Displays

The drivers for terrestrial carbon uptake remain unclear despite a clear signal that the land removes the equivalent of up to 25-30% of fossil fuel CO2 emissions each year. Recent work has confirmed sustained carbon uptake by the land that is proportional to anthropogenic emissions, meaning that the land 'sink' has strengthened over the past five decades, and with interannual variability driven by climate. Drivers responsible for sustained uptake include hypotheses related to lengthening growing season length, increasing nitrogen deposition, changes in the ratio of diffuse to direct radiation, and land-use and land cover change. More recently, land-use and land-cover change has been investigated as a driver of land carbon uptake owing to an emergence of global-scale datasets related to canopy disturbance, land use, and forest age. At the same time, land-surface models have increased their realism in terms of moving beyond 'big-leaf' model representation of ecosystems to including vertical structure and horizontal heteorogeneity via size-and-age structured approaches. This presentation will address recent work identified forest structure and vegetation dynamics as a driver for global carbon uptake and provide examples of how remote sensing observations have led to new datasets for initialization land-surface models. Compared to inventory-based approaches, land-surface models initialized with forest age show a lessor role in explaining net terrestrial carbon uptake at global scales, but at regional scales, vegetation structure is a key determinant of carbon exchange. New satellite missions improving forest structure observations are expected to reduce uncertainties and contribute substantially to ongoing land-surface model development.

How to cite: Poulter, B., Calle, L., Pugh, T., McDowell, N., Ciais, P., Besnard, S., Carvalhais, N., Neigh, C., and Montensano, P.: Forest structure and vegetation dynamics as a driver of global carbon uptake, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11676, https://doi.org/10.5194/egusphere-egu2020-11676, 2020.

Global change is affecting the capacity of terrestrial ecosystems to sequester carbon. While the effect of climate on ecosystem carbon balance has largely been explored, the role of other potentially important factors that may shift with global change, such as biodiversity and the concentration of nutrients remains elusive. More diverse ecosystems have been shown to be more productive and stable over time and differences in foliar concentrations of N and P are related to large differences in how primary producers function. Here, we used 89 eddy-covariance sites included in the FLUXNET 2015 database, from which we compiled information on climate, species abundance and elemental composition of the main species. With these data, we assessed the relative importance of climate, endogenous factors, biodiversity and community-weighted concentrations of foliar N and P on terrestrial carbon balance. Climate and endogenous factors, such as stand age, are the main determinants of terrestrial C balance and their interannual variability in all types of ecosystems. Elemental stoichiometry, though, played a significant role affecting photosynthesis, an effect that propagates through ecosystem respiration and carbon sequestration. Biodiversity, instead, had a very limited effect on terrestrial carbon balance. We found increased respiration rates and more stable gross primary production with increasing diversity. Our results are the first attempt to investigate the role of biodiversity and the elemental composition of terrestrial ecosystems in ecosystem carbon balance.

How to cite: Fernández-Martínez, M., Sardans, J., Peñuelas, J., and Janssens, I.: Exploring the effects of biodiversity and elemental stoichiometry on terrestrial carbon balance , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1589, https://doi.org/10.5194/egusphere-egu2020-1589, 2020.

Structurally intact tropical forests sequestered ~1 Pg C yr^-1 over the 1990s and early 2000s, equivalent to ~15% of fossil fuel emissions. Climate-driven vegetation models typically predict that this carbon sink will continue for the remainder of the 21^st century. However, recent plot inventories from Amazonia show a declining rate of carbon sequestration, potentially signaling an imminent end to the sink. Here we assess whether the African tropical forest sink is also declining.

Records from 244 multi-census plots across 11 countries reveal that the African tropical forest sink in aboveground live biomass has been stable for three decades, at 0.66 Mg C ha^-1 yr^-1, from 1985-2015 (95% CI, 0.53-0.79). Thus, the carbon sink responses of Earth’s two largest expanses of tropical forest have diverged over recent decades. A statistical model including CO₂, temperature, drought, and forest dynamics can account for the trends. Despite the past stability of the African carbon sink, our data and model show that very recently the sink has begun decreasing, and that it will continue to decline in the future.  This implies that the intact tropical forest carbon sink on both continents is set to end decades sooner than even the most extreme vegetation model estimates.

Published independent observations of inter-hemispheric atmospheric CO₂ concentration indicate increasing carbon uptake into the Northern hemisphere landmass, offsetting a weakening of the tropical forest sink, which reinforces our conclusion that the intact tropical forest carbon sink has already saturated. Nevertheless, continued on-the-ground monitoring of the world’s remaining intact tropical forests will be required to test our prediction that the intact tropical forest carbon sink will continue to decline. Our findings were recently published in Nature (March 2020) and have important policy implications: given tropical forests are likely to sequester less carbon in the future than Earth System Models predict, an earlier date to reach net zero anthropogenic greenhouse gas emissions will be required to meet any given commitment to limit the global heating of Earth.

How to cite: Hubau, W., Lewis, S. L., Phillips, O. L., and Beeckman, H. and the AfriTRON consortium & the RAINFOR consortium: The past and the future of the tropical forest carbon sink: insights from permanent forest inventory plots, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11069, https://doi.org/10.5194/egusphere-egu2020-11069, 2020.

Tropical forests are a crucial component of the global carbon cycle and importantly contribute to the global carbon land sink. Stem growth of tropical trees is a key component of carbon dynamics in tropical forests, but our understanding of how this is driven by climatic variation is poor. Such understanding is needed for predictive vegetation modelling of climate change effects.

Here, we help to fill this knowledge gap by conducting a meta-analysis of published tropical tree-ring width chronologies. We compiled >350 tropical chronologies (30°N - 30°S) from all tropical climate zones. We used this data set to explore i) common patterns in the tree-growth responses to monthly rainfall and temperature (Tmax) patterns (cluster analysis), ii) the relative importance of temperature and rainfall in determining tropical tree growth (glm), iii) how these climatic drivers shift along gradients of temperature and precipitation.

Our cluster analysis revealed 6-8 primary types of responses to monthly climate variables. These clusters are associated with mean climate, elevation, or geographic location. The seasonality of growth responses to temperature and rainfall differed clearly among clusters, but the signs of responses were consistent: higher Tmax reduces growth, more precipitation increases growth. Multiple regression analyses of growth responses to seasonal climate further confirmed the negative effects of temperature and positive effects of rainfall. Rainfall during the dry season had the strongest relative importance. Finally, we found that seasonal drivers of tropical tree growth are modified by mean climate. In drier regions, growth sensitivity to temperature increases; in warmer regions, growth sensitivity to rainfall increases. The latter may imply that global warming leads to stronger drought effects on tree growth and possibly enhances mortality risks of tropical trees.

Our meta-analysis shows that tree-ring studies help to improve understanding of climate-driven carbon dynamics in tropical forests. Insights from this study can be used to benchmark global vegetation modelling and to better understand responses of tropical tree species to climate change.

How to cite: Zuidema, P., Groenendijk, P., Trouet, V., and Babst, F.: Sensitivity of tropical tree growth to climatic variation: a global meta-analysis of tree-ring data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7770, https://doi.org/10.5194/egusphere-egu2020-7770, 2020.

