Kumar D (Orcid ID: 0000-0003-2253-4129)
Present and future changes in precipitation characteristics during Indian Summer
Monsoon in CORDEX-CORE simulations
Pyarimohan Maharana1,2, Dhirendra Kumar1,3@, Sushant Das4, P. R. Tiwari5,6
1
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi, India
2
3
4
Faculty of Science, Sri Sri University, Cuttack, Odisha, India
National Centre for Atmospheric Sciences, University of Reading, United Kingdom
Earth System Physics Section, The Abdus Salam International Centre for Theoretical
Physics, Trieste, Italy
5
Centre for Atmospheric and Climate Physics Research, University of Hertfordshire, United
Kingdom
6
Centre for Climate Change Research, University of Hertfordshire, United Kingdom
@
Correspondence: D. Kumar; Email: dhirendra.cub@gmail.com
1
https://orcid.org/0000-0003-3175-8714
3
https://orcid.org/0000-0003-2253-4129
4
https://orcid.org/0000-0003-2417-7970
5
http://orcid.org/0000-0002-7580-0446
This article has been accepted for publication and undergone full peer review but has not
been through the copyediting, typesetting, pagination and proofreading process which may
lead to differences between this version and the Version of Record. Please cite this article as
doi: 10.1002/joc.6951
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Abstract
This work examines the changes in precipitation characteristics over the Indian region using
the latest high-resolution CORDEX-CORE model simulations. Individual RCM (COSMO,
RegCM4.7, and REMO) experiments, their ensembles, and all RCM ensembles are examined
to evaluate their performance using various statistical metrics. It aims to provide a holistic
idea for choosing better models for specific analysis (variability, climatology, temporal
evolution, indices, etc.) of precipitation distribution over India. The COSMO model
experiments show lesser mean bias and have a high correlation coefficient (>0.8) compared
to REMO and RegCM4. Each RCM ensemble performs better than its members in
representing the spatial patterns of precipitation. Interestingly, the RCM experiments
downscaling the forcings from MPI_ESM GCMs and ERA-Interim outperforms other RCM
experiments in the present day period. The probability distribution function reflects that the
mean frequency and the intensity of precipitation are best represented in the RegCM4
ensemble. Further, most RCMs agree to a robust increase in low-intensity rainfall (>0.1 to 4
mm/day) in the future under the RCP8.5 scenario. The intraseasonal variability in terms of
active and break spells are best represented in the RegCM4 ensemble. There exists
uncertainty in terms of projection of active phases, while all model experiments agree to the
decrease in the break phases in the near future compared to the present day. The projected
consecutive dry (wet) days and their spells will decrease (increase) over India. The heavy
precipitation events are expected to increase (by 18-32 days) along with its contribution (by
14-48%) towards the total precipitation over entire India indicating a possible increase in
flooding events in the future.
Keywords: CORDEX-CORE, Indian Summer Monsoon, Precipitation extremes, RCP8.5.
1. Introduction
The Indian subcontinent is home to 1.3 billion people and a lot of its economy
depends on agricultural production. The Indian summer monsoon (ISM) precipitation, which
spans typically from June to September (JJAS) each year, plays a critical parameter in
providing water for agricultural purposes and other necessities. The ISM precipitation is
highly variable in terms of its spatial and temporal distribution, which can lead to extreme
events such as floods and droughts. Apart from the land-sea contrast, the spatial variability
of precipitation is also governed by the local drivers. These also include the orography, land
use, and land cover, terrestrial moisture sources, anthropogenic emissions, aerosols, etc.
(Shastri et al., 2014, Das et al., 2016, Salvi et al., 2019, Pathak et al., 2017). These regional
forcings could also shape the spatial patterns of rainfall. A thorough understanding of both
global and local drivers is extremely crucial to study the complexities associated with the
rainfall pattern over a particular region and its possible variations in the future. To address
this problem, the dynamical downscaling approaches are incorporated. Understanding the
ISM and its dependencies on various factors have been challenging and the topic of scientific
interest in the climate modeling community.
Within the context of climate modeling, many studies using global climate models
(GCMs) and regional climate models (RCMs) have been carried out to investigate the
complexity of ISM. GCMs are expected to understand the effect of forcings at both global
and regional levels for skillful projections. However, on account of their spatial resolution,
important subgrid features are missed, thereby affecting the skills of GCMs in capturing
important features of ISM. Also, the coarse resolution of GCMs acts as an impediment in
utilizing the data for impact assessment.
Most climate models struggle to capture the spatio-temporal characteristics of
precipitation over India and have reported the consistent mean dry bias tendencies in
simulating precipitation during the ISM season (e.g. Mishra et al., 2018, Preeti et al., 2019,
Narapusetty et al., 2016, Singh et al., 2016, Devanand et al., 2018, Nayak et al., 2019).
Another factor could be the inaccurate replication of precipitation extremes over India
(Mishra et al., 2014). One of the reasons for such discrepancy could be the inefficiency in
simulating regional processes due to their coarser resolution. Studies have shown that
improper representation of coastal lines and topography in coarser-resolution models could
be a deviating factor in developing errors in the mean variability and extremes and hence
regional climate models (RCMs) could be handy for the purpose (Kumar et al., 2013; Tiwari
et al., 2016).
