The global land carbon sink has increased in parallel with anthropogenic CO2 emissions over the last several decades, taking up ~25% of these emissions, and acting as a strong negative feedback to mitigate climate change. However, we have a limited ability to confidently attribute past changes. Here we use the suite of Dynamic Global Vegetation Models (DGVMs) from the Global Carbon Budget to develop a process-attribution framework to identify where models agree and, just as importantly, disagree, and thus guide future modelling efforts. We take a holistic approach to answer the following questions:

What are the 1) external drivers (concurrent rises in atmospheric CO2 and nitrogen deposition, climate, land-use and land-cover change (LULCC)), 2) main regions (tropics, extra tropics), and 3) processes (production vs turnover) primarily responsible for the changes in the net land carbon sink?

We find the observed global net land carbon sink is captured by current land models. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years. This uncertainty is driven by plant productivity, allocation, and turnover response to atmospheric CO2 (and to a smaller extent to LULCC), and the response of soil to LULCC (and to a lesser extent climate). Overall, differences in turnover explain ~70% of model spread in both vegetation and soil carbon changes.

Using a top-down constraint of net land-atmosphere carbon exchange from atmospheric inversions and remote-sensed products of vegetation functioning, we show that DGVMs underestimate carbon uptake in northern latitudes. A large portion of model error can be explained by the simulated LULCC flux. In tropical lands, models likely overestimate net carbon uptake due to too strong CO2 fertilisation, which can, in part, beexplained by too high modelled forest area and carbon densities.

How to cite: O'Sullivan, M., Friedlingstein, P., and Sitch, S.: Attributing trends in the land carbon cycle using process-based DGVMs and global scale observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13377, https://doi.org/10.5194/egusphere-egu23-13377, 2023.

Recent years have seen repeated record breaking temperatures and high impact events such as floods, storms, and droughts
across Europe, in line with the expected consequences of its +1.5 ^oC of warming over the past thirty years. In many metrics
the 2018 summer drought has ranked top of the list for severity and impacts, including in reductions of carbon exchange by
forests. Drought conditions thought unprecedented over the past 500 years trigger strong responses in forest, suffering
from leaf-level vapor-pressure deficits and root-level moisture deficits simultaneously. We report here that in the summer of
2022 close to 30% of the European continent was again under severe or exceptional drought, with temperatures exceeding
those even of 2018 and a similarly large size of area affected (3.4 million km²). Although a stationary blocking atmospheric
pressure pattern over the Atlantic was responsible for both droughts, the 2018 event mostly affected northwestern Europe while
the 2022 drought was centered over France. The more southerly centering exposed more drought resilient semi-arid vegetation
which dampened the peak loss of carbon uptake by forests relative to 2018. Observations and models suggest that vapor
pressure deficits rather than lack of soil moisture played a dominant role in reducing photosynthesis in 2022. Nevertheless we
find a similar cumulative reduction of net ecosystem carbon exchange (~50 TgC less uptake) in 2022, with specifically high
impacts in southern France where widespread summertime carbon release by forests, as well as extensive wildfires (emitting
close to 5 TgC) occurred. Our analysis demonstrates a much improved capacity in our community to rapidly quantify drought
impacts from the atmospheric and ecosystem monitoring network. However, strong impacts on eastern European broadleaf
forests suggested from observed near-infrared reflection by vegetation and simulated by terrestrial carbon cycle models can
not be confirmed currently through in-situ observations, signaling an important gap in our capacity to track carbon exchange in
the European terrestrial biosphere.

How to cite: van der Woude, A. and Peters, W. and the 2022 Drought Task Force: Reduced carbon uptake by European forests during the summer drought of 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13746, https://doi.org/10.5194/egusphere-egu23-13746, 2023.

