One of the constantly repeated, sympathy-seeking (and basis for wealth distribution) messages by politicians, by alarmists, by the mainstream media, and by brainwashed good-intentioned people, is that the world’s poorest countries have been hit hardest by human-induced global warming/climate change. Is it true or just some more of the constant stream of global warming/climate change disinformation? Let’s find out, shall we?
Berkeley Earth provides surface air temperature data for individual countries, so all we need to do is select a couple of logical references to compare with the poorest countries—that is, a couple of not-poorest references—and ways to present the surface air temperature data for the poorest and not-poorest countries so that we can confirm the claims, determining whether they’re real or outright baseless fabrications.
IMPORTANT INITIAL NOTE: I fully understood that poor people have the least financial resources with which to prepare for, and respond to, and shelter themselves from, changes in temperature and precipitation, i.e. from weather in general. That is NOT the topic of this post.
In this post, we’re going to illustrate and discuss if, since 1900, surface air temperature data confirm that the multidecadal rates of change in the surface air temperatures of the poorest countries have been greater or less than changes in a couple of reference not-poorest countries. We’ll also confirm something that should be obvious to anyone who had bothered to determine where those poorest countries are located around the globe. [End note.]
The ranking of countries in terms of wealth (GDP/capita) can be found at the Central Intelligence Agency’s The WORLD FACTBOOK webpage here. The list begins with the wealthiest country Liechtenstein—a teeny land-locked principality between Austria and Switzerland—and works its way down to the poorest country, which the CIA lists as Burundi.
Berkeley Earth provides near-surface land air temperature data broken down by countries here. Unfortunately, there is no data at the Berkeley Earth website for some of the poorest countries listed by the CIA. That is, according to the CIA, the countries of Tokelau, South Sudan, and Eritria are among the top-10 poorest countries in the world, but Berkeley Earth does not have data isolated for those countries. (I’m not pointing fingers. I just wanted to document why those countries weren’t included.) So I’ve included the next three poorest countries in terms of GDP/capita and one more for good measure, for which Berkeley Earth does have data. The countries included in this post are, in alphabetical order (links are to their respective CIA The WORLD FACTBOOK webpages), along with their latitudes according to Google:
Hmmm, looking at the latitudes, I believe I see something in common that might dispel some rumors, but let’s confirm that suspicion with data.
The countries I’ve selected as references are Liechtenstein and Norway. Why would I use those countries for references? First, according to the CIA’s The WORLD FACTBOOK, the richest country is Liechtenstein…the polar opposite in terms of wealth to the poorest countries. The Berkeley Earth data for Liechtenstein are here. Liechtenstein is found at 47N latitude, so it’s a European nation located in the mid-latitudes of the Northern Hemisphere. Second, considering that most if not all of the poorest countries are in the tropics (often described as the latitudes 24S-24N) and some are large enough so that they also extend into the subtropics (the subtropics include the latitudes from the tropics to 35N or 35S, depending on the hemisphere), I’ve selected Norway (data here) as the other reference. Norway is the northernmost country in the world. Undoubtedly, Norway has one of the harshest climates on Earth in terms of surface air temperatures for any country. Norway’s climate and the changes there are perfect counterpoints to those of tropical poor nations.
WHAT’S BEING PRESENTED?
Three comparison graphs of surface air temperature data are provided for each of the above-listed poorest countries, the curves for which are always shown in maroon. The curves for the reference country of Liechtenstein are in green, while for the other reference Norway they are in light blue.
Figure 1 includes a sample of one of the graphs. It includes surface air temperature data for Haiti along with those of Liechtenstein and Norway. Haiti is among the poorest countries in the world, but it’s not among the poorest 13, of which Berkeley Earth had data for 10. Also, Haiti neighbors the United States.
The graph in Figure 1 presents the multidecadal (30-year, actually 360-month) rates of change in surface air temperatures for the poor country (Haiti) and for the reference countries (Liechtenstein and Norway). 30-year rates of change in surface air temperatures are a great way to present the harshness of climate change for a country. Because weather is always changing, and because the standard definition of climate is weather averaged over periods of 3 decades or more, the 30-year rates of change (trends) in surface air temperatures are prime examples of climate change at work, regardless of whether you believe the changes are caused naturally or as a result of human activity.
But this is not how surface air temperature data are normally presented, so a graph like this may be confusing if you’re not familiar with it. For those requiring an explanation, first note the Title Block where the top line reads “30-Year Trends (Trailing) in Country Land Surface Air Temperatures”. Also note the units of the y-axis (the vertical axis) shown to the left of the graph. The units are Deg C/Decade, not simply Deg C. In other words, we’re illustrating how quickly or how slowly those 30-year trends were and whether, over those 30-year periods, the surface air temperatures were rising (positive data points) or falling (negative data points). The greater a positive [negative] data point is, the faster the surface air temperatures were rising [falling] over the 30-year period. Conversely, the closer a data point is to zero, the slower the surface air temperatures were rising (positive data points) or falling (negative data points) over the 30-year period. Also note the word “Trailing” in the top line of the title block. That means the trend for every 30-year (actually 360-month) period is shown in its final month. The x-axis, of course, is time in years.