The application and demand of RCMs have been continuously increasing due to their
ability to provide high-resolution regional climate information (Giorgi et al., 2019). They are
considered to be a modern-day tool for providing such continuous information by the means
of dynamically downscaling. For this purpose, the COordinated Regional climate
Downscaling EXperiment (CORDEX) project (Giorgi et al., 2009, 2012) was initiated. This
is aimed to attain high-resolution regional climate information over different CORDEX
domains across the globe. One such domain is the CORDEX-South Asia (CORDEX-SA)
that covers the Indian subcontinent, which is characterized by complex topographical
features as well as significant climate variability.
In the first phase of the CORDEX program, RCMs were utilized to downscale the
GCMs at 50 km (Giorgi et al., 2012) over all the CORDEX domains. Progressing further, in
the second phase of CORDEX, it is initiated to further achieve a downscaling resolution of
25km for studying the regional and local impacts in detail and to also highlight the added
value of the RCMs (Giorgi and Gutowski 2015). Several modeling groups have participated
in the ongoing project across different domains. Previously, several RCMs studies over the
South Asia CORDEX domain have been carried out to evaluate their performance in various
aspects e.g. spatial variability of parameters such as precipitation, temperature, winds, etc.
Choudhary et al. (2018) evaluated the performance of CORDEX RCMs in simulating the
ISM using a set of RCM simulations. They show that some models were able to simulate the
mean climate during ISM well over different sub-regions. Studies have shown the ability of
the RCMs to showcase the variability of the precipitation over the Himalayan region during
the ISM (Choudhary and Dimri, 2017). They also projected an increase in precipitation over
the eastern Himalayan region in the near future under a warming scenario. Moreover, a
reduction of dry bias in the regionally downscaled rainfall, onset dates and the interannual
variability by the CORDEX experiments have been reported previously (Varikoden et al.,
2018; Choudhary et al., 2019). Maharana et al. (2019) used RCMs to show that the earlier
onset of ISM is possible due to an increase in an anthropogenic influence on air temperature
under 1.5° and 2°C warming levels. No doubt that these studies give us confidence about the
reliability of the RCMs application on various aspects of ISM. In this regard, here we use the
CORDEX-CORE simulations over the South Asia domain to evaluate the characteristics of
ISM precipitation.
In the current framework, we have used the recent generation regional climate
simulations with improved resolution (~25km) and updated physical parameterizations. The
CORDEX-CORE experiment is a leap towards high-resolution modeling and thus, we
present first-hand information on their capabilities in representing the features of ISM.
Further, we aim to provide a holistic idea for choosing better models for specific analysis
(variability, climatology, temporal evolution, indices, etc.) of precipitation distribution over
India. To be precise, the present study focuses on the following aspects: i) Inter-comparison
of the models in simulating the mean spatial distribution of present-day (1979-2005) ISM
precipitation and its changes in two-time slices i.e. near future (NF; 2030-2055) and far future
(FF; 2070-2095). ii) We then look into the intraseasonal variability and its changes in terms
of active and break phases within the ISM in NF and FF relative to present-day and iii)
changes in extreme precipitation indices. Subsequent sections discuss the data and
methodology, results and discussion, and concluding remarks respectively.
2. Data and Methodology
The projected RCM outputs downscaled from CMIP5 GCMs over the South Asian
region under the World Climate Research Programme’s CORDEX (Giorgi et al., 2009)
project are procured for the present analysis. Here, the recent model experiments at much
higher horizontal resolution (25km) are utilized, which is termed as the CORDEX-CORE
experiments. All the available model experiments are examined for the study, which consists
of a total of 12 GCM (reanalysis)-RCM combination. The details of the model combination
used along with their abbreviation are provided in Table S1. The daily precipitation is
obtained from 1979 to 2100 (1979-2014) for GCM-RCM (reanalysis-RCM) experiments. As
mentioned before, the entire period has been divided into three-time slices; the present
(1979-2005), near future (NF; 2030-2055), and far future (FF; 2070-2095). The two-time
slices for the future are considered to study the short term and long term impacts of climate
change on the precipitation pattern, its distribution, and extremes. The experiments are
validated against the daily gridded rainfall from the India Meteorological Department (IMD)
available at a horizontal resolution of 25 km (Pai et al., 2014).
The 26 years (1979-2005) considered in the present-day period is selected based on
the common period among all experiments, which is based on the historical emission (Taylor
et al. 2012). In order to highlight the model performance, the widely used Taylor diagram
has been employed which relies upon the standard statistics like standard deviation (SD),
correlation coefficients (CC), and root mean square error (RMSE) (Taylor 2001; Maharana
and Dimri, 2014; Choudhary et al., 2017; Kumar and Dimri, 2019, 2020; Kumar et al., 2020).
