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

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

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

7. 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 the consequences their code has on the overall model performance Aerosol module

8. 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

9. Transport of gases Aerosol microphysics Transport of aerosols SO x, NO x Organic aerosol chemistry Direct effect Indirect effect Inorganic aerosol chemistry Development of global aerosol models

10. Increased primary sulfate Activation nucleation Primary emissions

13. 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

14. 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.)‏ INSOLUBLE NUCLEATION SOLUBLE AITKEN ACCUMULATION COARSE SU = sulfate BC = black carbon OC = organic carbon SS = sea salt DU = mineral dust SU BC OC SS DU

15. Modularisation of a global aerosol model

16. Emissions and fields Dust, sea salt, DMS Water, aerosols, SO 4 Online Fossil-fuel, SO 2 chemical fields: OH, H 2 O 2, NO 2, ozone Offline EmissionsFields Dust Black carbon 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 Examples of online/offline variables in a global model

17. Vertical discretisation Sigma coordinates Hybrid 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 Dense, terrain- following near surface Sparse, flat pressure-levels at top of atmosphere

18. 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

19. 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 (10 5 / timestep / gridbox) ~ 10 15 operations per simulation year

20. Computational demand: what is being calculated? Atmospheric circulation is calculated with primitive equations: … advected tracers Model dynamics: advection, Coriolis force Physical processes: all subgrid-scale non- adiabatic effects (friction, turbulence, phase change of water)‏

21. Computational demand: parameterizations and look-up tables 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

22. 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)‏

23. 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 single datapoint is unimportant Statistical tools have to be used to get useful information from results More complexity more noise more averaging needed Optical thickness at one gridpoint near Finland

24. 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

25. 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

26. 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

27. What are global models good 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?