@misc{VogelRivoireDeiddaetal.2021,
  author    = {Vogel, Johannes and Rivoire, Pauline and Deidda, Cristina and Rahimi, Leila and Sauter, Christoph A. and Tschumi, Elisabeth and van der Wiel, Karin and Zhang, Tianyi and Zscheischler, Jakob},
  title     = {Identifying meteorological drivers of extreme impacts},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1126},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-49627},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-496273},
  pages     = {151 -- 172},
  year      = {2021},
  abstract  = {Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.},
  language  = {en}
}
@article{VogelRivoireDeiddaetal.2021,
  author    = {Vogel, Johannes and Rivoire, Pauline and Deidda, Cristina and Rahimi, Leila and Sauter, Christoph A. and Tschumi, Elisabeth and van der Wiel, Karin and Zhang, Tianyi and Zscheischler, Jakob},
  title     = {Identifying meteorological drivers of extreme impacts},
  series = {Earth System Dynamics},
  volume    = {12},
  journal   = {Earth System Dynamics},
  issn      = {2190-4987},
  doi       = {10.5194/esd-12-151-2021},
  pages     = {151 -- 172},
  year      = {2021},
  abstract  = {Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study, we investigate whether key meteorological drivers of extreme impacts can be identified using the least absolute shrinkage and selection operator (LASSO) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the Agricultural Production Systems sIMulator (APSIM) crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply LASSO logistic regression to determine which weather conditions during the growing season lead to crop failure. We obtain good model performance in central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields; that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points, the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance. We conclude that the LASSO regression model is a useful tool to automatically detect compound drivers of extreme impacts and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.},
  language  = {en}
}
@misc{FrankReichsteinBahnetal.2015,
  author    = {Frank, Dorothe A. and Reichstein, Markus and Bahn, Michael and Thonicke, Kirsten and Frank, David and Mahecha, Miguel D. and Smith, Pete and Van der Velde, Marijn and Vicca, Sara and Babst, Flurin and Beer, Christian and Buchmann, Nina and Canadell, Josep G. and Ciais, Philippe and Cramer, Wolfgang and Ibrom, Andreas and Miglietta, Franco and Poulter, Ben and Rammig, Anja and Seneviratne, Sonia I. and Walz, Ariane and Wattenbach, Martin and Zavala, Miguel A. and Zscheischler, Jakob},
  title     = {Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts},
  series = {Global change biology},
  volume    = {21},
  journal   = {Global change biology},
  number    = {8},
  publisher = {Wiley-Blackwell},
  address   = {Hoboken},
  issn      = {1354-1013},
  doi       = {10.1111/gcb.12916},
  pages     = {2861 -- 2880},
  year      = {2015},
  abstract  = {Extreme droughts, heat waves, frosts, precipitation, wind storms and other climate extremes may impact the structure, composition and functioning of terrestrial ecosystems, and thus carbon cycling and its feedbacks to the climate system. Yet, the interconnected avenues through which climate extremes drive ecological and physiological processes and alter the carbon balance are poorly understood. Here, we review the literature on carbon cycle relevant responses of ecosystems to extreme climatic events. Given that impacts of climate extremes are considered disturbances, we assume the respective general disturbance-induced mechanisms and processes to also operate in an extreme context. The paucity of well-defined studies currently renders a quantitative meta-analysis impossible, but permits us to develop a deductive framework for identifying the main mechanisms (and coupling thereof) through which climate extremes may act on the carbon cycle. We find that ecosystem responses can exceed the duration of the climate impacts via lagged effects on the carbon cycle. The expected regional impacts of future climate extremes will depend on changes in the probability and severity of their occurrence, on the compound effects and timing of different climate extremes, and on the vulnerability of each land-cover type modulated by management. Although processes and sensitivities differ among biomes, based on expert opinion, we expect forests to exhibit the largest net effect of extremes due to their large carbon pools and fluxes, potentially large indirect and lagged impacts, and long recovery time to regain previous stocks. At the global scale, we presume that droughts have the strongest and most widespread effects on terrestrial carbon cycling. Comparing impacts of climate extremes identified via remote sensing vs. ground-based observational case studies reveals that many regions in the (sub-)tropics are understudied. Hence, regional investigations are needed to allow a global upscaling of the impacts of climate extremes on global carbon-climate feedbacks.},
