West coast India’s rainfall is becoming more convective

Abstract

A disastrous cloudburst and associated floods in Kerala during the 2019 monsoon season raise the hypothesis that rainfall over the west coast of India, much of which is stratiform, may be trending towards being more convective. As a first exploration, we sought statistically significant differences in monthly ERA-5 reanalysis data for the monsoon season between two epochs, 1980–1999 and 2000–2019. Results suggest a more convective (deeper, ice-rich) cloud population in recent decades, with patterns illustrated in ERA-5 spatial maps. Deepening of convection, above and beyond its trend in amount, is also indicated by the steeper regression slope of outgoing longwave radiation trends against precipitation than that exhibited in interannual variability. Our reanalysis results are strengthened by related trends manifested in more direct observations from satellite and gauge-based rainfall and a CAPE index from balloon soundings data.

Full text

ARTICLE OPEN
West coast India’s rainfall is becoming more convective
A. V. Sreenath
1
, S. Abhilash
1,2
✉, P. Vijaykumar
1,2
and B. E. Mapes
3
A disastrous cloudburst and associated floods in Kerala during the 2019 monsoon season raise the hypothesis that rainfall over the
west coast of India, much of which is stratiform, may be trending towards being more convective. As a first exploration, we sought
statistically significant differences in monthly ERA-5 reanalysis data for the monsoon season between two epochs, 1980–1999 and
2000–2019. Results suggest a more convective (deeper, ice-rich) cloud population in recent decades, with patterns illustrated in
ERA-5 spatial maps. Deepening of convection, above and beyond its trend in amount, is also indicated by the steeper regression
slope of outgoing longwave radiation trends against precipitation than that exhibited in interannual variability. Our reanalysis
results are strengthened by related trends manifested in more direct observations from satellite and gauge-based rainfall and a
CAPE index from balloon soundings data.
npj Climate and Atmospheric Science (2022) 5:36 ; https://doi.org/10.1038/s41612-022-00258-2
INTRODUCTION
Model studies predict that the geographic distribution of
precipitation will change in response to global warming
1,2
. In the
tropics, these changes have been reflected as’wet-gets-wetter’
(increased rainfall in presently rainy regions)
3,4
or’warmer-gets-
wetter’(increased rainfall where the rise in sea surface temperature
(SST) exceeds the mean surface warming)
4,5
pattern. Furthermore,
mean ocean warming leads to enhancement of extreme precipita-
tion and a decline in light and moderate rainfall
6,7
, especially in
regions where SST warms most. The secular SST warming trend of
the Indian Ocean is greater than most of the tropical Pacificand
Atlantic, especially since the 1950s
8
. The Arabian Sea has received
special attention because its geographical position substantially
impacts the densely populated bordering country, India
9
. Acceler-
ated warming of the Arabian Sea since the 1995s is creating
significant effects on intense cyclones over the Arabian Peninsula
and Indian subcontinent
10
. That study also found that warmer
winters and decreased decadal rainfall associated with warming of
the Arabian sea are accompanied by the decline of wheat
production and vegetation cover, respectively.
Studies revealed that there had been a decrease in the
probability of moderate rainfall events over central India during
the monsoon seasons between the 1951–2000 period, accom-
panied by enhanced extreme rainfall events
6
. Model projections
suggest that the Asian monsoon domain has larger changes in
extreme precipitation indices than other monsoon domains
11
,
indicating higher sensitivity of Asian monsoons to global warming.
Several hypotheses have been proposed to explain a long-lasting
drying trend in Indian Monsoon Summer Rainfall (ISMR). Based on
the Community Climate Model Version 3 simulations
12
, the first
hypothesis attributes the drying trend to the weakening of
meridional SST gradient from the equatorial region to the South
Asian coast during summer, weakens the monsoon circulation and
ultimately results in lower monsoon rainfall over India. Similarly,
Roxy et al.
13
suggested that the warming of the Western Indian
Ocean (WIO) has a primary role in the observed decrease of ISMR.
