Weather and Climate Science for Service Partnership (WCSSP) India - Calls- tender-PML
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Description
River outflow to the ocean in the NEMO model of the Regional Coupled System is currently prescribed as pure fresh water input, either at the surface or on a few ocean levels. However, in reality, fresh water gets mixed with marine water in estuaries, and the inflow into the ocean has a very different salinity and temperature profile with depth than what is currently done in the NEMO model at river outflow points. Current Met Office simulations do have stability issues at river outflow points, because of the current crude estimation of river flows. Estuary box models are considered as the way forward for ocean models which can't explicitly resolve estuaries (Matte et al. 2025). This will enable better numerical stability to run month-long or multi-decadal simulations and better oceanic circulation, due to improved temperature and salinity profiles near coastlines. This work will allow a better understanding of the interactions between river outflow and the ocean to enable partners to better assess potential impacts and thus mitigate against climate change. Primary beneficiaries would be the South Asian region, particularly those countries with an Indian Ocean coastline.
Objectives
Following a literature review of estuary box models (EBMs), we will develop a branch of NEMO4.0 that has the capability to run an EBM at any location specified. Development will be designed with future proofing in mind; - EBM inputs configurable at runtime for easy modification - Capability to have EBMs at some rivers and not at others - Allowing EBM parameters to differ for each river mouth - Designed with future support for FABM to include nutrients and other biogeochemical variables in the EBMs. A simulation will be set up with one or two EBMs at appropriate locations. In-country partners can provide recommendations for site(s) that have observational data available for comparison. The simulation will be benchmarked against the current configuration, with a sensitivity study into the tuning of the EBM parameters. In addition to understanding how the parameters affect the results, the sensitivity study could be used as a basis for machine learning to tune the parameters. We will investigate the feasibility of using a machine learning model for this purpose by identifying data requirements for training such a model.
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