NOAA-NASA RISE Seasonal Prediction

(Experimental local and regional sea level forecasts)

VERSION: 0.8beta
Notes: Initial test version. Links should all "work" but plots and downloaded data are not entirely vetted yet.

Experimental forecasts of sea level anomalies both globally and at two tide gauge stations (San Diego, CA and Charleston, SC). Anomalies represent monthly averages and are relative to a 1982-2011 monthly climatology.

Current San Diego and Charleston forecasts for Months 1-12:


Downloads

All gridded hindcast data are available in netCDF format for download here.
All hindcast time series are available for download (ASCII format) here.
The most current forecast output is available for download here.

Individual map files are also available through OPeNDAP. File names are structured as

https://psl.noaa.gov/thredds/dodsC/Projects/RISE/webData4OPeNDAP/DATANAME/DATANAME_ssh_YYYYMM_LagL.nc

where
YYYYMM = initialization date
DATANAME is the dataset/model forecast name id (CCSM4, CFSv2, CanCM3, CanCM4, GFDL_FLORB01, LIM, NMME_MEAN, AVISO, ORAs4)
L is the forecast lead in months (ranging from 0 to 12)

For more information on PSL OPeNDAP go here.

Note that verification data are stored similarly to model hindcast data. For example, the ORAs4_ssh_201001_Lag6.nc file contains the observed ORAs4 SSH anomalies used to verify the Month 6 forecast initialized January 2010 (i.e., the July 2010 observed anomaly).

All maps are on a 1 deg x 1 deg grid, interpolated/regridded as necessary.

Skill assessment



Link, maybe some brief text will go here. For full, more technical descriptions, see:

  • Long, X., et al. 2021: Seasonal Forecasting Skill of Sea-Level Anomalies in a Multi-Model Prediction Framework. J. Geophys. Res. Oceans, 126, doi: 10.1029/2020jc017060.
  • Shin, S.-I., and M. Newman, 2021: Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies. Geophys. Res. Lett., 48, e2020GL091886, doi: 10.1029/2020GL091886.


    Details of the techniques

    JPL ECCO adjoint:
    This hybrid dynamical method is based on the mathematical convolution of atmospheric forcing with the lead-time dependent sensitivities of sea level to atmospheric forcing. The sensitivities of sea level to atmospheric forcing are computed by the ECCO adjoint model with the observationally constrained ECCO ocean state estimate as the background ocean state. The atmospheric forcing is based on the combination of ECCO forcing (constrained by observations) prior to prediction time and ensemble NMME predictions of atmospheric forcing after prediction initialization time with bias corrections applied using ECCO forcing climatology that is observationally constrained. The convolution integrate in space, lead time, and forcing type give predictions of sea level anomalies.


    Decomposition of the convolution in space, lead time, and forcing type allows us to examine the sources of uncertainties for the sea level prediction.

    For full, more technical descriptions, see:

  • (Manuscript in preparation)

    NMME:
    [Some NMME boiler-plate.] For full, more technical descriptions, see:

  • Long, X., et al. 2021: Seasonal Forecasting Skill of Sea-Level Anomalies in a Multi-Model Prediction Framework. J. Geophys. Res. Oceans, 126, doi: 10.1029/2020jc017060.
  • Kirtman, B. P. et al., 2014: The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction. Bulletin of the American Meteorological Society, 95, 585–601.

    Linear Inverse Model (LIM):
    For full, more technical descriptions, see:

  • Shin, S.-I., and M. Newman, 2021: Seasonal Predictability of Global and North American Coastal Sea Surface Temperature and Height Anomalies. Geophys. Res. Lett., 48, e2020GL091886, doi: 10.1029/2020GL091886.
  • Penland, C., and P. D. Sardeshmukh, 1995: The Optimal Growth of Tropical Sea Surface Temperature Anomalies. J. Climate, 8, 1999-2024, doi: 10.1175/1520-0442(1995)008<1999:togots>2.0.co;2.

    Related web pages

    PSL WRIT pages (online oceanic and atmospheric data analysis)
    PSL tide gauge data
    NOAA sea level rise viewer
    CU-UH-JPL sea level explorer
    NASA sea level change portal

    Page credits

    Web page design: Don Hooper
    Realtime forecast code development: Yan Wang and Matt Newman

    Standard disclaimer: these forecasts are experimental. NOAA/PSL and CIRES/University of Colorado are not responsible for any loss occasioned by the use of these forecasts.