An alternative hypothesis suggests that large-scale deforestation
may have weakened evapotranspiration and, hence, ISM pre-
cipitation over land. Additionally, several studies propose that
higher aerosol emissions in northern India created a cooling effect
over the Indian subcontinent, eventually reducing ISMR
14,15
.Of
course, all these effects may occur together.
Greenhouse gas-driven modelling studies suggest that the
forced response may be a nearly uniform warming of the tropical
Indian Ocean, leading to intensified evaporation, enhanced
moisture availability in the atmosphere, and hence increased
monsoon rainfall in India
16,17
. Kitoh et al.
18
noted that despite a
modest weakening of the low-level monsoon winds over the
Arabian Sea, summer monsoon rainfall over India increased due to
enhanced moisture content in the warmer troposphere. On a
planetary scale, Wang et al.
19
reported that northern hemisphere
summer monsoon rainfall, and the Hadley and Walker circulations,
have exhibited substantial intensification during the last three
decades (1979–2011), with a striking increase of monsoon rainfall
by 9.5% per degree of global warming. Based on observational
data sets, Jin et al.
20
investigated whether and how the ISMR has
increased, specifically in the early part of the 21st century. They
find a robust ISMR increase since 2002, closely associated with a
favourable land-ocean temperature gradient, driven by a strong
warming signature over the Indian subcontinent and slower
warming rates over the Indian Ocean. Hari et al.
21
suggested that
this increase of ISMR since 2002 is related to the changes in ITCZ
dynamics, which is strengthened and displaced northward as a
part of changes in the energy and moisture budgets.
The agrarian-based economy of India crucially depends on the
summer monsoon rainfall
22
, and variation in rainfall has a massive
impact on the agricultural output. Both types of extremes (floods
and droughts) can adversely affect food security, inflation, and the
country’s Gross Domestic Product
22
. Hence, the impact of climate
change on the Indian summer monsoon circulation and precipita-
tion pattern is of great practical interest. Global warming is
projected to cause an increase in both the mean Indian monsoon
rainfall
23,24
and the frequency of extreme precipitation events
25,26
.
Romatschke et al.
27
identified eight geographical regions with
similar convective cloud system characteristics to those over south
Asia. They showed that convection over the Arabian Sea is in
systems often containing extensive stratiform regions. Orographi-
cally influenced large mesoscale convective systems are prolific
producers of precipitation in the Bay of Bengal, Arabian Sea, and
1
Department of Atmospheric Sciences, Cochin University of Science and Technology, Cochin 682016, India.
2
Advanced Centre for Atmospheric Radar Research, Cochin University
of Science and Technology, Cochin 682022, India.
3
University of Miami, Miami, FL, USA. ✉email: abhimets@gmail.com
www.nature.com/npjclimatsci
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surrounding areas during the monsoon
28
. They also observed less
frequent occurrences of broad stratiform regions over the eastern
coast of the Arabian Sea. Consistently, Hirose et al.
29
found that
horizontal dimensions of the precipitating systems over the
Arabian Sea are smaller than those observed over the Bay of
Bengal. When strong, moist lower level westerly wind from the
warm Arabian Sea encounters the Western Ghats mountain range,
the orographic uplift produces ample amounts of rainfall over the
windward side of the west coast
30
.
The west coast of India is considered highly vulnerable to
extreme rainfall events and has witnessed several disasters
31
.
Drastic changes in the precipitation pattern over the west coast of
India were noted by Lyngwa et al.
32
and Meenu et al.
33
. Kerala (the
southwestern state in India) witnessed extreme rainfall events in
August 2018 and 2019
34–37
. Vijaykumar et al.
35
detailed how the
rain pattern that caused the 2019 flood may be attributable to the
unprecedented warm near-coast SST anomalies and clouds’
unusually unstable and convective nature. We enquire whether
the rainfall characteristics over this focused region (73–77 °E,
8–20 °N) portray any transformations in the recent past. We show
that post-2000, there is an increased frequency of intense
precipitation rate and deep convective clouds over the west
coast, which may be associated with the rising SST of the eastern-
Arabian Sea, existence of greater instability and the intensification
and expansion of meridional overturning South Asian monsoon
circulation.
