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Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

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Page 1: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Toward a moist dynamics that takes account of cloud systems(in prep. for JMSJ)

Brian MapesUniversity of Miami

AGU 2011 YOTC session

Page 2: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Motivation

• Disconnect between detailed observations and large-scale desires that justify them

– Observations are 4+ dimensional (xyzt + scales)– rich mesoscale texture (cloud systems)

– How can this truly inform modeling?

Page 3: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Example: mixing in convection

Brooks Salzwedel Plume #1 2009 12" x 8” Mixed Media

“The authors identify the entrainment rate coefficient of the

convection scheme as the most important single parameter...

[out of 31]...[for]...HadSM3 climate sensitivity”

Rougier et al. 2009, J.Clim.doi:10.1175/2008JCLI2533.1.

Page 4: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Find the entrainment rate coefficient

Oct 18-19

30 hour loop

DYNAMO campaign, equatorial Indian Ocean (Maldives)

S-POL radar reflectivity

300 km

Page 5: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Our disconnect: like premodern medicineForm vs. Function

Page 6: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Connecting form to function

• Definitions & measures of function1. Offline diagnostic: sensitivity matrix

2. Test-harness performance: column with parameterized large-scale dynamics

3. Inline tests: global explicit cloud models

• Ways to control for form– Domain size and shape; vertical wind shear– Conditional sampling (obs??)

Page 7: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Connecting form to function: one model approach

• Definitions & measures of function1. sensitivity matrix

• Ways to control form– Domain size and shape

• Work of Zhiming Kuang (2010, 2012)

Page 8: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Sensitivity matrix M:

• a definition & measure of function– Kuang (2010 JAS) devised a way to build it

– using a CRM in eq’m, then matrix inversion– works because convection is linear enough

» as shown also in Tulich and Mapes 2010 JAS

Page 9: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

M from 128x128km 2km-mesh CRM in Rad. Conv. Eqm.

0

0 1 2 5 8 12 0 1 2 5 8 12 z (km) z (km)

0 1 2 5 8 12 0 1 2 5 8 12 z (km

)z (km

)

Page 10: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Effect of T’ on subsequent 4h heatingp coordinates view

each built from >100,000 days of CRM time

T’650 >0

inhibits heatingabove

(Kuang 2012)

Page 11: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Sensitivity of column integrated heatingto T’ at various pressure levels

T’70

0 >0

inhi

bits

he

ating

abov

e

Sensitivity of 4h rain to T’

Page 12: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Sensitivity of 4h small domain rain to T’ and q’

WARM AND MOIST PBL IS VERY FAVORABLE

Moisture in free troposphere is favorable

Warm air is a buoyancy barrier. This “effective inhibition“ layer extends up to 400mb!

Page 13: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

What is “organized?” Storms in 2048 x 64 km domain (unsheared RCE)

• Midlevel inflows, “layer overturning”• Coherent structures fewer of them, so ZK had

to use >200,000 days of CRM time for...

Organized convection: different sensitivity

Page 14: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Organized convection sensitivities to large-scale (domain-mean) T anomalies:

inhibits heatingabove

Page 15: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Organized 4h heating sensitivities to large-scale (domain-mean) T anomalies:

• The basic column vector (heating profile) is deep heating & PBL cooling

• +/-

• (plus local damping of T’, diagonal blue values)

Page 16: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Sensitivity of 4h big domain rain to T’ and q’

Much more sensitive to domain-mean moisture anomalies at various levels above PBL

Positive influence of T’ now up to 700mb (not just PBL “parcel” level)

“inhibition” layer now 600-300 mb

Kuang pers. comm. Saturday

Page 17: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Organization: A 2D-3D continuum?3D – small- No Shear

3D – With Shear

Strict 2DMapes (2004)

x (km)

doubly periodic

Page 18: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Connecting form to function:• Need definitions / measures of function

1.

2.

3. Full ‘inline’ tests: global models with explicit convection

Page 19: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Super-parameterization vs. Under-resolved convection

Page 20: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

‘Super’ vs ‘Under’ explicit convection global models:

• Teraflop for teraflop, which one gives better performance? (by what metrics?)

– ‘Under’ keeps the spectrum-tail mesoscale, but compromises on convection resolution

– ‘Super’ emphasizes convective scales, and accepts (hard-wires) a scale gap

Page 21: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Key points/ conclusions

• Mesoscale/multiscale structure confounds obs-model connections

• Need an account of how form relates to function• We have an accounting system (budgets, primes and bars),

but a scientific account is more than that

• Defining “function” is half the battle• Controlling form is the other half

Page 22: Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes University of Miami AGU 2011 YOTC session

Results• Offline diagnostic of function: matrix M• 4 hour rain sensitivity, from 128km CRM, shows:

– High sensitivity to PBL (“parcel”)– free trop q ~uniformly important at all levels– inhibition applies up to 600mb

• 4 hour sens. from 2048 x 128 w/ mesoscale org differs:– More sensitive to q’ in free troposphere– T’ at 700mb is a positive influence now– ‘inhibition’ layer extends up to 400-300 mb

• A continuum from isotropic 3D to strict 2D?

• Inline approaches: interesting comparison needs doing– Super-parameterization vs. under-resolved convection

• Working to bring in obs (having model predictions helps)