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Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process-based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system.

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N), and phosphorus (P) concentrations play important roles in plant economic strategies and ecosystem functioning. Various global maps of these foliar traits have been generated using statistical upscaling approaches based on in-situ trait observations. Here, we intercompare such global upscaled foliar trait maps at 0.5° spatial resolution (six maps for SLA, five for N, three for P), categorize the upscaling approaches used to generate them, and evaluate the maps with trait estimates from a global database of vegetation plots (sPlotOpen). We disentangled the contributions from different plant functional types (PFTs) to the upscaled maps and quantified the impacts of using different plot-level trait metrics on the evaluation with sPlotOpen: community weighted mean (CWM) and top-of-canopy weighted mean (TWM). We found that the global foliar trait maps of SLA and N differ drastically and fall into two groups that are almost uncorrelated (for P only maps from one group were available). The primary factor explaining the differences between these groups is the use of PFT information combined with remote sensing-derived land cover products in one group while the other group mostly relied on environmental predictors alone. The maps that used PFT and corresponding land cover information exhibit considerable similarities in spatial patterns that are strongly driven by land cover. The maps not using PFTs show a lower level of similarity and tend to be strongly driven by individual environmental variables. Upscaled maps of both groups were moderately correlated to sPlotOpen data aggregated to the grid-cell level (R = 0.2–0.6) when processing sPlotOpen in a way that is consistent with the respective trait upscaling approaches, including the plot-level trait metric (CWM or TWM) and the scaling to the grid cells with or without accounting for fractional land cover. The impact of using TWM or CWM was r

Interannual variability of vegetation activity (i.e., photosynthesis) is strongly correlated with El Niño Southern Oscillation (ENSO). Globally, a reduction in carbon uptake by terrestrial ecosystems has been observed during the ENSO warm phase (El Niño) and the opposite during the cold phase (La Niña). However, this global perspective obscures the heterogeneous impacts of ENSO at regional scales. Particularly, ENSO has contrasting impacts on climate in northern South America (NSA) depending on the ENSO phase and geographical location, which in turn affect the activity of vegetation. Furthermore, changes of vegetation activity during multiple ENSO events are not well understood yet. In this study, we investigated the spatial and temporal differences in vegetation activity associated with ENSO variability and its three phases (El Niño, La Niña, Neutral) to identify hotspots of ENSO impacts in NSA, a region dominated by rainforest and savannas. To achieve this, we investigated time series of vegetation variables from 2001 to 2014 at moderate spatial resolution (0.0083°). Data were aggregated through dimensionality reduction analysis (i.e., Global Principal Component Analysis). The leading principal component served as a proxy of vegetation activity (VAC). We calculated the cross-correlation between VAC and the multivariate ENSO index separately for each ENSO phase. Our results show that El Niño phase has a stronger impact on vegetation activity both in intensity and duration than La Niña phase. Moreover, seasonally dry ecoregions were more susceptible to El Niño impacts on vegetation activity. Understanding these differences is key for regional adaptation and differentiated management of ecosystems.

Soil is central to the complex interplay among biodiversity, climate, and society. This paper examines the interconnectedness of soil biodiversity, climate change, and societal impacts, emphasizing the urgent need for integrated solutions. Human-induced biodiversity loss and climate change intensify environmental degradation, threatening human well-being. Soils, rich in biodiversity and vital for ecosystem function regulation, are highly vulnerable to these pressures, affecting nutrient cycling, soil fertility, and resilience. Soil also crucially regulates climate, influencing energy, water cycles, and carbon storage. Yet, climate change poses significant challenges to soil health and carbon dynamics, amplifying global warming. Integrated approaches are essential, including sustainable land management, policy interventions, technological innovations, and societal engagement. Practices like agroforestry and organic farming improve soil health and mitigate climate impacts. Effective policies and governance are crucial for promoting sustainable practices and soil conservation. Recent technologies aid in monitoring soil biodiversity and implementing sustainable land management. Societal engagement, through education and collective action, is vital for environmental stewardship. By prioritizing interdisciplinary research and addressing key frontiers, scientists can advance understanding of the soil biodiversity–climate change–society nexus, informing strategies for environmental sustainability and social equity.

