constraints from atmospheric measurements on the...
TRANSCRIPT
CONSTRAINTS FROM ATMOSPHERIC MEASUREMENTS ON THE GLOBAL BUDGET OF
CARBONYL SULFIDE
Parv Suntharalingam
University of East Anglia
N. Krakauer1, A.J. Kettle2, D. J. Jacob3, S. Montzka4 1: City College; 2 : University of East Anglia; 3: Harvard; 4 : NOAA-ESRL
GEOS-Chem Meeting, April 7-10, 2009
WHY INVESTIGATE COS ?
COS
STRATOSPHERE
TROPOSPHERE
WHY INVESTIGATE COS ?
• Source of stratospheric sulfate aerosol; impacts on radiation budget and stratospheric chemistry
COS
Sulfate Aerosol
STRATOSPHERE
TROPOSPHERE
WHY INVESTIGATE COS ?
• Source of stratospheric sulfate aerosol; impacts on radiation budget and stratospheric chemistry
• Plant uptake of COS is proportional to that of CO2.
Can COS measurements provide a constraint on Global Primary Productivity ?
COS CO2
Sulfate Aerosol
STRATOSPHERE
TROPOSPHERE
SEASONAL CYCLES OF COS and CO2 COS measurements : S. Montzka (NOAA-ESRL)
CO2 SH
COS
J F M A M J J A S O N D J F M A M J J A S O N D
Similar seasonal cycles for COS and CO2, especially at forested sites.
CAN COS MEASUREMENTS CONSTRAIN BIOSPHERIC CO2 UPTAKE ?
Significant uncertainties remain on aspects of COS budget
Slide from S. Montzka
SOURCES AND SINKS OF ATMOSPHERIC CARBONYL SULFIDE
COS
Mean atmospheric conc. ~ 500 ppt
SOURCES AND SINKS OF ATMOSPHERIC CARBONYL SULFIDE
SOILS PLANTS ANTHROPOGENIC OCEAN
CS2
DMS
COS
Atmospheric Oxidation, Stratospheric Photolysis
Oxidation
Biomass Burning
SOURCES AND SINKS OF ATMOSPHERIC CARBONYL SULFIDE
SOILS PLANTS ANTHROPOGENIC OCEAN
CS2
DMS
COS
Atmospheric Oxidation, Stratospheric Photolysis
Oxidation
Biomass Burning
?? ?? ?? ??
Uncertainty remains on source/sink fluxes
Rainwater ??
PREVIOUS GLOBAL BUDGET ESTIMATES
OCEAN
ANTH
FIRE
PLANTS
SOILS
ATM LOSS
IMBALANCE IMBALANCE
PRECIP +280
-260
Chin and Davis 1993 Watts 2000
Gg S
/yr
• Large uncertainties
• Net imbalances of opposite sign
• Revision of soil flux term
SOILS
-400
-20
0
0
200
-400
-20
0
0
200
Spatial and Temporal Variation of COS Fluxes Kettle et al. [2002]
+65
Anthropogenic
Plants Soils
Ocean
• Process-based parameterizations of COS sources and sinks (monthly fields; gridded 1ox1o or 4ox5o)
• Budget closed with limits of uncertainties
Gg
S/yr
IMBALANCE Sources = 555 GgS/yr
(170-1010)
Sinks = 490 GgS/yr
(380-600)
OCEAN
ANTH
FIRE
PLANTS
SOILS ATM LOSS
Spatial and Temporal Variation of COS Fluxes Kettle et al. [2002]
+65
Anthropogenic
Plants Soils
Ocean
• Process-based parameterizations of COS sources and sinks (monthly fields; gridded 1ox1o or 4ox5o)
• Budget closed with limits of uncertainties
• Some uncertainties may be underestimated
Gg
S/yr
IMBALANCE Sources = 555 GgS/yr
(170-1010)
Sinks = 490 GgS/yr
(380-600)
OCEAN
ANTH
FIRE
PLANTS
SOILS ATM LOSS
RECENT ESTIMATES OF GLOBAL COS UPTAKE BY PLANTS SUGGEST LARGER SINK
Sandoval- Soto et al. 2005 (Method)
730 -1500
Xu et al. 2004 960 -1490
Montzka et al. 2007 730 - 1500
Units : Gg S/year
Kettle et al. 2002 210-266
Recent estimates are 3-6 times higher than Kettle et al. [2002]
ATMOSPHERIC CONSTRAINTS ON SEASONAL COS FLUXES Suntharalingam et al. [GRL, 2008]