RESULTS
Warming Eastern Arabian sea and accelerating rainfall
The Indian Ocean plays a regulatory role in the monsoon through
the seasonality of meridional oceanic heat transports, themselves
related to the seasonal monsoon winds
38
. As convenient indices of
this highly coupled system, here we considered SST anomalies
over the Eastern Arabian Sea (65–70 °E, 5–20 °N) and lower
tropospheric temperature (LTT) over the west coast of India
(73–77 °E, 8–20 °N) for time series analysis (Fig. 1a). These show an
in-phase relationship interannually and comparable positive 40-
year trends perhaps indicative of forced climate change
39
, with a
robust correlation (r=0.9, significant at 95% confidence level).
Recently, Jin and Wang (2017)
20
reported that there has also been
an increase in the land-sea thermal gradient, with more warming
inland than the Ocean and subsequently strengthened monsoon
over India in post-2002
21
.
Time series of seasonal mean rainfall and outgoing long-wave
radiation (OLR) from ERA-5 (see Data and methods) show a rising
and falling trend respectively over the west coast (Fig. 1b). These
variables are highly anti-correlated on interannual timescales (r=
−0.82 overall), but the strength of their trends appears to differ
distinctly. This is our first hint that cloud tops may be trending
upward in altitude, above and beyond the fact that surface
precipitation is rising in magnitude. A significant rainfall trend was
found by Kumar et al.
40
in seasonal monsoon rainfall over the west
coast. Arun et al.
41
noticed an increasing rainfall in the Konkan and
Goa (2.06 mm.year
−1
) and over the coastal Karnataka (1.79 mm.
year
−1
) along with a non-significant positive trend in peninsular
India (0.09 mm.year
−1
).
Trends in the character of precipitating clouds
Our time series replicates those prior findings of upward rainfall
trends (blue curve in Fig. 1b), although this gentle trend is not
significant at 95% confidence. We also find a downward trend of
OLR (orange curve), which does exceed 95% statistical confidence
tests, despite the similar year-to-year noise level in the two-time
series. Figure 2shows scatter and regression results for the data in
panel 1b. Precipitation and OLR are highly anti-correlated on
interannual timescales (r=−0.82, with a best-estimate regression
slope of −3.8 Wm
−2
per mm.day
−1
of rain rate). However, the
regression of their trends yields a much steeper slope of −13.3 in
the same units. This finding suggests that cloud tops may be
systematically rising in altitude or expanding in area, above and
beyond the secular increase in surface precipitation amount.
Either way, it suggests that the character of convective activity is
changing as a secular low-frequency trend, not merely the
amount. This finding remains somewhat indirect with monthly
or seasonal mean reanalysis data as the source. Still, this paper
attempts to dig as deeply into aspects of the evidence and
validate the sense of the results with more direct (but less
complete) observations. Future studies with less-averaged char-
acterisations will be required to fully interpret these results.
Fig. 1 Interannual variability of LTT, SST, rainfall and OLR.
aInterannual variability of anomalous sea surface tem- perature
(SST) (°C, black) and lower tropospheric temperature (LTT: 1000 hPa
to 700 hPa average) (°C, red) anomalies respectively over the Arabian
sea (65–70 °E and 15–20 °N) and the western coast of India (73–77 °E
and 8–20 °N) during JJAS. bInterannual variability of anomalous
rainfall (mm.day
−1
, Blue) and OLR(Wm
−2
, Orange) anomalies,
respectively, over the western coast of India (73–77 °E and 8–20 °
N) during JJAS. Fitted trends obtained by linear regression on year
are indicated by dashed lines.
