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Annual mean, maximum and minimum temperatures averaged over India during 1986–2015 show significant warming trend of 0.15 °C, 0.15 °C and 0.13 °C per decade, respectively (high confidence), which is consistent with dendroclimatic studies.
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Pre-monsoon temperatures displayed the highest warming trend followed by post-monsoon and monsoon seasons.
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The frequency of warm extremes over India has increased during 1951–2015, with accelerated warming trends during the recent 30 year period 1986–2015 (high confidence). Significant warming is observed for the warmest day, warmest night and coldest night since 1986.
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The CORDEX mean surface air temperature change over India for the mid-term (long-term) period 2040–2069 (2070–2099) relative to 1976–2005 is projected to be in the range of 1.39–2.70 °C (1.33–4.44 °C) across greenhouse gas warming scenarios. The ranges of these Indian mean temperature trends are broadly consistent with the CMIP5 based estimates.
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The frequency and intensity of warm days and warm nights are projected to increase over India in the next decades, while that of cold days and cold nights will decrease (high confidence). These changes will be more pronounced for cold nights and warm nights.
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The pre-monsoon season heatwave frequency, duration, intensity and areal coverage over India are projected to substantially increase during the twenty-first century (high confidence).
2.1 Introduction
2.2 Observed Temperature Changes Over India
2.2.1 Mean Temperature
Season | Temperature trends 1986–2015 (°C per decade) | ||
---|---|---|---|
Mean | Maximum | Minimum | |
Annual | 0.15*± 0.09 | 0.15*± 0.10 | 0.13*± 0.10 |
Winter (Dec–Feb) | 0.05 ± 0.16 | 0.03 ± 0.20 | 0.07 ± 0.18 |
Pre-monsoon (Mar–May) | 0.26*± 0.17 | 0.29*± 0.20 | 0.20*± 0.16 |
Monsoon (Jun–Sep) | 0.11 ± 0.12 | 0.10 ± 0.17 | 0.11*± 0.08 |
Post-monsoon (Oct–Nov) | 0.17 ± 0.17 | 0.14 ± 0.22 | 0.19 ± 0.20 |
Temperature dataset | Data resolution | Indian annual mean temperature trends (°C per decade) 1986–2015 |
---|---|---|
India Meteorological Department (IMD; Srivastava et al. 2017) | 1951–2015; daily; 395 stations over India; 1.0° × 1.0° gridded | 0.15 |
Climate Research Unit (CRU; Harris et al. 2014) | 1901–2016; monthly; global; 0.5° × 0.5° gridded | 0.20 |
University of Delaware (UDEL; Peterson et al. 1998) | 1901–2014; monthly; global; 0.5° × 0.5° gridded | 0.13 |
Berkeley Earth (BEST; Rhode et al. 2013) | 1750–2017; monthly; global; 1.0° × 1.0° gridded | 0.13 |
Global Meteorological Forcing Dataset (GMFD; Sheffield et al. 2006) | 1948–2016; daily; global; blended reanalysis with observations; 0.25° × 0.25° gridded | 0.19 |
1986–2007 | ||
1901–2007; monthly; 121 stations over India; 0.5° × 0.5° gridded | 0.26 | |
Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE; Yasutomi et al. 2011) | 1961–2007; daily; over Asia; 0.25° × 0.25° gridded | 0.21 |
Season | Specific humidity trends 1979–2015 (g kg−1 per decade) | Relative humidity trends 1979–2015 (% per decade) | ||
---|---|---|---|---|
HadISDH | ERA-interim | HadISDH | ERA-interim | |
Annual | 0.27*± 0.13 | 0.27*± 0.07 | 0.69*± 0.48 | 0.65*± 0.40 |
Winter (Dec–Feb) | 0.20*± 0.10 | 0.20*± 0.10 | 0.86*± 0.54 | 0.75*± 0.60 |
Pre-monsoon (Mar–May) | 0.37*± 0.11 | 0.36*± 0.11 | 0.79*± 0.58 | 0.85*± 0.64 |
Monsoon (Jun–Sep) | 0.22*± 0.11 | 0.25*± 0.09 | 0.47 ± 0.60 | 0.45 ± 0.51 |
Post-monsoon (Oct–Nov) | 0.33*± 0.25 | 0.37*± 0.16 | 1.00 ± 1.08 | 0.86*± 0.79 |
2.2.2 Causes of Observed Changes
2.2.3 Temperature Extremes
Season | Linear trends 1951–2015 (days per decade) | Linear trends 1986–2015 (days per decade) | ||||||
---|---|---|---|---|---|---|---|---|
Cold nights (TN10p) | Cold days (TX10p) | Warm nights (TN90p) | Warm days (TX90p) | Cold nights (TN10p) | Cold days (TX10p) | Warm nights (TN90p) | Warm days (TX90p) | |
Annual | −1.4 ± 2.3 | −1.4*± 1.1 | 3.1*± 2.2 | 7.4*± 1.7 | −6.9*± 3.8 | −1.2 ± 2.8 | 7.7*± 6.4 | 9.9*± 6.4 |
Winter (Dec–Feb) | −0.2 ± 0.5 | 0.2 ± 0.4 | 0.6 ± 0.6 | 2.2*± 0.7 | −0.5 ± 1.2 | 0.5 ± 1.4 | 1.3 ± 1.4 | 3.3*± 2.0 |
Pre-monsoon (Mar–May) | −0.2 ± 0.6 | −0.4 ± 0.5 | 0.2 ± 0.7 | 1.5*± 0.6 | −2.6*± 1.2 | −1.0 ± 1.4 | 1.4 ± 2.2 | 2.5*± 2.4 |
Monsoon (Jun–Sep) | −0.3 ± 0.9 | −0.6*± 0.4 | 1.7*± 0.9 | 2.4*± 0.8 | −3.0*± 1.5 | −0.6 ± 1.1 | 3.1*± 3.0 | 1.9 ± 3.3 |
Post-monsoon (Oct–Nov) | −0.6*± 0.4 | −0.6*± 0.5 | 0.7*± 0.6 | 1.3*± 0.6 | −0.8 ± 0.8 | −0.1 ± 1.1 | 1.9 ± 1.9 | 2.2 ± 2.4 |
Season | Linear trends 1951–2015 (°C per decade) | Linear trends 1986–2015 (°C per decade) | ||||||
---|---|---|---|---|---|---|---|---|
Coldest night (TNn) | Coldest day (TXn) | Warmest night (TNx) | Warmest day (TXx) | Coldest night (TNn) | Coldest day (TXn) | Warmest night (TNx) | Warmest day (TXx) | |
Annual | 0.00 ± 0.07 | −0.01 ± 0.06 | −0.02 ± 0.05 | 0.07*± 0.05 | 0.13*± 0.12 | 0.02 ± 0.13 | 0.12*± 0.10 | 0.21*± 0.11 |
Winter (Dec–Feb) | −0.01 ± 0.08 | −0.09*± 0.08 | −0.01 ± 0.08 | 0.02 ± 0.09 | −0.08 ± 0.19 | −0.10 ± 0.28 | 0.17*± 0.14 | 0.26*± 0.18 |
Pre-monsoon (Mar–May) | −0.02 ± 0.09 | −0.02 ± 0.10 | −0.09*± 0.07 | 0.05 ± 0.07 | 0.28*± 0.20 | −0.03 ± 0.32 | 0.10 ± 0.16 | 0.29*± 0.18 |
Monsoon (Jun–Sep) | −0.01 ± 0.05 | 0.04 ± 0.05 | −0.02 ± 0.04 | 0.09*± 0.06 | 0.15*± 0.09 | 0.10 ± 0.15 | 0.05 ± 0.09 | 0.12 ± 0.20 |
Post-monsoon (Oct–Nov) | 0.05 ± 0.09 | 0.04 ± 0.09 | 0.03 ± 0.08 | 0.10 ± 0.10 | 0.19 ± 0.19 | 0.01 ± 0.25 | 0.20 ± 0.22 | 0.17 ± 0.26 |
2.3 Projected Temperature Changes Over India
CORDEX South Asia RCM | RCM description | Contributing CORDEX modelling center | Driving CMIP5 AOGCM (see details at https://verc.enes.org/data/enes-model-data/cmip5/resolution) | Contributing CMIP5 modelling center |
---|---|---|---|---|
