NOAA Technical Memorandum, OAR AOML-98

PROCEDURES TO CREATE NEAR REAL-TIME SEASONAL AIR-SEA 

CO2 FLUX MAPS

G. Park

R. Wanninkhof

J. Triñanes

Atlantic Oceanographic and Meteorological Laboratory

Miami, Florida

February 2010

noaa NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

Office of Oceanic and

Atmospheric Research
NOAA Technical Memorandum, OAR AOML-98

PROCEDURES TO CREATE NEAR REAL-TIME SEASONAL AIR-SEA

CO2 FLUX MAPS 

Geun-Ha Park1

Rik Wanninkhof 2

Joaquin A. Triñanes3

1

University of Miami/Cooperative Institute for Marine and Atmospheric Studies

Miami, Florida

2

NOAA/Atlantic Oceanographic and Meteorological Laboratory

Miami, Florida

3

University of Santiago de Compostela/Technological Research Institute

Santiago de Compostela, Spain

February 2010

UNITED STATES NATIONAL OCEANIC AND Office of Oceanic and

DEPARTMENT OF COMMERCE ATMOSPHERIC ADMINISTRATION Atmospheric Research

Mr. Gary Locke Dr. Jane Lubchenco Dr. Richard W. Spinrad

Secretary Undersecretary for Oceans and Assistant Administrator

Atmosphere/Administrator
ii

Disclaimer

NOAA does not approve, recommend, or endorse any proprietary product or 

material mentioned in this document. No reference shall be made to NOAA or to 

this document in any advertising or sales promotion which would indicate or 

imply that NOAA approves, recommends, or endorses any proprietary product or 

proprietary material herein or which has as its purpose any intent to cause directly 

or indirectly the advertised product to be used or purchased because of this 

document.

The findings and conclusions in this report are those of the authors and do not 

necessarily represent the views of the funding agency.
iii

Table of Contents

List of Figures.................................................................................................................. iv

List of Acronyms............................................................................................................... v

Abstract .......................................................................................................................................... 1

1. Introduction .............................................................................................................................. 1

2. Calculation Method .................................................................................................................. 2

2.1 Global pCO2 Climatology............................................................................................ 3

2.2 Subannual Relationships between pCO2SW and SST, (pCO2SW/SST)2000m ................. 3

2.3 Sea Surface Temperature ................................................................................................ 6

2.4 Wind Speed..................................................................................................................... 7

2.5 Sea-Ice Cover.................................................................................................................. 7

3. Results and Discussion ........................................................................................................... 8

3.1 Overall Magnitude of Anomalies.................................................................................... 8

3.2 Regional Changes ........................................................................................................... 9

3.3 Seasonal Changes.......................................................................................................... 10

4. Caveats .................................................................................................................................. 11

5. The Near Real-Time CO2 Flux Product................................................................................. 11

6. Conclusions...............................................................................................................11

7. Acknowledgments.................................................................................................................. 12

8. References............................................................................................................................. 13
iv

List of Figures

Figure 1. A select comparison of three fixed seasonal and optimum subannual

pCO2SW-SST relationships..........................................................................................4

Figure 2. A select comparison of air-sea CO2 flux values estimated from three

fixed seasonal and optimum subannual pCO2SW-SST relationships in

pixels centered on 40°S, 52.5°E and 48°N, 152.5°E..................................................6

Figure 3. Net global air-sea CO2 fluxes in Pg C yr–1 for 1982-2007 and CO2

efflux anomalies relative to the 26-year mean value for the Equatorial

Pacific .........................................................................................................................8

Figure 4. Climatological air-sea CO2 flux map from Takahashi et al. (2009) and

magnitude of interannual air-sea CO2 flux variability in each pixel for

26 years.......................................................................................................................9

Figure 5. Interannual variability (1σ) of the net air-sea CO2 flux in each pixel for

four seasons ..............................................................................................................10

Figure 6. Interface from the website that enables the retrieval of graphic and

numeric data for the empirical air-sea CO2 flux product discussed in

this study...................................................................................................................12
v

