High-Resolution Satellite-Derived Ocean Surface Winds in the Nordic-Barents Seas Region in : Implications for Ocean Modeling

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1 High-Resolution Satellite-Derived Ocean Surface Winds in the Nordic-Barents Seas Region in : Implications for Ocean Modeling Векторные поля приповерхностных скоростей ветра над Cеверным и Баренцевым морями в , полученные на основе спутниковой съемки высокого разрешения: Значение для моделирования океанских процессов Dmitry Dukhovskoy Mark Bourassa Paul Hughes Center for Ocean-Atmospheric Prediction Studies Florida State University

2 Cyclones in the Nordic-Barents Seas Distribution of mean SLP and mean 500 hpa height (contours) fields for November - March, The Nordic-Barents Seas region is one of the most synoptically active and variable areas of the planet, especially during winter Total winter cyclone count (November March), Tsukernik et al., JGR, 2007 Tsukernik et al., JGR, 2007

3 Ocean Response to the Cyclone Q Q Turbulent Heat Fluxes lat sen a a LCE u qa C C u T p s a q s T s Upper ocean heat content Cyclone Momentum Flux Reed, JGR, 1983; Large et al., Rev. Geophys., 1994 a C d u u Barotropic response - Surge - Shelf waves Baroclinic response - Internal gravity waves - Inertial oscillations - Deepening of the pycnocline - Upwelling in the wake of cyclone

4 Schematics of generation of internal waves by a moving cyclone Internal waves Baroclinic topographic waves u 0

5 T-S Sections, December, GDEM 3 Salinity Greenland Barents Sea Temperature Greenland Barents Sea

6 Why Remote Sensing of Ocean Winds? Provides high-resolution and accurate measurements of wind speeds and directions over the ocean - Spatial resolution varies from 6 to 50 km - Resolves meso- and small-scale atmospheric features Covers large area within a short period of time - NSCAT covers 90% of the ocean in 2 days - QuickSCAT took 400,000 measurements over 93% of Earth s surface every day Captures short-living phenomena (O(12h)) in Polar regions - QuickSCAT circles the Earth from Pole to Pole every 100 min Wind speed and directions reveal good agreement (>90%) with surface observations especially for strong winds (>8 m/s) Drawbacks: - Cannot measure winds over the sea ice - Rain contaminates observations

7 What is a Scatterometer? A scatterometer is a microwave radar sensor used to measure the reflection or scattering effect produced while scanning the surface of the earth from an aircraft or a satellite. Unique among satellite remote sensors in their ability to determine wind direction Polar orbiting satellites Active microwave sensors

8 12 hour QuickSCAT Swaths over the Arctic

9 CCMP winds vs NCEPR 2 winds Cross-Calibrated Multi-Platform Ocean Surface Wind Components (CCMP) Project is funded by NASA Gridded data set of ocean surface vector winds at 0.25 resolution is created Period covered: July 1, 1987 December 31, 2008 The data set combines data derived from several scatterometer satellites Satellite data are assimilated into the ECMWF Operational Analysis fields National Center for Environmental Prediction Reanalysis (NCEPR 2) The Twentieth Century Reanalysis Project, supported by the Earth System Research Laboratory Physical Sciences Division from NOAA and the University of Colorado CIRES/Climate Diagnostics Center Global climatological data reconstruction from 1891 The product is obtained by assimilating surface observations of synoptic pressure, sea surface temperature and sea ice distribution Products include 3- and 6-hourly data on ~2 x 2 global grid, monthly, daily averages, etc. Fields: SST, SSS, atmospheric temperature, precipitation, heat fluxes, radiation, The primary source of forcing parameters in most Arctic Ocean models

10 Ocean vector winds from CCMP and NCEPR 2 in October April 2008 CCMP NCEPR 2

11 Spatial Spectra of the Wind Data

12 Flux Estimates on the Basis of CCMP and NCEPR 2

13 Coupled Ocean Sea Ice Modeling System of the Arctic Ocean HYbrid Coordiante Ocean Model (HYCOM) Developed in collaboration with the Naval Research Laboratory and the HYCOM Consortium Vertical coordinate system combines isopycnal, pressure (z level), and terrain-following (sigma) coordinates Orthogonal curvilinear grid Options for turbulence-closure schemes Open Source model: Simulated Sea Surface Temperature, February 2006 Sea Ice Model (CICE 4.0) State of the art sea-ice model - Multi-category ice thickness - Energy conserving thermodynamics - Energy-based ridging scheme - Elastic-viscous-plastic (EVP) ice dynamics Hybrid vertical coordinates in HYCOM Open Source ice model

14 Winds on Better resolve horizontal structure and intensity of large-scale features , 6:00 UTC CCMP NCEPR m/s Category 2 Hurricance on Saffir-Simpson scale m/s

15 -457W/m 2 Momentum and Heat Fluxes, December 19, :00 CCMP winds 210 NCEPR 2 winds Heat Fluxes, W/m Heat Fluxes, W/m W/m 2-11 TW TW Momentum flux, N/m 2 > Momentum flux, N/m N/m 7.5 N/m

