2018, Ostberg, Sebastian, Schewe, Jacob, Childers, Katelin, Frieler, Katja

An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.

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.

2018, Geiger, Tobias, Frieler, Katja, Bresch, David N.

Tropical cyclones pose a major risk to societies worldwide, with about 22 million directly affected people and damages of USD 29 billion on average per year over the last 20 years. While data on observed cyclones tracks (location of the center) and wind speeds are publicly available, these data sets do not contain information about the spatial extent of the storm and people or assets exposed. Here, we apply a simplified wind field model to estimate the areas exposed to wind speeds above 34, 64, and 96 knots (kn). Based on available spatially explicit data on population densities and gross domestic product (GDP) we estimate (1) the number of people and (2) the sum of assets exposed to wind speeds above these thresholds accounting for temporal changes in historical distribution of population and assets (TCE-hist) and assuming fixed 2015 patterns (TCE-2015). The associated spatially explicit and aggregated country-event-level exposure data (TCE-DAT) cover the period 1950 to 2015 and are freely available at https://doi.org/10.5880/pik.2017.011 (Geiger at al., 2017c). It is considered key information to (1) assess the contribution of climatological versus socioeconomic drivers of changes in exposure to tropical cyclones, (2) estimate changes in vulnerability from the difference in exposure and reported damages and calibrate associated damage functions, and (3) build improved exposure-based predictors to estimate higher-level societal impacts such as long-term effects on GDP, employment, or migration.

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).

2016, Schleussner, Carl-Friedrich, Lissner, Tabea K., Fischer, Erich M., Wohland, Jan, Perrette, Mahé, Golly, Antonius, Rogelj, Joeri, Childers, Katelin, Schewe, Jacob, Frieler, Katja, Mengel, Matthias, Hare, William, Schaeffer, Michiel

Robust appraisals of climate impacts at different levels of global-mean temperature increase are vital to guide assessments of dangerous anthropogenic interference with the climate system. The 2015 Paris Agreement includes a two-headed temperature goal: "holding the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C". Despite the prominence of these two temperature limits, a comprehensive overview of the differences in climate impacts at these levels is still missing. Here we provide an assessment of key impacts of climate change at warming levels of 1.5°C and 2°C, including extreme weather events, water availability, agricultural yields, sea-level rise and risk of coral reef loss. Our results reveal substantial differences in impacts between a 1.5°C and 2°C warming that are highly relevant for the assessment of dangerous anthropogenic interference with the climate system. For heat-related extremes, the additional 0.5°C increase in global-mean temperature marks the difference between events at the upper limit of present-day natural variability and a new climate regime, particularly in tropical regions. Similarly, this warming difference is likely to be decisive for the future of tropical coral reefs. In a scenario with an end-of-century warming of 2°C, virtually all tropical coral reefs are projected to be at risk of severe degradation due to temperature-induced bleaching from 2050 onwards. This fraction is reduced to about 90% in 2050 and projected to decline to 70% by 2100 for a 1.5°C scenario. Analyses of precipitation-related impacts reveal distinct regional differences and hot-spots of change emerge. Regional reduction in median water availability for the Mediterranean is found to nearly double from 9% to 17% between 1.5°C and 2°C, and the projected lengthening of regional dry spells increases from 7 to 11%. Projections for agricultural yields differ between crop types as well as world regions. While some (in particular high-latitude) regions may benefit, tropical regions like West Africa, South-East Asia, as well as Central and northern South America are projected to face substantial local yield reductions, particularly for wheat and maize. Best estimate sea-level rise projections based on two illustrative scenarios indicate a 50cm rise by 2100 relative to year 2000-levels for a 2°C scenario, and about 10 cm lower levels for a 1.5°C scenario. In a 1.5°C scenario, the rate of sea-level rise in 2100 would be reduced by about 30% compared to a 2°C scenario. Our findings highlight the importance of regional differentiation to assess both future climate risks and different vulnerabilities to incremental increases in global-mean temperature. The article provides a consistent and comprehensive assessment of existing projections and a good basis for future work on refining our understanding of the difference between impacts at 1.5°C and 2°C warming.

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, Rosenzweig, Cynthia, Arnell, Nigel W., Ebi, Kristie L., Lotze-Campen, Hermann, Raes, Frank, Rapley, Chris, Smith, Mark Stafford, Cramer, Wolfgang, Frieler, Katja, Reyer, Christopher P.O., Schewe, Jacob, van Vuuren, Detlef, Warszawski, Lila

The aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socio-economic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.

2016, Ruane, Alex C., Teichmann, Claas, Arnell, Nigel W., Carter, Timothy R., Ebi, Kristie L., Frieler, Katja, Goodess, Clare M., Hewitson, Bruce, Horton, Radley, Kovats, R. Sari, Lotze, Heike K., Mearns, Linda O., Navarra, Antonio, Ojima, Dennis S., Riahi, Keywan, Rosenzweig, Cynthia, Themessl, Matthias, Vincent, Katharine

This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.

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.

2017, Geiger, Tobias, Frieler, Katja, Levermann, Anders

Recently a multitude of empirically derived damage models have been applied to project future tropical cyclone (TC) losses for the United States. In their study (Geiger et al 2016 Environ. Res. Lett. 11 084012) compared two approaches that differ in the scaling of losses with socio-economic drivers: the commonly-used approach resulting in a sub-linear scaling of historical TC losses with a nation's affected gross domestic product (GDP), and the disentangled approach that shows a sub-linear increase with affected population and a super-linear scaling of relative losses with per capita income. Statistics cannot determine which approach is preferable but since process understanding demands that there is a dependence of the loss on both GDP per capita and population, an approach that accounts for both separately is preferable to one which assumes a specific relation between the two dependencies. In the accompanying comment, Rybski et al argued that there is no rigorous evidence to reach the conclusion that high-income does not protect against hurricane losses. Here we affirm that our conclusion is drawn correctly and reply to further remarks raised in the comment, highlighting the adequateness of our approach but also the potential for future extension of our research.