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Mark A. Bourassa, Paul Hughes and John Steffen Center for Ocean-Atmospheric Prediction Studies & EOAS, Florida State University Florida State University Funding by NASA Climate Data Records and NASA Ocean Vector Winds Science Team

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NEW: GCOS will consider adding missing surface fluxes to the list of Essential Climate Variables in 2015 I co-chair the Ocean Observations Panel (OOPC) Id welcome comments (written to mbourassa@fsu.edu) on The flux accuracy needs and size of signal for different processes Observational capability Existing now Likely to be available in the future The need to know fluxes is easy to justify Can we argue that fluxes can be well enough observed? Can information from reference sites be transferred to bulk observations of routine variables?

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Graphic from Bourassa et al. 2013, BAMS Flux Accuracies for High Latitude Applications

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Motivation For My Original Topic Satellite observations of surface winds and SST see coupled perturbations of winds and SSTs (Chelton and many others) These are confirmed with in situ observations (ONeill 2012) The dominate physical mechanism has been a topic of vigorous debate Changes in surface winds due to SST gradients are poorly modeled in NWP and climate models, potentially resulting in large errors in surface turbulent fluxes and the curl of the stress Our goal is to determine how large of a difference in surface turbulent fluxes of momentum, sensible heat, and latent heat occurs due to overlooking the correlated variability in SSTs and winds

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SST-Winds Relationship Wind stress magnitudes are relatively weak over colder water and strong over warmer water Wind stress divergence is strongest for flow perpendicular to isotherms (parallel to SST gradient) Wind stress curl is strongest for flow parallel to isotherms (perpendicular to SST gradient) From Chelton 2005

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Motivation Continued Why is this windSST coupling important? Pathway to transport moisture and diabatic heat from the marine atmospheric boundary layer (MABL) into the free atmosphere (Minobe et al. 2008; WCRP Grand Challenge) Atmospheric feedback can impact local oceanic currents, transport, temperature structure, eddy structure, and eddy kinetic energy (Chelton et al. 2007; Jin et al. 2009) Impact the large-scale (i.e., basin scale) ocean circulation (Hogg et al. 2009; WCRP Grand Challenge) Our other interests Modification of SST diurnal cycle Using SSTs to improve information in satellite wind fields Importance to ocean forcing and surface fluxes 6

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Spatial Smoothing in NWP Smoothing in NWP over oceans reduces signals on scales up to 8-10 times the grid spacing ECMWF operational grid spacing is now 15 km NWP winds had considerably less energy at spatial scales smaller than ~1000 km (Wikle et al. 1999; Milliff et al. 2004; Chelton et al. 2006). Currently, less than ~400 km for many products Along-track wavenumber spectra of wind speed in the eastern North Pacific for 2004 computed from QuikSCAT observations (heavy solid lines), NCEP analyses (thin solid lines), and ECMWF analyses (dashed lines) of 10 m winds bilinearly interpolated to the times and locations of the QuikSCAT observations. (Chelton et al. 2006)

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Larry ONeills List of Physics Behind Wind-SST Interactions on the Oceanic Mesoscale 1) SST-induced hydrostatic pressure gradients generated by cross-frontal boundary layer temperature and depth changes (locally low surface pressure over warm water and higher SLP over cooler water; e.g., Lindzen and Nigam 1987; Hashizume et al. 2001; Small et al. 2003JCLI, 2005JGR) 2) Cross-frontal boundary height changes in an equilibrium regime well downwind of front (e.g., Samelson et al. 2006; Spall 2007) 3) Secondary circulations (e.g., Hsu 1984; Wai and Stage 1989) from 4) SST-induced modulation of vertical turbulent momentum transport aloft to the surface (e.g., Sweet et al. 1981; Wallace et al. 1989; Hayes et al. 1989) 5) SST-induced modulation of surface layer vertical profile of horizontal wind by cross-frontal changes of surface buoyancy fluxes (e.g., Friehe et al. 1991; Liu et al. 2007) 6) Surface drag (tau/H) balancing SST-induced pressure driven flow (Small et al. 2005; ONeill et al. 2010) 7) Baroclinic modification of pressure gradients, vertical shear, and turbulent mixing in the surface layer and throughout the depth of the boundary layer (Song et al. 2006)

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The Model Used for Testing We used the University of Washington Planetary Boundary Layer (UWBPL) model Atmospheric model Coupled log-layer and Ekman layer Key Inputs are Geostrophic pressure gradients SST Air temperature (10m altitude) SST gradient Key Switches Baroclinic-related changes based on temperature gradient Stratification (SST Air Temperature) 9

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Change in Speed: Stability & Baroclinic For flow along the front e) Due only to stratification changes f) Due only to air temperature gradient changes 10

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Change in Speed: Stability & Baroclinic For flow across the front C) Due only to stratification changes D) Due only to air temperature gradient changes 11

