By Jim Steele
Published in Pacifica Tribune May 14, 2019
What’s Natural
Our Urban “Climate Crisis”
Based on a globally averaged statistic, some scientists and several politicians claim we are facing a climate crisis. Although it’s wise to think globally, organisms are never affected by global averages. Never! Organisms only respond to local conditions. Always! Given that weather stations around the globe only record local conditions, it is important to understand over one third of the earth’s weather stations report a cooling trend (i.e. Fig 4 below ) Cooling trends have various local and regional causes, but clearly, areas with cooling trends are not facing a “warming climate crisis”. Unfortunately, by averaging cooling and warming trends, the local factors affecting varied trends have been obscured.
It is well known as human populations grow, landscapes lose increasing amounts of natural vegetation, experience a loss of soil moisture and are increasingly covered by heat absorbing pavement and structures. All those factors raise temperatures so that a city’s downtown area can be 10°F higher than nearby rural areas. Despite urban areas representing less than 3% of the USA’s land surface, 82% of our weather stations are located in urbanized areas. This prompts critical thinkers to ask, “have warmer urbanized landscapes biased the globally averaged temperature?” (Arctic warming also biases the global average, but that dynamic must await a future article.)
Satellite data reveal that in forested areas the maximum surface temperatures are 36°F cooler than in grassy areas, and grassy areas’ maximum surface temperatures can be 36°F cooler than the unvegetated surfaces of deserts and cities. To appreciate the warming effects of altered landscapes, walk barefoot across a cool grassy lawn on a warm sunny day and then step onto a burning asphalt roadway.
In natural areas like Yosemite National Park, maximum air temperatures are cooler now than during the 1930s. In less densely populated and more heavily forested California, maximum air temperatures across the northern two thirds of the state have not exceeded temperatures of the 1930s. In contrast, recently urbanized communities in China report rapid warming of 3°F to 9°F in just 10 years, associated with the loss of vegetation.
Although altered urban landscapes undeniably raise local temperatures, some climate researchers suggest warmer urban temperatures do not bias the globally averaged warming trend. They argue warming trends in rural areas are similar to urbanized areas. So, they theorize a warmer global temperature is simply the result of a stronger greenhouse effect. However, such studies failed to analyze how changes in vegetation and wetness can similarly raise temperatures in both rural and urban areas. For example, researchers reported overgrazing had raised grassland temperatures 7°F higher compared to grassland that had not been grazed. Heat from asphalt will increase temperatures at rural weather stations just as readily as urban stations.
To truly determine the effects of climate change on natural habitats requires observing trends from tree ring data obtained from mostly pristine landscapes. Instrumental data are overwhelmingly measured in disturbed urbanized areas. Thus, the difference between instrumental and tree ring temperature trends can illustrate to what degree landscapes changes have biased natural temperature trends. And those trends are strikingly different!
The latest reconstructions of summer temperature trends from the best tree ring data suggest the warmest 30-year period happened between 1927 and 1956. After 1956, tree rings recorded a period of cooling that lowered global temperatures by over 1°F. In contrast, although tree rings and instrumental temperatures agreed up to 1950, the instrumental temperature trend, as presented in NASA graphs, suggests a temperature plateau from 1950 to 1970 and little or no cooling. So, are these contrasting trends the result of an increased urban warming effect offsetting natural cooling?
After decades of cooling, tree ring data recorded a global warming trend but with temperatures just now reaching a warmth that approaches the 1930s and 40s. In contrast, instrumental data suggests global temperatures have risen by more than 1°F above the 1940s. Some suggest tree rings have suddenly become insensitive to recent warmth? But the different warming trends are again better explained by a growing loss of vegetation and increasing areas covered by asphalt affecting temperatures measured by thermometers compared with temperatures determined from tree ring data in natural habitats.
Humans are increasingly inhabiting urban environments with 66% of humans projected to inhabit urban areas by 2030. High population densities typically reduce cooling vegetation, reduce wetlands and soil moisture, and increase landscape areas covered by heat retaining pavements. Thus, we should expect trends biased from urbanized landscapes to continue to rise. But there is a real solution to this “urban climate crisis.” It requires increasing vegetation, creating more parks and greenbelts, restoring wetlands and streams, and reducing heat absorbing pavements and roofs. Reducing CO2 concentrations will not reduce stifling urban temperatures.
Jim Steele is the retired director of San Francisco State University’s Sierra Nevada Field Campus and authored Landscapes and Cycles: An Environmentalist’s Journey to Climate Skepticism.
Contact: naturalclimatechange@earthlink.net
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Jim,
I enjoyed this article and it makes some great points.
On the tree rings, however I have always been a skeptic, especially in recent years.
