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首页> 外文期刊>Journal of Climate >Reconstruction of global monthly upper-level temperature and geopotential height fields back to 1880.
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Reconstruction of global monthly upper-level temperature and geopotential height fields back to 1880.

机译：重建1880年以来的全球每月高层温度和地势高度场。

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摘要

This work presents statistically reconstructed global monthly mean fields of temperature and geopotential height (GPH) up to 100 hPa for the period 1880-1957. For the statistical model several thousand predictors were used, comprising a large amount of historical upper-air data as well as data from the earth's surface. In the calibration period (1958-2001), the statistical models were fit using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) as the predictand. After the weighting of the predictors, principal component (PC) analyses were performed on both the predictand and predictor dataset. Multiple linear regression models relate each principal component time series from the predictand with an optimal subset of principal component time series from the predictor. To assess the quality of the reconstructions, statistical split-sample validation (SSV) experiments were performed within the calibration period. Furthermore, the reconstructions were compared with independent historical upper-air and total ozone data. Based on the SSV experiment, this study obtained good reconstructions for temperature and GPH in the Northern Hemisphere; however, the skill in the tropics and the Southern Hemisphere was much lower. The reconstruction skill shows a clear annual cycle with the highest values in January. The lower levels were better reconstructed except in the tropics where the highest levels showed the best skill. With the inclusion of a considerable amount of upper-air data after 1939 the skill increased substantially. The fields were analyzed for selected months in the 1920s and 1930s to demonstrate the usefulness of the reconstructions. It is shown that the reconstructions are able to capture regional-to-global dynamical features.
机译：这项工作提出了在1880年至1957年期间，统计重构的全球每月平均温度场和最高地势高度（GPH），最高可达100 hPa。对于统计模型，使用了数千个预测变量，其中包括大量的历史高空数据以及来自地球表面的数据。在校准期（1958-2001年）中，使用40年欧洲中距离天气预报中心（ECMWF）重新分析（ERA-40）作为预测模型，拟合了统计模型。在对预测变量进行加权之后，对预测变量和预测变量数据集都进行了主成分（PC）分析。多个线性回归模型将预测的每个主成分时间序列与预测变量的主成分时间序列的最佳子集相关联。为了评估重建的质量，在校准期内进行了统计拆分样本验证（SSV）实验。此外，将重建与独立的历史高空和总臭氧数据进行了比较。基于SSV实验，本研究获得了北半球温度和GPH的良好重构;但是，在热带和南半球的技能要低得多。重建技能显示出清晰的年度周期，其中一月份的值最高。较低级别的重建更好，除了在热带地区，最高级别显示的是最好的技能。 1939年以后，由于包含了大量的高空数据，该技能大大提高了。在1920年代和1930年代的选定月份对田野进行了分析，以证明重建的有用性。结果表明，重建能够捕获区域到全球的动态特征。

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