Back to opportunities | Category Index
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 | Coordinated CMIP experiments will be used to assess the predictability of global and regional climate as well as to understand the mechanisms yielding prediction skill in different parts of the Earth’s climate system. Furthermore, by leveraging the large datasets including DCPP hindcasts, comprehensive risk assessments can be performed for climate related extremes, including high impact weather events such as windstorms and precipitation, flooding, drought, marine heat waves. |
| expected_impacts | Climate predictions are used to study climate variability and climate change relevant to decision-making from a few seasons to ten years ahead. The enhanced list of variables compared to previous CMIP phases of decadal prediction experiments will allow the study of new aspects of climate predictability, including climate extremes and carbon cycle. The availability of high temporal frequency data will lead to improved physical understanding of processes shaping extremes and high-impact events and improved understanding of the differences between models and how they represent risks. |
| justification_of_resources | The demand for climate information, especially about future changes and the potential for climate extremes, has grown in demand for numerous sectors including energy, agriculture, water and ecosystem management, and human health. Therefore, reliable climate predictions are essential for long-term planning, for climate risk assessments and climate adaptation strategies. Furthermore, extreme events, such as high-impact weather events, or marine heat waves, can have large impacts on human health, society and ecosystems, but are, by definition, rare events and require large datasets to properly quantify and understand. CMIP experiments, and especially those related to DCPP, have potential to help fulfill this demand, and to underpin the development of robust climate services. To do so successfully requires the production of high temporal frequency data across the globe in a range of variables and across a range of domains, including atmospheric, oceanic, ice, and biogeochemical processes. |
| lead_theme | Earth System |
| minimum_ensemble_size | 10 |
| name | Multi-annual-to-decadal predictability of the Earth System and risk assessment of climate extremes |
| opportunity_id | 54 |
| data_request_themes | Impacts & Adaptation, Atmosphere, Land & Land-Ice, Ocean & Sea-Ice, Earth System |
| experiment_groups | dcpp, climate_extreme, picontrol, dcpp-cmip6-exps, historical |
| mips | DCPP, TIPMIP, DAMIP |
| time_subsets | 80ac3156-a698-11ef-914a-613c0433d878 |
| variable_groups | baseline_daily, baseline_subdaily, DCPP_Essential, DCPP_Wider |