A strong （weak） East Asian summer monsoon （EASM） is usually concurrent with the tripole pattern of North Atlantic SST anomalies on the interannual timescale during summer, which has positive （negative） SST anomalies in the northwestern North Atlantic and negative （positive） SST anomalies in the subpolar and tropical ocean. The mechanisms responsible for this linkage are diagnosed in the present study. It is shown that a barotropie wave-train pattern occurring over the Atlantic-Eurasia region likely acts as a link between the EASM and the SST tripole during summer. This wave-train pattern is concurrent with geopotential height anomalies over the Ural Mountains, which has a substantial effect on the EASM. Diagnosis based on observations and linear dynamical model results reveals that the mechanism for maintaining the wave-train pattern involves both the anomalous diabatic heating and synoptic eddy-vorticity forcing. Since the North Atlantic SST tripole is closely coupled with the North Atlantic Oscillation （NAO）, the relationships between these two factors and the EASM are also examined. It is found that the connection of the EASM with the summer SST tripole is sensitive to the meridional location of the tripole, which is characterized by large seasonal variations due to the north-south movement of the activity centers of the NAO. The SST tripole that has a strong relationship with the EASM appears to be closely coupled with the NAO in the previous spring rather than in the simultaneous summer.
The present paper presents a concise summary of our recent studies on the Asian summer monsoon,with highting decadal and inter- decadal scales. The studies on the long- term variations of the Asian summer monsoon and its impacts on the change in the summer precipitation in China are reviewed. Moreover,recent changes in the Asian summer monsoon and summer precipitation in East Asia(including Meiyu precipitation)are discussed. Finally,the future changes of the Asian summer monsoon are also pointed out in this paper.
The spectral version 1.1 of the Flexible Global Ocean–atmosphere–land System （FGOALS1.1-s） model was developed in the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophys- ical Fluid Dynamics at the Institute of Atmospheric Physics （LASG/IAP）. This paper reports the major modifications to the physical parameterization package in its atmospheric component, including the radiation scheme, convection scheme, and cloud scheme. Furthermore, the simulation of the East Asian Summer Monsoon （EASM） by FGOALS1.1-s is examined, both in terms of climatological mean state and interannual variability. The results indicate that FGOALS1.1-s exhibits significant improvements in the simulation of the balance of energy at the top of the atmosphere： the net radiative energy flux at the top was 0.003 W m-2 in the 40 years fully coupled integration. The distribution of simulated sea surface temperature was also quite reasonable, without obvious climate drift. FGOALS1.1-s is also capable of capturing the major features of the climatological mean state of the EASM： major rainfall maximum centers, the annual cycle of precipitation, and the lower-level monsoon circulation flow were highly consistent with observations in the EASM region. Regarding interannual variability, simulation of the EASM leading patterns and their relationship with sea surface temperature was examined. The results show that FGOALS1.1-s can reproduce the first leading pattern of the EASM and its close relationship with the decaying phase of the ENSO. However, the model lacked the ability to capture either the second major mode of the EASM or its relationship with the developing phase of the ENSO.
In this study, a method of analogue-based correction of errors(ACE) was introduced to improve El Ni?o-Southern Oscillation(ENSO) prediction produced by climate models. The ACE method is based on the hypothesis that the flow-dependent model prediction errors are to some degree similar under analogous historical climate states, and so the historical errors can be used to effectively reduce such flow-dependent errors. With this method, the unknown errors in current ENSO predictions can be empirically estimated by using the known prediction errors which are diagnosed by the same model based on historical analogue states. The authors first propose the basic idea for applying the ACE method to ENSO prediction and then establish an analogue-dynamical ENSO prediction system based on an operational climate prediction model. The authors present some experimental results which clearly show the possibility of correcting the flow-dependent errors in ENSO prediction, and thus the potential of applying the ACE method to operational ENSO prediction based on climate models.
