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)

2. How to quantify snow albedo feedback strength?
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

3. 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.

4. 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

5. 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.

6. 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.

7. 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.

8. 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.

9. 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.

10. 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.

11. Ts
s
calendar month

12. 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?

13. 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.

14. observational estimate based on ISCCP
It’s possible to calculate an observed value of s/Ts in the seasonal cycle context based on the ISCCP data set ( ) and the ERA40 reanalysis. This value falls near the center of the model distribution.

15. observational estimate based on ISCCP
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.
95% confidence interval

16. 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.

17. 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.

18. 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.

19. 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 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.
--These results map out a clear strategy for targeted climate system observation and further model analysis to reduce divergence in climate sensitivity.