Revolutionizing Climate Modeling with Project Athena: A Multi-Institutional, International Collaboration

The importance of using dedicated high-end computing resources to enable high spatial resolution in global climate models and advance knowledge of the climate system has been evaluated in an international collaboration called Project Athena. Inspired by the World Modeling Summit of 2008 and made possible by the availability of dedicated high-end computing resources provided by the National Science Foundation from October 2009 through March 2010, Project Athena demonstrated the sensitivity of climate simulations to spatial resolution and to the representation of subgrid-scale processes with horizontal resolutions up to 10 times higher than contemporary climate models. While many aspects of the mean climate were found to be reassuringly similar, beyond a suggested minimum resolution, the magnitudes and structure of regional effects can differ substantially. Project Athena served as a pilot project to demonstrate that an effective international collaboration can be formed to efficiently exploit dedicated sup...

[1]  Antonio J. Busalacchi,et al.  An Earth-system prediction initiative for the twenty-first century , 2010 .

[2]  Hiroaki Miura,et al.  A Madden-Julian Oscillation Event Realistically Simulated by a Global Cloud-Resolving Model , 2007, Science.

[3]  David A. Randall,et al.  An ocean‐atmosphere climate simulation with an embedded cloud resolving model , 2010 .

[4]  Nils Wedi,et al.  High-Resolution Global Climate Simulations with the ECMWF Model in Project Athena: Experimental Design, Model Climate, and Seasonal Forecast Skill , 2012 .

[5]  Masaki Satoh,et al.  Ensemble Simulation of Cyclone Nargis by a Global Cloud-System-Resolving Model—Modulation of Cyclogenesis by the Madden-Julian Oscillation , 2010 .

[6]  D. Randall,et al.  Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States , 2004 .

[7]  Tomoe Nasuno,et al.  The Intra-Seasonal Oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models , 2012, Climate Dynamics.

[8]  Harry H. Hendon,et al.  Prediction of the Madden–Julian oscillation with the POAMA dynamical prediction system , 2011 .

[9]  Franco Molteni,et al.  On the operational predictability of blocking , 1990 .

[10]  Nils Wedi,et al.  Evidence for Enhanced Land–Atmosphere Feedback in a Warming Climate , 2012 .

[11]  A. Dai.  Precipitation Characteristics in Eighteen Coupled Climate Models , 2006 .

[12]  Nils Wedi,et al.  Simulating the diurnal cycle of rainfall in global climate models: resolution versus parameterization , 2012, Climate Dynamics.

[13]  Joseph Tribbia,et al.  Crucial Experiments in Climate Science , 2010 .

[14]  I. Orlanski,et al.  The Rationale for Why Climate Models Should Adequately Resolve the Mesoscale , 2008 .

[15]  Madhuri S. Mulekar,et al.  A 15-Year Climatology of North Atlantic Tropical Cyclones. Part I: Size Parameters , 2004 .

[16]  Martin Köhler,et al.  Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time‐scales , 2008 .

[17]  Thomas M. Smith,et al.  Daily High-Resolution-Blended Analyses for Sea Surface Temperature , 2007 .

[18]  Kevin Hamilton,et al.  Numerical Resolution and Modeling of the Global Atmospheric Circulation: A Review of Our Current Understanding and Outstanding Issues , 2008 .

[19]  Thomas Jung,et al.  Sensitivity of extratropical cyclone characteristics to horizontal resolution in the ECMWF model , 2006 .

[20]  W. Winiwarter,et al.  Summary for policy makers , 2011 .

[21]  Shian-Jiann Lin,et al.  Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. , 2009 .

[22]  Renate Hagedorn,et al.  Strategies: Revolution in Climate Prediction is Both Necessary and Possible: A Declaration at the World Modelling Summit for Climate Prediction , 2009 .

[23]  Philip J. Rasch,et al.  Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals , 2006 .

[24]  M. Bauer,et al.  Composite Analysis of Winter Cyclones in a GCM: Influence on Climatological Humidity , 2006 .

[25]  E. Zipser,et al.  The Diurnal Cycle of Rainfall and Convective Intensity according to Three Years of TRMM Measurements , 2003 .

[26]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[27]  Bernard A. Chouet,et al.  Identifying bubble collapse in a hydrothermal system using hidden Markov models , 2012 .

[28]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[29]  Akira Noda,et al.  20-km-Mesh Global Climate Simulations Using JMA-GSM Model —Mean Climate States— , 2006 .

[30]  Masaki Satoh,et al.  Nonhydrostatic icosahedral atmospheric model (NICAM) for global cloud resolving simulations , 2008, J. Comput. Phys..

[31]  David L. Williamson,et al.  Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction , 2004 .

[32]  Renate Hagedorn,et al.  Toward a new generation of world climate research and computing facilities , 2010 .

[33]  Chun‐Chieh Wu,et al.  Current understanding of tropical cyclone structure and intensity changes – a review , 2004 .

[34]  Thomas Jung,et al.  Greenland’s Pressure Drag and the Atlantic Storm Track , 2007 .

[35]  Shaocheng Xie,et al.  Climate Model Forecast Experiments for TOGA COARE , 2008 .

[36]  A. Matthews,et al.  Intraseasonal oscillations in 15 atmospheric general circulation models: results from an AMIP diagnostic subproject , 1996 .

[37]  Bin Wang,et al.  Asian summer monsoon simulated by a global cloud‐system‐resolving model: Diurnal to intra‐seasonal variability , 2009 .

[38]  Nils Wedi,et al.  Tropical Cyclone Climatology in a 10-km Global Atmospheric GCM: Toward Weather-Resolving Climate Modeling , 2012 .

[39]  W. Collins,et al.  The Community Climate System Model Version 3 (CCSM3) , 2006 .

[40]  Masayuki Nakagawa,et al.  A Framework for Assessing Operational Madden–Julian Oscillation Forecasts: A CLIVAR MJO Working Group Project , 2010 .

[41]  Masahiro Sugiyama,et al.  A Cumulus Parameterization with State-Dependent Entrainment Rate. Part I: Description and Sensitivity to Temperature and Humidity Profiles , 2010 .

[42]  M. Rodwell,et al.  Toward Seamless Prediction: Calibration of Climate Change Projections Using Seasonal Forecasts , 2008 .

[43]  Andrew Dawson,et al.  Simulating regime structures in weather and climate prediction models , 2012 .