Experiments with Monthly Satellite Ocean Color Fields in a NCEP Operational Ocean Forecast System PI: Eric Bayler, NESDIS/STAR Co-I: David Behringer, NWS/NCEP/EMC/GCWMB Co-I: Avichal Mehra, NWS/NCEP/EMC/MMAB Sudhir Nadiga, NWS/NCEP/EMC – IMSG

Background Penetration and absorption of solar insolation in the upper layers of the ocean is affected by the optical properties of the water column The vertical distribution of heating due to the absorption of solar radiation affects near-surface heat content and vertical stability Affects surface heat flux Chlorophyll concentration has been found to correlate to the optical properties of water column NCEP operational models employ a limited climatology ( ) of satellite (SeaWiFS) ocean color data for modeling this process 2 SeaWiFS

Motivation Improved ocean and coupled ocean-atmosphere forecasts Better representation of vertical distribution of solar radiation over the water column by properly representing temporal changes Reduce biases and need for constraining the model (relaxation to observed surface and subsurface fields) Transition towards operational near-real-time use of VIIRS ocean color data in operational ocean modeling 3 NPP/JPSS VIIRS

NCEP OPERATIONAL OCEAN MODEL NOAA/OAR/GFDL Modular Ocean Model (MOM4) Global Ocean Data Assimilation System (GODAS) / Coupled Forecast System (CFS) Grid: Tripolar 1/2°×1/2° equatorial region: 1/2°×1/4° Relaxation: SST – 30-day to daily Reynolds ¼-degree optimally interpolated SST SSS – 30-day to WOA 2009 SSS annual mean Reference: Behringer, D. W., The Global Ocean Data Assimilation System at NCEP, AMS 87th Annual Meeting,

Investigation Design Data gaps: Filled by linear spatial and temporal interpolation Forcing: All runs atmospherically forced by daily Climate Forecast System Reanalysis (CFSR, 1979 – 2009) values. CFSR modified with NRL-added scatterometer data (wind stress, wind speed) Control: SeaWiFS chlorophyll monthly-mean climatology (1998 – 2001); Note: extreme ENSO event Annual chlorophyll cycle, no subsurface data assimilation 12-year model run: cyclical chlorophyll, sequential atmospheric forcing ( ) Experiment 1 (Exp1): Extended SeaWiFS chlorophyll monthly-mean climatology (1998 – 2010) Annual chlorophyll cycle, no subsurface data assimilation 12-year model run: cyclical chlorophyll, sequential atmospheric forcing ( ) Experiment 2 (Exp2): SeaWiFS monthly mean chlorophyll (1998 – 2010) Sequential monthly-mean chlorophyll no subsurface data assimilation 12-year model run: sequential chlorophyll and atmospheric forcing ( ) 5

Environmental State Reference Climate Forecast System Reanalysis (CFSR) Reanalysis defines the mean states of the atmosphere, ocean, land surface and sea ice Global, high-resolution, coupled atmosphere-ocean-land surface-sea ice model system provides the best estimate of the state of these coupled domains over the period 1979 – 2009 Continually assimilated the best possible observation data Hourly time resolution and 0.5° horizontal resolution Produced using the MOM model, the same model used for the control and experiment cases Employs control case chlorophyll climatology; however, the reanalysis continually assimilates the best available observations (vertical profiles of temperature, salinity, satellite altimeter data, etc.), largely correcting for the influences of chlorophyll Saha, S., et al., 2010, “The NCEP Climate Forecast System Reanalysis,” Bull. Amer. Meteor. Soc., 91, CFSR atmosphere forcing used for the control case and both experiments Results referenced to CFSR ocean state values 6