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. 
However, these models have so far not been benchmarked alongside each other.
We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

How to cite: Eckes-Shephard, A., Argles, A., Brzeziecki, B., Cox, P., De Kauwe, M. G., Esquivel Muelbert, A., Fisher, R. A., Knauer, J., Koven, C. D., Lehtonen, A., Longo, M., Luyssaert, S., Marqués, L., Moore, J., Needham, J. F., Olin, S., Peltoniemi, M., Sitch, S., Stocker, B., Weng, E., Zuleta, D., and Pugh, T.: Can we model forest demography globally? Benchmarking of state-of-the-art Demographic DGVMs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14120, https://doi.org/10.5194/egusphere-egu23-14120, 2023.

Afforestation and reforestation are necessary for many countries to meet their Nationally Determined Contributions (NDC) and Net Zero targets (Seddon, N. et al 2019). Many countries estimate carbon sequestered using simple land-based transitions (IPCC, 2006) or have more complicated empirical estimations of carbon sequestered through forestry (Thomson, A. et al. 2020). This often leads to differences between NDC inventory submissions and bio-geophysical land surface modelling (Grassi, G. et al 2022).   The increasing representation of plant/tree demography within the latest Dynamic Global Vegetation Models (DGVMs) (Argles, A. et al 2022) and the availability of higher resolution regional climate datasets, provides new scope to evaluate modelled estimates against national inventories and Net Zero policies. As much afforestation is likely to be in the form of managed forests (Bateman, I.J, et al. 2022), the impact of management needs to be represented within models. We evaluate the JULES-RED model (Argles, A. et al 2020) against measured C stocks and fluxes at Harwood Forest, a Sitka spruce plantation in the UK. Planted in 1973, we will show that the inclusion of thinning in JULES-RED contributes to a more realistic representation of the 2018 carbon stocks and tree size-structure. We estimate a non-linear initial age-effect followed by a linear divergence between fixed and varying CO₂ simulations, highlighting the need for better understanding of the effect of CO₂ fertilisation in even-age stands and plantations.

How to cite: Argles, A., Robertson, E., Harper, A., Morison, J., Xenakis, G., Hastings, A., Mccalmont, J., Moore, J., Bateman, I., Gannon, K., Betts, R., Bathgate, S., Thomas, J., Heard, M., and Cox, P.: Demography DGVMs, Forest Management, Reforestation, and Afforestation: Evaluations of JULES-RED at a Sitka Spruce Plantation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14276, https://doi.org/10.5194/egusphere-egu23-14276, 2023.

The carbon sequestration capacity of forest ecosystems is closely related to their stand age. However, land surface models (LSMs) usually omit the impact of stand age and calibrate the carbon fluxes from equilibrated states after the model spin-up followed by a perturbation of biomass only resulting from environmental factors such as CO₂ and climate. The mismatch between modelled and real forest stand ages will bring large uncertainties in the simulation of carbon stocks and the projection of future carbon sequestration potential. In this study, we implemented and calibrated age-dependent biomass in the Integrated Biosphere Simulator (IBIS) model using forest age and biomass information at 13 representative forests across the world. Specially, to avoid error compensation in model processes, we developed a stepwise optimization framework that integrates remotely sensed gross primary productivity (GPP), leaf area index (LAI), and age-dependent biomass curves into the IBIS model in three calibration steps. The modified adaptive surrogate modelling optimization (MASM) algorithm was applied in our framework to accelerate the parameterization based on surrogate modelling. Compared with the original model, our improved model leads an average error reduction (AER) in GPP, LAI and biomass by 23.7%, 28.6% and 65.7%, respectively, after each calibration step. The new parameters decreased the mean annual net biomass productivity (NBP) during 2000-2020 by 23.1 and 35.7 gC/m²/year in the mixed forests and deciduous broad-leaved forests, respectively, and increased NBP by 36.1-68.7 gC/m²/year in coniferous forests and evergreen broad-leaved forests. Our work highlights the necessity of considering forest age in LSMs, and provides a new framework for better calibrating LSMs under the constraints of multiple satellite products.

How to cite: Ma, R., Zhang, Y., Ciais, P., Xiao, J., and Liang, S.: Stepwise calibration of age-dependent biomass in the Integrated Biosphere Simulator (IBIS v2.6) model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5251, https://doi.org/10.5194/egusphere-egu23-5251, 2023.

