# Sebastian Sippel

## Forsker

0049 1517 432 2476

sebastian.sippel@nibio.no

Sted

Ås - Bygg H8

Besøksadresse

Høgskoleveien 8, 1433 Ås

#### Sammendrag

Many nonlinear methods of time series analysis require a minimal number of observations in the hundreds to thousands, which is not always easy to achieve for observations of environmental systems. Eddy Covariance (EC) measurements of the carbon exchange between the atmosphere and vegetation provide a noticeable exception. They are taken at high temporal resolution, typically at 20 Hz. This generates very long time series (many millions of data points) even for short measurement periods, rendering finite size effects unimportant. In this presentation, we investigate high-resolution raw data of 3D wind speed, CO2 concentrations, water vapor and temperature measured at a young forest plantation in Southeast Norway since July 2018. Guiding for the analysis is the gain or added value of the high resolution compared to more aggregated data, i.e. the scaling behavior of nonlinear properties of the time series. We present results of complexity analysis, Tarnopolski diagrams, q-Entropy, Hurst analysis, Empirical Mode Decomposition and Singular System Analysis. This provides detailed insights into the nature of dynamics of carbon fluxes across this system boundary at different temporal scales.

## Forfattere

Holger Lange Sebastian Sippel Britta Aufgebauer Michael Hauhs Christina Bogner Henning Meesenburg#### Sammendrag

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Remote sensing observations provide important information about vegetation and carbon dynamics on large scales, flux towers in situ measurements at the plot scale. Events important for ecological processes, such as hydrometeorological extremes, often happen at spatiotemporal scales between those covered by these two data sources. We discuss the event detection rates of ecological in situ networks as a function of their size and design. Using extreme reductions of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), available from satellite missions, as a proxy for substantial losses in Gross Primary Productivity (GPP), we rank historical events according to their severity, and show how many would have been detected with a given number of randomly placed sites, discuss the problem of clustering of sites, and compare the theoretical results with the existing networks FLUXNET and NEON. The further spatio-temporal expansion of the ICOS network should carefully consider the size distribution of extreme events in order to be able to monitor their impacts on the terrestrial biosphere.

## Forfattere

Milan Flach Sebastian Sippel Fabian Gans Ana Bastos Alexander Brenning Markus Reichstein Miguel D. Mahecha#### Sammendrag

Combined droughts and heatwaves are among those compound extreme events that induce severe impacts on the terrestrial biosphere and human health. A record breaking hot and dry compound event hit western Russia in summer 2010 (Russian heatwave, RHW). Events of this kind are relevant from a hydrometeorological perspective, but are also interesting from a biospheric point of view because of their impacts on ecosystems, e.g., reductions in the terrestrial carbon storage. Integrating both perspectives might facilitate our knowledge about the RHW. We revisit the RHW from both a biospheric and a hydrometeorological perspective. We apply a recently developed multivariate anomaly detection approach to a set of hydrometeorological variables, and then to multiple biospheric variables relevant to describe the RHW. One main finding is that the extreme event identified in the hydrometeorological variables leads to multidirectional responses in biospheric variables, e.g., positive and negative anomalies in gross primary production (GPP). In particular, the region of reduced summer ecosystem production does not match the area identified as extreme in the hydrometeorological variables. The reason is that forest-dominated ecosystems in the higher latitudes respond with unusually high productivity to the RHW. Furthermore, the RHW was preceded by an anomalously warm spring, which leads annually integrated to a partial compensation of 54% (36% in the preceding spring, 18% in summer) of the reduced GPP in southern agriculturally dominated ecosystems. Our results show that an ecosystem-specific and multivariate perspective on extreme events can reveal multiple facets of extreme events by simultaneously integrating several data streams irrespective of impact direction and the variables' domain. Our study exemplifies the need for robust multivariate analytic approaches to detect extreme events in both hydrometeorological conditions and associated biosphere responses to fully characterize the effects of extremes, including possible compensatory effects in space and time.

