Whole Atmosphere Community Climate Model (WACCM)

Because observations and model predictions both have errors, it is important to quantify this uncertainty when making forecasts. DART's ensemble data assimilation methods provide modelers with sets of equally likely initial conditions for producing an ensemble of forecasts. Differences between these forecasts give information that can help scientists identify the most important shortcomings in individual models. DART ensemble assimilation tools can also help modelers `tune' parameters in their models so model forecasts better fit observations.

A recent example of how DART benefits model development appears in the figure. Nick Pedatella (HAO) applied DART to the Whole Atmosphere Community Climate Model (WACCM) and used DART to quickly improve the model's ability to produce short-term forecasts. DART helped him analyze the model's outputs to identify the types of issues that could be involved. The model developers then addressed those issues in the model's code and significantly improved its predictions. This work is valuable to three NCAR labs: CGD developed the WACCM dynamics, HAO provided code development for WACCM to simulate space weather, and ACOM uses WACCM for high-altitude atmospheric chemistry. The figure compares model short-term forecasts generated with DART (top) to another method of making the model close to observations (specified dynamics WACCM, middle) and the available observations (bottom).

Data assimilation - with its comparison of model outputs to observations - is a key technology for modeling because it is the best method we have to evaluate and validate prediction models. We have only recently developed the capability to perform data assimilation on climate models. Historically, it has been too difficult and expensive for modeling groups to do that, but as computers have become more capable and software such as DART has been developed, validation of model predictions is now practical. The ongoing development and improvement of models requires scientific assurance that a model is producing accurate forecasts for the right reasons. Data assimilation helps developers progress from a model that produces something that isn't right, to understanding why it isn't right, to code changes that produce consistently accurate results. Finally, data assimilation adds meaning to those results by quantifying the uncertainty in model predictions.

People involved in this project are: N. Pedatella, H. Liu, J. Liu, and K. Raeder.