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The Deep Earth System Data Lab (DeepESDL, https://earthsystemdatalab.net) provides an AI-ready, collaborative environment enabling researchers to understand the complex dynamics of the Earth System using numerous datasets and multi-variate, empirical approaches. The solution builds on work done in previous projects funded by the European Space Agency (CAB-LAB and ESDL), which established the technical foundations and created measurable value for the scientific community, e.g., Mahecha et al. (2020, https://doi.org/10.5194/esd-11-201-2020) or Flach et al. (2018, https://doi.org/10.5194/bg-15-6067-2018 ). DeepESDL relies heavily on the well-established open-source technology stacks for data science in Python, thus ensuring usability and compatibility.The core of the DeepESDL is represented by the provision of programmatic access to various data sources in analysis-ready form, organised in data cubes combined with adequate computational resources and capabilities to allow researchers to immediately focus on efficient analysis and of multi-variate and high-dimensional data through empirical methods or AI approaches. To ensure proper documentation and discoverability, DeepESDL is building an informative catalogue to find all available data and to find the required metainformation describing them. This includes not only standard information, e.g., regarding spatial and temporal coverage, versioning, but also on specific transformation methods applied during data cube generation.The system design has openness, collaboration, and dissemination as key guiding principles. As science teams need proper tooling support to efficiently work together in this virtual environment, one of the key elements of the architecture is represented by the DeepESDL Hub, providing teams of scientific users with the means for collaboration and exchange of versioned results, source codes, models, execution parameters, and other artifacts and outcomes of their activities in a simple, safe and reliable way. The tools are complemented by an integrated, state-of-the-art application for the visualisation of all data in the virtual laboratory including input data, intermediate results, as well as the final products.Furthermore, the DeepESDL supports the implementation and execution of Machine Learning workflows on Analysis Ready Data Cubes in a reproducible and FAIR way, allowing sharing and versioning of all ML artifacts like code, data, models, execution parameters, metrics, and results as well as tracking each step in the ML workflows (supported by integration with Open-Source tools like TensorBoard or Mlflow) for an experiment so that others can reproduce them and contribute.Finally, dissemination is essential for the Open Science spirit of the DeepESDL. Two applications, xcube Viewer and 4D viewer, offer comprehensive user interfaces for interactive exploration of multi-variate data cubes. Both use the same RESTful data service API provided by xcube Server. The latter also provides OGC interfaces, so that other OGC-compliant applications, such as QGIS3, are able to visualise analysis-ready data cubes generated within DeepESDL.To foster collaboration, additional features such as publishing individual Jupyter Notebooks as storytelling documents or even books using Jupyter Books or the Executable Book Project are being explored, together with concepts such as storytelling and DeepESDL User Project Dashboards which may also link to the viewers and Notebooks.

The atmospheric CO2 growth rate (AGR) shows large year-to-year variations, which are mainly driven by land and ocean carbon uptake variations. Recent studies suggested an approximate doubling of the AGR regressed onto tropical mean temperature anomalies (“sensitivity of AGR to tropical mean temperature anomalies”; Wang et al., 2014; Luo et al., 2022), which was attributed to increasing drought in tropical land vegetation areas in a warming climate (Wang et al., 2014). We hypothesise that at least part of this apparent sensitivity change may instead be explained by extratropical areas and by internal climate variability.Here, we study the apparent sensitivity changes of AGR to tropical mean temperature in observations, atmospheric inversions, and a large climate model ensemble of historical simulations. First, we identify the main regional drivers of the apparent sensitivity change, including the ocean and extratropical regions in all datasets. Then, we evaluate whether these sensitivity changes can be attributed to anthropogenic forcing in a large climate model ensemble, or whether they are mostly driven by internal climate variability. Our results show that other regions beyond the land tropics contribute to the change in apparent sensitivity of AGR to tropical mean temperature anomalies in atmospheric inversions and in the period 1960 to 2006. Furthermore, the climate model large ensemble shows that such "doubling sensitivity" events can occur due to internal climate variability only. This points to the importance of distinguishing internal climate variability from forced signals when attributing causes to observed changes in the carbon cycle.Wang, X., Piao, S., Ciais, P. et al. A two-fold increase of carbon cycle sensitivity to tropical temperature variations. Nature 506, 212–215 (2014). https://doi.org/10.1038/nature12915Luo, X., Keenan, T. F. Tropical extreme droughts drive long-term increase in atmospheric CO2 growth rate variability. Nat Commun 13, 1193 (2022). https://doi.org/10.1038/s41467-022-28824-5

