Computational Earth Sciences

The Computational Earth Sciences group advances and communicates the predictive understanding of Earth Systems by developing and applying models, quantitative methods, and computational tools that exploit high performance computing resources.

Group Projects

Accelerated Climate Modeling for Energy - ACME

Oak Ridge National Laboratory (ORNL) is one of eight Department of Energy (DOE) laboratories that will use high-performance computing (HPC) to develop the most sophisticated Earth system model to date for climate change research with scientific and energy applications. The national labs are collaborating with the National Center for Atmospheric Research, four academic institutions, and one private-sector company on this long-term project, known as Accelerated Climate Modeling for Energy, or ACME. Many of the ORNL team members are also Climate Change Science Institute (CCSI) researchers.

DOE and ORNL have been major drivers of Earth system models in recent decades, and ACME will provide scientists with Earth system models that take advantage of upcoming milestones in computing capability. As new HPC architectures support computing power at hundreds of petaflops and then exaflops (a thousand petaflops), more expansive simulations will enable finer climate predictions.

And with more computing power comes significant expectations for scientific discovery.

"The model we are building will provide rapid science advances by including new processes such as phosphorous cycling in tropical ecosystems and by making it easier for domain science experts to participate in model development and application," said Peter Thornton, ORNL land modeler and ACME council member and Land Model Task Team leader. "Through innovative software architecture and intensive collaboration, we're building new science bridges to existing communities of observational and experimental expertise, which will challenge and ultimately improve our ability to predict future climate system dynamics."

Using DOE Office of Science Leadership Computing Facility resources, including ORNL's 27-petaflop Titan supercomputer, the team will set out on a 10-year plan to develop model codes that address key climate science questions, including those related to the water cycle, biogeochemistry, and cryosphere.

In the near term the team will simulate changes in river flow and other parts of the hydrologic cycle by modeling interactions between precipitation and the landscape within high-resolution, fully coupled atmosphere and land surface models. Over the next decade these models will help answer how the water cycle will evolve in a warmer climate and change land and water use.

Likewise, ACME models will explore fundamental questions about the impact of carbon, nitrogen, and phosphorus cycles on the climate system and then simulate changes in a warmer environment once new and developing models have been validated. Simulations of the cryosphere, or surface ice in the form of the continental Antarctic ice sheet, will also gain new depth and resolution, allowing researchers to study its stability in the climate system and the potential effects of sea level rise due to melting.

To contribute to this advanced model development, CCSI experts in terrestrial biogeochemistry and atmospheric chemistry will work with computational scientists to optimize workflow and engineer new software tools for calculating an increased number of scientific variables at higher resolutions on Titan, the Mira supercomputer at Argonne National Laboratory, and other DOE computing resources.

"Our goal is to build the best possible model to run on DOE computers, and with an optimized workflow and a strong software engineering infrastructure, we'll be able to broaden access for domain scientists to tackle detailed scientific problems," Thornton said.

CCSI and ORNL researchers involved in the program leadership are atmospheric scientist Kate Evans as Workflow Task Team colead, computer scientist Patrick Worley as Performance Task Team leader, and ORNL National Center for Computational Sciences Director James Hack as ex officio council member.

More information can be found in the Accelerated Climate Modeling for Energy: Project Strategy and Initial Implementation Plan - edited by Katie Elyce Jones



The Earth System Modeling (ESM) Program improves the Community Earth System Model’s (CESM’s) physical representations for clouds, aerosols, sea-ice, land-ice, ocean, land hydrology, land/ocean biogeochemistry and human activities, utilizing DOE computational expertise under the BER-ASCR (Office of Advanced Scientific Computing Research Scientific Discovery through Advanced Computing (SciDAC) program to optimize model performance on leadership computer systems and to construct variable and high resolution model versions for improved climate and process representation. Sophisticated frameworks to test, analyze, calibrate, visualize and validate model results are developed. The aim is to calibrate the model against measurements, including DOE atmospheric and terrestrial data, and to simulate climate over decadal to centennial time scales, providing the research that underpins the Regional and Global Modeling (RGCM) program. ESM in turn provides a climate modeling framework to guide research prioritization for the Atmospheric System Research (ASR) and Science Terrestrial Ecosystem (TES) programs.

The Program contributes to 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) through the CESMproject, the National Oceanic and Atmospheric Administration (NOAA), and the National Aeronautics and Space Administration (NASA).
Additional information is on the CESD climate modeling programs website.

