Scholarlink

<p>This study presents SD-EQM_GCMs_IT, a new high-resolution climate projection ensemble dataset over Italy, designed to support climate impact assessments and adaptation strategies. It is derived by statistically downscaling nine CMIP6 General Circulation Models (GCMs) using the Copernicus European Regional ReAnalysis (CERRA) as a training dataset. The Empirical Quantile Mapping (EQM) method ensures a realistic statistical distribution and enhances local climate characterization. The dataset provides daily values at 0.05° resolution from 1985 to 2100 for six Essential Climate Variables (ECVs), including temperature, humidity, wind speed, and precipitation. Two climate scenarios are considered: SSP1-2.6 and SSP3-7.0, for low and high challanges to mitigation and adaptation respectively. This dataset enhances the current climate information available for Italy by bridging the gap between existing CMIP6 global projections and the absence of an ensemble of regional climate models for the same scenarios. By offering high-resolution data, it equips policymakers, industries, and communities with refined climate insights to enhance resilience and adaptation efforts.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Statistical Downscaling over Italy using EQM: CMIP6 Climate Projections for the 1985–2100 Period

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月30日

查看全文
  • G. Fedele,
  • A. Reder,
  • P. Mercogliano

摘要

This study presents SD-EQM_GCMs_IT, a new high-resolution climate projection ensemble dataset over Italy, designed to support climate impact assessments and adaptation strategies. It is derived by statistically downscaling nine CMIP6 General Circulation Models (GCMs) using the Copernicus European Regional ReAnalysis (CERRA) as a training dataset. The Empirical Quantile Mapping (EQM) method ensures a realistic statistical distribution and enhances local climate characterization. The dataset provides daily values at 0.05° resolution from 1985 to 2100 for six Essential Climate Variables (ECVs), including temperature, humidity, wind speed, and precipitation. Two climate scenarios are considered: SSP1-2.6 and SSP3-7.0, for low and high challanges to mitigation and adaptation respectively. This dataset enhances the current climate information available for Italy by bridging the gap between existing CMIP6 global projections and the absence of an ensemble of regional climate models for the same scenarios. By offering high-resolution data, it equips policymakers, industries, and communities with refined climate insights to enhance resilience and adaptation efforts.