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一种混合再分析预测气象强迫数据,以促进农业的气候适应

Toshichika Iizumi¹, Takahiro Takimoto², Yoshimitsu Masaki²

¹Institute for Agro-Environmental Sciences, National Agriculture and Food Research Organization, 3-1-3 Kannondai, Tsukuba, Ibaraki, 305-8604, Japan. iizumit@affrc.go.jp.

Scientific data

|August 8, 2024

概括

一个新的每日全球气象数据集 (JCDS) 结合了历史记录和预测,以帮助农业食品系统适应气候变化和极端天气事件,如热浪和干旱.

科学领域:

气候科学气候科学
农业气象农业气象学
数据科学数据科学数据科学

背景情况:

气候变化对农业食品系统构成重大风险,影响生产者,运输商和制造商.
适应措施对于增强应对季节性极端现象和气候变化的弹性至关重要.
准确的气象数据对于开发和验证气候影响模型至关重要.

研究的目的:

引入一个全新的每日全球混合再分析预测气象强迫数据集 (JCDS).
为监测和预测应用提供一个框架,以支持农业食品系统的适应战略.
通过整合历史和预测气候数据,促进气候影响模型的使用.

主要方法:

开发了每日0.5°全球混合再分析预测气象强迫数据集,从2010年到2021年.
整合了日本55年再分析 (JRA55) 和JMA/MRI-CPS2 5个成员组合预测.
使用Delta方法应用偏差校正,并与S14FD基线气候学集成.

主要成果:

联合CDS数据集提供了统一的每日气象强迫数据时间序列.
该数据集结合了再分析和整体预测数据,提供了全面的覆盖范围.
偏差校正确保了综合气象数据的可靠性和准确性.

结论:

联合气候适应系统数据集是气候适应研究和应用的宝贵资源.
它可以改善农业食品系统的气候影响建模和预测.
在应对气候变化和极端天气的情况下,促进主动规划和风险管理.

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