Vegetation dynamics in northern south America on different time scales

The overarching goal of this doctoral thesis was to understand the dynamics of vegetation activity occurring across time scales globally and in a regional context. To achieve this, I took advantage of open data sets, novel mathematical approaches for time series analyses, and state-of-the-art technology to effectively manipulate and analyze time series data. Specifically, I disentangled the longest records of vegetation greenness (>30 years) in tandem with climate variables at 0.05° for a global scale analysis (Chapter 3). Later, I focused my analysis on a particular region, northern South America (NSA), to evaluate vegetation activity at seasonal (Chapter 4) and interannual scales (Chapter 5) using moderate spatial resolution (0.0083°). Two main approaches were used in this research; time series decomposition through the Fast Fourier Transformation (FFT), and dimensionality reduction analysis through Principal Component Analysis (PCA). Overall, assessing vegetation-climate dynamics at different temporal scales facilitates the observation and understanding of processes that are often obscured by one or few dominant processes. On the one hand, the global analysis showed the dominant seasonality of vegetation and temperature in northern latitudes in comparison with the heterogeneous patterns of the tropics, and the remarkable longer-term oscillations in the southern hemisphere. On the other hand, the regional analysis showed the complex and diverse land-atmosphere interactions in NSA when assessing seasonality and interannual variability of vegetation activity associated with ENSO. In conclusion, disentangling these processes and assessing them separately allows one to formulate new hypotheses of mechanisms in ecosystem functioning, reveal hidden patterns of climate-vegetation interactions, and inform about vegetation dynamics relevant for ecosystem conservation and management

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

The fate and transit time of carbon in a tropical forest

Tropical forests fix large quantities of carbon from the atmosphere every year; however, the fate of this carbon as it travels through ecosystem compartments is poorly understood. In particular, there is a large degree of uncertainty regarding the time carbon spends in an ecosystem before it is respired and returns to the atmosphere as CO2. We estimated the fate of carbon (trajectory of photosynthetically fixed carbon through a network of compartments) and its transit time (time it takes carbon to pass through the entire ecosystem, from fixation to respiration) for an old-growth tropical forest located in the foothills of the Andes of Colombia. We show that on average, 50% of the carbon fixed at any given time is respired in <0.5 years, and 95% is respired in <69 years. The transit time distribution shows that carbon in ecosystems is respired on a range of time-scales that span decades, but fast metabolic processes in vegetation dominate the return of carbon to the atmosphere. Synthesis. The transit time distribution integrates multiple ecosystem processes occurring at a wide range of time-scales. It reconciles measurements of the age of respired CO2 with estimates of mean residence time in woody biomass, and provides a new approach to interpret other ecosystem level metrics such as the ratio of net primary production to gross primary production