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Earth’s climate system involves multiple physical processes acting over a wide range of scales, and much of the physics of processes that influence climate and climate change occurs on scales smaller than typical global climate model (GCM) grid sizes. Differences in representation of the fast physics -- mainly cloud and precipitation processes -- are largely responsible for the spread in climate sensitivity and aerosol indirect effects predicted by current climate models. Improving parameterizations of these fast processes is thus essential to reducing uncertainty in climate sensitivity and to increasing confidence in the capability of projecting future climate.

Despite tremendous effort over the past decades progress has been frustratingly slow. The BNL-led multi-institutional Fast-physics System Testbed and Research, or FASTER project, aims to enhance and accelerate evaluation and improvement of fast physics in GCMs by integrating a spectrum of expertise ranging from observations to theoretical development to modeling, and by capitalizing on the high resolution data collected by the DOE ARM Climate Research Facility.  Supported by the DOE Climate Earth System (ESM) Modeling program, the FASTER project consists of six objectives.


  1. Construction of a Fast-Physics Testbed to rapidly evaluate fast physics in GCMs by comparing model results against continuous long-term cloud observations made by the ARM Climate Research Facility.
  2. Execution of a suite of CRM simulations for selected periods/cases to augment the Fast-Physics Testbed. We will run WRFs with different parameterizations as CRMs, CRMs with bin-microphysics, and multi-scale modeling framework.
  3. Continuous evaluation of model performance to identify and determine model errors by comparing the NWP and SCM results against continuous ARM observations, and to each other. The long-time data record at the ARM sites (e.g., SGP) permits evaluation of various statistical properties (e.g., PDFs) and recurring cloud regimes.
  4. Examination and improvement of parameterizations of key cloud processes/properties (e.g., convection, microphysics and aerosol-cloud interactions), thus narrowing the range of treatments of fast processes that exert strong influences on model sensitivity so as to better constrain climate sensitivity.
  5. Assessment and development of metrics of model performance. Different metrics will be applied and tested in the evaluation, and new metrics will be explored. Special care will be taken to address the issue of scale-mismatch between observations and models.
  6. Incorporation of newly acquired knowledge on parameterizations into the full participating GCMs to evaluate the impact of the refined parameterizations on GCM and ascertain the improvement in the representation of fast physics in the GCMs.