Representation of Physics in NEMS/GSM

Representation of Physics in NEMS/GSM

Shrinivas MoorthiGlobal Climate and Weather Modeling Branch

Environmental Modeling CenterNational Centers for Environmental Prediction

NEMS/GSM Physics

Representation of Physics in NCEP GSM

As mentioned yesterday, changes due to physics are applied in a time-split manner

Most components of physics are also time-split, applied one at a time

Time scheme in physics can be viewed as quasi-backward

With Leap-frog scheme operates on 2t i.e. (n-1) to (n+1)

With two time-level SL scheme operates on t - i.e. (n) to (n+1)

NEMS/GFS Modeling Summer School 2

NEMS/GSM Physics

Representation of Physics in NCEP GSM

Gloopr.f – radiation driver

Calls grrad.f which computes radiative fluxes and heating rates for some arbitrary number of vertical columns

Gloopb.f – physics driver

Calls gbphys .f which computes other non-radiation physics for some arbitrary number of vertical columns

NEMS/GFS Modeling Summer School 3

NEMS/GSM Physics

Radiation Parameterization

Longwave (LW) : (radlw_main.f, radlw_param.f radlw_datatb.f)

Based on AER’s Rapid Radiative Transfer Model (RRTM - Mlawer et al. 1997) Uses a correlated-k distribution method and a linear-in-tau transmittance table look-

up to achieve high accuracy and efficiency The algorithm contains 140 unevenly distributed intervals (g-point) in 16 broad

spectral bands. Absorbing gases - O3, H2O, CO2 , CH4, N2O, O2 , and up to four types of halocarbons (CFCs)

In water vapor continuum absorption calculations, an advanced CKD_2.4 scheme (Clough et al. 1992) used

A maximum-random cloud overlapping is used Cloud liquid/ice water path and effective radius are used for calculation of cloud-

radiative properties. Hu and Stamnes (1993) method for water clouds, and Ebert and Curry (1992) method for ice clouds

NEMS/GSM Modeling Summer School 4

NEMS/GSM Physics


Shortwave (SW) : (radsw_main.f, radsw_param.f radsw_datatb.f)

Based on AER’s Rapid Radiative Transfer Model version 2 (RRTM2) with NCEP updates/modification

A maximum-random cloud overlap is used, consistent with the maximum-random overlap used in the RRTM -LW

The SW aerosol single scattering albedo and asymmetry factor now reflect more recent data


NEMS/GSM Physics


For both LW and SW:

Atmospheric aerosols - both in the troposphere and stratosphere (capable of handling volcanic aerosols) optionally included

Realistic time varying observed global mean CO2 Hourly calculations for both LW and SW Additional advances in radiation parameterization such as McICA are being added

to NEMS/GSM

Other radiation modules:

radiation_astronomy.f, radiation_aerosols.f, radiation_clouds.f, radiation_surface.f,

and radiation_gases.f


NEMS/GSM Physics


• Surface energy (linearized) & water budgets; 4 soil layers

• Forcing: downward radiation, precip., temp., humidity, pressure, wind

• Land states: Tsfc, Tsoil*, soil water* and soil ice, canopy water*, snow depth and snow density

*prognostic

• Land data sets: veg. type, green vegetation fraction, soil type, snow-free albedo & maximum snow albedo

Noah land-surface model (sfc_drv.f, sflx.f)

Noah LSM is coupled to the NCEP GSM, CFS and other NCEP models.

NEMS/GSM Physics


Noah LSM - 4 soil layers (10, 30, 60, 100 cm) - Frozen soil physics included - Surface fluxes weighted by snow cover fraction - Improved seasonal cycle of vegetation cover - Spatially varying root depth - Runoff and infiltration account for subgrid variability in precipitation & soil moisture - Improved soil & snow thermal conductivity - Higher canopy resistance More

OSU LSM - 2 soil layers (10, 190 cm) - No frozen soil physics - Surface fluxes not weighted by snow fraction - Vegetation fraction never less than 50 percent - Spatially constant root depth - Runoff & infiltration do not account for subgrid variability of

precipitation & soil moisture - Poor soil and snow thermal conductivity, especially for thin snowpack and moist soils

NEMS/GSM PhysicsOcean surface in the NCEP GSM

(sfc_ocean.f)

SST from the OI analysis at the initial time relaxed to climatology with e-folding time of 90 days