Ultra High-resolution modeling (PI, Jim Hack)

The problem of predicting climate change and its consequences is motivated by the increasingly urgent need to adapt to near term trends in climate change and the potential changes in the frequency and intensity of extreme events. This project is developing the scientific framework to determine the benefit of employing very-high-resolution global models to investigate regional-scale phenomena. The team will test the hypothesis that higher resolution models are necessary to accomplish the related objectives of 1) the explicit simulation of non-linear phenomena and interactions on the small scale that have feedbacks on large scale climate features; and 2) the accurate and explicit simulation of local to regional scale phenomena, including low-probability, high-impact hydrological events. The focus will be sarigorous evaluation of our hypothesis with high-resolution simulations of observed climate and variability will be the focus. Using a series of stand-alone component and ensemble coupled present-day climate simulations, the project partners will perform comparisons of high- and low-resolution model configurations to determine the potential advantages of high resolution simulations. They will investigate the role of air-sea interaction, the quality of the basic state, and resolution sensitivity on a hierarchy of modes of variability, including the distribution and evolution of extreme events in control, historical, and time-slice experiments of future climate change. An integral component of this project will be the development of new objective diagnostics and metrics to gauge the potential benefit of employing high resolution to improve the representation of regional scale phenomena, especially those related to the hydrological cycle.

The pathway to the project's goals requires an improved understanding of critical sensitivities in model formulation that have been identified from preliminary very-high resolution coupled simulations. Our experimental protocol will enable the project team to investigate these sensitivities, which include the interaction of physics with the choice of dynamical core of the atmospheric model and the initialization of the ocean model, both of which contribute to the development of model biases relative to observations. Implicit on this pathway toward developing a more realistic very-high resolution coupled model for regional projections of climate change will be the exploration of the benefit of very-high resolution contributed by the atmospheric and ocean models, respectively.

Regional Modeling Frameworks (site PI, Moet Ashfaq)

Predicting the regional hydrologic cycle at time scales from seasons to centuries is one of the most challenging goals of climate modeling. Because hydrologic cycle processes are inherently multi-scale, increasing model resolution to more explicitly represent finer scale processes may be a key to improving simulations of the hydrologic cycle. The overall objective of the proposed research is to develop frameworks for robust modeling of regional climate and hydrologic cycle, and to improve understanding of factors contributing to uncertainties in simulating future changes in the regional hydrologic cycle. We propose a hierarchical approach to test the veracity of global high resolution, global variable resolution, and nested regional climate model for regional climate modeling. We hypothesize that hierarchical evaluation of different modeling approaches will lead to better understanding of their relative merits and improve the frameworks for robust regional climate simulation. Our evaluation hierarchy has four stages: 1) Idealized, no physics test cases, 2) Idealized, full physics test cases, 3) Real world, atmosphere-only and ocean-only simulations, and 4) Real world, coupled atmosphere-ocean simulations for both current and future climate.

At each stage, four types of experiments will be performed:

The GS-HR and GS-LR simulations will be performed using CCSM with three different dynamical cores—Spectral, HOMME, and Model for Prediction Across Scales (MPAS). The latter can be configured for GS-HR, GS-VR, and RS-HR so all three modeling approaches can be compared within a single framework. The RS-HR simulations will be performed using MPAS, WRF, and RegCM. For GS-VR and RS-HR, the high-resolution regions will be located in North and South America where the regional hydrologic cycle exemplifies scale interactions and atmosphere-land-ocean feedbacks that challenge regional climate modeling.

The global and regional simulations will be compared and evaluated to assess (a) the impacts of different dynamical cores for global high-resolution simulations, (b) multiple techniques for mesh refinement, (c) the upscaled impacts of the high resolution region, and (d) the overall value of regional climate simulation. We will also apply regional and global diagnostics, evaluation metrics, and process-based analysis to the simulations to determine (1) whether modeling frameworks that allow scale interactions through global high resolution or variable resolution may be more skillful in simulating the regional hydrologic cycle in climate regimes dominated by convection; (2) whether models that couple atmosphere and ocean at the regional scale are more skillful in simulating regional climate variability in the west coast of North and South America; and (3) whether differences in simulating feedbacks by different modeling approaches may be modulated by surface heterogeneities to amplify differences in simulating regional hydrologic cycle changes in the future climate.


The BER SciDAC Partnerships efforts focus on two main research thrusts: climate and environmental sciences and biological systems. Partnerships in climate and environmental sciences aim to advance the simulation and predictive capabilities of state-of-science climate modeling and provide improved models for better understanding the movement of subsurface contamination. Partnerships in biological systems seek to develop new methods for modeling complex biological systems, including molecular complexes, metabolic and signaling pathways, individual cells and, ultimately, interacting organisms and ecosystems.

