What controls the seasonal cycle of columnar methane ... · What controls the seasonal cycle of...
Transcript of What controls the seasonal cycle of columnar methane ... · What controls the seasonal cycle of...
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
Jan Jan Jan Jan Jan2011 2012 2013 2014
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
GOSAT ACTM_AGS ACTM_CTL
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
GOSAT ACTM_AGS ACTM_CTL
South IndiaEIGP Arid India
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
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
-5.00.0 5.0
5.0
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
0.00.0
5.0
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): AGS1750 1780 1810 1870
CH4(ppb)
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
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
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
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
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SRI
CT
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Gas samplingpoint
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Site S: Mitigation Experiments using alternate wetting and drying (AWD) system of rice intensification (SRI)
7.906.94
13.87
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SRI MSRI CT
g C
H4
kg-1
grai
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ice
Conventional TransplantingAWD +SRI
They succeeded to reduce CH4 emission from rice paddies ~ 50%
Oo et al., paper in preparation
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-12
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-4
-2
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2016.6
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2016.7
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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
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
83-93E
0N 10N 20N 30N 40N
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
-5.00.0 5.0
5.0
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
0.00.0
5.0
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
1750 1780 1810 1870CH4(ppb)
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
10.010.0
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
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)
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
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
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
Pre
ssu
re (
hP
a)
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)
0N 10N 20N 30N 40N1000
200
100
50
-10 0 10Convective transport
rate of CH4 (ppb/day)
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)
1000
200
100
50
Winter (JFM) 83-93E
Summer(JJA)
Deep convection -
- Inject CH4-rich
air mass into the
upper
tropospheric
region.
South IndiaEIGP Arid India
Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan Jan Apr Jul Oct Jan
GOSAT ACTM_AGS ACTM_CTL
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
GOSAT ACTM_AGS ACTM_CTL
South IndiaEIGP Arid India
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
GOSAT ACTM_AGS ACTM_CTL
GOSAT ACTM_AGS ACTM_CTL
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
Jan Apr Jul Oct Jan0
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
Jan Apr Jul Oct Jan0
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