A Strategy to Reduce the Persistent Spread in Projections of Future Climate

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A Strategy to Reduce the Persistent Spread in
Projections of Future Climate

• Alex Hall and Xin Qu
• UCLA Department of Atmospheric and Oceanic
  Sciences
• University of Washington
• May 21, 2007

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Divergence in future climate simulations This
plot shows the upper and lower limits of the
warming over the coming century predicted by
current GCM simulations.
This range is due to two factors (1)
uncertainty in emissions scenarios and (2)
different model sensitivities (i.e. different
simulations of climate feedbacks).
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The colors show 21st century warming taking place
in response to a plausible scenario of radiative
forcing. The values are averaged over all the
20 simulations used in the most recent UN
Intergovernmental Panel on Climate Change Report.
The warming is calculated by subtracting
temperatures at the end of the 20th century
(1961-1990) from temperatures at the end of the
21st century (2071-2100).
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The thin blue lines show the range in warming
across all the models.
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SURFACE ALBEDO FEEDBACK
Surface albedo feedback is thought to be a
positive feedback mechanism. Its effect is
strongest in mid to high latitudes, where there
is significant coverage of snow and sea ice.
Increase in temperature
Decrease in sea ice and snow cover
Increase in incoming sunshine
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Equilibrium annual-mean response of a coarse
resolution climate model when surface albedo
feedbacks are removed
All feedbacks present
No snow or ice albedo feedback. Note the effect
on Northern Hemisphere continents. This is
because of snow albedo feedback.
Hall 2004
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Simulated reduction in reflected solar radiation
due to CO2 doubling
---Snow and sea ice albedo feedbacks each account
for roughly half the total surface albedo
feedback in the northern hemisphere. ---Most
of the snow albedo feedback comes in springtime,
when both snow cover and insolation are
large. ---As we will see, there is a factor of
three divergence in the overall strength of snow
albedo feedback in current GCMs used in the IPCC
AR4.
(Hall, 2004)
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classical climate sensitivity framework
change in net incoming shortwave with SAT
climate sensitivity parameter
change in outgoing longwave with SAT
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Change in net incoming shortwave with SAT
Climate sensitivity parameter
How to quantify snow albedo feedback strength?
surface albedo feedback to dQ/dTs.
Change in outgoing longwave with SAT
dependence of planetary albedo on surface albedo
change in surface albedo with SAT
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We can easily cal-culate ??s/?Ts in models by
averaging surface albedo and surface air
tem-perature values from the beginning and end of
transient climate change experiments. Here is
the evolution of springtime Ts, snow extent, and
?s in one representative ex-periment used in the
AR4 assessment.
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We can easily cal-culate ??s/?Ts in models by
averaging surface albedo and surface air
tem-perature values from the beginning and end of
transient climate change experiments. Here is
the evolution of springtime Ts, snow extent, and
?s in one rep-resentative ex-periment used in the
AR4 assessment.
?Ts
??s
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The sensitivity of surface albedo to surface air
temperature in land areas poleward of 30N
exhibits a three-fold spread in the current
generation of climate models. This is a major
source of spread in projections of future climate
in the region.
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HOW TO REDUCE THE SPREAD? The work of Tsushima et
al. (2005) and Knutti and Meehl (2005) suggests
the seasonal cycle of temperature may be subject
to the same climate feedbacks as anthropogenic
warming. Therefore comparing simulated feedbacks
in the context of the seasonal cycle to
observations may offer a means of circumventing a
central difficulty of future climate research It
is impossible to evaluate future climate
feedbacks against observations that do not exist.
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calendar month
In the case of snow albedo feedback, the seasonal
cycle may be a particularly appropriate analog
for climate change because the interactions of
northern hemisphere continental temperature, snow
cover, and broadband surface albedo in the
context of the seasonal variation of insolation
are strikingly similar to the interactions of
these variables in the context of anthropogenic
forcing.
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April Ts
April ?s
calendar month
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May Ts
May ?s
calendar month
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?Ts
??s
calendar month
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So we can calculate springtime values of ??s/?Ts
for climate change and the current seasonal
cycle. What is the relationship between this
feedback parameter in these two contexts?
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Intermodel variations in ??s/?Ts in the seasonal
cycle context are highly correlated with ??s/?Ts
in the climate change context, so that models
exhibiting a strong springtime SAF in the
seasonal cycle context also exhibit a strong SAF
in anthropogenic climate change. Moreover, the
slope of the best-fit regression line is nearly
one, so values of ??s/?Ts based on the
present-day seasonal cycle are also excellent
predictors of the absolute magnitude of ??s/?Ts
in the climate change context.
Hall and Qu 2006
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observational estimate based on ISCCP
Its possible to calculate an observed value of
??s/?Ts in the seasonal cycle context based on
the ISCCP data set (1984-2000) and the ERA40
reanalysis. This value falls near the center of
the model distribution.
Hall and Qu 2006
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observational estimate based on ISCCP
Its also possible to calculate an estimate of
the statistical error in the observations, based
on the length of the ISCCP time series.
Comparison to the simulated values shows that
most models fall outside the observed
range. However, the observed error range may not
be large enough because of measurement error in
the observations.
95 confidence interval
Hall and Qu 2006
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What controls the strength of snow albedo
feedback?
We can break down snow albedo feedback strength