List of Acronyms

AOML Atlantic Oceanographic and Meteorological Laboratory

CO2 Carbon dioxide

DIC Dissolved inorganic carbon

DOE Department of Energy

ENSO El Niño-Southern Oscillation

ESRL Earth System Research Laboratory

NCEP National Centers for Environmental Prediction

NOAA National Oceanic and Atmospheric Administration

OI Optimum interpolation

pCO2 Partial pressure of carbon dioxide

pCO2AIR Partial pressure of carbon dioxide in atmosphere

pCO2SW Partial pressure of carbon dioxide in surface seawater

SOOP Ship of opportunity

SSS Sea surface salinity

SST Sea surface temperature

TA Total alkalinity
1

Procedures to Create Near Real-Time Seasonal Air-Sea CO2 Flux Maps

Abstract

Seasonal air-sea carbon dioxide (CO2) flux maps were calculated from 

wind speed data and the difference in CO2 partial pressure between surface 

seawater (pCO2SW) and the overlying atmosphere. To infer the seasonal variability 

of global net air-sea CO2 fluxes over the last three decades, we derived the 

optimum subannual relationships between pCO2SW and the sea surface temperature (SST). These optimum subannual relationships accounted for the variations 

between pCO2SW and SST and showed significantly better correlations than 

previous relationships with fixed monthly boundaries. The derived algorithms

were then applied to high-resolution SST data to yield changes in pCO2SW on a

monthly basis. The pCO2SW values were also combined with a gas transfer 

velocity estimate derived from high resolution wind products to estimate seasonal 

fluxes. The seasonal fluxes that are calculated with a three- to six-month lag 

from real-time can be obtained at http://cwcgom.aoml.noaa.gov/erddap/griddap/

aomlcarbonfluxes.graph. Here, we describe in detail the procedures and

uncertainties of this product.

1. Introduction

The ocean is an important sink for atmospheric CO2, absorbing 20-30% of the annual 

release of anthropogenic CO2 (Sabine et al., 2004). However, oceanic CO2 uptake is highly 

variable in time and space, and its interannual variability has not been well constrained. The

global surface CO2 database is currently too sparse, except for a few regions, to quantify the airsea CO2 flux variability on the required spatial and temporal scales, and other approaches need to 

be applied. The recent increase in CO2 partial pressure (pCO2) measurements from moorings and 

Ships of Opportunity (SOOP) now makes direct interpolation of these data possible for certain 

regions, particularly when auxiliary parameters such as SST and mixed layer depth, as well as

advanced interpolation procedures such as self-organizing maps and multi-linear regression, are 

used (e.g., Telszewski et al., 2009). The ultimate goal of NOAA’s Climate Observation Division

and Global Carbon Cycle Program is to establish a global observing network comprised of 

research ships, SOOP, and moorings that can resolve basin-wide fluxes of CO2 on seasonal 

timescales to ± 0.2 Pg (1015 g) C.

In the interim, CO2 seasonal fluxes can be estimated using the air-sea CO2 flux 

climatology of Takahashi et al. (2009), along with high resolution wind and SST data using the 

procedures outlined below. The interannual variability of global net air-sea CO2 flux is based on 

algorithms between pCO2SW and SST. In this study, we assumed that the subannual pCO2SW-SST 

relationships derived from the climatology for each pixel, along with interannual SST anomalies,

accounted for the interannual variations in pCO2SW. SST as a parameter has been available with 

global distribution over the past decade and can reasonably account for changes in pCO2SW

caused by thermodynamic, biological, and physical mixing effects.
2

2. Calculation Method

Changes in pCO2SW are controlled by variations of SST, sea surface salinity (SSS), 

dissolved inorganic carbon (DIC), and total alkalinity (TA). These changes can be expressed by 

the following deconvolution:

where ∂pCO2SW is the change in pCO2SW, ∂pCO2SW/∂XXX are the partial derivatives with respect 

to the varying quantity ∆XXX , and XXX is either SST, SSS, DIC, or TA. Among these, SST is 

the most important factor affecting the change in pCO2SW. The SST effect on pCO2SW under 

isochemical conditions (∂lnpCO2SW/∂SST) is ≈4.23% C –1 (Takahashi et al., 1993). Changes in 

DIC and TA concentrations in the surface ocean are caused by deep-water upwelling (transport 

effect) and photosynthesis (biological effect). These changes are often accompanied by SST 

changes. SSS represents only a small proportion of the overall variability of the total pCO2SW

(Takahashi et al., 1980). Therefore, SST accounted either directly or indirectly for the 

thermodynamic, transport, and biological effects that cause changes in pCO2SW. The decadal 

record of SST makes it a good parameter to account for the temporal variations in pCO2SW.

