Surface Observations in The Southern Ocean, Variability, and the Consequences

http://coaps.fsu.edu/~bourassa/[email protected]

Surface Observations in The Southern Ocean,

Variability, and the Consequences on Fluxes

Mark A. Bourassa

Center for Ocean-Atmospheric Prediction Studies, andDepartment of MeteorologyThe Florida State University

http://coaps.fsu.edu/[email protected]

With help from Shawn R. Smith

http://coaps.fsu.edu/~bourassa


mailto:[email protected]

The Florida State UniversitySouthern Ocean Working Group 2


Outline Observing Systems and Surface Products (emphasis on winds)

Historical data (ships and buoys) Satellites Numerical Weather Prediction (reanalyses)

Validation and Planning Tool: Research Ships and Buoys Validation of flux products and related variables Calibration/Validation of satellite sensors New: input in satellite sensor design.

High Frequency Fluxes Uncertainties in High Frequency Fluxes

Recent Developments in Flux Modeling





Zonal Wind Stress

Averages from Hellerman and Rosenstein (the classic), COADS, SOC, and ECMWF (all for different periods).





Four Net Heat Fluxes

Net heat fluxes climatologies from SOC, COADS, NCEP/NCAR Reanalysis, and ECMWF (all for different periods).





COADS Climatological Average

December long-term (1960-93) mean of (QS - Q)W, i.e., saturation specific humidity at sea surface temperature minus specific humidity, times wind speed (g kg-1 m s-1), based on the average of PACS enhanced year-month-1° box means. http://www.cdc.noaa.gov/coads/egs_paper_fig1.html





Average Monthly Number of Observations (COADS: 1988-1997)

Ship Coverage on a 1x1 grid.

Buoy Coverage

Average Monthly Number of Observations





Seasonal Coverage(DJF Based on COADS)

The quantity of ship data is insufficient for seasonal averages, except in isolated regions.

There are also a large number of buoy observations (or mislabeled ships).

Annual means based in in situ data have uncomfortably large uncertainty!





Standard Deviation of daSilva clim. Octobers





Strengths and Weakness of In Situ Observing System

Strengths: Observes key variables in calculating surface turbulent fluxes.

Atmospheric temperature and atmospheric humidity. Moored buoys – excellent temporal sampling Research ships & buoys– very high quality, high temporal

resolution observations. A excellent source of comparison/validation data.

Currently not ingested by models.

Weaknesses Ships – poor spatial and temporal sampling. Buoys – terrible spatial sampling. Questionable SSTs. Radiation data is rarely available.





Satellite Coverage Geosynchronos Satellites

Polar Orbiting Satellites Vector Winds

Scatterometers: ERS-1/2, NSCAT, SeaWinds, (ASCAT) Polarametric Radiometer: WindSat

Wind Speed Scatterometers: ERS-1/2, NSCAT, SeaWinds, (ASCAT) Polarametric Passive Radiometer: WindSat Non-Polarametric Passive Radiometers: e.g., SSM/I Active Radiometers: TMI,





South Pacific Surface WindsBased on Daily NSCAT Fields

Vector motion is based on gridded 10m wind from NSCAT alone. Arrow lengths are proportion to speed. Arrows are advected with the 10m winds.

Background color is vorticity.





Southern Ocean Examples





SeaWinds Daily (22 hour) CoverageAscending Node Descending Node

April 25, 2000From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/





SeaWinds Daily (22 hour) CoverageAscending Node Descending Node

Feb. 2, 2000From Paul Chang (NOAA/NESDIS): http://manati.wwb.noaa.gov/quikscat/



http://manati.wwb.noaa.gov/img/quikscatcur_as.map

http://manati.wwb.noaa.gov/img/quikscatcur_ds.map



QSCAT Coverage of Southern OceanExample of Daily Coverage





Coverage by Two SeaWinds Scatterometers









Are Two Scatterometers Better Than One?

