7.5-year global trends in GOME cloud cover and humidity - a signal of climate change? Institut für Umweltphysik, Uni-Heidelberg, Germany

Transcription

1 7.5-year global trends in GOME cloud cover and humidity - a signal of climate change? T. Wagner, S. Beirle, M. Grzegorski, S. Sanghavi, U. Platt Institut für Umweltphysik, Uni-Heidelberg, Germany The Greenhouse effect and the role of water vapor (feedback) GOME observations of H2O and clouds Comparison to other satellite sensors Results & Conclusions

2 Global Maps derived from GOME / SCIAMACHY NO2 VCD (S. Beirle, IUP Heidelberg) SO2 SCD (M.F. Khokhar, IUP Heidelberg) SCIAMACHY, 2003/04 GOME HCHO VCD (T. Marbach, IUP Heidelberg) BrO VCD (J. Hollwedel, IUP Heidelberg) 1997 GOME GOME

3 CH4 from SCIAMACHY C. Frankenberg, IUP Heidelberg CO2 CH4 CH4 columns are normalised with respect to CO2 columns Aug-Nov 2003 Together with KNMI team: Science-paper, March 2005

4 H2O VCD from GOME Jan Feb 1996

5 Greenhouse effect, historical remarks: Joseph Fourier, 1827: Greenhouse effect: Earth is relatively transparent to solar radiation, but highly absorbant to thermal radiation. => Increase of the temperature of the earth s surface John Tyndal, 1861: Role of trace gases, especially H2O: Not O2 or N2 are responsibel, but trace gases, predominantly H2O and CO2...they serve as a blanket, more necessary to the vegetable life of England than clothing is to man

6 Greenhouse effect, historical remarks: S. Arrhenius, 1896: Anthropogenic greenhouse effect, On the influence of carbonic acid in the air upon the temperature of the ground T. Chamberlain, 1905: Water vapor feedback: Water vapor, confessedly the greatest thermal absorbent in the atmosphere, is dependent on temperature for its amount, and if another agent, as CO2, not so dependent, raises the temperature of the surface, it calles into function a certain amount of water vapor which further absorbs heat, raises the temperature and calls for more vapor...

7 Illustration of the greenhouse effect:

8 Some more details:

9 Relative contributions of the different trace gases on the natural greenhouse effect: 63.5% 22.5% 7.2% 4.2% 2.7%

10 Spatial dependence of the H2O greenhouse effect 15 35% Altitude % 17.5% 20% 0 10% Latitude Held & Soden, 2000

11 Vertical scheme of the greenhouse effect: -At Ze the atmosphere gets transparent for IR emission -Lapse rate Γ = 0.65K/100m Tropopause Ts = Te + Γ * Ze([CO2]) Altitude Ze 1x CO2 Temperature Te = 255K Ts

12 Vertical scheme of the greenhouse effect: -At Ze the atmosphere gets transparent for IR emission -Lapse rate Γ = 0.65K/100m Tropopause Altitude Ze Ze 1x CO2 Ts = Te + Γ * Ze([CO2]) 2x CO2 Doubling of the CO2 concentration results in: (no H2O feedback) Ze = 150 m Ts = 1K E = 4W/m² Temperature Te = 255K Ts Ts

13 Anthropogenic influence on the greenhouse effect: UK Meteorological Office IPCC, 2001

14 Water vapor feedback Numbers for an assumed doubling of the CO2 concentration

15 Clausius Clapeyron: Dependence of the H2O partial pressure on temperature H2O partial pressure [hpa] Temperature [ C] Fractional change of H2O partial pressure is proportional to T -2 : A 1K change at 200K (300K) results in a change of 15% (6%)

16 IPCC on water vapor feedback: 1990: The best understood feedback, intuitively to understand 1992: No compelling evidence that water vapor feedback is anything other than positive although difficulties with upper tropospheric water vapor 1995: Feedback from redistribution of water vapor remains substantial source of uncertatinties in climate models especially feedback from the tropical upper troposphere

17 Temperature response to a doubling of CO2 for different feedback strengths Held & Soden, 2000

18 -if lapse rate Γ const? -if relative humidity const? => Two main questions: Tropopause A) How does atmospheric humidity react to a change in surface temperature? Altitude Ze Ze Ts = Te + Γ * Ze([CO2]) 1x CO2 2x CO2 Temperature Te = 255K Ts B) How does atmospheric temperature profile react to a change in surface temperature? (especially in tropical upper tropospere)

19 How is tropical free tropospheric humidity controlled? -convective updraft (small areas) -large scale subsidence Lindzen, 1991

20 Richard S. Lindzen, 1991: Some coolness concerning global warming T: altitude: humidity: T:

21 Reality seems to be more complex: -Mixing occurs at various atmospheric levels -relative humidity stays almost constant -Main question: Does relative humidity stays constant when climate changes?

