1 / 55

Challenges for DynVar

Challenges for DynVar. Ted Shepherd Grantham Chair in Climate Science Department of Meteorology University of Reading. Outline. Issues arising from Ozone Assessment Issues arising from CMIP5 and IPCC AR5

sovann
Download Presentation

Challenges for DynVar

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Challenges for DynVar Ted Shepherd Grantham Chair in Climate Science Department of Meteorology University of Reading

  2. Outline • Issues arising from Ozone Assessment • Issues arising from CMIP5 and IPCC AR5 • An outstanding puzzle: mechanisms of stratosphere-troposphere coupling on various timescales • The way ahead: science • The way ahead: programmatics

  3. Climate models consistently predict a strengthened Brewer-Dobson circulation in response to climate change Would increase ozone in NH midlat’s Red is obs Green is Cly There is a compensating decrease in the tropics CMAM simulations from Shepherd (2008 Atmos-Ocean)

  4. Do we understand why? • Was a major outstanding issue in the 2010 Ozone Assessment • Proposed (robust) mechanism of critical-layer control of Rossby-wave breaking, due to strengthening of upper flank of subtropical jet, has yet to be examined in other models • Plots show EP flux divergence and zonal wind Shepherd & McLandress (2011 JAS)

  5. Another view of this, a la Randel & Held (1991 JAS) • Picture is very similar for planetary-scale waves Shepherd & McLandress (2011 JAS)

  6. Although models are reasonably consistent in their prediction of strengthened upwelling, the contribution of resolved vs parameterized waves varies considerably • Is this a problem? Perhaps not, if there is compensation between them (mechanism is fundamentally similar if based on critical-layer control of wave breaking) Black – total Dark gray – resolved Light gray – GWD Butchart et al. (2010 J. Clim.)

  7. Models generally under-predict the observed Arctic ozone loss • May reflect deficiencies in representing PSC effects • May also reflect deficiencies in dynamics • Not clear whether the series of extremely cold winters in the 1990s, which aliased onto the ODS/ESC signal, lie within the natural interannual variability (gray band) CCMVal-1 Eyring et al. (2007) CCMVal-2 SPARC CCMVal (2010)

  8. Interannual variability in the NH may not be well characterized by the historical record (which is too short) 1979-1997 1955-2000 Polar temperatures at 30 hPa (approx 25 km) Yoden, Taguchi & Naito (2002 JMSJ)

  9. The oscillatory nature of NH polar vortex variability leads to a see-saw relationship between early-winter and late-winter decadal variability (here in 30 hPa polar T) • There is a lot of power in the decadal variations, which have tended to be interpreted as trends December March Updated from Labitzke & Kunze (2005 Meteor. Z.)

  10. Models can produce quite realistic simulations of Arctic polar vortex variability (here “PJO events”) • Simulations suggest considerable multi-decadal variability, even for three-member ensembles Hitchcock et al. (2013 J. Clim.)

  11. The QBO affects polar vortex variability through the Holton-Tan effect (1981 JAS); see recent review by Anstey & Shepherd (2013 QJRMS) • Does the lack of a QBO in most climate models compromise their polar vortex variability? If so, how? QBO is apparently responsible for the observed bimodality in NH variability (here the NAM index at 20 hPa) Years segregated by FUB QBO index (shaded is easterly) Easterly QBO Westerly QBO All Christiansen (2010 J. Clim.)

  12. Solar variability interacts with the Holton-Tan effect • The only differences that seem robust in the data are between QBO-W/SC-min and the other quadrants • However we have not sampled very much of phase space in the observational record February-March January-February Anstey & Shepherd (2013 QJRMS)

  13. The ozone hole has been the primary driver of past circulation-related summertime SH high-latitude changes • But how about Antarctic surface temperature? This is not so clear, due to a lack of observations in West Antarctica CMAM summertime surface temperature changes (to 2000) due to ODS changes alone Observed summertime surface changes (to 2000) Thompson & Solomon (2002 Science) McLandress et al. (2011 J. Clim.)

  14. CMAM predicts reduced downwelling in Antarctic late-spring/early-summer from climate change, leading to low total ozone and high UV radiation • Is this consistent with what is seen in other models? • What is the mechanism? Change in 70 hPa w bar star between 1960s and 2090s Change in clear-sky UV index between 1960s and 2090s (per cent) McLandress & Shepherd (2009 J. Clim.) Hegglin & Shepherd (2009 Nature Geosci.)

  15. The delayed late-spring breakup of the SH vortex from climate change is like the effect of the ozone hole Would have implications for summer-time SAM trends and all that follows therefrom McLandress et al. (2010 J. Clim.)

