HiCPC:新的中国上空 10 公里 CMIP6 降尺度每日气候预测。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Huihui Yuan, Like Ning, Jiewei Zhou, Wen Shi, Jianbin Huang, Yong Luo
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引用次数: 0

摘要

准确的气候预测对于环境科学和管理中的各种应用和影响评估至关重要。本研究介绍了 HiCPC(中国高分辨率 CMIP6 降尺度每日气候预测),这是一个针对中国具体需求量身定制的新型数据集。HiCPC 利用了耦合模式相互比较项目第六阶段(CMIP6)22 个全球气候模式(GCM)的输出结果。为解决每日 GCM 模拟中的固有偏差,以中国气象强迫数据集(CMFD)为参考,采用了先进的偏差校正和空间分解(BCSD)方法。HiCPC 以 0.1° × 0.1° 的增强空间分辨率提供了中国各地详细的日降水量和温度数据。它涵盖了历史时期(1979-2014 年)和基于 CMIP6 共同社会经济路径(SSPs)四种情景(SSP1-2.6、SSP2-4.5、SSP3-7.0 和 SSP5-8.5)的未来预测(2015-2100 年)。经过验证,HiCPC 表现出良好的性能,在整个历史时期超过了 CMIP6 GCM。这加强了其在中国气候变化评估及其相关影响方面的重要研究意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HiCPC: A new 10-km CMIP6 downscaled daily climate projections over China.

Accurate climate projections are critical for various applications and impact assessments in environmental science and management. This study presents HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China), a novel dataset tailored to China's specific needs. HiCPC leverages outputs from 22 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). To address inherent biases in daily GCM simulations, an advanced Bias Correction and Spatial Disaggregation (BCSD) method is employed, using the China Meteorological Forcing Dataset (CMFD) as a reference. HiCPC offers detailed daily precipitation and temperature data across China at an enhanced spatial resolution of 0.1° × 0.1°. It covers both the historical period (1979-2014) and future projections (2015-2100) based on four CMIP6 Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Upon validation, HiCPC demonstrates good performance, surpassing CMIP6 GCMs across the historical period. This reinforces its significance for essential research in climate change evaluation and its associated implications within China.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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