## Forfattere

Sebastian Sippel Markus Reichstein Xuanlong Ma Miguel D. Mahecha Holger Lange Milan Flach Dorothea Frank#### Sammendrag

Purpose of the Review Weather and climate extremes substantially affect global- and regional-scale carbon (C) cycling, and thus spatially or temporally extended climatic extreme events jeopardize terrestrial ecosystem carbon sequestration. We illustrate the relevance of drought and/or heat events (“DHE”) for the carbon cycle and highlight underlying concepts and complex impact mechanisms. We review recent results, discuss current research needs and emerging research topics. Recent Findings Our review covers topics critical to understanding, attributing and predicting the effects of DHE on the terrestrial carbon cycle: (1) ecophysiological impact mechanisms and mediating factors, (2) the role of timing, duration and dynamical effects through which DHE impacts on regional-scale carbon cycling are either attenuated or enhanced, and (3) large-scale atmospheric conditions under which DHE are likely to unfold and to affect the terrestrial carbon cycle. Recent research thus shows the need to view these events in a broader spatial and temporal perspective that extends assessments beyond local and concurrent C cycle impacts of DHE. Summary Novel data streams, model (ensemble) simulations, and analyses allow to better understand carbon cycle impacts not only in response to their proximate drivers (drought, heat, etc.) but also attributing them to underlying changes in drivers and large-scale atmospheric conditions. These attribution-type analyses increasingly address and disentangle various sequences or dynamical interactions of events and their impacts, including compensating or amplifying effects on terrestrial carbon cycling.

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Klimaendringer gir mer ekstremvær. Hetebølger og tørke kan gi redusert karbonlagring i skog og frigjøre klimagasser til atmosfæren.

## Forfattere

Daniel Mitchell Clare Heaviside Nathalie Schaller Myles Allen Kristie L. Ebi Erich M. Fischer Antonio Gasparrini Luke Harrington Viatcheslav Kharin Hideo Shiogama Jana Sillmann Sebastian Sippel Sotiris Vardoulakis#### Sammendrag

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Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks based on time series. Values (the nodes of the network) of the time series are linked to each other if there is no value higher between them. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytic results can be obtained for the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. We investigate a set of around 150 river runoff time series at daily resolution from Brazil with an average length of 65 years. Most of the rivers are exploited for power generation and thus represent heavily managed basins. We investigate both long-term trends and human influence (e.g. the effect of dam construction) in the runoff regimes (disregarding direct upstream operations). HVGs are used to determine the degree and distance distributions. Statistical and information-theoretic properties of these distributions are calculated: robust estimators of skewness and kurtosis, the maximum degree occurring in the time series, the Shannon entropy, permutation complexity and Fisher Information. For the latter, we also compare the information measures obtained from the degree distributions to those using the original time series directly, to investigate the impact of graph construction on the dynamical properties as reflected in these measures. We also show that a specific pretreatment of the time series conventional in hydrology, the elimination of seasonality by a separate z-transformation for each calendar day, changes long-term correlations and the overall dynamics substantially and towards more random behaviour. Moreover, hydrological time series are typically limited in length and may contain ties, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. Focus is on one hand on universal properties of the HVG, common to all runoff series, and on site-specific aspects on the other.

## Forfattere

Jannis von Buttlar Jakob Zscheischler Anja Rammig Sebastian Sippel Markus Reichstein Alexander Knohl Martin Jung Olaf Menzer M. Altaf Arain Nina Buchmann Alessandro Cescatti Damiano Gianelle Gerard Kiely Beverly Law Vincenzo Magliulo Hank Margolis Harry McCaughey Lutz Merbold Mirco Migliavacca Leonardo Montagnani Walter Oechel Marian Pavelka Matthias Peichl Serge Rambal Antonio Raschi Russell L. Scott Franceso P. Vaccari Eva van Gorsel Andrej Varlagin Georg Wohlfahrt Miguel Mahecha#### Sammendrag

