Data assimilation of satellite snow thickness products in the Arctic Ocean
Lead Research Organisation:
University College London
Department Name: Earth Sciences
Abstract
The low abundance of available observations means that the initial conditions used for Arctic forecasts are significantly less accurate than for lower latitudes, such that the assimilation of newly acquired data has the potential to significantly increase the accuracy of Arctic forecasts. While the repercussions of the changing Arctic climate on the sea ice extent and thickness are well documented, changes in the snow cover remain harder to monitor with limited in-situ and airborne observations. This project will investigate the representation of the snow-sea ice relationship in selected CMIP6 models, before exploring and assimilating new snow thickness products into a hierarchy of increasingly complex sea ice models, including the Met Office's Ocean and Sea-ice Forecasting Systems. Then, the impact of a more complex representation of snow structure will be investigated by coupling the CICE sea-ice model with CROCUS, a complex snow model. A preliminary coupled model code will be validated with existing data and revised accordingly, after which the resulting modelling system will be coupled with SMRT, a radiative transfer model. This will allow us to investigate the impact of snow thickness assimilation and more complex representation of snow structure on weather forecasts and sea-ice variables.
People |
ORCID iD |
Michel Tsamados (Primary Supervisor) | |
Carmen Nab (Student) |
Publications
Nab C
(2023)
Synoptic Variability in Satellite Altimeter-Derived Radar Freeboard of Arctic Sea Ice
in Geophysical Research Letters
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
NE/S007229/1 | 30/09/2019 | 29/09/2027 | |||
2390206 | Studentship | NE/S007229/1 | 30/09/2020 | 29/09/2024 | Carmen Nab |
Description | CASE Partnership |
Organisation | Meteorological Office UK |
Country | United Kingdom |
Sector | Academic/University |
PI Contribution | Data sharing, knowledge sharing |
Collaborator Contribution | Data sharing, knowledge sharing |
Impact | Data sharing, knowledge sharing |
Start Year | 2021 |