In this post, we’re going to present graphs that show the annual lowest TMIN and highest TMAX Near-Land Surface Air Temperatures (not in anomaly form) for ten (10) Countries in an effort to add some perspective to global warming. The list of countries, which follows, includes the countries with the highest populations.
And, as always with my posts, as part of the text, there are hyperlinks to the data that were used to prepare the graphs. Just click on the links if you’re looking for the data.
INITIAL NOTES
First of all, TMIN is described by Berkeley Earth as the “Mean of Daily Low Temperatures”, while TMAX is described as the “Mean of Daily High Temperatures”. Berkeley Earth provides monthly TMIN and TMAX data until partway through 2013. The start month for these individual-country datasets at Berkeley Earth depends on data availability from the individual country. Sometimes they start in the early 1800s, maybe even the mid-to-late 1700s for countries to be included in future posts (like the United Kingdom), and other times they start in the mid-to-late 1800s, so I’ve chosen 1900 as the start year for this post. The year 1900 is the end year of the IPCC’s new definition of “pre-industrial” times, so starting the graphs in 1900 is also appropriate in that respect.
The illustrations in this post are intended to show the difference in magnitude between (1) the rise in global mean land+ocean surface temperature anomalies, which is how “global warming” is normally presented, and (2) the range of the lowest annual TMIN and highest annual TMAX temperatures (not anomalies) for each country. Rephrased, we’re going to illustrate, and confirm something you already know, that the magnitude of the changes every year from the lowest TMIN to the highest TMAX for each country—that is, the wide range in the annual variations in surface temperatures—dwarf the 1-deg C rise in global mean surface temperatures that has been experienced since the end of pre-industrial times (per the IPCC’s new definition of pre-industrial time). These are being presented because the United Nations has recently established a goal of limiting global warming to 0.5-deg C above the 1.0-deg C rise already seen, when in reality a 0.5-deg C change in global mean surface temperatures would hardly be perceptible to anyone or anything on our lovely planet Earth, especially when we consider the wide variations in ambient outdoor temperatures we experience locally every year, every month, every week, every day.
COUNTRIES PRESENTED IN THIS POST
The countries for which data are presented in this post are the Top-Ten Most-Populated Countries, According to the website World Population Review (Source archived here because population estimates change often, daily at some websites). We’re presenting near-surface land air temperature extreme data. (The hyperlinks are to their data pages at Berkeley Earth, the source of data for this post):
COUNTRY: (Population in Millions)
-
- China: 1,415
- India: 1,354
- United States: 327
- Indonesia: 267
- Brazil: 211
- Pakistan: 201
- Nigeria: 196
- Bangladesh: 166
- Russia: 144
- Mexico: 131
- TOTAL Population of these Countries: 4,412
And if we assume a total global population of 7.6 billion, the countries included in this post are home to almost 60% of Earth’s human residents.
Let’s also take a look at the ranking of “Action on Climate Change” in the UN’s My World 2015 poll, where there were 16 topics the UN wanted ranked as priorities:
COUNTRY: (Ranking of “Action on Climate Change” Out of 16 choices)
- China: 4th
- India: 15th
- United States: 10th
- Indonesia: 13th
- Brazil: 12th
- Pakistan: 16th
- Nigeria: 16th
- Bangladesh: 12th
- Russia: 15th
- Mexico: 12th
Considering that “Action on Climate Change” ranked dead last globally (see the MyWorldAnalytics webpage here), having it rank 4th in China was not what I expected.
STANDARD INTRODUCTION FOR THE “GLOBAL WARMING IN PERSPECTIVE” SERIES
A small group of international unelected bureaucrats who serve the United Nations, of environmental activists, and of businesses with financial interests [in] climate change laws, now want to limit the rise of global land+ocean surface temperatures to no more 1.5 deg C from pre-industrial times…even though we’ve already seen about 1.0 deg C of global warming since then. So we’re going to put that 1.0 deg C change in global surface temperatures in perspective by examining the ranges of surface temperatures “we’ve been used to” on our lovely shared home Earth.
The source of the quote in the title of this post is Gavin Schmidt, who is the Director of the NASA GISS (Goddard Institute of Space Studies). It is from a 2014 post at the blog RealClimate, and, specifically, that quote comes from the post Absolute temperatures and relative anomalies (Archived here.). The topic of discussion for that post at RealClimate 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 grasp of reality 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 Earth has 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 near future.
Please keep that 0.5-deg C in mind as you view the graphs and read the text that follow.
BTW, there were two posts at WattsUpWithThat about global mean surface temperatures in absolute form that preceded Dr. Schmidt’s post, and they may have prompted his post. The posts I’m referring to at WattsUpWithThat were Willis Eschenbach’s post CMIP5 Model Temperature Results in Excel and my post On the Elusive Absolute Global Mean Surface Temperature – A Model-Data Comparison. (WattsUpWithThat cross post is here.)
DATA SOURCE
The source of the data presented in this post is Berkeley Earth. Why Berkeley Earth? In addition to furnishing their datasets in anomaly form, Berkeley Earth also provides monthly period-average surface temperatures in absolute form for the base period (1951-1980) they use for the anomalies. So with those monthly absolute values, it’s easy to convert the monthly long-term temperature anomaly data into absolute temperature values, which is what we want for this presentation. (And before someone complains about my use of the term absolute, it is commonly used by the climate science industry when describing temperatures in their observed, not anomaly, form.)
Mean near-land surface air temperature data for individual countries can be found here at Berkeley Earth. Specifically, for each of the countries this post, we’re presenting data for the TMIN (which are described as “Mean of Daily Low Temperatures”) and data for the TMAX (which are described as “Mean of Daily High Temperatures”).
As references for the lowest TMIN and highest TMAX time series graphs in this post, I’ve also included the curve of the monthly Berkeley Earth global mean land+ocean surface temperature anomalies data…found here. There are two versions on that webpage, I’ve used the data with air temperatures above sea ice, because it has a slightly higher long term linear trend. With a linear trend of 0.084 deg C/decade, over the 113+ year term of the graph, and based on that linear trend, the data show the average temperature of the Earth’s surface has risen about 1.0 deg C.