The monthly net air-sea CO2 fluxes (Fym) for each 4° latitude × 5° longitude pixel for 

individual years were calculated from the global ΔpCO2 climatology of Takahashi et al. (2009),

monthly mean wind speed, and SST anomalies compared to SST data for the reference year 2000 

in the following manner:

Fym = kym K0,ym {[pCO2SW 2000m + (∂pCO2SW/∂SST)2000m × ΔSSTym – 2000m] – pCO2AIR 2000m}, (2)

where the subscript ym is the year and month in the time series during the study period, and the 

subscript 2000m refers to the month in 2000. The solubility of CO2, K0, was estimated from 

monthly SST and climatological SSS estimates using the solubility equations of Weiss (1974):

K0 (mol/kg·atm) = exp(-60.2409 + 9345.17 / (SST + 273.15) + 23.3585 ×

 ln((SST + 273.15) / 100) + SSS × (0.023517 – 0.00023656 ×

 (SST + 273.15) + 0.00000047036 × (SST + 273.15)2

)). (3)

The monthly gas transfer velocity, kym, was determined from the second moment of the monthly 

mean wind speed:

kym = 0.217 × <U10 ym

2

> (Scym/660)-0.5, (4)

where <U10 ym

2

> is the second moment representing the variance of the 6-hour wind speed in 

each pixel, and Sc is the Schmidt number calculated according to Wanninkhof (1992) using the 

monthly SST for each pixel. The coefficient 0.217 is the adjusted value from the coefficient 0.26 

TA (1) TA

pCO DIC

DIC

pCO SSS

SSS

pCO SST

SST

pCO pCO 2SW 2SW 2SW 2SW

2SW ×∆

∂

∂

×∆ +

∂

∂

×∆ +

∂

∂

×∆ +

∂

∂ ∂ =
3

for the monthly wind speed normalized to a 10-m height above the surface (U10) as used by 

Takahashi et al. (2009) with <U10

2

>/<U10>2 of 1.2 (0.26/1.2 = 0.217). These coefficients are 

based on an improved assessment of the global 14C inventory used to constrain the global gas 

transfer velocity (Sweeney et al., 2007). In the polar regions where sea-ice forms seasonally, kym

was multiplied by (1–f), where f is the fractional sea-ice cover.

2.1 Global ΔpCO2 Climatology

The monthly global ΔpCO2 (pCO2SW – pCO2AIR) climatology of Takahashi et al. (2009) 

is for non-El Niño conditions. It was produced using approximately three million measurements 

of pCO2SW obtained from 1970 to 2007 by over 30 investigators. Atmospheric pCO2 (pCO2AIR) 

values (GLOBALVIEW-CO2, 2008) are for the reference year 2000. The ΔpCO2 climatology 

used in this study is available at www.ldeo.columbia.edu/res/pi/CO2/carbondioxide/pages/

air_sea_flux_2009.html. All pCO2SW measurements were normalized to the reference year 2000 

and interpolated to a 4° latitude × 5° longitude spatial grid using a two-dimensional diffusionadvection transport model for surface waters. Even with three million data points on a 4° × 5°

grid, the surface ocean remains undersampled (see Figure 1 in Takahashi et al., 2009). This

database is about three times larger than the 0.94 million measurements used for the previous 

climatology (Takahashi et al., 2002). Its observational density has been significantly increased 

and covers more geographic areas, particularly the subpolar Southern Ocean.

2.2 Subannual Relationships between pCO2SW and SST, (∂pCO2SW/∂SST)2000m

For areas outside of the central and eastern Equatorial Pacific, optimum subannual

pCO2SW-SST relationships for each 4° × 5° pixel were derived from least squares linear fits of 

monthly climatological pCO2SW and SST values. Optimum subannual pCO2SW-SST relationships

were made using at least three consecutive monthly values according to the monthly variation 

pattern of the two parameters for each pixel to avoid spurious values of (∂pCO2SW/∂SST)2000m

that can arise by determining the trend from month to month. Therefore, each pixel had from one 

to four subannual relationship(s) between pCO2SW and SST, and the monthly range of subannual 

relationships for each pixel could be different (Figure 1).

We used the following criteria to optimize the subannual relationships of pCO2SW and 

SST values and to minimize the number of subannual relationships for each pixel. First, a linear 

fit equation was made from 12 months of data. If its correlation coefficient (R2) was more than

0.9, one pCO2SW-SST relationship was accepted. If its correlation coefficient was below 0.9, we 

derived subannual relationships using at least three consecutive monthly values. Among them, 

we selected relationships in the following manner: (1) all correlation coefficients had to be

greater than 0.5; (2) relationships with a higher mean value of the correlation coefficient were

retained; (3) we strived for a minimum number of relationships and maximum number of months 

in each relationship (when relationships had similar correlation coefficients, this step (3) took

precedence over step (2)). If a particular pixel’s subannual relationships with all R2 greater than 

