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PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE 10.1002/2015JD024658 Key Points: • Idealized experiments with a uniform temperature increase of monsoon LPS are performed • Feedbacks in vertical velocity and atmospheric stability can explain the precipitation response • The LPSs are more intense in a warmer climate Correspondence to: S. L. Sørland, [email protected] Citation: Sørland, S. L., A. Sorteberg, C. Liu, and R. Rasmussen (2016), Precipitation response of monsoon low-pressure systems to an idealized uniform temperature increase, J. Geophys. Res. Atmos., 121, 6258–6272, doi:10.1002/ 2015JD024658. Received 17 DEC 2015 Accepted 24 MAY 2016 Accepted article online 28 MAY 2016 Published online 9 JUN 2016 Precipitation response of monsoon low-pressure systems to an idealized uniform temperature increase Silje Lund Sørland1,2, Asgeir Sorteberg2,3, Changhai Liu4, and Roy Rasmussen4 1 Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland, 2Geophysical Institute, University of Bergen, Bergen, Norway, 3Bjerknes Center for Climate Research, University of Bergen, Bergen, Norway, 4National Center for Atmospheric Research, Boulder, Colorado, USA Abstract The monsoon low-pressure systems (LPSs) are one of the most rain-bearing synoptic-scale systems developing during the Indian monsoon. We have performed high-resolution, convection-permitting experiments of 10 LPS cases with the Weather Research and Forecasting regional model, to investigate the effect of an idealized uniform temperature increase on the LPS intensification and precipitation. Perturbed runs follow a surrogate climate change approach, in which a uniform temperature perturbation is specified, but the large-scale flow and relative humidity are unchanged. The differences between control and perturbed simulations are therefore mainly due to the imposed warming and moisture changes and their feedbacks to the synoptic-scale flow. Results show that the LPS precipitation increases by 13%/K, twice the imposed moisture increase, which is on the same order as the Clausius-Clapeyron relation. This large precipitation increase is attributed to the feedbacks in vertical velocity and atmospheric stability, which together account for the high sensitivity. In the perturbed simulations the LPSs have higher propagation speeds and are more intense. The storms intensification to the uniform temperature perturbation can be interpreted in terms of the conditional instability of second kind mechanism where the condensational heating increases along with low-level convergence and vertical velocity in response to temperature and moisture increases. As a result, the surface low deepens. 1. Introduction Monsoon low-pressure systems (LPSs) are one of the high-impact synoptic-scale systems developing during the southwest Indian monsoon and are therefore important for precipitation over the Indian continent, especially in the central part [Mooley, 1973; Goswami et al., 2003; Ajayamohan et al., 2010; Krishnamurthy and Ajayamohan, 2010]. The LPSs are categorized by the strength of the surface wind, where the most common ones are the weaker monsoon lows and the more intense monsoon depressions [Tyagi et al., 2012]. There is an average of 14 LPSs during each monsoon season, where normally half of them develop into a depression [Sikka, 2006]. Multiple studies have reported that the frequency of the weaker lows has increased, while the occurrence of stronger monsoon depressions has decreased over the last few decades [Jadhav and Munot, 2009; Ajayamohan et al., 2010; Prajeesh et al., 2013]. This observed frequency shift is poorly understood, even though several mechanisms related to climate change and aerosol have been proposed [e.g., Dash et al., 2004; Jadhav and Munot, 2009; Prajeesh et al., 2013; Krishnamurti et al., 2013]. However, recently, Cohen and Boos [2014] posed the question whether there has been a decreasing trend in the depressions, since they could not detect a trend in the frequency of depressions in the distinctive LPS data sets constructed using different automated detection methods. ©2016. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. SØRLAND ET AL. Large rainfall rates are associated with the LPS, and during the monsoon the LPS produces about half of the Indian summer monsoon rainfall [Yoon and Chen, 2005]. Monsoon LPS can also trigger intense precipitation events in the vicinity of the propagation path [Goswami et al., 2006; Sikka, 2006; Ajayamohan et al., 2010]. Previous studies have reported an increase in extreme rainfall events in central India over last few decades and connected this to the monsoon LPS change [e.g., Goswami et al., 2006; Ajayamohan et al., 2010; Pattanaik and Rajeevan, 2010]. Intense rainfall over very short time periods can lead to fatal flooding and landslides, and thus, it is important to understand the meteorological conditions leading to these extreme events. Even though the warming of the climate system due to the increase in greenhouse gases is unequivocal [Intergovernmental Panel on Climate Change, 2013], it is not clear yet how the increase in atmospheric temperature will affect the monsoon LPS. With an increase in atmospheric temperature, the moisture content is expected to increase [e.g., Turner and Annamalai, 2012]. This moisture increase could have both a dynamic and thermodynamic effects. The dynamic effect is related to the importance of atmospheric moisture IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6258 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 content to tropical storms, such as the monsoon LPS. Several mechanisms have been suggested for the intensification/development of the LPS [e.g., Shukla, 1978; Sanders, 1984; Chen et al., 2005; Emanuel et al., 1994; Sikka, 2006; Boos et al., 2015]. In this paper we decided to examine the conditional instability of second kind (CISK) mechanism to explain the cooperative feedback between cumulus convection and the large-scale flow, as this has been proposed to play an important role for the strengthening of the monsoon LPS [e.g., Shukla, 1978; Chen et al., 2005]. Thus, in a warmer and more humid environment, the increase in atmospheric moisture can enhance latent heat release, leading to more intense LPS. The thermodynamic effect is related to the close correspondence between moisture content