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Simulations of the atmospheric circulation on a water-covered Earth

Peter Gleckler ( PCMDI) Brian Hoskins ( Reading) Richard Neale ( CDC, now NCAR) APE Modelling Groups. Mike Blackburn University of Reading. David Williamson NCAR. Simulations of the atmospheric circulation on a water-covered Earth.

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Simulations of the atmospheric circulation on a water-covered Earth

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  1. Peter Gleckler (PCMDI) Brian Hoskins (Reading) Richard Neale (CDC, now NCAR) APE Modelling Groups Mike Blackburn University of Reading David Williamson NCAR Simulations of the atmospheric circulation on a water-covered Earth - Space & Atmospheric Physics, Imperial College 28 February 2006 - Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  2. Outline “Compare idealised climates simulated by global atmospheric circulation models (AGCMs) being developed and used for numerical weather prediction and climate research.” “Provide a benchmark of current model behaviour and stimulate research to understand the causes of inter-model differences.” • Motivation and context • Experimental design + participants • Aspects of climate – mean and variability • Conclusions + next steps Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  3. Climate changes over the next few decades are predicted to be much larger than we have seen so far… Uncertainty in climate predictions- IPCC TAR (2001) - IPCC (2001)

  4. 1D / 2D idealised flows Dynamical core Idealised moist core Aqua-planet AMIP • Dry dynamics • - linear relaxation to climatology • - Rayleigh friction boundary layer • Unrealistic sensitivities • Full complexity GCM • - unique dynamics • - unique moist parameterizations • Difficult to isolate reasons for differences • Aqua-planet idealises the planet, not the model! • Aim for • - single idealised moist parameterization • - minimal complexity to represent processes • Use in all dynamical cores • Sensitivity of a moist atmosphere to dynamical formulation Evaluation of Atmospheric GCMs- an experimental hierarchy - Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  5. Dynamical Core behaviour dynamical core moist GCM • Moist processes are replaced by linear temperature relaxation + drag • Sensitivities to numerical options different from the complete GCM • Highlights moist feedbacks in climate zonal mean Temperature semi-Lagrangian versus Eulerian advection Chen & Bates (1996); Chen et al (1997) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  6. Historical aside …. Berlin Academy competition (1746): To determine “the order and the law which winds would have to observe if the Earth were surrounded everywhere by an ocean, so as to find at all times the direction and the velocity of the wind for every place” • Led to the first mathematical models of atmospheric motion • 11 entries, including d’Alembert and Bernoulli • Tidal oscillations only (rotation + gravitational attraction) • Expressly excluded effects of radiational heating, though recognised as important for the complete problem Won by d’Alembert: 2 layer model of atmosphere + ocean Egger and Pelkowski (2006) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  7. Symmetric SST profiles 1KEQ SST (degC) Latitude 3KEQ 3KW1 The Experiment • Complete GCMs but idealised planet • More constrained experiment than real-world benchmark (AMIP) • Facilitate understanding of model differences and sensitivities • No land / orography • 8 idealised sea surface temperature (SST) distributions • 5 symmetric SSTs span a range of tropical climates • Local and global-scale SST anomalies • 3-year climate (following spin-up) • Following Neale & Hoskins (2000) SST anomaly experiments Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  8. APE Modelling Groups Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  9. APE Modelling Groups Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  10. APE Modelling Groups Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  11. Range of tropical states Precipitation (mm/day) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  12. different scale Zonal Average Hydrological Cycle Precipitation (mm/day) Evaporation (mm/day) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  13. Convective / stratiform precip. Convective (mm/day) Stratiform (mm/day) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  14. Hydrological Cycle: NCAR model Precipitation: contributions to resolution dependence, T42 / T85 timestep grid params. at T42 Truncn. diffusion params. Courtesy of David Williamson, NCAR Working Group on Numerical Experimentation - WGNE

  15. [ Evap – Precip ] Surface Wind ECMWF - APE control (time average) (mm/day) Inter-tropical Convergence Zone Emanuel (1994) Cool30º lat. Eq.Warm • When does convection break through the trade-wind inversion? • Many interacting processes • ITCZ location sensitive to all these processes in models Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  16. (mm/day) Tropical Variability (precipitation) average 5ºN-5ºS Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  17. (mm/day) IFS Cy29r2 TL159 L60 ~125km grid GME Icosahedral L31 ~100km grid pre-HadGAM1 N96 L38 1.25° x 1.875° NICAM Icosahedral L54 7km grid non-hydrostatic no convective param. Tropical Variability (precipitation) Higher resolution models average 5ºN-5ºS Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  18. Observed sym. spectrum (OLR) Time UKMO_n96 sym. spectrum (precip) Frequency (CPD) Zonal Wavenumber Observations + Theory Observed variability (OLR) Time-longitude section of transient OLR averaged between the equator and 5N from May to July in 1980. (Nakazawa, 1988) • Hierarchy of convective organisation Images from Yoshi-Yuki Hayashi; Yukiko Yamada; NOAA CDC Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  19. HadGAM1 (umet) NCAR MGO N96 N48 LASG CSIRO FRCGC AGU GSFC Tropical rainfall: spectra APE control 10°S – 10°N 6hour grid-box averages Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  20. Tropical rainfall: Stratiform fraction APE control 10°S – 10°N AGU CGAM DWD ECMWF GFDL GSFC K1JAPAN LASG MGO MIT NCAR UMET_48 UMET_96 CSIRO_a CSIRO_b FRCGC Some correlation with spectral shape Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  21. cntl qobs SST (degC) latitude Response of zonal climate to SST qobs-control Wider SST maximum in tropics Stronger SST gradient : displaced poleward Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  22. 3kw1-control Zonal mean differences SST anomaly m=1 SST anomaly generates planetary waves Expect stationary momenum fluxes to alter zonal flow Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  23. Track Density Number per month in 5º radius Zonal speed latitude Mean Intensity peak cntl qobs flat cntl5n 1keq 3keq 3kw1 Zonal speed (ms-1) Pressure anomaly (hPa) latitude latitude Storm-track statistics • Tracking of storm features using 6 hourly sea-level pressure • NCAR model for all 8 SSTs Courtesy of Kevin Hodges, ESSC, Reading Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  24. Mean Intensity Track Density Pressure anomaly (hPa) Number per month in 5º radius latitude latitude Storm-track statistics • Tracking of storm features using 6 hourly sea-level pressure • 6 models for “flat” SST Courtesy of Kevin Hodges, ESSC, Reading Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  25. Low Frequency Variability 1-point correlation maps: 10-day low-pass surf. pressure Ref 51.6N • Significant zonal wavenumber m=5 in 3-year means • Slow propagation, c = 1.7ms-1 • Significant correlation with annular mode variability EOFs of 10-day low-pass streamfunction =0.3 Courtesy of Masahiro Watanabe, Hokkaido University. GRL 32, L05701. (2005) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  26. sw_dn TOA sw_up APE control experiment: 3 year averages + temporal variability Net flux (toa; surface) sw TOA lw Global Energy Balance Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  27. Cloud + Albedo Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

  28. Summary • Documenting a wide variety of model behaviours • No convergence for Δx>100km – basic tropical features not resolved? • Attempts to understand sensitivities in individual models • Additional experiments needed to understand model differences (e.g. no cloud-radiative feedback; fixed radiation; SCM) • Diagnostics focus: • Tropical wave activity • Diurnal cycle • Mid-latitude variability & storm-tracks • Issues: • Reference solution is unknown • Resolution convergence? (HPEs + parameterizations) Working Group on Numerical Experimentation - WGNE http://www.met.reading.ac.uk/~mike/APE/

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