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These are the intended use-case/justification for one or multiple variable groups. Opportunities are linked to relevant experiment groups. Identifying opportunities helps to provide a structure to map variables against requirements. Each opportunity description will convey why this combination of variables and experiments is important and how they contribute to impact.
| Attribute | Value |
|---|---|
| description | Supporting agricultural sector simulations of extreme event response, air pollution, water and land use for impacts and adaptation planning. This opportunity includes variable groups providing information needed for impacts and adaptation planning in the agricultural sector. Included are variable groups related to the minimum inputs required for agricultural modeling, extended agricultural modeling variables that allow for more detailed simulations of extreme event response, water resources, land use, and air pollution. Variable sets include both sub-daily and daily outputs but are grouped according to established applications. This opportunity requests data throughout the entire simulation period. However, if high temporal resolution data is difficult to provide for the entire period, this opportunity can be supported with high resolution data from the following time subsets: hist72 and histext + whole timeseries (2021-2100): first preference, hist43 and histext + whole timeseries (2021-2100): second preference, hist43 and histext + scenario30mid + scenario20: third preference, hist43 and histext + scenario20mid + scenario20: fourth preference, hist20 and histext + scenario20mid + scenario20: fifth preference. histext is included because the I&A community would like historical data that are as close to the present as possible. |
| desirable_ensemble_size | 10 |
| expected_impacts | Large communities of practice are well established and interested in using climate model outputs for agricultural applications, with previous applications resulting in a large number of high-profile publications and products relevant to policymakers, the private sector, and civil society. These include applications by the Agricultural Model Intercomparison and Improvement Project (AgMIP), who works along with the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP) to undertake multi-crop-model ensemble projections of future agricultural yields, water requirements, and the downstream ramifications on food prices, food security, socioeconomic development, and geopolitical stability. These process-based models can also investigate adapted land use or management strategies and can also search for regional systems with adaptation and mitigation co-benefits. In addition to process-based crop models, additional applications utilize empirical or machine learning models to anticipate crop risks or viability under different environments. In many cases these models use bias-adjusted versions of these CMIP outputs, but the process is rooted in CMIP outputs being available. Similarly, tile information helps us identify whether agricultural regions have exacerbated or reduced extremes compared to the larger grid cells. Agricultural water resource applications include planning for reactive and proactive adaptation strategies to deal with drought and the repercussions on food production, food trade, and food security. Outputs can also be used to plan large-scale water resource projects like the construction of reservoirs and canal systems. CMIP outputs can drive hydrology models from the global to landscape and local scales, and this information can also aid in more complex agricultural models. There is also great interest in evaluating the viability of different cropping systems including an analysis of whether crops will fare well given future water requirements and climate conditions. The air pollution variable group within this opportunity allows agricultural experts to understand how air pollution, volcanoes, and geoengineering activities may alter atmospheric chemistry and radiation profiles and lead to direct effects on agricultural production. For example, volcanoes and geoengineering may alter the balance of direct and diffuse solar radiation, which is important for agricultural production. The agricultural land use variable group allows experts to track how the models adjust functional land types and agricultural lands as the climate changes so that they can explore socioeconomic pressures on transitioning regions as well identify additional factors that may be accelerating or resisting such changes. |
| justification_of_resources | Agriculture and food security comprise one of the most prominent application areas for climate projections. Providing variables from this opportunity helps CMIP modeling groups to fulfill their aims of providing science that informs societal risk management and planning for adaptation and mitigation strategies. Forewarning of future risks allows for proactive planning and the development of strategies that will be effective, timely, and just. A large community with established models and high-profile products leads to a high likelihood of CMIP output use from this opportunity. The community is especially interested in DECK outputs, historical simulations and ScenarioMIP projections; with additional interest in the core simulations of DAMIP, DCPP, and HighResMIP which would allow for investigations and contextualization around today's climate and agricultural systems. Variable groups include core and enhanced driving information for a variety of crop models (e.g., biophysical process-based models and a variety of empirical and machine learning models), as well as variable groups that allow more nuanced discussion of climate impacts at the intersection of agriculture and water resources. These outputs are likely to be further bias-adjusted before application, but including them in CMIP7 outputs allows for core understanding of mean and distributional shifts that are important to farm and water managers. The enhanced agricultural variable groups within this opportunity allow CMIP outputs to be connected into more complex investigations of future crop yields and agroclimatological extremes. This includes through important fine-scale mechanisms across space and time, as well as through the consequences of near-surface air pollution and upper-atmosphere aerosols that can affect agricultural production in distinct ways. These investigations are critical to understanding acute extremes affecting the food system, and can help the agricultural impacts community evaluate important response risks. Agricultural impacts are highly prominent within climate change policy making and popular discourse, and there are established modeling groups requesting these variable groups. |
| lead_theme | Impacts & Adaptation |
| minimum_ensemble_size | 1 |
| name | Agriculture and Food System Impacts |
| opportunity_id | 19 |
| data_request_themes | Impacts & Adaptation, Land & Land-Ice, Earth System |
| experiment_groups | dcpp, irrmip-nonfasttrack, highresmip2-ia, deck, scenarios, historical |
| mips | ISIMIP, DCPP |
| time_subsets | hist20, hist43, histext, scenario20, scenario20mid, scenario30mid |
| variable_groups | AgAirPollution, AgCarbon, AgImpactsMisc, AgLandUse, AgModelCoreDaily, AgModelExpandedDaily, AgModelHourly, AgTile, AgWaterCore, AgWaterExt |