Patterns of woody plant diversity in the tropical savanna biome has received little research attention but is relevant to understanding the complex vegetation dynamics of a biome that have remained contentious for almost a century. Tropical savannas of Africa and Australia are defined by the co-existence of woody plants and grasses, and the evolution and assembly of the savanna biome trace back 3-10 million years. Here, we explored patterns of local (alpha-) diversity and species turnover (beta-diversity) of woody plant species across African and Australian savannas. We aimed test the relative role of the environmental gradients of rainfall, temperature, fire and soil in shaping the relative abundance of all of woody species, genera, and families. Using generalized additive models (GAMs) and generalised dissimilarity models (GDMs) of field inventory data from vegetation plots across sub-Saharan Africa and Northern Australia we analysed changes in alpha- and beta-diversity. Environmental gradients were characterised as effective rainfall (ER), rainfall seasonality (coefficient of variation of monthly rainfall), mean annual temperature (MAT), temperature seasonality, fire frequency, and cation exchange capacity (CEC) in soils.

Savannas in Australia are on average drier and hotter than in Africa likely as a product of lower altitude. Crucially, diversity across all taxonomic levels is approximately two to three times greater in Africa compared with Australia. Within each continent, rainfall seasonality was the strongest environmental correlate of both alpha- and beta-diversity. In Africa, there is a strongly negative relationship between alpha-diversity at all taxonomic levels and rainfall seasonality. In contrast, in Australia, the relationship between alpha-diversity and rainfall seasonality while relevant is non-linear. Surprisingly within continents, rainfall, temperature, soils and fire had little bearing in these data on patterns of alpha diversity.

In terms of beta-diversity, and likely linked to the overall differences in diversity between continents, the geographic distance equalling total species turnover is greater in Australia than in Africa. Effective rainfall was the only additional significant correlate of woody species turnover in Australia, but only in arid regions. In Australia, at higher taxonomic levels the capacity of GDMs to explain variation in the data diminished substantially as a product of low diversity in genera and families. When compared to Australia, species turnover in Africa increases when geographic distance, rainfall seasonality and mean annual temperature are relatively low.

Our findings highlight that with ongoing climate change specifically with shifts in rainfall distribution that will also affect local drought regimes, rainfall seasonality could substantially alter patterns of diversity, specifically in Africa. There have been persistent attempts to explain ecosystem dynamics in savannas with respect to climate, soils and fire with emphasis often on total rainfall, but our findings suggest that rainfall seasonality can have strong effects on diversity that may interact with other environmental correlates such as fire.

How to cite: Trotter, F. and Lehmann, C. and the Contributors: Alpha- and beta- diversity of woody species across environmental gradients in African and Australian tropical savannas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16130, https://doi.org/10.5194/egusphere-egu2020-16130, 2020.

Climate change is especially prominent in Arctic and sub-Arctic regions. Therefore, it is of great importance to investigate ecosystem processes and the dynamics of its components in the Siberian high latitudes. Plant biomass assessment is essential for estimating carbon stocks and further carbon balance and wildlife habitat modeling. Our study region in central Chukotka (north-eastern Siberia) is one of the least investigated sub-Arctic regions in terms of vegetation dynamics. We quantified changes in four vegetation classes: (1) larch closed-canopy forest, (2) forest tundra and shrub tundra, (3) graminoid tundra, and, (4) prostrate herb tundra and barren areas. We used Landsat spectral indices (Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Normalised Difference Snow Index (NDSI)) to map the vegetation classes in four focus areas in 2000/2001 and 2016/2017. In 2016, we collected field data on foliage projective cover (percentage cover) of dominant taxa from 52 sites along the tundra–taiga gradient. We applied constrained ordination for coupling projective cover with corresponding Landsat spectral indices from 2016/2017. Ordination scores were used in a k-means classification. We inferred significant shrubification in the tundra–taiga zone (20%) and in the northern taiga (40%), as well as notable tree infilling in the northern taiga (9%), and, no significant changes in the treeless tundra area. To estimate carbon stocks and its changes within and between differentiated vegetation classes, we aim to upscale above ground biomass across tundra, tundra-taiga and northern taiga zones and derive above ground biomass change for the 15 investigated years. In 2018, another expedition took part to this region with new 38 sites at witch we described projective cover and representatively harvested total above ground biomass (dominant taxa and rest). This data can be projected in the created previously ordination space with the use of projective cover similarities. Using interpolation, we plan to predict above ground biomass  in the whole ordination space. We will further use Landsat data and interpolation results to produce above ground biomass maps for the four focus areas in central Chukotka and derive difference maps from 2000/2001 to 2016/2017.

How to cite: Shevtsova, I., Kruse, S., Heim, B., and Herzschuh, U.: Recent vegetation composition and above ground biomass change in north-eastern Siberia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11085, https://doi.org/10.5194/egusphere-egu2020-11085, 2020.

The fate of boreal forests under global warming and forced rapid environmental changes is still highly uncertain, in terms of remaining a carbon sink or becoming a future carbon source. Forest dynamics and resulting ecosystem services are strongly interlinked in the vast permafrost-covered regions of the Siberian treeline ecotone. Consequently, understanding the role of current and future active layer dynamics is crucial for the prediction of aboveground biomass and thus carbon stock developments.

We present a coupled model version combining CryoGrid, a sophisticated one-dimensional permafrost land surface model adapted for the use in forest ecosystems, with LAVESI, a detailed, individual-based and spatially explicit larch forest model. Subsequently, parameterizing against an extensive field data set of >100 forest inventories conducted along the treeline of larch-dominated boreal forests in Siberia (97-169° E), we run simulations covering the upcoming decades under contrasting climatic change scenarios.

The model setup can reproduce the energy transfer and thermal regime in permafrost ground as well as the radiation budget, nitrogen and photosynthetic profiles, canopy turbulence and leaf fluxes and predict the expected establishment, die-off and treeline movements of larch forests. Our results will show vegetation and permafrost dynamics, quantify the magnitudes of different feedback processes between permafrost, vegetation, and climate and reveal their impact on carbon stocks in Northern Siberia.

How to cite: Stünzi, S., Kruse, S., Boike, J., Herzschuh, U., and Langer, M.: Coupling an individual-based boreal forest model with a permafrost land-surface model to forecast biomass development in boreal larch forests at the Siberian treeline, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14992, https://doi.org/10.5194/egusphere-egu2020-14992, 2020.

Carbon cycling in forest ecosystems is affected by a number of interacting environmental factors. Here we analyse carbon sequestration in temperate forests composed of three common Central European species: Norway spruce, European beech and oak along an extended environmental gradient across Central Europe using long-term monitoring data and process-based modelling of forest dynamics. For the analyses we used selected ICP forest monitoring plots, long-term forest research plots from thinning trials, and highly-equipped intensively monitored plots from five central European countries: Croatia, Hungary, Slovakia, Poland and the Czech Republic. Their temporal development was simulated using a process-based model Biome-BGCMuSo, which is sensitive to soil and climate conditions. Since such models of forest growth dynamics implicitly describe relationships between forest productivity and environmental conditions, their implementation can reveal the main factors affecting carbon cycling in forests along the gradients of latitude, altitude, or other environmental factors as long as they are included in the models. The study indicates that by linking long-term monitoring data and forest growth modelling we can not only test the model capacity to simulate forest dynamics, but above all we can increase our capacity to address main challenges faced by the central European forestry with respect to the global climate change.  

How to cite: Merganicova, K., Hollos, R., Barcza, Z., Merganic, J., Sitkova, Z., Kurjak, D., Mokros, M., Fleischer, P., Marjanovic, H., Hidy, D., Strelcova, K., and Hlasny, T.: Integrating multi-source data and model projections to address carbon cycling in central European forests , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13412, https://doi.org/10.5194/egusphere-egu2020-13412, 2020.