0.5 could not be found, we selected subannual boundaries with more relationships of R2 greater 

than 0.5 and a higher mean value of R2.
4

Figure 1. A select comparison of three fixed seasonal (a-d) and optimum subannual pCO2SW-SST relationships 

(e-h). The numbers correspond to the month of the year. The solid circles and solid lines show season 1 for three 

fixed seasonal relationships and Subannual Period 1 (labeled with SAP 1) for optimum subannual relationships, 

solid squares and dotted lines are for season 2 and Subannual Period 2, solid triangles and dashed lines are for 

season 3 and Subannual Period 3, and the open diamonds and dash-dotted lines depict Subannual Period 4 for 

the subannual relationships. The center of each pixel is listed in figures a−d and corresponds to the same 

location for the graph immediately to its right.
5

This approach differs from the way (∂pCO2SW/∂SST) has been determined in previous studies

(Lee et al., 1998; Park et al., 2006) that used seasonal pCO2SW-SST relationships derived from 

monthly pCO2SW and SST values for three fixed time periods: January-April (season 1), MayAugust (season 2), and September-December (season 3). Low correlations between pCO2SW and 

SST were found in several regions and seasons in these studies, particularly in the Southern and 

South Indian oceans. Moreover, with three fixed seasonal relationships, many pixels with low 

correlation coefficients (R2 < 0.5) were found in the high latitude oceans during the boreal winter 

season. For example, for season 1, 62% of the total number of pixels showed correlation 

coefficients lower than 0.5. This indicates that variations of pCO2SW were not well correlated 

with SST changes during the boreal winter season (season 1) with fixed monthly boundaries

(Figures 1c and 1d). To determine the reason for the low correlations, we checked the variation 

patterns of pCO2SW and SST in detail. Most of the low correlations were due to the criteria of 

fixed monthly boundaries. If the trend between pCO2SW and SST variations changed within the 

fixed monthly boundary, we obtained a low correlation value of the relationship. When flexible 

monthly boundaries were applied to each pixel, only 1% of the total pixels showed weak 

correlations between SST and pCO2SW. The mean correlation coefficient for all the pixels was 

0.83 ± 0.14. This good correlation indicates that SST changes can reasonably account for 

pCO2SW variability in most of the pixels.

Figure 1 shows the difference between the optimum subannual relationships and the 

previous approach of the three fixed seasonal relationships for select pixels. With the optimum 

subannual relationships, 18% of the total pixels, which are mainly distributed over northern 

subtropical regions, had one pCO2SW-SST relationship, and 24% of the total pixels had two 

subannual relationships. Half of the total pixels had three subannual relationships; however, only 

20% of them had the same monthly ranges as the three fixed seasonal relationships. Seven 

percent of the pixels had four subannual pCO2SW-SST relationships. At high latitudes, a few 

pixels (<10) showed no meaningful subannual pCO2SW-SST relationships because there were no 

significant temporal variations of the climatological pCO2SW or SST.

Examples of the effect of change in subannual relationships on the yearly air-sea CO2

flux values are shown in Figure 2. The larger changes and the greater increases of interannual 

variability in CO2 fluxes were found in the pixels that had low correlation coefficients of 

relationships within the fixed monthly boundaries and the larger SST variations (Figure 2b). 

However, the interannual variabilities of global air-sea CO2 flux values estimated from the three

fixed seasonal and optimum subannual relationships were the same (0.13 Pg C (1015g C) yr–1

, 

respectively). These similarities were due to a cancellation of positive and negative fluxes in 

pixels and damping of the interannual signal when the fluxes were summed over the global scale.

For the central and eastern Equatorial Pacific (6°N-10°S and 80°W-165°E), we used 

updated empirical pCO2SW-SST equations derived from multiyear data that were collected in the 

Equatorial Pacific between 1981 and 2004 (Feely et al., 2006). It is well known that the drivers 

of interannual variability in this region are different from those of seasonal variability, 

invalidating our basic assumptions. Moreover, this is one of the regions with a sufficient amount 

of pCO2SW data gathered over the past 30 years that enabled us to interpolate the data to discern 

their interannual variability (Feely et al., 2006) rather than rely on our empirical approach. 