Example of gridded fields showing Hurricane Fabian (2003) Single scatterometer uses observations from a 24 hours, and a 72 hours for background Duo scatterometer uses observations from a 10 hour period and background from 30 hours

Averaging period will vary for orbits that are not in phase Note the improved shape and speed in Fabian

SeaWinds on QuikSCAT SeaWinds on QuikSCAT and Midori





QSCAT Only Field Minus Duo Scatterometer Field

18+ ms-1 difference around the center of Fabian.

The area of low wind speeds off of Newfoundland is more sharply defined in the duo-scatterometer case, resulting in large differences in this region.

The large yellow area of near zero differences has similar input in the two products.

Vector Wind Speed Differences (ms-1)





Problems with Sampling: Tropics

The QSCAT-only fields (left) show a great deal of sampling-related variability in rapidly evolving features, such as hurricane Fabian.

The combined scatterometer fields (right) also suffer from this problem.





Strengths of Weakness of Satellite Observing System Strengths:

Vector winds (if there are appropriate satellites) Scalar wind speeds Sea Surface Height (SSH) Sea Surface Temperature (SST) Outgoing Radiation (at the top of the atmosphere) Spatially and temporally complete fields can be made.

Weaknesses Vector wind observations in peril! Near surface air temperature Near surface humidity Radiation at the ocean’s surface Precipitation (it’s just too variable)

Weaknesses are usually dealt with by incorporating model data, or assimilating surface data into models.





Testing the NCEP Reanalysis-1 Surface pressures are derived from surface winds observed from the

SeaWinds scatterometer.

To build confidence in our pressures, they are first compared to observations from the Tropical Prediction Center.

The remaining examples are typical of the Southern Ocean.





Scatterometer-Derived PressuresTS Keith

Statistics

Best Track:20.8N 94.9W988 mb70 mph

QSCAT:20.75N 94.75W989.9 mb

Development of this technique was inspired by Patoux and Brown (2001)





Southern Hemisphere Examples (day 1)

SeaWinds shows surface circulation. NCEP/NCAR Reanalysis has a very weak inverted trough.






Storm is in the correct location, but the reanalysis’ storm is far too weak.






Relatively good agreement.






However, upstream, the same old problems exist.





Southern Hemisphere Examples (days 11 and 12)

It starts again. Unfortunately, these examples are typical of the NCEP/NCAR’s accuracy in the Southern Ocean.





Numerical Weather Prediction Reanalyses

NWP reanalyses use a single package of parameterizations and assimilation to reprocess a long time series of data.

Advantages: Uniform methodology resulting in spatially and temporally complete

fields. Information can be propagated in time.

Disadvantages: The model is only as good as it’s physics and the data assimilated.

Surface physics, cloud physics, and radiative transfer are all relatively weak points in modeled physics.

A large fraction of surface data is rejected because of inconsistencies with the model.

This fraction is greater in data sparse regions. The type of coverage (satellite and in situ) changes substantially

with time.





High Quality In Situ Data Are Valuable Validation and Planning Tool: Research Ships and Buoys

Validation of flux products and related variables Calibration/Validation of satellite sensors New: input in satellite sensor design.

Research ships and research buoys provide much finer quality of data than VOS.

These observations have been used to test the quality of NCEP Reanalysis surface fields (winds, SST, air temperature, and humidity) and surface turbulent fluxes.

Winds and turbulent heat fluxes were found to be greatly biased! Much better sensible and latent heat fluxes could be calculated

using NCEP winds, temperatures, and humidities, along with better transfer coefficients.





Validation Example:SeaWinds on QSCAT Wind Speed

QSCAT-1 Model Function

Preliminary results 2 months of data Observations from

eight research vessels

< 25 km apart,< 20 minutes apart.

Uncertainty was calculated using PCA, assuming ships and satellite make equal contributions to uncertainty.

Likely to be unflagged rain contamination





Validation Example:SeaWinds on QSCAT Wind Direction

Preliminary results Same conditions as

the previous plot. Correctly selected

ambiguities are within 45 of the green line or the corners. Red dashed lines

indicates 180 errors.

Yellow dashed lines indicate 90 errors.

Statistics are for correctly selected ambiguities.





