What controls the seasonal cycle of columnar methane ... · What controls the seasonal cycle of columnar methane observed by GOSAT over different ... 1Nara Woman’s University, Japan

What controls the seasonal cycle of columnar

methane observed by GOSAT over different

regions in India?

N. Chandra1, S. Hayashida1, T. Saeki2 and P. K. Patra2

1Nara Woman’s University, Japan2Department of Environmental Geochemical Cycle Research, JAMSTEC, Japan

This is results from Atmospheric Methane from Agriculture in South Asia (AMASA）project, sponsored by MOE, Japan

The 3rd Third ACAM Workshop, June 5-9, 2017

Jinan University, Guangzhou

Why should we care for methane?

Resulting atmospheric drivers

-1 0 1 2Radiative forcing relative to 1750 (W m-2)

An

thro

po

gen

ic G

HG

s CO2

CH4

Halo-

carbons

N2O

CO

IPCC, 2013

Second most important driver of anthropogenic climate change.

Addresses climate change on time scales of decades.

Sectorial emissions of CH4 remain highly uncertain, particular from Asian

region due to limited observations.

AMASA

（Atmospheric Methane from Agriculture in South Asia）a project sponsored by the Environment Research and Technology Development :

April 2015-March 2018 Leader: Sachiko Hayashida

Goal 1: Improvement of Methane

Emission Estimate from South Asia

Goal 2: Development of an

Emission Mitigation Proposal

3

+A priori

posteriori

Emission estimate in regional scale

Mitigation scenarios from rice fields

by proper water management and/or

change in transplanting

Turner et al. [2015]

Satellite measurements of XCH4

SWIR -- XCH4 -- Integrated

measure of CH4 with

contributions from different

vertical atmospheric layers.Co

lum

nar M

ethan

e (XC

H4 )

GOSAT (2009 – present)

ACTM can be used

to investigate the

role of transport and

chemistry

Emission information could not drive straightforwardly without separating the

role of transport and chemistry in the XCH4.

High XCH4 – High surface emissions ?

Aim of this study

Understand the responsible factors for XCH4 seasonal cycle over the

Asian monsoon region.

Data Used in present study:

Period: 2011 - 2014

Observations and model: GOSAT and JAMSTEC’s ACTM

Simulations : (Anthropogenic: EDGARV4.2; Wetl. & Rice: VISIT; Termite: GISS;

Bio. Burn: GFED)

Two different emission scenarios (AGS and CTL) are used to examine

model sensitivity to change in the underlying fluxes in simulations of the

total atmospheric column.

AGS: All emission sectors in EDGAR42FT kept constant at the values for 2000, except for

the emissions from agricultural soils.

CTL: EDGAR32/VISIT/GISS

Jan Jan Jan Jan Jan2011 2012 2013 2014



XC

H4

(pp

b)

1750

1800

1850

1900

1750

1800

1850

1900

1750

1800

1850

1900

1750

1800

1850

1900

60E 70E 80E 90E 100E0N

10N

20N

30N

40Na. Indian region

NE IndiaWIGP

EIGPCI

SIAS

AI

WIEI

BOB

GOSAT ACTM_AGS ACTM_CTL

XCH4 from GOSAT and ACTM over Indian region

South IndiaEIGP Arid India

Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan


0246

1750

1835

1920

XC

H4

(pp

b)

gC

H4 m

-2 m

on

-1

0

1

0

1

Role of vertical layers in XCH4 mixing ratios

2011 - 2014

UA

UT

MT2

MT1

LT

Co

lum

nar M

ethan

e (XC

H4 )

0 hPa

200hPa

1000 hPa

800 hPa

600 hPa

400 hPa

200 hPa



LT (σp = 1.0 – 0.8)

MT1 (σp =0.8 – 0.6)

MT2 (σp =0.6 – 0.4)

UT (σp =0.4 – 0.2)

UA (σp =0.2 – 0.0)

380

415

450

305

315

325

345

360

375

345

360

375

400

425

450

XpC

H4 (p

pb

)

Contributions of vertical layers in XCH4 mixing ratios

UA

UT

MT2

MT1

LT

Co

lum

nar M

ethan

e (XC

H4 )

0 hPa

1000 hPa

800 hPa

600 hPa

400 hPa

200 hPa

-20

0

20

40

60

80

100

AI WIGP EIGP NEI EI CI WI SP AS BOB

Co

ntr

ibu

tio

n f

ro

m d

iffe

re

nt

lay

ers (

%)

Region

UA

UT

MT2

MT1

LT

SI

Chandra et al., ACPD (2017)

Source of higher CH4 in the upper troposphere

Chandra et al., ACPD (2017)

