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John Harries, John Harries, Space and Atmospheric Physics group, Blackett Laboratory, Imperial College, London,UK 30 November 2009 PG Lectures 1 Climate.

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Presentation on theme: "John Harries, John Harries, Space and Atmospheric Physics group, Blackett Laboratory, Imperial College, London,UK 30 November 2009 PG Lectures 1 Climate."— Presentation transcript:

1 John Harries, John Harries, Space and Atmospheric Physics group, Blackett Laboratory, Imperial College, London,UK 30 November 2009 PG Lectures 1 Climate Change: The Earth’s energy balance, IR spectral properties, and climate feedbacks

2 Acknowledgements: Many discussions with, and results provided by: Dr Helen Brindley, Dr Jacqui Russell, Dr Joanna Futyan, Dr Jenny Griggs, Dr Richard Bantges, Dr Claudio Belotti, Dr Claudine Chen… Dr Adrian Tuck, Professor Tony Slingo, Professor Brian Soden, Dr Bruce Wielicki, Professor James Anderson, Professor Richard Goody, Professor Tony del Genio….and many others. 30 November 2009 PG Lectures 2

3 30 November 2009 PG Lectures 3 Books: “Atmospheric Radiation”, Goody (1964) and revised by Goody and Yung (1989), OUP. “The Physics of Atmospheres”, Houghton (2002), CUP. “An Introduction to Atmospheric Physics”, Andrews (2000), CUP. “Atmospheric Science: An Introductory Survey”, 2 nd Ed., Wallace and Hobbs (2006), Elsevier/Academic Press.

4 Contents Part A: Basic Radiative Transfer 1. Introduction to Radiative Transfer (RT), Earth Radiation Budget (ERB) 2. Forcing and feedback processes, and variability Part B: The broad band signal 3. Pinatubo: a natural experiment; 4. Evidence for variability of broadband LW, SW, F N ; Part C: The spectrally resolved signal 5. Evidence for variability of resolved spectrum LW. 6. The water vapour feedback. Part D: Measurements of ERB and Spectrum 7. GERB 8. IRIS, IMG, AIRS, TES, and IASI 30 November 2009 PG Lectures 4

5 Part A: Basic Radiative Transfer 1. Introduction to Radiative Transfer (RT), Earth Radiation Budget (ERB) 2. Forcing and feedback processes, and variability 30 November 2009 PG Lectures 5

6 30 November 2009 PG Lectures 6 Global average terrestrial energy budget Albedo  1/3 F N = P in – P out =  p (stored or lost energy)

7 30 November 2009 PG Lectures 7 Spectral complexity

8 30 November 2009 PG Lectures 8

9 30 November 2009 PG Lectures 9 Some assumed relations Planck’s radiation law (gives the spectral radiance, or energy/sec ond/area/solid angle/frequency interval Rayleigh-Jeans approximation (long-wave limit,  ): Wien displacement law (differentiate : )  m K Stefan-Boltzmann law (integral of over all :

10 30 November 2009 PG Lectures 10 s n dA Radiant spectral intensity, (spectral radiance) = energy/unit area/solid angle/spectral frequency.  dd dd (if I isotropic) Flux Some radiative transfer theory  n s

11 30 November 2009 PG Lectures 11 dA s n Radiant intensity absorbed by ds at s: Radiant intensity emitted by ds at s: dA Radiant spectral intensity (spectral radiance) = energy/unit area/solid angle/spectral frequency. s s+ds

12 30 November 2009 PG Lectures 12 Here, k is the spectral extinction coefficient and  is the optical path (depth or thickness), both functions of frequency. It can be shown that, for a black body, J = B (T ), the Planck function for a black-body radiator at temperature,T. Schwarzchild’s equation of radiative transfer

13 30 November 2009 PG Lectures 13 Integrating Schwarzchild’s equation, using an integrating factor, exp(  ), provides the solution which is the basis of all radiative transfer and remote sensing techniques. Surface radiance, transmitted through whole atmosphere Radiance from layer ds transmitted through atmosphere above, integrated over all layers

