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基于模型的全球气候分类系统的不确定性地图。

Uncertainty maps for model-based global climate classification systems.

作者信息

Navarro Andrés, Merino Andrés, García-Ortega Eduardo, Tapiador Francisco J

机构信息

Earth and Space Sciences (ESS) Group, Institute of Environmental Sciences, University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, 45071, Toledo, Spain.

Atmospheric Physics Group (GFA), Environmental Institute, Universidad de León (ULE), Calle de la Serna 58, 24007, León, Spain.

出版信息

Sci Data. 2025 Jan 8;12(1):35. doi: 10.1038/s41597-025-04387-0.

Abstract

Climate classification systems (CCSs) are emerging as essential tools in climate change science for mitigation and adaptation. However, their limitations are often misunderstood by non-specialists. This situation is especially acute when the CCSs are derived from Global Climate Model outputs (GCMs). We present a set of uncertainty maps of four widely used schemes -Whittaker-Ricklefs, Holdridge, Thornthwaite-Feddema and Köppen- for present (1980-2014) and future (2015-2100) climate based on 52 models from the Coupled Intercomparison Model Project Phase six (CMIP6). Together with the classification maps, the uncertainty maps provide essential guidance on where the models perform within limits, and where sources of error lie. We share a digital resource that can be readily and freely integrated into mitigation and adaptation studies and which is helpful for scientists and practitioners using climate classifications, minimizing the risk of pitfalls or unsubstantiated conclusions.

摘要

气候分类系统(CCSs)正成为气候变化科学中用于缓解和适应的重要工具。然而,非专业人士常常误解其局限性。当气候分类系统源自全球气候模型输出(GCMs)时,这种情况尤为严重。我们基于耦合模式比较计划第六阶段(CMIP6)的52个模型,给出了当前(1980 - 2014年)和未来(2015 - 2100年)气候下四种广泛使用的方案——惠特克 - 里克利夫斯、霍尔德里奇、桑思韦特 - 费德马和柯本的一组不确定性地图。与分类地图一起,不确定性地图为模型在何处表现良好以及误差源在哪里提供了重要指导。我们共享了一个数字资源,它可以轻松且免费地整合到缓解和适应研究中,有助于使用气候分类的科学家和从业者,将陷入陷阱或得出无根据结论的风险降至最低。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b7c/11711615/dd76476e4601/41597_2025_4387_Fig1_HTML.jpg

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