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Winter (DJF) seasonal SST gradients (> 1 K/100 km) and data subset regions located over the Gulf Stream and the Kuroshio Extension Winter (DJF) seasonal wind speed difference and data subset regions located over the Gulf Stream and the Kuroshio Extension SST Gradients and Surface Winds

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Data Subset Regions Data subsets contain areas with largest SST gradients SST effects still occur outside of these regions, but to a lesser extent SSTs are slowly varying Winter (DJF) seasonal SST gradients (> 1 K/100 km) over the Kuroshio Extension Winter (DJF) seasonal SST gradients (> 1 K/100 km) over the Gulf Stream 145.125E 175.125W and 35.125N 45.125N73.375W 38.375W and 35.375N 50.375N 2 1.5 1 K / 100 km 2.2 2 1.8 1,6 1.4 1.2 1.0 K / 100km

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Data Subset Regions SST gradients are slightly reduced and displaced further north Maximum SST gradients still reach 2.2K/100 km Limit of solutions for UWPBL in this configuration Summer (JJA ) seasonal SST gradients (> 1 K/100 km) over the the Kuroshio Extension Summer (JJA) seasonal SST gradients (> 1 K/100 km) over the Gulf Stream 145.125E 175.125W and 35.125N 45.125N73.375W 38.375W and 35.375N 50.375N 2 1.5 1 2.2 2 1.8 1,6 1.4 1.2 1.0 K / 100km

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Experimental Setup Two data sets created: one that adjusted surface winds in response to small scale SST gradients and one the lacked this air-sea coupling (by Paul Hughes) Both data sets produced with surface pressures, 2-m air temperatures, and 2-m dew point temperatures from ERA-Interim and Reynolds Daily OISST Dec. 2002 Nov. 2003 and six DJF seasons of 1987 88, 1988 89, 1989 90, 1999 00, 2000 01, and 2001 02 Six hourly (0,6,12,18 Z) with 0.25 grid spacing covering Atlantic and Pacific Ocean basins Univ. of Washington Planetary Boundary Layer (UWPBL) model Results in 10m wind vectors. Fluxes calculated from these winds and above variables

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MFT12 Flux Model Parameters Bourassa (2006) surface roughness model, which includes the effects of capillary waves and sea state Clayson, Fairall, Curry (1996) roughness length parameterizations for potential temperature and moisture Zheng et al. (2013) transition from a smooth to rough surface Benoit (1977) parameterization for an unstable boundary layer Beljaars and Holtslag (1991) parameterization for a stable boundary layer Monin-Obukhov scale length (Liu et al. 1979)

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Seasonal Results 2002 2003 seasonal average differences in SHF (left), LHF (middle), and wind stress (right) for DJF (top row), MAM (2 nd row), JJA (3 rd row), and SON (bottom row) Sensible Heat FluxLatent Heat Flux Stress Fall Summer Spring Winter

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Seasonal SHF 2002-2003 seasonal PDFs of SHF difference over the Gulf Stream 2002-2003 seasonal box plots of SHF difference over the Gulf Stream PDFs show seasonally averaged values from each grid point in the domain

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Seasonal LHF 2002-2003 seasonal PDFs of LHF difference over the Gulf Stream 2002-2003 seasonal box plots of LHF difference over the Gulf Stream PDFs show seasonally averaged values from each grid point in the domain

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Seasonal Wind Stress 2002-2003 seasonal PDFs of wind stress difference over the Gulf Stream 2002-2003 seasonal box plots of wind stress difference over the Gulf Stream The change in stress is driving the change in sensible and latent heat fluxes Flux changes due to stability changes are relatively small

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Monthly Box Plots Dec. 2002 Nov. 2003 monthly box plots of SHF (top) and LHF (bottom) difference over the Gulf Stream (left) and Kuroshio Extension (right) Monthly averaged turbulent flux differences are more sensitive to the background environment More spatial variability than seasonal averages Annual cycle is better resolved SHF LHF

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Daily Results Daily PDFs of SHF (top) and LHF (bottom) difference over the Gulf Stream (left) and Kuroshio Extension (right) during selected high wind events Snapshots in the life cycle of individual synoptic-scale events that can impact storm evolution and upper oceanic properties Despite the same physical process taking place over the Gulf Stream and Kuroshio Extension, PDF shapes are different SHF LHF

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SST Gradients For Upwelling Example There are substantial SST gradients over most of the ocean 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 C/100km

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Ekman Upwelling Baroclinic Control

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Ekman Upwelling Changes Changes in Ekman Upwelling (Baroclinic case control) These are an order (1) impact Many areas with >30% changes

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Spatially Band-Pass Filtered Changes Biggest changes are on scales poorly captured in weather models Need finer resolution models with better boundary-layers Note that spatial scale of upwelling areas is smaller than that of downwelling events We need to couple models on fine spatial scales 100 to 200 km 200 to 300 km 300 to 400 km 400 to 500 km 500 to 600 km

