There is increasing concern in many advanced economies about the risks of disruption and crises in agri-food systems. Government departments and non-governmental organisations are working to identify and understand specific risks but struggle to take broad, holistic perspectives and therefore underestimate the potential for civil unrest. In the interests of helping move from understanding to action, we convened a group of experts through a Delphi process to map out potential pathways to acute UK food system crises and identify interventions that would build resilience and sustainability. To this end, we consulted 31 experts, carrying out 15 expert interviews, followed by three surveys and two workshops with a further 16 experts. The experts highlighted the many existing chronic issues creating a tinderbox for an acute risk to lead to a food crisis in the UK. These chronic issues include climate change, poor policy implementation, rising inequality, food supply chain consolidation and the risks from just-in-time supply of food. They voted to include three acute triggers-(a) cyber-attack, (b) a major extreme weather event and (c) a major new international conflict-and described how any combination of these could lead to (d) a UK food availability and/or price shock that could result in widespread fear of unsafe or inadequate food, leading to violence. A total of 7 system-wide interventions were prioritised to help address these pathway elements together and build sustainability, and a further 21 were identified to address elements individually.

The concept of climate resilient development pathways (CRDPs), introduced through the Intergovernmental Panel on Climate Change, presents a transformative framework for integrating climate adaptation, mitigation, and sustainable development. Despite its potential, practical guidance for implementing CRDPs remains limited. This study addresses a significant gap in practical implementation by developing and testing an operational approach to CRDPs within a bounded system—the UK wheat supply sector. While CRDPs offer a comprehensive way of orienting development towards climate resilience, the operationalisation of the concept requires pragmatic choices appropriate to the participants and context, which revealed several learning points. The mapping of multiple future pathways, as advocated in the literature, was constrained by decision-makers’ limited tolerance for uncertainty and their need for actionable policy recommendations. In addition, the process necessarily assumed the continuity of a market-led system, which constrained the range of potential directions of change. Key domestic and global system interconnections could not be fully accounted for because of the study’s bounded scope. Finally, fragmented ownership of required actions across the system highlighted a lack of guidance on mechanisms for ongoing monitoring and re-orientation within dispersed management systems. The developed methodology proposes a transferable approach for participatory pathway design that integrates stakeholder visioning, iterative co-production, and reflexive engagement. While the documentation of the unresolved challenges provides new insights into the realities of operationalising CRDPs, highlighting that operationalisation is not just a methodological challenge, but also a governance and system challenge.

We demonstrate a data-driven approach that gives skilful annual predictions of province-scale maize yield across China's Northeast Farming Region (NFR), using the June-August (JJA) mean temperature and total precipitation as predictors. Our work builds on a method used to explore climate-related risks to maize production in the USA and South Africa. The approach uses a two-dimensional Gaussian function to parametrise the relationship between the detrended maize yields and summer climate conditions, which allows for non-linear growth responses to the individual climate variables. To enable skilful annual yield predictions we extend the approach in three ways: i) testing and validating out-of-sample yield and yield shock predictions; ii) introducing partial pooling of data across the provinces to allow for systematic differences between them, resulting in more robust model parameter estimation; iii) iterative correction of biases introduced by yield detrending procedures. Maximal yields occur for mean JJA temperature 21 degrees C-22 degrees C and total JJA rainfall 400 mm (corresponding to monthly rainfall totals of similar to 130 mm), which broadly agree with previous applications of similar approaches in different countries, giving confidence that the approach is robust despite inherent approximations. The model also demonstrates skilful retrospective forecasts of province area-average maize yield, giving out-of-sample Pearson correlations of similar to 0.6 (statistically significant at 99.9% level) between forecasts and observations for 1979-2016. Furthermore, including the August Standardised Precipitation-Evapotranspiration Index (SPEI) as an extra predictor improves out-of-sample predictions for low yield events (e.g. yields at least 10% below average) associated with adverse climate conditions, as occurred in Liaoning in 2000. The extended modelling framework can provide maize yield predictions using either weather observations or seasonal climate forecasts, and offers the potential for climate services that could help manage impacts on the food system due to adverse weather conditions.

