Part 2 CFS Where It s Going S Lord

Part 2 CFS: Where It’s Going S. Lord, H-L Pan, S. Saha, D. Behringer, K. Mitchell And the NCEP (EMC-CPC CFSRR Team) 1

Overview • Current (CFS-v 1) description and status • CFS Reanalysis and Reforecast (CFSRR CFS-v 2) – – Atmosphere Ocean Land surface Sea ice • Future development (CFS-v 3) – Coupled A-O-L-S system – Long term Reanalysis strategy • Possibilities for Multi-Model Ensembles (MMEs) • Weather-Climate forecasting 2

Seasonal to Interannual Prediction at NCEP Operational System since August 2004 (CFS-v 1) Ocean Model MOMv 3 quasi-global 1 ox 1 o (1/3 o in tropics) 40 levels GODAS (2003) 3 DVAR XBT TAO Triton Pirata Argo Salinity (syn. ) TOPEX/Jason-1 Funded by NCPO/OCO Climate Forecast System (CFS) Daily Coupling Atmospheric Model GFS (2003) T 62 (~200 km) 64 levels Reanalysis-2 3 DVAR T 62 L 28 “Weather & Climate” Model OIv 2 SST Levitus SSS clim. Ocean reanalysis (1980 -present) provides initial conditions for retrospective CFS forecasts used for calibration and research Stand-alone version with a 14 -day lag updated routinely 3

Number of Temperature Observations per Month as a Function of Depth D. Behringer 4

CFS-v 2 Highlights 1. High resolution data assimilation – Produces better initial conditions for operational hindcasts and forecasts (e. g. MJO) – Enables new products for the monthly forecast system – Enables additional hindcast research 2. Coupled data assimilation – Reduces “coupling shock” – Improves spin up character of the forecasts 3. Consistent analysis-reanalysis and forecast-reforecast for – Improved calibration and skill estimates 4. Provide basis for a future coupled A-O-L-S forecast system running operationally at NCEP (1 day to 1 year) – (currently in parallel testing for “GFS” 1 -14 day prediction) Funded by NCPO/CDEP 5

CFSRR Components • Reanalysis – – – – 31 -year period (1979 -2009 and continued in NCEP ops) Atmosphere Ocean Land Seaice Coupled system (A-O-L-S) provides background for analysis Produces consistent initial conditions for climate and weather forecasts • Reforecast – 28 -year period (1982 -2009 and continued in NCEP ops ) – Provides stable calibration and skill estimates for new operational seasonal system • Includes upgrades for A-O-L-S developed since CFS originally implemented in 2004 – Upgrades developed and tested for both climate and weather prediction – “Unified weather-climate” strategy (1 day to 1 year) 6

CFSRR Component Upgrades Component Ops CFS 2010 CFS 1995 (R 2) model 200 km/28 sigma levels 2008 model (upgrades to all physics) 38 km/64 sigma-pressure levels Enthalpy-based thermodynamics Variable CO 2 (historical data, future scenarios) R 2 analysis Satellite retrievals GSI with simplified 4 d-var (FOTO) Radiances with bias-corrected spinup Ocean MOM-3 60 N – 65 S 1/3 x 1 deg. MOM-4 Global domain ¼ x ½ deg. Coupled sea ice forecast model Ocean data assim. 750 m depth 2000 m Land No separate land property analysis Global Land Data Assim. Sys (GLDAS) driven by observed precipitation 1995 land model (2 levels) 2008 Noah model Sea ice Daily analysis Daily hires analysis Coupling None Fully coupled background forecast (same as free 7 forecast) Atmosphere

One-day schematic of four 6 -hourly cycles of CFSRR Global Reanalysis: 00 Z GDAS Atmospheric Analysis 06 Z GDAS 12 Z GDAS 18 Z GSI 00 Z GSI 0 Z GODAS 6 Z GODAS 12 Z GODAS Ocean Analysis 18 Z GODAS 0 Z GLDAS 6 Z GLDAS 12 Z GLDAS Land Analysis 18 Z GLDAS 0 Z GLDAS Time S. Saha and S. Moorthi 8