Climate extremes are on the rise. Impacts of extreme climate and weather events on ecosystem services and ultimately human well-being can be partially attenuated by the organismic, structural, and functional diversity of the affected land surface. However, the ongoing transformation of terrestrial ecosystems through intensified exploitation and management may put this buffering capacity at risk. Here, we summarise the evidence that reductions in biodiversity can destabilise the functioning of ecosystems facing climate extremes. We then explore if impaired ecosystem functioning could, in turn, exacerbate climate extremes. We argue that only a comprehensive approach, incorporating both ecological and hydrometeorological perspectives, enables to understand and predict the entire feedback system between altered biodiversity and climate extremes. This ambition, however, requires a reformulation of current research priorities to emphasise the bidirectional effects that link ecology and atmospheric processes.

Sun-induced fluorescence in the far-red region (SIF) is increasingly used as a remote and proximal-sensing tool capable of tracking vegetation gross primary production (GPP). However, the use of SIF to probe changes in GPP is challenged during extreme climatic events, such as heatwaves. Here, we examined how the 2018 European heatwave (HW) affected the GPP–SIF relationship in evergreen broadleaved trees with a relatively invariant canopy structure. To do so, we combined canopy-scale SIF measurements, GPP estimated from an eddy covariance tower, and active pulse amplitude modulation fluorescence. The HW caused an inversion of the photosynthesis–fluorescence relationship at both the canopy and leaf scales. The highly nonlinear relationship was strongly shaped by nonphotochemical quenching (NPQ), that is, a dissipation mechanism to protect from the adverse effects of high light intensity. During the extreme heat stress, plants experienced a saturation of NPQ, causing a change in the allocation of energy dissipation pathways towards SIF. Our results show the complex modulation of the NPQ–SIF–GPP relationship at an extreme level of heat stress, which is not completely represented in state-of-the-art coupled radiative transfer and photosynthesis models.

While the eddy covariance (EC) technique is a well-established method for measuring water fluxes (i.e., evaporation or 'evapotranspiration’, ET), the measurement is susceptible to many uncertainties. One such issue is the potential underestimation of ET when relative humidity (RH) is high (>70%), due to low-pass filtering with some EC systems. Yet, this underestimation for different types of EC systems (e.g. open-path or closed-path sensors) has not been characterized for synthesis datasets such as the widely used FLUXNET2015 dataset. Here, we assess the RH-associated underestimation of latent heat fluxes (LE, or ET) from different EC systems for 163 sites in the FLUXNET2015 dataset. We found that the LE underestimation is most apparent during hours when RH is higher than 70%, predominantly observed at sites using closed-path EC systems, but the extent of the LE underestimation is highly site-specific. We then propose a machine learning based method to correct for this underestimation, and compare it to two energy balance closure based LE correction approaches (Bowen ratio correction, BRC, and attributing all errors to LE). Our correction increases LE by 189% for closed-path sites at high RH (>90%), while BRC increases LE by around 30% for all RH conditions. Additionally, we assess the influence of these corrections on ET-based transpiration (T) estimates using two different ET partitioning methods. Results show opposite responses (increasing vs. slightly decreasing T-to-ET ratios, T/ET) between the two methods when comparing T based on corrected and uncorrected LE. Overall, our results demonstrate the existence of a high RH bias in water fluxes in the FLUXNET2015 dataset and suggest that this bias is a pronounced source of uncertainty in ET measurements to be considered when estimating ecosystem T/ET and WUE.

