| Popis: |
The Surface Air Temperature (SAT) variability over Western South Asia (WSA) is highest during the early summer (May-June) season, which leads to frequent seasonal heatwaves over the region. Therefore, it is important to understand the model’s ability in predicting the regional anomalous temperature conditions, which is the focus of this study using the European Centre for MediumRange Weather Forecast fifth generation (ECMWF-SEAS5) seasonal prediction system dataset for the period 1981-2022. The model skilfully predicts the El Niño-Southern Oscillation (ENSO) related interannual variability of the SAT over WSA compared to the reanalysis, mediated through the upper-level positive 200-hPa geopotential height anomalies, that lead to warm conditions over the regions during the negative ENSO phase. A negative ENSO-SAT relationship shows that warmer extreme temperature events are more frequent during La Niña, while the opposite may happen for El Niño years. The model shows a reasonable actual prediction skill of the SAT anomalies over the WSA region compared to the higher resolution observational and reanalysis datasets, while a low skill is noted for coarser resolutions, which shows that a choice of the observational dataset is important for the estimation of the model skill. Overall, the Potential Predictability of the SAT is comparable to its actual prediction skill over the WSA region. Moreover, the higher Potential Predictability of the SAT over WSA is noted during La Niña compared to El Niño, which is mainly contributed through the higher signal compared to the noise. |