HOW SURFACE TEMPERATURE DATA ARE NORMALLY PRESENTED
Normally, global land+ocean surface temperature anomaly data are presented in anomaly form, with the scaling of the y-axis as tight as possible to make the long-term and short-term variations appear large, when, in reality, they’re very small…so small you’d never notice them if it wasn’t for the constant browbeating with alarmist propaganda we’re receiving daily from politicians, from the mainstream media, from businesses whose profits depend on the climate change scare, and from members of the publically funded climate data and modeling businesses, which have to keep their funding alive. An example of a normal presentation of global mean surface temperature (GMST) data can be seen in Reference Figure 1. It is a graph created by NASA GISS (Goddard Institute of Space Studies) and is available at their website here in .png form.
Reference Figure 1
When viewing the following time-series graphs, the black curves in my graphs are the Berkeley Earth-based monthly global mean land-ocean surface temperature anomalies equivalent of the curve above in Reference Figure 1.
AN EXAMPLE OF WHAT’S BEING PRESENTED
Reference Figure 2 is an example of what’s being presented in this post, but instead of TMIN and TMAX data for individual countries, the data in Reference Figure 2 is derived from the global mean data for near-surface land air temperatures. The Berkeley Earth global TMAX data for land surfaces are here and the global TMIN data are here. The blue curve toward the bottom includes the data for the annual lowest TMIN temperatures and red curve toward the top includes the data for the annual highest TMAX Near-Land Surface Air Temperatures (not in anomaly form). As noted above, the black curve toward the middle is for the Berkeley Earth annual global mean land+ocean surface temperature anomaly data, referred to on the graph as GMST for Global Mean Surface Temperature. For illustration purposes, and depending on the data for the individual country, I shift the curve [of the] GMST data so that it remains between the curves of the TMIN and TMAX data. With some countries, it’s not necessary and the GMST curve hugs 0.0 deg C. Also included on the graphs for each country are the trends—the warming rates as calculated by MS EXCEL—for the highest annual TMAX temperatures and the lowest annual TMIN temperatures.
Now notice how small the short- and long-term variations in global mean surface temperature (GMST) look. That’s because they are small, but you wouldn’t know that looking at a graph like the one prepared by NASA GISS in Reference Figure 1, above.
Reference Figure 2
As you can see, the trend of the highest annual global mean TMAX temperatures for land surfaces is slightly lower than the trend of the GMST data, which includes the surfaces of lands and oceans. On the other hand, the trend of the lowest annual global mean TMIN temperatures for land surfaces is noticeably higher than the trend of the GMST data, roughly twice as high. That is, globally, Earth’s daily high temperatures are warming much slower than Earth’s daily low temperatures. But data for the Earth’s countries hold surprises and the warming rates of the highest TMAX and lowest TMIN will differ with each country as you shall see. In one example in this post (for Mexico in Figure 10), the warming rate of the highest annual TMAX was slightly more than the lowest annual TMIN, though both were comparable to or lower than the trend for the annual GMST (land+ocean surface) data.
So there’s lots of information provided in each graph.
To aid in your understanding of what’s being presented in this post, see Reference Figure 3. It shows the monthly global TMIN and TMAX temperatures (not anomalies) for the global land surfaces with their wide annual variations. From the data used to create the graph in Reference Figure 3, I’ve extracted the highest annual values of the TMAX data and the lowest annual values of the TMIN data for Reference Figure 2. In other words looking at Reference Figure 3, I’ve gathered the annual extreme highest (peak) values of the red curve and the annual extreme lowest (valley) values of the blue curve to create Reference Figure 2. That way, as noted above, we can have the spreadsheet software calculate the linear trends of the high temperature extremes and low temperature extremes for the air temperatures near to the surfaces in each of the countries. And we can compare those to the warming rate of global mean surface temperatures, which is how global warming is normally presented.
Reference Figure 3
IMPORTANT NOTE: For the individual countries, if you were to attempt to extract the highest annual TMAX temperature and lowest annual TMIN temperature curves from anomaly data you will likely wind up with decidedly different results, as discussed and presented in the post here. (The WattsUpWithThat cross post is here.)
There seemed to be some disagreement about the use of actual temperatures (not anomalies) versus temperature anomalies in the comments at WattsUpWithThat for that post, so, as they say, a picture’s worth a thousand words.
In Reference Figure 4, I’ve plotted the mean TMAX temperatures for the Contiguous United States for two years (1958 in dark blue and 1959 in red). Also included is the respective average TMAX temperatures for the period 1951-1980 (black dotted curve), which is the 30-year period that Berkeley Earth uses when calculating temperature anomalies. As you can see, in 1959 (red curve), the highest TMAX temperature occurred in July, same month as the base-year average. But, in 1958 (blue curve), highest TMAX temperature occurred in August. For a time-series graph of the highest TMAX temperatures for the contiguous USA (Figure 3), the curve includes the July 1959 and August 1958 values, because they were the highest TMAX temperatures in those years.
Reference Figure 4
Now let’s take a look at the TMAX temperature anomalies for those two years. See Reference Figure 5. As you know, temperature anomalies are calculated as the difference between the actual temperature for a given month and the average temperature for that month base on a reference period, which is 1951-1980 for Berkeley Earth. If I were to plot the highest of the TMAX temperature anomalies in this post, they would likely have little to do with the highest actual TMAX temperatures, because the highest anomalies may occur randomly based on the local weather for a given month. In 1958, the highest TMAX anomaly happened in May, which was not the warmest TMAX month that year. Likewise, in 1959, the highest TMAX anomaly occurred in December, which was not the warmest TMAX month in 1959.
Reference Figure 5
[End note.]
I hope the preceding discussion and illustrations help you understand what’s being presented in Figures 1 to 10.
THE FOLLOWING TIMES SERIES GRAPHS HELP TO PUT INTO PERSPECTIVE THE 1-DEG C RISE IN GLOBAL MEAN SURFACE TEMPERATURES WE’VE ALREADY SEEN SINCE 1900
The initial 10 time-series graphs that follow (Figures 1 to 10) are provided for one simple reason: They compare the silly little 1-deg C rise in global mean surface temperature anomalies to the magnitude of the temperatures differences in the annual extremes we deal with every year, in terms of TMAX and TMIN surface temperatures (not anomalies) for individual countries. This allows viewers to the put into perspective the 1.0 deg C rise in global mean surface temperatures. In fact, there is even a note in dark blue in each graph that lists the difference in temperature between the Lowest Average TMIN and the Highest Average TMAX for the commonly used and WMO-recommended “climatological-standard normals” period of 1981-2010. (See the post here for a discussion of the WMO’s two sets of “normal” periods. The WattsUpWithThat cross post is here.) As you shall see, those differences can be many, many times greater than the 1 deg C rise in average surface temperatures the Earth has experienced since pre-industrial times.
