajectories. The small contribution of scenario uncertainty in near-term projections
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has been reported elsewhere (Hawkins and Sutton 2009). The spread of ToENN
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values in individual model realisations is lowest in the high-end RCP8.5 emissions
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scenario, where in all models a new normal for ∆T2015 is reached by 2040.
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When framed in terms of return times (Christidis et al. 2014) of future temperature
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anomalies exceeding ∆T2015, exceedance of this threshold very likely (>90%
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confidence) has a return period of 1-in-2 years in all RCP scenarios by 2006-2025
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(Table 3), but occurs infrequently in the historical simulations for 1976-2005. By
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2026-2045, such a temperature event occurs every year in the higher-end pathways
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(RCP8.5, RCP6.0 and RCP4.5). While it should be noted that the lack of volcanic
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eruptions necessarily required within the RCP forcing suite may be important in
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simulated temperatures the near past and near future, the frequent occurrence of
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contemporary extremes and emergence of a new normal of 2015 in the early part of
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the RCP pathways demonstrates rapid warming after the historical experiment
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finishes in 2005 (see Fig. 1).
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The ToENN for annual-mean temperatures is next explored at lower spatial-
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scales. The time of emergence of a new normal is first calculated for each model
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land surface gridbox for annual average temperatures (Fig. 3) using the current
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maximum temperature anomaly observed for each gridbox (as available up to 2015).
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An ensemble robustness assessment was also applied based on relative model
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agreement of sign and magnitude, demonstrating large spatial areas of model
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agreement on the sign and relative magnitude of simulated temperature changes,
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with the most notable exceptions over Antarctica where observations are poor, and
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hence not shown in Figure 3. While all scenarios show that a new normal for annual
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Tmean occurs for the majority of land surfaces (>70%) before the end of the century,
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there is a clearer scenario-dependence in the time of emergence of a new normal on
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the gridbox scale, where the signal-to-noise ratio is likely to be lower than for the
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global-average. Under aggressive greenhouse gases emission cuts (RCP2.6
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scenario), emergence occurs for 72% of land areas by 2100, but only for 1% of land
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areas in the first half of the century. In contrast, for the CMIP5 high-level emissions
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scenario, RCP8.5, which represents our current emissions trajectory (Peters et al.
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2012), the ToENN is consistently earlier than lower-end emission scenarios, and a
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robust emergence of a new normal occurs over most land surfaces (98%) by the end
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of the century and in 12% of locations before 2045 (Fig. 3d).
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In contrast to annual average temperatures, the corresponding ToENN for
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seasonal Tmean for various regions show a greater sensitivity to the various
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emissions scenario used in the model experiments (Figures 4-7). We focus on DJF
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(austral summer/boreal winter) regional-mean temperatures as an example. ToENN
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occurs comparatively earlier for Australia (Figure 5) than for Northern Hemisphere
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regional DJF ToENN, which are generally later in the 21st century and a larger model
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spread is simulated. In Australia, record summer (DJF) temperatures (∆T2013, (Lewis
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and Karoly 2013)) are a new normal in the majority of model realisations by 2035 in
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all scenarios. On these shorter temporal and lower spatial scales, the emergence of
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a new normal can be avoided in low emission pathways in some model realisations.
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For example, the mean ToENN for DJF temperatures in Europe and Asia, and
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particularly for North America, occurs later in the 21st century, or not at all, in the
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RCP2.6 scenario, compared with RCP6.0 or RCP8.5. The scenario dependence of
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regional ToENN is more complicated in the JJA (boreal winter) season in Asia and
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Europe, due to the complexity of the emissions trajectories in the RCP in the near-
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term (Peters et al. 2012). While the RCP2.6 scenario has a lower greenhouse gas
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forcing than RCP4.5, the near term warming projected is greater and more variable
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due to aerosol contributions that affect these highlighted regions (Chalmers et al.
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2012).