Forests cover about 30% of the terrestrial surface of our planet and store a large part of the terrestrial carbon (C), indicating their fundamental role in terrestrial C dynamics. In recent years, significant advances have been made in understanding terrestrial C cycling across scales, albeit uncertainties remain about fundamental processes, such as photosynthesis, allocation, and mortality, which exert dominant controls on vegetation C dynamics. Allocation plays a critical role in forest ecosystem C cycling by partitioning the products of net photosynthesis into leaves, wood, and below-ground components but is still poorly represented mostly given limitations in process understanding as well as in both suitable and commensurate observations.

Here, we explore different approaches in constraining C allocation alongside processes driving assimilation and out fluxes in a terrestrial ecosystem model based on novel forest biomass datasets. More specifically, we use a series of temporally changing above-ground biomass (AGB) data from local (i.e. in-situ forest inventory data) to global (i.e. long-term C-band satellite retrievals from 1992 to 2018) scales, in a multi-constraint approach. We explore the information contained in a novel AGB time series to diagnose the potential of using changes in vegetation C stocks, jointly with C and water fluxes, to constrain and parameterize different C allocation modeling approaches. Both at FLUXNET site level and global scale, we will: i) present these novel AGB datasets, their strengths and limitations, ii) demonstrate the relevance of constraining C allocation with such temporally changing AGB estimates, and iii) provide a comparison of different C allocation approaches (i.e. fixed versus dynamic allocation, and an hybrid modeling approach) and their implications in representing ecosystem dynamics.

How to cite: Besnard, S., Koirala, S., Santoro, M., Bao, S., Cartus, O., Gans, F., Jung, M., Trautmann, T., and Carvalhais, N.: Constraining carbon allocation in a terrestrial ecosystem model using long-term forest biomass time series, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10523, https://doi.org/10.5194/egusphere-egu2020-10523, 2020.

The future role of the terrestrial biosphere in the global carbon cycle is highly uncertain. Modeling and predicting the terrestrial net carbon balance is difficult due to the numerous processes driving variability of gross fluxes. Many approaches to reducing this model uncertainty have focused on model structure, namely by adding additional processes (e.g., nutrient dynamics or vegetation demography) and thus increasing complexity. While these developments seek to achieve greater structural realism by mirroring the complexity of the natural world, they often rely, by necessity, on poorly-determined or over-generalized parameters. Furthermore, increased structural complexity may increase the risk that parameters with compensating errors are found during model development, thereby reducing model accuracy in prediction. It is not clear whether or to what extent carbon cycle predictability scales with structural complexity, or whether an intermediate, optimum level of complexity exists that may balance the costs of a low (more biased) or high (more variant) complexity model. Here, we explore and define the relationship between carbon cycle model complexity and prediction accuracy. To do so, we leverage the CARbon Data MOdel fraMework (CARDAMOM), a Bayesian data assimilation system that retrieves terrestrial carbon cycle variables (including pools, fluxes, and static parameters) by combining multiple observations with a relatively simple ecosystem carbon balance model. CARDAMOM includes several ecological and dynamical constraints that can prevent ecologically unrealistic parameter combinations and reduce compensating errors between parameters (also known as equifinality). Furthermore, it is a flexible framework to which process representations, parameters, and constraints can easily be added and removed. We used CARDAMOM to develop a suite of model versions spanning a broad range of structural complexity, including the number of carbon pools and the allocation of carbon to the canopy. We assessed a model’s complexity based on its inherent dimensionality, determined via a principal component analysis that reduces the parameter space to its principal components. We tested and compared the training and forecast accuracies of net ecosystem exchange predictions using 14 increasingly complex versions of CARDAMOM, each with 48 different experimental designs (i.e., combinations of data constraints and error assumptions) at 5 globally-distributed eddy covariance sites representing a range of biomes and vegetation types across a total of 70 site-years. We also compared the model performance values against a range of machine learning approaches, which are assumed to represent the limit of infinite model complexity due to their large number of underlying parameters. In this presentation, we use this population to demonstrate and explain patterns in the mapping of model complexity and other assimilation choices to prediction accuracy, offering theoretical and empirical insights into the optimal structure of a carbon cycle model.

How to cite: Famiglietti, C. A., Smallman, T. L., Flack-Prain, S., Ge, R., Meyer, V., Parazoo, N. C., Quetin, G. R., Revill, A., Stettz, S. G., Yang, Y., Zhao, Y., Zhu, P., Bloom, A. A., Williams, M., and Konings, A. G.: Optimal model complexity for terrestrial carbon cycle prediction using data assimilation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10722, https://doi.org/10.5194/egusphere-egu2020-10722, 2020.

The HUMBOLDT-LSM_bio component is an expansion of the LPJ-GUESS dynamic vegetation model , including local diversity of plant traits and an organic matter module representing the Nitrogen and Phosphorus cycles. In the new trait variation module the initial full range of possible traits is filtered along the altitudinal gradient with the aim to predict the trait distribution of communities observed in the field. The model was parameterized using local trait data per species collected by field campaigns along the whole altitudinal gradient, considering the leaf and wood economics spectrum and tissue nutrient concentrations, and locally measured N and P flux data, in which we were able to use deposition and weathering rates, as well as soil organic and mineral layer nutrient concentrations. In order to evaluate the model with regards to nutrient limitation, the simulation experiment was designed with the NUMEX nutrient manipulation experiment in mind, meaning that the reference nutrient limited community was compared to simulations in which N or/and P limitations were deactivated (i.e. plants could grow independent of their N or P demands being met). Results in NUMEX suggested that the removal of nutrient limitation would produce more biotically homogenous communites, and taller trees with higher productivity and more allocation to belowground biomass.

Our results indicate that including trait diversity and nutrient limitation provide a significant improvement in relation to ecosystem representation especially at higher elevations. Deactivation of nutrient limitation suggests reduced community trait differentiation along the elevation gradient (e.g. specific leaf area), and increased productivity (i.e. Carbon and NPP values). Deactivation of trait diversity impels plant survival at higher altitudes. Significant model improvements are expected in the future with further field trait measurements from the RESPECT subprojects, and the inclusion of other significant processes such as leaf herbivory, seed dispersal and of course the coupled model runs with LSM_atmo and LSM_hydro.

How to cite: Dantas de Paula, M. and Hickler, T.: The Dynamic Vegetation Model HUMBOLDT (LSMbio) The role of biodiversity and nutrient limitation in driving ecosystem processes on a tropical altitudinal gradient, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21936, https://doi.org/10.5194/egusphere-egu2020-21936, 2020.

Climate change is expected to cause vegetation change in Africa, with profound impacts on ecosystems and biodiversity. Projections of future ecosystem states are constrained by uncertainties regarding relative impacts of climate change and CO₂ fertilisation effects. Rising atmospheric CO₂ drives climate change, but also directly affects plant physiological functions via carbon uptake, carbon allocation, water use efficiency, and growth. We use the adaptive Dynamic Global Vegetation Model (aDGVM) to quantify uncertainties in projected African vegetation until 2099. High-resolution climate forcing for the aDGVM, was generated by regional climate modelling. An ensemble of 24 aDGVM simulations based on six downscaled General Circulation Models (GCMs) under two Representative Concentration Pathways (RCPs 4.5 and 8.5) with plant-physiological CO₂ effects enabled and disabled was implemented.

