Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1111/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
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ISSN | 1354-1013 |
EISSN | 1365-2486 |
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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