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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 | Simulating clouds with global climate models is challenging as the relevant physics involves many nonlinear processes covering a wide range of spatial and temporal scales. As key components of the hydrological cycle and the climate system, an evaluation of clouds from models used for climate projections is an important prerequisite for assessing the confidence in the results from these models. So far, a quantitative evaluation of the representation of clouds in CMIP models with satellite observations has been challenging as only a limited number of parameters from a limited number of models has been available from satellite simulators. This opportunity aims at a quantitative evaluation of cloud parameters, radiation and precipitation using output from satellite simulators that has not been available in CMIP6. Focus will be on the coupled (historical) simulation to assess how well cloud are represented in the model configurations used for the projections. The sensitivities of cloud properties to climate change will then be investigated using the scenario simulations. Combining the daily cycle of cloud properties with precipitation and radiation fields will allow for a more process-based analysis and improved understanding of biases in and sensitivities of clouds in the coupled models. |
| expected_impacts | If a huge part of all CMIP7 models provide these requested variables it might be possible for us to answer e.g. the following science questions: How well is the representation of clouds in the newest model generation on different spatial and temporal scales in the coupled model configurations used for the projections? How well can climate models reproduce the daily cycle of cloud properties, radiation fields and precipitation and what are biases connected to relevant processes and how do there sensitivities change under different scenarios of climate change? |
| justification_of_resources | As we include mainly 2D fields in this opportunity the amount of resources is kept to a minimum. |
| lead_theme | Atmosphere |
| minimum_ensemble_size | 1 |
| name | Clouds, radiation & precipitation |
| opportunity_id | 71 |
| data_request_themes | Atmosphere |
| experiment_groups | scenarios, historical |
| mips | PMIP, DAMIP |
| time_subsets | hist43, scenario20 |
| variable_groups | clouds_basic_average, clouds_basic_instantaneous, cosp_output |