Testing with CMIP Runs (variable CO 2) OBS is CPC Analysis (Fan and van den Dool, 2008) CTRL is CMIP run with 1988 CO 2 settings (no variations in CO 2, current operations) CO 2 run is the ensemble mean of 3 NCEP CFS runs in CMIP mode – realistic CO 2 and aerosols in both troposphere and stratosphere Processing: 25 -month running mean applied to the time series of anomalies (deviations 9 from their own climatologies)

CFSRR at NCEP Climate Forecast System V 2 GDAS GSI LDAS 6 hr 24 hr Atmospheric Model GFS (2007) T 382 64 levels Land Model Ice Model Ocean Model MOMv 4 fully global 1/2 ox 1/2 o (1/4 o in tropics) 40 levels 6 hr Ice Ext GODAS 3 DVAR 10

Future Development • What’s going on and what’s needed – Land surface – Ocean & Sea ice – Atmosphere 11

Noah LSM replaces OSU LSM in new CFS • Noah LSM • OSU LSM – 2 soil layers (10, 190 cm) – 4 soil layers (10, 30, 60, 100 cm) – No frozen soil physics – Frozen soil physics included – Surface fluxes not weighted by – Surface fluxes weighted by snow fraction cover fraction – Vegetation fraction never less than – Improved seasonal cycle of 50 percent vegetation cover – Spatially constant root depth – Spatially varying root depth – Runoff & infiltration do not account – Runoff and infiltration account for subgrid variability of sub-grid variability in precipitation & soil moisture – Poor soil and snow thermal – Improved soil & snow thermal conductivity, especially for thin conductivity snowpack and moist soils – Higher canopy resistance – More Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005 Some Noah LSM upgrades & assessments were result of collaborations with CPPA PIs Funded by NCPO/CPPA K. Mitchell 12

CFSRR Reanalysis Land Component: Global Land Data Assimilation System (GLDAS) • Applies same Noah LSM as in new CFS • Uses same native grid (T 382 Gaussian) as CFSRR atmospheric analysis • Applies CFSRR atmospheric analysis forcing (except for precip) – hourly from previous 24 -hours of atmospheric analysis – Precipitation forcing is from CPC analyses of observed precipitation • Model precipitation is blended in only at very high latitudes • GLDAS daily update of the CFSRR reanalysis soil moisture states – Reprocesses last 6 -7 days to capture and apply most recent CPC precipitation analyses • Realtime GLDAS configuration will match reanalysis configuration – To sustain the relevance of the climatology of the retrospective reanalysis 13 • Applies LIS: uses the computational infrastructure of the NASA Land Information System (LIS), which is highly parallelized

LIS Capabilities • Flexible choice of 7 different land models – Includes Noah LSM used operationally by NCEP and AFWA • Flexible domain and grid choice – Global: such as NCEP global model Gaussian grid – Regional: including very high resolution (~. 1 -1 km) • Data Assimilation – Based on Kalman Filter approaches • High performance parallel computing – Scales efficiently across multiple CPUs • Interoperable and portable – Executes on several computational platforms – NCEP and AFWA computers included • Being coupled to NWP & CRTM radiative transfer models – Coupling to WRF model has been demonstrated – Coupling to NCEP global GFS model is under development – Coupling to JCSDA CRTM radiative transfer model is nearing completion • Next-gen AFWA AGRMET model will utilize LIS with Noah • NCEP’s Global Land Data Assimilation utilizes LIS K. Mitchell, C. Peters-Lidard 14

Impact of Noah vs. OSU Land Models and GLDAS Initial Land States in 25 -years of CFS Summer & Winter Reforecasts: Lessons Learned • Land surface model (LSM) for CFS forecast must be same as for supporting land data assimilation system (LDAS) • Impact of land surface upgrade on CFS seasonal precipitation forecast skill for is positive (but modest) – Significant only for summer season in neutral ENSO years (and then only small positive impact) – Essentially neutral impact for winter season and non-neutral ENSO summers • Differences in CFS precipitation skill over CONUS between neutral and non-neutral ENSO years exceeds skill differences between two different land configurations for same sample of years – Indicates that impact of SST anomaly is substantially greater than impact of land surface configuration 15

