Download Wind stress curl

Impacts of the ocean mesoscale on
mean state and coupled phenomena
Malcolm Roberts,
Met Office Hadley Centre
© Crown copyright Met Office
Thanks
• UK-Japan Climate Collaboration team: Pier Luigi Vidale,
Marie-Estelle Demory, Adam Clayton, John Donners
• Dave Stevens and HiGEM team
• Akira Kuwano-Yoshida and group
• Yevgeny Aksenov and NOCS group
• Arne Biastoch
• Eiji Watanabe and Hiroyasu Hasumi
• Frank Bryan, Wonsun Park, Tony Rosati
Motivation
• When building a coupled model, there is demand for resources
from all sides (atmosphere, ocean, sea-ice, land surface,
biogeochemistry etc etc) in many areas
• Resolution (horizontal+vertical)
• Complexity, either through new parameterisations, or from extra
components (e.g. Earth system components and feedbacks)
• Ensembles (to address uncertainty, signal to noise ratio)
• Run length – 100’s years for coupled models
• When arguing for resources, need evidence that making any
component (much) more expensive is necessary to produce an
improved simulation
• Cost implications (e.g. sea-ice can be multi-category, multi-level)
• Ocean biogeochemistry (many tracers required)
• Need longer timescale for ocean testing
Challenge
• Can we make a case for eddy-permitting ocean
models being essential for climate simulations?
• Or at least are there questions we cannot/should not
address without such a model?
• And if we choose an eddy-permitting ocean
model, what resolution atmosphere model do
we need?
• We also need to think about vertical resolution
• Diurnal cycle, intraseasonal coupled air-sea variability,
and near-surface vertical resolution
Mesoscale ocean and coupled
modelling
• Many studies using high resolution (eddy-permitting) ocean models that
show an improved simulation of the ocean (mean state and variability)
• More complex to show how increased ocean resolution directly impacts
on simulation of climate (e.g. atmosphere/land where people live) – may
well need sufficient atmospheric resolution to be able to respond (e.g.
HadCEM showed little change in climate, Roberts et al, 2004)
• Various studies using recent higher resolution observed datasets to
force atmosphere models are beginning to show how smaller scales
can have important effect on larger scales (e.g. TIWs, boundary
currents)
• Just beginning to be enough studies, using coupled climate models, to
show how enhanced ocean resolution does have important effects on
the mean state of the coupled model, and its variability
• This is important, both for long-term climate modelling (centuries), as
well as seasonal-decadal forecasting. Would like this to be a seamless
transition when modelling.
• It may also be very important for processes such as ocean
biogeochemistry, including air/sea flux exchanges and uptake of CO2
Selection of groups with “high” eddy-permitting
resolution coupled models (ready or planned)
Model/
Centre
UK MOHC/NERC
Model
name
Atmos
Ocean
Comments
HiGEM/NUGEM
1.25°x0.833
°/
0.833°x0.55
°
0.33°x0.33°
100’s years +
IPCC AR5 S2D
0.25°x0.25°
Later this year,
hierarchy
HadGEM3-H
(MOAtm/NEMO)
0.833°x0.55
°
GFDL
CM2.4
1°
0.25°
Soon to be 0.5°
atmos, working
on 0.1° ocean
NCAR, DOE
(LLNL, Los
Alamos)
CCSR/FRCGC
/NIES
LOCEAN,
IPSL, FRCGC
IFM-Geomar
CAM3.5
0.5°
0.1°
0.25°
0.1°
10-15 year
development
runs
MIROC
0.85° (T213)
0.25°x0.166
°
IPCC AR4
ECHAM/ NEMO
1.7° (T106)
0.25°x0.25°
Testing – 5-10
years so far
ECHAM5/
2.8° (T63)
0.5°, 0.25°
Later this year,
Overview of talk
• Ocean mesoscale and effects on atmosphere
• Ocean SST and wind-stress curl/divergence
• TIWs and Southern Ocean
• Boundary currents and precipitation
• Ocean mesoscale and sea-ice
• Impact of ocean mesoscale on ocean and coupled
climate mean state and variability
• TIWs
• Coastal processes
• Hawaiian countercurrent
• Agulhas
• Climate change and Southern Ocean
SST gradients and
atmosphere forcing
• Small-scale ocean SST gradients affect the atmospheric
winds
• This can cause a large scale mean response
• Examples are tropical instability waves (TIWs), Southern
Ocean features
• Following work by Chelton and others
(The following slides courtesy of Dave Stevens, UEA,
