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The global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), using a broad range of the latest available global observation-based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ-GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ-GUESS accurately captures both inter- and intra-annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ-GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML-V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ-GUESS aligns with ESA-CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use., The global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS), using a broad range of the latest available global observation-based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ-GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ-GUESS accurately captures both inter- and intra-annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ-GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML-V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ-GUESS aligns with ESA-CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

Aim: Earth observation-based estimates of land–atmosphere exchange of carbon are essential for understanding the response of the terrestrial biosphere to climatic change and other anthropogenic forcing. Temperature, soil water content and gross primary production are the main drivers of ecosystem respiration (Reco), and the main aims of this study are to develop an Reco model driven by long-term global-scale Earth observations and to study Reco spatiotemporal dynamics 1982–2015. Location: Global scale. Time Period: 1982–2015. Major Taxa Studied: Terrestrial ecosystems. Methods: We parameterized and applied a global Reco model for 1982–2015 using novel Earth observation-based data. We studied the relationships between Reco measured at field sites globally and land surface temperature, gross primary production and soil water content. Trends 1982–2015 were quantified, and the contributions from terrestrial regions to the spatiotemporal variability were evaluated. Results: The Reco model (LGS-Reco) captured the between-site and intra- and interannual variability in field-observed Reco and soil respiration well in comparison with other Earth observation-based products. The global annual Reco was on average 105.6 ± 2.3 Pg C for 1982–2015, which is close to 105 Pg C according to residuals of the carbon exchange processes within the global carbon budgets. The trend in global terrestrial Reco 1982–2015 was 0.19 ± 0.02 Pg C y−1, with the strongest positive trends found in cropland areas, whereas negative trends were primarily observed for savannah/shrublands of Southern Africa and South America. Trends were especially strong during the eighties and nineties, but substantially smaller 1998–2015. Main Conclusions: The LGS-Reco model revealed a substantial increase in global Reco during recent decades. However, the growth rates of glob

The ongoing climate change can modulate the behavior of global vegetation and influence the terrestrial biosphere carbon sink. Past observation-based studies have mainly focused on the linear trend or interannual variability of the vegetation greenness, but could not explicitly deal with the effect of natural decadal variability due to the short length of observations. Here we put the variabilities revealed by remote sensing-based global leaf area index (LAI) from 1982 to 2015 into a long-term perspective with the help of ensemble Earth system model simulations of the historical period 1850–2014, with a focus on the low-frequency variability in the global LAI during the growing season. Robust decadal variability in the observed and modelled LAI was revealed across global terrestrial ecosystems, and it became stronger toward higher latitudes, accounting for over 50% of the total variability north of 40°N. The linkage of LAI decadal variability to major natural decadal climate modes, such as the El Niño–Southern Oscillation decadal variability (ENSO-d), the Pacific decadal oscillation (PDO), and the Atlantic multidecadal oscillation (AMO), was analyzed. ENSO-d affects LAI by altering precipitation over large parts of tropical land. The PDO exerts opposite impacts on LAI in the tropics and extra-tropics due to the compensation between the effects of temperature and growing season length. The AMO effects are mainly associated with anomalous precipitation in North America and Europe but are mixed with long-term climate change impacts due to the coincident phase shift of the AMO which also induces North Atlantic basin warming. Our results suggest that the natural decadal variability of LAI can be largely explained by these decadal climate modes (on average 20% of the variance, comparable to linear changes, and over 40% in some ecosystems) which also can be potentially important in inducing the greening of the Earth of the past decades.

Over the past half-century, drastically increased chemical fertilizers have entered agricultural ecosystems to promote crop production on the Yangtze Plain, potentially enhancing agricultural nutrient sources for eutrophication in freshwater ecosystems. However, long-term trends of nitrogen dynamics in terrestrial ecosystems and their impacts on eutrophication changes in this region remain poorly studied. Using a process-based ecosystem model, we investigated the temporal and spatial patterns of nitrogen use efficiency (NUE) and nitrogen leaching on the Yangtze Plain from 1979–2018. The agricultural NUE for the Yangtze Plain significantly decreased from 50 % in 1979 to 25 % in 2018, with the largest decline of NUE in soybean, rice, and rapeseed. Simultaneously, the leached nitrogen from cropland and natural land increased, with annual rates of 4.5 and 0.22 kg N ha−1 yr−2, respectively, leading to an overall increase of nitrogen inputs to the 50 large lakes. We further examined the correlations between terrestrial nutrient sources (i.e., the leached nitrogen, total phosphorus sources, and industrial wastewater discharge) and the satellite-observed probability of eutrophication occurrence (PEO) at an annual scale and showed that PEO was positively correlated with the changes in terrestrial nutrient sources for most lakes. Agricultural nitrogen and phosphorus sources were found to explain the PEO trends in lakes in the western and central part of the Yangtze Plain, and industrial wastewater discharge was associated with the PEO trends in eastern lakes. Our results revealed the importance of terrestrial nutrient sources for long-term changes in eutrophic status over the 50 lakes of the Yangtze Plain. This calls for region-specific sustainable nutrient management (i.e., nitrogen and phosphorus applications in agriculture and industry) to improve the water quality of lake ecosystems.

LPJ-GUESS is an advanced DGVM including detailed forest demography and management, croplands, wetlands, specialised arctic processes, emissions of nonCO2 GHGs and a highly flexible land-use change scheme which tracks transitions between different land-uses. It is the vegetation component of the EC-Earth CMIP6 ESM, the RCA-GUESS regional ESM, and also has a European mode operating at tree species level.

Land biosphere processes are of central importance to the climate system. Specifically, ecosystems interact with the atmosphere through a variety of feedback loops that modulate energy, water, and CO2 fluxes between the land surface and the atmosphere across a wide range of temporal and spatial scales. Human land use and land cover modification add a further level of complexity to land-atmosphere interactions. Dynamic global vegetation models (DGVMs) attempt to capture land ecosystem processes and are increasingly incorporated into Earth system models (ESMs), which makes it possible to study the coupled dynamics of the land biosphere and the climate. In this work we describe a number of modifications to the LPJ-GUESS DGVM, aimed at enabling direct integration into an ESM. These include energy balance closure, the introduction of a sub-daily time step, a new radiative transfer scheme, and improved soil physics. The implemented modifications allow the model (LPJ-GUESS/LSM) to simulate the diurnal exchange of energy, water, and CO2 between the land ecosystem and the atmosphere and thus provide surface boundary conditions to an atmospheric model over land. A site-based evaluation against FLUXNET2015 data shows reasonable agreement between observed and modelled sensible and latent heat fluxes. Differences in predicted ecosystem function between standard LPJ-GUESS and LPJ-GUESS/LSM vary across land cover types. We find that the emerging ecosystem composition and carbon fluxes are sensitive to both the choice of stomatal conductance model and the response of plant water uptake to soil moisture. The new implementation described in this work lays the foundation for using the well-established LPJ-GUESS DGVM as an alternative land surface model (LSM) in coupled land-biosphere-climate studies, where an accurate representation of ecosystem processes is essential.