• Matching observed seasonal cycle requires :
- Increase in Plant uptake by 250 Gg S/yr (~100%)
- Decrease in Southern ocean fluxes by 50 Gg S/yr (~ 20%)
• Additional (tropical) source of 250 Gg S/yr required to close budget
Observations (NOAA-GMD)
Kettle2002 Model
‘Increased Plant Uptake’ model
THIS WORK : INVERSE ANALYSIS OF ATMOSPHERIC COS MEASUREMENTS
AIM : Derive optimal estimates of COS sources and sinks consistent with atmospheric measurements and prior flux distributions
APPROACH : Bayesian inverse analysis [Rodgers 2000]
INVERSE ESTIMATES OF COS FLUXES
Prior Fluxes (xa)
Kettle et al. [2002]
Forward Model
GEOS-Chem v. 7-03-06 2ox2.5o; 30 levels
INVERSE MODEL Minimize cost function of Model-Observation concentration mismatch, and deviation from
prior fluxes
J(x) = (y – ym)T Se –1 (y – ym)
+ (x – xa)T Sa –1 (x - xa)
Optimal Flux Estimates (xp)
Modelled Concentrations
Observations
ym
y
FLUX ESTIMATES FROM INVERSE ANALYSIS
Minimize cost function:
J(x) = (x – xa)T Sa –1 (x - xa) + (y – K x)T Sε –1 (y –K x)
Solution:
x = xa + G (y - K xa)
where, G = Sa KT (K Sa KT + Sε) -1
A posteriori errors : S = (KT Sε –1 K + Sa –1) -1
x = state vector (fluxes)
xa = a priori flux estimate
K = Jacobian matrix (model transport)
Sa = Error covariance matrix on fluxes
Sε = Error covariance matrix on concentration error
Rodgers, 2000
THIS ANALYSIS • Measurements : Monthly means of flask samples at 11 surface sites from NOAA-ESRL network (2001-2007) [Montzka et al 2007]
• Prior fluxes : Kettle et al. [2002].
• State vector : 8 COS fluxes (global, annual mean)
ONGOING WORK : Inverse analyses at higher spatial and temporal resolution. E.g., monthly estimates of regional COS fluxes
THIS WORK : INVERSE ANALYSIS OF ATMOSPHERIC COS MEASUREMENTS
AIM : Derive optimal estimates of COS sources and sinks consistent with atmospheric measurements and prior flux distributions
APPROACH : Bayesian inverse analysis [Rodgers 2000]
Soil Plant Anthropogenic Ocean Direct Ocean CS2 Ocean DMS Atmospheric Loss Biomass Burning
X =
COS SURFACE OBSERVATION NETWORK : NOAA-ESRL Flask measurements since 2000 [Montzka et al. 2007]
Barrow (BRW)
Mauna Loa (MLO)
South Pole (SPO)
•
•
• •
• • • •
•
• cgo
mhd
alt brw
kum mlo
smo
spo
nwr hfm lef
From S. Montzka
Measurements used here : Monthly averages from 11 sites (2001-2007)
ERROR COVARIANCE OF THE CONCENTRATION ERROR Specifying Matrix Sε
Elements of matrix Sε account for : 1. Instrument error in measurements
2. Representation error
3. Forward model error (transport, aggregation error, chemistry)
Characterization of Sε in this analysis :
• Diagonal matrix; errors derived from the residual standard deviation of variability in monthly measurements at each site [‘TransCom’ approach: Gurney et al. 2003]
• Sensitivity analyses conducted for different weightings of elements of Sε
WORK IN PROGRESS to separately account
for these terms
INVERSE ESTIMATES : Sensitivity Analyses
Cost function:
J(x) = (x – xa)T Sa –1 (x - xa) + (y – K x)T Sε –1 (y –K x)
Prior Uncertainties Sa • ‘Tight priors’ (Kettle 2002) • ‘Loose’ priors (Kettle 2002 x3; and plant uptake uncertainty x6) • Intermediate prior uncertainties (between bounds above)
