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Although the terrestrial biosphere absorbs about 25 per cent of anthropogenic carbon dioxide (CO.sub.2) emissions, the rate of land carbon uptake remains highly uncertain, leading to uncertainties in climate projections.sup.1,2. Understanding the factors that limit or drive land carbon storage is therefore important for improving climate predictions. One potential limiting factor for land carbon uptake is soil moisture, which can reduce gross primary production through ecosystem water stress.sup.3,4, cause vegetation mortality.sup.5 and further exacerbate climate extremes due to land-atmosphere feedbacks.sup.6. Previous work has explored the impact of soil-moisture availability on past carbon-flux variability.sup.3,7,8. However, the influence of soil-moisture variability and trends on the long-term carbon sink and the mechanisms responsible for associated carbon losses remain uncertain. Here we use the data output from four Earth system models.sup.9 from a series of experiments to analyse the responses of terrestrial net biome productivity to soil-moisture changes, and find that soil-moisture variability and trends induce large CO.sub.2 fluxes (about two to three gigatons of carbon per year; comparable with the land carbon sink itself.sup.1) throughout the twenty-first century. Subseasonal and interannual soil-moisture variability generate CO.sub.2 as a result of the nonlinear response of photosynthesis and net ecosystem exchange to soil-water availability and of the increased temperature and vapour pressure deficit caused by land-atmosphere interactions. Soil-moisture variability reduces the present land carbon sink, and its increase and drying trends in several regions are expected to reduce it further. Our results emphasize that the capacity of continents to act as a future carbon sink critically depends on the nonlinear response of carbon fluxes to soil moisture and on land-atmosphere interactions. This suggests that the increasing trend in carbon uptake rate may not be sustained past the middle of the century and could result in accelerated atmospheric CO.sub.2 growth.Earth system models suggest that soil-moisture variability and trends will induce large carbon releases throughout the twenty-first century., Author(s): Julia K. Green [sup.1] , Sonia I. Seneviratne [sup.2] , Alexis M. Berg [sup.3] , Kirsten L. Findell [sup.4] , Stefan Hagemann [sup.5] , David M. Lawrence [sup.6] , [...]

Terrestrial water storage (TWS) includes all forms of water stored on and below the land surface, and is a key determinant of global water and energy budgets. However, TWS data from measurements by the Gravity Recovery and Climate Experiment (GRACE) satellite mission are only available from 2002, limiting global and regional understanding of the long-term trends and variabilities in the terrestrial water cycle under climate change. This study presents long-term (i.e., 1940-2022) and high-resolution (i.e., 0.25°) monthly time series of TWS anomalies over the global land surface. The reconstruction is achieved by using a set of machine learning models with a large number of predictors, including climatic and hydrological variables, land use/land cover data, and vegetation indicators (e.g., leaf area index). The outcome, machine learning-reconstructed TWS estimates (i.e., GTWS-MLrec), fits well with the GRACE/GRACE-FO measurements, showing high correlation coefficients and low biases in the GRACE era. We also evaluate GTWS-MLrec with other independent products such as the land-ocean mass budget, atmospheric and terrestrial water budget in 341 large river basins, and streamflow measurements at 10,168 gauges. The results show that our proposed GTWS-MLrec performs overall as well as or is more reliable than previous TWS datasets. Moreover, our reconstructions successfully reproduce the consequences of climate variability, such as strong El Niño events. GTWS-MLrec dataset consists of three reconstructions based on JPL, CSR and GSFC mascons, three detrended and de-seasonalized reconstructions, and six global average TWS series over land areas, both with and without Greenland and Antarctica. Along with its extensive attributes, GTWS_MLrec can support a wide range of geoscience applications such as better understanding the global water budget, constraining and evaluating hydrological models, climate-carbon coupling, and water resources management. GTWS-MLrec is available on Zenodo through https://zenodo.org/record/8187432 (Yin et al., 2023c). [ABSTRACT FROM AUTHOR]

Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy‐covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi‐arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non‐linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep‐rooted plants when only using surface‐layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates. Plain Language Summary: Vapor pressure deficit (VPD) has increased over recent decades and is expected to continue to rise in the future with climate change. Projected changes in precipitation and soil moisture (SM) are more uncertain with higher spatial variability and larger projected intermodel spread. Given the global environmental changes underway, it is of paramount importance to independently assess the effects of high VPD and low SM on ecosystem productivity. Here, we assessed the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy‐covariance towers. We find that reductions in GPP are more attributable to high VPD rather than low SM, on average. The constraint of these two factors on GPP reduction is tightly linked with climates and plant functional types. We further emphasize the amplified impact of SM on GPP during droughts and the need to consider using deep SM. Overall, our findings provide a more comprehensive and quantitative attribution of the impact of increasing atmospheric dryness and changes in soil water deficit on ecosystem productivity in a changing climate. Key Points: Reductions in gross primary production (GPP) are more attributable to high vapor pressure deficit (VPD) rather than low soil moisture (SM) across all sitesThe constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional typesOur results highlight that the relative and non‐linear impacts of VPD and SM on GPP should be adequately considered [ABSTRACT FROM AUTHOR]