In other words, with reference to the above quote by Dr. Schmidt, every year, we, the residents of the countries presented in this post, are “used to” much greater variations in the surface temperatures of the countries where we live than the teeny little 1-deg C rise in global mean surface temperatures over a past period of 100+ years. How great? The following are those differences for the countries presented in this post:
Country: Average Temperature Difference Between Average Annual TMAX High and Average Annual TMIN Low for the Reference Period of 1981-2010
- China: 39.1 deg C
- India: 26.5 deg C
- United States: 37.3 deg C
- Indonesia: 9.8 deg C
- Brazil: 13.6 deg C
- Pakistan: 34.6 deg C
- Nigeria: 19.8 deg C
- Bangladesh: 21.5
- Russia: 50.9 deg C
- Mexico: 24.8
And to extend that range a bit more, of the countries presented in this post, Indonesia had the highest average TMAX high temperature for the period of 1981-2010 at 31.2 deg C (88 deg F) and Russia had the lowest average TMIN low temperature for that 30-year period at -29.5 deg C (-21 deg F). Now consider, in our hometowns, local ambient temperatures can easily change more than 20 deg F (11 Deg C) in one day, on top of the slower monthly variations. See Reference Figure 6 below. It is a graph of actual and average temperatures in Washington D.C.—where politicians propose goofy stuff like taxing U.S. citizens to reduce U.S. carbon emissions 90% by 2050 (Thanks, Willis). It’s for the month of November 2018, available from Accuweather, specifically their webpage here. The inhabitants of this planet are quite adaptable, obviously.
Reference Figure 6
All of those are good numbers to recall when you hear some goofy politician talking nonsense about raising our utility costs or taxing us to provide a “stable climate”. Oy vey!
And now the much-awaited time-series graphs for the ten countries with the highest populations:
Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 9
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Figure 10
A FEW THINGS STOOD OUT TO ME
And the things that stood out were the very low warming rates of the annual highest TMAX temperatures for the United States, China, India, and Pakistan. I’ve plotted the TMAX data separately for those countries and presented them in Figures 11 through 14 for your viewing pleasure and comment.
Figure 15 was added as an afterthought, because of the record high blips in the 1930s and 1950s. Sorry, the TMAX data end part way through 2013, so we have no way to know what happened more recently.
Figure 11
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Figure 12
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Figure 13
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Figure 14
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Figure 15
If you want to search for the possible sources of the tremendous downward spikes in the highest TMAX temperatures for two of the countries, they occurred in 1915, 1950 and 1992 in the Contiguous U.S. data, and for India, they occurred in 1917 and 1932.
The rest of the annual wiggles appear to be normal variations attributable to weather events.
TRENDS: SURFACE AREA-WEIGHTED AVERAGES VERSUS POPULATION-WEIGHTED AVERAGES
I took two approaches when determining the weighted averages of trends for the highest annual TMAX temperatures and the lowest annual TMIN temperatures. See Tables 1 and 2. I weighted the averages based on surface areas of the countries included in this post, Table 1. And for Table 2, I weighted the averages based on populations of the countries. The trends of the population-weighted averages are noticeably less than those of the area-weighted averages. In fact, the population-weighted trend for the highest annual TMAX temperatures for those ten countries, where almost 60% of Earth’s population reside, is only 0.035 deg C per decade.
Tables 1 & 2 (CLICK TO ENLARGE.)
And as noted below the tables, the trends listed in Tables 1 and 2 are for the highest annual TMAX temperatures (not anomalies) and the lowest annual TMIN temperatures (not anomalies).
CLOSING COMMENTS
Yes, I understand there can be wide differences in ambient temperatures within a country. The average annual surface temperatures in Chicago, IL and New York City, New York are roughly 12-deg C (22-deg F) cooler than they are in Tampa, Florida.
Further to this end, globally, in locations where humans, animals and plants reside, there can be very wide differences in the TMIN and TMAX temperatures as illustrated in Reference Figure 7a (Celsius) and 7b (Fahrenheit), which show the average annual cycle of TMAX temperatures for a “hot” country Oman (data here) and the average annual cycle of TMIN temperatures for a “cold” country Russia (data here), where the period used for the averages is the Berkeley Earth standard reference period of 1951-1980. Specifically, there’s a 70-deg C (126 deg F) temperature difference between the highest average TMAX in Oman and the lowest average TMIN in Russia. Obviously, the residents of this planet—animals, plants, and humans—are “used to” a very wide range of temperature extremes.
Reference Figure 7a
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Reference Figure 7b
But as alarmists would like us to believe, we’re all going to roast in our self-imposed, fossil-fuel-burning, CO2-emitting hells if global mean land+ocean surface temperatures rise another 0.5 deg C (0.9 deg F). And then they get all huffy when people disagree with them. Go figure.
NEXT POST IN THIS SERIES
The next post with time-series graphs of highest annual TMAX temperatures and lowest annual TMIN temperatures like the one in Figures 1 through 10 will include data for countries where I believe most of the visitors to WattsUpWithThat reside. They include (with links to the respective data webpages at Berkeley Earth):
- United States (Again)
- United Kingdom (Europe)
- Canada
- Australia
- Germany
- Norway
- India (Again)
And because they were requested at WattsUpWithThat in the thread of the preceding post in this series:
That’s it for this post. Thanks for taking time out of your day to read the text and examine the graphs.
Have fun in the comments below, and enjoy 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:
- Dad, Why Are You A Global Warming Denier? (For an overview, the blog post that introduced it is here.)
- Dad, Is Climate Getting Worse in the United States? (See the blog post here for an overview.)
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.
Regards,
Note: I repaired a few typos in Reference Figure 2 a few minutes after the initial publication.
In 1582 the Gregorian calendar was introduced and then was adopted by various countries around the world at different times with England not adopting it until 1752. Germany had waited until 1700. Orthodox countries hung on to old calendar for a lot longer. The result in every case was advancing the calendar by 11 days to get the seasons in sync with the Julian Calendar which was adopted in Europe in 46 BC.
This may very well affect temperature records in England since they go back that far.
Ah, yes14 days later, we still celebrate Xmas on 7th and NY on 14 Jan.
“This may very well affect temperature records in England since they go back that far.”