With the massive atmospheric fertilization from CO2 thru the law of photosynthesis, which effects woody stemmed plants/trees the most and is greatly accelerating growth rates of all trees around the planet, how does one separate out, the tiny effect of a slight increase in global warming from, what must be an order of magnitude greater effect from the 25% increase in CO2?
It’s tough enough to discern between weather elements that contributed to past tree growth/rings(too cold, too warm, too wet, too dry) but when the trees are growing 20% or more faster, it would seem impossible to detect these signals when imposed over a long period of time(individual years can tell us if their was some sort of weather adversity).
It’s also interesting to note in this study that urban trees are growing faster than rural trees, while of course all trees are growing around 20% faster since the 1960’s.
https://www.sciencedaily.com/releases/2017/11/171113111037.htm
So my point is, if all trees are growing 20% faster(from the beneficial CO2) and trees in urban areas grow even faster than that because of the additional beneficial warmth, how can you tell, when it comes to ALL trees, how much of the 20% was from beneficial CO2 and how much was from the beneficial, slight global warming?
Tree rings don’t leave a specific signature that I know which tell us that X amount came from more CO2 and Y amount came from temperature. If X is contributing 10 times more than Y, changes is Y are going to be overwhelmed by the value of X.
Hundreds of tree studies indicate different increased growing rates for different tree species and even individual studies for the same tree species can vary by more than how much a 1 degree global temperature increase would effect that species.
So its not like we can assign an accurate value to how much extra growth the added CO2 is causing and use the difference between that and the actual growth to assume temperature because the margin of error for the CO2 growth rate is many times greater than what the effect on growth of the average temperature over a long period would cause.
I’m happy to learn more about tree ring studies from somebody that knows more about them than me.
Mike,
Indeed tree ring reconstructions should be viewed with prudent skepticism. However I believe tree ring science has evolved and generally provides reliable trends. Tree ring series from arid regions are generally not used because indeed growth there is dominated by a response to precipitation. For temperature indicators, tree rings are collected from regions where temperature is the dominant limiting factor such as near tree line.
Furthermore there has been an evolution towards using Maximum Latewood Density (MXD) instead of tree ring width. MXD analyses correlated best with instrumental data and anatomical and physiological studies suggest why. A single tree ring will undergo a transition from early wood to latewood each year. Early wood tracheids are larger and less dense. Earlywood tracheids’ main function is to conduct water/sap up into the canopy. When water is more abundant the pressure increases the size of the new cells. So early wood is much more affected by precipitation. The seasonal transition to latewood creates smaller denser cells. The function of latewood is to strengthen support by investing more in cell wall thickness with smaller cells and thus a greater density. Cell wall thickness is driven mostly by temperature and not preciptation, so latewood density is most sensitive temperature
The MXD reconstructions correlate much better with nearby instrumental temperatures, but there is still a divergence problem with the global average in the most recent decades. As SmartRock relates below, the tree ring researchers addressed the divergence problem stating , “Modern warming is poorly represented in the new record questioning the ability of MXD to capture very warm temperatures” Such a statement appears to be an attempt to appease the global warming alarmists and avoid an outright confrontation over which method reconstructs temperature trends best, because it also calls into question the what factors are affecting the hockey-stick fabrications.
Thanks Jim. That tells me some things that I didn’t know before about tree rings.
This is interesting. Jim’s fourth figure (the tree-ring stuff) doesn’t have a caption to tell you what the blue and gold lines mean, so I followed his link to the paper (Schneider et al 2015), which basically takes tree ring numbers and pushes them through a whole load of statistics that I don’t understand. Jim’s figure is their figure 3(b). The blue line is is Schneider et al and the gold is from one of the Briffa et al 2001 papers from the climate factory in Norwich.
What’s interesting is that Schneider et al figure 3(c) is an expanded version of their own plot of temperature anomaly (summer months only) from 1900 to about 2002, with the CRUTEM “instrumental” record superimposed. They both track well from 1900 to about 1980, with a broad peak of about +0.3°C between 1930 and 1950, then go down to zero (the anomaly is from 1961 to 1990, so it has to average zero over that period) and then the tree ring goes up to about +0.2°C by 2000, while the “instrumental” plot does a perfect hockey stick up to +1.0°C.
What this tells me, as a casual observer, is that EITHER tree ring data do not reflect temperature trends at all, OR that the instrumental record has been stepped on to produce the desired result of post-1980 extreme warming. They simply cannot have it both ways.
But they try. They ignore it, and only make this absurdly disingenuous statement: “Modern warming is poorly represented in the new record questioning the ability of MXD to capture very warm temperatures”
They can’t very well say their methodology is useless (if so, why publish it?) and of course they can’t say that CRUTEM data don’t reflect reality, because really bad things would happen to them if they hinted at that.
Climate science – the oxymoron of the century.
