Our Urban “Climate Crisis”

Reblogged from Watts Up With That:

By Jim Steele

Published in Pacifica Tribune May 14, 2019

What’s Natural

Our Urban “Climate Crisis”

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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.)

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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.

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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?

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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.

Video exposé of the groundless Netflix bid to elevate walrus to climate change icon

polarbearscience

Last month, Netflix and WWF released a collaborative nature documentary that contained an egregiously: that Pacific walrus are being forced ashore by global warming where they suffer staggering population losses. But this is a story the film producers and WWF concocted for their own purposes, not a statement supported by scientific fact.

Video title screen

Over the last month, pointed questions have been asked about what really happened in Siberia while the film crew was there – and what didn’t. Scientific documents support the conclusion that Pacific walrus are currently thriving, have not been harmed by recent sea ice losses, and are not expected to be harmed in the foreseeable future, see here, here, here, and here.  This new video explains it all.

Netflix, Attenborough and cliff-falling walruses: The making of a false climate icon

Press release

In a GWPF video released today, Dr. Susan Crockford, a Canadian wildlife…

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Half of 21st Century Warming Due to El Nino

Reblogged from Dr.RoySpencer.com  [HiFast bold]

May 13th, 2019 by Roy W. Spencer, Ph. D.

A major uncertainty in figuring out how much of recent warming has been human-caused is knowing how much nature has caused. The IPCC is quite sure that nature is responsible for less than half of the warming since the mid-1900s, but politicians, activists, and various green energy pundits go even further, behaving as if warming is 100% human-caused.

The fact is we really don’t understand the causes of natural climate change on the time scale of an individual lifetime, although theories abound. For example, there is plenty of evidence that the Little Ice Age was real, and so some of the warming over the last 150 years (especially prior to 1940) was natural — but how much?

The answer makes as huge difference to energy policy. If global warming is only 50% as large as is predicted by the IPCC (which would make it only 20% of the problem portrayed by the media and politicians), then the immense cost of renewable energy can be avoided until we have new cost-competitive energy technologies.

The recently published paper Recent Global Warming as Confirmed by AIRS used 15 years of infrared satellite data to obtain a rather strong global surface warming trend of +0.24 C/decade. Objections have been made to that study by me (e.g. here) and others, not the least of which is the fact that the 2003-2017 period addressed had a record warm El Nino near the end (2015-16), which means the computed warming trend over that period is not entirely human-caused warming.

If we look at the warming over the 19-year period 2000-2018, we see the record El Nino event during 2015-16 (all monthly anomalies are relative to the 2001-2017 average seasonal cycle):

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Fig. 1. 21st Century global-average temperature trends (top) averaged across all CMIP5 climate models (gray), HadCRUT4 observations (green), and UAH tropospheric temperature (purple). The Multivariate ENSO Index (MEI, bottom) shows the upward trend in El Nino activity over the same period, which causes a natural enhancement of the observed warming trend.

We also see that the average of all of the CMIP5 models’ surface temperature trend projections (in which natural variability in the many models is averaged out) has a warmer trend than the observations, despite the trend-enhancing effect of the 2015-16 El Nino event.

So, how much of an influence did that warm event have on the computed trends? The simplest way to address that is to use only the data before that event. To be somewhat objective about it, we can take the period over which there is no trend in El Nino (and La Nina) activity, which happens to be 2000 through June, 2015 (15.5 years):

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Fig. 2. As in Fig. 1, but for the 15.5 year period 2000 to June 2015, which is the period over which there was no trend in El Nino and La Nina activity.

Note that the observed trend in HadCRUT4 surface temperatures is nearly cut in half compared to the CMIP5 model average warming over the same period, and the UAH tropospheric temperature trend is almost zero.

One might wonder why the UAH LT trend is so low for this period, even though in Fig. 1 it is not that far below the surface temperature observations (+0.12 C/decade versus +0.16 C/decade for the full period through 2018). So, I examined the RSS version of LT for 2000 through June 2015, which had a +0.10 C/decade trend. For a more apples-to-apples comparison, the CMIP5 surface-to-500 hPa layer average temperature averaged across all models is +0.20 C/decade, so even RSS LT (which usually has a warmer trend than UAH LT) has only one-half the warming trend as the average CMIP5 model during this period.

