DSpace :: Browsing by Author "Müller, Christoph"

Browsing by Author "Müller, Christoph"

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An AgMIP framework for improved agricultural representation in integrated assessment models
(Bristol : IOP Publishing, 2017) Ruane, Alex C.; Rosenzweig, Cynthia; Asseng, Senthold; Boote, Kenneth J.; Elliott, Joshua; Ewert, Frank; Jones, James W.; Martre, Pierre; McDermid, Sonali P.; Müller, Christoph; Snyder, Abigail; Thorburn, Peter J.
Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications.
Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change
(Amsterdam [u.a.] : Elsevier, 2017) Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco; Asseng, Senthold; Baranowski, Piotr; Basso, Bruno; Bodin, Per; Buis, Samuel; Cammarano, Davide; Deligios, Paola; Destain, Marie-France; Dumont, Benjamin; Ewert, Frank; Ferrise, Roberto; François, Louis; Gaiser, Thomas; Hlavinka, Petr; Jacquemin, Ingrid; Kersebaum, Kurt Christian; Kollas, Chris; Krzyszczak, Jaromir; Lorite, Ignacio J.; Minet, Julien; Minguez, M. Ines; Montesino, Manuel; Moriondo, Marco; Müller, Christoph; Nendel, Claas; Öztürk, Isik; Perego, Alessia; Rodríguez, Alfredo; Ruane, Alex C.; Ruget, Françoise; Sanna, Mattia; Semenov, Mikhail A.; Slawinski, Cezary; Stratonovitch, Pierre; Supit, Iwan; Waha, Katharina; Wang, Enli; Wu, Lianhai; Zhao, Zhigan; Rötter, Reimund P.
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (−2 to +9°C) and precipitation (−50 to +50%). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the application of the EDA and SDA approaches revealed their capability to distinguish: (i) stronger yield responses to precipitation for winter wheat than spring wheat; (ii) differing strengths of response to climate changes for years with anomalous weather conditions compared to period-average conditions; (iii) the influence of site conditions on yield patterns; (iv) similarities in IRS patterns among models with related genealogy; (v) similarities in IRS patterns for models with simpler process descriptions of root growth and water uptake compared to those with more complex descriptions; and (vi) a closer correspondence of IRS patterns in models using partitioning schemes to represent yield formation than in those using a harvest index. Such results can inform future crop modelling studies that seek to exploit the diversity of multi-model ensembles, by distinguishing ensemble members that span a wide range of responses as well as those that display implausible behaviour or strong mutual similarities.
Climate change impacts on agriculture in 2050 under a range of plausible socioeconomic and emissions scenarios
(Bristol : IOP Publishing, 2015) Wiebe, Keith; Lotze-Campen, Hermann; Sands, Ronald; Tabeau, Andrzej; van der Mensbrugghe, Dominique; Biewald, Anne; Bodirsky, Benjamin; Islam, Shahnila; Kavallari, Aikaterini; Mason-D'Croz, Daniel; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; van Meijl, Hans; Willenbockel, Dirk
Previous studies have combined climate, crop and economic models to examine the impact of climate change on agricultural production and food security, but results have varied widely due to differences in models, scenarios and input data. Recent work has examined (and narrowed) these differences through systematic model intercomparison using a high-emissions pathway to highlight the differences. This paper extends that analysis to explore a range of plausible socioeconomic scenarios and emission pathways. Results from multiple climate and economic models are combined to examine the global and regional impacts of climate change on agricultural yields, area, production, consumption, prices and trade for coarse grains, rice, wheat, oilseeds and sugar crops to 2050. We find that climate impacts on global average yields, area, production and consumption are similar across shared socioeconomic pathways (SSP 1, 2 and 3, as we implement them based on population, income and productivity drivers), except when changes in trade policies are included. Impacts on trade and prices are higher for SSP 3 than SSP 2, and higher for SSP 2 than for SSP 1. Climate impacts for all variables are similar across low to moderate emissions pathways (RCP 4.5 and RCP 6.0), but increase for a higher emissions pathway (RCP 8.5). It is important to note that these global averages may hide regional variations. Projected reductions in agricultural yields due to climate change by 2050 are larger for some crops than those estimated for the past half century, but smaller than projected increases to 2050 due to rising demand and intrinsic productivity growth. Results illustrate the sensitivity of climate change impacts to differences in socioeconomic and emissions pathways. Yield impacts increase at high emissions levels and vary with changes in population, income and technology, but are reduced in all cases by endogenous changes in prices and other variables.
