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 An outstanding puzzle: mechanisms of stratosphere- troposphere coupling on various timescales The way ahead: science The way ahead: programmatics

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

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 Shepherd & McLandress (2011 JAS) Plots show EP flux divergence and zonal wind

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

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) Butchart et al. (2010 J. Clim.) Black – total Dark gray – resolved Light gray – GWD

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)

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

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 Updated from Labitzke & Kunze (2005 Meteor. Z.) December March

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.)

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? Christiansen (2010 J. Clim.) 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 All Westerly QBO

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 Anstey & Shepherd (2013 QJRMS) January-February February-March

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 Observed summertime surface changes (to 2000) CMAM summertime surface temperature changes (to 2000) due to ODS changes alone McLandress et al. (2011 J. Clim.) Thompson & Solomon (2002 Science)

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 clear-sky UV index between 1960s and 2090s (per cent) Change in 70 hPa w bar star between 1960s and 2090s Hegglin & Shepherd (2009 Nature Geosci.) McLandress & Shepherd (2009 J. Clim.)

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.)

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

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

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

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

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

CMIP5 projections of mean precipitation changes between and ; 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)

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)

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)

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.)

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.)

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

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?

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

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 and The midlatitudes are either blue or gray (off scale) Deser et al. (2012 Clim. Dyn.): A1B scenario used

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

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 Deser et al. (2012 Clim. Dyn.) PDFs of DJF trends from 2005 to 2060 in the Eurasian/North Atlantic sector Control With climate change

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

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)

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

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. Vertical EP flux anomalies Hitchcock et al. (2013 J. Clim.); see also Hitchcock et al. (2013 JAS) So strat-trop response is complex

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 Hitchcock, Shepherd, Yoden, Noguchi & Taguchi (2013 JAS) Idealised experiments with wave-1 (left) and wave-2 (right) topography

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 4xCO 2 : right is effect of stratosphere Scaife et al. (2012 Clim. Dyn.)

Morgenstern et al. (2010 JGR) 850 hPa NAM index 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?

In CMAM, the Arctic wintertime mean sea level response to doubled CO 2 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 Sigmond & Scinocca (2010 J. Clim.) (DRAG)

The difference was not due to the different orographic GWD response to doubled CO 2 – 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 CO 2 in T63 dynamical CMAM Sigmond & Scinocca (J. Clim., in press) Sigmond & Scinocca (2010 J. Clim.) Contours show zonal wind response

Rather, whether the CMAM Arctic vortex strengthened or weakened under doubled CO 2 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 CO 2 DJF zonal wind and OGWD response to doubled CO 2 in T63 dynamical CMAM Sigmond & Scinocca (J. Clim., in press) Sigmond & Scinocca (2010 J. Clim.)

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

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

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 day surface variance predicted by persistence Gerber et al. (2010 JGR)

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

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

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.)

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

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? Butchart et al. (2011 JGR) 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!

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! McLandress, Shepherd, Polavarapu & Beagley (2012 JAS) OGWD in CMAM

When CMAM is run in data assimilation mode, increments imply missing drag at these latitudes, which descends from the upper stratosphere as the zero wind line descends (left) There is other evidence for the role of oro GWD at these latitudes An ad hoc inclusion of extra oro GWD in this latitude belt substantially reduces the zonal-wind bias in CMAM (right) Zonal wind increments from data assimilation McLandress, Shepherd, Polavarapu & Beagley (2012 JAS)

Models tend to locate the tropospheric eddy-driven jet too far equatorward, in both hemispheres (black are obs) – Reflected here in the location of the node of annular-mode variability – Biases are similar when observed SSTs are imposed, implying the errors arise from atmospheric processes Gerber et al. (2010 JGR)

Bias-correcting the climatological tropospheric jet in CMAM does not reduce the bias in SAM timescale Simpson, Hitchcock, Shepherd & Scinocca (J. Clim., in press) Contradicts Kidston & Gerber (2010 J. Clim.) claim that the SAM timescale bias results from jet latitude bias Lesson: cannot rely on correlations; need to break feedback loop between eddies and mean flow to identify biases CMAM Obs

SPARC is expected to encompass more of the troposphere CLIVAR is scaling back, and leaving annular modes to SPARC – Is it time to declare victory on high-top/low-top? – Is it time to forget the strat-trop distinction and focus on atmospheric processes not covered by GEWEX and CLIVAR? Cross-cutting initiatives will be catalysed by the WCRP Grand Challenges, so core project “turf” will become less critical – Polar climate predictability (Bitz and Shepherd, leads) Includes a number of initiatives of relevance to DynVar – Clouds, circulation and climate sensitivity (Bony and Stevens, leads) Includes initiative on “changing patterns” (Sobel and Shepherd, leads) of clear relevance to DynVar WCRP context

There are several outstanding DynVar issues to be dealt with for the next Ozone Assessment Atmospheric circulation represents a major uncertainty for regional climate change: systematic biases, and variability Strat-trop coupling is an essential process in extratropical climate variability and change (consistent across timescales) – Exact mechanism is complex and remains elusive, but models can simulate the process fairly well Need to address model sensitivity and bias arising from parameterisations (especially oro GWD); also to understand nature of variability and implications for the observed record The role of SPARC within WCRP is ready to expand significantly, through the Grand Challenges – DynVar is needed more than ever! Summary