Congo Basin forests are among the most diverse, carbon-rich and CO2-absorbing areas in the World (1,2) and play an increasingly important role in international climate policy (3). On the pivotal CoP26 in Glasgow, more than 100 World leaders promised to stop deforestation by 2030, including specific pledges to focus on protecting Congo Basin forests. However, there is a striking discrepancy between the Congo Basin’s paramount importance versus its poor scientific coverage (4). As a result of this data gap, Earth System Models are not capturing present-day tropical forest carbon dynamics (5). Therefore, our consortium is contributing to closing the Congo Basin forest data gap and improve Land Surface Models to capture its biodiversity and carbon dynamics. To reach this ambition, we are collecting field data on permanent forest inventory plots scattered across the Congo basin.

The data covers multiple time scales by combining different methodological approaches: (i) weakly monitoring of cambial and foliar phenology of selected trees in the plots provides seasonal- and annual-scale changes in carbon uptake, (ii) repeated tree diameter and height measurements of all trees in the plots reveal decadal-scale changes in the carbon balance and tree community composition, (iii) measuring whole-tree, wood and leaf traits on selected trees in the plots allow in-depth analysis of decadal-scale changes in taxonomic and functional composition, (iv) identification of radiocarbon dated soil charcoal sampled in the plots reveal century-scale and millennial-scale changes in biodiversity, (v) continuous monitoring of climate variables provides yearly and decadal-scale changes in temperature and water availability.

By themselves, those data shed light on the short- and long-term resilience of critical Congo Basin forest ecosystem functions. Here we present an overview of recently published and preliminary results showing how our consortium contributes to advance our understanding of the effects of environmental change on vegetation dynamics, tree mortality and carbon dynamics of Congo Basin forests. Combining all these collected field data will ultimately allow to parameterize and validate Land Surface Models specifically for the Congo Basin.

1.Hubau, W. et al. Nature 579, 80–87 (2020). 2. Jung, M. et al. Nat. Ecol. Evol. 5, 1499–1509 (2021). 3. Rockström, J. et al. PNAS 118, 1–5 (2021). 4. White, L. J. T. et al. Nature 598, 411–414 (2021). 5. Koch, A., Hubau, W. & Lewis, S. L. Earth’s Future. 9, 1–19 (2021).

How to cite: Hubau, W. and the DAMOCO, PILOTMAB & CANOPI consortium: Closing the data gap to develop Land Surface Models for Congo Basin forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4183, https://doi.org/10.5194/egusphere-egu24-4183, 2024.

In Austria, the Austrian Federal Office and Research Centre for Forests (BFW) provides basic forest data such as volume stock or the national carbon inventory. The Austrian carbon inventory currently records the carbon stored in the bark as part of the forest's total tree carbon stock. However, tree bark plays a vital role in the life span of a tree and is also important for the decomposition process following tree death. There is a veritable need to quantify the proportion of bark accurately because it has an important impact on economic calculations. In this study we assess the amount of carbon stored in the stem bark of Austrian commercial forests (in German “Wirtschaftswald”) as it can be derived from (i) the taper curve, (ii) the bark percentage, (iii) the bark density, and (iv) the bark fissure index. We applied this “bark carbon model" to the latest data from the Austrian National Forest Inventory (NFI). The model predicts for each tree the corresponding bark carbon content, which can be easily aggregated to plot or regional level for further use. Our results suggest, that about 7% of the total carbon in Austrian commercial forest is stored in the bark of the tree stems. The findings provide bark carbon information that can also be used for other purposes like potential harvesting of bark, or determining the fire adaptation of tree species. Moreover, this study helps to provide information for the bark carbon share of the Austrian National carbon inventory.

How to cite: Sinan, M., Neumann, M., and Hasenauer, H.: The bark carbon storage of Austrian commercial forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12243, https://doi.org/10.5194/egusphere-egu24-12243, 2024.