The lowest model layer is assumed to be the surface layer (sigma=0.996) and the Monin-Obukhov similarity profile relationship is applied to obtain the turbulent exchange coefficients for momentum, heat and moisture following Miyakoda and Sirutis (1986) with modifications by P. Long for both very stable and unstable situations

Sensible and latent heat fluxes are computed using bulk aerodynamic formula with turbulent exchange coefficients calculated in sfc_diff.f

Ocean roughness lengths are determined from the surface wind stress using Charnock (1955) method

Thermal roughness over the ocean is based on a formulation derived from TOGA COARE(Zeng et al, 1998)


NEMS/GSM Physics

Near-Surface Sea Temperature (NSST) Model

(sfc_nst.f)

NSST is a T-Profile just below the sea surface. Here, only the vertical thermal structure due to Diurnal Thermocline Layer (DTL) warming and Thermal Skin Layer (TSL) cooling is resolved

NSST Model DTL warming: Modified Fairall (1996) warming model.

A prognostic control equation of the warming layer thickness is derived.

The free convection process is introduced TSL cooling: The same as Fairall (1996)

This is an option in GSM but not used at this time


NEMS/GSM Physics

Sea Ice in the NCEP GSM

(sfc_sice.f)

A three-layer thermodynamic sea ice model was embedded into GFS (May 2005) It predicts sea ice/snow thickness, the surface temperature and ice temperature In each model grid box, the heat and moisture fluxes and albedo are treated

separately for ice and open water Sea-ice initial condition is obtained from the daily operational analysis The surface temperature of sea ice is determined from an energy balance that includes

the surface heat fluxes and the heat capacity of the ice Surface fluxes are computed using turbulent exchange coefficients (sfc_diff.f) and

bulk aerodynamic formula


NEMS/GSM Physics

Snow Cover

Snow cover is obtained from an analysis by NESDIS (the IMS system) and the Air Force, updated daily When the snow cover analysis is not available, the predicted snow is used Precipitation falls as snow if the temperature T at 850hPa < 0o C Snow mass is determined prognostically from a budget equation that accounts for accumulation and melting Snow melt contributes to soil moisture, and sublimation of snow to surface evaporation Snow cover affects the surface albedo and heat transfer/capacity of the soil, but not of sea ice

It predicts sea ice/snow thickness, the surface temperature and ice temperature structure. In each model grid box, the heat and moisture fluxes and albedo are treated separately for ice and open

water. Sea-ice initial condition is obtained from the daily analysis


NEMS/GSM Physics

Planetary Boundary Layer & Vertical Diffusion

(moninp.f/moninq.f)PBL: The nonlocal planetary boundary layer (PBL) scheme in the NCEP

GSM - originally proposed by Troen and Mahrt (1986) and

implemented by Hong and Pan (1996) First-order vertical diffusion scheme PBL Height diagnostically determined via bulk-Richardson approach Coefficient of diffusivity specified as a cubic function of height Counter-gradient flux parameterization based on fluxes at the surface

and convective velocity scale Background vertical diffusion for heat and tracers exponentially

decreasing with height


NEMS/GSM Physics

Planetary Boundary Layer & Vertical Diffusion

(moninp.f/moninq.f)Free Atmosphere:

In the free atmosphere, the local diffusion scheme (called local-K approach, Louis, 1979) is used

In this approach the vertical diffusivity is represented in terms of a mixing length, stability functions and vertical wind shear

The stability functions depend on local gradient Richardson number at a given height

The stability functions are different for stable and neutral/unstable stratifications

Mixing length is 30m for stable 150m for unstable environment Background vertical diffusion exponentially decreasing with height


NEMS/GSM Physics

Planetary Boundary Layer/Vertical Diffusion

(moninq.f)Recent update (Han and Pan 2010)

A stratocumulus top driven vertical diffusion scheme is incorporated to increase vertical diffusion in the cloudy region of the lower troposphere. The stratocumulus top driven diffusion is further enhanced when CTEI is met

For the nighttime stable PBL, a local diffusion scheme is used To reduce erosion of stratocumulus along the costal oceans, the background

diffusivity in the lower inversion layers is further reduced to 30% of that at the surface

Background diffusivity for momentum has been substantially increased to 3.0 m2s-1 up to ~200 hPa, which significantly reduces wind forecast errors


NEMS/GSM Physics

Gravity-wave Drag and Mountain Blocking Parameterization (gwdps.f)

Original Gravity-wave drag parameterization implemented by

Alpert et al. (1988) following Pierrehumbert (1987)

