2019, Schewe, Jacob, Gosling, Simon N., Reyer, Christopher, Zhao, Fang, Ciais, Philippe, Elliott, Joshua, Francois, Louis, Huber, Veronika, Lotze, Heike K., Seneviratne, Sonia I., van Vliet, Michelle T. H., Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, de Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter, Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Orth, René, Ostberg, Sebastian, Pokhrel, Yadu, Pugh, Thomas A. M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Jörg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, Warszawski, Lila

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

2020, Lange, Stefan, Volkholz, Jan, Geiger, Tobias, Zhao, Fang, Vega, Iliusi, Veldkamp, Ted, Reyer, Christopher P.O., Warszawski, Lila, Huber, Veronika, Jägermeyr, Jonas, Schewe, Jacob, Bresch, David N., Büchner, Matthias, Chang, Jinfeng, Ciais, Philippe, Dury, Marie, Emanuel, Kerry, Folberth, Christian, Gerten, Dieter, Gosling, Simon N., Grillakis, Manolis, Hanasaki, Naota, Henrot, Alexandra-Jane, Hickler, Thomas, Honda, Yasushi, Ito, Akihiko, Khabarov, Nikolay, Koutroulis, Aristeidis, Liu, Wenfeng, Müller, Christoph, Nishina, Kazuya, Ostberg, Sebastian, Müller Schmied, Hannes, Seneviratne, Sonia I., Stacke, Tobias, Steinkamp, Jörg, Thiery, Wim, Wada, Yoshihide, Willner, Sven, Yang, Hong, Yoshikawa, Minoru, Yue, Chao, Frieler, Katja

The extent and impact of climate-related 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 Inter-Sectoral 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 cross-category 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. ©2020. The Authors.

2020, Reyer, Christopher P.O., Silveyra Gonzalez, Ramiro, Dolos, Klara, Hartig, Florian, Hauf, Ylva, Noack, Matthias, Lasch-Born, Petra, Rötzer, Thomas, Pretzsch, Hans, Meesenburg, Henning, Fleck, Stefan, Wagner, Markus, Bolte, Andreas, Sanders, Tanja G.M., Kolari, Pasi, Mäkelä, Annikki, Vesala, Timo, Mammarella, Ivan, Pumpanen, Jukka, Collalti, Alessio, Trotta, Carlo, Matteucci, Giorgio, D'Andrea, Ettore, Foltýnová, Lenka, Krejza, Jan, Ibrom, Andreas, Pilegaard, Kim, Loustau, Denis, Bonnefond, Jean-Marc, Berbigier, Paul, Picart, Delphine, Lafont, Sébastien, Dietze, Michael, Cameron, David, Vieno, Massimo, Tian, Hanqin, Palacios-Orueta, Alicia, Cicuendez, Victor, Recuero, Laura, Wiese, Klaus, Büchner, Matthias, Lange, Stefan, Volkholz, Jan, Kim, Hyungjun, Horemans, Joanna A., Bohn, Friedrich, Steinkamp, Jörg, Chikalanov, Alexander, Weedon, Graham P., Sheffield, Justin, Babst, Flurin, Vega del Valle, Iliusi, Suckow, Felicitas, Martel, Simon, Mahnken, Mats, Gutsch, Martin, Frieler, Katja

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.