Multiscale Project (Jim Hack, site PI)

The MULTISCALE project’s primary goal is to produce better models for these critical processes and constituents, from ocean-eddy and cloud-system to global scales, through improved physical and computational implementations. An integrated team of climate and computational scientists will accelerate the development and integration of multiscale atmospheric and oceanic parameterizations into the DOE-NSF Community Earth System Model (CESM). The team’s technical objective is to introduce accurate and computationally efficient treatments of interactive clouds, convection, and eddies into the next generation of CESM at resolutions approaching the characteristic scales of these structures. The project delivers treatments of these processes and constituents, which are scientifically useful over resolutions ranging from 2 to 1/16 degrees.

The MULTISCALE team will develop, validate, and apply multiscale models of the climate system based upon atmospheric and oceanic components with variable resolution. The project will exploit new variable-resolution unstructured grids based on finite-element and finite-volume formulations developed by team members. Effective deployment of these dynamical cores will require significant and concurrent advances in time-stepping methods, grid generation, and automated optimization methods for next-generation computer architectures.

Predicting Ice Sheet and Climate Evolution at Extreme Scales (PISCEES)(Kate Evans, site PI)

PISCEES aims to enable quantitative predictions of coupled ice-sheet/climate evolution using a new generation of high-performance computers and computational tools.

ACES2BCG(Forrest Hoffman, PI)

The ACES4BGC Project seeks to advance the predictive capabilities of Earth System Models (ESMs) by reducing two of the largest sources of uncertainty, aerosols and biospheric feedbacks, with a highly efficient computational approach. In particular, this project will implement and optimize new computationally efficient tracer advection algorithms for large numbers of tracer species; add important biogeochemical interactions between the atmosphere, land, and ocean models; and apply uncertainty quantification (UQ) techniques to constrain process parameters and evaluate uncertainties in feedbacks between biogeochemical cycles and the climate system. The resulting improvements to theCommunity Earth System Model (CESM)will deliver new scientific capabilities and significantly improve the representation of biogeochemical interactions at the canopy-to-atmosphere, rivers-to-coastal oceans, and open oceans-to-atmosphere interfaces. ACES4BGC partners modelers with decades of cumulative research experience and a team of computer and computational scientist building scalable solvers and tools, developing advanced UQ methods, and applying technologies for performance optimization through U.S. DOESciDAC Institutes. The project began April 15, 2012.

Research Sponsors

Selected Recent Publications from Group Members and affiliates:

Ashfaq, M., L. C. Bowling, K. Cherkauer, J. S. Pal, and N. S. Diffenbaugh (2010), Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: A case study of the United States, J. Geophys. Res., 115, D14116, doi:10.1029/2009JD012965.

Diffenbaugh N. S., M. A. White, M. Ashfaq, and G. V. Jones (2011), Climate adaptation wedges: a case study of premium wine in the western United States, Environ. Res. Lett., in press.

Evans, K.J., P. H. Lauritzen, S. K. Mishra, R. Neale, M. A. Taylor, J. J. Tribbia (2013). AMIP Simulation with the CAM4 Spectral Element Dynamical Core. J. Climate, in press. doi: 10.1175/JCLI-D-11-00448.1.

Evans, K. J. A. G. Salinger, P. H. Worley, S. Price, W. Lipscomb, J. Nichols, J. B. White III, M. Perego, J. Edwards, M. Vertenstein, J.-F. Lemieux (2012). A modern solver template to manage solution algorithms in the Community Earth System Model. Int. J. High Perf. Comp. App., 26: 54-62, doi:10.1177/1094342011435159.

Hargrove, W. W., J. P. Spruce, G. E. Gasser, and F. M. Hoffman (2009), Toward a national early warning system for forest disturbances using remotely sensed phenology, Photogramm. Eng. Rem. Sens., 75 (10), 1150-1156.

Kumar, J., R. T. Mills, F. M. Homan, and W. W. Hargrove (2011), Parallel k-means clustering for quantitative ecoregion delineation using large data sets, in Proceedings of the International Conference on Computational Science (ICCS 2011), Procedia Comput. Sci., vol. 4, edited by M. Sato, S. Matsuoka, P. M. Sloot, G. D. van Albada, and J. Dongarra, pp. 1602{1611, Elsevier, Amsterdam, doi:10.1016/j.procs.2011.04.173.

Mahajan, S. K. J. Evans, J. Truesdale, J. Hack, J.-F. Lamarque (2012). Inter-annual Tropospheric Aerosol Variability in Late Twentieth Century and its Impact on Tropical Atlantic and West African Climate by Direct and Semi-direct Effects. J. Climate, 25:8031-8056, doi:10.1175/JCLI-D-12-00029.1.



Group Leader: Kate Evans
Phone: 865-576-6517
Administrative Assistant: Teresa Hurt
Phone: 576-9448


Postdocs and Students

Extended Staff