Released on November 16, 2015
Revised on November 30, 2015 (1st)Revised on May 20, 2016 (2nd)
Revised on September 29, 2016 (3rd)GOSAT Project, Satellite Observation Center, NIES
Calculation of whole-atmosphere monthly CO2 mean concentration based on GOSAT
observations
1. Introduction
The concentration of carbon dioxide (CO2) derived from space-based spectral
measurements by GOSAT encompasses levels from the top of the atmosphere to the earth’s
surface; this is referred to as column-averaged dry air mole fraction of CO2 or XCO2. The
column-averaged CO2 concentration thus contains more information for understanding
overall trends of CO2 in the atmosphere than CO2 measured at the surface. For the retrievalof XCO2, measurement of light absorption spectra in the short-wave infrared (SWIR)
radiation range is made with a Fourier Transform Spectrometer (FTS) onboard the satellite.
These spectra are, however, only collected over sunlit parts of the globe without cloud
cover and where the local solar altitude is above a specific threshold. Therefore, the area
over which GOSAT XCO2 data exist changes with season (Figure 1).

(a) April 2013（boreal spring）(b) July 2013（boreal summer）(c) October 2013（boreal fall）(d) January 2014（boreal winter）

Figure 1. Distribution of GOSAT XCO2 (GOSAT FTS SWIR Level 2 data product) gridded to1

2.5-degree mesh and monthly averaged. Circles indicate the locations of XCO2. The color shows
the level of XCO2 (low: blue; high: red).

Here, we considered an approach to estimate the whole-atmosphere mean CO2
concentration from GOSAT XCO2 data stored in GOSAT FTS SWIR Level 2 data product.
The estimation approach is explained below.
2. Estimation of whole-atmosphere mean CO2 concentration
The data gaps found in the distribution of GOSAT XCO2, as shown in Figure 1, mustbe filled out before the XCO2 data can be used for the calculation of the whole-atmospheremean CO2. The gaps were filled by using latitudinal band mean XCO2 calculated fromthree-dimensional CO2 distribution based on atmospheric tracer transport modeling(GOSAT Level 4B data product).
2.1 Calibration of GOSAT XCO2 data
For the evaluation of precision and accuracy, GOSAT XCO2 values were comparedwith surface-based XCO2 measurements taken at monitoring sites of the Total Carbon
Column Observing Network [1] (TCCON; http://tccon.ornl.gov). Biases found through thisdata validation process were used to correct GOSAT XCO2 values. These biases were
found to be dependent on changes in the characteristics of the observational instruments,
spectral measurement errors, and the version of the algorithm for retrieving GOSAT XCO2
that went through several updates in the past. The versions of GOSAT FTS SWIR Level 2
data product used here, which cover different time periods, are as follows: V02.21 (May2009 – May 2014); V02.31 (mid-June – mid-December 2014); V02.40 (February – earlyAugust 2015); V02.50 (early August – mid September 2015); V02.60 (mid-September2015 and onward).
From the comparison to the TCCON data, biases in V02.21 data were found to betime-dependent to a small extent. The dependency is expressed as follows:
Bias = -1.76 + (2.30 × 10-3 × t ) + (-7.83 × 10-7 × t2 ) (ppm),(1)

where t represents days past since the launch of the satellite (January 23, 2009). The biases
in V02.21 data were corrected using the above regression equation.
In the case of V02.31, the time-dependency of the biases was not considered since thetime span is only half a year; the biases were corrected by raising all XCO2 by 0.62 ppm,
preliminary validation result compared with TCCON data. The same approach was applied
to the half-year-long V02.40 data (raising all XCO2 by 1.35 ppm). For V02.50 and V02.60,2

