1. Downscaling Methodology Dr Jack KatzfeyDecember 2012 CLIMATE ADAPTATION FLAGSHIP

2. Process of understanding: Observations What time and space scales are needed? Understanding Explaining the process of what we see Modelling What complexity is needed for what scale? Prediction Forecasts versus projections Adaptation What will help reduce affect of climate change?

3. 1 km climate surfaces 1 km climate surfaces SPATIAL SCALES – WORLDCLIM example GCM 200-300 km Scaled ✗ ✗ (‘Delta method’*) Pattern scaling ✔ ✗ (Marksim Weather Generator) Statistical downscaling ✔ ✗ Dynamical downscaling ✔ ✗ (PRECIS) Global- Regional Regional- Site *The method makes the following gross assumptions: 1. Changes in climates vary only over large distances (i.e. as large as GCM side cell size)…..

4. Downscaling aims To provide more detailed (and hopefully more accurate) information on current and future regional climate through higher resolution simulations with better resolved physical processes and surface inputs. The more detailed orography and land use information input to the regional model provides more information than coarser resolution analyses and more spatially consistent information than gridded observations. In addition, the finer resolution of the regional model should more realistically represent atmospheric phenomena and dynamics.

5. Downscaling aims To simulate the current climate accurately. Future climate change will then be superimposed on a more realistic current climate, which in turn should give more confidence in climate change projections. To produce a range of downscaled climate change signals in order to capture the range of possible future climate projections, consistent with the range seen in the large-scale GCMs on which the RCMs are based. This is the approach taken in the IPCC projections of climate change, where it is acknowledged that there are biases and uncertainty in model projections, so that the consideration of the output of as many models as feasible, in what is known as ensemble predictions, is the most reasonable way to deal with uncertainty.

6. What is Regional Climate Modelling? RCMs are based around three main components: Nudging regional atmospheric behaviour at ‘boundaries’ towards a host GCM Modelling dynamical and physical processes at regional scales Inclusion of surface forcings, including orographic and coastal effects Regional Climate Model Surface forcings Global climate (GCM host) Nudging/forcing/LBC Land-surface schemes/ specification Dynamical and physical parameterisations Domain/Resolution Regional Climate Modelling

7. Motivation for bias correction GCM (~200 km) RCM (~60km) RCM (~8 km) Bias correction

8. What is Regional Climate Modelling? RCMs are based around three main components: Nudging regional atmospheric behaviour at ‘boundaries’ towards a host GCM Modelling dynamical and physical processes at regional scales Inclusion of surface forcings, including orographic and coastal effects Regional Climate Model Surface forcings Global climate (GCM host) Nudging/forcing/LBC Land-surface schemes/ specification Dynamical and physical parameterisations Domain/Resolution Regional Climate Modelling

9. What is Regional Climate Modelling? RCMs are based around three main components: Nudging regional atmospheric behaviour at ‘boundaries’ towards a host GCM Modelling dynamical and physical processes at regional scales Inclusion of surface forcings, including orographic and coastal effects Regional Climate Model Surface forcings Global climate (GCM host) Nudging/forcing/LBC Land-surface schemes/ specification Dynamical and physical parameterisations Domain/Resolution Regional Climate Modelling

10. Regional Climate Modelling Approaches Also need to consider: Domain size Resolution Two-way interaction Internal variability Limited area Lateral boundary influence Computational expense None High Low High Variable resolution Global high-resolution Regional Climate Modelling

11. Comparison of GCM, LAM (ACCESS RCM) and SGRCM (CCAM) grids Scale- selective filter (frequency domain) Interpolated lateral boundaries Global Stretched Grid Model (SGM) Limited Area Model (LAM) One-way nesting GCM

12. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Processes in Climate Models Mike Manton APN Symposium 2004

13. Limited Area Models - BC Large amounts of data are needed in order to specify the vertical profile of data at all lateral boundary points frequently enough (typically 6 hourly) in order to capture the atmospheric flow realistically, including possible diurnal effects. There may be undesirable effects in the LAM when the host data is interpolated to the finer LAM grid. Potentially, large gradients can occur in the boundary region as the internal model (LAM) drifts to a different climate then the host model (GCM). Differing resolution, physics and dynamics can lead to differences in evolution of weather systems within the LAM, so great care must be used in formulating the lateral boundary scheme.

14. Limited Area Models - BC The location of lateral boundaries (i.e., they should ideally not be in regions of significant orography) The amount of change in resolution between host and LAM (one does not want to introduce damping or inconsistent data when the host model data needs to be interpolated in space and in time) How much two-way interaction is allowed (i.e., interaction of the regional simulation with the larger scale), to ensure that larger scale variability from phenomena such as El Niño-Southern Oscillation (ENSO) is transferred into the domain.

