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Soil respiration (RS), the soil‐to‐atmosphere CO2 flux that is a major component of the global carbon cycle, is strongly influenced by local soil temperature (Tsoil) and water content (SWC). Regional to global‐scale RS modelling thus requires this information at local scales, but few high‐quality, wall‐to‐wall (global) Tsoil and SWC data exist. As a result, such modelling efforts commonly use air temperature (Tair) and monthly precipitation (Pm) as surrogate predictors, but their site‐scale accuracy and potential bias are unknown. Here, we used monthly data from 880 sites across a wide variety of different environmental conditions (i.e., climate, ecosystem type, elevation, vegetation leaf habit and drainage conditions) to determine the suitability of Tair as a surrogate for Tsoil, and data from 507 sites to examine the suitability of Pm as a surrogate for SWC. Site‐specific linear and second‐order exponential non‐linear models were compared using model evaluation metrics (i.e., slope, p‐value of slope, root mean square error [RMSE], index of agreement and model efficiency). We found that Tsoil and Tair are highly correlated and explain similar RS variability. In contrast, Pm is not a good surrogate for SWC, even though Pm explains a similar amount of RS variability to SWC. The wide variability in the site‐specific relationships between RS and SWC means that no single relationship can be used for large‐scale modelling. The results from this study support the use of Tair in continental‐to‐global scale RS models, and highlight the urgent need for continental‐to‐global scale SWC datasets for the modelling and evaluation of future soil carbon dynamics under global climate change. Highlights: The accuracy of air temperature and precipitation as surrogates in global soil respiration modelling is unknown.Monthly air temperature and soil temperature are strongly correlated and explained similar amounts of variability in soil respiration.Relationships between precipitation and soil water content are extremely variable by region, thus precipitation is a poor surrogate in global modelling.There is a need for accurate multiscale soil moisture datasets to evaluate future soil carbon dynamics. [ABSTRACT FROM AUTHOR]

The net ecosystem exchange (NEE) is the difference between ecosystem CO 2 assimilation and CO 2 losses to the atmosphere. Ecosystem respiration (R eco ), the efflux of CO 2 from the ecosystem to the atmosphere, includes the soil-to-atmosphere carbon flux (i.e., soil respiration; R soil ) and aboveground plant respiration. Therefore, R soil is a fraction of R eco and theoretically has to be smaller than R eco at daily, seasonal, and annual scales. However, several studies estimating R eco with the eddy covariance technique and measuring R soil within the footprint of the tower have reported higher R soil than R eco at different time scales. Here, we compare four different and contrasting ecosystems (from forest to grasslands, and from boreal to semiarid) to test if measurements of R eco are consistently higher than R soil . In general, both fluxes showed similar temporal patterns, but R eco was not consistently higher than R soil from daily to annual scales across sites. We identified several issues that apply for measuring NEE and measuring/upscaling R soil that could result in an underestimation of R eco and/or an overestimation of R soil . These issues are discussed based on (a) nighttime measurements of NEE, (b) R soil measurements, and (c) the interpretation of the functional relationships of these fluxes with temperature (i.e., Q 10 ). We highlight that there is still a need for better integration of R soil with eddy covariance measurements to address challenges related to the spatial and temporal variability of R eco and R soil . [ABSTRACT FROM AUTHOR]

Limiting atmospheric carbon dioxide (CO2) concentrations to low levels requires strategies to manage anthropogenic carbon emissions from terrestrial systems as well as fossil fuel and industrial sources. We explore the implications of fully integrating terrestrial systems and the energy system into a comprehensive mitigation regime that Limits atmospheric CO2 concentrations. We find that this comprehensive approach lowers the cost of meeting environmental goals but also carries with it profound implications for agriculture: Unmanaged ecosystems and forests expand, and food crop and livestock prices rise. Finally, we find that future improvement in food crop productivity directly affects land-use change emissions, making the technology for growing crops potentially important for limiting atmospheric CO2 concentrations. [ABSTRACT FROM AUTHOR]