XCO2 values were raised by 0.52 ppm (preliminary validation result compared withTCCON data over the period between April 2009 and December 2015), as corresponding
TCCON measurements are not made available yet [2].
The FTS instrument onboard GOSAT can switch the gain of the observed signal
amplifier depending on the intensity of surface-reflected sunlight observed (high, medium,and low gains). The instrument uses the medium gain when measuring over highlyreflective surfaces such as deserts, and switches the gain to high elsewhere over land andocean. Therefore, GOSAT FTS SWIR Level 2 data product contains XCO2 values based
measurements with high gain over land, medium gain over land, and high gain over ocean.
Past studies suggested that these three types of XCO2 values, found in the same time
periods and latitudinal bands, may have biases that differ slightly from one to another.
Since the majority of the TCCON data used for the XCO2 data validation are collected
over land surfaces above which GOSAT takes measurements with high gain, biases foundin XCO2 values retrieved from measurements with high gain over land may be mostreliable. We therefore selected these high-gain GOSAT XCO2 values (biases corrected) forthe estimation of whole-atmosphere mean CO2.
Notes:[1] A global network of monitoring sites where high-resolution Fourier transform infrared
spectrometers are installed. Column-averaged concentrations of atmospheric tracegases, such as CO2, methane, carbon monoxide, and nitrous oxide, can be retrieved
from the spectral measurements by these instruments.
[2] At present, whole-atmosphere CO2 mean concentrations estimated for the period afterFebruary 2015 are preliminary. These estimates are planned to be updated after thecompletion of the XCO2 data validation activities.
2.2 Estimation of monthly XCO2 distribution in longitudinal bands
GOSAT Level 4B data product stores the six-hourly three-dimensional distribution ofCO2 on a 2.5-degree mesh. These concentration data were obtained by simulating
atmospheric CO2 transport based on surface CO2 flux data (GOSAT Level 4A product)
estimated from GOSAT FTS SWIR Level 2 XCO2 and surface-based CO2 data over a
period between June 2009 and May 2012. To estimate XCO2 values over the entire surfaceof the globe, including the gaps seen in the GOSAT XCO2 distribution, we estimatedmonthly latitudinal distribution of XCO2 in several large areas from GOSAT Level 4B data
product as follows.

3January 2011

July 2011

Figure 2. Upper panel: distribution of monthly mean model-simulated XCO2 for January and July
2011. Lower panel: XCO2 averaged over a 10° latitude×60° longitude grid box in each of thecolored large areas, as shown in the upper panel. The line color indicates the location of each area.

First, we divided the globe into six large areas, each has a width of 60° longitude (seeupper panel of Figure 2). Within each of the large areas, we defined 18 grid boxes with asize of 10° latitude by 60° longitude. We then calculated the monthly mean ofmodel-simulated XCO2 (GOSAT Level 4B data) over each of these grid boxes (lowerpanel of Figure 2).
Regions from 80°S to the South Pole have similar geographical features and are also
away from major sources and sinks of CO2; monthly mean XCO2 over these regions arethus relatively stable and can serve as a reference in calculating the latitudinal distributionof model-simulated XCO2. For each of the six large areas, we calculated the vector
deviation D as follows:D(year, month, latitudinal-grid, longitudinal-grid)= (mean XCO2 over each 10°lat.×60°lon. grid box) – (mean XCO2 over 80°S-90°S).

(2)

We calculated D for all months between June 2009 and May 2012 (three years). For every
4month from January to December, we then computed the mean of D (described as Dmean)over the three-year period. Dmean(month, lat.-grid, lon.-grid) values were used for theestimation of monthly global XCO2 described in Section 2.3. Samples of the latitudinal
distribution of Dmean are shown in Figure 3 (January and July).
July

Dmean (ppm)

JanuaryFigure 3. Mean deviation (Dmean) of grid-box (10°×60°) mean XCO2 from southern (80°S-90°S)
XCO2 (model-simulated XCO2). Latitudinal distribution for each of the large areas defined inFigure 2 is shown (same color scheme used). Distribution for January (left) and July (right) arepresented. Error bars indicate the standard deviation of model-simulated XCO2 over the three-year
period.

2.3 Estimation of monthly global XCO2
For each of the six large areas defined in Section 2.2, we then calculated monthly mean
GOSAT XCO2 (biases were corrected as in Sect. 2.1) over each 10°×60° grid box. The
monthly mean value was calculated if the number of GOSAT XCO2 found in a given gridbox in a month was greater than five.
To obtain gap-filled distribution of XCO2, we used the following regression equationand found vector value a by the least squares method:(monthly mean GOSAT XCO2 over 10°×60° grid boxes)= a(year, month) + Dmean(month, lat.-grid, lon.-grid).