22 Clouds can absorb IR and warm the climate (which is warmer - cloudy nights or clear ones?). Clouds also can reflect energy to space and can cool the climate.

23 Cloud forcing strongly depends on cloud height: General rule: low clouds cool, high clouds warm -Main question: Does cloud amount and/or distribution change when climate changes?

24 Such questions have been addressed in previous studies: -dependence on season and location (using seasonal cycle) -dependence on ENSO -dependece on atmospheric cooling due to Pinatubo eruption -Here we investigate the dependence for monthly and yearly mean values during the period temporal variations -spatial variations

25 Decrease of temperature and atmospheric humidity after the Pinatubo eruption a test case for the investigation of the water vapor feedback Soden et al., 2002

26 GOME covers the years , here we investigate the period The amplitude of the temperature changes is -0.3K for yearly averages Up to -2.1K for monthly averages Temperature anomaly [K]

27 Trends of surface-near temperatures, nasa.gov/data/u pdate/gistemp

28 Role of transport:

29 -humidity is remote controlled, especially over the continents

30 What can Satellite observations contribute? Global coverage: -Investigation of spatial patterns -Averages over large areas (e.g. Tropics) Long time series -correlation with temperature evolution Water vapor observations in different spectral ranges: - Microwave (SSM/I) - thermal IR (TOVS) - Visible Spectral range (GOME / SCIA)

31 Advantages in the microwave and IR-Spektral range: SSM/I: High temporal and spatial resolution and coverage (except oceans) High accuracy No cloud interference TOVS: (limited) vertical resolution

32 Advantages of GOME/SCIAMACHY-H2O-observations High sensitivity for the whole atmospheric column High precission Similar sensitivity over land and ocean global coverage Data retrieval is not depending on additional or a-priori information

33 GOME & SCIAMACHY-viewing geometry

34 GOME & SCIAMACHY-Spectral regions

35 Set Atmospheric of Atmospheric Trace Abosrbers gases analysed Identified in satellite GOME Spectra spectra at the Satellite Group at the Institut für Umweltphysik O 4 O 3 UV OClO H 2 O HCHO O 2 Intensity [arbitrary units 1E+16 1E+14 1E+12 1E+10 1E+08 Satellite group: Wavelength [nm] SO 2 NO 2 BrO O 3 vis

36 Optical density H2O O2 O4 GOME, , 08:30 UT SZA: 33, Lat: 5, Long 31 Raw Spectrum Ring residual Analysis Details: Wavelength range: nm Shift and squeeze: spectra linked to sun spectrum. Reference spectra: O2: Hitran, 273 K H2O: Hitran, 280 K O4: Greenblatt, 293 K Ring: calculated from sun spectrum ( ) Wavelength [nm]

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38 Radiative transport corrections A): Zenit Geometrical elongation of the absorption path due to sun elevation Direktlicht- Beobachtungen SZA The final water vapor product:: Vertical column density (VCD): vertically integrated H2O-concentration

39 Radiative transport corrections B): Correction of changing sensitivity due to -clouds - aerosols - surface albedo

40 Radiative transport corrections C): Influence of spatial inhomogeneities inside the (large) ground pixel 40 km 320 km

41 -Our H2O data product is based on measured AMF (from molecular (O2) or dimer (O4) absorption) a) The concentrations of both absorbers are known and almost constant b) The influence of radiative transport variations is similar as for H2O 2 VCD H 2O = = SCD SCD O 2 H O VCD O 2 SCD H AMF 2 O O 2

42 Especially for trend studies it is important to minimise the influence of clouds => selection of mostly clear skies: Only measurements for O2 absorption between 80% and 95% of the maximum value are used Average of the H2O VCD all observations only clear sky observations

43 Vergleich der H2O VCD für April 1997 SSM/I 2 6 GOME H2O VCD [1023 molec/cm ] Total column precipitable water [g/cm ]

44 Correlation of GOME and SSMI H2O VCD (over ocean only) April 1997

45 Two cloud data sets derived from GOME: A) Intensity-based (HICRU-Algorithm, Michael Grzegorski, IUP Heidelberg) - clouds are bright - almost inependent on cloud altitude B) O2-observations - clouds shield part of the O2 profile - depends on brightness and cloud altitude - high precission

