On the persistent spread in snow-albedo feedback

Abstract

Snow-albedo feedback (SAF) is examined in 25 climate change simulations participating in the Coupled Model Intercomparison Project version 5 (CMIP5). SAF behavior is compared to the feedback’s behavior in the previous (CMIP3) generation of global models. SAF strength exhibits a fivefold spread across CMIP5 models, ranging from 0.03 to 0.16 W m⁻² K⁻¹ (ensemble-mean = 0.08 W m⁻² K⁻¹). This accounts for much of the spread in 21st century warming of Northern Hemisphere land masses, and is very similar to the spread found in CMIP3 models. As with the CMIP3 models, there is a high degree of correspondence between the magnitudes of seasonal cycle and climate change versions of the feedback. Here we also show that their geographical footprint is similar. The ensemble-mean SAF strength is close to an observed estimate of the real climate’s seasonal cycle feedback strength. SAF strength is strongly correlated with the climatological surface albedo when the ground is covered by snow. The inter-model variation in this quantity is surprisingly large, ranging from 0.39 to 0.75. Models with large surface albedo when these regions are snow-covered will also have a large surface albedo contrast between snow-covered and snow-free regions, and therefore a correspondingly large SAF. Widely-varying treatments of vegetation masking of snow-covered surfaces are probably responsible for the spread in surface albedo where snow occurs, and the persistent spread in SAF in global climate models.

Appendix: Seasonal cycle and climate change relationship in \(\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}\)

Using the methodology in Hall and Qu (2006), we examine the seasonal cycle and climate change relationship in $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}.$ In the context of seasonal cycle, $\Updelta \bar{\alpha}_{s}$ is quantified by the difference in regionally-averaged, climatological values of α _s between April and May in NH extratropical land masses. Likewise, $\Updelta {\bar{T}}_{s}$ is quantified by the difference in regionally-averaged, climatological values of T _s between April and May. In the case of climate change, $\Updelta \bar{\alpha}_{s}$ is quantified by the difference in regionally-averaged, climatological April values of α _s between the current (1980–1999) and future (2080–2099) climates. Likewise, $\Updelta {\bar{T}}_{s}$ is quantified by the difference in regionally-averaged, climatological April value of T _s between the current and future climates.

Figure 9 scatters the climate change values of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ versus the corresponding seasonal cycle values of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ in the 25 CMIP5 models. Consistent with Hall and Qu (2006), there is a high degree of correspondence between the two quantities (r = 0.86). This is further supported by simple regression analysis, which reveals that the slope of the best-fit line between the two quantities (s = 1.11) is not significantly different from one and the intercept of the line (i = 0.05 % K⁻¹) is not significantly different from zero.

Fig. 9

Scatterplot of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ in the context of climate change versus $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ in the context of seasonal cycle. To obtain $\Updelta \bar{\alpha}_{s}$ and $\Updelta {\bar{T}}_{s},$ we average $\Updelta \alpha_{s}$ and $\Updelta T_{s}$ over NH extratropical land masses. In both contexts, $\Updelta \alpha_{s}$ is weighted by the climatological April incoming solar radiation at the surface prior to averaging. The thick dashed line is the best-fit regression line. The observed estimate of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ (the thin vertical line) and statistical uncertainty of the observed estimate (the gray area) are estimated based on MODIS surface albedo and ERA-Interim surface air temperature (see the text for details). Observed climatological April incoming solar radiation at the surface is calculated based on the Clouds and Earth’s Radiant Energy System (CERES) data (Wielicki et al. 1996)

As in Hall and Qu (2006), we constrain the climate change values of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ using observed $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ value in the context of seasonal cycle. To obtain an updated observed estimate of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s},$ we use the recent (2001–2012) surface albedo measurements from MODIS (Jin et al. 2003) and surface air temperature from ERA-Interim (Dee et al. 2011) for the same period. By performing procedures similar to those described in Sect. 3, we obtain an observed estimate of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s} (-0.87 \,\%\, \hbox{K}^{-1},$ represented by the thin vertical line in Fig. 9), and the lower and upper bounds of the estimate (respectively: −0.78 and −0.96 % K⁻¹, represented by the gray area in Fig. 9). As in the case of the overall feedback strength, the simulated seasonal cycle values of $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s}$ in models S and T are overestimated by about 50 %, while the respective values in models P, Q and R are underestimated by the same amount. Therefore, the assessment of model biases is qualitatively similar no matter which measure (the overall feedback strength or $\Updelta \bar{\alpha}_{s}/\Updelta {\bar{T}}_{s})$ is used in the analysis.