Presentation on theme: "Understanding climate model biases in Southern Hemisphere mid-latitude variability Isla Simpson 1 Ted Shepherd 2, Peter Hitchcock 3, John Scinocca 4 (1)"— Presentation transcript:
Understanding climate model biases in Southern Hemisphere mid-latitude variability Isla Simpson 1 Ted Shepherd 2, Peter Hitchcock 3, John Scinocca 4 (1) LDEO, Columbia University, USA (2) Dept of Meteorology, University of Reading, UK (3) DAMTP, University of Cambridge, UK (4) CCCma, Environment Canada, Canada
The Southern Annular Mode Dominant mode of variability in SH extra-tropical circulation Climatology First EOF ERA-Interim re-analysis
The SAM timescale Calculate the autocorrelation function Calculate the e-folding timescale. =7 days
The SAM timescale bias – CMIP3 Climate models exhibit much too persistent SAM anomalies in the summer season. Gerber et al (2008) Obs IPCC models
The SAM timescale bias – CCMVal2 Climate models exhibit much too persistent SAM anomalies in the summer season. Obs CCMVal models Gerber et al (2010)
Climate Models exhibit a SAM that is much too persistent in the summer season. Gerber et al (2010) The SAM timescale bias – CCMVal2
Climate Models exhibit a SAM that is much too persistent in the summer season. The SAM timescale bias – CMIP5
Many climate forcings produce a mid-latitude circulation response that projects onto the SAM. Ozone depletion Son et al (2010), JGR
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.
Why is this potentially of concern for simulating forced responses? Eddy Feedbacks (Lorenz and Hartmann 2001, 2003), Robinson 2000) Dissipative processes e.g. surface friction Intraseasonal Forcing e.g. forcing from the stratosphere (Keeley et al 2009)
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?
A stratospheric influence on SAM timescales? Thought to be stratospheric variability that gives rise to this maximum…variability in the timing of the vortex breakdown (Baldwin et al 2003) 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?
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)
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.
The Nudging Process In spectral space Only acting on the zonal mean K
The Nudging Process In spectral space Only acting on the zonal mean time climatology
FREE and NUDGED have the same climatologies, but FREE has stratospheric variability, NUDGED does not. Vortex Breakdown Dates FREE NUDGED
There does seem to be a problem in the “internal” dynamics of the tropospheric circulation. Is this caused by climatological circulation biases?
Relationship between climatological jet bias and SAM timescales Kidston and Gerber (2010) If we improve the jet position, do we improve the timescale of SAM variability?
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 Different from nudging in that variability can still occur, just around a new climatological state. Time Bias Correcting Experiments
Two different experiments Bias correcting at all levels – BC Bias correcting in the troposphere and nudging the zonal mean toward ERA-Interim in the stratosphere - BCNUDG Both stratospheric and tropospheric variability but around an improved climatological state. Improved timing of the vortex breakdown and improved tropospheric jet structure. Removed stratospheric variability but has an improved climatological timing of the vortex breakdown. Improved the tropospheric jet structure.
The SAM timescale bias in CMAM does not seem to be caused by climatological circulation biases. Eddy feedback biases?
Eddy feedbacks on the SAM Eddies driving the SAM SAM driving eddies i.e., a positive feedback See Lorenz and Hartmann (2001), Simpson et al (2013) Eddy forcing of the SAM regressed onto the SAM Index
Quantify the feedback strengths for each simulation and the reanalysis. Focus on the DJF season.
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.
Relation to climatological circulation biases? Our bias corrected runs tell us that climatological circulation biases are NOT the CAUSE of the eddy feedback bias. But the climatological circulation biases and eddy feedback biases could be related e.g. they could have a common cause.
Climatologically there is wave activity propagating into the mid-latitudes to the S-W of New Zealand ERA FREE
There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height ERAFREE-ERA
There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height FREE-ERA CMIP5 - ERA
There are common climatological biases in the region around New Zealand 300hPa eddy geopotential height CMIP5 - ERACMIP5 CONSENSUS
Conclusions Overly persistent SAM variability in the SH summer season is a common model bias. The CMAM experiments demonstrated a bias in internal tropospheric dynamics that is not alleviated by improving the climatological circulation. The problem is associated with a bias in the feedback by planetary scale waves in the model in the summer season. This is true of the majority of other models in the CMIP-5 archive.
Conclusions In order to have faith in the future predictions for the SH mid-latitude circulation in the summer season, we need to understand the planetary wave feedback localised to the SW of New Zealand and why it is biased in the models.
But…. Models do reasonably well at simulating past SAM trends. CMIP-5 DJF SAM Trends Gillett and Fyfe (2013), GRL Are we able to simulate recent SH SAM trends correctly for the correct reason? Is our ability to simulate SAM eddy feedbacks correctly somehow less important than we imagine for our ability to simulate forced responses?