Role of remote sensing in evaluating global carbon cycle

1
Role of remote sensing in evaluating global
carbon cycle

• J. Boutin (LODYC)

Many thanks to Lodyc team (J. Etcheto, L.
Merlivat, Y. Rangama), D. Antoine, H. Loisiel,
Y. Dandonneau, D. Glover, C. Lequéré, T.
Takahashi, B. Delille
2
Some applications of remote sensing

• CO2 transfer velocity
• Use of SST and Ocean color for pCO2
  interpretations extrapolations
• Southern Ocean
• Determination of Carbon related parameters
• Primary production
• POC
• Analysis of factors controlling phytoplankton
  distribution
• At regional scale
• At local scale
• Present and future sensors

3
CO2 transfer velocity exchange coefficient (Kk
S) Monitoring using satellite wind speed
(Geosat, SSM/I, ERS, QSCAT) 1985-2002
K over Global ocean (k-U Wanninkhof(1992)
relationship)
2002
1985
Geosat altimeter
SSMI Microwave radiometer
ERS1 and ERS2 scatterometer
QSCAT scatterometer
(Boutin et al., 2003)
4
CO2 transfer velocity Use of altimeter
measurements to relate k to the mean square
slope (MSS) of small waves (6cm-16cm) (Glover et
al., 2002)
k from TOPEX-POSEIDON and k-MSS
relationship (Glover et al., 2002)
k from ERS scatterometer wind speeds and k-U
Liss Merlivat (1986) relationship
5
k from TOPEX-POSEIDON and k-MSS
relationship (Glover et al., 2002)
k from ERS scatterometer wind speeds and k-U
Wanninkhof (1992) relationship
6
CO2 transfer velocity summary and issues
Monitoring of k using satellite wind speed
(Geosat, SSM/I, ERS, QSCAT) 1985-2002 and a
given k-U relationship Strong k variability
(including interannual) Need for
intercalibration of U retrieved from various
instruments
K deduced from various k-U relationships differ
(Boutin et al., GRL, 2002) Calibration of k-U
relationships still needed
Use of altimeter measurements and k-MSS
relationship (Glover et al., 2002) k-MSS
estimates close to k-U Liss and Merlivat
estimates (k-altimeter relationship calibrated
with laboratory (wind/wave tank) measurements).
Calibration of k-MSS relationships still needed
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pCO2 and air-sea flux from SST and Chla

• Location of in-situ measurements in their
  physical-biogeochemical context
• Interpretation of space and time pCO2 variations
• Identification of biogeochemical provinces with
  dominant mechanisms controlling pCO2 variations
• Development of extrapolation methods in
  biogeochemical provinces
• Space and time extrapolation of pCO2
• Monitoring of the extent of biogeochemical
  provinces using SST or Chl criteria
• Use of pCO2(SST,Chl) relationships in each
  province
• Space and time monitoring of air-sea flux at
  regional scale
• pCO2 from SST, Chl
• pCO2air deduced from xCO2, SST and Patm
  (meteorological models)
• K from satellite wind speed

8
CARIOCA drifters through the subantarctic zone of
the Indian Ocean
SubAntarctic Zone (SAZ)
2 CARIOCA buoys were deployed during an OISO
campaign in January 2002. They drifted during 3
and 6 months respectively. They measured ocean
pCO2, SST, fluorescence, atmospheric pressure and
wind speed. CARIOCA 2 also measured SSS. Buoys
data were intercalibrated precision determined
by comparison with OISO ship measurements (N.
Metzl, pers. comm.) within /-3
matm (Boutin, Etcheto, Rangama,
Merlivat, 2003)
9
Influence of Chla and SST on pCO2 observed during
AESOPS and Astrolabe campaigns
40S 50S 60S
130 140 150 160 170 180 -170 -160
(Rangama, Boutin, et al., 2003)
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pCO2 regressions South of Tasmania and New Zealand
pCO2 versus SST in low Chl area by seasons
pCO2 versus Chl in high Chl area
(Rangama, Boutin, et al., 2003)
11
(Rangama, Boutin, et al., 2003)
12
Net air-sea flux deduced from pCO2(SST,Chl) and
from Takahashi (2002) pCO2 extrapolations (45S-60S
125E-205E zone area about ¼ of the 45S-60S
band of the Southern Ocean)
Air-sea flux deduced from exchange coefficient
calculated from satellite wind speeds (ERS2) and
K-U Wanninkhof relationship and from either -Our
interpolated pCO2 and pCO2_atm gt yearly flux
-0.08GtC yr-1 -DpCO2 from Takahashi et al.
(2002) gt yearly flux -0.13 GtC yr-1 Need
in-situ measurements to validate pCO2 fields
50 difference
(Rangama, Boutin, et al., 2003)
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pCO2 and air-sea flux from SST and Chla summary
and issues

