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Radiation Spectra at TOA and Climate 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

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Radiation spectra at toa and climate diagnoses l.jpg

Radiation Spectra atTOA andClimate Diagnoses

V. Ramaswamy and Yi Huang

NOAA/ GFDL, Princeton University


Scope l.jpg

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 l.jpg

Introduction (1/3)Global annual mean energy budget

[Kiehl&Trenberth 1997]


Introduction 3 3 motivation l.jpg

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)


Slide5 l.jpg

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


Contribution by water vapor continuum l.jpg

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]


Applications of jacobians l.jpg

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]


Airs zonal mean olr spectra l.jpg

Clear-sky radiances

All-sky radiances

CO2

Window

O3

H2O

AIRS zonal mean OLR spectra

[W m-2 / cm-1 / sr]

Latitude

Latitude


Airs zonal mean spectral greenhouse effect l.jpg

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


Olr spectra l.jpg

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


Spectral breakdown of olr t s relationship 1 4 case study super greenhouse effect sge l.jpg

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]


Sge 2 4 airs observations regression coefficients r v a t s b l.jpg

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]


Sge 3 4 airs vs am2 l.jpg

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]


Sge 4 4 cause of bias stratification of olr spectra with sst l.jpg

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 l.jpg

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


Radiative forcings l.jpg

Radiative Forcings


Unforced natural variabilities of olr spectrum l.jpg

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 l.jpg

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.


1980 2004 evolution of olr spectrum l.jpg

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 l.jpg

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 l.jpg

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.


140 year end to end difference l.jpg

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.


240 year end to end difference l.jpg

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.


Slide25 l.jpg

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


Slide27 l.jpg

Schwarzkopf and Ramaswamy (2008)


Slide29 l.jpg

(96-00) – (56-60)

WmgggO3

AllForc

Anthro.

Aerosol

Anthro.

Nat.

BC,OC


Slide30 l.jpg

Soden et al. [Science, 2005]


Slide31 l.jpg

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.


Slide32 l.jpg

END


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