The recent IPCC report (ARS) has documented that there is unprecedented warming of the earth's atmosphere in last few decades, which can substantially impact the hydrological cycle and precipitation patterns over the globe. The global temperatures have shown an increase of 0.85°C during the period 1880-2012. The land temperatures over India have also shown unequivocal warming. The annual average temperatures over the Indian landmass have shown an increasing trend of about 0.6'c (100 yrs)“ during the period 1901-2010. The highest trend is observed in post monsoon season 0.79°C (100 yrs)", and the lowest in the monsoon season 0.43°C (100yrs)
Over the last thirty years there has been consistent warming over Indian landmass. It has been documented that the land-ocean thermal contrast and the monsoon circulation have weakened during the recent few decades, while the frequency of cyclonic disturbances, tropical cyclones and severe tropical cyclones has reduced over the Indian Ocean as well as Bay of Bengal during the monsoon and post monsoon seasons. The overall monsoon does not show any significant change mainly because the decreasing trend in moderate rain events has been compensated by an increasing trend in heavy rain events.
India receives almost 75% ofthe annual rainfall in the summer monsoon (southwest monsoon) season from June through September. The summer monsoon rainfall plays a vital role in agriculture, water resource management and power management. The survival of the large population as well as the economy of India depends highly on the quantity and distribution of rainfall received during the summer monsoon season. It depicts high variability on a variety of space-time scales, from diurnal to inter-annual to decadal. A large year-to-year variability is characterized by years of excess and deficit monsoons. Deficit monsoons have large adverse impact on crop production, while it is observed that good /excess monsoons do not compensate for the loss in crop yield during droughts. Hence, it is essential to examine the variability and changes in Indian rainfall based on the most recent data. This chapter provides a brief overview of the observed changes in temperature and precipitation over India. We also present analyses of extreme temperature and precipitation indices. Detailed descriptions ofthe observed climate variability and change over India are described in Rajeevan and Nayak (2016).
Highlights
(a) Annual mean, maximum and minimum temperatures averaged over the country as a whole show significant warming trend of 0. 16', 0.17 and 0.14' C per decade, respectively since 1981. Maximum warming trend is seen during the post-monsoon season. The annual average temperature over the Indian landmass has significantly increased in the region north of 20' N.
(b) The number of warm days and warm nigh ts has significantly increased over the last 35 years.
(c) The annual as well as seasonal (June through September) monsoon rainfall over India shows signmcant decreasing trend over the core monsoon zone, north-eastem parts and southern parts of west coast.
(d) The total number of consecutive dry days with spell length more than five days has increased significantly, while the total number of consecutive wet days has shown significant decrease.
Spatial maps of linear trend in mean temperature for different seasons (a) Winter (Dec-Jan-Feb), (b) Pre-Monsoon (Mar-Apr-May), (c) Monsoon (Jun-Jul-Aug-Sep), (d) Post- Monsoon (Oct-Nov) during
1981 - 2015 . Trendsare expressed as change over 35 years and only significant grids are shaded. Note that maximum warming trends are observed during the pre-monsoon and post-monsoon seasons.
1981 - 2015 . Trendsare expressed as change over 35 years and only significant grids are shaded. Note that maximum warming trends are observed during the pre-monsoon and post-monsoon seasons.
Changes in temperature extremes
The Indian region experienced extreme weather events in recent years such as heat waves (Andhra
Pradesh and Telangana, 2016); cold waves (Jammu and Kashmir, 2017), extremely heavy rain events
(Mumbai, 2005, Uttarakhand 2013) and many others. These events happen to be more severe and
more frequent in recent years, producing devastating impacts on human life, agriculture, water resources, health etc. In this section we examine changes and trends in some of the extreme indices.
The number of warm days averaged over the entire Indian landmass depict significant increasing trend -1 4.5 days (35 years) during the period 1981-2015, while the number of warm nights has also increased -1 significantly ~ 3.5 days (35 years) (Fig. 1.4). On the other hand, the number of cold days and cold nights do not show any significant trend (Fig. 1.4c, 1.4d). The occurrence of hot days and hot nights, particularly during the pre-monsoon (Mar-Apr-May) season, were significantly more during the recent 35 years as compared to the previous time epoch.