Analysis Definitions Anomaly: Difference from designated reference (specified monthly-mean annual cycle) Index (i) represents month of data record, e.g. (i = 1) = Jan 1998 Root Mean Square Error (RMSE) RMSE includes: Differences in monthly-mean annual cycles Differences in anomalies Index (i) represents month of data record; e.g. (i = 1) = Jan 1998 Normalized RMSE difference RMSE comparison of specified cases with respect to a common reference Expressed in terms of percentage of the Control’s RMSE with respect to the common reference 7

Control Case: Limited Ocean Color Climatology Upper-ocean temperature variability Pacific “Cold Tongue”(2S – 2N, 120W) Temperature (C) 8

Control Case: Limited Ocean Color Climatology Seasonal and interannual anomalies Temperature (C) * Anomalies with respect to Control case mean-monthly cycle 9 Pacific “Cold Tongue”(2S – 2N, 120W)

Control Case: Limited Ocean Color Climatology Ocean Temperature RMSE (CFSR reference) Temperature (C) 10 Pacific “Cold Tongue”(2S – 2N, 120W)

Exp2: Sequential Monthly-mean Ocean Color Ocean Temperature RMSE (CFSR reference) Temperature (C) 11 Pacific “Cold Tongue”(2S – 2N, 120W)

Exp2 vs Control: Ocean Temperature RMSE (CFSR reference) Pacific “Cold Tongue” (2S – 2N, 120W) RMSE (°C) Near-surface improvement of order 0.2C, ~ 10% 12

Control Case: Limited Ocean Color Climatology Upper-ocean temperature variability Temperature (C) 13 Pacific “Warm Pool” (2S – 2N, 165E)

Temperature (C) Control Case: Limited Ocean Color Climatology Seasonal and interannual anomalies 14 Pacific “Warm Pool” (2S – 2N, 165E)

15 Temperature (C) Control Case: Limited Ocean Color Climatology Ocean Temperature RMSE (CFSR reference) Pacific “Warm Pool” (2S – 2N, 165E)

16 Temperature (C) Exp2: Sequential Monthly-mean Ocean Color Ocean Temperature (CFSR reference) Pacific “Warm Pool” (2S – 2N, 165E)

17 Exp2 vs Control: Ocean Temperature RMSE (CFSR reference) Pacific “Warm Pool” (2S – 2N, 165E) RMSE (°C) Minor near-surface improvement

18 Near-surface Temperature RMSE Control Case (CFSR reference) Equatorial Zonal Cross-section (2S – 2N) Temperature (C)

19 Near-surface Temperature RMSE Exp1 (CFSR reference) Equatorial Zonal Cross-section (2S – 2N) Temperature (C)

20 Temperature (C) Equatorial Zonal Cross-section (2S – 2N) Near-surface Temperature RMSE Exp2 (CFSR reference)

21 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Exp1 – Control: Impact magnitude of extended ocean color climatology 0.05

22 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Exp2 – Exp1: Additional impact magnitude from sequential ocean color data 0.05

23 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Exp2 – Control: Net impact magnitude from sequential ocean color data 0.05

24 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Differences: Extended Ocean Color Climatology RMSE (Exp1) – RMSE (Control): CFSR reference

25 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Differences: Sequential Ocean Color Data RMSE (Exp2) – RMSE (Exp1): CFSR reference

26 Equatorial Zonal Cross-section (2S – 2N) Temperature (C) Near-surface Temperature RMSE Differences: Net = Extended Climatology “+” Sequential Data RMSE (Exp2) – RMSE (Control): CFSR reference

27 Equatorial Zonal Cross-section (2S – 2N) Percent Normalized Near-surface Temperature RMSE Differences Extended Ocean Color Climatology

28 Equatorial Zonal Cross-section (2S – 2N) Percent Normalized Near-surface Temperature RMSE Differences Sequential Ocean Color Data

29 Equatorial Zonal Cross-section (2S – 2N) Percent Normalized Near-surface Temperature RMSE Differences Net = Extended Climatology “+” Sequential Data

