Analyzing Spatio-temporal variability of clouds over the Arabian Sea using ERA5 reanalysis dataset

Clouds cover 67% of the earth's surface hence they play an essential role in governing the energy balance of the earth. The combined effect of two properties, i.e., emissivity and albedo of clouds, defines the net radiative effect and their relative importance changes from day to night. In this study, we analyze four decades (1979-2018) of high-resolution (0.25°×0.25°) hourly cloud data from ECMWF fifth-generation reanalysis ERA5 dataset to study the long-term changes in Spatio-temporal variability of clouds over the Arabian Sea. The rationale behind choosing the ERA5 data is that, unlike any other climate variables, the long-term ground truth data for clouds do not exist, and satellite datasets have discrepancies. Ship-observation compiled Extended Edited Synoptic Cloud Reports Archive (EECRA) is a multidecadal data but has a coarse resolution (10°×10°) and suffers from human observational error. In this study, we used a combination of wind speed, air temperature, sea surface temperature (SST), and cloud cover data from ERA5  to explain the observed diurnal behavior and long-term changes in diurnal amplitude and local time of maximum clouds. The clouds over the Arabian Sea show two distinct diurnal peaks during June - August (JJA), but a single diurnal peak is found during the rest of the year. The seasonal and spatial variability in the diurnal behavior of clouds can be characterized in terms of the local thermodynamics of the Arabian Sea. The diurnal amplitude and local time of a maximum of low, mid, and high-level clouds have changed from 1979 to 2018, and the changes are spatially heterogeneous across all seasons. The diurnal amplitude of high-level clouds has increased through all seasons except during JJA. During the JJA season, the entire Arabian Sea shows a decrease in the diurnal amplitude of high-level clouds, with the largest decrease, observed in the eastern Arabian Sea along the west coast of India.