Recent advances in the spatial resolution and sensitivity of satellite sensors have allowed the mapping of aboveground biomass (AGB) with enhanced levels of detail and a wall-to-wall worldwide coverage. However, determining the magnitude and direction of AGB changes over time remains challenging due to large uncertainties in AGB estimates (biases and random errors), inconsistencies across sensors/instruments and limited availability of ground-truth data (national forest inventories, multi-census plots and airborne lidar). Combining multiple environmental descriptors derived from independent (mainly optical) satellite-based data sources, we apply a framework that infers evidence of pressures and impacts to characterise temporal changes in vegetation (fast or slow, gain or loss) and check agreement with the changes detected in the global ESA CCI Biomass time series product. We deploy the approach at the global scale focusing on forests that we define with a tree cover greater than 10% and tree height greater than 5 m. We illustrate the comparison with local case studies, highlighting processes such as regrowth, degradation and disturbances, and differentiating between natural and anthropogenic causes (e.g., wildfire, flooding, harvest, plantations). Selected sites represent different biomes and continents, including tropical moist forests in the Amazon, tropical drylands in Africa, temperate forests in Europe, Mediterranean woodlands in Australia and boreal forests in Siberia and North America. The results provide enhanced understanding of the processes underlying AGB changes in different regions and allow new insights into the quality of remotely-sensed AGB for tracking changes in carbon stocks and informing decision-making.

How to cite: Acil, N., Lucas, R., Santoro, M., and Balzter, H.: Characterising space-based aboveground biomass change: from global to local, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21672, https://doi.org/10.5194/egusphere-egu24-21672, 2024.

How to cite: Zotta, R.-M., Moesinger, L., van der Schalie, R., Vreugdenhil, M., Preimesberger, W., Frederikse, T., de Jeu, R., and Dorigo, W.: VODCA v2: Multi-sensor, multi-frequency vegetation optical depth data for long-term canopy dynamics and biomass monitoring , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7557, https://doi.org/10.5194/egusphere-egu24-7557, 2024.

Reliable predictions of ecosystem dynamics and carbon stocks depend on accurate initialization of ecosystem states in process-based model simulations. Unlike traditional potential vegetation simulations which assume that ecosystems equilibrate with long-term climate, observation-initialized simulations integrate the impacts of previous history of disturbance events and human activities on ecosystem structure and composition. However, observation-constrained initialization is challenging at regional scales due to limited availability of spatially-comprehensive measurement data. In this study, we assimilate remote-sensing estimates of canopy structure from Global Ecosystem Dynamics Investigation (GEDI) and canopy composition from AVIRIS imaging spectrometry into Ecosystem Demography version 2 (ED2), a cohort-based Terrestrial Biosphere Model. We drive model simulations with future climate scenarios and rising atmospheric CO2 concentrations to predict ecosystem responses to environmental changes over an elevational transect region in California’s Sierra Nevada by the end of the century. Our simulations suggest that predictions are significantly impacted by ecosystem initial condition at the multi-decadal (50+ year) scale. The impacts are stronger in dense-canopy forests at mid-to-high elevations than woody savannahs at low elevations. Under a hotter and drier future climate with CO2 enrichment, ecosystems across the elevational transect are predicted to act as a net carbon sink but with marked changes in composition. Aboveground biomass (AGB) is predicted to increase at low elevations due to increasing abundance in both deciduous and coniferous trees. However, at mid-to-high elevations, AGB increases are caused by increasing abundance of coniferous trees but large declines in the abundance of deciduous trees. Our research demonstrates how large-scale remote-sensing data can be assimilated into process-based model simulations to improve future predictions of ecosystem dynamics.  

How to cite: Chang, L., Liu, S., Antonarakis, A., Longo, M., Tang, H., Armston, J., and Moorcroft, P.: Future Predictions of Ecosystem Changes in California’s Sierra Nevada over the coming century using Remote-Sensing Constrained Terrestrial Biosphere Model Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11047, https://doi.org/10.5194/egusphere-egu24-11047, 2024.

