Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA Langley Research Center 2 Science System & Applications Inc. CLARREO SDT Meeting The National Institute of Aerospace April 10-12, 2012

Objective of this study Search for the method to reduce the fingerprinting error – Find out “mean” atmospheric and cloud properties to match the temporally and spatially averaged spectral radiance Find out whether separating clear-sky from all-sky scenes improves the retrieval of temperature and humidity change from climatological mean (anomaly)

MERRA DATA 1983 – 2010 : 28 years – Global (540,361) ( 0.66 Lon x 0.50 Lat ) – 6 Hourly: T, Q, O 3 Profiles at 42 vertical levels – Hourly : Tskin, T2m, Q2m, Sfc_emiss Random Overlap Cloud Fraction (High, Mid, Low) Cloud Optical Depth (High, Mid, Low) Cloud Pressure ( 1 st layer seen from space) NO Phase, NO Particle Size, Limited Cloud Height info. – No Cloud LWC/IWC profile files Clear and Total Sky OLR ( MERRA Rt code) – Files used : inst6_3d_ana_Np., tavg1_2d_slv_Nx, tavg1_2d_rad_Nx On /ASDC_archive

PCRTM Principal Component Radiative Transfer Model – Xu Liu, William L. Smith, Daniel K. Zhou, and Allen Larar Applied Optics Vol 45, No. 1, 1 Jan 2006 Spectral longwave radiance ( cm -1 ) 0.5cm -1 effective resolution – Obtained using 280 principal components Cloud properties (P. Yang) – Optical depth, Particle size, Phase,Effective pressure Includes Multiple Scattering Variable Gases: H 2 O, O 3 - CO 2, CH 4, N 2 O, CO can vary in PCRTM but not in this simulation. -Other minor trace gas concentrations fixed example: CFC’s -No Aerosol in this simulation

Radiance simulation with MERRA 90deg CLARREO ‘like’ Orbit – Repeats Annually Assuming 30 second sampling interval FOV is taken as closest MERRA grid-hour box Spectral radiance is computed for every FOV

Orbit Coverage

Monthly mean computations 10 zonal monthly mean temperature and humidity profiles 3 cloud types within a zone Emissivity weighted logarithmic mean optical thickness for each cloud type Spectral radiances computed with monthly zonal mean properties agree with instantaneous spectral radiance well.

Monthly mean properties Retrieve from All cloud and atmospheric properties are sampled by the 90° CLARREO orbit Kato et al Forward modeling of using monthly mean cloud and atmospheric properties

Difference between and Difference of 28-year mean radiance (10°S to 0°) RMS difference Blue: Red: Annual mean – 28-year mean Difference of annual anomalies

Retrieval of annual anomalies Use spectral radiance computed at a high resolution (instrument sampling) Compute ΔI by taking annual mean spectral radiance minus 28-year mean Retrieve cloud and atmospheric property anomalies (deviation from 28-year mean) from annual mean radiance anomalies using spectral kernels computed by perturbing monthly mean properties ( ) Compare retrieved atmospheric and cloud properties with annual property anomalies ( )

Clear-sky occurrence MERRA clear fraction: excludes clouds with optical thickness less than 0.3 Error bars indicate standard deviation of 28-year clear fractions CALIPSO CloudSat derived clear fraction (2007 to 2009) also exclude clouds of which optical thickness less than 0.3 Error bars are max and min of three year clear fraction

Clear-sky temperature change retrieval Temperature 100 – 10 hPa (20°S to 10°S) Red: retrieved Blue: Truth (deviation from 28 year mean) ΔT (K) Surface temperature (40°S - 30°S)

Clear-sky versus All-sky Clear-sky only sampling does not affect retrieval All-sky and clear-sky with cloud removed have similar RMS and correlation coef.

Clear-sky versus All-sky hPa temperature Tropics: fixed vertical grid of hPa might be a problem

Clear-sky versus all-sky: surface temperature SH ocean ~60°S is mostly cloudy: All-sky had a larger RMS Tropics and midlatitude (NH): all-sky has slightly larger RMS and smaller correlation coef. Compared with cloud removed

Upper tropospheric relative humidity Clear-sky fraction weight has a larger RMS and smaller correlation coef.

Clouds: cloud fraction exposed to space Low-level cloud fraction 50°S – 40°S 0°N – 10°N High-level cloud fraction 0°N – 10°N 30°N – 40°N Blue: truth (deviation from 28 mean) Red: retrieved

Cloud properties: cloud fraction

Cloud top height Need to retrieve cloud top effective temperature instead of cloud top height

Summary and conclusions Using only clear-sky scenes to detect temperature and humidity changes does not seem to improve the retrieval result significantly. Using all-sky scene to detect temperature and humidity change does not increase error significantly compared with those retrieved from cloud removed. Fingerprinting retrieves effective cloud properties (e.g. weighted by emissivity). Combination of forward modeling and retrieval simulations is needed to understand retrieved cloud properties.

Back-ups

Instantaneous Vs Mthly Average