and precipitation intensity. Several studies have investigated how precipitation intensity changes in a warming world [i.e., Allen and Ingram, 2002; Pall et al., 2007; O’Gorman and Schneider, 2009; Muller et al., 2011; Romps, 2011; Loriaux et al., 2013; Attema et al., 2014]. Allen and Ingram [2002] found that the global mean precipitation increases at a lower rate than the Clausius-Clapeyron (CC) ratio, and this is because of the constraint imposed by the atmospheric energy budget rather than the moisture. However, the extreme precipitation increases at a higher rate than mean precipitation [Pall et al., 2007; O’Gorman and Schneider, 2009]. Loriaux et al. [2013] showed that stratiform precipitation, which has a longer duration and is more common in colder weather, follows the CC scaling, while convective precipitation, with a shorter duration associated with warmer temperatures, has an increase of 2 times the CC ratio. The use of global climate models (GCMs) has been the traditional approach to study how the climate system changes for a given future scenario. However, GCMs cannot give a satisfactory projection on regional scales, since the horizontal resolution is too coarse. A high-resolution regional climate model (RCM) nested within a GCM, the so-called dynamical downscaling, has been applied to improve the GCM results. Refined results are a consequence of improved representation of complex terrain, better resolved smaller-scale features, and improved representation of convective and mesoscale processes [e.g., Maraun et al., 2010; Feser et al., 2011]. Even though RCMs may provide some added values compared to the driving GCMs, there are still some difficulties when interpreting the results, due to the uncertainties regarding the model physical representation, horizontal resolution, and lateral boundary conditions (LBCs). Moreover, RCMs may not be an ideal tool to study physical processes in a given climate scenario, since their results can be influenced by unrealistic features of the driving GCM. Furthermore, downscaling GCM results with RCMs involves typically month-long ensembles or multiyear simulations and is computationally expensive. In light of these concerns with the traditional dynamic downscaling, new approaches have been developed allowing an investigation of climate changes on smaller scales that are not adequately represented in GCMs. One such method is the Pseudo Global Warming (PGW) approach, consisting of adding a climate change signal to the reanalysis-derived LBC [e.g., Schär et al., 1996; Frei et al., 1998; Lynn et al., 2009; Im et al., 2010; Rasmussen et al., 2011; Manda et al., 2014]. This method is appropriate for sensitivity studies, and it provides a first-order estimate of the impact of climate change. There are different ways to perform PGW simulations. Lynn et al. [2009] and Rasmussen et al. [2011] added a mean climate change signal, taken from a future scenario of a GCM, to the reanalysis-derived LBC. Schär et al. [1996] introduced surrogate climate change simulations, in which only the temperature field in the LBC is perturbed and the relative humidity (RH) is kept unchanged. As a result, the specific humidity (q) is being changed according to the CC relation (i.e., 6–7%/K). One main difference between these two methods is the vertical temperature profile that is added to the perturbed simulations. In Lynn et al. [2009] and Rasmussen et al. [2011] the imposed temperature profile has a vertical signal, with an enhanced warming aloft compared to lower levels, consistent with the tropospheric temperature trend predictions from climate models [e.g., Thorne et al., 2011]. In Schär et al. [1996] a uniform vertical temperature profile is imposed on the perturbed simulations. The Coupled Model Intercomparison Project 5 (CMIP5) underestimates the precipitation in the central Indian region [e.g., Sooraj et al., 2014], and this dry bias can be linked to a poor representation of the monsoon LPS in the GCMs [e.g., Praveen et al., 2015]. Hence, we decided to use the surrogate climate change approach to investigate the monsoon LPS precipitation response to a warmer and moistener atmosphere. We adopt the surrogate climate change approach in Schär et al. [1996]. As a uniform vertical temperature change cannot take into account the enhanced tropical upper troposphere warming induced by the water vapor feedback as projected by state of the art coupled GCMs, our results should not be interpreted as a climate change scenario but the response to an idealized temperature perturbation. It should also be mentioned that the strength of the upper tropospheric temperature trend is still a debatable issue [e.g., Thorne et al., 2011; SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6259 Journal of Geophysical Research: Atmospheres a Table 1. The Lifetime of the 10 Different LPS Simulated in This Study LPS # Lifetime MPE 1 2 3 4 5 6 7 8 9 10 Mean 06 UTC 15/9/1980 to 12 UTC 25/9/1980 12 UTC 24/6/1983 to 06 UTC 2/7/1983 18 UTC 26/7/1991 to 18 UTC 9/8/1991 00 UTC 6/9/1993 to 18 UTC 12/9/1993 12 UTC 28/8/1997 to 00 UTC 6/9/1997 12 UTC 8/6/1999 to 18 UTC 12/6/1999 06 UTC 29/8/2000 to 06 UTC 5/9/2000 18 UTC 19/6/2002 to 06 UTC 27/6/2002 18 UTC 31/7/2004 to 00 UTC 8/8/2004 12 UTC 1/9/2009 to 00 UTC 11/9/2009 21.5% 1.1% 7.0% 14.4% 19.1% 48.6% 19.8% 8.1% 32.8% 3.7% 4.9% a The column to the right lists the central India mean percentage error (MPE) of total accumulated precipitation at the end of the simulation. The mean MPE for all the LPS simulations is given at the bottom of the column. 