83-93EConvection

CH4 (ppb/day)

Advection

a. Winter (JFM); 83-93E

0.0

0.0

0.015.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

b. Spring (AMJ); 83-93E

-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

c. Summer (JAS); 83-93E

-30.0-15.0

-15.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

d. Autumn (OND); 83-93E

-30.0

-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

-10 -8 -6 -4 -2 0 2 4 6 8 10

Convective transport rate of CH 4 (ppb day-1) -10 0 10

1840

e. Winter (JFM); 83-93E

-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

f. Spring (AMJ); 83-93E

-20.0

-10.0

-5.0

0.0 5.05.0

10.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

g. Summer (JAS); 83-93E

-20.0

-10.0-5.0

0.0

0.05.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

h. Autumn (OND); 83-93E

-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

Winter (JFM)

0N 10N 20N 30N 40N


-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0

-5.00.0 5.0

5.0

10.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0-5.0

0.00.0

5.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS1750 1780 1810 1870

CH4(ppb)


-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0

-5.0

0.0 5.05.0

10.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0-5.0

0.0

0.05.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

60E 80E 100E 120E

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N

i. Winter (JFM); 200 hPa

10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N

j. Spring (AMJ); 200 hPa

10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N

k. Summer (JAS); 200 hPa

10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N

l. Autumn (OND); 200 hPa

10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

200 hPa

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

Summer (JJA)

Conclusion

Both convection and advection play significant role in transport and

redistribution of CH4 over the South Asian monsoon region.

A direct link between surface emissions and higher levels of XCH4

can not be established straightforwardly.

Upper troposphere contribute strongly in the peak of XCH4 over

most of the regions lying in the northern part of India.

Thank You for your kind

attention

V1

V2 V1

V2

V1

V2

V2

V1

V1

V1

V1

V2

V2

V2

V2

V2

V1

V1

Pla

tfo

rm f

or

gas

sam

plin

g

5m

7m

SRI

CT

MSR

I

R1

R2

R3

Gas samplingpoint

12

Site S: Mitigation Experiments using alternate wetting and drying (AWD) system of rice intensification (SRI)

7.906.94

13.87

0

2

4

6

8

10

12

14

16

SRI MSRI CT

g C

H4

kg-1

grai

n r

ice

Conventional TransplantingAWD +SRI

They succeeded to reduce CH4 emission from rice paddies ~ 50%

Oo et al., paper in preparation

-16

-14

-12

-10

-8

-6

-4

-2

0

2

4

6

2016.6

.2

2016.6

.6

2016.6

.10

2016.6

.14

2016.6

.18

2016.6

.22

2016.6

.26

2016.6

.30

2016.7

.4

2016.7

.8

2016.7

.12

2016.7

.16

2016.7

.20

2016.7

.24

2016.7

.28

2016.8

.1

2016.8

.5

2016.8

.9

2016.8

.13

2016.8

.17

2016.8

.21

2016.8

.25

2016.8

.29

2016.9

.2

2016.9

.6

Wa

ter

dep

th (

cm)

S

R…

Soil

Wet

Dry

AMASA: 6 Sub-themes

1. Analysis of GOSAT and in-situ measurements of methane

(NWU).

2. Methane measurements in South Asia (NIES).

3. Mitigation options of methane emissions (NIAES).

4. Methane flux measurements in South Asia (Chiba Univ.).

5. Continuous measurements of methane by a laser Instrument.

6. Inverse Analysis of Methane (JAMSTEC).

Summer -- Higher

CH4 emissions as

well as higher

convective

transport.

Source of higher CH4 in the upper troposphere

Chandra et al., ACP (2017) (in review)

500

1840


-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0

-5.0

0.0 5.05.0

10.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0-5.0

0.0

0.05.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

83-93E

0N 10N 20N 30N 40N


-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0

-5.00.0 5.0

5.0

10.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0-5.0

0.00.0

5.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

1750 1780 1810 1870CH4(ppb)


-5.0

0.0 5.0

5.0

10.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0

-5.0

0.0 5.05.0

10.010.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-20.0

-10.0-5.0

0.0

0.05.0

5.0

10.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-5.0

-5.0

0.0 5.010.020.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS


0.0

0.0

0.015.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0-15.0

-15.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0

-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

-10 -8 -6 -4 -2 0 2 4 6 8 10

Convective transport rate of CH 4 (ppb day-1)

Anticyclonic winds --Traps CH4-rich air mass and further spread over the larger south

Asian region.