2009+ Land Surface Model Development 1 - Unify all NCEP model land components to use MODIS-based hi-res global land use with IGBP classes 2 - Improve global fields of land surface characteristics (vegetation cover, albedo, emissivity) using satellite data (with Joint Center for Satellite Data Assimilation) 3 - Enhance land surface subgrid-variability with high-resolution sub-grid tiles 4 - Increase number of soil layers (from 4 to about 10) 5 - Introduce dynamic seasonality of vegetation (to replace pre-specified seasonal cycle) 6 - Improve hydrology including addition of groundwater 7 - Add multi-layer treatment to snowpack physics 8 - Introduce carbon fluxes Items 5 -8 are being transitioned from the CPPA-funded work of PI Prof Z. -L. Yang and Dr. G. -Y. Niu of U. Texas/Austin K. Mitchell 16

GODAS in the CFSRR • Operational in 2010 • MOMv 4 (1/2 o x 1/2 o, 1/4 o in the tropics, 40 levels) • Updated 3 DVAR assimilation scheme – – Temperature profiles (XBT, Argo, TAO, TRITON, PIRATA) Synthetic salinity profiles derived from seasonal T-S relationship TOPEX/Jason-1 Altimetry Data window is asymmetrical extending from 10 -days before the analysis date – Surface temperature relaxation to (or assimilation of) Reynolds new daily, 1/4 o OIv 2 SST – Surface salinity relaxation Levitus climatological SSS – Coupled atmosphere-ocean background • Current stand-alone operational GODAS will be upgraded in 2009 to the higher resolution MOMv 4 and be available for comparison with the coupled version – Updated with new techniques and observations 17 D. Behringer

In the west, assimilating Argo salinity corrects the bias at the surface and the depth of the undercurrent core and captures the complex structure at 165 o. E. Assimilating Argo Salinity Comparison with independent ADCP currents. ADCP GODAS In the east, assimilating Argo salinity reduces the bias at the surface and sharpens the profile below thermocline at 110 o. W. GODAS-A/S D. Behringer 18

2009+ GODAS Activities • Complete CFSRR – Evaluate ODA results • Add ARGO salinity • Improve climatological T-S relationships and synthetic salinity formulation • ENVISAT data? • Improve use of surface observations – Vertical correlations (mixed layer) • Situation-dependent error covariances (recursive filter formulation) • Investigate advanced ODA techniques – Experimental Ensemble Data Assimilation system (with GFDL) – Reduced Kalman filtering (with JPL) – Improved observation representativeness errors (with Bob Miller, OSUJCSDA) • Impact of the GODAS mixed layer analysis on subseasonal forecasting with the CFS. Augustin Vintzileos (EMC) 19 D. Behringer

Comparison of GODAS/KF and GODAS/3 DVAR with TAO temperature and zonal velocity anomalies Re = [model explained variance] / [data variance] 20 o. C Dyn. Ht U KF SST 3 DVAR - A KF SST 3 DVAR - B For points toward the top (GKF) and toward the right (G 3 DV) the models are closer to the data. For points above (below) the diagonal GKF (G 3 DV) is closer to the data. in collaboration with I Fukumori (JPL) 20

Sea Ice Analysis from CFSRR 21 R. Grumbine

Atmospheric Model • Improve CFS climatology and predictive skill with improved physical parameterizations – Deep and/or shallow convection – Cloud/radiation/aerosol interaction and feedback – Boundary layer processes – Orographic forcing – Gravity wave drag – Stochastic forcing – Cryosphere 22

Shallow Cloud Development H. -L. Pan and J. Han • Use a bulk mass-flux parameterization • Based on the simplified Arakawa-Shubert (SAS) deep convection scheme, which is being operationally used in the NCEP GFS model • Separation of deep and shallow convection is determined by cloud depth (currently 150 mb) • Main difference between deep and shallow convection is specification of entrainment and detrainment rates • Only precipitating updraft in shallow convection scheme is considered; downdraft is ignored 23

Development based on LES studies Siebesma & Cuijpers (1995, JAS) Siebesma et al. (2003, JAS) LES studies 24

ISCCP Combined Impact of Revised PBL & New Shallow Convection For CFS Control Revised PBL & new shallow convection Cloud cover improved J. Han 25

Revised PBL + New shallow (Winter 2007) 500 h. Pa Height Anomaly Correlation NH(20 N-80 N) Skill scores are better (1) SH(20 S-80 S) 26

CONUS Precipitation skill score Winter 2007 12 -36 hrs 36 -60 hrs Skill scores are better (2) 60 -84 hrs 27

