A long-standing challenge in studying the global carbon cycle has been understanding the factors controlling inter–annual variation (IAV) of carbon fluxes related to vegetation photosynthesis and respiration, and improving their representations in existing biogeochemical models. Here, we compared an optimality-based mechanistic model and a semi-empirical light use efficiency model to understand how current models can be improved to simulate IAV of gross primary production (GPP). Both models simulated hourly GPP and were parameterized for (1) each site–year, (2) each site with an additional constraint on IAV (CostIAV), (3) each site, (4) each plant–functional type, and (5) globally. This was followed by forward runs using calibrated parameters, and model evaluations at different temporal scales across 198 eddy covariance sites. Both models performed better on hourly scale than annual scale for most sites. Specifically, the mechanistic model substantially improved when drought stress was explicitly included. Most of the variability in model performances was due to model types and parameterization strategies. The semi-empirical model produced statistically better hourly simulations than the mechanistic model, and site–year parameterization yielded better annual performance for both models. Annual model performance did not improve even when parameterized using CostIAV. Furthermore, both models underestimated the peaks of diurnal GPP in each site–year, suggesting that improving predictions of peaks could produce a comparatively better annual model performance. GPP of forests were better simulated than grassland or savanna sites by both models. Our findings reveal current model deficiencies in representing IAV of carbon fluxes and guide improvements in further model development.

Fire is a crucial factor in terrestrial ecosystems playing a role in disturbance for vegetation dynamics. Process-based fire models quantify fire disturbance effects in stand-alone dynamic global vegetation models (DGVMs) and their advances have incorporated both descriptions of natural processes and anthropogenic drivers. Nevertheless, these models show limited skill in modeling fire events at the global scale, due to stochastic characteristics of fire occurrence and behavior as well as the limits in empirical parameterizations in process-based models. As an alternative, machine learning has shown the capability of providing robust diagnostics of fire regimes. Here, we develop a deep-learning-based fire model (DL-fire) to estimate daily burnt area fraction at the global scale and couple it within JSBACH4, the land surface model used in the ICON ESM. The stand-alone DL-fire model forced with meteorological, terrestrial and socio-economic variables is able to simulate global total burnt area, showing 0.8 of monthly correlation (rm) with GFED4 during the evaluation period (2011-15). The performance remains similar with the hybrid modeling approach JSB4-DL-fire (rm=0.79) outperforming the currently used uncalibrated standard fire model in JSBACH4 (rm=-0.07). We further quantify the importance of each predictor by applying layer-wise relevance propagation (LRP). Overall, land properties, such as fuel amount and water content in soil layers, stand out as the major factors determining burnt fraction in DL-fire, paralleled by meteorological conditions over tropical and high latitude regions. Our study demonstrates the potential of hybrid modeling in advancing fire prediction in ESMs by integrating deep learning approaches in physics-based dynamical models.

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.

The spatial contribution to the global land-atmosphere carbon dioxide (CO\textsubscript{2}) exchange is crucial in understanding and projecting the global carbon cycle, yet different studies diverge on the dominant regions. Informing land models with observational data is a promising way to reduce the parameter and structural uncertainties and advance our understanding. Here, we develop a parsimonious diagnostic process-based model of land carbon cycles, constraining parameters with observation-based products. We compare CO\textsubscript{2} flux estimates from our model with observational constraints and Trends in Net Land-Atmosphere Carbon Exchange (TRENDY) model ensemble to show that our model reasonably reproduces the seasonality of net ecosystem exchange (NEE) and GPP and interannual variability (IAV) of NEE. Finally, we use the developed model, TRENDY models, and observational constraints to attribute variability in global NEE and gross primary productivity (GPP) to regional variability. The attribution analysis confirms the dominance of Northern temperate and boreal regions in the seasonality of CO\textsubscript{2} fluxes. Regarding NEE IAV, we identify a significant contribution from tropical savanna regions as previously perceived. Furthermore, we highlight that tropical humid regions are also identified as at least equally relevant contributors as semi-arid regions. At the same time, the largest uncertainty among ensemble members of NEE constraint and TRENDY models in the tropical humid regions underscore the necessity of better process understanding and more observations in these regions. Overall, our study identifies tropical humid regions as key regions for global land-atmosphere CO\textsubscript{2} exchanges and the inter-model spread of its modeling.

Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

Vegetation turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found that the forest ecosystem is close to a steady state at global scale, contrasting with the larger differences between τ under steady state and τ under non-steady state at the grid cell level. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.

Next to precipitation, secondary water sources emerging from shallow groundwater and lateral redistribution of soil moisture, together with soil properties modulating their accessibility are highly important in water-limited ecosystems. However, effects of these land-associated secondary inputs are not well known over large domains given the mismatch of spatial scales of processes. Here, we quantify the role of land properties on the spatial variations of seasonal decay rate of vegetation cover over water-limited regions of Africa, using machine learning. Over the study domain, 17 % of these variations are directly attributed to land properties, and 16 % are attributed to interaction effects of land properties with climate and vegetation. Locally, total land attributed variations account for more than 60 % in hotspots with different land properties like shallow groundwater, complex topography, and favourable soil properties. Our findings lend empirical evidence for the importance of local-scale secondary water inputs over large domains.