15. Strengths of LAMs: There is less computational cost than for SGM, since you only compute for the area of interest. Only regional surface input datasets are required. The model can be more optimally configured for the model’s given resolution, which is not possible if there are a variety of resolutions, as for stretched-grid RCMs. Larger user communities for technical support.

16. Weaknesses of LAMs: Treatment of the lateral boundary conditions is difficult. There are problems of passing information from the host model through the lateral boundaries and potential incompatibilities of internal systems passing into the boundary zone. There is potential incompatibility of the regional model with the boundary data due to different scales. A large amount of atmospheric data is required to provide sufficient information at the lateral boundaries to drive the model. Internal model climatology may be different than boundary data, leading to boundary problems.

17. Strengths of Stretched-Grid RCMs: There are no lateral boundaries. There is a potential to correct some of the biases in the input data through bias correction techniques. There is a potential for two-way interaction between the high resolution area and outside regions. (Teleconnections?) Potentially less data is required to run than for a LAM if only surface input data from host model is required. (Unless large-scale nudging is used, than more data is required than for RCMs)

18. Weaknesses of Stretched Grid RCMs The model needs to be configured to run correctly at a range of horizontal resolutions. Global surface datasets are required to run the model. Global datasets of other atmospheric variables are needed if atmospheric nudging is required. Because they are global, these datasets will be larger than the regional ones required for LAMs. Greater need for conservation of atmospheric properties is required. Potential weakness is if run with only lower boundary condition, model may generate different atmospheric response – interpretation?

19. Regional Climate Modelling overview A RCM is not simply a ‘long’ NWP run `Boundary’ conditions are particularly important for determining how well a regional model captures all drivers that influence a region’s climate (including all forcings from the host GCM) A RCM will tend to spin-up its own local climate behaviour based on reconciling the atmospheric and surface forcings in a way that is consistent with the model’s dynamical and physical formulation (but what about model errors?) Seamless prediction? (forecast errors=climate errors) Regional Climate Modelling

20. Multiple downscaling to higher resolution Simulated annual rainfall for Tasmania at different resolutions Global Model CCAM 60 kmCCAM 14 km Bias-correc. Spectral forcing Increased resolution Climate Futures for Tasmania project

21. Regional Climate Modelling Ensembles 21 Climate Futures for Tasmania project Change in annual rainfall 1961:1990 to 2070:2099 14 km results Although mean changes, pattern fairly consistent

22. Validation of DDS (CCiP, 2011) When forced by GCM (via CCAM), slight degradation

23. Annual rainfall change (mm/d) Figure 7.17: Change in projected annual rainfall (mm/day) from additional downscaling simulations for period 2055 relative to 1990, for the A2 scenario. Note that the changes for the Zetac model are for the Jan- Feb-Mar period only. The host global climate model was the GFDL2.1 model. (CCiP, 2011)

24. Regional Climate Modelling Future directions – Urban modeling From H. Schluenzen, Univ. of Hamburg Urban model within RCM

25. The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology Methodology – urban climate The aTEB scheme includes the following additional features: Alternative in-canyon aerodynamical resistance scheme including recirculating and venting regions (based Harman et al 2004) Two canyon walls rotated through 180 o instead of a single TEB wall rotated through 360 o Modified in-canyon reflections for radiation to account for the extra wall Big-leaf model for in-canyon vegetation (similar to Sang-Hyun and Soon-Ung 2008, but using Kowalczyk et al 1994) Simple AC heat flux into canyon (see also Ohashi et al, 2007) AC traffic Schematic representation of the aerodynamic resistances. Note snow and water have been omitted in this diagram roof wall road

26. CCAM intialised with NCEP fnl analysis at 00 UTC 5 January 2003 Used aTEB (Thatcher and Hurley, 2012) with default Melbourne urban settings (Coutts et al, 2007) Forecasts for 3 days at: 60 km over all of Australia 8 km over Victoria with spectral forcing from 60 km forecast 1 km over Melbourne with spectral forcing from 8 km forecast Two experiments using CABLE land-surface scheme: One without urban scheme (nu) One with Urban tiles (u) Simple forecast experiment of impact of urban model of forecast due to resolution

27. T2m (°C) time series in city (Day 2-3) Note greater affect or urban at higher resolution Melb. R.O.: Tmax: 34.9 Tmin: 16.6 Tmax: 40.2 60 km uf=.25 8 km uf=.97 1 km uf=1 Black with urban Red w/o urban Difference U-NU 18 42 2 6

28. Nocturnal temperature diff. due to urban Left: T2m (urban)- T2m(non-urban) avg. 11pm-5am (12-18 UTC) Right: Urban fraction Top: 60km, Middle: 8 km and Bottom: 1 km forecasts

29. Winds(10m) 12pm-6am Winds (scale 10m/s) Wind differences (u-nu) (scale 2m/s) Shading surface height (m)  Note land breese and affect of urban on winds