Revised PBL + New shallow (Summer 2005) 500 h. Pa Height Anomaly Correlation NH(20 N-80 N) Skill scores are better (3) SH(20 S-80 S) 28

CONUS Precipitation skill score Summer 2005 12 -36 hrs 36 -60 hrs Skill scores are possibly better (4) 60 -84 hrs 29

ENSO Signal Observed SST Anomaly Nino 3. 4 OIV 2 Control SST Anomaly 50 year CMIP Run ENSO too weak (early) Too strong later RESULT: no implementation for Weather or climate 30

Downward Shortwave Radiation at Ground 2 S-2 N Annual Mean 50 Year Run Year 1 -20 Control Year 21 -50 Year 1 -20 Experiment Year 21 -50 Can be Improved With Shallow-Deep Cloud Tuning Observed Clouds Too Thick in SE Pacific (DSWR too small) Observed DSWR from Visiting Scientist (Mechoso – UCLA, CPPA sponsored through VOCALS) 31

Phase (local time) of Maximum Precipitation (24 -hour cycle) Myong-In Lee and Sieg Schubert (NASA/GMAO) Five-member ensembles driven by Climatological SST forcing (1983 -2002 avg) 32

Impact of Diurnal SST (Xu Li) 33

RMS Error Growth Tropical Intraseasonal Forecasts (MJO) Resolution does not affect skill. Forecasts initialized by GDAS are better (a gain of ~3 -5 days). T 254 T 126 T 62 GDAS CDAS-2 Pattern Correlation Time evolution of mean energy at wave numbers 10 -40 when CFS is initialized by R 2 (red) or by GDAS (blue). drift 34 A. Vintzileos

Ongoing Reanalysis Project • CFS will be upgraded every ~7 years – New forecast system • • • Upgrades from operations New techniques Higher resolution analysis Aerosol and trace gas analysis Carbon cycle Hydrology, ground water, etc. – New observations from data mining – Satellite data treatment (e. g. bias correction) • Evolution to Integrated Earth System Analysis • Ongoing work to incorporate these improvements – Preparation for Reanalysis production phase – All additions carefully tested 35

Proposed Concept of Operations Development Phase 3 -4 years Production Phase 2 -3 years 36

Future Model Component Upgrades Component Possible Upgrades - AER RRTM shortwave & longwave radiation - Variable CO 2 & aerosols - Maximum random cloud overlap Enthalpy-based thermodynamics - Fractional cloudiness (impacts surface solar flux) - Possible neural network emulation for radiation (trained on hindcasts) - Sigma-pressure-theta hybrid - Prognostic cloud water - Non-local PBL - Simplified Arakawa-Schubert conv. Atmosphere 2010 CFS - Ferrier microphysics (impacts radiation and precipitation type) - Shallow convection (mass flux) - Convective gravity wave - Conservative, positive definite tracer advection Land - Global Land Data Assim. Sys (GLDAS) driven by observed precipitation - Dynamic vegetation (impacts drought) - Groundwater (impacts soil wetness) Ocean - MOM-4 -Ocean ensemble (HYCOM + MOM ? ) -Salinity assimilation - Situation-dependent background errors and other advanced techniques Comprehensive Testing in Weather and Climate Modes • Daily data assimilation and 15 day forecasts • LDAS for balanced land states • CMIP runs (> 50 years) • Sample seasonal runs (May & October) 37

Multi-Model Ensemble Strategy • International MME (IMME) with EUROSIP is under negotiation – Operational delivery – Consolidated products – Use for official duty only • Full set of hindcasts required for bias correction and skill masking • National MME – COLA is generating hindcasts for NCAR system – Issues are • developing concept of operations (how partners will participate) • identifying metrics for value added (e. g. consolidation) • building computing resources (particularly for reforecasts) into computer acquisitions 38

IMME Status (1) • Goal: produce operational ensemble products from CFS and EUROSIP seasonal climate products • EUROSIP – ECMWF – Met Office – Meteo France • Prospectus has been submitted to EUROSIP Counsel – Covers • Licensing • Commercial interest and revenue sharing – Consistent with EUROSIP general provisions • Formal Memorandum of Understanding will be drafted 39