• SST and Chl efficient to monitor pCO2 in several
  areas (Equatorial Pacific south of Australia
  north Atlantic )
• Spatial extent of biogeochemical provinces
• pCO2 variability in these provinces using
  regressions with respect to SST, Chl or
  parameters derived from SST and Chl (e.g. SST
  anomalies)

• Extend these studies to other areas identify
  provinces and useful parameters for pCO2
  extrapolations (use of altimetry?)
• Need for in-situ measurements made in various
  provinces, at various seasons for
• Developing accurate extrapolation schemes
• Validating extrapolations

14
Carbon related parameters derived from Ocean
color Primary production
Regional and global estimates of PP from SEAWIFS
Chla, SST and PAR Total PP in Southern Ocean
(gt30S) (Moore and Abott, JGR, dec 2000) gt 80 of
PP between 30S and 50S Global NPP El-Niño - La
Niña transition (Behrenfeld et al., Science,
March 2001)
15
Total PP in Southern Ocean (gt30S) (Moore and
Abott, JGR, dec 2000)
PP per unit area in each region maximum in the
MCR (Midlatitude coastal region)
Definition of ecological regions
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Total PP in Southern Ocean (gt30S) (Moore and
Abott, JGR, dec 2000)
PP integrated over each region maximum in the
SWR (Subantarctic water ring region (35S-gtPF))
Definition of ecological regions
17
Carbon related parameters derived from Ocean
color Primary production
Intercomparison exercise of Primary Production
deduced from ocean color Primary Production
Algorithms Round Robin (PPARR) (NASA) a-
Comparison of PP deduced with various algorithms
to in situ measurements (coordinated by J.
Campbell) (Campbell J et al., GBC, 2002). b-
Comparison of PP retrieved at regional to global
scale from satellite measurements (coordinated
by M.H. Carr) (in progress)
Main issues to derive NPP from ocean color 1-
Model the algae physiology and predict it in
function of environmental conditions
(temperature, light,nutriments, physics) 2-
Derive Net PP from Total PP depends on
phytoplankton species ongoing in situ studies
to measure NPP and regionalize NPP/PP (e.g.
GepCo, Y. Dandonneau)
18
Carbon related parameters derived from Ocean
color Particulate organic carbon (POC)
POC shown to be linearly related to
backscattering coefficient of suspended marine
particles (bbp)(in the absence of mineral
particles) and bbp can be derived from spectral
remote-sensing reflectance in the optical domain
(555nm) (Stramski et al., 1999)
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Carbon related parameters derived from Ocean
color Particulate organic carbon (POC)
Global POC distribution in April 1998 (POC
linearly related to bbp bbp derived from
SEAWIFS) (Loisel et al., 2002)
gt study of spatial and temporal variations of
bbp (proxy for POC) and Chl
20
Chl and bbp derived from Polder and SEAWIFS data
-Seasonal variations relatively larger for bbp
than for Chl -Extreme values shifted in time
(max bbp 3months after Chl possibly because of
accumulation of dead phytoplancton cells
zooplankton detritus)
(Loisel et al., 2002)
21
Carbon related parameters derived from Ocean
color Organic carbon
Main issues -improve POC relationships at
regional scale -DOC from ocean color? (pb ocean
color sensitive only to ColoredDOM)
22
Carbon related parameters derived from Ocean
color Some phytoplancton species
(Coccolithophorids, Trychodesmiums) measurable
from space

• Coccolithophores in the Bering Sea from SeaWiFS
  on April 25, 1998.
• The bright aquamarine water is caused by the huge
  numbers of coccolithophores. This bloom was
  present in 1997 and 1998, and appears to be
  re-occuring in 1999. (Image courtesy Norman
  Kuring, SeaWiFS Project)

