Revolutionizing Climate Modeling – Project Athena: a multi-Institutional, International Collaboration
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Parry, M.L., O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson (eds), 2007: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.Phillips, Thomas J., and Coauthors, 2004: Evaluating Parameterizations in General Circulation Models: Climate Simulation Meets Weather Prediction. Bull. Amer. Meteor. Soc., 85, 1903–1915. Pickart, R. S., F. Straneo, and G. W. K. Moore, 2003: Is Labrador Sea Water Formed in the Irminger Basin? Deep-Sea Res., 50A, 23–52. Rashid, H.A., H. H. Hendon, M. C. Wheeler, and O. Alves, 2011: Prediction of the Madden-Julian oscillation with the POAMA dynamical prediction system. Climate Dyn., 36, 649-661, doi:10.1007/s00382-010-0754-x. Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey and M. G. Schlax, 2007: Daily High-resolution Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496. Satoh, M., T. Matsuno, H. Tomita, H. Miura, T. Nasuno, S. Iga, 2008: Nonhydrostatic Icosahedral Atmospheric Model (NICAM) for Global Cloud Resolving Simulations. J. Comp. Phys., 227, 3486-3514, doi:10.1016/j.jcp.2007.02.006. Satoh, M., Oouchi, K., Nasuno, T., Taniguchi, H., Yamada, Y., Tomita, H., Kodama, C., Kinter III, J., Achuthavarier, D. Manganello, J, Cash, B., Jung, T., Palmer, T. and Wedi, N., 2011: Intra-Seasonal Oscillation and its control of tropical cyclones simulated by high-resolution global atmospheric models. Climate Dyn., doi10.1007/s00382-011-1235-6. Satoh, M., K. Oouchi, T. Nasuno, H. Taniguchi, Y. Yamada, H. Tomita, C. Kodama, J. L. Kinter III, 2011: Intra-seasonal oscillation and its control of tropical cyclones simulated by high resolution global atmospheric model. (in preparation). Shapiro, M., and Coauthors, 2010: An Earth-System Prediction Initiative for the Twenty-First Century. Bull. Amer. Meteor. Soc., 91, 1377–1388. doi: 10.1175/2010BAMS2944.1 Shukla, J., R. Hagedorn, H. B. J., J. Kinter, J. Marotzke, M. Miller, T. Palmer, and S. J., 2009: Strategies: Revolution in Climate Prediction is Both Necessary and Possible: A Declaration at the World Modeling Summit for Climate Prediction. Bull. Amer. Meteor. Soc., 90, 16–19. Shukla, J., T. N. Palmer, R. Hagedorn, B. Hoskins, J. Kinter, J. Marotzke, M. Miller, and J. Slingo, 2010: Toward a New Generation of World Climate Research and Computing Facilities. Bull. Amer. Meteor. Soc., 91, 1407-1412. Slingo, J. M., K. R. Sperber, J. S. Boyle, J.-P. Ceron, M. Dix, B. Dugas, W. Ebisuzaki, J. Fyfe, D. Gregory and J.-F. Gueremy, et al., 1996: Intraseasonal oscillation in 15 atmospheric general circulation models: Results from an AMIP diagnostic subproject. Clim. Dyn., 12, 325– 357, doi:10.1007/BF00231106. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Avery, M. Tignor and H.L. Miller (eds.), 2007: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA). Stan, C., M. Khairoutdinov, C. A. DeMott, V. Krishnamurthy, D. M. Straus, D. A. Randall, J. L. Kinter III, and J. Shukla, 2010: An ocean-atmosphere climate simulation with an embedded cloud resolving model. Geophys. Res. Lett., 37, L01702. doi:101029/2009GL040822. Taniguchi, H., W. Yanase, M. Satoh, 2010: Ensemble simulation of cyclone Nargis by a Global Cloud-system-resolving Model - modulation of cyclogenesis by the Madden-Julian Oscillation. J. Meteor. Soc. Japan, 88, 571-591. Tibaldi, S. and F. Molteni, 1990: On the Operational Predictability of Blocking. Tellus, 42A, 343–365. Wang, Y., and C.-C. Wu, 2004: Current Understanding of Tropical Cyclone Structure and Intensity Changes - A Review. Meteor. Atmos. Phys., 87 (4), 257–278. Zhao, M., I. M. Held, S-J. Lin, and G. A. Vecchi, 2009: Simulations of Global Hurricane Climatology, Interannual Variability, and Response to Global Warming Using a 50km Resolution GCM. J. Climate, 33, 6653-6678. Table 1: Project Athena experiments 
