11 research outputs found
Attribution of global lake systems change to anthropogenic forcing
Lake ecosystems are jeopardized by the impacts of climate change on ice seasonality and water temperatures. Yet historical simulations have not been used to formally attribute changes in lake ice and temperature to anthropogenic drivers. In addition, future projections of these properties are limited to individual lakes or global simulations from single lake models. Here we uncover the human imprint on lakes worldwide using hindcasts and projections from five lake models. Reanalysed trends in lake temperature and ice cover in recent decades are extremely unlikely to be explained by pre-industrial climate variability alone. Ice-cover trends in reanalysis are consistent with lake model simulations under historical conditions, providing attribution of lake changes to anthropogenic climate change. Moreover, lake temperature, ice thickness and duration scale robustly with global mean air temperature across future climate scenarios (+0.9 °C °Cairâ1, â0.033 m °Cairâ1 and â9.7 d °Cairâ1, respectively). These impacts would profoundly alter the functioning of lake ecosystems and the services they provide
Accuracy, realism and general applicability of European forest models
Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models\u27 performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe\u27s common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests
Accuracy, realism and general applicability of European forest models
Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests.Peer reviewe
The PROFOUND Database for evaluating vegetation models and simulating climate impacts on European forests
Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data on European forests to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand level, and remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a "SQLite" relational database or "ASCII" flat file version (at https://doi.org/10.5880/PIK.2020.006/; Reyer et al., 2020). The data policies of the individual contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R package (https://CRAN.R- project.org/package=ProfoundData; Silveyra Gonzalez et al., 2020), which provides basic functions to explore, plot and extract the data for model set-up, calibration and evaluation.Peer reviewe
Scenario set-up and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)
This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, www.isimip.org) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6
Rekonstruktion und Analyse des Zustandsraumes zur Identifizierung von dynamischen ZustÀnden in realen Zeitreihen
One of the main goals of analyzing a high-dimensional time series is to
identify structures in it. Some of these structures correspond to important
dynamical features in the underlying system, like different dynamical states
and the transitions between these. In this thesis we introduce two new
methodologies for the identification of different dynamical features in a
system from the analysis of a real-world time series. We focus in the
dynamical features corresponding to the different dynamical metastable states
(in a system with multiple and well distinguished time scales, these can be
understood as the attractors associated to each of the different time scales)
in a system and the transitions between dynamical regimes in a system. Our
first method is designed for the identification of different dynamical
metastable states, and takes a recurrence analysis approach. The results
provided by this method seem to be robust to the introduction of noise and
missing points. Our second method is designed for the identification of
transitions between different dynamical regimes, and takes an algebraic
topological approach. It seems that our second method is, by construction,
also robust to the noise and outliers in the data. However, it is still not
sensitive enough to identify dynamical transitions where the shape of the
attractors in a system suffer small changes. Given that both methods
introduced in this thesis rely on the geometrical analysis of the state space,
another issue treated in this thesis is the reconstruction of the state space
from a complex time series. In this thesis, our criteria for an adequate state
space reconstruction are given in terms of the gain or loss of geometrical
information. These criteria are specifically developed for each of the
approaches taken for every method: recurrence analysis and persistent
homology.Die Identifizierung der Strukturen ist ein der Hauptziele der Analyse einer
hochdimensionalen Zeitreihe. Einige dieser Strukturen entsprechen wichtigen
dynamischen Eigenschaften in einem System, wie verschiedene dynamische
ZustĂ€nde und die ĂbergĂ€nge zwischen denen. In dieser Dissertation stellen wir
zwei neue Methoden fĂŒr die Analyse des Real-World- Zeitreihen dar, die
verschiedene dynamische Eigenschaften in einem System identifizieren. Wir
fokussieren uns auf die Identifizierung der unterschiedlichen dynamischen