60E 80E 100E 120E

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

200 hPa

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

60E 70E 80E 90E 100E 110E 120E0

10N

20N

30N

40N


10ms-110

10ms-1

60E 80E 100E 120E0

10N

20N

30N

40N

1750 1770 1790 1810 1830 1850 1870

CH4 (ppb): AGS

Pre

ssu

re (

hP

a)


0.0

0.0

0.015.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0-15.0

-15.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0

-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

-10 -8 -6 -4 -2 0 2 4 6 8 10


0N 10N 20N 30N 40N1000

200

100

50

-10 0 10Convective transport

rate of CH4 (ppb/day)


0.0

0.0

0.015.0

30.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0-15.0

-15.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50


-30.0

-15.0

0.0

0.0

0N 10N 20N 30N 40N1000

500

300

200

100

50

-10 -8 -6 -4 -2 0 2 4 6 8 10


1000

200

100

50

Winter (JFM) 83-93E

Summer(JJA)

Deep convection -

- Inject CH4-rich

air mass into the

upper

tropospheric

region.


Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan


0246

1750

1835

1920

XC

H4

(pp

b)

gC

H4 m

-2 m

on

-1

0

1

0

1

Contributions of vertical layers in XCH4 mixing ratios

2011 - 2014



LT (σp = 1.0 – 0.8)

MT1 (σp =0.8 – 0.6)

MT2 (σp =0.6 – 0.4)

UT (σp =0.4 – 0.2)

UA (σp =0.2 – 0.0)

380

415

450

305

315

325

345

360

375

345

360

375

400

425

450

XpC

H4 (p

pb

)

-20

0

20

40

60

80

100

AI WIGP EIGP NEI EI CI WI SP AS BOB

Co

ntr

ibu

tio

n f

rom

dif

fere

nt

lay

ers

(%

)

Region

UA

UT

MT2

MT1

LT

SI

Calculation of XCH4

XCH4 is calculated from the ACTM profile using the formula i CH4 (i)

pi, where i is the model level of thickness pi

XCH4 = Σn=260 CH4 (n) * [(σp (n) + σp (n-1))/2 - (σp (n) + σp (n+1))/2]

For the first layer (n =1)

XCH4 = Σn=1 CH4 (n) * [1- (σp (n) + σp (n+1))/2]

where

n = number of vertical sigma pressure layer,

σp = sigma pressure level



Arid India

Jan Apr Jul Oct Jan0

4

8

12

16WIGP

Jan Apr Jul Oct Jan

EIGP

Jan Apr Jul Oct Jan

Northeast India

Jan Apr Jul Oct Jan

Western India


4

8

12

16Central India

Jan Apr Jul Oct Jan

East India

Jan Apr Jul Oct Jan

South India

Jan Apr Jul Oct Jan

Arabian Sea


4

8

12

16Bay of Bengal

Jan Apr Jul Oct Jan

Tota

l colu

mnar

CH

4 loss r

ate

(ppb/d

ay)

Columnar loss rate of CH4

Resulting atmospheric drivers

-1 0 1 2Radiative forcing relative to 1750 (W m-2)

An

thro

po

gen

ic G

HG

s CO2

CH4

Halo-

carbons

N2O

CO

Why do we care about methane?

Source: IPCC, 2013

Second most important drivers of climate change.

• Importance of methane as a short

lived climate forcer (SLCF).

• Curbing CH4 emissions will more

helpful than CO2 to fight against

global warming at inter-decadal time

scale.

Time Horizon (yr)

Tem

per

atu

re im

pac

t (1

0-3K

)

Temperature response to 1-year

pulse (2008) of emissions

CH4 emission : complexities and uniqueness of ACTM inversion

Inversion results are dependent on the choice of prior flux

So inversions are run for 7 ensemble cases

The outliers are decided by independent aircraft measurements

Source types

Natural:VISIT: Wetl & RiceGISS: TermiteGFED: Bio. BurnSRON: OceanSRON: MudVolcano

Anthropogenic:(EDGAR4.2)IPCC_1A (transport)IPCC_1B (Fugitive)IPCC_2 (Industry)IPCC_4A (Ent. Ferm.)

Soil sink: VISIT

Chemical loss: OH: Spivakovsky/sclCl/O1D: ACTM

CH4ags CH4e42 CH4fix CH4fug CH4ong CH4enf CH4ctl

Trends : come from Anthropogenic emissionsVariability : are mainly due to Natural emissions

Challenges

• Individual sources of CH4 remain highly uncertain.

• In-situ observations - Improve our understanding of various CH4

sources, but the observation stations are sparsely distributed.

Observations from space have transformed the

condition from data-poor to data-rich over past 20

years.

Challenges

• Regional emissions of CH4 remains highly uncertain particular

over Asian region, which is one of the most significant areas of

CH4 emissions.

