6th International Workshop on Climate Informatics (CI 2016)

22.09 - 23.09.2016  
Boulder, USA

The 6th International Workshop on Climate Informatics (CI 2016) will be held 22-23 September 2016 in Boulder, Colorado, USA.


Mesa Lab
National Center for Atmospheric Research (NCAR)
Boulder, Colorado


Climate informatics broadly refers to any research combining climate science with approaches from statistics, machine learning and data mining. The Climate Informatics workshop series, now in its sixth year, seeks to bring together researchers from all of these areas.

Workshop aims

We aim to stimulate the discussion of new ideas, foster new collaborations, grow the climate informatics community, and thus accelerate discovery across disciplinary boundaries. Since earth system sciences can be brought to bear on the study of climate, the scope of the workshop also includes data science approaches to problems at the nexus of climate and the earth system sciences.

Workshop format

The format of the workshop seeks to overcome cross-disciplinary language barriers and to emphasize communication between participants by featuring tutorials, invited talks, panel discussions, posters and breakout sessions. The programs of previous workshops from 2011 to 2015 can be found here:

We invite all researchers interested in learning about critical issues and opportunities in the field of climate informatics to join us, whether established in the field or just starting out.

Short papers

We encourage submissions on topics anywhere at the interface of climate science and machine learning, statistics, data mining, or related fields. Reviews, position papers, and works in progress, are also encouraged. Topics include but are NOT limited to:

- Machine learning, statistics, or data mining, applied to climate science
- Management and processing of large climate datasets
- Long and short term climate prediction
- Ensemble characterization of climate model projections
- Paleoclimate reconstruction
- Uncertainty quantification
- Spatiotemporal methods applied to climate data
- Time series methods applied to climate data
- Methods for modeling, detecting and predicting climate extremes
- Climate change attribution
- Dependence and causality among climate variables
- Detection and characterization of climate teleconnections
- Data assimilation
- Climate model parameterizations
- Hybrid methods
- Other data science approaches at the nexus of climate and earth system sciences


Short paper submission deadline is Friday 15 July. Papers may be up to three pages long excluding references and up to one page of references (double column format) and submission will be through EasyChair. Latex and Word templates and detailed submission instructions are available on the web page:

Travel fellowships

We are excited to announce that travel fellowships for early-career scientists will again be available, thanks to NSF support. Details will be available on the website (address at top or bottom of this page).


We will have a data science "hackathon" on the afternoon of 21 September, to solve a challenging problem in climate informatics. Details will be available on the website (address at top or bottom of this page).

Important dates

Friday 15 July: Short paper deadline.

Wednesday 10 August: Author notification and travel fellowship notification.

Saturday 10 September: Final revisions to papers due and registration deadline.

Wednesday 21 September: Climate Informatics Hackathon at NCAR, in Boulder, CO.

22-23 September: Workshop takes place at NCAR, in Boulder, CO.

Keynote speakers

Sudipto Banerjee, University of California Los Angeles, USA
Yulia Gel, University of Texas at Dallas, USA
Doug Nychka, National Center for Atmospheric Research, USA
Pradeep Ravikumar, University of Texas at Austin, USA
Jason Smerdon, Columbia University, USA

Background information

We have greatly increased the volume and diversity of climate data from satellites, environmental sensors and climate models in order to improve our understanding of the climate system.  However, this very increase in volume and diversity can make the use of traditional analysis tools impractical and necessitate the need to carry out knowledge discovery from data. Machine learning has made significant impacts in fields ranging from web search to bioinformatics, and the impact of machine learning on climate science could be as profound. However, because the goal of machine learning in climate science is to improve our understanding of the climate system, it is necessary to employ techniques that go beyond simply taking advantage of co-occurence, and, instead, enable increased understanding.

The Climate Informatics workshop series seeks to build collaborative relationships between researchers from statistics, machine learning and data mining and researchers in climate science.  Because climate models and observed datasets are increasing in complexity and volume, and because the nature of our changing climate is an urgent area of discovery, there are many opportunities for such partnerships.

Organizing Committee

Workshop Co-Chairs:
Arindam Banerjee, University of Minnesota
Jennifer Dy, Northeastern University

Program Committee Co-Chairs:
Slava Lyubchich, University of Maryland Center for Environmental Science (UMCES)
Andrew Rhines, Harvard University

Publicity and Publications Chair:
Wei Ding, University of Massachusetts Boston

Steering Committee:
Imme Ebert-Uphoff, Colorado State University

Claire Monteleoni, George Washington University

Doug Nychka, National Center for Atmospheric Research

Local Administrative Support:
Kathy Peczkowicz, NCAR
Cecilia Banner, NCAR

Further information

Go to the website:

Twitter: @Climformatics

To learn more about Climate Informatics, go to: