10.3.2 Patterns of Change in the 21st Century
The TAR noted that much of the regional variation of the annual mean warming in the multi-model means is associated with high- to low-latitude contrast. This can be better quantified from the new multi-model mean in terms of zonal averages. A further contrast is provided by partitioning the land and ocean values based on model data interpolated to a standard grid. Figure 10.6 illustrates the late-century A2 case, with all values shown both in absolute terms and relative to the global mean warming. Warming over land is greater than the mean except in the southern mid-latitudes, where the warming over ocean is a minimum. Warming over ocean is smaller than the mean except at high latitudes, where sea ice changes have an influence. This pattern of change illustrated by the ratios is quite similar across the scenarios. The commitment case (shown), discussed in Section 10.7.1, has relatively smaller warming of land, except in the far south, which warms closer to the global rate. At nearly all latitudes, the A1B and B1 warming ratios lie between A2 and commitment, with A1B particularly close to the A2 results. Aside from the commitment case, the ratios for the other time periods are also quite similar to those for A2. Regional patterns and precipitation contrasts are discussed in Section 10.3.2.3.
Figure 10.6. Zonal means over land and ocean separately, for annual mean surface warming (a, b) and precipitation (c, d), shown as ratios scaled with the global mean warming (a, c) and not scaled (b, d). Multi-model mean results are shown for two scenarios, A2 and Commitment (see Section 10.7), for the period 2080 to 2099 relative to the zonal means for 1980 to 1999. Results for individual models can be seen in the Supplementary Material for this chapter.
Figure 10.7 shows the zonal mean warming for the A1B scenario at each latitude from the bottom of the ocean to the top of the atmosphere for the three 21st-century periods used in Table 10.5. To produce this ensemble mean, the model data were first interpolated to standard ocean depths and atmospheric pressures. Consistent with the global transfer of excess heat from the atmosphere to the ocean, and the difference between warming over land and ocean, there is some discontinuity between the plotted means of the lower atmosphere and the upper ocean. The relatively uniform warming of the troposphere and cooling of the stratosphere in this multi-model mean are consistent with the changes shown in Figure 9.8 of the TAR, but now its evolution during the 21st century under this scenario can also be seen. Upper-tropospheric warming reaches a maximum in the tropics and is seen even in the early-century time period. The pattern is very similar over the three periods, consistent with the rapid adjustment of the atmosphere to the forcing. These changes are simulated with good consistency among the models. The larger values of both signs are stippled, indicating that the ensemble mean is larger in magnitude than the inter-model standard deviation. The ratio of mean to standard deviation can be related to formal tests of statistical significance and confidence intervals, if the individual model results were to be considered a sample.
Figure 10.7. Zonal means of change in atmospheric (top) and oceanic (bottom) temperatures (°C), shown as cross sections. Values are the multi-model means for the A1B scenario for three periods (a–c). Stippling denotes regions where the multi-model ensemble mean divided by the multi-model standard deviation exceeds 1.0 (in magnitude). Anomalies are relative to the average of the period 1980 to 1999. Results for individual models can be seen in the Supplementary Material for this chapter.
The ocean warming evolves more slowly. There is initially little warming below the mixed layer, except at some high latitudes. Even as a ratio with mean surface warming, later in the century the temperature increases more rapidly in the deep ocean, consistent with results from individual models (e.g., Watterson, 2003; Stouffer, 2004). This rapid warming of the atmosphere and the slow penetration of the warming into the ocean has implications for the time scales of climate change commitment (Section 10.7). It has been noted in a five-member multi-model ensemble analysis that, associated with the changes in temperature of the upper ocean in Figure 10.7, the tropical Pacific Ocean heat transport remains nearly constant with increasing greenhouse gases due to the compensation of the subtropical cells and the horizontal gyre variations, even as the subtropical cells change in response to changes in the trade winds (Hazeleger, 2005). Additionally, a southward shift of the Antarctic Circumpolar Current is projected to occur in a 15-member multi-model ensemble, due to changes in surface winds in a future warmer climate (Fyfe and Saenko, 2005). This is associated with a poleward shift of the westerlies at the surface (see Section 10.3.6) and in the upper troposphere particularly notable in the Southern Hemisphere (SH) (Stone and Fyfe, 2005), and increased relative angular momentum from stronger westerlies (Räisänen, 2003) and westerly momentum flux in the lower stratosphere particularly in the tropics and southern mid-latitudes (Watanabe et al., 2005). The surface wind changes are associated with corresponding changes in wind stress curl and horizontal mass transport in the ocean (Saenko et al., 2005).