Changing environmental conditions impact ecosystem dynamics which have important implications for the land–atmosphere carbon and water exchanges. Land surface models coupled with dynamic vegetation models can be used to understand the impact of changes in terrestrial ecosystems on carbon and water cycles and their interactions with climate. However, process-based vegetation models are highly parameterized and have a large number of uncertain parameters, which lead to uncertainties in the model outputs. Here, we use a dynamic vegetation model, the Functionally Assembled Terrestrial Simulator (FATES) coupled to the Community Land Model (CLM v5) to analyze parameter sensitivities and its effects on forest growth, carbon storage and fluxes. We first calibrate allometry parameters to accurately describe plant functional types, representative of most abundant tree species across Europe (such as Norway spruce and European Beach), at three different European sites. Further, an ensemble of model simulations with perturbed parameters were performed and compared against observations to explore uncertainties in simulated vegetation structure and distributions (forest density, tree basal areas and above ground biomass) and their effects on ecosystem functioning (carbon, water and energy fluxes). Comparison with observation shows that the CLM5-FATES model is able to capture the interannual variability well for water and carbon fluxes (such as ET and GPP), but shows larger uncertainties for simulated forest structure (growth, establishment, and mortality). Future work will focus on parameter optimization to further improve model performance in simulating vegetation growth and composition for different vegetation distributions and climate conditions.

How to cite: Naz, B. S., Poppe, C., and Hendricks Franssen, H.-J.: Parameter sensitivity analysis of vegetation and carbon dynamics using land surface model (CLM5-FATES) at European forest sites., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11629, https://doi.org/10.5194/egusphere-egu23-11629, 2023.

Climate change, driven by rising atmospheric CO₂ concentrations, is well under way, and we are already starting to see significant shifts in the function and distribution of vegetation as a result. Dynamic vegetation models, the main platform used to predict the likely magnitude, rate and nature of these shifts, were originally rooted in theories of successional dynamics following disturbance. A key question for these models is how well they can capture vegetation responses to climatic change, which includes both press and pulse disturbances. Here we develop a general framework for representing climate-driven successional dynamics in vegetation models. The framework is illustrated with a series of case studies from Australia of vegetation responses to the major global change drivers of rising CO₂, warming, drought and fire. The Australian environment, intrinsically characterized by high climate variability, has experienced increasingly challenging climate extremes in recent years and thus provides an excellent testbed for predictive models.

How to cite: Medlyn, B., Williams, L., Knauer, J., Inbar, A., Stephens, C., Gallagher, R., Nolan, R., Choat, B., Boer, M., and Smith, B.: Climate succession: a framework for predicting vegetation dynamics driven by climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11134, https://doi.org/10.5194/egusphere-egu23-11134, 2023.

The inclusion of tree hydraulic processes into ecosystem models provides opportunities to better capture instantaneous tree drought responses as well as drought legacy effects. Here we are presenting a simple tree hydrologic approach implemented into a process-oriented ecosystem model that simulates instantaneous tree water potential dynamics based on soil water availability and transpiration demand. Reductions in tree water potential are then calculated into a loss of hydraulic functioning leading to sap wood and leaf area losses. This affects within-tree allocation as tissue becomes damaged, and finally may result in tree death if either hydraulic function is impaired beyond repair or tissues for resource acquisition cannot be sufficiently recovered anymore. This approach further provides potential explanations for various medium- and long-term legacy effects of drought, as well as mortality rates in dependence on environmental conditions.

Here we describe the model and evaluate the approach at a number of different sites over several decades, illustrating the species-specific sensitivity to drought stress and the dependency on precipitation pattern, potential soil water storage, and specific tree physiological traits such as xylem vulnerability. The importance of considering stem water storage and depletion as well as the possibility to link this water pool to micro-dendrometer measurements for evaluation is emphasized. Also, we indicate a possible way to integrate the hydraulic failure hypothesis with the theory of carbon starvation and discuss which process may be dominating under specific environmental conditions.

