Coupled model intercomparison project phase 6 (CMIP6) high resolution model intercomparison project (HighResMIP) bias in extreme rainfall drives underestimation of amazonian precipitation

IF 2.5 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Robinson Negron-Juarez, Michael Wehner, Maria Assunção F Silva Dias, Paul Ullrich, Jeffrey Q Chambers, William J Riley
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Abstract

Extreme rainfall events drive the amount and spatial distribution of rainfall in the Amazon and are a key driver of forest dynamics across the basin. This study investigates how the 3-hourly predictions in the High Resolution Model Intercomparison Project (HighResMIP, a component of the recent Coupled Model Intercomparison Project, CMIP6) represent extreme rainfall events at annual, seasonal, and sub-daily time scales. TRMM 3B42 (Tropical Rainfall Measuring Mission) 3 h data were used as observations. Our results showed that eleven out of seventeen HighResMIP models showed the observed association between rainfall and number of extreme events at the annual and seasonal scales. Two models captured the spatial pattern of number of extreme events at the seasonal and annual scales better (higher correlation) than the other models. None of the models captured the sub-daily timing of extreme rainfall, though some reproduced daily totals. Our results suggest that higher model resolution is a crucial factor for capturing extreme rainfall events in the Amazon, but it might not be the sole factor. Improving the representation of Amazon extreme rainfall events in HighResMIP models can help reduce model rainfall biases and uncertainties and enable more reliable assessments of the water cycle and forest dynamics in the Amazon.
耦合模式相互比较项目第 6 阶段(CMIP6) 高分辨率模式相互比较项目(HighResMIP) 极端降雨量的偏差导致低估了亚马孙降水量
极端降雨事件驱动着亚马逊流域的降雨量和空间分布,是整个流域森林动态的关键驱动因素。本研究调查了高分辨率模式相互比较项目(HighResMIP,最近的耦合模式相互比较项目 CMIP6 的一个组成部分)中的 3 小时预测如何代表年、季节和亚日时间尺度的极端降雨事件。TRMM 3B42(热带降雨测量任务)的 3 小时数据被用作观测数据。结果表明,在 17 个 HighResMIP 模型中,有 11 个模型显示了观测到的降雨量与年度和季节尺度极端事件数量之间的联系。有两个模式比其他模式更好地捕捉到了季节和年度尺度上极端事件数量的空间模式(相关性更高)。尽管一些模式再现了日降雨总量,但没有一个模式捕捉到极端降雨的次日时间。我们的研究结果表明,较高的模式分辨率是捕捉亚马逊极端降雨事件的一个关键因素,但它可能不是唯一的因素。改进 HighResMIP 模型对亚马逊极端降雨事件的表现有助于减少模型的降雨偏差和不确定性,并能对亚马逊的水循环和森林动态进行更可靠的评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Environmental Research Communications
Environmental Research Communications ENVIRONMENTAL SCIENCES-
CiteScore
3.50
自引率
0.00%
发文量
136
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