So, once again, we see that the observed rate of warming — when we ignore the natural fluctuations in the climate system (which, along with severe weather events dominate “climate change” news) — is only about one-half of that projected by climate models at this point in the 21st Century. This fraction is consistent with the global energy budget study of Lewis & Curry (2018) which analyzed 100 years of global temperatures and ocean heat content changes, and also found that the climate system is only about 1/2 as sensitive to increasing CO2 as climate models assume.

It will be interesting to see if the new climate model assessment (CMIP6) produces warming more in line with the observations. From what I have heard so far, this appears unlikely. If history is any guide, this means the observations will continue to need adjustments to fit the models, rather than the other way around.

Chinese UHI study finds 0.34C/century inflation effect on average temperature estimate.

Tallbloke's Talkshop

New study published by Springer today makes interesting reading. Phil Jones’ ears will be burning brightly.

Abstract:
Historical temperature records are often partially biased by the urban heat island (UHI) effect. However, the exact magnitude of these biases is an ongoing, controversial scientific question, especially in regions like China where urbanization has greatly increased in recent decades. Previous studies have mainly used statistical information and selected static population targets, or urban areas in a particular year, to classify urban-rural stations and estimate the influence of urbanization on observed warming trends. However, there is a lack of consideration for the dynamic processes of urbanization. The Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are three major urban agglomerations in China which were selected to investigate the spatiotemporal heterogeneity of urban expansion effects on observed warming trends in this study. Based on remote sensing (RS) data, urban area expansion…

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What’s Wrong With The Surface Temperature Record? Guest: Dr. Roger Pielke Sr.

Reblogged from Watts Up With That:

Dr. Roger Pielke Sr. joins [Anthony Watts] on a podcast to discuss the surface temperature record, the upcoming IPCC report, and climate science moving forward.

Dr. Roger Pielke Sr. explains how the Intergovernmental Panel on Climate Change (IPCC) is incorrectly explaining climate change to the media and public. Pielke highlights how the IPCC ignores numerous drivers of climate aside from CO2, leading to numerous factual inaccuracies in the IPCC reports.

Climate monitoring station in a parking lot at University of Arizona, Tucson

We also cover what is wrong with the surface temperature record – specifically why many temperature readings are higher than the actual temperature.

Available on Amazon at a special low price – click image

Pielke is currently a Senior Research Scientist in CIRES and a Senior Research Associate at the University of Colorado-Boulder in the Department of Atmospheric and Oceanic Sciences (ATOC) at the University of Colorado in Boulder (November 2005 -present). He is also an Emeritus Professor of Atmospheric Science at Colorado State University.

BIG NEWS – Verified by NOAA – Poor Weather Station Siting Leads To Artificial Long Term Warming

Sierra Foothill Commentary

Based on the data collected for the Surface Station Project and analysis papers describing the results, my friend Anthony Watts has been saying for years that “surface temperature measurements (and long term trends) have been affected by encroachment of urbanization on the placement of weather stations used to measure surface air temperature, and track long term climate.”

When Ellen and I traveled across the country in the RV we visited weather stations in the historical weather network and took photos of the temperature measurement stations and the surrounding environments.

Now, NOAA has validated Anthony’s findings — weather station siting can influence the surface station long temperature record. Here some samples that were taken by other volunteers :

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Impacts of Small-Scale Urban Encroachment on Air Temperature Observations

Ronald D. Leeper, John Kochendorfer, Timothy Henderson, and Michael A. Palecki
https://journals.ametsoc.org/doi/10.1175/JAMC-D-19-0002.1

Abstract

A field experiment was performed in Oak Ridge, TN, with four…

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Bramston Reef Corals – The Other Side of the Mud Flat

Reblogged from Watts Up With That:

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Reposted from Jennifer Marohasy’s blog

May 6, 2019 By jennifer

THE First Finding handed down by Judge Salvador Vasta in the Peter Ridd court case concerned Bramston reef off Bowen and a photograph taken in 1994 that Terry Hughes from James Cook University has been claiming proves Acropora corals that were alive in 1890 are now all dead – the fringing reef reduced to mudflat.

Meanwhile, Peter Ridd from the same university, had photographs taken in 2015 showing live Acropora and the need for quality assurance of Hughes’ claims.