IITM-RegCM4 (6 members) | The Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climatic Model Version 4 (RegCM4; Giorgi et al. 2012) | Centre for Climate Change Research (CCCR), Indian Institute of Tropical Meteorology (IITM), India | CCCma-CanESM2 | Canadian Centre for Climate Modelling and Analysis (CCCma), Canada |
NOAA-GFDL-GFDL-ESM2M | National Oceanic and Atmospheric Administration (NOAA), Geophysical Fluid Dynamics Laboratory (GFDL), USA | |||
CNRM-CM5 | Centre National de RecherchesMe´te´orologiques (CNRM), France | |||
MPI-ESM-MR | Max Planck Institute for Meteorology (MPI-M), Germany | |||
IPSL-CM5A-LR | Institut Pierre-Simon Laplace (IPSL), France | |||
CSIRO-Mk3.6 | Commonwealth Scientific and Industrial Research Organization (CSIRO), Australia | |||
SMHI-RCA4 (10 members) | Rossby Centre Regional Atmospheric Model Version 4 (RCA4; Samuelsson et al. 2011) | Rossby Centre, Swedish Meteorological and Hydrological Institute (SMHI), Sweden | ICHEC-EC-EARTH | Irish Centre for High-End Computing (ICHEC), European Consortium (EC) |
MIROC-MIROC5 | Model for Interdisciplinary Research On Climate (MIROC), Japan Agency for Marine-Earth Sci. & Tech., Japan | |||
NCC-NorESM1 | Norwegian Climate Centre (NCC), Norway | |||
MOHC-HadGEM2-ES | Met Office Hadley Centre for Climate Change (MOHC), United Kingdom | |||
CCCma-CanESM2 | CCCma, Canada | |||
NOAA-GFDL-GFDL-ESM2M | NOAA, GFDL, USA | |||
CNRM-CM5 | CNRM, France | |||
MPI-ESM-LR | MPI-M, Germany | |||
IPSL-CM5A-MR | IPSL, France | |||
CSIRO-Mk3.6 | CSIRO, Australia |
2.3.1 Mean Temperature
Emission scenario | Model ensemble (members) | Annual mean temperature (°C) | |
---|---|---|---|
2040–2069 | 2070–2099 | ||
RCP2.6 | CDX-ENS(5) | 1.38 ± 0.17 (12.3%) | 1.31 ± 0.24 (18.3%) |
CDX-REA(5) | 1.39 ± 0.18 (12.9%) | 1.33 ± 0.24 (18.0%) | |
RCP4.5 | CDX-ENS(16) | 1.92 ± 0.30 (15.6%) | 2.34 ± 0.44 (18.8%) |
CDX-REA(16) | 2.03 ± 0.28 (13.8%) | 2.44 ± 0.41 (16.8%) | |
RCP8.5 | CDX-ENS(15) | 2.66 ± 0.37 (13.9%) | 4.31 ± 0.56 (13.0%) |
CDX-REA(15) | 2.70 ± 0.31 (11.5%) | 4.44 ± 0.45 (10.1%) |
Emission scenario | Model ensemble (members) | Annual maximum temperature (°C) | |
---|---|---|---|
2040–2069 | 2070–2099 | ||
RCP2.6 | CDX-ENS(5) | 1.29 ± 0.14 (10.9%) | 1.23 ± 0.22 (17.9%) |
CDX-REA(5) | 1.29 ± 0.14 (10.9%) | 1.25 ± 0.23 (18.4%) | |
RCP4.5 | CDX-ENS(16) | 1.79 ± 0.29 (16.2%) | 2.14 ± 0.39 (18.2%) |
CDX-REA(16) | 1.88 ± 0.26 (13.8%) | 2.33 ± 0.37 (15.9%) | |
NEX-REA(10) | 1.91 ± 0.28 (14.7%) | 2.35 ± 0.42 (17.9%) | |
RCP8.5 | CDX-ENS(15) | 2.44 ± 0.34 (13.9%) | 3.93 ± 0.53 (13.5%) |
CDX-REA(15) | 2.59 ± 0.36 (13.9%) | 4.10 ± 0.45 (11.0%) | |
NEX-REA(10) | 2.51 ± 0.46 (18.3%) | 4.38 ± 0.65 (14.8%) |
Emission scenario | Model ensemble (members) | Annual minimum temperature (°C) | |
---|---|---|---|
2040–2069 | 2070–2099 | ||
RCP2.6 | CDX-ENS(5) | 1.49 ± 0.28 (18.8%) | 1.42 ± 0.31 (21.8%) |
CDX-REA(5) | 1.45 ± 0.24 (16.6%) | 1.33 ± 0.27 (20.3%) | |
RCP4.5 | CDX-ENS(16) | 2.09 ± 0.38 (18.2%) | 2.58 ± 0.54 (20.9%) |
CDX-REA(16) | 2.24 ± 0.29 (12.9%) | 2.66 ± 0.38 (14.3%) | |
NEX-REA(10) | 2.10 ± 0.30 (14.3%) | 2.38 ± 0.41 (17.2%) | |