In the UK the ten warmest years since 1884 have all occurred after 2002. Summer temperatures have been increasing with a peak of 40.3 degrees C in 2022. Predictions are that temperatures will regularly exceed 40 degrees C in the future. Currently not all refrigeration plant is able to cope with the high ambient temperatures being experienced. End users currently have to regularly reduce load to cope with the climatic conditions. This will only increase in the future with a greater need for short term and longerterm adaptation strategies. This paper provides information on how prepared the UK food industry is for climatic changes. Results from the study identified that there is low adaptive maturity to cope with climate change. Generally businesses are unprepared for the maximum temperatures that are likely to occur in the future. Some businesses have already begun applying adaptation strategies, but these have limitations in terms of long term viability.

Climate adaptation policies have been developed at global, national and local levels, however, significant implementation gaps persist. Adaptation targets – achieved through metrics to assess the effectiveness of an adaptation action or policy – offer a potential solution to improve implementation. If adaptation actions can be compared and tracked, it should be possible to identify which actions are most effective, where more support is needed, the extent to which vulnerabilities are addressed, and evidence what progress is made. Despite this potential, the development and delivery of adaptation targets has been challenging because: (1) adaptation is context-specific - a target in one place may not be suitable in another; and (2) there is often a lack of clarity over how metrics should be designed. We aim to stimulate debate in this area through development of guiding principles for creating climate adaptation targets. These principles aim to increase the robustness of targets through the lens of equity and vulnerability as well as highlighting some key challenges and limitations in the development and implementation of adaptation targets at the local level.

We review how the international modelling community, encompassing integrated assessment models, global and regional Earth system and climate models, and impact models, has worked together over the past few decades to advance understanding of Earth system change and its impacts on society and the environment and thereby support international climate policy. We go on to recommend a number of priority research areas for the coming decade, a timescale that encompasses a number of newly starting international modelling activities, as well as the IPCC Seventh Assessment Report (AR7) and the second UNFCCC Global Stocktake. Progress in these priority areas will significantly advance our understanding of Earth system change and its impacts, increasing the quality and utility of science support to climate policy.We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris climate targets - for example, pathways that overshoot 1.5 or 2 degrees C global warming, before returning to these levels at some later date. Earth system models need to be capable of thoroughly assessing such warming overshoots - in particular, the efficacy of mitigation measures, such as negative CO2 emissions, in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 or 2 degrees C above pre-industrial temperatures is also required. We recommend Earth system models run overshoot scenarios in CO2-emission mode to more fully represent coupled climate-carbon-cycle feedbacks and, wherever possible, interactively simulate other key Earth system phenomena at risk of rapid change during overshoot. Regional downscaling and impact models should use forcing data from these simulations, so impact and regional climate projections cover a more complete range of potential responses to a warming overshoot. An accurate simulation of the observed, historical record remains a fundamental requirement of models, as does accurate simulation of key metrics, such as the effective climate sensitivity and the transient climate response to cumulative carbon emissions. For adaptation, a key demand is improved guidance on potential changes in climate extremes and the modes of variability these extremes develop within. Such improvements will most likely be realized through a combination of increased model resolution, improvement of key model parameterizations, and enhanced representation of important Earth system processes, combined with targeted use of new artificial intelligence (AI) and machine learning (ML) techniques. We propose a deeper collaboration across such efforts over the coming decade.With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend an increased focus on high-impact-low-likelihood (HILL) outcomes - in particular, the risk and consequences of exceeding critical tipping points during a warming overshoot and the potential impacts arising from this. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly as a result of climate mitigation actions, it is important that spatially detailed, disaggregated information used to generate future scenarios in integrated assessment models be available for use in impact models. Conversely, there is a need to develop methods that enable potential societal responses to projected Earth system change to be incorporated into scenario development.The new models, simulations, data, and scientific advances proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable by modelling communities across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

