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You can add links, pictures etc and then write your comment or question and hit Publish!

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E-driven models - way CORDEX select data you likely would not select e-driven models but are more applicable to future so need to think about applicability of future as well as representation of the past

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Decsion about scenarios; might be more difficult in CMIP7

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Taking a step back: Do we want a common set of criteria for model selection?

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Taking a step back: Do we want a common set of criteria for model selection?

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Need for central source of derived quantities 

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Need for central source of derived quantities 

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Gavin Schmidt: How hard to assess when data not all in one place. Climate sensitivity/metrics, skills of ENSO, other derived quantities should be available somewhere.

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Important to be clear about what we mean by uncertainty

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Will the REF be a 'game changer' for model selection?

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difficult to find a common strategy in different activites

climate sensitivy is often used

find common set and then critera that are specific for regions

ECS might be different for region compared to global

model familiy; models can be different even if they share components

use same set of critera across activities and use specific critera for regions

be pracmmatic; data must be avail.

length of simulation might also be an issue

understand stakeholder needs is important; model selection might depend on that

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By doing model selection in the first place, could be losing important future information, e.g. tipping points

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John Dunne: In CMIP7 hope to address representing regional SST and higher res to represent extremes.

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Alberto Meucci: High resolution is not only aspect to give the extremes but also improved physics. How much is confidence around high resolution models?

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data for selecting models based on GCM processes

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data for selecting models based on GCM processes

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Could we create a table that summarizes the implementation of GCM model components, such as aerosols and clouds? This would help us select models that demonstrate a wide range of process differences or a more consistent level of process reliability.

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Decisions can be made on

1) whether a process is represented

2) how well the process is represented

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Often a lack of observations which can limit you, especially when considering different geographical regions

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Two levels, one generic and one region/initiative-specific for model selection

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Two levels, one generic and one region/initiative-specific for model selection

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We could think of two levels, one of common metrics/criteria from a global perspective, common for all initiatives (sensitivity, biases, etc.), and another region/initiative-specific, dealing with the specificities of each initiative (e.g. key regional drivers for CORDEX, etc.). The first level, combined with data availability could yield a sub-ensemble of common models for all initiatives. The general level could be coordinated with/aligned to the Rapid Evaluation Framework (REF):

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ESMValTool might be good starting point

Rapid Evaluation framework in CMIP7 will also be an option

for CORDEX it is important to know early which groups save boundary conditions for downscaling

regular updates from the groups on the status of the simulations from CMIP would be useful

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ECS: regional results are rather independent of the driving models ECS; plausible range normally includes the 10-90% range but not the full range, thus maybe not a very good criteria to disregard models.

Model families: selected ESMs from Euro-CORDEX include many NEMO-models in the ocean. These show quite different results at the regional scale. Thus, having one component in common should not be a criteria to disregard models.

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How does the energy section look at the uncertainties and choose what output to use for planning?

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We no longer use SSP scenarios to show results, but instead look at Global Warming Levels, and choose models which fit this. For this we need other uncertainty assessments.

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core scenarios across different activities

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core scenarios across different activities

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It would be useful to decide on a set of ESMs that would be used by both CORDEX, ISIMIP .... This would help to provide consistent information to IPCC, users ....

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As an academician and hydrologist researcher, a lot of research queries that we face are reflecting the communication of uncertainty that further propagates to applications in hydrology. So if we could borrow the existing time-tested methods, from modellers and convince ourselves the local impacts etc.

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Challenges when with selecting the best criteria

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Challenges when with selecting the best criteria

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we need to ask us why not more ESMs provide the required boundary data? One reason is that it is extremely expensive, 10 times more data need to be stored for dynamical regional downscaling. Not choosing the ESMs that make the effort to store and cmorize all data will lead to even less ESMs that will provide the data.

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Collaborating with the global community models

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Collaborating with the global community models

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Here is the paper containing the plot I mentioned (Fig 3): https://esd.copernicus.org/articles/12/1543/2021/esd-12-1543-2021.pdf

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What would make the biggest difference for CMIP7?

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What would make the biggest difference for CMIP7?

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regarding the sampling of internal variablity mentioned by Paco, there's also a limitation on the availability of LBCs to feed dynamical downscaling: https://wcrp-cordex.github.io/cmip6-for-cordex/CMIP6_studies_table_EUR.html#all

Still, there are some efforts to downscale different members: https://wcrp-cordex.github.io/simulation-status/CORDEX_CMIP6_status_by_experiment.html#EUR-12-EURintvar

There could be room for ML/AI here

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Models need to be more of Regional Based and the Spatial Resolution need to be improved based on Integration of AI & ML Technologies

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Emergence of coupled(ESM) regional models?

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Emergence of coupled(ESM) regional models?

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Traditionally our regional CORDEX models have been atmospheric only, with targetted regional ocean, sea ice etc models. Is there a view to moving toward coupled regional models in CMIP7?