(3)

Value a, common for all the six large areas, is an estimated monthly mean XCO2
concentration over the 80°S-90°S region. Using a, we estimated monthly mean XCO2 over10°×60° grid boxes as the sum of a and Dmean. The latitudinal distribution of the estimated
XCO2 as well as monthly mean GOSAT XCO2 for 10° ×60° grid boxes, are shown inFigure 4.
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The whole-atmosphere monthly mean CO2 concentration was obtained by calculating aweighted average of the estimated XCO2 values. Weight was assigned to each estimated
XCO2 value according to the latitude of its 10°×60° grid box center as
weight = cos (-85° + 10° × n),(4)
where n is an integer that indicates the north-to-south location of the grid box in a given
large area (0-17).

180°-120°W

120°-60°W

60°-0°W

0°-60°E

120°-60°E

120°-180°E

LatitudeFigure 4. Latitudinal distribution of monthly mean GOSAT XCO2 (circle) and estimated XCO2(diamond).

3. Trend line of whole-atmosphere monthly mean CO2
The result obtained in this analysis shows that the whole-atmosphere monthly mean
CO2 rises continually, showing seasonal oscillations. The trend line of the time series of
the whole-atmosphere monthly mean CO2 can be obtained by calculating mean seasonalvariation and subtracting it from the time series (red line in the upper part of Figure 5). A
trend line value for a given month is nearly equal to a one-year average.
The slope of the derived trend line is not constant over the analyzed period. The redline in the lower part of Figure 5 shows the CO2 annual growth, which is given by taking
the time derivative of the trend line. The growth rate was small in 2011, and became large
from late 2012 to early 2013. Overlaid onto the figure are a trend (blue line) derived frommeasurements taken at surface-level sites operated by US National Oceanic andAtmospheric Administration (NOAA) and the annual CO2 growth calculated(DlugokenckyandTans,NOAA/ESRL(dataavailableat
www.esrl.noaa.gov/gmd/ccgg/trends/)). Both growth rate curves (red and blue) show
similar changes, although their phases are slightly different from one another. At each of
the NOAA monitoring sites, high-precision CO2 measurement is guaranteed with the use
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of reference CO2 gases. The reasonable agreement of both results (red and blue) suggests
long-term stability of the space-based CO2 observation by GOSAT.

Figure 5. Whole-atmosphere mean CO2 based on GOSAT XCO2 data (red) and global mean CO2
based on surface measurements (blue). Upper: CO2 trend line. Lower: CO2 annual growth.

4. Characteristics of whole-atmosphere mean CO2
Figure 6 shows the whole-atmosphere monthly mean CO2 values and their trend
calculated using GOSAT FTS SWIR Level 2 data between May 2009 and April 2016. InDecember 2015, the whole-atmosphere monthly mean, which increases gradually withseasonal oscillation, reached the level of 400 ppm for the first time. The trend of thewhole-atmospheric CO2 mean, which increases monotonically, first exceeded 400 ppm inFebruary 2016 based on the calculation result including GOSAT data up to April 2016.

Figure 6. Whole-atmosphere monthly mean CO2 (red dots) and their trend (black diamonds).
7Appendix: Summary of this revision (2nd rev.)- Biases in GOSAT FTS SWIR Level 2 data product were updated based on the latest
result of GOSAT data validation activities (Section 2.1).
- This lead to slight changes in the coefficients of Equation 1 (the time dependency ofbiases in V02.21 data product).
- The whole-atmosphere mean CO2 was then re-calculated based on the above updated
information.
- With this update, the extent of uncertainty associated with the whole-atmosphere CO2
estimate must be re-evaluated, and thus Chapter 3 was withheld at this time.
- Chapter 4 (Characteristics of whole-atmosphere mean CO2) was modified according to
the new results released in May 2016.
- Figure 6 was updated with the new results released in May 2016.
Summary of this revision (3rd rev.)- Chapter 4 (Characteristics of whole-atmosphere mean CO2) was modified according to
the new results released in September 2016.
- Figure 6 was updated with the new results released in September 2016.
Note that the calculation method of the whole-atmosphere monthly CO2 mean
concentration is just the same as the method described in the 2nd revision.
Acknowledgement
The GPV (Grid Point Value) weather forecast data, provided by the Japan MeteorologicalAgency, were used for the retrieval of GOSAT FTS SWIR Level 2 XCO2. Also, for thevalidation of GOSAT XCO2, surface-based XCO2 data by the Total Carbon Column
Observing Network (TCCON; http://tccon.ornl.gov) were used. Thanks are extended tothese two organizations.

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