1. Key considerations for simulating Arctic weather and climate with limited area models Nicole Mölders University of Alaska Fairbanks, Geophysical Institute and College of Natural Sciences, and Mathematics, Atmospheric Science Program

2. Various scales have to be bridged Step 1: Observe process at laboratory/field scale Step 2: Generalized constitutive laws Step 3: Conservation laws to derive partial differential equation Step 4: Predict system behavior at different scales than lab/field scale

3. Small scales imbedded in large scales stochastic quantity

4. Scales in atmosphere, soil, ocean differ E kin ≈ v 2 /2 ≈L p 2 /T p 2 Modified after Mölders 1999

5. Methods for scaling of processes at the atmosphere-surface interface Aggregation (upscaling) distribution to mean (lumping of info) Disaggregation (downscaling) mean to distribution!!!! (transfer to more detail) ?

6. atmospheric grid cell at the surface Schematic view of aggregation methods explicit subgrid scheme (e.g. Seth et al. 1994) GESIMA several km strategy of dominance e.g. MM5, WRF mosaic approach (e.g. Avissar & Pielke1989) RAMS, GESIMA,CCSM, most GCMs

7. Aggregation method may affect results From Mölders 2001 mosaic approach dominance strategy explicit subgrid

8. >0.0 0.01 0.03 0.05 0.07 0.09 Scales problematic in coupling Modified after Mölders et al. 1999 >0.0 0.01 0.03 0.05 0.07 0.09 No feedback (one-way)With feedback (two-way) 1mm/h=10 6 l/h=10 3 m 3 /h

9. river discharge runoff generation Hydrological approach lower saturated zone upper unsaturated zone soil moisture gravity lakes snow ET interception Research interest-specific modeling approaches gravity runoff generation ET interception Meteorological approach snow soil moisture 1 soil moisture 2 soil moisture n diffusivity runoff generation

10. Model inconsistency potential error source 0 1 2 3 From Mölders et al. 1994 Cloud properties differ in meteorological & chemistry part of EURAD Gas phase concentrations after cloud event differ for all species affected by cloud processes Consistency required Investigate uncertainty range resulting from parameterizations

11. Fulfill six important evaluation criteria for scientific credibility Comparison to known analytical solutions Determination of mass and energy budgets to determine conservations of these quantities Comparison of model results with those of other models (model inter-comparison) Comparison of model results to observations Publication of model description/parts/modules in peer- reviewed journals Code must be available on request

12. Sparse data, network design/density aggravate evaluation Historic Network Modified after PaiMazumder & Mölders 2007 100 sites network 400 sites network Arctic networks along haul ways Less 1 st class sites for precip than WMO recommends Is error within uncertainty range of observations? Right or wrong for what reasons?

13. Lateral boundary & initial conditions introduce errors

14. Skill scores, methods for identification of error sources Modified after Mölders 2007

15. External forcing may introduce errors Develop evaluation strategies Which errors are due to external forcing? Which errors are due to the limited area model? Modified after Brown & Mölders 2007

16. Key considerations Identify state-of-the-art and work/start from there Bridging of scales Couple where necessary, not everywhere you could Check whether one or two-way coupling is required Define data exchange (bottleneck in parallel processing!) Consistency within the model Evaluation and analysis strategies 6 criteria Identification of external error sources Identification of “imported errors” from driving model Determination/definition of “investigation area” Identify & reduce uncertainty Heuristic/indirect evaluations may be a chance …

17. Acknowledgements I thank The IARC scientists for inviting me M.E. Brown, D. Henderson, T. Fathauer, Z. Li, D. PaiMazumder, and G. Kramm for collaboration NSF for support under contract OPP0327664 You for your attention