Skip to main content

Who We Are

US CLIVAR is a national research program with a mission to foster understanding and prediction of climate variability and change on intraseasonal-to-centennial timescales, through observations and modeling with emphasis on the role of the ocean and its interaction with other elements of the Earth system, and to serve the climate community and society through the coordination and facilitation of research on outstanding climate questions.

Our Research

The ocean plays a key role in providing a major long-term "memory" for the climate system, generating or enhancing variability on a range of climatic timescales. Understanding the ocean's role in climate variability is therefore crucial for quantifying and harnessing the predictability inherent to the Earth system. US CLIVAR-led research has played a substantial role in advancing understanding of, and skill in predicting climate variability and change.

Science and Research Challenges

Cracked earth

Subseasonal-to-   
Seasonal Prediction

Forest

Decadal Variability   
and Predictability

Flooding in neighborhood

Climate Change

Tornado and lightning

Climate and Extreme       
Events

Ice in polar landscape

Polar Climate Changes

Fish swimming undersea

Climate and Marine       
Carbon/Biogeochemistry

Coast with cliffs and waves

Climate at the Coasts

Announcements

See all announcements

2026 Annual AMS meeting logo.

US CLIVAR Related Sessions at the 2026 Annual AMS Meeting

Abstract submission deadlines vary by session, with many having a deadline of August 14, 2025.

June Newsgram is Available

June Newsgram is Available

Stay informed with the latest news, research highlights, webinars, data sets, meetings, funding, career opportunities, and jobs for the climate science community.

A new observational benchmark for equatorial upwelling figure caption.

A new observational benchmark for equatorial upwelling

Published in Journal of Climate, Karnauskas (2025) re-examined a collection of historical estimates made since 1961, as well as drifters and other modern observations and state estimates, to provide new constraints on this key variable.

Figure caption: Illustration of the neural network architecture, including SST inputs (left) and the region over which a conditional Gaussian distribution of 500 mb heights is predicted (right).

Detection of state-dependent prediction skill using an adaptable, machine learning-based approach

Kyle Shackleford (Colorado State University) presents a new framework for identifying state-dependent prediction skill in this new Research Highlights..

Upcoming Webinars

There are no upcoming Webinars at this time.

US CLIVAR Climate Variability and Predictability Program