Fig. 2 Trends in rainfall and OLR. Scatter plot between detrend
values of rainfall and OLR (red dots) and red shade indicating 95%
confidence level. The blue line indicates the linear trend of rainfall
and OLR.
A.V. Sreenath et al.
2
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Studies show that the link between SST and convection plays a
crucial role in shaping the precipitation over the tropics
42,43
, but
other factors matter too. Besides mean SST warming, SST
gradients are also important in determining the tropical circula-
tion and precipitation characteristics
44
. Furthermore, even if the
SSTs over a region are conducive for increased convective activity,
large-scale circulations could suppress the local convective
rainfall
45
. Henceforth, the term “anomaly”or “change”will refer
to the difference between post-2000 and pre-2000 composites.
Figure 3a indicates that positive SST anomalies (significant at a
99% confidence level) existed over vast areas of the Indian Ocean
during post-2000 monsoon seasons, although with negative
anomalies over the far western Arabian Sea and spots in the
head Bay of Bengal. Because of a shallow mixed layer in summer,
SST in the western Arabian Sea is very sensitive to wind-driven
upwelling variations
46,47
, similar to the more famous cold tongue
in the equatorial Pacific
48,49
. Low-level southerly wind anomalies
near 10 °N display two branches in Fig. 3a: an Arabian Sea branch
and a Bay of Bengal branch. The northward wind in the Arabian
Sea strengthens where the mean flow (panel b) is directed
towards the west coast of India, where its interaction with Western
Ghats orography produces copious amounts of rainfall on the
west coast of India.
Panel c shows the change of ERA-5 Outgoing Longwave Radiation
(OLR). Low values correspond to more cloud amount and/or radiative
emission from higher cloud-top altitudes and therefore may be
partially a proxy for the top heights of deep clouds
50,51
.Duringthe
post-2000s, extreme negative OLR anomalies are prominent over the
north of northwest coast of India. In addition to this, minor but
significant (stippled) minima are spotted over southern peninsular
India and the central Bay of Bengal. Might these OLR anomalies
during the recent epoch partly reflect the deepening of clouds over
the west coast, not merely an increased frequency of occurrence of
the same pre-2000 types of clouds?
Intensifying South Asian monsoon circulation
To elucidate the coupling between convection depth and larger-
scale South Asian monsoon circulations, consider the vertical-
meridional section in the longitude belt 73–77 °E (boxed in
Fig. 3d), where the flow is illustrated by arrows in Fig. 4a, b. This
monsoon circulation exhibits stronger ascending motion throughout
latitudes >10 °N during the post-2000 period (Fig. 4a).Inaddition,the
ascent has become more top-heavy, with the strongest and most
significant (stippled) increases above the 500 hPa level, up to 150 hPa
in the 15–20 °N latitudes. The corresponding vertical distribution and
changes of ERA-5’sspecific cloud ice water (SCIW) and specificcloud
liquid water (SCLW) over the west coast are depicted in Fig. 4c–f.
Climatologically (Fig. 4d), three main ice cores lie between 0–8°N(1st
core), 8–15 °N (2nd core), and 18–25 °N (3rd core).
The climatology of SCLW (panel f) exhibits two well-separated
horizontal bands, one in the lower levels (1000–800 hPa) and the
other in the middle levels (600–450 hPa) enhanced between 8
and 25 °N. These elongated bands are an averaged view of ERA-
5’s lower and middle-level clouds over the west coast and
suggest characteristic features of the precipitation mechanism.
The more flattened nature of the mid-level band may suggest
slowly falling recently melted snow and graupel near the 0 °C
isotherm, while the lower-level condensate may indicate cumulus
Fig. 3 Changes in SST and OLR. a Anomaly (post-20 minus pre-20) JJAS composite SST(unit:°C) with stippling indicates statistically significant
areas at a 99% confidence level, overlaid with 850 hPa vector winds(unit:ms
−1
) significant at a 99% confidence level. bSST Climatology. c,dAs
above but for OLR(unit:Wm
−2
). The boxes in aand drepresents the eastern-Arabian sea (5–20 °N, 65–70 °E) and west coast of India (8–20 °N,
73–77 °E), respectively.