10.1002/2015JD024658 Flannaghan et al., 2014]. We use the ERA-Interim reanalysis [Dee et al., 2011] for initial conditions and LBC and perform idealized experiments of 10 monsoon LPS cases that are connected to an observed extreme rainfall event. The surrogate climate change approach allows us to interpret the result with respect to the thermodynamical changes without the complication of the altered large-scale dynamics. How the change in monsoon circulation in a warmer climate affects the intensity and frequency of LPS is beyond the scope of this study. We use the same model experiments as in Sørland and Sorteberg [2015b, hereafter SS2015b]. In SS2015b the focus was on the changes in short-duration extreme precipitation and runoff over the central India, to assess possible impacts important for the society when the LPSs are developing in a warmer and moister atmosphere. One of the main results in SS2015b was that the LPSs in a warmer and more humid atmosphere are able to bring the moisture farther inland where it is released as precipitation, giving the largest change in precipitation at the end of the LPS track. The present study uses different analysis from SS2015b; herein, the analysis is performed following the low-pressure systems, instead of in the central Indian region as in SS2015b. We concentrate on the changes in precipitation generation processes, and as such, with respect to the center of the low we perform composite analysis of important parameters for precipitation. If precipitation intensity were moisture limited, we would expect an increase according to the CC relation. However, other processes, such as changes in vertical velocity and atmospheric stability, could also play a role. It is therefore not clear a priori if precipitation intensity should follow the CC relation. This study is investigating how precipitation intensity responds to the imposed warming and relates the precipitation response to changes in physical variables that are critical to the precipitation. With this we aim to quantify the contribution of different factors to precipitation changes. In section 2 the experimental design is described together with the model evaluation and a description of analysis methods. The results are presented in section 3. We end with a summary and concluding remarks in section 4. 2. Experimental Design, Model Evaluation, and Analysis Methods 2.1. Idealized Experiment Approach The design of the sensitivity experiments is detailed in SS2015b. So only the main points are briefed here for convenience of discussion. 2.1.1. LPS Cases The 10 LPS cases are picked from a data set described in Sørland and Sorteberg [2015a, hereafter SS2015a]. The data set consists of the lifetime and position (the trajectory) of 39 LPS cases that developed over the Bay of Bengal (BoB) and propagated toward the Indian continent from 1979 to 2010. All the LPSs are associated with an observed extreme rainfall event in the vicinity of the propagation path (for details see SS2015a). Extreme precipitation is defined as the precipitation exceeding the 99.5th percentile. Table 1 lists the 10 selected LPSs with their respective lifetime. 2.1.2. Model Setup The regional Weather Research and Forecasting (WRF) model [Skamarock et al., 2008] version 3.4.1 is used to perform the simulations. The Advanced Research WRF dynamical core is used, and since WRF is a fully compressible and nonhydrostatic model, it is suitable for very high resolution simulations. The performed simulations have a horizontal resolution of 4 km and 50 vertical (eta) model levels. The computational domain contains 694 × 694 grid points, covering India and parts of BoB, Himalaya, and Arabian Sea (Figure 1). The domain size is a compromise between two competing factors. The domain must be small enough SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6260 Journal of Geophysical Research: Atmospheres Figure 1. The domain used for the WRF simulations. The black box covering central India is the area of where the 24 h accumulated precipitation is averaged to compare with the observationally based IMD rainfall estimates. 10.1002/2015JD024658 to be constrained by the large-scale boundary conditions but large enough so that the model can develop its own LPS dynamics. The employed physics parameterizations include the Thompson microphysical scheme [Thompson et al., 2008], Yonsei University Planetary boundary layer scheme (YSU) [Hong et al., 2006], Community Atmosphere Model’s (CAM) longwave and shortwave radiation scheme [Collins et al., 2006], and the Noah land surface model scheme [Niu et al., 2011]. Note that these simulations are convection permitting, since no convection scheme is used. The model was run with sea surface temperature (SST) update every 6 h, and also, an alternative initialization of the lake water temperature was included. This method uses the diurnal average skin temperature, instead of the SST from the closest ocean. 2.1.3. Control and Perturbed Boundary Conditions To make sure that the LPSs are present in the simulation, we start the model simulation close to when the LPS is identified and present in the initial condition. The simulations start 24 h prior to the initiation of the LPS and continue throughout the LPS lifetime. The reason for the short spin-up time was to make sure that the lowpressure system would be present in the initial conditions. We also tested a start time of 12 h before the identification of the LPS but found no large differences in the model results. The initial and lateral boundary conditions are provided from the 6-hourly ERA-Interim reanalysis data [Dee et al., 2011] for both the control and perturbed runs. The control run uses unperturbed ERA-Interim boundary conditions. The perturbed runs are generated using the surrogate climate change method [Schär et al., 1996], which involves changing all the temperature fields in the initial and boundary conditions uniformly. Specifically, a temperature increment, ΔT, is added to the temperature fields on all pressure levels in the atmosphere, the SST, the skin temperature, and all the soil temperature fields in the ERA-Interim data while keeping the relative humidity constant. All the temperature fields in the whole atmospheric column are equally altered to avoid any changes in the gradients, which would lead to changes in surface and soil fluxes. When generating initial and lateral boundary conditions for the perturbed simulations, the surface pressure is adjusted to keep pressure gradients unchanged using the hypsometric equation. To summarize, for each LPS case, three simulations are conducted with three different initial and boundary conditions, consisting of one unperturbed control run (CTR), and two perturbed runs, which respectively corresponds to a 2 K perturbation where ΔT = +2 K and Δq ≈ +13% and a 4 K perturbation where ΔT = +4 K and Δq ≈ +26%. 