The EU aims at reaching carbon neutrality by 2050 and Finland by 2035. Net negative greenhouse gas emissions are needed to comply with the targets of the Paris climate agreement. We integrated results of three spatially distributed model systems (FRES, PREBAS, Zonation) to evaluate the potential to reach this goal at both national and regional scale in Finland, by simultaneously considering protection targets of the EU biodiversity strategy. Modelling of both anthropogenic emissions and forestry measures were carried out, and forested areas important for biodiversity protection were identified based on spatial prioritization. We used scenarios until 2050 based on mitigation measures of the national climate and energy strategy, forestry policies and predicted climate change, and evaluated how implementation of these scenarios would affect greenhouse gas fluxes, carbon storages, and the possibility to reach the carbon neutrality target. Potential new forested areas for biodiversity protection according to the EU 10% strict protection target provided a significant carbon storage (426-452 TgC) and sequestration potential (-12 to -17.5 TgCO₂eq a^-1) by 2050, indicating complementarity of emission mitigation and conservation measures. Assuming a price of ca. 80 € ton^-1 CO₂eq according to the current level of the EU emission trading system (EU ETS), the economic value of the carbon sequestration of the current protected areas in Finland would be about 500 million € per year. These areas thus provide ecosystem services of significant economic value. The results of our study can be utilized for integrating climate and biodiversity policies, accounting of ecosystem services for climate regulation, and delimitation of areas for conservation.

How to cite: Forsius, M., Junttila, V., Kujala, H., Savolahti, M., and Schulz, T.: Present and future importance of protected areas as carbon sinks and storages in Finland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1283, https://doi.org/10.5194/egusphere-egu24-1283, 2024.

Climate change and biodiversity loss are increasingly considered jointly, particularly to find optimal solutions for both crises and to avoid negative side-effects and feedbacks. Much research has been devoted to predicting the effects of climatic changes on the distribution of species, but the consequences of biodiversity changes for the climate system are less understood. For instance, what are the main aspects (species richness, functional diversity, land cover patchiness) and mechanisms through which biodiversity interacts with the climate? Do landscapes with different levels of diversity contribute differently to climate regulation or feedbacks? How do human choices such as nature conservation or natural resources production affect the climate? To address these questions, we combine observational and modelling approaches in a collaborative effort of ecologists and climate scientists.

First, we present how ecosystem diversity affects forests’ climate response (indicated by interannual variability in summer NDVI) and climate effect (indicated by interannual variability in summer LST), using 20 years (2003-2022) of remote sensing data at 1 km resolution over Europe. We consider different diversity levels (taxonomic, functional, structural) together with various ecosystem, topography, soil, and climate predictors in a multiple linear regression with Ridge regularisation. This approach allows isolating the effects of specific biodiversity aspects (e.g. tree species richness, forest edge density), functional properties (e.g. leaf type, leaf traits), and structure (e.g. canopy height, tree cover density), and determining the sign and magnitude of their contribution. We show which aspects and scales of biodiversity are relevant for ecosystem stability and climate regulation, respectively, and classify forests into response and effect types that could be considered in coupled biosphere-atmosphere models.

Second, we discuss how biodiversity aspects can be integrated into the coupled biosphere-atmosphere regional climate model COSMO-CLM² to quantify their effects on land-atmosphere interactions and feedbacks over Europe. We demonstrate one approach, utilising future land cover scenarios derived from the Nature Futures Framework that represent different value perspectives on nature (intrinsic, instrumental, and relational), habitat types from EUNIS (European Nature Information System), and species abundances from EVA (European Vegetation Archive). Our results show temperature differences of up to several degrees locally, with enhanced temperature sensitivities under hot and dry conditions. Such findings can help identify synergies between biodiversity conservation, climate change mitigation, and adaptation, and support the development of effective policy solutions.

Finally, this presentation will provide perspectives for research at the interface of biodiversity and climate change.

How to cite: Sieber, P., Schwaab, J., Karger, D., and Seneviratne, S. and the FeedBaCks consortium: Exploring climate-biodiversity interactions in observational data and models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19043, https://doi.org/10.5194/egusphere-egu24-19043, 2024.

