Post on 31-Jul-2020
Thomas Wagner
Satellite group Mainz-HeidelbergMax-Planck-Institute for Chemistry, Mainz
Institute for Environmental Physics, Uni-Heidelberg
Investigating climate feedback through water vapor and cloud cover from GOME and
SCIAMACHY satellite observations
Outline:
• New generation of UV/vis satellite instruments (since 1995) => observations of many tropospheric trace gases
• Analysis of water vapor and cloud properties
• Investigation of climate feedbacks
(Why) do we need satellite observations?
Wish: ‘to measure everything everywhere’
=> Satellite observations are ‘closest’ to this ideal
• global observations:-remote areas can be ‚reached‘-similar sensitivity over whole globe-comparison to models: test of our understanding of the system earth
• separation of spatial and temporal variability
Tropospheric composition can only be retrieved from Nadir observations
From UV/VIS/NIR instruments in particular the boundary layer can be observed
(…this is the layer where we live and most of the sources are!)
240 260 280 300 320 340 360 380 400Wavelength [nm]
GOME spectrum
SBUV wavelengths
TOMS wavelengthsOzone hole (17.10. 1994)
measured by TOMS
SBUV and TOMS instruments measure light atselected spectral intervals
Early (and still succesful) UV/VIS concepts:
SBUV => O3 Profiles TOMS => Total O3 column
O3 cross section
300 400 500 600 700 800Wavelength [nm]
0.0
0.5
1.0
1.5
2.0
2.5
Spec
tral I
rradi
ance
[W/m
2 /nm
]
GOME solar spectrum
Planck function for 5800K
GOME (since 1995) and SCIAMACHY (since 2002):spectral information with moderate resolution
=> Information on many tropospheric trace gases
John Burrows et al., SCIAMACHY - A European Proposal for Atmospheric Remote Sensing from the ESA Polar Platform, published by the Max-Planck-Institute for Chemistry, Mainz, Germany, 1988.
GOME & SCIA spectral propertiesO3 UV
O3 vis
HCHO
OClO
O4
O2
H2O
SO2 NO2 BrO
Satellite group: http://satellite.iup.uni-heidelberg.de
Set of Atmospheric Absorbers Identified in GOME Spectra at the Satellite Group at the Institut für Umweltphysik
0.0
0.2
0.4
0.6
300 400 500 600 700 800Wavelength [nm]
Spec
tral a
lbed
o
SCIAMACHY:
240 – 2300nm
=> CO, CH4, CO2
DOAS satellite algorithms developed in our group during the last 10 years
SCIAMACHY
OMI
GOME-II
GOME-I
1990 2000 2010 2020 2030year
GOME-IIGOME-II
GOME on ERS-2
SCIAMACHY on ENVISAT
GOME-2 on METOP
OMI on AURA
UV / vis (+NIR) satellite instruments
DOAS analysis of BrO
345 350 355 360Wavelength [nm]
BrO
O3
O4
Residual
Atmospheric spectrum
Divided by Sun Spectrum
60 %
Ring Spectrum
7 %
7 %
0.3 %
0.2 %
1.2 %
2.2 %
I) Spectroscopy
DOAS fit yields SCD:Slant column density
Mag
nitu
de o
f the
sp
ectra
l stru
ctur
es SCD depends on the light path and the trace gas distribution
Desired Quantity: VCD, Vertical column density
VCD = SCD / AMF
AMF: Air Mass Factor, derived from radiative transfer modelling
II) Radiative transport modelling
dz
20 30 40 50 60 70 80 90SZA [°]
0
4
8
12
AMF
albedo 0.8
albedo 0.0
stratospheric AMF
tropospheric AMF
geometric AMF
-Compromise between spatio-temporal and spectralcoverage/resolution
=> only few overpasses (one per day – one per 6 days)