Socio-economic drought impacts often occur concomitantly across multiple sectors, leading to more severe consequences than if they affected single sectors. Improved management of such disasters requires cross-sectoral impact assessments and analyses. As such, analyzing how regions are affected by multiple impacts can provide crucial information for mitigating their consequences. Here, we characterize the multivariate distributions of socio-economic drought impacts. Our aim is to understand patterns by which diverse drought impacts co-occur. We introduce the concept of drought impact profiles, which describe characteristic distributions of co-occurring impacts. To this end, we use a unique spatio-temporal dataset generated with text mining and machine learning applied to newspaper articles. This dataset describes reported socio-economic drought impacts along seven categories (agriculture, forestry, fires, social, aquaculture, livestock, waterways) in Germany between 2000-2022. We combine several dimensionality reduction algorithms (PCA, ISOmap, self-organizing maps) to generate robust and interpretable representations of the drought impacts. Our results show characteristic patterns for both particular drought events and regions. Also, the applied methods provide a low-dimensional representation of the multivariate socio-economic drought impacts. This research provides a methodological contribution to the holistic, empirical investigation of co-occurring drought impacts. The proposed methods can inform risk models, and policy-makers on the urgency of cross-sectoral governance approaches. Also, the proposed method could apply to other hazards or compound events.

As global change accelerates, the urgency for a solid understanding of biosphere-environment interactions grows. However, we need more data on plant functional traits to test such relationships reliably across ecosystems. The TRY database contains an impressive collection of plant trait measurements for thousands of species already, and there have been some approaches to spatially extrapolate them using geospatial predictors and remote sensing data; however, the original data is spatially sparse so that extrapolations come with substantial uncertainties. At the same time, citizen scientists have collected increasingly dense observations of species occurrences around the globe. Here, we test if we can link species occurrences from the citizen science project iNaturalist with trait observations from TRY to produce global trait maps without the need for spatial extrapolation. We generated spatial grids for 18 traits, calculating a mean for each grid cell by averaging trait values associated with observations within that cell. We compared mean trait values from iNaturalist observations to community-weighted mean traits from sPlotOpen, a globally sampled dataset of vegetation plot data. Our results show correlations between the two datasets of up to r = 0.69, especially in biomes with higher iNaturalist observation density and those not dominated by trees. Also, we show that iNaturalist-derived maps have higher correlations to sPlotOpen-derived maps than previously published trait maps. This strong correlation between two fundamentally different datasets is astounding and unexpected. iNaturalist is noisy and heterogenous, sampled by citizen scientists who share the species they encounter and find interesting; sPlotOpen is a data collection of vegetation plots that were measured and recorded within the framework of specific research questions. The fact that these two datasets exhibit such a strong resemblance opens up a promising avenue for using the data treasure trove that is crowd-sourced data to help fill the gaps in plant trait data and demonstrates that crowd-sourced data, such as the iNaturalist observations, can be used to complement professional data collection efforts.

Hot temperature extremes are changing in intensity and frequency. Quantifying these changes is key for developing adaptation strategies [1]. The conventional approach to study changes in hot extremes is based on air temperatures. However, hydrology [2] and many biogeochemical processes, e.g. decomposition of organic material and release of CO2 [3]- are more sensitive to soil rather than air temperature. In this study, we show that soil hot extremes are increasing faster than air hot extremes by 0.7° C/Decade in intensity and twice as fast in frequency on average over Central Europe. Furthermore, we identify soil temperature as a factor in the soil moisture–temperature feedback. During dry conditions, increases in net radiation yield higher soil temperature and a consequent release of sensible heat while latent heat flux is constrained by soil moisture deficits. The release of sensible heat from soils leads to increases in air temperature and vapour pressure deficit that may further dry out and warm up the soil. This study further highlights the contribution of soil moisture–temperature feedback to the evolution of hot extremes in a warming climate. The rapid increase in soil heat extremes shown in these results may have important implications for climate and ecological risk applications.

Foliar traits such as specific leaf area (SLA), leaf nitrogen (N) and phosphorus (P) concentrations play an important role 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 calculated a top-of-canopy-weighted mean (TWM) and the community-weighted mean (CWM) of sPlotOpen trait estimates.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 land cover products in one group while the other group relied only on environmental predictors. The impact of using TWM or CWM on spatial patterns is considerably smaller than that of including PFT and land cover information. The maps that used PFT and 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.Overall, the maps using PFT and land cover information better reproduce the between-PFT trait differences and trait distributions of the plot-level sPlotOpen data, while the two groups performed similarly in capturing within-PFT trait variation. Upscaled maps of both groups were moderately correlated to grid-cell-level sPlotOpen data (R = 0.2-0.6), but only when accounting for the differences in processing in the upscaling approaches by applying similar scaling to the sPlotOpen data.Our findings indicate the importance of accounting for within-grid-cell trait variation, which has important implications for applications using existing maps and future upscaling efforts.