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This result demonstrates that at the regional-scale, a new normal for
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contemporary extremes can be avoided for certain seasonal-scale extremes with
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aggressive greenhouse gas emission reductions. The larger model spread of ToENN
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on regional and seasonal scales occurs as the signal to noise ratio is lower
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regionally than for global annual average-temperatures. Furthermore, the temporal
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and spatial scale dependence of extremes is widely noted in attribution approaches
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that largely focus on large-scale phenomena such as coherent heatwaves (e.g. Stott
345
et al. 2004; Schär et al. 2004) or continental-scale seasonal heat (e.g. Lewis and
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Karoly 2014; Knutson et al. 2014). Previous studies investigating the time of
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emergence of mean and extreme climate indices also identify regional and seasonal-
348
scale differences which are dependent on variability (Mora et al. 2013; King et al.
349
2015).
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4. Communicating changing extremes
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We have proposed a formal definition of a new normal based on when a
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particular extreme event emerges as a new normal in a future climate states, which
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we call the time of emergence of a new normal. We have demonstrated this general
355
framework for a new normal using the case study of the record-breaking global
356
average 2015 temperatures. Based on CMIP5 simulations of alternative future
357
emission scenarios and realisations of multiple contributing models, we show 2015
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global annual-mean temperature anomalies will emerge as the new normal by 2040
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at the latest and is unavoidable even in emissions scenarios representing aggressive
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greenhouse gas cuts (i.e. RCP2.6 (Peters et al. 2012)). In contrast, a new normal in
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local- and regional-scale temperatures can be delayed in low emissions scenarios,
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suggesting that if greenhouse gas emissions were to fall, the majority of places
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would benefit, as the extremes of the world today would not become the new normal
364
in the future. This framework can be employed at a multitude of spatial and temporal
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scales and for a variety of extreme event types in order to understand the
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occurrence of contemporary extremes of high societal and economic risk in the
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future.
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The results of this specific case study support previous examinations of historical
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records in future projections under varying emissions scenarios. For example,
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Mueller and co-authors (2016) examined summer mean temperatures and
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determined that historically hot summers would be the norm within the next 20 years
373
for half the world’s population. Furthermore, many areas are likely to move into a
374
new seasonal heat regime within the next four decades (Diffenbaugh and Scherer
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2011). On a regional scale, historically hot summers are projected to be the norm in
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Eastern China within two decades in the CMIP5 mid-range RCP4.5 scenario (Sun et
377
al. 2014). Later dates were determined by Mora and co-authors’ (2013) analysis,
378
however, this instead focused on a distinct emergence from the range of historical
379
variability, rather than on changing frequency of contemporary extremes in future
380
projections.
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The consistency of these results looking at the increased frequency of historical
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record-breaking temperatures occurring on regional and seasonal-scales in future
384
projections, together with our ToENN example presented here, collectively
16
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demonstrate the value of the concept of a new normal. While event attribution
386
studies are increasingly useful for understanding and disentangling the multiple
387
contributing factors to observed extremes, they are limited in providing insight into
388
climate extremes in the future. That is, attribution studies determining that
389
anthropogenic climate change substantially influenced an observed event infer that
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type of event will occurring more frequently in a future of enhanced warming (Lewis
391
and Karoly 2013) and focus narrowly on the frequency of historical extremes.
392
Alternatively, studies centred on the future occurrence of historical extremes (Mueller
393
et al. 2016; Sun et al. 2014) encompass an assessment of both event frequency and
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the increasing magnitude of events, relative to a historical baseline. This is
395
demonstrated in Figure 1 where the 2015 record-breaking global-average
396
temperatures are projected to rapidly become cooler than average in the near future.
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Our case study of the 2015 global-average record temperatures also
399
demonstrates the utility of the new normal concept for the communication of climate
400
change, where it is clearly defined. This is one proposal for understanding this
401
concept from a scientific perspective, prompted by the description of observed
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seasonal- to annual-scale extremes as a ‘new normal’ (Lewis and Perkins-Kirkpatrick
403
2016). Alternative definitions to the time of emergence of a new normal are possible
404
and potentially equally useful. For example, a new normal could alternatively be
405
defined using the variability in observed climates and future climate projections.
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Other useful explorations could investigate specific classes of events, such as so-
407
called ‘extreme extremes’ like as heatwaves (e.g. Perkins and Pitman 2014) or
408
floods, or holistically examine climatic conditions in a specific region (e.g. Wood et al.
17
409
2013). The generic framework provided here is ideal for expansion to other
410
categories of events and their definition, and for exploration in other model datasets.