Forest carbon sequestration is a key part of the European transition to carbon neutrality. Quantification of forest carbon sequestration rates relies on relies on successful integration of high volumes of remote sensing and in-situ data arriving at ever increasing velocities with a bewildering variety of “long tail” and legacy data. Research Infrastructures (RIs) can add value to these data by supporting their harmonised, cross-site collection, curation and publication and by providing a platform for assessing data veracity. Integration of RI networks through site co-location and standardised observation methods has been proposed as one way of dealing with the Big Data needed to quantify societally relevant environmental processes including those related to the carbon cycle. However, the full potential of RI network integration as a tool to improve environmental understanding has yet to be realised.

Here, we review current successes, identify challenges to better integration, and suggest ways forward. We provide recommendations for scientists, site managers and policy makers that will support the transition to a Big Data approach to quantifying and communicating forest carbon sequestration using the Swedish situation as an example.

How to cite: Futter, M., Högbom, L., Moldan, F., Peacock, M., and Villwock, H.: Challenges and Opportunities for Research Infrastructure Co-location to Improve Understanding of Terrestrial Carbon Cycling in Northern European Forests, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15707, https://doi.org/10.5194/egusphere-egu24-15707, 2024.

The loss of biodiversity from human activities on land is a widely-recognized, worldwide problem. Since the advent of the industrial revolution the loss of plant and animal species has increased dramatically, with 25% of species now at risk of extinction. Conventions and targets to protect biodiversity have been implemented, but with limited success. The Aichi targets for 2020, for example, were almost all missed, with worsening trends for 12 out of the 20 targets. One reason for this failure is the ineffective application of broad-scale measures that are not tailored to the underlying causes of biodiversity loss. Knowledge on the spatial and temporal distribution of anthropogenic drivers of biodiversity loss would therefore enable targeted interventions that address location-specific stressors and thus would be better-adapted measures to protect biodiversity.

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has identified five main drivers of anthropogenic origin as the causes of biodiversity loss: land use, natural resource extraction, climate change, pollution, and invasive alien species. However, when seeking to quantify impacts on biodiversity, these drivers are still usually treated separately. We develop a Biodiversity Pressure Index (BPI) by quantifying and mapping data for nine indicators of the five drivers into a single, annually changing index with a spatial resolution of 0.1° at global scale covering the period 1990-2020.

We find that large areas (approximately 86%, including Antarctica, Greenland) are under major human pressure and that almost all areas have experienced an increase (about 96% of land) in pressure over the past thirty years. Industrialised regions had high pressure levels already in 1990 and continue to do so in 2020, whereas regions with rapid economic growth setting in after 2000 where low in pressure in 1990, but show high pressure levels today. Whilst areas impacted by human activities are increasing, areas of wilderness are decreasing to a point that in 2020, only 0.02% of the terrestrial land are entirely free from human influence. (Sub-) tropical wetlands and temperate grasslands are the biomes with the highest pressures today. And whilst land use is still one of the main factors, climate change - especially increasing temperature - is one of the major recent and future threats to biodiversity.

How to cite: Ramm, K., Brown, C., Arneth, A., and Rounsevell, M.: Human pressure on global land ecosystems and biodiversity increases notably from 1990-2020 - Development of a spatially explicit Biodiversity Pressure Index (BPI), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5708, https://doi.org/10.5194/egusphere-egu24-5708, 2024.

The significance of considering vertical layers in studying soil organic carbon (SOC) dynamics within wetlands arises from the interplay of hydrological and ecological factors across various soil depths, where anaerobic conditions prevail in the deeper layers. This anaerobic environment significantly influences microbial processes, leading to methane production rather than carbon dioxide. Factors such as the accumulation of organic material, temperature gradients, and fluctuations in the water table contribute to diverse SOC dynamics across different vertical strata. Understanding these variations in vertical layers is crucial for accurate assessments of carbon stocks, greenhouse gas emissions, and the overall role of wetlands in the global carbon cycle. Such understanding is essential for devising effective conservation and management strategies, particularly in the face of climate change and land-use modifications impacting wetlands.  To model these dynamics, a vertical extension of the Rothamsted Carbon (RothC) model can be successfully employed in conjunction with the Richardson equation. This combined approach simulates the influence of soil moisture flux on the transport of carbon throughout the soil column. The specific scenario examined is focused on the growth of rice in the Ebro Delta lands and on the carbon flux emissions in the Ria de Aveiro Coastal lagoon, both sites being part of the Long-Term Ecological Research (LTER) network and the eLTER RI community.  This work contributes to the research activities carried out by the authors within the projects H2020 eLTER PLUS, HE RESTORE4Cs, and PNRR - “National Biodiversity Future Centre”, funded by the European Union – NextGenerationEU.

