raymond t. pierrehumbert the university of...

Idealized GCM studies, AGU Fall Meeting 2007

Idealized GCM Studies of Snowball Earth and of Titan

Raymond T. Pierrehumbert

The University of Chicago

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Idealized GCM vs. Intermediate Complexity Models

• ICM: Parameterized dynamics (e.g. synoptic eddy heat flux)

• IGCM

– Full representation of dynamics

– Perhaps reduced resolution

– Simplified physics

– ...and/or idealized forcing, bdd. conditions, etc.

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Limitations of Intermediate Complexity Models

• Winds, momentum fluxes (anything dynamical)

• Hadley cell, water vapor feedback (c.f. CLIMBER)

• Synoptic heat and moisture fluxes (c.f. static stability effects)

• Precipitation and hydrological cycle

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”Intermediate Complexity” a misnomer

• Often more complex than GCM, because of ad hoc parameterizations

• Too complex to allow good understanding of behavior

• But, even when behavior is understood, too far from basic physics toallow conclusions about general principles to be drawn

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Two paths to IGCM

• Strip down a full GCM, simplify physics, experimental design

• Purpose-built idealized GCM

The former is mostly a matter of philosophy about how to use a GCM. Thelatter engages question of flexible modelling software design.

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Two Examples

• Snowball Earth Climate

• The General Circulation of Titan

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Neoproterozoic Snowball Earth simulations

• Initiation (Poulsen et al. GRL 2001 and follow-ons)

• Deglaciation threshold (Pierrehumbert Nature 2004, JGR 2005)

• Surface wind during partial deglaciation

• Postglacial hothouse and recovery

Conducted with FOAM GCM

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Surface wind during Snowball Deglaciation

• Motivation: Giant wave ripples in sediment indicate strong prevailingsurface wind

• Conjectured to be due to temperature gradient between ice marginand hot ocean

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Surface wind during Snowball Deglaciation

Cold Ice

Hot Ocean

Surface Wind??

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But the basic GFD works like this

EqPole

Cold Hot

Surface dragbalances u’v’

Baroclinically Unstable JetimpliesEddiesimplies

Angular Momentum Transport

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Simulations at .2 bar CO2

90

-90-180 0 180

Longitude

Lat

itud

e

Individual Jan. Mon. Mean Surf. U

-180 0 180Longitude

Individual Jul. Mon. Mean Surf. U m/s

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Postglacial Hothouse

• High CO2 needed to deglaciate

• After deglaciation, left with low albedo, high CO2 Hothouse

• Precip determines weathering rate and recovery time

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Precip vs. CO2

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0

50

100

150

10 100 1000

B.L Sat. Specific Hum.BL Specific Hum

Precip

Spec

ific

Hum

idity

Precip (mm

/mo)

CO2 (xPAL)

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The Surface Budget

0

50

100

150

200

10 100 1000

S_{abs}L

F_{sens}IR

Flux

(W

/m2 )

CO2 (xPAL)

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Winds at 100x CO2

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Winds at 200x CO2

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Winds at 400x CO2 – Superrotation!

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Summary: Precip and surface conditions on Hot Worlds

• Clausius-Clapeyron means that small ∆T yields big L

• When L approaches surface absorbed solar, temperature inversionsform which choke off evaporation (cf Pierrehumbert Nature 2002)

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Titan, at 95K is a ”Hot World” !

• Surface solar radiation under 2W/m2

• Methane so volatile that in saturation, 30 % of surface layer atmo-sphere is Methane

• That’s like Earth water vapor at 345K

• Leads to > 400W/m2 of Methane latent heat flux if surface is 1K

warmer than atmosphere

• Strong surface inversions are a dominant feature of Titan climate

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Moist Methane Climate Dynamcics of Titan

• Axisymmetric GCM

• Grey-gas radiative transfer

• Simplified Betts-Miller methane moist convection

• Simplified Monin-Obukhov stable boundary layer scheme

• Thesis work of Jonathan Mitchell (also collaborations with DarganFrierson and Rodrigo Caballero)

• See Mitchell et al. PNAS 2006.

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About the software issues

• Based on interpreted Python script

• Numerically intensive routines written in compiled Fortran or c , andbuilt into new Python commands using SWIGor PyFort .

• Model construction using Caballero’s ClimT object-oriented toolkit

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Convection patterns: dry case

121

(a) Dry seasonality (b) Dry convection

(c) Moist seasonality (d) Moist convection

(e) Intermediate seasonality (f) Intermediate convection

Figure 4.7: Left column: Contour plots of precipitation (filled contours) for our threesimulations, with the pattern of solar forcing at the surface overlaid (black lines) forreference. Right column: Contour plots of the logarithm of the averages of convectiveperturbations for the 10 terrestrial years bracketing southern summer solstice; filledcontours are the convective heating rate in K/day on the same color scale (10−6: coolcolors to 10−1.5: warm colors) and dashed contours are the convective drying ratein g/kg/day. “Stepping” patterns at the edges of contours are representative of theresolution of our simulations.

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Convection patterns: moist case

121

(a) Dry seasonality (b) Dry convection

(c) Moist seasonality (d) Moist convection

(e) Intermediate seasonality (f) Intermediate convection

Figure 4.7: Left column: Contour plots of precipitation (filled contours) for our threesimulations, with the pattern of solar forcing at the surface overlaid (black lines) forreference. Right column: Contour plots of the logarithm of the averages of convectiveperturbations for the 10 terrestrial years bracketing southern summer solstice; filledcontours are the convective heating rate in K/day on the same color scale (10−6: coolcolors to 10−1.5: warm colors) and dashed contours are the convective drying ratein g/kg/day. “Stepping” patterns at the edges of contours are representative of theresolution of our simulations.

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