Because this dataset covers all phases of El Niño and La Niña periods for the past three decades, 

these pCO2SW-SST equations could account for pCO2SW changes caused by the El Niño-Southern 
6

Oscillation (ENSO) better than seasonal relationships derived from the monthly climatology 

based on non-El Niño conditions. Twelve pCO2SW-SST equations representing the warm 

(January to June) and cool (July to December) seasons of both El Niño and non-El Niño 

conditions for three time periods (1979-1989, 1990-1997, and 1998-2004) were used in this 

study (see Table 3 in Feely et al., 2006). El Niño and non-El Niño periods are based on SST 

anomalies in the Niño 3.4 region (5°N-5°S and 120°W-170°W) (Trenberth, 1997). The pCO2SW

values after 2004 were calculated from the equations for the period 1998-2004. To estimate a net 

air-sea CO2 flux in the central and eastern Equatorial Pacific, mean pCO2AIR values for the

three time periods were calculated from mole fractions of CO2 obtained from the National 

Oceanic and Atmospheric Administration’s Earth System Research Laboratory (NOAA-ESRL) 

(GLOBALVIEW-CO2, 2008).

2.3 Sea Surface Temperature

The monthly mean SST data used in this work were from NOAA’s Optimum 

Interpolation (OI) SST V2 data processed from 1982 onward (available at www.cdc.noaa.gov/ 

data/gridded/data.noaa.oisst.v2.html). These smoothed assimilated data are a blend of in-situ and 

satellite SSTs and are available on a 1° latitude × 1° longitude grid. The original monthly data

were re-gridded onto a 4° × 5° grid to match the grid size of the ΔpCO2 climatology.

Figure 2. A select comparison of air-sea CO2 flux values estimated from three fixed seasonal and optimum 

subannual pCO2SW-SST relationships in pixels centered on 40°S, 52.5°E (a) and 48°N, 152.5°E (b). The 

corresponding pCO2SW-SST relationships are shown in Figures 1c, 1d, 1g, and 1h, respectively.
7

NOAA’s OI SST V2 database is the second version of the OI SST product recomputed 

for late 1981 onward using consistent methods. The most significant change for the OI SST V2 

data is a reduction of biases in the OI SST at higher latitudes due to an improved simulation 

technique that uses sea-ice data. Improved ship data coverage used in the update and extension of 

the Comprehensive Ocean-Atmosphere Data Set (COADS) has also reduced the satellite SST 

biases in otherwise data-sparse regions (Reynolds et al., 2002).

2.4 Wind Speed

The monthly mean wind speed data used in this study were acquired from the 6-hour 

(four times a day) data of the National Centers for Environmental Prediction-Department 

of Energy (NCEP-DOE) reanalysis 2 product plotted on a Gaussian grid1 (available at

www.cdc.noaa.gov/data/gridded/data.ncep.reanalysis2.html). The statistical analysis uses all 

wind data within each 4° × 5° pixel, properly masked for land. The land mask for computing the 

4° × 5° monthly wind fields is from NOAA’s OI SST V2 product.

The NCEP-DOE reanalysis product was created to produce a 40-year record of global 

atmospheric fields of research quality data suitable for many uses including weather and climate 

applications. This reanalysis 2 product is an improved version of the original NCEP reanalysis 

product in that it fixed errors and updated parameterizations of physical processes (Kanamitsu 

et al., 2002).

Wind speed directly controls the magnitude of the air-sea CO2 flux for a given ΔpCO2

(see eqn. 2). The higher wind speeds yield higher air-sea CO2 fluxes. Therefore, wind speed 

variability is an important factor for quantifying changes in the air-sea CO2 flux. The NCEP 

wind speed product contains significant different values and temporal trends at high latitudes 

when compared with satellite-based wind speed data (Wallcraft et al., 2009). However, the 

degree of bias and its effect on the variability of the air-sea CO2 fluxes has not yet been fully 

resolved.

2.5 Sea-Ice Cover

At high latitudes, ice cover has a significant effect on the air-sea CO2 flux. In winter, 

sea-ice covering the surface ocean inhibits CO2 gas exchange at the air-sea interface. In summer, 

sea-ice melts and the air-sea CO2 exchange resumes. Moreover, pCO2SW levels near the ice edge 

show large seasonal changes with large minima in the spring due to high biological productivity 

and high values in the fall and winter due to vertical advection and mixed layer deepening.

The monthly fractional sea-ice cover values (f) were obtained from the monthly NOAA 

OI SST V2 surface ice concentration fields (available at www.cdc.noaa.gov/data/gridded/data. 

noaa.oisst.v2.html). The original data were plotted on a 1° latitude × 1° longitude grid for the 

___________________________

1

A Gaussian grid is used in the earth sciences as a grid for scientific modeling on a sphere. It can be uniquely accessed by onedimensional latitude and longitude arrays (orthogonal coordinates). The longitudes are equally spaced, while the latitudes are 

unequally spaced according to the Gaussian quadrature. Gaussian grids do not have points at the poles, and the grid cells typically 

have an aspect ratio of longitude to latitude of 1:2. There are different Gaussian grids for different models and data providers. 