14 30 November 2009 PG Lectures 14 Studies of the Physics of the Earth’s Climate Balance, and the new Geostationary Earth Radiation Budget experiment (GERB) Professor John Harries Head, Space and Atmospheric Physics Real spectral radiances (Schwarzchild) Surface term (atmos. window) Integral emission (atmos. layers )  3  2  1 T2T2 T1T1 T3T3 TsTs Surface term Integral emission Weighting function

15 30 November 2009 PG Lectures 15 Studies of the Physics of the Earth’s Climate Balance, and the new Geostationary Earth Radiation Budget experiment (GERB) Professor John Harries Head, Space and Atmospheric Physics Real spectral radiances (Schwarzchild) Surface term (atmos. window) Integral emission (atmos. layers )  3  2  1 T2T2 T1T1 T3T3 TsTs Surface term Integral emission

16 30 November 2009 PG Lectures 16  r  (z) z Weighting function

17 30 November 2009 PG Lectures 17 A simple interpretation of the effect of clouds on radiance and OLR Surface term Integral emission  3  2  1 T2T2 T1T1 T3T3 TsTs Surface term Integral emission

18 30 November 2009 PG Lectures 18 Fluxes: in quasi steady state at TOA, with time dependence: (Note: S = I TS /4) where the radiance has a time dependence as follows (from Schwarzchild equation of radiative transfer): Absorbed short-wave flux Emitted long-wave flux Surface term Integral emission I

19 30 November 2009 PG Lectures 19 Heating (cooling) rates Another important concept is the heating rate, the energy per second being absorbed (heating) or emitted (cooling) in a layer z to z + dz. Within such a layer, we may write for the rate of change of energy: Note: (a)  is total density, not just of the absorber; (b) convention for F N can be reversed, in which case minus sign before the dF/dz term; (c) flux units are energy/m 2 /s, so that dF/dz is energy/m 3 /s.

20 30 November 2009 PG Lectures 20 Manabe and Stricker, JAS, 21, p373, 1964

21 30 November 2009 PG Lectures 21 The significance of transmittance and heating rates

22 Part A: Basic Radiative Transfer 1. Introduction to Radiative Transfer (RT), Earth Radiation Budget (ERB) 2. Forcing and feedback processes, and variability 30 November 2009 PG Lectures 22

23 30 November 2009 PG Lectures ‘Forcing’ and ‘feedback’ processes. Forcing processes ( External processes which impose a change of climate balance): greenhouse gas changes; solar variations; volcanic eruptions. Feedback processes ( Internal processes which respond to a forced change): water vapour feedback; cloud feedback; land surface feedback; ice feedback; ocean feedback.

24 30 November 2009 PG Lectures 24 Global Terrestrial Energy Budget (per unit surface area) Input SW Power P in = I TS (1 – A) / 4 = S (1 – A) Output LW Power P out =  T E 4 =  (1 – g)T S 4 Power deposited =  p G = greenhouse radiative forcing (in Wm-2) =  (T S 4- T E 4) g = normalised greenhouse effect, g = G / (  T S 4 )  G / 390  0.40 A = planetary albedo  0.31  = Stefan-Boltzmann constant =  Wm -2 K -4 T E = effective temperature of Earth / atmosphere  254K T S = mean surface temperature of Earth  288K P in = P out +  p  1 Wm -2 (Hansen et al., Science, 2005)  235 Wm -2 ( I TS  1366 Wm -2 )

25 30 November 2009 PG Lectures 25 Terrestrial Energy Budget: feedbacks and forcings S (1 – A) =  (1 – g) T S 4 +  p 1 +  p 2 + … Feedback loops, eg: hydrological cycle, circulation patterns, cloud, vegetation, etc. greenhouse forcing delayed responses Delay due to slow feedback processes: eg. deep ocean warming SW LW Measures of the SW, LW and F N (= ) fluxes at TOA give information on greenhouse forcing, cloud and water vapour feedback…..i.e. on climate processes and climate change Forcings,eg: Direct increase in A (and smaller increase in g) due to volcano