The present work examines the RCP8.5 (Riahi et al., 2011), the most pessimistic warming
scenario to account for the worst possible impacts of climate change. To understand the
variability in the magnitude of mean ISM precipitation, we used probability distribution
functions (PDFs). It is a useful analysis for comparing the simulated precipitation intensities
over a region by fitting the gamma distribution to the daily precipitation data. The PDFs have
been computed in time for the JJAS months while considering the Indian landmass area (Fan
et al., 2015; Kumar et al., 2020; Kumar and Dimri, 2020). After analyzing the present-day
conditions, near and future changes in the mean ISM precipitation under the high warming
scenario (RCP8.5) are explored. The initial analysis is based on the individual model’s
experiment, but also, each RCM ensemble has been carried out to have robustness in the
results.
Secondly, we investigate the changes in the frequency of active and break phases in
the NF and FF periods relative to the present day. The mean date of monsoon onset over the
Kerala coast is 1st June with an SD of 8 days (Pai and Nair, 2009). The monsoon progresses
northward and reaches Delhi around 25th June and by the end of June, the entire country is
influenced by the monsoon. It starts to retreat from the northern parts of India in early
September. Ideally, July and August are the two months where the entire season is under the
influence of monsoon. However, the rainfall during these months is not constant due to the
oscillation of monsoon trough between the central Indian region and foothills of the
Himalayas (Joseph and Gadgil, 2003), which lead to the active and break phases of the ISM.
Therefore, all India averaged rainfall is not a good measure to study this intraseasonal
oscillation and hence a representative zone signifying the all India rainfall is a better approach
(Maharana and Dimri, 2016; Rajeevean et al., 2010; Mandke et al., 2007). The daily areaaveraged rainfall over central India was extracted following Mandke et al. (2007) for the
study of active and break phases. The standardized rainfall anomaly is prepared following
their methodology i.e. the dates corresponding to the standardized anomaly value with +1 (1) for three consecutive days is defined as the active (break) spells.
Finally, the precipitation indices for different time-slices are computed in this study
following
the
ETCCDI
climate
change
indices
(http://cccma.seos.uvic.ca/ETCCDI/list27indices.shtml). This is important to understand the
overall behavior of the rainfall and its projected change over India. The details of these
indices, their definition, and the abbreviation used are mentioned in Table S2.
3. Results and Discussion
Based on different analyses discussed in the previous section, the performance of
different experiments, RCM ensembles, and all model ensemble is explored and presented
with the relevant discussion.
3.1 Evaluation of RCMs in simulating the spatial distribution of ISM precipitation
The daily mean JJAS precipitation patterns from the observation and different
experiments suggest significant spatial heterogeneity across the Indian landmass (Fig. 1). The
observed precipitation maxima are located over the Western Ghats, Northeast India, central
India, Eastern India, and the Himalayan foothills (Fig. 1a). By and large, the major
precipitation features over these regions are well reproduced in the experiments and their
corresponding ensemble (Fig. 1n-p). The further comparison suggests a differential behavior
in the magnitude of simulated precipitation across different regions and in different RCMs.
This holds in particular if seen in RCMs with similar boundary conditions e.g. ERAIN (Fig.
1b-d) and NorESM1 (Fig. 1k-m). This indicates a lesser dominance of the boundary forcing
from GCM on the precipitation simulation during JJAS and can be implied from all RCM
experiments. Such a strong imprint of RCMs on the ISM simulation was reported previously
over the CORDEX-South Asia domain using RCA4 experiments (Rana et al., 2020). This
underlines the role of different physical parameterizations and the internal dynamical
formulation of the individual RCMs. The Indian region lies in the center of the CORDEXSouth Asia domain and very far from the boundary of the RCM to avoid unrealistic values
(Seth and Giorgi, 2007); hence model physics becomes dominant in the evolution of
meteorological parameters. Also, the CORDEX-SA domain is reasonably large to produce
its own circulation (Jones et al., 1995). The simulation from the evaluation runs (driven by
ERA-Interim) characteristically represents different spatial patterns for different RCMs.
Focusing on the particular suites of RCMs and their ensembles, the spatial
precipitation maxima, and its magnitude is better simulated by COSMO model experiments.
The orographic precipitation along the Western Ghats, Northeast India, and rain shadow
region over the southern peninsula are well represented. A similar distribution is found for
the REMO experiments except that there is a shift of high precipitation areas below the core
central region compared to IMD. In RegCM4 experiments, there is high precipitation over
the leeward side of the Western Ghats which is more aggravated in case the experiment is
forced with MIROC5 (Fig. 1f). Such unrealistic precipitation has also been reported
previously while using the RegCM4 framework (Kumar et al., 2020; Pattnayak et al., 2013)
and this further affects the model ensemble in computing the biases during ISM. On the
contrary, there is a high underestimation of precipitation over northern India (> 5 mm/day)
in all the experiments (Fig. 2). The experiments with the COSMO model have widespread
and moderate dry biases across the major parts of India. In a study using the COSMO-CLM,
the dry biases over the central Indian region were attributed to the removal of moisture due
to the warming over the adjoining oceans and the overestimation of the precipitation over the
Western Ghats (Rockel & Geyer, 2008). In most cases in this suite, such dry biases range in
the order of 1-5 mm/day. Focusing on the regional features, the Western Ghats and parts of
Northeast India are characterized by moderate to strong wet biases (3-6mm/day).
