1. Narrowing the Divergence in Simulations of Climate Feedbacks Alex Hall and Xin Qu UCLA Department of Atmospheric and Oceanic Sciences Berkeley Atmospheric Science Symposium October 14, 2005

2. 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).

3. 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 Hall 2004

4. Simulated reduction in reflected solar radiation due to CO 2 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. ---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. (Hall, 2004)

5. climate sensitivity parameter change in outgoing longwave with SAT change in net incoming shortwave with SAT classical climate sensitivity framework

6. surface albedo feedback to dQ/dT s. Climate sensitivity parameter Change in outgoing longwave with SAT Change in net incoming shortwave with SAT dependence of planetary albedo on surface albedo change in surface albedo with SAT

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

8. Climate sensitivity parameter Change in outgoing longwave with SAT Change in net incoming shortwave with SAT dependence of planetary albedo on surface albedo change in surface albedo with SAT surface albedo feedback to dQ/dT s.

9. 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?

10. 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. How to estimate ?

11. an analytical model for planetary albedo Qu and Hall 2005 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.

12. planetary albedo an analytical model for planetary albedo

13. contribution from atmosphere, composed of a contribution from the clear-sky atmosphere (effective albedo of the clear- sky atmosphere), and a contribution from cloud, proportional to the product of cloud cover and the logarithm of cloud optical thickness. an analytical model for planetary albedo

14. contribution from the surface, which is the surface albedo modulated by two components: (1) the clear- sky transmissivity of the atmosphere, and (2) the cloudy sky transmissivity of the atmosphere, proportional to the product of cloud cover and the logarithm of cloud optical thickness an analytical model for planetary albedo

15. 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.

16. Because it captures observed and simulated planetary albedo variations so well, we can use the analytical model to calculate a true partial derivative of planetary albedo with respect to surface albedo. an analytical model for planetary albedo

17. This term does not contain any variables that depend on surface albedo. an analytical model for planetary albedo

18. attenuation effect of the clear-sky atmosphere on surface albedo, represented by the clear-sky atmospheric transmissivity

19. an analytical model for planetary albedo attenuation effect of clouds on surface albedo anomalies, proportional to the product of cloud cover and the logarithm of cloud optical thickness

20. Here is the contribution of the clear-sky atmosphere over both Eurasia and North America in the transient climate change experiments with current generation of models used in the IPCC AR4 assessment for springtime. The calculation was done for the present climate (dark green) and the climate 100 years from now (light green). The models agree in this quantity to within a few percent. In a clear-sky atmosphere, surface albedo anomalies typically result in planetary albedo anomalies about 75% as large.

21. Here is the con- tribution of clouds in springtime in the same experiments. There is substant- ially more inter- model variability than in the clear-sky case, with the models agreeing to within about 20%.

22. Here is the sum of the clear and cloud sky contributions. These also largely converge, mainly because the clear- sky component is more than three times as large as the cloudy-sky component. In North America and Eurasia, planetary albedo anomalies are typically about half as large as associated surface albedo anomalies.

23. Climate sensitivity parameter Change in outgoing longwave with SAT Change in net incoming shortwave with SAT dependence of planetary albedo on surface albedo change in surface albedo with SAT surface albedo feedback to dQ/dT s.

24. Climate sensitivity parameter Change in outgoing longwave with SAT Change in net incoming shortwave with SAT dependence of planetary albedo on surface albedo change in surface albedo with SAT surface albedo feedback to dQ/dT s.

25. We can easily cal- culate  s /  T s 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 T s, snow extent, and  s in one rep-resentative ex- periment used in the AR4 assessment. Hall and Qu 2005

26. We can easily cal- culate  s /  T s 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 T s, snow extent, and  s in one rep-resentative ex- periment used in the AR4 assessment.  s TsTs

27. 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.

28. 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.

29. 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. calendar month

30. April  s April T s

31. calendar month May  s May T s

32. calendar month  s TsTs

33. So we can calculate springtime values of  s /  T s for climate change and the current seasonal cycle. What is the relationship between this feedback parameter in these two contexts?

34. Intermodel variations in  s /  T s in the seasonal cycle context are highly correlated with  s /  T s 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 /  T s based on the present- day seasonal cycle are also excellent predictors of the absolute magnitude of  s /  T s in the climate change context.

35. observational estimate based on ISCCP It’s possible to calculate an observed value of  s /  T s 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.

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

37. Conclusions, Part I To within 10%, surface albedo anomalies over the NH land masses result in planetary albedo anomalies about half as large in climate simulations and in the satellite-based ISCCP data set. This component of snow albedo feedback is therefore not a main source of divergence in climate simulations.

38. Conclusions, Part II On the other hand the sensitivity of surface albedo to surface temperature in NH land masses exhibits a factor-of-three spread in transient climate change experiments. This spread may be dramatically reduced by exploiting the northern hemisphere springtime warming and simultaneous snow retreat as an analog for anthropogenic climate change. Large intermodel variations in snow albedo feedback's strength in human-induced climate change are nearly perfectly correlated with comparably large intermodel variations in its strength in the context of the present- day seasonal cycle.

39. Conclusions, Part III 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.