Hot temperature extremes are changing in intensity and frequency. Quantifying these changes is key for developing adaptation and mitigation strategies. The conventional approach to study changes in hot extremes is based on air temperatures. However, many biogeochemical processes, i.e. decomposition of organic material and release of CO2, are triggered by soil temperature and it remains unclear whether it changes as does air temperature. Here, we demonstrate that soil hot extremes are intensifying and becoming even more frequent faster than air hot extremes over central eastern and western Europe. Based on existing model simulations, we also show that the increase in hot soil extremes could amplify or spread future heat waves by releasing sensible heat during hot days. We find an increase of 3 (7) % in the number of hot days with a contribution of heat from the soil under a warming level of 2.0 (3.0) °C than under a warming level of 1.5 °C. Furthermore, defining intensity and frequency extreme indices based on soil and air temperatures leads to a difference of more than 1 °C in intensity and 10% in frequency regionally during the last decades of the 21st century under the SPP5 8.5 emission scenario. In light of these results, maximum soil temperatures should be included in ecological risk studies as a complementary perspective to the conventional approach using extreme indices based on air temperatures.

Extreme weather and climate events have direct impacts on ecosystems and can further trigger ecosystem disturbances, often having impacts that last longer than the event’s duration. The projected increased frequency or intensity of extreme events could thus amplify ecological impacts and reduce the biosphere’s CO2 mitigation potential, but multiple feedbacks between ecosystems and climate extremes are often not considered in risk assessments. In this Perspective, we propose a systemic framework to analyse the causal relationships between climate extremes, disturbance regimes and ecosystems, building on two broadly used perspectives: climate risk assessment and disturbance ecology. Each has strengths and limitations, as each perspective places a different — and partly disjointed — focus on the physical and ecological processes that drive high-impact ecological events. We unify these approaches into a framework (compound ecoclimatic events) that decomposes events into climatic drivers, stressors, environmental factors, impacts and their sources of variability, and further incorporates feedbacks between ecosystem processes and stressors. This framework can be used to develop ecoclimatic storylines to better understand the role of each factor in influencing high-impact events; to incorporate uncertainties associated with internal climate and ecological variability, with scenario definitions, and with epistemic uncertainties; and to quantify the human fingerprint on high-impact ecoclimatic events.

Quantifying changes in hot temperature extremes is key for developing adaptation strategies. Changes in hot extremes are often determined on the basis of air temperatures; however, hydrology and many biogeochemical processes are more sensitive to soil temperature. Here we show that soil hot extremes are increasing faster than air hot extremes by 0.7 °C per decade in intensity and twice as fast in frequency on average over Central Europe. Furthermore, we identify soil temperature as a key factor in the soil moisture–temperature feedback. During dry and warm conditions, the energy absorbed by the soil is used to warm the soil, increasing the release of sensible heat flux and surface air temperatures. This increase in surface air temperature leads to a higher atmospheric demand for water, increasing soil evaporation, which may further dry and warm the soil highlighting the contribution of soil moisture–temperature feedback to the evolution of hot extremes in a warming climate.

The leaf economics spectrum and the global spectrum of plant forms and functions3 revealed fundamental axes of variation in plant traits, which represent different ecological strategies that are shaped by the evolutionary development of plant species. Ecosystem functions depend on environmental conditions and the traits of species that comprise the ecological communities. However, the axes of variation of ecosystem functions are largely unknown, which limits our understanding of how ecosystems respond as a whole to anthropogenic drivers, climate and environmental variability. Here we derive a set of ecosystem functions from a dataset of surface gas exchange measurements across major terrestrial biomes. We find that most of the variability within ecosystem functions (71.8%) is captured by three key axes. The first axis reflects maximum ecosystem productivity and is mostly explained by vegetation structure. The second axis reflects ecosystem water-use strategies and is jointly explained by variation in vegetation height and climate. The third axis, which represents ecosystem carbon-use efficiency, features a gradient related to aridity, and is explained primarily by variation in vegetation structure. We show that two state-of-the-art land surface models reproduce the first and most important axis of ecosystem functions. However, the models tend to simulate more strongly correlated functions than those observed, which limits their ability to accurately predict the full range of responses to environmental changes in carbon, water and energy cycling in terrestrial ecosystems.

