Data-Assimilative Hindcast and Climate Forecast of Storm Surges with an ASGF Regression Model |
| |
Authors: | Zhigang Xu Jean-Pierre Savard Denis Lefaivre |
| |
Affiliation: | 1. Maurice Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Quebec, Canadazhigang.xu@dfo-mpo.gc.ca;3. Ouranos Consortium, Montréal, Quebec, Canada;4. Maurice Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Quebec, Canada |
| |
Abstract: | ABSTRACTThis study demonstrates that long-term climate model solutions can be efficiently converted to storm surge time series at points of interest (POIs) for the future. The all-source Green's function (ASGF) regression model is used for this conversion. In addition to being data assimilative, the ASGF regression model can also simulate storm surges at a POI faster than the traditional modelling approach by orders of magnitude. This is demonstrated using the tidal gauge at Sept-Îles (Quebec, Canada) in the Gulf of St. Lawrence as the POI. First the ASGF regression model is used to assimilate 32 years of tidal gauge data, producing a continuous hindcast of storm surges and a set of best-estimate regression parameters. Second, the ASGF regression model with the best-estimate parameters is used to convert a Canadian Regional Climate Model solution (CRCM/AHJ) to an hourly time series of storm surges from 1961 to 2100. Gumbel's extreme value analysis (EVA) is then applied to the time series as a whole and also to tri-decadal segments. The tri-decadal approach is used to investigate whether there is any progressive shortening or lengthening of storm surge return periods as a result of future climate change. A method for correcting for bias due to the forcing field at the EVA level is also demonstrated. |
| |
Keywords: | all-source Green's function (ASGF) ASGF regression model storm surges climate forcing field |
|
|