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Abstract. This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH4 and CO2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Abstract: Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, and when they are, it is often at a national scale, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries. Our study reveals three key findings: First, we find that the overall potential losses to fisheries is higher than potential losses to agriculture, but there is substantial within-country variability. Second, while more than two-thirds of locations will bear a double burden of potential losses to both fisheries and agriculture simultaneously, mitigation could reduce the proportion of places facing a double burden. Third, lower socioeconomic status communities are more likely to experience potential impacts than higher socioeconomic status communities.

Climate change is expected to profoundly affect key food production sectors, including fisheries and agriculture. However, the potential impacts of climate change on these sectors are rarely considered jointly, especially below national scales, which can mask substantial variability in how communities will be affected. Here, we combine socioeconomic surveys of 3,008 households and intersectoral multi-model simulation outputs to conduct a sub-national analysis of the potential impacts of climate change on fisheries and agriculture in 72 coastal communities across five Indo-Pacific countries (Indonesia, Madagascar, Papua New Guinea, Philippines, and Tanzania). Our study reveals three key findings: First, overall potential losses to fisheries are higher than potential losses to agriculture. Second, while most locations (> 2/3) will experience potential losses to both fisheries and agriculture simultaneously, climate change mitigation could reduce the proportion of places facing that double burden. Third, potential impacts are more likely in communities with lower socioeconomic status.

This analysis assesses the financial viability of legally investing in native Cerrado vegetation deforestation for crop production, considering climate change. The study uses data from twelve different crop models based on three different climate models to predict potential future crop yields in cleared land for growing soy and maize. The outcomes show that in many micro-regions, investments in clearing land for crop production would destroy economic value, that is, generate a negative net present value because of low/negative and volatile cashflows driven primarily by future yields as affected by climate. Our analysis was carried out based on present agricultural practices and technology. As climate changes, farmers may adapt their practices, which can lead to more resilient and productive crops, or grow different crops, which could provide better returns on investment in clearing land than the ones resulting from our analysis. Despite various uncertainties, farmers, policy makers and financial institutions should be aware of the climatic and financial risks associated with land clearing in Brazil, mainly in micro-regions in which all scenarios resulted in negative outcomes in the investment analysis. [ABSTRACT FROM AUTHOR]

Elevated surface ozone (O 3) concentrations can negatively impact growth and development of crop production by reducing photosynthesis and accelerating leaf senescence. Under unabated climate change, future global O 3 concentrations are expected to increase in many regions, adding additional challenges to global agricultural production. Presently, few global process-based crop models consider the effects of O 3 stress on crop growth. Here, we incorporated the effects of O 3 stress on photosynthesis and leaf senescence into the Decision Support System for Agrotechnology Transfer (DSSAT) crop models for maize, rice, soybean, and wheat. The advanced models reproduced the reported yield declines from observed O 3 -dose field experiments and O 3 exposure responses reported in the literature (O 3 relative yield loss RMSE <10 % across all calibrated models). Simulated crop yields decreased as daily O 3 concentrations increased above 25 ppb, with average yield losses of 0.16 % to 0.82 % (maize), 0.05 % to 0.63 % (rice), 0.36 % to 0.96 % (soybean), and 0.26 % to 1.23 % (wheat) per ppb O 3 increase, depending on the cultivar O 3 sensitivity. Increased water deficit stress and elevated CO 2 lessen the negative impact of elevated O 3 on crop yield, but potential yield gains from CO 2 concentration increases may be counteracted by higher O 3 concentrations in the future, a potentially important constraint to global change projections for the latest process-based crop models. The improved DSSAT models with O 3 representation simulate the effects of O 3 stress on crop growth and yield in interaction with other growth factors and can be run in the parallel DSSAT global gridded modeling framework for future studies on O 3 impacts under climate change and air pollution scenarios across agroecosystems globally. [ABSTRACT FROM AUTHOR]

