Transcript Integrated Systems for Weather and Air Quality Forecasting

HIRLAM-A
Work Plan 2007
Draft 2007-01-25
Integrated systems for weather
and air quality forecasting
Leif Laursen,
Alexander Baklanov, Ulrik Korsholm, Alexander Mahura
Danish Meteorological Institute,
DMI, Research Department, Lyngbyvej 100, Copenhagen, DK-2100, Denmark
In cooperation with COST728, HIRLAM and MEGAPOLI consortiums
Environmental Prediction into the Next Decade:
Weather, Climate, Water and the Air We Breathe
Technical Conference preceding CAS XV
Incheon, Republic of Korea, 16-17 November 2009
Keywords in integrated modelling:
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Chemical weather
Coupling/Integration/on-line/two-way feedbacks
Practical; fewer operational models
Prediction of consequences of climate change on
pollution levels
• Models more consistent
• Air pollutants interact with meteorology: aerosols,
trace gases affecting radiation balance and clouds
• Verification more difficult
Chemical weather forecast: common concept
• Chemical weather forecasting (CWF) - is a new quickly
developing and growing area of atmospheric modelling.
• Possible due to quick growing supercomputer capability and
operationally available NWP data as a driver for atmospheric
chemical transport models (ACTMs).
• The most common simplified concept includes only operational
air quality forecast for the main pollutants significant for health
effects and uses numerical ACTMs with operational NWP data as
a driver.
• Such a way is very limited due to the off-line way of coupling the
ACTMs with NWP models (which are running completely
independently and NWP does not get any benefits from the
ACTM) and not considering the feedback mechanisms.
Chemical weather forecast: new concept
• To account for variability in trace gases and aerosols with time
scales less than the off-line coupling interval on-line models with
a 2-way coupling between radiatively active species and
meteorology must be used.
• Aerosols affect the radiation balance through: direct interaction
with incoming/outgoing radiation, changes in cloud top
reflectance, changes in precipitation development (and thereby
cloud lifetime).
• Clouds and radiation affect aerosols through: in-cloud / belowcloud scavenging, heterogeneous chemistry, local and regional
thermally induced circulation cells, reaction rates depends on
temperature, photolysis strongly modified by cloud cover.
• CWF should include not only health-affecting pollutants (air
quality components) but also GHGs and aerosols affecting
climate, meteorological processes, etc.
• Improvement of NWP itself
Examples of aerosol-meteorology feedbacks
Direct effect - Decrease solar/thermal-infrared radiation and visibility:
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Processes involved: radiation (scattering, absorption, refraction, etc.);
Key variables: refractive indices, extinction coefficient, single-scattering albedo,
asymmetry factor, aerosol optical depth, visual range;
Key species: - cooling: water, sulphate, nitrate, most OC;
- warming: BC, OC, Fe, Al, polycyclic/nitrated aromatic compounds;
Semi-direct effect - Affect PBL meteorology and photochemistry:
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Processes involved: PBL, surface layer, photolysis, meteorology-dependent processes;
Key variables: temperature, pressure, relative and water vapour specific humidity, wind speed and direction,
clouds fraction, stability, PBL height, photolysis rates, emission rates of meteorology-dependent primary
species (dust, sea-salt, pollen and other biogenic);
First indirect effect (so called the Twomey effect) – Affect clouds drop size, number, reflectivity,
and optical depth via CCN or ice nuclei:
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Processes involved: aerodynamic activation / resuspension, clouds microphysics, hydrometeor dynamics;
Key variables: int./act. fractions, CCN size/compound, clouds drop size / number / liquid water content, cloud
optical depth, updraft velocity;
Second indirect effect (also called as the lifetime or suppression effect) - Affect cloud liquid water
content, lifetime and precipitation:
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Processes involved: clouds microphysics, washout, rainout, droplet sedimentation;
Key variables: scavenging efficiency, precipitation rate, sedimentation rate.
 High-resolution on-line models with a detailed description of the PBL structure are necessary to
simulate such effects.
 Online integrated models are necessary to simulate correctly the effects involved 2nd feedbacks
Chemical weather forecast: The new concept
Several model developments in Europe and international
projects and collaboration points in this direction:
Model name
On-line coupled chemistry
BOLCHEM
Ozone as prognostic chemically
active tracer
Only European short range
models with aerosol
indirect effects
ENVIRO-HIRLAM
Gas phase, aerosol and
heterogeneous chemistry
Each HIRLAM time Yes
step
WRF-Chem
RADM+Carbon Bond,
Madronich+Fast-J photolysis,
modal+sectional aerosol
Each model time step Yes
WMO-COST728 GAW 177
COSMO LM-ART
Gas phase chem (58 variables),
aerosol physics (102 variables),
pollen grains
each LM time step
Yes (*
COSMO LM-MUSCAT (** Several gas phase mechanisms,
aerosol physics
Each time step or
time step multiple
None
MCCM
RADM and RACM, photolysis
(Madronich), modal aerosol
Each model time step (Yes) (***
MESSy: ECHAM5
Gases and aerosols
Yes
MESSy: ECHAM5COSMO LM (planned)
Gases and aerosols
Yes
MC2-AQ
Gas phase: 47 species, 98
chemical reactions and 16
photolysis reactions
each model time step None
GEM/LAM-AQ
Gas phase, aerosol and
heterogeneous chemistry
Set up by user – in
most cases every
time step
Operational ECMWF model Prog. stratos passive O3 tracer
(IFS)
ECMWF GEMS modelling GEMS chemistry
GME
OPANA=MEMO+CBMIV
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Time step for
coupling
Feedback
None
None
Each model time ste
Each model time step Yes
Progn. stratos passive O3 tracer Each model time step
Each model time step
Direct effects only; **) On-line access model; ***) Only via photolysis
Chemical weather forecast: The new concept
European COST Actions 728 (2005-2009):
"Enhancing Meso-scale Meteorological Modelling Capabilities for Air Pollution
and Dispersion Applications"
Coord. – Ranjeet S Sokhi , University of Hertfordshire
The main objective is to develop advanced conceptual and computational frameworks to
enhance significantly European capabilities in mesoscale meteorological modelling for
air pollution and dispersion applications.
