With the motivation to identify whether a reasonably simulated atmospheric circulation would necessarily lead to a successful reproduction of monsoon precipitation, the performances of five sets of reanalysis data [NCEP-U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project II (AMIP-II) reanalysis (NCEP-2), 40-yr ECMWF Re-Analysis (ERA-40), Japanese 25-yr Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)] in reproducing the climatology, interannual variation, and long-term trend of global monsoon (GM) precipitation are comprehensively evaluated. To better understand the variability and longterm trend of GM precipitation, the authors also examined the major components of water budget, including evaporation, water vapor convergence, and the change in local column water vapor, based on the five reanalysis datasets. Results show that all five reanalysis datasets reasonably reproduce the climatology of GM precipitation. ERA-Interim (NCEP-2) shows the highest (lowest) skill among the five datasets. The observed GM precipitation shows an increasing tendency during 1979-2011 along with a strong interannual variability, which is reasonably reproduced by five reanalysis datasets. The observed increasing trend of GM precipitation is dominated by contributions from the Asian, North American, Southern African, and Australian monsoons. All five datasets fail in reproducing the increasing tendency of the North African monsoon precipitation. The wind convergence term in the water budget equation dominates the GM precipitation variation, indicating a consistency between the GM precipitation and the seasonal change of prevailing wind.

No abstract

No abstract

No abstract

The East Asian summer monsoon (EASM) experienced decadal transitions over the past few decades, and the associated "wetter-South-drier-North" shifts in rainfall patterns in China significantly affected the social and economic development in China. Two viewpoints stand out to explain these decadal shifts, regarding the shifts either a result of internal variability of climate system or that of external forcings (e.g. greenhouse gases (GHGs) and anthropogenic aerosols). However, most climate models, for example, the Atmospheric Model Intercomparison Project (AMIP)-type simulations and the Coupled Model Intercomparison Project (CMIP)-type simulations, fail to simulate the variation patterns, leaving the mechanisms responsible for these shifts still open to dispute. In this study, we conducted a successful simulation of these decadal transitions in a coupled model where we applied ocean data assimilation in the model free of explicit aerosols and GHGs forcing. The associated decadal shifts of the three-dimensional spatial structure in the 1990s, including the eastward retreat, the northward shift of the western Pacific subtropical high (WPSH), and the south-cool-north-warm pattern of the upper-level tropospheric temperature, were all well captured. Our simulation supports the argument that the variations of the oceanic fields are the dominant factor responsible for the EASM decadal transitions.

No abstract

No abstract

Salinity is a key ocean state property, changes in which reveal the variation of the water cycle and the ocean thermohaline circulation. However, prior to this century, in situ salinity observations were extremely sparse, which decreased the reliability of simulations of ocean general circulation by ocean and climate models. In 2009, sea surface salinity (SSS) observations covered the global ocean via the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission, and several versions of global SSS products were subsequently released. How can these data benefit model performance? Previous studies found contradictory results. In this work, we assimilated SMOS-SSS data into the LASG/IAP Climate system Ocean Model (LICOM) using the Ensemble Optimum Interpolation (EnOI) assimilation scheme. To assess and quantify the contribution of SMOS-SSS data to model performance, several tests were conducted. The results indicate that the CECOS/CATDS 2010.V02 SMOS-SSS product can significantly improve model simulations of sea surface/subsurface salinity fields. This study provides the basis for the future assimilation of SMOS-SSS data for short-range climate forecasting. Key Points: SMOS sea surface salinity observations were assimilated into the LICOM ocean model SMOS-SSS data play a complementary role in model salinity simulations Observation error covariances and the choice of SMOS product products are important (2016), The complementary role of SMOS sea surface salinity observations for estimating global ocean salinity state,