Radiation spectra at toa and climate diagnoses
Download
1 / 32

Radiation Spectra at TOA and Climate Diagnoses - PowerPoint PPT Presentation


  • 162 Views
  • Uploaded on
  • Presentation posted in: General

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha

Download Presentation

Radiation Spectra at TOA and Climate Diagnoses

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


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 [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)


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 [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


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


Schwarzkopf and Ramaswamy (2008)


(96-00) – (56-60)

WmgggO3

AllForc

Anthro.

Aerosol

Anthro.

Nat.

BC,OC


Soden et al. [Science, 2005]


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.


END


ad
  • Login