Development of probability density functions for future South American rainfall
Development of probability density functions for future South American rainfall
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DOI：
10.1111/j.1469-8137.2010.03368.x
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发表时间：
2010-01-01
期刊：
影响因子：
9.4
通讯作者：
Cramer, Wolfgang
中科院分区：
文献类型：
--
作者：
Jupp, Tim E.;Cox, Peter M.;Cramer, Wolfgang
我们通过对24个耦合模式相互比较档案项目3（CMIP 3）大气环流模式（GCM）的预测进行加权，估计南美洲五个地区未来降雨的概率密度函数（PDF）。这些模式根据其再现季节降雨年际变化的相对能力进行评级。气候模式的相对权重根据贝叶斯定理顺序更新，基于预测时间序列平均值的偏差和偏差校正时间序列的分布拟合。根据季节和地区，我们发现GCM的排名非常不同，没有一个模型在所有情况下都表现良好。然而，在某些地区和季节，差分加权的模型导致显着的变化，在推导出的降雨PDFs.Using每个季节的相对模型权重的组合，我们也推导出一组整体模型权重为每个区域，可用于生产PDF的森林生物量从模拟的Lund-Potsdam-Jena动态全球植被模型管理的土地（LPJmL）。
P>We estimate probability density functions (PDFs) for future rainfall in five regions of South America, by weighting the predictions of the 24 Coupled Model Intercomparison Archive Project 3 (CMIP3) General Circulation Models (GCMs). The models are rated according to their relative abilities to reproduce the inter-annual variability in seasonal rainfall.The relative weighting of the climate models is updated sequentially according to Bayes' theorem, based on the biases in the mean of the predicted time-series and the distributional fit of the bias-corrected time-series.Depending on the season and the region, we find very different rankings of the GCMs, with no single model doing well in all cases. However, in some regions and seasons, differential weighting of the models leads to significant shifts in the derived rainfall PDFs.Using a combination of the relative model weightings for each season we have also derived a set of overall model weightings for each region that can be used to produce PDFs of forest biomass from the simulations of the Lund-Potsdam-Jena Dynamic Global Vegetation Model for managed land (LPJmL).