How to cite: Grote, R., Nadal-Sala, D., Petrík, P., and Ruehr, N.: Simulating forest drought legacy-effects from tree hydraulic damage: An integrated modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3362, https://doi.org/10.5194/egusphere-egu23-3362, 2023.

Tree mortality caused by natural disturbances, such as droughts, insects, and wildfires, is a global issue due to increased frequency and severity of extreme weather. California has been a major hotspot of large-scale tree mortality since 2012-2015 drought. Despite many local studies, there is no confident count of dead trees at the state level. Here we mapped all individual dead trees in California using submeter aerial images and Conventional Neural Network (i.e. EfficientUnet architecture). The model accuracy is about 96% and 83% when comparing to visually interpreted samples from aerial photos and in-situ observations, respectively. In total, we found more than 80 million dead trees from NAIP imagery in 2020, which accounts for 2% of trees reported in 2011. About half of the dead trees belongs to California mixed conifer group. North coast and central and southern Serrie Nevada are the most affected regions. Based on the localization and segmentation of every single dead tree, we retrieved mortality traits (i.e. dead tree density, dead crown size, and classification of old or recent death) and identified hotspots that have emerging mortality and high wildfire fuel load. The mortality traits, along with individual dead tree location at the state scale, provides unprecedented detailed information for forest management and improved carbon accounting, helping to understand dynamics and causes of tree mortality in a changing climate.

How to cite: Cheng, Y., Oehmcke, S., Brandt, M., Das, A., Rosenthal, L., Saatchi, S., Wagner, F., Verbruggen, W., Vrieling, A., Beier, C., and Horion, S.: Mapping and characterising tree mortality in California at individual tree level using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5917, https://doi.org/10.5194/egusphere-egu23-5917, 2023.

In 2022, Europe experienced a widespread severe summer edaphic drought and heat event, as well as abnormally hot autumn temperatures. By contrasting year 2022 with previous ones and using high-frequency Eddy-covariance and meteorological monitoring from 16 ecosystem ICOS forest stations across Europe, we(i) characterized the impact on carbon uptake and evapotranspiration rates, and (ii) disentangled the effects of soil water deficit from effects of atmospheric dryness on the forest fluxes.

Reduction of observed CO₂ uptake and evapotranspiration varied across Europe relative to drought intensity. Scandinavian forests were minimally affected in 2022, unlike the 2018 drought. On the contrary, several sites in southern Europe became a carbon source during the 2022 drought. Specifically, in southern France, some sites experienced a reduction of GPP of up to 70% relative to 2015-2021. Using artificial neural networks to analyze the responses of forests' CO2 uptake to soil water content and atmospheric dryness, showed the very high vapor pressure deficit experienced in 2022 was the major driver of the ecosystem responses in southern France this particular year.  

How to cite: Joetzjer, E., Lafont, S., Loubet, B., Destouet, G., Jacotot, A., Cuntz, M., Ciais, P., Fu, Z., Herig-Coimbra, P., Domec, J.-C., and Lousteau, D.: Responses of European forest fluxes to the 2022 heatwave and drought recorded by ICOS Eddy-covariance stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14228, https://doi.org/10.5194/egusphere-egu23-14228, 2023.

Rising carbon dioxide (CO₂) levels can lead to more carbon sequestration in plant biomass, and forests’ natural ability to store carbonin long-lived woody tissue is of particular interest. However, the extent of CO2-fertilization in trees varies across age, species and the availability of other resources. Woody tissue encompasses more than just the tree’s trunk, and a critical knowledge gap lies in the allocation of carbon to the other woody components like branches and twigs. In addition, the flux of woody carbon from the tree to the forest floor (turnover) is more than events of single tree mortality. These fluxes come in the form of litterfall, breakage of whole branches or complete tree mortality. The goal of this study is to quantify biomass allocation patterns and subsequent turnover rates within the woody carbon pool of two contrasting forest FACE experiments, BIFoR FACE in Staffordshire UK, and EucFACE in Sydney, Australia and answer the following questions: how do these allocation patterns determine the potential for carbon sequestration and how do patterns shift with elevated CO₂ concentrations?