The treatment of the gravity-wave drag parameterization is improved by

using Kim and Arakawa (1995) formulation

Mountain blocking is parameterized following Lott and Miller (1997)

Stationary convection forced gravity wave drag parameterization is

optional (gwdc.f) - based on Chun and Baik (1998)


NEMS/GSM Physics

Convection Parameterization

Deep Convection (sascnvn.f)

Simplified Arakawa Schubert (SAS) scheme is operational in GFS (Pan and Wu, 1994, based on Arakawa-Schubert (1974) as simplified by Grell (1993))

Includes saturated downdraft and evaporation of precipitation One cloud-type per every time step Until July 2010, random clouds were invoked Entrainment of the updraft and detrainment of the downdraft in the sub-cloud

layers Downdraft strength is based on the vertical wind shear through the cloud. Momentum transport is parameterized in terms of mass flux and vertical wind

shear (Han and Pan, 2006)


NEMS/GSM Physics


Deep Convection (sascnvn.f)

Significant changes to SAS were made during July 2010 implementation which helped reduce excessive grid-scale precipitation occurrences

No random cloud top – single deep cloud assumed

Cloud water is detrained from every cloud layer

Specified finite entrainment and detrainment rates for heat, moisture, and momentum

In the sub-cloud layers, the entrainment rate is inversely proportional to height and the detrainment rate is set to be a constant equal to the cloud base entrainment rate

Above cloud base, an organized entrainment is added, which is a function of environmental relative humidity


NEMS/GSM Physics


Shallow Convection (shalcv.f)

Until July 2010, the shallow convection parameterization was based on Tiedtke (1983) formulation in the form of enhanced vertical diffusion within the cloudy layers

In July 2010, a new mass flux based shallow convection scheme based on Han and pan (2010) was implemented operationally (shalcnv.f)

Updated old shallow convection scheme is still an option (set old_monin=.true.) with an option to limit the cloud top to below low-level inversion when CTEI does not exist


NEMS/GSM Physics


Shallow Convection (shalcnv.f)

New massflux based shallow convection scheme is currently operational

Detrains cloud water from every updraft layer

Convection initiating level is defined as the level of maximum moist static energy within PBL

Cloud top is limited to 700 hPa

Entrainment rate is inversely proportional to height and detrainment rate is equal to the cloud base entrainment rate

Cloud base mass flux at cloud base is specified as a function of convective boundary layer velocity scale


NEMS/GSM Physics


GSM also has optionally another convection parameterization scheme – the Relaxed Arakawa-Schubert (RAS) scheme (rascnvv2.f)

(Moorthi and Suarez, 1992, 1999)

Moist convective adjustment scheme (mstcnv.f) is also optionally available


NEMS/GSM Physics

Grid-scale Condensation and Precipitation

(gscond.f & precpd.f)

The large-scale condensation and precipitation are parameterized following Zhao and Carr (1997) and Sundqvist et al (1989)

Implemented in GFS along with prognostic cloud condensate in 2001 (Moorthi et al, 2001)

Partitioning between cloud water and ice is based on the temperature. Convective cloud detrainment is a source of cloud condensate which can

either be precipitated or evaporated through large scale cloud microphysics Evaporation of rain in the unsaturated layers below the level of

condensation is taken into account All precipitation that penetrates the bottom atmospheric layer is allowed to

fall to the surface (rain or snow depending on 850hPa temperature)


NEMS/GSM Physics

Grid-scale Condensation and Precipitation

(gscond.f & precpd.f)

Condensation or evaporation of cloud is done in the routine gscond.f Conversion from condensation to precipitation (snow or rain) or evaporation

of rain is done in precpd.f Important tunable parameter are:

Auto conversion coefficients (for both ice and water)

Minimum value of cloud condensate before the conversion from condensate

to precipitation occurs

Coefficient for evaporation of precipitation

These parameters can be set through namelist

These parameters determine the amount of cloud condensate in the atmosphere and thus the cloud properties for radiation


NEMS/GSM Physics

Cloud Fraction

Following Xu and Randall (1996), the fractional cloud cover within grid box (σ) is given by

where RH is the environmental relative humidity, ql the liquid water mixing ratio, qs the saturation specific humidity, k1, k2 and k3 the empirical coefficients

Following Xu and Randall, the values of k1=0.25, k2=100, and k3=0.49 are used

in the current operational setting


NEMS/GSM Physics

Ozone sources and sinks

(ozphys.f)

Current OPR version based on Naval Research Laboratory’s CHEM2D model - McCormack et al, (2006)