Extreme climatic events, such as droughts and heat stress, induce anomalies in ecosystem–atmosphere CO2 fluxes, such as gross primary production (GPP) and ecosystem respiration (Reco), and, hence, can change the net ecosystem carbon balance. However, despite our increasing understanding of the underlying mechanisms, the magnitudes of the impacts of different types of extremes on GPP and Reco within and between ecosystems remain poorly predicted. Here we aim to identify the major factors controlling the amplitude of extreme-event impacts on GPP, Reco, and the resulting net ecosystem production (NEP). We focus on the impacts of heat and drought and their combination. We identified hydrometeorological extreme events in consistently downscaled water availability and temperature measurements over a 30-year time period. We then used FLUXNET eddy covariance flux measurements to estimate the CO2 flux anomalies during these extreme events across dominant vegetation types and climate zones. Overall, our results indicate that short-term heat extremes increased respiration more strongly than they downregulated GPP, resulting in a moderate reduction in the ecosystem's carbon sink potential. In the absence of heat stress, droughts tended to have smaller and similarly dampening effects on both GPP and Reco and, hence, often resulted in neutral NEP responses. The combination of drought and heat typically led to a strong decrease in GPP, whereas heat and drought impacts on respiration partially offset each other. Taken together, compound heat and drought events led to the strongest C sink reduction compared to any single-factor extreme. A key insight of this paper, however, is that duration matters most: for heat stress during droughts, the magnitude of impacts systematically increased with duration, whereas under heat stress without drought, the response of Reco over time turned from an initial increase to a downregulation after about 2 weeks. This confirms earlier theories that not only the magnitude but also the duration of an extreme event determines its impact. Our study corroborates the results of several local site-level case studies but as a novelty generalizes these findings on the global scale. Specifically, we find that the different response functions of the two antipodal land–atmosphere fluxes GPP and Reco can also result in increasing NEP during certain extreme conditions. Apparently counterintuitive findings of this kind bear great potential for scrutinizing the mechanisms implemented in state-of-the-art terrestrial biosphere models and provide a benchmark for future model development and testing.

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Horizontal Visibility Graphs (HVGs) are a recently developed method to construct networks from time series. The values of the time series are considered as the nodes of the network and are linked to each other if there is no larger value between them, such as they can “see” each other. The network properties reflect the nonlinear dynamics of the time series. For some classes of stochastic processes and for periodic time series, analytical results can be obtained for network-derived quantities such as the degree distribution, the local clustering coefficient distribution, the mean path length, and others. HVGs have the potential to discern between deterministic-chaotic and correlated-stochastic time series. Here, we investigate the sensitivity of the HVG methodology to properties and pre-processing of real-world data, i.e., time series length, the presence of ties, and deseasonalization, using a set of around 150 runoff time series from managed rivers at daily resolution from Brazil with an average length of 65 years. We show that an application of HVGs on real-world time series requires a careful consideration of data pre-processing steps and analysis methodology before robust results and interpretations can be obtained. For example, one recent analysis of the degree distribution of runoff records reported pronounced sub-exponential “long-tailed” behavior of North American rivers, whereas another study of South American rivers showed hyper-exponential “short-tailed” behavior resembling correlated noise.We demonstrate, using the dataset of Brazilian rivers, that these apparently contradictory results can be reconciled by minor differences in data-preprocessing (here: small differences in subtracting the seasonal cycle). Hence, data-preprocessing that is conventional in hydrology (“deseasonalization”) changes long-term correlations and the overall runoff dynamics substantially, and we present empirical consequences and extensive simulations to investigate these issues from a HVG methodological perspective. After carefully accounting for these methodological aspects, the HVG analysis reveals that the river runoff dataset shows indeed complex behavior that appears to stem from a superposition of short-term correlated noise and “long-tailed behaviour,” i.e., highly connected nodes. Moreover, the construction of a dam along a river tends to increase short-term correlations in runoff series. In summary, the present study illustrates the (often substantial) effects of methodological and data-preprocessing choices for the interpretation of river runoff dynamics in the HVG framework and its general applicability for real-world time series.