Terrestrial laser scanning provided the tools to determine canopy structure on a stand scale, and the use of algorithms on the resulting point cloud trees supplied data on the partitioning of biomass among twigs, branches and stems. These results were then used to test general hypotheses about canopy structure and how it changes with elevated CO₂. The fluxes from the different wood components were quantified with monthly observations and collections, litter traps to collect the smallest material and transects to make an inventory of larger compartments like branches. The results of these studies will be combined with the canopy structure partitioning fractions to determine if the allocation patterns vary over time and between two contrasting forest types. It is worthwhile to increase our understanding of the dynamics of all woody components within forests and on a global scale beyond single tree mortality to improve the accuracy of predictive carbon budget models.

How to cite: van Wijngaarden, K., Smith, B., Medlyn, B., Larsen, J., and Pugh, T.: Beyond single tree mortality: understanding biomass allocation and turnover of twigs and branches in forest FACE experiments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11686, https://doi.org/10.5194/egusphere-egu23-11686, 2023.

Storms are natural weather events, varying greatly in frequency and intensity across the world. They are characterised by strong winds that can disturb forests via tree defoliation, breakage and uprooting. Despite close monitoring of forest disturbance occurrences in recent years, we still lack information on the contribution of storms in driving forest dynamics worldwide. Here, we build a machine learning classification model to identify wind-related forest disturbances at the global scale. Forest disturbance patches detected between 2002 and 2014 were associated with multiple covariates as potential indicators of wind damage. These covariates include structural metrics inherent to the disturbances, such as patch size, elongation and spatiotemporal clustering, as well as environmental variables describing topography, weather, and soil conditions. We used these data for 20,000 reference patches (10,000 wind and 10,000 non-wind), widely distributed across forest biomes, to train a random forest classifier. Cross-validation with 20,000 other reference patches over 10 runs showed that the model achieved satisfactory performance scores. It yielded omission errors of ca. 20% and commission errors of ca. 5%, mostly associated with harvest, selective logging and biotic outbreaks.  The most important variable was maximum wind speed, followed by patch temporal clustering. Model deployment at the global scale will provide quantitative insights into storm damage biogeography, regime characteristics and relative contributions to global carbon fluxes and forest dynamics.

How to cite: Acil, N., Wayman, J., Suvanto, S., Senf, C., Sadler, J., and Pugh, T.: Mapping storm damage across the world’s forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9939, https://doi.org/10.5194/egusphere-egu23-9939, 2023.

Knowledge about the state of the vegetation at fire occurrence is essential for estimating fire behavior and fire emissions. The spatial distribution and the temporal dynamics of the biomass in various vegetation components, surface litter and woody debris are important controls on fire spread and emissions. So far, no large-scale product exists that combines all these requirements. Maps of canopy height and above ground biomass (AGB) from satellite retrievals provide information on the regional variability of forest biomass, but they have a limited use for fire-related applications because they do not provide information on different fuel components such as biomass in the canopy, wood, grass or litter. Fire-targeted products like the global fuelbed database and the North American Wildland Fuel Database (NAWFD) combine land cover maps with representative values or statistical distributions of fuel properties such as biomass values for trees, shrubs, grass, woody debris and litter. However, those maps do not provide information on the spatial variability of fuel loads within one vegetation type (fuelbed). In addition, information on the temporal dynamics of the fuels is missing. Temporal dynamics of fuels can be approximated by satellite-derived time series of vegetation indices or biophysical parameters. 