We utilize a global warming level (GWL) lens to evaluate global and regional patterns of agricultural impacts as global surface temperature increases, providing a unique perspective on the experience of stakeholders with continued warming in the 21st century. We analyze crop productivity outputs from 11 crop models simulating 5 climate models under 3 emissions scenarios across 4 crops within the AgMIP/ISIMIP Phase 3 ensemble. We categorize regional productivity changes (without adaptation) into 9 characteristic climate change response patterns, identifying consistent increases and decreases as well as non‐linear (peak or dip) responses indicative of inflection points reversing trends as GWLs increase. Many maize regions and pockets of wheat, rice and soybean show peak decrease patterns where initial increases may lull stakeholders into complacency or maladaptation before productivity shifts to losses at higher GWLs. Although the GWL perspective has proven useful in connecting diverse climate models and emissions scenarios, we identify multiple pitfalls that recommend proceeding with caution when applying this approach to climate impacts. Chief among these is that carbon dioxide (CO2) concentrations at any GWL depend on a climate model's transient climate response (TCR). Higher CO2 concentrations generally benefit crop productivity, so this leads to more pessimistic agricultural projections for so‐called "hot" models and can skew multi‐model ensemble results as models with high TCR are disproportionately likely to reach higher GWLs. While there are strong connections between many climatic impact‐drivers and GWLs, vulnerability and exposure components of food system risk are strongly dependent on development pathways. Plain Language Summary: This study uses the latest ensemble of crop and climate models with a framework to identify cropping systems and regions where non‐linear aspects of agricultural system response to climate change could lead to complacency or maladaptation. Results will help readers understand the different experiences and contextual adaptation needs for food systems in the coming decades. This framework could be widely applied across all impacts sectors. Analysis also highlights that climate models with particularly strong climate sensitivity lead more pessimistic projections by crop impacts, underscoring the need for special care in the use of climate model ensembles for impact assessments. Key Points: Climate change response patterns show regional farming systems where initial positive changes give way to long‐term detrimental impactsRegional and species‐dependent patterns of agricultural impacts on benchmark global warming levels prioritize risk management strategiesClimate models with high climate sensitivity have less CO2 benefit on a given global warming level (GWL) and are over‐represented in ensembles of higher GWLs [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: 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. Key Points: Crop models show strong differences in input sensitivitiesStandardized modeling experiments reveal differences in emergent functional relationshipsNew standards in model evaluation are needed [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replace the models, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with a machine learning algorithm to reproduce the response of year-to-year variation of four crop yields to CO2 (C), temperature (T), water (W), and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2. The emulators were able to explain more than 52 % of the variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CO2 –temperature–water–nitrogen (CTWN) perturbations, the emulators could reproduce the year-to-year variation of crop yield well over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual-factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future and serving as a lightweight tool for multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulations of crop yield under climate change. [ABSTRACT FROM AUTHOR]

Globally, we are facing an emerging climate crisis, with impacts to be notably felt in semiarid regions across the world. Cultivation of drought‐adapted succulent plants has been suggested as a nature‐based solution that could: (i) reduce land degradation, (ii) increase agricultural diversification and provide both economic and environmentally sustainable income through derived bioproducts and bioenergy, (iii) help mitigate atmospheric CO2 emissions and (iv) increase soil sequestration of CO2. Identifying where succulents can grow and thrive is an important prerequisite for the advent of a sustainable alternative 'bioeconomy'. Here, we first explore the viability of succulent cultivation in Africa under future climate projections to 2100 using species distribution modelling to identify climatic parameters of greatest importance and regions of environmental suitability. Minimum temperatures and temperature variability are shown to be key controls in defining the theoretical distribution of three succulent species explored, and under both current and future SSP5 8.5 projections, the conditions required for the growth of at least one of the species are met in most parts of sub‐Saharan Africa. These results are supplemented with an analysis of potentially available land for alternative succulent crop cultivation. In total, up to 1.5 billion ha could be considered ecophysiologically suitable and available for succulent cultivation due to projected declines in rangeland biomass and yields of traditional crops. These findings may serve to highlight new opportunities for farmers, governments and key stakeholders in the agriculture and energy sectors to invest in sustainable bioeconomic alternatives that deliver on environmental, social and economic goals. [ABSTRACT FROM AUTHOR]