IMME Status (2) • Some tenets of a potential agreement – E-partners and NCEP will be free to process individual forecasts into combined IMME products with their own procedures – NCEP will distribute its combined IMME product to its internal users for official duty use in time to meet NCEP forecast schedules – NCEP will distribute its combined products to the E-Partners as soon as possible each month, using ECMWF as the distributing agent – NCEP and E-partners will coordinate distribution of IMME products to their users on a regular monthly schedule – Product delivery will not compromise any organization’s operational delivery schedules and commitments – NCEP wishes to join the EUROSIP Steering Group as a nonvoting member and will participate in future meetings 40

Weather-Climate Forecasting 41

NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems Current - 2007 Current (2007) GFS anal NAM anal AQ GENS/NAEFS RDAS SREF GDAS NAM GFS HUR RTOFS CFS 42

Global Model Suite Daily to S/I Forecasts • • • All forecasts are Atmosphere-Land-Ocean coupled All systems are ensemble-based except daily, high-resolution run All forecasts initialized with LDAS, GODAS, GSI from GFS initial conditions Physics and dynamics packages may vary • – Forecast Product Anticipated that the weekly forecast will have most rapid implementations and code changes, seasonal configuration may be one (or at most two) versions behind weekly Membership refresh period Runs/day Number of members per refresh period Horizontal resolution (ratio, current value) Forecast Length Initialization technique Computing Resource ratio 4 x/day 4 1 1. 0, T 382 15 days GSI 1. 0 Weekly daily 80 80 0. 5, T 170 15 days ET breeding 2. 5 Monthly weekly 8 56 0. 5, T 170 60 days ? ? 1. 0 Seasonal monthly 2 60 0. 33, T 126 1 year Lagged analysis 4 x daily 0. 44 Daily-hires 43

NCEP Production Suite Weather, Ocean, Land & Climate Forecast Systems Next Generation Prototype CFS MFS Global NAM RTOFS AQ SREF RTOFS Reforecast HENS HUR GENS/NAEFS Rap Refresh GFS AQ Hydro / NIDIS/FF CFS & MFS & GODAS WAV Regional Hydro RDAS GDAS 44

Summary • CFSRR CFS-v 2 – – High resolution reanalysis CO 2 trend Upgrades models and data assimilation Foundation for coupled “earth-system” reanalysis • Beginning scientific development of CFS-v 3 – Fully coupled A-O-L-S system for IESA – Advanced data assimilation techniques • Building a MME system with International and US contributions • Focusing on Weather-Climate forecasting – 1 day to 3 years 45

Thanks Questions? 46

Comparison of GODAS/M 4 and GODAS/M 3 with TAO temperature and zonal velocity In thermocline both GM 4 and GM 3 are warm at 140 w, while GM 4 is warm and GM 3 is cold at 110 w. The undercurrent is stronger than observed in GM 4 and weaker in GM 3. The vertical structure at 165 e is better in GM 4 than in GM 3. 47

Land Information System (LIS) • NOAA-NASA-USAF collaboration – K. Mitchell (NOAA) – C. Peters-Lidard (NASA) – J. Eylander (USAF) • LIS hosts – Land surface models – Land surface data assimilation and provides – Regional or global land surface conditions for use in • Coupled NWP models • Stand-alone land surface applications K. Mitchell, C. Peters-Lidard 48

Science Plan for the CFS (II) • Most effective way to improve the CFS (climate) GFS/CFS (weather) as one package • We want to improve weather and climate forecasts by making physically based improvements to the atmospheric model parameterization packages. • We have been successful when we apply rigorous tests to physically based parameterization improvements to both weather and climate models and want to continue along this way. 49

Science plan for the CFS (III) • Deep and/or shallow convection • These processes transport sub-grid scale heat and moisture vertically, which is especially important for climate prediction. Boundary layer processes As the CFS is a coupled model, the boundary layer is critical for communication of the ocean and land conditions with the atmosphere. • Cloud/radiation/aerosol interaction and feedback Clouds and aerosols modulate the sources and sinks of thermal energy in to the earth system. This interaction is crucial on climate time scales. • Orographic forcing Orography determines many climate variables through form-drag, mountain blocking, and land/sea contrast. 50