Both sides were preparing evidence for over a year – with the lawyers apparently pocketing in excess of one million dollars – yet there was no interest in an independent assessment of the state of Bramston reef.

It more than once crossed my mind, that with all the money floating around for reef research and lawyers … there could perhaps be some mapping, or just one transect, at this most contentious of locations supposedly indicative of the state of the Great Barrier Reef more generally.

In his judgment Judge Salvadore Vasta was left to simply conclude that it was unclear whether there was now mudflat or coral reef where an extensive area of Acropora coral had been photographed back in 1890, but that Peter Ridd nevertheless had the right to ask the question.

Indeed, the court case and the appeal which must be lodged by tomorrow (Tuesday 7th May), is apparently all about ‘academic freedom’ and ‘employment law’, while the average Australian would perhaps be more likely to care if they got to see some coral and some fish – dead or alive.

I visited Bramston Reef over Easter because I couldn’t wait any longer to know if the corals in Peter Ridd’s 2015 photographs had been smashed by Cyclone Debbie that hovered over Bowen two years later, in April 2017.

As I drove into Bowen, I took a detour towards Edgecombe Bay, but I didn’t stop and explore – because I saw the signage warning of crocodiles.

Peter Ridd had told me that his technicians had approached from the south south-east in a rubber dinghy to get their photographs. The day I arrived (April 18, 2019), and the next, there was a strong south south-easterly wind blowing, and no-one prepared to launch a boat to take me out.

On the afternoon of Easter Friday – ignoring the signage warning of crocodiles – I walked through the mangroves to the water’s edge. I found the mudflat which Terry Hughes had claimed now covers once healthy Acropora coral and walked across it. The other side of the mudflat there was reef flat with beds of healthy Halimeda. This area of reef flat over sand extended for nearly one kilometre – before it gave way to hectares of Acropora coral.

Professor Hughes had just not walked far enough.

When, with much excitement, I showed my photographs of all the Acropora to a Bowen local. He described them as, “rubbish corals”. He seemed ashamed that the corals I had photographed at Bramston reef were not colourful.

For a coral to make the front cover of National Geographic it does need to be exceptionally colourful. Indeed, for a woman model to make the cover of Vogue magazine she needs to be exceptionally thin. But neither thin, nor colourful, is necessarily healthy. Indeed, Acropora corals are generally tan or brown in colour when they have masses of zooxanthellae and are thus growing quickly – and are healthy.

White corals have no zooxanthellae and are often dead, because they have been exposed to temperatures that are too high. Colourful corals, like thin women, are more nutrient starved and often exist in environments of intense illumination – existing near the limits of what might be considered healthy.

Such basic facts are not well understood. Instead there is an obsession with saving the Great Barrier Reef from imminent catastrophe while we are either shown pictures of bleached white dead coral, or spectacularly colourful corals from outer reefs in nutrient-starved waters … while thousands of square kilometres of healthy brown coral is ignored.

Peter Ridd did win his high-profile court case for the right to suggest there is a need for some quality assurance of the research – but I can’t see anyone getting on with this. The Science Show on our National Broadcaster, hosted by a most acclaimed scientist journalist, has reported on the case just this last weekend. Rather than launching a dinghy and having a look at Bramston Reef, Robyn Williams has replayed part of a 2008 interview with Peter Ridd, and let it be concluded that because Peter Ridd holds a minority view he is likely wrong.

Understanding the real state of the Great Barrier Reef is not a trivial question: it has implications for tourism, and the allocation of billions of dollars of public monies … with most currently allocated to those properly networked – but not necessarily knowledgeable or prepared to walk beyond a mudflat to find the corals.

Signage warning of crocodiles.

Signage warning of crocodiles.

Photographs of the Acropora out of the water where taken about here.

Photographs of the Acropora out of the water where taken about here

There is a mudflat to the west of Bramston Reef.

There is a mudflat to the west of Bramston Reef.

That mudflat is teeming with life, as expected in an intertidal zone.

That mudflat is teeming with life, as expected in an intertidal zone.

This Porites coral is a healthy tan colour.

This Porites coral is a healthy tan colour.

After the mud flat there was reef flat, with coarse sand and lots of Halimeda. All healthy, and typical of an inner Great Barrier Reef.

After the mud flat there was reef flat, with coarse sand and lots of Halimeda. All healthy, and typical of an inner Great Barrier Reef.

