Variability of the earth s olr inferred from reanalyses and satellite observations
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Variability of the Earth’s OLR inferred from reanalyses and satellite observations. Richard Bantges, Claudio Belotti, Helen Brindley and Jon Murray. Space & Atmospheric Physics. CLARREO Science Team Meeting, July 2010. Outline. Modelled ‘clear-sky’ IR variability from ERA Interim reanalyses

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Variability of the earth s olr inferred from reanalyses and satellite observations l.jpg

Variability of the Earth’s OLR inferred from reanalyses and satellite observations

Richard Bantges, Claudio Belotti, Helen Brindley and Jon Murray

Space & Atmospheric Physics

CLARREO Science Team Meeting, July 2010

© Imperial College London


Outline l.jpg
Outline and satellite observations

  • Modelled ‘clear-sky’ IR variability from ERA Interim reanalyses

  • Clear-sky IR spectral signatures from satellite interferometer data

  • All-sky sampling studies using SEVIRI

  • Future developments and activities

© Imperial College London


1 modelled clear sky variability from era interim l.jpg
(1) Modelled ‘clear-sky’ variability from ERA Interim and satellite observations

  • ERA Interim covers period 1989 onward

  • Profiles of T, H2O(g), O3 from reanalyses

  • CO2, CH4, N2O from UKMO records (total column, 5 year global mean, interpolation)

  • Surface emissivity constant at 0.99 globally

  • Spectral radiances simulated at nadir using Oxford RFM

© Imperial College London


Rfm model runs l.jpg
RFM model runs and satellite observations

  • So far: 1989, 1994, 1999, 2004-2008

  • monthly mean fields (‘clear-sky’ but using all profiles)

  • 37 atmospheric levels (1000-1mb)

  • spatially resolved 1.5°x1.5°

  • 100-2500cm-1, spectral resolution 0.5cm-1

  • ~29000 RT simulations per month

© Imperial College London


Slide5 l.jpg

ERA Interim – and satellite observations

temperature anomalies

Step change: 1998: SSU replaced by AMSU-A

Temperature anomaly (K)

© Imperial College London


Era interim 3 5 yr global average differences l.jpg
ERA Interim: 3 & 5 yr global average differences and satellite observations

O3

Std. dev. (1σ) BT (K)

CO2

CH4

CO2

H2O

© Imperial College London


Era interim annual mean atmospheric profiles l.jpg
ERA Interim – Annual mean atmospheric profiles and satellite observations

© Imperial College London


Era interim 3 year global variability l.jpg
ERA Interim: 3 year global variability and satellite observations

2004-2006

2005-2007

2006-2008

Std. dev. (1σ) BT (K)

© Imperial College London


El ni o southern oscillation index l.jpg
El Niño Southern Oscillation Index and satellite observations

2008

2004

© Imperial College London


Era interim 3 5 year zonal averages l.jpg
ERA Interim: 3 & 5 year zonal averages and satellite observations

Std. dev. (1σ) BT (K)

Std. dev. (1σ) BT (K)

© Imperial College London


2 spectral signatures from satellite data l.jpg
(2) Spectral signatures from satellite data and satellite observations

© Imperial College London


Data methodology l.jpg
Data / Methodology and satellite observations

  • IMG – level 1D (band3, calibrated, unapodized)

  • IASI – level 1C (band1&2, calibrated, apodized) (±2.5° off nadir)

  • IMG & IASI resampled to 0.02cm-1 dispersion grid

  • IMG data convolved with the IASI instrument function

  • Semi-empirical wavenumber shift applied to IMG to match IASI

  • SST – ERSST v3b (Extended Reconstruction Sea Surface Temperature)

  • Central Pacific region (±10°N, 130-180°W)

  • AMJ average, cloudy spectra removed using brightness temperature at 10.8μm contrast with SST. (5K).



3 all sky sampling studies using seviri l.jpg
(3) All-sky sampling studies using SEVIRI and satellite observations

  • Investigate impact of sampling strategies using narrow-band spectral channels

  • What is the effect of increased spectral resolution on previous results? (e.g. Doelling, Kirk-Davidoff)

  • What is seen at different wavelengths?

  • What degree of averaging is required to meet desired accuracy (i.e. do we need all wavelengths – e.g. different requirements may have different needs)?

© Imperial College London


All sky sampling method l.jpg
All-sky sampling: method and satellite observations

  • Channels: 6.2, 7.3, 8.7, 10.8, 12.0, 13.4 microns. Temporal/Spatial resolution: Data every fifteen minutes, from approx 75N-75S, 75W-75E.

  • 3 months of data processed so far (from 2010)

  • Sampling strategy: Fly through 1 true-polar orbiter with SEVIRI pixel size (~10-25 km) footprint sampling every 200 km along ‘pseudo’ satellite track

  • Results binned to 15 x 15 degree averages and compared to fully sampled fields.

© Imperial College London


Slide16 l.jpg

8.7 and satellite observations

13.4

12.0

10.8

7.3

6.2

SEVIRI narrow-band channels

A typical clear-sky spectrum of outgoing thermal energy


Slide17 l.jpg

SEVIRI – 10.8 and satellite observationsmm, 1 month average

True

Sampled

NOTE: Questions were raised over the validity of the satellite tracks used. These results are pending further investigation.

© Imperial College London

Sampled - true


Slide18 l.jpg

SEVIRI – 10.8 and satellite observationsmm, 3 month average

True

Sampled

NOTE: Questions were raised over the validity of the satellite tracks used. These results are pending further investigation.

Sampled - true


Slide19 l.jpg

SEVIRI – 6.2 and satellite observationsmm, 3 month average

NOTE: Questions were raised over the validity of the satellite tracks used. These results are pending further investigation.

© Imperial College London


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and satellite observationsZonal’ averages (across SEVIRI disk)

NOTE: Questions were raised over the validity of the satellite tracks used. These results are pending further investigation.

© Imperial College London


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Summary (1) and satellite observations

  • Shown from ERA Interim model studies that 3 yr lifetime may not be sufficient to fully sample the “natural” variability

  • ENSO record suggest that 5 year would be better at capturing variability

  • Initial studies using (IASI & IMG) satellite data show consistency with inter annual and longer term ERA interim mean profiles (for well mixed GHGs)

  • Water vapour and ozone require more detailed anaylses

© Imperial College London


Summary 2 l.jpg
Summary (2) and satellite observations

  • Initial investigations of sampling strategy highlight that different levels of sampling accuracy will be obtained in different wavelength bands

  • Caution - the maximum IR differences aren’t always observed at 10.8mm

© Imperial College London


Future developments l.jpg
Future developments and satellite observations

  • Extend the period of ERA interim to look at a full 10 year cycle

  • Treatment / assessment of uncertainties introduced by cloud fields within ERA interim modelling

  • Extend IASI / IMG comparisons to 60N/S

  • IASI clear / all sky inter-annual variability

  • Full year (+) sampling strategy with SEVIRI (can adapt sampling)

  • Extend letter of agreement between NCEO & NASA

  • Submitted proposal SDT- TIR & FIR studies- Calibration / Validation

© Imperial College London


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