Abstract. We review how the international modelling community, encompassing Integrated Assessment models, global and regional Earth system and climate models, and impact models, have worked together over the past few decades, to advance understanding of Earth system change and its impacts on society and the environment, and support international climate policy. We then recommend a number of priority research areas for the coming ~6 years (i.e. until ~2030), a timescale that matches a number of newly starting international modelling activities and encompasses the IPCC 7th Assessment Report (AR7) and the 2nd UNFCCC Global Stocktake. Progress in these areas will significantly advance our understanding of Earth system change and its impacts and increase the quality and utility of science support to climate policy. We emphasize the need for continued improvement in our understanding of, and ability to simulate, the coupled Earth system and the impacts of Earth system change. There is an urgent need to investigate plausible pathways and emission scenarios that realize the Paris Climate Targets, including pathways that overshoot the 1.5 °C and 2 °C targets, before later returning to them. Earth System models (ESMs) need to be capable of thoroughly assessing such warming overshoots, in particular, the efficacy of negative CO2 emission actions in reducing atmospheric CO2 and driving global cooling. An improved assessment of the long-term consequences of stabilizing climate at 1.5 °C or 2 °C above pre-industrial temperatures is also required. We recommend ESMs run overshoot scenarios in CO2-emission mode, to more fully represent coupled climate - carbon cycle feedbacks. Regional downscaling and impact models should also use forcing data from these simulations, so impact and regional climate projections are as realistic as possible. An accurate simulation of the observed record remains a key requirement of models, as does accurate simulation of key metrics, such as the Effective Climate Sensitivity. For adaptation, improved guidance on potential changes in climate extremes and the modes of variability these extremes develop in, is a key demand. Such improvements will most likely be realized through a combination of increased model resolution and improvement of key parameterizations. We propose a deeper collaboration across modelling efforts targeting increased process realism and coupling, enhanced model resolution, parameterization improvement, and data-driven Machine Learning methods. With respect to sampling future uncertainty, increased collaboration between approaches that emphasize large model ensembles and those focussed on statistical emulation is required. We recommend increased attention is paid to High Impact Low Likelihood (HILL) outcomes. In particular, the risk and consequences of exceeding critical tipping points during a warming overshoot. For a comprehensive assessment of the impacts of Earth system change, including impacts arising directly from specific mitigation actions, it is important detailed, disaggregated information from the Integrated Assessment Models (IAMs) used to generate future scenarios is available to impact models. Conversely, methods need to be developed to incorporate potential future societal responses to the impacts of Earth system change into scenario development. Finally, the new models, simulations, data, and scientific advances, proposed in this article will not be possible without long-term development and maintenance of a robust, globally connected infrastructure ecosystem. This system must be easily accessible and useable across all modelling communities and across the world, allowing the global research community to be fully engaged in developing and delivering new scientific knowledge to support international climate policy.

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models. Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models. Crop models show strong differences in input sensitivities Standardized modeling experiments reveal differences in emergent functional relationships New standards in model evaluation are needed

We report the results of a survey of farmers and landowners to identify the most likely potential food system disruption scenarios for the UK and compare these with a previous expert elicitation with a much wider set of food system stakeholders. We found that 60% of farmers think a Societal Event in which 1 in 2000 people are injured in the UK is at least 20% likely to occur over the coming decade. Over a timeframe of 50 years, this increased to almost 90% of farmers. These results show that farmers and landowners are considerably more concerned about the vulnerability of the food system in the UK than the wider group of food system experts are. Farmers agreed with experts on the majority of potential causes of such vulnerability, which are climate change, trade policies (import and export), competition for land and ecological collapse (over 50 years). However, they also highlighted the importance of the power structure within the food system, with large corporations supplying to, or buying from, farmers creating lower revenue, making farming an unsustainable business. We conclude that an urgent systematic review of potential interventions that would improve resilience be conducted by the UK Government, in partnership with farmers.

it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed.The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge.Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing.All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community.At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians.Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews.Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view.By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation.

