by Judith Curry
“Climate dice”, describing the chance of unusually warm or cool seasons relative to climatology, have become progressively “loaded” in the past 30 years, coincident with rapid global warming. The distribution of seasonal mean temperature anomalies has shifted toward higher temperatures and the range of anomalies has increased.
Jim Hansen et al. have posted a new draft paper entitled: Perceptions of climate change: the new climate dice.
This text summarizes their approach:
We use the Goddard Institute for Space Studies (GISS) surface air temperature analysis to examine seasonal mean temperature variability and how that variability has changed in recent decades.
We illustrate observed variability of seasonal mean surface air temperature emphasizing the standard deviation (“bell curve”), which the lay public may appreciate. We choose 1951-1980 as the base period for most of our illustrations, because that is a time of little global temperature trend just prior to the rapid global warming in recent decades. It is a period that older people today, particularly those of the “baby boom” generation, can remember. Global temperature in 1951-1980 is also within the Holocene temperature range, and thus it is a climate that the natural world and civilization is adapted to. In contrast, global temperature in the first decade of the 21st century is probably already outside the Holocene range, as evidenced by the fact that the Greenland and Antarctic ice sheets are losing mass rapidly and sea level is now rising at a rate (3 m/millennium). (see the manuscript for reference citations).
JC comment: I like the general approach used in this study. However, the last sentence about the Holocene threw me for a loop. Even accepting the statements about Antarctic ice sheets and sea level rise at face value, how does this evidence lead to a conclusion that global temperature in the first decade of the 21st century is probably already outside the Holocene range? I agree that the ‘detection’ problem should be framed in the context of climate variability over the entire Holocene, but I have not seen anyone do that convincingly.
The results are clearly and simply presented. Maps of spatial variability of anomalies, as well as global averages are presented, mostly on a decade by decade basis.
“Loading” of the “climate dice” describes the systematic shift of the frequency distribution of temperature anomalies. Hansen et al. (2) represented the climate of 1951-1980 by colored dice with two sides colored red for “hot”, two sides blue for “cold”, and two sides white for near average temperatures. With a normal distribution of temperatures the dividing point would be at 0.43σ to achieve equal (one third) chances of being in each of these three categories in the period of climatology (1951-1980).
Fig. 5 confirms that the global occurrence of “hot” anomalies (seasonal mean temperature anomaly exceeding +0.43σ) has approximately reached the level of 67% required to make four sides of the dice red, with the odds of either an unusually “cool” season or an “average” season now each approximately corresponding to one side of the six-sided dice. However, the loading of the dice over land area in summer is even stronger (Fig. 5, lower row).
Probably the most important change is the emergence of a new category of “extremely hot” summers, more than 3σ warmer than climatology. For practical purposes it is important to look at the changes over land areas, where most people live, rather than the global mean for which anomalies are more constrained by the ocean’s thermal inertia. Fig. 6 illustrates that +3σ anomalies practically did not exist in the period of climatology (1951-1980), but in the past several years these extreme anomalies have covered of the order of 10% of the land area.
The study also focuses on the U.S., back to 1900, to compare recent temperatures with those in the 1930’s.
Nevertheless, it is apparent that the long-term trend toward hot summers is not as pronounced in the United States as it is in hemispheric land as a whole. Also note that the extreme summer heat of the 1930s, especially 1934 and 1936, is comparable to the most extreme recent years.
Year-to-year variability, which is mainly unforced weather variability, is so large for an area the size of the United States that it is perhaps unessential to find an “explanation” for either the large 1930s anomalies or the relatively slow upturn in hot anomalies during the past few decades. However, this matter warrants discussion, because, if the absence of a stronger warming in recent years is a statistical fluke, the United States may have in store a relatively rapid trend toward more extreme anomalies.
Some researchers have suggested that the high summer temperatures and drought in the United States in the 1930s can be accounted for by sea surface temperature patterns plus natural variability (10, 11). Other researchers (12-14), have presented evidence that agricultural changes and crop failure in the 1930s contributed to changed surface albedo, aerosol (dust) production, high temperatures, and drying conditions. Furthermore, both empirical evidence and climate simulations (14, 15) indicate that agricultural irrigation has a significant regional cooling effect. Thus increasing amounts of irrigation over the second half of the 20th century may have contributed a summer cooling tendency in the United States that partially offset greenhouse warming. Such regionally-varying effects may be partly responsible for differences between observed regional temperature trends and the global trend.
