The accuracy of climate projections is improved by increasing the number of samples from ensemble experiments, leading to a decrease in the confidence interval of a target climatological variable. The improvement in the accuracy depends on the degree of independence of each ensemble member in the experiments. When the members of ensemble experiments are dependent on each other, the introduction of an effective sample size (ESS) is necessary to correctly estimate the confidence interval. This study is the first attempt to estimate the ESS for precipitation as a function of the number of ensemble members, although some previous studies have investigated another type of ESS in terms of the length of simulation period. The ESS in the present study is intrinsic to the atmospheric general circulation models (AGCM) forced by the ocean boundary condition because the outputs of AGCM ensemble members are similar or dependent on each other due to the commonly used boundary condition, i.e., the distribution of sea surface temperature, sea ice concentration, and sea ice thickness. Looking at the values of ESS as a function of geographical location, those in the tropics and over the ocean are smaller than those at higher latitudes and over continents; precipitation events in areas with smaller (larger) ESS are strongly (weakly) constrained by the ocean boundary condition. The increase in temporal and spatial averaging scales for precipitation estimation leads to the decrease in the ESS, of whose trend is attributed to the spatio-temporal characteristics of the precipitation events as represented by the power spectrum and co-spectrum.

中文翻译：

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大气环流模式集合实验中降水估计的有效样本大小：对时间和空间平均尺度的依赖

通过增加来自集合实验的样本数量来提高气候预测的准确性，从而降低目标气候变量的置信区间。准确率的提高取决于每个集成成员在实验中的独立程度。当集合实验的成员相互依赖时，需要引入有效样本量（ESS）以正确估计置信区间。这项研究是首次尝试将降水的 ESS 估计为集合成员数量的函数，尽管之前的一些研究已经根据模拟周期的长度研究了另一种类型的 ESS。本研究中的 ESS 是海洋边界条件强迫的大气环流模型 (AGCM) 所固有的，因为 AGCM 集合成员的输出由于常用边界条件而相似或相互依赖，即海面温度、海冰浓度和海冰厚度。从 ESS 值作为地理位置的函数来看，热带和海洋上的值小于高纬度和大陆上的值；ESS 较小（较大）地区的降水事件受到海洋边界条件的强（弱）约束。降水估计时空平均尺度的增加导致 ESS 的减少，