Project Athena Overview Project Athena: Origins  The World Modeling Summit (WMS) in May 2008 called for a revolution in climate modeling to more rapidly advance improvements in accuracy and reliability  The WMS recommended petascale supercomputers dedicated to climate modeling based in at least 3 international facilities  Dedicated petascale machines are needed to provide enough computational capability and a controlled environment to support long runs and the management, analysis and stewardship of very large (petabyte) data sets  The U.S. National Science Foundation, recognizing the importance of the problem, realized that a resource (Athena) was available to meet the challenge of the World Modeling Summit and offered to dedicate the Athena supercomputer for 6 months in  An international collaboration was formed among groups in the U.S., Japan and the U.K. to use Athena to take up the challenge

Project Athena Overview COLA - Center for Ocean-Land-Atmosphere Studies, USA (NSF- funded) ECMWF - European Centre for Medium-range Weather Forecasts, UK JAMSTEC - Japan Agency for Marine-Earth Science and Technology, Research Institute for Global Change, Japan University of Tokyo, Japan NICS - National Institute for Computational Sciences, USA (NSF- funded) Cray Inc. Project Athena: Collaborating Groups Codes NICAM: Nonhydrostatic Icosahedral Atmospheric Model IFS: ECMWF Integrated Forecast System Supercomputers Athena: Cray XT quad-core Opteron nodes (18048) #30 on Top500 list (November 2009) – dedicated Oct’09 – Mar’10 Kraken: Cray XT dual hex-core Opteron nodes (99072) #3 on Top500 list (November 2009) replaced Athena – allocation of 5M SUs

Athena Experiments 3Straus/GMU/COLA AGU Dec 2010

Surface pressure Potential Vorticity

Project Athena Overview Blocking Index. 13 month integrations of ECMWF model (at T159 and T1259). DJFM ERA-40 T159 T1259

500 hPa Geopotential height – ERA DJFM DJFM Weather Regimes Euro-Atlantic Region

Project Athena Overview hPa: DJF

Aircraft observations showing spectra of wind components and T, plotting log(E) vs. log(k), so that the slope of the straight lines indicate the exponent n in the previous slide. Other in-situ observations have confirmed these results! 8Straus/GMU/COLA AGU Dec 2010 Atmospheric Spectra Power Laws Two scaling regimes: Log-log plot of Energy vs. wavenumber 100 – 10 km

ECMWF Dec-March Simulations: Eddy Kinetic Energy Spectrum 250 hPa Total Eddy Kinetic Energy ECMWF Black: T511 (40 km grid) Red:T1279 (16 km grid) Blue:T2047 (10 km grid) 250 hPa 5 DJF seasons Note sudden downturn in spectra: suggests dissipation regime 9Straus/GMU/COLA AGU Dec 2010 Hint of two regimes at T1279 and T2047 but not at T511

Straus/GMU/COLA AGU Dec

Slope of Total Eddy Kinetic Energy Black: T511 (40 km grid) Red:T1279 (16 km grid) Blue:T2047 (10 km grid) 250 hPa level 5 DJF seasons Local Spectral Slope b E n ~ n -b Least squares fit of log10(eddy kinetic energy) to line with slope –b, locally over a range of constant log10(n) Large slope indicates dissipation regime 11Straus/GMU/COLA AGU Dec 2010 y-axis is b x-axis is log 10 (n) T2047, T1279 show weak shallowing of spectra at higher wavenumbers

Project Athena Overview North Atlantic Tropical Cyclones Track Density IFS, March-November, T1279 T511 T159 OBS

Project Athena Overview Blocking: DJFM

Project Athena Overview Blocking: DJFM

Project Athena Overview Annual Mean Precipitation Change Europe: 21 st C minus 20 th C T159 (125-km)T1279 (16-km) “Time-slice” runs of the ECMWF IFS global atmospheric model with observed SST for the 20 th century and CMIP3 projections of SST for the 21 st century at two different model resolutions The continental-scale pattern of precipitation change associated with global warming is the same, but the regional details are quite different, particularly in southern Europe.

Project Athena Overview Precipitation Change (21 st C – 20 th C) 128-km 16-km diff JJA Less rainfall in tropical cyclone regions of Atlantic and eastern Pacific in 21 st C Monsoon changes make northern region of south Asia wetter

Athena Results Seasonal Length Runs Results shown for 5 DJF seasons and 5 JJA seasons Results for both ECMWF and NICAM models 17Straus/GMU/COLA AGU Dec 2010

Project Athena Overview

Cluster analysis methodology 1)Identification of clusters in the reduced phase space defined by the empirical orthogonal functions (EOFs. The leading EOFs (to explain about 80% of the space-time variance) are retained. 2)For a given number k of clusters, the optimum partition of data into k clusters is found by an algorithm that takes an initial cluster assignment (based on the distance from pseudorandom seed points), and iteratively changes it by assigning each element to the cluster with the closest centroid, until a ‘‘stable’’ classification is achieved. (A cluster centroid is defined by the average of the PC coordinates of all states that lie in that cluster.) 3)This process is repeated many times (using different seeds), and for each partition the ratio r* k of variance among cluster centroids (weighted by the population) to the average intra-cluster variance is recorded. 4)The partition that maximises this ratio is the optimal one. The (modified) K-means cluster analysis method (K is the number of clusters into which the data will be grouped, this number must be specified in advance) (Straus et al. 2007) can be summarized in the following four steps:

Cluster analysis - Significance The goal is to assess the strength of the clustering compared to that expected from an appropriate reference distribution, such as a multidimensional Gaussian distribution.  In assessing whether the null hypothesis of multi-normality can be rejected, it is therefore necessary to perform Monte-Carlo simulations using a large number M of synthetic data sets.  Each synthetic data set has precisely the same length as the original data set against which it is compared, and it is generated from a series of n dimensional Markov processes, whose mean, variance and first-order auto-correlation are obtained from the observed data set.  A cluster analysis is performed for each one of the simulated data sets. For each k-partition the ratio r mk of variance among cluster centroids to the average intra-cluster variance is recorded.  Since the synthetic data are assumed to have a unimodal distribution, the proportion P k of red-noise samples for which r mk < r* k is a measure of the significance of the k-cluster partition of the actual data, and 1- P k is the corresponding confidence level for the existence of k clusters.

Cluster analysis - How many clusters? The need of specifying the number of clusters can be a disadvantage of K-means method if we don’t know in advance what is the best cluster partition of the data set in question. However there are some criteria that can be used to choose the optimal partition.  Significance: partition with the highest significance with respect to predefined Multinormal distributions  Reproducibility: We can use as a measure of reproducibility the ratio of the mean-squared error of best matching cluster centroids from a N pairs of randomly chosen half-length datasets from the full actual one. The partition with the highest reproducibility will be chosen.  Consistency: The consistency can be calculated both with respect to variable (for example comparing clusters obtained from dynamically linked variables) and with respect to domain (test of sensitivities with respect to the lateral or vertical domain).