1. Constraining snow albedo feedback with the present-day seasonal cycle
Alex Hall and Xin Qu
UCLA Department of Atmospheric and Oceanic Sciences
CCSM Climate Variability and Polar Climate Working Groups meeting

2. dependence of planetary albedo on surface albedo
Change in net incoming shortwave with SAT
Climate sensitivity
parameter
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.
Change in outgoing longwave with SAT
dependence of planetary albedo on surface albedo
change in surface albedo with SAT

3. surface albedo feedback to dQ/dTs.
Change in net incoming shortwave with SAT
Climate sensitivity
parameter
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

4. 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?

5. How to estimate ?
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.

6. an analytical model for planetary albedo
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

7. The performance of the analytical model…
…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 thick-ness, 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.

8. an analytical model for planetary albedo
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.

9. an analytical model for planetary albedo

10. an analytical model for planetary albedo
There is rough convergence in this quantity in AR4 models

11. surface albedo feedback to dQ/dTs.
Change in net incoming shortwave with SAT
Climate sensitivity
parameter
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

12. surface albedo feedback to dQ/dTs.
Change in net incoming shortwave with SAT
Climate sensitivity
parameter
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

13. 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.
Hall and Qu 2005

14. 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

15. While there is convergence for the most part in simulations of the de-pendence of planetary albedo on surface albedo, the sensitivity of surface albedo to surface air temp-erature exhibits a three-fold spread in the current generation of climate models. This is likely due to differing surface albedo paramet-erizations.

16. 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.

17. 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.

18. Ts
s
calendar month

19. 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?

20. 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.

21. 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.

22. 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

23. NCAR CCSM3
The old and new NCAR models tend both fall below the observed estimate.
NCAR PCM1

24. 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.

25. Correlation between seasonal cycle and climate change snow albedo feedback parameters as a function of calendar month

26. snow albedo dependent on snow age
snow albedo dependent on temperature