Transcript UCLA_IOE_Feb2007 - UCLA: Atmospheric and Oceanic Sciences

How Much Will the
Climate Warm?
Alex Hall and Xin Qu
UCLA Department of Atmospheric and Oceanic Sciences
UCLA Institute of the Environment
Environmental Science Colloquium
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).
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 third 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).
The thin blue lines show the range in warming across all the models.
WATER VAPOR FEEDBACK
Increase in
temperature
Enhancement
of the
greenhouse
effect
Water vapor feedback is
thought to be a positive
feedback mechanism.
Water vapor feedback
might amplify the
climate’s equilibrium
response to increasing
greenhouse gases by as
much as a factor of two.
It acts globally.
Increase in
water vapor
in the
atmosphere
SURFACE ALBEDO FEEDBACK
Increase in
temperature
Increase in
incoming
sunshine
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.
Decrease in
sea ice and
snow cover
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
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.
(Hall, 2004)
---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.
classical climate sensitivity framework
climate sensitivity
parameter
change in net incoming
shortwave with SAT
dF dQ


dTs dTs
change in outgoing
longwave with SAT
How to quantify snow
albedo feedback
strength?
Climate sensitivity
parameter
surface albedo
feedback to
dQ/dTs.
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
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
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
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.
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.
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
April Ts
April s
calendar month
 s Ts
May Ts
May s
calendar month
 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.
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.
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.
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.
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% snowcovered areas. Improving the realism of effective snow albedo in models will
lead directly to reductions in the divergence of snow albedo feedback.
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.
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.
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.
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 feedback’s 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.
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 feedback’s 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.
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 feedback’s 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.
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 feedback’s 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.