A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth

Kearney, Michael R.; Gillingham, Phillipa K.; Bramer, Isobel; Duffy, James P.; Maclean, Ilya M. D.

doi:10.1111/2041-210x.13330

Cited by 107 publications

(116 citation statements)

References 23 publications

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“…However, the ‘microclima’ models must be calibrated with local observations of temperature at the height of interest, whereas ‘NicheMapR’ computes local temperatures from first principles. Elements of both models have subsequently been combined into a single framework, enabling the computation of hourly, historical, terrain‐corrected microclimate anywhere on the earth (Kearney et al, in press). Building on the earlier development of high‐resolution models of soil and surface water conditions (Maclean, Bennie, Scott, & Wilson, ), the capabilities of ‘microclima’ have since been extended to enable the estimation of soil water content using the package ‘ecohydrotools’ (Maclean & Mosedale, ).…”

Section: Introductionmentioning

confidence: 99%

Predicting future climate at high spatial and temporal resolution

Maclean

2019

Global Change Biology

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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.

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Section: Introductionmentioning

confidence: 99%

Predicting future climate at high spatial and temporal resolution

Maclean

2019

Global Change Biology

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“…Ecologists studying snow‐affected systems therefore need accessible tools to capture the underlying micrometeorological processes. The modelling framework developed here should provide an accessible means for ecologists to make accurate inferences of the microclimatic consequences of snow on biological processes in the USA and alsewhere (Kearney, Gillingham, Bramer, Duffy, & Maclean, 2020).…”

Section: Discussionmentioning

confidence: 99%

How will snow alter exposure of organisms to cold stress under climate warming?

Kearney

2020

Global Ecol Biogeogr

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Aim:The aim was to test the capacity of an ecologically focused microclimate model to capture the interaction between snow cover and soil temperature and use it to assess how historical and future climate warming affects exposure of organisms to stressfully cold conditions in shallow soil.Location: Continental USA. Methods:A snow heat budget algorithm was developed and integrated with the general-purpose microclimate model of the NicheMapR package. The gridMET daily historical weather grids were used as environmental forcing. The model was tested against hourly observations of soil temperature and snow cover for 590 Soil Climate Analysis Network/SNOw TELemetry sites across the USA. It was then used to simulate cold stress exposure of ectotherms and hibernation costs for endotherms. Results: The model captured soil and snow observations to within c. 10%-15% of the range (r c. near 0.85). Air temperature exhibited a mean warming rate of 0.37°C per decade across sites, but in snow-affected regions the predicted shallow-soil warming rates were one-third lower. Biophysical analyses predicted an increase in the activity time of ectotherms as a result of contemporary climate change but little change in cold stress at most sites. For endotherms, hibernation costs were predicted to decline in general. However, both reduced and increased cold stress was simulated to occur for ectotherms and endotherms under simulated air temperature warming of ≤3 °C.Main conclusions: Many of the direct biological consequences of climate warming will be mediated through soil temperature. Microclimatic processes linked to snow cover mean that exposure to cold stress for both ectotherms and endotherms might stay constant, decrease or even increase under climate warming, depending on the local circumstances and species-specific biology. The idiosyncratic and season-specific decoupling of above-and below-ground environments under climate warming will have important ecological consequences in snow-affected regions and can now be computed mechanistically.

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“…We have demonstrated methods for connecting dynamic energy budget growth models to microclimate datasets across plant ontogeny. This formulation can produce complex, realistic growth dynamics in response to multiple environmental stresses, and can be scaled up to produce mapped distributions using globally available microclimatic inputs (Kearney et al 2020).…”

Section: Discussionmentioning

confidence: 99%

Integrating dynamic plant growth models and microclimates for species distribution modelling

Schouten

Vesk

Kearney

2020

Ecological Modelling

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Climate is a major factor determining the distribution of plant species. Correlative models are frequently used to model the relationships between species distributions and climatic drivers but, increasingly, their use for prediction in novel scenarios such as climate change is being questioned. Mechanistic models, where processes limiting plant distribution are explicitly included, are regarded as preferable but more challenging. The availability of tools for simulating microclimates with high spatial and temporal definition has also opened new possibilities for simulating the limiting environmental stresses experienced by plant over their ontogeny. However, the field of mechanistic species distribution modelling is relatively new and the tools and theory for constructing these models are underdeveloped. In this paper we explore the potential for using a Dynamic Energy Budget model of organism growth integrated with microclimate and photosynthesis models. We model the interactions of plant growth and microclimatic stressors over the life stages of plant growth, and scale them up to demonstrate predictions of distribution at the continental scale. We develop the model using Julia, a new language for scientific computing, as a set of generic modelling packages. These have a modular, toolkit structure that has the potential to increase the efficiency and transparency of developing mechanistic SDMs.

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A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth

Cited by 107 publications

References 23 publications

Predicting future climate at high spatial and temporal resolution

Predicting future climate at high spatial and temporal resolution

How will snow alter exposure of organisms to cold stress under climate warming?

Integrating dynamic plant growth models and microclimates for species distribution modelling

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