1 / 22

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

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)

terra
Download Presentation

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

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. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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?

  3. Example: mixing in convection “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. Brooks Salzwedel Plume #1 2009 12" x 8” Mixed Media

  4. Find the entrainment rate coefficient Oct 18-19 30 hour loop DYNAMO campaign, equatorial Indian Ocean (Maldives) S-POL radar reflectivity 300 km

  5. Our disconnect: like premodern medicineForm vs. Function

  6. Connecting form to function • Definitions & measures of function • Offline diagnostic: sensitivity matrix • Test-harness performance: column with parameterized large-scale dynamics • Inline tests: global explicit cloud models • Ways to control for form • Domain size and shape; vertical wind shear • Conditional sampling (obs??)

  7. Connecting form to function: one model approach • Definitions & measures of function • sensitivity matrix • Ways to control form • Domain size and shape • Work of ZhimingKuang (2010, 2012)

  8. 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

  9. M from 128x128km 2km-mesh CRM in Rad. Conv. Eqm. z (km) z (km) z (km) z (km) 0 1 2 5 8 12 0 1 2 5 8 12 0 1 2 5 8 12 0 1 2 5 8 12 0

  10. Effect of T’ on subsequent 4h heatingp coordinates view each built from >100,000 days of CRM time inhibits heating above T’650 >0 (Kuang 2012)

  11. Sensitivity of column integrated heatingto T’ at various pressure levels Sensitivity of 4h rain to T’ inhibits heating above T’700 >0

  12. Sensitivity of 4h small domain rain to T’ and q’ Moisture in free troposphere is favorable Warm air is a buoyancy barrier. This “effective inhibition“ layer extends up to 400mb! WARM AND MOIST PBL IS VERY FAVORABLE

  13. Organized convection: different sensitivity 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...

  14. Organized convection sensitivities to large-scale (domain-mean) T anomalies: inhibits heating above

  15. 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)

  16. Sensitivity of 4h big domain rain to T’ and q’ “inhibition” layer now 600-300 mb 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) Kuang pers. comm. Saturday

  17. Organization: A 2D-3D continuum? 3D – With Shear 3D – small- No Shear doubly periodic x (km) Strict 2D Mapes (2004)

  18. Connecting form to function: • Need definitions / measures of function • Full ‘inline’ tests: global models with explicit convection

  19. Super-parameterization vs. Under-resolved convection

  20. ‘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

  21. 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

  22. 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)

More Related