Daily mean SAT gridded data (Srivastava et al. 2009) from the India Meteorological Department (IMD) for March, April, and May (Spring season - MAM) during the period 1951 to 2021 is utilized for analysis. This daily gridded SAT data is available at 1°x1° resolution is used to compute the climatological seasonal means and standard deviation of SAT. We also compute the seasonal mean SAT and the composite anomalies of SAT during decay El Niño years (1973, 1983, 1995, 1998, 2003, 2010, 2016). This decay El Niño years are identified based on November, December and January (NDJ) Niño3.4 SST anomalies similar to those of Chowdary et al. (2017).
ERA5 [ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis 5th generation] (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) global real analysis monthly mean U-wind, V-winds for the 850hPa and 200hPa levels, Mean Sea Level Pressure (MSLP), Soil Moisture, Specific Humidity, Shortwave radiation, Total cloud cover, Sea Surface Temperature (SST) and surface sensible heat flux data at 30km resolution are also used for the analysis. Further, to understand the spatial distribution of atmospheric circulation during the decay El-Niño events, composite anomalies of rotational wind and stream function at 850hPa, divergence and velocity potential at 200hPa are computed, and in addition, Soil Moisture, Specific Humidity, Shortwave radiation, Total cloud cover and surface sensible heat flux, in spring season for the period 1951–2021 (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) are also utilized.
The UTCI data at the 30km horizontal resolution at daily for the period 1979 to 2021 available from the ERA5 [ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis 5th generation] (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5) is used to compute composite anomalies of El Niño decay years (Eq. (1)).
UTCI = Ta + Offset (Ta, TMRT, Uwind, pvapour) --------- (1)
Where, Ta - is ambient air temperature; TMRT - is mean radiant temperature; Uwind is wind speed; pvapour is water vapour pressure.
The use of the Universal Thermal Climate Index (UTCI) for outdoor thermal evaluation around the world is advised under a variety of climatic settings because of its good performance in comparison to internationally standardised assessment providers (Brode et al. 2013). Due to its sensitivity to climatic influences and thermo-physiological importance across the full heat exchange range, the UTCI can be used to access the thermal stress under a variety of climatic situations (Di Napoli et al. 2018; Wu et al. 2019; Antonescu et al. 2021; Odnoletkova and Patzek 2021).
The ERA 5 hourly temperature and Relative Humidity (RH) data is used to compute Discomfort index (DI) suggested by the Thom (1959) (Eq. (2)). If the air temperature (T) measured in degrees Celsius and RH in %, then the DI can be computed by
DI (°C) = T − 0.05(1-0.01 RH) (T-14.5) --------- (2)
Higher DI value indicates the higher thermal human discomfort and the suggested above 28oC, 30oC of DI values are used in the studies (Stathopoulou et al. 2005; de Freitas and Grigorieva 2015; Nedel et al. 2015; Roghanchi and Kocsis 2018; Yasmeen and Liu 2019; Dasari et al. 2021)
In addition to analysis of the observations, we have examined the predictability of the spring SAT during decaying El Niño years based on the Asia-Pacific Economic Cooperation Climate Center (APCC) models (e.g, Min et al. 2017). The present study uses 28 years (1983–2010) of SAT, SST, Sea Level Pressure (SLP), U-850, V-850, U-200hPa, and V-200 hPa weather variables of hindcast data sets with a 7-month integration. These simulations starting on the first and fifth days of each month, with hindcast experiments conducted in a group of 5 operational climate prediction models, which are used by APCC in the multi-model ensemble prediction system (https://cliks.apcc21.org/dataset/model). Models used are SCoPS, TCWB1Tv1.1, CanSPISv2.1, CFSv2, CGCMv2.0. Detailed description of APCC global climate models is presented in the Table 1. Further, with December and February initial conditions we have computed El-Niño decay year’s composite anomalies during spring season.
Table 1
Institute | Model | Spatial resolution |
APCC (APEC Climate Center) | SCoPS (Seamless Coupled Prediction System) (Ham et al. 2019) | 2.5° x 2.5° |
CWB (Central Weather Bureau) | TCWB1Tv1.1 (Paek et al. 2015) | 2.5° x 2.5° |
ECCC (Environment Climate Change Canada) | CanSPISv2.1 (The Canadian Seasonal to Interannual Prediction System version 2) (McFarlane et al. 1992) | 2.5° x 2.5° |
NCEP (National Centers for Environmental Prediction) | CFSv2(Climate Forecast System Version 2) (Saha et al. 2014) | 2.5° x 2.5° |
PNU (Pusan National University) | CGCMv2.0 (Ahn et al. 2018) | 2.5° x 2.5° |