In semi-arid regions, grasslands naturally display a self-organized pattern that optimizes resource utilization and productivity. Representing this type of vegetation in land surface model constitutes a difficult challenge. To simulate these grasses, the ORCHIDEE land surface model treats grass density as the ratio of the area occupied by individuals to the Plant Functional Type (PFT) area, assuming a fixed grass density of 1 for maximal occupancy. However, the fixed maximal grass density lacks the response of grassland to environmental perturbations. In addition, the low biomass contained in certain pixels results in frequent mortality, indicative of resource limitations at the plant individual level. To address this considerable limitation, we introduced dynamic reduction of grass density based on mortality indicators, hence enhancing individual biomass and alleviating mortality occurrences. The adaptive approach significantly decreased mortality events across most pixels while enhancing leaf area index (LAI) for the majority of them. Our findings suggest that optimizing resource through grass density reduction in response to environmental condition, could not only improve individual biomass to alleviate mortality but also enhance overall grassland production.

How to cite: Xu, S., Balkanski, Y., Luyssaert, S., Ciais, P., and Sciare, J.: Representation of dynamic grass density in land surface model ORCHIDEE trunk v4.1, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17051, https://doi.org/10.5194/egusphere-egu24-17051, 2024.

Several efforts are being pursued to improve the representation of ecological demographic processes that govern terrestrial vegetation canopy structure within Dynamic Global Vegetation Models (DGVM) as it influences critical fluxes of carbon, nutrients, and water. How trees are structured within the canopy determines how they absorb incoming solar radiation and is partitioned between different tree cohorts. Here we present two new schemes with more detailed vegetation canopy structure representation in the DGVM LPJ-GUESS. These new schemes provide a closer linkage to observations to better constrain processes of growth and mortality, improve the representation of species coexistence as well as the capability to represent reestablishment in small canopy gaps following small-scale mortality or selective harvest. LPJ-GUESS is currently structuring its canopy with vertically overlapping cohort crowns without horizontal spatial structure as the crowns are distributed uniformly across the entire patch area. This original approach does not provide a realistic representation of competition between trees of different heights and sizes, nor canopy gaps following mortalities. A first solution to amend the model is to adopt an approach similar to the perfect plasticity approximation in which cohorts are sorted according to tree height and perfectly fill the patch area with each cohort crown area. When the patch is filled an understory layer is created with the next tallest tree and so on for each consecutive layer. A second solution is to explicitly position cohorts within the patch according to forest floor light conditions during establishment. The death of a tree will result in a gap formation which will persist over time and allow new cohorts to establish within the gap exposed to full light conditions. All schemes have been evaluated against aboveground woody biomass, aboveground woody mortality, and aboveground woody productivity split into diameter at breast height size classes and how forestry thinning generates re-establishment of a woody understory. Also, their capability to represent species coexistence has been evaluated. We see improvements in the capability to simulate stand biomass-size distributions, species coexistence, and reestablishment in small canopy gaps with a more detailed canopy structure scheme.

How to cite: Wårlind, D., Elena Stoebke, J., Olin, S., A. Miller, P., and A. M. Pugh, T.: Representing canopy structure dynamics within the LPJ-GUESS dynamic global vegetation model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20269, https://doi.org/10.5194/egusphere-egu24-20269, 2024.

The relationship between carbon dioxide emissions and their accumulation in the atmosphere is one of the most important elements of the function of the Earth system.  Exertion of control over the terrestrial carbon budget, via afforestation, reforestation, bioenergy production and other methods to enhance land carbon storage (biochar, enhanced weathering) all imply a need to forecast and understand the dynamics of these carbon stores as they evolve in changing atmospheric CO2 and climatic conditions. The dynamics of the carbon cycle, however, are notably complex and require comprehension of models representing the functioning of numerous coupled systems which must produce predictions under these no-analog conditions, and so must necessarily embed process understanding to allow for meaningful extrapolation into the future. 

Models of the terrestrial biosphere, often embedded in Earth system models, thus contain advanced representations of a large set of processes that are known to impact ecosystem carbon storage.  This complexity, however, has presented a substantial barrier to objective calibration using conventional statistical approaches, as the number of model parameters and the computational expense of the models means that comprehensive exploration of the parameter space is effectively unmanageable. Further, many ecosystem processes exhibit non-linear and threshold properties (notably, vegetation death, competitive interactions, fire thresholds) and thus are challenging for methods that assume linearity. 

