Multi-wheat-model ensemble responses to interannual climate variability

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 ≤ 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade

Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator

Using climate scenarios from only 1 or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time consuming to run process-based models (e.g. hydrological and crop models) in climate change impact studies using numerous climate scenarios. It would be interesting to investigate if using a reduced number of climate scenarios could lead to a reasonable estimate of the ensemble mean. In this study, we generated a single ensemble-mean climate scenario (En-WG scenario) using ensemble means of the change factors derived from 20 GCMs included in CMIP5 to perturb the parameters in a weather generator, LARS-WG, for selected locations across Canada. We used En-WG scenarios to drive crop growth models in DSSAT ver. 4.7 to simulate crop yields for canola and spring wheat under RCP4.5 and RCP8.5 emission scenarios. We evaluated the potential of using the En-WG scenario to simulate crop yields by comparing them with crop yields simulated with the LARS-WG generated climate scenarios based on each of the 20 GCMs (WG scenarios). Our results showed that simulated crop yields using the En-WG scenarios were often close to the ensemble means of simulated crop yields using the 20 WG scenarios with a high probability of outperforming simulations based on a randomly selected GCM. Further studies are required, as the results of the proposed approach may be influenced by selected crop types, crop models, weather generators, and GCM ensembles

Local impacts of climate change on winter wheat in Great Britain

Under future CMIP5 climate change scenarios for 2050, an increase in wheat yield of about 10% is predicted in Great Britain (GB) as a result of the combined effect of CO2 fertilization and a shift in phenology. Compared to the present day, crops escape increases in the climate impacts of drought and heat stresses on grain yield by developing before these stresses can occur. In the future, yield losses from water stress over a growing season will remain about the same across Great Britain with losses reaching around 20% of potential yield, while losses from drought around flowering will decrease and account for about 9% of water limited yield. Yield losses from heat stress around flowering will remain negligible in the future. These conclusions are drawn from a modelling study based on the response of the Sirius wheat simulation model to local-scale 2050-climate scenarios derived from 19 Global Climate Models from the CMIP5 ensemble at 25 locations representing current or potential wheat-growing areas in GB. However, depending on susceptibility to water stress, substantial interannual yield variation between locations is predicted, in some cases suggesting low wheat yield stability. For this reason, local-scale studies should be performed to evaluate uncertainties in yield prediction related to future weather patterns

Towards Useful Decadal Climate Services

The decadal time scale (∼1–10 years) bridges the gap between seasonal predictions and longer-term climate projections. It is a key planning time scale for users in many sectors as they seek to adapt to our rapidly changing climate. While significant advances in using initialized climate models to make skillful decadal predictions have been made in the last decades, including coordinated international experiments and multimodel forecast exchanges, few user-focused decadal climate services have been developed. Here we highlight the potential of decadal climate services using four case studies from a project led by four institutions that produce real-time decadal climate predictions. Working in co-development with users in agriculture, energy, infrastructure, and insurance sectors, four prototype climate service products were developed. This study describes the challenge of trying to match user needs with the current scientific capability. For example, the use of large ensembles (achieved via a multisystem approach) and skillfully predicted large-scale environmental conditions, are found to improve regional predictions, particularly in midlatitudes. For each climate service, a two-page “product sheet” template was developed that provides users with both a concise probabilistic forecast and information on retrospective performance. We describe the development cycle, where valuable feedback was obtained from a “showcase event” where a wider group of sector users were engaged. We conclude that for society to take full and rapid advantage of useful decadal climate services, easier and more timely access to decadal climate prediction data are required, along with building wider community expertise in their use.This study received support from the C3S_34c contract (ECMWF/COPERNICUS/2019/C3S_34c_DWD) of the Copernicus Climate Change Service (C3S) operated by ECMWF. DS, AS, and HT were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra. AP, KP, and BF were funded by the Deutscher Wetterdienst.Peer Reviewed"Article signat per 22 autors/es: Nick Dunstone, Julia Lockwood, Balakrishnan Solaraju-Murali, Katja Reinhardt, Eirini E. Tsartsali, Panos J. Athanasiadis, Alessio Bellucci, Anca Brookshaw, Louis-Philippe Caron, Francisco J. Doblas-Reyes, Barbara Früh, Nube González-Reviriego, Silvio Gualdi, Leon Hermanson, Stefano Materia, Andria Nicodemou, Dario Nicolì, Klaus Pankatz, Andreas Paxian, Adam Scaife, Doug Smith, and Hazel E. Thornton"Postprint (published version