Temperature and precipitation extremes
The Expert Team on Climate Change Detection and lndices (ETCCDI) defined a set of climate extreme indices based on daily maximum, minimum precipitation and temperature and daily precipitation amounts.
in this chapter, the temperature indices have been computed on 1' x 1' grid while the precipitation indices are computed on 0.25“ x0. 25' grid based on the entire period 1951 -2015.
To examine the observed changes and trend in extreme precipitation and temperature indices we select few from the 27 indices (see Table 1 in Sillmann et al. [2013]).
The percentile indices for temperature extremes considered are cold nights and days (during winter Dec-lanFeb) and warm nights and days (during Mar-Apr-May), which describe the threshold exceedance rate of days
where minimum or maximum temperature is below the 10th orabove the 90th percentile, respectively.
The extreme indices considered for precipi tation are 96 contribution by moderate rain days ( those between 70" and 90" percentile values) and by very heavy rain days (those exceeding 95" percentile).
The number of consecutive dry day index (CDD) represents the number of dry days (days with daily rainfall < 1 mm) in the spells where consecutive dry days are atleast5 (i. e., days with PR<1 mm) in a year. Similarly number of consecutive wet day index (CWO) is the number of wet days (days with daily rainfall > 1 mm) in the spells where consecutive wet days are at least 5 (ie days with PR > 1 mm) in a year.
Highlights
The all lndia mean surface air temperature change for the near-term period 2016-2045 relative to 1976-2005 is projected to be in the range of 1.08'C to 1.44'C, and is larger than the natural internal variability. This assessment is based on a reliability ensemble average (REA) estimate incorporating each RCM performance and convergence, and is associated With less than 16% uncertainty range (Table 2.1, Box 2.4).
The all India mean surface air temperature is projected to increase in the far future (2066-2095) by 1.35 t
0.23 ’C under RCPZ.6, 2.41 1 0.40’C under RCP4.5 and 4.19 t 0.46'C under RCP8.5 scenario respectively. These changes are relative to the period 1976-2005. The semi-arid north-west and north lndia wrll likely
warm more rapidly than the all lndia mean (Table 2.1, Fig. 2.1).
0 Monthly increase in all lndia mean surface air temperature based on REA estimate is relatively higher during winter months than in the summer monsoon months throughout the 21' century under the three
RCP scenarios (Fig. 2.3).
0 The REA changes for all lndia annual minimum temperature of 4.43 1 0.34' C is more pronounced than that of 3.94 t 0.45' C and 4.19 1 0.46‘C increases estimated for the respective annual maximum and mean
temperatures respectively the end of the 21$t century under RCP8.5 scenario (Tables 2.1, 2.2 and 2.3).
© The models project substantial changes in temperature extremes aver lndia by the end of the 21' century, with a likely overall decrease in the number of cold days and nights, and increase in the number of warm days and nights.
° Although the all India annual precipitation is found to increase as temperature increases. the REA assessment indicates that precipitation changes throughout the 21" century remain highly uncertain.
0 The all lndia annual precipitation extremes are projected to increase with relatively higher uncertainty under RCP8.5 scenario by the end ofthe 21" centurv.
0 The downscaled projections suggest that intensification o/‘both dry and wet seasons is expected along the west coast of India and in the adjoining peninsular region.
Figure 2.2 Time series of Indian annual mean surface air temperature (°C) anomalies (relative to 1976-2005) from CORDEX South Asia concentrationdriven experiments. The historical simulations (grey) and the downscaled projections are shown for RCP2.6 (green), RCP4.5 (blue) and RCP8.5 (red) scenarios for the multi-RCM ensemble mean (solid lines) and the minimum to maximum range of the individual RCMs (shading). The black line shows the observed anomalies during 1951 2015 based on IMD gridded data.
Figure 2.6 Time series of Indian annual mean precipitation (mm d ) anomalies (relative to 1976–2005) from CORDEX South Asia concentration-driven experiments. The historical simulations (grey) and the downscaled projections are shown for RCP2.6 (green), RCP4.5 (blue) and RCP8.5 (red) scenarios for the multi-RCM ensemble mean (solid lines) and the minimum to maximum range of the individual RCMs (shading). The black line shows the observed anomalies during 1951-2015 based on IMD gridded data.