Comparative analysis of the spatial distribution indicates that the ensemble of the
COSMO model seems to have relatively less bias over the central Indian region and hence
stands close to observation (Fig. 2m). Therefore, it tends to outperform the other two with a
lesser magnitude of dry (wet) biases across India. The ensemble of RegCM4 experiments
seems to be dominated by the greater wet biases over the southern peninsula (Fig. 2n). In
fact, the downscaling carried out from the NorESM contributes more to this bias in the
RegCM4 compared to REMO and COSMO ensemble. Such features further suggest an
important role of the large-scale circulation and the associated moisture transport in the
RegCM4 suite of experiments (Ghosh et al., 2019) and are subject to further verification.
Commenting on the overall performance in representing the spatial features of precipitation
climatology, the ensemble of individual experiments is better than many of the individual
experiments. A similar feat of ensembles has been previously reported from CORDEX-SA
experiments, where the ensemble outperformed the individual experiments (Choudhary et
al., 2018). More importantly, the model experiments in the current framework also seem to
improve the mean precipitation climatology over the central Indian region, which were
previously strongly underestimated in CORDEX-South Asia experiments (Choudhary et al.,
2018, Singh et al., 2017, Rana et al., 2020).
3.2 Statistical Validation
3.2.1 Taylor Diagram
Taylor’s diagram is one of the instrumental and widely used ways of summarizing
the major statistics of a meteorological variable (Taylor, 2001). It uses the correlation
coefficient (CC), SD, and the RMSE to provide comparative information about the model
performance. The capabilities of different models in representing the spatial distribution of
daily mean JJAS precipitation over the Indian region is explored (Fig. 3). Most of the models
can capture the spatial patterns of precipitation (CC>0.6). The COSMO suite of experiments
including the evaluation run outperform the other experiment with higher CC (>0.8), lower
RMSE, and the SD closer to that of the observation. Among these, the EIN_COSMO and
MPI-COSMO experiments have greater proximity to the observation. Besides, few of the
REMO experiments lie within the 0.7-0.8 correlation range and among them, the MPIREMO experiment better resembles the observation. The RegCM4 experiments portray a
wide range of behavior with CC varying between 0.5 to 0.7 with EIN_RegCM4 and MPIRegCM4 resembling closer to the observation. Such performance aligns well with the spatial
distribution of the bias (Fig. 2).
The mean annual cycle (or the temporal evolution) is an important indicator of the
seasonal behavior of precipitation within the year. The performance of individual models in
representing the mean annual cycle and the inter-annual variability of precipitation is
investigated over the Indian landmass as a whole and the Monsoon CORE Region (MCR);
defined by Mandke et al., (2007) using Taylor’s metrics (Fig 4a&b respectively). All the
models tend to capture the temporal evolution of the precipitation over India fairly well with
CC >0.8. Although the COSMO experiments were outstanding in representing the spatial
patterns of mean JJAS precipitation, they display a range of behavior in capturing the mean
annual. From the RegCM4 experiments, again the MPI-RegCM4 (CC=0.92) stands out
compared to the other two models. Considering the overall performances, the REMO
experiments stand better in simulating the mean annual cycle with the CC>0.95. In particular,
the MPI-REMO resembles the observation with greater proximity. Interestingly, this
experiment has considerable dry biases over the Himalayan foothills and northeast India.
This means, despite the REMO model’s shortcoming in representing the spatial patterns, it
certainly better simulates the temporal evolution of precipitation within the year. Moreover,
all the experiments perform satisfactorily well in representing the mean annual cycle over
the MCR region (Fig. 4b). Again, the MPI-REMO experiment performs exceptionally well
with a lesser RMSE (~ 0.5) and greater CC (~0.99). It is important to note that this experiment
is characterized by a mixed pattern of positive (negative) bias over the MCR during the
monsoon (Fig. 2i).
We also examined the ability of the model experiments to represent the Interannual
variability over India and the MCR through the correlation matrices (Fig. 4c-d). The
simulated all India rainfall for all the experiments are not well correlated (correlation
coefficient ~0.4) with the IMD observed value (Fig 4c), while the RMSE is also high
(between 0.5 to 1.2) among the experiments. The less value of the CC indicates that the model
experiments, in general, fail to capture the year to year variability. A similar line of results
was reported earlier over Africa and Indian domains using RegCM3; where models
particularly fail to reproduce the excess rainfall years (Sylla et al., 2009, Maharana and
Dimri, 2014). Further, a weak correlation (< 0.4) with relatively higher RMSE (0.9-1.8) is
obtained from the analysis of precipitation over the MCR (Fig. 4d). As mentioned earlier, the
model physics and internal dynamics play a major role in determining the interannual
variability of rainfall as compared to the initial and lateral boundary conditions. The marked
improvement in the representation of rainfall in the model experiments (CORDEX-CORE)
can be attributed to the improvement in physics and resolution as compared to the previous
CORDEX-SA experiments. Therefore, the inability of the model experiments to represents
the year to year variability should not be considered as a weakness of the RCMs; rather it
reflects that that improved model physics are good enough to produce the climatological
picture of the rainfall distribution and hence these high-resolution RCMs can be considered
as a good tool to study the underlying atmospheric processes associated with the ISM with
much conviction.
