Issues surrounding NH high-latitude climate change

1. Issues surrounding NH high-latitude climate change Alex Hall UCLA Department of Atmospheric and Oceanic Sciences

2. surface albedo feedback has been thought for quite some time to be a possible contributor to high-latitude climate sensitivity… --Budyko (1969), Sellers (1969) --Manabe and Stouffer (1980) --Robock (1983) --Ingram et al. (1989) --Meehl and Washington (1990) --Bitz and Holland (2003)

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

4. Change in net incoming shortwave with SAT Climate sensitivity parameter surface albedo feedback to dQ/dTs. Change in outgoing longwave with SAT change in solar radiation with surface albedo change in surface albedo with SAT

5. Simulated reduction in reflected solar radiation at the top of the atmosphere due to CO2 doubling (Hall, 2004)

6. Geographical and seasonal distribution of the quasi-equilibrium SAT response to CO2-doubling.

7. Geographical and seasonal distribution of the SAT sensitivity when surface albedo feedback is suppressed.

8. Latitude-height cross-section of warming due to CO2 doubling when surface albedo feedback is present.

9. Latitude-height cross-section of warming due to CO2 doubling when surface albedo feedback is absent.

10. Change in net incoming shortwave with SAT Climate sensitivity parameter Surface albedo feedback to dQ/dTs. Change in outgoing longwave with SAT change in solar radiation with surface albedo change in surface albedo with SAT

11. Could clouds neutralize ice albedo feedback? To address this question, we examine the controls on planetary albedo in the recent climate record.

12. What is the surface contribution to planetary albedo variations? (Qu and Hall, 2005) standard deviation of seasonal-mean planetary albedo (%) in the ISCCP D2 data set broken down by season

13. We assess the controls on planetary albedo variability by examining the ISCCP D2 data set (1983-2000). For both clear and all-sky cases, the ISCCP data set (D2) contains (1) surface radiation fluxes (2) TOA radiation fluxes These were generated based on observations at 3 different channels (visible, near IR, and IR) and a radiative transfer model. Rossow and Gardner (1993a and b) J Clim. Rossow and Schiffer (1999) BAMS.

14. Planetary albedo variability can be divided into contributions from four components: SURFACE: the portion unambiguously related in linear fashion to surface albedo variability CLOUD: The portion unambiguously related in linear fashion to cloud cover and optical depth variability RESIDUAL: The portion that cannot be linearly related to either surface or cloud variability COVARIANCE: The portion linearly related to surface and cloud variability but not unambiguously attributable to either. (1)(2)(3)(4) SURFACECLOUDRESIDUALCOVARIANCE

15. We defined six regions, guided by known differences in the behavior of surface albedo variability: northern hemisphere snow-covered lands northern hemisphere sea ice zone southern hemisphere sea ice zone snow-free lands ice-free ocean Antarctica We averaged the contributions of the four components over each region for each season and normalized by the total planetary albedo variability. Note that the definition of the regions varies seasonally. SURFACE CLOUD RESIDUALCOVARIANCE

16. contributions from the four components normalized by total planetary albedo variability in ISCCP SURFACE CLOUD RESIDUALCOVARIANCE

17. The magnitude of the covariance term is generally small, indicating that interannual variability in surface albedo and cloud are largely uncorrelated. However, on land and in the SH sea ice zone, it is not negligible, suggesting surface-cloud interaction. SURFACE CLOUD RESIDUAL COVARIANCE

18. The magnitude of the residual term is larger than the covariance term but is also generally small, except over Antarctica and the sea ice zones during winter. This may be due to nonlinear dependence of planetary albedo on surface albedo and cloud SURFACE CLOUD RESIDUAL COVARIANCE

19. The cloud contribution to planetary albedo variability in ISCCP dominates the snow-free lands and the ice-free oceans SURFACE CLOUD RESIDUAL COVARIANCE

20. The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. It is dominant in the SH sea ice zone year around, and in the other cryosphere regions for most of the year. SURFACE CLOUD RESIDUAL COVARIANCE

21. The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. It is dominant in the SH sea ice zone year around, and in the other cryosphere regions for most of the year. SURFACE CLOUD RESIDUAL COVARIANCE

22. The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. It is dominant in the SH sea ice zone year around, and in the other cryosphere regions for most of the year. SURFACE CLOUD RESIDUAL COVARIANCE

23. The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. It is dominant in the SH sea ice zone year around, and in the other cryosphere regions for most of the year. SURFACE CLOUD RESIDUAL COVARIANCE

24. The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. It is dominant in the SH sea ice zone year around, and in the other cryosphere regions for most of the year. SURFACE CLOUD RESIDUAL COVARIANCE

25. Comparison to a climate model To allow for as direct a comparison with the ISCCP data as possible, we used a simulated time series with approximately the same mix of internal variability and externally-forced climate change: a recent CCSM3 scenario run (T85) data taken from the same time period as ISCCP (1983-2000).

26. Controls on planetary albedo variability in CCSM3 SURFACE CLOUD RESIDUAL COVARIANCE

27. CCSM3 ISCCP A side-by-side comparison of CCSM3 and ISCCP reveals much more contribution from the surface in ISCCP to interannual planetary albedo variability in all regions except for ice-free oceans. SURFACE CLOUD RESIDUAL COVARIANCE

28. ISCCP DJF MAM JJA SON DJF MAM JJA SON CCSM3 DJF MAM JJA SON DJF MAM JJA SON The surface contribution is larger in both sea ice and snow zones of the NH. The difference is particularly striking over the NH snow-covered lands, with planetary albedo variability in CCSM3 being overwhelmingly determined by cloud rather than surface variations.

29. Why is the contribution of the surface so much smaller in CCSM3? --Is it that clouds are more variable, increasing the relative contribution of clouds? --Or is it that the CCSM3 atmosphere is more opaque to solar radiation, attenuating the effect of surface albedo anomalies? --Or is surface albedo itself less variable in CCSM3?

30. ISCCP Clear-sky surface albedo standard deviation (%) in ISCCP and CCSM3, broken down by season. ISCCP has consistently more surface albedo variability. CCSM3

31. ISCCP Clear-sky surface albedo standard deviation (%) in ISCCP and CCSM3, broken down by season. The larger surface albedo variability in ISCCP is particularly apparent in the interior of the northern hemisphere snowpack. (e.g. DJF) CCSM3

32. ISCCP Clear-sky surface albedo standard deviation (%) in ISCCP and CCSM3, broken down by season. A similar pattern is seen in NH spring. CCSM3

33. a couple of conclusions  The surface contribution to planetary albedo variability in ISCCP is significant everywhere except for the ice-free oceans. If surface albedo were to vary in cryosphere regions in the future, the planet’s shortwave radiation absorption would be significantly affected.  CCSM3 has substantially less surface albedo variability than ISCCP, particularly in the interior of ice and snow packs, resulting in a much smaller simulated contribution of the surface to planetary albedo variability. This may lead to ways to improve the model’s surface albedo parameterization.