Figure Captions Figure 1 – Change in April through October mean precipitation from simulations of the 1961-2007 period to the 2071-2117 period over western Eurasia, as a percentage of the 1961-2007 mean precipitation. Observed sea surface temperature (SST) and sea ice was specified for the 1961-2007 simulations. An estimate of the late 21st century SST and sea ice, computed by adding the change in mean annual cycle averaged over multiple CMIP3 simulations to that specified for the 1961-2007 simulation, was used in the 2071-2117 simulations. Upper panel: 125-km resolution simulations. Lower panel: 16-km resolution simulations. Figure 2 – Phase of the diurnal cycle of precipitation in observations and models. The upper panel shows the June-July-August mean hour of maximum rainfall estimated from Tropical Rainfall Measurement Mission (TRMM) data over 1998-2009. The colors correspond to local time on the 24-hour clock shown as an inset at upper left. The lower panels show the hour of maximum rainfall for transects along lines A-B (lower left panel) and C-D (lower right panel) Two transects are shown (A-B) and (C-D) for TRMM (green circles), NICAM (red squares), IFS 125-km simulation (blue dashes), and IFS 10-km simulation (cyan triangles). Data from all sources were first interpolated to the NICAM grid for ease of comparison. Values over ocean points where the amplitude of the diurnal cycle is less than half the seasonal mean (JJA) rainfall and values over land points where the rainfall rate is less than 0.2 mm d-1, are not shown. Figure 3a – A snapshot at 05 UTC on 23 May 2009 from the NICAM model simulation of 21 May – 31 August 2009, with cloudiness (based on the simulated outgoing longwave radiation) in shades of gray and precipitation rate in colors. The cloudiness is shaded in brighter gray for thicker clouds, and the colors range from shades of green, indicating precipitation rates less than 1 mm d-1, to yellow and orange (1-16 mm d-1), to red (16-64 mm d-1) and magenta (> 64 mm d-1). Figure 3b - Distribution of maximum attained 10-m wind speed (upper panel) and minimum SLP (lower panel) in the North Atlantic hurricanes from the IBTrACS data (black bars), IFS 10-km simulation (red bars) and IFS 39-km simulation (green bars) for the May to November seasons of 1990-2008. The inset plots show the tails of the distributions. Figure 3c – Distribution of minimum attained SLP over the entire globe (all basins) from the IBTrACS data (black bars), IFS 10-km simulation (red bars) and NICAM simulation (blue bars) for June – August of 2001-02, 2004-09. The inset plots show the tails of the distributions. Figure 3d - Distributions of 10-m tangential wind (left panels; m s-1) and total column liquid water and ice (TCLWI; right panels; kg m-2) for the most intense TCs at the peak of their intensity from the NICAM simulation (panels a and d, labeled “NICAM”), the IFS 10-km simulation (panels b and e, labeled “T2047”), the IFS 39-km simulation (panels c and f, labeled “T159”), respectively. Radius is 2°. Contour interval is 3 m s-1 for wind Dashed black contours in panels d, e and f show the radius of maximum winds for each case with respect to the center of the storm determined from the location of maximum vorticity at 925 hPa (1000 hPa for the IFS cases). Figure 4 – Time-latitude sections of daily precipitation anomalies averaged over 60°E-90°E for NICAM 7km (middle) and IFS 10km (bottom) 103-day long hindcasts for the period 22 May - 30 Aug 2006 and for the corresponding observations from TRMM (top). Annual cycle for the period 2001-2009 (2003 omitted) is removed from the daily mean to obtain the daily anomaly. Figure 5 – Frequency of occurrence (in %) of days at which the winter (December - March) Northern Hemisphere extratropical flow is blocked: Reanalysis (black with 95% confidence level shaded in gray), 125-km simulation (blue curve labeled T159), and 16-km simulation (red curve labeled T1279) for the AMIP experiment over the period 1960 - 2007. The dots indicate significant differences of model simulated values from reanalysis. 
Figure 1 – Change in April through October mean precipitation from simulations of the 1961-2007 period to the 2071-2117 period over western Eurasia, as a percentage of the 1961-2007 mean precipitation. Observed sea surface temperature (SST) and sea ice was specified for the 1961-2007 simulations. An estimate of the late 21st century SST and sea ice, computed by adding the change in mean annual cycle averaged over multiple CMIP3 simulations to that specified for the 1961-2007 simulation, was used in the 2071-2117 simulations. Upper panel: 125-km resolution simulations. Lower panel: 16-km resolution simulations. 
