Download Precipitation response of monsoon low

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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
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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.
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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
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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
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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
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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
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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.
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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
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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 ).
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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 ).
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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)
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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.
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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
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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. In spite of these limitations, the
results from these idealized experiments
can help to understand more complex
dynamical and thermodynamical feedbacks
that might occur as temperature and atmospheric water content increase in more realistic climate change simulations.
References
Ajayamohan, R. S., W. J. Merryfield, and V. V. Kharin (2010), Increasing trend of synoptic activity and its relationship with extreme rain events
over central India, J. Clim., 23, 1004–1013.
Allen, M. R., and W. J. Ingram (2002), Constraints on future changes in climate and the hydrologic cycle, Nature, 419, 224–232.
Attema, J. J., J. M. Loriaux, and G. Lenderink (2014), Extreme precipitation response to climate perturbations in an atmospheric mesoscale
model, Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/1/014003.
Azad, R., and A. Sorteberg (2014), The vorticity budgets of North Atlantic winter extratropical cyclone life cycles in MERRA Reanalysis. Part I:
Development phase, J. Atmos. Sci., 71, 3109–3128.
Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J. J. Luo, and T. Yamagata (2007), How may tropical cyclones change in a
warmer climate?, Tellus Ser. a-Dyn. Meteorol. Oceanogr., 59, 539–561.
Boos, W. R., J. V. Hurley, and V. S. Murthy (2015), Adiabatic westward drift of Indian monsoon depressions, Q. J. R. Meteorol. Soc., 141,
1035–1048, doi:10.1002/qj.2454.
Chen, T. C., J. H. Yoon, and S. Y. Wang (2005), Westward propagation of the Indian monsoon depression, Tellus Ser. a-Dynamic Meteorol.
Oceanogr., 57, 758–769.
Cohen, N. Y., and W. R. Boos (2014), Has the number of Indian summer monsoon depressions decreased over the last 30 years? Geophys. Res.
Lett., 41, 7846–7853, doi:10.1002/2014GL061895.
Collins, W. D., et al. (2006), The Community Climate System Model version 3 (CCSM3), J. Clim., 19, 2122–2143.
Dash, S. K., J. R. Kumar, and M. S. Shekhar (2004), On the decreasing frequency of monsoon depressions over the Indian region, Curr. Sci., 86, 1404–1411.
Dee, D. P., et al. (2011), The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137,
553–597.
Emanuel, K. A., J. David Neelin, and C. S. Bretherton (1994), On large-scale circulations in convecting atmospheres, Q. J. R. Meteorol. Soc., 120,
1111–1143, doi:10.1002/qj.49712051902.
Feser, F., B. Rockel, H. von Storch, J. Winterfeldt, and M. Zahn (2011), Regional climate models add value to global model data: A review and
selected examples, Bull. Am. Meteorol. Soc., 92, 1181–1192.
Flannaghan, T. J., S. Fueglistaler, I. M. Held, S. Po-Chedley, B. Wyman, and M. Zhao (2014), Tropical temperature trends in Atmospheric
General Circulation Model simulations and the impact of uncertainties in observed SSTs, J. Geophys. Res. Atmos., 119, 13,327–13,337,
doi:10.1002/2014JD022365.
Frei, C., C. Schär, D. Luthi, and H. C. Davies (1998), Heavy precipitation processes in a warmer climate, Geophys. Res. Lett., 25, 1431–1434,
doi:10.1029/98GL51099.
Goswami, B. N., R. S. Ajayamohan, P. K. Xavier, and D. Sengupta (2003), Clustering of synoptic activity by Indian summer monsoon intraseasonal
oscillations, Geophys. Res. Lett., 30(8), 1431, doi:10.1029/2002GL016734.
Goswami, B. N., V. Venugopal, D. Sengupta, M. S. Madhusoodanan, and P. K. Xavier (2006), Increasing trend of extreme rain events over India
in a warming environment, Science, 314, 1442–1445.
Hong, S. Y., Y. Noh, and J. Dudhia (2006), A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather
Rev., 134, 2318–2341.
Im, E. S., E. Coppola, F. Giorgi, and X. Bi (2010), Local effects of climate change over the Alpine region: A study with a high resolution regional
climate model with a surrogate climate change scenario, Geophys. Res. Lett., 37, L05704, doi:10.1029/2009GL041801.
Intergovernmental Panel on Climate Change (2013), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T. F. Stocker et al., 1535 pp., Cambridge Univ. Press,
Cambridge, U. K., and New York, doi:10.1017/CBO9781107415324.
IDEALIZED UNIFORM TEMPERATURE EXPERIMENT
6271
Journal of Geophysical Research: Atmospheres
10.1002/2015JD024658
Jadhav, S. K., and A. A. Munot (2009), Warming SST of Bay of Bengal and decrease in formation of cyclonic disturbances over the Indian
region during southwest monsoon season, Theor. Appl. Climatol., 96, 327–336.
Krishnamurthy, V., and R. S. Ajayamohan (2010), Composite structure of monsoon low pressure systems and its relation to Indian rainfall,
J. Clim., 23, 4285–4305.
Krishnamurti, T. N., A. Martin, R. Krishnamurti, A. Simon, A. Thomas, and V. Kumar (2013), Impacts of enhanced CCN on the organization of
convection and recent reduced counts of monsoon depressions, Clim. Dyn., 41, 117–134.
