challenges for dynvar

Challenges for DynVar

Ted ShepherdGrantham Chair in Climate Science

Department of MeteorologyUniversity 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 – totalDark gray – resolvedLight 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-1Eyring et al. (2007)

CCMVal-2SPARC CCMVal (2010)

1979-1997 1955-2000

Yoden, Taguchi & Naito (2002 JMSJ)

Pola

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30

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25 k

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• 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 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)

• 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


• 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


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!


• 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.)

Spring Summer Fall

• 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

• 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 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

Hitchcock, Shepherd & Manney (2013 J. Clim.)

2007-2008 2008-2009

• 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 4xCO2: 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 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

Sigmond & Scinocca (2010 J. Clim.)

(DRAG) (DRAG)

• 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)

Sigmond & Scinocca (2010 J. Clim.)Contours show zonal

wind response

• 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.)

• 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 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.)

• 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)

• 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.)

• 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

challenges for dynvar

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