The NCEP-DOE reanalysis 2 product uses the global T2 Gaussian grid with 192 × 94 points.
8

study period and were re-gridded to a 4° × 5° grid. When the ice cover value was less than 0.1,

each pixel was regarded as a sea-ice-free area. When the ice cover value was greater than 0.9, we 

assumed that each pixel had 10% ice-free open water (f = 0.9) because air-sea CO2 fluxes can be 

enhanced by leads caused by dynamic motions of sea-ice (Takahashi et al., 2009).

3. Results and Discussion

3.1 Overall Magnitude of Anomalies

Our empirical model obtained a 26-year global mean net air-sea CO2 flux of 

1.49 Pg C yr–1 for the period 1982-2007 (Figure 3a). The global net air-sea CO2 fluxes showed a 

strong correlation with the ENSO cycle, which confirms that large-scale climate reorganizations 

have a key effect on the interannual variability of air-sea CO2 fluxes. Higher oceanic CO2

uptakes have occurred during El Niño periods that are mainly due to relatively low CO2 efflux in 

the Equatorial Pacific (Figures 3a and 3b). Our analysis showed that the interannual variability of 

the global net air-sea CO2 flux was ±0.13 Pg C yr–1 (1σ) for the period 1982-2007.

Figure 3. (a) Net global air-sea CO2 fluxes in Pg C yr–1 for 1982-2007. Negative values denote net oceanic CO2

uptake. Shaded bars indicate El Niño periods (based on SST changes in the Niño 3.4 region). The dotted line 

denotes a 26-year global mean air-sea CO2 flux. (b) CO2 efflux anomalies relative to the 26-year mean value for 

the Equatorial Pacific (10o

N-10oS, 80oW-135oE).
9

3.2 Regional Changes

Large interannual variabilities were found in the high latitude North Atlantic, Equatorial 

Pacific, Southern Ocean, and mid North Pacific which correspond to the CO2 sink and source 

regions on the global net air-sea CO2 flux map (Figures 4a and 4b). The high latitude North 

Atlantic (north of 50°N, Figure 4a), the most intense CO2 uptake area, had a large interannual 

variability due mainly to large changes in SST anomalies and the steep seasonal relationships 

between pCO2SW and SST. The Equatorial Pacific, the most permanent CO2 source area, showed

large interannual variability closely connected with the ENSO cycle that determines the intensity 

of the upwelling of deep waters rich in CO2 (Figures 3b and 4). Although a seasonal upwelling of 

deep water occurs in the Indian Ocean due to the Southwest Monsoon winds, the Indian Ocean 

had relatively low interannual variability when compared to the other two major oceans.

Figure 4. (a) Climatological air-sea CO2 flux map from Takahashi et al. (2009) and (b) magnitude of interannual 

air-sea CO2 flux variability in each pixel for 26 years. The magnitude of interannual variability is expressed as a 

standard deviation (1σ) from the 26-year annual mean of net air-sea CO2 fluxes in each pixel.
10

The yearly net air-sea CO2 flux in the Southern Ocean is controlled by the net difference 

between a significant drawdown of surface pCO2SW by biological production in summer and the 

upwelling of CO2-rich deep waters in winter. The pCO2SW change in this region is 180 degrees

out of phase with SST, and its seasonal magnitude is as great as 100 μatm. This allowed for

negative seasonal pCO2SW-SST relationships (with high sensitivity for SST anomalies), leading 

to high interannual variability in the air-sea CO2 flux.

3.3 Seasonal Changes

Figure 5 shows the interannual air-sea CO2 flux anomalies from four seasons for the 26-

year mean seasonal CO2 flux values based on the total interannual variability of each pixel. Each

season has a range of three months: December-February, March-May, June-August, and 

September-November. The North Pacific and North Atlantic oceans showed large interannual

variabilities in the boreal winter and spring seasons (Figures 5a and 5b) and small variabilities in 

the summer (Figure 5c). The El Niño phenomenon that mainly controls surface pCO2SW in the 

Equatorial Pacific typically persists from late fall to the following spring. The efflux in the 

Equatorial Pacific showed large interannual changes in that season (Figure 5a). In the Southern 

Ocean, large variabilities were found near the ice edge in all of the seasons except austral 

summer (Figures 5b, 5c, and 5d).