411
412
The term ‘new normal’ has been used widely to redress the misplaced perception
413
that climate change impacts will occur only as a future problem, rather than as a
414
influence on present climates (Perkins and Pitman 2014). Framing contemporary
415
phenomena as a new normal readily encapsulates the contribution of climate change
416
to current weather and climate events (Trenberth et al. 2015). However, the use of
417
the term without precise definition in both scientific literature and public commentary
418
on climate change limits its utility. First, the vague use of the term negates the
419
importance of natural climate variability. From a simple perspective, if 2015 is a new
420
normal for global temperatures, what if 2016, 2017 or 2018 are cooler? Second, the
421
use of the term without definition provides limited information about climate change
422
impacts and risks, which are circumvented by a precise description. Applying the
423
new normal definition based on a time of emergence approach and the case study
424
that we offer here, we expect with high confidence that global average temperatures
425
greater than those observed in 2015 will occur at least every second year by 2040.
426
This precisely defined new normal effectively communicates the influence of climate
427
change in the record-breaking temperatures and their changing occurrence in the
428
future. In combination with attribution and time of emergence studies, this approach
429
can help inform adaptation strategic for climate change.
430
431
Acknowledgements
432
This research was supported by ARC DECRAs (160100092 to S.C.L.) and
433
(DE140100952 to S.E.P.-K), the ARC Centre of Excellence for Climate System
18
434
Science (grant CE 110001028) and the NCI National Facility. We thank the Bureau
435
of Meteorology, the Bureau of Rural Sciences, and CSIRO for providing AWAP data.
436
We acknowledge the WCRP’s Working Group on Coupled Modelling, which is
437
responsible for CMIP. The U.S. Department of Energy’s PCMDI provides CMIP5
438
coordinating support.
439
440
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Figure Captions
541
Figure 1 Multi-model mean global temperature change relative to 1961-1990 for
542
CMIP5 historicalNat, historical (1850-2005) and RCP (2006-2100) scenarios. The
543
5th-95th percentile model range is shown for the historical and RCP scenarios. The
544
multi-model mean values area shown in solid lines (left) for each CMIP5 experiment
545
and black dots (right) for means across the entire time period. The horizontal line
546
indicates the global annual-average temperature anomaly observed in 2015 (∆T2015).
547
548
Figure 2 Emergence of new normal for global annual-mean temperatures. Global
549
average temperature changes are shown for each RCP scenario for multi-model
550
mean (and 5th-95th percentile range) relative to 1961-1990. Plot circles indicate the
551
time of emergence of a new normal in each model realisation and the corresponding
552
average annual-mean temperature in the 20-year period centred on the year of
553
emergence. The horizontal line shows the highest observed anomaly (∆T 2015).
554
Vertical lines show the median (>50%) and very likely (>90%) time of emergence of
555
new normal in each scenario for the multi-model ensemble (where n=11).
556
557
Figure 3 Maps of the multi-model median decade of emergence of a new normal for
558
annual average temperatures in each gridbox for various RCP scenarios. Grey
559
stippling marks areas of high robustness (>0.8), where corresponding to a relatively
560
high level of model agreement on sign and magnitude. White areas (where no
561
ToENN value is shown) indicate areas of missing data in observational temperature
562
products and Antarctica is excluded due to poor observational coverage.
563
564
22
565
Figure 4 As for figure 2, but showing the emergence of new normal for Australian
566
DJF temperatures. A black cross at the end of the century represents model
567
realisations where emergence does not occur prior to 2100. In this case, vertical
568
mean and very likely lines are not shown for that experiment (i.e. RCP2.6). In cases
569
where ToENN occurs in between 2090-2100, markers are plotted at the
570
corresponding for the mean temperature over this decade.
571
572
Figure 5 As for figure 4, but showing the emergence of new normal for European
573
DJF temperatures.
574
575
Figure 6 As for figure 4, but showing the emergence of new normal for Asian DJF
576
temperatures.
577
578
Figure 7 As for figure 4, but showing the emergence of new normal for North
579
American DJF temperatures.
580
581
Table 1 Details of CMIP5 experiments analysed.
582
583
Table 2 CMIP5 models and observational datasets analysed. .
584
585
Table 3 Projected return times (years) of global mean annual, DJF and JJA average
586
temperature anomalies exceeding the largest magnitude in the observed record.
