Understanding and constraining snow albedo feedback

1
Understanding and constraining snow albedo
feedback

• Alex Hall and Xin Qu
• UCLA Department of Atmospheric and Oceanic
  Sciences
• Polar and global climate modeling workshop, IARC,
  U of Alaska
• How do we measure it? (Qu and Hall 2005)
• How important is it? (Qu and Hall 2006)
• How can we constrain it observationally? (Hall
  and Qu 2006a)
• What processes control its strength? (Hall and Qu
  2006b)

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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 important is snow albedo feedback?
Correlation between local annual-mean temperature
response and 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.
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Correlation between zonal mean temperature
response over continents and snow albedo feedback
strength
Eurasia
North America
latitude
calendar month
calendar month
In late winter and spring, the temperature
response over land is highly correlated with snow
albedo feedback strength over both landmasses.
The region of high correlation generally migrates
poleward with the retreat of the snow margin.
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Correlation between zonal mean temperature
response over continents and snow albedo feedback
strength
Eurasia
North America
latitude
calendar month
calendar month
Interestingly, the summertime temperature
response over land is also highly correlated with
snow albedo feedback strength! This is evident
evident over both continents, but the effect is
particularly strong over North America.
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HOW TO REDUCE THE DIVERGENCE? 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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?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.
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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.
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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
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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.
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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.
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feedback strength
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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Conclusions --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 divergence contributes to much
of the spread in the temperature response of
global climate models in northern hemisphere land
masses, even in summertime. --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.
--These results map out a clear strategy for
targeted climate system observation and further
model analysis to reduce divergence in climate
sensitivity.