Here, we aim to develop an approach that combines the spatial information from remotely-sensed AGB and canopy height maps, the annual temporal information from land cover maps with the high temporal information from leaf area index (LAI) time series to retrieve information on the spatial variability and temporal dynamics of fuel loads. Therefore, we developed a data-model fusion approach that uses the 10-daily LAI product from Sentinel-3 OLCI and Proba-V and land cover maps as input. We apply the approach to a spatial resolution of 333 x 333 m across different study regions in the Amazon, southern Africa, Siberia and the United States. In a first step, the temporal dynamics in tree height is computed from long-term changes in mean LAI and the fractional tree cover by taking observation of canopy height from GEDI as reference. Since canopy height is closely related to AGB through allometric relations between tree height and biomass, the estimated tree height is then used to estimate stem biomass and consecutively branches and leaf biomass, which is calibrated against maps of AGB. The Biomass and Allometry Database is used to calibrate model parameters that regulate the relationships between canopy height, leaf and woody biomass. Estimated temporal changes in tree height directly translate into changes in stem, branches and leaf biomass and hence result in a carbon turnover (e.g. leaf fall, transfer of woody biomass). Based on a simple decomposition model we then compute the dynamics of surface litter and woody debris. The estimates of litter, fine and coarse woody debris correspond well with databases of in situ observations. Our fuel data-model fusion approach allows estimating spatial patterns and temporal dynamics of vegetation and surface carbon pools for the analysis of carbon turnover and pools and as input into fire behaviour and emission models.

How to cite: Wessollek, C. and Forkel, M.: Estimating biomass compartments and surface fuel loads by integrating various satellite products with a data-model fusion approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12187, https://doi.org/10.5194/egusphere-egu23-12187, 2023.

Deadwood is a prominent part of forest ecosystems. It is important for multiple forest functions, including habitat, water storage, nutrient cycling and carbon storage. Deadwood volume is now routinely measured in many large-scale inventory programs, including the national forest inventory or inventory of nationalparks or nature reserves. Policy changes, increasing climatic stress, more frequent and more intense disturbances and/or abandoning forest management will likely lead to increasing deadwood volumes in the next decades. We will explore here the available knowledge on decay of deadwood in Central Europe, focussing on carbon (C) and nitrogen (N) content and release.

Decay classes can be assessed in the field and provide a potent proxy for deadwood density, pore volume and C:N ratios, based on pilot studies in Eastern Austria. Using C:N as proxy for decomposability suggest that decomposition is non-linear and that advanced decay stages have faster decomposition. Our results highlight that smaller sized deadwood (2-10 cm diameter) can store substantial amounts of C and N. Additional field work, non-destructive methods and modelling can link decay stages with time since death and allow estimating mass and volume loss by decomposing organisms.

For understanding the dynamics between standing and lying deadwood we will need models that are able to predict the disintegration of trees, considering loss of less stable stem parts, like bark or sapwood. We will need continuous deadwood monitoring as well as more complex models to understand the main pathways for deadwood decay, the effects of climate on decomposition and the role of management in deadwood accumulation and dynamics. Complementing available data with new methods and models will allow us to quantify the capacity of managed and unmanaged forests for deadwood, the carbon sequestration in deadwood and its persistance in the future.

References:

Gschwantner T (2019) Totholz-Zunahme ausschließlich positiv? BFW Praxisinformation 20:

Müller-Using S, Bartsch N (2009) Decay dynamic of coarse and fine woody debris of a beech (Fagus sylvatica L.) forest in Central Germany. European Journal of Forest Research 128, 287–296. doi:10.1007/s10342-009-0264-8.

Neumann M, Hasenauer H (2021) Thinning Response and Potential Basal Area — A Case Study in a Mixed Sub‐Humid Low‐Elevation Oak‐Hornbeam Forest. Forests 12:. https://doi.org/10.3390/f12101354

Neumann M, Hasenauer H (in review) A simple concept for estimating deadwood carbon in forests. Carbon Management

Oettel J, Lapin K, Kindermann G, et al (2020) Patterns and drivers of deadwood volume and composition in different forest types of the Austrian natural forest reserves. For Ecol Manage 463:118016. https://doi.org/10.1016/j.foreco.2020.118016

Pietsch SA, Hasenauer H (2006) Evaluating the self-initialization procedure for large-scale ecosystem models. Glob Chang Biol 12:1658–1669. https://doi.org/10.1111/j.1365-2486.2006.01211.x

How to cite: Neumann, M., Pucher, C., and Hasenauer, H.: Decay of deadwood carbon – current knowledge and opportunities for modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1466, https://doi.org/10.5194/egusphere-egu23-1466, 2023.

