ISCCP at its 30th

(New York, 22 – 25 April, 2013)

Congratulations to the “Core Team” and to “Patient Outside-Supporters”: Our best wishes for a perspective future and for a continuing stream of ..s each day!

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The true “New York Style” bagel

A few Aspects of Cloud Impact on the Radiation Budget

… by “satellite observations” and … by model simulations

Ehrhard Raschke1 and Stefan Kinne MPI Meteorology & (1) U. of Hamburg, Germany

ISCCP at its 30th , New York, 22 – 25 April, 2013

(Influence of clouds on the radiation budget of the atmosphere and at its boundaries estimated with satellite-based and model data sets)

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Further reading: E. Raschke, S. Kinne, P. Stackhouse Jr. et al., WCRP Report 19/2012, >500 pp, >150 MB

Classical Questions:

How & where do clouds modulate the transfer of radiative energy within the Earth-Atmosphere-System ?

How well are clouds observed and modeled?

We know now many details qualitatively on the CRE. But can we rely on this state to justify climate variations as they might be manifested in variations of cloud and radiation fields?

Our tools: Two data sets

Based on Observations: Central value: {CERES, ISCCP, SRB} average [CIS]Uncertainty: {CERES, ISCCP, SRB} local spread* [ΔCIS] period 4 years (03/2000-02/2004)

Models of IPCC-4Central value: Interquartile average [IPCC]Uncertainty: Interquartile spread range* [ΔIPCC]

period 12 years (1984-1995)

* means deseasonalized

all-sky CISS=342

IPCC CMIP 3S=342

Trenberth2007

S=342

Stephens2012

S=340

Wild 2013

S=340

TOA total net + 2.5 + 2 0 0 0

SW up at TOA - 101 - 102 - 102 - 100 - 100

LW up at TOA - 238 - 235 - 239 - 240 - 239

IR-GHE 154 157 157 158 158

SW dn at sfc 190 187 184 188 185

SW up at sfc - 25 - 24 - 23 - 23 - 24

LW dn at sfc 344 334 333 345 342

LW up at sfc - 394 - 393 - 396 - 398 - 397

SW net at sfc 165 163 161 165 161

LW net at sfc - 50 - 59 - 63 - 53 - 55

Tot net at sfc 115 104 98 112 106

SW div in atm 76 75 79 75 79

LW div in atm - 188 - 176 - 176 - 187 - 185

Total div in atm - 112 - 101 - 97 - 112 - 106

Annual global averages of CIS, IPCC and other datasets for all-sky conditions

(in Wm-2)

Rounding errors: > ± 0.5 Wm-2

Net at TOA

CRE of Net at TOA

CIS: Net and CRE on Net radiation at TOA (4 years)

Net Radiation

CRE of Net Radiation

CIS: Net and CRE on Net Radiation at Surface (4 years)

IPCC minus CIS (in Wm-2) at TOA

Annual all-sky netflux at TOA

cc

0

Annual CRE on netflux at TOA

1

IPCC minus CIS (in Wm-2) at surface

cc

-11

-12

Annual CRE on netflux at surface

Annual all-sky netflux at surface

Relative spread ranges: 100x(ΔIPCC - ΔCIS) / ΔCIS

TOA: diversity is much larger in modeling Surface: mixed message

Major reasons for disagreement:

1.Clouds: optical depth, vertical distribution – both “measured” and modeled.

2.Ancillary data, which describe the state of the atmosphere and ground. Aerosols, GH-gases, the temperature and also the insolation at TOA are ancillary; also the reflectance and temperature of the surface.

3.Errors in computational procedures

CIS average

ΔCIS(spread)

SW surface albedo LW, up flux / 600 Wm-2

CIS: Ancillary data of surface are quite uncertain

Total divergence and LW Greenhouse-E.

CIS: Total divergence and greenhouse Clouds increase and decrease total divergence and

contribute up to 25% of local GHE

LW greenhouse effectSW+LW divergence

CRE CRE+

+

++

-

+ +

CIS: Spread of divergence and GHELocal spread of CRE ~ 20 to 40% of local flux

CRE

all-skyavg

SW+LW divergence LW greenhouse effect

CRE CRE

Are there typical differences between radiation fields over “deep ocean

regions” and over “land surfaces” ?