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WG1: Meteorological parameterization/ applications (Maria Athanassiadou,
UK MetOffice)
WG2: Integrated systems of MetM and CTM: strategy, interfaces and module
unification (Alexander Baklanov, DMI)
WG3: Mesoscale models for air pollution and dispersion applications
(Mihkail Sofiev, FMI)
WG4: Development of evaluation tools and methodologies (Heinke
Schluenzen, University of Hamburg)
New Cost action ES0602, CWF
MEGAPOLI EU FP7 project
Megacities: Emissions, Impact on Air Quality and Climate, and
Improved Tools for Mitigation Assessments
Project duration: Oct. 2008 – Sep. 2011
27 European research organisations from 11 countries are involved.
Coordinator: A. Baklanov (DMI)
Vice-coordinators: M. Lawrence (MPIC) and S. Pandis (FRTHUP)
(see: Nature, 455, 142-143 (2008), http://megapoli.info )
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The main aim of the project is
(i) to assess impacts of growing
megacities and large air-pollution
“hot-spots” on air pollution and
feedbacks between air quality,
climate and climate change on
different scales, and
(ii) to develop improved integrated
tools for prediction of air pollution
in cities.
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1 Level
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Level
3 Level
Paris,
London,
Rhine-Ruhr,
Po Valley
Moscow, Istanbul, Mexico City,
Beijing, Shanghai, Santiago, Delhi,
Mumbai, Bangkok, New York,
Cairo, St.Petersburg, Tokyo
All megacities:
cities with a population > 5 Million
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Urban (and Regional and Global
and some Street) Scale Modelling
Available and New Observations
Tool Application and Evaluation
Mitigation
Policy
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Regional (and Global and
some Urban) Modelling
Available Observations
Implementation of
Integrated Tools
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Global Modelling
Satellite studies
DMI-HIRLAM Modelling Domains
Multy-scale Modelling and M2UE nesting
Urban Areas
M2UE
Hor. Resol.:
T: 15 km
S: 5 km
U01: 1.4 km
I01: 1.4 km
M2UE resol.:
10-300 m
Enviro-HIRLAM results:
Korsholm et al., 2009
445 km
Effect of Paris on regional thermal structure
665 km
Horizontal resolution: 0.05º x 0.05º
Vertical resolution: 40 levels
Model top: 10 hPa
MSG1 satellite image 2005-06-30, 12 UTC
Case with low winds, deep convective clouds, little precipitation
Reference run without feedbacks (REF), Perturbed run with first (1IE) and second
(2IE) indirect effects and urban heat fluxes (HEA) and roughness (DYN).
•Domain covering 665 x 445 km around Paris, France,
•Case study days: 2005-06-28 - 2005-07-03,
•300 s time step, NWP-Chem chemistry (18 species),
T2m comparison at a measurement station downwind from Paris
Aerosol indirect effects
Korsholm et al., 2009
Difference from measurements (Cº)
T2m comparison; average over all 31 stations
Daytime improvement
Korsholm et al., 2009
Findings
In this particular meteorological case: 2IE led to a general better
T2m comparison during Daytime; only small changes during night,
1IE was small in comparison (larger for thin clouds),
urban parameterization had negligible effect (strong large scale forcing).
Dominating process in this case:
Paris
Aerosols
Increased Cloud cover (2nd aerosol indirect effect)
Shortwave, long wave response
Daytime cooling, night time heating
Vertical NO2 profile in point of max.
increase (49.2N;2.7E) during daytime
2005-06-29 at 12 UTC for the REF
simulation (red) and the simulation
including the indirect effects (green)
Pressure (pa)
Additionally: local thermally induced circulations redistribute the aerosols and trace gases:
concentration (μg m-3)
Korsholm et al., 2009
Conclusions
In this particular case (Korsholm et al., EMS, 2008):
• Indirect effects induce large changes in NO2
• Changes mediated through changes in dynamcis
• Residual circulation induced by temperature
changes
• Redistribution both vertically and horizontally
• Also applies for night-time conditions
• Chem vs dynamics
• Fist indirect effect is much smaller than second one
• Large non-linear component
Integrated Atmospheric System Model Structure
Aerosol Dynamics
Model
Transport &
Chemistry Models
Climate /
Meteorological Models
Interface / Coupler
Atmospheric
Contamination Models
Atmospheric
Dynamics /
Climate Model
Ocean and
Ecosystem Models
One-way: 1. NWP meteo-fields as a driver for ACTM (off-line);
2. ACTM chemical composition fields as a driver for R/GCM (or for NWP)
Two-way: 1. Driver + partly feedback NWP (data exchange via an interface with a limited time
period: offline or online access coupling, with or without second iteration with
corrected fields);
2. Full chain feedbacks included on each time step (on-line coupling/integration)
Thank You !