Altering management of disturbances in savanna-grasslands, a biome spanning >20 million km² and under extensive human management, can offset a substantial proportion of anthropogenic carbon emissions. Quantifying the long-term carbon accrual and storage can be challenging because much of the change occurs belowground and it requires an understanding of ecological processes in a diverse set of environmental conditions. Here, we focus on the role of altered fire regimes given ca. 3 million km² of savanna-grasslands burn annually. Combining repeated measurements of total ecosystem carbon stocks in a 58-yearlong fire-manipulation experiment with a global dataset, we demonstrate that while fire management leads to large changes in carbon sequestration, its magnitude and persistence over time is highly variable. In the experiment, fire exclusion resulted in large increases in carbon in soil organic matter via increased aboveground biomass inputs and faster decomposition. Increased burning frequency decreased carbon but this was partly offset by increased inputs from fine root turnover. However, repeated measurements illustrated both the magnitude of the sequestration within a plot and the differences across treatments changed—and even reversed—through time due to changes in tree inputs following a disease outbreak. The global dataset revealed that in wet sites, carbon sequestered in trees is most important but in drier sites the sequestration in soil organic matter is most important. Within soils, much of the variability in carbon accrual was due to variability in how much woody biomass inputs changed, with drier sites experiencing large changes. Consequently, much of the variability in carbon accrual is due to variability in the amount of woody biomass inputs and decomposition into soils. Because trees can be highly sensitive to changing disturbance regimes in drylands we propose that using fire management to sequester carbon in soils can be highly uncertain and unstable through time.

How to cite: Pellegrini, A., Hobbie, S., and Reich, P.: Nature-based solutions in savanna-grasslands can be both uncertain and unstable, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11573, https://doi.org/10.5194/egusphere-egu23-11573, 2023.

How to cite: Dorigo, W., Zotta, R., van der Schalie, R., Moesinger, L., and de Jeu, R.: VODCA v2: An updated long-term vegetation optical depth dataset for ecosystem monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13458, https://doi.org/10.5194/egusphere-egu23-13458, 2023.

Global forests play a key role in the global carbon cycle and are a cornerstone in international policy-making to prevent global warming from exceeding 1.5°C and reach carbon neutrality. In line with recent climate science, the actions taken in the current decade are crucial for obtaining the goals laid out in international agreements. One forest-based strategy with high short-term climate benefits is the return of global forests to their carbon storage potential, by ceasing forest management, but the ecological boundaries of increasing biomass in existing forests remain poorly quantified. Recent studies preferentially focus on the mitigation potential of reforestation, without explicitly accounting for the carbon dynamics in existing forests, thus providing an incomplete evaluation of the possible expansion of the forest carbon stock. Here we integrate satellite remote sensing estimates of current forest biomass with a machine learning framework to show that existing global forests could increase their above-ground biomass by 44.1 PgC at most (an increase of 16% over current levels) if allowed to reach their natural equilibrium state. In total, the maximum carbon storage potential in this hypothetical scenario equates to just about 4 years of global anthropogenic CO2 emissions (at the 2019 rate). This maximum potential would require the complete stop of forest management and harvesting for decades. Therefore, without first strongly reducing CO2 emissions, this strategy holds low climate change mitigation potential. This urges to view storing additional carbon in existing forests as an effective strategy to offset carbon emission from sectors that will be hard to decarbonise, rather than as a tool to compensate all business-as-usual emissions.

How to cite: Roebroek, C., Duveiller, G., Seneviratne, S., Davin, E., and Cescatti, A.: Releasing global forests from management: how much more carbon could be stored?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13184, https://doi.org/10.5194/egusphere-egu23-13184, 2023.