Figure 5. Interannual variability (1σ) of the net air-sea CO2 flux in each pixel for the four seasons of 

(a) December-February, (b) March-May, (c) June-August, and (d) September-November for 26 years.
11

4. Caveats

While we believe, based on a comparison with models and time series stations, that the 

approach outlined in this study faithfully reproduces the interannual variability of air-sea CO2

fluxes, there are some caveats that could lead to an underestimation of the variability. The most 

important caveat is that this method is unable to detect changes that are not correlated with 

temperature. We also implicitly assumed that for all regions/pixels, except the eastern and central 

Equatorial Pacific, that the subannual trends in SST and pCO2SW could be used to determine 

interannual variations. For large-scale climate reorganizations such as the North Atlantic 

Oscillation and the Southern Annular Mode, there are other possible trends that are not captured 

with this approach. Finally, the approach assumed that there is no long-term trend in ∆pCO2

other than changes that correspond to long-term SST trends (see eqn 1). We, therefore, believe 

that while interannual variability is well represented by this approach, decadal trends in the 

fluxes are not adequately captured.

5. The Near Real-Time CO2 Flux Product

The release of pCO2SW data generally does not occur within two years of their acquisition 

due to the time it takes to quality control the data, agreements on when the data can be released,

and the ad-hoc arrangements to collate and release uniform/global datasets. Since global SST 

and wind data can be obtained within months of their collection, our approach provides a 

powerful means to determine month-to-month changes in near real-time. The near real-time flux 

maps can be obtained at http://cwcgom.aoml.noaa.gov/erddap/griddap/aomlcarbonfluxes.graph. 

A graphical user interface allows a user to select regional and global monthly maps from 1982

onward, and data can be exported in a variety of graphic and numeric formats. Moreover, time 

series plots can be created for any particular pixel. A screen shot of the web-based interface is 

presented in Figure 6.

6. Conclusions

An improved understanding of the year-to-year variations in CO2 uptake is necessary to 

predict the future response of the carbon cycle to climate change. To accomplish this, the regions 

and processes that control the interannual variations of the ocean and land biosphere must be 

determined. Here, we modeled interannual variability in the net air-sea CO2 fluxes using an

updated subannual pCO2SW-SST algorithms. These improved subannual algorithms, along with 

improved SST and wind speed data sets, allowed us to produce more reliable air-sea CO2 flux 

maps than previous endeavors. These near real-time air-sea CO2 flux maps can be obtained from 

the above mentioned website and are useful in detecting basin-wide variations of net air-sea CO2

fluxes on seasonal scales and improve our understanding of the carbon cycle. This material 

serves an important outreach function for the public and other groups by enabling them to 

become better informed about NOAA’s ocean carbon research efforts. With the ongoing 

collection of SOOP, mooring, and satellite data, we will be able to resolve temporal variations of 

air-sea CO2 fluxes with more confidence and higher accuracy in the future.
12

Figure 6. Interface from the web site (http://cwcgom.aoml.noaa.gov/erddap/griddap/ aomlcarbonfluxes.graph)

that enables the retrieval of graphic and numeric data for the empirical air-sea CO2 flux product discussed in this 

study.

7. Acknowledgments

This work was supported by NOAA’s Global Carbon Cycle Program, which sponsored 

the development of the new algorithms described in the proposal entitled “Observation-Based 

Quantification of Seasonal to Interannual Changes in Air-Sea CO2 Fluxes” (GC07-193). Funds 

for managing the CO2 flux product are provided by NOAA’s Climate Observation Division 

under the Global Carbon Data Management and Synthesis Project. The program managers, Mike 

Johnson, Joel Levy, Ken Mooney, and Kathy Tedesco, are gratefully acknowledged for their 

support. This research was also carried out in part under the auspices of the Cooperative Institute 

for Marine and Atmospheric Studies (CIMAS), a Joint Institute of the University of Miami and 

NOAA (cooperative agreement NA17RJ1226).
13

8. References

Feely, R.A., T. Takahashi, R. Wanninkhof, M.J. McPhaden, C.E. Cosca, S.C. Sutherland, and M.-E. Carr 

(2006): Decadal variability of the air-sea CO2 fluxes in the equatorial Pacific Ocean. J. Geophys. 

Res., 111, C08S90, doi:10.1029/2005JC003129.

GLOBALVIEW-CO2 (2008): Cooperative Atmospheric Data Integration Project-Carbon Dioxide. CDROM, NOAA-ESRL, Boulder, Colorado (also available on the Internet via anonymous FTP to 

ftp.cmdl.noaa.gov, Path: ccg/co2/GLOBALVIEW).

Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J.J. Hnilo, M. Fiorino, and G.L. Potter (2002): 

NCEP-DOE AMIP-II reanalysis (R-2). Bull. Amer. Meteorol. Soc., 83, 1631-1643.