3.2.2 Probability Density Function
The temporal characteristics of the daily mean JJAS precipitation are explored using the PDF
by fitting the gamma distribution to the daily precipitation values from JJAS averaged over
the Indian landmass. The PDF indicates the relative frequency of a particular precipitation
category and hence provides a broader sense of the mean and extreme distribution of the
variable in a more coherent manner. The gamma distribution can be used to summarize the
mean and intensity of the precipitation distribution with the low mean and high variability
(Fan et al., 2015; Husak et al., 2007). Moreover, the right long tail of the distribution is
indicative of the extreme values present within the distribution (Ananthakrishnan & Soman,
1989).
Fig. 5 shows the PDF of daily mean JJAS precipitation from different experiments
and the IMD observation. To investigate the possible changes in the mean precipitation
distribution under the near and far future climate in the RCP8.5 scenario, the corresponding
distribution is also considered. For the present climate, a wide range of behavior is exhibited
by the suite of experiments, with many of them underestimating the precipitation magnitude.
The distribution of observation suggests the peak frequency of the precipitation magnitude
around 4mm/day while the extremes range up to 11-12 mm/day. Owing to the stronger dry
biases in the REMO suite of experiments, the peak frequency in these experiments is found
at 1-2 mm/day while the magnitude of extremes is also underestimated by 2-3 mm/day.
While most of the RegCM4 experiments tend to resemble the observed distribution with a
higher degree of proximity to the mean frequency and intensity of the precipitation. This may
be attributed to a wide range of spatial variability in the precipitation magnitudes in the
RegCM4 suite of experiments, which are canceled out on spatial averaging and hence closer
to the observed distribution. This also indicates that the total all India precipitation may be
better captured in these model experiments, although significant biases are present in spatial
distribution across all the models. The COSMO suite of experiments exhibits a wide range
of behavior in PDF depending upon the driving GCM. This also indicates a wide range of
uncertainty within the same RCM suite as well as among the whole pool of experiments.
While deliberating over the time slices under future climate, most of the experiments
suggest a gradual shift of the JJAS precipitation regime towards a drier climate from near to
far future. In due course, the experiments overestimating the mean and extremes shift towards
a drier climate and thus reach close to the distribution of observation (Fig. 5b&c). The
patterns of disagreement in the precipitation dominates through the NF and FF as well, thus
again giving rise to a large degree of uncertainty. As seen in the present, NorESM_RegCM4,
MPI-RegCM4, and the NorESM_COSMO experiments are closer to the observed
distribution. These experiments also show decreasing precipitation with the shift in the peak
and tail of their distribution under the warming scenario implying a robust increase in lowintensity precipitation (>0.1 to 4 mm/day) during the NF and FF periods.
3.3 Intraseasonal Variability
This section examines the behavior of active and break spells of the IMD observation
for the present time and RCM ensembles for the present, NF and FF (Fig. 6a-b). The observed
number of active spells during the present time is 39 (Fig. 6a). The simulated active spells
for the ensemble of COSMO experiments are underestimated for the present time (28), which
increases in the NF (33 days) and then declines in the FF (28). A different pattern is found
for the ensembles of RegCM4 models, where the number of active spells in the NF (32)
declines as compared to the present time (39) whereas the FF shows an increase (38). The
ensemble of REMO experiments shows a constant decline in the active spells from the
present (28) to NF (26) and FF (23). The ensemble of COSMO and RegCM4 experiments
reflect an increase in the active days at FF with respect to the present time. A similar projected
increase in the short active spells using CMIP5 model experiments over India during FF has
been reported (Sudeepkumar et al., 2019; Sharmila et al., 2015). This suggests that despite
the consistent dry (wet) biases in the daily mean ISM precipitation in RegCM4 experiments,
the distribution of precipitation within the season is better captured during the present
climate. This is indicative of the interplay of the synoptic disturbances inside the environment
of a particular RCM and the ensemble. This also strengthens the confidence in the projected
number of active spells by their ensemble.
The number of observed break spells during the present time is 34 (Fig. 6b). The
ensemble of COSMO experiments overestimates the break spells in the present time (38),
which declines during FF (27) as compared to NF (30). Similarly, a higher number of
simulated break spells (41) are found in the ensemble of RegCM4 models for the present
time. This declines in the NF (31) and then increases in the FF (33). The number of break
spells (36) in the ensemble of REMO experiments is very close to the observation in the
present time. Following the pattern of active spells, the breaks spell also declines in the NF
(29) and FF (22). However, the CMIP5 model experiments illustrated a non-significant
increase in the break spells and its length in FF over Indian regions (Sudeepkumar et al.,
2019; Sharmila et al., 2015). In addition, there are considerable inter-model differences in
the changes in rainfall anomalies during the active spells of ISM under future warming (Fig.