Loriaux, J. M., G. Lenderink, S. R. De Roode, and A. P. Siebesma (2013), Understanding convective extreme precipitation scaling using
observations and an entraining plume model, J. Atmos. Sci., 70, 3641–3655.
Lynn, B. H., R. Healy, and L. M. Druyan (2009), Investigation of Hurricane Katrina characteristics for future, warmer climates, Clim. Res., 39,
75–86.
Manda, A., H. Nakamura, N. Asano, S. Iizuka, T. Miyama, Q. Moteki, M. Yoshida, K. Nishi, and T. Miyasaka (2014), Impacts of a warming marginal
sea on torrential rainfall organized under the Asian summer monsoon, Sci. Rep. Nat. Commun., 4, 5741, doi:10.1038/srep05741.
Maraun, D., et al. (2010), Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical
models and the end user, Rev. Geophys., 48, RG3003, doi:10.1029/2009RG000314.
Mooley, D. A. (1973), Some aspects of Indian monsoon depressions and associated rainfall, Mon. Weather Rev., 101, 271–280.
Muller, C. J., P. A. O’Gorman, and L. E. Back (2011), Intensification of precipitation extremes with warming in a cloud-resolving model, J. Clim.,
24, 2784–2800.
Niu, G.-Y., et al. (2011), The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and
evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, doi:10.1029/2010JD015139.
O’Gorman, P. A., and T. Schneider (2009), Scaling of precipitation extremes over a wide range of climates simulated with an idealized GCM,
J. Clim., 22, 5676–5685.
Pall, P., M. R. Allen, and D. A. Stone (2007), Testing the Clausius-Clapeyron constraint on changes in extreme precipitation under CO2
warming, Clim. Dyn., 28, 351–363.
Pattanaik, D. R., and M. Rajeevan (2010), Variability of extreme rainfall events over India during southwest monsoon season, Meteorol. Appl.,
17, 88–104.
Prajeesh, A. G., K. Ashok, and D. V. B. Rao (2013), Falling monsoon depression frequency: A Gray-Sikka conditions perspective, Sci. Reports, 3,
doi:10.1038/srep02989.
Praveen, V., S. Sandeep, and R. S. Ajayamohan (2015), On the relationship between mean monsoon precipitation and low pressure systems in
climate model simulations, J. Clim., 28(13), 5305–5324.
Rajeevan, M., J. Bhate, J. A. Kale, and B. Lal (2006), High resolution daily gridded rainfall data for the Indian region: Analysis of break and active
monsoon spells, Curr. Sci., 91, 296–306.
Rasmussen, R., et al. (2011), High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: A process study of
current and warmer climate, J. Clim., 24, 3015–3048.
Romps, D. M. (2011), Response of tropical precipitation to global warming, J. Atmos. Sci., 68, 123–138.
Saha, K., and S. Saha (1988), Thermal budget of a monsoon depression in the Bay of Bengal during Fgge-Monex 1979, Mon. Weather Rev., 116,
242–254.
Sanders, F. (1984), Quasi-geostrophic diagnosis of the monsoon depression of 5–8 July 1979, J. Atmos. Sci., 41, 538–552.
Schär, C., C. Frei, D. Luthi, and H. C. Davies (1996), Surrogate climate-change scenarios for regional climate models, Geophys. Res. Lett., 23,
669–672, doi:10.1029/96GL00265.
Shukla, J. (1978), Cisk-barotropic-baroclinic instability and growth of monsoon depressions, J. Atmos. Sci., 35, 495–508.
Sikka, D. R. (2006), A study on the monsoon Low pressure systems over the Indian region and their relationship with drought and excess
monsoon seasonal rainfall, COLA Tech Rep., 217, 61 pp., Center for Ocean-Land-Atmosphere Studies, Univ. of Maryland, College Park.
Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and J. G. Powers (2008), A description of the
advanced research WRF version 3.
Sooraj, K. P., P. Terray, and M. Mujumdar (2014), Global warming and the weakening of the Asian summer monsoon circulation: Assessments
from the CMIP5 models, Clim. Dyn., 45, 233–252.
Sørland, S., and A. Sorteberg (2015a), The dynamic and thermodynamic structure of monsoon low-pressure systems during extreme rainfall
events, Tellus Ser. a-Dynamic Meteorol. Oceanogr., 67, doi:10.3402/tellusa.v67.27039.
Sørland, S., and A. Sorteberg (2015b), Low-pressure systems and extreme precipitation in central India: Sensitivity to temperature changes,
Clim. Dyn., doi:10.1007/s00382-015-2850-4.
Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall (2008), Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization, Mon. Weather Rev., 136, 5095–5115.
Thorne, P. W., J. R. Lanzante, T. C. Peterson, D. J. Seidel, and K. P. Shine (2011), Tropospheric temperature trends: History of an ongoing
controversy, WIREs Clim. Change, 2, 66–88, doi:10.1002/wcc.80.
Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons (2003), The changing character of precipitation, Bull. Am. Meteorol. Soc., 84,
1205–1217.
Turner, A. G., and H. Annamalai (2012), Climate change and the South Asian summer monsoon, Nat. Clim. Change, 2, 587–595.
Tyagi, A., P. G. Asnani, U. S. De, H. R. Hatwar, and A. B. Mazumdar (2012), The Monsoon Monograph, vols. 1 and 2, Indian Meteorological
Department, New Delhi.
Yoon, J. H., and T. C. Chen (2005), Water vapor budget of the Indian monsoon depression, Tellus Ser. a-Dyn. Meteorol. Oceanogr., 57, 770–782.
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