From the concluding discussion:
Seasonal-mean temperatures have changed dramatically in the past three decades. The global shift of the probability distribution for seasonal mean temperature anomalies is more than one standard deviation and the shift is even larger for land areas. In addition, there is a broadening of the probability distribution, the warming shift being greater at the high temperature tail of the distribution than at the low temperature tail.
Seasonal-mean temperatures in the category defined as “cold” in 1951-1980 climatology (mean temperature below -0.43σ), which occurred about one-third of the time in 1951-1980, still occur with a probability about 10% over land areas. Thus an occasional unusually cool winter is not evidence against global warming. Temperature is less “noisy” in the summer than winter. The chance of summer falling in the “hot” category of 1951-1980 is now about 80% (Fig. 7). The climate dice are now loaded to a degree that the perceptive person (old enough to remember the climate of 1951-1980) should recognize the existence of climate change.
The most important change of the climate dice is the appearance of a new category of extremely hot summer anomalies, with mean temperature at least three standard deviations greater than climatology. These extreme temperatures were practically absent in the period of climatology, covering only a few tenths of one percent of the land area, but they have occurred over about 10% of land area in recent years. The increased frequency of these extreme anomalies, by more than an order of magnitude, implies that we can say with a high degree of confidence that events such as the extreme summer heat in the Moscow region in 2010 and Texas in 2011 were a consequence of global warming. Rahmstorf and Coumou (23), using a more elegant mathematical analysis, reached a similar conclusion for the Moscow anomaly.
It is not uncommon for meteorologists to reject global warming as a cause of these extreme events, offering instead a meteorological explanation. For example, it is said that the Moscow heat wave was caused by an atmospheric “blocking” situation, or the Texas heat wave was caused by La Nina ocean temperature patterns. Certainly the locations of the extreme anomalies in any given case are related to specific weather patterns. However, blocking patterns and La Ninas have always been common, yet the large areas of extreme warming have come into existence only with large global warming. Today’s extreme anomalies occur because of simultaneous contributions of specific weather patterns and global warming.
JC conclusion: I like several aspects of this paper. It puts longer term climate change into context of year to year natural variability (both globally and regionally). The analysis is straightforward and clearly presented. The writing is accessible to a general audience.
The problem that I have with the paper is that the analysis does not support some of the inferences. The major conclusion (stated in the abstract) is:
We conclude that extreme heat waves, such as that in Texas and Oklahoma in 2011 and Moscow in 2010, were “caused” by global warming, because their likelihood was negligible prior to the recent rapid global warming.
First, an anomalously warm season may not correlate with the existence of an extreme heat wave. The study did not systematically consider all major heat waves during the period (they only mentioned the 2010 and 2011 heat waves), and assess whether these were predominantly associated with anomalously warm seasons.
Second, the paper concluded from an analysis of U.S. temperatures in the 1930’s that “Also note that the extreme summer heat of the 1930s, especially 1934 and 1936, is comparable to the most extreme recent years.”
A critical issue IMO is interpretation of the variability in context of the major multidecadal ocean oscillations, e.g. AMO and PDO. The paper implicitly assumes that all of the warming since 1980 is AGW (whereas even the IPCC only says “most”, implying >50%). These modes of atmospheric and oceanic circulation are more likely to be associated with the blocking patterns that produce heat waves (this was particularly the case for the Moscow heat wave).
IMO, if Hansen wants to draw this conclusion, the following analysis needs to be done. Go through the temperature data records since 1900, and search out the individual heat wave events. I would define heat waves in the context of two different definitions: 1) relative to the average local temperature for the entire period; 2) relative to the average local temperature for the decade. The combination of these allows interpretation of what is associated with the trend, vs interannual/decadal variability. Interpret the statistics globally and regionally, in the context of known modes of internal variability (e.g. ENSO, AMO) and the global warming trend. Then we would have the basis for assessing whether their conclusion is true or not.