  16. Global aspects of climate change are robust both in observations and in physically-based climate models; uncertainties involve: • How much the radiative forcing will increase in the future (mitigation options, and carbon uptake) • How much warming results from a given radiative forcing (“climate sensitivity”) • Regional aspects of climate change are generally not robust, either in observations or in models • Strongly determined by atmospheric circulation patterns • Subject to chaotic variability on decadal time scales • Strongly affected by model biases

  17. We tend to present climate in terms of radiative forcing IPCC AR4 (2007)

  18. This graphic is iconic, and is found everywhere IPCC AR4 (2007)

  19. But it’s much harder to find a graphic concerning atmospheric circulation…. PaulMirocha.com

  20. Pretty much everything we have any confidence in when it comes to climate change is energetically controlled • And is backed up with basic physical understanding • We generally have very little confidence in anything involving dynamical aspects of climate change • There is generally no basic physical understanding of predicted changes in atmospheric circulation • An example is the model-predicted poleward migration of the eddy-driven jets • A symptom is that atmospheric circulation is generally discussed in terms of empirical circulation indices whose physical basis is unclear

  21. CMIP5 projections of mean precipitation changes between 1986-2005 and 2016-2035; where the changes are robust, they are stippled • Not much stippling over large parts of the globe! • Hatching means no significant change wrt natural variability Knutti & Sedlacek (2012 Nature CC)

  22. Centennial timescale changes are stronger, and statistically significant, but the regions of robustness are about the same • Suggests CMIP5 non-robustness is dominated by model differences, which are systematic • The midlatitudes seem especially challenging Knutti & Sedlacek (2012 Nature CC)

  23. Contrast this with the surface temperature projections: nearly everything is stippled even for the near-term projections • Suggests that the non-robustness of projected precipitation changes is related to non-robustness of projected changes in atmospheric circulation Knutti & Sedlacek (2012 Nature CC)

  24. Circulation patterns in climate models can exhibit severe biases(systematic errors) Midlatitude jet generally lies too far equatorward in the models After Woollings (2010 Phil. Trans.)

  25. These systematic errors in circulation lead to large differences in the predicted changes • 850 hPa zonal wind speed in four leading climate models (shading), with predicted 100-year changes (contours) Woollings & Blackburn (2012 J. Clim.)

  26. There is no evidence of improvement between CMIP3 (right) and CMIP5 (left): circulation-related errors are stubborn! Knutti & Sedlacek (2012 Nature CC)

  27. In the extratropics, surface pressure is related to surface wind, and is dynamically controlled by upper tropospheric eddy momentum fluxes • Surface temperature is, in contrast, generally controlled energetically/thermodynamically • Provides mechanism for chaotic variability, which can involve decadal timescales; also related to climate extremes • In the early 2000’s the NAO trend since 1960 was “attributed” to climate change; what would we say now?

  28. The recent spate of wet summers in the UK is driven by multi-decadal variations in North Atlantic SSTs, reversing an earlier trend Spring Summer Fall Sutton & Dong (2012 Nature Geosci.)

  29. Using a single model (here NCAR CCSM3), the importance of internal variability can be assessed • Plots show number of ensemble members needed to detect an anthropogenic signal in SLP between 2005-2014 and 2028-2037 • The midlatitudes are either blue or gray (off scale) Deser et al. (2012 Clim. Dyn.): A1B scenario used

  30. Another way to look at this: the decade at which the decadal-mean SLP or precipitation change in a 5-member ensemble becomes statistically significant (at 95% level) • Midlatitudes are generally gray (meaning beyond 2050) • And of course the real atmosphere has only one ensemble member! Deser et al. (2012 Clim. Dyn.): A1B scenario used

  31. In contrast, surface temperature changes are far more predictable • For Eurasia/North Atlantic, there is about a 30% chance of 55-year trends in SLP or precip being of opposite sign to anthropogenic signal; not so for temperature PDFs of DJF trends from 2005 to 2060 in the Eurasian/North Atlantic sector With climate change Control Deser et al. (2012 Clim. Dyn.)

  32. Hence in midlatitudes (of both hemispheres), to provide meaningful climate information at a regional scale, the main limitations are arguably: • Systematic uncertainties in model projections of changes in atmospheric circulation • Likely related to systematic errors in the climatologies • Assumption that errors in mean state do not lead to errors in the response to forcings is linear thinking: however the circulation response is likely to be very nonlinear • Internal variability of the atmospheric circulation • Model error is important here too • The lack of a clear improvement in the robustness of model projections of circulation-related features suggests that the model uncertainties are related to physical parameterisations

  33. Stratosphere-troposphere coupling: the apparent downward propagation of annular mode anomalies • A warmer polar stratosphere (weaker vortex) leads to an equatorward shift in the midlatitude tropospheric jet • Mechanism is not well understood, but is robust in models Composites of Northern Annular Mode (NAM) indices Baldwin & Dunkerton (2001 Science)

  34. About half of all SSWs are short-lived, as in 2007-2008 (left), while half have extended recovery periods, as in 2008-2009 (right) • The extended recovery periods are highly repeatable (i.e. predictable) — hence persistent impact on troposphere • Figures show MLS polar-cap average temperatures 2007-2008 2008-2009 Hitchcock, Shepherd & Manney (2013 J. Clim.)