Science Plan for the CFS (IV) • Gravity wave drag Gravity waves generated by the sub-grid scale orography and/or cumulus convection transport wave energy from the troposphere to the stratosphere and mesosphere and thus control the climate of those regions. • Stochastic forcing is not in the CFS at this time, but is important for parameterizing random, unresolved physical forcing. • Cryosphere The cryosphere (glaciers, frozen land, sea ice) plays a crucial role in determining the earth's climate. Modeling of sea-ice and its interaction with the ocean and atmosphere, and modeling frozen land its interaction with the atmosphere all important to climate. 51

Science Plan for the CFS (V) • Testing procedures are key to the road to making model implementations • While transition to operation for MMEs requires only seasonal hindcasts to be evaluated, it is done because we expect the team maintaining the MME models to do their own rigorous tests. • Tests in data assimilation modes and evaluated with forecasts are crucial for weather forecasts. • Tests in multi-year coupled simulations and seasonal hindcasts are crucial for climate forecasts • CTB computer resource is not sufficient and NCEP computer must be used when full-scale testing is needed 52

Gaps • • Insufficient EMC staff to collaborate with external investigators, train their staff (often post-docs) on use of the CFS, and develop new parameterization codes suitable for the CFS for the broad spectrum of possible areas listed above (O 2 R); Insufficient computing resources for experimentation and transition changes to the CFS; Insufficient EMC and NCEP Central Operations (NCO) staff to support the R 2 O (implementation) process; Insufficient knowledge within the research community about the tests needed to complete an implementation 53

We built a new shallow convection scheme a few years ago • Use a bulk mass-flux parameterization • Based on the simplified Arakawa-Shubert (SAS) deep convection scheme, which is being operationally used in the NCEP GFS model • Separation of deep and shallow convection is determined by cloud depth (currently 150 mb) • Main difference between deep and shallow convection is specification of entrainment and detrainment rates • Only precipitating updraft in shallow convection scheme is considered; downdraft is ignored 54

We build it based on LES studies Siebesma & Cuijpers (1995, JAS) Siebesma et al. (2003, JAS) LES studies 55

Cloud water cross-section looks better 56

PBL & Low clouds combined (CFS run) ISCCP Control Revised PBL & new shallow convection Cloud cover looks better 57

Revised PBL + New shallow (Winter, 2007) 500 h. Pa Height Anomaly Correlation NH(20 N-80 N) Skill scores were better SH(20 S-80 S) 58

Precipitation skill score over US continent 12 -36 hrs 36 -60 hrs Skill scores were better 60 -84 hrs 59

Revised PBL + New shallow (Summer, 2005) 500 h. Pa Height Anomaly Correlation NH(20 N-80 N) Skill scores were better SH(20 S-80 S) 60

Precipitation skill score over US continent 12 -36 hrs 36 -60 hrs Skill scores were slightly better 60 -84 hrs 61

NINO 3. 4 OIV 2 Observed ENSO signal 62

NINO 3. 4 set 22 Multi-year simulation of the control looks ok 63

NINO 3. 4 set 28 b The test version showed too weak ENSO in early years and too strong ENSO in later years. RESULTS : no implementation 64

Srb 2 is observation With a VOCALS grant from CPPA, Mechoso worked with us to examine these runs. This is the downward short wave radiation reaching ground for the control 65

There is too much radiation reaching ground for the new package over western Pacific but too little over central Pacific. More changes will have to be made. 66

Cloud water cross-section looks better 67

Climate Requirements for NCEP’s Next Operational System (2011) Application Operational System Seasonal-Monthly Climate CFS 32 Climate Prediction Center Monthly fcst system 10+ “ GLDAS, NLDAS 2. 5* NIDIS, CPC Reforecast Computing X factor 9* • Ratio of ops: R 2 O computing – Currently 1: 1. 3 – Requesting • 2011: 1: 2. 0 • 2013: 1: 3. 0 • 2015: 1: 4. 0 Requirement Generator “ Extension of Week 2 system * New system + Climate (and other) computing requirements total a factor of 3 X in additional funding (above Moore’s Law – constant $$ capability) 68

NOAA Computing Resources and Operational Requirements for Climate Forecasting at NCEP • Research – Including CFSRR • Operations 69

Climate R&D Computing for Week 2 to S/I Enables CFSRR to execute ¾ of required production rate June 2008+ New Power 6 system for CTB, CDEV, JCSDA, MTB (same % as previous) CFSRR will use all Power 5 system 70