26 30 November 2009 PG Lectures 26 Hansen 2005

27 30 November 2009 PG Lectures 27 Hansen, 2005

28 30 November 2009 PG Lectures 28 Feedback processes. Let us assume that CO 2 is changing, and the other parameters can give rise to feedbacks on this basic forcing process. We can express dependence of P out and P in on cloud (C), ice (I), water vapour (H 2 O), temperature (T) and carbon dioxide (CO 2 ) as follows (ignoring  p): and write the dependences explicitly as follows (ignoring ice, which should be added later); Held & Soden, 2002

29 30 November 2009 PG Lectures 29 where: is the temperature response to a CO 2 change with no feedbacks, and: is a non-dimensional ratio giving the strength of the water vapour or cloud feedback process (  = 0 is no feedback;  = 1.0 is complete damping) from which re-arranging gives:

30 30 November 2009 PG Lectures 30 The change in surface temperature  Ts for doubled CO2 as a function of the water vapour feedback parameter  H2O. Results are shown for two different scenarios of other temperature dependent feedbacks that encompass the current range of predictions in  Ts  1.5– 4.5K when H 2 O  0:4. Held and Soden, 2002

31 30 November 2009 PG Lectures 31 This simple model takes account of feedback processes that affect both the SW (P in ) and LW (P out ), and can produce both positive (  0) and negative (  0) feedbacks.

32 30 November 2009 PG Lectures 32 The End

33 Part B: The broad band signal 3. Pinatubo: a natural experiment; 4. Evidence for variability of broadband LW, SW, FN; 30 November 2009 PG Lectures 33

34 30 November 2009 PG Lectures 34

35 First, some questions: How does the radiative energy budget of the Earth vary in response to a perturbation, such as increasing CO2 or a volcanic eruption? How close to steady state is the Earth at any instant and place, and also in the average? Is the TOA net flux always zero: how quickly does it restore to zero after a perturbation: how large are any excursions from zero? Are broadband measurements a useful technique to monitor global climate change from space (as opposed to specific processes), or do we need spectrally resolved observations? 30 November 2009 PG Lectures 35

36 Next a few comments: Earth is not in thermodynamic equilibrium, since energy flows into and out of the boundaries of the system: it is not isolated. The Earth is a non-equilibrium (quasi) steady state system, and therefore is described by non-equilibrium thermodynamics and statistical mechanics. As a steady-state, non-equilibrium system with many degrees of freedom (ie many routes for energy to cascade between the incoming solar beam and the outgoing thermal radiation), the Earth presumably follows the principle of maximised entropy production (MEP), [eg Paltridge (1975, 1978, 1979, 1981, 2001); see excellent review book Edited by Kleidon and Lorenz, 2003]. 30 November 2009 PG Lectures 36

37 The total response of the climate system including the feedback processes, in response to a perturbation, such as increasing CO 2, is the summation of all the individual feedback processes, each having an individual magnitude of response and time constant for the response [Soden, 2002; Hallegate et al, 2005]. It can be shown (eg Harries and Futyan, 2006) that a volcanic eruption proves to be a useful natural experiment in separating faster and slower processes (eg those which involve radiative signals of rapid processes, or other, slower dynamical processes, eg involving large scale motions of water vapour fields). 30 November 2009 PG Lectures 37

38 How large and long-lived a signal in the net radiation at the TOA, FN, is expected following a perturbation? The evidence from ERB measurements (Wielicki et al.) and models (Hansen et al.), including extremely careful analyses and error studies, indicates that FN may change by up to 10 Wm-2, but quickly (TBD) returns to zero, showing very small, essentially not significant departures form zero. Altogether, the few studies to date support the view that the Earth maintains a zero FN at TOA. Except in the case of a volcanic eruption, such as that of Mt Pinatubo in November 2009 PG Lectures 38

39 30 November 2009 PG Lectures 39 3.Pinatubo: a natural experiment Pinatubo (Phillipines, June 1991) was powerful (20 Mt), and directed vertically: so, a large mass of injecta quickly reached the stratosphere. Tropospheric material was quickly washed out. Stratospheric zonal circulation is strong, and particles quickly circulated equatorial zone, spreading N and S more slowly. Decay from stratosphere slow.