Global-scale patterns for each of the three scenarios and time periods are given in Figure 10.8. In each case, greater warming over most land areas is evident (e.g., Kunkel and Liang, 2005). Over the ocean, warming is relatively large in the Arctic and along the equator in the eastern Pacific (see Sections 10.3.5.2 and 10.3.5.3), with less warming over the North Atlantic and the Southern Ocean (e.g., Xu et al., 2005). Enhanced oceanic warming along the equator is also evident in the zonal means of Figure 10.6, and can be associated with oceanic heat flux changes (Watterson, 2003) and forced by the atmosphere (Liu et al., 2005).
Fields of temperature change have a similar structure, with the linear correlation coefficient as high as 0.994 between the late-century A2 and A1B cases. As for the zonal means, the fields normalised by the mean warming are very similar. The strict agreement between the A1B field, as a standard, and the others is quantified in Table 10.5, by the absolute measure M (Watterson, 1996; a transformation of a measure of Mielke, 1991), with unity meaning identical fields and zero meaning no similarity (the expected value under random rearrangement of the data on the grid of the measure prior to the arcsin transformation). Values of M become progressively larger later in the 21st century, with values of 0.9 or larger for the late 21st century, thus confirming the closeness of the scaled patterns in the late-century cases. The deviation from unity is approximately proportional to the mean absolute difference. The earlier warming patterns are also similar to the standard case, particularly for the same scenario A1B. Furthermore, the zonal means over land and ocean considered above are representative of much of the small differences in warming ratio. While there is some influence of differences in forcing patterns among the scenarios, and of effects of oceanic uptake and heat transport in modifying the patterns over time, there is also support for the role of atmospheric heat transport in offsetting such influences (e.g., Boer and Yu, 2003b; Watterson and Dix, 2005). Dufresne et al. (2005) show that aerosol contributes a modest cooling of the Northern Hemisphere (NH) up to the mid-21st century in the A2 scenario.
Such similarities in patterns of change have been described by Mitchell (2003) and Harvey (2004). They aid the efficient presentation of the broad scale multi-model results, as patterns depicted for the standard A1B 2080 to 2099 case are usually typical of other cases. This largely applies to other seasons and also other variables under consideration here. Where there is similarity of normalised changes, values for other cases can be estimated by scaling by the appropriate ratio of global means from Table 10.5. Note that for some quantities like variability and extremes, such scaling is unlikely to work. The use of such scaled results in combination with global warmings from simple models is discussed in Section 11.10.1.
As for the zonal means (aside from the Arctic Ocean), consistency in local warmings among the models is high (stippling is omitted in Figure 10.8 for clarity). Only in the central North Atlantic and the far south Pacific in 2011 to 2030 is the mean change less than the standard deviation, in part a result of ocean model limitations there (Section 8.3.2). Some regions of high-latitude surface cooling occur in individual models.
Figure 10.8. Multi-model mean of annual mean surface warming (surface air temperature change, °C) for the scenarios B1 (top), A1B (middle) and A2 (bottom), and three time periods, 2011 to 2030 (left), 2046 to 2065 (middle) and 2080 to 2099 (right). Stippling is omitted for clarity (see text). Anomalies are relative to the average of the period 1980 to 1999. Results for individual models can be seen in the Supplementary Material for this chapter.
The surface warming fields for the extratropical winter and summer seasons, December to February (DJF) and June to August (JJA), are shown for scenario A1B in Figure 10.9. The high-latitude warming is rather seasonal, being larger in winter as a result of sea ice and snow, as noted in Chapter 9 of the TAR. However, the relatively small warming in southern South America is more extensive in southern winter. Similar patterns of change in earlier model simulations are described by Giorgi et al. (2001).
Figure 10.9. Multi-model mean changes in surface air temperature (°C, left), precipitation (mm day–1, middle) and sea level pressure (hPa, right) for boreal winter (DJF, top) and summer (JJA, bottom). Changes are given for the SRES A1B scenario, for the period 2080 to 2099 relative to 1980 to 1999. Stippling denotes areas where the magnitude of the multi-model ensemble mean exceeds the inter-model standard deviation. Results for individual models can be seen in the Supplementary Material for this chapter.