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Conclusions Differences in surface turbulent fluxes exhibit a seasonal cycle with a peak in winter (DJF), a transitional period in spring (MAM) and fall (SON), and a minimum in summer (JJA) Winter averages for SHF (4 W/m), LHF (6 W/m), and Tau (0.03 N/m) and non-negligible for many applications differences are important, even in summer, for very long time scale applications such as the upper ocean energy budget (Levitus et al. 2005) The local daily variations are much larger, and are presumably important for cyclogenesis and ocean circulation and certainly for ocean mixing Models require finer resolution and better boundary-layer parameterizations to capture these processes Great care must be taken when assimilating point data from ships and buoys, particularly to tune L3&4 gridded products

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Mark A. Bourassa, Paul Hughes and John Steffen mbourassa@fsu.edu Florida State University Funding by NASA Climate Data Records and NASA Ocean Vector Winds Science Team

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Boundary Layer Response Flow from cold to warm SST with (a) strong background winds and (b) weak background winds Horizontal acr0ss-front profiles of SST and air temperature below Vertical profiles of downstream anomalies in air temperature and pressure From Small 2008

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Small-scale Relationship Between SST and Wind Stress Filtered surface stress has anomalies associated with SST features. 30 Spatial High-Pass Filtered Wind Stress Curl (from Chelton et al. 2004) -2 -1 0 1 2 Curl Anomalies (N m -3 x 10 7 )

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Small-scale Relationship Between SST and Wind Stress Results are linear for SST when both signs of SST gradients are included! 31

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SHF: Additional DJF Seasons Figure 14: DJF seasonal PDFs of SHF difference (top) and LHF difference (bottom) over the Gulf Stream (left) and Kuroshio Extension (right) for the years 87 88, 88 89, 89 90, 99 00, 00 01, 01 02. Consistency in PDFs among all DJF seasons is a surprising result for the Kuroshio Extension Low-frequency variability in synoptic- scale environment and SST fields has a marginal effect on PDF shapes, especially for the Gulf Stream

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Monthly Results Figure 15: 2003 monthly average differences in SHF (left), LHF (middle), and wind stress (right) for January (top row), April (2 nd row), July (3 rd row), and October (bottom row)

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References Beljaars, A.C. M., and A. A. M. Holtslag, (1991), Flux parameterization over land surfaces for atmospheric models, J. Appl. Meteorol., 30, 327-341. Benoit, R., (1977), On the integral of the surface layer profile-gradient functions, J. Appl. Meteorol., 16, 859-860. Bourassa, M. A., (2006), Satellite-based observations of surface turbulent stress during severe weather, Atmosphere - Ocean Interactions, 2, 35 - 52. Chelton, D. B., (2005), The impact of SST specification on ECMWF surface wind stress fields in the eastern tropical Pacific, J. Climate, 18, 530-550. Chelton, D. B., M. H. Freilich, J. M. Sienkiewicz, and J. M. Von Ahn, (2006), On the use of QuickSCAT scatterometer measurements of surface winds for marine weather prediction, Mon. Wea. Rev., 134, 2055-2071. Clayson, C. A., C. W. Fairall, and J. A. Curry, (1996), Evaluation of turbulent fluxes at the ocean surface using surface renewal theory, J. Geophys. Res., 101, 28,503-28,513. Levitus, S., J. I. Antonov, and T. Boyer, (2005), Warming of the World Ocean, 19552003, Geophys. Res. Lett., 32, L02604. Liu, W. T., K. B. Katsaros, and J. A. Businger, (1979), Parameterization of air-sea exchanges of heat and water vapor including the molecular constraints at the interface, J. Atmos. Sci., 36, 1722-1735. Milliff, R. F., J. Morzel, D. B. Chelton, and M. H. Freilich, (2004), Wind stress curl and wind stress divergence biases from rain effects on QSCAT surface wind retrievals, J. Atmos. Ocean. Technol., 21, 1216-1231. ONeill, L. W., (2012), Wind speed and stability effects on coupling between surface wind stress and SST observed from buoys and satellite, J. Climate, 25, 1544-1569. Small, R. J., S. P. deSzoeke, S.-P. Xie, L. ONeill, H. Seo, Q. Song, P. Cornillon, M. Spall, and S. Minobe, (2008), Air-sea interaction over ocean fronts and eddies, Dyn. Atmos. Oceans, 45, 274-319. Wikle, C. K., R. F. Milliff, and W. G. Large, (1999), Surface wind variability on spatial scales from 1 to 1000 km observed during TOGA COARE, J. Atmos. Sci., 56, 2222-2231. Zheng, Y., M. A. Bourassa, and P. J. Hughes, (2013), Influences of sea surface temperature gradients and surface roughness changes on the motion of surface oil: A simple idealized study, J. Appl. Meteor. Clim., 52, 1561 - 1575.