I’m pretty sure CET takes into account the change to the calendar. Gordon Manley makes specific reference to the problem in his paper. But if the change was ignored I’d expect there would be a pretty obvious pattern of discontinuities in the monthly data. I’d guess some months should change by at least a degree, but there’s no obvious change in any month over this period.
You might be surprised. Until I wrote to the Met office couple of years ago they did not take into account, when calculating annual temperature data, that not all months are equally long, giving February same weight as July or August etc making some years at the extreme nearly 0.1C cooler. (the value is aproaching the extend attributed to cooling during sunspot cycles minima according to LSvalgaard.)
Doesn’t surprise me that you ignored the point I was making. If Gorden Manley was not taking into account the change in the Calendar I think this would be quite evident in the monthly data, with some months showing a change after 1752 of a degree or so.
Unless you can demonstrate this discontinuity I think this is one are where I’ll trust Gorden Manley.
I have no idea what G Manley may have or have not said or done.
In 1752 Wednesday 2 September was followed by Thursday 14 September by dropping 11 days thus making year 1752 shorter,
down to 355 days.
It is not clear to me how records should be adjusted to be correct, perhaps you know, since I don’t.
I would just kept data as it was with a note referencing the change.
As an example I would quote Wilcox Solar Obsevatry case when optical filters were changed to give superior data quality; instead correcting old data they kept records with the appropriate note.
see here
On the other hand sunspots data administratiors kept doing their individual corrections making historic data questionable, to be eventually re-evaluated by Dr Svalgaard&co.
Historic observations or instrumental records should be preserved in the original form, and if there is a discontinuity so be it, as long as it is referenced.
P.s to make the above link work replace the ‘htm’ with ‘gif’
“It is not clear to me how records should be adjusted to be correct, perhaps you know, since I don’t.”
It’s pretty simple if you have daily data – just convert dates prior to the switch from old style to new style. If you don’t do this than the records for any give month prior to the switch will not be comparing like for like with the same month after the switch. May 1751 using the Julian Calendar would be the 12th May through the 11th June using the current calendar, hence quite a bit warmer.
vukcevic did not ignore the point you were making, he was just pointing out that your faith in the capabilities of the Met are not well founded.
Mr Bellman
Daily data is available only from 1772, while prior to 1772 there are only monthly temperatures estimates.
https://www.metoffice.gov.uk/hadobs/hadcet/data/download.html
Since September 1752 lasted only 19 days, I can not see an acceptable method of somehow shifting previous 100+ months by 11 days going back to 1659.
vukcevic,
I think you are misunderstanding the daily CET. This is a separate data set designed to be compatible with Manley’s monthly set. But the reason Parker et al only took the daily data back to 1772 was not because daily data didn’t exist, but because it was based on indoor temperatures, which would be difficult to adjust on a daily basis.
If you want to discount CET before 1752 that’s OK by me. I don’t believe the early data is very reliable for a whole lot of reasons besides the Julian Calendar. But I do think it’s unfair to claim that Manley simply ignored the issue.
To back up some of my guesswork from yesterday, if you compare recent daily values ( 1981 – 2010) then the first 11 days of May are around 3°C cooler than the first 11 days of June. This means if you were using the Julian Calendar you would expect May to be on average around 1°C warmer than under the current calendar. September would be around 1°C cooler under the Julian system. Yet the average May temperature for the 30 years before the switch are almost identical to the 30 years after it. (11.4°C before, 11.3°C after) September temperature drop a little after 1752 (from 14.0°C to 13.4°C) which is the opposite direction to what you’d expect if the Julian definition of September was being used before 1752.
In short I can see no evidence that there was the expected shift in temperatures you would expect if the change in calendars was being ignored.
Few days back I presented in my observations on an article presented on this wesite on max and min temperature for India [infact I had more countries data]. The trend in min is higher than max. Same is seen here. The high min trend is associated with urban factor — as the data sets from rural are few compared to urban. Otherwise both max and min should have been flat with zero trend.
Dr. S. Jeevananda Reddy
Exactly my thoughts when I got to that bit in the article. Man made global warming substantially consists of the UHI effect plus ‘homogenization’.
Couldn’t agree more.
In the UK the adjustment for UHI is around 1.5C and yet weather forecasts in the late Autumn (Fall) and Winter routinely warn that overnight temperatures in the rural areas near London will be 3-4C COLDER than in the urban areas.
Correct and accurate adjustment for UHI would, I suspect, show no warming trend in TMIN. That would significantly reduce the claimed warming.
Dr. S. Jeevananda Reddy – December 15, 2018 at 2:42 am
But, but, but there are two “wild cards” in your above deck that you haven’t accounted for.
#1 – the “urban (heat island) factor” of pre-1930 is probably 1/20th of what it now is, post-2000.
#2 – the naturally occurring post-LIA Interglacial Warming has never been considered, calculated or included in any near-surface temperature data.
Thus, given #2 above, …… all post-1880 Interglacial Warming has been “highjacked” by the proponents of AGW who have been blaming all of the aforesaid IG warming on increases in atmospheric CO2 quantities. A blatant falsehood that can only be proven on the backside of dingy, dirty, damp “barroom” napkins. 😊
The IPCC implies that LIA warming is “less than half” of 1.0 C.
This is of course ignored when the “experts” claim we have had 1.0 C of warning and you will be shouted down if you dare point this out.
Dr. S. Jeevananda Reddy
I agree that the thermal mass of urban areas will cause TMin to increase as cities are expanded. However, the ‘Greenhouse Gas’ hypothesis predicts that CO2 will impede night-time cooling, thus one would expect an increase in TMin in both urban and rural areas. However, CO2 and H2O are probably elevated in urban areas because of combustion of fossil fuels and different water use patterns.
In urban areas, the pollutants released are short lived unlike CO2 — with no cumulative impact. In winter, the formation of temperature inversions [called mixing depth], common in higher latitudes, cause the min temperature raise. This is positive in urban areas and negative in rural areas. The concrete-thar roads and vertical buildings give input energy to raise in urban areas and colder water vapour in rural areas. Because of this the original satellite data showed no warming trend. This was removed from the internet. The current satellite data follows the ground data. The main falacy is, even though ground based temperature lack uniformity in space and time, satellite data matches with that, Don’t you think it is a manipulated data?
There is no warming at global level. The temperature and other meteorological parameters change with climate system existing in the respective areas. We never take them into account.
Few decades back, one scientist from IITM Pune [later he became the director – a friend of Prof. Sukla when he was in IITM] while interpreting MONEX data, he drew isolines over Himalayan Mountains forgetting the fact of Jet Stream. DDGA from IMD openly scolded him in the workshop.