Assessment of Small Scale Variability in

Surface Vector Winds-

An Estimate of Beam to Beam Variability of Vector

Winds Within a Vector Wind Cell

Mark A. BourassaDept. of Meteorology, The Florida State University, andThe Center for Ocean-Atmospheric Prediction Studies





The Problem The different looks within a vector wind cell do not occur at the same

time. The winds can and do change between looks, and short water waves respond very rapidly to those changes.

These changes can be thought of as appearing as noise in the observed backscatter. When individual footprints are averaged over sufficient space/time (space in this case), the variability due to smaller scale processes can be greatly reduced.

If we have smaller footprints or have a longer time between looks, then this apparent noise will increase.

The looks are also not at identical locations within the ocean vector wind cell. That is not yet considered within this study.





The Approach The following approach will overestimate the problem, because it

approximates the variability in a slice through a footprint. Averaging in the second dimension should further reduce the uncertainty in the mean, and hence reduce the differences between means calculated at slightly different times.

Taylor’s hypothesis is used to convert spatial scale (in these examples 25, 20, 15, 10, and 5 km) to time scale. Time scale = spatial scale / mean wind speed.

A maximum time scale of 40 minutes is used. The weighting in space (translated to time) is equal to a Gaussian

distribution, centered on the center of the footprint, and dropping by one standard deviation at the edge of the footprint.

Mean speeds and directions are calculated, and differences are calculated for time steps of 1 through 20 minutes.

Ideally, this variability would be negligible for the space time scales we are considering.





Example of Variability in 60s Averagesfor Various Difference In Time

Variance in wind speed differences (ms-1) as a function of the difference in time (minutes) for individual observations (one minute averages).

This is not what we are interested in, but it is a nice standard of comparison.

0 to 4 ms-1

4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1





Examples for 25km footprints

Variance in wind speed differences (left; ms-1) and directional differences (right; degrees) as a function of the difference in time (minutes).

High wind speeds have more variability in speed, but less in direction. Directional variability for low wind speeds is very sensitive to the

differences in time.

0 to 4 ms-1

4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1






Variance in wind speed (left; ms-1) and direction (right; degrees) as a function of the difference in time (minutes). 0 to 4 ms-1

4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1







4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1







4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1






Variance in wind speed (left; ms-1) and direction (right; degrees) as a function of the difference in time (minutes).

Speeds, for large wind speeds, are highly sensitive to the differences in observationtime.

0 to 4 ms-1

4 to 8 ms-1

8 to 12 ms-1

12 to 16 ms-1

16 to 20 ms-1





Comments I am not convinced that this problem is negligible for direction at low

wind speeds (<4 ms-1) and for speed at large wind speeds (>16 ms-1). Particularly so for speeds in the 5 km footprint. We may have to run an atmospheric model to see if considering

the extra dimension helps. The number of surface layer cells within a footprint (think of this

as N when converting from a variance to an uncertainty in the mean) drops as the footprint decreases in area. I doubt that we are seeing the limit where N=1, so I that an atmospheric model will show decreased variability. The boundary-layer height varies regionally, making the

number of cells per area change regionally The high wind speed & small footprint variability could be due to

a relatively small number of observations used to calculate the means. I consider that combination to be the least reliable.

I can provide a table of number if someone want to test the impacts of these estimates of variability.





High Frequency Fluxes What resolution in space and time is obtainable? The answer to this question is highly dependent on the sampling of the

observational system. Temporal resolution is more difficult to achieve than temporal

resolution. For data-based products, this is the key constraint. For NWP reanalyses, the quality of the modeled physics is also a

constraining factor. We can expect to have access to daily (or better) fields of stress, so

long as there is at least one wide swath scatterometer in orbit. Strengths and weaknesses of WindSat remain to be seen.

Current indications are not good for Southern Ocean For energy fluxes, the availability of atmospheric temperature and

moisture data might be the key constraint. High quality SST is also essential for ALL energy fluxes.

The products must be internally consistent!





Graphical Example of AVHRR SST Snapshot

Frontal Instabilities

Wavelength: 50-100 km

Cold Filament Width Scale:

10-30 km





SST During Hurricane Danielle

SSTs from two satellites (TMI on TRMM and AVHRR), and Reynolds’ blend of in situ and satellite SST.

