Spatial variations in sea surface temperature (SST) and rainfall changes over the tropics are investigated based on ensemble simulations for the first half of the twenty-first century under the greenhouse gas (GHG) emission scenario A1B with coupled ocean–atmosphere general circulation models of the Geophysical Fluid Dynamics Laboratory (GFDL) and National Center for Atmospheric Research (NCAR). Despite a GHG increase that is nearly uniform in space, pronounced patterns emerge in both SST and precipitation. Regional differences in SST warming can be as large as the tropical-mean warming. Specifically, the tropical Pacific warming features a conspicuous maximum along the equator and a minimum in the southeast subtropics. The former is associated with westerly wind anomalies whereas the latter is linked to intensified southeast trade winds, suggestive of wind–evaporation–SST feedback. There is a tendency for a greater warming in the northern subtropics than in the southern subtropics in accordance ...

Global Warming Pattern Formation: Sea Surface Temperature and Rainfall

Climate change is altering the seasonal distribution, interannual variability and overall magnitude of precipitation. A new global measure of precipitation seasonality is proposed, and application of this method to observations from the tropics shows that increases in variability were accompanied by shifts in seasonal magnitude, timing and duration.

Changes in rainfall seasonality in the tropics

The latest Coupled Model Intercomparison Project phase 6 (CMIP6) dataset was analyzed to examine the projected changes in temperature and precipitation over six South Asian countries during the twenty-first century. The CMIP6 model simulations reveal biases in annual mean temperature and precipitation over South Asia in the present climate. In the historical period, the median of the CMIP6 model ensemble systematically underestimates the annual mean temperature for all the South Asian countries, while a mixed behavior is shown in the case of precipitation. In the future climate, the CMIP6 models display higher sensitivity to greenhouse gas emissions over South Asia compared with the CMIP5 models. The multimodel ensemble from 27 CMIP6 models projects a continuous increase in the annual mean temperature over South Asia during the twenty-first century under three future scenarios. The projected temperature shows a large increase (over 6 °C under SSP5-8.5 scenario) over the northwestern parts of South Asia, comprising the complex Karakorum and Himalayan mountain ranges. Any large increase in the mean temperature over this region will most likely result in a faster rate of glacier melting. By the end of the twenty-first century, the annual mean temperature (uncertainty range) over South Asia is projected to increase by 1.2 (0.7–2.1) °C, 2.1 (1.5–3.3) °C, and 4.3 (3.2–6.6) °C under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively, relative to the present (1995–2014) climate. The warming over South Asia is also continuous on the seasonal time scale. The CMIP6 models projected higher warming in the winter season than in the summer over South Asia, which if verified will have repercussions for snow/ice accumulations as well as winter cropping patterns. The annual mean precipitation is also projected to increase over South Asia during the twenty-first century under all scenarios. The rate of change in the projected annual mean precipitation varies considerably between the South Asian countries. By the end of the twenty-first century, the country-averaged annual mean precipitation (uncertainty range) is projected to increase by 17.1 (2.2–49.1)% in Bangladesh, 18.9 (−4.9 to 72)% in Bhutan, 27.3 (5.3–160.5)% in India, 19.5 (−5.9 to 95.6)% in Nepal, 26.4 (6.4–159.7)% in Pakistan, and 25.1 (−8.5 to 61.0)% in Sri Lanka under the SSP5-8.5 scenario. The seasonal precipitation projections also shows large variability. The projected winter precipitation reveals a robust increase over the western Himalayas, with a corresponding decrease over the eastern Himalayas. On the other hand, the summer precipitation shows a robust increase over most of the South Asia region, with the largest increase over the arid region of southern Pakistan and adjacent areas of India, under the high-emission scenario. The results presented in this study give detailed insights into CMIP6 model performance over the South Asia region, which could be extended further to develop adaptation strategies, and may act as a guideline document for climate change related policymaking in the region.

