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Seasonal variations of greenhouse gas Seasonal variations of greenhouse gas column-averaged dry air mole fraction column-averaged dry air mole fraction

retrieved from SWIR spectra of retrieved from SWIR spectra of GOSAT TANSO-FTS GOSAT TANSO-FTS

Nawo Eguchi*1, Yukio Yoshida2, Isamu Morino2, Nobuyuki Kikuchi2, Tazu Saeki2, Makoto Inoue2, Osamu Uchino2,

Shamil Maksyutov2, Hiroshi Watanabe2 and Tatsuya Yokota2

1: Tohoku University (Now at Kyushu University)

2: National Institute for Environmental Studies

* nawo@riam.kyushu-u.ac.jp

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Contents

• Status of SWIR Level 2 current version (Ver01.xx)

• Seasonal variations of XCO2 and XCH4

– Comparison with SCIAMACHY(2003-05)• Summary

– Possibility to scientific research use

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Interferogram 　 ( 干渉光 )

Greenhouse gases Observing SATelliteGreenhouse gases Observing SATelliteTop-Down approachSynoptic scale – GlobalIntra-seasonal, Seasonal, Inter-Annual scalesCO2, CH4, H2O, Clouds, Aerosol

フーリエ変換分光器TANSO-FTS(Thermal And Near-infrared Sensor for carbon Observation - Fourier Transform Spectrometer

JAXA/NIES/MOE( 宇宙航空研究開発機構・環境研・環境省 )

23 January 2009

種子島宇宙セン

ター

SWIR Band2

Complex Fourier Inverse Transform

Solar

CO2

CH4

H2O

Cirrus

Aerosol

IFOV ( 観測視野 )FTS 10.5km

Sun

Alt. 666km

Measurement of reflection from Surface, clouds and so on

MAP method [e.g., Rodgers, 2000]Column & Profile : CO2, CH4, H2O

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Optimal Estimation Method (Rodgers [2000])

aiaiTiiai

Tiii xxSxFySKDSKSKxx

111211

1

.min11 aa

Ta

TJ xxSxxxFySxFyx

Measurement residual Difference from a prior

eq. (1)

eq. (2)

The optimal x is found when an iterated solution Cost function J (x) is a minimum value.

Factor: Error covariance of observation

:Optimal concentration:

Observed radiance spectra:

εSxy

a

a

Error covariance of prior:Prior concentration :

Unit matrix for scaling :

SxD

Simulated spectra:)(xF

• The columns and profiles of CO2 and CH4 are retrieved by the optimal estimation method based on Rodgers [2002] from the GOSAT TANSO-FTS SWIR (Shortwave InfraRed; 0.76, 1.6, and 2 micron) and TIR (Shortwave InfraRed; 0.76, 1.6, and 2 micron) spectrum data.

• Optimal solution from eq.(1) eventually required the accurate Sa (a priori error covariance matrix) and its assessment.

In the GOSAT retrieval, a priori (Xa) and its covariance matrices (Sa) of CO2 and CH4 are obtained from the simulated data of NIES Transport Model [Maksyutov et al., 2008]. Prior covariance matrix is consisted of variances on the three temporal scales:(1) Synoptic scale variability (SSynoptic) in 2-week using NIES TM to obtain concentrations on global (every grids),(2) Interannual variability (VInteranuual) using observed concentration to obtain variability for a long term (several decade),(3) Seasonal cycle bias (BSeason): to estimate the effects of the errors in the simulated seasonal cycles.

SWIR L2 ATBD [2010]

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Status of SWIR Level2 Ver.01.xx

• Improved point from previous version (Ver.00.xx)– Cirrus detection method– Surface Pressure retrievals by using TANSO-FTS

SWIR Band 1 (O2A band)• Explicitly-retrieval of equivalent path length which is closely

related with aerosol and surface pressure in retrieval field

– Spectroscopic parameter of CH4 , line-mixing etc…

• Period of data available to General User (GU)– 6 April 2009 to 19 April 2011 (except May 2009)

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Comparison Ver. 01.xx with Ver. 00.xx

Yoshida　et　al.　(MSJ　meeting　2010)

High and low retrieved values are removed because of improved method treating cirrus and surface pressure (aerosol)

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Screening strategy of TANSO-FTS SWIR Level 2 data

To keep a certain quality of retrieved parameter, the filtering and screening of data are conducted before and after the retrieval process, respectively.

