Search Results
State-of-the-art global models underestimate impacts from climate extremes
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.
Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
2018, Wartenburger, Richard, Seneviratne, Sonia I, Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N, Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A M, Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P, Yang, Hong, Zhou, Tian
Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%–40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia. Although most of the ET estimates capture these extreme events, the generally large spread among the entire ensemble indicates substantial uncertainties.
Assessing the impacts of 1.5 °C global warming – simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)
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).
The Global Gridded Crop Model Intercomparison phase 1 simulation dataset
2019, Müller, Christoph, Elliott, Joshua, Kelly, David, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Hoek, Steven, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A. M., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Wang, Xuhui, de Wit, Allard, Yang, Hong
The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives. © 2019, The Author(s).
Global irrigation contribution to wheat and maize yield
2021, Wang, Xuhui, Müller, Christoph, Elliot, Joshua, Mueller, Nathaniel D., Ciais, Philippe, Jägermeyr, Jonas, Gerber, James, Dumas, Patrice, Wang, Chenzhi, Yang, Hui, Li, Laurent, Deryng, Delphine, Folberth, Christian, Liu, Wenfeng, Makowski, David, Olin, Stefan, Pugh, Thomas A. M., Reddy, Ashwan, Schmid, Erwin, Jeong, Sujong, Zhou, Feng, Piao, Shilong
Irrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.
Understanding the weather signal in national crop‐yield variability
2017, Frieler, Katja, Schauberger, Bernhard, Arneth, Almut, Balkovič, Juraj, Chryssanthacopoulos, James, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Khabarov, Nikolay, Müller, Christoph, Olin, Stefan, Smith, Steven J., Pugh, Thomas A.M., Schaphoff, Sibyll, Schewe, Jacob, Schmid, Erwin, Warszawski, Lila, Levermann, Anders
Year‐to‐year variations in crop yields can have major impacts on the livelihoods of subsistence farmers and may trigger significant global price fluctuations, with severe consequences for people in developing countries. Fluctuations can be induced by weather conditions, management decisions, weeds, diseases, and pests. Although an explicit quantification and deeper understanding of weather‐induced crop‐yield variability is essential for adaptation strategies, so far it has only been addressed by empirical models. Here, we provide conservative estimates of the fraction of reported national yield variabilities that can be attributed to weather by state‐of‐the‐art, process‐based crop model simulations. We find that observed weather variations can explain more than 50% of the variability in wheat yields in Australia, Canada, Spain, Hungary, and Romania. For maize, weather sensitivities exceed 50% in seven countries, including the United States. The explained variance exceeds 50% for rice in Japan and South Korea and for soy in Argentina. Avoiding water stress by simulating yields assuming full irrigation shows that water limitation is a major driver of the observed variations in most of these countries. Identifying the mechanisms leading to crop‐yield fluctuations is not only fundamental for dampening fluctuations, but is also important in the context of the debate on the attribution of loss and damage to climate change. Since process‐based crop models not only account for weather influences on crop yields, but also provide options to represent human‐management measures, they could become essential tools for differentiating these drivers, and for exploring options to reduce future yield fluctuations.
Global gridded crop model evaluation: Benchmarking, skills, deficiencies and implications
2017, Müller, Christoph, Elliott, Joshua, Chryssanthacopoulos, James, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Glotter, Michael, Hoek, Steven, Iizumi, Toshichika, Izaurralde, Roberto C., Jones, Curtis, Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Ray, Deepak K., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Song, Carol X., Wang, Xuhui, de Wit, Allard, Yang, Hong
Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.
Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty
2018, Schleussner, Carl-Friedrich, Deryng, Delphine, Müller, Christoph, Elliott, Joshua, Saeed, Fahad, Folberth, Christian, Liu, Wenfeng, Wang, Xuhui, Pugh, Thomas A. M., Thiery, Wim, Seneviratne, Sonia I., Rogelj, Joeri
Following the adoption of the Paris Agreement, there has been an increasing interest in quantifying impacts at discrete levels of global mean temperature (GMT) increase such as 1.5 °C and 2 °C above pre-industrial levels. Consequences of anthropogenic greenhouse gas emissions on agricultural productivity have direct and immediate relevance for human societies. Future crop yields will be affected by anthropogenic climate change as well as direct effects of emissions such as CO2 fertilization. At the same time, the climate sensitivity to future emissions is uncertain. Here we investigate the sensitivity of future crop yield projections with a set of global gridded crop models for four major staple crops at 1.5 °C and 2 °C warming above pre-industrial levels, as well as at different CO2 levels determined by similar probabilities to lead to 1.5 °C and 2 °C, using climate forcing data from the Half a degree Additional warming, Prognosis and Projected Impacts project. For the same CO2 forcing, we find consistent negative effects of half a degree warming on productivity in most world regions. Increasing CO2 concentrations consistent with these warming levels have potentially stronger but highly uncertain effects than 0.5 °C warming increments. Half a degree warming will also lead to more extreme low yields, in particular over tropical regions. Our results indicate that GMT change alone is insufficient to determine future impacts on crop productivity.
Implications of climate mitigation for future agricultural production
2015, Müller, Christoph, Elliott, Joshua, Chryssanthacopoulos, James, Deryng, Delphine, Folberth, Christian, Pugh, Thomas A.M., Schmid, Erwin
Climate change is projected to negatively impact biophysical agricultural productivity in much of the world. Actions taken to reduce greenhouse gas emissions and mitigate future climate changes, are thus of central importance for agricultural production. Climate impacts are, however, not unidirectional; some crops in some regions (primarily higher latitudes) are projected to benefit, particularly if increased atmospheric carbon dioxide is assumed to strongly increase crop productivity at large spatial and temporal scales. Climate mitigation measures that are implemented by reducing atmospheric carbon dioxide concentrations lead to reductions both in the strength of climate change and in the benefits of carbon dioxide fertilization. Consequently, analysis of the effects of climate mitigation on agricultural productivity must address not only regions for which mitigation is likely to reduce or even reverse climate damages. There are also regions that are likely to see increased crop yields due to climate change, which may lose these added potentials under mitigation action. Comparing data from the most comprehensive archive of crop yield projections publicly available, we find that climate mitigation leads to overall benefits from avoided damages at the global scale and especially in many regions that are already at risk of food insecurity today. Ignoring controversial carbon dioxide fertilization effects on crop productivity, we find that for the median projection aggressive mitigation could eliminate ~81% of the negative impacts of climate change on biophysical agricultural productivity globally by the end of the century. In this case, the benefits of mitigation typically extend well into temperate regions, but vary by crop and underlying climate model projections. Should large benefits to crop yields from carbon dioxide fertilization be realized, the effects of mitigation become much more mixed, though still positive globally and beneficial in many food insecure countries.
Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble
2019, Folberth, Christian, Elliott, Joshua, Müller, Christoph, Balkovič, Juraj, Chryssanthacopoulos, James, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Liu, Wenfeng, Reddy, Ashwan, Schmid, Erwin, Skalský, Rastislav, Yang, Hong, Arneth, Almut, Ciais, Philippe, Deryng, Delphine, Lawrence, Peter J., Olin, Stefan, Pugh, Thomas A.M., Ruane, Alex C., Wang, Xuhui
Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.