Regional & Global Climate Modeling

The goal of the RGCM program is to advance the predictive understanding of Earth’s climate by focusing on scientific analysis of the dominant sets of governing processes that describe climate change on regional scales; evaluating robust methods to obtain higher spatial resolution for projections of climate and earth system change; and diagnosing model systems that are cause for uncertainty in regional climate projections. The program goal is accomplished through sensitivity studies and applications of regional and global earth system models that focus on various aspects of the climate system, including but not limited to, the understanding of feedbacks within the climate system, detection and attribution studies, developing capabilities for decadal predictability, and uncertainty characterization. RGCM investments are also dedicated to development of metrics for model validation, that in turn may be used to inform the model development strategies of Earth System Modeling (ESM), and to inform the process research priorities of the Terrestrial Ecosystem Sciences (TES) and the Atmospheric System Research (ASR) programs. RGCM also coordinates with the Integrated Assessment Research (IAR) program on understanding individual and select coupled systems, such as water resources, critical for the energy mission.

RGCM Priorities:

1. Development of robust analytical frameworks and model hierarchies to advance Earth system projections, predictions, and hindcasts, and to understand climate evolution at multiple scales. This priority also includes decadal predictions for specific regions, using high-resolution and variable scale climate modeling, and applying a combination of dynamical and statistical downscaling methodologies. Metrics are developed and assessed depending on measurement availability and quality, and depending on temporal and spatial scales.

2. Focused investigation of regions that are climatically sensitive or vital to climate assessments.

  • Arctic focus: Analyze the complex interactions between sea ice, ice sheets, cold oceans, regional climate, and permafrost stability in the context of both high-resolution regional and global models. This links closely with the vegetation and biogeochemical focus of the Next Generation Ecosystem Experiment (NGEE) Arctic and informs ESM model development.
  • Tropical focus: Includes an emphasis on understanding and identifying tropical biases, such as cloud-precipitation biases, in collaboration with ASR, and in the carbon cycle, in collaboration with TES and NGEE tropics.
  • Regional focus: Analysis of the integrated water cycle as climate changes will be done in collaboration with IAR.

3. The assessment and delineation of natural and forced climate variability. Understanding the relative importance of anthropogenic versus natural climate change, i.e., taking into account natural variability, requires a combination of modeling and observational research to extend this understanding. This also includes resolving different long- and short-term modes of climate variability (e.g., El Niño Southern Oscillation, Madden-Julian Oscillation) and describing how these change in a changing climate.

4. The analysis and understanding of climate extreme events, including floods and droughts, potential abrupt system changes, and tipping points, and how these are affected in a changing climate. Further emphasis is placed on multivariate and multi-stressor extremes, such as simultaneous combinations of hot, dry, and windy conditions and hot, moist, and stagnant conditions, and characterizing the number and amount of exceedences above given thresholds and quantifying  uncertainties. Climate system resilience, reversibility, and tipping points are investigated.

5. The characterization of climate feedbacks and their uncertainties to quantify the cloud-climate, carbon-cycle climate, high-latitude feedback processes and address the fidelity of the models that capture these processes at regional and global scales.

6. Model evaluation, analysis, uncertainty characterization, diagnostics, and visualization tools to improve and facilitate comparison among models and between models and measurements in order to challenge and inform model development. Metrics to evaluate components of the Earth system, such as the carbon cycle, ocean eddies, and cloud-aerosol interactions, represent a practical approach to help guide the planning process for  observational and process research.

7. Dissemination of data through the Earth System Grid Federation (ESGF). The ESGF is an interagency and international effort led by DOE and co-funded by national and international agencies for the management and dissemination of CMIP5 model output and observational data. Efforts will soon be placed on developing a roadmap to upgrade the ESGF to handle data emerging post-CMIP5.

Why the Program's Research is Important

Achieving greater detail about uncertainty and future variability of the earth climate system is critical for decision makers. There is a need to ascertain shifts in major modes of climate variability and climate extremes, to detect and attribute regional manifestations of climate change. This program also provides support for national and international climate modeling research and assessments. An understanding of the model biases seamlessly feeds back to the model development needs of the Earth System Modeling program, the process research needs of the Atmospheric System Research and Terrestrial Ecosystem Science programs.

RGCM also contributes to elements of the Interagency Group on Integrated Modeling (IGIM) of the U.S. Global Change Research Program (USGCRP), and coordinates its activities with the climate modeling programs at other federal agencies, particularly the National Science Foundation (NSF), the National Oceanic and Atmospheric Administration (NOAA), and the National Aeronautics and Space Administration (NASA).

Solicitations

Funding opportunity announcements are posted on the DOE Office of Science Grants and Contracts Website and at grants.gov. Information about preparing and submitting applications, as well as the DOE Office of Science merit review process, is at the DOE Office of Science Grants and Contracts Web Site.

Data Sharing Policy

Funding of projects by the program is contingent on adherence to the BER data sharing policy.

Recent Content

Recent Highlights

With an eye towards future higher resolution RASM simulations we evaluated extreme surface wind statistics in three reanalyses (ERA-I, CFSR, and CORE2) and two regional climate simulations (WRF50 and WRF10). This analysis compares wind speed probability distribution function, 99th percentile...
Identify wind regimes over the ocean adjacent to southeastern Greenland in 10 years of ERA-Interim and WRF data and assess the surface turbulent fluxes associated with these wind regimes.
Use the self organizing map (SOM) artificial neural network clustering algorithm to identify the southeastern Greenland oceanic wind regimes in regional climate model (WRF) and reanalysis (ERA-Interim) data. Assess if these patterns are correlated with the North Atlantic Oscillation and compare the...
This research uses the Regional Arctic System Model (RASM) to analyze the impact of the strong mesoscale wind events along the southeastern coast of Greenland on ocean heat loss, buoyancy flux, and mixed layer depth change. This is the first high-resolution, fully coupled, realistically forced...
This manuscript describes the initial version of the Regional Arctic System Model (RASM) and assesses the near surface climate in this new model.

Publications

Strong, mesoscale tip jets and barrier winds that occur over the ocean near southern Greenland have the potential for impacting deep convection in the ocean. The self-organizing map (SOM) training algorithm was used to identify and classify the range of 10 m wind patterns present during ten winters...
Strong, mesoscale tip jets and barrier winds that occur along the southeastern Greenland coast have the potential to impact deep convection in the Irminger Sea. The self-organizing map (SOM) training algorithm was used to identify 12 wind patterns that represent the range of winter [November–March...
The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near- surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is...
We implement a variance-based distance metric (Dn) to objectively assess skill of sea ice models when multiple output variables or uncertainties in both model predictions and observations need to be considered. The metric compares observations and model data pairs on common spatial and temporal...