Here we propose a method for decomposing the complexity of one such model, the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) that allows investigation, calibration and comprehension of individual parts of the system in isolation (driven by observed data fields). This ‘modular complexity’ approach allows the full complexity model to be run in a series of ‘modes’ that can operate as domain-specific models for, e.g. ecohydrology, community ecology, biogeochemistry etc. while also allowing the full complexity version to be used for higher order problems, such a predicting global vegetation dynamics under future climate scenarios.  We describe a series of investigations using FATES that illustrate the potential for this model decomposition approach and discuss the potential for further application of this philosophy. 

How to cite: Fisher, R., Koven, C., Knox, R., Needham, J., Lemiuex, G., Xu, C., Foster, A., Chitra-Tarak, R., and Robbins, Z.: How to model all the plants on Earth?  An approach to managing system complexity in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES). , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19555, https://doi.org/10.5194/egusphere-egu24-19555, 2024.

Insect and disease outbreaks in forests are biotic disturbances that can profoundly alter ecosystem dynamics. In many parts of the world, these disturbance regimes are intensifying as the climate changes and shifts the distribution of species and biomes. As a result, key forest ecosystem services, such as carbon sequestration, regulation of water flows, wood production, protection of soils, and the conservation of biodiversity could be increasingly compromised. Despite the relevance of these detrimental effects, there are currently no spatially detailed databases that record insect and disease disturbances on forests at the pan-European scale. Here, we present the new Database of European Forest Insect and Disease Disturbances (DEFID2). It comprises over 650,000 harmonized georeferenced records, mapped as polygons or points, of insects and disease disturbances that occurred between 1963 and 2021 in European forests. The records currently span eight different countries and were acquired through diverse methods (e.g., ground surveys, remote sensing techniques). The records in DEFID2 are described by a set of qualitative attributes, including severity and patterns of damage symptoms, agents, host tree species, climate-driven trigger factors, silvicultural practices, and eventual sanitary interventions. They are further complemented with a satellite-based quantitative characterization of the affected forest areas based on Landsat Normalized Burn Ratio time series, and damage metrics derived from them using the LandTrendr spectral-temporal segmentation algorithm (including onset, duration, magnitude, and rate of the disturbance), and possible interactions with windthrow and wildfire events. The DEFID2 database is a novel resource for many large-scale applications dealing with biotic disturbances. It offers a unique contribution to design networks of experiments, improve our understanding of ecological processes underlying biotic forest disturbances, monitor their dynamics and enhance their representation in land-climate models. Further data sharing is encouraged to extend and improve the DEFID2 database continuously. The database is freely available at https://jeodpp.jrc.ec.europa.eu/ftp/jrc-opendata/FOREST/DISTURBANCES/DEFID2/.

How to cite: Forzieri, G. and the DEFID2 team: The Database of European Forest Insect and Disease Disturbances: DEFID2, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2558, https://doi.org/10.5194/egusphere-egu24-2558, 2024.

An increasing incidence and intensity of droughts under anthropogenic climate change jeopardizes the potential of tropical forests and woodlands to capture carbon in woody biomass and act as CO₂ sink. A pantropical quantification of drought impacts on tree stem growth is needed to evaluate this risk.

We assessed drought impacts in a pantropical network of 477 tree-ring chronologies (>10,000 trees from >150 species and 35 plant families) and found modest stem growth declines (median: 2-4%) during drought years. Growth declines were larger for dry-season than wet-season droughts, specifically for Gymnosperms, and at hotter and more arid sites. Lagged growth reductions during post-drought years were rare. Over half of the growth reduction during drought years was mitigated during wet extreme years.

Thus, drought impacts on tropical forest carbon sequestration through stem growth have been small and short-lived. Yet, risks of increasing drought-induced carbon loss is expected to aggravate under climate change, in particular through elevated mortality associated with droughts.

How to cite: Zuidema, P., Babst, F., Groenendijk, P., Rahman, M., and Trouet, V. and the Tropical Tree-ring Network: Pantropical tree growth resilience to drought , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16050, https://doi.org/10.5194/egusphere-egu24-16050, 2024.

Climate change poses a significant threat to the carbon (C) sequestration capacity of Irish forests, exacerbated by heightened risks from pests, pathogens, and escalating climate extremes, including drought and intense rainfall. Building resilience in forest ecosystems is vital to protecting the ecosystem services they deliver and has therefore gained increased attention from both research and industry. ADAPTForRes is a project dedicated to assessing forest management options and identifying enhanced climate-smart mitigation strategies.

