Towards a global understanding of vegetation-climate dynamics at multiple timescales

Funding Information: Acknowledgements. This paper has been realized within the Earth System Data Lab project funded by the European Space Agency. The authors acknowledge Lina Fürst for initiation of the preliminary study laying the foundation for this project. The authors acknowledge support from Ulrich Weber for data management and preprocessing. Lina M. Estupinan-Suarez acknowledges the support of the DAAD and its Graduate School Scholarship Programme (57395813). Nora Linscheid acknowledges the support of the TUM Graduate School. Lina M. Estupinan-Suarez and Nora Linscheid acknowledge the continuous support of the International Max Planck Research School for Global Biogeochemical Cycles. Felix Cre-mer acknowledges the support of the German Research Foundation project HyperSense (grant no. TH 1435/4-1). Publisher Copyright: © Author(s) 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Climate variables carry signatures of variability at multiple timescales. How these modes of variability are reflected in the state of the terrestrial biosphere is still not quantified or discussed at the global scale. Here, we set out to gain a global understanding of the relevance of different modes of variability in vegetation greenness and its covariability with climate. We used > 30 years of remote sensing records of the normalized difference vegetation index (NDVI) to characterize biosphere variability across timescales from submonthly oscillations to decadal trends using discrete Fourier decomposition. Climate data of air temperature (Tair) and precipitation (Prec) were used to characterize atmosphere-biosphere covariability at each timescale. Our results show that short-term (intra-annual) and longerterm (interannual and longer) modes of variability make regionally highly important contributions to NDVI variability: short-term oscillations focus in the tropics where they shape 27% of NDVI variability. Longer-term oscillations shape 9% of NDVI variability, dominantly in semiarid shrublands. Assessing dominant timescales of vegetation-climate covariation, a natural surface classification emerges which captures patterns not represented by conventional classifications, especially in the tropics. Finally, we find that correlations between variables can differ and even invert signs across timescales. For southern Africa for example, correlation between NDVI and Tair is positive for the seasonal signal but negative for short-term and longer-term oscillations, indicating that both short- and long-term temperature anomalies can induce stress on vegetation dynamics. Such contrasting correlations between timescales exist for 15% of vegetated areas for NDVI with Tair and 27% with Prec, indicating global relevance of scale-specific climate sensitivities. Our analysis provides a detailed picture of vegetation-climate covariability globally, characterizing ecosystems by their intrinsic modes of temporal variability. We find that (i) correlations of NDVI with climate can differ between scales, (ii) nondominant subsignals in climate variables may dominate the biospheric response, and (iii) possible links may exist between short-term and longer-term scales. These heterogeneous ecosystem responses on different timescales may depend on climate zone and vegetation type, and they are to date not well understood and do not always correspond to transitions in dominant vegetation types. These scale dependencies can be a benchmark for vegetation model evaluation and for comparing remote sensing products.publishersversionpublishe

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Earth system data cubes unravel global multivariate dynamics

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model-data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model-data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved

Reclassifying historical disasters : from single to multi-hazards

Multi-hazard events, characterized by the simultaneous, cascading, or cumulative occurrence of multiple natural hazards, pose a significant threat to human lives and assets. This is primarily due to the cumulative and cascading effects arising from the interplay of various natural hazards across space and time. However, their identification is challenging, which is attributable to the complex nature of natural hazard interactions and the limited availability of multi-hazard observations. This study presents an approach for identifying multi-hazard events during the past 123 years (1900–2023) using the EM-DAT global disaster database. Leveraging the ‘associated hazard’ information in EM-DAT, multi-hazard events are detected and assessed in relation to their frequency, impact on human lives and assets, and reporting trends. The interactions between various combinations of natural hazard pairs are explored, reclassifying them into four categories: preconditioned/triggering, multivariate, temporally compounding, and spatially compounding multi-hazard events. The results show, globally, approximately 19 % of the 16,535 disasters recorded in EM-DAT can be classified as multi-hazard events. However, the multi-hazard events recorded in EM-DAT are disproportionately responsible for nearly 59 % of the estimated global economic losses. Conversely, single hazard events resulted in higher fatalities compared to multi-hazard events. The largest proportion of multi-hazard events are associated with floods, storms, and earthquakes. Landslides emerge as the predominant secondary hazards within multi-hazard pairs, primarily triggered by floods, storms, and earthquakes, with the majority of multi-hazard events exhibiting preconditioned/triggering and multivariate characteristics. There is a higher prevalence of multi-hazard events in Asia and North America, whilst temporal overlaps of multiple hazards predominate in Europe. These results can be used to increase the integration of multi-hazard thinking in risk assessments, emergency management response plans and mitigation policies at both national and international levels

