Monsoon season local control on precipitation over warm tropical oceans

by K Rajendran, Sulochana Gadgil and Sajani Surendran

Understanding local SST control on precipitation during monsoon is important for deducing climate change due to global warming, particularly for warm oceans. Studies of the relationship of the precipitation over tropical oceans with local sea surface temperature (SST), on the monthly scale, have shown that the propensity for precipitation is high for SST above a threshold of 27.5∘C/28∘C. However, for warm oceans with SST above the threshold such as the Bay of Bengal and South China Sea, for each SST, there is a large variation of precipitation and the SST-precipitation relationship is weak. On daily scale mean precipitation increases slightly with SST when precipitation lags SST by a few days, but the relationship between them is rather weak. But, when SST is above the threshold, for daily, pentad, 10-day and monthly time scales, and with or without time lag, the curve depicting the variation of mean precipitation with SST explains only a small fraction of precipitation variance and hence cannot be considered to be representative of the SST–precipitation relationship, or used to deduce the impact of SST on precipitation. On the other hand, the local control on precipitation is predominantly atmospheric dynamics with the relationship of variation of precipitation to low-level convergence on all timescales, being strong. This suggests that for warm oceans, the limiting resource for precipitation/convection is not SST but dynamics.

Citation

Rajendran, K., Gadgil, S. & Surendran, S. Meteorology and Atmospheric Physics (2018). https://doi.org/10.1007/s00703-018-0649-7, 1-15pp

Evaluation of WRF-simulated multilevel soil moisture, 2-m air temperature, and 2-m relative humidity against in situ observations in India

by Kantha Rao, B. & Rakesh, V

The ability of the Weather Research and Forecasting (WRF) model to simulate multilevel soil moisture (SM), 2-m air temperature (T2m), and 2-m relative humidity (RH2m) was evaluated for five different locations in India. WRF model simulations were carried out for 30 cases during different seasons with two different land surface schemes, viz. Noah and Rapid Update Cycle (RUC). The simulations were compared with in situ observations taken routinely at 30-min time intervals at the five selected locations. Statistical evaluation showed that, although the model could simulate SM reasonably well [with the majority of cases falling in the < 25% relative error (RE) category] at different depths for Delhi (DLH) and Gulbarga (GLB), the model errors were high (with most cases falling in the > 50% RE category) for Almora (ALR), Hyderabad (HYD), and Cochin (CHN). In case of T2m, model errors were high (RE > 15%) over hilly terrain, e.g., at ALR, while errors were relatively lower (RE < 10%) for plane areas such as HYD, GLB, DLH, and CHN. In general, the diurnal variation showed that the model underestimated (overestimated) afternoon temperatures during nonrainy (rainy) days. RH2m was also well simulated by the model at the locations HYD, GLB, and CHN, although it underestimated RH2m during morning hours at the locations ALR and DLH. Overall, the comparison showed that the WRF model could reproduce the near-surface temperature and humidity for plane areas such as HYD, GLB, and CHN reasonably well, but has limitations for complex terrains, e.g., at ALR, and highly polluted cities such as DLH.

Source: https://link.springer.com/article/10.1007/s00024-018-2022-7