Climate change is increasing the risk of extreme weather events, potentially threatening crop yields and global food security. A key benefit of international free trade is risk sharing, because global aggregate production is more stable than that of individual countries which may be adversely affected by extremes. Here we test the hypothesis that diverse sourcing of crops from multiple trading partners reduces exposure to extreme weather, using a detailed trade matrix and range of extreme weather indices. We find that countries with high source diversity have moderate exposure, but that there is wide variation in the degree of exposure in countries reliant on domestic crop production. Global aggregate production and export volatility is stable or declining for most crops, suggesting that source diversification will increase resilience to both climatic and non-climatic supply shocks.

We report the results of a structured expert elicitation to identify the most likely types of potential food system disruption scenarios for the UK, focusing on routes to civil unrest. We take a backcasting approach by defining as an end-point a societal event in which 1 in 2000 people have been injured in the UK, which 40% of experts rated as “Possible (20–50%)”, “More likely than not (50–80%)” or “Very likely (>80%)” over the coming decade. Over a timeframe of 50 years, this increased to 80% of experts. The experts considered two food system scenarios and ranked their plausibility of contributing to the given societal scenario. For a timescale of 10 years, the majority identified a food distribution problem as the most likely. Over a timescale of 50 years, the experts were more evenly split between the two scenarios, but over half thought the most likely route to civil unrest would be a lack of total food in the UK. However, the experts stressed that the various causes of food system disruption are interconnected and can create cascading risks, highlighting the importance of a systems approach. We encourage food system stakeholders to use these results in their risk planning and recommend future work to support prevention, preparedness, response and recovery planning.

Record-breaking high temperatures were experienced across the United Kingdom during summer 2022. The impacts of these extreme climatic conditions were felt across the food system, including increased energy costs for cold storage, the failure of refrigeration systems in numerous retail facilities, and impacts on livestock including heat stress. Future climate projections indicate an increased likelihood and duration of extreme high temperatures like those experienced in 2022. Learning from the impacts of the 2022 heatwave on the United Kingdom food system can help identify adaptations that build resilience to climate change. We explore the impacts through two case studies (United Kingdom poultry and wheat sectors), discuss potential adaptation options required for a climate-resilient, net-zero United Kingdom food system and consider future research needs. United Kingdom chicken meat production was 9% lower in July 2022 than July 2021; in contrast, energy costs increased for both production and refrigeration. Potential heatwave adaptation measures for poultry include transitioning to heat tolerant chicken breeds, lower stocking density, dehumidification cooling and misting systems, nutritional supplements, and improving retail refrigeration resilience and efficiency. United Kingdom wheat yields were 8% higher in 2022 than the 2017–2021 average. Increases were observed in every United Kingdom region but were least in the South and East where the heatwave intensity was strongest. Future adaptation measures to avoid negative impacts of summer heat stress on winter wheat could include earlier maturing and heat/drought tolerant varieties, earlier autumn sowing, targeted irrigation for drought around anthesis, and soil and water conservation measures.

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

We identify major knowledge gaps in the primary impacts of extreme weather and climate change across the UK’s food system, its functioning and their interactions to provide information to support adaptation and resilience planning. Future shocks and stresses due to changes in weather and climate extremes will have significant impacts on the UK food system. Key knowledge gaps remain in our understanding of their impacts on non-cereal crops, livestock and fisheries production, on the food chain beyond primary production, on the longer-term impacts, and in an integrated, full system view of impacts that accounts for cumulative impacts, interactions, feedbacks and the interplay between domestic and overseas elements of the UK food system. These knowledge gaps need to be urgently addressed to ensure future climate resilience of the UK food system. There are several areas where research could better support decision-making towards increased resilience to weather and climate shocks in both food policy and business sectors. We note the need for a step change in the collection, quality, synthesis and application of a broad range of weather and food chain data and information across time and space. There is a need to develop tools to support the inclusion of the “missing middle” of food chain and policy discussions that incorporate weather and climate impacts: processing/packaging, transport, storage, wholesale, retail and disposing/reusing. Greater integration of climate, biophysical, social, political and economic research is required to characterise geo-political influences on food system climate resilience. Further work is needed to assess adaptation actions needed in response, and their knock-on trade-offs and consequences across sectors, and their interactions. The challenges identified here suggest the need for challenge-led, connective, interdisciplinary approaches to future funding initiatives in support of achieving food system resilience to weather and climate shocks.