Anthropogenic nitrogen inputs cause major negative environmental impacts, including emissions of the important greenhouse gas N2O. Despite their importance, shifts in terrestrial N loss pathways driven by global change are highly uncertain. Here we present a coupled soil-atmosphere isotope model (IsoTONE) to quantify terrestrial N losses and N2O emission factors from 1850-2020. We find that N inputs from atmospheric deposition caused 51% of anthropogenic N2O emissions from soils in 2020. The mean effective global emission factor for N2O was 4.3 ± 0.3% in 2020 (weighted by N inputs), much higher than the surface area-weighted mean (1.1 ± 0.1%). Climate change and spatial redistribution of fertilisation N inputs have driven an increase in global emission factor over the past century, which accounts for 18% of the anthropogenic soil flux in 2020. Predicted increases in fertilisation in emerging economies will accelerate N2O-driven climate warming in coming decades, unless targeted mitigation measures are introduced.

With this position paper the DKN highlights pertinent research gaps and suggests forthcoming fields of research. It combines insights from global environmental change research and the environmental social sciences and humanities. This programmatic framework will form the basis of the future work program of the committee itself. Furthermore, it is hoped that it will motivate other scientists to engage across disciplinary boundaries with the manifold epistemological, theoretical, and methodological challenges of sustainability science. This contribution addresses agents in science, science management, and science funding in a national and an international context.

Whilst the last decades have seen a clear shift in emphasis from managing natural hazards to managing risk, the majority of natural-hazard risk research still focuses on single hazards. Internationally, there are calls for more attention for multi-hazards and multi-risks. Within the European Union (EU), the concepts of multi-hazard and multi-risk assessment and management have taken centre stage in recent years. In this perspective paper, we outline several key developments in multi-(hazard-)risk research in the last decade, with a particular focus on the EU. We present challenges for multi-(hazard-)risk management as outlined in several research projects and papers. We then present a research agenda for addressing these challenges. We argue for an approach that addresses multi-(hazard-)risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards. In this approach, the starting point is a specific sustainability challenge, rather than an individual hazard or sector, and trade-offs and synergies are examined across sectors, regions, and hazards. We argue for in-depth case studies in which various approaches for multi-(hazard-)risk management are co-developed and tested in practice. Finally, we present a new pan-European research project in which our proposed research agenda will be implemented, with the goal of enabling stakeholders to develop forward-looking disaster risk management pathways that assess trade-offs and synergies of various strategies across sectors, hazards, and spatial scales.

Tree-ring chronologies encode interannual variability in forest growth rates over long time periods from decades to centuries or even millennia. However, each chronology is a highly localized measurement describing conditions at specific sites where wood samples have been collected. The question whether these local growth variabilites are representative for large geographical regions remains an open issue. To overcome the limitations of interpreting a sparse network of sites, we propose an upscaling approach for annual tree-ring indices that approximate forest growth variability and compute gridded data products that generalize the available information for multiple tree genera. Using regression approaches from machine learning, we predict tree-ring indices in space and time based on climate variables, but considering also species range maps as constraints for the upscaling. We compare various prediction strategies in cross-validation experiments to identify the best performing setup. Our estimated maps of tree-ring indices are the first data products that provide a dense view on forest growth variability at the continental level with 0.5° and 0.0083° spatial resolution covering the years 1902–2013. Furthermore, we find that different genera show very variable spatial patterns of anomalies. We have selected Europe as study region and focused on the six most prominent tree genera, but our approach is very generic and can easily be applied elsewhere. Overall, the study shows perspectives but also limitations for reconstructing spatiotemporal dynamics of complex biological processes. The data products are available at https://www.doi.org/10.17871/BACI.248.

