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A Correction to this paper has been published: https://doi.org/10.1038/s41558-021-00991-1.

Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

In the version of this Analysis originally published, the following affiliation for A. Lohila was missing: ‘Finnish Meteorological Institute, Climate System Research, Helsinki, Finland’. This affiliation has now been added, and subsequent affiliations renumbered accordingly, in the online versions of the Analysis.

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. The carbon balance of peatlands is predicted to shift from a sink to a source this century. However, peatland ecosystems are still omitted from the main Earth system models that are used for future climate change projections, and they are not considered in integrated assessment models that are used in impact and mitigation studies. By using evidence synthesized from the literature and an expert elicitation, we define and quantify the leading drivers of change that have impacted peatland carbon stocks during the Holocene and predict their effect during this century and in the far future. We also identify uncertainties and knowledge gaps in the scientific community and provide insight towards better integration of peatlands into modelling frameworks. Given the importance of the contribution by peatlands to the global carbon cycle, this study shows that peatland science is a critical research area and that we still have a long way to go to fully understand the peatland–carbon–climate nexus.

Current peatland models generally treat vegetation as static, although plant community structure is known to alter as a response to environmental change. Because the vegetation structure and ecosystem functioning are tightly linked, realistic projections of peatland response to climate change require the inclusion of vegetation dynamics in ecosystem models. In peatlands, Sphagnum mosses are key engineers. Moss community composition primarily follows habitat moisture conditions. The known species habitat preference along the prevailing moisture gradient might not directly serve as a reliable predictor for future species compositions, as water table fluctuation is likely to increase. Hence, modelling the mechanisms that control the habitat preference of Sphagna is a good first step for modelling community dynamics in peatlands. In this study, we developed the Peatland Moss Simulator (PMS), which simulates the community dynamics of the peatland moss layer. PMS is a process-based model that employs a stochastic, individual-based approach for simulating competition within the peatland moss layer based on species differences in functional traits. At the shoot-level, growth and competition were driven by net photosynthesis, which was regulated by hydrological processes via the capitulum water content. The model was tested by predicting the habitat preferences of Sphagnum magellanicum and Sphagnum fallax – two key species representing dry (hummock) and wet (lawn) habitats in a poor fen peatland (Lakkasuo, Finland). PMS successfully captured the habitat preferences of the two Sphagnum species based on observed variations in trait properties. Our model simulation further showed that the validity of PMS depended on the interspecific differences in the capitulum water content being correctly specified. Neglecting the water content differences led to the failure of PMS to predict the habitat preferences of the species in stochastic simulations. Our work highlights the importance of the capitulum water content with respect to the dynamics and carbon functioning of Sphagnum communities in peatland ecosystems. Thus, studies of peatland responses to changing environmental conditions need to include capitulum water processes as a control on moss community dynamics. Our PMS model could be used as an elemental design for the future development of dynamic vegetation models for peatland ecosystems.

The effect of Land Use Change and Forestry (LUCF) on terrestrial carbon fluxes can be regarded as a carbon credit or debit under the UNFCCC, but scientific uncertainty in the estimates for LUCF remains large. Here, we assess the LUCF estimates by examining a variety of models of different types with different land cover change maps in the 1990s. Annual carbon pools and their changes are separated into different components for separate geographical regions, while annual land cover change areas and carbon fluxes are disaggregated into different LUCF activities and the biospheric response due to CO2 fertilization and climate change. We developed a consolidated estimate of the terrestrial carbon fluxes that combines book-keeping models with process-based biogeochemical models and inventory estimates and yields an estimate of the global terrestrial carbon flux that is within the uncertainty range developed in the IPCC 4th Assessment Report. We examined the USA and Brazil as case studies in order to assess the cause of differences from the UNFCCC reported carbon fluxes. Major differences in the litter and soil organic matter components are found for the USA. Differences in Brazil result from assumptions about the LUC for agricultural purposes. The effects of CO2 fertilization and climate change also vary significantly in Brazil. Our consolidated estimate shows that the small sink in Latin America is within the uncertainty range from inverse models, but that the sink in the USA is significantly smaller than the inverse models estimates. Because there are different sources of errors at the country level, there is no easy reconciliation of different estimates of carbon fluxes at the global level. Clearly, further work is required to develop data sets for historical land cover change areas and models of biogeochemical changes for an accurate representation of carbon uptake or emissions due to LUC.