587
Return times are expressed as >N where the probability of occurrence of
588
temperatures above this threshold are too small to be accurately estimated, or do not
589
occur at all in N simulated years and as <2 where simulated temperatures greater
23
590
than the most extreme observed are very likely to occur more frequently than in
591
every second projected year.
592
593
24
Manuscript (non-LaTeX) Tables
Click here to download Manuscript (non-LaTeX) tables.docx
Table 1 Details of CMIP5 experiments analysed.
Model Experiment
historical
historicalNat
RCP2.6
RCP4.5
RCP6.0
RCP8.5
Major forcings
Anthropogenic (greenhouse gases, aerosols, ozone) and natural (solar,
volcanics)
Solar, volcanics
Anthropogenic (greenhouse gases, aerosols, ozone scenarios) and
natural (solar). Radiative forcing reaches a maximum near the middle of
the twenty-first century before decreasing to 2.6 W m−2.
Anthropogenic (intermediate greenhouse gases, aerosols, ozone
scenarios) and natural (solar).
Anthropogenic (intermediate greenhouse gases, aerosols, ozone
scenarios) and natural (solar)
Anthropogenic (greenhouse gases, aerosols, ozone scenarios) and
natural (solar). Radiative forcing reaches a level of about 8.5 W m−2 at
the end of the century.
Table 2 CMIP5 models and observational datasets analysed. .
Models
bcc-csm1-1
CCSM4
CESM1-CAM5
GFDL-ESM2M
GISS-E2-H
GISS-E2-R
IPSL-CM5A-LR
IPSL-CM5A-MR
MIROC-ESM
MRI-CGCM3
NorESM1-M
Observations
GISTEMP
CRUTEM4
Table 3 Projected return times (years) of global mean annual, DJF and JJA
average temperature anomalies exceeding the largest magnitude in the
observed record. Return times are expressed as >N where the probability of
occurrence of temperatures above this threshold are too small to be
accurately estimated, or do not occur at all in N simulated years and as <2
where simulated temperatures greater than the most extreme observed are
very likely to occur more frequently than in every second projected year.
ANNUAL
HistoricalNat
Historical(1976-2005)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
DJF
HistoricalNat
Historical(1976-2005)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
JJA
HistoricalNat
Historical(1976-2005)
RCP2.6
RCP4.5
RCP6.0
RCP8.5
20062025
Return Times
2026- 20462045
2065
20662085
20862100
>N
150
2.5
2.5
2.5
2.5
<2
<2
<2
<2
<2
1
1
1
<2
1
1
1
<2
1
1
1
3.2
3.4
3.3
3.3
<2
<2
<2
1
<2
1
1
1
<2
1
1
1
<2
1
1
1
<2
<2
<2
<2
<2
<2
1
1
<2
1
1
1
<2
1
1
1
<2
1
1
1
>N
>N
>N
75
Rendered Figure 1
Click here to download Rendered Figure fig1.pdf
Annual Tmean anomalies (K)
RCP8.5
RCP6.0
RCP4.5
RCP2.6
RCP8.5
6
Historical
7
Observations
Historical (1850-2005)
RCP (2006-2100)
HistoricalNat
HistoricalNat
8
b. Ensemble range
Observations
a. Global average Tmean
5
RCP6.0
4
3
RCP4.5
2
1
RCP2.6
∆ T2015
0
-1
1900
1950
Year
2000
2050
Figure 1 Multi-model mean global temperature change relative to 1961-1990 for CMIP5
historicalNat, historical (1850-2005) and RCP (2006-2100) scenarios. The 5th-95th percentile model range is shown for the historical and RCP scenarios. The multi-model mean
values area shown in solid lines (left) for each CMIP5 experiment and black dots (right) for
means across the entire time period. The horizontal line indicates the global annual-average
temperature anomaly observed in 2015 (∆T2015
Rendered Figure 2
Click here to download Rendered Figure fig2.pdf
a. RCP2.6
c. RCP6.0
Annual Tmean anomalies (K)
7
Annual Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
1
2020
2040
2060
Year
2080
2100
0
2020
b. RCP4.5
2060
Year
2080
2100
2080
2100
d. RCP8.5
Annual Tmean anomalies (K)
7
Annual Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
2040
1
2020
2040
2060
Year
2080
2100
0
2020
2040
2060
Year
Figure 2 Emergence of new normal for global annual-mean temperatures. Global average
temperature changes are shown for each RCP scenario for multi-model mean (and 5th-95th
percentile range) relative to 1961-1990. Plot circles indicate the time of emergence of a new
normal in each model realisation and the corresponding average annual-mean temperature
in the 20-year period centred on the year of emergence. The horizontal line shows the highest observed anomaly (∆T2015). Vertical lines show the median (>50%) and very likely
(>90%) time of emergence of new normal in each scenario for the multi-model ensemble
(where n=11).
