Current American political dissatisfaction with the 2015 Paris climate accord hinges on its perceived economic risks to the industries that powered the country's 20th-century explosion in productivity and living standards for much of the population. While futurists promise everyone will be better off when the transition to cleaner and more-efficient energy is complete, many still fear those promises are hollow -- leading to a society dominated by overlords of the finance-technology-energy complex -- and are based on a false premise of imminent global catastrophe. Harborers of these reservations naturally advocate for applying the brakes to investments in measuring, preparing for, and adapting to climate changes of any stripe, and consider such efforts a gigantic boondoggle. Independent of climate projections, geopolitics, and the subtleties of national moods, I thought it worthwhile to take a moment to assess the indisputable evidence at hand for the observed connection between temperature and various economic measures. In other words, to point out there while there is a real cost to doing something about a warming world, there is also a cost to doing nothing.
The most-salient point is that there exists abundant evidence that high temperatures, like poverty, exact a kind of "cognitive tax" that seems to be an evolutionary adaptation to scarcity of a resource critical for survival. This temperature-productivity relationship has long been observed anecdotally, starting with Montesquieu in the 18th century (an "excess of heat" makes people "slothful and dispirited"). Quantitative studies have estimated that combating this heat with air conditioning results in increases in productivity from 5% (sedentary call-center work) to 25% (active kitchen work), and that the amount of outdoor labor -- from construction to gardening -- able to be performed during the hottest months could fall by half by the year 2200. Even in current conditions, outdoor laborers in Nicaragua opt to work about 20 fewer minutes per day (about 4%) when daily-maximum wet-bulb temperatures exceed the threshold of 26 C, according to public-policy graduate student Tim Foreman whom I spoke with at a recent conference. A listing of the results of some previous studies are shown in the figure below. Physiologically, these relationships are logical, given the body's reluctance to risk overheating itself and the decreasing effectiveness of our main cooling mechanism (sweat) at higher and higher temperatures.
On an international scale, for poorer (primary-resource-dependent) countries, every 1 C temperature increase is associated with a decrease in economic growth of 1.3 percentage points. A different study found productivity peaks at an annual-average temperature of 13 C, irrespective of a country's wealth, though it is only at daily maximum temperatures above 30 C that the relationship becomes meaningful. Thus warming is expected to help contribute to raising economic growth in cool countries, while subtracting from growth in warm ones. This is even the case within the United States, but as with the country comparisons, the definitive reason for this relationship's persistence despite electricity, air conditioning, and the increasing prevalence of indoor work is still at large.
Less expected, perhaps, is the link between temperature and mood, which is a nontrivial consideration on a personal level (e.g. in deciding where to accept a job or attend a school), but is rarely mentioned as it relates to society-wide impacts (e.g. with respect to possible changes in temperature or storminess in a particular region). I argue that this cost/benefit of climate change should be included in estimations of the overall effects. Leaving aside for a moment all other changes, the historical distribution of weather conditions sets a baseline for the culture of a region, month by month throughout the annual cycle, and this expectation may be considerably disrupted if monsoons, beach weather, snowstorms, etc. arrive at different times or not at all. It may be that this mood-weather relationship helps explain the observation that countries enduring clusters of repeated meteorological shocks are less able to recover on a unit basis than those for which the shocks are more evenly spaced out. And it's also likely a contributing factor to the expected erosion of economic gains in already-hot countries, which will face not only more-extreme heat but also more-severe storms.
All in all, it's abundantly clear that we not only shape the climate to some extent, but that the climate also shapes us -- our productivity, our mood -- in subtle yet unrelenting ways. It's even the case that temperature affects things as intimate, individual, and seemingly intrinsic as condom usage (and resultant HIV transmission rates). And this is not to mention the myriad other effects on our health. All of which goes to show that, especially going forward, the very term 'natural disaster' must be re-evaluated as regards anthropogenically-influenced changes in temperature, pollutants, and storms. While there may be reasonable debate about the location and magnitude of these changes, and the cost of the interventions intended to address them, there should be no doubt that they affect us all, no matter who we are or what we do.