## Forfattere

Holger Lange Jogeir N. Stokland Bernhard Ahrens Lise Dalsgaard Stephanie Eisner Hanna Lee Toby Marthews Helge Meissner Sebastian Sippel#### Sammendrag

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## Forfattere

Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus Reichstein#### Sammendrag

Daily precipitation extremes and annual totals have increased in large parts of the global land area over the past decades. These observations are consistent with theoretical considerations of a warming climate. However, until recently these trends have not been shown to consistently affect dry regions over land. A recent study, published by Donat et al. (2016), now identified significant increases in annual-maximum daily extreme precipitation (Rx1d) and annual precipitation totals (PRCPTOT) in dry regions. Here, we revisit the applied methods and explore the sensitivity of changes in precipitation extremes and annual totals to alternative choices of defining a dry region (i.e. in terms of aridity as opposed to precipitation characteristics alone). We find that (a) statistical artifacts introduced by data pre-processing based on a time-invariant reference period lead to an overestimation of the reported trends by up to 40 %, and that (b) the reported trends of globally aggregated extremes and annual totals are highly sensitive to the definition of a "dry region of the globe". For example, using the same observational dataset, accounting for the statistical artifacts, and based on different aridity-based dryness definitions, we find a reduction in the positive trend of Rx1d from the originally reported +1.6 % decade−1 to +0.2 to +0.9 % decade−1 (period changes for 1981–2010 averages relative to 1951–1980 are reduced to −1.32 to +0.97 % as opposed to +4.85 % in the original study). If we include additional but less homogenized data to cover larger regions, the global trend increases slightly (Rx1d: +0.4 to +1.1 % decade−1), and in this case we can indeed confirm (partly) significant increases in Rx1d. However, these globally aggregated estimates remain uncertain as considerable gaps in long-term observations in the Earth's arid and semi-arid regions remain. In summary, adequate data pre-processing and accounting for uncertainties regarding the definition of dryness are crucial to the quantification of spatially aggregated trends in precipitation extremes in the world's dry regions. In view of the high relevance of the question to many potentially affected stakeholders, we call for a well-reflected choice of specific data processing methods and the inclusion of alternative dryness definitions to guarantee that communicated results related to climate change be robust.

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## Forfattere

Sebastian Sippel Holger Lange Miguel D. Mahecha Michael Hauhs Paul Bodesheim Thomas W. Kaminski Fabian Gans Osvaldo A. Rosso#### Sammendrag

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide dataanalytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

## Forfattere

Sebastian Sippel Jakob Zscheischler Martin Heimann Holger Lange Miguel D. Mahecha Geert Jan van Oldenborgh Friederike E.L. Otto Markus Reichstein#### Sammendrag

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## Forfattere

Holger Lange Sebastian Sippel Miguel D. Mahecha Osvaldo A. Rosso Kirsten Thonicke Susanne Rolinski Anja Rammig Christian Beer#### Sammendrag

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#### Sammendrag

In an attempt to discern stochastic and deterministic parts of measured signals, we analyze time series from the viewpoint of ordinal pattern statistics. After choosing a suitable embedding dimension $D$, the occurrencies of all $D!$ patterns form a probability distribution $P$. The latter is input to information and complexity functionals describing, e.g., chaotic regimes or stochastic properties due to long-range correlations. Here, we use an information quantifier which is local in pattern probability space, the Fisher information $F$. This is calculable only after fixing a pattern coding scheme, i.e. numbering each and every pattern. It has been demonstrated that $F$ discerns different dynamic regimes for the logistic map to a certain extent; however, this depends on the details of the coding scheme. Here, we seek to find an optimal coding scheme for long-range correlated stochastic processes, mimicking many records e.g. from the geosciences. To increase the contrast between colored noise and deterministic processes, $F$ should be minimal for the former. Structurally similar ordinal patterns should be located adjacent to each other. Similarity is related to the number of inversions in the respective patterns. In practical terms, it is impossible to try all $D!!$ coding schemes whenever$D > 3$; however, we demonstrate a classification of coding schemes into equivalence classes based on the number of "jumps" in the patterns. These are used to improve the Keller and Lehmer coding schemes. The approach has a potential to provide an analytical understanding of the Fisher information for stochastic processes. Results for these optimizations will be shown for both the logistic map and colored ($k$-) noise. As a byproduct, an innovative method to estimate the scaling exponent $k$ emerges. Finally, we comment shortly on the importance of finite size effects, which is always an issue when dealing with observed data.

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