[1] Six dynamical seasonal model outputs, which are currently used in the APEC Climate Center Multimodel Ensemble (MME) prediction system, are employed for statistical downscaling prediction of station-scale precipitation in the Philippines and Thailand. Correlation analysis and Singular Value Decomposition Analysis are used to reveal atmosphere dynamic linkage based on the observed data other than model data. The observed linkage provides a robust basis for the choice of predictor and its range in predicted fields. In order to avoid spatial shift of predicted field away from observed climate, a movable window is set to select the most sensible area within the range of predictor for downscaling. The downscaled MME prediction is verified against observed station precipitation in a cross-validation manner, and the prediction skill is apparently improved compared with the simple composite of raw model predictions for most of the stations. Citation:
The ability of the CLImate GENerator (CLIGEN) weather generator to reproduce daily precipitation characteristics for Korea was assessed on the basis of 55-year long historical daily precipitation records from eight weather stations (Seoul, Incheon, Daegu, Ulsan, Gwangju, Busan, Kangneung, and Jeonju) representing different parts of the Korean peninsula. The basic statistics of daily precipitation (mean, standard deviation, skewness of daily precipitation, number of rainy days, and the lengths of wet/dry period), probability distribution characteristics of daily precipitation (percentiles and maximum value), and the spatial covariance statistic generated by CLIGEN were compared with those derived from the observed weather series. Significance tests were conducted on the difference between the historical and generated statistics with the 1% significance level. The results show that CLIGEN simulates most of the daily precipitation characteristics satisfactorily with a tendency to slightly underestimate the mean and variability of daily precipitation. Especially, the number of rainy days is perfectly reproduced with mean relative error of 0.4% across all the stations. It is also found that the spatial covariance statistic from eight different stations is well reproduced by CLIGEN with respect to the leading EOF mode of summer season daily precipitation.
[1] Seasonal predictability of global surface air temperature for the 100 years of 20th century is examined using the Climate of the 20th Century international project (C20C) AGCM experiment. The C20C experiments reproduce reasonably well the observed warming trend over the globe. The perfect model concept, one simulation being considered as observation, is utilized to examine the changes of seasonal mean predictability for the last 100 years. The global pattern correlations of seasonal mean temperature show clearly the seasonal mean predictability being increased since 1920s. The analysis of the ensemble mean and deviation also shows that the signal to noise ratio is much increased for the recent 30 years, particularly in the tropical and subtropical Pacific. The increase of the seasonal predictability is found to be related to the enhancement of SST variability over the tropical Pacific, which appears to be related to the global warming.
This study investigates the role of model tropical diabatic heating error on the boreal summer northeast Asian monsoon (NEAM) simulation given by a general circulation model (GCM). A numerical experiment is carried out in which the GCM diabatic heating is adjusted toward more realistic values in the tropics. It is found that the seasonal mean NEAM circulation and rainfall are improved in the GCM. This can be attributed to the reduced positive heating bias in the western Pacific Ocean around 108-158N in the model, which in turn leads to better-simulated low-level southerly winds over eastern Asia and more moisture supply to the NEAM region. The GCM's ability in capturing the year-to-year variation of NEAM rainfall is also markedly improved in the experiment. These results show that the diabatic heating error over the western Pacific can be one reason for poor NEAM simulations in GCMs. The authors also suggest a simple method to reduce model heating biases that can be readily applied to dynamical seasonal prediction systems.
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