Predicting future climate at high spatial and temporal resolution

Abstract

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.

SoilTemp: A global database of near‐surface temperature

“…Accurately predicting how species' ranges will shift under climate change requires a good understanding of the variety of climate niches truly available to them (Lenoir et al, 2017;Maclean, Hopkins, Bennie, Lawson, & Wilson, 2015). The latter requires both a good understanding of what defines current microclimates and of how climate change will interact with the drivers of microclimatic conditions (Maclean, 2019).…”

Microclimatic conditions anywhere at any time!

“…Yet, a paradigm shift is happening, and that is why we are writing this commentary. That paradigm shift is happening thanks, among others, to the work of Ilya Maclean, which culminated in a recent ‘technical advances’ paper in Global Change Biology (Maclean, ). In that paper, he presents the methods for providing fine‐grained, hourly and daily estimates of current and future temperature and soil moisture over decadal timescales (Figure ).…”

“…Conceptual figure summarizing the process of modelling present and future microclimatic data, as used in Maclean (). Present‐day climate with a coarse spatiotemporal resolution (e.g.…”

“…You also have to consider observation height, and the biases introduced by vegetation cover, snow cover and soil depth. The mechanistic modelling family from Maclean () allows incorporating this vertical variability, thanks to an energy balance equation in which the difference between microclimatic and mesoclimatic reference temperature at 2 m height is modelled as a function of energy fluxes occurring at the surface.…”

On the importance of getting fine‐scale temperature records near any surface

“…Given the accumulating evidence that fine-scale microclimates can play a crucial role in the fate of organisms, strong collaborative work among multiple disciplines (including computer and atmospheric sciences, ecology and physiology) is likely to be needed to generate a global assessment of microclimate buffering. This empirical approach should be combined with mechanistic modeling (Maclean, 2020) in particular to generate microclimatic data for places and/or periods that were not covered by dataloggers. These advances in microclimate ecology will bring global change biology one major step ahead, notably by generating accurate and comprehensive predictive models available for ecologists.…”