30. Regional Climate Modelling Future Work Linking to observed trends Relationship to statistical downscaling Linking into Climate Futures tools GCM selections (regional SST changes?) More analysis of storm tracks and teleconnections New version of CCAM and ACCESS RCM Coastal Effects Hydrology, waves, salinity Development of a Regional Earth System Model Air quality Renewables Integrated assessment

31. Summary The effect of resolution on the urban environment Partly due to urban fraction at the coarse resolution? Note in regional climate mode, we cannot afford to run at very high- resolutions for long simulations CCAM capability of forecasts (as shown here) CCAM capability as a regional climate model (as shown by Marcus Thatcher) Ensembles Bias-correction Many experiments can be run… Cost effective – Not that expensive!

32. Adding Value Through Downscaling Finer resolution More realistic surface forcing Higher resolution surface land-use and orography Multiply downscaling to finer resolutions Reducing GCM errors Bias-adjusted SSTs Ensembles Downscale multiple GCMs Multiple RCMs Multiple parameterizations/parameter ensemble? Regional Climate Modelling

33. Why use regional climate models? Can provide a spatially and temporally consistent dataset Can provide finer resolution datasets Can provide physically-based effects caused by local forcings (especially orography) Small, statistically based corrections can be applied if needed Caution: Technique can influence results Needs to be validated

34. Regional Climate Modelling Applications Current climate Potential to assess climate in regions without observations (higher resolution than traditional ‘reanalyses’ –But not site specific (like wind farm siting) Assessment of ability of models to simulate current climate

35. Regional Climate Modelling Downscaling from Reanalyses Wind parameters derived from REMO data are in agreement with observations, and on average, they describe the wind magnitude slightly better than the NCEP/NCAR re-analysis data. Larsén et al., Wind Energy 2010; 13:279–296

36. Multi-year forecasts or downscaling from analyses: Provides statistics of winds, even when no observations are available

37. Wind farm research CCAM is being used to study the aggregate behaviour of multiple wind farms, including: Modelling the wind climatology Modelling the variance spectra Modelling spatial correlations between sites The results are then used to anticipate the impact of wind farms on the electricity market (i.e., highly correlated output between wind farms can lead to network instability) Results from WERU Regional Climate Modelling

38. Applications Future climate projections

39. Cascade of uncertainty? Regional Climate Modelling Need for ensembles (modified after Jones, 2000, and "cascading pyramid of uncertainties" in Schneider, 1983)

40. Cascade of uncertainty? Uncertainty may not increase Regional Climate Modelling Increased resolution Additional surface forcing Bias-correction (modified after Jones, 2000, and "cascading pyramid of uncertainties" in Schneider, 1983)

41. Regional Climate Modelling Climate change vs model uncertainty Future climate change signal composed of: Climate change sensitivity/signal Model errors (especially current climate) Inaccurate responses of models to climate change forcing Increasing confidence of regional climate change projections Higher resolution More realistic surface forcing Possibly reduced errors in current climate Ensembles

42. Regional Climate Modelling Large-scale SST bias-correction In addition to fixing biases, allows simulation to have more realistic weather systems and how they may change in response to climate change Surface temperature average 115 E to 155 E, 40 S to 10 S 3 year running average Model uncertainty/error (2.3°C) Model uncertainty plus change (3.7°C) Spread of change signal (1.4°C) Example of SST bias in a GCM Same mean

43. Regional Climate Modelling Bias adjustment of sea surface temperatures Sea surface temperatures is main influence on climate (ENSO, climate change) Dommenget, Dietmar, 2009: The Ocean’s Role in Continental Climate Variability and Change. J. Climate, 22, 4939–4952 Can improve the representation of the current climate by fixing some of the biases Ensemble using only one downscale model does not decrease spread of climate change signal 43

44. Modes of running CCAM Numerical Weather Prediction Seasonal Prediction Regional Climate Prediction

45. Numerical Weather Prediction Model set-up as usual, but potentially at higher resolution Multiple downscaling since runs are for shorter time scales (1 day to couple weeks) Initial condition very important Atmospheric fields Surface fields, such as soil moisture and temperatures Time varying ocean temperatures and GH gases not as important

46. Seasonal Prediction Model set-up as usual, but potentially at `modest’ resolution Multiple downscaling since runs are for shorter time scales (from months to 1 year?) Initial condition important (but maybe less so than for NWP) Atmospheric fields Surface fields, such as soil moisture and temperatures Time varying ocean temperatures are very important But GH gases may be fixed

47. Regional Climate Prediction Model set-up as usual, but potentially at `lowest’ resolution Multiple downscaling (but costly) Initial condition not as important Atmospheric fields Surface fields, such as soil moisture and temperatures Time varying ocean temperatures are very important GH gases and aerosols need to vary over time