If a series of data points is growing farther and farther away from zero with time, that means the 30-year trends are accelerating. Conversely, if a series of data points is growing closer and closer toward zero with time, that means the 30-year trends are decelerating. That is, a drop in the 30-year trends does not mean the surface air temperatures are cooling. To be showing cooling, the data points have to drop into negative values. For example, referring to the trends of our reference country Norway in blue, they peaked at the 30-year period ending about January 2008 at a rate of roughly 0.7 deg C/decade. But as you can plainly see, the 30-year trends dropped off after that so that five years later, the 30-year trends ending in 2013 were down to 0.4 deg C/decade for Norway’s changes in surface air temperatures. That is, the warming there continued through to the 30-year period ending in 2013, but the warming wasn’t happening as fast over the more-recent 30-year periods ending in 2013 as it had been during the 30-year period ending early in 2008.
The massive variations in the rates of surface air temperature change for Norway should come as no surprise to anyone who understands the naturally occurring phenomenon called polar amplification, which not only amplifies the warming at high latitudes of the Northern Hemisphere, but it also amplifies the cooling taking place there during cooling periods (see Figure 3 in the post Notes on Polar Amplification)…except in climate models as we illustrated and discussed in the post Polar Amplification – Observations Versus IPCC Climate Models.
NOTE: The surface air temperature data for individual countries at Berkeley Earth end in 2013, so we don’t know whether the 30-year rates of change in the surface air temperatures of Norway continued their decline…or whether they rose again. I’m not trying to hide anything by stopping the graphs with the 360-month period ending in 2013. The data from mid-2013 to now simply doesn’t exist on those Berkeley Earth webpages for individual countries. [End note.]
There are many noteworthy things shown in Figure 1—one of them being, because the number of positive trend data points exceed the number of data points for negative trends, the overall trend is positive. Another thing that I feel I have to mention now is, even before 1950—after which it is said that mankind began to be the primary influence on global climate—with the reference countries of Liechtenstein and Norway, there were major variations in the 30-year rates at which the surface temperatures for these countries were changing, sometimes warming and sometimes cooling over multidecadal timeframes. Sadly, the rest of the noteworthy things would keep us away from the intent of this post. But feel free to discuss them in the comments.
IMPORTANT NOTE ABOUT THE TREND GRAPHS: As you can plainly see, the variations in the 30-year rates of change in the surface air temperatures of the two reference countries far exceeded the variations in the 30-year rates of change in the surface air temperatures of the poor country Haiti. SPOILER ALERT: You’ll find that to be the case for all of the poorest countries presented in this post. In other words, with these examples, you’ll see that the poorest countries have NOT been hit hardest by climate change in terms of surface air temperature changes. Far from it. In fact, compared to the two references, especially those of Norway, the multidecadal variations in temperature-change rates for the poor country Haiti (and other poor countries, as you shall see) are quite mild…as we might expect for any country located in the tropics. [End note.]
Examples of the second two graphs provided for each country are shown in Figure 2. Both graphs present the average annual variations in monthly surface air temperature for the subject poor nation—Haiti for the example poor country—and the two reference countries Liechtenstein and Norway. The top graph is presented in Deg C, while the bottom graph is presented in Deg F…for visitors from the United States who are more familiar with those units. The curves are based on the average monthly surface air temperatures for the period of 1951-1980, which Berkeley Earth lists on their individual data webpages for each country, along with temperature anomalies. So, we can plainly see that the annual variations in the surface air temperatures of the reference countries are far greater than annual variations of the poor country Haiti. Also, the average monthly surface air temperatures in both reference countries fall below freezing for much of the year, while the average surface air temperatures for the poor country Haiti remain balmy.
And as we all know, cold ambient outdoor temperatures are many times more deadly to humans than hot ambient temperatures. I don’t recall ever discussing that point in a blog post, but I did in my well-illustrated ebook Dad, Why Are You A Global Warming Denier?- A Short Story That’s Right for the Times. In it, the Dad character was having a conversation about that subject with his daughter Anna. That exchange in the ebook, written in the first person by Anna, reads:
Dad was silent while he surfed the web, but then he suddenly said, “Change of topics. Consider this study from a few years ago that confirmed what many people have always thought: far and away more human deaths are caused by cold than heat. The paper Mortality risk attributable to high and low ambient temperature: a multicountry observational study, by Gasparrini, et al., was published in a medical journal, The Lancet, not a science journal, in July, 2015. There was an article in USA Today about it. Ah, there it is. Study: Cold kills 20 times more people than heat. Would you like to read it, Anna?”
“I remember that story. I wondered then why global warming was supposed to be bad, if cold kills more people than heat. Back then I figured it meant that precipitation and sea level were the key problems with global warming. But, you’ve already straightened me out on those two aspects.”
IMPORTANT NOTE ABOUT THE COMPARISON GRAPHS SHOWING THE ANNUAL CYCLES IN SURFACE TEMPERATURES: Not only are the 30-year changes in the surface temperatures noticeably more extreme in the reference not-poor countries than they are in the poor country (Figure 1), the local climates in the reference not-poor countries are also much harsher in terms of annual variations in surface temperatures than they are in the poor country, as shown in Figure 2. Of course the annual variations in temperatures are more temperate in the poorest countries; the poorest countries are located in the tropics. [End note.]