model output from the HiGEM coupled climate model,
ocean resolution 1/3x1/3°, atmosphere 5/4x5/6°)
Observations
Wind curl obs
Uniform wind above the boundary layer
Mechanism
SST
Colder
Warmer
Uniform wind above the boundary layer
Mechanism
Stable
Boundary
Layer
Turbulent
Boundary
Layer
SST
Colder
Warmer
Uniform wind above the boundary layer
Mechanism
Stable
Boundary
Layer
Turbulent
Boundary
Layer
Weaker surface wind
Stronger surface wind
SST
Colder
Warmer
Uniform wind above the boundary layer
Mechanism
Stable
Boundary
Layer
Turbulent
Boundary
Layer
Divergence
Weaker surface wind
Stronger surface wind
SST
Colder
Warmer
Wind above the boundary layer
Warmer
Colder
South easterly wind blowing across a perturbed oceanic front
Surface wind stress
.  0

 0
Warmer
.  0
 0
.  0


Colder

Surface wind stress

Wind stress divergence linearly related to the down wind SST gradient
. T. | T ||  | cos
where  is the angle between the wind stress and SST gradient
Similarly wind stress curl linearly related to the cross wind SST gradient
Mean tausst
Mean divsst
Wind stress divergence (shaded) and SST (contoured) - High resolution
Wind divergence and sst
Wind stress divergence (shaded) and SST (contoured) - High resolution
Wind divergence and sst
Wind stress divergence (shaded) and SST (contoured) - High resolution
Wind divergence and sst
Wind stress divergence (shaded) and SST (contoured) - High resolution
Wind divergence and sst
Wind stress divergence (shaded) and SST (contoured) - High resolution
Wind divergence and sst
30 year mean Wind stress curl coloured and SST contoured
4 wind curl
Low resolution ocean - High resolution atmosphere
Low resolution ocean - Low resolution atmosphere
High resolution ocean - High resolution atmosphere
High resolution ocean - Low resolution atmosphere
Summary
• The atmosphere can respond to the ocean on oceanic length
scales
• The persistent small scale features in Southern Ocean wind
stress curl and divergence arise from mesoscale ocean features
• The overlying wind field is strongly coupled to the Tropical
Instability Waves
• A high resolution model can capture this behaviour
• There are implications for coupling strength between
atmosphere and ocean, which may depend on such small-scale
processes
Atmospheric response to the
Gulf Stream in an AGCM
Akira Kuwano-Yoshida1, Shoshiro
Minobe2, and Shang-Ping Xie3
1. Earth Simulator Center,
JAMSTEC
2. Hokkaido University
3. IPRC, University of Hawaii
To be submitted to J. Climate
Model and experiment design
Model
Resolution
SST
AGCM for the Earth Simulator
(AFES, Ohfuchi et al. 2004) ver.2
(Enomoto et al. 2008)
T239 (~ 50km) L48
NCEP RTG SST (0.5 degree, daily)
Initial condition ERA40
Integration
11. Feb. 2001 ~ 28. Feb. 2006
The data from 1. Mar. 2001 are
analyzed.
Sensitivity experiment for SST gradient
• CNTL: Original RTG
SST data
• SMTH: 2 dimensional 12-1 filtering was
conducted to 25N - 55N,
100W - 30W region
(blue square) 100 times
for daily RTG SST data.
Seasonal variation : precipitation
TRMM3B43
ANN
MAM
JJA
SON
DJF
CNTL
SMTH
•
The precipitation band
appears over the Gulf
Stream through the
year in TRMM3B43
and CNTL run, while
the band disappears in
SMTH run.
Precipitation form
Convective precipitation
TRMM
Large-scale precipitation
•
CNTL
SMTH
Convective precipitation
is more sensitive to the
Gulf Stream SST in
AFES and observation.
Composite for Convective precipitation in DJF
CNTL
SMTH
CNTL
SMTH
• CNTL: Cumulus precipitation (color in left panels) associated with large
CAPE (bold contour in left panels) elongates along surface
moisture convergence (bold contour in right panels) induced by -SST
Laplacian maximum (color in right panels).
• SMTH: Cumulus precipitation passes away eastward because smoothed
SST front does not induce surface convergence.
Summary and conclusion
• Precipitation is trapped by the Gulf Stream
through the year, while the strength and
width have seasonal variation.
• Cumulus precipitation is more sensitive to
the Gulf Stream SST front.
• Surface convergence and evaporation
induced by sharp SST gradient form
favorable environment for cumulus
convection occurrence and maintenance.