How to cite: Marangi, C., Bohaienko, V., Diele, F., Martiradonna, A., and Provenzale, A.: A vertical RothC model for simulating the Soil Organic Carbon  dynamics in coastal wetland environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10869, https://doi.org/10.5194/egusphere-egu24-10869, 2024.

Mounting evidence from field observations has shown that high functional diversity is associated with strong ecosystem resilience and stability. However, plant ecology studies have focused on the passive response of global ecosystems to climatic changes while the impacts of plant-functional diversity on climate including its feedback are seldom addressed. Moreover, state-of-the-art climate models are insufficient to address such topics. Their land component models cover only a restricted range of present-day plant features, so that adaptation at the sub-grid scale is ignored. Based on a process-based plant functional trade-off scheme developed by Kleidon and Mooney (2000), we have set up a new vegetation model JeDi-BACH into the land component of the ICON-Earth System Model (ICON-ESM). The advantage of this new model is that the representation of global vegetation is an emergent outcome of environmental filtering following several well-known fundamental functional trade-offs that link plant functions to abiotic and biotic attributes. In such a way, plants dynamically adjust to the changing environment and meanwhile modify climate. With this new model, we present a series of sensitivity studies investigating the effect of plant trait diversity on the coupled vegetation-climate system in a coupled land-atmosphere setup. We found that high plant diversity ecosystems tend to stabilize terrestrial climate in a high water-turnover state, leading to a wet and cool climate. The enhancement in evapotranspiration with increasing diversity found in our study is consistent with the BEF (Biodiversity-Ecosystem Functioning) relationship derived from the field studies. Our modeling results demonstrate the importance of the "biodiversity-climate feedback" and highlight the role of plant functional diversity in shaping a robust climate.

Kleidon, A. and Mooney, H. A.: A global distribution of biodiversity inferred from climatic constraints: Results from a process-based modelling study, Glob. Chang. Biol., 6(5), 507–523, doi:10.1046/j.1365-2486.2000.00332.x, 2000.

How to cite: Hu, P., Reick, C. H., Kleidon, A., and Claussen, M.: Plant diversity-climate interactions from a modeling perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10432, https://doi.org/10.5194/egusphere-egu24-10432, 2024.

There exists an urgent need to address the ongoing nature crisis, and businesses must play a pivotal role in fostering positive change. As a result, there has been a significant increase in corporate attention on biodiversity. In response to this attention, several frameworks for companies to report their impacts on nature have emerged, including the EU’s Corporate Sustainability Reporting Directive (CSRD) and the Taskforce on Nature-related Financial Disclosures (TNFD). These frameworks set out steps for companies wanting to make a positive impact and include nature in business, particularly through determining their proximity to ecologically sensitive locations.

Our advanced prioritisation tool enables screening of any site in the world (both terrestrial and marine assets) for its proximity to ecologically sensitive locations. This tool incorporates metrics including Ecological Integrity, Decline in Ecological Integrity, Areas of High Physical Water Stress, Areas of High Potential Ecosystem Services and Biodiversity Sensitive Areas. Our tool aligns with best practices and with reporting guidance and standards (TNFD and CSRD).

By leveraging our screening tool, businesses can turn data-driven insights into responsible nature-positive actions.

How to cite: Piovano, T., Jenkins, R., Burnell, L., Burke, C., and Wilebore, B.: A comprehensive tool for prioritising ecologically sensitive locations and driving nature-positive actions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16765, https://doi.org/10.5194/egusphere-egu24-16765, 2024.

