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 Sea level rise (SLR) is a global concern in the era of climate change, prompting the exploration of interventions such as solar radiation modification through stratospheric aerosol injection (SAI). This intervention could affect the physical system in various ways. The present study analyzes the global and regional impacts of SAI using ARISE-SAI-1.5 (SAI-1.5) simulations with the Community Earth System Model 2. We calculated the regional thermosteric sea level under different scenarios. After validating our methodology for sea level components over the period 1995–2014, we determined changes in sea level variables under both SAI-1.5 and the underlying Shared Socioeconomic Pathway 2–4.5 (SSP2-4.5) relative to the reference period (1995–2014). In contrast to sea surface temperature, which under this SAI strategy should be maintained near 1.5 °C above preindustrial values, global SLR would continue increasing under SAI-1.5. However, SAI would significantly impact thermal expansion in SSP2-4.5 simulations, reducing the global long-term sea level trend from 3.7 ± 0.03 mm yr⁻¹for SSP2-4.5–1.9 ± 0.04 mm yr⁻¹for SAI-1.5, a 49% reduction. The associated ocean heat content is reduced from (2.0 ± 0.3) × 10²²J yr⁻¹under SSP2-4.5 to (1.17 ± 0.30) × 10²²J yr⁻¹under SAI, a 42% reduction. Additionally, SAI would impact the regional and global ocean by reducing the SLR rate. These findings underscore the potential of SAI as a climate intervention strategy with significant implications for sea level change. 

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. 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.