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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 | This opportunity enables the diagnosis of radiative forcing - the perturbation in Earth's radiative energy budget directly due to a change in composition, such as rising greenhouse gas concentrations or aerosol emissions. Fundamentally, all anthropogenically-induced climate change is a response to the energy imbalance caused by the radiative forcing. Therefore, the systematic diagnosis of radiative forcing in climate models is crucial for interpreting projections of climate change, evaluating the climate impacts of proposed emission reduction strategies, and for understanding and ultimately reducing climate model uncertainty. The opportunity is dedicated to quantifying the total or "effective" radiative forcing in CMIP simulations, along with its components such as the instantaneous radiative forcing and radiative adjustments. The contents of the opportunity will enable users to employ common, well-established methods for diagnosing radiative forcing globally and is particularly relevant for participation in the Radiative Forcing Model Intercomparison Project (RFMIP). The request consists entirely of monthly output, mostly for standard variables that are also included in the CMIP7 Baseline data request. |
| expected_impacts | The data and experiments referenced in this opportunity will enable a comprehensive diagnosis of radiative forcing in CMIP7, which is key for model evaluation and scientific understanding. For example, the radiative fluxes can be directly used to estimate the effective radiative forcing in fixed-SST or, when combined with regression techniques, in fully coupled simulations. Then, radiation diagnostic tools like radiative kernels can be applied to the output of the atmospheric state (e.g. temperature, humidity) to further decompose the effective radiative forcing and understand its physical drivers and sources of uncertainty. |
| justification_of_resources | Given its focus on low-frequency output, this opportunity will not be burdensome to data providers and will provide a considerable return on investment, considering that the diagnosis of radiative forcing is fundamental to a variety of science applications. Furthermore, three experiments now required as part of the DECK (piClim-control, piClim-4xCO2, piClim-anthro) are designed for radiative forcing diagnosis and will serve little scientific value without the output contained in this opportunity. |
| lead_theme | Atmosphere |
| minimum_ensemble_size | 1 |
| name | Diagnosing Radiative Forcing |
| opportunity_id | 72 |
| data_request_themes | Atmosphere |
| experiment_groups | deck, fast-track, scenarios, historical |
| mips | RFMIP, DCPP, PMIP, DAMIP |
| time_subsets | 80ac3156-a698-11ef-914a-613c0433d878 |
| variable_groups | radiativeforcing_monthly |