8.6.3.3 Cryosphere Feedbacks

A number of feedbacks that significantly contribute to the global climate sensitivity are due to the cryosphere. A robust feature of the response of climate models to increases in atmospheric concentrations of greenhouse gases is the poleward retreat of terrestrial snow and sea ice, and the polar amplification of increases in lower-tropospheric temperature. At the same time, the high-latitude response to increased greenhouse gas concentrations is highly variable among climate models (e.g., Holland and Bitz, 2003) and does not show substantial convergence in the latest generation of AOGCMs (Chapman and Walsh, 2007; see also Section 11.8). The possibility of threshold behaviour also contributes to the uncertainty of how the cryosphere may evolve in future climate scenarios.

Arguably, the most important simulated feedback associated with the cryosphere is an increase in absorbed solar radiation resulting from a retreat of highly reflective snow or ice cover in a warmer climate. Since the TAR, some progress has been made in quantifying the surface albedo feedback associated with the cryosphere. Hall (2004) found that the albedo feedback was responsible for about half the high-latitude response to a doubling of atmospheric CO₂. However, an analysis of long control simulations showed that it accounted for surprisingly little internal variability. Hall and Qu (2006) show that biases of a number of MMD models in reproducing the observed seasonal cycle of land snow cover (especially the spring melt) are tightly related to the large variations in snow albedo feedback strength simulated by the same models in climate change scenarios. Addressing the seasonal cycle biases would therefore provide a constraint that would reduce divergence in simulations of snow albedo feedback under climate change. However, possible use of seasonal snow albedo feedback to evaluate snow albedo feedback under climate change conditions is of course dependent upon the realism of the correlation between the two feedbacks suggested by GCMs (Figure 8.16). A new result found independently by Winton (2006a) and Qu and Hall (2005) is that surface processes are the main source of divergence in climate simulations of surface albedo feedback, rather than simulated differences in cloud fields in cryospheric regions.

Figure 8.16. Scatter plot of simulated springtime Δα_s/ΔT_s values in climate change (ordinate) vs simulated springtime Δα_s/ΔT_s values in the seasonal cycle (abscissa) in transient climate change experiments with 17 AOGCMs used in this report (Δα_s and T_s are surface albedo and surface air temperature, respectively). The climate change Δα_s/ΔT_s values are the reduction in springtime surface albedo averaged over Northern Hemisphere continents between the 20th and 22nd centuries divided by the increase in surface air temperature in the region over the same time period. Seasonal cycle Δα_s/ΔT_s values are the difference between 20th-century mean April and May α_s averaged over Northern Hemisphere continents divided by the difference between April and May T_s averaged over the same area and time period. A least-squares fit regression line for the simulations (solid line) and the observed seasonal cycle Δα_s/ΔT_s value based on ISCCP and ERA40 reanalysis (dashed vertical line) are also shown. The grey bar gives an estimate of statistical error, according to a standard formula for error in the estimate of the mean of a time series (in this case the observed time series of Δα_s/ΔT_s) given the time series’ length and variance. If this statistical error only is taken into account, the probability that the actual observed value lies outside the grey bar is 5%. Each number corresponds to a particular AOGCM (see Table 8.1). Adapted from Hall and Qu (2006).

Understanding of other feedbacks associated with the cryosphere (e.g., ice insulating feedback, MOC/SST-sea ice feedback, ice thickness/ice growth feedback) has improved since the TAR (NRC, 2003; Bony et al., 2006). However, the relative influence on climate sensitivity of these feedbacks has not been quantified.

Understanding and evaluating sea ice feedbacks is complicated by their strong coupling to processes in the high-latitude atmosphere and ocean, particularly to polar cloud processes and ocean heat and freshwater transport. Additionally, while impressive advances have occurred in developing sea ice components of the AOGCMs since the TAR, particularly by the inclusion of more sophisticated dynamics in most of them (see Section 8.2.4), evaluation of cryospheric feedbacks through the testing of model parametrizations against observations is hampered by the scarcity of observational data in the polar regions. In particular, the lack of sea ice thickness observations is a considerable problem.

The role of sea ice dynamics in climate sensitivity has remained uncertain for years. Some recent results with AGCMs coupled to slab ocean models (Hewitt et al., 2001; Vavrus and Harrison, 2003) support the hypothesis that a representation of sea ice dynamics in climate models has a moderating impact on climate sensitivity. However, experiments with full AOGCMs (Holland and Bitz, 2003) show no compelling relationship between the transient climate response and the presence or absence of ice dynamics, with numerous model differences presumably overwhelming whatever signal might be due to ice dynamics. A substantial connection between the initial (i.e., control) simulation of sea ice and the response to greenhouse gas forcing (Holland and Bitz, 2003; Flato, 2004) further hampers ‘clean’ experiments aimed at identifying or quantifying the role of sea ice dynamics.

A number of processes, other than surface albedo feedback, have been shown to also contribute to the polar amplification of warming in models (Alexeev, 2003, 2005; Holland and Bitz, 2003; Vavrus, 2004; Cai, 2005; Winton, 2006b). An important one is additional poleward energy transport, but contributions from local high-latitude water vapour, cloud and temperature feedbacks have also been found. The processes and their interactions are complex, however, with substantial variation between models (Winton, 2006b), and their relative importance contributing to or dampening high-latitude amplification has not yet been properly resolved.