Climate change impacts on European arable crop yields: Sensitivity to assumptions about rotations and residue management
(Amsterdam [u.a.] : Elsevier, 2022) Faye, Babacar; Webber, Heidi; Gaiser, Thomas; Müller, Christoph; Zhang, Yinan; Stella, Tommaso; Latka, Catharina; Reckling, Moritz; Heckelei, Thomas; Helming, Katharina; Ewert, Frank
Most large scale studies assessing climate change impacts on crops are performed with simulations of single crops and with annual re-initialization of the initial soil conditions. This is in contrast to the reality that crops are grown in rotations, often with sizable proportion of the preceding crop residue to be left in the fields and varying soil initial conditions from year to year. In this study, the sensitivity of climate change impacts on crop yield and soil organic carbon to assumptions about annual model re-initialization, specification of crop rotations and the amount of residue retained in fields was assessed for seven main crops across Europe. Simulations were conducted for a scenario period 2040–2065 relative to a baseline from 1980 to 2005 using the SIMPLACE1 framework. Results indicated across Europe positive climate change impacts on yield for C3 crops and negative impacts for maize. The consideration of simulating rotations did not have a benefit on yield variability but on relative yield change in response to climate change which slightly increased for C3 crops and decreased for C4 crops when rotation was considered. Soil organic carbon decreased under climate change in both simulations assuming a continuous monocrop and plausible rotations by between 1% and 2% depending on the residue management strategy.
Comparing impacts of climate change and mitigation on global agriculture by 2050
(Bristol : IOP Publ., 2018) van Meijl, Hans; Havlik, Petr; Lotze-Campen, Hermann; Stehfest, Elke; Witzke, Peter; Pérez Domínguez, Ignacio; Bodirsky, Benjamin Leon; van Dijk, Michiel; Doelman, Jonathan; Fellmann, Thomas; Humpenöder, Florian; Koopman, Jason F. L.; Müller, Christoph; Popp, Alexander; Tabeau, Andrzej; Valin, Hugo; van Zeist, Willem-Jan
Systematic model inter-comparison helps to narrow discrepancies in the analysis of the future impact of climate change on agricultural production. This paper presents a set of alternative scenarios by five global climate and agro-economic models. Covering integrated assessment (IMAGE), partial equilibrium (CAPRI, GLOBIOM, MAgPIE) and computable general equilibrium (MAGNET) models ensures a good coverage of biophysical and economic agricultural features. These models are harmonized with respect to basic model drivers, to assess the range of potential impacts of climate change on the agricultural sector by 2050. Moreover, they quantify the economic consequences of stringent global emission mitigation efforts, such as non-CO2 emission taxes and land-based mitigation options, to stabilize global warming at 2 °C by the end of the century under different Shared Socioeconomic Pathways. A key contribution of the paper is a vis-à-vis comparison of climate change impacts relative to the impact of mitigation measures. In addition, our scenario design allows assessing the impact of the residual climate change on the mitigation challenge. From a global perspective, the impact of climate change on agricultural production by mid-century is negative but small. A larger negative effect on agricultural production, most pronounced for ruminant meat production, is observed when emission mitigation measures compliant with a 2 °C target are put in place. Our results indicate that a mitigation strategy that embeds residual climate change effects (RCP2.6) has a negative impact on global agricultural production relative to a no-mitigation strategy with stronger climate impacts (RCP6.0). However, this is partially due to the limited impact of the climate change scenarios by 2050. The magnitude of price changes is different amongst models due to methodological differences. Further research to achieve a better harmonization is needed, especially regarding endogenous food and feed demand, including substitution across individual commodities, and endogenous technological change.
Crop productivity changes in 1.5 °C and 2 °C worlds under climate sensitivity uncertainty
(Bristol : IOP Publ., 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.