A.V. Sreenath et al.
3
Published in partnership with CECCR at King Abdulaziz University npj Climate and Atmospheric Science (2022) 36

convection near the surface. Statistically significant SCIW changes
(Fig. 4c) exhibit two main positive cores: a weak trend over 8–12 °
N and a stronger trend over 15–25 °N. Both of these are aligned
with mid-level SCLW anomalies (panel e), suggestive of anomalies
in the melting ice amounts. However, SCLW has significantly
decreased near the surface in these same columns, whether by
reduced cumulus activity or by scavenging cloud water from
SCIW-related precipitation. Whatever the mechanism, the ERA-5
cloud has become more vertically extended, top-heavy and more
massive on average.
Checking ERA-5 results against available observations
This section seeks to validate the sense of the ERA-5 results above
by using radiosonde, satellite and gauge-based rainfall
observations. Direct observations are valuable, but their space
and time coverage is limited, and observing technologies can
have spurious drifts, so careful laborious work will be required in
the years ahead to confirm these results more fully.
Seeking changes in convective instability, we calculated the
twice-daily (at 0000 UTC and 1200 UTC) convective available
potential energy (CAPE) by using radiosonde observation down-
loaded from the University of Wyoming (see Data Sources) for
Mangalore (12.9 °N, 74.8 °E), during 1980–2019 JJAS season. A
statistical analysis of these instantaneous CAPE values during the
pre-2000 and post-2000 monsoon seasons is shown in box plots
(Fig. 5a). The results certainly suggest that CAPE values were
significantly higher during the post-2000 epoch (for instance, the
median shifted from ~700–1100 Jkg
−1
), although the formal
Fig. 4 Changes in SAM circulation and cloud structure. a Composite of anomalous negative Omega (shaded) and meridional-vertical wind
vectors (units of ms
−1
for v and −100 Pa s
−1
for Omega) overlaid with stippling, indicating areas that are statistically significant at a 99%
confidence level. bClimatology of negative Omega (shaded) and meridional vertical wind vectors. All variables are averaged over 73–77 °E.
Composite of meridional vertical structures of anomalous (c) SCIW(unit:gkg
−1
)(e) SCLW(unit:gkg
−1
) overlaid with stippling indicating
statistically significance at a 99% confidence level. Climatology of (d) SCIW and (f) SCLW. SCIW and SCLW are averaged over 73–77 °E in JJAS.
A.V. Sreenath et al.
4
npj Climate and Atmospheric Science (2022) 36 Published in partnership with CECCR at King Abdulaziz University

significance is not trivial to estimate for this derived variable with
its clipped and skewed distribution.
To more directly estimate cloud changes, Fig. 5b–f displays
linear trends over the available period of record in cloud cover
discriminated by type in ISCCP, cloud top pressure in CERES-
MODIS, and a convective rainfall estimate from TRMM (see Data
and Methods section). In the climatology of deep convective
clouds (Fig. 5c), the distribution varies between 8–16% along the
west coast. Trends are all positive over the west coast (warm
colours in Fig. 5b). A significant change in deep convective clouds
appears from 8 °N to 14 °N (southern parts of the west coast and
offshore), where the background was already fairly large, but a
broad area north of about 20 °N has also experienced significant
(stippled) positive changes.
Convective rainfall from TRMM is more spatially confined than
convective cloud, both in its climatology (panel e) and in its
trend (panel d), but these changes are in broad agreement with
panel b along the coast. Convective clouds have increased more
robustly than convective rainfall, another indication of our
general hypothesis that the convective process may have
deepened, not merely become more frequent. Since satellite
precipitation estimates are indirect, gauge-based rainfall are
examined in Fig. 5g–j as probability density distributions of daily
rainfall in grids obtained from the India Meteorological Depart-
ment (IMD). Extremely heavy rain events (>244.5 mm.day
−1
)
increased during the recent epoch, consistent with the titular
notion and indications above of greater instability and deepen-
ing of clouds over the west coast.
