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CASIX Science Update
Measuring atmospheric CO2 from space using Full Spectral Initiation (FSI) WFM-DOAS
Michael Barkley and Paul Monks, EOS Group, University of Leicester
The major focus of the University of Leicester efforts within Project 3 has been the development of the Full Spectral Initiation (FSI) algorithm (described in detail in Barkley et al., 2006) which is designed to retrieve CO2 from space using the SCIAMACHY instrument, on-board ENVSIAT. The FSI algorithm is based on the WFM-DOAS technique (Buchwitz et al., 2000), whereby the total columns of a range of greenhouse gases can be retrieved from spectral measurements in the near-infrared, through the fitting of a model reference spectra plus its derivatives, to the measured sun-normalised radiance. In order to minimize the errors on the retrieved CO2 columns, the FSI algorithm is biased towards the use of a-priori data, generating a model reference spectrum for each individual SCIAMACHY observation using the known, or rather, estimated properties of the atmosphere and the surface at the time of the measurement. This approach is chosen in preference to performing global retrievals using only a finite number of reference spectra (e.g. Buchwitz et al., 2005). To read more click here.
From silk to satellite: A half century of ocean color
Dionysios Raitsos and Samantha Lavender, University of Plymouth
Phytoplankton play a major role in the absorption and storage of carbon dioxide in the oceans. Because phytoplankton contain chlorophyll, it is possible to estimate their abundance by measuring the color of ocean waters. Since 1931, researchers on ships have collected plankton on silk and estimated phytoplankton biomass based on the resulting color of the silk. Beginning in 1997, images from NASA's SeaWiFS satellite made it possible to measure the color of large regions of the ocean and estimate phytoplankton abundance. The usefulness of the SeaWiFS dataset is, however, limited by its short time span. Raitsos et al. compared the satellite data and the data collected from ships for a region of the Northeast Atlantic from 1997 to 2002. They found that the two datasets strongly agreed in their estimates of phytoplankton abundance. This allowed them to use the ship-collected data to extrapolate the SeaWiFS dataset back to 1948. They suggest that this extended dataset will improve models of marine ecosystems and our understanding of biogeochemical cycling and climate change.
Title: Extending the SeaWiFS chlorophyll data set back 50 years in the northeast Atlantic
Authors: Dionysios E. Raitsos, Philip C. Reid, Samantha J. Lavender, Martin Edwards, and Anthony J. Richardson, University of Plymouth, Plymouth, United Kingdom.
Source: Geophysical Research Letters (GL) paper 10.1029/2004GL022484, 2005.
From AGU Journal Highlights: http://www.eurekalert.org/pub_releases/2005-04/agu-ajh041405.php
Interface Modelling Tool now available to all CASIX members
Helen Kettle and Chris Merchant, University of Edinburgh
A 1-D bio-geochemical ocean turbulence model for air-sea carbon flux is now available to members of CASIX. Using this model you can simulate the ocean carbon cycle and resulting CO2 fluxes at any time and space resolution. The model is ideal for studying short time scale at the air-sea interface. For example, diurnal variations in carbon flux governed by diurnal variation of light and turbulent mixing through the water column. To read more click here.
Systematic errors in global air-sea carbon fluxes caused by temporal averaging of sea-level pressure (Project 8)
Helen Kettle and Chris Merchant, University of Edinburgh
Long-term time averaging of meteorological data, such as wind speed and air pressure, can cause large errors in air-sea carbon flux estimates. Other researchers have already shown that time averaging of wind speed data creates large errors in flux due to the non-linear dependence of the gas transfer velocity on wind speed (Bates and Merlivat, 2001). Here we show that long-term time averaging of the partial pressure of CO2 in the atmosphere (pCO2air) also causes significant errors in flux. To read more click here.