16 Summary (1) Satellite scatterometer wind products provide more detailed wind forcing for ocean models compared to NCEPR 2: - Better spatial resolution enables CCMP data capture meso- and smallscale atmospheric features absent or misrepresented in NCEPR 2 - Intensity of large and mesoscale features is higher in CCMP fields compared to NCEPR 2 - Short-time living atmospheric processes are captured in CCMP data due to higher temporal resolution of the CCMP fields (2) Forced with the scatterometer winds ocean models will have stronger heat and momentum fluxes (3) Stronger air-sea interaction will lead to: - More intense transformation of water masses - Different dynamics (internal waves, topographically trapped waves, upwellings) - Different characteristics of the mixed layers

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18 km T C Upwelling and topographic waves generated by a cyclone N S Y X km

19 Winds on Better resolve horizontal structure and intensity of large-scale features , 6:00 UTC CCMP NCEPR m/s Category 2 Hurricance on Saffir-Simpson scale m/s

20 Winds on CCMP NCEPR

21 Momentum and Heat Fluxes, CCMP winds 210 NCEPR 2 winds Heat Fluxes, W/m 2-8.9TW Heat Fluxes, W/m 2-6.5TW -210 W/m W/m Momentum flux, N/m 2 > Momentum flux, N/m N/m N/m

22 Temperature Change in the Ocean Model Ocean dynamics Turbulent mixing Solar radiation

23 Wind Stress in the Momentum Equation z u K z F x p fv uu t u M u 0 1 z v K z F y p fu uv t v M v 0 1 Open boundary conditions (sea surface): y x z M z v u K, 0,

24 Atmospheric and Oceanic Heat Transports to the Arctic The surface heat flux (Simonsen & Haugen, JGR, 1996): the Nordic Seas TW the Barents Sea - 42 to 162 TW Near-surface T, February, GDEM3 From: Dukhovskoy et al., JGR, 2006

25 Arctic Ocean Oscillation (Proshutinksy & Johnson) Meridional heat transport via transient eddies controls regime shift in the Arctic From: Proshutinsky & Johnson, 1997 (updated) From: Dukhovskoy et al., GRL, 2004

26 Cyclone classification Large-scale low-pressure systems: Spatial scale: O(1e3) km Time scale: days-week Cyclones Meso-, small-scale low pressure systems: Spatial scale: O(100) km Time scale: hours - day Simulated warm-core Polar Low over the Japan Sea Polar Low in NOAA satellite image NOAA 04:33 UTC 20 December, 2002 From: G. Fu, Ph.D. Thesis, 1999 From: L. Hamilton, The European Polar Low Working Group, 2004

27 Launched 6/19/ /23/2009, flied at 800 km, circling the Earth from pole to pole every 100 minutes. The sensor took 400,000 measurements over 93% of Earth s surface every day QuickSCAT

28 Satellite Scatterometry

29 Simulate Sea Ice Thickness, December 19, 2007 Courtesy to: P. Posey and A. Walcraft, NRL SSC

30 Advantages of Scaterometer Wind Products Resolve small-scale features that are missing in NCEPR , 12:00 UTC CCMP NCEPR 2

31 Advantages of Scaterometer Wind Products Better resolve horizontal structure and intensity of large-scale features , 6:00 UTC CCMP NCEPR 2

32 National Center for Environmental Prediction Reanalysis (NCEPR 2) The NCEP/NCAR Reanalysis 1 project is using a state-of-the-art analysis/forecast system to perform data assimilation using past data from 1948 to the present. A large subset of this data is available from PSD in its original 4 times daily format and as daily averages. However, the data from is a little different, in the regular (non-gaussian) gridded data. That data was done at 8 times daily in the model, because the inputs available in that era were available at NOAA-CIRES 3Z, 9Z, 15Z, Twentieth and 21Z, whereas Century the Global 4x daily Reanalysis data has Version been available II ( ) at The Twentieth 0Z, 6Z, Century 12Z, and Reanalysis 18Z. These Project, latter times supported were forecasted by the Earth and System the Research Laboratory combined Physical result for Sciences this early Division era is from 8x daily. NOAA The and local the ingestion University of Colorado CIRES/Climate process took Diagnostics only the 0Z, Center, 6Z, 12Z, is and an effort 18Z forecasted to produce values, a global and reanalysis dataset thus spanning only those the were entire used twentieth to make century, the daily assimilating time series and only monthly surface observations means here. of synoptic pressure, monthly sea surface temperature and sea ice distribution (Version II includes the years 1891 to 2008). Products include 6- hourly ensemble mean and spread analysis fields on a 2x2 degree global lat/lon grid, and 3 and 6-hourly ensemble mean and spread forecast (first guess) fields on a global Gaussian T-62 grid. Fields are accessible in yearly time series files (1 file/parameter). Ensemble grids, spectral coefficients, and other information will available by offline request in the near future.

33 Impact of intense cyclones on the ocean Atmosphere Q Q Momentum / Heat Fluxes lat sen a C d a a u u LCE u qa C C u T p s a q s T s Ocean Thermodynamics: surface cooling Dynamics: mixing Reed, JGR, 1983; Large et al., Rev. Geophys., 1994

34 Why care to resolve cyclones?