For a couple of days after most everyone else had left the thread, the poster known as Bendidon and I carried on the conversation. He insisted that gridding had to be done to prevent the overwhelming preponderance of stations in the US, Northern Europe, and Southeast Oz from biasing the global average. As evidence in favor of his position, he posted two sets of graphs, one with gridding and one without, and both of the sets calculating the global anomalies with and without US data, ostensibly to show how gridding corrects for the US overload.
What I thought was interesting was that with the gridding, he was able to remove the entire US data set from the chart and see no difference in the trend at all. Is it right that a calculation should allow one to remove practically an entire continent’s worth of data without having any significant effect on the average anomalies and trends?
These were the trends of the two sets of graphs.
No gridding, no US data: 0.15C/decade
Gridding, with or without US data: 0.10C/decade
No gridding, with US data: 0.08C/decade
My simple calculation gave a trend of 0.09C/decade.
What did the gridding do, though? Seems it smoothed the data so much that you could take away the entire US data set, which caused the ungridded trends to be different by a factor of two, and have the trends remain indistinguishable.
Is that really what we want?
Hey Jim,
Great article. In terms of warming are you actually saying that mild naturally occurring warming trend was amplified by UHI bias? Or in fact we cannot even confirm with certainty any ‘global’ warming trend as observed in historical records warming trend is purely artifact of urbanization?
Progressive land-use changes outside of urban areas can have a profound effect upon the apparent “trend” in long records from nominally non-urban stations. That is the reason why linear trends vary so widely in any given region. Those trends cannot be assumed to be representative of truly pristine locations, which constitute the vast majority of the globe, yet form only a tiny fraction of of the global data base.
Given that high trend-variability and paucity of truly pristine sites, it’s easy to construct severely misleading time-histories of spatial averages. Through ad hoc algorithms and various other devices. the strong cooling that took place in the third quarter of the last century has been virtually eliminated in all of the widely trumpeted “global” indices. Although there should be many reservations about the efficacy of tree-ring records as temperature indicators, the presentation here is noteworthy in clearly indicating that we are not in any uncharted territory, but only now are reaching the high yearly temperatures experienced many decades ago. A very similar result can be obtained from carefully vetted station records throughout the globe.
A further answer to James Schrumpf (hopefully the last one)
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“What I thought was interesting was that with the gridding, he was able to remove the entire US data set from the chart and see no difference in the trend at all.”
You seem to have, maybe intentionally, left out the central point of the discussion.
Thus for the readers who might eventually visit this head post, I think it would be honest to show them again.
1. Regardless which GHCN global data set we consider, the US stations account for about 50% of the total stock. This is of course the case for the data set I was using (GHCN daily).
2. If we distribute all stations worldwide over a latitude/longitude grid, e.g. of 2.5 ° size , i.e. 77,000 km² (I prefer it because it has the same size as UAH’s grid) we see this:
https://drive.google.com/file/d/17ZgjmYUL43320EoLQ5bL0Hs3aYwas-gt/view
Even this picture still does not reflect reality: within the 100 most populated grid cells of the 413 having more stations than the average (14), 96 are in CONUS; the most populated one in the US has… 363!
The top 96 CONUS grid cells totalise about 16,000 stations; this no more than 4.5 % of the Globe’s land surfaces.
The effect is that if you generate a monthly time series out of all the worldwide station data, these 4.5 % will account for as much as 50 % of Globe’s land.
This is bare nonsense, not only at the global level, but even within CONUS: here you have 50 % of the grid cells with 16,000 stations, and 50 % of the grid cells containing the rest, i.e. 2,000 stations.
This evidently calls for a more equitable distribution of station measurement data, with the goal of avoiding that the Globe’s average temperature looks like CONUS’s backyard.
Let us look at two graphs showing this – not with data in anomaly form, depending on the baseline selection strategy chosen, but rather with data in absolute form.
The red plot in thre graphs shows The Globe, the other one the Globe minus CONUS.
3.1 Without gridding:
https://drive.google.com/file/d/1U0b9TfcSxUDp7EiDfDK85-8f7gMYCmwI/view
Linear estimates in °C / decade
– Globe: 0.07
– Globe minus CONUS: 0.27
3.2 With gridding:
https://drive.google.com/file/d/15aZe2L45P9AL2AjcM-prh509xrKuLKvz/view
Linear estimates in °C / decade
– Globe: 0.11
– Globe minus CONUS: 0.11
4. Why are the estimates in the gridding case so similar?
Simply because now, we compare two averages where 200 CONUS grid cells (at best 6 % of the Globe’s land) compete with 2000 grid cells (94 % of the Globe’s land).
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“Is it right that a calculation should allow one to remove practically an entire continent’s worth of data without having any significant effect on the average anomalies and trends?”
An entire continent ? Really, James Schrumpf?
Don’t you suffer a bit of a little mental disease we Europeans use to call “americanocentrism” ?
Regards
J.-P. D.