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

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 thus fail to reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions are controlled 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 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 of 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 2 m 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 warmer than gridded air temperature), whereas soils in warm and humid environments are on average slightly cooler (0.7 ± 2.3°C cooler). The observed substantial and biome-specific offsets underpin that the projected impacts of climate and climate change on 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 global gaps by collecting more in-situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications.

Research in environmental science relies heavily on global climatic grids derived from estimates of air temperature at around 2 meter above ground1-3. These climatic grids however fail to reflect conditions near and below the soil surface, where critical ecosystem functions such as soil carbon storage are controlled and most biodiversity resides4-8. By using soil temperature time series from over 8500 locations across all of the world’s terrestrial biomes4, we derived global maps of soil temperature-related variables at 1 km resolution for the 0–5 and 5–15 cm depth horizons. Based on these maps, we show that mean annual soil temperature differs markedly from the corresponding 2 m gridded air temperature, by up to 10°C, with substantial variation across biomes and seasons. Soils in cold and/or dry biomes are annually substantially warmer (3.6°C ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are slightly cooler (0.7 ± 2.3°C). As a result, annual soil temperature varies less (by 17%) across the globe than air temperature. The effect of macroclimatic conditions on the difference between soil and air temperature highlights the importance of considering that macroclimate warming may not result in the same level of soil temperature warming. Similarly, changes in precipitation could alter the relationship between soil and air temperature, with implications for soil-atmosphere feedbacks9. Our results underpin that the impacts of climate and climate change on biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments.

Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Observed global mean annual‐maximum precipitation is significantly stronger (49.5%) for persistent extremes than daily extremes. However, both globally observed and modeled rates of relative increases are lower for persistent extremes compared to daily extremes, especially for Southern Hemisphere and large regions in the 0‐45°N latitude band. Climate models also show significant differences in the magnitude and partly even the sign of local mean changes between daily and persistent extremes in global warming projections. Changes in extreme precipitation therefore are more complex than previously reported, and extreme precipitation events with varying duration should be taken into account for future climate change assessments.

[1] Extreme climate events have inflicted severe and adverse effects on human life, social economy, and natural ecosystems. In this study, the precipitation time series from a network of 90 weather stations in Northeast China (NEC) and for the period of 1961-2009 are used. An objective method, the multifractal detrended fluctuation analysis method, is applied to determine the thresholds of extreme events. Notable occurrence frequency and strong intensity of extreme precipitation (EP) mainly occur in Liaoning Province and the piedmont regions in Changbai Mountains and Xiao Hinggan Mountains. Generally, EP frequency shows a nonsignificant negative trend, whereas EP intensity has a weak and nonsignificant positive trend for the entire NEC in the period of 1961-2009. To assess EP severity, we propose an EP severity index (EPSI) combining both EP frequency and intensity, rather than separately analyze the EP frequency or intensity. Spatial gradients of EPSI are observed in northeast-southwest and northwest-southeast directions over NEC. The EPSI in northwestern and southeastern NEC are low (0.02-0.3), whereas high EPSI (0.34-0.83) occurs in the southwestern and northeastern portions of the region. Higher EPSI (0.4-0.83) occurs in southern Liaoning Province, which decreases along the southwest-northeast direction. The spatial patterns of EPSI are associated with the circulation over East Asia. Areas that have a short distance from sea and that locate in the windward slope of mountain will probably accompany high EP severity over NEC.

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes and biogeochemical cycling. We also compare studies of natural snow gradients with snow manipulation studies, altering snow depth and duration, to assess time scale difference of these approaches. The number of studies on snow in tundra ecosystems has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. In specific, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by manipulative studies (average 7.9 days advance, 5.5 days delay) were substantially lower than those observed over spatial gradients (mean range of 56 days) or due to interannual variation (mean range of 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates.

Climate involves different combinations of temperature and precipitation, and each year's combination of factors can be assigned a climatic year type (CYT; e.g. Warm‐Humid). Describing the changes in the CYT provides more information than describing the temperature or precipitation data alone. In this study, we defined nine CYTs using the probability density function of annual temperature and precipitation. Recent and future spatiotemporal changes in CYT were analysed using 507‐station observational data and projected data obtained from the CMIP5 multi‐model ensemble under RCP2.6, RCP4.5, and RCP8.5 scenarios. China was divided into six subregions to analyse the spatiotemporal changes. Obvious differences in spatial patterns among the various CYTs reflect the climate regime throughout China. The warmth‐associated CYTs (Warm‐Humid, Warm‐Dry, and Warm‐Normal) mainly occur in West China (e.g. Southwest China). The cold‐associated CYTs (Cold‐Humid, Cold‐Dry, and Cold‐Normal) dominate at high latitudes and high altitudes (e.g. Northeast China and the Tibetan Plateau). The climate in China changed from cold to warm in the last half‐century, accompanying the transformation of Cold‐Humid, Cold‐Dry, and Cold‐Normal before the early 1990s to Warm‐Humid, Warm‐Dry, and Warm‐Normal from the early 1990s onward. In the 21st century, the projected CYTs are mainly Warm‐Humid, Warm‐Dry, and Warm‐Normal in China. Warm‐Humid dominates in West China, North China, and Northeast China. Warm‐Dry is mainly projected in the Yellow River Valley and South China. High‐frequency Warm‐Normal is projected in the Yellow River Valley. Warm‐Humid is projected to increase whereas Warm‐Dry and Warm‐Normal are projected to decrease from 2015 to 2099. All three CYTs are projected to exhibit larger changes in trends under stronger versus weaker RCPs (RCP8.5 > RCP4.5 > RCP2.6). Compared with temperature or precipitation data alone, CYTs provide more complete information on climate change and more accurately characterize regional differences in climate throughout China.

On the basis of the daily precipitation records of 442 meteorological stations throughout Northeast Asia (NEA) from 1961 to 2009, this study investigates spatiotemporal changes in summer extreme precipitation (SEP) and the relationship between SEP and East Asia summer monsoon (EASM) in NEA. The results show that the spatial patterns of the trends in SEP indices are similar, with obvious clustering, except for the consecutive dry days (CDD) index. Stations exhibiting increasing trends are mainly located in Eastern China (EC) and the western part of Western Japan (WJ). Stations with decreasing trends are mostly distributed in Northern China (NC) and the southern region of Eastern Japan (EJ). The spatial patterns of the correlations between the EASM index EASMI and the SEP indices are similar, except for the CDD index. Stations that show decreasing trends mostly exhibit positive correlations between SEP indices and the EASMI, and vice versa. The spatial patterns of trends (increasing trends in EC, decreasing trends in NC and Northeast China) in SEP indices between 1961 and 2009 have a correlation with a weakening EASM. This study demonstrates that the mechanism dominating the mean state change of the EASM also applies to SEP. A strong mid‐latitude cyclone could result in a regional SEP (R99p occurred in more than 20 stations over a subregion in 1 day) in EC and Japan in weak EASM years, and could result in a regional SEP in NC in strong EASM years.