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  • 1
    Online Resource
    Online Resource
    Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques
    Copernicus GmbH ; 2017
    In:  Earth System Dynamics Vol. 8, No. 3 ( 2017-08-08), p. 677-696
    Details
    In: Earth System Dynamics, Copernicus GmbH, Vol. 8, No. 3 ( 2017-08-08), p. 677-696
    Abstract: Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.
    Type of Medium: Online Resource
    ISSN: 2190-4987
    URL: Article
    URL: Article
    DOI: 10.5194/esd-8-677-2017
    DOI: 10.5194/esd-8-677-2017-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2578793-7
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  • 2
    Online Resource
    Online Resource
    Increasing interannual climate variability during crop flowering in Europe
    IOP Publishing ; 2023
    In:  Environmental Research Letters Vol. 18, No. 4 ( 2023-04-01), p. 044037-
    Details
    In: Environmental Research Letters, IOP Publishing, Vol. 18, No. 4 ( 2023-04-01), p. 044037-
    Abstract: Climate change has increasingly adverse effects on global crop yields through the occurrence of heat waves, water stress, and other weather-related extremes. Besides losses of average yields, a decrease in yield stability—i.e. an increase in variability of yields from year to year—poses economic risks and threatens food security. Here we investigate a number of climate indices related to adverse weather events during the flowering of wheat, maize and rapeseed, in the current cultivation areas as well as the main European producer countries. In 52 projections from regional climate models, we identify robust increases in the interannual variability of temperature, precipitation and soil moisture by ∼+20% in standard deviation in the model median. We find that winter wheat is most exposed to variability increases, whereas rapeseed flowering escapes the largest increases due to the early flowering time and the northern locations of cultivation areas, while the opposite (escape due to southern locations and late flowering) is true for maize to some extent. Considering the timing of crop development stages, we also find a robust increase in the variability of the temporal occurrence of flowering, which suggests a decreased reliability in the timing of crop stages, hampering management steps like fertilization, irrigation or harvesting. Our study raises concerns for European crop yield stability in a warmer climate and highlights the need for risk diversification strategies in agricultural adaptation.
    Type of Medium: Online Resource
    ISSN: 1748-9326
    URL: Article
    DOI: 10.1088/1748-9326/acc87e
    Language: Unknown
    Publisher: IOP Publishing
    Publication Date: 2023
    detail.hit.zdb_id: 2255379-4
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  • 3
    Online Resource
    Online Resource
    Inferring causation from time series in Earth system sciences
    Springer Science and Business Media LLC ; 2019
    In:  Nature Communications Vol. 10, No. 1 ( 2019-06-14)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 10, No. 1 ( 2019-06-14)
    Abstract: The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-019-10105-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
    detail.hit.zdb_id: 2553671-0
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  • 4
    Online Resource
    Online Resource
    Lessons on Climate Sensitivity From Past Climate Changes
    Springer Science and Business Media LLC ; 2016
    In:  Current Climate Change Reports Vol. 2, No. 4 ( 2016-12), p. 148-158
    Details
    In: Current Climate Change Reports, Springer Science and Business Media LLC, Vol. 2, No. 4 ( 2016-12), p. 148-158
    Type of Medium: Online Resource
    ISSN: 2198-6061
    URL: Article
    DOI: 10.1007/s40641-016-0049-3
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
    detail.hit.zdb_id: 2808618-1
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  • 5
    Online Resource
    Online Resource
    Why CO〈sub〉2〈/sub〉 cools the middle atmosphere – a consolidating model perspective
    Copernicus GmbH ; 2016
    In:  Earth System Dynamics Vol. 7, No. 3 ( 2016-08-29), p. 697-715
    Details
    In: Earth System Dynamics, Copernicus GmbH, Vol. 7, No. 3 ( 2016-08-29), p. 697-715
    Abstract: Abstract. Complex models of the atmosphere show that increased carbon dioxide (CO2) concentrations, while warming the surface and troposphere, lead to lower temperatures in the stratosphere and mesosphere. This cooling, which is often referred to as “stratospheric cooling”, is evident also in observations and considered to be one of the fingerprints of anthropogenic global warming. Although the responsible mechanisms have been identified, they have mostly been discussed heuristically, incompletely, or in combination with other effects such as ozone depletion, leaving the subject prone to misconceptions. Here we use a one-dimensional window-grey radiation model of the atmosphere to illustrate the physical essence of the mechanisms by which CO2 cools the stratosphere and mesosphere: (i) the blocking effect, associated with a cooling due to the fact that CO2 absorbs radiation at wavelengths where the atmosphere is already relatively opaque, and (ii) the indirect solar effect, associated with a cooling in places where an additional (solar) heating term is present (which on Earth is particularly the case in the upper parts of the ozone layer). By contrast, in the grey model without solar heating within the atmosphere, the cooling aloft is only a transient blocking phenomenon that is completely compensated as the surface attains its warmer equilibrium. Moreover, we quantify the relative contribution of these effects by simulating the response to an abrupt increase in CO2 (and chlorofluorocarbon) concentrations with an atmospheric general circulation model. We find that the two permanent effects contribute roughly equally to the CO2-induced cooling, with the indirect solar effect dominating around the stratopause and the blocking effect dominating otherwise.
