| DOI: | 10.1111/gcb.14876
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| 论文题名: | Predicting future climate at high spatial and temporal resolution |
| 作者: | Maclean I.M.D.
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| 刊名: | Global Change Biology
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| ISSN: | 13541013
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| 出版年: | 2020
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| 卷: | 26, 期:2 | | 语种: | 英语
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| 英文关键词: | ecology
; mechanistic model
; microclimate
; soil moisture
; soil temperature
; species distributions
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| Scopus关键词: | climate change
; ecological modeling
; future prospect
; microclimate
; soil moisture
; soil temperature
; spatial distribution
; spatial resolution
; spatial variation
; temporal variation
; Cornwall [England]
; England
; Lizard Peninsula
; United Kingdom
; United Kingdom
; Squamata
; animal experiment
; article
; biologist
; climate change
; cold air
; energy balance
; lizard
; microclimate
; nonhuman
; physical model
; running
; soil moisture
; soil temperature
; species distribution
; United Kingdom
; vegetation
; water transport
; weather
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| 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. © 2019 The Author. Global Change Biology published by John Wiley & Sons Ltd |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/159658
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| Appears in Collections: | 气候变化与战略
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作者单位: | Environment and Sustainability Institute, University of Exeter, Penryn, United Kingdom
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| Recommended Citation: | |
Maclean I.M.D.. Predicting future climate at high spatial and temporal resolution[J]. Global Change Biology,2020-01-01,26(2)
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