46 Average cloud cover HICRU cloud fraction O2 cloud cover

47 Influence of ENSO on the H2O VCD January 1997 January 1998 H2O VCD [1023 molec/cm²] Total column precipitable water vapor [g/cm²]

48 Relative anomaly of the H2O VCD during El-Nino (average of Oct Mar 1998 compared to average over the same period of the yaers 1996/97, 1998/99, 1999/2000, 2000/01)

49 Relative anomaly of the O2 and HICRU cloud cover during El-Nino HICRU O2

50 Relative anomalies during different years H2O HICRU O2

51 GOME H2O VCD as a function of time and latitude (mean values over one month and 10 latitude) H 2 O VCD [10 22 molec/cm²] Precipitable water [g/cm²]

52 Manthly mean H2O VCDs for the whole globe, and for the northern and southern hemisphere 1.4E E H2O VCD [molec/cm] 1.0E E E E E+22 SH NH Global Precipitable water [g/cm²] 0.0E+00 Jan.96 Dez.96 Jan.98 Jan.99 Jan.00 Jan.01 Jan.02 Jan.03 0

53 Trend patterns H2O VCD T [K]

54 Trend patterns H2O VCD T [K]

55 Trend patterns H2O VCD T [K]

56 Trend patterns H2O VCD T [K]

57 Temporal evolution of the H2O VCD over Germany Winter: strong decrease (-15%) Summer: strong increase (+13%) 1.2E E E E E E+22 H2O over Germany Winter: -14.6% Spring: -4.2% Summer: +12.6% Autumn: +4.8% 0.0E+00 Jan. 96 Jan. 97 Jan. 98 Jan. 99 Jan. 00 Jan. 01 Jan. 02 Jan. 03 Jan. 04

58 For the correlation of monthly data anomalies with respect to the long year monthly mean values are calculated: Tropical (30 S to 0 N) H2O VCD H2O VCD [molec/cm²] 1.32E E E E E E+23 tropical average Trop_-30 bis+30 tropical_h2o_anomaly 2.4E E E+22 9E+21 4E+21-1E+21 H2O anomaly [molec/cm²] 1.02E+23-6E Time

59 Evolution of globally averaged anomalies of temperature and H2O VCD E E E+21 Temperature anomaly [K] E+21 1.E+21 0.E+00-1.E+21 H2O anomaly [molec/cm²] E temp_anomaly H2O_anomaly -3.E Time -4.E+21

60 Evolution of tropical anomalies of temperature and H2O VCD 6.00E E+21 Trop_3030_H2O_anomaly Trop_-30 bis E H2O anomaly [molec/cm²] 3.00E E E E E temperature anomaly [K] -2.00E E E Jan. 96 Jan. 97 Jan. 98 Jan. 99 Jan. 00 Jan. 01 Jan. 02 Jan. 03

61 Correlation of globally and yearly averaged values H2O VCD 8.6E E+22 Average H2O VCD [molec/cm²] 8.5E E E E E E E+22 y = 10.0E+21x E+21 R 2 = Temperature anomaly [K]

62 Correlation of globally and monthly averaged values H2O VCD 4E+21 Anomaly of the H2O VCD [molec/cm²] 2E+21 0E+00-2E+21-4E+21 y = 7.4E+21x - 2.8E+21 R 2 = Temperature anomaly [K]

63 Correlation of tropical monthly averaged values H2O VCD 7E+21 H2O VCD anomaly [molec/cm²] 5E+21 3E+21 1E+21-1E+21-3E+21-5E+21 y = 9.6E+21x - 3.2E+21 R 2 = Temperature anomaly [K]

64 What can we learn from the tropical H2O trends? It seems that relative humidity stays constant when climate changes. Similar conclusion from study of temperature decrease after Pinatubo eruption (Soden et al., 2002) 7E+21 H2O VCD anomaly [molec/cm²] 5E+21 3E+21 1E+21-1E+21-3E+21-5E+21 y = 9.6E+21x - 3.2E+21 R 2 = Temperature anomaly [K] => Strong positive water vapor feedback

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66 Conclusions 1: Temporal and spatial patterns of 1997/1998 El Nino are clearly visible in H2O and cloud data, for H2O significant changes even in mid and high latitude H2O and cloud trends are strongly variable, overall H2O increases by about 2.9±0.5% percent from For clouds no significant trend was identified (O2 cloud cover: 1.5 ±1.5%, HICRU cloud cover: ±2%) Over oceans trend patterns of H2O VCD are mostly correlated with temperature trends

67 Conclusions 2: Temporal variations of the H2O VCD are closely related to temperature changes: -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 overlap of about 1 year between SCIAMACHY and GOME => continuation of the time series