    Type of Medium: Online Resource
    ISSN: 2190-4987
    URL: Article
    DOI: 10.5194/esd-7-697-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
    detail.hit.zdb_id: 2578793-7
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  • 6
    Online Resource
    Online Resource
    Observed trends in the magnitude and persistence of monthly temperature variability
    Springer Science and Business Media LLC ; 2017
    In:  Scientific Reports Vol. 7, No. 1 ( 2017-07-19)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2017-07-19)
    Abstract: Climate variability is critically important for nature and society, especially if it increases in amplitude and/or fluctuations become more persistent. However, the issues of whether climate variability is changing, and if so, whether this is due to anthropogenic forcing, are subjects of ongoing debate. Increases in the amplitude and persistence of temperature fluctuations have been detected in some regions, e.g. the North Pacific, but there is no agreed global signal. Here we systematically scan monthly surface temperature indices and spatial datasets to look for trends in variance and autocorrelation (persistence). We show that monthly temperature variability and autocorrelation increased over 1957–2002 across large parts of the North Pacific, North Atlantic, North America and the Mediterranean. Furthermore, (multi)decadal internal climate variability appears to influence trends in monthly temperature variability and autocorrelation. Historically-forced climate models do not reproduce the observed trends in temperature variance and autocorrelation, consistent with the models poorly capturing (multi)decadal internal climate variability. Based on a review of established spatial correlations and corresponding mechanistic ‘teleconnections’ we hypothesise that observed slowing down of sea surface temperature variability contributed to observed increases in land temperature variability and autocorrelation, which in turn contributed to persistent droughts in North America and the Mediterranean.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-017-06382-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2615211-3
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  • 7
    Online Resource
    Online Resource
    Beyond bifurcation: using complex models to understand and predict abrupt climate change
    Oxford University Press (OUP) ; 2016
    In:  Dynamics and Statistics of the Climate System
    Details
    In: Dynamics and Statistics of the Climate System, Oxford University Press (OUP)
    Type of Medium: Online Resource
    ISSN: 2059-6987
    URL: Article
    DOI: 10.1093/climsys/dzw004
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 2855397-4
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  • 8
    Online Resource
    Online Resource
    Past abrupt changes, tipping points and cascading impacts in the Earth system
    Springer Science and Business Media LLC ; 2021
    In:  Nature Geoscience Vol. 14, No. 8 ( 2021-08), p. 550-558
    Details
    In: Nature Geoscience, Springer Science and Business Media LLC, Vol. 14, No. 8 ( 2021-08), p. 550-558
    Type of Medium: Online Resource
    ISSN: 1752-0894 , 1752-0908
    URL: Article
    DOI: 10.1038/s41561-021-00790-5
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 2396648-8
    detail.hit.zdb_id: 2405323-5
    SSG: 16,13
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  • 9
    Online Resource
    Online Resource
    Hydrological forecasting at impact scale: the integrated ParFlow hydrological model at 0.6 km for climate resilient water resource management over Germany
    Frontiers Media SA ; 2023
    In:  Frontiers in Water Vol. 5 ( 2023-5-30)
    Details
    In: Frontiers in Water, Frontiers Media SA, Vol. 5 ( 2023-5-30)
    Abstract: In the context of the repeated droughts that have affected central Europe over the last years (2018–2020, 2022), climate-resilient management of water resources, based on timely information about the current state of the terrestrial water cycle and forecasts of its evolution, has gained an increasing importance. To achieve this, we propose a new setup for simulations of the terrestrial water cycle using the integrated hydrological model ParFlow/CLM at high spatial and temporal resolution (i.e., 0.611 km, hourly time step) over Germany and the neighboring regions. We show that this setup can be used as a basis for a monitoring and forecasting system that aims to provide stakeholders from many sectors, but especially agriculture, with diagnostics and indicators highlighting different aspects of subsurface water states and fluxes, such as subsurface water storage, seepage water, capillary rise, or fraction of plant available water for different (root-)depths. The validation of the new simulation setup with observation-based data monthly over the period 2011–2020 yields good results for all major components of the terrestrial water cycle analyzed here, i.e., volumetric soil moisture, evapotranspiration, water table depth, and river discharge. As this setup relies on a standardized grid definition and recent globally available static fields and parameters (e.g., topography, soil hydraulic properties, land cover), the workflow could easily be transferred to many regions of the Earth, including sparsely gauged regions, since ParFlow/CLM does not require calibration.
    Type of Medium: Online Resource
    ISSN: 2624-9375
    URL: Article
    DOI: 10.3389/frwa.2023.1183642
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2986721-6
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  • 10
    Online Resource
    Online Resource
    Edge Detection Reveals Abrupt and Extreme Climate Events
    American Meteorological Society ; 2020
    In:  Journal of Climate Vol. 33, No. 15 ( 2020-08-01), p. 6399-6421
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 33, No. 15 ( 2020-08-01), p. 6399-6421
    Abstract: The most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions (“extreme events”) or abrupt shifts to a new persistent climate state (“tipping points”). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edges in 2D images to many dimensions (including time). Our edge detector identifies abrupt or extreme climate events in spatiotemporal data, quantifies their abruptness (or extremeness), and provides diagnostics that help one to understand the causes of these shifts. We also publish a comprehensive toolset of code that is documented and free to use. We document the performance of the new edge detector by analyzing several datasets of observations and models. In particular, we apply it to all monthly 2D variables of the RCP8.5 scenario of the Coupled Model Intercomparison Project (CMIP5). More than half of all simulations show abrupt shifts of more than 4 standard deviations on a time scale of 10 years. These shifts are mostly related to the loss of sea ice and permafrost in the Arctic. Our results demonstrate that the edge detector is particularly useful to scan large datasets in an efficient way, for example multimodel or perturbed-physics ensembles. It can thus help to reveal hidden “climate surprises” and to assess the uncertainties of dangerous climate events.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-19-0449.1
    DOI: 10.1175/JCLI-D-19-0449.s1
    RVK:
    UA 4548
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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