Transcript The Effects of Sea Surface Temperature Gradients on

Mark A. Bourassa, Paul Hughes and John Steffen
Center for Ocean-Atmospheric Prediction Studies &
EOAS, Florida State University
Funding by NASA Climate Data Records and NASA Ocean Vector Winds Science Team
Florida State University
 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)
 I’d welcome comments (written to [email protected]) 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?
Flux Accuracies for High Latitude Applications
Graphic from Bourassa et al. 2013, BAMS
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 (O’Neill
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
SST-Winds Relationship
From Chelton 2005
 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)
Motivation Continued
Why is this wind–SST 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
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)
Larry O’Neill’s List of Physics Behind
Wind-SST Interactions on the Oceanic Mesoscale
1) SST-induced hydrostatic pressure gradients generated by cross-frontal
2)
3)
4)
5)
6)
7)
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)
Cross-frontal boundary height changes in an equilibrium regime well
downwind of front (e.g., Samelson et al. 2006; Spall 2007)
Secondary circulations (e.g., Hsu 1984; Wai and Stage 1989) from
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)
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)
Surface drag (tau/H) balancing SST-induced pressure driven flow (Small et
al. 2005; O’Neill et al. 2010)
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)
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
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
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
SST Gradients and Surface Winds
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
1.5
1
Winter (DJF) seasonal SST gradients (> 1
K/100 km) over the Kuroshio Extension
145.125°E – 175.125°W and 35.125°N – 45.125°N
Winter (DJF) seasonal SST gradients
(> 1 K/100 km) over the Gulf Stream
73.375°W – 38.375°W and 35.375°N – 50.375°N
 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
2.2
2
1.8
1,6
1.4
1.2
1.0
K / 100km
2
K / 100km
Data Subset Regions
1.5
1
Summer (JJA ) seasonal SST gradients (> 1
K/100 km) over the the Kuroshio Extension
145.125°E – 175.125°W and 35.125°N – 45.125°N
2.2
2
1.8
1,6
1.4
1.2
1.0
Summer (JJA) seasonal SST gradients
(> 1 K/100 km) over the Gulf Stream
73.375°W – 38.375°W and 35.375°N – 50.375°N
 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
K / 100km
2
K / 100km
Data Subset Regions
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
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)
Seasonal Results
Latent Heat Flux
Stress
Fall Summer Spring Winter
Sensible Heat Flux
2002 – 2003 seasonal average differences in SHF (left), LHF (middle),
and wind stress (right) for DJF (top row), MAM (2nd row), JJA (3rd row),
and SON (bottom row)
Seasonal SHF
2002-2003 seasonal PDF’s 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
Seasonal LHF
2002-2003 seasonal PDF’s 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
Seasonal Wind Stress
2002-2003 seasonal PDF’s 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
Monthly Box Plots
SHF
SHF
• Monthly averaged
turbulent flux
differences are more
sensitive to the
background
environment
LHF
LHF
• More spatial
variability than
seasonal averages
• Annual cycle is
better resolved
Dec. 2002 – Nov. 2003 monthly box plots of SHF (top) and
LHF (bottom) difference over the Gulf Stream (left) and
Kuroshio Extension (right)
Daily Results
SHF
SHF
LHF
LHF
Daily PDF’s 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
SST Gradients For Upwelling Example
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2C/100km
 There are substantial SST gradients over most of the ocean
Ekman Upwelling
 Baroclinic
 Control
Ekman Upwelling Changes
 Changes in Ekman Upwelling (Baroclinic case – control)
 These are an order (1) impact
 Many areas with >30% changes
Spatially Band-Pass Filtered Changes
 Biggest changes are on
100 to 200 km
200 to 300 km
300 to 400 km
400 to 500 km
500 to 600 km
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
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/m²), LHF (6 W/m²), and Tau
(0.03 N/m²) 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
Mark A. Bourassa, Paul Hughes and John Steffen
[email protected]
Funding by NASA Climate Data Records and NASA Ocean Vector Winds Science Team
Florida State University
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
Small-scale Relationship Between
SST
and
Wind
Stress
Spatial High-Pass Filtered Wind Stress Curl (from Chelton et al. 2004)
-2
-1
0
Curl Anomalies (N m-3 x 107)
1
2
Filtered surface stress has anomalies associated with SST features.
30
Small-scale Relationship Between
SST and Wind Stress
 Results are linear for SST when both signs of SST gradients are
included!
31
SHF: Additional DJF Seasons
• Consistency in PDFs
among all DJF seasons
is a surprising result for
the Kuroshio Extension
• Low-frequency
variability in synopticscale environment and
SST fields has a
marginal effect on PDF
shapes, especially for
the Gulf Stream
Figure 14: DJF seasonal PDF’s 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.
Monthly Results
Figure 15: 2003 monthly average differences in SHF (left), LHF (middle), and wind stress
(right) for January (top row), April (2nd row), July (3rd row), and October (bottom row)
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