Comparing CAM3 Surface
Winds with Observed 10-m
Surface Winds (SeaWinds)
Scott B. Capps
Advisor: Dr. Charles S. Zender
University of California, Irvine
SST/ICE Data provided by:
Dennis Shea, NCAR
Temporal Filtering done by:
Daniel Wang, UCI
OUTLINE:
CAM3 vs. SeaWinds Comparison

Implementation of 4-bin Wind Speed PDF

Physically-based Wind Speed PDF

QuikSCAT Surface Winds – 2005
A
A
Shape=2.16
Scale=10.67 m s-1
B
A
B
B
Shape=7.01
Scale=7.94 m s-1
A
B
QuikSCAT 10m Wind Speeds (m s-1)
July 2, 2006 at 6pm local time
QuikSCAT Observations:
1999 - Current
10m surface wind speeds (m/s)
Empirically derived relationship
between ocean roughness and wind
speed
Asc/desc pass 6:00am/6:00pm

QuikSCAT Grid
Cell (0.25oX0.25o)
T42 Grid Cell
Known Issues:




Rain cells discarded
Underestimates wind speeds > 25
m/s
Directional errors at low wind
speeds
In situ vs. QuikSCAT RMS error
differences <1 m/s and ~15o
(Bourassa et al. 2003)
A Fair Comparison: CAM3 vs SeaWinds
QuikSCAT:
2x/day x 365 days/year x 6 years = 4,380
0.25x0.25 degree resolution
10-m winds (neutral stability)

CAM3.1.p2(uncoupled):
72x/day x 365 days/year x 6 years = 157,680
T42 resolution
DOM (observed SST/ICE for 2000-2005)
Lowest level winds (~50m AGL)

A Fair Comparison: CAM3 vs SeaWinds
Sub-sample from CAM output
QuikSCAT:
2x/day x 365 days/year x 6 years = 4,380
0.25x0.25 degree resolution
10-m winds (neutral stability)
QuikSCAT:
2x/day x 365 days/year x 6 years = 4,380
Spatial average of the temporal PDF
10-m winds (neutral stability)


CAM(uncoupled):
CAM:
72x/day x 365 days/year x 6 years = 157,680
T42 resolution
DOM (observed SST/ICE for 2000-2005)
Lowest level winds (~50m AGL)

2x/day x 365 days/year x 6 years = 4,380
Temporal PDF
DOM (observed SST/ICE for 2000-2005)
10-m winds

2000-2005 SeaWinds Climatology and CAM3 Biases




0.19 m/s global mean bias
Northern Hemisphere trade wind regions and mid-latitude storm tracks
2.5-3.0 m/s positive bias in circumpolar region
1.0-3.0 m/s negative bias in ITCZ




0.27 m/s global mean bias
Overestimates variability in the North Atlantic storm track
Equatorial periphery of subtropical anticyclones
Underestimates variability in Indian Ocean



-0.18 m/s global mean bias
1.5 positive bias near Australia
Negative biases in the ITCZ




-0.12 m/s global mean bias
Underestimates peak winds in doldrums
Overestimates peak winds in trade wind regions
5 m/s bias in south Indian Ocean
Zonally-Averaged Statistical Parameters


Mean wind speed and shape positive differences in NH trade wind regions
Largest positive mean speed and shape differences in NH/SH storm track regions
CAM
underestimates
variability in the
trades
2000-2005 DJF Climatology
2000-2005 JJA Climatology
TOGA TAO Region
CAM minus SeaWinds
The Forcing of a 4-bin Wind Speed PDF
June 2000-2005 Climatology
The Implementation of a 4-bin Wind Speed PDF
Slab Ocean Model 4-bin PDF minus 1-bin
climatology (lowest level winds)
A significant decrease in mean
winds within the circumpolar and
trade wind regions
Future Research: Implementing a Physically-based PDF
Velocity Scales
(Cakmur et al. 2004)
Turbulence Kinetic
Energy
Dry (Free)
Convection
Turbulence
Gust Fronts
Richardson Number
R. V. Cakmur and R. L. Miller; Incorporating the effect of small-scale circulations upon dust emission in an atmospheric
general circulation model, Journal of Geophys. Research, 109; 2004
Future Research: Implementing a
Physically-based PDF
Relative Frequency
TKE Diagnostic is not consistent with observations:
Observed TKE values (Stull(1988), Yamada &
Mellor(1975)):
2 2
 Convectively mixed: 3 m /s (sfc-300m)
2 2
 Neutral: 3.4 m /s
2 2
 Stable: < 0.4 m /s
Need to Evaluate other PBL schemes
CONCLUSIONS:
Consistent Positive Shape and Mean Wind Biases in Trade Wind and Circumpolar
Regions
4-bin PDF Improved Positive Biases
Physically-Based PDF: Need More Turbulence Parameters (Shallow Convection
Scheme Downdraft Mass Flux)