Radiation Spectra atTOA andClimate Diagnoses V. Ramaswamy and Yi Huang NOAA/ GFDL, Princeton University
Scope • Sensitivity of spectrally resolved outgoing longwave radiation (OLR) • Radiative Jacobians: • Characteristics of observed outgoing longwave spectra and the climate system • AIRS observation • GCM simulations • Spectral signatures of climate change • Natural variability • Recent evolution • Long-term change • Possibilities of further climate information?
Introduction (1/3)Global annual mean energy budget [Kiehl&Trenberth 1997]
Surface T Change Planck Damping TOA Radiation Imbalance Sensitivity Introduction (3/3) – Motivation • Wetherald and Manabe  Bony et al. 2006 Feedbacks Ts: surface temperature R: radiation flux Xi: meteorological variable (e.g. atmospheric temperature, water vapor concentration, or cloud properties.) Notations: Water vapor (WV), clouds (C), lapse rate (LR), albedo (A)
Radiative Jacobians Spectrally decomposed sensitivity of clear-sky OLR at each 10 cm-1 interval to 10% perturbation of specific humidity at each 50-mb layer. [Huang, Ramaswamy and Soden 2007 JGR] Window H2O vib-rot - Window region: most sensitive to lower troposphere (water vapor continuum absorption) - H2O bands: middle- and upper-troposphere - Reduced sensitivity in CO2 and O3 bands. H2O rot [mW / m2 / cm-1] H2O rot Window H2O vib-rot CO2 O3
With continuum 7.5 0.5 Spectrally integrated Sensitivity Without continuum without cont with cont Contribution by water vapor continuum • Continuum controls the sensitivity in window region. • Noticeable contribution in rotation band. [mW / m2 / cm-1] H2O vib-rot Window H2O rot [Huang, Ramaswamy and Soden 2007 JGR]
El Nino El Nino La Nina La Nina La Nina El Nino Applications of Jacobians • Reconstruction of the clear-sky OLR time series • 20-year AMIP run • Linear additivity of T and H2O contributions to total dOLR [Huang, Ramaswamy and Soden 2007 JGR]
Clear-sky radiances All-sky radiances CO2 Window O3 H2O AIRS zonal mean OLR spectra [W m-2 / cm-1 / sr] Latitude Latitude
Greenhouse effect (ghe) of gases (Rsfc – Rclr) / Rsfc Additional ghe due to clouds (Rclr – Rall) / Rsfc CO2 Window O3 H2O AIRS zonal mean spectral greenhouse effect 1 – Surface emission completely trapped Latitude 0 – Surface emission completely escapes Rsfc: Surface emission (Planck function) Rclr: Clear-sky outgoing radiance Rall: All-sky outgoing radiance Latitude
650 1650 [ K ] CO2 [ cm-1 ] AIRS radiance anomaly (tropical mean) Window H2O O3 NCEP SST anomaly OLR spectra • Data and Model - AIRS (Atmospheric Infrared Sounder) on Aqua Over 5 years (since Aug. 2002) L1B: all-sky; L2: clear-sky 0.5 K precision - MODEL GFDL GCM + MODTRAN Consistent sampling with obs. Random cloud overlap - Convoluted into 2 cm-1 regularly spaced frequency grids • Irradiances – CERES • Surface temperature – NCEP
Planck damping + Feedbacks – Spectral breakdown of OLR-TS relationship (1/4)Case study: Super-greenhouse Effect (SGE) Correlation between OLR and Ts (seasonal cycle; CERES obs.) + Clear-sky • SGE • Anti-correlation between outgoing radiation and surface temperature. [Ramanathan and Collins 1991] • Evident in both seasonal and interannual variations. [Allan et al. 1999] • Strong water vapor and cloud feedbacks • Goal: Spectral perspective - All-sky Significance level: 95% [Huang and Ramaswamy 2008 GRL]
Window, H2O continuum + – H2O vib-rot band SGE (2/4): AIRS observationsRegression Coefficients Rv= a*Ts+b Clear-sky CERES (broadband flux) dOLR/dTS = -2.3 [W m-2 / K] ? H2O rot All-sky dOLR/dTS = -7.2 [W m-2 / K] [Huang and Ramaswamy 2008 GRL]
Underestimate of cloud radiative response SGE (3/4): AIRS Vs. AM2 Window, H2O continuum Clear-sky dOLR/dT = -2.3 (CERES) -2.1 (MODEL) [ W m-2 / K ] H2O vib-rot band H2O rot dOLR/dT = -7.2 (CERES) -6.1 (MODEL) [ W m-2 / K ] [Huang and Ramaswamy 2008 GRL]
SST SST Clear-sky All-sky AIRS AIRS 304 304 304 300 300 300 294 296 296 MODEL MODEL MODEL–AIRS MODEL–AIRS 304 300 • Bias in the convectively active regime (SST>300K) is the main cause of the underestimated window region radiance response to SST. 294 1650 1650 650 wavenumber 650 wavenumber • The water vapor band bias is persistent regardless of SST. SGE (4/4): Cause of bias –Stratification of OLR spectra with SST Normalized radiance anomalies ( ) binned into 1-K SST intervals ( ) [Huang and Ramaswamy 2008 GRL]