Albedo, defined as the ratio between reflected radiation and total incoming radiation, is a key variable in the Earth radiative budget. In a fast changing climate with more frequent extreme events, such as droughts and excessive heat, vegetation is under constant stress. Such stress factors might modify the tree physiology, the reflectivity of individual leaves, and, eventually, the forest albedo as an entity. This might alter the local radiative budget and contribute to changes in the local climate, e.g., intensifying drought - a potential feedback loop. The understating of those effects might be further complicated by the occurrence of clouds. Therefore, this study presents spectral solar measurements of upward and downward irradiance that are used to determine the spectral albedo over a forest canopy. Since June 2021, ongoing measurements are performed on top of the Leipzig Canopy Crane located in the Leipzig floodplain forest. The measurements are separated for illumination geometries, i.e., the solar zenith angle, as well as for different cloud conditions. The interpretation of the measurements is aided and validated by coupled radiative transfer simulations using the library for radiative transfer model (libRadtran) and the Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE2.0) model. Both models allow for simulations in the visible, near- and far-infrared wavelength range. By that, the impact of clouds on the spectral and broad band albedo, as well as the net radiative budget can be investigated. First simulations revealed that the presence of clouds enhance the spectral forest albedo. The magnitude of the effect is controlled by the cloud optical thickness, i.e., the ratio of direct and diffuse radiation. The enhancement is more pronounced for small solar zenith angles. However, the effect from clouds appears to be smaller than influences of variations in the surface properties. The presentation aims to outline the measurement set-up and strategy, and to discuss preliminary results. Furthermore, the new, iterative coupling of the atmosphere and soil-vegetation model is presented, which aims to improve the understating of cloud-vegetation radiation interactions.

How to cite: Wolf, K., Schäfer, M., Shekhar Jha, S., Weigelt, A., Richter, R., Kühne, T., Ehrlich, A., Jäkel, E., and Wendisch, M.: Impact of clouds on the forest albedo measured at the Leipzig Canopy Crane - A pilot study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3894, https://doi.org/10.5194/egusphere-egu24-3894, 2024.

To develop climate change mitigation strategies, it is necessary to identify variables that facilitate the modeling of prospective scenarios. There are a large number of variables that must be analyzed in an integrated manner in order for scenarios to be proposed that include the particularities of a given area, measuring the possible effects of this phenomenon in terms of productivity. Identifying and analyzing variables and their variations over time enables fundamental predictions to understand the potential environmental impacts on ecosystems and human activity. Understanding these variables is important to support decision-making, policy development and implementing actions that help reduce greenhouse gas emissions and guarantee food security. This research study not only seeks to determine the technical variables, which are fundamental in predictive models, but also sets out to emphasize the importance of integrating social and economic aspects that can become decisive factors.

Rural areas in Colombia, with the department of Cundinamarca used as a case study, have been affected in various ways by climate change [1]. This scenario represents a challenge that needs to be addressed in a prioritized manner to ensure food security and independence, economic development, sustainability, livestock and human health, among other aspects that precisely relate to the development of a region. To propose solutions, artificial intelligence (AI) is emerging as an innovative alternative that makes it possible to process large amounts of data and find patterns, correlations and trends that can provide an understanding of the variables’ behavior, as well as develop systems to adapt to climate change. Therefore, identifying variables to apply advanced AI models to forecast the effects of climate change in a given region is a fundamental step towards generating an efficient and accurate tool to establish mitigation actions in a region that, together with the implementation of policies and actions that promote sustainability, will strengthen communities’ current capacity for action.

The variables identified include economic structure, access to technological resources, governance models, education levels, access to public services, poverty rate, demographics and crop price references. Through AI models and an in-depth analysis of available information, these types of models will become more precise for the implementation of early warning systems (EWS) and sustainable practices, as well as strengthen infrastructure. Historically in Colombia, rural areas are the most vulnerable to climate change given that they have fewer economic and technological resources that enable them to adapt to its impacts, with the most frequent phenomena being torrential rainfall, extreme flooding and forest fires; events associated with climate change.

• Peña Q, Andrés J, Arce B, Blanca A, Boshell V, J. Francisco, Paternina Q, María J, Ayarza M, Miguel A, & Rojas B, Edwin O. (2011). Trend analysis to determine hazards related to climate change in the Andean agricultural areas of Cundinamarca and Boyacá. Agronomía Colombiana, 29(2), 467-478. Retrieved January 09, 2024, from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0120-99652011000200014&lng=en&tlng=en.

How to cite: Felibert Álvarez, D. J., Guineme Baracaldo, M. E., Triana Forero, J. A., Solano Meza, J. K., and Rodrigo-Ilarri, J.: Identification of socio-economic variables to implement advanced artificial intelligence models to manage climate change risk, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12455, https://doi.org/10.5194/egusphere-egu24-12455, 2024.