In this study, we utilise Eddy Covariance (EC), soil chamber and biometric methodologies to investigate the C stock and flux dynamics of three distinct forest types: commercial Sitka spruce coniferous forest on mineral soil, broadleaf-dominated native woodland on mineral soil, and a mixed species (Norway spruce and Birch) forest on peat soil. Initial results suggest that the Sitka spruce forest nearing the end of its first rotation assimilates the most C, while the native deciduous broadleaved forest shows near C neutrality due to the age/maturity of the stand and high quantities of decaying biomass on the forest floor. The C dynamics of the Norway spruce/Silver birch mixed forest were dominated by high levels of ecosystem respiration driven by the high organic content of the soil and low water table heights in summer.

Furthermore, advanced footprint analyses have been employed to address the heterogenous nature of the native Irish forest studied here – acknowledging challenges posed by dynamic forest management practices, diversity in vegetative distribution and complex terrain. This approach provides additional insight into the flux dynamics from the forest compartments and encompasses management practices (thinning, clearfelling, underplanting), phenology (budburst, leaf expansion, senescence), inventory (species, ages, height), NDVI and disease outbreak information. The additional parameters generated from this analysis enhances data richness, allowing for a greater understanding of ecosystem C dynamics. Additionally, biometric stocks of C and soil derived C flux measurements including auto- and heterotrophic partitioning experiments have been conducted to further explore the impacts of forest composition and management on fluxes from wider ecosystem carbon pools.

How to cite: Byrne, S., Byrne, K., Tobin, B., Caldararu, S., Ruffing, B., Dowd, L., and Saunders, M.: ADAPTForRes: Assessing Forest resilience and carbon dynamics in differing Irish forest types to promote more sustainable sinks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16058, https://doi.org/10.5194/egusphere-egu24-16058, 2024.

In the evergreen boreal forest, field studies show that vegetation does not always regenerate to its previous state after disturbance but instead transitions to systems dominated by deciduous trees or non-forest vegetation. Gaining a better understanding of drivers and impacts of post-disturbance recovery is thus crucial to accurately project future vegetation dynamics and associated impacts on the carbon, water, and energy balance of the region. We here perform simulations with the dynamic vegetation model LPJ-GUESS to investigate (1) if observations of post-disturbance recovery dynamics can be reproduced in the model, (2) which environmental factors control such shifts, and (3) how these in turn influence land surface properties such as albedo and evapotranspiration. We find that post-disturbance recovery trajectories can be clustered into distinct response patterns of recovery and shifts to alternative plant types. These shifts occur even in places where multiple plant types can in theory establish in the model and thus emerge due to shifts in competitive advantage mediated by warming and soil properties. We further find that shifts from forested to non-forested ecosystems have strong impacts on land-surface properties while shifts between different forest types are less impactful. We conclude that LPJ-GUESS is capable of reproducing observed disturbance-induced changes in vegetation dynamics following disturbances. Post-disturbance recovery is a key process driving accelerated vegetation change under climate change, further stressing the importance of accurately representing disturbance impact and recovery processes in land surface and coupled modeling.

How to cite: Layritz, L. S., Gregor, K., Krause, A., Kruse, S., Meyer, B., Pugh, T. A. M., Boettiger, C., and Rammig, A.: Post-Disturbance Recovery Shifts in Boreal Evergreen Landscapes: Impacts on Carbon Dynamics and Land Surface Properties., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10400, https://doi.org/10.5194/egusphere-egu24-10400, 2024.

Within the framework of the project IMPOSE (Emit now, mitigate later? IMPlications of temperature OverShoots for the Earth system) six idealized emission-overshoot simulations have been performed with the Earth System Model NorESM2-LM2 and used as forcing for the 2^nd generation dynamic global vegetation model LPJ-GUESS to investigate the impact of different CO₂ overshoots on global vegetation.