Rendered Figure 3
90 o N
Click here to download Rendered Figure fig3.pdf
a. RCP2.6
c.RCP6.0
b. RCP4.5
d. RCP8.5
60 o N
30 o N
0 o
30 o S
60 o S
90 o N
60 o N
30 o N
0 o
30 o S
60 o S
00
Af
te
r
21
5
-2
86
08
20
-2
09
5
5
20
76
-2
66
06
20
-2
56
20
07
5
5
05
-2
20
46
04
-2
36
20
20
26
-2
03
5
5
5
02
-2
16
20
20
06
-2
01
5
(Deacde of Emergence of New Normal)
Figure 3 Maps of the multi-model median decade of emergence of a new normal for annual average temperatures in each gridbox for various
RCP scenarios. Grey stippling marks areas of high robustness (>0.8), where corresponding to a relatively high level of model agreement on sign
and magnitude. White areas (where no ToENN value is shown) indicate areas of missing data in observational temperature products and Antarctica
is excluded due to poor observational coverage.
Rendered Figure 4
Click here to download Rendered Figure fig4.pdf
a. RCP2.6
c. RCP6.0
Australia DJF
Tmean anomalies (K)
7
Australia DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
1
2020
2040
2060
Year
2080
2100
0
2020
b. RCP4.5
2060
Year
2080
2100
2080
2100
d. RCP8.5
Australia DJF
Tmean anomalies (K)
7
Australia DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
2040
1
2020
2040
2060
Year
2080
2100
0
2020
2040
2060
Year
Figure 4 As for figure 2, but showing the emergence of new normal for Australian DJF temperatures. A black cross at the end of the century represents model realisations where emergence
does not occur prior to 2100. In this case, vertical mean and very likely lines are not shown for
that experiment (i.e. RCP2.6). In cases where ToENN occurs in between 2090-2100, markers
are plotted at the corresponding for the mean temperature over this decade.
Rendered Figure 5
Click here to download Rendered Figure fig5.pdf
a. RCP2.6
c. RCP6.0
Europe DJF
Tmean anomalies (K)
7
Europe DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
1
2020
2040
2060
Year
2080
2100
0
2020
b. RCP4.5
2060
Year
2080
2100
2080
2100
d. RCP8.5
Europe DJF
Tmean anomalies (K)
7
Europe DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
2040
1
2020
2040
2060
Year
2080
2100
0
2020
2040
2060
Year
Figure 5 As for figure 4, but showing the emergence of new normal for European DJF temperatures.
Rendered Figure 6
Click here to download Rendered Figure fig6.pdf
a. RCP2.6
c. RCP6.0
Asia DJF
Tmean anomalies (K)
7
Asia DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
1
2020
2040
2060
Year
2080
2100
0
2020
b. RCP4.5
2060
Year
2080
2100
2080
2100
d. RCP8.5
Asia DJF
Tmean anomalies (K)
7
Asia DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
2040
1
2020
2040
2060
Year
2080
2100
0
2020
2040
2060
Year
Figure 6 As for figure 4, but showing the emergence of new normal for Asian DJF
temperatures.
Rendered Figure 7
Click here to download Rendered Figure fig7.pdf
a. RCP2.6
c. RCP6.0
N. America DJF
Tmean anomalies (K)
7
N. America DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
1
2020
2040
2060
Year
2080
2100
0
2020
b. RCP4.5
2060
Year
2080
2100
2080
2100
d. RCP8.5
N. America DJF
Tmean anomalies (K)
7
N. America DJF
Tmean anomalies (K)
7
6
6
5
5
4
4
3
3
2
2
1
0
2040
1
2020
2040
2060
Year
2080
2100
0
2020
2040
2060
Year
Figure 7 As for figure 4, but showing the emergence of new normal for North American
DJF temperatures.