40 30 November 2009 PG Lectures 40 Recent work in USA has attempted to make measurements of stability of TOA radiation balance, and of evidence for “stored energy”,  p, by measuring net flux anomaly at TOA.  p =  F N Question: what value should F N be, and how much can it vary (  F N (t))? 20N -20S: Wielicki et al, (2001): revision Wong et al (2005). Mt. Pinatubo, June 1991

41 30 November 2009 PG Lectures 41 Following work on Pinatubo by Soden et al. [2002], we have used the perturbation caused by Pinatubo to study some of the process time constants in the system; We have analysed the time series of the following parameters, and measured the (assumed exponential) rise and decay of the perturbation in each parameter:  Longwave and shortwave TOA fluxes for latitudes 60N– 60S and for 1991–1996 (note: latitude range is maximum extent of observations from ERBS); Soden et al. [2002] confirmed no significant differences from 90N–90S).  The TOA net flux anomaly, formed from the difference between absorbed SW and emitted LW fluxes.  Observed total column water vapour, and lower tropospheric temperature, for 90N–90S (NVAP project; Randel et al., [1996]).  Observed 6.7 mm brightness temperature for 90N–90S (TOVS Radiances Pathfinder project [Bates et al., 1996].

42 30 November 2009 PG Lectures 42 Time series of the anomalies of the following parameters: (top to bottom) observed longwave and shortwave TOA fluxes for latitudes 60N–60S and for 1991–1996; Observed net flux formed from the difference between absorbed SW and emitted LW fluxes; Observed total column water vapour and lower tropospheric temperature for 90N–90S; Observed 6.7 mm brightness temperature for 90N–90S SW and LW flux anomalies Net flux anomalies (“stored or lost energy”) T 6.7 Water vapour column and T Harries & Futyan, 2006

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44 30 November 2009 PG Lectures 44 Concluding remarks on Pinatubo study: Pinatubo offers a natural perturbation to the climate system; Radiative processes which can respond immediately to the “instantaneous” insertion of aerosol from the volcano show very short time constants (few months), driven by the time taken for aerosols to become distributed; Radiative processes which involve slower dynamical response, eg moving water vapour around, take much longer (1-2 years); Rise and decay process time constants differ; Models ought to reproduce these relaxation times as validation.

45 30 November 2009 PG Lectures 45 Part B: The broad band signal 3. Pinatubo: a natural experiment; 4. Evidence for variability of broadband LW, SW, FN;

46 30 November 2009 PG Lectures 46 4.Evidence for variability of Broadband SW,LW, F N. 4.Evidence for variability of Broadband SW, LW, F N. …..a preliminary look

47 30 November 2009 PG Lectures 47 SW Wong et al, (2005), 20 N/S

48 30 November 2009 PG Lectures 48 (Top) Annual mean anomalies of: 1984–2000 ES albedo reconstruction (broken, black); 1999–2004 measured ES albedo anomalies (solid, black); SBSRN (green); OBTGOME (red); CERES (magenta) ERBE (broken, red) shortwave flux anomalies; Smod (blue). All anomalies are relative to the 1999–2001 period. (Bottom) Expansion for 2000– 2005 period. Some of the Figure 1 (top) data sets are plotted here as monthly means when available. Anomalies are normalized to For the ES observations, ±1 sd error bars are also plotted. SW Palle et al., 2004, 2005

49 30 November 2009 PG Lectures 49 Apparent agreement. Monthly mean annual cycle and standard deviation (vertical bars) of albedo from six models. These and other models are used by IPCC for pre- industrial control simulations. SW Charlson et al, 2005