S1). In such a case, the rainfall during the active phases is likely to be more intense during
the future with a more profound signal from the ensemble of the REGCM4 over central India.
On the contrary, the ensemble of other RCMs does not project such anomalous rainfall during
the warming climate. It is noteworthy that, all the models agree to a greater extent in
representing the decline in the rainfall during the break phases of ISM in the NF and FF (Fig.
S2).
This analysis reflects that all the RCM ensembles have a different signature in terms
of the number of projected active and break spells and have uncertainties. Such behavior of
the ensemble of particular RCMs underlines the uncertainty among themselves as well as in
their contributing individual experiments. However, the ensemble of RegCM4 is close to the
observed value in terms of presenting both, the number of active as well as break spells
during the present climate. Importantly,
the different RCM ensembles have different
characteristics of the active (break) climatology which may be affected through how the mean
is represented with them.
3.4 Extreme Precipitation Indices
In this section, we discuss, the changes in the behavior of various precipitation
indices over India using all model ensembles. In general, the observed consecutive dry days
(CDD) per year are much higher (100 to more than 200) in number over Rajasthan and
Gujarat region due to less rainfall; whereas the Western Ghats region, northeast India, and
the eastern coast of India shows relatively less count of CDD per year (20 - 80 days). Similar
spatial patterns are also found for all the model experiments (figure not shown). The projected
change in the CDD per year for the ensemble is found to decline all over the Indian region in
the NF except for a few regions in Uttarakhand and northern Arunachal Pradesh (Fig. 7a).
The decline is higher (15–21 days) over western and southern India as compared to the central
and eastern Indian region (3 – 12 days). The pattern further intensifies during FF, where the
CDDs have further declined over most of western and southern India (Fig. 7b). Interestingly,
there is a slight increase in the CDD during the FF over the central and eastern Indian region
as compared to NF, which is evident from the values which range from 3 – 9 days. It increases
by 3 - 6 days per year over Uttarakhand and northern Arunachal Pradesh as compared to the
present time. The increasing frequency of CDD (3-5 days) during FF under RCP8.5 over the
west-central and western Rajasthan region was also reported (Rai et al., 2019a; Kundeti el
at., 2013). In contrast, a declining trend of dry extremes such as the CDD has been projected
to occur in CORDEX-South Asia simulations (Rai et al. 2019b). Further, the analysis reflects
an overall decline of CDDS over the entire Indian region except for the western Rajasthan
and Gujarat region under NF and FF (Fig. 7c&d). The decrease in the CDDS is highest over
the J&K region (> 4) followed by southern, northeast India (1-2), and the central Indian
region (1). The CDDS over the western Rajasthan and Gujarat region is normally very long
due to the lack of rainfall. The increase of CDDS over these regions may be attributed to the
possible increase in the projected rainfall which splits the longer spells into a few smaller
spells.
The observed consecutive wet days (CWD) per year are highest over the Western
Ghats (more than 70 days). Moreover, northeast India experiences around 20 to 60 days
CWD per year while it varies from 10 to 25 over the central and east Indian region. The
lowest numbers (5-10) are observed for the southern peninsula and western Indian region
(figure not shown). The projected CWD of all model ensembles in the NF w.r.t the present
time reflects an increase over the entire Indian landmass (Fig. 8a). This increase follows a
pattern with the maxima over the southern peninsular region (36-48 days) followed by the
central and eastern Indian region (20-36) and western India and part of the western part of
Gangetic plains (4-20 days). The previous suite of CORDEX-SA experiments also suggests
a marked increase in the frequency of CWD over central India (Rai et al. 2019b).
Interestingly, the eastern J&K which consists of the western Himalayas and northern
Arunachal Pradesh also reflects an increase of CWD per year (36-48). A similar increasing
pattern of change of CWD is also found for FF w.r.t present time (Fig. 8b). This could be
due to the intensification of local precipitation caused by moisture feedback provided by the
melting of snow there. However, careful observation of the spatial patterns reflects that the
CWD in FF has declined throughout the study domain as compared to NF. This is evident
from the decrease in the spatial extent of higher CWD over southern and central India and
the northeast part. The rise in the CWD in NF increases the CWDS throughout the Indian
landmass (Fig. 8c). The increase (1-4) is greater over the dry regions of India such as the
J&K and Rajasthan region. However, most of the central Indian region and Gangetic plains
show an increase of 1 spell per year. Besides, the CWDS tends to decrease over the major
rainfall peaks of the summer monsoon region (northeast and east coast) and northeast
monsoon (southern tip of India). The rise in CWD over these rainfall peaks probably
increases the length of the CWDS, which ultimately decreases their count. The change of
CWDS at FF with respect to present follows an almost similar pattern like NF, but again the
magnitude decline at higher temperatures rises following the behavior of CWD (Fig. 8d).
This is apparent from the decrease of the spatial coverage of the magnitude of CWDS
particularly over the western, southern, eastern, and northeast parts of India.