metastabilen ZustÀnden in einem System (in einem System mit mehreren,
unterscheidbaren Zeitskalen, kann jede Zeitskala mit verschiedenen Attraktoren
verbunden sein) und auf die ĂbergĂ€nge zwischen verschiedenen dynamischen
Regimen in einem System. Unsere erste Methode identifiziert verschiedene
dynamische metastabile ZustÀnde. Diese Methode ist in dem Rekurrenz-Analyse-
Ansatz geankert. Die Ergebnisse dieser Methode sind scheinbar gegen die
RauscheneinfĂŒhrung und fehlende Datenpunkte robust. Unsere zweite Methode
identifiziert, die ĂbergĂ€nge zwischen verschiedenen dynamischen Regimen. Diese
Methode ist auf einer algebraischen topologischen Ansatz basiert. Es scheint,
dass unsere zweite Methode gegen die Rauschen-EinfĂŒhrung und Ausreisser in den
Daten robust ist. Es ist jedoch immer noch nicht empfindlich genug, dynamische
ĂbergĂ€ngen, wo die Form der Attraktoren in einem System kleine Ănderungen
ausweist, zu identifizieren. Da beide in dieser Arbeit vorgestellte Methoden
auf die geometrische Analyse des Phasenraums beruhen, wird in dieser Arbeit
des Weiteren die Rekonstruktion des Phasenraums von komplexen Zeitreihen
behandelt. In dieser Dissertation, beziehen sich unsere Kriterien fĂŒr eine
angemessene Phasenraumrekonstruktion auf den Gewinn oder Verlust der
geometrischen Informationen. Diese Kriterien sind speziell fĂŒr jeden Ansatz
bei den jeweiligen Methoden entwickelt worden: Rekurrenz-Analyse und
persistente Homologie
Attribution of global lake systems change to anthropogenic forcing
Lake ecosystems are jeopardized by the impacts of climate change on ice seasonality and water temperatures. Yet historical simulations have not been used to formally attribute changes in lake ice and temperature to anthropogenic drivers. In addition, future projections of these properties are limited to individual lakes or global simulations from single lake models. Here we uncover the human imprint on lakes worldwide using hindcasts and projections from five lake models. Reanalysed trends in lake temperature and ice cover in recent decades are extremely unlikely to be explained by pre-industrial climate variability alone. Ice-cover trends in reanalysis are consistent with lake model simulations under historical conditions, providing attribution of lake changes to anthropogenic climate change. Moreover, lake temperature, ice thickness and duration scale robustly with global mean air temperature across future climate scenarios (+0.9â°Câ°Cairâ1, â0.033âmâ°Cairâ1 and â9.7âdâ°Cairâ1, respectively). These impacts would profoundly alter the functioning of lake ecosystems and the services they provide
Accuracy, realism and general applicability of European forest models
Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state-of-the-art, stand-scale forest models against field measurements of forest structure and eddy-covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi-model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests
Projecting exposure to extreme climate impact events across six event categories and three spatial scales
Summarization: The extent and impact of climateârelated extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the InterâSectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in crossâcategory aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.Presented on: Earth's Futur
The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests
Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database (PROFOUND DB) provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale. A particular advantage of this database is its wide coverage of multiple data sources at different hierarchical and temporal scales, together with environmental driving data as well as the latest climate scenarios. Specifically, the PROFOUND DB provides general site descriptions, soil, climate, CO2, nitrogen deposition, tree and forest stand-level, as well as remote sensing data for nine contrasting forest stands distributed across Europe. Moreover, for a subset of five sites, time series of carbon fluxes, atmospheric heat conduction, and soil water are also available. The climate and nitrogen deposition data contain several datasets for the historic period and a wide range of future climate change scenarios following the Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, RCP8.5). We also provide pre-industrial climate simulations that allow for model runs aimed at disentangling the contribution of climate change to observed forest productivity changes. The PROFOUND DB is available freely as a SQLite relational database or ASCII flat file version (at https://doi.org/10.5880/PIK.2019.008). The data policies of the individual, contributing datasets are provided in the metadata of each data file. The PROFOUND DB can also be accessed via the ProfoundData R-package (https://github.com/COST-FP1304-PROFOUND/ProfoundData), which provides basic functions to explore, plot, and extract the data for model set-up, calibration and evaluation