=> large ground pixels (GOME: 320x40km², SCIA: 60X30km², OMI: 13x13km²)
=> Usually observation for fixed local time (no diurnal cycle)
Limitations: Number of photons and satellites is limited
14 orbits per day
Limitations: low vertical resolution & large uncertainties
• Typically only column averaged quantities are derived
• Often decreased sensitivity close to the ground
• Often large uncertainties (e.g. due to clouds)
Advantages:
….new view on earth:
• (Nearly) global coverage with similar sensitivity
• Stability of instruments
• Observations in remote regions
Trop. NO2 VCD (S. Beirle) SO2 SCD (M.F. Khokhar)
Global Maps reflect distribution of sources
HCHO SCD (T. Marbach) BrO VCD (J. Hollwedel)
SCIAMACHY, 2003/04
GOME, 1997 GOME, 1996-2001
GOME, 1996-2001
Similar results at Uni-Bremen, BIRA, Brussels, SAO, Cambridge
C. FrankenbergCO VCD
SCIAMACHY, Jan., Feb. 2004
H2O VCD T. Wagner
SCIAMACHY, Aug.-Nov. 2003 HICRU, GOME, 1996 - 2002
CH4 VCD M. GrzegorskiCloud fractionC. Frankenberg
GOME, 1996 - 2002
Global Maps reflect distribution of sources
Similar results at Uni-Bremen, BIRA, Brussels, SAO, Cambridge
CH4 VCD from SCIAMACHY
C. Frankenberg, IUP Heidelberg
J.F. Meirink, KNMI, Utrecht
Science, March 2005
Comparison with model results
SCIAMACHY, Aug-Nov 2003
CH4 VCD from SCIAMACHY
C. Frankenberg, IUP Heidelberg
J.F. Meirink, KNMI, Utrecht
Science, March 2005
Comparison with model results
SCIAMACHY, Aug-Nov 2003
TM3 model, Aug-Nov 2003
MODIS Enhanced Vegetation Index
The largest differences can be seen in tropical broadleaf evergreen forests
Science, March 2005
C. Frankenberg, IUP Heidelberg
In agreement with recent findings of a new CH4source from plants under aerobic conditions
Keppler et al., Nature 2006
Difference SCIAMACHY – Model, Aug-Nov 2003
Original CH4emission inventory
Changes from comparison to SCIAMACHY-data
Emissions [Tg CH4/yr]
Wetlands: 174Rice: 60
Wetlands: 201Rice: 52
Inverse modelling of the global distribution of CH4
=> Adjustement of CH4 emission inventories for different sources and regions Peter Bergamaschi et al., JGR, 2007
The rest of the talk is on the analysis and results of the
a) atmospheric water vapor column (VCD) and
b) effective cloud fraction
b) Effective cloud top height (from O2 absorption)
Climate feedbacks are investigated by comparing these results to surface near temperatures
Without feedbacks climate predictions would be rather easy:
Doubling the CO2concentration:
=> temperature increase of about 1K
Because of feedbacks climate predictions are rather difficult:
Cloud forcing strongly depends on cloud height:
Clear sky Water cloud at 2km Thin ice cloud at 10km
+16 -115 +32
General rule: low clouds tend to cool, high clouds tend to warm
-Main question: (How) does cloud amount and/or distribution change when climate changes?
Analysis of H2O, O2,and O2 - O2 (O4) in the red spectral range
610 630 650 670
Wavelength [nm]
-0.03
-0.01
-0.10
-0.05
0.00
-0.10
-0.05
0.00
Opt
ical
den
sity
-0.01
0.00
0.01
GOME, 04.12.1996, 08:30 UT SZA: 33°, Lat: 5°, Long 31°
O4
O2
H2O
residual
29.6
30.0
30.4 Raw Spectrum
-0.06
-0.04Ring
Analysis Details:
Wavelength range: 611.6 - 675.4 nm
Shift and squeeze: spectra linked to sun spectrum.
