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 stratospheretroposphere coupling on various timescales
• The way ahead: science
• The way ahead: programmatics
• Climate models consistently predict a strengthened BrewerDobson circulation in response to climate change
Red is obs
Green is Cly
Would
increase
ozone in NH
midlat’s
There is a
compensating
decrease in
the tropics
CMAM simulations from Shepherd (2008 Atmos-Ocean)
• 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)
• 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)
Black – total
Dark gray – resolved
Light gray – GWD
Butchart et al. (2010 J. Clim.)
CCMVal-1
Eyring et al. (2007)
CCMVal-2
SPARC CCMVal (2010)
• 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)
• Interannual variability in the NH may not be well
characterized by the historical record (which is too short)
1955-2000
Polar temperatures at 30 hPa (approx 25 km)
1979-1997
Yoden, Taguchi & Naito (2002 JMSJ)
• 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.)
• 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 HoltonTan 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?
Easterly
QBO
Westerly
QBO
All
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)
Christiansen (2010 J. Clim.)
• 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
January-February
February-March
Anstey & Shepherd (2013 QJRMS)
• 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)
Thompson & Solomon
(2002 Science)
CMAM summertime surface
temperature changes (to 2000)
due to ODS changes alone
McLandress et al. (2011 J. Clim.)
• CMAM predicts reduced downwelling in Antarctic latespring/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
McLandress & Shepherd
(2009 J. Clim.)
Change in clear-sky UV index between
1960s and 2090s (per cent)
Hegglin & Shepherd
(2009 Nature Geosci.)
• The delayed late-spring breakup of the SH vortex from climate
change is like the effect of the ozone hole
Would have
implications
for summertime 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 20162035; 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 nonrobustness 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 nonrobustness of projected
precipitation changes is
related to nonrobustness 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 multidecadal variations in North Atlantic SSTs, reversing an earlier trend
Spring
Summer
Fall
Sutton & Dong (2012 Nature Geosci.)
• 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 20052014 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 5member 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
PDFs of DJF trends from 2005 to 2060 in the Eurasian/North Atlantic sector
Control
With
climate
change
Deser et al. (2012 Clim. Dyn.)
• 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
2007-2008
2008-2009
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.
So strat-trop
response is
complex
Hitchcock et al.
(2013 J. Clim.);
see also
Hitchcock et al.
(2013 JAS)
Vertical EP flux anomalies
• 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)
• 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.)
• 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)
• 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.)
• 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.)
• 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)
McLandress &
Shepherd
(2009 J. Clim.)
CMAM (CCMVal-1)
CMAM (CCMVal-2)
Butchart et al. (2011 JGR)
• 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)
Effect of ozone recovery
Effect of ozone loss
SAM trends also
have implications
for Southern Ocean
heat and carbon
uptake, and
potentially for icesheet stability
McLandress,
Shepherd, et al.
(2011 J. Clim.)
CMAM
• 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
Butchart et al. (2011 JGR)
WMO (2011) said seaice 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!
OGWD in
CMAM
McLandress, Shepherd,
Polavarapu & Beagley
(2012 JAS)
• 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
• Contradicts Kidston
& Gerber (2010 J.
Clim.) claim that the
SAM timescale bias
results from jet
latitude bias
CMAM
Obs
• Lesson: cannot rely
on correlations; need
to break feedback
loop between eddies
and mean flow to
identify biases
Simpson, Hitchcock, Shepherd & Scinocca (J. Clim., in press)
WCRP context
• 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
Summary
• 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!