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Regional modelling with CCAM

Regional modelling with CCAM. John McGregor Centre for Australian Weather and Climate Research CSIRO/BOM, Melbourne Ausaid Workshop Aspendale 18 May 2009 With contributions from Kim Nguyen, Jack Katzfey, Marcus Thatcher. Description of regional climate modelling and CCAM

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Regional modelling with CCAM

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  1. Regional modelling with CCAM John McGregor Centre for Australian Weather and Climate Research CSIRO/BOM, Melbourne Ausaid Workshop Aspendale 18 May 2009 With contributions from Kim Nguyen, Jack Katzfey, Marcus Thatcher

  2. Description of regional climate modelling and CCAM 60 km simulations over Australia 60 km present-day simulations over Asia 60 km simulations over the Indonesian region 8 km simulations of present-day and future climate over Fiji Outline

  3. CCAM is formulated on the conformal-cubic grid Orthogonal Isotropic The conformal-cubic atmospheric model Example of quasi-uniform C48 grid with resolution about 210 km

  4. atmospheric GCM with variable resolution 18 atmospheric layers semi-Lagrangian, semi-implicit – allows long time steps TVD vertical advection reversible staggering - good behaviour of atmospheric waves conservation of mass and moisture CCAM dynamics CCAM physics • cumulus convection - CSIRO mass-flux scheme, including downdrafts - up to 3 simultaneous plumes permitted • includes liquid and ice cloud-water - used to derive cloud distributions • stability-dependent boundary layer with non-local vertical mixing • vegetation/canopy scheme - 6 layers for soil temperatures - 6 layers for soil moisture • GFDL parameterization for long and short wave radiation

  5. Location of variables in grid cells All variables are located at the centres of quadrilateral grid cells. However, during semi-implicit/gravity-wave calculations, u and v are transformed reversibly to the indicated C-grid locations.

  6. MPI implementation Remapped region 0 Original Remapping of off-processor neighbour indices to buffer region Indirect addressing is used extensively in CCAM - simplifies coding Preferred number of processors: 1, 2, 3, 4, 6, 12, 16, 18, 24, …

  7. CCAM present-day simulation with quasi-uniform 210 km grid - using specified monthly sea-surface-temperatures (SSTs) Observed 1979-95 JJA rainfall Observed 1979-95 DJF rainfall CCAM 1979-95 JJA rainfall CCAM 1979-95 DJF rainfall

  8. Conformal-cubic C48 grid used for Australian simulations, Schmidt = 0.3 Resolution over Australia is about 60 km

  9. Limited-area models using 1-way nesting at their boundaries Variable-resolution global models avoid any boundary mismatch problems For moderate stretching can run as stand-alone (just using SSTs & sea-ice) For strong stretching can nudge with selected “host model” fields, e.g. winds above 500 hPa but with correction of present-day monthly SST biases of host GCM Dynamical downscaling techniques

  10. Global nudging Far-field nudging Nudging of far-field winds above a certain level, with e-folding time ~24 h Nudging of selected fields above a certain level, with e-folding time 24-48 h No nudging (only SSTs) Partial nudging Model uses observed or GCM winds only outside the inner red boundary Model uses observed or GCM upper winds (and optionally other fields) at all points

  11. Uses a sequence of 1D passes over all panels to efficiently evaluate broad-scale digitally-filtered host-model fields (typically above 900 hPa or above 500 hPa). These periodically (6-hourly or 12-hourly) replace the corresponding broad-scale CCAM fields. Gaussian filter typically uses a length-scale approximately the width of finest panel Suitable for both NWP and regional climate. Using to downscale from the range of AR4 GCMs (once-daily information is OK for 60 km simulation). New digital-filter downscaling method

  12. SST bias-correction • Unfortunately, coupled GCMs possess SST biases • A significant common bias is the equatorial “cold tongue” • Nudging from Mk 3 atmospheric fields is no longer employed • - provides better self-consistency of CCAM runs

  13. Observed CCAM Mk3 DJF JJA Present-day rainfall for DJF and JJA From a recent 60 km simulation downscaling 1961-2100from CSIRO Mk3 GCM, using digital filter for MSL and winds above 500 hPa