Detection possible only for species with clear
signature on ocean color measurements!
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Analysis of factors controlling phytoplankton
distribution Chl Sea surface height anomalies
(altimetry) SST
Use of Chla Sea Surface Height anomalies SST
to infer circulation of the ocean -in the
Agulhas Current System (Machu and Garçon, JMR,
Dec2001) gt use wavelet analysis to relate
phytoplankon distribution to physical
environment -in the Southern Ocean (Lequéré et
al., GRL, 2002) gtbiological productivity
response to ocean stratification from SSHa and
Chl correlations
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Analysis of factors controlling phytoplankton
distribution Chl Sea surface height anomalies
(altimetry) SST
Signature of Antarctic Circumpolar Waves (97-01)
clear on SST, SSH ano., not on Chla gtbiological
productivity response to ocean stratification
(Lequéré et al., GRL, 2002) Assumptions -low
SSH SST lt-gt ocean mixing -high SSH SST
lt-gt ocean stratification
Temporal correlation (09/97 to 08/01) between SSH
and Chl a anomalies.
Correlationgt0 marine productivity favored by
ocean stratification (control by light, dust
input from the atmosphere, SST) Correlationlt0
productivity favored by ocean mixing (nutrient
limitation).
Response of primary production to a generalized
stratification of the Southern Ocean could be
gt productive regions more productive, poor
regions more sterile
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Analysis of factors controlling phytoplankton
distribution Time evolution of Chl after the
SOIREE experiment
61S
141E
Seawifs Chl a 42 days after the initial iron
release 150km ribbon of high Chl
concentration gt Mesoscale stirring of a tracer
patch in the surface ocean (Abraham et al., 2000)
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Analysis of factors controlling phytoplankton
distribution

• Identification of dominant processes controlling
  biological variability gt constraints for
  modelling
• Main limitations
• Chl remote sensing is limited to surface ocean
• Clouds

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SUMMARY

• Remote sensing is a powerful tool to monitor time
  and space variations of SOME parameters
  influencing carbon distribution and air-sea
  fluxes (U,SST,SSH,Chl) and allows to derive
  variations of specific parameters (e.g. PP,POC)
• Help to interpret and extend in space and time in
  situ measurements
• Constraints for biogeochemical modelling

• In situ measurements are essential to
• Identify/Analyse the origin of variations
  observed by remote sensing measurements of
  parameters not accessible from space, depth
  profiles
• validate remotely sensed parameters and
  parameters derived from remote sensing
  measurements covering various provinces at
  various seasons

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Current sensors
Wind speed (2 scatterometers in the
air) -Scatterometer QSCAT July99-gt Seawinds
on ADEOS2 Dec 02
Sea Surface Temperature -Visible/IR radiometer
AVHRR 1982-gt New generation of geostationnary
satellite (IR channels) GOES -gt Meteosat 2nd
generation August 02 -Microwave radiometer
TMI (40S-40N) Dec 97-gt (no cloud
contamination) AMSR-E on Aqua May 2002 AMSR
on ADEOS2 Dec 02
Ocean Color (6 radiometers in the
air) -Visible/IR radiometer Seawifs Sept 97
-gt MODIS on Terra feb 00 -gt MERIS on
ENVISAT March 2002 MODIS on Aqua May
2002? POLDER 2 GLI on ADEOS2 Dec 02
Sea Surface Height anomalies (3
altimeters) -Altimeter Topex-Poseidon August
92 -gt Jason Dec01-gt RA on ENVISAT March
2002
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Current sensors
New era with a lot of remote sensing measurements
available
Main challenge Manage the huge amount of data
coming from various instruments - need
for uniform data quality check (consistence
between instruments and with truth data) -
need for synthesized products well suited for
thematic research (easily usable compatible
formats)
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Future sensors (new technology)

• Atmospheric CO2 concentrations
• OCO (Orbiting Carbon Observatory) selected by
  NASA launch 2006
• Sea surface salinity
• SMOS (Soil Moisture and Ocean Salinity) accepted
  by ESA launch 2006
• Aquarius selected by NASA launch 2006

µatm
16
370
CARIOCA pCO2 at 10 degrees versus SST and
SSS (Southern Ocean)
330
0.5 in SSS
290
8
33.6
35
31
Preliminary estimate of SSS error retrieved from
SMOS measurements(SSS averaged over 200km 10
days)
0.15
0.1
0.05
0.02

Boutin et al., 2003