hicle for studying abrupt climate changes. Time-slices run the risk of mixing observations from just before and just after regional or larger abrupt changes. However, pending the still distant availability of high-time-resolution global maps of climate for all periods, anomaly-mapping efforts (e.g., perhaps DryMAP for the Younger Dryas, 8MAP for the event about 8,200 years ago, and LIMAP for the Little Ice Age) would be of considerable value. Similar studies of megadroughts are especially important. These efforts need to focus on the spatial and temporal variability of climatic changes and the resulting economic and ecological impacts. To place the warming and associated changes of the last hundred years in context and compare them with natural fluctuations, focus on the last 2,000 years is required.

IMPROVE STATISTICAL APPROACHES

Recommendation 4. Current practices in the development and use of statistics related to climate and climate-related variables generally assume a simple, unchanging distribution of outcomes. This assumption leads to serious underestimation of the likelihood of extreme events. The conceptual basis and the application of climatic statistics should be re-examined with an eye to providing realistic estimates of the likelihood of extreme events.

The term “climate” implies a relatively persistent set of environmental conditions. Because of this, some early reports of “abrupt climate change” were considered oxymorons, because if climate is a 30-year average, it cannot change in less than 30 years. This underscores the difficulty in recognizing, documenting, and discussing abrupt climate change. The difficulty is exacerbated by the observation of increased variability in some records near climate transitions and by the tendency of people to observe mode shifts in system behavior when only normal, uncorrelated random behavior is involved (Albright, 1993).

Many societal decisions are based on assumptions about the distributions of extreme weather-related events. Large capital projects—such as dams, airports, tunnels, subway systems, roads, levees, hydroelectric projects, and bridges—have embedded safety margins that are derived from data and assumptions about the frequency distribution of floods, hurricanes, storms, precipitation, and snowpacks. Many economic and business decisions depend on explicit or implicit assumptions about the distribution



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