Table 2 ： Data number of data passed by L1B quality flag and CAI cloud flag

Clear sky ratio (from MODIS) 16 ～ 17 %

Eguchi and Yokota [GRL, 2008]

Before the retrieval process, the level 1B data are filtered out by Level 1B quality flag (spike noise, saturation and so on)

approximately ６０％ NG (Ver01.10), approximately ２０ % NG (Ver01.20, 30) CAI cloud flag (remove scan which having cloud pixels)

approximately ８０ % NG Totally, ９３％ ( ８２％ ) NG before the retrieval process

Period Total L1BFlag CAIcldL1BFlag

+ CAIcld

L2 for GU

XCO2 XCH4

2009/04/23-25 27543 9522 (34.6) 4347 (19.6*) 1688 (6.3*) 703 681

2009/07/24-26 27973 11484 (41.1) 4253 (19.8*) 1728 (6.6*) 640 590

2010/01/16-18 26185 9518 (36.4) 4259 (20.3*) 1832 (7.2*) 959 951

2010/03/20-22 27096 21455 (79.2) 4550 (20.3*) 4116 (18.4*) 785 739

Finally 2 ～ 3%

* With respect to CAI available data number

Ver 01.30 (Ver.01.20 is also same feature)

The L1B quality flag check is weak.Most of added data are low SNR data.

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Table3 ： Surviving ratio of retrieved data by screening items （ function of land/ocean 、 clear-sky ratio ） 2009 7/24-26 (Shade indicates less than 50%)

Effective screening item is AOD (variety of path length) for land and CAI coherent test for ocean. χ2

and 　 2μ scatering material (cirrus) determinations are closely correlated with clear-sky ratio within FOV.

Spectrum fitting Sufficiency information of spectrum

Check cloud remain

Evaluation of simultaneous retrieved parameter

Convergence of retrieval process

Screening (After the retrieval process)

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Seasonal characteristic of XCO2 Apr 2009 ～ Jun 2011 (GU : Apr 2009 – Apr 2011)

• White color indicates that the data are removed by screening.

• The sunglint region is primary measurement area over ocean.

Ave. XCO2 (whole period)

• The retrieved values at high latitudes are low because the GOSAT measured summer time over there. The CO2 value at summer time is lowest through the year.

• The Level 1B quality is low at the tropics and Asian monsoon regions where the clouds cover frequently.

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Northern Hemisphere

Southern Hemisphere

Max. May / Min. SeptemberMonthly mean STD 3 ppmvAmplitude 5 ～ 10 ppmvDiff from prior 8 ppmv ( ～ 2% low bias)

Only the grids with more than six months of data were taken into consideration.

Seasonal variation of XCO2 (Monthly mean) Amplitude (peak-to-peak)

Month with the maximum

prior(NIES Transport Model Ver05)

2009 Apr 2010 Apr 2011 Apr

SCIAMACHY

Interhemispheric Difference (NH-SH)

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Regional Characteristic of XCO2

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Seasonal characteristic of XCH4 Apr 2009 ～ 2011 Jun (GU : Apr 2009 – Apr 2011)

• White color indicates that the data are removed by screenings.

• The sunglint region is primary measurement area over ocean.

Ave. XCH4 (whole period)

• The seasonal variation in L2 current version is consistent with the previous knowledge.

• The contrasts of inter-hemispheric and between east and west North America are seen, also the high XCH4 is seen over Asia.

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Land

Ocean

Land Max : Sep-Nov Min : Apr- JulAmplitude : 20 ppbvOcean ???

Seasonal variation of XCH4 (Monthly mean) Amplitude (peak-to-peak)

Month of the maximum value

The dip is caused by the seasonal march of observation latitudinal band.

delay

2009 Apr 2010 Apr 2011 Apr

Only the grids with more than six months of data were taken into consideration.

Non-correction by factor Higher　than　a　prior　( 〜 1%)

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Regional characteristic of XCH4

Non-correction by factor

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Summary

Quality check of Level 2 current version (Ver.01.xx) Most of level 1B data (93%) are removed by L1B quality

check and CAI cloud flag. There is room for improvement of the screening method of

cirrus and aerosol (effective path length), esp. thin cirrus rejection and its effect on retrieved value.

Seasonal Variations of XCO2, XCH4

It is found that the seasonal variation on the continental scale is similar to the variation by a prior (NIESTM-05) (phase and amplitude), but the XCH4 seasonal variation (at several regions) is more complex than that of XCO2.

XCO2

Large differences from a prior are found in the areas of NH where plant activity is high.

XCH4

Large variances are found over Asia and North America.

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Potential to scientific research use

Remain negative bias of 〜 2% ( 〜 9ppmv) for XCO2, 〜 1% ( 〜 20ppbv) for XCH4

[Morino et al., AMT, 2011] Improvement of retrieval process Further validation is needed (discussion for seasonal and regional

biases). Impacting on flux estimation (Level 4 product) research

Seasonal cycle (phase and amplitude) and annual mean (low and middle latitudes) are consistent with the previous knowledge.

XCO2: Large differences from prior are located over high activity regions of plant.

XCH4: Large variances are located over East Asia.

Research of Inter-annual variation requires data accumulation. Rejection of abnormal values near sources and sinks

Analysis considering synoptic scales can be done, if the data quality and number meet the level of quality for science.

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Thank you for your attention