Graphic from http://www.ssmi.com/hurricane/example_data_applications.html#sst_comparison





Observed Surface Stresses Preliminary data form the

SWS2 (Severe Wind Storms 2) experiment. The drag coefficients

for high wind speeds are large and plentiful.

The atypically large drag coefficients are associated with rising seas.

Many models overestimate these fluxes.

Excellent empirical fit to means of these data and many other by P.K. Taylor & M. Yelland (2001).





Bourassa (2003) Surface Stress Model: Comparison to Observations

A modified version of Bourassa et al. (1999).

Modifies the wind speed by the orbital velocity. Better accounts for

the spread. Does not match the

four high wind speed points.

Surface turbulent fluxes respond to the shear on the RHS of the top equation.

Scatterometers appear to be similar, but the 0.8 is uncertain.

100.60.8 log s

EN current orbitalv o

z HuU u Uk z

0.52 22

2

0.019 0.035ouz

u g





Bourassa (2003) Means & UncertaintiesComparison of Model to SWS2 Observations

Error bars indicate 3 standard deviations from the binned mean.

Each bin contains at least 10 observations.

The lower stress bins contain several hundred observations.

Greatly improved fit in the mid-range.

Consideration of sea state can greatly reduce random errors and biases.





High Resolution Fluxes Physically-based models that have been shown to be good for stress

and (different model) for turbulent heat fluxes have great promise. Members SEAFLUX group are working on blending the best

features of these models. These models consider sea state and net radiative flux in addition

to the usual parameters. Given reasonable input data, it should be possible to calculate

reasonable accurate surface turbulent fluxes applicable to small spatial and temporal scales.

The great difficulty is finding (producing) internally consistent input for these flux models. Again, recent improvements in the our understanding of the

physical links between these variables should lead to improved products.





Recommendations (1 of 3) Of the current satellite instruments, only the scatterometer is capable

of observing synoptic scale variability including winds around low pressure systems. There are currently no plans for another US scatterometer mission.

The loss of the existing instrument or one of the planned instruments will result in a very large gap in the observational record.

Daily average wind fields can be produced with one satellite provided that spatial variability in uncertainty is acceptable.

Two scatterometers could be used to produce much better quality daily and 12 hourly fields.

The quality of numerical weather prediction products for the Southern Ocean improved dramatically after assimilation of scatterometer data. Reanalyses that assimilate these data should be encouraged.

The continuity of scatterometer data (or equivalent) is essential.





Recommendations (2 of 3) Uses for high quality in situ data (from research vessels and buoys)

High quality in situ wind speed data, for U10 > 20 ms-1, is needed for the calibration of many satellite instruments. Observations of winds and waves from the Southern Ocean should be ideal for examining sea state influence on scatterometer winds and stresses. Very preliminary results suggest that scatterometer winds (and stresses in general) are sensitive to sea state, and that scatteromter-derived stresses are much less sensitive to sea state.

Gridded flux products can easily be evaluated with in situ data. The variability observed from the in situ data can be used as

design input for future satellite missions. For U10 > 20 ms-1, this variability is large and appears to a significant source of error for some proposed sensor instruments (scatterometer, polarimetric radiometers, and perhaps SAR).

The data for research vessels (national and international) should be quality assured and archived.

Resupply vessels should be outfitted with automated sensors.





Recommendations (3 of 3) There is two much variability in surface stress and net energy flux

gridded products because of inconsistencies in the products. For example, different SST product are used in products, and

different time averaging is used in parameters that used in flux calculations.

Recommend the development of surface flux products for which the variables are all treated in an internally consistent manner.

Consider the physics that are of interest, thereby allowing the spatial and temporal characteristics of interest to be determined. Ask people providing the products used in the studies to identify what is needed to accurately identify variability on the scales of interest. In other words, understand the apparent limits of the observing

system, and work to insure there is an observing system that is adequate for the desired studies.



Documents

Surface Observations in The Southern Ocean, Variability, and the Consequences