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Projections of Precipitation and Temperature over the South Asian Countries in CMIP6

. The climate modelling community has trialled a large
number of metrics for evaluating the temporal performance of general circulation
models (GCMs), while very little attention has been given to the assessment
of their spatial performance, which is equally important. This study
evaluated the performance of 36 Coupled Model Intercomparison Project 5
(CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan using state-of-the-art spatial metrics, SPAtial
EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V,
Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The
multi-model ensemble (MME) precipitation and maximum and minimum temperature
data were generated through the intelligent merging of simulated
precipitation and maximum and minimum temperature of selected GCMs employing
random forest (RF) regression and simple mean (SM) techniques. The results indicated
some differences in the ranks of GCMs for different spatial metrics. The
overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the
best GCMs in simulating the spatial patterns of mean annual, monsoon,
winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum
temperature over Pakistan. MME precipitation and maximum and minimum
temperature generated based on the best-performing GCMs showed more
similarities with observed precipitation and maximum and minimum temperature
compared to precipitation and maximum and minimum temperature simulated by
individual GCMs. The MMEs developed using RF displayed better performance
than the MMEs based on SM. Multiple spatial metrics have been used for the
first time for selecting GCMs based on their capability to mimic the spatial
patterns of annual and seasonal precipitation and maximum and minimum
temperature. The approach proposed in the present study can be extended to
any number of GCMs and climate variables and applicable to any region for
the suitable selection of an ensemble of GCMs to reduce uncertainties in
climate projections.

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Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics

We review the skill of thirty coupled climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in terms of reproducing properties of the seasonal cycle of precipitation over the major river basins of South and Southeast Asia (Indus, Ganges, Brahmaputra and Mekong) for the historical period (1961–2000). We also present how these models represent the impact of climate change by the end of century (2061–2100) under the extreme scenario RCP8.5. First, we assess the models' ability to reproduce the observed timings of the monsoon onset and the rate of rapid fractional accumulation (RFA) slope — a measure of seasonality within the active monsoon period. Secondly, we apply a threshold-independent seasonality index (SI) — a multiplicative measure of precipitation (P) and extent of its concentration relative to uniform distribution (relative entropy — RE). We apply SI distinctly over the monsoonal precipitation regime (MPR), westerly precipitation regime (WPR) and annual precipitation. For the present climate, neither any single model nor the multi-model mean performs best in all chosen metrics. Models show overall a modest skill in suggesting right timings of the monsoon onset while the RFA slope is generally underestimated. One third of the models fail to capture the monsoon signal over the Indus basin. Mostly, the estimates for SI during WPR are higher than observed for all basins. When looking at MPR, the models typically simulate an SI higher (lower) than observed for the Ganges and Brahmaputra (Indus and Mekong) basins, following the pattern of overestimation (underestimation) of precipitation. Most of the models are biased negative (positive) for RE estimates over the Brahmaputra and Mekong (Indus and Ganges) basins, implying the extent of precipitation concentration for MPR and number of dry days within WPR lower (higher) than observed for these basins. Such skill of the CMIP5 models in representing the present-day monsoonal hydroclimatology poses some caveats on their ability to represent correctly the climate change signal. Nevertheless, considering the majority-model agreement as a measure of robustness for the qualitative scale projected future changes, we find a slightly delayed onset, and a general increase in the RFA slope and in the extent of precipitation concentration (RE) for MPR. Overall, a modest inter-model agreement suggests an increase in the seasonality of MPR and a less intermittent WPR for all basins and for most of the study domain. The SI-based indicator of change in the monsoonal domain suggests its extension westward over northwest India and Pakistan and northward over China. These findings have serious implications for the food and water security of the region in the future.