  35. The extended recoveries from SSWs (right) are associated with a strong suppression of planetary-wave fluxes (colour) from the troposphere (contours show zonal winds). Also seen in models. So strat-trop response is complex Hitchcock et al. (2013 J. Clim.); see also Hitchcock et al. (2013 JAS) Vertical EP flux anomalies

  36. Momentum budget shows equatorward shift of zonal wind is driven by synoptic-scale eddy momentum fluxes (Ms), but is strongly mitigated by planetary-scale eddy momentum fluxes and mountain torque (Mp) • Presumably related to suppression of planetary-wave forcing Idealised experiments with wave-1 (left) and wave-2 (right) topography Hitchcock, Shepherd, Yoden, Noguchi & Taguchi (2013 JAS)

  37. Stratosphere-resolving climate models generally predict less of a poleward shift in the wintertime North Atlantic storm track • Attributed to weakening of Arctic stratospheric polar vortex (as with response to SSWs) as a result of climate change • Figure shows percentage change in frequency of extreme wintertime rainfall from 4xCO2: right is effect of stratosphere Scaife et al. (2012 Clim. Dyn.)

  38. Yet stratosphere-resolving climate models do not provide a robust prediction of how the surface NAM will respond to climate change • How much of this spread is related to biases in climatology? 850 hPa NAM index Morgenstern et al. (2010 JGR)

  39. In CMAM, the Arctic wintertime mean sea level response to doubled CO2 changed dramatically between two different (but plausible) parameter settings in the orographic GWD scheme • Difference consistent with Scaife et al. (2012): weakened stratospheric vortex / weaker poleward shift in tropospheric jet (DRAG) (DRAG) Sigmond & Scinocca (2010 J. Clim.)

  40. The difference was not due to the different orographic GWD response to doubled CO2 • The orographic GWD response (colours) is a vertical dipole, reflecting momentum conservation (Shepherd & Shaw 2004 JAS), so has a negligible effect on surface pressure DJF zonal wind and OGWD response to doubled CO2 in T63 dynamical CMAM Sigmond & Scinocca (J. Clim., in press) Contours show zonal wind response Sigmond & Scinocca (2010 J. Clim.)

  41. Rather, whether the CMAM Arctic vortex strengthened or weakened under doubled CO2 depended on the mean state • So the sensitivity to orographic GWD is via its effect on the climatological winds, which affect the planetary-wave response (shown below) to doubled CO2 DJF zonal wind and OGWD response to doubled CO2 in T63 dynamical CMAM Sigmond & Scinocca (J. Clim., in press) Sigmond & Scinocca (2010 J. Clim.)

  42. In general, stratosphere-resolving climate models simulate SSWs fairly well • However the models need to be tuned carefully to achieve this (gravity-wave drag) McLandress & Shepherd (2009 J. Clim.) CMAM (CCMVal-1) CMAM (CCMVal-2) Butchart et al. (2011 JGR)

  43. Stratosphere-resolving models can correctly predict the surface response to SSWs when initialized at the time of the SSW • Figure shows response averaged over 16-60 days after the SSW, for 20 SSWs from 1970-2009 (model: ensemble of 10) • Provides opportunity to really test model parameterisations Sigmond, Scinocca, Kharin & Shepherd (2013 Nature Geosci.)

  44. There has been considerable interest in annular-mode timescales, motivated by the fluctuation-dissipation theorem • In the NH, the long-timescale variability in models seems to occur too late in the season, also the “predictability” of 850 hPa NAM • i.e. fraction of 10-40 day surface variance predicted by persistence Gerber et al. (2010 JGR)

  45. However ensembles of simulations suggest that the seasonality of the stratospheric NAM timescale is not well characterized by the half-century observational record 2007-2008 2008-2009 Hitchcock, Shepherd & Manney (2013 J. Clim.)

  46. Long simulations with an idealised model show no clear relationship between AM timescales and the persistence of AM anomalies • Hitchcock et al. (JAS); the lower panels have weakened radiative damping

  47. The ozone hole causes a poleward shift in upper tropospheric eddy momentum flux convergence at subpolar latitudes, which can explain the SAM trend (consistent with response to SSWs) • DJF trends at 250 hPa; colours show climatology (red is positive) McLandress et al. (2011 J. Clim.)

  48. SAM trends also have implications for Southern Ocean heat and carbon uptake, and potentially for ice-sheet stability Ozone recovery needs to be accounted forin projections of SH summertime climate change (cf. Son et al. 2009) Effect of ozone recovery Effect of ozone loss McLandress, Shepherd, et al. (2011 J. Clim.) CMAM

  49. But do we trust the CCMs in the Antarctic? • Climate models tend to have a systematic bias towards a too-late Antarctic vortex breakup • To what extent does this compromise projections of summertime SH high-latitude climate? Other relevant likely model biases include the ocean response to surface wind changes and associated sea-ice changes WMO (2011) said sea-ice increase was due to the ozone hole, but the climate models don’t support this! Butchart et al. (2011 JGR)

  50. The SH jet has a maximum around 60°S • At this latitude band, the surface is represented entirely as ocean in the models, hence no orographic GWD! OGWD in CMAM McLandress, Shepherd, Polavarapu & Beagley (2012 JAS)

More Related