SST
Significant reduction of cold bias of global mean sea surface temperature (SST) by ~0.8oC
Robust simulations of drivers of natural modes of global climate variability [eg., El Nino/Southern
Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)] well-captured in IITM-ESMv1
Teleconnections between ENSO and the Indian monsoon rainfall well captured in IITM-ESMv1
SST
Significant reduction of cold bias of global mean sea surface temperature (SST) by ~0.8oC
Robust simulations of drivers of natural modes of global climate variability [eg., El Nino/Southern
Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)] well-captured in IITM-ESMv1
Teleconnections between ENSO and the Indian monsoon rainfall well captured in IITM-ESMv1
Spatial map of the observed climatology
(a) Annual mean sea surface temperature (SST oC) (b) Mean precipitation (mm day-1) during the June-July-August-September (JJAS) boreal summer monsoon season. The SST data is based on Hadley Centre dataset (HadISST, Rayer et al. 2003) and the precipitation data is estimated from the Tropical Rainfall Measurement Mission (TRMM, Huffman etal., 2010) satellite. The warm pool region in the tropical eastern Indian Ocean and western Pacific Ocean are associated with SST > 29oC, while cooler SSTs prevail in the eastern equatorial Pacific (< 22oC) giving rise to a strong east-west gradient in the tropical Pacific Ocean. Cold SSTs (< 18oC) are seen in the extra-tropical oceanic areas. The boreal summer monsoon precipitation is dominated by tropical precipitation over the Indian subcontinent, Southeast and East Asia, Tropical Eastern Indian Ocean and Western Pacific, the Inter-Tropical-Convergence-Zone (ITCZ) over the equatorial Pacific and Atlantic, Central and Latin America, equatorial Africa. Extra-tropical precipitation is seen over the East Asian region
including eastern China, Korea and Japan, and the east coast of North America.
Spatial map of climatological (a) Mean SST ( C)
(b) Mean JJAS precipitation (mm day-1) simulated by the
IITM-ESMv2 from the PI control simulation. The mean
values are based on the last 100 years of the PI Control
simulation. The broad spatial patterns of the simulated SST
and rainfall are consistent with the observed patterns. The
magnitude of the simulated SST and rainfall in the tropical
Indo-Pacific warm pool are underestimated as compared
to observations.
Spatial map of mean boreal summer monsoon
(JJAS) precipitation (mm day-1) over India (a) Observed
precipitation from the India Meteorological Department
(IMD) based on the Pai et al. (2015) dataset (b) Simulated
precipitation from IITM-ESMv1 (c) Simulated precipitation
from IITM-ESMv2 (d) Difference (IITM-ESMv1 minus IMD
observation) shows the systematic bias in the IITM-ESMv1
simulation (e) Difference (IITM-ESMv2 minus IMD
observation) shows the systematic bias in the IITM-ESMv2
simulation. It can be noticed that the IITM-ESMv1 has a
large dry bias over north-central India. The magnitude of
the negative bias in precipitation has been reduced in the
IITM-ESMv2 simulation.
Figure 3.6 Spatial maps of climatological mean chlorophyll concentration (mg m3) (a) Satellite estimates (SeaWifs) (b) llTM-ESMVZ simulation. High chlorophyll concentrations off the Somali Coast and Arabian Sea, and the eastern Pacific are associated with oceanic upwelling. The llTM-ESMvZ captures the high-chlorophyll patterns in the northern Indian Ocean and eastern Pacific, although the magnitudes are somewhat underestimated in the Arabian Sea. Also the simulated chlorophyll in the Pacific Ocean extends far westward from the Eastern to the Central PaCIfic, as compared to observations.
Figure 3.9 (a) Time-series of the observed global mean surface temperature (° C) anomalies for the period 1900-2016. A clear increasing trend ["' 0.85°C (116 years)'1] in the global mean temperatures can be noted (b) Spatial map of linear trend in surface temperature over the period (19002016). Notice that the spatial pattern of warming is not uniform. The warming trend is stronger over the extra-tropical regions of North America, Europe, Asia, South America, Africa and Australia, and relatively smaller in magnitude over the tropical and near-equatorial areas. Special modeling experiments are included as part of the CMIP6 activity for detection, attribution and future projection of spatio-temporal variability of the climate change signal.
Special thanks to IMD, NOAA, NASA
Thank you
By P.ghosh
Update: 22:00 IST
25/01/2018
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