Lee, K., R. Wanninkhof, T. Takahashi, S.C. Doney, and R.A. Feely (1998): Low interannual variability in 

recent oceanic uptake of atmospheric carbon dioxide. Nature, 396, 155-159.

Park, G.-H., K. Lee, R. Wanninkhof, and R.A. Feely (2006): Empirical temperature-based estimates of 

variability in the oceanic uptake of CO2 over the past 2 decades. J. Geophys. Res., 111, C07S07, 

doi:10.1029/2005JC003090.

Reynolds, R.W., N.A. Rayner, T.M. Smith, D.C. Stokes, and W. Wang (2002): An improved in situ and 

satellite SST analysis for climate. J. Climate, 15, 1609-1625.

Sabine, C.L., R.A. Feely, N. Gruber, R.M. Key, K. Lee, J.L. Bullister, R. Wanninkhof, C.S. Wong, 

D.W.R. Wallace, B. Tilbrook, F.J. Millero, T.-H. Peng, A. Kozyr, T. Ono, and A.F. Rios (2004): The 

oceanic sink for anthropogenic CO2. Science, 305, 367-371.

Sweeney, C., E. Gloor, A.R. Jacobson, R.M. Key, G. McKinley, J.L. Sarmiento, and R. Wanninkhof 

(2007): Constraining global air-sea gas exchange for CO2 with recent bomb 14C measurements.

Global Biogeochem. Cycles, 21, GB2015, doi:10.1029/ 2006GB002784.

Takahashi, T., W.S. Broecker, S.R. Werner, and A.E. Bainbridge (1980): Carbonate chemistry of the 

surface waters of the world oceans. In Isotope Marine Chemistry, E.D. Goldberg, Y. Horibe, and K. 

Saruhashi (eds.). Uchida Rokakuho, Tokyo, 291-326.

Takahashi, T., J. Olafsson, J.G. Goddard, D.W. Chipman, and S.C. Sutherland (1993): Seasonal variation 

of CO2 and nutrients in the high-latitude surface oceans: A comparative study. Global Biogeochem. 

Cycles, 7, 843-878.

Takahashi, T., S. Sutherland, C. Sweeney, A. Poisson, N. Metzl, B. Tilbrook, N. Bates, R. Wanninkhof, 

R. Feely, and C. Sabine (2002): Global sea-air CO2 flux based on climatological surface ocean pCO2

and seasonal biological and temperature effects. Deep-Sea Res., Part II, 49, 1601-1622.

Takahashi, T., S.C. Sutherland, R. Wanninkhof, C. Sweeney, R.A. Feely, D.W. Chipman, B. Hales, G. 

Friederich, F. Chavez, C. Sabine, A. Watson, D.C.E. Bakker, U. Schuster, N. Metzl, H. YoshikawaInoue, M. Ishii, T. Midorikawa, Y. Nojiri, A. Körtzinger, T. Steinhoff, M. Hoppema, J. Olafsson, 

T.S. Arnarson, B. Tilbrook, T. Johannessen, A. Olsen, R. Bellerby, C.S. Wong, B. Delille, N.R. 

Bates, and H.J.W. de Baar (2009): Climatological mean and decadal change in surface ocean pCO2, 

and net sea-air CO2 flux over the global oceans. Deep-Sea Res., Part II, 56, 554-577.
14

Telszewski, M., A. Chazottes, U. Schuster, A.J. Watson, C. Moulin, D.C.E. Bakker, M. González-Dávila, 

T. Johannessen, A. Körtzinger, H. Lüger, A. Olsen, A. Omar, X.A. Padin, A.F. Ríos, T. Steinhoff, 

M. Santana-Casiano, D.W.R. Wallace, and R. Wanninkhof (2009): Estimating the monthly pCO2

distribution in the North Atlantic using a self-organizing neural network. Biogeosci., 6, 1405-1421.

Trenberth, K.E. (1997): The definition of El Niño. Bull. Amer. Meteorol. Soc., 78, 2771-2777.

Wallcraft, A.J., A.B. Kara, C.N. Barron, E.J. Metzger, R.L. Pauley, and M.A. Bourassa (2009):

Comparisons of monthly mean 10 m wind speeds from satellites and NWP products over the global 

ocean. J. Geophys. Res., 114, D16109, doi:10.1029/2008JD011696.

Wanninkhof, R. (1992): Relationship between wind speed and gas exchange over the Ocean. J. 

Geophys. Res., 97, 7373-7382.

Weiss, R.F. (1974): Carbon dioxide in water and seawater: The solubility of a non-ideal gas. Mar. 

Chem., 2, 203-215.