Diverging importance of drought stress for maize and winter wheat in Europe
([London] : Nature Publishing Group UK, 2018) Webber, Heidi; Ewert, Frank; Olesen, Jørgen E.; Müller, Christoph; Fronzek, Stefan; Ruane, Alex C.; Bourgault, Maryse; Martre, Pierre; Ababaei, Behnam; Bindi, Marco; Ferrise, Roberto; Finger, Robert; Fodor, Nándor; Gabaldón-Leal, Clara; Gaiser, Thomas; Jabloun, Mohamed; Kersebaum, Kurt-Christian; Lizaso, Jon I.; Lorite, Ignacio J.; Manceau, Loic; Moriondo, Marco; Nendel, Claas; Rodríguez, Alfredo; Ruiz-Ramos, Margarita; Semenov, Mikhail A.; Siebert, Stefan; Stella, Tommaso; Stratonovitch, Pierre; Trombi, Giacomo; Wallach, Daniel
Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.
Drivers and patterns of land biosphere carbon balance reversal
(Bristol : IOP Publishing, 2016) Müller, Christoph; Stehfest, Elke; van Minnen, Jelle G; Strengers, Bart; von Bloh, Werner; Beusen, Arthur H W; Schaphoff, Sibyll; Kram, Tom; Lucht, Wolfgang
The carbon balance of the land biosphere is the result of complex interactions between land, atmosphere and oceans, including climatic change, carbon dioxide fertilization and land-use change. While the land biosphere currently absorbs carbon dioxide from the atmosphere, this carbon balance might be reversed under climate and land-use change ('carbon balance reversal'). A carbon balance reversal would render climate mitigation much more difficult, as net negative emissions would be needed to even stabilize atmospheric carbon dioxide concentrations. We investigate the robustness of the land biosphere carbon sink under different socio-economic pathways by systematically varying climate sensitivity, spatial patterns of climate change and resulting land-use changes. For this, we employ a modelling framework designed to account for all relevant feedback mechanisms by coupling the integrated assessment model IMAGE with the process-based dynamic vegetation, hydrology and crop growth model LPJmL. We find that carbon balance reversal can occur under a broad range of forcings and is connected to changes in tree cover and soil carbon mainly in northern latitudes. These changes are largely a consequence of vegetation responses to varying climate and only partially of land-use change and the rate of climate change. Spatial patterns of climate change as deduced from different climate models, substantially determine how much pressure in terms of global warming and land-use change the land biosphere will tolerate before the carbon balance is reversed. A reversal of the land biosphere carbon balance can occur as early as 2030, although at very low probability, and should be considered in the design of so-called peak-and-decline strategies.
Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use
(Berlin ; Heidelberg ; New York : Springer, 2021) Rolinski, Susanne; Prishchepov, Alexander V.; Guggenberger, Georg; Bischoff, Norbert; Kurganova, Irina; Schierhorn, Florian; Müller, Daniel; Müller, Christoph
Changes in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes.
Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm
(Amsterdam : Elsevier, 2016) van Vuuren, Detlef P.; Stehfest, Elke; Gernaat, David E.H.J.; Doelman, Jonathan C.; van den Berg, Maarten; Harmsen, Mathijs; de Boer, Harmen Sytze; Bouwman, Lex F.; Daioglou, Vassilis; Edelenbosch, Oreane Y.; Girod, Bastien; Kram, Tom; Lassaletta, Luis; Lucas, Paul L.; van Meijl, Hans; Müller, Christoph; van Ruijven, Bas J.; van der Sluis, Sietske; Tabeau, Andrzej
This paper describes the possible developments in global energy use and production, land use, emissions and climate changes following the SSP1 storyline, a development consistent with the green growth (or sustainable development) paradigm (a more inclusive development respecting environmental boundaries). The results are based on the implementation using the IMAGE 3.0 integrated assessment model and are compared with a) other IMAGE implementations of the SSPs (SSP2 and SSP3) and b) the SSP1 implementation of other integrated assessment models. The results show that a combination of resource efficiency, preferences for sustainable production methods and investment in human development could lead to a strong transition towards a more renewable energy supply, less land use and lower anthropogenic greenhouse gas emissions in 2100 than in 2010, even in the absence of explicit climate policies. At the same time, climate policy would still be needed to reduce emissions further, in order to reduce the projected increase of global mean temperature from 3 °C (SSP1 reference scenario) to 2 or 1.5 °C (in line with current policy targets). The SSP1 storyline could be a basis for further discussions on how climate policy can be combined with achieving other societal goals.
Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets
(Bristol : IOP Publ., 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.
First process-based simulations of climate change impacts on global tea production indicate large effects in the World’s major producer countries
(Bristol : IOP Publ., 2020) Beringer, Tim; Kulak, Michal; Müller, Christoph; Schaphoff, Sibyll; Jans, Yvonne
Modeling of climate change impacts have mainly been focused on a small number of annual staple crops that provide most of the world's calories. Crop models typically do not represent perennial crops despite their high economic, nutritional, or cultural value. Here we assess climate change impacts on global tea production, chosen because of its high importance in culture and livelihoods of people around the world. We extended the dynamic global vegetation model with managed land, LPJmL4, global crop model to simulate the cultivation of tea plants. Simulated tea yields were validated and found in good agreement with historical observations as well as experiments on the effects of increasing CO2 concentrations. We then projected yields into the future under a range of climate scenarios from the Inter-Sectoral Impact Model Intercomparison Project. Under current irrigation levels and lowest climate change scenarios, tea yields are expected to decrease in major producing countries. In most climate scenarios, we project that tea yields are set to increase in China, India, and Vietnam. However, yield losses are expected to affect Kenya, Indonesia, and Sri Lanka. If abundant water supply and full irrigation is assumed for all tea cultivation areas, yields are projected to increase in all regions.
Fossil-fueled development (SSP5): An energy and resource intensive scenario for the 21st century
(Amsterdam : Elsevier, 2016) Kriegler, Elmar; Bauer, Nico; Popp, Alexander; Humpenöder, Florian; Leimbach, Marian; Strefler, Jessica; Baumstark, Lavinia; Bodirsky, Benjamin Leon; Hilaire, Jérôme; Klein, David; Mouratiadou, Ioanna; Weindl, Isabelle; Bertram, Christoph; Dietrich, Jan-Philipp; Luderer, Gunnar; Pehl, Michaja; Pietzcker, Robert; Piontek, Franziska; Lotze-Campen, Hermann; Biewald, Anne; Bonsch, Markus; Giannousakis, Anastasis; Kreidenweis, Ulrich; Müller, Christoph; Rolinski, Susanne; Schultes, Anselm; Schwanitz, Jana; Stevanovic, Miodrag; Calvin, Katherine; Emmerling, Johannes; Fujimori, Shinichiro; Edenhofer, Ottmar
This paper presents a set of energy and resource intensive scenarios based on the concept of Shared Socio-Economic Pathways (SSPs). The scenario family is characterized by rapid and fossil-fueled development with high socio-economic challenges to mitigation and low socio-economic challenges to adaptation (SSP5). A special focus is placed on the SSP5 marker scenario developed by the REMIND-MAgPIE integrated assessment modeling framework. The SSP5 baseline scenarios exhibit very high levels of fossil fuel use, up to a doubling of global food demand, and up to a tripling of energy demand and greenhouse gas emissions over the course of the century, marking the upper end of the scenario literature in several dimensions. These scenarios are currently the only SSP scenarios that result in a radiative forcing pathway as high as the highest Representative Concentration Pathway (RCP8.5). This paper further investigates the direct impact of mitigation policies on the SSP5 energy, land and emissions dynamics confirming high socio-economic challenges to mitigation in SSP5. Nonetheless, mitigation policies reaching climate forcing levels as low as in the lowest Representative Concentration Pathway (RCP2.6) are accessible in SSP5. The SSP5 scenarios presented in this paper aim to provide useful reference points for future climate change, climate impact, adaption and mitigation analysis, and broader questions of sustainable development.