Dr. S. Jeevananda Reddy
”This was removed from the internet. The current satellite data follows the ground data.”
What?
Great post, Bob. Wandering through the dramatic temperature differences for various countries reminds me of the dramatic variation in the flora and fauna inhabiting our planet Earth. If you transplanted a cactus from Mexico to Russia I don’t think it would be “happy”. But if you took a human from Miami to Vail during January, they would ski and think it was terrific (my personal tendency is the reverse, going to Miami Beach in July and thinking it is terrific). Your post clearly shows there is something for everyone and their favorite climate on our planet Earth. And, since there tends to be deserts nearer the equator and frozen tundra nearer the poles, we humans can adapt to either temperature trend.
Thanks, Bob, for your continuous hard work in assembling the climate information. Unfortunately, after the first thousand and one graphs (adjusted or not) viewed in any one year, one’s mind glazes over and one’s attention moves to the World outside the window where nothing of Climatic significance has occurred in the last seventy years at least.
So true
“In this post, we’re going to present graphs…”
And the problem is, they look much the same for different countries. That is just one of the problems with absolute temperature; all years look much the same and places look pretty similar too. The space on the graph is devoted to spreading out the picture of seasonal change, which damps out everything else.
Now you’ll say, yes, that is the intention, to show how small the changes are. You don’t need a long series of time series graphs just to show you think the time progression is small. But comparing change in average to transient oscillations is misleading. The global change in glaciation is only about 6°C, but it makes a radical difference to the world. Even though a weather change change of 6°C makes very little difference at all.
Which is because the ocean which are 70% of the surface moderates global averages and why either sort of analysis is stupid.
Living in central WI, I dress each day according to the weather I see when I look out the window (there’s no such thing as bad weather–only the wrong clothes). I don’t dress according to the average annual temperature for the Earth, or even for my location. Any species that has evolved to be so sensitive to temperature that a rise of a mere 1 or 2 deg in average temps would affect its survival is doomed anyway because it will frequently experience much greater temp swings throughout the year. Survival depends on the extremes, not the average.
A change in average weather drastic enough to cause a change in biome is climate change. Anything less is just weather variability.
You’re too practical. You might try dressing according to feelings and the desire for social justice (or not).
You’re doing it wrong, then. You should dress according to the daily predicted relative change since the previous day. So if it’s 1 deg C warmer, you can toss off a scarf; 2 deg C warmer, off goes the jacket, etc. It’s not the absolute temperature that matters, but the change of the average!
/sarc
Nick, in response to my opening statement in the post, “In this post, we’re going to present graphs…”
You began your comment, “And the problem is, they look much the same for different countries…”
Your limited powers of observation are showing, Nick.
Adios,
Bob
Yes, Bob. I agree with you. I can see differences. But then I was looking for differences.
It’s true that there isn’t a wide global difference in temperature between a glacial state and an interglacial. But that’s not because a *global* change in average makes a large difference no matter what direction it is in — it’s because we are in an ice age and dangerously close to slipping back into an ice age, and that seems to be triggered by *local* changes in glacial inception regions rather than the magnitude of the global effect. Since we are in an interglacial, appealing to the dramatic effects of cooling says nothing about the impact of an equal amount of warming — to the contrary, an equal amount of warming could extend the interglacial, which is very very good news for mankind. If Anthropegenic Global Cooling were a thing, that could absolutely slip into CAGC. For AGW, CAGW is science-free partisanship.
This is also true of CO2 atmospheric concentrations. Doubling pre-industrial CO2 concentrations is beneficial to life, halving pre-industrial CO2 concentrations would take us dangerously close to extinguishing it. The effects are not symmetrical between raising and lowering, and appealing to the dangerous consequences of *lowering* to imply that *raising* must be similarly risky is the same bad logic (or intentional deception) as appealing to a catastrophic cooling risk to imply that a catastrophic warming risk also exists.
“And the problem [with the graphs] is, they look much the same for different countries.”
I noticed that. The TMax charts for all those nations show the 1930’s to be as warm or warmer than subsequent years. Yes, they look very much the same.
I guess Bob will show us some graphs like this for nations in the southern hemisphere in his coming posts. I’m curious to see if they too show the 1930’s to be as warm or warmer than subsequent years.
If they do, why, then that would mean we are looking at a global pattern. Wouldn’t it?
If the global pattern is that the 1930’s were just as warm as today, and this 1930’s warmth was generated before CO2 became a potentially significant factor (according to the IPCC), then there is no reason to blame current warming on CO2, when it could just as easily be Mother Nature/Natural Variation causing the current warming just like She did in the 1930’s
Which means the weather we are currently experiencing is not unprecedented. We have been this warm in the recent past. We are not getting “hotter and hotter”. There is no need to bankrupt the world’s economies and impoverish the people of the Earth by trying to power the world with windmills.
I guess that’s why the Alarmists use anomalies instead of real temperatures, so they can keep this CAGW fraud going.
That pretty much captures the situation. Good comment.
Tom Abbott – December 15, 2018 at 6:22 am
We have been getting “warmer and warmer” ….. but that increase in temperature has been restricted to the daily “low” or daily minimum temps, ……. not the daily “high” or daily maximum temps.
If one looks at most any recent multi-year Annual Average Temperature graph it will show an increase in the Average Temperatures for the specified time frame, …… but how does one know if said increase is due to an increase in the Average Winter Temperatures or an increase in the Average Summer Temperatures? WE DON’T KNOW.
If the Average Winter Temperatures were steadily getting less cold (warmer) over the past 60 years …. which we know is an observational fact …… and the Average Summer Temperatures remained about the same, ……. then wouldn’t that produce an increase in Average Temperatures over said 60 year time frame? ABSOLUTELY IT WOULD.
And if so, wouldn’t that rule out the presumed “greenhouse” effect of atmospheric CO2? ABSOLUTELY IT WOULD.
If the atmospheric CO2 is increasing but the Summer temperatures are not getting hotter …… then atmospheric CO2 is not affecting near earth temperatures.
If the Average Summer Temperatures had been increasing at the same rate as the Average Winter Temperatures, which they should have been if atmospheric CO2 is the culprit, then 100+ degree F days would now be commonplace throughout the United States during the Summer months. But they are not commonplace and still only rarely happen except in the desert Southwest where they have always been commonplace.
