Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0

IF 4 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Climate Services Pub Date : 2024-01-01 DOI:10.1016/j.cliser.2023.100443
Anikó Kern , Laura Dobor , Roland Hollós , Hrvoje Marjanović , Csaba Zsolt Torma , Anna Kis , Nándor Fodor , Zoltán Barcza
{"title":"Seamlessly combined historical and projected daily meteorological datasets for impact studies in Central Europe: The FORESEE v4.0 and the FORESEE-HUN v1.0","authors":"Anikó Kern ,&nbsp;Laura Dobor ,&nbsp;Roland Hollós ,&nbsp;Hrvoje Marjanović ,&nbsp;Csaba Zsolt Torma ,&nbsp;Anna Kis ,&nbsp;Nándor Fodor ,&nbsp;Zoltán Barcza","doi":"10.1016/j.cliser.2023.100443","DOIUrl":null,"url":null,"abstract":"<div><p>The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.</p></div>","PeriodicalId":51332,"journal":{"name":"Climate Services","volume":"33 ","pages":"Article 100443"},"PeriodicalIF":4.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240588072300105X/pdfft?md5=a34d12845a9886670b99f87eb037f145&pid=1-s2.0-S240588072300105X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Climate Services","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S240588072300105X","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

The FORESEE is an open access, climatological database for Central Europe containing observed and projected meteorological data for the 1951–2100 period. As a climate service, FORESEE disseminates basic meteorological variables at a daily time step with a 0.1° × 0.1° spatial resolution including maximum/minimum temperature, precipitation, incoming shortwave solar radiation and daylight vapour pressure deficit. The future climate in FORESEE v4.0 and FORESEE-HUN v1.0 is projected by 14 regional climate models from the EURO-CORDEX database using the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. Based on RCP4.5 the country-specific results indicate similar projected mean changes in annual mean temperature (1.5–1.7 °C) but considerable differences in precipitation (from −1.6 to 6.9%) in the region for 2071–2100 relative to 1991–2020. We present two case studies to demonstrate the applicability of FORESEE in climate change impact studies using the ensemble approach. Climate change induced negative weather effect (15.4% and 28.9% mean loss for 2071–2100 according to RCP4.5 and RCP8.5, respectively) might dominate the future winter wheat yields in Hungary that is superimposed to the overall trend determined by other factors. The projections provide consistent results about the mean advance in the start of the growing season for forests in Hungary up to 2100 with ensemble mean of 9.1 days (RCP4.5) and 19.8 days (RCP8.5). We also demonstrate that the representative model selection method might lead to misleading results in impact studies that should be considered. The updated FORESEE is a way forward in the dissemination of policy-relevant essential climate data in Central Europe.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
无缝结合历史和预测日气象数据集,用于中欧影响研究:FORESEE v4.0 和 FORESEE-HUN v1.0
FORESEE 是一个开放式的中欧气候数据库,包含 1951-2100 年期间的观测和预测气象数据。作为一项气候服务,FORESEE 以 0.1° × 0.1° 的空间分辨率发布每日时间步长的基本气象变量,包括最高/最低气温、降水量、太阳短波入射辐射量和日照蒸汽压力赤字。FORESEE v4.0 和 FORESEE-HUN v1.0 中的未来气候是由 EURO-CORDEX 数据库中的 14 个区域气候模式使用代表性气候路径 (RCP) 4.5 和 8.5 情景预测的。基于 RCP4.5,具体国家的结果表明,与 1991-2020 年相比,2071-2100 年该地区年平均气温的预计平均变化相似(1.5-1.7 °C),但降水量有相当大的差异(-1.6% 到 6.9%)。我们介绍了两个案例研究,以展示 FORESEE 在使用集合方法进行气候变化影响研究方面的适用性。气候变化引起的负面天气效应(根据 RCP4.5 和 RCP8.5,2071-2100 年的平均损失分别为 15.4% 和 28.9%)可能会主导匈牙利未来的冬小麦产量,并与其他因素决定的总体趋势相叠加。这些预测对 2100 年前匈牙利森林生长季节开始的平均提前期提供了一致的结果,集合平均值为 9.1 天(RCP4.5)和 19.8 天(RCP8.5)。我们还证明,在影响研究中,代表性模型选择方法可能会导致误导性结果,这一点应加以考虑。更新后的 FORESEE 是在中欧传播与政策相关的基本气候数据的前进方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Climate Services
Climate Services Multiple-
CiteScore
5.30
自引率
15.60%
发文量
62
期刊介绍: The journal Climate Services publishes research with a focus on science-based and user-specific climate information underpinning climate services, ultimately to assist society to adapt to climate change. Climate Services brings science and practice closer together. The journal addresses both researchers in the field of climate service research, and stakeholders and practitioners interested in or already applying climate services. It serves as a means of communication, dialogue and exchange between researchers and stakeholders. Climate services pioneers novel research areas that directly refer to how climate information can be applied in methodologies and tools for adaptation to climate change. It publishes best practice examples, case studies as well as theories, methods and data analysis with a clear connection to climate services. The focus of the published work is often multi-disciplinary, case-specific, tailored to specific sectors and strongly application-oriented. To offer a suitable outlet for such studies, Climate Services journal introduced a new section in the research article type. The research article contains a classical scientific part as well as a section with easily understandable practical implications for policy makers and practitioners. The journal''s focus is on the use and usability of climate information for adaptation purposes underpinning climate services.
期刊最新文献
Scalingclimate information services and climate smart agriculture through bundled business models Improving smallholder farmers’ access to and utilization of climate information services in sub-Saharan Africa through social networks: A systematic review Do climate-smart agricultural practices impact the livelihoods of vulnerable farmers in the Southern part of Bangladesh? The impact of use of climate information services on smallholder welfare: Evidence from the hub of cashew production in Ghana Adaptation to climate variability and household welfare outcomes in Uganda
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1