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Abstract Climate change poses significant threats to global agriculture, impacting food quantity, quality, and safety. The world is far from meeting crucial climate targets, prompting the exploration of alternative strategies such as stratospheric aerosol intervention (SAI) to reduce the impacts. This study investigates the potential impacts of SAI on rice and wheat production in India, a nation highly vulnerable to climate change given its substantial dependence on agriculture. We compare the results from the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection‐1.5°C (ARISE‐SAI‐1.5) experiment, which aims to keep global average surface air temperatures at 1.5°C above preindustrial in the Shared Socioeconomic Pathway 2‐4.5 (SSP2‐4.5) global warming scenario. Yield results show ARISE‐SAI‐1.5 leads to higher production for rainfed rice and wheat. We use 10 agroclimatic indices during the vegetative, reproductive, and ripening stages to evaluate these yield changes. ARISE‐SAI‐1.5 benefits rainfed wheat yields the most, compared to rice, due to its ability to prevent rising winter and spring temperatures while increasing wheat season precipitation. For rice, SSP2‐4.5 leads to many more warm extremes than the control period during all three growth stages and may cause a delay in the monsoon. ARISE‐SAI‐1.5 largely preserves monsoon rainfall, improving yields for rainfed rice in most regions. Even without the use of SAI, adaptation strategies such as adjusting planting dates could offer partial relief under SSP2‐4.5 if it is feasible to adjust established rice‐wheat cropping systems. 

The Geoengineering Model Intercomparison Project (GeoMIP) held its 14th annual workshop, with almost 70 in-person participants and 15 remote participants for a robust discussion about future experiments and community needs in light of phase 7 of the Coupled Model Intercomparison Project (CMIP7). 

Abstract Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. 

Abstract. The Geoengineering Model Intercomparison Project (GeoMIP) has proposed multiple model experiments during phases 5 and 6 of the Climate Model Intercomparison Project (CMIP), with the latest set of model experiments proposed in 2015. With phase 7 of CMIP in preparation and with multiple efforts ongoing to better explore the potential space of outcomes for different solar radiation modifications (SRMs) both in terms of deployment strategies and scenarios and in terms of potential impacts, the GeoMIP community has identified the need to propose and conduct a new experiment that could serve as a bridge between past iterations and future CMIP7 experiments. Here we report the details of such a proposed experiment, named G6-1.5K-SAI, to be conducted with the current generation of scenarios and models from CMIP6 and clarify the reasoning behind many of the new choices introduced. Namely, compared to the CMIP6 GeoMIP scenario G6sulfur, we decided on (1) an intermediate emission scenario as a baseline (the Shared Socioeconomic Pathway 2-4.5), (2) a start date set in the future that includes both considerations for the likelihood of exceeding 1.5 °C above preindustrial levels and some considerations for a likely start date for an SRM implementation, and (3) a deployment strategy for stratospheric aerosol injection that does not inject in the tropical pipe in order to obtain a more latitudinally uniform aerosol distribution. We also offer more details regarding the preferred experiment length and number of ensemble members and include potential options for second-tier experiments that some modeling groups might want to run. The specifics of the proposed experiment will further allow for a more direct comparison between results obtained from CMIP6 models and those obtained from future scenarios for CMIP7. 

Abstract As the severity of climate change and its associated impacts continue to worsen, schemes for artificially cooling surface temperatures via planetary albedo modification are being studied. The method with the most attention in the literature is stratospheric sulfate aerosol intervention (SAI). Placing reflective aerosols in the stratosphere would have profound impacts on the entire Earth system, with potentially far‐reaching societal impacts. How global crop productivity would be affected by such an intervention strategy is still uncertain, and existing evidence is based on theoretical experiments or isolated modeling studies that use crop models missing key processes associated with SAI that affect plant growth, development, and ultimately yield. Here, we utilize three global gridded process‐based crop models to better understand the potential impacts of one SAI scenario on global maize productivity. Two of the crop models that simulate diffuse radiation fertilization show similar, yet small increases in global maize productivity from increased diffuse radiation. Three crop models show diverse responses to the same climate perturbation from SAI relative to the reference future climate change scenario. We find that future SAI implementation relative to a climate change scenario benefits global maize productivity ranging between 0% and 11% depending on the crop model. These production increases are attributed to reduced surface temperatures and higher fractions of diffuse radiation. The range across model outcomes highlights the need for more systematic multi‐model ensemble assessments using multiple climate model forcings under different SAI scenarios. 

Abstract. Farmers around the world time the planting of their crops to optimize growing season conditions and choose varieties that grow slowly enough to take advantage of the entire growing season while minimizing the risk of late-season kill. As climate changes, these strategies will be an important component of agricultural adaptation. Thus, it is critical that the global models used to project crop productivity under future conditions are able to realistically simulate growing season timing. This is especially important for climate- and hydrosphere-coupled crop models, where the intra-annual timing of crop growth and management affects regional weather and water availability. We have improved the crop module of the Community Land Model (CLM) to allow the use of externally specified crop planting dates and maturity requirements. In this way, CLM can use alternative algorithms for future crop calendars that are potentially more accurate and/or flexible than the built-in methods. Using observation-derived planting and maturity inputs reduces bias in the mean simulated global yield of sugarcane and cotton but increases bias for corn, spring wheat, and especially rice. These inputs also reduce simulated global irrigation demand by 15 %, much of which is associated with particular regions of corn and rice cultivation. Finally, we discuss how our results suggest areas for improvement in CLM and, potentially, similar crop models.