Responses of Agroecosystems to Climate Change: Specifics of Resilience in the Mid-Latitude Region

This study examines the productivity and resilience of agroecosystems in the Korean Peninsula. Having learned valuable lessons from a Chapman University project funded by the United States Department of Agriculture which concentrated on the semi-arid region of southwestern United States, our joint Korea—Chapman University team has applied similar methodologies to the Korean Peninsula, which is itself an interesting study case in the mid-latitude region. In particular, the Korean Peninsula has unique agricultural environments due to differences in political and socioeconomic systems between South Korea and North Korea. Specifically, North Korea has been suffering from food shortages due to natural disasters, land degradation and political failure. The neighboring developed country, South Korea, has a better agricultural system but a low food self-sufficiency rate. Therefore, assessing crop yield potential (Yp) in the two distinct regions will reveal vulnerability and risks of agroecosystems in the mid-latitude region under climate change and variability and for different conditions

Atlas of Global Change Risk of Population and Economic Systems

This book is open access and illustrates the spatial distribution of the global change risk of population and economic systems with the maps of environment, global climate change, global population and economic systems, and global change risk. The risks of global change are mapped at 0.25 degree grid unit. The risk results and their contribution rates of the world at national level are unprecedentedly derived and ranked. The book can be a good reference for researchers and students in the field of global climate change and natural disaster risk management, as well as risk managers and enterpriser to understand the global change risk of population and economic systems

Crop production and global food security in relation to climate variation: an empirical analysis

The challenge of meeting increasing global food demand is amplified by climate change. Crop yield is vulnerable to extreme conditions, including heatwaves, droughts and downpours, leading to widespread concern about negative effects of climate change on food security. This thesis describes a novel empirical analysis of total production, yields and harvested area data for three major crops (wheat, maize and soybean), using a unique, global, gridded agricultural time-series data set. Trend analysis is applied to changes in production, yield and harvested area of these three crops. Machine learning is used to quantify their responses to climate. A new methodology is introduced to identify “shocks”. Results show a more complex dynamics of agricultural production than is suggested by current liter- ature. Large changes in regional production, driven by harvested area rather than yield, have been driven by policy shifts. A large “killing degree-day” sum depresses yields for some regions and crops, but enhances them in others. Heat deficits can be as deleterious as heatwaves. Shocks can be negative or positive. Production variability has increased, but major negative shocks have been few, and have not become more frequent. Production shocks have been caused as often by changes in harvested area as in yield. These findings do not support a universal negative effect of climate change on crop production. More- over, stable global food supplies will not be assured by maximizing yields. It is equally important that farmers in different countries and environments grow a variety of crops. Climate-related risk is currently concentrated in the most productive baskets, exposing the global food supply to avoidably high risk. Increasing frequencies of climate extremes in the main producing areas only make such shocks more likely. Various measures that are not directly related to climate would help to make global food supplies more resilient.Open Acces

When Will Current Climate Extremes Affecting Maize Production Become the Norm?

We estimate the effects of climate anomalies (heat stress and drought) on annual maize production, variability, and trend from the country level to the global scale using a statistical model. Moderate climate anomalies and extremes are diagnosed with two indicators of heat stress and drought computed over maize growing regions during the most relevant period of maize growth. The calibrated model linearly combines these two indicators into a single Combined Stress Index. The Combined Stress Index explains 50% of the observed global production variability in the period 1980?2010. We apply the model on an ensemble of high-resolution global climate model simulations. Global maize losses, due to extreme climate events with 10-year return times during the period 1980?2010, will become the new normal already at 1.5 °C global warming levels (approximately 2020s). At 2 °C warming (late 2030s), maize areas will be affected by heat stress and drought never experienced before, affecting many major and minor production regions.Fil: Zampieri, M.. European Commission Joint Research Centre; ItaliaFil: Ceglar, A.. European Commission Joint Research Centre; ItaliaFil: Dentener, F.. European Commission Joint Research Centre; ItaliaFil: Dosio, A.. European Commission Joint Research Centre; ItaliaFil: Naumann, Gustavo. European Commission Joint Research Centre; Italia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: van den Berg, M.. European Commission Joint Research Centre; ItaliaFil: Toreti, A.. European Commission Joint Research Centre; Itali