Under strong climatic change with high CO₂ increases (RCP 8.5), almost a third of terrestrial Africa is projected to experience biome changes with woody encroachment into grassy biomes dominating biome changes. Projections under medium-impact scenarios (RCP 4.5) still predict biome changes for around a quarter of Africa. With climate change only and elevated-CO₂ effects disabled, woody encroachment is weak and reduction of forest cover in favour of savannas prevails. Change in aboveground vegetation carbon until 2099 varied from a strong increase under elevated CO₂ (61.5%, RCP 8.5; 33.9%, RCP 4.5) to a small increase of 5.4% (RCP 4.5) and a decrease of -13.6% (RCP 8.5) without CO₂ effects.

CO₂ effects in combination with RCP scenarios caused the greatest uncertainty in projected ecosystem changes. Downscaled GCM projections caused weaker uncertainties in the simulations. Future biome changes due to climate and CO₂ change are therefore likely in large parts of Africa. Their magnitude and location often remain uncertain. Climate mitigation and adaptation response measures that rely upon vegetation-derived ecosystem services will need to account for alternative climate futures.

How to cite: Martens, C., Hickler, T., Davis-Reddy, C., Engelbrecht, F., Higgins, S. I., von Maltitz, G. P., Midgley, G. F., Pfeiffer, M., and Scheiter, S.: Uncertainties of climate and CO2 impacts on carbon stocks and biome distribution in Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21790, https://doi.org/10.5194/egusphere-egu2020-21790, 2020.

The carbon balance of terrestrial ecosystems is determined by environmental drivers (chiefly related to climate and land use) which interact with each other and change over time. In particular, ecosystems are presently still affected by past environmental changes because they have not yet reached equilibrium with their environment. However, the magnitude and drivers of this legacy effect for the upcoming decades are still unclear. Here, we use the dynamic global vegetation model LPJ-GUESS to calculate the effects of historical (1850-2015) and future (2015-2099, exemplarily for the high emission/moderate deforestation scenario SSP5-8.5) environmental changes on historical and future terrestrial carbon cycling and to quantify the contributions of the following environmental drivers: climate change, CO₂ fertilization, agricultural expansion, shifting cultivation frequency, wood harvest, nitrogen deposition, and nitrogen fertilization.

According to our simulations, the land represented a cumulative net carbon source (-154 GtC) over the historical period mainly due to deforestation, wood harvest, and negative climate change impacts partly offset by carbon uptake via increased CO₂ levels and nitrogen input. In contrast, the land is simulated to act as a net carbon sink (+118 GtC) over the 21^st century. This is mostly a result of historical environmental changes as ecosystems still adapt to present-day CO₂ and nitrogen availability as well as long-term vegetation regrowth following agricultural abandonment and wood harvest. The net impact of future environmental changes on future carbon cycling is much smaller because effects from individual environmental drivers largely compensate. Historical environmental changes dominate future terrestrial carbon cycling at least until mid-century when legacy effects gradually diminish and future environmental changes start to trigger carbon accumulation. Our results suggest that legacy effects persist even many decades after environmental changes occurred and need to be considered when interpreting alterations of the terrestrial carbon cycle.

How to cite: Krause, A., Arneth, A., and Rammig, A.: Legacy effects from historical environmental changes dominate future terrestrial carbon uptake , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8031, https://doi.org/10.5194/egusphere-egu2020-8031, 2020.

The vulnerability of terrestrial carbon sequestration to increases in fossil fuel emissions is one of the most important feedbacks in the Earth System.  However, the relative importance of temperature and moisture controls on regional terrestrial CO2 fluxes varies substantially and yet critical to unraveling their roles in carbon-climate feedbacks. Here, we employ the Hierarchical Emergent Constraint (HEC) to quantify an emergent relationship between spatially- explicit sensitivities of carbon fluxes to atmospheric aridity across an ensemble of Earth System Models (ESMs) and the long-term sensitivity of tropical land-carbon storage to atmospheric aridity.  Our results show that interannual fluctuations in atmospheric aridity, as an important driver of atmospheric water demand for plants, substantially impact the terrestrial carbon sink. However, this analysis, which is conditioned on observations, leads to a substantially lower feedback than predicted by ESMs alone. Furthermore, we show that a relatively small number of regions have an out-sized impact on global carbon climate-feedbacks.  These findings underscore the role of both water and temperature on carbon-climate feedbacks while the regional attribution provided by HEC points to areas for further process-based research.

How to cite: Barkhordarian, A., Bowman, K. W., Cressie, N., Jewell, J., and Baehr, J.: Emergent constraints on global carbon-climate feedbacks from regional atmospheric aridity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10830, https://doi.org/10.5194/egusphere-egu2020-10830, 2020.

Three-quarters of Finland’s land surface area (22.8 million hectares) is filled with forests. The role of remote sensing in large area inventories is crucial. The forests of Finland serve as an important resource for the nation’s nature conservation as well as for the forestry industry. Furthermore, forests are significant carbon sinks and play a great role in climate change mitigation. Research on vegetation parameter retrieval is of special relevance in order to extend our knowledge about the vegetation dynamics and terrestrial carbon stocks at regional and global scales.

In future in addition to multispectral satellites, hyperspectral satellite missions will start to provide remote sensing data to support the needs of forestry and other natural resource management practices. We investigated the influence of spectral and spatial resolution of remote sensing data on retrieval of biomass and other forest properties. The study contributed to better information productivity on forest variables in boreal forest ecosystem.

We used the remote sensing data by Sentinel-2 (10 bands, resolution 10 m) and hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m). As reference data, we used new forest resource dataset provided by the Finnish Forest Centre and additional independent in situ measurements. We applied kernel-based regression methods to relate the forest variables of interest with the remotely sensed data. Based on recent studies, we selected Gaussian process regression (GPR) and support vector regression (SVR), which have proven to work well with hyperspectral and multispectral remote sensing data. Regression estimations were performed for stem biomass, basal area, mean height, leaf area index (LAI) and main tree species. The estimation accuracies were examined with absolute and relative root-mean-square errors.

Successful forest variable estimations showed that kernel-based regression algorithms are suitable tools for quantification of forest structure and assessment of its change. The estimation accuracies between the two algorithms were similar. However, the faster SVR algorithm was found to be more practical, especially considering large scale mapping and future near real-time applications. Based on the study results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as basal area or stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.

How to cite: Halme, E., Pellikka, P., and Mõttus, M.: Utility of hyperspectral remote sensing data in estimating biomass and structure variables in boreal forest of Finland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3437, https://doi.org/10.5194/egusphere-egu2020-3437, 2020.

Land vegetation growth in the northern high latitudes (north of 50˚N) is strongly temperature limited, thus anomalously warm years are expected to result in an increased drawdown of Carbon Dioxide (CO₂) and vice versa. Piao et al (2017) concluded in an analysis of climate and CO₂ data from Point Barrow, Alaska that there was a weakening response of northern high latitude spring carbon uptake to temperature anomalies over the last 40 years. They proposed that this is due to a weakening control of temperature on productivity. We have analysed northern high latitude climate and remote sensing vegetation indices, as well as atmospheric CO₂ data at Point Barrow, with atmospheric transport analyses of the footprint seen at Barrow. Our results show no large-scale significant change in the spring NDVI-temperature relationship inside the footprint of Barrow, and across the high northern latitudes as a whole. This casts doubt on the assertion that the changing relationship between CO₂ uptake and temperature is driven by a change in vegetation response to temperature. We thus tested several alternative mechanisms that could explain the apparent weakening, including a change in interannual variability of atmospheric transport (i.e. the footprint seen by Barrow) and the spatial agreement of temperature anomalies. We find that the heterogeneity of temperature anomalies increased over time, whereas there is no significant change in interannual variation in the footprint seen by Barrow. These results offer an additional explanation for the apparent decrease in spring temperature sensitivity of northern high latitude CO₂ uptake.