Just in case: While you’re running through the following series of graphs, you might start to think, as I did, that the temperatures of the poor countries are what you want see while you’re on your winter holidays, when you’re trying to get away from Old Man Winter. You’re probably right. Refer to Figure 3, which includes the annual monthly cycles in surface temperatures for Hawaii, Miami and Puerto Vallarta. The data for Hawaii are here, Miami are here, and Puerto Vallarta are here.
That’s enough for the introductory stuff, Bob. Let’s get on with the rest of the presentation.
And I’ll do it without commentary about what the graphs indicate, because, for the most part, I’d be repeating the commentary for the comparisons using Haiti in Figures 1 and 2.
NOTE: Many of the curves for the poor countries are very similar in the 30-year trend graphs. I can assure you they are different. With the annual cycle graphs, the curves for the poor countries can also be similar. But, sometimes, they are different enough to justify changing the scale of the y-axis, as you shall see. But you will find that a couple of the poorest countries have wider variations than the others. Look to see where those countries are located, and you’ll find the answer to those additional variations. [End note.]
Again, the data presentations for the poorest countries run in alphabetical order.
Figures 4 and 5 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Burundi (data here) compared with the reference countries of Liechtenstein and Norway.
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CENTRAL AFRICAN REPUBLIC
Figures 6 and 7 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Central African Republic (data here) compared with the reference countries of Liechtenstein and Norway.
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DEMOCRATIC REPUBLIC OF THE CONGO
Figures 8 and 9 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Democratic Republic of the Congo (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 10 and 11 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Liberia (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 12 and 13 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Madagascar (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 14 and 15 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Malawi (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 16 and 17 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Mozambique (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 18 and 19 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Niger (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 20 and 21 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Sierra Leone (data here) compared with the reference countries of Liechtenstein and Norway.
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Figures 22 and 23 present the comparisons of 30-trends and of annual cycles in surface air temperatures for Yemen (data here) compared with the reference countries of Liechtenstein and Norway.
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Well, the data confirms something most of us understand, and it is, climate is always changing, so we humans and the non-human residents of this planet are always used to changes in climate, which result from changes in weather, which is chaotic. And that brings to mind…
THE QUOTE FROM DR. GAVIN SCHMIDT, THE DIRECTOR OF THE NASA GODDARD INSTITUTE OF SPACE STUDIES (GISS)
And that quote, if you’ve been following my recent posts, is:
To be clear, no particular absolute global temperature provides a risk to society, it is the change in temperature compared to what we’ve been used to that matters.
Of course, the above quote comes from Gavin Schmidt, who is the Director of the NASA Goddard Institute of Space Studies. It is from a 2014 post at the “Climate science from real climate scientists” blog RealClimate, and that quote comes from the blog post Absolute temperatures and relative anomalies (Archived here, just in case.). So not to be accused of quoting Gavin out of context, I’ll present the full paragraph. The topic of discussion for the post was the wide span of absolute global mean temperatures [GMT, in the following quote] found in climate models. Gavin wrote (my boldface):
Most scientific discussions implicitly assume that these differences aren’t important i.e. the changes in temperature are robust to errors in the base GMT value, which is true, and perhaps more importantly, are focussed on the change of temperature anyway, since that is what impacts will be tied to. To be clear, no particular absolute global temperature provides a risk to society, it is the change in temperature compared to what we’ve been used to that matters.
Anyone with the slightest bit of common sense knows that, annually, the local ambient temperatures where they live vary much more than the 1-deg C change in global surface temperatures that data show we’ve experienced since preindustrial times and way much more than the 0.5-deg C additional change in global mean surface temperatures the UN has set its sights on trying to prevent in the future.
THAT’S ALL FOR NOW, FOLKS!
Makes you wonder, doesn’t it? With temperature-based climates like those and a little wealth, the poorest countries could be paradises…vacation spots that more and more people from wealthier countries would want to visit on their holidays. I’ll leave it to you to discuss what could help the poorest countries rise up from being poorest, and I think I can guess your answers.
Well, I’ve been stuck indoors for a number of days preparing this post. And it’s another beautiful day in Newfoundland, if you like snow and wind and cold…that’s what they’re forecasting anyway. I’m going to wander outside to go sit on a dock of the bay and watch the tide roll away—Thank you, Otis—so I may be a little slow responding to comments.
If necessary, for a Part 2 to this post, we’ll discuss and illustrate the horrendous climate model performance in their hindcasts of surface temperature and precipitation variability for the tropics/subtropics and for the mid-latitudes.
Have fun in the comments, and enjoy the rest of your day.
STANDARD CLOSING REQUEST
Please purchase my recently published ebooks. As many of you know, this year I published 2 ebooks that are available through Amazon in Kindle format:
And please purchase Anthony Watts’s et al. Climate Change: The Facts – 2017.
To those of you who have purchased them, thank you. To those of you who will purchase them, thank you, too.
via Watts Up With That?
December 7, 2018 at 12:02PM