Elevated surface ozone (O3) concentrations can negatively impact growth and development of crop production by reducing photosynthesis and accelerating leaf senescence. Under unabated climate change, future global O3 concentrations are expected to increase in many regions, adding additional challenges to global agricultural production. Presently, few global process-based crop models consider the effects of O3 stress on crop growth. Here, we incorporated the effects of O3 stress on photosynthesis and leaf senescence into the Decision Support System for Agrotechnology Transfer (DSSAT) crop models for maize, rice, soybean, and wheat. The advanced models reproduced the reported yield declines from observed O3-dose field experiments and O3 exposure responses reported in the literature (O3 relative yield loss RMSE < 10 % across all calibrated models). Simulated crop yields decreased as daily O3 concentrations increased above 25 ppb, with average yield losses of 0.16 % to 0.82 % (maize), 0.05 % to 0.63 % (rice), 0.36 % to 0.96 % (soybean), and 0.26 % to 1.23 % (wheat) per ppb O3 increase, depending on the cultivar O3 sensitivity. Increased water deficit stress and elevated CO2 lessen the negative impact of elevated O3 on crop yield, but potential yield gains from CO2 concentration increases may be counteracted by higher O3 concentrations in the future, a potentially important constraint to global change projections for the latest process-based crop models. The improved DSSAT models with O3 representation simulate the effects of O3 stress on crop growth and yield in interaction with other growth factors and can be run in the pDSSAT global gridded modeling framework for future studies on O3 impacts under climate change and air pollution scenarios across agroecosystems globally. [ABSTRACT FROM AUTHOR]

Simultaneous harvest failures across major crop-producing regions are a threat to global food security. Concurrent weather extremes driven by a strongly meandering jet stream could trigger such events, but so far this has not been quantified. Specifically, the ability of state-of-the art crop and climate models to adequately reproduce such high impact events is a crucial component for estimating risks to global food security. Here we find an increased likelihood of concurrent low yields during summers featuring meandering jets in observations and models. While climate models accurately simulate atmospheric patterns, associated surface weather anomalies and negative effects on crop responses are mostly underestimated in bias-adjusted simulations. Given the identified model biases, future assessments of regional and concurrent crop losses from meandering jet states remain highly uncertain. Our results suggest that model-blind spots for such high-impact but deeply-uncertain hazards have to be anticipated and accounted for in meaningful climate risk assessments. Simultaneous harvest failures across crop-producing regions are major threats to global food security. A strongly meandering jet can trigger these, however, climate and crop models underestimate effects with consequences for climate risk assessments. [ABSTRACT FROM AUTHOR]

Understanding the impact of climate change on year-to-year variation of crop yield is critical to global food stability and security. While crop model emulators are believed to be lightweight tools to replaces the models per se, few emulators have been developed to capture such interannual variation of crop yield in response to climate variability. In this study, we developed a statistical emulator with machine learning algorithm to reproduce the response of year-to-year variation of four crop yield to CO2 (C), temperature (T), water (W) and nitrogen (N) perturbations defined in the Global Gridded Crop Model Intercomparison Project (GGCMI) phase 2 experiment. The emulators were able to explain more than 92% variance of simulated yield and performed well in capturing the year-to-year variation of global average and gridded crop yield over current croplands in the baseline. With the changes in CTWN perturbations, the emulators could well reproduce the year-to-year variation of crop yield over most current cropland. The variation of R and the mean absolute error was small under the single CTWN perturbations and dual factor perturbations. These emulators thus provide statistical response surfaces of yield, including both its mean and interannual variability, to climate factors. They could facilitate spatiotemporal downscaling of crop model simulation, projecting the changes in crop yield variability in the future, and serving as a lightweight tool of multi-model ensemble simulation. The emulators enhanced the flexibility of crop yield estimates and expanded the application of large-ensemble simulation of crop yield under climate change. [ABSTRACT FROM AUTHOR]