The simulations describe a set of scenarios with high, medium, and low cumulative CO₂ emissions, each of which has a short (immediate) and a long (100 years) peak of cumulative CO₂ emissions before declining towards a baseline simulation where a cumulative 1500 PgC is emitted within the first 100 years. The simulations have been performed in a “World without humans”, i.e. without land-use change, urban areas, fire-suppression, etc. to eliminate the somewhat arbitrary human factor.

The results clearly show that the height of the overshoot has a large impact on global vegetation distribution and composition while its duration does not seem to play a significant role. Overall, we can state that any overshoot scenario results in vegetation patterns that are different from (though converging towards) the non-overshoot baseline simulation. The higher the overshoot, the larger the initial deviation.

We have observed that there is less savannah after an overshoot and less so, the higher the overshoot, due to a reduced amount of tropical rain-green trees. As a result, there is also significantly less potential for fire. Further, there is more boreal vegetation, partly at the expense of temperate summer-green trees. A convergence towards the baseline simulation seems to be possible but isn’t reached by the end of the simulation window.

Furthermore, it can be observed that overshoots are asymmetrical when it comes to succession, i.e. while there are well-known succession patterns when global temperatures rise and vegetation is expanding into previously colder regions, patterns are different when the temperatures on the decline.

Finally, we like to state that dynamic vegetation is an important feature in Earth-system models w.r.t. vegetation carbon sequestration. Not only do the biogeophysical feedbacks matter, the total amount of carbon sequestered is about 16% higher than in simulations in which dynamic vegetation was supressed. These, and other feedbacks will be investigated in more detail in the ongoing Horizon Europe projects OptimESM and RESCUE.

How to cite: Nieradzik, L., Lee, H., Miller, P., Schwinger, J., and Wårlind, D.: Changes in global vegetation distribution and composition under idealized overshoot scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9771, https://doi.org/10.5194/egusphere-egu24-9771, 2024.

The land carbon cycle is fundamental in regulating atmospheric CO₂ dynamics from seasonal to centennial scales. The fine equilibrium between photosynthetic gains spent in metabolic costs and/or lost in mortality underpins the contribution of terrestrial ecosystems to the global carbon cycle. Uncertainties and divergent hypotheses on the role of climate in regulating their underlying mechanisms hamper our current diagnostic and prognostic abilities despite growing evidence on ecosystem vulnerability to present and future changes in climate. However, quantitative knowledge of the contributions of different carbon cycle processes regulating carbon uptake and release still need to be improved, inflating the uncertainties in future projections of net land-atmosphere carbon exchanges. In this study, we rely on satellite-based Earth observation retrievals of above-ground biomass and vegetation primary productivity to reconstruct the land-atmosphere carbon exchange dynamics over the last two decades, through the application of a three-box model at the pixel level. Our approach confidently reproduces 60% of the observed variability in atmospheric CO₂ growth rate (CGR) over throughout 1997-2019 (R = 0.78, p-val < 0.05), with a low RMSE of 1.0 PgC yr^-1. We further detail CO₂ release from vegetation dynamics emerging from quick turnover induced by wildfires and leaves senescence, as well as the slow turnover from plant and soil decomposition mechanisms. This allows us to differentiate between transient and lagged effects on land-to-atmosphere fluxes. The carbon release, characterized by a lag of over one year, referred to as lagged effects. Globally, the lagged responses accounted for 50% of the variability in CGR, exceeding three times the contribution of transient fluxes from live vegetation. We have yet to a change or trend in the total contributions of vegetation dynamics to CGR. Yet, the relative role of lagged effects to CGR via decomposition fluxes increased by 50%, possibly due to accelerated mortality and decomposition fluxes. As global warming imposes higher stress on vegetation while increasing temperature-mediated decomposition, our results highlight the importance of quantifying their underlying metabolic responses. Such understanding is instrumental for assessing the contribution of adaptation and mitigation measures that will shape the contribution of the terrestrial carbon cycle to dampen the effects of anthropogenic emissions on global climate.

How to cite: Yang, H., Santoro, M., Luijkx, I., Ciais, P., Wang, S., Reichstein, M., Besnard, S., and Carvalhais, N.: Transient and lagged effects of vegetation dynamics on the global CO2 growth rate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13194, https://doi.org/10.5194/egusphere-egu24-13194, 2024.