A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities

Potential and limitations of inferring ecosystem photosynthetic capacity from leaf functional traits

The aim of this study was to systematically analyze the potential and limitations of using plant functional trait observations from global databases versus in situ data to improve our understanding of vegetation impacts on ecosystem functional properties (EFPs). Using ecosystem photosynthetic capacity as an example, we first provide an objective approach to derive robust EFP estimates from gross primary productivity (GPP) obtained from eddy covariance flux measurements. Second, we investigate the impact of synchronizing EFPs and plant functional traits in time and space to evaluate their relationships, and the extent to which we can benefit from global plant trait databases to explain the variability of ecosystem photosynthetic capacity. Finally, we identify a set of plant functional traits controlling ecosystem photosynthetic capacity at selected sites. Suitable estimates of the ecosystem photosynthetic capacity can be derived from light response curve of GPP responding to radiation (photosynthetically active radiation or absorbed photosynthetically active radiation). Although the effect of climate is minimized in these calculations, the estimates indicate substantial interannual variation of the photosynthetic capacity, even after removing site-years with confounding factors like disturbance such as fire events. The relationships between foliar nitrogen concentration and ecosystem photosynthetic capacity are tighter when both of the measurements are synchronized in space and time. When using multiple plant traits simultaneously as predictors for ecosystem photosynthetic capacity variation, the combination of leaf carbon to nitrogen ratio with leaf phosphorus content explains the variance of ecosystem photosynthetic capacity best (adjusted R-2 = 0.55). Overall, this study provides an objective approach to identify links between leaf level traits and canopy level processes and highlights the relevance of the dynamic nature of ecosystems. Synchronizing measurements of eddy covariance fluxes and plant traits in time and space is shown to be highly relevant to better understand the importance of intra-and interspecific trait variation on ecosystem functioning.Peer reviewe

Elk–testing climate–carbon cycle models: a case for pattern–oriented system analysis

Process–oriented models are a primary tool being used to project future states of climate and ecosystems in the Earth system in response to anthropogenic and other forcing. Coupled climate–carbon cycle models receive tremendous attention, especially in the context of the 5th assessment report of the IPCC (International Panel on Climate Change). However, intercomparison of model scenarios indicate large uncertainties regarding predictions of global interactions between atmosphere and biosphere. Rigorous scientific testing of these models is essential but very challenging, largely because it is neither technically nor ethically possible to perform global earth– scale experiments—we do not have replicate Earths for hypothesis testing. Hence, model evaluations have to rely on monitoring data such as ecological observation networks, global remote sensing, paleo proxy data, or small–scale manipulative experiments. Here, we critically examine strategies of how model evaluations should be performed. We put a particular emphasis on the representation of terrestrial ecosystems, where the two key problems are: 1. weak (or inconclusive) ‘validations’ which do not take advantage of all the relevant information in the observed data, and 2. apparent falsifications: “false alarms” likely to occur when individual system processes (in the model) are compared to the overall emergent system behaviour (of the observed world)

Performance of Singular Spectrum Analysis in Separating Seasonal and Fast Physiological Dynamics of Solar-Induced Chlorophyll Fluorescence and PRI Optical Signals

High temporal resolution measurements of solar-induced chlorophyll fluorescence (F) and the Photochemical Reflectance Index (PRI) encode vegetation functioning. However, these signals are modulated by time-dependent processes. We tested the applicability of the Singular Spectrum Analysis (SSA) for disentangling fast components (physiology-driven) and slow components (controlled by structural and biochemical properties) from PRI, far-red F (F-760), and far-red apparent fluorescence yield (Fy*(760)). The proof of concept was developed on spectral and flux time series simulated with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. This allowed the evaluation of SSA decomposition against variables that are independent of physiology or are modified by it. Slow SSA-decomposed components of PRI and Fy*(760) showed high correlations with the reference variables (R-2 = 0.97 and 0.96, respectively). Fast SSA-decomposed components of PRI and Fy*(760) were better related to the physiological reference variables than the original signals during periods when leaf area index (LAI) was above 1 m(2) m(-2). The method was also successfully applied to predict light-use efficiency (LUE) from the fast SSA-decomposed components of PRI (R-2 = 0.70) and Fy*(760) (R-2 = 0.68) when discarding data modeled with LAI R-in < 250 W m(-2). The method was then tested on data acquired in a Mediterranean grassland. In this case, the fast SSA-decomposed component of apparent LUE* showed a stronger correlation with the fast SSA-decomposed component of Fy*(760) (R-2 = 0.42) than with original Fy*(760) (R-2 = 0.01). SSA-based approach is a promising tool for decoupling physiological information from measurements acquired with automated proximal sensing systems