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Understanding climate model biases in Southern Hemisphere mid-latitude variability

Understanding climate model biases in Southern Hemisphere mid-latitude variability. Isla Simpson 1. Ted Shepherd 2 , Peter Hitchcock 3 , John Scinocca 4.

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Understanding climate model biases in Southern Hemisphere mid-latitude variability

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  1. Understanding climate model biases in Southern Hemisphere mid-latitude variability Isla Simpson1 Ted Shepherd2, Peter Hitchcock3, John Scinocca4 (1) LDEO, Columbia University, USA (2) Dept of Meteorology, University of Reading, UK (3) DAMTP, University of Cambridge, UK (4) CCCma, Environment Canada, Canada

  2. The Southern Annular Mode Dominant mode of variability in SH extra-tropical circulation Climatology First EOF ERA-Interim re-analysis

  3. The SAM timescale Calculate the autocorrelation function  =7 days Calculate the e-folding timescale.

  4. The SAM timescale bias – CMIP3 • Climate models exhibit much too persistent SAM anomalies in the summer season. Obs IPCC models Gerber et al (2008)

  5. The SAM timescale bias – CCMVal2 • Climate models exhibit much too persistent SAM anomalies in the summer season. Obs CCMVal models Gerber et al (2010)

  6. The SAM timescale bias – CCMVal2 • Climate Models exhibit a SAM that is much too persistent in the summer season. Gerber et al (2010)

  7. The SAM timescale bias – CMIP5 • Climate Models exhibit a SAM that is much too persistent in the summer season.

  8. Many climate forcings produce a mid-latitude circulation response that projects onto the SAM. Ozone depletion Son et al (2010), JGR

  9. Why is this potentially of concern for simulating forced responses? May indicate that we’re getting an important process wrong in the simulation of the SH extra-tropical circulation.

  10. Eddy Feedbacks (Lorenz and Hartmann 2001, 2003), Robinson 2000) Why is this potentially of concern for simulating forced responses? • Dissipative processes e.g. surface friction • Intraseasonal Forcing e.g. forcing from the stratosphere (Keeley et al 2009)

  11. Can we isolate the role for “internal” tropospheric dynamics on the SAM timescale bias from the influence of stratospheric variability as an intraseasonal forcing on the SAM?

  12. A stratospheric influence on SAM timescales? The SH vortex breaks down too late in GCMs, maybe this is resulting in enhanced stratospheric variability in the summer and contributing to the SAM timescale bias? Thought to be stratospheric variability that gives rise to this maximum…variability in the timing of the vortex breakdown (Baldwin et al 2003)

  13. The Canadian Middle Atmosphere Model • Comprehensive stratosphere resolving GCM • T63L71, lid=0.0006hPa • Without interactive chemistry • Prescribed SSTs • No QBO • Constant GHG’s (1990’s concentrations)

  14. Model Experiments • 100 year free running control simulation (FREE) • 100 year nudged simulation (NUDGED) In NUDGED, the zonal mean vorticity, divergence and temperature in the stratosphere are nudged toward the zonal mean, seasonally varying climatology of FREE. We eliminate zonal mean stratospheric variability but keep the climatology the same.

  15. The Nudging Process In spectral space Only acting on the zonal mean K

  16. The Nudging Process In spectral space Only acting on the zonal mean climatology time

  17. FREE and NUDGED have the same climatologies, but FREE has stratospheric variability, NUDGED does not. NUDGED FREE Vortex Breakdown Dates

  18. ERA-Interim FREE

  19. FREE NUDGED

  20. Contribution from stratospheric variability Stratospheric variability enhances the SAM timescales in the SH spring.

  21. ERA-Interim NUDGED

  22. There does seem to be a problem in the “internal” dynamics of the tropospheric circulation. Is this caused by climatological circulation biases?

  23. Relationship between climatological jet bias and SAM timescales If we improve the jet position, do we improve the timescale of SAM variability? Kidston and Gerber (2010)

  24. Bias Correcting Experiments Obtain the mean tendency that is required to bring the model toward the ERA climatology (Kharin and Scinocca, 2012, GRL)  applying that constant seasonally varying tendency to the model. Model Climatology Observed Climatology Time Different from nudging in that variability can still occur, just around a new climatological state.

  25. Two different experiments • Bias correcting at all levels – BC • Both stratospheric and tropospheric variability but around an improved climatological state. Improved timing of the vortex breakdown and improved tropospheric jet structure. • Bias correcting in the troposphere and nudging the zonal mean toward ERA-Interim in the stratosphere - BCNUDG • Removed stratospheric variability but has an improved climatological timing of the vortex breakdown. Improved the tropospheric jet structure.

  26. Improved tropospheric jet structure?

  27. Annual Mean Timescales Kidston and Gerber (2010)

  28. Annual Mean Timescales

  29. Annual Mean Timescales

  30. Annual Mean Timescales

  31. The SAM timescale bias in CMAM does not seem to be caused by climatological circulation biases. Eddy feedback biases?

  32. Eddy feedbacks on the SAM Eddy forcing of the SAM regressed onto the SAM Index Eddies driving the SAM SAM driving eddies i.e., a positive feedback See Lorenz and Hartmann (2001), Simpson et al (2013)

  33. Quantify the feedback strengths for each simulation and the reanalysis. Focus on the DJF season.

  34. Synoptic scale eddy feedback (k>3)

  35. Planetary scale eddy feedback (k=1-3)

  36. Summary of DJF feedback strengths This is mostly coming from wavenumber 3

  37. DJF regressions averaged over lags +7 to + 14 days FREE ERA u -u’v’, k=3

  38. Regressions on the 300hPa (+7 to +14 lag average) ERA-Interim -u’v’ (k=1-3)

  39. Regressions on the 300hPa (+7 to +14 lag average) ERA-Interim FREE -u’v’ (k=1-3) -u’v’ (k=1-3)

  40. Comparison with CMIP-5 historical simulations • 20 models: those with 6 hourly u and v available • Quantify DJF feedback strength

  41. Eddy feedback, All k

  42. Eddy feedback, All k

  43. Eddy feedback, k=1-3

  44. Eddy feedback, k=1-3

  45. Virtually all GCMs exhibit this same bias in planetary wave feedbacks. Models don’t capture the negative feedback by planetary scale waves that is localised to the south west of New Zealand in the summer season.

  46. Relation to climatological circulation biases? Our bias corrected runs tell us that climatological circulation biases areNOTtheCAUSEof the eddy feedback bias. But the climatological circulation biases and eddy feedback biases could be related e.g. they could have a common cause.

  47. Climatologically there is wave activity propagating into the mid-latitudes to the S-W of New Zealand ERA FREE

  48. There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height ERA FREE-ERA

  49. There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height FREE-ERA CMIP5 - ERA

  50. There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height CMIP5 - ERA CMIP5 CONSENSUS

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