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Description

GODAS consists of a dataset of ocean properties, maintained by the National Centers for Environmental Prediction (NCEP). The dataset consists of real time analysis of ocean temperatures and salinity, as well as historical estimates from the years 1979 - 2004. The goal of GODAS is to enable study of global climate, as well as analysis of sub-seasonal, seasonal, and interannual variability of the ocean.

Toolbox tags

This toolbox entry has been labelled with the following tags:

Sector: global context; climate
Spatial scale: global; regional
Temporal focus: present
Onset: slow
Role in decision process: diagnostic
Level of skills required:
Data requirements:
Adaptation tasks: Detection and attribution

Applicability

As mentioned in the , GODAS can provide inputs into studies of climate change impacts, as well as studies of ocean variability, as the dataset provides estimates of ocean temperature, salinity, and movement at a global scale.

Accessibility

There is no charge for use of the data, and users can download datasets via web browser. Knowledge of FORTRAN may be helpful to extract data, but specific plots can also be downloaded via the GODAS website. All relevant documentation is provided on the website.

Further Reading and References

Further documentation on the GODAS data can be found at: http://www.cpc.ncep.noaa.gov/products/GODAS/pl/introduction_godas_web.pdf and the GODAS website.

Access to the data is provided at: http://www.esrl.noaa.gov/psd/data/gridded/data.godas.html

Further reading:

Behringer, D.W. and Y. Xue. 2004. Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Eighth Symposium on Integrated Observing and Assimilation Systems for Atmosphere, Oceans, and Land Surface, AMS 84th Annual Meeting, Washington State Convention and Trade Center, Seattle, Washington.

Behringer, D.W. 2007. The Global Ocean Data Assimilation System (GODAS) at NCEP. 11th Symposium on Integrated Observing and Assimilation Systems for the Atmosphere, Oceans, and Land Surface, AMS 87th Annual Meeting, San Antonia, TX.

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Detection of trends via statistical methods

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