ForestTemp – Sub‐canopy microclimate temperatures of European forests

Abstract

Ecological research heavily relies on coarse-gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub-canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate-change-related impacts on forest-floor biodiversity and functioning, high-resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near-surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub-canopy temperature at 15 cm above the surface and free-air temperature over the period 2000-2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub-canopy air temperatures differ substantially from free-air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high-resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub-canopy temperature maps will enable future research to model below-canopy biological processes and patterns, as well as species distributions more accurately.

The role of geology in creating stream climate-change refugia along climate gradients

“…The cooling effects associated with forest canopy cover is also widely reported in the terrestrial and freshwater realms Nakamura 1999, Haesen et al 2021). Extensive monitoring networks in European forests revealed that subcanopy air temperatures were cooler than the free air temperatures by 2.0 ℃ on average (Haesen et al 2021). The Earth's subsurface factors, namely the geology, also contribute to local thermal heterogeneity.…”

“…In the context of Antarctic active layers, Hrbáček et al (2017) reported a distinct ground thermal regime between lithologically different sites due to thermal conductivity controlled by the geology. More importantly, effect of these covariates on local thermal conditions are not always uniform and can vary along environmental gradients (Hilderbrand et al 2014, Jucker et al 2018, Ehbrecht et al 2019, De Frenne et al 2021). These potential interactions among landscape and climatic factors imply that identification and management of climate-change refugia based on independent effects cannot effectively mitigate biodiversity decline in the future.…”

Trait phenology and fire seasonality co‐drive seasonal variation in fire effects on tree crowns

“…1.5 m above a level, grassy surface. As such, these data do not account for forest canopy effects on temperature microclimate (Haesen et al ., 2021). Climate data were extracted using the weathercan package in R (LaZerte & Albers, 2018).…”

“…This is a standard Environment Canada weather station that records air temperature in the open, c. 1.5 m above a level, grassy surface. As such, these data do not account for forest canopy effects on temperature microclimate (Haesen et al, 2021). Climate data were extracted using the WEATHERCAN package in R (LaZerte & Albers, 2018).…”

Predicting plant species distributions using climate‐based model ensembles with corresponding measures of congruence and uncertainty

“…WorldClim is one of the most widely used sources of climate data for ecological studies; however, many alternative methods of generating such datasets have been demonstrated at ecologically meaningful scales. These include downscaling and empirical adjustment of pre‐existing climate surfaces (Abatzoglou et al, 2018; Karger et al, 2017; Kearney et al, 2014, 2020; Kriticos et al, 2012; Poggio et al, 2018), correlative interpolations calibrated using micro‐loggers in forested ecosystems (Ashcroft & Gollan, 2012; Haesen et al, 2021; Lembrechts et al, 2021; Meineri & Hylander, 2017) and techniques that apply geostatistical and spline‐based methods in conjunction with earth observation data (Funk et al, 2015; Hengl et al, 2012; Oyler et al, 2015; Stewart et al, 2017; Stewart & Nitschke, 2017e).…”