The Ocean-Land-Atmosphere Model (OLAM) Robert L. Walko Roni Avissar Rosenstiel School of Marine and Atmospheric Science University of Miami, Miami, FL Martin Otte U.S. Environmental Protection Agency Research Triangle Park, NC David Medvigy Department of Geosciences and Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ

Motivation for OLAM originated in our work with the Regional Atmospheric Modeling System (RAMS) RAMS, begun in 1986, is a limited-area model similar to WRF and MM5 Features include 2-way interactive grid nesting, microphysics and other physics parameterizations designed for mesoscale & microscale simulations But, there are significant disadvantages to limited-area models External GCM domain RAMS domain Information flow Numerical noise at lateral boundary OLAM global lower resolution domain OLAM local high resolution region Well behaved transition region Information flow So, OLAM was originally planned as a global version of RAMS. OLAM began with all of RAMS’ physics parameterizations in place.

Global RAMS 1997: “Chimera Grid” approach Lateral boundary values interpolated from interior of opposite grid Not flux conservative

OLAM dynamic core is a complete replacement from RAMS Based on icosahedral grid Seamless local mesh refinement

OLAM: Relationship between triangular and hexagonal cells (either choice uses Arakawa-C grid stagger)

OLAM: Hexagonal grid cells

Downscaling Regional Climate Model Simulations to the Model Simulations to the Spatial Scale of the Observations Downscaling Regional Climate Model Simulations to the Model Simulations to the Spatial Scale of the Observations

Terrain-following coordinates used in most models OLAM uses cut cell method

One reason to avoid terrain-following grids: Error in horizontal gradient computation (especially for pressure) P V P P P V

Wind Terrain-following coordinate levels Terrain Another reason: Anomalous vertical dispersion Thin cloud layer

Continuous equations in conservation form Momentum conservation (component i) Total mass conservation conservation Scalar conservation (e.g. ) Equation of State Momentum density = potential temperature = ice-liquid potential temperature Total density

,. Discretized equations: Finite-volume formulation: Integrate over finite volumes and apply Gauss Divergence Theorem

Conservation equations in discretized finite-volume form cell face area cell volume (SGS = “subgrid-scale eddy correlation”) Discretized momentum density is consistent between all conservation equations

Grid cells A and B have reduced volume and surface area Fully-underground cells have zero surface area A B

Land cells are defined such that each one interacts with only a single atmospheric level Land grid cells

Cut cells vs. terrain-following coordinates  High vertical resolution near ground  Direction of atmospheric isolines

C-staggered momentum advection method of Perot (JCP 2002) 3D wind vector diagnosed Normal wind prognosed

iw iw1 iw6 iw5 iw4 iw3 iw2 iw7 iv1 iv7 iv6 iv5 iv4 iv3 iv2 im7 im1 im2 im3 im4 im5 im6 Neighbors of W point on hexagonal mesh itab_w(iw)%im(1:7) itab_w(iw)%iv(1:7) itab_w(iw)%iw(1:7)

iv iu im1 im2 im3 im5 im6 im4 iv1 iv3iv9 iv15 iv13 iv5 iv12 iv16 iv14 iv8 iv4 iv2 iv11 iv10 iv7 iv6 iw1 iw2 iw3 iw4 Neighbors of V point on hexagonal mesh itab_v(iv)%im(1:6) itab_v(iv)%iv(1:16) itab_v(iv)%iw(1:4)

im iw1 iw2 iw3 iv3 iv1 iv2 Neighbors of M point on hexagonal mesh itab_m(im)%iv(1:3) itab_m(im)%iw(1:3)

RAMS/OLAM Bulk Microphysics Parameterization Physics based scheme – emphasizes individual microphysical processes rather than the statistical end result of atmospheric systems Intended to apply universally to any atmospheric system (e.g., convective or stratiform clouds, tropical or arctic clouds, etc.) Represents microphysical processes that are considered most important for most modeling applications Designed to be computationally efficient

Physical Processes Represented Cloud droplet nucleation Ice nucleation Vapor diffusional growth Evaporation/sublimation Heat diffusion Freezing/melting Shedding Sedimentation Collisions between hydrometeors Secondary ice production

Hydrometeor Types 1.Cloud droplets 2.Drizzle 3.Rain 4.Pristine ice (crystals) 5.Snow 6.Aggregates 7.Graupel 8.Hail H G S A C P R D

Stochastic Collection Equation Table Lookup Form of Collection Equation

LANDCELL 1LANDCELL 2 LEAF–4 fluxes wgg wcahca rvc hvcwvc hvc wgvc1 A wav C V V G2 G1 hav rav C G2 S2 S1 rsa hscwsc hca has was wca wss wgs wgg hgg hgs hss rsv hvswvs wgvc2 hgcwgc rga hgg rgv wgvc2 wgvc1 G1 longwave radiation sensible heat water

K s = saturation hydraulic conductivity  s = saturation water potential  w = density of water [ /  s ] = soil moisture fraction b = 4.05, 5.39, 11.4 for sand, loam, clay Water flux between soil layers Hydraulic conductivity(m/s) Soil water potential (m)

How should models represent convection at different grid resolutions? Conventional thinking is to resolve convection where possible and to parameterize it otherwise Horizontal grid spacing (km) Deep Convection Shallow Convection resolve parameterize ? ?