How to cite: Joyce, P., Gloor, M., Brienen, R., and Buermann, W.: Influence of spatial coherence of temperature anomalies on the supposed breakdown of the warmer spring – larger carbon uptake mechanism in northern high latitudes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10261, https://doi.org/10.5194/egusphere-egu2020-10261, 2020.

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, where a hypothesis test is performed for each grid cell. When the whole image or set of grid cells is analyzed for a global effect, the problem of multiple testing arises – this applies to the study of global greening trends, which have been widely reported. Although there is a consensus on the greening patterns, there is still much debate about the attribution to CO2 fertilization, temperature rise, and land use intensification. We argue that none of the studies uses a proper statistical methodology and hence fail to identify the hotspots of “real greening". To perform statistical inference, we need to account for this multiplicity of hypothesis tests. In this work, we demonstrate how to address this issue with a permutation method based on clustering, which allows us to make robust inference on regions or patterns, using the Mann-Kendall Test as the basis. The method is illustrated by comparing global greening trends derived from five different data products which contain global data for Leaf Area Index and/or Fraction of Absorbed Photosynthetically Active Radiation: GIMMS 3g, NOAA CDR, Land Long Term Data Record, LTDR MOD15A2, and SPOT/PROBA-V data. We find that many greening trends detected in earlier studies do not withstand our rigorous significance testing. Yet we do find consistent greening trends in South East China. Our results show substantial differences in statistically significant patterns of greening and browning among the products used, but greatly reduce the focal areas of greening that should be investigated in detail with proper trend-attribution methods. 

How to cite: Cortés, J., Mahecha, M., Reichstein, M., and Brenning, A.: On the significance of global greening trends with multiple testing – an application to five data products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20245, https://doi.org/10.5194/egusphere-egu2020-20245, 2020.

New satellite products hold promise to improve our understanding of terrestrial ecosystem functioning, yet it remains a key challenge to measure global gross primary productivity (GPP) and its climate-induced fluctuations. While global estimates of GPP exist and several new satellite products hold potential for better GPP estimation, the best proxy of GPP may depend on the temporal and spatial scale considered, because available satellite products may differentially represent different time scales of vegetation dynamics. For example, vegetation indices such as NDVI and EVI may capture seasonal phenology well, while sun-induced fluorescence (SIF) may be more sensitive to short-term fluctuations of photosynthesis, and vegetation optical depth (VOD) may best represent slower changes in aboveground biomass. SIF in particular is proposed as a promising proxy for GPP as they show linear relationships with ecosystem-dependent slopes, but this may not be the case at all time scales.

In this study, we compare different Earth Observation vegetation proxies to FLUXCOM GPP in order to understand which vegetation proxy best represents GPP at sub-annual, annual and long-term scales with the aim to enable more accurate short- and long-term prediction of GPP and its drivers. We further assess the dominant climatic drivers of vegetation productivity and the vegetation’s sensitivity from sub-annual to inter-annual time scales using a multiple linear regression approach. We find the dominant drivers of vegetation productivity to differ across time scales in relation to land cover and climate.

In summary, depending on the time-scale, different satellite products best represent GPP and its climatic drivers. Considering this may help improve GPP estimates and predictions of long-term land carbon sink dynamics in the future.

How to cite: Linscheid, N., Carvalhais, N., Mahecha, M., Rammig, A., and Reichstein, M.: Time-scale dependent relations of vegetation productivity with Earth Observation based proxies and with climate drivers, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18660, https://doi.org/10.5194/egusphere-egu2020-18660, 2020.

Carbon sinks play an important role in absorbing almost half of the CO₂ emissions emanating from anthropogenic activities. However, regional contributions of atmospheric CO₂ are not well known in Southern Africa. This is partly attributed to a shortage of in-situ data, data gaps, and limitation in the theory in modeling atmospheric CO₂ dynamics. The shortage of in-situ observations and poor model skills have created a need for assimilation of observations into models to assess the variability of atmospheric levels in near real-time globally. In this study, we investigated the variabilities of XCO₂ at multi-temporal scales based on reanalysis data from the carbon tracker (CT) assimilation model over Southern Africa from the year 2000 to 2016. The ensemble empirical mode decomposition (EEMD) statistical technique was used to decompose the CO₂ time series into signals with different periodicities. The results demonstrate that the different component signals are driven by atmospheric, surface and oceanic forcings (e.g., rainfall, temperature, soil moisture, and SST).

How to cite: Thande, B. B., Mengistu Tsidu, G., and Getachew Mengistu, A.: Multi-scale Atmospheric CO2 Variabilities over Southern Africa, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13171, https://doi.org/10.5194/egusphere-egu2020-13171, 2020.

Remote sensing is widely used for monitoring vegetation status and ecosystem productivity. The increasing interest in connecting satellite vegetation indices to actual forest productivity has led to explore their relationship mainly at coarse spatial resolution and continental scale. The aim of this study is to find a connection and predict tree growth using medium resolution multispectral images and tree ring data for a sample of Italian and Austrian beech forests along latitudinal and altitudinal gradients. Beech tree ring data were collected and analyzed during the last 20 years, recording tree positions with a GPS device. MODIS pre-composite 250 m 16 days images (MOD13Q1) from 2000 to 2018 were first re-projected and quality checked using the MODIS quality assessment. Vegetation indices (NDVI and EVI) were extracted within a distance of 750 meters from every site centroid. Only deciduous forests (assessed by Corine Land Cover) with a dense canopy cover (assessed by Global Forest Change tree cover) were selected. Eight different phenology metrics were calculated using a threshold method and a modified one and then correlated with tree ring data (Basal Area Increment, BAI). The overall network and the relationship between metrics were characterized first with a Principal Component Analysis (PCA), and then evaluating the mean phenology, exploring its relationship with environmental variables (elevation, temperature). Last, the model for predicting BAI at every site was calculated for the period 2000-2009 using the metrics as predictors in a multiple linear regression. Two group of metrics were identified from PCA: the first is made of metrics based on dates (named DOY, e.g. start of growing season), the second on the vegetation index values (named VALUE, e.g. peak value,). BAI was modeled using as predictors the highest correlate from each of the two groups of metrics. BAI predictions for every site were generally significant: the 61% of the sites had at least one significant predictor, with a mean R-squared of 0.55 over the 41 sampled sites. DOY metrics were significantly related to altitude and temperature. Because of the wide latitudinal gradient of the study sites, mean annual temperatures showed higher correlations than the altitude with the DOY metrics. The mean growing season was longer for warm sites at low altitude. The relation between multispectral images and beech populations actual growth at medium spatial resolution is consistent even for those sites that are in complex environmental conditions, making possible to predict the annual diameter growth.

How to cite: Di Fiore, L., Piovesan, G., Baliva, M., and Di Filippo, A.: Combined analysis of tree rings and MODIS images to evaluate beech forest productivity along geographic gradients , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5335, https://doi.org/10.5194/egusphere-egu2020-5335, 2020.

