GSTDTAP
DOI10.1111/gcb.14876
Predicting future climate at high spatial and temporal resolution
Maclean, Ilya M. D.
2019-11-16
发表期刊GLOBAL CHANGE BIOLOGY
ISSN1354-1013
EISSN1365-2486
出版年2019
文章类型Article;Early Access
语种英语
国家England
英文摘要

Most studies on the biological effects of future climatic changes rely on seasonally aggregated, coarse-resolution data. Such data mask spatial and temporal variability in microclimate driven by terrain, wind and vegetation, and ultimately bear little resemblance to the conditions that organisms experience in the wild. Here, I present the methods for providing fine-grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales. Observed climate data and spatially coherent probabilistic projections of daily future weather were disaggregated to hourly and used to drive empirically calibrated physical models of thermal and hydrological microclimates. Mesoclimatic effects (cold-air drainage, coastal exposure and elevation) were determined from the coarse-resolution climate surfaces using thin-plate spline models with coastal exposure and elevation as predictors. Differences between micro and mesoclimate temperatures were determined from terrain, vegetation and ground properties using energy balance equations. Soil moisture was computed in a thin upper layer and an underlying deeper layer, and the exchange of water between these layers was calculated using the van Genuchten equation. Code for processing the data and running the models is provided as a series of R packages. The methods were applied to the Lizard Peninsula, United Kingdom, to provide hourly estimates of temperature (100 m grid resolution over entire area, 1 m for a selected area) for the periods 1983-2017 and 2041-2049. Results indicated that there is a fine-resolution variability in climatic changes, driven primarily by interactions between landscape features and decadal trends in weather conditions. High-temporal resolution extremes in conditions under future climate change were predicted to be considerably less novel than the extremes estimated using seasonally aggregated variables. The study highlights the need to more accurately estimate the future climatic conditions experienced by organisms and equips biologists with the means to do so.


英文关键词ecology mechanistic model microclimate soil moisture soil temperature species distributions
领域气候变化 ; 资源环境
收录类别SCI-E
WOS记录号WOS:000496645300001
WOS关键词WEATHER ; MODEL ; MICROCLIMATE ; SOIL
WOS类目Biodiversity Conservation ; Ecology ; Environmental Sciences
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
引用统计
文献类型期刊论文
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/225279
专题环境与发展全球科技态势
作者单位Univ Exeter, Environm & Sustainabil Inst, Penryn TR10 9FE, England
推荐引用方式
GB/T 7714
Maclean, Ilya M. D.. Predicting future climate at high spatial and temporal resolution[J]. GLOBAL CHANGE BIOLOGY,2019.
APA Maclean, Ilya M. D..(2019).Predicting future climate at high spatial and temporal resolution.GLOBAL CHANGE BIOLOGY.
MLA Maclean, Ilya M. D.."Predicting future climate at high spatial and temporal resolution".GLOBAL CHANGE BIOLOGY (2019).
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