Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow Observations from 9372 Catchments
Bias Correction of Global High-Resolution Precipitation Climatologies Using Streamflow Observations from 9372 Catchments
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DOI：
10.1175/jcli-d-19-0332.1
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发表时间：
2020-02-01
影响因子：
4.9
通讯作者：
Karger, Dirk N.
中科院分区：
文献类型：
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作者：
Beck, Hylke E.;Wood, Eric F.;Karger, Dirk N.
我们介绍了一套全球高分辨率（0.05度）降水（P）气候校正偏差使用径流（Q）观测来自全球9372站。对于每个站，我们推断出“真正的”长期P使用Budyko曲线，这是一个经验方程长期P，Q和潜在的蒸发。随后，我们计算了三个最先进的P气候的长期偏差校正因子[“WorldClim第2版”数据库（WorldClim V2）;地球陆地表面积高分辨率气候学，1.2版（CHELSA V1.2）;降水气候学，第1版（CHPclim V1）]，之后，我们使用随机森林回归生成全球无间隙偏差校正图的P气候。每月的气候偏差校正因子的计算是通过分解的长期偏差校正因子的基础上的测量渔获量效率。我们发现，所有三个气候系统低估了P在全球所有主要山脉的部分地区，尽管在生产的每一个气候的地形明确考虑。此外，所有的气候低估P在纬度>60度N，可能是因为测量不足。阿拉斯加、亚洲高山和智利地区的所有三种P气候都获得了异常高的长期校正因子（>1.5），这些地区的特征是显著的海拔梯度、稀疏的测量网络和显著的降雪。使用偏差校正的WorldClim V2，我们证明了其他广泛使用的P数据集（GPCC V2015，GPCP V2.3和MERRA-2）严重低估了智利，喜马拉雅山脉和沿着北美太平洋沿岸的P。基于偏差校正的WorldClim V2的全球陆地表面平均P为862 mm yr（-1）（比原始WorldClim V2增加9.4%）。年和月偏差校正的P气候学已作为降水偏差校正（PBCOR）数据集发布，可在线获取（）。
We introduce a set of global high-resolution (0.05 degrees) precipitation (P) climatologies corrected for bias using streamflow (Q) observations from 9372 stations worldwide. For each station, we inferred the "true" long-term P using a Budyko curve, which is an empirical equation relating long-term P, Q, and potential evaporation. We subsequently calculated long-term bias correction factors for three state-of-the-art P climatologies [the "WorldClim version 2" database (WorldClim V2); Climatologies at High Resolution for the Earth's Land Surface Areas, version 1.2 (CHELSA V1.2 ); and Climate Hazards Group Precipitation Climatology, version 1 (CHPclim V1)], after which we used random-forest regression to produce global gap-free bias correction maps for the P climatologies. Monthly climatological bias correction factors were calculated by disaggregating the long-term bias correction factors on the basis of gauge catch efficiencies. We found that all three climatologies systematically underestimate P over parts of all major mountain ranges globally, despite the explicit consideration of orography in the production of each climatology. In addition, all climatologies underestimate P at latitudes >60 degrees N, likely because of gauge undercatch. Exceptionally high long-term correction factors (>1.5) were obtained for all three P climatologies in Alaska, High Mountain Asia, and Chile-regions characterized by marked elevation gradients, sparse gauge networks, and significant snowfall. Using the bias-corrected WorldClim V2, we demonstrated that other widely used P datasets (GPCC V2015, GPCP V2.3, and MERRA-2) severely underestimate P over Chile, the Himalayas, and along the Pacific coast of North America. Mean P for the global land surface based on the bias-corrected WorldClim V2 is 862 mm yr(-1) (a 9.4% increase over the original WorldClim V2). The annual and monthly bias-corrected P climatologies have been released as the Precipitation Bias Correction (PBCOR) dataset, which is available online ().