Tropical cyclone (TC) Amphan is analyzed in terms of the various factors that governed the intensification process associated with it and compared with Fani. Furthermore, the TC radial characteristics and ocean productivity are examined. Notably, both TCs formed in the Bay of Bengal during the pre-monsoon seasons of 2020 and 2019, respectively. For this study, both ocean and atmospheric parameters from various sources including global analyses, satellite observations, and outputs from Model for Prediction Across Scales-Atmosphere (MPAS-A) and Advanced Research Weather Research and Forecasting (WRF-ARW), are considered. The results indicate a gradual decrease in vertical wind shear during Fani. In the case of Amphan, the increase in mid-tropospheric relative humidity values is found to be substantial. The sea surface cooling after the passage of Amphan was higher than in the case of Fani. The higher sea surface temperature in the Amphan case corresponds to the lower aerosol loading (partly because of lockdown measures) than that of Fani in the pre-cyclone phase. And the decrease (increase) in aerosol loading coincides with an increase (decrease) in the direct radiative forcing at the ocean surface. The Madden-Julian Oscillation played a greater role in the cyclogenesis of Fani, but Kelvin waves offered a major support in the case of Amphan. The warmer sea surface, higher tropical cyclone heat potential, and conducive ocean and atmospheric setting together supported the further intensification of Amphan to the supercyclone stage. The difference in chlorophyll concentration showed a significant variation, with higher positive values seen in the case of Amphan implicating greater vertical mixing. The numerical modeling effort indicated superior performance of MPAS-A compared to WRF-ARW in simulating the radial parameters of the TCs.

As a vital source of the climate change predictability, the snow depth predictability originates from its own persistence and the external forcing factors. In order to investigate the root of snow depth predictability at the North Hemisphere, this study conducted an ensemble of 20 simulations spanning 50 years with the Community Earth System Model (CESM). With a regression model constructed via the canonical correlation analysis method, we analyzed the temporal and spatial distribution characteristics of snow depth predictability on the global scale, as well as the effects of snow depth persistence and sea surface temperature (SST) on snow depth predictability. The results show that the predictability due to snow depth persistence depends on both season and location. The persistence of snow depth can reach more than 3 months in high latitude region. After considering the SST forcing, the predictability is increased in many parts of the Northern Hemisphere, such as northern North America, Europe, and central Siberia. The areas where SST significantly influences snow depth predictability mainly overlap the snow cover transition zones. We further investigated the possible pathways of the impact of SST on snow depth predictability, and found that in North America and Europe, SST improves the predictability mainly through affecting the surface temperature, while in central Siberia and eastern Europe, the pathway also includes snowfall and shortwave radiation, respectively. Additionally, we conducted a similar analysis with three other climate models from the Atmospheric Model Intercomparison Project phase 6 (AMIP6), and the results can also verify the conclusions of CESM ensemble simulations.