In summary, preliminary indications are that summer monsoon
clouds systems and precipitation have indeed trended towards
more deep-convective characteristics over the west coast of India,
as well as increasing in frequency and amount, consistent with
changes in the analyzed South Asian monsoon circulation of Fig.
4a during recent decades.
DISCUSSION
In summary, this study inquired whether observed trends in
rainfall, rainfall intensity and cloud depth (suggested by down-
ward trends in OLR and upward trends in ice water) may be
indicative of an increasingly convective character of rain-making
processes over the west coast of India. We further speculated that
these may be connected to Arabian Sea warming, greater
instability, and strengthened monsoon ascent over western India.
If these trends are part of a forced climate signal, they may be
likely to continue. Zonal and meridional SST gradients over the
Indian Ocean region have also changed, suggesting how nonlocal
effects (circulation changes) may also be playing a substantial role,
for instance, steering low-level moisture transport towards the
west coast. Even larger scales (beyond the basin) also mediate
Fig. 5 Trends in CAPE, cloud depth and rainfall from direct observations. a Box plot illustrating changes in CAPE during JJAS over the west
coast (12.9 °N and 74.8 °E) from 1980 to 1999 and from 2000 to 2019. In box-part (shaded), the centre line represents the median, bottom and
top-line represents the 25th and 75th percentile of data, respectively. The whiskers represent 1.5 times the inner quartile range (IQR), and data
outside the 1.5*IQR is identified as outliers. Direct observations of trends by satellite. bSpatial patterns of the 1998–2014 linear trends of the
average JJAS deep convective cloud cover from ISCCP-H series data (unit:%.day
−1
.17year
−1
) with stippling indicates statistically significant
areas at 95% confidence level. cClimatology of deep convective cloud cover (unit: %). dSpatial patterns of the 1998–2014 linear trends of the
average JJAS convective rainfall from TRMM (unit: mm.day
−1
.17year
−1
) with stippling indicates statistically significant areas at 95% confidence
level. eClimatology of convective rainfall (unit: mm.day
−1
). fBox plot illustrating the monthly averaged CERES-MODIS cloud top pressure
changes during JJAS over the west coast from 2000 to 2009 and from 2010 to 2019. In box-part (shaded), the centre line represents the
median, bottom and top-line represents the 25th and 75th percentile of data, respectively. The whiskers represent 1.5 times the inner quartile
range (IQR), and data outside the 1.5*IQR is identified as outliers. g–j PDF of JJAS rainfall (50 mm.day
−1
bins between 0 and 400 mm.day
−1
)
prepared with the aid of gridded rainfall data from IMD.
A.V. Sreenath et al.
5
Published in partnership with CECCR at King Abdulaziz University npj Climate and Atmospheric Science (2022) 36

forced global changes, so more observational and modelling work
to answer and extend our title. Internal consistencies among the
available data sources examined here bolster confidence in the
observed signal, but more big-data work with instantaneous (not
seasonally averaged) data is needed to establish and understand
the trends. Convection’s character is ultimately expressed in the
details of thermodynamic profiles, the fine texture of clouds in
satellite imagery, and short but impactful extremes in rainfall time
series. If trends in these facets of convection continue, ecological
and social consequences may be adverse even in places where
bulk water resources are and will remain abundant.
METHODS
Reanalysis data
ERA-Interim has been used in many climate studies on clouds in the
past
52–54
. As a successor of ERA-Interim, ERA-5 offers several improve-
ments. Recent studies reported better performance of ERA-5 compared to
other reanalysis products in representing various climatic variables,
including precipitation, soil moisture, and evaporation, even in the ISMR
region
55
. The significant improvements in the horizontal and vertical
resolutions of ERA-5 data can result in better estimates of precipitation
intensity and orographic precipitation in the western Ghat and foothills of
Himalaya
55
. We use monthly averaged ERA-5 data during monsoon season
(June to September) from 1980 to 2019 with 0.25° spatial resolution for
dynamic (horizontal and vertical wind components), thermodynamic (air
temperature, specific humidity), cloud properties (OLR and rainfall) and sea
surface temperature. The cloud profile data utilised in this study include
specific cloud ice water (SCIW) and specific cloud liquid water (SCLW) with
a vertical resolution of 50 hPa. To delineate the changes in circulation and
rainfall characteristics during the recent period, we further segregated the
years into periods before and after 2000.