• Where is the source of CH4 in Asia?

Satellite observations from space

have the potential to captured the

spatial and temporal variability in

CH4 for most part of glob landin

Model Descriptions

Atmospheric general circulation model (AGCM)-based CTM (i.e., JAMSTEC’s

ACTM).

Meteorological field Japan Meteorological Agency reanalysis fields (vr., JRA-55).

Anthropogenic EDGAR42FT2012 (2013)

Wetlands and rice paddies VISIT terrestrial ecosystem model

Biomass burning Goddard Institute for Space Studies (GISS) and Global and

Global Fire Emission Database (GFED) version 3.2

Resolutions ~2.8 × 2.8° horizontal and 67 vertical sigma-pressure levels

Atmospheric molar fractions of CH4 have been simulated using an

ensemble of 3 cases of a priori emission scenarios .

AGS All emission sectors in EDGAR42FT kept constant at the

values for 2000, except for the emissions from agricultural

soils.

CTL EDGAR32/VISIT/GISS

UA

UT

MT2

MT1

LT

Co

lum

nar M

ethan

e (XC

H4 )

0 hPa

200hPa

1000 hPa

800 hPa

600 hPa

400 hPa

200 hPa

Methane should be part of climate policy

…but for reasons totally different than CO2

• It addresses climate change on time scales of decades – which we care about

Loss of Arctic sea ice, seal level rise, rain during ski season

• It is an alternative to geoengineering by aerosol injections

Both address near-term climate change – which do you prefer?

• It has air quality co-benefits

Methane is a major precursor of background tropospheric ozone

• Reducing methane emissions can be easy to do and make money

Fix leaks from oil/gas super-emitters, capture methane from landfills…

Climate policy should not use a single time horizon for metrics;

reporting both 20-year and 100-year GWPs would be a simple solution

The GOSAT observational record

3 cross-track 10-km pixels 250 km apart

orbit

track

• CO2 proxy retrieval [Parker et al, 2011]

• Mean single-retrieval precision 13 ppb

• Use here data for 6/2009-12/2011

THE ART AND SCIENCE OF CLIMATE MODEL TUNING

(this has relevance to solving OH issues in CTMs)

Hourdin et al., BAMS, 2017

We survey the rationale and diversity of

approaches for tuning, a fundamental

aspect of climate modeling, which should

be more systematically documented and

taken into account in multimodel analysis.

The colored curves correspond to three configurations of the GFDL CM3

model. CM3 denotes the CMIP5 model, while CM3c and CM3w denote

alternate configurations with large and smaller, respectively, cooling from cloud

aerosol interactions.

Global absorbed shortwave

radiation (ASR, full curve) and

outgoing radiation (OLR,

dashed) at top of atmosphere.

The squares and diamonds

correspond to default values

retained after a tuning phase

(for GFDL and IPSL-CM they

correspond to the values

retained for CMIP5, but

because the experiments were

redone with recent versions of

the same models, the balance

is not completely satisfied with

the selected values).

Example of tuning approach for the ECHAM model (after Mauritsen

et al. 2012). The figure illustrates the major uncertainty in climate-

related stratiform liquid and ice clouds and shallow and deep

convective clouds. The gray curve to the left represents tropospheric

temperatures, and the dashed line is the top of the boundary layer.

Parameters are (a) convective cloud mass flux above the level of

nonbuoyancy, (b) shallow convective cloud lateral entrainment rate,

(c) deep convective cloud lateral entrainment rate, (d) convective

cloud water conversion rate to rain, (e) liquid cloud homogeneity, (f)

liquid cloud water conversion rate to rain, (g) ice cloud homogeneity,

and (h) ice particle fall velocity.

Launch of the Climate and Clean Air Coalition to Reduce Short Lived Climate Pollutants, Feb 17, 2012. Source: US Department of State

Climate and Clean Air Coalition/CCAC)

29

SLCP:CH4Tropospheric OzoneBlack Carbon

What is GOSAT?

GOSAT(Greenhouse gases Observing SATellite)

– GOSAT is the first satellite that dedicated to greenhouse-gas-monitoring.

– Targets of GOSAT are CO2 and CH4

– Onboard sensor is named “TANSO-FTS” (Fourier Transform Spectrometer)

– GOSAT collected more than 6-years of record as of 2015.

2009 2010 2011 2012 2013 2014 2015 -Milestone *

Launch

* * * *in operation in space

Current ground-based observation network

Advantage of GOSAT is its wide spatial coverage

Documents

What controls the seasonal cycle of columnar methane ... · What controls the seasonal cycle of columnar methane observed by GOSAT over different ... 1Nara Woman’s University, Japan