Seasonal cycle of precipitation over major river basins in South and Southeast Asia: A review of the CMIP5 climate models data for present climate and future climate projections

The annual cycle of Indian monsoon rainfall plays a critical role in the agricultural as well as the industrial sector. Thus, it is necessary to evaluate the behaviour of the monsoon annual cycle in a warming climate. There are several studies on the variability and uncertainty of the Indian monsoon. This study, examines the impact of climate change on the annual cycle of monsoon rainfall in India from 1871–2100 by applying 20 model simulations designed by the World Climate Research Programme (WCRP) coupled with the model inter-comparison Project 5 (CMIP5). It is found that the models MPI-ESM-LR, INM-CM4 and MRI-CGCM3 best capture the spatial patterns of the monsoon rainfall peak month (MRPM) of the winter monsoon compared to observations, whereas HadGEM2-AO and MIROC-ESM-CHEM best capture the MRPM of the summer monsoon. The MIROC, MIROC-ESM, and MIROC-ESM-CHEM models best capture the average rainfall intensity as well as the MRPM of all-India rainfall. This paper examines the future spatial distribution of the MRPM for meteorological sub-divisions of India, that can have crucial implications for water resources and management. Although the future projections as per the CMIP5 models indicate no changes in the MRPM of the all-India rainfall, a reduction in average intensity can be expected. The projections indicate a shift in the MRPM in some meteorological sub-divisions, particularly with regard to the summer monsoon but no significant change has been projected for the winter monsoon. For example, the summer monsoon MRPM is projected to move from July to August in northern and central India.

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CMIP5 Projected Changes in the Annual Cycle of Indian Monsoon Rainfall

Monsoons are the life and soul of India’s financial aspects, especially that of agribusiness in deciding cropping patterns. Around 80% of the yearly precipitation occurs from June to September amid monsoon season across India. Thus, its seasonal mean precipitation is crucial for agriculture and the national water supply. From the start of the 19th century, several studies have been conducted on the possible increments in Indian summer monsoon precipitation in the future. Unfortunately, none of them has endeavoured to discover the models whose yield give the best fit to the observed data. Here some statistical tests are performed to quantify the models of Coupled Model Inter-comparison Project 5 (CMIP5). Then, after, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to select optimum models. It shows that four models, CCSM4, CESM1-CAM5, GFDL-CM3, and GFDL-ESM2G, best capture the pattern in Indian summer monsoon rainfall over the historical period (1871–2005). Further, Student’s t-test is utilized to estimate the significant changes in meteorological subdivisions of selected optimum models. Also, our results reveal the Indian meteorological subdivisions which are liable to encounter significant changes in mean at confidence levels that differ from 80% to 99%.

Statistical Selection of the Optimum Models in the CMIP5 Dataset for Climate Change Projections of Indian Monsoon Rainfall

The presence of a sparse rain gauge network in complex terrain like Himalaya has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-b...

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Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

Drought is a function of time as well as climate variables such as temperature and precipitation. The process of drought forming is slow, and it manifests at different time scales, which adversely affects the economy of a country. The identification and characterization of droughts at various spatiotemporal scales are of great importance. It helps in the planning and management of water resources, policymaking, and agribusiness industries. In the present paper, the Cauvery River basin is chosen as a study area to analyze the changes in the frequency distribution of extreme droughts and duration, with the combined effect of evapotranspiration and rainfall. The drought indices such as Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI) are implemented on monthly rainfall data and potential evapotranspiration of resolution 0.25° × 0.25° long./lat. for the period 1931–2010. The results reveal that the frequency of the extreme droughts over the basin has significantly increased over the post-era of global warming. The increased rate of extreme droughts is particularly evident in downstream of the basin, mainly due to the increase in temperature and deficit rainfall. Further, the implementation of continuous wavelet transform reveals that SPI at 3-(SPI-3) and 12-(SPI-12) month scale are associated with extended reconstruction of sea surface temperature (ERSST) in anti-phase and in-phase, respectively. It is concluded that the in-phase association of SPI-12 and ERSST enhances the drought situation compared to the anti-phase link of SPI-3 and ERSST.

Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2019EA000584

Pravat Jena

Papers

CMIP5 Projected Changes in the Annual Cycle of Indian Monsoon Rainfall

Statistical Selection of the Optimum Models in the CMIP5 Dataset for Climate Change Projections of Indian Monsoon Rainfall

Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India

Weakening of Triennial Oscillation of the Indian Summer Monsoon Rainfall (at 1° × 1° Gridded Scale) Under Future Global Warming