Land-cover / land-cover change together with varying land-use intensity are forces of global importance especially within the last decades. This concerns the conversion of natural ecosystems into agricultural land, but also the intensified use of agricultural areas which both translate into respective (decreasing) carbon stocks. Unfortunately, land-cover information at field level is often missing at larger scales. While there has been some progress for a more detailed spatial and thematic characterisation within the domain of arable land, especially forest and grassland ecosystems are largely rudimentarily described. Here we present a framework to address to above mentioned issues which provides field level information on crop types, tree species and land-use intensity in grasslands as well as the underlying phenology at a national scale (Germany).

We used Sentinel 2 a/b time series (all observations between 2016-2019) at 20 m spatial resolution. For training and validation InVeKoS (LPIS) data, forest inventories as well as farmland management data covering considerable parts of Germany were employed. The number of mowing and fertilisation events as well as livestock density served as indicators to estimate land-use intensity. Crop type and tree species classification relied on a novel compositing approach which is tailor-made to operate in temperate (often cloudy) climates and is easily scalable from a local to a national level. Land-use intensity in grasslands together with land-surface phenology could be inferred via time series analysis on the seasonal evolution of vegetation indices. Both classification as well as intensity estimates also included machine learning methods (randomForest).

We could achieve ca. 90 % overall classification accuracy for crops (19 types) and ca. 75 % for tree species (4 deciduous, 4 conifers) across Germany. Crop type and tree species specific phenology varied according to underlying topography and climate conditions. We identified between 1-5 annual mowing events across Germany, for most regions 2-3. Land-use intensity estimates were in line with areas typical of high/low livestock density. Altogether, this framework and its products can well serve as a basis to support robust carbon stock estimates for different ecosystem up to the national scale.

How to cite: Doktor, D., Lange, M., and Preidl, S.: Using vegetation dynamics to generate forest species and crop type maps as well as land-use intensity measures to support carbon stock estimations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8185, https://doi.org/10.5194/egusphere-egu2020-8185, 2020.

The boreal biome accounts for approximately one third of the terrestrial carbon (C) sink. However, estimates of its individual C pools remain uncertain and are often limited to specific points in time. Here, focusing on the southern edge of the boreal forest in central Canada, we quantified the magnitude and temporal dynamics of C allocation to aboveground tree growth at a mature black spruce (Picea mariana)-dominated forest stand in Saskatchewan. We reconstructed annual total live aboveground tree biomass increment (AGBi) using a biometric approach, i.e., species-specific allometry combined with forest stand characteristics and tree ring widths collected with a C-oriented sampling design. We explored the links between boreal tree growth and ecosystem C input by comparing AGBi with eddy-covariance-derived ecosystem C fluxes from 1999 to 2015. Mean AGBi at the study site was 71 ± 7 g C m^–2 (1999–2015), which is only a minor fraction of gross ecosystem production (GEP; i.e., AGBi / GEP ≈ 9 %). Ecosystem C input and AGBi were decoupled, highlighting the potential role of direct sink limitations (temperature, water availability) on boreal tree wood formation. Moreover, C allocation to AGBi remained stable over time, with a temporal trend of near zero (–0.0001 yr^–1; p-value=0.775), contrary to significant trends in GEP (+5.72 g C m^–2 yr^–2; p-value=0.02) and ecosystem C use efficiency (i.e., NPP / GEP; –0.0041 yr^–1, p-value=0.007). These findings highlight the importance of belowground tree C investments, together with the substantial contribution of understory, ground cover and soil to the boreal forest C balance. Our quantitative insights into the magnitude and temporal dynamics of aboveground boreal tree C allocation offer additional observational constraints for terrestrial ecosystem models that, to date, are biased in converting C input to biomass, and can guide forest-management strategies for mitigating carbon dioxide emissions.

How to cite: Pappas, C., Maillet, J., Rakowski, S., Baltzer, J., Barr, A., Black, A., Fatichi, S., Laroque, C., Matheny, A., Roy, A., Sonnentag, O., and Zha, T.: Aboveground tree growth is a minor and decoupled fraction of boreal forest carbon input, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8637, https://doi.org/10.5194/egusphere-egu2020-8637, 2020.

Forest growth is considered as an important global carbon sink but its responses to environmental changes remain uncertain. Tree stems are a predominant carbon pool in temperate eucalypt forests, representing a susbstantive component of their net productivity and carbon dynamics. Despite their importance, our understanding of factors controlling stem growth in these evergreen forests remains limited partly because the dominant eucalypts lack distinct growth rings. Unravelling eucalypt species' growth responses to climate from other factors, such as competition and disturbances like fire, is challenging due to the lack of long-term growth data. To address this gap, we present six years of monthly measurements of stem-diamter changes (as basal area increment, BAI) of two co-occurring dominant eucalypts from different sub-genera (Eucalyptus obliqua and E. rubida) across seven sites in a natural temperate forest of south-eastern Australia. We used linear mixed-effect models to examine the relative importance to monthly BAI of species, monthly climate indices and their potential lag effects (temperature and rainfall), inter-tree competition, and recent fire history (long-unburnt, prescribed fires, wildfire). Monthly BAI peaked in spring and autumn and was lowest in summer with signficant differences between species during spring and summer. Overall BAI variation was most clearly associated with maximum mean temperature, having a hyperbolic relationship with increases in BAI up to species-specific temperature optima and decreases thereafter. Rainfall, particularly autumn rainfall, influenced seasonal patterns in BAI, while inter-tree competition and recent fire history were of comparatively minor importance. BAI also varied strongly between years reflecting the opportunistic growth behaviour of eucalypts including higher annual growth rates during and after periods of high rainfall and transient decreases in BAI during extended drier periods. Our study provides field-based evidence of different growth niches for co-existing eucalypts in natural temperate mixed forests and highlights the importance of intra-annual climate variation to better understand overall productivity in temperate evergreen forests.

How to cite: Hinko-Najera, N., Najera Umaña, J. C., Smith, M. G., Löw, M., Griebel, A., and Bennett, L. T.: Intra-annual stem growth of co-occurring temperate eucalypts in relation to climate variability, competition and fire history, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12798, https://doi.org/10.5194/egusphere-egu2020-12798, 2020.

Bamboo forest is an important forest type in subtropical China and is characterized by fast growth and high carbon sequestration capacity. However, the dynamics of carbon fluxes during the fast growing period of bamboo shoots and their correlation with environment factors are poorly understood. We measured carbon dioxide exchange and climate variables using open-path eddy covariance methods during the 2011 growing season in a Moso bam-boo forest (MB, Phyllostchys edulis) and a Lei bamboo. forest (LB, Phyllostachys violascens) in Zhejiang province, China. The bamboo forests were carbon sinks during the growing season. The minimum diurnal net ecosystem exchange (NEE) at MB and LB sites were - 0.64 and - 0.66 mg C m-2 s-1, respectively. The minimum monthly NEE, ecosystem respiration (RE), and gross ecosystem exchange (GEE) were - 99.3 ± 4.03, 76.2 ± 2.46, and - 191.5 ± 4.98 g C m-2 month-1, respectively, at MB site, compared with - 31.8 ± 3.44, 70.4 ± 1.41, and - 157.9 ± 4.86 g C m-2 month-1, respectively, at LB site. Maximum RE was 92.1 ± 1.32 g C m-2 month-1 at MB site and 151.0 ± 2.38 g C m-2 month-1 at LB site. Key control factors varied by month during the growing season, but across the whole growing season, NEE and GEE at both sites showed similar trends in sensitivities to photosynthetic active radiation and vapor pressure deficit, and air temperature had the strongest correlation with RE at both sites. Carbon fluxes at LB site were more sensitive to soil water content compared to those at MB site. Both on-year (years when many new shoots are produced) and off-year (years when none or few new shoots are produced) should be studied in bamboo forests to better understand their role in global carbon cycling.

