volume feedback, f in, contributes to an increase in the ice volume by allowing more space for the ice sheets to grow; the second one, the vegetation-snow albedo feedback,
I jo (and the synergism between the two, l\ ] )> contributes to slow this increase in ice volume through the reduction of the global albedo. It remains true, however, that these conclusions can be only partial because only two factors have been considered. One next step would be to reconsider the main role played by the ice albedo and the water vapor-tcmperaturc feedbacks (Berger et al, 1993b), but this time in conjunction w ith the sea level and vegetation-snow albedo factors. Additional factors might also be included, such as the isostatic rebound of the bedrock beneath the ice sheets, contincntalitv, and snow aging factors. However, it must be remembered that four factors require 16 experiments, which involves not only a considerable amount of computer time but also a great deal of human effort to analyze the results.
All the sensitivity experiments made with the LLN 2.5-D models, including those discussed here, show that the response - both in amplitude and in phase-of the climate system to a given forcing is clearly a function of the actual climatic state, the latitude, and the season. This seriously complicates the elaboration of any simple universal rule to describe the causes-to-effects relationship in a glacial-interglacial cycle. According to the simulations done with the LLN models, the following factors plav a key role in shaping the 100 kyr cycle: insolation, albedo, water vapor, and vegetation-temperature feedbacks, and the isostatic response of the lithosphcre to ice sheet loading. This means that when one of these parameters is kept fixed, the model produces or melts too much ice, preventing it from sustaining the 100 kyr cycle. It is therefore almost impossible to claim w hich process is the most important for explaining an entrance into or termination of glaciation. It is much more a series of events, to be seen in a dynamical and synergistic point of view; that explains the w axing and waning of the ice sheets at the astronomical time scale. Figure 8J gives such a flow chart, describing the response of the climate system between isotopic stages 5e and 5d. The ice volume maximum occurs at 109 kyr BP in our simulation, 7 kyr later than the minimum of the June 65 N insolation (Berger et at, 1996), The time lag between the ice volume and the insolation forcing depends on the month that is chosen as a reference (Loutre and Berger, 1995), but this does not affect the overall conclusions, Moreover, a similar flow chart can be made for any other climatic variable, such as the air surface temperature at a given latitude lor a whole zonal belt, over the oceans, or over the continents, but the timing of the variations w ill not be the same. For example, the surface temperature of the 50-55 N belt from July to September is almost in phase w ith the 65°N June insolation (lagging behind by only — 1 to 2 kyr), a much faster response than at higher latitudes, where a much more profound influence of the ice sheets is discernable.
W ith a global average surface temperature of 0.58 C warmer than the 19611990 baseline, 1998 was the hottest year on record since 1860 (WMO, 1998) and maybe the warmest over the past 1200 years (Overpeck, 1998), More analysis of temperature
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