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!