2.2. Analysis Methods The ERA-Interim LPS detection follows a tracking algorithm described in SS2015a. The LPS trajectories in the WRF simulations (CTR, 2 K, and 4 K runs) are determined by visual inspection of the 850 hPa geopotential height field every 6 h. All simulations have a well-defined low-pressure system. A subjective assessment of the position of the low is made by identifying the lowest (deepest) local minimum pressure in the vicinity of the low-pressure center detected in the previous time step. In addition, the low has to contain at least two or more closed contours (where the contour interval is 5 m); otherwise, the low-pressure system is assumed to have dissipated. On the basis of the information of timing and position of low-pressure centers, we make composites of different parameters. The composite methodology consists of centering a radial coordinate system on the center of the low and rotating it in the propagation direction [Bengtsson et al., 2007; Azad and Sorteberg, 2014; SS2015a]. The SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6261 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Figure 2. (first and third columns) The central India mean 24 h accumulated precipitation during the simulation of each LPS case. (second and fourth columns) The cyclone trajectories for the different LPS cases. For the accumulated precipitation (trajectories) the green line is the IMD precipitation (the trajectory from the ERA-Interim), the blue line is from the CTR simulations, and the black and red lines are from the 2 and 4 K runs. composites have a radius of 5°, which we will call the cyclone-related area. For the composited vertical structures, we have taken the spatial mean over the circular area of a 5° radius around the low center at all pressure levels. 2.3. Model Evaluation A detailed description of the model evaluation is given in SS2015b, where the WRF control precipitation is evaluated against the Indian Meteorological Department (IMD) rainfall data [Rajeevan et al., 2006] in the central Indian region (Figure 1). On average, the modeled accumulated precipitation during the LPS lifetime is underestimated by 4.9% (Table 1). By removing the outliers, the modeled precipitation goes from being slightly underestimated to slightly overestimated. Nevertheless, there is a great variation in the model performance for different LPS cases, indicating no systematic errors in the WRF precipitation as compared to the IMD precipitation. WRF reasonably reproduces the accumulated precipitation over the central India during the LPS lifetime, but the precipitation timing is not always correct (Figure 2). The LPS trajectories in the control simulations are compared with those in the ERA-Interim reanalysis. The trajectories from the reanalysis and the control simulation are reasonably well colocated in most of the LPS cases (Figure 2). Since we start the integration 24 h before the identification of the LPS in the ERA-Interim, the start position is not exactly the same. 3. Results 3.1. General Features of the Monsoon LPS Composite analysis was used to show the general features of the ERA-Interim monsoon LPS in SS2015a. To see how well the WRF model captures the observed characteristics, we perform the same composite analysis SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6262 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Figure 3. Composites of (a and b) cyclone-related temperature and (c and d) specific humidity at 950 hPa (Figures 3a and 3c) and 750 hPa (Figures 3b and 3d). The composite is the ensemble mean of the control simulations, and the radius around the center of the low is 5°. The temperature is given in Kelvin (K), while the specific humidity is given in kg/kg. The cyclone center is given by the black dot in the middle, and the fields have been rotated to have the same propagation direction (given by the arrow) before the averaging. For details see section 3.1. of several parameters in the CTR runs, and the results are presented in Figures 3 and 4. Note that the composites are relative to the propagation direction, which assumed is from east to west in the figures. As in SS2015a, a cold core at lower levels and a warm core aloft are apparent (Figures 3a and 3b), and the moisture accumulation at the center is also evident (Figures 3c and 3d). The strong upward vertical velocity and the precipitation are colocated (Figures 4a and 4j). The ERA-Interim composite in SS2015a places the precipitation and upward motion ahead and to the northwest of the center of the low (see Figure 4 in SS2015a), while in the WRF simulations they are located in the southwest quadrant, in agreement with observational studies [e.g., Saha and Saha, 1988; Sikka, 2006], and also in the Tropical Rainfall Measuring Mission composite in SS2015a. This improvement in the precipitation pattern may be a result of explicit convection due to the use of high resolution. A more detailed representation of the gradients in the temperature and specific humidity fields is seen (Figure 3), which is an additional benefit from the fine resolution. The vertical velocity is not as smooth as seen in the ERA-Interim composite (see Figures 4 and 5 in SS2015a); especially, the smallscale features associated with convective cells are discernible in the WRF composite (Figure 4a). 3.2. Response to the Imposed Warming In the following we examine the response to the imposed warming by comparing each control run with the two corresponding perturbed runs. We focus on the differences in the parameters that are important for the evolution of LPS. 3.2.1. Comparison of the LPS Trajectories The large-scale flow from the ERA-Interim reanalysis, which is imposed as the LBC, is kept unchanged in the surrogate climate change method. However, the large domain size enables the development of different LPS dynamics in the three simulations. Figure 2 compares the trajectories between the perturbed runs, the control run, and the ERA-Interim. The great similarity which is present among the three runs should be noted, suggestive of a dynamical consistency between the control run and perturbed runs. However, in a couple of cases the trajectories of the 4 K run differ considerably from the control run (e.g., case 5). The large domain allows the LPS dynamics to develop its own mesoscale solution. Due to the different SSTs and the moisture increase in the perturbed runs, which leads to increased latent heat release, feedback processes may affect the propagation of the LPS. During the LPS lifetime the different feedback SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6263 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Figure 4. Composite of the cyclone-related vertical velocity at (a–c) 750 hPa, (d–f) 900 hPa divergence, (g–i) MSLP, and (j–l) 6 h accumulated precipitation, for the CTR simulations (left column), 2 K simulations (middle column), and 4 K simulations (right columns). The radius around the center of the low is 5°, and the vertical velocity is given as cm/s, divergence as 1/6 h, MSLP in hPa, and 6 h accumulated precipitation in mm/6 h. For negative (positive) values of the divergence the flow converges (diverges). The cyclone center is given by the black dot in the middle, and the fields have been rotated to have the same propagation direction (given by the arrow) before the averaging. For details see section 3.1. processes will add up, giving rise to the largest change in trajectory at the end of the simulation. Moreover, as the intensity of the lows has changed in the perturbed runs and the paths are rather similar, it is possible that stronger winds are hitting perpendicularly on some of the main central Indian mountain ranges, thereby increasing the orographic enhancement of precipitation. In Figure 2 the average precipitation in the central Indian region in the perturbed runs is compared with the corresponding CTR runs and the IMD rainfall. The magnitude has increased, but the precipitation evolution generally matches the CTR runs. In the CTR runs LPSs propagate with an average speed of 2.75 m/s. The speed increases by 28% to 3.53 m/s in the 4 K runs (Table 2). The lifetime of the LPS has decreased from 8.5 days in the CTR runs to 7.8 days in the 4 K runs (Table 2). The effect of a warmer and moistener atmosphere is stronger in the 4 K runs than the 2 K, especially regarding the different propagation speeds and the LPS lifetime. SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6264 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Table 2. The LPS Ensemble Average Lifetime, Propagation Speed, and Cyclone-Related (5° Radius Around the Surface Low) Mean MSLP, 6 h Accumulated Precipitation (P), the Atmospheric Column Weighted Mean Specific Humidity (q), Positive a Vertical Velocity (w), and Atmospheric Stability (Γ), for the Control Simulations and the 2 K and 4 K Simulations CTR 2K 4K a Lifetime (Days) Propagation Speed (m/s) MSLP (hPa) P (mm/6 h) q (g/kg) w (cm/s) Γ (K/km) 8.5 8.5 7.8 2.75 3.07 3.53 1000.6 1000.4 999.7 3.45 4.21 5.27 15.4 16.5 18.0 6.70 7.34 8.17 4.97 5.04 5.10 See text for further information. 3.2.2. Cyclone Composite Analysis Figure 4 compares the LPS composites of different physical variables. The maximum vertical velocity is located at the same place in the perturbed runs as in the CTR runs (Figures 4a–4c). However, the area of strong updraft expands with the temperature increase. As shown in Figures 4d–4f, at 900 hPa the flow mainly converges, and the convergence is strongest to the south-southwest of the low-pressure center, where strong updrafts occur. The convergence is stronger in the 4 K runs than the CTR runs, but there is little difference between 2 K runs and CTR runs. The surface low is clearly seen in the composite of mean sea level pressure (MSLP), and it is evident that the minimum pressure at the center of the low decreases with the temperature increase, being 1–2 hPa (2–3 hPa) lower in the 2 K (4 K) runs than in the CTR runs (Figures 4g–4i). The area mean MSLP is given in Table 2. The MSLP gradients across the low also increase in the warmer climate; the 4 K (CTR) runs have a pressure difference of 8.6 hPa (7.5 hPa) from the center of the low and to the edge of the 5° radius. The gradient enhancement indicates that the storms are more intense in the warmer scenario. The 6 h accumulated precipitation in Figures 4j–4l indicates that the position of the precipitation pattern is insensitive to the temperature increase, with the heavy precipitation being located to the south-southwest of the surface low. The mean maximum precipitation has not increased, but the high precipitation rate is covering a larger area in the perturbed runs than in the CTR runs. 3.2.3. Differences in the Vertical Structure Vertical profiles (taken as the spatial mean over the 5° radius circular area around the low-pressure center at each pressure level) of the different cloud hydrometeors, namely, cloud water (QCLOUD), raindrops (QRAIN), cloud ice (QICE), snow (QSNOW), and graupel (QGRAUP) for the three sets of simulations, are shown in Figure 5, together with the sum of all the cloud hydrometeors (Qtot). The vertical structure is the same for all the runs, but the magnitude differs. The cloud water has a maximum concentration in the layers from 925 to 700 hPa. The cloud water increases in magnitude from the CTR to the perturbed runs at all levels, except below 925 hPa, where there is little change. Above 450 hPa there is not much cloud water in any of the runs, and cloud hydrometeors that do develop are snow and graupel when temperature is below freezing. Note that the vertical level where these cloud hydrometeors start to form increases with the increased temperature perturbation, because the zero isotherm is located further aloft in the warmer atmospheric column. The raindrops are uniformly distributed from 550 hPa down to the surface in all the three sets of simulations. The raindrops make up the largest component of the cloud hydrometeors, and the mass increases from the CTR runs to the perturbed runs, with the largest increase in the 4 K runs as expected. A vertical cross section of the total cloud hydrometeors is shown in Figure 6. The cross section is taken 5° ahead of and behind the low center, along the propagation direction. An increase with the temperature increase is clearly seen, and the horizontal extent of the cloud mass is also increasing in the perturbed runs. The significant peak between 925 and up to 750 hPa is an interesting feature of the cloud microphysics in the monsoon LPS. It is also notable that the zero isotherms are located at higher levels in the perturbed runs. Figure 7 shows the difference in the vertical structure of specific humidity (q), relative humidity (RH), positive vertical velocity (w), temperature (T), and condensational heating (Hcond) between the perturbed simulations and the control runs. The condensational heating is diagnosed at each pressure level by taking the time difference of the saturated specific humidity (qsat) in the following way: Hcond ¼ dqsat Lv Cp dt for dqsat < 0 ; w > 0 and RH ≥ 