50 30 November 2009 PG Lectures 50 For the tropical oceans. In addition to ERA40, NCEP and SRB products there are observational and empirical data (OBS) comprising (a) HadISST ocean surface temperature; (b) column integrated water vapor from SMMR and SSM/I; (c) the Prata formula estimate of clear sky surface net LW radiation (SNLc) using SMMR and SSM/I data; (d) radiation budget satellite data from ERBS (1985– 1990), ScaRaB (1994/1995), CERES on TRMM (1998) and CERES on TERRA (2000– 2004); and (e) combined ERBS/ScaRaB/CERES and Prata-SSM/I estimates of SNLc. Allan (2006) LW: global: annual cycle removed

51 30 November 2009 PG Lectures 51 LW Wong et al,2005

52 30 November 2009 PG Lectures 52 Wong et al, (2005) FNFN

53 30 November 2009 PG Lectures 53 Wong, et al, J Clim FNFN

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56 30 November 2009 PG Lectures 56 Part C: The spectrally resolved signal 5.Evidence for variability of resolved spectrum LW. 6.The Water Vapour feedback

57 30 November 2009 PG Lectures 57 HH 2 O pure rotation band CO 2 2 band O3O3 HH 2 O 2 band CFCl 3 CH 4 H2O continuum

58 30 November 2009 PG Lectures 58 Hanel et al, Applied Optics, 1971 Thermal emission spectra recorded by IRIS-D on Nimbus 4. The apodized spectra have a spectral resolution between 2.8 and 3 cm-1. A hot desert case, an intermediate case over water, and an extremely cold spectrum recorded over the Antarctic are shown. Radiances of blackbodies at several temperatures are superimposed.

59 30 November 2009 PG Lectures 59 Feedback parameter, G = B (T s ) - F TOA

60 30 November 2009 PG Lectures 60 The IRIS experiment measured the Earth’s spectrum in 1970: The ADEOS experiment measured the Earth’s spectrum in How does the IR spectrum of the Earth change as the climate changes? Can we detect spectral signatures of climate change? Are there measurements of the Earth’s spectrum to the required accuracy? Does the variability of the Earth’s spectrum (in time and space) mask any changes due to climate?

61 Available Satellite Instruments InstrumentIRISIMGAIRSTESIASI SatelliteNimbus 4ADEOSAQUAAURAMETOP-A Spectro- meter type FTS grating spectrometer FTS Data available Apr 1970 – Jan 1971 Oct 1996 – Jun present present present Spectral coverage (cm -1 ) 400 – 1600 cm -1 continuous 715 – 3030 cm -1 3 bands 650 – 2700 cm bands 650 – 1350 cm -1 3 bands 645 – 2760 cm -1 3 bands Spectral resolution 2.8 cm cm –1.0 cm cm cm -1 Footprint (nadir) 95 km diameter 8km x 8km13 km diameter 5x9 km12 km diameter 30 November PG Lectures

62 30 November 2009 PG Lectures 62 New data from high resolution FT Spectrometers in US and Europe.

63 30 November 2009 PG Lectures 63 Do we have evidence of “climate forcing” by increasing greenhouse gases? Harries et al., Nature, March Yes!

64 30 November 2009 PG Lectures 64 OK! But what about feedback signatures?

65 30 November 2009 PG Lectures 65 6.The Water Vapour feedback

66 30 November 2009 PG Lectures 66 HH 2 O pure rotation band CO 2 2 band O3O3 HH 2 O 2 band CFCl 3 CH 4 H2O continuum

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70 30 November 2009 PG Lectures 70 One problem with using observed spectra is that the pure rotation band is not accessed (experimental difficulties). So, while that work continues, we have also used re-analysis data to simulate the IR spectrum at different places and times. Now we look at difference in re-analysis based simulations for different regions and times: There is a great deal of inter-annual fluctuation, and the change in water vapour feedback is soometimes +ve, sometimes –ve.