Reference spectra: O2: Hitran, 273 KH2O: Hitran, 280 KO4: Greenblatt, 293 KRing: calculated from sun spectrum
(01.06.1997)
-Our H2O data product is based on ‚measured AMF‘
(from molecular (O2) or dimer (O4) absorption)
a) The concentrations of bothabsorbers are known and almost constant
b) The influence of radiativetransport variations is similar asfor H2O
2
2
2
2
2
2O
OH
O
O
OHOH AMF
SCD
VCDSCD
SCDVCD ==
0
0.5
1
1.5
2
H2O
VC
D [1
023
mol
ec/c
m]
0
1
2
3
4
5
6
Tota
l col
umn
prec
ipita
ble
wat
er [g
/cm
]
ComparisonH2O VCD April 1997
SSM/I
GOME
??
Comparison to SSM/I data: April 1997 (left), April,15, 1997 (right)
Correlation analysis for monthly mean data (left, for April 1997) and for collocated measurements (right, for April 15, 1997) of SSM/I and GOME on a 0.5x0.5 degree grid. The SSM/I data were taken from instrument F10 in ascending mode (version 5, data from http://www.ssmi.com/) which measures about one hour before GOME.
Two cloud data sets derived from GOME:
A) Intensity-based (HICRU-Algorithm, Michael Grzegorski, IUP Heidelberg)- clouds are bright- almost independent on cloud altitude
B) O2-absorption- clouds shield part of the O2 profile- depends on cloud fraction and cloud altitude
-60 -30 0 30 60Latitude
0.2
0.4
0.6
[rel.
units
]
O2 630nm
GOME orbit, 81024135 (narrow mode)
0.0
0.5
1.0C
loud
Fra
ctio
nHICRU
High cloud fraction => low O2 absorption
Results of radiative transfer modelling (from our Monte-Carlo model TRACY-2)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Cloud fraction
1km_20°2km_20°4km_20°7km_20°9km_20°12km_20°
Dependence of the normalised O2 absorption on cloud top heightand effective cloud fraction.
rela
tive
O2
abso
rptio
n
ISCCP cloud amount 1983-2005
Effective cloud fraction [%]
GOME (HICRU) cloud fraction 1996-2002
ISCCP cloud amount 1983-2005
Effective cloud fraction [%]
GOME (HICRU) cloud fraction 1996-2002
ISCCP cloud top pressure 1983-2005 GOME cloud top height 1996-2002
024681012km
Investigation of spatial variability of trends
Trends of surface-near temperatures, 1996 – 2002
http://www.giss.nasa.gov/data/update/gistemp
Trend patterns 1996 - 2002
T [K] Absolute temperature change Relative change of H2O VCD
increase of temperature
=> increase of water vapor column
Trend patterns 1996 - 2002
-0.02
-0.01
0.00
0.01
0.02
T [K] Absolute temperature change Absolute change of cloud fraction
Increase of temperature
=> decrease of cloud fraction
Trend patterns 1996 - 2002-0.05
-0.03
0.00
0.03
0.05
T [K] Absolute temperature change Relative change of O2 absorption
Increase of temperature
=> decrease of O2 absorption
Investigation of temporal variability
0
0.1
0.2
0.3
0.4
0.5
0.6
1996 1997 1998 1999 2000 2001 2002
Tem
pera
ture
ano
mal
y [K
]
GOME covers the years 1995 – 2003,
here we investigate the period 1996 – 2002
The amplitude of the temperaturechanges is
-0.3K for yearly averages
Up to -2.1K for monthly averages!
