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Radiation Spectra at TOA and Climate Diagnoses

Radiation Spectra at TOA and Climate Diagnoses

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Radiation Spectra at TOA and Climate Diagnoses

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  1. Radiation Spectra atTOA andClimate Diagnoses V. Ramaswamy and Yi Huang NOAA/ GFDL, Princeton University

  2. 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?

  3. Introduction (1/3)Global annual mean energy budget [Kiehl&Trenberth 1997]

  4. Surface T Change Planck Damping TOA Radiation Imbalance Sensitivity Introduction (3/3) – Motivation • Wetherald and Manabe [1988] 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)

  5. 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

  6. 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]

  7. 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]

  8. Clear-sky radiances All-sky radiances CO2 Window O3 H2O AIRS zonal mean OLR spectra [W m-2 / cm-1 / sr] Latitude Latitude

  9. 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

  10. 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

  11. 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]

  12. 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]

  13. 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]

  14. 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]

  15. 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 [1983], Charlock [1984], and Slingo and Webb [1997] • Observation: Harries et al. [2001] • 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

  16. Radiative Forcings

  17. 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

  18. 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.

  19. 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.

  20. 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].

  21. 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.

  22. 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.

  23. 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.

  24. Long term changes in a) atmospheric temperature, b) specific humidity, relative humidity, and d) cloud condensate

  25. Schwarzkopf and Ramaswamy (2008)

  26. (96-00) – (56-60) WmgggO3 AllForc Anthro. Aerosol Anthro. Nat. BC,OC

  27. Soden et al. [Science, 2005]

  28. 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.

  29. END