This rather complex table summarizes the results of many earlier studies investigating the effects of temperature and pollutants on the quality of work performed in sedentary indoor settings, with strong negative effects reported especially for volatile organic compounds [VOCs] and high temperatures. Source: Mendell and Heath, 2005.
In billions of years,
Us. And this is what we do?
The world keeps turning.
In this post I'll recap the full year 2016 in the continental US — as well as winter 2017 — using comfort scores, as a supplement to the many other high-quality summaries out there that use more-traditional metrics (i.e. average temperature). Essentially, I intend it to capture the perceived pleasantness of a given 6-hour period, with temperatures between 70 F and 80 F considered ideal, and temperatures below 70 F or above 80 F accumulating 'discomfort' points in accordance with their wind chill or heat index respectively. Points are apportioned so that, for example, a wind chill of 30 F is considered roughly equivalent in comfort to a heat index of 92 F. This definition is applied to NCEP reanalysis data to produce the below figures and calculations. Additional comfort-score maps are found over on the Recent Weather page.
Averaged over the entire year (first figure below), it's clear that heat and humidity over the Gulf of Mexico combine to make it, and areas immediately adjacent to it, quite uncomfortable. In fact, due to high heat indices, these areas lead the country in discomfort three seasons of the year. With its lower moisture, despite high summer temperatures, most of the West is considerably more comfortable than the Southeast, with the sole exception of North-American-Monsoon-influenced southern Arizona. The entire Pacific coast, along with eastern New England, stand out as the most comfortable spots. In the northern tier of the country and into southern Canada, the annual-average discomfort is dominated by the winter months, and thus it increases going further north. In terms of individual cities, the most comfortable year-round are San Francisco (mild winters and cool summers) and Honolulu (cool winters and warm summers). The most uncomfortable are Brownsville, TX and Miami.
In the next figure, we can see the same data computed relative to the normal picture. This shows that 2016 was a warm year in nearly all of the country: the southern parts were less comfortable than normal (percentiles > 50), and the northern parts were more comfortable (percentiles < 50). Where the color scale maxes out in dark red — in the northern Gulf of Mexico and the western Atlantic — discomfort was record-high due to record high SSTs, whereas in the Pacific Northwest it was record-low, due mostly to a lack of cold wintertime temperatures in concurrence with the cloudiness and precipitation of the strong El Niño.
Plots for winter 2017 can be found on the Recent Weather page. In winter, moisture is low enough that it doesn't really play a role in affecting comfort, and so comfort is a temperature-only story. Thus, as is typical, the most comfortable areas were Hawaii and Florida, and the least comfortable the Upper Midwest across to the inland Northwest (with anomalous warmth in the inland Northeast taking it out of this competition in which it is normally a contender). The Southeast and lower Midwest were much more comfortable than normal due to persistent anomalous warmth that has also resulted in those areas having phenological spring arrive several weeks early. In contrast to last year, cool conditions have dominated the Pacific Northwest and made it less comfortable than normal (the conclusion also reached by the Weather Channel after a parade of storms there).
As in 2016, a recap and discussion of the past winter in terms of snowfall (also tracked regularly on the Recent Weather page) will happen once the snow stops falling at the highest ski resorts, probably sometime in May. Regular blog posts will of course occur in the interim, so keep an eye out for those!
The faintest ink is stronger than the strongest memory — paraphrasation of a Chinese proverb
In the world today, we have data galore, and yet many issues of substantial public policy are decided largely through the perceptions of the deciders, whether the issue is economic, climatic, or otherwise, and whether or not the perception is substantiated by the data. I write this post on the presupposition that the truth still matters, though some cynics may argue that narrative is now everything and reality is nothing.