  14. Present-day rainfall for MAM and SON Observed CCAM Mk3 MAM SON

  15. Methodology • Recent SEACi run downscales from bias-corrected sea surface temperatures (SSTs) of the host Mk 3.5 GCM (A2 scenario) to 200 km • The 200 km runs are then downscaled to 20 km applying a digital filter every 6 h above 900 hPa to preserve features having approx. length-scale of central panel • Revised methodology provides improved self-consistency of runs Quasi-uniform C48 CCAM grid with resolution about 200 km. Grid is orthogonal and isotropic Stretched C48 grid with resolution about 20 km over eastern Australia

  16. Present-day rainfall from 20 km simulation downscaling 1961-2000 Produces good present-day rainfall with generally small biases. Also good max/min temperatures. Obs 20 km 20 km biases

  17. Rainfall trends 1961-2100 mm/day • DJF MAM JJA SON ANN Mk 3.5 Produces similar broadscale patterns of changes between 200 km and 20 km runs Also gives broadly similar changes to Mk 3.5, but less so in tropics in DJF All runs show drying over MDB in most seasons 200 km 20 km

  18. Rainfall trends 1961-2030 mm/day • DJF MAM JJA SON ANN Mk 3.5 All runs show drying over MDB for most seasons More differences from Mk 3.5 than for 1961-2100 Many of changes occur by 2030 200 km 20 km

  19. DJF MAM JJA SON ANN 2070 Tmax 2070 Tmin Simulated changes in daily maximum and minimum temperatures compared to 1980

  20. Pan evaporation changes in 2050 in mm/day compared to1980 DJF MAM JJA SON ANN 2050 The larger increases in DJF and SON are consistent with a reduction of relative humidity and rainfall and an increase of solar radiation, air temperature and diurnal temperature range Net evaporation changes in mb/day compared to1980 In SON; the regions with reduced evaporation are those where there has been reduced rainfall, and hence there is less available soil water to evaporate.

  21. DJF MAM JJA SON ANN 2050 Net evaporation changes in 2050 in mm/day compared to1980 In SON (especially) the regions with reduced evaporation are those where there has been reduced rainfall, and hence there is less available soil water to evaporate.

  22. Conformal-cubic C63 60 km Asia simulations, Schmidt factor = 0.37 • C63 global grid (6 x 63 x 63 grid points) • Performed for Regional Model Intercomparison Project • Schmidt stretching factor of 0.37 was used • 10 years (1989-1998) downscaled from NCEP reanalysis • Global nudging of winds above 500 hPa with an e-folding time of 24 h

  23. JJA rainfall (mm/day) from CMAP/CRU (observed) and CCAM

  24. JJA maximum temperatures (oC) from IPCC and CCAM • - captures well the maximum temperatures over east and southeast Asia • cold bias ( 2 oC to 4 oC ) over south India • warm bias to the north of the Plateau

  25. JJA minimum temperatures (oC) from IPCC and CCAM - good agreement, with bias +2oC

  26. 5-day area-mean rainfall (mm/day) over southeast Asia (105-130E, 20-30N) • Black= CCAM;blue = CMAP • captures well the distribution • - slightly under-estimates the rainfall at monsoon onset. • Pentads: • 19-24: Apr, 25-30: May, 31-36: Jun, 37-42: Jul, 43-48: Aug

  27. 5-day area-mean rainfall (mm/day) over Japan (130-140E, 32-37N) • captures rainfall pattern well • under-estimates the amounts • Pentads: • 19-24: Apr, 25-30: May, 31-36: Jun, 37-42: Jul, 43-48: Aug

  28. 5-day area-mean rainfall (mm/day) over South China Sea (110-120E, 10-17N). • captures rainfall pattern well • slightly over-estimates amounts for Apr • under-estimates amounts for May-Jul. • Pentads: • 19-24: Apr, 25-30: May, 31-36: Jun, 37-42: Jul, 43-48: Aug

  29. 5-day area-mean rainfall (mm/day) over India (75-80E, 16-26N). • captures rainfall pattern well • Pentads: • 19-24: Apr, 25-30: May, 31-36: Jun, 37-42: Jul, 43-48: Aug

