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- ItemImproving the use of crop models for risk assessment and climate change adaptation(Amsterdam : Elsevier, 2017) Challinor, Andrew J.; Müller, Christoph; Asseng, Senthold; Deva, Chetan; Nicklin, Kathryn Jane; Wallach, Daniel; Vanuytrecht, Eline; Whitfield, Stephen; Ramirez-Villegas, Julian; Koehler, Ann-KristinCrop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.
- ItemUncertainty of biomass contributions from agriculture and forestry to renewable energy resources under climate change(Stuttgart : Gebrueder Borntraeger Verlagsbuchhandlung, 2015) Gutsch, M.; Lasch-Born, P.; Lüttger, A.B.; Suckow, F.; Murawski, A.; Pilz, T.
- ItemPresentation of uncertainties on web platforms for climate change information(Amsterdam : Elsevier B.V., 2011) Reusser, D.E.; Wrobel, M.; Nocke, T.; Sterzel, T.; Förster, H.; Kropp, J.P.Adaptation to climate change is gaining attention and is very challenging because it requires action at a local scale in response to global problems. At the same time, spatial and temporal uncertainty about climate impacts and effects of adaptation projects is large. Data on climate impacts and adaptation is collected and presented in web-based platforms such as ci:grasp, which is unique in its structuredness and by explicitly linking adaptation projects to the addressed climate impacts. The challenge to find an adequate and readable representation of uncertainty in this context is large and research is just in the initial phase to provide solutions to the problem. Our goal is to present the structure required to address spatial and temporal uncertainty within ci:grasp. We compare existing concepts and representations for uncertainty communication with current practices on web-based platforms. From our review we derive an uncertainty framework for climate information going beyond what is currently present in the web. We make use of a multi-step approach in communicating the uncertainty and a typology of uncertainty distinguishing between epistemic, natural stochastic, and human reflexive uncertainty. While our suggestions are a step forward, much remains to be done.
- ItemEvapotranspiration 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, TianActual 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.
- ItemExploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios(Bristol : IOP Publ., 2021) Mueller, Christoph; Franke, James; Jaegermeyr, Jonas; Ruane, Alex C.; Elliott, Joshua; Moyer, Elisabeth; Heinke, Jens; Falloon, Pete D.; Folberth, Christian; Francois, LouisConcerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.
- ItemDo new sea spray aerosol source functions improve the results of a regional aerosol model?(Amsterdam [u.a.] : Elsevier Science, 2018) Barthel, Stefan; Tegen, Ina; Wolke, RalfSea spray aerosol particle is a dominating part of the global aerosol mass load of natural origin. Thus, it strongly influences the atmospheric radiation balance and cloud properties especially over the oceans. Uncertainties of the estimated climate impacts by this aerosol type are partly caused by the uncertainties in the particle size dependent emission fluxes of sea spray aerosol particle. We present simulations with a regional aerosol transport model system in two domains, for three months and compared the model results to measurements at four stations using various sea spray aerosol particle source source functions. Despite these limitations we found the results using different source functions are within the range of most model uncertainties. Especially the model's ability to produce realistic wind speeds is crucial. Furthermore, the model results are more affected by a function correcting the emission flux for the effect of the sea surface temperature than by the use of different source functions. © 2018 The Authors
- ItemComparison of two model calibration approaches and their influence on future projections under climate change in the Upper Indus Basin(Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Ismail, Muhammad Fraz; Naz, Bibi S.; Wortmann, Michel; Disse, Markus; Bowling, Laura C.; Bogacki, WolfgangThis study performs a comparison of two model calibration/validation approaches and their influence on future hydrological projections under climate change by employing two climate scenarios (RCP2.6 and 8.5) projected by four global climate models. Two hydrological models (HMs), snowmelt runoff model + glaciers and variable infiltration capacity model coupled with a glacier model, were used to simulate streamflow in the highly snow and glacier melt–driven Upper Indus Basin. In the first (conventional) calibration approach, the models were calibrated only at the basin outlet, while in the second (enhanced) approach intermediate gauges, different climate conditions and glacier mass balance were considered. Using the conventional and enhanced calibration approaches, the monthly Nash-Sutcliffe Efficiency (NSE) for both HMs ranged from 0.71 to 0.93 and 0.79 to 0.90 in the calibration, while 0.57–0.92 and 0.54–0.83 in the validation periods, respectively. For the future impact assessment, comparison of differences based on the two calibration/validation methods at the annual scale (i.e. 2011–2099) shows small to moderate differences of up to 10%, whereas differences at the monthly scale reached up to 19% in the cold months (i.e. October–March) for the far future period. Comparison of sources of uncertainty using analysis of variance showed that the contribution of HM parameter uncertainty to the overall uncertainty is becoming very small by the end of the century using the enhanced approach. This indicates that enhanced approach could potentially help to reduce uncertainties in the hydrological projections when compared to the conventional calibration approach. © 2020, The Author(s).