Water storage is an important way to cope with temporal variation in water supply anddemand. The storage capacity and the lifetime of water storage reservoirs can besignificantly reduced by the inflow of sediments. A global, spatially explicit assessment ofreservoir storage loss in conjunction with vulnerability to storage loss has not been done.We estimated the loss in reservoir capacity for a global data set of large reservoirs from1901 to 2010, using modeled sediment flux data. We use spatially explicit population datasets as a proxy for storage demand and calculate storage capacity for all river basinsglobally. Simulations suggest that the net reservoir capacity is declining as a result ofsedimentation ( $5% compared to the installed capacity). Combined with increasing needfor storage, these losses challenge the sustainable management of reservoir operation andwater resources management in many regions. River basins that are most vulnerable includethose with a strong seasonal flow pattern and high population growth rates such as the majorriver basins in India and China. Decreasing storage capacity globally suggests that the roleof reservoir water storage in offsetting sea-level rise is likely weakening and may bechanging sign.

Climate sensitivity in its most basic form is defined as the equilibrium change in global surface temperature that occurs in response to a climate forcing, or externally imposed perturbation of the planetary energy balance.Within this general definition, several specific forms of climate sensitivity exist that differ in terms of the types of climate feedbacks they include. Based on evidence from Earth���s history, we suggest here that the relevant form of climate sensitivity in the Anthropocene (e.g. from which to base future greenhouse gas (GHG) stabilization targets) is the Earth system sensitivity including fast feedbacks from changes in water vapour, natural aerosols, clouds and sea ice, slower surface albedo feedbacks from changes in continental ice sheets and vegetation, and climate���GHG feedbacks from changes in natural (land and ocean) carbon sinks. Traditionally, only fast feedbacks have been considered (with the other feedbacks either ignored or treated as forcing), which has led to estimates of the climate sensitivity for doubled CO2 concentrations of about 3���C. The 2��CO2 Earth system sensitivity is higher than this, being ���4���6���C if the ice sheet/vegetation albedo feedback is included in addition to the fast feedbacks, and higher still if climate���GHG feedbacks are also included. The inclusion of climate���GHG feedbacks due to changes in the natural carbon sinks has the advantage of more directly linking anthropogenic GHG emissions with the ensuing global temperature increase, thus providing a truer indication of the climate sensitivity to human perturbations. The Earth system climate sensitivity is difficult to quantify due to the lack of palaeo-analogues for the present-day anthropogenic forcing, and the fact that ice sheet and climate���GHG feedbacks have yet to become globally significant in the Anthropocene. Furthermore, current models are unable to adequately simulate the physics of ice sheet decay and certain aspects of the natural carbon and nitrogen cycles. Obtaining quantitative estimates of the Earth system sensitivity is therefore a high priority for future work.

Water storage is an important way to cope with temporal variation in water supply anddemand. The storage capacity and the lifetime of water storage reservoirs can besignificantly reduced by the inflow of sediments. A global, spatially explicit assessment ofreservoir storage loss in conjunction with vulnerability to storage loss has not been done.We estimated the loss in reservoir capacity for a global data set of large reservoirs from1901 to 2010, using modeled sediment flux data. We use spatially explicit population datasets as a proxy for storage demand and calculate storage capacity for all river basinsglobally. Simulations suggest that the net reservoir capacity is declining as a result ofsedimentation ( $5% compared to the installed capacity). Combined with increasing needfor storage, these losses challenge the sustainable management of reservoir operation andwater resources management in many regions. River basins that are most vulnerable includethose with a strong seasonal flow pattern and high population growth rates such as the majorriver basins in India and China. Decreasing storage capacity globally suggests that the roleof reservoir water storage in offsetting sea-level rise is likely weakening and may bechanging sign.

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e. g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005-2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500-2100 at 0.5° × 0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500-2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005-2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system. © 2011 The Author(s).

Nine ecosystem process models were used to predict CO2 and water vapor exchanges by a 150-year-old black spruce forest in central Canada during 1994–1996 to evaluate and improve the models. Three models had hourly time steps, five had daily time steps, and one had monthly time steps. Model input included site ecosystem characteristics and meteorology. Model predictions were compared to eddy covariance (EC) measurements of whole-ecosystem CO2 exchange and evapotranspiration, to chamber measurements of nighttime moss-surface CO2 release, and to ground-based estimates of annual gross primary production, net primary production, net ecosystem production (NEP), plant respiration, and decomposition. Model-model differences were apparent for all variables. Model-measurement agreement was good in some cases but poor in others. Modeled annual NEP ranged from −11 g C m−2 (weak CO2 source) to 85 g C m−2 (moderate CO2 sink). The models generally predicted greater annual CO2 sink activity than measured by EC, a discrepancy consistent with the fact that model parameterizations represented the more productive fraction of the EC tower “footprint.” At hourly to monthly timescales, predictions bracketed EC measurements so median predictions were similar to measurements, but there were quantitatively important model-measurement discrepancies found for all models at subannual timescales. For these models and input data, hourly time steps (and greater complexity) compared to daily time steps tended to improve model-measurement agreement for daily scale CO2 exchange and evapotranspiration (as judged by root-mean-squared error). Model time step and complexity played only small roles in monthly to annual predictions.