Toward a moist dynamics that takes account of cloud systems (in prep. for JMSJ) Brian Mapes...
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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.](https://reader034.fdocuments.in/reader034/viewer/2022051621/5697bfc41a28abf838ca5c13/html5/thumbnails/1.jpg)
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.](https://reader034.fdocuments.in/reader034/viewer/2022051621/5697bfc41a28abf838ca5c13/html5/thumbnails/2.jpg)
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?
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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.
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Find the entrainment rate coefficient
Oct 18-19
30 hour loop
DYNAMO campaign, equatorial Indian Ocean (Maldives)
S-POL radar reflectivity
300 km
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Our disconnect: like premodern medicineForm vs. Function
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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??)
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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)
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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
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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
)
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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)
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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’
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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!
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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
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Organized convection sensitivities to large-scale (domain-mean) T anomalies:
inhibits heatingabove
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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)
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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
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Organization: A 2D-3D continuum?3D – small- No Shear
3D – With Shear
Strict 2DMapes (2004)
x (km)
doubly periodic
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Connecting form to function:• Need definitions / measures of function
1.
2.
3. Full ‘inline’ tests: global models with explicit convection
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Super-parameterization vs. Under-resolved convection
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‘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
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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
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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)