Search for: All records

Abstract The deliberate addition of sulfur dioxide in the stratosphere to form reflective sulfate aerosols, reflect sunlight, and reduce surface temperatures is increasingly being considered as an option for minimizing the impacts of climate change. This strategy would create an unprecedented climate where the relationship between surface temperature and carbon dioxide concentration is decoupled. The implications of stratospheric aerosol intervention (SAI) for global crop protein concentrations have not yet been explored. While elevated CO₂concentrations are expected to reduce crop protein, higher temperatures may increase crop protein concentrations. Here we report changes of maize, rice, soybean, and wheat protein concentrations under a medium emissions climate change scenario and a SAI scenario to maintain global average temperatures at 1.5 °C above preindustrial levels, as simulated by three global gridded crop models. We show that using SAI to offset surface temperature increases would create decreases in the global protein concentrations of maize and rice, with minimal impact on wheat and soybean. Some already protein-deficient and malnourished nations that rely heavily on these crops to meet protein demands would show large decreases in protein intake under SAI with the current diet pattern, which could exacerbate their nutrient scarcity. The range of results between crop models highlights the need for a more comprehensive analysis using additional crop models, climate models, a broader range of climate intervention scenarios, and advancements in crop models to better represent protein responses to climate changes. 

ABSTRACT Solar radiation modification (SRM) is a potential strategy to rapidly mitigate global warming by reflecting more sunlight into space. However, its impact on tropical hydrological cycles remains underexplored. This study investigates the potential effects of SRM on streamflow in the Kelantan River Basin (KRB) by incorporating climate projections from the Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6) into the Soil and Water Assessment Tool plus (SWAT+) model. Results indicate that UKESM1-0-LL and MPI-ESM1-2-LR exhibit higher uncertainty in representing KRB's climate compared to CNRM-ESM2-1 and IPSL-CM6A-LR. Under SSP5-8.5, maximum and minimum temperatures are projected to increase by up to 3.52 °C by the late 21st century, while SRM scenarios may limit warming to 1.72-2.33 °C, similar to 1.96-2.22 °C under SSP2-4.5. The multi-model ensemble mean projected an inverse V-shaped trend in annual precipitation, with a peak in the mid-21st century before declining, except for G6sulfur, which exhibits a steady decrease. Increases in monthly precipitation from November to January during the 2045-2064 period under all evaluated scenarios may intensify flooding in the KRB. Meanwhile, decreases in streamflow during dry months are projected for the periods 2045-2064 and 2065-2085 under G6sulfur, particularly in the middle and upper basins. 

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. 

Abstract Following a nuclear war, destruction would extend well beyond the blast zones due to the onset of a nuclear winter that can devastate the biosphere, including agriculture. Understanding the damage magnitude and preparing for the folly of its occurrence are critical given current geopolitical tensions. We developed and applied a framework to simulate global crop production under a nuclear winter using the Cycles agroecosystem model, incorporating ultraviolet (UV)-B radiation effects on plant growth and adaptive selection of crop maturity types (shorter cycle the lower the temperature). Using maize (Zea maizeL.) as a sentinel crop, we found that annual maize production could decline from 7% after a small-scale regional nuclear war with 5 Tg soot injection, to 80% after a global nuclear war with 150 Tg soot injection, with recovery taking from 7 to 12 years. UV-B damage would peak 6–8 years post-war and can further decrease annual maize production by 7%. Over the recovery period, adaptive selection of maize maturity types to track changing temperatures could increase production by 10% compared to a no-adaptation strategy. Seed availability may become a critical adaptation bottleneck; this and prior studies might underestimate food production declines. We propose that adaptation must include the development of Agricultural Resilience Kits consisting of region- and climate-specific seed and technology packages designed to buffer against uncertainty while supply chains recover. These kits would be congenial with the transient conditions during the recovery period, and would also be applicable to other catastrophes affecting food production. 

Abstract. The global food trade system is resilient to minor disruptions but vulnerable to major ones. Major shocks can arise from global catastrophic risks, such as abrupt sunlight reduction scenarios (e.g. nuclear war) or global catastrophic infrastructure loss (e.g. due to severe geomagnetic storms or a global pandemic). We use a network model to examine how these two scenarios could impact global food trade, focusing on wheat, maize, soybeans, and rice, accounting for about 60 % of global calorie intake. Our findings indicate that an abrupt sunlight reduction scenario, with soot emissions equivalent to a major nuclear war between India and Pakistan (37 Tg), could severely disrupt trade, causing most countries to lose the vast majority of their food imports (50 %–100 % decrease), primarily due to the main exporting countries being heavily affected. Global catastrophic infrastructure loss with a comparable impact on yields as the abrupt sunlight reduction has a more homogeneous distribution of yield declines, resulting in most countries losing up to half of their food imports (25 %–50 % decrease). Thus, our analysis shows that both scenarios could significantly impact the food trade. However, the abrupt sunlight reduction scenario is likely more disruptive than global catastrophic infrastructure loss regarding the effects of yield reductions on food trade. This study underscores the vulnerabilities of the global food trade network to catastrophic risks and the need for enhanced preparedness. 

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 Middle East and North Africa (MENA) region is the dustiestregion in the world, and understanding the projected changes in the dustconcentrations in the region is crucial. Stratospheric aerosolinjection (SAI) geoengineering aims to reduce global warming by increasingthe reflection of a small amount of the incoming solar radiation to space,hence reducing the global surface temperatures. Using the output fromthe Geoengineering Large Ensemble Project (GLENS), we show areduction in the dust concentration in the MENA region under both the globalwarming (RCP8.5) and GLENS-SAI scenarios compared to the present-dayclimate. This reduction in dust over the whole MENA region is stronger underthe SAI scenario, except over dust hotspots and for the dry season. In otherwords, in the summer, with the strongest dust events, more reduction has beenprojected for the global warming scenario compared to the SAI scenario.The maximum reduction in the dust concentrations in the MENA region (underboth global warming and SAI) is due to the weakening of the dusthotspot emissions from the sources of the Middle East. Further analysis ofthe differences in the surface temperature, soil water, precipitation, leafarea index and near-surface wind speed provides some insights into theunderlying physical mechanisms that determine the changes in the future dustconcentrations in the MENA region. Detailed correlation analysis over dusthotspots indicates that lower future dust concentrations are controlled bylower wind speed and higher precipitation in these regions under boththe RCP8.5 and 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. 

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.