Extreme events, such as those caused by climate change, economic or geopolitical shocks, and pest or disease epidemics, threaten global food security. The complexity of causation, as well as the myriad ways that an event, or a sequence of events, creates cascading and systemic impacts, poses significant challenges to food systems research and policy alike. To identify priority food security risks and research opportunities, we asked experts from a range of fields and geographies to describe key threats to global food security over the next two decades and to suggest key research questions and gaps on this topic. Here, we present a prioritization of threats to global food security from extreme events, as well as emerging research questions that highlight the conceptual and practical challenges that exist in designing, adopting, and governing resilient food systems. We hope that these findings help in directing research funding and resources toward food system transformations needed to help society tackle major food system risks and food insecurity under extreme events.

Estimating gross primary production (GPP), the gross uptake of CO2 by vegetation, is a fundamental prerequisite for understanding and quantifying the terrestrial carbon cycle. Over the last decade, multiple approaches have been developed to derive spatiotemporal dynamics of GPP combining in situ observations and remote sensing data using machine learning techniques or semiempirical models. However, no high spatial resolution GPP product exists so far that is derived entirely from satellite-based remote sensing data. Sentinel-2 satellites are expected to open new opportunities to analyze ecosystem processes with spectral bands chosen to study vegetation between 10- and 20-m spatial resolutions with five-day revisit frequency. Of particular relevance is the availability of red-edge bands that are suitable for deriving estimates of canopy chlorophyll content that are expected to be much better than any previous global mission. Here, we analyzed whether red-edge-based and near-infrared-based vegetation indices (VIs) or machine learning techniques that consider VIs, all spectral bands, and their nonlinear interactions could predict daily GPP derived from 58 eddy covariance sites. Using linear regressions based on classic VIs, including near-infrared reflectance of vegetation (NIRv), we achieved prediction powers of $R^{2}_{\mathrm{10-fold}} = 0.51$ and an $RMSE_{\mathrm{10-fold}} = 2.95 $ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ] in a 10-fold cross validation. Chlorophyll index red (CIR) and the novel kernel NDVI (kNVDI) achieved significantly higher prediction powers of around $R^{2}_{\mathrm{10-fold}} \approx 0.61$ and $RMSE_{\mathrm{10-fold}} \approx 2.57$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. Using all spectral bands and VIs jointly in a machine learning prediction framework allowed us to predict GPP with $R^{2}_{\mathrm{10-fold}} = 0.71$ and $RMSE_{\mathrm{10-fold}} = 2.68$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. Despite the high-power prediction when machin

The input of liquid water to terrestrial ecosystems is composed of rain and non-rainfall water (NRW). The latter comprises dew, fog, and the adsorption of atmospheric vapor on soil particle surfaces. Although NRW inputs can be relevant to support ecosystem functioning in seasonally dry ecosystems, they are understudied, being relatively small, and therefore hard to measure. In this study, we apply a partitioning routine focusing on NRW inputs over 1 year of data from large, high-precision weighing lysimeters at a semi-arid Mediterranean site. NRW inputs occur for at least 3 h on 297 d (81 % of the year), with a mean diel duration of 6 h. They reflect a pronounced seasonality as modulated by environmental conditions (i.e., temperature and net radiation). During the wet season, both dew and fog dominate NRW, while during the dry season it is mostly the soil adsorption of atmospheric water vapor. Although NRW contributes only 7.4 % to the annual water input, NRW is the only water input to the ecosystem during 15 weeks, mainly in the dry season. Benefitting from the comprehensive set of measurements at our experimental site, we show that our findings are in line with (i) independent measurements and (ii) independent model simulations forced with (near-) surface energy and moisture measurements. Furthermore, we discuss the simultaneous occurrence of soil vapor adsorption and negative eddy-covariance-derived latent heat fluxes. This study shows that NRW inputs can be reliably detected through high-resolution weighing lysimeters and a few additional measurements. Their main occurrence during nighttime underlines the necessity to consider ecosystem water fluxes at a high temporal resolution and with 24 h coverage.