Now, instead of saying that “the Earth is warming” it is more technically correct to say “the earth has not been cooling off as much during its cold/cool periods or seasons”.
One example of said “short term” non-cooling occcurs quite frequently and is commonly referred to as “Indian Summer”.
Given the above, anytime the earth’s average calculated temperature fails to decrease to the temperature recorded for the previous year(s), it will cause an INCREASE or spike in the Average Temperature Calculation results for that period ….. which is cause for many people to falsely believe “the earth is getting hotter”.
What you are missing is that it shows that although the World has been warming it has been getting less Cold rather than hotter.
Which the graphs show nicely thankyou.
I really enjoyed this analysis. I have long wondered if human activity ( island heat effect ) would affect the minimum temperature more (keeping nights warm ?). So what would a population rise (numbers or urban area increase) normalised temperature rise look like ?
Mike D. I agree. Most of the charts that are shown focus on the daily high temperatures. I have noticed, where I live, that the daily highs have stayed around the norms, while the nights rarely have reached the average low temp for the area. All of the TMAX/TMIN charts show a larger rise in the minimums than the maximums. Only in Mexico was the increase high for the max.
Sir David Attenborough states that his opinion on climate change was influenced by a lecture given by the late Professor Ralph Cicerone. The main points of this lecture are explained in a Word file which covers a presentation to the US Senate in July, 2005. Details may be found at-
https://www.carbonbrief.org/the-2004-lecture-that-finally-convinced-david-attenborough-about-global-warmingwarming
This presentation includes a graph showing a world temperature increase of about 0.7 degrees C from 1880.
Professor Cicerone comments on the warming from the 1900’s to the 1940’s and the 1970’s to 2005 with a stable period in between but does not note that this does not match the steady increase of CO2 since 1880.
Would it be possible to prepare a graph showing both temperature rise and CO2 levels from 1880 showing that the increase in temperature does not follow the same even rise as CO2, please? Also temperature graphs showing the effect of different vertical scales?
“Professor Cicerone comments on the warming from the 1900’s to the 1940’s and the 1970’s to 2005 with a stable period in between but does not note that this does not match the steady increase of CO2 since 1880.”
Correct – it doesn’t.
However there were NV factors at play.
A +ve PDO ENSO regime in the 30’s/40’s and atmospheric aerosols after that (global dimming).
Aside from that the forcing from CO2 did not really cancel the -ve forcings until post 1970.
In short there other factors at play and no one ever said the CO2 will cause a monotonous global temp rise without modulation from natural factors overlaying – though that modulation will become increasingly faint with time.
http://2.bp.blogspot.com/-Fkg790Q3b8o/VMRGN17t2oI/AAAAAAAAHwo/GTCVnmku248/s1600/GISTempPDO.gif
“though that modulation will become increasingly faint with time.”
That is your opinion AP Banton, and you need to say that to avoid the claim being treated as fact.
AP Banton, you make some interesting points:
“no one ever said the CO2 will cause a monotonous global temp rise”
The Global Climate Model ensembles project a pretty monotonous global temperature rise.
“…though that modulation will become increasingly faint with time”
I’d be interested in a link to a paper that demonstrates this.
LittleOil, the only planets on which global temperatures follow the rise on CO2 are the virtual planets in climate models, which aren’t simulating Earth’s climate.
I include a model-data comparison of the 30-year trends in global mean surface temperatures in my monthly updates here at WUWT which are cross posted at my blog. An example:
The text of the post regarding that graph reads:
“There are numerous things to note in the trend comparison. First, there is a growing divergence between models and data starting in the early 2000s. The continued rise in the model trends indicates global surface warming is supposed to be accelerating, but the data indicate little to no acceleration since then. Second, the plateau in the data warming rates begins in the early 1990s, indicating that there has been very little acceleration of global warming for more than 2 decades. This suggests that there MAY BE a maximum rate at which surface temperatures can warm. Third, note that the observed 30-year trend ending in the mid-1940s is comparable to the recent 30-year trends. (That, of course, is a function of the new NOAA ERSST.v5 data used by GISS.) Fourth, yet that high 30-year warming ending about 1945 occurred without being caused by the forcings that drive the climate models. That is, the climate models indicate that global surface temperatures should have warmed at about a third that fast if global surface temperatures were dictated by the forcings used to drive the models. In other words, if the models can’t explain the observed 30-year warming ending around 1945, then the warming must have occurred naturally. And that, in turns, generates the question: how much of the current warming occurred naturally? Fifth, the agreement between model and data trends for the 30-year periods ending in the 1960s to about 2000 suggests the models were tuned to that period or at least part of it. Sixth, going back further in time, the models can’t explain the cooling seen during the 30-year periods before the 1920s, which is why they fail to properly simulate the warming in the early 20th Century.
“One last note, the monumental difference in modeled and observed warming rates at about 1945 confirms my earlier statement that the models can’t simulate the warming that occurred during the early warming period of the 20th Century.”
A link to the update:
https://bobtisdale.wordpress.com/2018/11/15/october-2018-global-surface-landocean-and-lower-troposphere-temperature-anomaly-update/
Cheers,
Bob
Bob
A couple of points:
Why can you post images and we can’t, at least I can’t?
I looked at your graph and thought, “Oh then the ‘pause’ really is still there, so I plotted out GISS LOTI 30 year running average temperatures and realized (finally) that it was 30 year running TRENDS that were plotted out. I’m sure I’m not the only one to misinterpret that graph. I need to learn to read (-:
So yes, the current 30 year trend is a 0.2°C/decade increase. And the point is it looks like it won’t get any higher than that based on the fact that it didn’t continue the climb in 1945.
So based on GISS LOTI as “Observations” and that we are talking about 0.2°C increments per decade, the issues are:
Will the global average temperature stop going up or not?
Will rising global temperature ever be a problem or not?
Steve
My understanding is that figures for atmospheric CO2 are not all that accurate before 1950, but we do have a reasonable idea how much fossil fuel was used, well back into the 1800’s. Bearing in mind that the effect of CO2 follows a natural log law, does this make for a correlation between the very nonlinear rise of fossil fuel usage and the more linear temperature increase?
My thoughts are that no, it probably doesn’t, because the main effect of CO2 occurs at much lower levels than that already present in the 1800’s. Even allowing for the log law, the amounts of fossil fuel used before 1950 cannot account for the 1910-40 warming.
https://iwrconsultancy.co.uk/science/twentieth-century
that we can measure things does not mean we can control things.