  language  = {en}
}
@article{SollySchoeningBochetal.2014,
  author    = {Solly, Emily F. and Sch{\"o}ning, Ingo and Boch, Steffen and Kandeler, Ellen and Marhan, Sven and Michalzik, Beate and M{\"u}ller, J{\"o}rg and Zscheischler, Jakob and Trumbore, Susan E. and Schrumpf, Marion},
  title     = {Factors controlling decomposition rates of fine root litter in temperate forests and grasslands},
  series = {Plant and soil},
  volume    = {382},
  journal   = {Plant and soil},
  number    = {1-2},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {0032-079X},
  doi       = {10.1007/s11104-014-2151-4},
  pages     = {203 -- 218},
  year      = {2014},
  abstract  = {Fine root decomposition contributes significantly to element cycling in terrestrial ecosystems. However, studies on root decomposition rates and on the factors that potentially influence them are fewer than those on leaf litter decomposition. To study the effects of region and land use intensity on fine root decomposition, we established a large scale study in three German regions with different climate regimes and soil properties. Methods In 150 forest and 150 grassland sites we deployed litterbags (100 mu m mesh size) with standardized litter consisting of fine roots from European beech in forests and from a lowland mesophilous hay meadow in grasslands. In the central study region, we compared decomposition rates of this standardized litter with root litter collected on-site to separate the effect of litter quality from environmental factors. Standardized herbaceous roots in grassland soils decomposed on average significantly faster (24 +/- 6 \% mass loss after 12 months, mean +/- SD) than beech roots in forest soils (12 +/- 4 \%; p < 0.001). Fine root decomposition varied among the three study regions. Land use intensity, in particular N addition, decreased fine root decomposition in grasslands. The initial lignin:N ratio explained 15 \% of the variance in grasslands and 11 \% in forests. Soil moisture, soil temperature, and C:N ratios of soils together explained 34 \% of the variance of the fine root mass loss in grasslands, and 24 \% in forests. Grasslands, which have higher fine root biomass and root turnover compared to forests, also have higher rates of root decomposition. Our results further show that at the regional scale fine root decomposition is influenced by environmental variables such as soil moisture, soil temperature and soil nutrient content. Additional variation is explained by root litter quality.},
  language  = {en}
}
@article{ReichsteinBahnCiaisetal.2013,
  author    = {Reichstein, Markus and Bahn, Michael and Ciais, Philippe and Frank, Dorothea and Mahecha, Miguel D. and Seneviratne, Sonia I. and Zscheischler, Jakob and Beer, Christian and Buchmann, Nina and Frank, David C. and Papale, Dario and Rammig, Anja and Smith, Pete and Thonicke, Kirsten and van der Velde, Marijn and Vicca, Sara and Walz, Ariane and Wattenbach, Martin},
  title     = {Climate extremes and the carbon cycle},
  series = {Nature : the international weekly journal of science},
  volume    = {500},
  journal   = {Nature : the international weekly journal of science},
  number    = {7462},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {0028-0836},
  doi       = {10.1038/nature12350},
  pages     = {287 -- 295},
  year      = {2013},
  abstract  = {The terrestrial biosphere is a key component of the global carbon cycle and its carbon balance is strongly influenced by climate. Continuing environmental changes are thought to increase global terrestrial carbon uptake. But evidence is mounting that climate extremes such as droughts or storms can lead to a decrease in regional ecosystem carbon stocks and therefore have the potential to negate an expected increase in terrestrial carbon uptake. Here we explore the mechanisms and impacts of climate extremes on the terrestrial carbon cycle, and propose a pathway to improve our understanding of present and future impacts of climate extremes on the terrestrial carbon budget.},
  language  = {en}
}