How to cite: Chen, L.: Diurnal and seasonal variations in carbon fluxes in bamboo forests during the growing season in Zhejiang province, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21912, https://doi.org/10.5194/egusphere-egu2020-21912, 2020.

Drought-sensitive European beech forests are increasingly challenged by climate change. Admixing other, preferably more deep-rooting, tree species has been proposed to increase the resilience of beech forests to summer drought. This might not only alter soil water dynamics and availability, but also soil organic carbon (SOC) and total nitrogen (TN) storage in soils. Since information of these effects is scattered, our aim was to synthesize results from studies that compared SOC/TN stocks of beech monocultures with those of mixed beech stands as well as of other monocultures. We conducted a meta-analysis including 40 studies with 208, 231 and 166 observations for forest floor, mineral soil and the total soil profile, respectively. Pure conifer stands had higher SOC stocks compared to beech in general, especially in the forest floor with up to 200% (larch forests). Other broadleaved tree species (ash, oak, lime, maple, hornbeam) showed in comparison to beech lower SOC storage in the forest floor, with little impact on total stocks.  Similarly, for mixed beech-conifer stands we found significantly increased SOC stocks of >10% and a small increase in TN stocks of approx. 4% compared to beech monocultures, which means a potential SOC storage increase of >0.1 t ha^-1yr^-1 (transformation of mineral soil to 100 cm depth). In contrast, mixed beech-broadleaved stands did not show a significant change in total SOC stocks. Currently, the influence climatic and soil parameters on SOC changes due to admixture of other tree species is analyzed based on this dataset. This is expected to facilitate an assessment which mixtures with beech have the largest potential towards increasing SOC stocks.

How to cite: Rehschuh, S. and Dannenmann, M.: Admixing other tree species to European beech forests: Effects on soil organic carbon and total nitrogen stocks. A review., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10430, https://doi.org/10.5194/egusphere-egu2020-10430, 2020.

EGU Abstract, 3-8 May, Vienna 2020
Session BG3.19 
Climate change and adaptive forest management: Effects, Methods, and Objectives

Positive feedback from climate warming to carbon sequestration in boreal forests
Pekka Kauppi1,2, Tomas Lundmark2 and Annika Nordin2
1University of Helsinki, Department of Forest Sciences, POBOX 27, Fin-00014 University of Helsinki, Finland
2 Swedish University of Agricultural Sciences, Dpt Forest Ecology and Management, 90183 Umeå, Sweden
pekka.kauppi@helsinki.fi
tomas.lundmark@slu.se
annika.nordin@slu.se

'Wovon man nicht sprechen kann, darüber muß man schweigen.' (“Whereof one cannot speak, thereof one must be silent.”). This quote of Ludwig Wittgenstein is thought-provoking regarding beneficial effects of climate change. Logically, climate warming must provoke favorable environmental effects in some regions and over certain periods of time despite the prospects of dramatic detrimental effects of global warming on the environment in the long term. Our focus is on boreal forests in recent past.
Devastating effects of climate warming on terrestrial ecosystems have been recorded in many parts of the world. Heat waves have enhanced wildfires. In Australia alone, wildfires disturbed more than six million hectares of land in 2019-2020. Will climate warming undermine the contribution of land use management to climate change mitigation? - Most surprisingly, we report here a reverse relationship from north Europe. Climate warming has amplified the favorable impacts of land management on carbon sequestration. This is a forest-climate paradox, maybe temporary and anecdotal but persistent and firmly documented in Finland, Norway and Sweden since 1990.
Springtime is the most interesting season for forest biota in north Europe. During spring in north Europe, soil is rich in moisture from the snow melt. Days are long as of the beginning of April. Cloudy weather is unusual in the springtime. When spring comes early, there is plenty of solar radiation and water available for photosynthesis and growth. Warm spring evokes an early bud burst. Conversely, cold spring delays the onset of the growing season. April and May temperatures were exceptionally high during the period 1990-2013 (Figs. 1a and 1b) . Similar patterns of climate warming were observed in Norway and Sweden.
 
 
Figure 1a. Average temperature in Finland in April during 1847-2013 (degrees centigrade).
 
 
Figure 1b. Average temperature in Finland in May during 1847-2013 (degrees centigrade).
Especially during 1990-2019 the growing seasons in north Europe turned out to be long. The Net Primary Production and forest carbon sink improved. Forest increment in north Europe approximately doubled from 1970 to 2010 responding to multiple drivers . A combination of successful forest management and environmental change created an interesting paradox promoting forest ecosystem services. Carbon sink improved concomitantly with increasing harvests for the forest industries, an important economic sector in the region.
In so far, climate warming specifically in north Europe has contributed significantly to the evolution and persistence of the carbon sink and to fossil fuel substitution. Future research is needed to monitor this feedback from climate warming to carbon sequestration.

How to cite: Kauppi, P., Lundmark, T., and Nordin, A.: Positive feedback from climate warming to carbon sequestration in boreal forests, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12784, https://doi.org/10.5194/egusphere-egu2020-12784, 2020.

Ecosystems are expected to face a significantly warmer and drier climate in the coming decades. Experiments have tried to unravel drought responses of ecosystems in mesic and humid biomes, but the structure and functioning of these systems may change when climatic regime shifts occur. Here, we summarize major mechanisms typical of drylands and indicate how these may come into play when current mesic ecosystems face tipping points in a warmer and drier world.

These dryland mechanisms of ecosystem functioning encompass (i) processes of vegetation development, such as self-organization of vegetation patchiness and formation of biological soil crust, (ii) biologically driven biogeochemical and physiological processes, such as drying-wetting cycles and hydraulic redistribution, and (iii) abiotically driven biogeochemical processes, such as photochemical degradation of organic matter and soil hydrophobicity. We present insights from published studies and original model simulations and mapping, and formulate hypotheses on thresholds and spatial locations beyond which dryland mechanisms are expected to operate in non-xeric ecosystems. Notably, for dryland mechanisms to get activated elsewhere there is no need for non-xeric biomes to become actual drylands. With a globally increasing area exposed to gradually rising temperatures, moderate decline in precipitation, and increasing frequency, duration and intensity of extreme heat and drought events, we envision that dryland mechanisms will increasingly control ecosystem functioning in many regions of the world.

How to cite: Grünzweig, J. M., De Boeck, H. J., Rey, A., Tzuk, O., Meron, E., Flores, O., Santos, M. J., and Bahn, M.: Emerging mechanisms of ecosystem functioning in a warmer and drier world , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13465, https://doi.org/10.5194/egusphere-egu2020-13465, 2020.

The modelled global vegetation for the end of the 21st century is currently is insufficiently constrained
by climate models. A significant proportion of that uncertainty has been attributed to the limitations
of current Dynamic Global Vegetation Models (DGVMs), and the misrepresentation of mortality, dis-
turbance and regrowth within forests. Improving the simulation of the underlying processes of de-
mographic change is of primary importance in the development of predictors of future climate.

