Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 5–15 cm 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 8500 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

Global maps of soil temperature

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 5–15 cm 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\u27s 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

Global maps of soil temperature.

Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m 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 5-15 cm 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

Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions

<div><p>The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions.</p></div

Mean budbreak and flowering times under different climate conditions using two types of phenology model: (i) spring warming budbreak and flowering models [58, 60, 62], and (ii) winter chilling (vernalization) models [53].

<p>Mean budbreak and flowering times under different climate conditions using two types of phenology model: (i) spring warming budbreak and flowering models [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141218#pone.0141218.ref058" target="_blank">58</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141218#pone.0141218.ref060" target="_blank">60</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141218#pone.0141218.ref062" target="_blank">62</a>], and (ii) winter chilling (vernalization) models [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0141218#pone.0141218.ref053" target="_blank">53</a>].</p

Changes in cumulative growing season GDD (1^st April to 31^st October) calculated from baseline (1960–90) weather generator runs, daily historic weather data (1960–2011) and future climate weather generator runs using three emissions scenarios (low, middle and high emissions from left to right) and time periods.

<p>Baseline quantile box plots calculated from 9,000 30-year time sequences; future box plots from 1,000 30-year time sequences.</p

Modelled times of budbreak, flowering and veraison (from bottom to top) calculated from baseline (1960–90) weather generator runs, daily observed weather (1960–2011) and future climate weather generator runs using three emissions scenarios (low, middle and high emissions from left to right) and time periods.

<p>Modelled times of budbreak, flowering and veraison (from bottom to top) calculated from baseline (1960–90) weather generator runs, daily observed weather (1960–2011) and future climate weather generator runs using three emissions scenarios (low, middle and high emissions from left to right) and time periods.</p

Mean seasonal growing degree days, measures of late frost risk and of adverse flowering weather under different climate scenarios.

<p>Late frost risk expressed as (i) probability of a frost day (minimum temperature < = 0°C) after budbreak; (ii) mean number of these frost days, and (iii) mean accumulated degree days under 2°C after budbreak. Adverse flowering weather defined as a mean daily temperature <15°C or total precipitation>5mm and expressed as (i) the probability of 10 or more adverse days during the 7 days before and after flowering, and (ii) mean number of adverse days during the same 15 day period. The timing of budbreak and flowering calculated using spring warming models.</p

Parameters & forcing equation for spring warming models [58, 60, 62] used to simulate the timing of budbreak, flowering and veraison.

<p>The same forcing equation, using different starting times and base temperatures and where <i>T</i>_<i>mean</i> is the mean temperature of day <i>t</i>, applies to all three phenophases.</p

Parameters and equations of winter-chilling models [53] used to simulate the timing of dormancy break, budbreak and flowering.

<p>Daily chilling and forcing states calculated from daily mean temperature (T_mean) and curve shape parameters a = 0.005 and c = 2.8. The same forcing equation applies to budbreak and flowering. The critical forcing state at which budburst occurs is calculated from the chilling state and curve shape parameters: co1 = 176 and co2 = 0.015.</p