Influence of small scales on
mean climate and variability
• TIWs and impact on mean climate in tropical Pacific (may
be partly model specific here)
• Example here from Met Office work, but many other
authors have investigated this area in much greater detail
• Work by Jochum and co-workers has shown:
• how TIWs increase the atmosphere-ocean heat flux
• how intraseasonal eddies such as TIWs cause
interseasonal-interannual variability in the climate model
(through mixed layer changes)
• Influence of improved mean climate on atmosphere
(including precipitation)
• Teleconnections can spread this impact globally
Eddies, SSTs and ENSO
• Rôle of resolved versus
parametrised ocean mixing, for
example:
L
– Tropical Instability Waves
emerge in 90km-1/3o model
(HiGEM), performing meridional
mixing near Equator;
• ENSO is poorly represented in
the standard Hadley Centre
climate model (HadGEM1); a
more realistic ENSO is simulated
by the high-resolution HiGEM
model;
H
Eddy heat flux
convergence
Obs
Ocean temperature
profile along Equator
– is this because the HiGEM mean
state in the tropical Pacific is so
much closer to reality ?
– if so, this is a good example of a
smaller scale phenomenon
affecting the large scale mean
state and, through it, a major
element of climate variability.
Roberts et al, 2009, in press
SST errors
compared to
HadISST
climatology for
HadGEM1.1 and
HiGEM1.1
El Niño DJF precipitation anomalies (mm/day)
HadGEM1.1
HiGEM1.1
CMAP observations
Ocean and sea-ice
interaction
• Models with different ocean resolutions
• in Arctic, clear differences in the inflow routes
• Important impact on sea-ice extent
• definite improvement in the Norwegian Atlantic
Current,
• the Arctic Circumpolar Current,
• the inflow of the Pacific Water through the Chukchi
Sea,
• in southward re-circulation of the Atlantic water in
the Nordic Seas and in the West Greenland
Current in the Baffin bay
Potential Temperature at ca. 100 m depth in September in NEMO and OCCAM
NEMO 1°
CICE4
95 m
Sep 2001
NEMO 1°
LIM2
95 m
Sep 2001
NEMO 1/4°
LIM2
95 m
Sep 2001
OCCAM 1/12°
98 m
Sep 2004
Courtesy Y. Aksenov, NOCS
Pacific inflow – Watanabe and
Hasumi, 2009, JPO (submitted)
• This study deals with how the water entering the Arctic Ocean
from the Pacific behaves.
• It first flows along the Alaskan coast in the clockwise direction,
and then is transported toward the centre of the Beaufort Gyre
by baroclinic eddies in summer.
• The eddy activity depends on the summer-time sea ice margin.
• When the Alaskan coastal area is open, the coastal current tends to
be strong, which favours baroclinic eddy generation.
• In such a situation, eddies also transport solar heat absorbed in the
open coastal region toward the central Canada Basin.
• This heat transport induces further retreat of the sea-ice margin or
delays re-freezing of the offshore region.
• Could be important feedback mechanism, both for simulation of
sea-ice extent for future climate, as well as improving presentday simulated mean state.
• Uses regional ocean-sea-ice model with 2.5 km resolution
Model bathymetry in the spherical coordinate system which is rotated so
that the singular points are on the equator. B.S. = Bering Strait, H.C. =
Herald Canyon, C.C. = Central Channel, N.R. = Northwind Ridge, C.R. =
Colville River mouth,
Coastal processes
• Coastal upwelling regions are a strongly coupled system
of clouds, winds and SST
• Important for ocean primary productivity (hence sink for
CO2), fisheries (20% of global catch), and coastal
communities
• Global models often have large warm biases (poor
stratocumulus/SST simulation) off western coasts of
America/Africa
• Work done by John Donners (UJCC/NCAS-Climate,
Univ. of Reading)
Change in summer SSTs at 4 locations when
increasing atmosphere res. only from N96 (150km)
to N144 (90km)
California
Africa
S. America
Africa
Ocean upwelling
winter
summer
Deep upwelling
Warning: different color scales!
~ 5mm/s ==> 13 m/month
Low resolution ocean (1°)
~ 25mm/s ==> 65 m/month
High resolution ocean (1/3°)
Observations: tens to hundreds m/month
Seasonal cycle of SST – need high resolution in
both components
Summary
• Global coupled models lacked resolution to represent
upwelling adequately; regional studies used atmosphereor ocean-only models.
• Both higher atmosphere and ocean resolution improve
the seasonal cycle of SSTs: either due to cloud or
upwelling effects.
• Upwelling at higher ocean resolution is concentrated
near the coastline, drawing colder water from deeper
down the water column.
• Cloud feedback enhances SST cooling due to upwelling.