Several previous studies have highlighted the irrigation‐induced impacts on the global and regional water cycle, energy budget, and near‐surface climate. While land models are widely used to address this question, the implementations of irrigation in these models vary in complexity. Here, we expand the representation of irrigation in Community Land Model to enable six different irrigation methods. We find that using a combination of irrigation methods, including default, sprinkler, flood and paddy techniques performs best as determined by evaluating the simulated irrigation water withdrawals against observations, and therefore select this combination as the new irrigation scheme. Then, the impact of the new irrigation scheme on surface fluxes is evaluated and detected using single‐point simulations. Finally, the global and regional irrigation‐induced impacts on surface energy and water fluxes are compared using both the original and the new irrigation scheme. The new irrigation scheme substantially reduces the bias and root‐mean‐square error of simulated irrigation water withdrawal in the USA and other countries, but considerably overestimates withdrawals in Central China. Results of single‐point experiments show that different irrigation methods have different effects on surface fluxes, while the magnitudes are small. At the global scale, the new scheme enlarges the irrigation‐induced impacts on water and energy variables relative to the original scheme, with varying magnitudes across regions. Overall, our results suggest that this newly developed scheme is a better tool for simulating irrigation‐induced impacts on climate, and highlight the added value of incorporating human water management in Earth system models. Plain Language Summary: Knowing the effects of irrigation on the water and energy cycle is important, as it helps us to understand better how irrigation may affect the near‐surface climate such as dampening heat extremes and increasing local air humidity. Land models are widely used for this purpose. However, in most of these models, different irrigation techniques are currently not considered. In this study, we develop a new irrigation scheme for the Community Land Model, and evaluate it by comparing modeled and observed irrigation water withdrawals and surface energy and water fluxes. Results show that this new scheme performs better in simulating irrigation water withdrawals in most countries. Results of one‐dimensional simulations show that different irrigation methods have small but varying impacts on surface fluxes. At the global and regional scale, incorporating more realistic irrigation methods enlarges the effects of irrigation on water and energy variables. We therefore conclude that improving the realism of irrigation in models can help us to improve our understanding of how irrigation affects climate through altered water and energy fluxes. Key Points: A newly‐developed irrigation module considering different irrigation methods is implemented in Community Land ModelThe new irrigation scheme shows a better performance of simulating irrigation water withdrawal against the original moduleDifferent irrigation methods have different effects on water cycle and energy budgets, changing regional irrigation‐induced impacts [ABSTRACT FROM AUTHOR]

Non‐continuous flooding is an effective practice for reducing greenhouse gas (GHG) emissions and irrigation water use (IRR) in rice fields. However, advancing global implementation is hampered by the lack of comprehensive understanding of GHGs and IRR reduction benefits without compromising rice yield. Here, we present the largest observational data set for such effects as of yet. By using Random Forest regression models based on 636 field trials at 105 globally georeferenced sites, we identified the key drivers of effects of non‐continuous flooding practices and mapped maximum GHGs or IRR reduction benefits under optimal non‐continuous flooding strategies. The results show that variation in effects of non‐continuous flooding practices are primarily explained by the UnFlooded days Ratio (UFR, that is the ratio of the number of days without standing water in the field to total days of the growing period). Non‐continuous flooding practices could be feasible to be adopted in 76% of global rice harvested areas. This would reduce the global warming potential (GWP) of CH4 and N2O combined from rice production by 47% or the total GWP by 7% and alleviate IRR by 25%, while maintaining yield levels. The identified UFR targets far exceed currently observed levels particularly in South and Southeast Asia, suggesting large opportunities for climate mitigation and water use conservation, associated with the rigorous implementation of non‐continuous flooding practices in global rice cultivation. [ABSTRACT FROM AUTHOR]

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT + (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT + model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models. [ABSTRACT FROM AUTHOR]

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate‐related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process‐based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no‐adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing‐zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies. [ABSTRACT FROM AUTHOR]

For the first time, we use a modern climate model with interactive chemistry including the effects of aerosols on photolysis rates to simulate the consequences of regional and global scale nuclear wars (injecting 5 and 150 Tg of soot respectively) for the ozone layer and surface ultraviolet (UV) light. For a global nuclear war, heating in the stratosphere, reduced photolysis, and an increase in catalytic loss from the HOx cycle cause a 15 year‐long reduction in the ozone column, with a peak loss of 75% globally and 65% in the tropics. This is larger than predictions from the 1980s, which assumed large injections of nitrogen oxides (NOx), but did not include the effects of smoke. NOx from the fireball and the fires provide a small (5%) increase to the global average ozone loss for the first few years. Initially, soot would shield the surface from UV‐B, but UV Index values would become extreme: greater than 35 in the tropics for 4 years, and greater than 45 during the summer in the southern polar regions for 3 years. For a regional war, global column ozone would be reduced by 25% with recovery taking 12 years. This is similar to previous simulations, but with a faster recovery time due to a shorter lifetime for soot in our simulations. In‐line photolysis provides process specific action spectra enabling future integration with biogeochemistry models and allows output that quantifies the potential health impacts from changes in surface UV for this and other larger aerosol injections. Plain Language Summary: Nuclear war would result in many immediate fatalities from the blast, heat, and radiation, but smoke from fires started by these weapons could also cause climate change lasting up to 15 years threatening food production. For the first time with a modern climate model, we have simulated the effects on ozone chemistry and surface ultraviolet (UV) light caused by absorption of sunlight by smoke from a global nuclear war. This could lead to a loss of most of our protective ozone layer taking a decade to recover and resulting in several years of extremely high UV light at the surface further endangering human health and food supplies. Key Points: Global nuclear war injecting 150 Tg of stratospheric smoke causes a peak global ozone loss of 75% with depletion lasting 15 yearsOzone loss leads to a tropical ultraviolet (UV) Index above 35 after 3 years lasting 4 years, and a 20% global average UV‐B increase, a hazard to lifeIncluding smoke decreases photolytic rates, increasing ozone loss by 15% with decreasing NOx and increasing HOx catalytic cycle losses [ABSTRACT FROM AUTHOR]