The very wet days, which are represented by the daily rainfall exceeding the 95th
percentile (ECAR95P) of the period, is an indicator of the very heavy rainfall events. In
general, the ECAR95P is high (low) over the drier regions like northwest India (high rainfall
locations such as the Western Ghats, eastern and northeast India). The per-year count of
ECAR95P is highest over the Rajasthan region (30-50 days) followed by the western part of
Gangetic plains (20-30 days) and most parts of southern peninsular India except for the west
coast (20-30 days) (figure not shown). Whereas, the major precipitation dominated peaks
such as the Western Ghats, eastern and northeast India reflects an ECAR95P value ranging
from 2-15 days. These regions during JJAS, in general, receive rainfall of higher magnitude.
Therefore, the chances of rainfall exceeding 95 percentiles are less. On the other hand, the
drier region like Rajasthan and southern peninsular India experiences a smaller number of
wet days, and hence the chances of rainfall exceeding 95 percentiles are higher. The projected
change in the ECAR95P at NF w.r.t. the present time illustrates an increase in the heavy
rainfall event all over India (Fig. 9a). The increase of ECAR95P over northern J&K, eastern
Rajasthan, eastern Maharashtra, and eastern Tamilnadu is higher ranging from 26-28 days,
while the rest of India shows an increase of 18-22 days. The very heavy rainfall events further
intensify over the entire Indian region at FF with the global temperature rise (Fig. 9b). The
ECAR95P exceeds 32 days over northern J&K. The Rajasthan and Gujarat region along with
the entire Maharashtra and southern peninsula shows a rise by 26-28 days, while the rest of
India shows an increase of 24-26 days. Rai et al. (2019b) also found an increase in the
ECAR95P over the entire Indian landmass with the rise in global temperature from present
to FF. With the increase in the projected ECAR95P in NF and FF, the percentage contribution
of the rainfall due to 95 percentile rainfall or above (ECAR95PTOT) has increased over the
Indian landmass (Fig. 9c). The increase of ECAR95PTOT during NF w.r.t. the present time
is highest over the northern J&K region (>30%); while over the Western Ghats and northeast
region, it varies between 22-26%. The lowest increase is found over the Rajasthan region
(14-20%)n with further intensification during the FF. This is reflected by the increase of
ECAR95PTOT up to more than 48% over J&K, 24-28% over the entire west coast, the
southern tip, northeast, and few parts of the east coast. Also, it will contribute around 2024% more rainfall over the central Indian region w.r.t. the present time. The intensification
of heavy rainfall in FF due to the ECAR95PTOT declines over Rajasthan as compared to NF.
The observed ECAR95P distributed over all the grid points over the Indian region is
examined using a violin plot (Fig. 10). This violin plot is a combination of the box plot and
the distribution of data points. The observed median value of ECAR95P is around 21
mm/day. The range of the rainfall over these grids varies from 8-62 mm/day. However, the
4th quartile indicates a higher spread of ECAR95P from 25-62 mm/day, which indicates the
heavy rainfall range. The distribution in the violin plots indicates the maximum density
around 19 mm/day, which means the maximum number of the grid points receives this
amount of precipitation. The all model ensemble shows a lower median of 18 mm/day as
compared to the observed values with a much higher spread (6-67 mm/day). The 4th quartile
also has a greater range from 22-67 mm/day. The maximum density is found in a range of
16-22 mm/year. There is a significant rise of the median value to 45 mm/day at NF for all
model ensembles. The NF range shifted to 33-84 mm/day, while the 4th quarter shows a
higher spread (47-84 mm/day). Interestingly, the very heavy rainfall distribution reflects a
bimodal rainfall peak at 39 mm/day and 47 mm/day. Similarly, the median of ECAR95P
further rises to 47 mm/day during FF. The rise of rainfall over the entire domain further
increases the range to 35-85 mm/day, while the 4th quartile ranges from 50-85 mm/day. The
distribution of ECAR95P at FF also indicates a bimodal distribution with maximum numbers
of grids receiving rainfall either in 42 mm/day and 49 mm/day. The overall distribution of
ECAR95P shows a drastic increase from the present time to NF and FF under the global
warming scenario. The increase of heavy rainfall events under the higher warming scenario
has been reported earlier (Sharmila et al., 2015; Rai et al., 2019a, Sudeepkumar et al., 2019);
which may increase the future flood events over India.
4. Conclusions
The present study examines the precipitation characteristics over India using the model
experiments from the second-generation high resolution (25km) CORDEX-CORE
experiments. Various statistical metrics are employed to evaluate the performance of the
individual model experiments, RCM ensembles, and all model ensembles. The main aim of
the study is to provide a holistic idea for the CORDEX community in choosing the better
RCM or their ensembles for specific applications over the Indian region. Specifically, as
carried out in this study e.g. climatology, the temporal evolution of precipitation, i.e.
interannual and intraseasonal variability during JJAS and annual extreme rainfall indices.
The concluding remarks of the study based on this work are as follows:
● The spatial distribution of rainfall is well represented in the COSMO model
experiments (high correlation, low RMSE, and SD) compared to the REMO and
RegCM4. Each RCM ensemble is better than its members in representing the spatial
patterns of precipitation over India. In general, the experiments forced with ERA-
Interim portrays good agreement with the observation across all the RCMs.