Freshwater resources under success and failure of the Paris climate agreement
(Göttingen : Copernicus Publ., 2019) Heinke, Jens; Müller, Christoph; Lannerstad, Mats; Gerten, Dieter; Lucht, Wolfgang
Population growth will in many regions increase the pressure on water resources and likely increase the number of people affected by water scarcity. In parallel, global warming causes hydrological changes which will affect freshwater supply for human use in many regions. This study estimates the exposure of future population to severe hydrological changes relevant from a freshwater resource perspective at different levels of global mean temperature rise above pre-industrial level (ΔTglob). The analysis is complemented by an assessment of water scarcity that would occur without additional climate change due to population change alone; this is done to identify the population groups that are faced with particularly high adaptation challenges. The results are analysed in the context of success and failure of implementing the Paris Agreement to evaluate how climate mitigation can reduce the future number of people exposed to severe hydrological change. The results show that without climate mitigation efforts, in the year 2100 about 4.9 billion people in the SSP2 population scenario would more likely than not be exposed to severe hydrological change, and about 2.1 billion of them would be faced with particularly high adaptation challenges due to already prevailing water scarcity. Limiting warming to 2 °C by a successful implementation of the Paris Agreement would strongly reduce these numbers to 615 million and 290 million, respectively. At the regional scale, substantial water-related risks remain at 2 °C, with more than 12% of the population exposed to severe hydrological change and high adaptation challenges in Latin America and the Middle East and north Africa region. Constraining δTglob to 1.5 °C would limit this share to about 5% in these regions. ©2019 Author(s).
Future climate change significantly alters interannual wheat yield variability over half of harvested areas
(Bristol : IOP Publ., 2021-9-3) Liu, Weihang; Ye, Tao; Jägermeyr, Jonas; Müller, Christoph; Chen, Shuo; Liu, Xiaoyan; Shi, Peijun
Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.
Generating a rule-based global gridded tillage dataset
(Katlenburg-Lindau : Copernics Publications, 2020) Porwollik, Vera; Rolinski, Susanne; Heinke, Jens; Müller, Christoph
Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models, but global assessments are hampered by lack of information on the type of tillage and their spatial distribution. This study describes the generation of a classification of tillage practices and presents the spatially explicit mapping of these crop-specific tillage systems for around the year 2005. Tillage practices differ by the kind of equipment used, soil surface and depth affected, timing, and their purpose within the cropping systems. We classified the broad variety of globally relevant tillage practices into six categories: no-tillage in the context of Conservation Agriculture, traditional annual, traditional rotational, rotational, reduced, and conventional annual tillage. The identified tillage systems were allocated to gridded crop-specific cropland areas with a resolution of 5 arcmin. Allocation rules were based on literature findings and combine area information on crop type, water management regime, field size, water erosion, income, and aridity. We scaled reported national Conservation Agriculture areas down to grid cells via a probability-based approach for 54 countries. We provide area estimates of the six tillage systems aggregated to global and country scale. We found that 8.67Mkm2 of global cropland area was tilled intensively at least once a year, whereas the remaining 2.65Mkm2 was tilled less intensely. Further, we identified 4.67Mkm2 of cropland as an area where Conservation Agriculture could be expanded to under current conditions. The tillage classification enables the parameterization of different soil management practices in various kinds of model simulations. The crop-specific tillage dataset indicates the spatial distribution of soil management practices, which is a prerequisite to assess erosion, carbon sequestration potential, as well as water, and nutrient dynamics of cropland soils. The dynamic definition of the allocation rules and accounting for national statistics, such as the share of Conservation Agriculture per country, also allow for derivation of datasets for historical and future global soil management scenarios. The resulting tillage system dataset and source code are accessible via an open-data repository (DOIs: https://doi.org/10.5880/PIK.2019.009 and https://doi.org/10.5880/PIK.2019.010, Porwollik et al., 2019a, b). © Author(s) 2019.
The GGCMI Phase 2 emulators: Global gridded crop model responses to changes in CO2, temperature, water, and nitrogen (version 1.0)
(Katlenburg-Lindau : Copernicus, 2020) Franke, James A.; Müller, Christoph; Elliott, Joshua; Ruane, Alex C.; Jägermeyr, Jonas; Snyder, Abigail; Dury, Marie; Falloon, Pete D.; Folberth, Christian; François, Louis; Hank, Tobias; Izaurralde, R. Cesar; Jacquemin, Ingrid; Jones, Curtis; Li, Michelle; Liu, Wenfeng; Olin, Stefan; Phillips, Meridel; Pugh, Thomas A. M.; Reddy, Ashwan; Williams, Karina; Wang, Ziwei; Zabel, Florian; Moyer, Elisabeth J.
Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: Atmospheric carbon dioxide (CO2) concentrations, temperature, water supply, and nitrogen inputs (CTWN). Simulations are run under two different adaptation assumptions: That growing seasons shorten in warmer climates, and that cultivar choice allows growing seasons to remain fixed. The dataset allows emulating the climatological-mean yield response of all models with a simple polynomial in mean growing-season values. Climatological-mean yields are a central metric in climate change impact analysis; we show here that they can be captured without relying on interannual variations. In general, emulation errors are negligible relative to differences across crop models or even across climate model scenarios; errors become significant only in some marginal lands where crops are not currently grown. We demonstrate that the resulting GGCMI emulators can reproduce yields under realistic future climate simulations, even though the GGCMI Phase 2 dataset is constructed with uniform CTWN offsets, suggesting that the effects of changes in temperature and precipitation distributions are small relative to those of changing means. The resulting emulators therefore capture relevant crop model responses in a lightweight, computationally tractable form, providing a tool that can facilitate model comparison, diagnosis of interacting factors affecting yields, and integrated assessment of climate impacts. © 2020 EDP Sciences. All rights reserved.
The GGCMI Phase 2 experiment: Global gridded crop model simulations under uniform changes in CO2, temperature, water, and nitrogen levels (protocol version 1.0)
(Katlenburg-Lindau : Copernicus, 2020) Franke, James A.; Müller, Christoph; Elliott, Joshua; Ruane, Alex C.; Jägermeyr, Jonas; Balkovic, Juraj; Ciais, Philippe; Dury, Marie; Falloon, Pete D.; Folberth, Christian; François, Louis; Hank, Tobias; Hoffmann, Munir; Izaurralde, R. Cesar; Jacquemin, Ingrid; Jones, Curtis; Khabarov, Nikolay; Koch, Marian; Li, Michelle; Liu, Wenfeng; Olin, Stefan; Phillips, Meridel; Pugh, Thomas A. M.; Reddy, Ashwan; Wang, Xuhui; Williams, Karina; Zabel, Florian; Moyer, Elisabeth J.
Concerns about food security under climate change motivate efforts to better understand future changes in crop yields. Process-based crop models, which represent plant physiological and soil processes, are necessary tools for this purpose since they allow representing future climate and management conditions not sampled in the historical record and new locations to which cultivation may shift. However, process-based crop models differ in many critical details, and their responses to different interacting factors remain only poorly understood. The Global Gridded Crop Model Intercomparison (GGCMI) Phase 2 experiment, an activity of the Agricultural Model Intercomparison and Improvement Project (AgMIP), is designed to provide a systematic parameter sweep focused on climate change factors and their interaction with overall soil fertility, to allow both evaluating model behavior and emulating model responses in impact assessment tools. In this paper we describe the GGCMI Phase 2 experimental protocol and its simulation data archive. A total of 12 crop models simulate five crops with systematic uniform perturbations of historical climate, varying CO2, temperature, water supply, and applied nitrogen (“CTWN”) for rainfed and irrigated agriculture, and a second set of simulations represents a type of adaptation by allowing the adjustment of growing season length. We present some crop yield results to illustrate general characteristics of the simulations and potential uses of the GGCMI Phase 2 archive. For example, in cases without adaptation, modeled yields show robust decreases to warmer temperatures in almost all regions, with a nonlinear dependence that means yields in warmer baseline locations have greater temperature sensitivity. Inter-model uncertainty is qualitatively similar across all the four input dimensions but is largest in high-latitude regions where crops may be grown in the future.
Global bioenergy potentials from agricultural land in 2050: Sensitivity to climate change, diets and yields
(Amsterdam : Elsevier, 2011) Haberl, Helmut; Erb, Karl-Heinz; Krausmann, Fridolin; Bondeau, Alberte; Lauk, Christian; Müller, Christoph; Plutzar, Christoph; Steinberger, Julia K.