80%: dt Lv is the latent heat of condensation and Cp is the specific heat at constant pressure. The heating is only calculated where there is upward vertical motion (w > 0), condensation (dqs/dt < 0), and RH greater than SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6265 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Figure 5. The ensemble mean vertical structure of the cyclone-related (average over an area with a 5° radius around the center of the low) of cloud hydrometeors cloud water (QCLOUD), raindrops (QRAIN), cloud snow (QSNOW), cloud ice (QICE), graupel (QGRAUP), and the sum of all the cloud hydrometeors (Qtot = QCLOUD + QRAIN + QSNOW + QICE + QGRAUP), for the three sets of simulations CTR (black line), 2 K (blue line), and 4 K (red line). The cloud hydrometeors are given as mass per 3 volume (g/m ). or equal to 80%. The temperature, RH, w, and Hcond are given as the difference between the perturbed and control simulations (xK CTR). The x represents the temperature perturbation for the 2 and 4 K runs. q is given as the relative difference: dq ðqxK qCTR Þ 100% ≈ : dT qCTR xK The difference is found by first calculating the mean of the parameters during each LPS simulation, then averaging over all LPS cases, before finding the difference between the perturbed and control runs. The initial difference between the perturbed and control runs of q, RH, and temperature is also shown in Figure 7. For the temperature profile we see that the imposed warming has been reduced for both the perturbed runs below 450 hPa; the temperature difference is less than the initial difference. For the 2 K runs the vertical Figure 6. Vertical cross section of the ensemble mean sum of all the cloud hydrometeors (Qtot) in the (left) CTR, (middle) 2 K, and (right) 4 K simulations. The cross section is taken along the propagation direction of the cyclone, and the radius 3 around the center is 5°. Qtot is given as mass per volume (g/m ). SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6266 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 Figure 7. Ensemble mean differences between the perturbed and control simulations of the cyclone-related (average over an area with a 5° radius around the center of the low) vertical profile of specific humidity (q), relative humidity (RH), positive vertical velocity (w), atmospheric temperature (T), and condensational heating (Hcond). The units are given at the bottom of each figure. The black line (gray) is the difference between 2 K (4 K) and CTR. For q, RH, and T the initial difference is shown as dashed lines. temperature difference is in the range 0.2–0.4 K less than the 2 K initial differences, while for the 4 K run the temperature difference is around 3 K (3.5 K) below (above) 825 hPa. RH has not changed much with a 1–2.5% reduction below 550 hPa for both the 2 K and 4 K runs. Since RH was kept constant, the initial relative difference for the specific humidity follows the CC relation, namely, around 6.5%/K in the lower part of the atmosphere. Owing to the small RH change, the change in q will approximately follow the change in saturated specific humidity, qs, which is a function of the temperature. Thus, as a result of smaller temperature differences, the relative difference in q is less than the initial value. The vertical velocity difference represents only the difference in the upward motions. Above 950 hPa the vertical velocity increases with increased temperature, with the 4 K runs having the strongest upward motion. The condensation heating is an important energy source driving monsoon LPS, possibly through the CISK mechanism [Shukla, 1978]. The difference in condensational heating between the perturbed and CTR runs is largest around 875 hPa, with a secondary peak at 550 hPa. The 4 K simulations display maximum differences of 0.14 K/6 h around 925 hPa and 0.08 K/6 h at 550 hPa. These two maxima are colocated with the largest increase in cloud water (Figure 5). 3.2.4. Precipitation Response to the Uniform Temperature Increase The mean 6 h accumulated precipitation (P), taken as the spatial mean over a circular area of 5° radius around the center of the surface low, is presented in Table 2. The mean percentage change in precipitation relative to CTR runs is given in Table 3. The mean precipitation increases by 22.0% (52.8%) in the 2 K (4 K) runs over the CTR runs. This is much higher than the imposed moisture increase, which follows the CC relation, namely, 13% (26%) for the 2 K (4 K) runs. Other processes besides the change in the available moisture are needed to explain the increase in precipitation. In order to understand the high sensitivity of precipitation to a warming, we hypothesize that the precipitation change can be understood as a response to the imposed change in the specific humidity (Δq) which led to an increase in condensation and the feedback of enhanced latent heat release, which induced a positive upward velocity anomaly (Δw) as seen in Figures 4 and 7. In addition, as the imposed temperature change in the initial and lateral boundary conditions has not been sustained throughout the integration, the temperature changes are nonuniform in the vertical (Figure 7). Thus, changes in the stability have occurred (ΔΓ; Γ ¼ dT dz ). SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6267 Journal of Geophysical Research: Atmospheres Table 3. The Ensemble Mean Relative Change (%) Between the Perturbed Simulations and the Control Simulations, of Cyclone-Related 6 h Accumulated Precipitation From WRF ( ΔPWRF ), the Diagnostic Linear Model ( ΔPREG ), the Atmospheric Column Weighted Mean Specific a Humidity (Δ), Vertical Velocity (Δw ), and the Atmospheric Stability (ΔΓ) ΔPWRF ΔPReg Δq Δw ΔΓ 2K 4K 22.0% (11.0%/K) 25.6% (12.7%/K) 7.3% (3.7%/K) 9.6% (4.8%/K) 1.4% (0.07 K/km) (0.7%/K) 52.8% (13.2%/K) 59.1% (15.6%/K) 17.5% (4.4%/K) 21.9% (5.5%/K) 2.6% (0.13 K/km) (0.7%/K) a The numbers in the parenthesis is the relative change scaled with the initial temperature change (%/K). x¼ 10.1002/2015JD024658 The above arguments suggest that the change in precipitation should be a function of changes in q, w, and Γ, i.e., ΔP = f(Δq, Δw, ΔΓ). To find mean values of q, w, and Γ, we first take the spatial mean of these parameters over the 5° radius circular area around the center of the low at each time step, at all pressure levels. As we are only interested in the changes