30 Sea-Surface Height (SSH) RMSE Differences: Extended Ocean Color Climatology RMSE (Exp1) – RMSE (Control): CFSR reference Height Anomaly Difference (cm) Equatorial Pacific Ocean

31 Sea-Surface Height (SSH) RMSE Differences: Sequential Ocean Color Data RMSE (Exp2) – RMSE (Exp1): CFSR reference Equatorial Pacific Ocean Height Anomaly Difference (cm)

32 Height Anomaly Difference (cm) Equatorial Pacific Ocean Sea-Surface Height (SSH) RMSE Differences: Net = Extended Climatology “+” Sequential Data RMSE (Exp2) – RMSE (Control): CFSR reference Generalized reduction of SSH errors

33 Normalized Sea-Surface Height (SSH) RMSE Differences: Extended Ocean Color Climatology RMSE (Exp1) – RMSE (Control): CFSR reference Equatorial Pacific Ocean Percent

34 Percent Equatorial Pacific Ocean Normalized Sea-Surface Height (SSH) RMSE Differences: Sequential Ocean Color Data RMSE (Exp2) – RMSE (Exp1): CFSR reference

35 Equatorial Pacific Ocean Percent Normalized Sea-Surface Height (SSH) RMSE Differences: Net = Extended Climatology “+” Sequential Data RMSE (Exp2) – RMSE (Control): CFSR reference Generalized reduction of SSH errors

36 Equatorial Pacific Ocean Heat Content (exp -6 J/m 2 ) Ocean Heat Content (OHC) RMSE Differences: Extended Ocean Color Climatology RMSE (Exp1) – RMSE (Control): CFSR reference

37 Equatorial Pacific Ocean Heat Content (exp -6 J/m 2 ) Ocean Heat Content (OHC) RMSE Differences: Sequential Ocean Color Data RMSE (Exp2) – RMSE (Exp1): CFSR reference

38 Equatorial Pacific Ocean Heat Content (exp -6 J/m 2 ) Ocean Heat Content (OHC) RMSE Differences: Net = Extended Climatology “+” Sequential Data RMSE (Exp2) – RMSE (Control): CFSR reference Air-sea heat flux impact important to fully coupled modeling (GODAS/CFS)

39 Equatorial Pacific Ocean Percent Normalized Ocean Heat Content (OHC) RMSE Differences: Extended Ocean Color Climatology RMSE (Exp1) – RMSE (Control): CFSR reference

40 Equatorial Pacific Ocean Percent Normalized Ocean Heat Content (OHC) RMSE Differences: Sequential Ocean Color Data RMSE (Exp2) – RMSE (Exp1): CFSR reference

41 Equatorial Pacific Ocean Percent Normalized Ocean Heat Content (OHC) RMSE Differences: Net = Extended Climatology “+” Sequential Data RMSE (Exp2) – RMSE (Control): CFSR reference Air-sea heat flux impact important to fully coupled modeling (GODAS/CFS)

Summary Chlorophyll: Simulations with monthly SeaWiFS ocean chlorophyll data reduce subsurface temperature errors. Most changes in temperature are found just above the seasonal thermocline (20C isotherm) Sea-Surface Height (SSH): Reductions of SSH errors in the equatorial cold tongue region and north of the equator are in the 5-10% range Ocean Heat Content (OHC): Reductions of ocean heat content errors south of the equator and in the cold tongue region are in the 1-10% range Currently constrained at surface. When fully coupled (GODAS/CFS), differences will influence air-sea heat fluxes. NEXT: Comparisons of model output with real data for validation ; (in situ vertical profiles of temperature, salinity, velocity) from Pacific/Atlantic/Indian Ocean arrays and satellite altimetry Near-real-time ocean color assimilation Extend study to assess ocean color assimilation impact on the operational results for NOAA’s Real-Time Ocean Forecast System (RTOFS), based on the HYCOM model Unify NOAA’s ocean color data assimilation methodology for the operational models (GODAS, RTOFS) 42