There is a growing recognition that the interrelations between agriculture, food, bioenergy, and climate change have to be better understood in order to derive more realistic estimates of future bioenergy potentials. This article estimates global bioenergy potentials in the year 2050, following a “food first” approach. It presents integrated food, livestock, agriculture, and bioenergy scenarios for the year 2050 based on a consistent representation of FAO projections of future agricultural development in a global biomass balance model. The model discerns 11 regions, 10 crop aggregates, 2 livestock aggregates, and 10 food aggregates. It incorporates detailed accounts of land use, global net primary production (NPP) and its human appropriation as well as socioeconomic biomass flow balances for the year 2000 that are modified according to a set of scenario assumptions to derive the biomass potential for 2050. We calculate the amount of biomass required to feed humans and livestock, considering losses between biomass supply and provision of final products. Based on this biomass balance as well as on global land-use data, we evaluate the potential to grow bioenergy crops and estimate the residue potentials from cropland (forestry is outside the scope of this study). We assess the sensitivity of the biomass potential to assumptions on diets, agricultural yields, cropland expansion and climate change. We use the dynamic global vegetation model LPJmL to evaluate possible impacts of changes in temperature, precipitation, and elevated CO2 on agricultural yields. We find that the gross (primary) bioenergy potential ranges from 64 to 161 EJ y−1, depending on climate impact, yields and diet, while the dependency on cropland expansion is weak. We conclude that food requirements for a growing world population, in particular feed required for livestock, strongly influence bioenergy potentials, and that integrated approaches are needed to optimize food and bioenergy supply.
Global cotton production under climate change – Implications for yield and water consumption
(Munich : EGU, 2021) Jans, Yvonne; von Bloh, Werner; Schaphoff, Sibyll; Müller, Christoph
Being an extensively produced natural fiber on earth, cotton is of importance for economies. Although the plant is broadly adapted to varying environments, the growth of and irrigation water demand on cotton may be challenged by future climate change. To study the impacts of climate change on cotton productivity in different regions across the world and the irrigation water requirements related to it, we use the process-based, spatially detailed biosphere and hydrology model LPJmL (Lund Potsdam Jena managed land). We find our modeled cotton yield levels in good agreement with reported values and simulated water consumption of cotton production similar to published estimates. Following the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) protocol, we employ an ensemble of five general circulation models under four representative concentration pathways (RCPs) for the 2011 2099 period to simulate future cotton yields. We find that irrigated cotton production does not suffer from climate change if CO2 effects are considered, whereas rainfed production is more sensitive to varying climate conditions. Considering the overall effect of a changing climate and CO2 fertilization, cotton production on current cropland steadily increases for most of the RCPs. Starting from _ 65 million tonnes in 2010, cotton production for RCP4.5 and RCP6.0 equates to 83 and 92 million tonnes at the end of the century, respectively. Under RCP8.5, simulated global cotton production rises by more than 50% by 2099. Taking only climate change into account, projected cotton production considerably shrinks in most scenarios, by up to one-Third or 43 million tonnes under RCP8.5. The simulation of future virtual water content (VWC) of cotton grown under elevated CO2 results for all scenarios in less VWC compared to ambient CO2 conditions. Under RCP6.0 and RCP8.5, VWC is notably decreased by more than 2000m3 t1 in areas where cotton is produced under purely rainfed conditions. By 2040, the average global VWC for cotton declines in all scenarios from currently 3300 to 3000m3 t1, and reduction continues by up to 30% in 2100 under RCP8.5. While the VWC decreases by the CO2 effect, elevated temperature acts in the opposite direction. Ignoring beneficial CO2 effects, global VWC of cotton would increase for all RCPs except RCP2.6, reaching more than 5000m3 t1 by the end of the simulation period under RCP8.5. Given the economic relevance of cotton production, climate change poses an additional stress and deserves special attention. Changes in VWC and water demands for cotton production are of special importance, as cotton production is known for its intense water consumption. The implications of climate impacts on cotton production on the one hand and the impact of cotton production on water resources on the other hand illustrate the need to assess how future climate change may affect cotton production and its resource requirements. Our results should be regarded as optimistic, because of high uncertainty with respect to CO2 fertilization and the lack of implementing processes of boll abscission under heat stress. Still, the inclusion of cotton in LPJmL allows for various large-scale studies to assess impacts of climate change on hydrological factors and the implications for agricultural production and carbon sequestration. © 2021 BMJ Publishing Group. All rights reserved.

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