in the meteorological parameters where precipitation occurs, we make an atmospheric column weighted mean of q, Γ, and w (only upward motions are considered) in the layers where there is condensation: i¼n X W i xi i¼1 x is either q, Γ, or w, n is the number of vertical layers from 975 hPa and up to 350 hPa, and the weight, W, is given as 8 dqs > > > > > dqs dt i > < <0 Xi¼n dqs dt Wi ¼ : > i¼1 dt > > i > > dqs > : 0 ≥0 dt The LPS mean of the weighted parameters (q; w; and Γ) for the three sets of simulations is listed in Table 2. The specific humidity and vertical velocity increase in the two perturbed simulations as compared to the CTR simulations. The stability slightly decreases from 4.97 K/km in the CTR runs to 5.10 K/km in the 4 K runs. For each LPS case we take the 5° spatial mean of the 6 h accumulated precipitation (P) and the 6-hourly atmospheric column weighted mean of q, w, and Γ, average over the lifecycle of the LPS and combine the parameters for the CTR, 2 K, and 4 K runs. Each set of parameters then gets 30 samples. A multiple linear regression is performed where the dependent variable is the precipitation and the independent variables are q, w, and Γ. Note that there are some correlations between these variables, and therefore, we have to interpret the results with caution. The linear regression estimate is PREG ¼ c0 þ c1 q þ c2 w þ c3 Γ ; where q, w, and Γ are the weighted column averages for all the three types of simulations (CTR, 2 K, and 4 K) ∂P merged. The coefficients c1, c2, and c3 can be seen as approximations to the local derivatives ∂P ; ∂P ; and ∂Γ , ∂q ∂w and c0 is the intercept. From the above equation we estimate the sensitivity of the precipitation to q, w, and Γ. Our hypothesis is that the change in precipitation is a simple function of changes in q, w, and Γ, namely, ΔP ¼ f Δq; Δw; ΔΓ . Using the Taylor expansion and only keeping the first-order terms, we get a linear approximation for the precipitation change in each perturbed simulation based on the above sensitivities from the multiple regression: ∂P ∂P ∂P ΔPREG ¼ Δq þ Δw þ ΔΓ; ∂q ∂w ∂Γ where Δq, Δw, and ΔΓ are the column weighted (averaged over the 5° radius around the center of the low) values. The scatterplot of the cyclone-related precipitation change from the linear estimate and WRF is shown in Figure 8. The estimated precipitation changes are strongly correlated to the changes in WRF simulations with a correlation coefficient of 0.94, and the LPS mean change in the precipitation derived from the linear equation is 12.7%/K (2 K) SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6268 Journal of Geophysical Research: Atmospheres Figure 8. Scatterplots with the best fit regression line between the precipitation difference in the different cases from WRF (ΔPWRF) and the linear estimate (ΔPREG). The correlation is listed at the top of the figure. The changes are given in mm/6 h. 10.1002/2015JD024658 and 15.6%/K (4 K). Table 3 compares the precipitation change between WRF and the linear regression. The linear regression model skillfully reproduces the variability between the different simulations. On average, the regression model overestimates the WRF results by 2.5%/K. Next, the linear algorithm is used to assess the contribution from the changes in each independent variable (i.e., q, w , and Γ ). The LPS mean relative changes of q, w, and Γ are listed in Table 3, and Figure 9 shows the percentage change for each LPS case for the 2 and 4 K simulations, along with the relative precipitation change from WRF and the linear model. As far as the LPS mean is concerned, the contribution to the change in precipitation is mainly from changes in specific humidity (5.0–6.2%/K) and vertical velocity (6.7–8.9%/K) (Figure 9). The change in stability only gives a very small contribution to the precipitation change (0.8–1%/K). When we investigate the reason for the different precipitation response in the individual LPS cases, we see that this is mainly attributed to the variable change in vertical velocity among the different cases, while the variability in specific humidity change is smaller. In the cases with a large precipitation response, the increase in positive vertical velocities is also large, and vice versa. In the latter case, the change in stability is small or the atmosphere has become slightly more stable. There is only one case where a reduction in precipitation is observed (see Figure 9, case 5 in the 2 K simulation). This outlier is not reproduced by the linear estimate. In this case the atmospheric stability has decreased and the moisture has increased, but there is little change in vertical velocity. Thus, the air has most likely not reached saturation; therefore, no condensation heating has occurred, which can be the reason for the lack of an increase in upward motion. As there is no indicator of deviation from saturation in the simple linear estimate, this is not captured. 4. Summary and Concluding Remarks Based on the method of Schär et al. [1996], we have performed idealized experiments of 10 monsoon LPS cases that developed during the Indian monsoon and were associated with observed extreme precipitation. Three sets of simulations have been performed with the high-resolution WRF model, consisting of one set of unperturbed control simulations and two sets of perturbed simulations where a uniform 2 K or 4 K temperature perturbation is specified in the initial and lateral boundary conditions. As the relative humidity is kept unchanged, the imposed temperature increase leads to a specific humidity increase of 13% and 26% in the 2 and 4 K runs, respectively. The mean precipitation increase (within a 5° radius of the low-pressure center) is as much as 13.2%/K in the 4 K simulations, which is 2 times as large as expected from the CC relation. To investigate the simulated precipitation response to imposed warming, we have derived a linear regression model based on the assumption that the precipitation response in the perturbed simulations is determined by the change in specific humidity, a dynamical feedback-induced change in vertical velocity and a thermodynamical feedbackinduced change in stability of the atmosphere. The comparison with WRF results indicates that the linear model overestimates the ensemble mean precipitation change by less than 3%/K and reproduces 88% of the variability among the different