S i l M M A R YWe attempt to construct a logical framework for the deciphering of the physical processes that determine the interannual variability of the coupled climate system. Of particular interest are the causes of the 'predictability barrier' in the boreal spring when observation-prediction correlations rapidly decline. The barrier is a property of many models and occurs irrespective of what time of year a forecast is initiated. Noting that most models used in interannual prediction emphasize the coupled physics of the Pacific Ocean basin, with the intent of encapsulating the essential structure of the El Nino-Southern Oscillation (ENSO) system, lagged Southern Oscillation Index (SOI) correlations are compared with the model results. The lagged SO1 correlations also decrease rapidly in springtime. In that sensc. the coupled ocean-atmosphere models are behaving in a manner very similar to the real system, at least as it is defined by the SOI.We propose that (i) the springtime is a period where errors may grow most rapidly in a coupled oceanatmosphere forecast model or (ii) there are other influences on the system that are not included in the simple coupled-model formulations. Both propositions are based on observations. By examining the period of correlation decrease, it is noticed that the equatorial pressure gradients tend to be a minimum at the time of the correlation decrease, suggesting that the occan-atmosphere system may be least robust during the spring and, thus, subject to error growth. At the same time the south Asian summer monsoon is growing very rapidly. As the monsoon circulation is highly variable in hoth phase and amplitude from year to year. the oceanatmosphere system may be subject to variable and impulsive forcing each spring.A monsoon intensity index. based on the magnitude of the mean summer vertical shear in the 'South Asia' region, was defined for the broad-scale monsoon. 'Strong' and 'weak' monsoon seasons wxre determined by the index and were shown to he consistent with the independent broad-scale outgoing long-wave-radiation fields. Associated with the anomalous monsoons were global scale. coherent summer circulation patterns. Of particular importance was that stronger (weaker) than average summer trade winds were associated with strong (weak) monsoon periods. Thus, a signal of the variable monsoon was detected in the low-level wind fields over the Pacific Ocean that would be communicated to the Pacific Ocean through surface stresses.A longer-period context for the anomalous summer monsoon circulation fields was sought. Based on the summer monsoon index, annual cycles for the ycars in which there were strong and weak monsoon seasons were composited. Large-scale coherent differcnccs were apparent in the circulation fields over most of the globe including south Asia and the tropical Indian Ocean as far as the previous winter and spring. Although the limited data period renders the absoluteness of thc conclusions difficult to confirm. the results indicate that the variable monsoon (an...

This paper describes the latest improvements applied to the Goddard profiling algorithm (GPROF), particularly as they apply to the Tropical Rainfall Measuring Mission (TRMM). Most of these improvements, however, are conceptual in nature and apply equally to other passive microwave sensors. The improvements were motivated by a notable overestimation of precipitation in the intertropical convergence zone. This problem was traced back to the algorithm's poor separation between convective and stratiform precipitation coupled with a poor separation between stratiform and transition regions in the a priori cloud model database. In addition to now using an improved convective-stratiform classification scheme, the new algorithm also makes use of emission and scattering indices instead of individual brightness temperatures. Brightness temperature indices have the advantage of being monotonic functions of rainfall. This, in turn, has allowed the algorithm to better define the uncertainties needed by the scheme's Bayesian inversion approach. Last, the algorithm over land has been modified primarily to better account for ambiguous classification where the scattering signature of precipitation could be confused with surface signals. All these changes have been implemented for both the TRMM Microwave Imager (TMI) and the Special Sensor Microwave Imager (SSM/I). Results from both sensors are very similar at the storm scale and for global averages. Surface rainfall products from the algorithm's operational version have been compared with conventional rainfall data over both land and oceans. Over oceans, GPROF results compare well with atoll gauge data. GPROF is biased negatively by 9% with a correlation of 0.86 for monthly 2.5Њ averages over the atolls. If only grid boxes with two or more atolls are used, the correlation increases to 0.91 but GPROF becomes positively biased by 6%. Comparisons with TRMM ground validation products from Kwajalein reveal that GPROF is negatively biased by 32%, with a correlation of 0.95 when coincident images of the TMI and Kwajalein radar are used. The absolute magnitude of rainfall measured from the Kwajalein radar, however, remains uncertain, and GPROF overestimates the rainfall by approximately 18% when compared with estimates done by a different research group. Over land, GPROF shows a positive bias of 17% and a correlation of 0.80 over monthly 5Њ grids when compared with the Global Precipitation Climatology Center (GPCC) gauge network. When compared with the precipitation radar (PR) over land, GPROF also retrieves higher rainfall amounts (20%). No vertical hydrometeor profile information is available over land. The correlation with the TRMM precipitation radar is 0.92 over monthly 5Њ grids, but GPROF is positively biased by 24% relative to the radar over oceans. Differences between TMI-and PR-derived vertical hydrometeor profiles below 2 km are consistent with this bias but become more significant with altitude. Above 8 km, the sensors disagree significantly, but the information content is low...

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[1] In this work, the authors analyze the observed long-term variations of seasonal climate in China and then investigate the possible influence of increases in greenhouse gas concentrations on these variations by comparing the observations with the simulations of the second phase of the Coupled Model Intercomparison Project (CMIP2). The long-term variations of precipitation and temperature in China are highly seasonally dependent. The main characteristic of summer precipitation in China is a drying trend in the north and a wetting trend in the central part. The precipitation in winter shows an increasing trend in southern and eastern-central China. Interesting features have also been found in the transitional seasons. In spring, precipitation variations are almost opposite to those in summer. In autumn the precipitation decreases in almost the whole country except for the middle and lower reaches of the Yangtze River Valley. In addition, the seasonality of precipitation has become slightly weaker in recent decades in southern and eastern China. Pronounced warming is observed in the entire country in winter, spring, and autumn, particularly in the northern part of China. In summer a cooling trend in central China is particularly interesting, and cooling (warming) trends generally coexist with wetting (drying) trends. The correlativity between precipitation and temperature variations is weak in spring, autumn, and winter. It has also been found that the long-term climate variations in winter and summer in China may be connected to the warming trend in the sea surface temperature of the Indian Ocean. A comparison between the observed seasonal climate variations and the CMIP2 simulations of 16 models indicates that the observed long-term variations of winter, spring, and autumn temperature in China may be associated with increases in greenhouse gas concentrations. However, such a connection is not found for the summer temperature. The tremendous uncertainties among the models in precipitation simulations make it difficult to link the precipitation variations to global warming.