All-sky Clear sky Cloud radiative effects

CIS IPCC CIS IPCC CIS IPCC

oceanS=364

land S=311

oceanS=364

landS=311

oceanS=364

landS=311

oceanS=364

landS=311

oceanS=364

landS=310

oceanS=364

landS=311

TOA total net 0.06 - 0.08 0.06 - 0.07 0.14 - 0.03 0.13 - 0.04 - 0.09 - 0.05 - 0.07 - 0.04

SWup at TOA 0.26 0.35 0.28 0.35 0.11 0.22 0.11 0.23 0.15 0.13 0.16 0.12

LWup at TOA 0.68 0.73 0.67 0.72 0.75 0.81 0.75 0.80 - 0.08 - 0.08 - 0.08 - 0.08

IR-GHE at TOA 0.47 0.44 0.48 0.43 0.39 0.36 0.39 0.37 0.08 0.08 0.09 0.06

SWdn at sfc 0.56 0.55 0.54 0.56 0.73 0.71 0.74 0.74 - 0.17 - 0.16 - 0.20 - 0.17

SWup at sfc 0.03 0.15 0.04 0.14 0.03 0.15 0.04 0.15 - 0.01 - 0.00 - 0.00 - 0.01

LWdn at sfc 1.01 1.00 0.99 0.95 0.92 0.90 0.91 0.86 0.09 0.10 0.09 0.09

LWup at sfc 1.14 1.17 1.15 1.15 1.14 1.17 1.14 1.17 0.00 0.00 0.00 -0.02

SWnet at sfc 0.54 0.40 0.50 0.42 0.70 0.56 0.70 0.58 - 0.15 - 0.16 - 0.20 - 0.16

LWnet at sfc - 0.13 - 0.17 - 0.16 - 0.20 - 0.22 - 0.27 - 0.24 - 0.31 - 0.09 - 0.10 0.08 0.11

Totnet at sfc 0.40 0.23 0.35 0.22 0.47 0.29 0.46 0.28 - 0.07 - 0.06 - 0.11 - 0.05

SWdiv in atm 0.21 0.22 0.23 0.22 0.21 0.22 0.20 0.20 0.00 0.00 0.04 0.02

LWdiv in atm - 0.54 - 0.56 - 0.54 - 0.56 - 0.53 - 0.54 - 0.52 - 0.52 - 0.01 - 0.02 - 0.02 - 0.04

Total div in atm - 0.33 - 0.34 - 0.31 - 0.34 - 0.32 - 0.32 - 0.32 - 0.32 - 0.01 - 0.02 0.02 - 0.02

Radiation products scaled vs. insolation at TOA

Summary and recommendations1. Clouds reduce (enhance) downward SW and upward LW (upward

SW and downward LW) radiation. CRE on net fluxes and on divergences are mixed depending on cloud top height and wavelength.

2. Uncertainties and diversities are often higher in IPCC than in CIS data. They are caused by uncertainties in ancillary data and in cloud treatment.

3. Specific problems occur over mountainous continental and over both sub-arctic regions (What is the radiation budget of a grid element over the Andes?).

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We recommend to re-analyze all datasets and to agree on same properties for the surface albedo and emission!

We encourage for international competition!

Apply unique and stringent quality control procedures!

Plan careful for next radiation assessment with more recent data.

Thank You!

Further reading: E. Raschke, S. Kinne, P. Stackhouse Jr. et al., WCRP Report 19/2012, > 500 pp, > 150 MB.

SW & LW divergence & GHE

CIS: Mixed messages for atm. CRECRE on SW divergence in the atmosphere

CIS: Is it correct?ISCCP: Clouds increase solar absorption in atm

SRB: Clouds increase solar absorption in atm … over oceans

CERES: Clouds decrease solar absorption in

atm …

TOA

Surface

IPCC minus CIS of net fluxes at

CREAll-sky net flux

TOA : CRE difference dominate all-sky difference Surface: CIS CRE are much weaker (trop oceans)

IPCC minus CIS of SW-div, LW-div, LW-GHE

All-sky

Clear-sky

CRE

ΔIPCC minus ΔCIS of SW-div, LW-div, LW-GHE

All-sky

Clear-sky

CRE

CIS

Annual net flux at TOA

Annual net flux at surface

Clouds reduce the annual net flux at TOA and surface. CIS-CRE

CRE on net flux at TOA

CRE on net flux at surface

CISaverage

ΔCISspread

CRE on SW, dn CRE on LW, dn

CIS: CRE of down fluxes and their uncertainty at surface