into a contribution from the reduction in albedo
of the snowpack due to snow metamorphosis, and a
contribution from the reduction in albedo due to
the snow cover retreat.
Qu and Hall 2007a
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What controls the strength of snow albedo
feedback?
snow cover component
snow metamorphosis component
It turns out that the snow cover component is
overwhelmingly responsible not only for the
overall strength of snow albedo feedback in any
particular model, but also the intermodel
divergence of the feedback.
Qu and Hall 2007a
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feedback strength
Qu and Hall 2007a
effective snow albedo
Because of the dominance of the snow cover
component, snow albedo feedback strength is
highly correlated with a nearly three-fold spread
in simulated effective snow albedo, defined as
the albedo of 100 snow-covered areas. Improving
the realism of effective snow albedo in models
will lead directly to reductions in the
divergence of snow albedo feedback.
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How important is snow albedo feedback?
Correlation between local annual-mean temperature
response and springtime snow albedo feedback
strength. Variations in snow albedo feedback
strength are primarily responsible for the
variations in temperature response over large
portions of northern hemisphere landmasses.
Hall et al. 2007
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Correlation between local soil moisture response
during summer (JJAS) and springtime snow albedo
feedback strength over North America. Models
with strong snow albedo feedback lead to large
reductions in summertime soil moisture over the
continental U.S. and southern Canada. This occurs
because strong snow albedo feedback leads to
earlier springtime snowmelt, so that the
summertime evaporation season lasts longer.
Hall et al. 2007
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Correlation between local temperature response
during summer (JJAS) and springtime snow albedo
feedback strength over North America.
Variations in snow albedo feedback strength lead
to large variations in the temperature response
over the continental U.S. and southern Canada.
Hall et al. 2007
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3 Main Conclusions (1) We can measure the
strength of snow albedo feedback accurately in
climate change simulations, and there is a
roughly three-fold spread in simulations of snow
albedo feedback strength. This spread causes
much of the spread in the temperature response of
current global climate models in northern
hemisphere land masses. (2) The feedbacks
simulated strength in the seasonal cycle is
highly correlated with its strength in climate
change. We compared snow albedo feedback's
strength in the real seasonal cycle to simulated
values. They mostly fall well outside the range
of the observed estimate, suggesting many models
have an unrealistic snow albedo feedback. The
range in the feedback strength can be attributed
mostly to differing estimates of the albedo of
100 snow-covered surfaces. (3) These results
map out a clear strategy for targeted climate
system observation and further model analysis to
reduce spread in snow albedo feedback. If we
could eliminate the spread in this feedback, it
would constrain many critical aspects of future
climate change, including the summertime soil
moisture reduction in northern hemisphere land
masses.
29
3 Main Conclusions (1) We can measure the
strength of snow albedo feedback accurately in
climate change simulations, and there is a
roughly three-fold spread in simulations of snow
albedo feedback strength. This spread causes
much of the spread in the temperature response of
current global climate models in northern
hemisphere land masses. (2) The feedbacks
simulated strength in the seasonal cycle is
highly correlated with its strength in climate
change. We compared snow albedo feedback's
strength in the real seasonal cycle to simulated
values. They mostly fall well outside the range
of the observed estimate, suggesting many models
have an unrealistic snow albedo feedback. The
range in the feedback strength can be attributed
mostly to differing estimates of the albedo of
100 snow-covered surfaces. (3) These results
map out a clear strategy for targeted climate
system observation and further model analysis to
reduce spread in snow albedo feedback. If we
could eliminate the spread in this feedback, it
would constrain many critical aspects of future
climate change, including the summertime soil
moisture reduction in northern hemisphere land
masses.
30
3 Main Conclusions (1) We can measure the
strength of snow albedo feedback accurately in
climate change simulations, and there is a
roughly three-fold spread in simulations of snow
albedo feedback strength. This spread causes
much of the spread in the temperature response of
current global climate models in northern
hemisphere land masses. (2) The feedbacks
simulated strength in the seasonal cycle is
highly correlated with its strength in climate
change. We compared snow albedo feedback's
strength in the real seasonal cycle to simulated
values. They mostly fall well outside the range
of the observed estimate, suggesting many models
have an unrealistic snow albedo feedback. The
range in the feedback strength can be attributed
mostly to differing estimates of the albedo of
100 snow-covered surfaces. (3) These results
map out a clear strategy for targeted climate
system observation and further model analysis to
reduce spread in snow albedo feedback. If we
could eliminate the spread in this feedback, it
would constrain many critical aspects of future
climate change, including the summertime soil
moisture reduction in northern hemisphere land
masses.
31
3 Main Conclusions (1) We can measure the
strength of snow albedo feedback accurately in
climate change simulations, and there is a
roughly three-fold spread in simulations of snow
albedo feedback strength. This spread causes
much of the spread in the temperature response of
current global climate models in northern
hemisphere land masses. (2) The feedbacks
simulated strength in the seasonal cycle is
highly correlated with its strength in climate
change. We compared snow albedo feedback's
strength in the real seasonal cycle to simulated
values. They mostly fall well outside the range
of the observed estimate, suggesting many models
have an unrealistic snow albedo feedback. The
range in the feedback strength can be attributed
mostly to differing estimates of the albedo of
100 snow-covered surfaces. (3) These results
map out a clear strategy for targeted climate
system observation and further model analysis to
reduce spread in snow albedo feedback. If we
could eliminate the spread in this feedback, it
would constrain many critical aspects of future
climate change, including the summertime soil
moisture reduction in northern hemisphere land
masses.