Decadal Variability Of Land Precipitation In Northern Hemisphere And Its Relationship With The Sea Surface Temperature

Based on the Global Precipitation Climatology Centre(GPCC)observation precipitation data and the Coupled Model Intercomparison Project Phase 6(CMIP6)historical simulations,this paper employs the ensemble empirical mode decomposition to extract the decadal-to-multidecadal variability and trend of the seasonal land precipitation in the northern hemisphere from 1891 to 2014,and it is found that the variance contribution of the i decadal-to-multidecadal variability is larger than the trend.The spatial distributions and temporal characteristics of the decadal-to-multidecadal variability of land precipitation dominant mode from the empirical orthogonal function are different in four seasons.Regions with the same sign of precipitation anomalies are likely to be teleconnected through oceanic forcing.In winter and spring,the Interdecadal Pacific Oscillation(IPO)and the Atlantic Multidecadal Oscillation(AMO)play a joint role.They were in phase before late 1970 s and out of phase after then,weakening/strengthening the impacts of the North Pacific and North Atlantic on the decadal-to-multidecadal variability of land precipitation before/after late 1970 s.In summer and autumn,AMO alone plays a part and the amplitude of time series does not vary as in winter and spring.By dividing winter into two periods(1891-1975/1976-2014),we found that the main mode of precipitation distribution was different in two periods.It is the variety of SST that cause the abnormality of atmospheric circulation,then affects the precipitation.The ability of the coupled models from CMIP6 historical simulations is also evaluated.The good-models average largely captures the spatial structure in four seasons and the associated oceanic signals.The poor-models average is hardly or weakly correlated with observation.The model's ability to simulate the main mode of land precipitation is better in winter and spring than in summer and autumn,which may be related to an strong IPO signal simulated in winter and spring by these models.