Halimeda is a green macroalgae, it was healthy.

Halimeda is a green macroalgae, it was healthy.

Acropora corals with a view to Gloucester Island.

I did find one bleached coral.

I did find one bleached coral.

Most of the Acropora was a healthy brown colour suggesting good growth, rather than beauty.

Most of the Acropora was a healthy brown colour suggesting good growth, rather than beauty.

There were also corals to the south east.

There were also corals to the south east.

Looking across to Gloucester Island, in front of the mangroves when the tide was in, early on 19 April.

Looking across to Gloucester Island, in front of the mangroves when the tide was in, early on 19 April.

Looking towards Gloucester Island, the day before.

Looking towards Gloucester Island, the day before.

To be sure to know when I post pictures at this blog, and to get the latest news regarding the Peter Ridd court case including the possible appeal by James Cook University, subscribe for my irregular email updates.

Curious Correlations

Reblogged from Watts Up With That:

Guest Post by Willis Eschenbach

I got to thinking about the relationship between the Equatorial Pacific, where we find the El Nino/La Nina phenomenon, and the rest of the world. I’ve seen various claims about what happens to the temperature in various places at various lag-times after the Nino/Nina changes. So I decided to take a look.

To do that, I’ve gotten the temperature of the NINO34 region of the Equatorial Pacific. The NINO34 region stretches from 90°W, near South America, out to 170° West in the mid-Pacific, and from 5° North to 5° South of the Equator. I’ve calculated how well correlated that temperature is with the temperatures in the whole world, at various time lags.

To start with, here’s the correlation of what the temperature of the NINO34 region is doing with what the rest of the world is doing, with no time lag. Figure 1 shows which areas of the planet move in step with or in opposition to the NINO34 region with no lag.

Figure 1. Correlation of the temperature of the NINO34 region (90°-170°W, 5°N/S) with gridcell temperatures of the rest of the globe. Correlation values greater than 0.6 are all shown in red.

Now, perfect correlation is where two variables move in total lockstep. It has a value of 1.0. And if there is perfect anti-correlation, meaning whenever one variable moves up the other moves down, that has a value of minus 1.0.

There are a couple of interesting points about that first look, showing correlations with no lag. The Indian Ocean moves very strongly in harmony with the NINO34 region (red). Hmmm. However, the Atlantic doesn’t do that. Again hmmm. Also, on average northern hemisphere land is positively correlated with the NINO34 region (orange), and southern hemisphere land is the opposite, negatively correlated (blue).

Next, with a one-month lag to give the Nino/Nina effects time to start spreading around the planet, we see the following:

Figure 2. As in Figure 1, but with a one month lag between the NINO34 temperature and the rest of the world. In other words, we’re comparing each month’s temperature with the previous month’s NINO34 temperature.

Here, after a month, the North Pacific and the North Atlantic both start to feel the effects. Their correlation switches from negative (blues and greens) to positive (red-orange). Next, here’s the situation after a two-month lag.

Figure 3. As in previous figures, but with a two month lag.

I found this result most surprising. Two months after a Nino/Nina change, the entire Northern Hemisphere strongly tends to move in the same direction as the NINO34 region moved two months earlier … and at the same time, the entire Southern Hemisphere moves in opposition to what the NINO34 region did two months earlier.

Hmmm …

And here’s the three-month lag:

Figure 4. As in previous figures, but with a three month lag.

An interesting feature of the above figure is that the good correlation of the north-eastern Pacific Ocean off the west coast of North America does not extend over the continent itself.

Finally, after four months, the hemispherical pattern begins to fall apart.

Figure 5. As in previous figures, but with a four & five month lag.

Even at five months, curious patterns remain. In the northern hemisphere, the land is all negatively correlated with NINO34, and the ocean is positively correlated. But in the southern hemisphere, the land is all positively correlated and the ocean negative.

Note that this hemispheric land-ocean difference with a five-month lag is the exact opposite of the land-ocean difference with no lag shown in Figure 1.

Now … what do I make of all this?

The first thing that it brings up for me is the astounding complexity of the climate system. I mean, who would have guessed that the two hemispheres would have totally opposite strong responses to the Nino/Nina phenomenon? And who would have predicted that the land and the ocean would react in opposite directions to the Nino/Nina changes right up to the very coastlines?