That we measure things poorly suggests that we might control them poorly, if we could at all.
Bob thanks for the post. Minor housekeeping below:
“A small group of international unelected bureaucrats who serve the United Nations, of environmental activists, and of businesses with financial interests climate change laws. . . ”
I dunno about this one, maybe it’s convention:
“For illustration purposes, and depending on the data for the individual country, I shift the curve GMST data . . . “
Thank you for finding those, sycomputing. I really hate it when I can’t find my typos. I’ve added what’s required with brackets.
Cheers,
Bob
Is there an acceptable definition for “global mean surface temperatures”? How many countries are included to be
qualified to be called “global” (30, 50, 100)? Over what time period is the country’s average temperature taken (5,10 20 years)? Presumably it is the “mean” or average temperatures of these countries and then averaged again to obtain a”global mean surface” temperature. Anybody?
Global average temperature is a mathematical construct. It does not exist is reality. It is meaningless! So, if there is a “1-deg C change in global surface temperatures” (presumably, this means global average temperature), how is this 1-deg C change applied to each country in practical terms? Does it mean that one should add 1-deg C to, say, USA’s or China’s
average temperature?
Yes it is mathematical construct.
It allows us to say things like
1. the sun is warmer than the earth
2. The MWP was warmer than today.
3. the climate changes, no one denies the climate changes.
mathematical construct. extremely useful
In China air pollution s a very serious issue. Particulate matter especially from industry and the Takla Makan desert.
If Climate change came 4th in the pool in China I suspect there was confusion over pollution of the air and release of CO2.
As always, we need to see the question when evaluating a survey. And the translation of the question, in this case.
M Courtney-
“…I suspect there was confusion over pollution of the air and release of CO2. ”
I believe you are correct. Not only is there confusion in the average person’s mind between air pollution and CO2 release, but the CAGW alarmist actively promote this confusion.
Yes, OE, it’s why the kids at school [awa those of similar levels of intellect] are so easily brainwashed.
Bob
Great post. Lots to digest.
Sorry for being dense, but in looking at the figures for individual countries, specifically, Highest Annual T Max, there appears to be lower annual variability for the higher latitude countries ((China, USA, Russia) than the lower latitude countries (Indonesia, Mexico, Bangladesh). I’m trying to think of why that could be. It seems to Be counterintuitive.
Do you agree there appears to be greater annual variability for the lower latitude countries
and if so why might that be?
As mentioned in another thread, the anomaly method requires a solid baseline and in this case is does not have one.
Not only is the quality of the data from 1951-1980 just as questionable as data from other years but the number, location, and siting quality of the 1951-1980 data does not match the number, location, and siting quality of any of the subsequent or previous periods. Unless only the exact same stations are used, any supposed “anomaly” would not even be from the same dataset as the questionable baseline.
I’m just asking here, but isn’t that a major problem with the anomaly presentation?
Anyone who has to work outside at night (think LEO’s and fire departments and EMT’s) are happy to have night time temperatures be warmer, don’t you think? Shouldn’t we have mercy on them and warm it up a little more? Just sayin’.
Interesting point that trends in Tmin are what make up most of the change. Therefore, as ‘climate change’ progresses, the seasonal temperature swings in each region get smaller, not larger. That would seem to be at odds with the alarmists’ predictions of more extreme weather. Weather is driven by short term temperature change, after all.
So, we should see more stable conditions as a consequence of global warming. In fact we do, with USA storm frequency having reduced somewhat since the 1930’s. Just don’t tell the alarmists, they have enough to worry them right now. Mostly, from the Acolytes of the Yellow Sign. 😉
Precisely! This also explains why accumulated cyclone energy is trending down, and tornado counts and energy are also trending down. Weather is becoming more stable, not less, because the daily variances (probably due to increasing UHI) are falling. Climate change is actually a GOOD thing – we’re not getting hotter, we’re just not getting as cold at night, and we’re benefiting by less storm energy.
“YUP”, that’s what the data suggests, ….. but the literal fact is, ….Earth’s daily high temperatures are not cooling off as much at nighttime or during the wintertime.
Therefore, when the near-surface temperatures have not been cooling down (decreasing) as much during the past few years/several decades ….. then it is only logical for some folks to assume (wrongly) that the daily “low” near-surface temperatures have been increasing.
No, that is not how it is working, if the high goes up 0.1C and the low goes up 0.2C then the Low is warming faster.
Some of this is due to UHI around the Climate Stations.
NOPE, because the “high” of the day increased to 0.1C above what the “high” was the previous day, ……….. and/or …….. the high” of the previous day only decreased to 0.2C above what it had decreased to the previous day before that.
The subject is “global warming” (adding heat energy), ……. not “global freezing” (removing heat energy). 🙂 🙂
If descriptive statistics demonstrate that there is no there, there (which is the kind of analysis Bob has used) further analysis with parametized calculations (such as with anomalies, Nick’s favorite calculation)) leads to a rabbit hole at least as often as it does a discovery.
Great post Bob!
Well done Bob, thanks.
Politics is the only Religion that promise it can control Climate.
it is the change in temperature compared to what we’ve been used to that matters.
=======
So really, where most people live. The trend is in the direction TOWARDS what we are used to (increasing tmin), not away from what we are used to.
Gavin got it wrong. He failed to consider that change can either be more or less like what you are used to.
Gavin failed to consider that change can yield positive, negative, or zero. Gavin considered only the case where the difference was negative.
Great post Bob. It exposes the main weakness in the CAGW argument. And it centers on their use of anomalies to imply that any “departures from average” in the climate are bad. We live on a planet with a stable climate system. Anyone that examines the evidence with an impartial view would have to agree with that. In this stable system there are factors that change. Stability happens because we have negative feedbacks in the system, that happen over time. That does not mean we will be “stuck on average”. It means that over time we will oscillate around the average.
All life does not care about departures from average. We care about the Goldilocks Porridge Ranges. We care much more about: Too Hot, getting hotter, than we care about Just Right, going up a little over decades. The same with Too Cold. Too Cold going down is much more of a problem, than the same occurring in the Just Right situation.
All departures from average are not the same. In fact the vast majority are not noticeable. And there is no evidence that all those “unnoticeable departures from average” will accumulate in disaster. So far they are “moderating the cold”, which is not a threat to anyone or anything.
Bob, always enjoy your posts. Even though you are explicit about your methodology and explain your results, it often takes me several days to go through the post to understand what you have done, and what the results show.
