EAGER: An International, Dedicated High-End Collaborative Project to Revolutionize Climate Modeling

Institute Of Global Environment And Society, Rockville MD

Investigators

Abstract

Atmospheric convection must be parameterized in present day global climate models because available dedicated computational resources have not been sufficient to run the models at fine enough resolution to simulate convection explicitly. This project will explore whether dedicated high-end computing support for ultra high resolution experiments can truly accelerate progress in the area of climate modeling. The approach will be three-pronged: 1. State-of-the-art high-resolution numerical weather prediction models will be run on multi-year timescales to assess the impact of high resolution on systematic error. Specifically, global numerical weather prediction models will be run at 10-15 km resolution with 20th century forcings and analysis will be performed on the improved ability to simulate the statistics of weather, including severe weather, with high resolution. As well as being relevant for the climate change problem, such integrations will be of importance to numerical weather prediction centers in guiding their future operational strategies on the need for high resolution models in monthly and seasonal forecast mode. 2. Time-slice climate change simulations will be made using available climate models run at strikingly higher resolution than is typically used. Dedicated computational resources will be used to run state-of-the-art global models at 10-15 km resolution to produce simulations for the 20th and the 21st centuries. Analysis of these runs will be done, focusing on the impact of greenhouse gases on changes in the statistics of weather, extreme events and the hydrologic cycle, in comparison with identical runs made at 100-150 km resolution, downscaled to regional scales. 3. Convection-permitting atmospheric models capable of resolving cloud systems in the atmosphere (4-8 km grids) and energetic eddies in the ocean (10 km grids) will be used to evaluate the impact of resolving these processes on simulation of seasonal climate. In particular, global cloud-system-resolving atmospheric models will be used in two-tier, regionally-coupled mode to produce a series of hindcasts to be compared with hindcasts made using conventional resolution models. The predicted surface fluxes of heat, momentum and fresh water will be used to drive an eddy-resolving ocean model, to determine if the simulated ocean climate is significantly different from that simulated using coarser resolution fluxes. An international team of experts has formed to contribute models, advice on experimental design, and effort to run and evaluate the model simulations. The research has strong potential to be transformative and influence the future directions of computational geo-fluid-dynamics. The research is exploratory, the work is in its early stages and untested on the time and spatial scales consistent with climate process physics and dynamics. It is therefore, high-risk-high payoff research. Broader Impact: The international weather and climate modeling community came together in 2008 at the World Modeling Summit (WMS) to reach a consensus that the time is ripe to revolutionize the application of numerical models for prediction of climate through the development of seamless prediction methodologies which unify the weather and climate forecast problems. The rationale includes: a. a recognition that the climate models of the current generation have reached a plateau in their ability to simulate salient features of Earth's climate, b. a societal demand for reducing the uncertainty in projections of climate change in the future, c. a complementary demand for greater spatial discrimination in the climate changes that may be anticipated in the next 30 years, especially concerning changes in extreme events, and d. a hypothesis that resolving important processes in the atmosphere and ocean and at the land surface, as well as interactions among them, as is already the case in weather prediction models, can dramatically improve the fidelity of the models.