RCP8.5 | CDX-ENS(15) | 2.92 ± 0.45 (15.4%) | 4.77 ± 0.70 (14.7%) |
CDX-REA(15) | 2.90 ± 0.25 (8.6%) | 4.71 ± 0.35 (7.4%) | |
NEX-REA(10) | 2.79 ± 0.40 (14.3%) | 4.87 ± 0.55 (11.3%) |
2.3.2 Temperature Extremes
2.4 Knowledge Gaps
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The uneven spatial distribution of temperature observation sites over India may lead to errors in the assessment of present-day temperature changes, particularly over the northern parts of the country with a very sparse network.
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Confidence in the assessed long-term temperature trends may be constrained by the data inhomogeneity due to changing observation site locations.
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There has been an increase in costly extreme temperature events (e.g. heat waves) across India. Hence urgent research studies are needed on event attribution that evaluates how the probability or intensity of a heatwave event, or more generally, a class of extreme temperature events, has changed as a result of increases in atmospheric greenhouse gases from human activity.
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The CMIP5 multi-model ensemble members sampling structural uncertainty and internal variability cannot be treated as purely independent because some climate models have been developed by sharing model components leading to shared biases. This implies a reduction in the effective number of independent CMIP5 models. The CORDEX South Asia ensemble consists of two RCMs driven by a subset of CMIP5 AOGCMs, implying a very little effective number of independent members in this multi-RCM ensemble.
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The contribution of natural internally generated variability to the total uncertainty in the sub-regional/local temperature projections need to be quantitatively assessed using an ensemble of high-resolution future climate projections for India. The existing ensemble of dynamically downscaled temperature projections from CORDEX South Asia multi-RCMs does not sample initial conditions, which are needed to quantify the contribution of internal variability to the total uncertainty at smaller spatial scales.
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More research is needed to understand whether the increased water vapour under conditions of regional warming is leading to significant positive feedback on human-induced climate change, as water vapour is the most important contributor to the natural greenhouse effect.
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Assessment of joint projections of multiple variables over India are needed to understand the key processes relevant to future projected significant increases in temperature variability and extremes, for example, projected changes by combining mean temperature and precipitation; linking soil moisture, precipitation and temperature mean and variability; combining temperature, humidity, etc.