Monthly and zonal mean ozone production rate and ozone destruction rate per unit ozone mixing ratio were provided by NRL based on CHEM2D model

Original version of these terms were provided by NASA/DAO based on NASA 2D Chemistry model

GSM is capable of running both versions


NEMS/GSM Physics


Kij = Stabilization of ith cloud by unit cloud base

mass flux of cloud j

Fi = large-scale destabilization of cloud i

MBj = cloud base mass flux of cloud j

MiforMand

MiforM

jiFM

ijK

Bi

Bj

,1,0

,1,01

Quasi-equilibrium closure

Arakawa-Schubert (1974) parameterization

=exp(z)

NEMS/GSM Physics

Relaxed Arakawa-Schubert Convective parameterization (rascnvv2.f)

RAS version 1 (MWR 1992) was developed in early 90s as a simple and economical alternative to the original Arakawa-Schubert (1974) parameterization as implemented by Lord (1978)

Two Major simplification are made:

1) Entrainment relation is modified to avoid costly calculation needed to find entrainment parameter associated with clouds detraining at given model level.

= 1 + z

2) To “relax” the conditionally unstable state toward equilibrium each time the parameterization is invoked rather than requiring “quasi-equilibrium” of the cloud ensemble


NEMS/GSM PhysicsRelaxed Arakawa-Schubert Convective parameterization ( rascnvv2.f)


RAS invokes multiple clouds detraining at different model levels every time step

Each clouds modify the environment by a fraction (t/) of the mass flux needed to fully stabilize s single cloud, thus relaxing the state towards equilibrium

Clouds are chosen randomly

No downdrafts in RASV1

RASV1 assumed that liquid water detrained only at the cloud top where it partially evaporated and rest rained without reevaporation in the environment

A slightly modified RASv1 with reevaporation of rain was used in the NCEP Seasonal Forecast Model

NEMS/GSM PhysicsRelaxed Arakawa-Schubert (V2) Convective parameterization (rascnvv2.f)


RASV2 relaxes some of the simplifications made in RASV1(Moorthi and Suarez, 1999)

Normalized mass flux can be a quadratic function of height = 1 + z + (/2)(z)2

=1 for deep clouds = 3 shallow

Simple ice phase for the cloud condensate included

Cheng and Arakawa (1997) downdraft is included (saturated or unsaturated)

Downdraft can penetrate the boundary layer and influence surface evaporation

NEMS/GSM Physics

Relaxed Arakawa-Schubert (V2) Convective parameterization (rascnvv2.f)


• Virtual affects and condensate loading on the buoyancy included (drag due to suspended rain not included)

• Full cloud condensate budget with entrainment of environmental condensate and detrainment of cloud condensate

• Detrainment of rain at cloud edges

• Positive definite mass flux advection term (quasi -TVD scheme) Mass flux can advect environmental cloud condensate and tracers without producing negative values

• Evaporation of falling precipitation (Sud and Molod, 1998)



• Downdrafts driven by precipitation loading and evaporation

• Precipitation flux available for downdraft is obtained as a steady state solution to a tilted updraft

• Downdraft tilting angle is pre-assigned depending on cloud depth (~35 to 7.5 degrees)

• Precipitation is transported within the updraft; may be available for downdraft at different levels than where it was generated

• Downdrafts can start anywhere and end anywhere in the domain

• If downdraft solution does not exist, only updraft is used (downdraft is limited to deep clouds only P(top) < 500hPa)



• Precipitation scavenging of aerosols included (used in NGAC)

• Momentum transport by convection included

• Triggers:

Sub cloud layer mean RH > RHc P(kbl) – P(lcl) < 150 hPa P(sfc) – P(kbl) < 300 hPa DP(neg wrkfun) < 150 hPa mag(neg_wrkfun/tot_wrkfun) < max(0.05,min(cd*200,0.15))



Single column model result with GATE data – run done in 2002

NEMS/GSM PhysicsRelaxed Arakawa-Schubert Convective parameterization (rascnv.f)


T1534 GSM run Hurricane Sandy

Semi-Lagrangian dynamics

RASV2.1

No shallow convection

T=450stphys=225s

NEMS/GSM PhysicsRelaxed Arakawa-Schubert Convective parameterization (rascnv.f)


T1534 GSM run Hurricane Sandy

Semi-Lagrangian dynamics

RASV2.1

No shallow convection

T=450stphys=225s

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Representation of Physics in NEMS/GSM