DMI-ENVIRO-HIRLAM

An On-Line Coupled Multi-Purpose Environment Model

U. Korsholm*, A. Baklanov, A. Mahura, C. Petersen, K. Lindberg, A. Gross, A. Rasmussen, J.H. Sørensen, J. Chenevez

The Danish Meteorological Institute, Copenhagen, Denmark * usn@dmi.dk, phone: +45 39157439

ACCENT/GLOREAM Workshop 2006

Purpose

• Plans and current status, main model features

• Preliminary results: aerosol–meteorology feedbacks

Motivation

• Climate: direct, indirect, semi-direct effects, large scale dynamical feedback (Kim & Lee, 2006; Kim et al., 2006)

• Local: direct, indirect, semi-direct effects, local scale feedbacks

• Importance for short range weather forecasting ? (Perez et al. 2006)

Radiation budgets

Temperature profiles

Chemistry/Aerosols

CloudCondensation

Nuclei

Precipitation

Chemistry/Aerosols

Examples of feedbacks

Cloud-radiationinteraction

Temperature profiles

Chemistry/Aerosols

Definitions

off-line models comprise:• Separate CTMs driven by meteorological input data from meteo-

preprocessors, measurements or diagnostic models• Separate CTMs driven by analysed or forecasted meteo-data from

NWP archives or datasets• Separate CTMs reading output-files from operational NWP models

or specific MetMs with limited temporal resolution (e.g. 1, 3, 6 hours)

on-line models comprise: • On-line access models, when meteo-data is available at each time-

step• On-line integration of a CTM into a MetM; feedbacks are possible:

on-line coupled modeling

Advantages of On-line & Off-line modeling

On-line coupling• Only one grid; no interpolation

in space• No time interpolation• Physical parameterizations are

the same; no inconsistencies• Possibility of feedbacks with

meteorology• All 3D meteorological variables

are available at the right time (each time step); no restriction in variability of met. fields

• Does not need meteorological- pre/post-processors

Off-line• Possibility of independent

parameterizations• Low computational cost; more

suitable for ensembles and operational activities

• Independence of meteorological model computations

Future plans

Radiative & optic properties models

General Circulation & Climate models

Cloud condensation nuclei (CCN) model

Aerosol dynamics models

Ecosystem

models

Ocean dynamics

model

Atmospheric chemistry and transport models

Emission databases, models and scenarios

Inverse methods and adjoint models

WP7

Integrated Assessment Model

WP5

Numerical Weather Prediction Model

Why develop an on-line coupled model ?Climate; NWP; Research; Operational; Emergency

1. Model nesting for high resolutions 2. Improved representation of pbl. and sl. 3. ‘Urbanisation’ of the NWP model 4. Improvement of advection schemes 5. Implementation of chemical mechanisms6. Implementation of aerosol dynamics 7. Realisation of feedback mechanisms

8. Assimilation of monitoring data

EmissionTransportDispersionDeposition

Current status

Observational database

ECMWF

Surface analysis Upper air analysis

Boundaries Output

Initialisation

Forecast

DMI-ENVIRO-HIRLAM

Model Description 1

Model identification T15 S05

grid points (mlon) 610 496

grid points (mlat) 568 372

number of vertical levels

40 40

horizontal resolution (deg)

0.15°0.05

°

time step (dynamics) 360s 120s

time step (physics) 360s 120s

host model ECMWF T15

Model Description 2

Transport and dispersion

• Bott advection (Bott, 1989) + Easter update for tracers (Easter, 1993); Semi-Lagrangian for meteorology– Risk of mass-wind

inconsistency• No horizontal diffusion• Vertical diffusion: CBR-

scheme (Cuxart et al., 2000)– Coefficient defined by

mixing length formulation in stable/unstable conditions

Mass conservation test for ETEX release

ETEX 1, 48 hours after start of release

Semi-Lagrangian Bott scheme Bott-Easter sheme

Model Description 3

Model Description 4Deposition• Particle size dependent parameterizations for dry and wet deposition• Resistance approach for dry deposition (Wesley, 1989; Zanetti, 1990) • Terminal settling velocity in different regimes:

– Stokes law– non-stationary turbulence regime– correction for small particles

• Dependent on land use classification• Below-cloud scavenging (washout); precipitation rates (Baklanov & Sorensen,

2001) • Scavenging by snow (Maryon et al., 1996)• Different scavenging of particles and gases

Next step• Rainout into 3D clouds (based on on-line coupling):

– convective precipitation – stratiform precipitation

Preliminary results 1: Deposition

Accumulated dry deposition [kBq/m2]

Chernobyl accident; point source emissions (Devell et al., 1995, persson et al., 1986)

Date: 19860501 18:00 UTC

Accumulated wet deposition [kBq/m2]

Preliminary results 2: Feedback

For water clouds: r³eff = kr³v

r³eff =3L/(4lkN)

(Wyser et al. 1999)

L : Cloud condensate contentN: Number concentration of cloud

droplets

ΔNcont = 108.06 conc0.48

ΔNmarine = 102.24 conc0.26

(Boucher & Lohmann, 1995)

Emission rate: 7.95 gs-1; ETEXDiameter: 1 µm

k N [m-3]

Marine 0.81 108

Cont 0.69 4х108

Urban fractions [%; dark green – dark red]

Preliminary results 2: Feedback

Difference (ref - perturbation) in accumulated dry deposition [ng/m2]

Difference (ref - perturbation) in accumulated wet deposition [ng/m2]

Accumulated (reference) dry deposition [μg/m2] +48 h

Accumulated (reference) wet deposition [μg/m2] +48 h

• DMI is developing an on-line coupled environment model: DMI-ENVIRO-HIRLAM

– emission module, inventories– transport, dispersion, dry and wet deposition– aerosol dynamics – gas-phase and heteorogeneos chemistry– data assimilation– cloud, radiation coupling

• To be used for: research, operational, emergency ? • Main advantages of on-line coupled meso-scale NWP model and CTM

– no restriction in variability of input fields– possibility of feedbacks

• Previously tested for mass conservation• Deposition being tested on Chernobyl accident

– looks promising, local hot-spots• Investigation into cloud-aerosol coupling

– model sensitivity, large changes in deposition

Summary

Acknowledgements

The HIRLAM development program at DMICopenhagen Global Change Initiative (COGCI)

References• Baklanov A. & Sorensen, H., J., 2001, Physics and Chemistry of the Earth,

vol. 26, No. 10, 787-799• Bott, A., 1989, Mon. Wea. Rev., 117, 1006-1015• Boucher, O. & Lohmann, U., 1995, Tellus 47, Ser. B, 281-300• Cuxart, J. et al., 2000, Q.J.R. Meteo. Soc., 126, 1-30• Devell et al., 1995, CSNI report, OECD/NEA, Paris• Easter, C., Mon. Wea. Rev., vol. 121, 297-304• Kim, M., K. et al., J. Clim., 2006, in press• Kim, M., K. & Lee, W., S., GRL, vol. 33, L16704, 2006• Maryon R., H. et al., 1996, Depart. Of Env., UK, Met. Office. DoE Report #

DOE/RAS/96.011• Perez, C. et al., JGR, vol. 111, D16206, 2006• Persson et al., SMHI/RMK report No. 55, 1986• Wesley, M.,L., 1989, Atm. Env., vol. 23, No. 6, 1293-1304• Wyser et al., 1999, Contr. Atmos. Phys., vol. 72, No. 3, 205-218• Zanetti, P., 1990, Air Pollution Modelling – Theories, Computational

Methods and Available Software. Southhampton: Computational Mechanics and New York: Van Nostrand Reinhold