  30. 70-85E Monthly patterns of rainfall over India and Bay of Bengal 86-95E

  31. Monthly patterns of rainfall over South China Sea and Western Pacific 110-120E 121-140E

  32. Methodology for Indonesian simulations Coarse grid of a coupled atmosphere-ocean GCM.Mk3 model has 200 km grid and 18 levelsCan be dynamically downscaled to finer grids Quasi-uniform C48 CCAM grid with resolution about 200 km. Grid is orthogonal and isotropic Stretched C48 grid with resolution about 60 km over Indonesia

  33. Known that fine resolution is needed to simulate good rainfall patterns over the maritime continent 6 very long simulations driven by 6 different IPCC AR4 coupled GCMs: Mk3.5, GFDL2.0, GFDL2.1, ECHAM5, HadCM3, MIROC-Med - from 1971-2000, 2041-2060, 2081-2100 for the A2 emission scenario Uses monthly bias-corrected SSTs from the 6 GCMs Proceeding via 200 km quasi-uniform CCAM simulations Final grid resolution is about 60 km preserving largest-scale fields by using digital filter Methodology provides self-consistency of runs CCAM 60 km simulations centred on Indonesia Stretched C48 grid with resolution about 60 km over Indonesia

  34. Simulations of present-day rainfall (mm/day) for DJF and MAM Acceptable present-day rainfall and temperatures are produced by all the simulations and for all seasons (DJF, MAM, JJA, SON) Obs 60 km CCAM (Mk 3.5) The finer resolution 60 km runs produce improvements over the 200 km runs, as anticipated 200 km CCAM with SSTs from Mk 3.5 200 km CCAM with SSTs from GFDL 2.1

  35. Simulations of present-day rainfall (mm/day) for JJA and SON Obs 60 km CCAM (Mk 3.5) 200 km CCAM with SSTs from Mk 3.5 200 km CCAM with SSTs from GFDL 2.1

  36. DJF maximum and minimum temperatures

  37. JJA maximum and minimum temperatures

  38. Rainfall changes (mm/day) to 2090 downscaling from GFDL 2.1 for DJF, JJA, ANN 200 km run 60 km run DJF JJA Fairly similar changes for 200 km and 60 km runs ANN

  39. GFDL2.1 ECHAM HADCM Rainfall changes to 2050 from 60 km runs DJF MAM • Preliminary analysis shows interesting differences between the simulations, but some similarities: • seem to become wetter on average over Sumatra and Borneo • tendency to become drier over Java JJA SON ANN

  40. GFDL2.1 ECHAM HADCM Changes to daily maximum temperatures 2050 from 60 km runs DJF MAM Increasing over land by 0.5 to 1.5 degrees. By 2100 some increases over 3 degrees. JJA SON ANN

  41. GFDL2.1 ECHAM HADCM Changes to daily minimum temperatures 2050 from 60 km runs DJF MAM Increasing over land by 1 to 2 degrees (about 0.5 degrees more than daily maximum). By 2100 some increases over 3 degrees. JJA SON ANN

  42. Many diagnostic fields are available Saved 6-hourly Examples include pan evaporation and net evaporation

  43. GFDL2.1 ECHAM HADCM Changes to pan evaporation in mm/day by 2050 from 60 km runs DJF MAM Increases by up to 1 mm/day over land JJA SON ANN

  44. GFDL2.1 ECHAM HADCM Changes to net evaporation in mm/day by 2050 from 60 km runs DJF MAM Mostly small increases over land JJA SON ANN

  45. 8 km simulation over Fijidownscaling NCEP reanalyses for 10 years C48 grid Model orography For these strongly-stretched simulations, “global nudging” from the broad-scale fields was used

  46. Fiji rainfall Downscaled to 8 km from NCEP reanalysis, 1975-1984 January February March April August July June May November December September October

  47. Observed MRI GFDL 2.1 UK - GEM GFDL 2.0 UK – CM3 Pacific MSL pressure patterns for DJF from some IPCC AR4 models- note deficiencies in their trade winds

  48. Simulated present-day Jan and July for Fiji- downscaled from Mk3 via 200 km C48 CCAM (from NCEP simulation – note colours reversed)

  49. Simulated future Jan and July rain in 2050- downscaled from Mk3 via 200 km C48 CCAM Winds become more northerly – less rain on east coast Winds become stronger – more rain on east (and west) coast

  50. Simulated rainfall change (2050-present) Jan and July for Fiji - downscaled from Mk3

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