The document discusses the importance of accurate predictions for weather and climate variability using global earth system models, highlighting their societal relevance across various time scales. While existing prediction efforts show promise, challenges include observational data limitations, model fidelity, and resource requirements. To enhance predictions, it is essential to prioritize improvements in modeling and observational data collection.

1. Computer Modeling Capabilities and
Challenges for Earth System Predictions
Hosted by the University Corporation for Atmospheric Research • April 24, 2018
Gokhan Danabasoglu, NCAR
Senior Scientist and CESM Chief Scientist

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Outline
• Importance of predictions
• Role of Earth System Models
• Examples of promising predictions
• Challenges and summary

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Predictions: Societal Relevance
• Days to a week
• Weather forecasts
• Subseasonal
• Natural hazards preparedness
• Seasonal-to-interannual
• Shifts in likelihood of weather regimes
• Resource management (e.g., water, fire, agriculture)
• Transportation changes (e.g., Arctic shipping routes)
• Decadal
• Long-term resource management (e.g., water, fire, agriculture,
forests)
• Infrastructure investment
• Natural hazard mitigation and adaptation
• Demographic and socioeconomic strains
• (Re)-insurance

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Building Blocks of Predictions
Computer Models
Advanced simulations of the
Earth System run on
supercomputers
Observations
Satellite, buoys, hydrographic,
etc.
Initial Conditions
Advanced techniques to
incorporate current weather /
climate state into the models,
i.e., a data assimilation system

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The primary tools to make
future predictions of weather
and climate variability
Global Earth System Models
The models use physical
equations to simulate key
fields in the atmosphere,
ocean, land, and sea-ice,
such as temperature and
humidity.

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Global Earth System Models
Community Earth System Model

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Yeager et al. (2018, BAMS)
Kirtman et al. (2014, BAMS)
Subseasonal-Seasonal
Predictions
Decadal Predictions
Coupled Model Inter-
Comparison Project (CMIP)
Decadal Climate Prediction
Project (DCPP)
Community Climate Prediction Efforts

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Surface Air Temperature Predictions over North
America with CESM on Monthly Time Scales
01 January starts; verifying January-means for 1982-2010 Courtesy of Kirtman
Old version New Version
Skill Score
betterworse
Better models and better (ocean) initial conditions lead
to better surface temperature predictions over land

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CESM prediction of ice loss
Satellite observations
Prediction of 10-year Trends in Arctic
Winter Sea Ice with CESM
Yeager et al. (2015, GRL)
observations; predictions; hindcast
1997-2006 period

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• Observational data limitations
Observations for initializing ocean, land surface, sea
ice are limited
• Model quality / fidelity
Spatial resolution; representation of processes
• Forecasting method limitations
• Resource requirements
People, computational, and storage
Challenges for Predictions

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Summary
• Global Earth System Models are the primary tools for
future predictions of weather and climate variability
• Existing prediction efforts show promising results that are
beneficial to the society on multiple time scales
• To improve predictions:
• Better models and better / more observations are
needed to produce better initialization of predictions;
• It is crucial to sustain efforts in both observations and
model developments