Observed data
For the present study, the radiosonde data available at 0000 and 1200 UTC
for the 1980–2019 monsoon season at Mangalore station (12.9 °N and
74.8 °E) are used to monitor the thermodynamic state (CAPE) of the
atmosphere. For more direct observational confirmation, this study uses 17
years of (1998–2014) of monthly averaged TRMM (Tropical Rainfall
Measuring Mission)-3A25v7 data during monsoon season for convective
rainfall. The 3A25 product is obtained from the space-time average
accumulation of TRMM level-1 and level-2 data (i.e. 1B21, 1C21, 2A21,
2A23, 2A25) on each grid point. The monthly average of convective rain
fraction (horizontal resolution: 0.5° (latitude) × 0.5° (longitude)) is obtained
by averaging the accumulated passes over each bin for the entire month
(For more information: https://www.eorc.jaxa.jp/TRMM/documents/ PR_al-
gorithm_product_information/pr_manual/PR_Instruction_Manual_V7_L1.
pdf). The ISCCP (International Satellite Cloud Climatology Project) H-series
data contain products for monitoring the cloud and surface properties to
better understand the effects of clouds on climate, the global hydrologic
cycle and the radiation budget. Trends of deep convective cloud cover
(10 < =cloud top pressure < =440hPa, 22.63 < optical thickness < =450,
Cloud top temperature < 253 K) on an equal-area map grid (1° × 1°) were
evaluated during the (1998–2014) monsoon season from ISCCP H-series
data (Fig. 6a). Cloud top pressure is taken from the CERES-MODIS (Clouds
and the Earth’s Radiant Energy System-Moderate Resolution Imaging
Spectroradiometer) data (1° × 1°) from 2000 to 2019 during JJAS
56
, and a
boxplot analysis (Fig. 6e) of cloud top pressure was performed for two
decades ((2000–2009) and (2010–2019)) over the west coast. The daily
gridded rainfall data from the India Meteorological Department (IMD),
available at 0.25° × 0.25° resolution, is prepared using a dense network of
rain gauges
57
, are also used to examine the PDF of rain events.
DATA AVAILABILITY
The Era-5, IMD rainfall, radiosonde observation, ISCCP, TRMM and CERES-MODIS data
sets can be accessed from the web links, https://www.ecmwf.int/en/forec asts/
datasets/reanalysis-datasets/era5,https://www.imdpune.gov.in/Clim_Pred_LRF_New
/Grided_Data_Download.html,http://weather.uwyo.edu/upperair/sounding.html,https://
www.ncei.noaa.gov/products/climate-data-records/cloud-properties-isccp,https://disc.
gsfc.nasa.gov/datasets and https://ceres.larc.nasa.gov/data/, respectively.
CODE AVAILABILITY
The computer codes generated during the current study are available from the
corresponding author on reasonable request.
Received: 20 June 2021; Accepted: 7 April 2022;
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ACKNOWLEDGEMENTS
The authors would like to thank ECMWF, the University of Wyoming, NOAA, NASA
and IMD for making available the datasets. In addition, Sreenath A V acknowledges
the Council of Scientific and Industrial Research (CSIR), India, for providing financial
support.
AUTHOR CONTRIBUTIONS
A.S., S.A.V. and M.B.E designed the study. S.A.V. performed the analysis and wrote the
paper with feedback from A.S., V.P. and M.B.E.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Correspondence and requests for materials should be addressed to S. Abhilash.
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