Calculation of monthly mean anomalies:
Tropical (30°S to 30°N) H2O VCD
1.02E+23
1.07E+23
1.12E+23
1.17E+23
1.22E+23
1.27E+23
1.32E+23
1996 1997 1998 1999 2000 2001 2002 2003 2004
Time
H2O
VC
D [m
olec
/cm
²]
-6E+21
-1E+21
4E+21
9E+21
1.4E+22
1.9E+22
2.4E+22
H2O
ano
mal
y [m
olec
/cm
²]
tropical averageTrop_-30°bis+30°tropical_H2O_anomaly
Time series of monthly mean values
Average seasonal cycle
Monthly anomalies
1996 1997 1998 1999 2000 2001 2002 2003 2004Time
0.00
0.40
0.80
tem
p.
anom
. [K]
-0.04
0.00
0.04H
ICR
U
anom
aly
-0.04
0.00
0.04
O2
anom
aly
temperature +0.10 K over 7 years
HICRU cloud fraction+0.33% over 7 years
O2 absorption-0.80% over 7 years
Monthly anomalies from 60°S to 60°N
-4E+21
0E+0
4E+21
H2O
ano
mal
y [m
olec
/cm
]
H2O VCD+2.1% over 7 years
+0.2km over 7 years
Global average trends
Evolution of tropical anomalies oftemperature and H2O VCD Correlation analysis
y = 9.6E+21x - 3.2E+21R2 = 0.57
-5E+21
-3E+21
-1E+21
1E+21
3E+21
5E+21
7E+21
0 0.2 0.4 0.6 0.8Temperature anomaly [K]
H2O
VC
D a
nom
aly
[mol
ec/c
m²]
-4.00E+21
-3.00E+21
-2.00E+21
-1.00E+21
0.00E+00
1.00E+21
2.00E+21
3.00E+21
4.00E+21
5.00E+21
6.00E+21
Jan. 96 Jan. 97 Jan. 98 Jan. 99 Jan. 00 Jan. 01 Jan. 02 Jan. 03-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Trop_3030_H2O_anomalyTrop_-30°bis+30°
=> Strong positive water vapor feedback
Relative anomaly of the H2O VCD during El-Nino(average of Oct. 1997 – Mar 1998 compared to average over the same period of the yaers 1996/97, 1998/99, 1999/2000, 2000/01)
Investigation of spatial variability of (monthly) anomalies
The amplitude of monthly mean anomalies shows large variations(temperature data 1996-2003)
max
imum
am
plitu
de [K
]
http://www.giss.nasa.gov/data/update/gistemp
Dependence of the H2O VCD on temperature derived from correlation analysis
Change of the H2O VCD per Kelvin
[1021 molec/cm²]
Dependence of the cloud fraction on temperature derived from correlation analysis
Change of the effective cloud fraction per Kelvin
[%]
Dependence of the O2 absorption on temperature derived from correlation analysis
Change of the normalised O2 absorption per Kelvin
[%]
Dependence of the cloud top height on temperature derived from correlation analysis
Change of the cloud top heightper Kelvin
[km]
Conclusions:
Global trends:
• H2O and cloud trends are strongly variable, overall we find for1996 – 2002 :
-H2O VCD: + 2.1±0.5%-cloud fraction: +0.3±2%-cloud top height: +200m ±100m-temperature: +0.1K
Correlation with temperature:
-H2O VCD: positive-cloud fraction: negative (except tropical oceans)-cloud top height: positive
Conclusions 2:
• Water vapor feedback: -globally averaged the H2O VCDs increase by about 9 to 12% per K-tropical H2O VCDs increase by about 8% per K
• The increase in the tropics indicates that relative humidity stays constant, indicating a very effective, positive water vapor feedback
• Cloud feedback:-slight decrease of cloud fraction (except tropics)-increase of cloud top height
=> Positive cloud feedback
• Deeper insight in involved processes from detailed comparison with model results
The timeline of UV/VIS/NIR satellite instruments (1995 -2021) covers an interesting period….
SCIAMACHY
OMI
GOME-II
GOME-I
1990 2000 2010 2020 2030year
GOME-II
?
GOME-II
global temperature evolution (IPCC)
Many thanks for your attention!
Deutschmann Pukite Kühl Heue Hollwedel Khokhar Beirle Marbach Frankenberg Wilms-Grabe Sanghavi Grezegorski WagnerTim Janis Sven Klaus-Peter Jens, Fahim Steffen Thierry Christian Walburga Suniti Michael Thomas
Special Thanks to the satellite group!