Imperial studies on spectral signatures: Part I

1. Imperial studies on spectral signatures: Part I Helen Brindley and John Harries CLARREO meeting, 30th April-2nd May, 2008 © Imperial College London

2. Aim: Use both GCM output and observations to attempt to ‘attribute’ signals seen in the latter to particular causes © Imperial College London

3. Aim: Use both GCM output and observations to attempt to ‘attribute’ signals seen in the latter to particular causes Steps: • Produce consistent observational data (spectra from multiple instruments) © Imperial College London

4. © Imperial College London Griggs and Harries, 2007

5. Aim: Use both GCM output and observations to attempt to ‘attribute’ signals seen in the latter to particular causes Steps: • Produce consistent observational data (spectra from multiple instruments) • Simulate expected spectral signals using GCM fields for relevant periods given different forcing scenarios (offline) • Compare and contrast… © Imperial College London

6. Easiest(!) case: clear-sky Overall match © Imperial College London

7. Decomposing simulations: © Imperial College London

8. But are observations representative? e.g. I: ‘Instantaneous’ sampling issues • FOV: characterisation of scene vs coverage? © Imperial College London

9. But are observations representative? e.g. I: ‘Instantaneous’ sampling issues • FOV: characterisation of scene vs coverage? • Satellite tracks: scope for redundancy? © Imperial College London

12. But are observations representative? e.g. I: ‘Instantaneous’ sampling issues • FOV: characterisation of scene vs coverage? • Satellite tracks: scope for redundancy? • Simulations shown: clear-sky only. Much more difficult to accurately capture all-sky conditions (factor of at least 3 higher deviation from true regional monthly mean over ~ 40° x 40°) (e.g. Brindley and Harries, 2003) © Imperial College London

13. But are observations representative? e.g. II: ‘Climate scale’ sampling issues • Length of mission? Gaps? Brindley and Allan, 2003 © Imperial College London

14. Suggestions for future studies Dependent on what the overall aim of the project is, but suggest a strong need for observational based scoping: • Select a limited, well-characterised region with known strong variability. • Classify what is seen on different time-scales (e.g. seasonal/annual). • How is change/variability manifested in spectral observations? • How well is this variability captured in GCM (or NWP) simulations, ideally using satellite ‘fly-through’ methodology? © Imperial College London

15. Imperial studies on spectral signatures: Part II Claudine Chen - Imperial College John Harries - Imperial College Helen Brindley - Imperial College Mark Ringer - UK Met Office

16. IRIS and TES comparison • Central Pacific case: Latitude: 10°S to 10°N, Longitude: 180°E to 230°E • Tropical Oceans case: Latitude: 10°S to 10°N, islands masked • ‘Avoid’ seasonality: AMJ only used • Remove cloudy spectra using a two-step threshold filter: • compare brightness temperature at 1127.7 cm-1 with the skin temperature from the NCEP reanalysis [Haskins, et al., 1997]. • Remove residual contamination from ice clouds by exploiting the difference in absorption coefficient in ice and water between the 8 mm and 11 mm bands. [Ackerman, et al., 1990] • Match spectral resolution • Temperature and specific humidity monthly mean profiles from UKMO HadGEM1 or NCEP reanalysis data. O3, CH4, CO2, CFCs and N2O concentrations taken from HadGEM1 input fields • Profiles averaged spatially and temporally before use in radiative transfer code Modelled spectra with LBLRTM

17. 1970 differences 2006 differences 2006-1970 spectra

18. Right: Brightness temperature differences between simulations and observations for1970 and 2006, Tropical Oceans, AMJ Left: Observed and simulated 2006 – 1970 brightness temperature differences, Tropical Oceans, AMJ

19. Altitude Resolved Sensitivity Study

20. Summary: Part II • Preliminary study shows how TES can be used to extend previous comparisons of TOA spectrally resolved radiances • Observed TES – IRIS difference spectrum is broadly consistent with previous findings • Developed methodology for routinely producing TES/IRIS-like spectra from reanalysis model simulated fields • Initial studies help diagnose systematic differences between observed and modelled spectra Ongoing work: • Extending comparison to encompass greater temporal and spatial domain • Extending to all sky cases – modelling/interpretation?

21. Overall thoughts • To study the potential for monitoring climate using spectral signatures, it is important to study not only the variability of theoretical spectra, but the actual variability of real spectra. • The signatures obtained from theoretical spectra are a valuable basis, but do not include natural variability of the real atmosphere, nor sampling errors introduced by satellite orbital tracks in space and time. How well can climate models capture the variability in existing spectral observations, particularly when these sample cloudy and mixed scenes? Could currently non-(satellite) sampled regions of the spectrum (e.g. far IR) add useful information? • The UK wants to participate. • What can we contribute to? • Continue studies of all-sky spectra from TES, IASI, to study spectral signatures in real atmosphere; • Ground-based calibration of FTS, using existing GERB facility, with transfer standards from NPL; • NPL short-wave calibration in space.

22. GERB calibration at Imperial GERB being loaded for calibration The EOCF vacuum chamber http://www.sp.ph.ic.ac.uk/eocf/main.html 23