Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil
Performance evaluation of Eta/HadGEM2-ES and Eta/MIROC5 precipitation simulations over Brazil
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DOI：
10.1016/j.atmosres.2020.105053
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发表时间：
2020-11-01
影响因子：
5.5
通讯作者：
Nobre, Carlos A.
中科院分区：
文献类型：
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作者：
Almagro, Andre;Oliveira, Paulo Tarso S.;Nobre, Carlos A.
气候变化的影响可以在全世界范围内产生重大影响。极端事件可以改变水的供应和农业生产，使气候变化规划成为一项重要任务。巴西国家空间研究所提供了一个区域气候模型输出（模拟和预测）的大型数据集，这为开展高分辨率气候变化研究提供了许多可能性。然而，仍然没有根据考虑到巴西生物群系的高分辨率地面观测数据对模型得出的降雨量输出进行性能评价。本文试图填补这一空白，并评估整个巴西的模拟降水。我们使用网格化观测的降水数据和历史气候模拟模型的跨学科研究气候，第5版（MIROC 5）和哈德利中心全球环境模型，第2版（HadGEM 2-ES），这是缩减的埃塔RCM（区域气候模型）。对于重叠的时期（1980-2005年），有很好的协议（PBIAS高达10%）的缩小规模的年度模拟亚马逊和塞拉多生物群落和大的偏见（达到40%）在潘帕生物群落相比，观察。我们的研究结果表明，HadGEM 2-ES能够很好地代表大面积地区的长期平均月降水量，如亚马逊和塞拉多。此外，Eta RCM大大改善了驾驶GCM MIROC 5模拟。总之，我们建议对亚马逊河使用HadGEM 2-ES模拟，对大西洋森林、塞拉多和潘帕使用Eta/HadGEM 2-ES模拟，对卡廷加和潘塔纳尔使用Eta/MIROC 5模拟。我们的研究概述了巴西的两个缩小规模的模拟数据集，这可能有助于验证模型对进一步气候变化评估的适用性。
Climate change effects can have significant impacts worldwide. Extreme events can modify water availability and agricultural production, making climate change planning an essential task. The National Institute for Space Research (INPE in Portuguese) in Brazil has made a large dataset of regional climate model outputs (simulations and projections) available, which opens up many possibilities of carrying out high-resolution climate change studies. However, there is still no performance evaluation of the model-derived rainfall output against high-resolution ground-based observation data considering the Brazilian biomes. This paper attempts to fill this gap and evaluates the simulated precipitation throughout Brazil. We used gridded observed precipitation data and historical climate simulations from the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) and from the Hadley Center Global Environment Model, version 2 (HadGEM2-ES), which were downscaled by the Eta RCM (Regional Climate Model). For the overlapping period (1980-2005), there is good agreement (PBIAS up to 10%) of downscaled annual simulations for the Amazon and Cerrado biomes and large biases (reaching 40%) in the Pampa biome, compared to the observations. Our results showed that HadGEM2-ES is capable of representing long-term mean monthly precipitation for large areas well, such as the Amazon and Cerrado. Furthermore, the Eta RCM has considerably improved the driving GCM MIROC5 simulations. In conclusion, we recommend using the HadGEM2-ES simulations for the Amazon, Eta/HadGEM2-ES for the Atlantic Forest, Cerrado, and Pampa, and Eta/MIROC5 for the Caatinga and Pantanal. Our study provides an overview of two downscaled simulation datasets in Brazil that may help verify the models' suitability for further climate change assessments.