Quantifying Surface Slicks for CO2 flux studies (Project 2)
Rob Potter and Gay
Mitchelson-Jacob, University of Wales Bangor
For the selected test sites Envisat ASAR data will be obtained and analysed with the aim to quantify natural and anthropogenic slicks (by size, thickness, type). ESA BEST software will be used to calibrate the ASAR backscatter in dB values (Rosich and Meadows 2004). An analysis of the differences between natural and man made slicks can be determined using fractal analysis – man made slicks tend to have a smooth boundary (Jolly et al, 1999). Considering the variance of slick features both regionally and temporally will lead to an estimate for the upper and lower bounds for the backscatter at selected test locations. CASIX partners at SOC responsible for Project 1, are using dual frequency altimetry to detect surface slicks at scales of the altimeter footprint O(10km). This data will be used to compare with wide swath ASAR data acquired for Project2, to validate the altimeter measurements. A recent development by Horstmann and Koch (2003) explores the potential for retrieving Wind Fields from ASAR. This will allow for the dependency of air-sea gas exchange on the wind velocity to be further investigated. Additionally, Ekman horizontal and vertical velocities could be derived (e.g. sites of upwelling of nutrients) leading to specific studies on the processes that effect air-sea gas transfer.
For regions with known biological blooms the
use of MERIS data ought to help us to determine the origin of slicks
imaged by ASAR (Da Silva et al 2004). Whilst AATSR SST data should help
us to assess the influence of stability of the atmospheric boundary
layer that might lead to ambiguity in slick detection imaged by SAR (Wu
1991). The results of this work should therefore lead to a climatology
of slicks in a number of test regions and to the development of a robust
algorithm to detect slicks. With this developed skill we should then be
able to improve the models for calculation of gas transfer in the
presence of slicks.
Binding,C.E., D.G.Bowers, E.G.Mitchelson-Jacob, An algorithm for the
retrieval of suspended sediment concentrations in the Irish Sea from
SeaWiFS ocean colour satellite imagery, International Journal of Remote
Sensing, 24, Number 19, 10 October 2003.
November 2003 (ESA SP-549, May’04).
M., and W.Alpers, Using ERS-2 SAR images for routine observations of marine
pollution in European coastal waters, Science of the Total Environment,
Heywood,K.J., A.C.Naveira Garabato and D.P.Stevens, High mixing rates in the
abyssal Southern Ocean, Nature 415, 1011 - 1014, 28 February 2002.
Horstmann, J. and W. Koch, High resolution ocean surface wind fields
retrieved from spaceborne SAR operating at C-band, 2003
Jolly, G.W., et al, The Clean Seas Project – A Final Report, November 1999.
Kettle, H. and C.J. Merchant, Systematic errors in global air-sea CO2 flux
caused by temporal averaging of sea-level pressure, Atmos. Chem. Phys.
Discuss., 5, pp325-346, 2005.
Kvenvolden, K.A., and C.K. Cooper, Natural seepage of crude oil into the
marine environment, Geo.Mar.Lett., 23, 140-146, 2003.
Mitchelson-Jacob, E.G., and S.Sundby, Eddies of Vestfjorden, Norway,
Continental Shelf Research, 21(16), pp1901-1918, 2001.
Romeiser, R, et al, On the remote sensing of oceanic and atmospheric
convection in the Greenland Seas by SAR, JGR(oceans), 109, NoC3, 02 March
Rosich, B. and P. Meadows, Absolute calibration of ASAR Level 1 products
generated with PF-ASAR, Technical Note, rev. 5, 07 October 2004.
Scott, J., Ocean Surface Slicks – Pollution, Productivity, Climate, and
Life-Saving, IEEE, IGARSS 1999.
Takahashi,T., et al, Global sea-air CO2 flux based on climatological surface
ocean pCO2 and seasonal biological and temperature effects, Deep Sea
Research, II, 49, pp1601-1622, 2002.
Wassmann. P., T.Ratkova, I.Andreassen, M.Vernet, G.Pedersen and F.Rey,
Spring Bloom Development in the Marginal Ice Zone and the Central Barents
Sea, Marine Ecology, Volume 20, Issue 3-4 P321,December 1999
Wu, J., Effects of atmospheric stability on oceanic ripples: A comparison between optical and microwave measurements, J.G.R, 96, pp7265-7269, 1991.