Getting the leaves right matters for estimating temperature extremes

IF 4 3区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Geoscientific Model Development Pub Date : 2023-12-19 DOI:10.5194/gmd-16-7357-2023

G. Duveiller, M. Pickering, J. Muñoz‐Sabater, L. Caporaso, S. Boussetta, G. Balsamo, A. Cescatti

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引用次数: 1

Abstract

Abstract. Atmospheric reanalyses combine observations and models through data assimilation techniques to provide spatio-temporally continuous fields of key surface variables. They can do so for extended historical periods whilst ensuring a coherent representation of the main Earth system cycles. ERA5 and its enhanced land surface component, ERA5-Land, are widely used in Earth system science and form the flagship products of the Copernicus Climate Change Service (C3S) of the European Commission. Such land surface modelling frameworks generally rely on a state variable called leaf area index (LAI), representing the number of leaves in a grid cell at a given time, to quantify the fluxes of carbon, water and energy between the vegetation and the atmosphere. However, the LAI within the modelling framework behind ERA5 and ERA5-Land is prescribed as a climatological seasonal cycle, neglecting any interannual variability and the potential consequences that this uncoupling between vegetation and atmosphere may have on the surface energy balance and the climate. To evaluate the impact of this mismatch in LAI, we analyse the corresponding effect it has on land surface temperature (LST) by comparing what is simulated to satellite observations. We characterise a hysteretic behaviour between LST biases and LAI biases that evolves differently along the year depending on the background climate. We further analyse the repercussions for the reconstructed climate during more extreme conditions in terms of LAI deviations, with a specific focus on the 2003, 2010 and 2018 heat waves in Europe for which LST mismatches are exacerbated. We anticipate that our results will assist users of ERA5 and ERA5-Land data in understanding where and when the larger discrepancies can be expected, but also guide developers towards improving the modelling framework. Finally, this study could provide a blueprint for a wider benchmarking framework for land surface model evaluation that exploits the capacity of LST to integrate the effects of both radiative and non-radiative processes affecting the surface energy.

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正确选择树叶对估算极端温度至关重要

摘要。大气再分析通过数据同化技术将观测数据和模式结合起来，提供关键地表变量的时空连续场。大气再分析可提供较长历史时期的数据，同时确保对主要地球系统周期的连贯表述。ERA5及其增强型陆地表面组件ERA5-Land被广泛应用于地球系统科学，是欧盟委员会哥白尼气候变化服务（C3S）的旗舰产品。此类陆地表面建模框架通常依赖于一个称为叶面积指数（LAI）的状态变量，代表某一时间网格单元中的叶片数量，以量化植被与大气之间的碳、水和能量通量。然而，ERA5 和 ERA5-Land 模拟框架中的叶面积指数被规定为气候学上的季节周期，忽略了任何年际变化以及植被与大气之间的这种不耦合可能对地表能量平衡和气候造成的潜在后果。为了评估这种 LAI 不匹配的影响，我们将模拟结果与卫星观测结果进行比较，分析其对地表温度（LST）的相应影响。我们描述了地表温度偏差和 LAI 偏差之间的滞后行为，这种行为根据背景气候的不同而在一年中发生不同的变化。我们进一步分析了在更极端条件下 LAI 偏差对重建气候的影响，重点关注欧洲 2003 年、2010 年和 2018 年的热浪，这些热浪加剧了 LST 的不匹配。我们预计，我们的研究结果将有助于ERA5和ERA5-Land数据的用户了解何时何地会出现较大的差异，同时也将指导开发人员改进建模框架。最后，这项研究可以为更广泛的陆地表面模式评估基准框架提供一个蓝图，利用 LST 的能力来综合影响地表能量的辐射和非辐射过程的影响。

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

Geoscientific Model Development GEOSCIENCES, MULTIDISCIPLINARY-

CiteScore

8.60

自引率

9.80%

发文量

352

审稿时长

6-12 weeks

期刊介绍： Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication: * geoscientific model descriptions, from statistical models to box models to GCMs; * development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results; * new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data; * papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data; * model experiment descriptions, including experimental details and project protocols; * full evaluations of previously published models.