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81 30 November 2009 PG Lectures 81 Now we try to overcome the natural variability by taking big averages, and , both global and tropical zone. We find that between the first and the second 15-year periods, both the tropical and global, and both NCEP and ERA-40 show that the water vapour feedback grew weaker. The re-analyses are known to have larger uncertainties associated with upper troposphere water vapour, so we are now testing the result by using other data. At least, we have learned that the water vapour feedback needs to be very carefully modelled in climate change runs if the change in climate is to be accurate.

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86 30 November 2009 PG Lectures 86 Conclusion on variability of resolved spectrum: - Today’s new resolved spectral measurements, by AIRS, TES, IASI, etc can be used for spectral signature of climate detection; It appears from this work that monitoring the climate from the TOA is better done using the resolved spectrum than integrated measurements CLARREO - The magnitude of spectral signatures of climate change are significantly larger than noise levels; - IASI will fly on operational series (MetOp) for at least a decade, so that more progress should be made in detecting long term climate change signals. - The water vapour feedback changes both positively and negatively with time, not just monotonically increasing.

87 30 November 2009 PG Lectures 87 The end

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93 30 November 2009 PG Lectures 93 Concluding remarks: How close to steady state is the Earth at any instant and place, and also in the average? Evidence from observations + models indicates that the Earth (steady-state, non-equilibrium system, maximising entropy production rate) maintains zero net TOA radiative anomaly to  few Wm-2. How does the radiative energy budget of the Earth vary in response to a perturbation, such as increasing CO 2, or a volcano? Perturbations show rise (and/or) decay controlled by the forcing process. Long term (decadal) net flux departures from zero indicate slow processes delaying temperature response to forcing. Pinatubo rise and decay times indicate radiative time constants respond to speed of underlying dynamical processes. Time constants from weeks to years: should be added constraint on models.

94 30 November 2009 PG Lectures 94 Is the net flux always zero: how quickly does it restore to zero after a perturbation: how large are excursions from zero? Variability of broadband net flux, F N, lies within  2 W m-2;  F N up to – 5 Wm-2 for Pinatubo. Evidence is that net flux returns to zero between months and years, determined by processes. Non-zero net flux anomaly associated with delayed response processes and “stored energy”. Magnitude in latest model by Hansen is  0.75 Wm-2; analysis of earlier work, experimental and model, however, gives more like +2 Wm-2. Evidence in observations is inadequate to draw any conclusions about evidence for stored energy in broadband signal Can only say that net flux anomaly is maintained within 0  2 Wm-2.

95 30 November 2009 PG Lectures 95 Are broadband measurements a useful technique to monitor climate change from space, or do we need spectrally resolved observations? Climate change processes cause a variety of changes at different wavelengths, which may tend to compensate in broadband. Moreover, we have seen a limit of  2 Wm-2 in practice in background variability: if Hansen is right, signal is  1 Wm-2. The resolved IR emission spectrum can now be measured to relative accuracy of 0.1% or so, and spectral signatures may change by as much as 1% or more in modern spectra (eg IASI, TES). So, climate trends should be monitored using resolved spectrum (broad band, however, is powerful for regional and process studies). Less complex spectrometers can be designed, since very high spectral resolution not needed (1 cm-1 adequate).

96 30 November 2009 PG Lectures 96 End

97 30 November 2009 PG Lectures 97 CERES (polar orbiter) monthly averages : What observational evidence do we have? LW SW

98 30 November 2009 PG Lectures 98 GERB (geostationary) 15 minute averages: LW SW Visible (SW) Total LW = Total - SW GERB-2 measurements

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100 30 November 2009 PG Lectures 100 Iacono and Clough, JGR, 1996

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102 30 November 2009 PG Lectures 102 Iacono and Clough, JGR, 1996