Climate change affects global agricultural production and threatens food security. Faster phenological development of crops due to climate warming is one of the main drivers for potential future yield reductions. To counter the effect of faster maturity, adapted varieties would require more heat units to regain the previous growing period length. In this study, we investigate the effects of variety adaptation on global caloric production under four different future climate change scenarios for maize, rice, soybean, and wheat. Thereby, we empirically identify areas that could require new varieties and areas where variety adaptation could be achieved by shifting existing varieties into new regions. The study uses an ensemble of seven global gridded crop models and five CMIP6 climate models. We found that 39% (SSP5‐8.5) of global cropland could require new crop varieties to avoid yield loss from climate change by the end of the century. At low levels of warming (SSP1‐2.6), 85% of currently cultivated land can draw from existing varieties to shift within an agro‐ecological zone for adaptation. The assumptions on available varieties for adaptation have major impacts on the effectiveness of variety adaptation, which could more than half in SSP5‐8.5. The results highlight that region‐specific breeding efforts are required to allow for a successful adaptation to climate change. [ABSTRACT FROM AUTHOR]

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, harvesting). The SWAT+ model represents agricultural land by default in a generic way where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for Northeast Africa. We evaluate the influence of the crop phenology representation on simulations of Leaf Area Index (LAI) and Evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR ET data respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural Hydrologic Response Units (HRUs) which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in regional to global hydrological models. [ABSTRACT FROM AUTHOR]

The extent and impact of climate‐related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter‐Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross‐category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia. Plain Language Summary: Global warming changes the frequency, intensity, and spatial distribution of extreme events. We analyze computer simulations of river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves under past, present‐day, and potential future climate conditions. Our results show that global warming increases the number of people around the world that are affected by these events each year, both for all event types combined and each type individually. Changes in the chance of being affected by extreme events are unevenly distributed in space. Particularly large increases are simulated for tropical and subtropical regions. Key Points: We quantify the pure effect of climate change on the exposure to extreme climate impact events, for both historical and future time periodsGlobal warming increases the global population exposure to river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwavesThe largest increases in exposure are projected for tropical and subtropical regions [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Climate change may have major influences on surface runoff, which would consequently result in important implications for terrestrial ecosystems and human well‐being. At global scale there is limited understanding of these issues with respect to the warming targets stipulated in the Paris Agreement. Here we use a well‐established hydrological model (Variable Infiltration Capacity [VIC]) forced with a representative ensemble of latest climate projections from four global circulation models (GCMs) to estimate potential future changes in runoff and Terrestrial Ecosystem Water Retention (TEWR), as well as changes in extreme runoff and their impacts on population, and overall gross domestic product (GDP) worldwide. Results suggest that annual runoff generally would have larger increases, while annual TEWR generally would have larger decreases under the 2.0°C warming scenario as opposed to 1.5°C warming scenario. Global mean warming of 2°C versus 1.5°C would lead to more distinct spatial patterns in runoff change, with a general shift of the runoff distribution towards more extreme low runoff in Mexico, western United States, Western Europe, southeastern China, West Siberian Plain and more extreme high runoff in Alaska, northern Canada, and large parts of Asia. More people and GDP would be exposed to extreme low runoff decrease, extreme high runoff increase, extreme low runoff decrease as well as extreme high runoff increase under a higher warming scenario. This study differentiates hydrological impacts between the two warming scenarios and illustrates higher runoff, lower TEWR, larger potential drought and flood hazards and adverse impacts on population and GDP under 2°C than 1.5°C. Key Points: Annual runoff generally increases while terrestrial ecosystem water retention generally decreases under 2.0 than 1.5°C warmingMore areas would experience droughts (34.6%), floods (54.4%), droughts and floods (14.5%) under 2.0 than 1.5°C warmingMore people (85.9% totally) and GDP (85.9% totally) would be affected by droughts or/and floods under 2.0 than 1.5°C warming [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]