Figure 2 – Phase of the diurnal cycle of precipitation in observations and models. The upper panel shows the June-July-August mean hour of maximum rainfall estimated from Tropical Rainfall Measurement Mission (TRMM) data over 1998-2009. The colors correspond to local time on the 24-hour clock shown as an inset at upper left. The lower panels show the hour of maximum rainfall for transects along lines A-B (lower left panel) and C-D (lower right panel) Two transects are shown (A-B) and (C-D) for TRMM (green circles), NICAM (red squares), IFS 125-km simulation (blue dashes), and IFS 10-km simulation (cyan triangles). Data from all sources were first interpolated to the NICAM grid for ease of comparison. Values over ocean points where the amplitude of the diurnal cycle is less than half the seasonal mean (JJA) rainfall and values over land points where the rainfall rate is less than 0.2 mm d-1, are not shown. 
Figure 3a – A snapshot at 05 UTC on 23 May 2009 from the NICAM model simulation of 21 May – 31 August 2009, with cloudiness (based on the simulated outgoing longwave radiation) in shades of gray and precipitation rate in colors. The cloudiness is shaded in brighter gray for thicker clouds, and the colors range from shades of green, indicating precipitation rates less than 1 mm d-1, to yellow and orange (1-16 mm d-1), to red (16-64 mm d-1) and magenta (> 64 mm d-1). 
Figure 3b - Distribution of maximum attained 10-m wind speed (upper panel) and minimum SLP (lower panel) in the North Atlantic hurricanes from the IBTrACS data (black bars), IFS 10-km simulation (red bars) and IFS 39-km simulation (green bars) for the May to November seasons of 1990-2008. The inset plots show the tails of the distributions. Figure 3c – Distribution of minimum attained SLP over the entire globe (all basins) from the IBTrACS data (black bars), IFS 10-km simulation (red bars) and NICAM simulation (blue bars) for June – August of 2001-02, 2004-09. The inset plots show the tails of the distributions. 
Figure 3d - Distributions of 10-m tangential wind (left panels; m s-1) and total column liquid water and ice (TCLWI; right panels; kg m-2) for the most intense TCs at the peak of their intensity from the NICAM simulation (panels a and d, labeled “NICAM”), the IFS 10-km simulation (panels b and e, labeled “T2047”), the IFS 39-km simulation (panels c and f, labeled “T159”), respectively. Radius is 2°. Contour interval is 3 m s-1 for wind Dashed black contours in panels d, e and f show the radius of maximum winds for each case with respect to the center of the storm determined from the location of maximum vorticity at 925 hPa (1000 hPa for the IFS cases). 
Figure 4 – Time-latitude sections of daily precipitation anomalies averaged over 60°E-90°E for NICAM 7km (middle) and IFS 10km (bottom) 103-day long hindcasts for the period 22 May - 30 Aug 2006 and for the corresponding observations from TRMM (top). Annual cycle for the period 2001-2009 (2003 omitted) is removed from the daily mean to obtain the daily anomaly. 
Figure 5 – Frequency of occurrence (in %) of days at which the winter (December - March) Northern Hemisphere extratropical flow is blocked: Reanalysis (black with 95% confidence level shaded in gray), 125-km simulation (blue curve labeled T159), and 16-km simulation (red curve labeled T1279) for the AMIP experiment over the period 1960 - 2007. The dots indicate significant differences of model simulated values from reanalysis. 1 Throughout this article, model resolution will be measured in terms of the average spacing between gridpoints. 2 http://www.nics.tennessee.edu/computing-resources/kraken 3 http://www.top500.org/ 4 As originally envisioned, the hypothesis also involves resolving mesoscale phenomena in the ocean (eddies); however, the pilot project was limited to land-atmosphere models only. 5 http://www-pcmdi.llnl.gov/projects/amip/NEWS/overview.php 6 T indicates triangular spectral truncation of atmospheric states represented as wave structures on a sphere. T159 indicates that 159 such waves are retained in the truncation. 7 http://wxmaps.org/athena/home/mov/NICAM_p09.mov 8 http://www.ucar.edu/yotc/documents/mjo/korea_poster_presentations/ses1/Achuthavarier_post.pdf 9 http://wxmaps.org/athena/home/index.html 10 Grid Analysis and Display System (GrADS) - http://www.iges.org/grads/grads.html Directory: pub -> kinter -> Projects pub -> Acm word Template for sig site pub -> The german unification, 1815-1870 pub -> Baltic Olympiads in Informatics: Challenges for Training Together pub -> Preparation of Papers for ieee transactions on medical imaging pub -> Forthcoming meetings and conferences pub -> Asilomar Conference on Circuits, Systems & Computers, Pacific Grove, ca, November 1978, pp. 55 58 pub -> Forthcoming meetings and conferences pub -> Harmonised compatibility and sharing conditions for video pmse in the 7 9 ghz frequency band, taking into account radar use Projects -> Table of contents (30 pp limit, approved by nsf on 19 October 2012) – 1 pg introduction Download 114.48 Kb. Share with your friends:
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