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- Lembrechts, Jonas J., et al.
(författare)
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SoilTemp : A global database of near-surface temperature
- 2020
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 26:11, s. 6616-6629
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Tidskriftsartikel (refereegranskat)abstract
- Current analyses and predictions of spatially explicit patterns and processes in ecology most often rely on climate data interpolated from standardized weather stations. This interpolated climate data represents long-term average thermal conditions at coarse spatial resolutions only. Hence, many climate-forcing factors that operate at fine spatiotemporal resolutions are overlooked. This is particularly important in relation to effects of observation height (e.g. vegetation, snow and soil characteristics) and in habitats varying in their exposure to radiation, moisture and wind (e.g. topography, radiative forcing or cold-air pooling). Since organisms living close to the ground relate more strongly to these microclimatic conditions than to free-air temperatures, microclimatic ground and near-surface data are needed to provide realistic forecasts of the fate of such organisms under anthropogenic climate change, as well as of the functioning of the ecosystems they live in. To fill this critical gap, we highlight a call for temperature time series submissions to SoilTemp, a geospatial database initiative compiling soil and near-surface temperature data from all over the world. Currently, this database contains time series from 7,538 temperature sensors from 51 countries across all key biomes. The database will pave the way toward an improved global understanding of microclimate and bridge the gap between the available climate data and the climate at fine spatiotemporal resolutions relevant to most organisms and ecosystem processes.
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| 2. |
- Lembrechts, Jonas J., et al.
(författare)
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Global maps of soil temperature
- 2022
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Ingår i: Global Change Biology. - : Wiley. - 1354-1013 .- 1365-2486. ; 28:9, s. 3110-3144
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Tidskriftsartikel (refereegranskat)abstract
- Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 pixels (summarized from 8519 unique temperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean=3.0±2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6±2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7±2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.
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| 3. |
- Lembrechts, Jonas J., et al.
(författare)
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Comparing temperature data sources for use in species distribution models : From in-situ logging to remote sensing
- 2019
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Ingår i: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 28:11, s. 1578-1596
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Tidskriftsartikel (refereegranskat)abstract
- Aim Although species distribution models (SDMs) traditionally link species occurrences to free-air temperature data at coarse spatio-temporal resolution, the distribution of organisms might instead be driven by temperatures more proximal to their habitats. Several solutions are currently available, such as downscaled or interpolated coarse-grained free-air temperatures, satellite-measured land surface temperatures (LST) or in-situ-measured soil temperatures. A comprehensive comparison of temperature data sources and their performance in SDMs is, however, currently lacking. Location Northern Scandinavia. Time period 1970-2017. Major taxa studied Higher plants. Methods We evaluated different sources of temperature data (WorldClim, CHELSA, MODIS, E-OBS, topoclimate and soil temperature from miniature data loggers), differing in spatial resolution (from 1 '' to 0.1 degrees), measurement focus (free-air, ground-surface or soil temperature) and temporal extent (year-long versus long-term averages), and used them to fit SDMs for 50 plant species with different growth forms in a high-latitudinal mountain region. Results Differences between these temperature data sources originating from measurement focus and temporal extent overshadow the effects of temporal climatic differences and spatio-temporal resolution, with elevational lapse rates ranging from -0.6 degrees C per 100 m for long-term free-air temperature data to -0.2 degrees C per 100 m for in-situ soil temperatures. Most importantly, we found that the performance of the temperature data in SDMs depended on the growth forms of species. The use of in-situ soil temperatures improved the explanatory power of our SDMs (R-2 on average +16%), especially for forbs and graminoids (R-2 +24 and +21% on average, respectively) compared with the other data sources. Main conclusions We suggest that future studies using SDMs should use the temperature dataset that best reflects the ecology of the species, rather than automatically using coarse-grained data from WorldClim or CHELSA.
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