Concerns 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.

Land-surface models (LSMs) typically simulate a single crop per year in a field or location. However, actual cropping systems are characterized by a succession of distinct crop cycles that are sometimes interspersed with long periods of bare soil. Sequential cropping (also known as multiple or double cropping) is particularly common in tropical regions, where the crop seasons are largely dictated by the main wet season. In this paper, we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at sites in France and India. We simulate all the crops grown within a year in a field or location in a seamless way to understand how sequential cropping influences the surface fluxes of a land-surface model. We evaluate JULES with sequential cropping in Avignon, France, providing over 15 years of continuous flux observations (a point simulation). We apply JULES with sequential cropping to simulate the rice–wheat rotation in a regional 25 km resolution gridded simulation for the northern Indian states of Uttar Pradesh and Bihar and four single-grid-box simulations across these states, where each simulation is a 25 km grid box. The inclusion of a secondary crop in JULES using the sequential cropping method presented does not change the crop growth or development of the primary crop. During the secondary crop growing period, the carbon and energy fluxes for Avignon and India are modified; they are largely unchanged for the primary crop growing period. For India, the inclusion of a secondary crop using this sequential cropping method affects the available soil moisture in the top 1.0 m throughout the year, with larger fluctuations in sequential crops compared with single-crop simulations even outside the secondary crop growing period. JULES simulates sequential cropping in Avignon, the four India locations and the regional run, representing both crops within one growing season in each of the crop rotations presented. This development is a step forward in the ability of JULES to simulate crops in tropical regions where this cropping system is already prevalent. It also provides the opportunity to assess the potential for other regions to implement sequential cropping as an adaptation to climate change.

Common bean is the most consumed legume in the world and an important source of protein in Latin America, Eastern, and Southern Africa. It is grown in a variety of environments with mean air temperatures of between 14°C and 35°C and is more sensitive to high temperatures than other legumes. As global heating continues, breeding for heat tolerance in common bean is an urgent priority. Transpirational cooling has been shown to be an important mechanism for heat avoidance in many crops, and leaf cooling traits have been used to breed for both drought and heat tolerance. As yet, little is known about the magnitude of leaf cooling in common bean, nor whether this trait is functionally linked to heat tolerance. Accordingly, we explore the extent and genotypic variation of transpirational cooling in common bean. Our results show that leaf cooling is an important heat avoidance mechanism in common bean. On average, leaf temperatures are 5°C cooler than air temperatures, and can range from between 13°C cooler and 2°C warmer. We show that the magnitude of leaf cooling keeps leaf temperatures within a photosynthetically functional range. Heat tolerant genotypes cool more than heat sensitive genotypes and the magnitude of this difference increases at elevated temperatures. Furthermore, we find that differences in leaf cooling are largest at the top of the canopy where determinate bush beans are most sensitive to the impact of high temperatures during the flowering period. Our results suggest that heat tolerant genotypes cool more than heat sensitive genotypes as a result of higher stomatal conductance and enhanced transpirational cooling. We demonstrate that it is possible to accurately simulate the temperature of the leaf by genotype using only air temperature and relative humidity. Our work suggests that greater leaf cooling is a pathway to heat tolerance. Bean breeders can use the difference between air and leaf temperature to screen for genotypes with enhanced capacity for heat avoidance. Once evaluated for a particular target population of environments, breeders can use our model for modeling leaf temperatures by genotype to assess the value of selecting for cooler beans.

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 II. The GGCMI Phase II 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) concen5 trations, 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 without relying on interannual variations; we show that these 1 https://doi.org/10.5194/gmd-2019-365 Preprint. Discussion started: 13 February 2020 c © Author(s) 2020. CC BY 4.0 License.

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. Intermodel 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.