© Crown copyright Met Office
Wind stress curl/ocean
current interaction
• Scale interactions between atmosphere and
ocean can cause changes to the mean state –
often involve wind stress curl
• Changes to both atmosphere and ocean
resolutions both play a role – an important
question as to how one should balance their
relative resolutions
• Sasaki and Nonaka (2006) showed how
different forcing fields (NCEP vs QuikSCAT
winds) can also cause differences to the
Hawaiian Lee Countercurrent – due to wind
stress curl propagated from Hawaiian Islands
• The following uses the coupled HiGEM model
Wind stress curl (colours, N/m2) and ocean current at 35m (contours
at 5cm/s intervals) for a matrix of coupled atmos/ocean model
resolutions (150 or 90km Atm, 1 or 1/3° Ocn models)
LoA-LoO
HiA-LoO
LoA-HiO
HiA-HiO
Agulhas
• Transport of properties (heat, salt) between Indian and
Atlantic Oceans potentially very important for long-term
Atlantic meridional overturning
• This is (almost) always wrong in climate model
simulations – since their resolution is rarely sufficient to
resolve the retroflection
• Impact on mean model properties, but also makes
models more difficult to use and compare to observations
(changing water mass properties in the Atlantic)
Agulhas region
in HadGEM1.1
(top) and
HiGEM1.1
(bottom)
Retroflection
and eddyshedding at high
resolution,
mean flow at
low resolution –
significant
impacts on
water mass
properties ->
climate change
detection
Agulhas
• Need high resolution for retroflection (better that
½ degree?), and even higher for Agulhas ring
variability
• Important implications for ocean and coupled
climate:
• Water mass erosion
• Heat/freshwater transport and long-term MOC
• Understanding of the mechanisms of MOC variability,
for comparison with observations
• The following courtesy of A. Biastoch, IFMGEOMAR
Nested Agulhas Model using AGRIF
DRAKKAR Hierarchy [DRAKKAR Group, 2007], based on NEMO v2.3 [Madec, 2008]
•
Tripolar grid, coupled configuration with 2-way interaction between
 Global coarse-resolution model (1/2° )
 Regional high-resolution model (1/10° )
•
46 vertical levels, partial cell topography
•
State-of-the-art physics / parameterizations
•
Thermodynamic-dynamic sea-ice model
•
20-yr spinup with global model
•
O(50-yr) atmospheric forcing:
NCEP/NCAR-derived (“CORE”),
applied via Bulk formulae,
6h/1d-resolution, inter-annual
variability (1958-2004)
•
~O(months) on (NEC SX-8/9)
•
5-daily output  several TB
ORCA05
Courtesy A. Biastoch
AG01
Agulhas-Induced MOC Variability
Difference in MOC and
North Brasil Current at
6°S
MOC-difference at
1000m: Exp. with minus
without Agulhas nest
(interannually filtered)
 ±1.5 Sv decadal variability
 rapid communication to the North Atlantic
[Biastoch, Böning, Lutjeharms; Nature, 2008]
Principle of Signal Propagation
Topographic Shelf Waves
Large-scale horizontal
circulation and eddy kinetic
energy
[Biastoch, Böning, Lutjeharms; Nature, 2008]
Agulhas Variability vs. Subpolar Deepwater Formation
Complete Forcing
Effect of heat
flux variability
Effect of Agulhas
mesoscale variability
Standard deviation of interannual MOC strength
 Agulhas influence reaches into northern hemisphere
 … in tropics of similar amplitude as those by subpolar deepwater
formation
[Biastoch, Böning, Lutjeharms; Nature, 2008]
Ocean-atmosphere coupling
• Couple 0.5° atmosphere with either 1° or
0.1° ocean model (CAM3.5)
• Look at relationship between ocean SST and
atmospheric winds
• Although much more variability in high
resolution ocean case, the regression
coefficient (coupling strength) is unchanged –
and weaker than observed
• Suggests missing or misrepresented physics in the
atmosphere model
• Supplied by F. Bryan, NCAR
Lower resolution
ocean
Higher resolution
ocean
Climate change and Southern
Ocean
• Look at the response of models with different horizontal
resolution to climate change (1% to 2x CO2 forcing) - 1° vs
¼° ocean resolution
• Westerly winds shift poleward under climate change in both
models
• Response of ocean and hence climate is different
• Eddy transport works against mean flow, and changes compensate
the increased Ekman flow
• Hence change in oceanic energy transport much reduced at higher
resolution
• Atmospheric energy transport reduced in turn
• Hence response of coupled climate to forcing is different at
higher resolution
• Work from R. Farneti, GFDL, using CM2.4 and CM2.1
Change in ocean energy transport
(2x CO2 – control)
Blue = eddy-permitting
Change in atmosphere energy
transport (2x CO2 – control)
Blue = eddy-permitting
Heat transport components of the control and 2xCO2 integrations
using the higher resolution ocean model CM2.4
Summary/issues
• Increasing number of examples of how
mesoscale ocean processes play important role
in ocean, atmosphere and climate mean state
and variability
• Are there recommendations to make with
regards to ocean resolution and e.g. IPCCclass models
• Is there enough evidence that eddy-permitting
models are needed in order to produce “good”
climate projections?
• Does the timescale under consideration
(annual/decades vs centuries) make any difference
Questions
© Crown copyright Met Office