Extreme hydrological and meteorological conditions can severely affect ecosystems, parts of the economy, and consequently society. These impacts are expected to be aggravated by climate change. Here we analyze and compare the impacts of multiple types of extreme events across several domains in Europe, to reveal corresponding impact signatures. We characterize the distinct impacts of droughts, floods, heat waves, frosts and storms on a variety of biophysical and social variables at national level and half-monthly time scale. We find strong biophysical impacts of droughts, floods, heat waves and frosts, while public attention and property damage are more affected by storms and floods. We show unexpected impact patterns such as reduced human mortality during floods and storms. Comparing public attention anomalies with impacts across all other considered domains we find that attention on droughts is comparatively low despite the significant overall impacts. Resolving these impact patterns highlights large-scale vulnerability and supports regional extreme event management to consequently reduce disaster risks.

Terrestrial ecosystems are essential for human life, as they provide several key services, such as food and water security, sequestration of anthropogenic carbon dioxide (CO2) and evaporative cooling. Healthy ecosystems perform photosynthesis, during which the plants’ stomata, tiny apertures on the leaf surface, are open to take up CO2 and evaporate water into the atmosphere, thereby providing a cooling effect. At the Earth’s surface, all the energy that is available from solar radiation that is not used to evaporate water, is used to heat near-surface air. Thereby, ecosystems play a key role in connecting the energy and the water balance.In Chapter 1, I introduce the energy and the water balance, which are linked through the evaporation of terrestrial water by ecosystems. How much of the available energy is used to evaporate water from the land surface, the evaporative fraction (EF), depends on water supply (soil moisture); When the soil is too dry for plants to extract water from the soil, plants close their stomata to prevent water loss. As the soil moistens, plants can access this water and increase their transpiration with further soil moisture increases until the maximum EF is reached. At this point, any additional soil moisture increases will no longer affect the EF, rendering the magnitude of the evaporation of water, the terrestrial evaporation (ET), solely dependent on available energy. The soil moisture and the corresponding terrestrial evaporation regime can dampen or amplify heat waves and other extreme events. Therefore, distinguishing energy versus water-limited conditions and accurately representing in Earth system models is essential.The aim of this thesis is to study the relevance of water versus energy limited conditions and associated regime transitions for weather, climate and related extremes under climate change. In this data-driven analysis, a multitude of data streams is used, amongst which satellite observations, in-situ observations, such

Systemic risk is associated with cascading impacts that spread within and across systems and sectors (e.g. ecosystems, health, infrastructure and the food sector) via the movements of people, goods, capital and information within and across boundaries (e.g. regions, countries and continents). The spread of these impacts can lead to potentially existential consequences and system collapse across a range of time horizons. Globalization contributes to systemic risk affecting people worldwide. The impacts of climate change or COVID-19 show how the challenges of addressing systemic risk go beyond conventional risk management and governance. Critical system interdependencies, amplified by underlying vulnerabilities, highlight that there is a growing need to better understand cascading impacts, systemic risks and the possible political (governance) and societal responses. This includes improving our understanding of the root causes of systemic risk, both biophysical and socio-economic, and related information needs. Addressing contemporary challenges in terms of systemic risk requires integrating different systems perspectives and fostering system thinking, while implementing key intergovernmental agendas, such as the Paris Agreement, the Sendai Framework for Disaster Risk Reduction and the Sustainable Development Goals. This Briefing Note represents an integrated perspective of climate, environmental and disaster risk science and practice regarding systemic risk. It provides an overview of the concepts of systemic risk that have evolved over time and identifies commonalities across terminologies and perspectives associated with systemic risk used in different contexts. Key attributes of systemic risk are outlined without prescribing a single definition, and information and data requirements that are essential for a better and more actionable understanding of the systemic nature of risk are discussed. Finally, the opportunities to connect research and policy for addressing