Future projection of summer surface air temperature trend over central India: Role of external forcing and internal variability

2019 ◽  
Vol 40 (2) ◽  
pp. 1107-1117
Author(s):  
Reshmita Nath ◽  
Yong Luo ◽  
Wen Chen
2020 ◽  
Author(s):  
Bin Yu ◽  
Guilong Li ◽  
Shangfeng Chen ◽  
Hai Lin

<p>Recent studies indicated that the internal climate variability plays an important role in various aspects of projected climate changes on regional and local scales. Here we present results of the spreads in projected trends of wintertime North American surface air temperature and extremes indices of warm and cold days over the next half-century, by analyzing a 50-member large ensemble of climate simulations conducted with CanESM2. CanESM2 simulations confirm the important role of internal variability in projected surface temperature trends as demonstrated in previous studies. Yet the spread in North American warming trends in CanESM2 is generally smaller than those obtained from CCSM3 and ECHAM5 large ensemble simulations. Despite this, large spreads in the climate means as well as climate change trends of North American temperature extremes are apparent in CanESM2, especially in the projected cold day trends. The ensemble mean of forced climate simulations reveals high risks of warm days over the western coast and north Canada, as well as a weakening belt of cold days extending from Alaska to the northeast US. The individual ensemble members differ from the ensemble mean mainly in magnitude of the warm day trends, but depart from the ensemble mean in conspicuous ways, including spatial pattern and magnitude, of the cold day trends. The signal-to-noise ratio pattern of the warm day trend resembles that of the surface air temperature trend; with stronger signals over north Canada, Alaska, and the southwestern US than the midsection of the continent. The projected cold day patterns reveal strong signals over the southwestern US, north Canada, and the northeastern US. In addition, the internally generated components of temperature and temperature extreme trends exhibit spatial coherences over North America, and are comparable to the externally forced trends. The large-scale atmospheric circulation-induced temperature variability influences these trends. Overall, our results suggest that climate change trends of North American temperature extremes are likely very uncertain and need to be applied with caution.</p>


2019 ◽  
Vol 32 (6) ◽  
pp. 1693-1706 ◽  
Author(s):  
Zhen-Qiang Zhou ◽  
Renhe Zhang ◽  
Shang-Ping Xie

Abstract Year-to-year variability of surface air temperature (SAT) over central India is most pronounced in June. Climatologically over central India, SAT peaks in May, and the transition from the hot premonsoon to the cooler monsoon period takes place around 9 June, associated with the northeastward propagation of intraseasonal convective anomalies from the western equatorial Indian Ocean. Positive (negative) SAT anomalies during June correspond to a delayed (early) Indian summer monsoon onset and tend to occur during post–El Niño summers. On the interannual time scale, positive SAT anomalies of June over central India are associated with positive SST anomalies over both the equatorial eastern–central Pacific and Indian Oceans, representing El Niño effects in developing and decay years, respectively. Although El Niño peaks in winter, the correlations between winter El Niño and Indian SAT peak in the subsequent June, representing a post–El Niño summer capacitor effect associated with positive SST anomalies over the north Indian Ocean. These results have important implications for the prediction of Indian summer climate including both SAT and summer monsoon onset over central India.


2016 ◽  
Vol 43 (2) ◽  
pp. 902-909 ◽  
Author(s):  
Nikola Jajcay ◽  
Jaroslav Hlinka ◽  
Sergey Kravtsov ◽  
Anastasios A. Tsonis ◽  
Milan Paluš

2014 ◽  
Vol 4 (6) ◽  
pp. 462-466 ◽  
Author(s):  
Fei Ji ◽  
Zhaohua Wu ◽  
Jianping Huang ◽  
Eric P. Chassignet

2020 ◽  
Vol 11 (3) ◽  
pp. 185-197 ◽  
Author(s):  
Shu-Yue YIN ◽  
Tao WANG ◽  
Wei HUA ◽  
Jia-Peng MIAO ◽  
Yong-Qi GAO ◽  
...  

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