Land-atmosphere carbon exchanges and feedbacks constitute one of the largest uncertainties in future climate projections. Seasonal variations in atmospheric CO₂ content depend on uptake by photosynthesis and release by autotrophic and heterotrophic respiration, providing an atmospheric signal of land ecosystem activity. Large increases in the seasonal cycle amplitude (SCA) of CO₂ have occurred since the 1950s, especially in northern high latitudes. However, land surface and dynamic vegetation models have produced a wide range of magnitudes for the SCA, and have generally underestimated its increase. We explored the controls of the SCA by using a parameter-sparse eco-evolutionary optimality (EEO) model, the ‘P model’, combined with generic representations of plant and decomposer respiration, to simulate seasonal cycles and decadal trends of net ecosystem exchange (NEE). Simulated NEE fields were used to model near-surface CO₂ concentrations during the satellite era, with the help of the atmospheric chemistry-transport model TM5. The P model has previously been shown to reproduce trends of gross primary production (GPP) at flux sites with long records. Our model set-up also generated a realistic simulation of global net terrestrial carbon uptake, comparable with results produced by more complex dynamic vegetation models; and allowed us to attribute causes to observed SCA increases at high-latitude CO₂ monitoring stations.

How to cite: Cai, W., Prentice, I. C., and Hooghiem, J.: Simulating CO2 seasonal cycle amplitude in northern high latitudes with an eco-evolutionary optimality model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6618, https://doi.org/10.5194/egusphere-egu24-6618, 2024.

Reducing the net CO2 emissions is a key target worldwide, as shown, for instance, by the commitment of the Paris Agreement, and in this context, forests are being scrutinized for their capacity to act as carbon sinks and store large amounts of carbon over long periods. Quantifying the substitution effect of wood remains very difficult as it depends on many factors difficult to measure, such as the distribution of wood products into types of products having different lifetimes and which can substitute for different materials.

In Romania, forests have a large overall biomass stock, even in managed forests, since the management is operating at an intensity much lower than in many other European countries. Increased regionality in the global change effects requires a more local investigation. Therefore, we used a dynamic forest landscape model (LandClim model) to compare the three opposed mitigation strategies of forests and quantify their potential for sequestration of carbon and substitution of carbon in the context of global changes.

Under the mild climate RCP26 the carbon stocks were kept at levels roughly similar to the current stocks. The Set Aside 100% (SA100) managed stands stored the highest quantity of carbon, showing a capping of growth at the end of the 200 simulated years. Under the extreme climate RCP85, stocks increased for three decades but then plummeted. The highest stocks were obtained by the Set Aside 0% (SA0) management.

The cumulative harvest showed two surges under the climate scenario RCP26, first at the beginning of the simulation (2020-2060) and then during the 2170-2210 period. Under mild climate change RCP26, the effect of substitution from wood procurement clearly exceeds the increase in storage that can be expected. Under the RCP85 climate, harvest occurred exclusively during 2020-2070, then practically stopped when all stocks and fluxes became a lot more similar among management scenarios, given the catastrophic drop of stocks past 2080.

Wind-related disturbances had relatively constant consequences under RCP26, albeit with more fluctuations and a much higher intensity in SA100. SA0 and SA30 had similar magnitudes until 2120, and then wind-induced losses increased more strongly for Set Aside 30% (SA30). By 2210 the amounts of wind-induced carbon losses were 50% larger for SA100 than for SA30. Under scenario RCP85, the management strategy did not influence these losses which were near zero after 2080, as a result of the very small stocks.

The literature suggests no management strategies for carbon storage in mild climates, but in extreme climates cannot be a solution. Therefore, under the cloud of increased disturbance and pressure of climate change, the substitution strategy is more effective and safer than sequestration.

How to cite: Cosofret, C., Bouriaud, O., and Bouriaud, L.: Comparing the efficiency of forest mitigation policies: Is sequestering more efficient than using wood?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5483, https://doi.org/10.5194/egusphere-egu24-5483, 2024.

