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.

• It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem‐level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem‐level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem–climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.

Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two‐leaf light use efficiency (TL‐LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big‐leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL‐LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL‐LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL‐LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (ε_msh) was 2.63 to 4.59 times that of sunlit leaves (ε_msu). Generally, the relationships of ε_msh and ε_msu with ε_max were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL‐LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL‐LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within‐canopy distribution of PAR.

Background and aims Ecosystem respiration (R _eco ) is controlled by thermal and hydrologic regimes, but their relative importance in defining the CO₂ emissions in peatlands seems to be site specific. The aim of the paper is to investigate the sensitivity of R _eco to variations in temperature and water table depth (WTD) in a wet, geogenous temperate peatland with a wide variety of vegetation community groups. Methods The CO₂ fluxes were measured using chambers. Measurements were made at four microsites with different vegetation communities and peat moisture and temperature conditions every 3 to 4 weeks during the period 2008–2009, 2 years with contrasting WTD patterns. Models were used to examine the relative response of each microsite to variations in peat temperature and WTD and used to estimate annual total R _eco . Results Temporal variations in R _eco were strongly related to peat temperature at the 5 cm depth. However, two of the microsites did not show any significant change in this relationship while two others showed contrasting responses including an increase and decrease in temperature sensitivity with deeper WTD. Average R _eco varied among the microsites and tended to be greatest for those with greatest leaf area which also positively correlated with deeper WTD, ash content and degree of peat decomposition at 20 cm. A combined temperature and WTD model explained up to 94 % of the temporal variation in daily average R _eco and was used to show that on an annual basis, R _eco was between 5 and 18 % greater in the warmer year with deeper WTD. Conclusion Microsite-specific responses were related to differences in vegetation and peat characteristics among microsites. R _eco may have remained insensitive to WTD variations at one microsite due to the dominance of autotrophic respiration from abundant sedge biomass. At a Sphagnum-dominated microsite, a lack of response may have been due to relatively small variations in WTD that did not greatly influence microbial respiration or due to offsets between decreasing and increasing respiration rates in near-surface and deeper peat. The microsite with the most recalcitrant peat had reduced R _eco sensitivity to temperature under more aerobic conditions while another microsite showed the opposite response, perhaps due to less nutrient availability during the wet year. Ultimately, micro-site specific models with both soil temperature and WTD as explanatory variables described temporal variations in R _eco and highlighted the significant spatial variations in respiration rates that may occur within a single wetland.

Peatlands store substantial amounts of carbon and are vulnerable to climate change. We present a modified version of the Organising Carbon and Hydrology In Dynamic Ecosystems (ORCHIDEE) land surface model for simulating the hydrology, surface energy, and CO₂ fluxes of peatlands on daily to annual timescales. The model includes a separate soil tile in each 0.5° grid cell, defined from a global peatland map and identified with peat-specific soil hydraulic properties. Runoff from non-peat vegetation within a grid cell containing a fraction of peat is routed to this peat soil tile, which maintains shallow water tables. The water table position separates oxic from anoxic decomposition. The model was evaluated against eddy-covariance (EC) observations from 30 northern peatland sites, with the maximum rate of carboxylation (V_cmax) being optimized at each site. Regarding short-term day-to-day variations, the model performance was good for gross primary production (GPP) (r² = 0.76; Nash–Sutcliffe modeling efficiency, MEF = 0.76) and ecosystem respiration (ER, r² = 0.78, MEF = 0.75), with lesser accuracy for latent heat fluxes (LE, r² = 0.42, MEF = 0.14) and and net ecosystem CO₂ exchange (NEE, r² = 0.38, MEF = 0.26). Seasonal variations in GPP, ER, NEE, and energy fluxes on monthly scales showed moderate to high r² values (0.57–0.86). For spatial across-site gradients of annual mean GPP, ER, NEE, and LE, r² values of 0.93, 0.89, 0.27, and 0.71 were achieved, respectively. Water table (WT) variation was not well predicted (r² < 0.1), likely due to the uncertain water input to the peat from surrounding areas. However, the poor performance of WT simulation did not greatly affect predictions of ER and NEE. We found a significant relationship between optimized V_cmax and latitude (temperature), which better reflects the spatial gradients of annual NEE than using an average V_cmax value.