Understanding and managing systemic risk is more important than ever due to our immense global connectivity (e.g., between sectors, such as food-health-water-energy, countries and continents, down to individuals). Despite the fact that the notion of systemic risk is several decades old, the term is used in diverse ways across different disciplines (e.g., financial systems, medicine, earth system sciences, disaster risk research and climate science). Triggered by the repercussions of the global financial crisis of the late 2000s, and more recently the COVID-19 pandemic, which are clear realization of systemic risk, the perception of systemic risk has often been focused on global and catastrophic or even existential risks. Systemic risk, however, can be seen as a feature of systems at all possible scales (e.g., global, national, regional, local) with system boundaries varying depending on the context.Addressing current societal challenges, such as climate change, 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.Based on insights gained and knowledge collected from an expert workshop, literature review and expert elicitation, we give an integrated perspective of climate, environmental and disaster risk science and practice on systemic risk as summarized in a Briefing Note to the International Science Council. We provide an overview of concepts of systemic risk that have evolved over time and identify 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 are discussed that are essential for a better and more actionable understanding of the systemic nature of risk. Finally, the opportunities to connect research and policy for addressing systemic risk are highlighted.

The ecosystem carbon turnover time (τ) is an emergent ecosystem property that partly determines the feedback between the terrestrial carbon cycle and climate which is strongly controlled by temperature1–3. However, it remains uncertain to what extent hydrometeorological conditions may influence the apparent temperature sensitivity of τ, defined as the change in the carbon turnover times with a 10ºC rise in temperature (Q10). Here, we show that Q10 is unlikely temperature dependent and that it converges to 1.6 ± 0.1 globally once hydrometeorological conditions are taken into account. By investigating the responses of τ to temperature and hydrometeorological factors using an ensemble of observation-based global datasets and a global compilation of in-situ measurements, we show that temperature and hydrometeorology are almost equally important in shaping the spatial pattern of τ, explaining 60% and 40% of its global variability, respectively. Our findings reveal a strong constraint on Q10 values with significant reduction in magnitude and uncertainties, by accounting for the hydrometeorological effects on spatial variability of τ. These findings suggest that hydrometeorological conditions modulate the apparent temperature sensitivity of terrestrial carbon turnover times, confounding the role of temperature in quantifying the response of carbon cycle to climate change.

NFDI4Earth (www.nfdi4earth.de) is proposed as the consortium of the German NFDI (National Research Data Infrastructure) to address the digital needs of researchers in Earth System Sciences (ESS). The NFDI4Earth consortium has been created in a bottom-up process and comprises currently 58 members from German universities, research institutions, infrastructure providers, public authorities and different research organizations.The large number and diversity of observational, analytical, and model data sets in very high spatial, temporal and thematic resolution, confronts the ESS with a strongly increasing amount of data in great heterogeneity and of inherent complexity. Earth system processes constantly change on various time scales and strongly influence each other. Describing and evaluating these processes urgently requires efficient workflows and extremely powerful data analytic frameworks like datacubes as well as appropriate levels of harmonizing related data services and their underlying standards. Research data are currently managed by an unstructured plethora of services that are scattered, heterogeneous and often only project-based without a long-term perspective. A variety of measures and services become bundled under the umbrella of NFDI4Earth in a one-stop service framework. With a common approach to openness and FAIRness, they form a united, sustainable and coherent solution.In addition to existing links between German and international partners in ESS, NFDI4Earth will establish itself as a single point of contact and the voice for German Earth system scientists in both existing and emerging networks and alliances. NFDI4Earth is for example already striving to establish linkages with federative e-infrastructures like the European Open Science Cloud (EOSC) at an early stage.

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.

“No recuerdo que me hayan tildado nunca de indeciso.Distraído, abstraído, un poco au-delà de la bataille esosí. Pero no carente de convicciones ni de disposición aactuar. No, no llegué a ser hombre de acción. Nunca seme ocurrió entrar en negocios, aunque eso hubiese sidolo familiar y ‘obvio’. Creo que estuve más interesado enconstruir mundos posibles que en tratar de probar queestaba en el lugar adecuado”.

Technological developments from last decades offer unprecedented opportunities to monitor the Earth system. International research projects like BACI are joint efforts to provide free-of-charge, unified and high quality Earth Observations and the development of tools to analyze them. The ability to detect and monitor anomalous behaviour in multivariate environmental time series is crucial. These events are signals of changes in the underlying dynamical system and their detection can be used as an early-warning system for land ecosystems. In this study we present a methodology to detect these anomalies in biosphere data by a combination of a multivariate autoregressive model together with a distance measure. This work is framed within the EU-funded project BACI 'Detecting changes in essential ecosystem and biodiversity properties - towards a Biosphere Atmosphere Change Index'.

The mission of NFDI4Earth is to address the digital needs of the Earth System Sciences (ESS). We develop several components, services and concepts within NFDI4Earth. To improve the internal and external communication, we provide One-Pagers for selected central components, services and concepts, which describe them from the usage / user perspective on one to a maximum of two pages. The One-Pagers follow a common structure, starting with the overall aim of the related NFDI4Earth software component or concept. After that, we briefly describe core problem(s) and innovative approaches to solutions. NFDI4Earth is a community-driven project. We therefore identify users of the components / concept and the intended benefits for them. Moreover, innovations have adoption units (see Rogers, 20031). These can be individuals (e.g., a researcher) or organizations (e.g., a research institute that commits to operate a service after its development). We point out the intended adoption units with respect to the below-mentioned expected outcomes and evaluation criteria.