The first thing I ran into was exactly what you were plotting with “lowest TMIN” and highest TMAX” . You gave Berkeley Earth’s definition of TMIN as “Mean of daily low temperatures” Then you said “Berkeley Earth provides monthly TMIN and TMAX data until partway through 2013.”
My interpretation of this is that for TMIN Berkeley finds the monthly mean of the daily minimum temperature for each station. Then the lowest annual TMIN would be the lowest monthly mean daily minimum for the year. Unless anomalies are used this would always be in the winter.
But what then, for the whole country? Is the TMIN for the country, the lowest station TMIN, or is the mean of all station TMIN’s? ?
Since this is Berkeley Earth data you may not know answer. Regardless of how is it done, does the resulting annual lowest TMIN have any meaning? I don’t mean for these to be snarky questions, I am genuinely confused.
Bob,
I know a lot of work went into preparing the graphs. So, don’t take my following comment as a criticism. You demonstrate clearly (even if Nick Stokes can’t see it), that the Earth isn’t warming as a monolithic block. Warming varies around the world. To best appreciate that, I think that professional (paid!) climatologists should be compiling work similar to yours, only using the Köppen climate classifications instead of political boundaries. I think that would provide more insight on not only where the temperatures are changing, but it would also allow for a better understanding of what is responsible for the changes. You, and those of us like you, are at the mercy of how those maintaining databases provide the data.
Incidentally, when I did my guest piece on warming I demonstrated that the difference between TMax and TMin is not constant throughout time. That suggests the driving forces are not constant. See Figure 1 at this link:
https://wattsupwiththat.com/2015/08/11/an-analysis-of-best-data-for-the-question-is-earth-warming-or-cooling/
Thank you. Your Figure 1 about TMAX TMIN diffense is interesting, because if gives a hint of which greenhouse gas (if any) might be behind the changes.
If Earth has blanket like H2O and clouds TMAX would reduce and TMIN increase. As this seems to be the case, the next question is what drives clouds and humidity. CO2 does not have a good correlation with it in real world.
Slicing and dicing up to the weather station level could give use other insights about the mechanisms behind the changes in our weather.
Is there a graph showing the TMIN for urban areas only? Urban meaning only those stations that are 1 kilometre or more from road, building etc.
For those who would like a very practical example of what the supposedly catastrophic warming of “another 0.5 C” would do to the US of A, in terms of its average temperature, I provide the following, because no one else did.
The distance from New York to Tampa is 1136 miles. The average temperature difference is 12 C. So warming 0.5 C is the equivalent of moving New York City 50 miles south, “on average”.
I find it impossible to get concerned about such a “catastrophic” change. For every fifty miles one drives towards Tampa, it really does get half a degree C warmer, on average. When the scale of the change is considered in some realistic context, one easily sees the unreasonableness of the catastrophic arguments.
If I moved fifty miles from Waterloo Ontario south I wouldn’t get to Lake Erie. To suppose that the birds, critters and flora would perish en masse with such a “drastic” change is silly. Even a child would not believe it.
Gavin says it is about a change in the temperature you are used to. It is also about the guff you are used to. A change in the guff doesn’t make it true. (Guff means BS.)
There is a clear, worldwide trend: winter low temperatures are rising, on average, raising the annual global mean, but the maxima are not following suit. Delta T is reducing, globally, and there is a real possibility it is mostly due to urbanization and construction.
Another cause of a temperature variation is that a person on the coast gets the benefit of a sea breeze, cooling things down in the summer, but warming things up in the winter. Move inland and it all changes.
Both Napoliasn and Hitler made the same mistake as they moved away from the sea influenced climate of Europe to the vast steeps of Russia.
Another factor is each persons feelings are not the same. My wife does not feel the cold, but I do, but come the summer and she suffers and I do not, until it gets past about 35 C.
The Green warmers are not interested in facts, but only what I would say is pretend emotion. Lots of “Think of the children” stuff. They want “World Government, ii.e. Communism.
Try to explain to them that humans evolved in the much warmer climate of Africa, and as a result we need to wear clothes to keep warm. True many of us look a lot better dressed than undressed.
I think that we will lose the battle of ideas, as the media and the politicians like it just the way it is. Its the usual,” ” Only we can save you from the wrath to come” Its religion and we all know just how powerful a effect that makes on peoples minds.
We will have to wait till the lights start to go out, and our present high standard of living declines, then we will do what the French in Paris are doing, demonstrate. Only then in the name of their survival will the politicians change their ways.
MJE
Bob still doesn’t seem to get it. Averages of absolute temperatures obscure the variance.
Humans can adapt to changes. They can move, they can use technology and engineering (building sea walls, for instance), they are able to plan and make decisions. Other living organisms are limited in the ways they can adapt. Some will be fine, but others won’t. It depends largely on the rate of change, though amount and nature of the change comes into it, too. It seems to me that this is largely ignored by skeptics in general. People don’t realize how dependent we are on the stability of ecosystems, or the economic cost when they are damaged or unstable. The cost in forest productivity alone could be billions of dollars simply because insect populations are not restricted by low temperatures. And dead trees are a fire hazard. Or there’s the cost in fishery production due to higher stream temperatures.
1.5 C doesn’t seem like a lot to us, but that’s an average, and natural systems are very sensitive to not just averages, but extremes and thresholds and seasonal variation. Apart from that are the effects on other weather and climate parameters, particularly precipitation – not just totals, but when it happens, and intensity of events.
“But as alarmists would like us to believe, we’re all going to roast in our self-imposed, fossil-fuel-burning, CO2-emitting hells if global mean land+ocean surface temperatures rise another 0.5 deg C (0.9 deg F). And then they get all huffy when people disagree with them. Go figure.”
…And skeptics would like us to believe that alarmists are fools, making a big deal out of nothing. But by looking at climate change only in terms of the obvious and the averages, skeptics are ignoring the variety and scope of repercussions of climate change.
I suggest that you read some history, 1.5C over 150-200 years is nothing compared to the daily, weekly, monthly and annual changes that the whole world experiences.
I also suggest that you read Crispin in Waterloo at December 16, 2018 at 3:31 am for a reality check.
You really have no clue of what you’re talking or you’re simply trolling for the sake of it.
This reply was for Kristi
a simple example how a small change in temperature can be important in certain areas
https://phys.org/news/2018-12-weve-glacier-peru-years.html#nRlv