Imperial studies on spectral signatures: Part I CLARREO meeting, 30 th April-2 nd May, 2008 Imperial College LondonPage 1 Helen Brindley and John Harries Aim: Use both GCM output and observations to attempt to attribute signals seen in the latter to particular causes Imperial College LondonPage 2 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 LondonPage 3 Imperial College LondonPage 4 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 2250 cm -1 4 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 Griggs and Harries, 2007 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 LondonPage 5 Easiest(!) case: clear-sky Imperial College LondonPage 6 Overall match Decomposing simulations: Imperial College LondonPage 7 But are observations representative? e.g. I: Instantaneous sampling issues FOV: characterisation of scene vs coverage? Imperial College LondonPage 8 But are observations representative? e.g. I: Instantaneous sampling issues FOV: characterisation of scene vs coverage? Satellite tracks: scope for redundancy? Imperial College LondonPage 9 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 LondonPage 12 But are observations representative? e.g. II: Climate scale sampling issues Length of mission? Gaps? Imperial College LondonPage 13 Brindley and Allan, 2003 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 LondonPage 14 Imperial studies on spectral signatures: Part II Claudine Chen - Imperial College John Harries - Imperial College Helen Brindley - Imperial College Mark Ringer - UK Met Office Central Pacific case: Latitude: 10S to 10N, Longitude: 180E to 230E Tropical Oceans case: Latitude: 10S to 10N, islands masked Avoid seasonality: AMJ only used Remove cloudy spectra using a two-step threshold filter: compare brightness temperature at 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 m and 11 m bands. [Ackerman, et al., 1990] Match spectral resolution IRIS and TES comparison Temperature and specific humidity monthly mean profiles from UKMO HadGEM1 or NCEP reanalysis data. O 3, CH 4, CO 2, CFCs and N 2 O concentrations taken from HadGEM1 input fields Profiles averaged spatially and temporally before use in radiative transfer code Modelled spectra with LBLRTM Spatial distribution Footprint (km) IRIS 95 IMG 8x8 AIRS 13 TES 5x9 Spectra before cloud removal Spectra after cloud removal Percent spectra remaining 1%33%14%3% IMG (AMJ 1997)AIRS (AMJ 2003) TES (AMJ 2006) IRIS (AMJ 1970) 1970 differences2006 differences spectra Right: Brightness temperature differences between simulations and observations for 1970 and 2006, Tropical Oceans, AMJ Left: Observed and simulated 2006 1970 brightness temperature differences, Tropical Oceans, AMJ Altitude Resolved Sensitivity Study 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? Overall thoughts 1.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. 2.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? 3.The UK wants to participate. 4.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. 23 GERB calibration at Imperial The EOCF vacuum chamber GERB being loaded for calibration