Vegetation plays a fundamental role in modulating the exchange of water, energy, and carbon fluxes between the land and the atmosphere. These exchanges are modelled by Land Surface Models (LSMs), which are an essential part of numerical weather prediction and data assimilation. However, most current LSMs implemented specifically in weather forecasting systems use climatological vegetation indices, and land use/land cover datasets in these models are often outdated. In this study, we update land surface data in the ECMWF land surface modelling system ECLand using Earth observation-based time varying leaf area index and land use/land cover data, and evaluate the impact of vegetation dynamics on model performance. The performance of the simulated latent heat flux and soil moisture is then evaluated against global gridded observation-based datasets. Updating the vegetation information does not always yield better model performances because the model’s parameters are adapted to the previously employed land surface information. Therefore we recalibrate key soil and vegetation-related parameters at individual grid cells to adjust the model parameterizations to the new land surface information. This substantially improves model performance and demonstrates the benefits of updated vegetation information. Interestingly, we find that a regional parameter calibration outperforms a globally uniform adjustment of parameters, indicating that parameters should sufficiently reflect spatial variability in the land surface. Our results highlight that newly available Earth-observation products of vegetation dynamics and land cover changes can improve land surface model performances, which in turn can contribute to more accurate weather forecasts.

In the framework of the RECCAP2 initiative, we present the greenhouse gas (GHG) and carbon (C) budget of Europe. For the decade of the 2010s, we present a bottom-up (BU) estimate of GHG net-emissions of 3.9 Pg CO2-eq. yr-1 (global warming potential on 100 year horizon), and are largely dominated by fossil fuel emissions. In this decade, terrestrial ecosystems are a net GHG sink of 0.9 Pg CO2-eq. yr-1, dominated by a CO2 sink. For CH4 and N2O, we find good agreement between BU and top-down (TD) estimates from atmospheric inversions. However, our BU land CO2 sink is significantly higher than TD estimates. We further show that decadal averages of GHG net-emissions have declined by 1.2 Pg CO2-eq. yr-1 since the 1990s, mainly due to a reduction in fossil fuel emissions. In addition, based on both data driven BU and TD estimates, we also find that the land CO2 sink has weakened over the past two decades. In particular, we identified a decreasing sink strength over Scandinavia, which can be attributed to an intensification of forest management. These are partly offset by increasing CO2 sinks in parts of Eastern Europe and Northern Spain, attributed in part to land use change. Extensive regions of high CH4 and N2O emissions are mainly attributed to agricultural activities and are found in Belgium, the Netherlands and the southern UK. We further analyzed interannual variability in the GHG budgets. The drought year of 2003 shows the highest net-emissions of CO2 and of all GHGs combined.

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarise the evidence that reductions in biodiversity can destabilise the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasise the bidirectional effects that link ecology and atmospheric processes.

Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production (GPP) also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management.

Quantifying the anthropogenic fluxes of CO2 is important to understand the evolution of carbon sink capacities, on which the required strength of our mitigation efforts directly depends. For the historical period, the global carbon budget (GCB) can be compiled from observations and model simulations as is done annually in the Global Carbon Project’s (GCP) carbon budgets. However, the historical budget only considers a single realization of the Earth system and cannot account for internal climate variability. Understanding the distribution of internal climate variability is critical for predicting the future carbon budget terms and uncertainties. We present here a decomposition of the GCB for the historical period and the RCP4.5 scenario using single model large ensemble simulations from the Max Planck Institute Grand Ensemble (MPI-GE) to capture internal variability. We calculate uncertainty ranges for the natural sinks and anthropogenic emissions that arise from internal climate variability, and by using this distribution, we investigate the likelihood of historical fluxes with respect to plausible climate states. Our results show these likelihoods have substantial fluctuations due to internal variability, which are partially related to ENSO. We find that the largest internal variability in the MPI-GE stems from the natural land sink and its increasing carbon stocks over time. The allowable fossil fuel emissions consistent with 3°C warming may be between 9–18 PgCyr-1. The MPI-GE is generally consistent with GCP’s global budgets with the notable exception of land-use change emissions in recent decades, highlighting that human action is inconsistent with climate mitigation goals.

Compound weather and climate events are combinations of climate drivers and/or hazards that contribute to societal or environmental risk. Studying compound events often requires a multidisciplinary approach combining domain knowledge of the underlying processes with, for example, statistical methods and climate model outputs. Recently, to aid the development of research on compound events, four compound event types were introduced, namely (1) preconditioned, (2) multivariate, (3) temporally compounding, and (4) spatially compounding events. However, guidelines on how to study these types of events are still lacking. Here, based on a bottom-up approach, we consider four case studies, each associated with a specific event type and a research question, to illustrate how the key elements of compound events (e.g., analytical tools and relevant physical effects) can be identified. These case studies show that (1) impacts on crops from hot and dry summers can be exacerbated by preconditioning effects of dry and bright springs. (2) Assessing compound coastal flooding in Perth (Australia) requires considering the dynamics of a non-stationary multivariate process. For instance, future mean sea-level rise will lead to the emergence of concurrent coastal and fluvial extremes, enhancing compound flooding risk. (3) In Portugal, deep-landslides are often caused by temporal clusters of moderate precipitation events. Finally, (4) crop yield failures in France and Germany are strongly correlated, threatening European food security through spatially compounding effects. These analyses allow for identifying general recommendations for studying compound events. Overall, our insights can serve as a blueprint for compound event analysis across disciplines and sectors.