Semi-arid ecosystems, common across the Australian continent, strongly influence the inter-annual variability and trend in the global terrestrial net carbon sink. Here we explore the future Australian terrestrial carbon cycle using the CMIP6 ensemble, and the dynamic global vegetation model LPJ-GUESS. Uncertainty in Australia’s carbon storage in vegetation ranged between 6 and 49 PgC at the end of the century and was strongly linked to biases in the meteorological forcing. Using LPJ-GUESS with bias-corrected meteorological forcing reduced uncertainty in the vegetation carbon storage to between 14 and 20 PgC, with the remaining range linked to model sensitivities to rising atmospheric CO₂ concentration, temperature, and precipitation variability. Reducing this uncertainty will require improved terrestrial biosphere models, but also major improvements in the simulation of regional precipitation by Global Climate Models.

How to cite: Teckentrup, L., De Kauwe, M., Pitman, A., Ukkola, A., Wårlind, D., and Smith, B.: Resolving uncertainty in the response of Australia's terrestrial carbon cycle to projected climate change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14716, https://doi.org/10.5194/egusphere-egu24-14716, 2024.

Understanding the relationship between forest age, an indicator of successional stages, and net carbon fluxes is crucial for effective forest management and climate mitigation. Using the satellite-based Global Age Mapping Integration (GAMI) v1.0 dataset, we analyzed forest age shifts from 2010 to 2020 and their correlation with net carbon dioxide (CO₂) flux changes from independent atmospheric inversions. Globally, we do not report substantial forest age shifts during this period. The total area of young (1-20 years old), intermediate (21-60 years old), mature (61-150 years old), and old-growth (>150 years old) forests in 2020 compared to 2010 changed by approximately -0.07 (-7.7% compared to 2010), +0.03 (+6.0%), +0.03 (+2.1%), +0.01 (+1.1%) billion hectares, respectively. Despite these relatively stable global trends in forest age classes, we observe substantial changes at the regional scales. The Amazon, Congo basin, and Southeast Asia regionally experienced significant forest age decreases with local changes of up to 30% compared to 2010, attributed to deforestation and degradation. Siberian forests maintained their older age structure; however, large areas are transitioning to younger ages (0.09 billion hectares, 7.2% of Eurasia Boreal region), likely driven by increased fire frequency, logging activities, or climate-induced changes. Most European and North American forests trended toward older ages. However, those changes were heterogeneous at the sub-pixel level, revealing a complex mix of stand-replacement and aging dynamics across the different forest age spectrums. Stand-replaced forests, followed by regrowth, constitute a relatively minor fraction (6%) of the overall forested ecosystems, primarily dominated by aging forests (64%) and "stable" old-growth tropical forests (30%). Stand-replaced forests were prominent in young forests (0.1 billion hectares, 54.3% of total stand-replaced forests), while intermediate, mature, and old-growth forests accounted for 13.2%, 17.9%, and 14.6% of the total area of stand-replaced forests. Conversely, aging forests (excluding old-growth "stable" tropical forests) were primarily observed in the mature age classes, encompassing 1.2 billion hectares and constituting around 53% of the total aging forests. When coupling GAMI data with CO₂ flux estimates, we observe a significant correlation between the spatial patterns of the stand-replaced forest fraction and net CO₂ flux changes (R² = 0.37, slope = 118.7 gC m^-2 year^-1 [a positive slope indicates increased carbon released], p-val = 0.05) across the eleven TRANSCOM-land regions. This correlation surpasses the correlation with aging forests (R² = 0.02, slope = -3.7 gC m^-2 year^-1, p-val = 0.69). We attribute this significant correlation to the net above-ground biomass (AGB) losses in stand-replaced forests per unit area, substantially exceeding the magnitude of the net AGB gains observed in aging and old-growth "stable" tropical forests throughout 2010-2020. Our study highlights the importance of rapid forest turnover through stand-replacement, despite its limited spatial extent, on regional net carbon balance, especially when contrasted with the more gradual process of forest maturation.

How to cite: Besnard, S., Heinrich, V. H. A., Carvalhais, N., Herold, M., Peters, W., Luijkx, I., Santoro, M., and Yang, H.: The covariation of forest age shifts and net carbon balance over the period 2010 to 2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3888, https://doi.org/10.5194/egusphere-egu24-3888, 2024.