Part of the problem is that data is meek and tends to be overwritten (even among the scientifically literate and open-minded) by anecdotal-type perceptions that are dominated by memorable events and are in many cases not representative of the climate at a particular time and place. As a plausible hypothetical, the one year when there were two large snowstorms a week apart, and not the 5 years in between with no such storms at all. One recent study showed that perceptions of weather 5-20 years prior were pretty far off (the only category where people had any skill at all was for recent summer temperatures), except for flashbulb memories on personally momentous days, such as Danes' very accurate recollections, decades later, of the weather on the days of the German invasion and liberation of their country during WWII. For flashbulb memories, bias — where it exists — tends to be positive when moods are high (liberation) and negative when moods are low (invasion), termed the “pleasantness bias” by psychologists. In line with this, respondents from Colorado stated the weather on Sep 11 was worse than average, although in actuality it was sunny and warm across nearly the whole of the United States. These misperceptions are quite likely a function of several factors, such as the human tendency to remember the most ‘impressive’ thing (as it makes a better story), and the survivalist need to remember as much as possible about the conditions that surrounded an extreme, so as to be prepared for its next occurrence. People, not being machines, are also subject to heuristics like the availability heuristic of extending recent trends into the future, and in fact we often have more-accurate memories when the weather is pleasant.
An example of the corruptibility of human perception of weather & climate issues: for people who are on the fence about anthropogenic climate change, the percent professing belief increases linearly with recent temperature anomalies (Source: Hamilton & Stampone 2013, "Short-Term Weather and Belief in Anthropogenic Climate Change").
As a result, memories will never be entirely representative; however I think there are ways to leverage them to help people better understand their “personal climate history”, by linking their memories more clearly to the supporting data. For example, by pointing out where their memories and the data line up (and thus where their memories provide local color to the data), and where they don’t musing about the reasons why they remember something that is on the whole different from what actually happened on the preponderance of days. Establishing a trustworthy and viscerally true-seeming linkage between data and perception is in my view key for determining whether climate events, whatever and wherever they are, will be responded to proportionately and appropriately for a given location.
One step in the direction of remedying this issue is the Common Sense Climate Index [CSCI], developed to quantify the 'feeling' people have as to whether or not the climate in their area has changed significantly during their lifetime. It is fairly simple in concept: it compares average temperatures and the frequency of extreme temperatures to the values they had in a baseline period, e.g. 1951-1980. When the index in a year exceeds one standard deviation relative to the baseline distribution, that means the year's temperatures would have been expected only out of every 6 years previously, and the devisers of the index presume this difference is large enough to be noticed by the casual observer. Four illustrative figures of the index are shown below; almost the whole world exceeded 1.0 in 2016, and it's been at or above 1.0 in recent years for the world, the United States, and many individual cities. Some areas, particularly those with high interannual temperature variability or land-atmosphere interactions, like the US High Plains, have yet to see a consistent upward trend.
Endeavors like the CSCI are no doubt beneficial in the communication of science at a gut level, but this is not to say that memories should be discounted or considered passé as a mechanism of understanding climate. On the contrary, they play an essential role by filling in where data could not be obtained any other way, or exists but is of uncertain quality. A study in Iceland gives the example of diarists recording glacial positions, or encountering polar bears where none now exist. As this type of information is inherently qualitative, it is especially valuable when it describes something binary, where the accuracy is hard to dispute — someone either saw a polar bear or they didn't, no room for ambiguity.
Also, memories of climate are significantly better than those of weather, particularly among people who work the land and have a good sense of conditions in a place over multiple generations, and who have precise and unambiguous points of reference that they note as a matter of habit: dates of ice breakup, dates of spring planting, locations of glacial tongues, etc. Even so, quantitatively, the best correlation between recollection and fact is pretty imperfect.
I'll close this post with some thoughts on how an index like the CSCI could be improved. Very simply, breaking out the components would allow people to see for example how extremes or particular months have changed in their region, helping them gain insight into the present climate (e.g. 'this past December seemed very unusual -- was it really?') and connect it with their own past. Another modification could be customizing the index based on a basket of indicators in each region that are regionally meaningful — special events, ski-resort opening/closing dates, days of snow cover, leaf-out dates, when the window A/C has to be brought out of the garage, the first autumn night requiring a winter coat, etc. Ideally, this would make the index maximally relevant to everyone's lives. Shifting the reference period for people of different ages, or those who have moved, would also help accomplish that goal. Finally, using more crowdsourced data, in the CSCI and in general, would likely stimulate greater interest and investment in local-climate problems, as people are always more emotionally tied to data they have a hand in producing and that they know matters to them. Scientists are trained to think that all data matters, but outside of science the relevance has to first be felt in order to be believed.