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Cattle farming is a major source of global food production and livelihoods that is being impacted by climate change. However, despite numerous studies reporting local-scale heat impacts, quantifying the global risk of heat stress to cattle from climate change remains challenging. We conducted a global synthesis of documented heat stress for cattle using 164 records to identify temperature-humidity conditions associated with decreased production and increased mortality, then projected how future greenhouse gas emissions and land-use decisions will limit or exacerbate heat stress, and mapped this globally. The median threshold for the onset of negative impacts on cattle was a temperature-humidity index of 68.8 (95% C.I.: 67.3–70.7). Currently, almost 80% of cattle globally are exposed to conditions exceeding this threshold for at least 30 days a year. For global warming above 4°C, heat stress of over 180 days per year emerges in temperate regions, and year-round heat stress expands across all tropical regions by 2100. Limiting global warming to 2°C, limits expansion of 180 days of heat stress to sub-tropical regions. In all scenarios, severity of heat stress increases most in tropical regions, reducing global milk yields. Future land-use decisions are an important driver of risk. Under a low environmental protection scenario (SSP3-RCP7.0), the greatest expansion of cattle farming is projected for tropical regions (especially Amazon, Congo Basin, and India), where heat stress is projected to increase the most. This would expose over 500 million more cattle in these regions to severe heat risk by 2090 compared to 2010. A less resource-intensive and higher environmental protection scenario (SSP1-RCP2.6) reduces heat risk for cattle by at least 50% in Asia, 63% in South America, and 84% in Africa. These results highlight how societal choices that expand cattle production in tropical forest regions are unsustainable, both worsening climate change and exposing hundreds of millions more cattle to large increases in severe, year-round heat stress. 

Abstract Convective available potential energy (CAPE) is of strong interest in climate modeling because of its role in both severe weather and in model construction. Extreme levels of CAPE (>2000 J kg −1 ) are associated with high-impact weather events, and CAPE is widely used in convective parameterizations to help determine the strength and timing of convection. However, to date few studies have systematically evaluated CAPE biases in models in a climatological context, and none have addressed bias in the high tail of CAPE distributions. This work compares CAPE distributions in ~200 000 summertime proximity soundings from four sources: the observational radiosonde network [Integrated Global Radiosonde Archive (IGRA)], 0.125° reanalyses (ERA-Interim and ERA5), and a 4-km convection-permitting regional WRF simulation driven by ERA-Interim. Both reanalyses and the WRF Model consistently show too-narrow distributions of CAPE, with the high tail (>90th percentile) systematically biased low by up to 10% in surface-based CAPE and even more in alternate CAPE definitions. This “missing tail” corresponds to the most impacts-relevant conditions. CAPE bias in all datasets is driven by surface temperature and humidity: reanalyses and the WRF Model underpredict observed cases of extreme heat and moisture. These results suggest that reducing inaccuracies in land surface and boundary layer models is critical for accurately reproducing CAPE. 

A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30°N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history. 

Abstract. 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.