Prior Fluxes xa • Kettle et al. 2002 • Montzka et al. 2007
Observational error covariance Sε
• ‘TransCom’ weighting of sites • Constant weighting of all sites • Subsets of sites : Land vs. Ocean sites
Sensitivity analyses for combinations of the above : 16 cases
SENSITIVITY ANALYSES: Variation of Cost Function Parameters
MODEL REPRESENTATIONS OF DIFFERENT PRIOR COS FLUXES HAVE SIMILAR ATMOSPHERIC SIGNATURES
2 x Plant uptake
3 x Soil uptake
2 x Ocean direct flux
2 x Ocean DMS oxidation flux
• This results from colocation of fluxes, and similar seasonal variations
• Prior fluxes derived from limited information (e.g., 1 soil study; 1 DMS-to-COS yield study). Need for improved characterization of underlying processes
Prior Fluxes : Kettle et al. [2002]
ERROR CORRELATION MATRIX (Prior fluxes : Kettle et al. 2002)
1.00 -0.81 0.23 -0.13 -0.07 0.09 0.04 0.01
-0.81 1.00 0.22 0.31 -0.01 -0.19 -0.17 0.32
0.23 0.22 1.00 0.29 0.20 -0.13 -0.09 0.05
-0.13 0.31 0.29 1.00 0.15 -0.64 -0.08 0.25
-0.07 -0.01 0.20 0.15 1.00 -0.55 0.24 -0.52
0.09 -0.19 -0.13 -0.64 -0.55 1.00 0.02 -0.16
0.04 -0.17 -0.09 -0.08 0.24 0.02 1.00 0.21
0.01 0.32 0.05 0.25 -0.52 -0.16 0.21 1.00
SOIL
PLANT
ANTH
OCEAN Direct
OCEAN CS2
OCEAN DMS ATM. LOSS
FIRES
SOIL
PLAN
T
ANTH
OCEA
N Di
rect
OCEA
N CS
2
OCEA
N DM
S
ATM.
LOS
S
FIRE
S
Large correlation between Plant and Soil fluxes
Large correlation between Ocean fluxes
SUMMARY OF POSTERIOR FLUX ESTIMATES Comparison to Kettle et al. [2002] Prior
PLANT + SOIL OCEAN
ANTH
ATM LOSS
“FIRES”
Gg S
/yr
• Plant and Soil : Increased uptake (by ~50%)
• Ocean fluxes reduced - close to zero. May be due to aggregation error
• “Fires” : significant increase. May represent unidentified tropical sources
• Ongoing work : flux estimates on regional and monthly basis Kettle 2002 Prior Kettle 2002 Prior
LATITUDINAL DISTRIBUTION OF PRIOR FLUXES [Kettle et al. 2002]
FIRES
PLANT
SOIL
OCEAN
ANTHROPOGENIC
Gg
S / (
degr
ee y
r)
SUMMARY
Preliminary inverse estimates of global COS fluxes indicate: Increased plant and soil uptake (by ~ 50%)
Significant decreases in ocean flux (may be due to aggregation error)
Increased COS fluxes from “Fires” (may be accounting for unrepresented tropical sources)
ONGOING AND FUTURE WORK • Inverse analyses at higher temporal and spatial resolution.
• Improved representation of error covariance matrix for concentration error
• Improved characterization of prior fluxes.
EXTRA
SEASONAL VARIABLITY OF COS FLUXES Flux Distributions : Kettle et al [2002]
Northern Hemisphere Southern Hemisphere OCEAN
ANTH.
PLANT
SOILS FIRE
AGGREGATED FLUXES
• N Hemisphere variability driven by plant uptake and ocean fluxes
• S Hemisphere variability driven by ocean fluxes
TOTAL SURFACE COS FLUX
OCEAN
WHERE ARE THE MISSING FLUXES ?
Largest discrepancies in the tropics
Latitudinal Variation of Annual Mean Mixing Ratios Observations and ‘Increased Sink’ Model (Global Mean Subtracted)
60S 60N 30S 30N 0
Observations
Model
RANGE OF POSTERIOR FLUX ESTIMATES
SOIL
PLANT
ANTH
OCEAN Direct
OCEAN CS2
OCEAN DMS
ATM LOSS
“FIRES”
Gg S
/yr
Kettle 2002 Prior