Transcript CCSM_Feb2006 - UCLA: Atmospheric and Oceanic Sciences

Constraining snow albedo
feedback with the presentday seasonal cycle
Alex Hall and Xin Qu
UCLA Department of Atmospheric and Oceanic
Sciences
CCSM Climate Variability and Polar Climate
Working Groups meeting
In this talk, we explore a strategy
to reduce the divergence in IPCC
AR4 simulations of snow albedo
feedback. The idea is to split the
feedback
into
its
two
components and assess the
divergence in each separately.
The focus is on springtime, when
most of the snow albedo
feedback effect is concentrated.
Climate sensitivity
parameter
Change in net incoming
shortwave with SAT
dF dQ


dTs dTs
Change in outgoing
longwave with SAT
Q 
 p  s


  I 
 s Ts
Ts SAF
dependence of
planetary albedo
on surface albedo
change in surface
albedo with SAT
Climate sensitivity
parameter
Change in net incoming
shortwave with SAT
dF dQ


dTs dTs
surface albedo
feedback to
dQ/dTs.
Change in outgoing
longwave with SAT
Q 
 p  s


  I 
 s Ts
Ts SAF
dependence of
planetary albedo
on surface albedo
change in surface
albedo with SAT
THE ROLE OF CLOUD
To what extent do
clouds attenuate surface
albedo anomalies, and
hence weaken the
positive feedbacks
associated with the
cryosphere?
And how relevant are
cloud changes
associated with
anthropogenic climate
change in altering snow
and sea ice albedo
feedback?
How to estimate  p
 s?
Generating accurate estimates of p/s
from model output or from satellite data is
not straightforward, because of the
possibility that surface and cloud
variations could be correlated. This rules
out simply regressing planetary albedo
onto surface albedo.

 p  s
an analytical model
for planetary albedo
 p    1  c  ln  1 T  2  c  ln  1  s
cr
a
cr
a

The analytical model for planetary albedo gives
planetary albedo as a function of common
model output, such as cloud cover, cloud
optical thickness, and surface albedo. The
idea is to come up with an accurate analytical
expression for planetary albedo that can be
used to calculate a true partial derivative with
respect to surface albedo for any simulation or
satellite-derived data set.
Qu and Hall 2005
 p  s
The performance
analytical model…

of
the
…is extremely good. These
scatterplots show predicted
geographical and temporal
variability
in
springtime
planetary albedo values based
on input values required by
the analytical model (cloud
cover, cloud optical thickness, surface albedo, etc.)
against
actual
planetary
albedo variations in North
American and Eurasian land
masses. The analytical model
nearly
perfectly
captures
planetary albedo variability in
ISCCP as well as two current
simulations.
 p  s
an analytical model
for planetary albedo
 p    1  c  ln  1 T  2  c  ln  1  s
cr
a
cr
a

Because it captures observed and
simulated planetary albedo variations so
well, we can use the analytical model to
calculate accurately a true partial
derivative of planetary albedo with respect
to surface albedo.
 p  s
an analytical model
for planetary albedo
 p    1  c  ln  1 T  2  c  ln  1  s
cr
a

cr
a
 p
cr
T
a  2  c  ln   1

 s
 p  s
an analytical model
for planetary albedo
 p    1  c  ln  1 T  2  c  ln  1  s
cr
a
cr
a
 p
cr
T
a  2  c  ln   1

 s

There is rough convergence in this quantity in AR4 models
Climate sensitivity
parameter
Change in net incoming
shortwave with SAT
dF dQ


dTs dTs
surface albedo
feedback to
dQ/dTs.
Change in outgoing
longwave with SAT
Q 
 p  s


  I 
 s Ts
Ts SAF
dependence of
planetary albedo
on surface albedo
change in surface
albedo with SAT
Climate sensitivity
parameter
Change in net incoming
shortwave with SAT
dF dQ


dTs dTs
surface albedo
feedback to
dQ/dTs.
Change in outgoing
longwave with SAT
Q 
 p  s


  I 
 s Ts
Ts SAF
dependence of
planetary albedo
on surface albedo
change in surface
albedo with SAT
We can easily calculate
s/Ts
in
models by averaging
surface albedo and
surface
air
temperature 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
experiment used in the
AR4 assessment.
 s Ts
Hall and Qu 2005
We can easily calculate
s/Ts
in
models by averaging
surface albedo and
surface
air
temperature 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
experiment used in the
AR4 assessment.
 s Ts
Ts
s
 p  s

While
there
is
convergence for the
most
part
in
simulations of the dependence of planetary
albedo on surface
albedo, the sensitivity
of surface albedo to
surface
air
temperature
exhibits
a
three-fold spread in
the current generation
of climate models.
This is likely due to
differing
surface
albedo
parameterizations.
 s Ts
HOW TO REDUCE THIS 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.
 s Ts
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.
 s Ts
Ts
s
calendar month
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?
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 presentday seasonal cycle are also
excellent predictors of the
absolute magnitude of s/Ts
in the climate change context.
 s Ts
observational
estimate based
on ISCCP
 s Ts
It’s 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.
observational
estimate based
on ISCCP
95%
confidence
interval
 s Ts
It’s 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.
NCAR
CCSM3
The old and new NCAR models
tend both fall below the
observed estimate.
NCAR
PCM1
 s Ts
Conclusions
We were able to isolate the surface component as the main
source of an approximately three-fold divergence in
simulations of snow albedo feedback.
Focusing on the surface component, 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. Though this comparison
may put the models in an unduly harsh light because of
uncertainties in the observed estimate that are difficult to
quantify, these results map out a clear strategy for targeted
climate system observation and analysis to reduce
divergence in climate sensitivity.
Identifying and correcting model biases in simulations of
snow albedo feedback in the current seasonal cycle will lead
directly to a reduction in the spread of simulations of snow
albedo feedback in anthropogenic climate change.
Correlation between seasonal cycle and climate
change snow albedo feedback parameters as a
function of calendar month
snow albedo dependent
on snow age
snow albedo dependent
on temperature