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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 | In order to assess the regional impact of future climate scenarios it is necessary to downscale global climate models (GCMs) into spatial and temporal scales that solve sub-mesoscale to mesoscale dynamics (e.g., eddies in the ocean, convective precipitation). We are interested in conducting atmospheric and oceanic dynamical downscaling of GCMs for Mexico. With outputs from the downscaling exercise a variety of research questions and impact assessment can be tackled. For instance: \- In the ocean, we could anticipate marine species’ range shifts through the evaluation of changes in circulation and bio-dispersal as well as changes in temperature and consequent optimal habitat re-distribution. With respect to temperature, recent marine heatwaves (MHWs) have caused the death of foundation species, habitat loss, and community reconfiguration. Thus, evaluating MHWs’ future occurrence, as well as that of upwelling, a mechanism that regulates their depth extent and duration, is of primary importance. This is of particular interest for kelp forests in Baja California and corals in the Caribbean Sea; two oceanic regions with highly valued ecosystem services and for which we envision performing downscaling. \- In the atmosphere, it would allow solving the dynamics of the North American Monsoon (NAM) along the Mexican occidental mountain range, and consequently, a proper evaluation of changes in regional convective precipitation. This is crucial for adequate planning of irrigation infrastructure, and to assess the sustainability of crops that depend on seasonal rainfall. Similarly, atmospheric downscaling of GCMs is necessary to understand the response of the Caribbean Low-Level Jet (CLLJ) to warming climate and associated changes in frequency and intensity of extreme weather events as heatwaves and droughts in the Mesoamerican region. Often, due to the lack of some global climate model outputs stored at high frequency (e.g., 3-hourly for atmospheric variables or daily ocean current velocities), the high frequency signal used in downscaling exercises does not come from the global climate models but from historic reanalyses or synthetic data. We believe a great improvement would be achieved by forcing downscaling with GCM high frequency output rather than historic or synthetic data. Particularly, \- In the oceanic realm, the high frequency signal (daily) is particularly important to identify and study extreme temperature events (i.e., MHWs, MCSs), upwelling, and biodispersal; all processes that we are particularly interested in reproducing when downscaling and that ideally, should be inherited from the simulated historic and climate scenarios. \- Comparably, realistic atmospheric simulations on climate time scales require a coupled land surface model (LSM) to implicitly solve the heat and mass exchanges between the atmosphere and the land surface, including those involving vegetation, which are central to the hydrological cycle. The use of LSMs in regional climate simulations, in turn, requires high frequency (6hr) 3D fields of soil temperature and moisture at specified soil depths, which ideally should be sourced from the same global climate simulations used to force the atmospheric model. Thus, this opportunity proposes the storage of variables used for dynamical downscaling at high frequency; specifically: To force the ocean model 3D Daily @ the generic ocean model vertical coordinates thetao so uo vo zos 2D Daily @ the generic model grid rsds rlds pr friver To force the ocean and atmospheric models Baseline\_fixed 2D every 6hr @ the generic model grid tas huss uas vas psl To force the atmospheric model 2D every 6hr @ the generic model grid ps ts snd snc sivol siconc 3D every 6hr @ the generic pressure levels (19) ta hus ua va zg 3D every 6hr @ generic vertical levels (3) tsl mrsos We envisage proposing a variable group baseline daily ocean conformed by the necessary variables to force an oceanic model for dynamical downscaling at a daily temporal resolution. |
| expected_impacts | The requested variables are the necessary to run a hydrodynamic and an atmospheric model to perform downscaling and capture the dynamics recognized of processes that affect marine ecosystems health and hydrological cycles (e.g., MHWs, convective precipitation). We are interested in performing downscaling for historic and climate scenarios. The historic runs will complement observations to explore mechanisms behind identified historic long-term trends, and extreme events. Additionally, the historic run and downscaled product is the baseline to evaluate change in future climate scenarios. |
| justification_of_resources | Having modelling centres saving these variables at high frequency will aid GCM data processing for downscaling as well as standardizing downscaling practices; both facilitate research centres with little computational resources getting involved in climate modelling. |
| lead_theme | Impacts & Adaptation |
| minimum_ensemble_size | 1 |
| name | Atmospheric and Oceanic Dynamical downscaling |
| notes | For consideration when merging/refinining this opportunity: [from vertdict on ID 47] VH: this is relevant to I&A because the outputs of these ocean models are required as inputs to regional models that investigate impacts/adaptation. Typically variables required (for I&A) are: thetao so uo vo zos tos rsds rlds pr tas huss uas vas psl ps ts ta hus ua va zg vsi usi This opportunity is not necessary for I&A as long as Dynamical downscaling 22 (merged downscaling) opportunity does include these listed variables, essential for regional impacts modelling use. |
| opportunity_id | 28 |
| data_request_themes | Impacts & Adaptation, Atmosphere, Ocean & Sea-Ice |
| experiment_groups | scenarios, historical |
| mips | PMIP |
| time_subsets | all |
| variable_groups | DynDownscale_day, baseline_fixed |