Atmospheric Modelling. Roland von Glasow University of East Anglia School of Environmental Sciences Norwich, UK

Transcription

1 Atmospheric Modelling Roland von Glasow University of East Anglia School of Environmental Sciences Norwich, UK

2 Outline 1. What do we mean with modelling? 2. How to make a model 3. Model examples: Dispersion model Weather forecast Global climate model Atmospheric chemistry model 4. Model evaluation

3 (1) What is a model?

4 Physical model e.g. wind tunnel model of flow in a city wind tunnel Univ. Hamburg

5 Conceptual model Description of a process by making simplifications, appropriate assumptions

6 Mathematical vs. numerical model Mathematical model: a conceptual model translated into mathematical equations [ CO] t = k[ CO][ OH ] Numerical model: a mathematical model implemented on a computer do i=1,n CO(i)=CO(i-1) dt*k*co(i-1)*oh(i-1) enddo

7 What is a model - summary Physical model e.g. wind tunnel model of flow in a city something you can touch Conceptual model describe a process by omitting unimportant details and surroundings, by simplifying it, by making appropriate assumptions a collection of ideas / a concept Mathematical model a conceptual model translated into mathematical equations a set of equations Numerical model a mathematical model implemented on a computer a computer model

8 Benefits of models Diagnostic tool: Detailed process studies Model data comparisons: test our current understanding of the processes involved Upscaling bigger picture Models provide 4D info which experiments can never do Budgets extractable (esp. chemistry, energy, H 2 O) Prognostic tools: Prediction (NWP, climate, ) What if.. scenarios/numerical experiments: switching off different processes future/past scenarios 2xCO 2, 4xCO 2, more/less aerosol,.. Time slice experiments to test internal variability of model/atmosphere

9 Limitations of models Not perfect Can only represent current knowledge it comes out, what you put in (however, new ideas/reactions/ speculations can be tested) Not all processes can be included (CPU time and space limitations) Resolution limitations (time, space) Approximations are necessary Meteorology never exactly correct Emissions never exactly correct Sometimes: good results for the wrong reasons Only as good as modeller (i.e. chemist/ physicist/meteorologist) Only as good as programmer also: no model is bug-free

10 (2) How to make a model

11 How to make a numerical model What is the question in mind? What is the impact of NO x emission controls on regional ozone? What is the response of temperature to a doubling of CO 2? What complexity is needed? Chemistry only or with meteorology, transport, clouds etc? Energy balance model or 3D model? What processes have to be included? Emissions: Industrial, biogenic, lightning? Transport / Dynamics: Convection? What turbulence scheme? Cloud microphysics / cloud macrophysics? What is the time resolution needed? How long do we need to run the simulation for, a day, a year, 100 years? What time step do we need to consider, 1 second, hourly, daily, yearly?

12 How to make a numerical model, cont/d What dimension is appropriate (0D 3D)? Do we need to consider space in all 3 dimensions? Can we just consider latitude v altitude, for example? May be we only need to consider what is happening at a single point? Graedel and Crutzen, 1995

13 How to make a numerical model, cont/d What is the spatial resolution needed? What grid resolution is required? 5 x5 latitude and longitude, or 1x1 km? Vertical resolution? TRACE

14 Step 1: Reality

15 Step 2: Conceptual model Which processes need to be included for question in mind? Graedel and Crutzen, 1997

16 Step 3: Single process Example: Chemical reaction CO + OH CO 2 +H carbon monoxide hydroxyl radical carbon dioxide hydrogen radical

17 Step 4 6: Translate single process to computer code Single process: CO + OH CO 2 + H Prognostic equation: [ CO] t = k[ CO][ OH ] Discretised equation: CO(t+ t) = CO(t) t *k*co(t)*oh(t) Computer code: do i=1,n CO(i)=CO(i-1) dt*k*co(i-1)*oh(i-1) enddo

18 (3) Model examples

19 (3.1) Dispersion model

20 Most simple model: Gaussian plume Key assumption: Dispersion follows Gaussian distribution Colls, 2002

21 Trajectory models Gaussian plume models limited to ~50km from source what to do then? Basic idea: use meteorological fields to drive Lagrangian particle transport As good/bad as input data Limitations: how long does one air parcel remain one parcel? vertical transport (convection) often not well reproduced Very successful in last ~10 years: Retro plume models: cloud of particles that are followed individually, e.g. FLEXPART

22 HYSPLIT (NOAA)

23 (3.2) Numerical weather forecast Operational: great time and success pressure Initial and boundary conditions crucial! Initial conditions: state of system/atmosphere at model start each parameter has to be defined in each grid cell problem: data often not available Boundary conditions: surface temperature and moisture ground albedo snow/ice cover, conditions at model top (and sides if not global 3D) Data assimilation: Talagrand (1997): using all the available information, to determine as accurately as possible the state of the atmospheric (or oceanic) flow filter the model-relevant information from observed data interpolate observations onto model grid (space and time) translate observations to model variables, e.g. satellite data

24 Input data for NWP land stations, ships polar orbiting satellites buoys geostationary satellites But: Global model GME of DWD needs 2.6x10 7 initial data points, about 10 4 data points are available from observations DWD, , 10:30 13:29 UTC aircraft

