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.

2017, Frieler, Katja, Lange, Stefan, Piontek, Franziska, Reyer, Christopher P.O., Schewe, Jacob, Warszawski, Lila, Zhao, Fang, Chini, Louise, Denvil, Sebastien, Emanuel, Kerry, Geiger, Tobias, Halladay, Kate, Hurtt, George, Mengel, Matthias, Murakami, Daisuke, Ostberg, Sebastian, Popp, Alexander, Riva, Riccardo, Stevanovic, Miodrag, Suzuki, Tatsuo, Volkholz, Jan, Burke, Eleanor, Ciais, Philippe, Ebi, Kristie, Eddy, Tyler D., Elliott, Joshua, Galbraith, Eric, Gosling, Simon N., Hattermann, Fred, Hickler, Thomas, Hinkel, Jochen, Hof, Christian, Huber, Veronika, Jägermeyr, Jonas, Krysanova, Valentina, Marcé, Rafael, Müller Schmied, Hannes, Mouratiadou, Ioanna, Pierson, Don, Tittensor, Derek P., Vautard, Robert, van Vliet, Michelle, Biber, Matthias F., Betts, Richard A., Bodirsky, Benjamin Leon, Deryng, Delphine, Frolking, Steve, Jones, Chris D., Lotze, Heike K., Lotze-Campen, Hermann, Sahajpal, Ritvik, Thonicke, Kirsten, Tian, Hanqin, Yamagata, Yoshiki

In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Convention on Climate Change (UNFCCC) invited the Intergovernmental Panel on Climate Change (IPCC) to provide a "special report in 2018 on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways". In Nairobi, Kenya, April 2016, the IPCC panel accepted the invitation. Here we describe the response devised within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) to provide tailored, cross-sectorally consistent impact projections to broaden the scientific basis for the report. The simulation protocol is designed to allow for (1) separation of the impacts of historical warming starting from pre-industrial conditions from impacts of other drivers such as historical land-use changes (based on pre-industrial and historical impact model simulations); (2) quantification of the impacts of additional warming up to 1.5°C, including a potential overshoot and long-term impacts up to 2299, and comparison to higher levels of global mean temperature change (based on the low-emissions Representative Concentration Pathway RCP2.6 and a no-mitigation pathway RCP6.0) with socio-economic conditions fixed at 2005 levels; and (3) assessment of the climate effects based on the same climate scenarios while accounting for simultaneous changes in socio-economic conditions following the middle-of-the-road Shared Socioeconomic Pathway (SSP2, Fricko et al., 2016) and in particular differential bioenergy requirements associated with the transformation of the energy system to comply with RCP2.6 compared to RCP6.0. With the aim of providing the scientific basis for an aggregation of impacts across sectors and analysis of cross-sectoral interactions that may dampen or amplify sectoral impacts, the protocol is designed to facilitate consistent impact projections from a range of impact models across different sectors (global and regional hydrology, lakes, global crops, global vegetation, regional forests, global and regional marine ecosystems and fisheries, global and regional coastal infrastructure, energy supply and demand, temperature-related mortality, and global terrestrial biodiversity).

2013, Warszawski, Lila, Friend, Andrew, Ostberg, Sebastian, Frieler, Katja, Lucht, Wolfgang, Schaphoff, Sibyll, Beerling, David, Cadule, Patricia, Ciais, Philippe, Clark, Douglas B., Kahana, Ron, Ito, Akihiko, Keribin, Rozenn, Kleidon, Axel, Lomas, Mark, Nishina, Kazuya, Pavlick, Ryan, Rademacher, Tim Tito, Buechner, Matthias, Piontek, Franziska, Schewe, Jacob, Serdeczny, Olivia, Schellnhuber, Hans Joachim

Climate change may pose a high risk of change to Earth's ecosystems: shifting climatic boundaries may induce changes in the biogeochemical functioning and structures of ecosystems that render it difficult for endemic plant and animal species to survive in their current habitats. Here we aggregate changes in the biogeochemical ecosystem state as a proxy for the risk of these shifts at different levels of global warming. Estimates are based on simulations from seven global vegetation models (GVMs) driven by future climate scenarios, allowing for a quantification of the related uncertainties. 5–19% of the naturally vegetated land surface is projected to be at risk of severe ecosystem change at 2 ° C of global warming (ΔGMT) above 1980–2010 levels. However, there is limited agreement across the models about which geographical regions face the highest risk of change. The extent of regions at risk of severe ecosystem change is projected to rise with ΔGMT, approximately doubling between ΔGMT = 2 and 3 ° C, and reaching a median value of 35% of the naturally vegetated land surface for ΔGMT = 4 °C. The regions projected to face the highest risk of severe ecosystem changes above ΔGMT = 4 °C or earlier include the tundra and shrublands of the Tibetan Plateau, grasslands of eastern India, the boreal forests of northern Canada and Russia, the savanna region in the Horn of Africa, and the Amazon rainforest.

2017, Zhao, Fang, Veldkamp, Ted I.E., Frieler, Katja, Schewe, Jacob, Ostberg, Sebastian, Willner, Sven, Schauberger, Bernhard, Gosling, Simon N., Müller Schmied, Hannes, Portmann, Felix T., Leng, Guoyong, Huang, Maoyi, Liu, Xingcai, Tang, Qiuhong, Hanasaki, Naota, Biemans, Hester, Gerten, Dieter, Satoh, Yusuke, Pokhrel, Yadu, Stacke, Tobias, Ciais, Philippe, Chang, Jinfeng, Ducharne, Agnes, Guimberteau, Matthieu, Wada, Yoshihide, Kim, Hyungjun, Yamazaki, Dai

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

2017, Chang, Jinfeng, Ciais, Philippe, Wang, Xuhui, Piao, Shilong, Asrar, Ghassem, Betts, Richard, Chevallier, Frédéric, Dury, Marie, François, Louis, Frieler, Katja, Ros, Anselmo García Cantú, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Morfopoulos, Catherine, Munhoven, Guy, Nishina, Kazuya, Ostberg, Sebastian, Pan, Shufen, Peng, Shushi, Rafique, Rashid, Reyer, Christopher, Rödenbeck, Christian, Schaphoff, Sibyll, Steinkamp, Jörg, Tian, Hanqin, Viovy, Nicolas, Yang, Jia, Zeng, Ning, Zhao, Fang

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.