Here we present the Robust Ecosystem Demography (RED), a new dynamical vegetation model which
simulates the size-structure of forests by partitioning the population of a Plant Functional Type (PFT)
into mass classes. Allometric scaling of mortality and growth across mass classes allows for a variety
of complex demographic processes to be captured, such as disturbances and regrowth. Competition
among PFTs is done purely through restricting the recruitment of new vegetation to unshaded space.
RED represents a reduction of complexity from more numerically unwieldy cohort DGVMs which
simulate both size and patch dimensions. The limited number of dimensions and simple competitive
regime allows the equilibrium state to be solved for analytically, providing two potential functions - (i)
Avoiding-spinning up by providing an equilibrium state for intilisation. (ii) Insights into the demog-
raphy of vegetated areas, arising from parameter tuning to fit observation, such as coverage or carbon
mass. When paired with a rate of mortality or carbon assimilate rate gives, respectfully, required as-
similate or mortality rates.
We demonstrate the model functionality using offline UKESM PFT carbon assimilate rates, paired
with observed vegetation cover from the ESA LC_CCI datasets for the 9 different PFTs. From this
dataset we calibrate a novel global equilibrium mortality map for each PFT and show the competitive
and successional behaviour of dynamical runs with convergence to the fitted equilibrium. Finally, we
explore underlying ecological questions that emerge from the equilibrium solutions.

How to cite: Argles, A., Moore, J., and Cox, P.: RED DGVM: simple approach to modelling vegetation with novel implications., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16276, https://doi.org/10.5194/egusphere-egu2020-16276, 2020.

With human land-use activities expected to increase in the future it is important to understand how LULCC (Land-Use Land-Cover Change) activities affect the Earth’s surface, climate and biogeochemical cycles. Here we use the CMIP6 version of the EC-Earth3 Earth System Model (ESM) to assess the impacts of LULCC on surface fluxes of carbon (C) and nitrogen (N). EC-Earth is one of the first ESMs to interactively couple a 2nd generation dynamical vegetation model (LPJ-GUESS) with mechanistic C-N dynamics in soil and vegetation to an atmospheric model. The size, age structure, temporal dynamics and spatial heterogeneity of the vegetated landscape are represented and simulated dynamically in LPJ-GUESS. Such functionality has been argued to be essential to correctly capture biogeochemical and biophysical land-atmosphere interactions on longer timescales and has been shown to improve their representation compared to more common area-based vegetation schemes. The patch based structure of LPJ-GUESS also makes it possible to represent the history (soil, litter status) of a single patch as it might have been involved in several land-use transitions. We focus on the effects of gross land-use transitions (“land-hist”), net land-use transitions (“land-noShiftcultivate”) and fixing land-use at 1850 levels (“land-noLu”).

Global carbon pools increase in simulations without LUC while they decline in those applying LUC, with gross-transitions resulting in values around 3% (or 75 Pg) lower than simulations with net-transitions. This is mainly driven by vegetation carbon changes in the tropical to mid-latitude regions where gross-transitions lead to a significantly higher decrease in high vegetation cover. Furthermore, this is reflected differently for different species, e.g. while there is no change in the LAI of boreal needleleaf trees in net-transitions, their presence is significantly reduced in gross transition scenarios giving way to the growth of fast-growing shade-intolerant species. Moreover, fire-fluxes, which in these experiments are mainly driven by fuel-availability, are also lowest in the gross-transition simulations.

Finally, we will show that the level of complexity with which shifting cultivation is treated has implications for the biogeophysical feedbacks in ESMs resulting from changes to surface albedo and latent heat exchange.

The experiments we conducted clearly indicate the benefits of dynamic vegetation and the importance of using gross transitions in land use-change (LUC) studies. 

How to cite: Nieradzik, L., Wårlind, D., Miller, P., Lindeskog, M., Anthoni, P., Arneth, A., and Smith, B.: Effects of an advanced land-use scheme and dynamic vegetation on the terrestrial carbon cycle in EC-Earth, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19730, https://doi.org/10.5194/egusphere-egu2020-19730, 2020.

South Asia is one of the world’s most vulnerable regions to climate change and provides a home to approximately 1.7 billion people. South Asian vegetation is essential for ecosystem services, biodiversity and carbon storage in the region. Vegetation distribution and biome niches are likely to be severely altered by future climate change and rising atmospheric CO₂ concentration. Assessing how ecosystems will respond to these changes is of vital importance. We used the aDGVM2 to simulate vegetation patterns of South Asia under RCP4.5 and RCP8.5. We found good agreement between observed and simulated biomass, height and potential vegetation maps. 

Model results show that large areas are susceptible to biome shift by the end of the 21st century. Woody encroachment is predicted in open savanna regions which are at high risk of transitioning into forest. We simulated vegetation under both scenarios with fixed CO₂ concentration and found decreased tree dominance and biomass. Simulations under elevated CO₂ concentrations predicted an increase in biomass, canopy cover, tree height and decrease in evapotranspiration. Changes in above ground biomass and canopy cover trigger biome shifts toward trees dominated the system. C3 vegetation is not saturated at current CO₂ concentrations as the model simulated strong CO₂ fertilization effect which will increase further with the rising CO₂. Although there is considerable uncertainty in the biome projections, the geographic patterns of biomes are generally consistent across the RCP4.5 and RCP8.5 scenarios. The results provide potential future trajectories of the response of South Asian vegetation to the climate change. The results will help to understand the regional climate-vegetation interaction and to develop regional strategies for biodiversity conservation to cope with climate change. 

How to cite: Kumar, D., Pfeiffer, M., Gaillard, C., Langan, L., Martens, C., and Scheiter, S.: Impact of climate change on biome distribution and productivity of the tropical ecosystems under RCP scenarios in South Asia, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9111, https://doi.org/10.5194/egusphere-egu2020-9111, 2020.

El Niño-Southern Oscillation (ENSO) is the primary cause of interannual variations in the global carbon cycle because ENSO-driven extensive teleconnection over continents affects the terrestrial ecosystem process. ENSO is an interannual phenomenon, but it also has decadal variability. The ENSO-like SST pattern and ENSO characteristic, e.g. ENSO amplitude, change on decadal timescales. However, the influence of decadal ENSO variability on global carbon cycle has not yet been fully examined. Here we examined the impacts of decadal ENSO variability on decadal variation of terrestrial carbon flux by analyzing fully coupled pre-industrial control simulation of the Community Earth System Model 1 large ensemble (CESM1-LE). Considerable decadal variability of atmosphere-to-land carbon flux exists and this terrestiral carbon flux is mainly modulated by the tropical biosphere on decadal timescales as well as on interannual timescales. We found that there are two different pathways, which can explain about 36% of the decadal variations in terrestrial carbon flux. First, long-term climate change over tropics induced by decadal tropical Pacific SST variability regulates the terrestrial productivity and hence atmospheric CO₂ on decadal time scale. Second, decadal changes in asymmetric terrestrial ecosystem’s response to ENSO events, resulted from decadal modulation of ENSO amplitude, generate decadal variability of terrestrial carbon flux.

Key words: Global Carbon Cycle, El Niño-Southern Oscillation (ENSO), Pacific Decadal Variability, ENSO asymmetry, Decadal NBP variability

How to cite: Park, S., Kim, J.-S., Kug, J.-S., Stuecker, M. F., Kim, I.-W., and Williams, M.: Two aspects of decadal ENSO variability modulating the long-term global carbon cycle, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6491, https://doi.org/10.5194/egusphere-egu2020-6491, 2020.