- ItemConstraining ocean diffusivity from the 8.2 ka event(Berlin : Springer Verlag, 2010) Lorenz, A.; Held, H.; Bauer, E.; von Deimling, T.S.Greenland ice-core data containing the 8.2 ka event are utilized by a model-data intercomparison within the Earth system model of intermediate complexity, CLIMBER-2.3 to investigate their potential for constraining the range of uncertain ocean diffusivity properties. Within a stochastic version of the model (Bauer et al. in Paleoceanography 19:PA3014, 2004) it has been possible to mimic the pronounced cooling of the 8.2 ka event with relatively good accuracy considering the timing of the event in comparison to other modelling exercises. When statistically inferring from the 8.2 ka event on diffusivity the technical difficulty arises to establish the related likelihood numerically per realisation of the uncertain model parameters: while mainstream uncertainty analyses can assume a quasi-Gaussian shape of likelihood, with weather fluctuating around a long term mean, the 8.2 ka event as a highly nonlinear effect precludes such an a priori assumption. As a result of this study the Bayesian Analysis leads to a sharp single-mode likelihood for ocean diffusivity parameters within CLIMBER-2.3. Depending on the prior distribution this likelihood leads to a reduction of uncertainty in ocean diffusivity parameters (e. g. for flat prior uncertainty in the vertical ocean diffusivity parameter is reduced by factor 2). These results highlight the potential of paleo data to constrain uncertain system properties and strongly suggest to make further steps with more complex models and richer data sets to harvest this potential. © The Author(s) 2009.
- ItemEvidence-based narratives in European research programming(Heidelberg : Springer, 2021) Schepelmann, Philipp; Fischer, Susanne; Drews, Martin; Bastein, Ton; Kropp, Jürgen; Krummenauer, Linda; Augenstein, KarolineThe article introduces and exemplifies the approach of evidence-based narratives (EBN). The methodology is a product of co-design between policy-making and science, generating robust intelligence for evidence-based policy-making in the Directorate General for Research and Innovation of the European Commission (DG RTD) under the condition of high uncertainty and fragmented evidence. The EBN transdisciplinary approach tackles practical problems of future-oriented policy-making, in this case in the area of programming for research and innovation addressing the Grand Societal Challenge related to climate change and natural resources. Between 2013 and 2018, the EU-funded RECREATE project developed 20 EBNs in a co-development process between scientists and policy-makers. All EBNs are supported with evidence about the underlying innovation system applying the technological innovation systems (TIS) framework. Each TIS analysis features the innovation, its current state of market diffusion and a description of the innovation investment case. Indicators include potential future market sizes, effects on employment and environmental and social benefits. Based on the innovation and TIS function analyses, the EBNs offer policy recommendations. The article ends with a critical discussion of the EBN approach.
- ItemResponsibility Under Uncertainty: Which Climate Decisions Matter Most?(Amsterdam : Baltzer Science Publ., 2023) Botta, Nicola; Brede, Nuria; Crucifix, Michel; Ionescu, Cezar; Jansson, Patrik; Li, Zheng; Martínez, Marina; Richter, TimWe propose a new method for estimating how much decisions under monadic uncertainty matter. The method is generic and suitable for measuring responsibility in finite horizon sequential decision processes. It fulfills “fairness” requirements and three natural conditions for responsibility measures: agency, avoidance and causal relevance. We apply the method to study how much decisions matter in a stylized greenhouse gas emissions process in which a decision maker repeatedly faces two options: start a “green” transition to a decarbonized society or further delay such a transition. We account for the fact that climate decisions are rarely implemented with certainty and that their consequences on the climate and on the global economy are uncertain. We discover that a “moral” approach towards decision making — doing the right thing even though the probability of success becomes increasingly small — is rational over a wide range of uncertainties.