103 30 November 2009 PG Lectures 103 Placeholder for variability of spectrum

104 30 November 2009 PG Lectures 104 Hansen JGR 2005

105 30 November 2009 PG Lectures 105 SourceYears analysedAnnual, global-mean data OLR (Wm־²) Albedo, A, ( %) Net flux, F N (Wm־²) Observations Early measurements [Vonder Haar and Suomi (1971)] 1962 – Pre-1972 experiments¹ [Ellis and Vonder Haar (1976)] 1959– # Pre-1972 experiments plus Nimbus 6 ERBE¹ [Campbell and Vonder Haar 1980] 1959– # Nimbus 6 ERB wide field of view¹ [Jacobowitz et al. (1979)] 1975 – # (-0.1) Nimbus 6 ERB narrow field of view¹ [Campbell and Vonder Haar 1980] 1975 – # Nimbus 7 ERB wide field of view¹ [Jacobowitz et al. (1984)] 1978 – Nimbus 7 ERB narrow field of view [Jacobowitz et al. (1984)] 1978 – NOAA scanning radiometers (AVHRR)*ª [Lucas et al. (2001)] Table 1: Globally and annually averaged OLR flux, albedo and net flux from a number of sources What about historical satellite measurements of F N ?

106 30 November 2009 PG Lectures 106 ScaRaB² [Kandel et al. (1998)] 03/ / ERBE scanner² [Barkstrom et al. (1989)] 03/1985 – 02/ CERES (ES9 Terra Edition 2) [Wielicki et al. (1996)] 01/2001 – 12/ ? (+5.8) Best estimate from all instruments (Mean and standard deviation) ± ± ± 3.6 Best estimate from later instruments (this page) 03/1985 – 12/ ± ± ± 1.7

107 30 November 2009 PG Lectures 107 Model results HadAM2ª [Pope et al. (2000)] HadAM3ª [Pope et al. (2000)] GISS SI2000 [Hansen et al (2002)]

108 30 November 2009 PG Lectures 108 Summary (Decadal scale anomalies, Wm-2) SW:LW:Net: /+7-1/+2-1/ /+5-1/+1-1/ /+2-1.5/+2+0.5/ /+4-1.5/ Model+/ /+3.5 Very preliminary! Scatter +/- 1 to +/- 2.5 Wm-2: inconsistent reports of albedo and net flux uncertainties

109 30 November 2009 PG Lectures 109 Looking for accuracies < 0.3 Wm-2 or better to detect “stored energy”. Unlikely that at present, given sampling problems, we can detect these changes in broad band: GERB may help.

110 30 November 2009 PG Lectures 110 Spectrally resolved measurements in 1970, 1997, 2001-now

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112 30 November 2009 PG Lectures 112 Some of the evidence for climate change, and the uncertainties

113 30 November 2009 PG Lectures 113 The temperature signal at the surface and the coincident changes in CO 2, CH 4, sulphates, etc…

114 30 November 2009 PG Lectures 114 There are, of course, uncertainties in many forcing processes….. IPCC

115 MEP in a complex non-equilibrium system allows for a multiplicity of MEP states for the same, or very similar energy configuration. Could this be a principle underlying the existence of transitions between ‘metastable’ states, that are very close together in energy….popular expression “tipping points”? 30 November 2009 PG Lectures 115

116 30 November 2009 PG Lectures 116 But the major uncertainties are in feedbacks, not the forcings: Should we believe that we understand “climate change” well enough to predict our future? No! The feedback processes, especially clouds, water vapour, oceans, cause large uncertainty Climate change runs by different models for same conditions

117 30 November 2009 PG Lectures 117 Variability and complexity in climate

118 30 November 2009 PG Lectures 118 Studies of the Physics of the Earth’s Climate Balance, and the new Geostationary Earth Radiation Budget experiment (GERB) Professor John Harries Head, Space and Atmospheric Physics Climate is highly variable: + Many processes are non-linear; + Some processes are chaotic; + Natural variability in climate components; + Feedback processes cause variability. Climate is very complex: + Many greenhouse absorbers (CO2, CH4, H2O, FCC, O3, clouds..); + Many SW scatterers (clouds, aerosols, dust); + Both Forcing and Feedback processes; + Wide range of time and space scales are significant; Variability is in spectral, spatial and temporal space.


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