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Global models

Global models. 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. 1-D models

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Global models

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  1. Global models

  2. 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

  3. 1-D models Representative of surrounding area Timestep: seconds Vertical levels: even 100 Timescale: usually days 3-D global models Grid box represents ~100 km x 100 km Timestep: >10 minutes Vertical levels: few tens Timescale: years to centuries Parameterizations

  4. Earth System (Model)‏ Circulation Aerosols Clouds Aerosol emissions Gaseous emissions Deposition Heat transfer Momentum flux Aerosol emissions Vegetation Land use Soil moisture Circulation Biogeochemistry Heat transport

  5. 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

  6. Earth System Model: black box modeling • ESM can easily have >200 000 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

  7. 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

  8. 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

  9. Increased primary sulfateActivation nucleationPrimary emissions

  10. 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

  11. SU SU SU BC BC BC OC OC SU OC SS SS DU DU DU DU BC OC 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

  12. Modularisationof a global aerosol model

  13. Fields Emissions Offline chemical fields: OH, H2O2, NO2, ozone Fossil-fuel, SO2 Online Water, aerosols, SO4 Dust, sea salt, DMS 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

  14. Vertical discretisation Sigma coordinates • Pressure/height coordinate is not a good choice for a vertical coordinate • Typically 20-30 hybrid levels are used • Choice of model vertical extent: • troposphere+lower stratosphere • +stratosphere + lower mesosphere • + mesosphere + lower thermosphere Sparse, flat pressure-levels at top of atmosphere Dense, terrain-following near surface Hybrid coordinates

  15. 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

  16. 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

  17. advected tracers … Computational demand:what 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)‏

  18. Computational demand:parameterizations 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

  19. 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)‏

  20. 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

  21. 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

  22. 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

  23. 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

  24. 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?

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