Seasonal Coastal Sea Level 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:

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.


  Bias corrected?  no   yes    Anomalies   Probabilities

Time Series

Bias corrected?  no   yes   Anomalies   Probabilities

RISE Hindcasts

Anomalies   Probabilities

Details of the techniques


For full, more technical descriptions, see:

Linear Inverse Model (LIM):

For full, more technical descriptions, see:

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:

Statistical downscaling:

Currently, the large computer models that form the basis of seasonal climate prediction systems produce coastal sea level forecasts spaced about 100 km apart. This is too coarse to meet the needs of U.S. coastal ocean management and services, which are becoming increasingly important as sea levels rise in a warming climate. Here we use a method to generate monthly sea level predictions on distances as small as 10 km apart, by applying the observed statistical relationship between sea level variations on scales of 100–1,000 km and finer-scale coastal ocean observations to the original coarser model predictions. For the RISE project, this was applied to the SEAS5, CCSM4, and SPEAR hindcasts.

For full, more technical descriptions, see:

Skill Assessment page to come


All RISE Hindcasts time series are available for download (ASCII format) here.
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

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 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.