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‐km²resolution 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‐km²pixels (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.

Policies for climate mitigation on land rarely acknowledge biophysical factors, such as reflectivity, evaporation, and surface roughness. Yet such factors can alter temperatures much more than carbon sequestration does, and often in a conflicting way. We outline a framework for examining biophysical factors in mitigation policies and provide some best-practice recommendations based on that framework. Tropical projects—avoided deforestation, forest restoration, and afforestation—provide the greatest climate value, because carbon storage and biophysics align to cool the Earth. In contrast, the climate benefits of carbon storage are often counteracted in boreal and other snow-covered regions, where darker trees trap more heat than snow does. Managers can increase the climate benefit of some forest projects by using more reflective and deciduous species and through urban forestry projects that reduce energy use. Ignoring biophysical interactions could result in millions of dollars being invested in some mitigation projects that provide little climate benefit or, worse, are counter-productive.

In arid regions throughout the world, shallow phreatic aquifers feed natural oases of much higher productivity than would be expected solely from local rainfall. In South America, the presence of well‐developed Prosopis flexuosa woodlands in the Monte Desert region east of the Andes has puzzled scientists for decades. Today these woodlands provide crucial subsistence to local populations, including descendants of the indigenous Huarpes. We explore the vulnerability and importance of phreatic groundwater for the productivity of the region, comparing the contributions of local rainfall to that of remote mountain recharge that is increasingly being diverted for irrigated agriculture before it reaches the desert. We combined deep soil coring, plant measurements, direct water‐table observations, and stable‐isotopic analyses (²H and ¹⁸O) of meteoric, surface, and ground waters at three study sites across the region, comparing woodland stands, bare dunes, and surrounding shrublands. The isotopic composition of phreatic groundwaters (δ²H: −137‰ ± 5‰) closely matched the signature of water brought to the region by the Mendoza River (−137‰ ± 6‰), suggesting that mountain‐river infiltration rather than in situ rainfall deep drainage (−39‰ ± 19‰) was the dominant mechanism of recharge. Similarly, chloride mass balances determined from deep soil profiles (>6 m) suggested very low recharge rates. Vegetation in woodland ecosystems, where significant groundwater discharge losses, likely >100 mm/yr occurred, relied on regionally derived groundwater located from 6.5 to 9.5 m underground. At these locations, daily water‐table fluctuations of ∼10 mm, and stable‐isotopic measurements of plant water, indicated groundwater uptake rates of 200–300 mm/yr. Regional scaling suggests that groundwater evapotranspiration reaches 18–42 mm/yr across the landscape, accounting for 7–17% of the Mendoza River flow regionally. Our study highlights the reliance of ecosystem productivity in natural oases on Andean snowmelt, which is increasingly being diverted to one of the largest irrigated regions of the continent. Understanding the ecohydrological coupling of mountain and desert ecosystems here and elsewhere should help managers balance production agriculture and conservation of unique woodland ecosystems and the rural communities that rely on them.

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR “Normalized Difference Vegetation Index” (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the “Eastern Paraguay” and “Uruguay River margins” focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative “Land ecosystem change utility for South America”, which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.