25 Analysis in NWP DWD: first start summer 1989 observation error atmosphere first guess analysis analysis forecast DWD

26 (3.3) Climate models Goal: gain information about past, current and future climate on a global scale History of climate models 0D energy balance models Sun Earth

27 (3.3) Climate models Goal: gain information about past, current and future climate on a global scale History of climate models 0D energy balance models 1D energy balance models 1D radiative convective models 2D models 3D general circulation models

28 Processes included in a GCM McGuffie & Henderson-Sellers, 1997

29 IPCC, 2001

30 Brasseur et al., 1999 Mixing time scales

31 Weather forecast vs. climate fore(hind)cast Climate GCM s started as pure AGCMs in 1960s as spinoff from NWP Only atmosphere is NOT enough development of more detailed models Today still strong interaction NWP climate modeling, but different foci: climate models: stronger physical consistency needed on > week timescales spatial resolution moderate (esp. for long timescales) boundary value problem NWP: no need to look at timescales longer than 2 weeks high spatial resolution needed initial value problem Main differences between different GCMs: physical parameterizations, sub models (land, ocean, etc), (resolution)

32 (3.4) Atmospheric chemistry models Examples of addressed problems: Oxidation capacity / self cleansing capacity Air quality - health Aerosol particle transformations Acidity of precipitation, other deposition effects Stratospheric ozone: UV shield Chemical weather forecast support for field campaigns

33 Special considerations Transport is essential! Mass conservation is key! Emissions and deposition Rate coefficients for chemical reactions: depend on T, p, rh atmospheric chemistry is photochemistry: photolysis rates are function of T, p, O 3, H 2 O, CO 2, clouds, aerosol,.. in column aerosol and cloud processes are important (uptake, wash-out, reactions within particle, reactions on particle surface) Often very large chemical mechanisms that need special considerations: lump species/reactions combine similar compounds, reactions merge fast reaction cycles into one reaction

34 (4) Model evaluation

35 Comparison - verification Verification establishment of truth models are not closed, something is always missing (underdetermined) But use in NWP: Are forecasts correct? Validation no logic errors internally consistent no coding errors same results on different computers agree with analytic solutions But use in NWP: Why are forecasts not correct? Evaluation comparison with field data: variances explained, etc Some models are being used for policy decisions, so they should be valid and evaluated but don t claim them to be verified

36 Evaluation What data is available, how reliable is it? What model output to look at? Maybe only data at a few observational sites, or along tracks of observational platforms (e.g. aircraft, ship, zeppelin)? Only vertical columns for comparison with some satellite data? Maybe we only need annual averages? Models produce huge amounts of data - challenging! Key question: Does our current knowledge as implemented in the model lead to good results when compared with field data? or: Do we understand what is happening in the atmosphere?

37 Evaluation cont/d Exact 1-to-1 agreement cannot be expected, so model performance as a whole might be better than it appears based on a single point etc (keep in mind what the model was designed for) Model vs. satellite (or other indirect ) data: comparison of 2 models: radiative transfer model for satellite retrieval vs. stand-alone radiation model Interesting science starts when model and field data DON'T agree: What is missing/wrong in model/our understanding of the processes? How good is (field-)data? How representative is (field-)data? Are we comparing the same parameters?? A not-so-useful approach: A good model - fits my data a bad model - doesn't fit my data

38 Summary Conceptual model mathematical model numerical model Model has to be the right tool for the question in mind Importance of initial and boundary conditions: weather forecast: initial conditions climate: boundary conditions Model evaluation key step in model development often new understanding about the atmosphere is being developed

39 If you want to know more.. M. Z. Jacobson, Fundamentals of Atmospheric Modeling, Cambridge, 2005, atmospheric modeling with focus on aerosols and chemistry very good, al the nifty equations and params., main drawback: "turbulence = K-theory" J. H. Seinfeld and S. N. Pandis, Atmospheric Chemistry and Physics, Wiley, 2005, they touch all the major topics good for people with decent maths/physics background G. P. Brasseur, J. J. Orlando and G. S. Tyndall (eds), Atmospheric Chemistry and Global Change, Oxford University Press, 1999, very good overview but: out of print, check your library B. J. Finlayson-Pitts und J.N. Pitts, Chemistry of the Upper and Lower Atmosphere, Academic Press, San Diego, 2000 atmospheric chemistry from the viewpoint of laboratory chemists K. McGuffie and A. Henderson-Sellers, A climate modelling primer, Wiley, 1996 intro to climate modeling from energy-balance models to Earth system models R. A. Pielke, Mesoscale Meteorological Modeling, Academic Press, 2002 regional modeling many equations derived, formal E. Kalnay, Atmospheric Modeling, Data Assimilation and Predictability numerical weather prediction all the basic concepts somewhat formal Trenberth, K. E. (ed), Climate System Modeling very detailed description of single processes and techniques Randall, D. A. (ed), General Circulation Model Development: Past, Present, and Future history of climate modeling, US centric T. Warner, Numerical weather and climate prediction, Cambridge University Press, 2011 covers the field in great (mathematical) detail