用 WRF-Chem 模拟东亚上空 10 年的高分辨率大气二氧化碳浓度数据

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Geoscience Data Journal Pub Date : 2024-09-13 DOI:10.1002/gdj3.273
Min-Gyung Seo, Hyun Mee Kim, Dae-Hui Kim
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引用次数: 0

摘要

东亚是了解全球碳循环的重要地区,因此本研究生成了东亚地区的高分辨率二氧化碳浓度数据,以分析大气中二氧化碳浓度的长期变化。利用天气研究和预报与化学耦合模式(WRF-Chem),以 9 千米的分辨率模拟了东亚地区 10 年内(2009-2018 年)的大气二氧化碳浓度。生成的二氧化碳浓度数据包括二氧化碳浓度、生物源二氧化碳浓度、人为二氧化碳浓度、海洋二氧化碳浓度、生物圈二氧化碳吸收量、生物圈二氧化碳释放量以及以 3 小时为间隔的气象变量。模拟的高分辨率二氧化碳浓度、生物圈二氧化碳浓度和人为二氧化碳浓度以 NetCDF-4(网络通用数据表,第 4 版)格式存储,可在 https://doi.org/10.7910/DVN/PJTBF3 上下载。模拟的东亚地表二氧化碳年均浓度在2009年为391.027 ppm,2018年为412.949 ppm,表明在10年期间增加了21.922 ppm,并有适当的季节变化。利用地表二氧化碳观测数据和轨道碳观测站2号(OCO-2)的卫星柱平均二氧化碳摩尔分数(XCO2)验证了东亚地区的月平均二氧化碳浓度。基于地表二氧化碳观测数据和OCO-2 XCO2浓度,WRF-Chem模拟的二氧化碳浓度平均均方根误差(RMSE)分别为2.474和0.374 ppm,小于低分辨率CarbonTracker 2019B(CT2019B)模拟的平均均方根误差。因此,WRF-Chem模拟的东亚地区10年高分辨率大气二氧化碳浓度是与观测值相似的可靠数据,对了解东亚地区碳循环具有重要价值。
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High-resolution atmospheric CO2 concentration data simulated in WRF-Chem over East Asia for 10 years

In this study, high-resolution CO2 concentration data were generated for East Asia to analyse long-term changes in atmospheric CO2 concentrations, as East Asia is an important region for understanding the global carbon cycle. Using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), atmospheric CO2 concentrations were simulated in East Asia at a resolution of 9 km for a period of 10 years (2009–2018). The generated CO2 concentration data include CO2 concentrations, biogenic CO2 concentrations, anthropogenic CO2 concentrations, oceanic CO2 concentrations, biospheric CO2 uptake, biospheric CO2 release and meteorological variables at 3-h intervals. The simulated high-resolution CO2 concentrations, biogenic CO2 concentrations and anthropogenic CO2 concentrations are stored in NetCDF-4 (Network Common Data Form, version 4) format and are available for download at https://doi.org/10.7910/DVN/PJTBF3. The simulated annual mean surface CO2 concentrations in East Asia were 391.027 ppm in 2009 and 412.949 ppm in 2018, indicating an increase of 21.922 ppm over the 10-year period with appropriate seasonal variabilities. The monthly mean CO2 concentrations in East Asia were verified using surface CO2 observations and satellite column-averaged CO2 mole fraction (XCO2) from Orbiting Carbon Observatory 2 (OCO-2). Based on surface CO2 observations and OCO-2 XCO2 concentrations, the average root-mean-square error (RMSE) of the simulated CO2 concentrations in WRF-Chem was 2.474 and 0.374 ppm, respectively, which is smaller than the average RMSE of the low-resolution CarbonTracker 2019B (CT2019B) simulation. Therefore, the simulated high-resolution atmospheric CO2 concentrations in East Asia in WRF-Chem over 10 years are reliable data that resemble the observed values and could be highly valuable in understanding the carbon cycle in East Asia.

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来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
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