Accurately predicting terrestrial ecosystem responses to climate change is crucial for addressing global challenges. This relies on mechanistic modelling of ecosystem processes through Land Surface Models (LSMs). Despite their importance, LSMs face significant uncertainties due to poorly constrained parameters, especially in carbon cycle predictions. This paper reviews the progress made in using data assimilation (DA) for LSM parameter optimisation, focusing on carbon-water-vegetation interactions, as well as discussing the technical challenges faced by the community. These challenges include identifying sensitive model parameters and their prior distributions, characterising errors due to observation biases and model-data inconsistencies, developing observation operators to interface between the model and the observations, tackling spatial and temporal heterogeneity as well as dealing with large and multiple datasets, and including the spin-up and historical period in the assimilation window. We then outline how machine learning (ML) can help address these issues, proposing different avenues for future work that integrate ML and DA to reduce uncertainties in LSMs. We conclude by highlighting future priorities, including the need for international collaborations, to fully leverage the wealth of available Earth observation datasets, harness machine learning advances, and enhance the predictive capabilities of LSMs.

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.

We present a comparison of atmospheric transport models that simulate carbonyl sulfide (COS). This is part II of the ongoing Atmospheric Transport Model (ATM) Inter-comparison Project (TransCom–COS). Differently from part I, we focus on seven model intercomparison by transporting two recent COS inversions of NOAA surface data within TM5-4DVAR and LMDz models. The main goals of TransCom-COS part II are (a) to compare the COS simulations using the two sets of optimized fluxes with simulations that use a control scenario (part I) and (b) to evaluate the simulated tropospheric COS abundance with aircraft-based observations from various sources. The output of the seven transport models are grouped in terms of their vertical mixing strength: strong and weak mixing. The results indicate that all transport models capture the meridional distribution of COS at the surface well. Model simulations generally match the aircraft campaigns HIPPO and ATom. Comparisons to HIPPO and ATom demonstrate a gap between observed and modelled COS over the Pacific Ocean at 0–40N, indicating a potential missing source in the free troposphere. The effects of seasonal continental COS uptake by the biosphere, observed on HIPPO and ATom over oceans, is well reproduced by the simulations. We found that the strength of the vertical mixing within the column as represented in the various atmospheric transport models explains much of the model to model differences. We also found that weak-mixing models transporting the optimized flux derived from the strong-mixing TM5 model show a too strong seasonal cycle at high latitudes.

We present a comparison of atmospheric transport model simulations for carbonyl sulfide (COS), within the framework of the ongoing atmospheric tracer transport model intercomparison project “TransCom”. Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 9-year period (2010–2018), using prescribed state-of-the-art surface fluxes for various components of the atmospheric COS budget: biospheric sink, oceanic source, sources from fire and industry. Since the biosphere is the largest sink of COS, we tested sink estimates produced by two different biosphere models. The main goals of TransCom-COS are (a) to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratios in the troposphere, and (b) to assess the sensitivity of simulated tropospheric COS mixing ratios to the seasonal and diurnal variability of the COS biosphere fluxes. To this end, a control case with state-of-the-art seasonal fluxes of COS was constructed. Models were run with the same fluxes and without chemistry to isolate transport differences. Further, two COS flux scenarios were compared: one using a biosphere flux with a monthly time resolution and the other using a biosphere flux with a three-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biosphere fluxes and to indirect oceanic emissions through dimethylsulfide (DMS) and carbon disulfide (CS2). The modelled COS mixing ratios were assessed against in-situ observations from surface stations and aircraft.