Sabine Undorf’s research while affiliated with Potsdam Institute for Climate Impact Research and other places

Sub-Saharan Africa is projected to be exposed to substantial climate change hazards, especially in its agricultural sector, so adaptation will be necessary to safeguard crop yields. Tropical and subtropical maize production regions approach critical temperature thresholds in the growing season already in today’s climate, and climate change might already be contributing to this. In this study we analyse the impact of anthropogenic climate change on maize yields and the potential for adaptation in Cameroon. We innovate by introducing a counterfactual climate as baseline to a definition for adaptation potential proposed in the literature to assess the relative benefit heat-tolerant crop varieties have already under current and under projected climate change. Spatially detailed simulations of maize yields are performed using the process-based crop model APSIM with W5E5 reanalysis data and bias-corrected and downscaled climate model data from CMIP6/ISIMIP3b for counterfactual, historical and projected future climate scenarios SSP1-2.6 and SSP3-7.0. It is found that unadapted maize yields experience significant losses under all climate change scenarios, with mean losses of 0.3 t ha⁻¹ for the current period compared to the counterfactual climate without anthropogenic climate forcings and that yields are significantly higher for the heat-tolerant varieties across all scenarios simulated. Yield impacts of heat tolerance are highest under projected climate change, making it effective climate change adaptation. This result is robust to the exact value of parameterised heat tolerance. Breeding heat-tolerant varieties as parameterised in this study can be an effective adaptation but is still not enough to mitigate simulated losses under a high-emissions scenario.

Northern Kazakhstan is a major wheat exporter, contributing to food security in Central Asia and beyond. However, wheat yields fluctuate and low-producing years occur frequently. It is currently unclear to what extent human-induced climate change contributes to this. The most severe low-producing year in this century was in 2010, which had severe consequences for the food security of wheat-importing countries. Here, we present a climate impact attribution study that quantifies the impact of human-induced climate change on the average wheat production and associated economic revenues in northern Kazakhstan in the 21st century and on the likelihood of a low-production year like 2010. The study uses bias-adjusted counterfactual and factual climate model data from two large ensembles of latest-generation climate models as input to a statistical subnational yield model. We consider the climate data and the yield model as fit for purpose as first, the factual climate simulations represent the observations, second, the out-of-sample validation of the yield model performs reasonably well with a mean R ² of 0.54, and third, the results are robust under the performed sensitivity tests. Human-induced climate change has had a critical impact on wheat production, specifically through increases in daily-minimum temperatures and extreme heat. This has resulted in a decrease in yields during 2000–2019 by approximately 6.2%–8.2% (uncertainty range of two climate models) and an increased likelihood of the 2010 low-production event by 1.5–4.7 times (10th to 90th percentile uncertainty range covering both climate models). During 2000–2019, human-induced climate change caused economic losses estimated at between 96 and 180 million USD per year (10th to 90th percentile uncertainty range covering both climate models). These results highlight the necessity for ambitious global mitigation efforts and measures to adapt wheat production to increasing temperatures, ensuring regional and global food security.

Aerosol effects on cloud properties are notoriously difficult to disentangle from variations driven by meteorological factors. Here, a machine learning model is trained on reanalysis data and satellite retrievals to predict cloud microphysical properties, as a way to illustrate the relative importance of meteorology and aerosol, respectively, on cloud properties. It is found that cloud droplet effective radius can be predicted with some skill from only meteorological information, including estimated air mass origin and cloud top height. For ten geographical regions the mean coefficient of determination is 0.41 and normalised root-mean square error 24%. The machine learning model thereby performs better than a reference linear regression model, and a model predicting the climatological mean. A gradient boosting regression performs on par with a neural network regression model. Adding aerosol information as input to the model improves its skill somewhat, but the difference is small and the direction of the influence of changing aerosol burden on cloud droplet effective radius is not consistent across regions, and thereby also not always consistent with what is expected from cloud brightening.

Changes in anthropogenic aerosol emissions have strongly contributed to global and regional trends in temperature, precipitation, and other climate characteristics and have been one of the dominant drivers of decadal trends in Asian and African precipitation. These and other influences on regional climate from changes in aerosol emissions are expected to continue and potentially strengthen in the coming decades. However, a combination of large uncertainties in emission pathways, radiative forcing, and the dynamical response to forcing makes anthropogenic aerosol a key factor in the spread of near-term climate projections, particularly on regional scales, and therefore an important one to constrain. For example, in terms of future emission pathways, the uncertainty in future global aerosol and precursor gas emissions by 2050 is as large as the total increase in emissions since 1850. In terms of aerosol effective radiative forcing, which remains the largest source of uncertainty in future climate change projections, CMIP6 models span a factor of 5, from -0.3 to -1.5 W m-2. Both of these sources of uncertainty are exacerbated on regional scales. The Regional Aerosol Model Intercomparison Project (RAMIP) will deliver experiments designed to quantify the role of regional aerosol emissions changes in near-term projections. This is unlike any prior MIP, where the focus has been on changes in global emissions and/or very idealised aerosol experiments. Perturbing regional emissions makes RAMIP novel from a scientific standpoint and links the intended analyses more directly to mitigation and adaptation policy issues. From a science perspective, there is limited information on how realistic regional aerosol emissions impact local as well as remote climate conditions. Here, RAMIP will enable an evaluation of the full range of potential influences of realistic and regionally varied aerosol emission changes on near-future climate. From the policy perspective, RAMIP addresses the burning question of how local and remote decisions affecting emissions of aerosols influence climate change in any given region. Here, RAMIP will provide the information needed to make direct links between regional climate policies and regional climate change. RAMIP experiments are designed to explore sensitivities to aerosol type and location and provide improved constraints on uncertainties driven by aerosol radiative forcing and the dynamical response to aerosol changes. The core experiments will assess the effects of differences in future global and regional (Africa and the Middle East, East Asia, North America and Europe, and South Asia) aerosol emission trajectories through 2051, while optional experiments will test the nonlinear effects of varying emission locations and aerosol types along this future trajectory. All experiments are based on the shared socioeconomic pathways and are intended to be performed with 6th Climate Model Intercomparison Project (CMIP6) generation models, initialised from the CMIP6 historical experiments, to facilitate comparisons with existing projections. Requested outputs will enable the analysis of the role of aerosol in near-future changes in, for example, temperature and precipitation means and extremes, storms, and air quality.

