Interactions between parameterization schemes, where each scheme contains its
own set of errors and assumptions (for example, a soil model and radiation ...
Parameterizations Sound Climate Change Scenario Modeling Panama City 22‐25 May 2012
5/29/2012
1
What is parameterization? • the representation of important physical processes that cannot be directly included in a model
Why parameterize? y Parameterization is necessary for several reasons: y Computers are not yet powerful enough to treat many physical processes explicitly because the processes are either too small or too complex to be resolved y Many other physical processes cannot be explicitly modeled because they are not sufficiently understood to be represented in equation format or there are no appropriate data
y Physical process that cannot be directly predicted require parameterization schemes based on reasonable physical (e.g., radiation) or statistical (e.g., inferring cloudiness from relative humidity) representations. y Several types of assumptions are used to "create" information. y Empirical/statistical: Assumes that a given relationship holds in every case. y Dynamical/thermodynamical: A complex process is summarized through a simplified relationship. y Model within a model: Although the use of nested models pushes the assumption back to a finer detail, eventually assumptions must still be made. Running a model within a model requires far more development by modelers and takes longer to run.
y Key problem of parameterization is trying to predict with incomplete information (i.e.., the effects of sub grid‐scale processes using only information at the grid scale).
y Problems associated with using parameterizations can result from y Interactions between parameterization schemes, where each scheme contains its own set of errors and assumptions (for example, a soil model and radiation scheme passing back and forth information about heating the boundary layer) y The increasing complexity and interconnectedness of parameterizations, which result in forecast errors that are more difficult to trace back to specific processes
y The largest impact of using parameterization schemes is usually on predictions of sensible weather at the surface. y These problems and impacts require that users apply physical reasoning on a case‐by‐case basis when the processes being parameterized are important to the interpretation.
Parameterization “philosophies” • CCSM3 single parameterizations chosen for public release of each model version allows developers to choose parameterization schemes that work together well
• WRF multiple parameterizations included in each release allows users to select parameterization schemes best suited for their use
Major parameterizations in models y Convective parameterization y deep convection y shallow convection
y Microphysics (cloud processes) y Radiation y shortwave y longwave
y Planetary boundary layer y Surface layer y Land surface
Convective parameterization
Deep Convective Parameterization CCSM3 y Zhang and McFarlane [1995] scheme: a plume ensemble approach where it is assumed that an ensemble of convective scale updrafts (and associated saturated downdrafts) may exist whenever the atmosphere is conditionally unstable in the lower troposphere. y Sundqvist [1988] style evaporation of the convective precipitation as it makes its way to the surface.
CESM1 y No major changes
WRF (cu_physics) 1. 2. 3. 4. 5.
6. 7. 8. 9. 10.
Kain‐Fritsch scheme: Deep and shallow convection sub‐grid scheme using a mass flux approach with downdrafts and CAPE removal time scale Betts‐Miller‐Janjic scheme. Operational Eta scheme. Column moist adjustment scheme relaxing towards a well‐ mixed profile Grell‐Devenyi (GD) ensemble scheme: Multi‐closure, multi‐ parameter, ensemble method with typically 144 sub‐grid members Simplified Arakawa‐Schubert:Simple mass‐flux scheme with quasi‐equilibrium closure with shallow mixing scheme (and momentum transport in NMM only). Grell 3D is an improved version of the GD scheme that may also be used on high resolution (in addition to coarser resolutions) if subsidence spreading (option cugd_avedx) is turned on. Tiedtke scheme. Mass‐flux type scheme with CAPE removal time scale, shallow component and momentum transport. Zhang‐McFarlane scheme . Mass‐flux CAPE‐removal type deep convection from CESM climate model with momentum transport. New Simplified Arakawa‐Schubert. New mass‐flux scheme with deep and shallow components and momentum transport. New Simplified Arakawa‐Schubert. New mass‐flux scheme with deep and shallow components and momentum transport. Old Kain‐Fritsch scheme: Deep convection scheme using a mass flux approach with downdrafts and CAPE removal time scale .
Shallow Convective Parameterization CCSM3 • mass‐flux approach: based on Hack 1994. No entrainment and limited detrainment. CESM1 • mass‐flux approach: based on Park and Bretherton (2009).
WRF ishallow = 1 (works together with Grell 3D scheme) 1. UW (Bretherton and Park) scheme. Shallow cumulus option from CESM climate model with momentum transport.
• Convective parameterization schemes were designed to reduce instability in model atmospheres. • Prediction of precipitation is actually just a by‐ product of the way in which a scheme does this. • Consequently, these schemes may not predict the location and timing of convective precipitation as well as users might expect. For climate models, the location and timing of precipitation is less important than for weather forecast models.
Microphysics (cloud) parameterization
Microphysics Parameterization CCSM3 • Rasch and Kristjánsson [1998] and Zhang et al. [2003] three‐class (vapor, liquid, ice) scheme.
WRF (mp_physics) 1. 2. 3. 4. 5. 6. 7. 8.
CESM1 • Morrison and Gettelman [2008] three‐class (vapor, liquid, ice) double‐moment scheme.
9. 10. 11. 12. 13. 14.
Kessler scheme: A warm‐rain (i.e. no ice) scheme used commonly in idealized cloud modeling studies. Lin et al. scheme: A sophisticated scheme that has ice, snow and graupel processes, suitable for real‐data high‐resolution simulations. WRF Single‐Moment 3‐class scheme: Simple, efficient scheme with ice and snow processes suitable for mesoscale grid sizes. WRF Single‐Moment 5‐class scheme: A slightly more sophisticated version that allows for mixed‐phase processes and super‐cooled water. Eta microphysics: The operational microphysics in NCEP models. A simple efficient scheme with diagnostic mixed‐phase processes. WRF Single‐Moment 6‐class scheme: Ice, snow and graupel processes suitable for high‐resolution simulations. Goddard microphysics scheme. Ice, snow and graupel processes suitable for high‐resolution simulations. New Thompson et al. scheme: Ice, snow and graupel processes suitable for high‐resolution simulations. This adds rain number concentration and updates the scheme from previous. Milbrandt‐Yau Double‐Moment 7‐class scheme. Includes separate categories for hail and graupel with double‐moment cloud, rain, ice, snow, graupel and hail. Morrison double‐moment scheme. Double‐moment ice, snow, rain and graupel for cloud‐resolving simulations. WRF Double‐Moment 5‐class scheme. Double‐moment rain. Cloud and CCN for warm processes, but is otherwise like WSM5. WRF Double‐Moment 6‐class scheme. Double‐moment rain. Cloud and CCN for warm processes, but is otherwise like WSM6. Stony Brook University (Y. Lin) scheme. 5‐class scheme with riming intensity predicted to account for mixed‐phase processes. NSSL 2‐moment scheme. Two‐moment scheme for cloud droplets, rain drops, ice crystals, snow, graupel, and hail. Also predicts average graupel particle density. Intended for cloud‐ resolving simulations (dx