Spectral signatures of climate change • Why infrared radiances? • Globally observed by satellites; • Can be accurately calibrated and thus self-traceable [Goody and Haskins, 1998; Anderson et al., 2004]. • Distinguishable spectral signatures • Modeling: Kiehl , Charlock , and Slingo and Webb  • Observation: Harries et al.  • Questions: • Spectral range, resolution? Radiometer accuracy, stability? Footprint size? Orbit type (sampling frequency, pattern)? … • Experiments • GFDL CM2.1 runs for IPCC AR4 [period from 1860 to 2004] • Unforced variability: • “Nat” run in a period (1861-1880) with unchanged external radiative forcings • Forced changes: • “Allforc” – prescribed with all observed forcings (WMGHG, O3, aerosol, volcano, solar incidence, etc.) • “Anth” – anthropogenic forcing only” • “WmGhgO3” –well-mixed greenhouse gases and O3 only • “CO2” – CO2 only
Inter-annual variability 0.1 Inter-month variability 0.5 Decomposition of inter-annual variation Unforced natural variabilities of OLR spectrum • Experiment setup: • 20-year (1861-1880) “Nat” run with fixed forcings • Results • - Interannual variability < 0.1K • - Intermonth variability < 0.5K in window, H2O bands; > 1K in CO2 and O3 bands • - Agreement with AIRS observation (5 years). • - The small variability results from compensating water vapor and temperature contributions of much larger amplitude. CO2 CO2 O3 H2O vi-rot. H2O rot. Window
1980-2004 evolution of atmosphere and surface conditions T_sfc T_atm H2O OLR Cld OLR_c Blue lines and color contours: Evolution of the variables in Allforc experiment. Red dotted lines and black dots: change (relative to 1980) larger than 3 times the standard deviation in Nat.
Resolution: 2 cm-1 Clear-sky Increase in outgoing radiation Resolution: 5 cm-1 decrease in outgoing radiation CO2 Window CH4 H2O vib.-rot. CO2 [K] H2O rot. All-sky O3 Resolution: 10 cm-1 1980-2004 evolution of OLR spectrum Global ocean annual mean radiance changes relative to 1980 in “Allforc” experiment; Black dots: larger than 3 times the standard deviation in Nat.
Linear trends Clear-sky H2O rot. Window H2O vib.-rot. CO2 O3 CH4 CO2 All-sky Red dashed line: trend estimated from linear regression; Green shaded areas: a measure of the uncertainty [Weatherhead et al. 1998].
Decomposition of radiance change in the water vapor vibration-rotational band ‘MODEL’: simulated difference spectrum between 1980-1984 mean and 2000-2004 mean in model-simulated time series; ‘Jacobian’: reproduced difference by using temperature and water vapor Jacobians; Jacb-Tsurf: surface temperature contribution; Jacb-Ttrop: tropospheric temperature contribution; Jacb-Tstrat: stratospheric temperature contribution; Jacb-q: water vapor contribution.
H2O rot Window H2O vib-rot. O3 CO2 CO2 CH4 Global Mean 140-year end-to-end difference • Notations: • Red: <2000-2004> minus <1861-1865> spectral difference • Blue: variability among ensemble members (3xSTD) • Green: unforced natural variability (3xSTD) • Results: • window regions – surface warming; • CO2 bands – stratospheric cooling partly offset by the raised emitting level (similar in O3 and CH4 bands); • H2O bands – atmospheric warming is compensated by water vapor feedbacks.
Window H2O rot H2O vib-rot. O3 CH4 CO2 CO2 240-year end-to-end difference • Notations: • Red: <2000-2004> minus <1861-1865> spectral difference • Blue: <2100-2104> minus <1861-1865> • Green: unforced natural variability (3xSTD) • Results: • window regions – surface warming; • CO2 bands – stratospheric cooling partly offset by the raised emitting level (similar in O3 and CH4 bands); • H2O bands – atmospheric warming is compensated by water vapor feedbacks.
Long term changes in a) atmospheric temperature, b) specific humidity, relative humidity, and d) cloud condensate
(96-00) – (56-60) WmgggO3 AllForc Anthro. Aerosol Anthro. Nat. BC,OC
Percent change 2090-2099 minus 1980-1999 • Key Points: • Precipitation changes more uncertain than temperature changes. • Models do not agree on sign of the change in many areas. • High latitudes tend to receive more precipitation, especially in winter. • The Mediterranean region tends to dry.