Global models

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Global models
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Content

• Principles of Earth System Models and global
  models
• Global aerosol models as part of Earth System
  Models
• Model input
• Computation
• Spatial discretization
• Parameterizations, look-up tables
• Output
• Evaluating model results
• Postprosessing

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• 3-D global models
• Grid box represents 100 km x 100 km
• Timestep gt10 minutes
• Vertical levels few tens
• Timescale years to centuries

• 1-D models
• Representative of surrounding area
• Timestep seconds
• Vertical levels even 100
• Timescale usually days

Parameterizations
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Earth System (Model)?
Circulation Aerosols Clouds
Aerosol emissions Gaseous emissions Deposition
Heat transfer Momentum flux Aerosol emissions
Circulation Biogeochemistry Heat transport
Vegetation Land use Soil moisture
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Earth System Model choice of components

• Choice of ESM components is based on
• timescale of the experiment years, decades or
  millenia
• variables of interest air quality, climate
  change, process study
• availability of computational resources

Model of everything related to Earth
Population model
Dynamic vegetation model
Complexity Computational expense Model noise
Ocean circulation model
Prescribed vegetation (type, leaf area index)?
Mixed layer ocean
Cloud microdynamics
Prescribed sea surface temperatures and sea ice
Prescribed meteorology
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Earth System Model black box modeling

• ESM can easily have gt200 000 lines of code
• A single researcher usually contributes only to a
  single module
• Rest of the model is considered black box (need
  to know basis)?
• Not a significant problem with ESM users, but
  developers do not always know all of
  theconsequences their codehas on the overall
  modelperformance

Aerosol module
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Global aerosol models

• Global aerosol model has to describe all possible
  combinations of atmospheric aerosol composition
  and size
• Dust, seasalt, black carbon, organic carbon,
  sulfate, ...
• Atmospherically relevant aerosol processes
• Nucleation, condensation, coagulation,
  deposition, ...
• Model must be easily coupled with the host-model
• Emissions
• Parameters for radiative effects
• Formation of cloud droplets
• Still, the model has to be computationally
  efficient

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Transport of gases
Transport of aerosols
SOx, NOx
Direct effect
Aerosol microphysics
Inorganic aerosol chemistry
Indirect effect
Development of global aerosol models
Organic aerosol chemistry
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Increased primary sulfateActivation
nucleationPrimary emissions
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Global aerosol models

• Fixed aerosol climatologies
• Monthly/yearly average radiative properties of
  aerosol
• Based on simulations and satellite observations
• Aerosol mass-only models
• No aerosol microphysical processes
• Modal size-resolved aerosol microphysics models
• Aerosol distribution is represented with
  superposition of several log-normal modes
• Sectional size-resolved aerosol microphysics
  models
• Better representation of aerosol processes

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Example model setup ECHAM5-HAM

• ECHAM5 is an atmospheric General Circulation
  Model developed from ECMWF (global weather
  forecast model)?
• HAM module describes aerosol population with
  seven log-normal distributions and solves related
  microphysics (condensation, coagulation, wet
  deposition, etc.)?

SU sulfate BC black carbon OC organic
carbon SS sea salt DU mineral dust
SOLUBLE
INSOLUBLE
AITKEN
COARSE
NUCLEATION
ACCUMULATION
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Modularisationof a global aerosol model
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Emissions and fields

• Emission inventories usually contain static
  monthly or yearly average emission fields
• Online emissions use meteorological conditions
  and surface properties to calculate emission of
  e.g. dust and sea salt

Black carbon
Dust
Examples of online/offline variables in a global
model
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Vertical discretisation

• Pressure/height coordinate is not a good choice
  for a vertical coordinate
• Typically 20-30 hybrid levels are used
• Choice of model vertical extent
• tropospherelower stratosphere
• stratosphere lower mesosphere
• mesosphere lower thermosphere

Sparse, flat pressure-levels at top of atmosphere
Dense, terrain-following near surface

• Sigma coordinates

Hybrid coordinates
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Horizontal discretisation

• Linear terms of temperature, divergence,
  vorticity and surface pressure are usually
  presented in spectral space using spherical
  functions with a certain truncation (21, 42, 63,
  ...)?
• Other terms (humidity, concentrations) are
  calculated in gridspace

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Computational demand

• If memory use (number of vertical levels) x
• (number of latitudes) x
• (number of longitudes) x
• (number of tracers)?
• Common resolution with simple aerosol model
• 19 x 64 x 128 x 20 x 8 bytes 25 Megabytes
• Slightly better resolution and a sectional
  aerosol model
• 31 x 128 x 192 x 50 x 8 bytes 305 Megabytes
• Arithmetic operations (105 / timestep / gridbox)
• 1015 operations per simulation year

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Computational demandwhat is being calculated?

• Atmospheric circulation is calculated with
  primitive equations

Model dynamics advection, Coriolis force
Physical processes all subgrid-scale
non-adiabatic effects (friction, turbulence,
phase change of water)?
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Computational demandparameterizations and
look-uptables

• To decrease computation time, included submodels
  are usually parameterized
• Parameterization is not as accurate as original
  model, and cannot be used outside
  parameterization limits
• Parameterizations are also needed to include
  subgrid-scale processes, such as
• Convection scheme
• Cloud structure
• Aerosol processes
• Look-up tables are used to store frequently
  needed data for fast access

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Evaluation of results

• Results of global models can be evaluated against
  field observations
• Flight observations
• Long-term and campaign in situ observations
• Satellite observations
• Inter-model comparison
• Global models have differences in representations
  of atmospheric physics
• Running experiments with several models (e.g.
  IPCC)?

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Model output

• Status of the climate every 30 minutes
• Direct (predicted) variables
• Temperature, winds, humidity, aerosol
  concentrations
• Derived variables
• AOD, aerosol forcing
• Due to model noise, a singledatapoint is
  unimportant
• Statistical tools have to beused to get useful
  informationfrom results

Optical thickness at one gridpoint near Finland
More complexity more noise more
averaging needed
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Model output averaging

• Selection of averaging dimension
• Time, latitude, longitude, vertical
• Global averaging (both latitude and longitude)
  decreases noise significantly
• Shows the effect on global climate
• Averaging over few (tens) of years makes it
  possible to investigate local changes
• Averaging dimension depends also on variable of
  interest
• Comparing AOD to satellite observation
• Studying effect on global 2-meter temperature

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Model output length of simulation

• When planning the duration of the model run,
  response time of different model components must
  be taken into account
• With an ocean model included, it might take a few
  decades for the temperature to reach a new stable
  state
• Response time of mixed-layer ocean model is much
  shorter due to lower mass of water

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Model tuning

• Why do climate models produce so good results?
• Partly because they are tuned to do so
• Climate system includes several variables whose
  values are poorly known
• For e.g. cloud-related variables (convective
  cloud systems)?
• Values can be taken from a hat, or used in
  tuning
• Usually modeled Top-of-Atmosphere radiation flux
  is matched to observed
• This makes the overall climate (temperatures
  etc.) look close to observed
• Almost all models are tuned with different
  variables and different tuning criteria

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What are global modelsgood for?

• Importance of individual processes in the Earth
  system
• add/remove/modify a single process
• e.g. role of new particle formation in climate
  system
• Predicting the future
• e.g. climate change in 100 years
• need to construct scenarios for
  emissions/conditions
• validity of parameterizations in new conditions?