cases. We use the simple model to evaluate the relative contributions from the three different predictors. The results can be summarized as the following. The contribution to the precipitation change is mainly from changes in specific humidity and vertical velocity, whereas the variability in precipitation response is mainly from changes in vertical velocity; the atmospheric stability has a minor contribution. In the cases with a large (small) increase in precipitation, the vertical velocities have increased SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6269 Journal of Geophysical Research: Atmospheres 10.1002/2015JD024658 (slightly increased). The precipitation response to the temperature increase is conceptualized in Figure 10. The initial temperature increase leads to a change in specific humidity (~6.5%/K). This increase in the atmospheric moisture enhances condensation, and the associated increase in latent heating will influence the vertical velocity and the atmospheric stability. If the precipitation were scalable in terms of the changes in atmospheric moisture, given by the CC relation, the precipitation increase would be close to the initial specific humidity increase. However, changes in vertical velocity and atmospheric stability amplify the precipitation response. Therefore, the precipitation change ends up being about twice as large as the change in specific humidity. The effect of a vertically uniform temperature increase on the intensification of LPS is very clear, especially in the 4 K simulations. More intense LPSs have developed, where both the horizontal extent of the storm and the mean precipitation have increased. The LPSs in the perturbed runs seem to be intensified through the CISK-like mechanism, where the condensational heating acts to spinFigure 9. The contribution to the precipitation change from changes in up the low, increase the updrafts column-integrated specific humidity (q, black), positive vertical velocity and the low-level convergence, and (w, grey), and atmospheric stability (Γ, white) to the scaled relative change (%/K) (in cyclone-related precipitation) for the 10 cases with the (top) 2 K deepen the surface low. The lowperturbations and (bottom) 4 K perturbations. The scale relative change from level convergence is acting to pump the linear model (ΔPREG) is given as a red circles and the precipitation moisture upward in the atmospheric change from WRF (ΔPWRF) as blue circles). The ensemble mean of the 10 column. This will increase the amount cases from 2 K and 4 K is shown to the right in each figure. of condensed water and precipitation. A more intense storm with higher propagation speed has developed. Trenberth et al. [2003] argued that by increasing the moisture content of the atmosphere, the storms will become more vigorous with larger precipitation rate, and also experience a reduced lifetime, consistent with our results. As shown in SS2015b, the LPSs in the warmer climate are bringing more moisture farther inland, and therefore, the precipitation change is also large in the western region of central India. It is hypothesized that the LPSs in the perturbed runs are able to transport more moisture across the central India as a result of reduced detrainment rate due to faster propagation of the storms. To properly assess this speculation, however, a cloud-resolving model is needed. The experiments reported herein provide an estimate of how LPS precipitation could respond to a uniform increase in temperature and corresponding atmospheric moisture content. For the 4 K runs the precipitation increased for all the 10 cases, and for the 2 K runs 9 out of the 10 cases experienced an increase in the mean cyclone-related precipitation. However, the idealized setting of these experiments should be emphasized. In the global climate model-projected climate change scenarios the warming of the tropical and subtropical SØRLAND ET AL. IDEALIZED UNIFORM TEMPERATURE EXPERIMENT 6270 Journal of Geophysical Research: Atmospheres Figure 10. A conceptualization of the proposed reasons to the simulated change in cyclone-related precipitation. The initial temperature change (ΔT) leads to a change in specific humidity (Δq) that yields a change in the condensation (Δc). The change in the condensation gives positive feedbacks (marked with +) through the change in vertical velocity (Δw) and atmospheric stability (ΔΓ), which leads to an enhanced change in the amount of condensed water. As a result of these feedbacks the final precipitation response (ΔP) is enhanced compared to the initial specific humidity changes, given by the initial temperature change. Acknowledgments We thank the Editor and the three reviewers for their comments and suggestions, which greatly improved the quality of the manuscript. This work has been partly funded by the Centre for Climate Dynamics at the Bjerknes Centre for Climate Research, University of Bergen, and the Norwegian Research Council projects NorIndia, SnowHim, and Norklima. The authors acknowledge NCAR Research Applications Laboratory staff and the Water System scientists for providing a productive research environment and the computer time to conduct the high-resolution simulations on the Yellowstone computer cluster. For information regarding the WRF simulations data, contact Silje Lund Sørland ([email protected]. ch). The IMD daily gridded rainfall data set can be obtained from the National Climate Centre in Pune, India (http:// www.imdpune.gov.in/). The authors acknowledge the National Climate Centre for sharing the IMD daily gridded rainfall data set and are grateful to Mari Sandvik for informative discussions. SØRLAND ET AL. 10.1002/2015JD024658 troposphere is predicted to have a vertical profile where the warming aloft is larger than at the surface [e.g., Thorne et al., 2011; Flannaghan et al., 2014], and the uniform temperature profile applied here may not be representative for the future. Moreover, in a warming world we would expect the large-scale dynamics to change, and thus, the LPS will be embedded in a different large-scale background flow. The change in the atmospheric stratification and circulation will affect the LPS frequency and evolution, and this is not assessed in this study but is crucial to include to get the whole picture of how the monsoon LPS will change in the future. 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