Moreover, within each of the model ensembles, the MPI_ESM GCMs (i.e. MPIESM-MR and MPI-ESM-LR) driven simulations outperform all the other GCM
driven experiments. However, the individual model experiments have improved in
representing the mean precipitation climatology over central India as compared to the
same from the previous CORDEX-SA experiments.
● The examination of the PDF shows that the precipitation distribution in terms of mean
frequency and the intensity in RegCM4 experiments tend to closely resemble the
observation. Also, the intraseasonal variability (active and break spells) of
precipitation over the MCR is best represented in the RegCM4 ensemble compared
to COSMO and REMO models. Most RCMs show decreasing precipitation with the
shift in the peak and tail of their distribution under the warming scenario implying a
robust increase in low-intensity rainfall (>0.1 to 4 mm/day) in the NF and FF.
Ensemble of RegCM4 shows a projected increase in the active cases and a decrease
in break cases in the FF compared to the present day. The respective ensemble of the
RegCM4 and REMO shows a decrease in the active cases while all the ensembles
show a robust decline in the break cases during NF.
● All model ensemble analysis indicates that projected CDD and CDDS will decline
with the global temperature rise from present to FF. However, the projected increase
in CDDS over rain scarce western India can be attributed to the splitting of prolonged
CDDS into smaller ones due to the projected increase in the rainfall. The projected
CWD and CWDS increase from present to NF and then declines in FF.
● The very heavy rainfall and its contribution (ECAR95P and ECAR95PTOT)
consistently increase from the present to NF and FF under RCP8.5, with a larger
increase over the western Himalayas; which has also been reported earlier over the
Indian region (Rai et al., 2019a, Sharmila et al., 2015; Sudeepkumar et al., 2019).
This may result in an increase in flood events in the future.
The present study focuses mostly on the evaluation and possible changes in precipitation
characteristics over India. As a next assignment, the authors will try to examine the change
in the monsoon dynamics driving these precipitation distributions.
Acknowledgments
The authors are thankful to two anonymous reviewers and the editor for the constructive
comments, which have helped in improving the quality of the manuscript. The authors would
like to thank the WCRP and the ESGF (https://www.esgf-data.dkrz.de/projects/esgf-dkrz) for
the provision of CORDEX-CORE simulations. PM acknowledges the Council of Scientific
and Industrial Research (CSIR), India for providing a timely stipend to the Research
Associate (CSIR sanction letter no.-09/263(1184)/2019/EMR-I). SD acknowledges ICTP
postdoctoral fellowship. PRT acknowledges funding support from ECR programme
operational at Centre for Atmospheric and Climate Physics Research, University of
Hertfordshire, United Kingdom.
Author Contribution
Conceptualization: P. Maharana and S. Das; Analysis: P. Maharana and D. Kumar;
Discussion, Writing, and Editing: P. R. Tiwari with all the authors.
Data Availability
Data will be available upon request.
Conflict of Interests
The authors declare no conflict of interest.
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Accepted Article
Fig. 1. The JJAS precipitation climatology (mm/day) from (a) IMD observation, (b-m)
different model experiments, and (n–p) ensembles for the COSMO, RegCM4 and REMO
experiments respectively for the present period (1979-2005).
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Fig. 2. The JJAS precipitation climatology bias (mm/day) for (a-l) different model
experiments and ensembles (m–p) for the COSMO, RegCM4 and REMO experiments
respectively with respect to the IMD observation.
Fig. 3. The Taylor diagram for daily mean JJAS precipitation climatology (mm/day) during
the present period over the Indian region from different model experiments.
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Fig. 4. The Taylor diagram for the mean annual cycle of daily precipitation and the
interannual variability over the (a&c) Indian region and (b&d) Monsoon Core Region
respectively during the present period.
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Fig. 5. The probability density function of the daily mean JJAS climatological precipitation
area-averaged over the Indian landmass for the IMD observation and different model
experiments for (a) Present (P), (b) Near Future (NF, 2030 – 2055) and (c) Far Future (FF,
2070 – 2095). Future simulations correspond to RCP8.5 scenarios.
Fig. 6. The number of (a) active and (b) break spells of precipitation for IMD observation and
different ensembles from the experiments for P, NF and FF periods.
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Fig. 7. The difference of all model ensemble consecutive dry days (CDD) for (a) NF – P, (b)
FF – P. (c-d) same as (a-b) but for the difference of consecutive dry days spells (CDDS).
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Fig. 8. The difference of all model ensemble consecutive wet days (CWD) for (a) NF – P, (b)
FF – P. (c-d) same as (a-b) but for the difference of consecutive wet days spells (CWDS).
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Fig. 9. The difference of all model ensemble very wet days w.r.t. 95th percentile (ECAR95P)
for (a) NF – P, (b) FF – P. (c-d) same as (a-b) but for the difference of precipitation
percentage of ECAR95P (ECAR95PTOT).
Fig. 10. The violin plot of the very wet days w.r.t. 95th percentile precipitation over India for
IMD observation and all model ensembles for P, NF, and FF periods.
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