Anthropogenic aerosol emissions are expected to change rapidly over the coming decades, driving strong, spatially complex trends in temperature, hydroclimate, and extreme events both near and far from emission sources. Under-resourced, highly populated regions often bear the brunt of aerosols’ climate and air quality effects, amplifying risk through heightened exposure and vulnerability. However, many policy-facing evaluations of near-term climate risk, including those in the latest Intergovernmental Panel on Climate Change assessment report, underrepresent aerosols’ complex and regionally diverse climate effects, reducing them to a globally averaged offset to greenhouse gas warming. We argue that this constitutes a major missing element in society’s ability to prepare for future climate change. We outline a pathway towards progress and call for greater interaction between the aerosol research, impact modeling, scenario development, and risk assessment communities.

Changes in anthropogenic aerosol emissions have strongly contributed to global and regional trends in temperature, precipitation, and other climate characteristics, and have been one of the dominant drivers of decadal trends in Asian and African precipitation. These, and other, influences on regional climate from changes in aerosol emissions are expected to continue, and potentially strengthen, in the coming decades. However, a combination of large uncertainties in emissions pathways, radiative forcing, and the dynamical response to forcing makes anthropogenic aerosol a key factor in the spread in near-term climate projections, particularly on regional scales, and therefore an important one to constrain. For example, in terms of future emissions pathways, the uncertainty in future global aerosol and precursor gas emissions by 2050 is as large as the total increase in emissions since 1850. In terms of aerosol effective radiative forcing, which remains the largest source of uncertainty in future climate change projections, CMIP6 models span a factor of five, from -0.3 to -1.5 W m-2. Both of these sources of uncertainty are exacerbated on regional scales. The Regional Aerosol Model Intercomparison Project (RAMIP) will deliver experiments designed to quantify the role of regional aerosol emissions changes in near-term projections. This is unlike any prior MIP, where the focus has been on changes in global emissions and/or very idealized aerosol experiments. Perturbing regional emissions makes RAMIP novel from a scientific standpoint, and links the intended analyses more directly to mitigation and adaptation policy issues. From a science perspective, there is limited information on how realistic regional aerosol emissions impact local as well as remote climate conditions. Here, RAMIP will enable an evaluation of the full range of potential influences of realistic and regionally varied aerosol emission changes on near-future climate. From the policy perspective, RAMIP addresses the burning question of how local and remote decisions affecting emissions of aerosols influence climate change in any given region. Here, RAMIP will provide the information needed to make direct links between regional climate policies and regional climate change. RAMIP experiments are designed to explore sensitivities to aerosol type and location, and provide improved constraints on uncertainties driven by aerosol radiative forcing and the dynamical response to aerosol changes. The core experiments will assess the effects of differences in future global and regional (East Asia, South Asia, Africa and the Middle East) aerosol emission trajectories through 2051, while optional experiments will test the nonlinear effects of varying emission location and aerosol types along this future trajectory. All experiments are based on the Shared Socioeconomic Pathways, and are intended to be performed with sixth Climate Model Intercomparison Project (CMIP6) generation models, initialised from the CMIP6 historical experiments, to facilitate comparisons with existing projections. Requested outputs will enable analysis of the role of aerosol in near-future changes in, for example, temperature and precipitation means and extremes, storms, and air quality.

There is much debate on how social values should influence scientific research. However, the question of practical applicability of philosophers’ normative proposals has received less attention. Here, we test the attainability of Matthew J. Brown’s (2020) Moral Imagination ideal (MI ideal), which aims to help scientists to make warranted value-judgements through reflecting on goals, options, values, and stakeholders of research. Here, the tools of the MI ideal are applied to a climate modelling setting, where researchers are developing aerosol-cloud interaction (ACI) parametrizations in an Earth System Model with the broader goal of improving climate sensitivity estimation. After the identification of minor obstacles to applying the MI ideal, we propose two ways to increase its applicability. First, its tools should be accompanied with more concrete guidance for identifying how social values enter more technical decisions in scientific research. Second, since research projects can have multiple goals, examining the alignment between broader societal aims of research and more technical goals should be part of the tools of the MI ideal.

Philosophers argue that many choices in science are influenced by values or have value-implications, ranging from the preference for some research method’s qualities to ethical estimation of the consequences of error. Based on the argument that awareness of values in the scientific process is a necessary first step to both avoid bias and attune science best to the needs of society, an analysis of the role of values in the physical climate science production process is provided. Model-based assessment of climate sensitivity is taken as an illustrative example; climate sensitivity is useful here because of its key role in climate science and relevance for policy, by having been the subject of several assessments over the past decades including a recent shift in assessment method, and because it enables insights that apply to numerous other aspects of climate science. It is found that value-judgements are relevant at every step of the model-based assessment process, with a differentiated role of non-epistemic values across the steps, impacting the assessment in various ways. Scrutiny of current philosophical norms for value-management highlights the need for those norms to be re-worked for broader applicability to climate science. Recent development in climate science turning away from direct use of models for climate sensitivity assessment also gives the opportunity to start investigating the role of values in alternative assessment methods, highlighting similarities and differences in terms of the role of values that encourage further study.