<p>The variability of spring and early summer (April–May–June; AMJ) Surface Air Temperature (SAT) over India during post-El Niño years is investigated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In this study, we analysed the historical simulations of 25 CMIP6 models and compared them with the observed SAT for 1951–2014. Across various CMIP6 models, the standard deviation and coefficient of variation for AMJ SAT are generally higher than those observed over most parts of India. Analysis revealed a significant increase in AMJ SAT over India during post-El Niño years in the observations, with SAT warming 52% higher in south-central India compared to other regions. Most of the CMIP6 models have captured the spatial patterns of El Niño-induced SAT warming over India&#xa0;reasonably well. During AMJ of El Niño decaying years, easterly wind anomalies on the southern flank of the Western North Pacific (WNP) anticyclone extend into southern peninsular India, generating a weak anticyclonic circulation. This circulation initiates local downdrafts that reduce cloud cover, allowing greater solar radiation to reach the surface and enhancing SAT over parts of India. Furthermore, most of the CMIP6 models capture the SAT variability during the decay phase of El Niño and the associated processes responsible for SAT warming reasonably well. About 50% of CMIP6 models showed that 6 out of 10 warmest years (AMJ) over India are associated with post-El Niño years during the study period, which is consistent with the observations. The strong positive Sea Surface Temperature (SST) anomalies associated with El Niño are found to be extended too far west in most of the CMIP6 models, unlike in the observations. This discrepancy in SST distribution altered the atmospheric circulation aloft over the WNP and north Indian Ocean region and impacted the SAT variability over India in some models.</p>

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Surface air temperature variability over India in CMIP6 models during spring and early summer: after effect of El Niño

  • Sambasivarao Velivelli,
  • G. Ch. Satyanarayana,
  • Jasti S. Chowdary,
  • G. Srinivas,
  • Patekar Darshana,
  • Gopinadh Konda,
  • Raju Attada,
  • Anant Parekh,
  • C. Gnanaseelan

摘要

The variability of spring and early summer (April–May–June; AMJ) Surface Air Temperature (SAT) over India during post-El Niño years is investigated in the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. In this study, we analysed the historical simulations of 25 CMIP6 models and compared them with the observed SAT for 1951–2014. Across various CMIP6 models, the standard deviation and coefficient of variation for AMJ SAT are generally higher than those observed over most parts of India. Analysis revealed a significant increase in AMJ SAT over India during post-El Niño years in the observations, with SAT warming 52% higher in south-central India compared to other regions. Most of the CMIP6 models have captured the spatial patterns of El Niño-induced SAT warming over India reasonably well. During AMJ of El Niño decaying years, easterly wind anomalies on the southern flank of the Western North Pacific (WNP) anticyclone extend into southern peninsular India, generating a weak anticyclonic circulation. This circulation initiates local downdrafts that reduce cloud cover, allowing greater solar radiation to reach the surface and enhancing SAT over parts of India. Furthermore, most of the CMIP6 models capture the SAT variability during the decay phase of El Niño and the associated processes responsible for SAT warming reasonably well. About 50% of CMIP6 models showed that 6 out of 10 warmest years (AMJ) over India are associated with post-El Niño years during the study period, which is consistent with the observations. The strong positive Sea Surface Temperature (SST) anomalies associated with El Niño are found to be extended too far west in most of the CMIP6 models, unlike in the observations. This discrepancy in SST distribution altered the atmospheric circulation aloft over the WNP and north Indian Ocean region and impacted the SAT variability over India in some models.