Second, it would seem to offer some ability to improve long-range forecasting for certain specific areas. Positive correlation with Hawaii, North Australia, Southern Africa, and Brazil is good up to four-five months out.

Finally, it strikes me that I can run this in reverse. By that, I mean I can find all areas of the planet that are able to predict the future temperature at some pre-selected location. Like, say, what areas of the globe correlate well with whatever the UK will be doing two months from now?

Hmmm indeed …

Warmest regards to all, the mysteries of this wondrous world are endless.

w.

Willis’ Favorite Airport

Reblogged from Watts Up With That:

By Steven Mosher,

AC Osborn made an interesting comment about airports that will give me an opportunity to do two things: Pay tribute to Willis for inspiring me and give you all a few more details about airports and GHCN v4 stations. Think of this as a brief but necessary sideline before returning to the investigation of how many stations in GHCNv4 are “ruralish” or “urbanish”. In his comments AC was most interested in how placement at airports would bias the records and my response was that he was talking about microsite and I would get to that eventually. Also a few other folks had some questions about microsite versus LCZ, so let’s start with a super simple diagram.

fig01

We can define microsite bias as any disturbance/encroachment at the site location which biases the measurement up or down within the “footprint” of the sensor. For a thermometer at 1.5meters, this range varies from a few meters in unstable conditions to hundreds of meters in stable conditions . In the recent NOAA study, they found bias up to 50 meters away from a disturbance. I’ve drawn this as the red circle, but in practice, depending on prevailing wind, it is an ellipse. The NOAA experiment (more on that in a future post) put sensors at 4m, 50m, and 124m from a building and found

The mean urban bias for these conditions quickly dropped from 0.84 °C at tower-A (4 m) to 0.55 and 0.01 °C at towers-B` and -C located 50 and 124 meters from the small-scale built environment. Despite a mean urban signal near 0.9 °C at tower-A, the mean urban biases were not statistically significant given the magnitude of the towers standard 2 deviations; 0.44, 0.40, 0.37, and 0.31 °C for tower-A, -B, -B’, and -C respectively.

While not statistically significant, however, they still recommend precaution and suggest that the first 100m of a site be free of encroachments. In field experiments of the effect of roads on air temperature measured at 1.5m, a bias of .1C was found as far as 10m away from roads. At airports this distance should probably be increased. At an airport where the runway is 50m+ wide, the effect the asphalt has on the air temperature is roughly 1.2C at the edge of the runway and diminishes to ~.1c by 150m away from the runway. (Kinoshita, N. (2014). An Evaluation Method of the Effect of Observation Environment on Air Temperature Measurement. Boundary-Layer Meteorology) Exercising even more caution, I’ve extended this out to 500m, although it should be noted that this could classify good sites as “bad” sites and reduce differences in a good/bad comparison. Obviously, this range can be tested by sensitivity analysis.

Outside the red circle I’ve depicted the “Local Climate Zone”. Per Oke/Stewart this region can extend for kilometers. In simple terms you can think of two kinds of biases: Those biases that arise from the immediate vicinity within the view of the sensor and have a direct impact of the sensor, and those that are outside the view of the sensor and act indirectly– say that tall set of buildings 800m away that disturb the natural airflow to the site. In the previous post, we were discussing the local scale; this is the scale at which we would term the bias “UHI.”

There is another source of bias, from far away areas, and I will cover that in another post. For now, we will use airports to understand the difference between these two scales. Let’s do that by merely picturing some extremes in our mind: An airport in Hong Kong, and an airport on a small island in the middle of the ocean. Both airports might have microsite bias, but the Hong Kong temperature would be influenced by the urban local climate zone with its artificial ground cover. The airport on the island is surrounded by nonurban ocean, with no UHI from the ocean. Simplistically, the total bias a site might be seen as a combination of a micro bias, local bias, and distant bias.

There are, logically, six conditions we can outline:

Rural–natural No Micro Bias Warm Micro Bias Cool Micro Bias
Urban–artificial No Micro Bias Warm Micro Bias Cool Micro Bias

It is important to remember that micro disturbances can bias in both directions, cooling by shading for example. And note that logically you could find a well sited site in an urban location. This was hypothesized by Peterson long ago:

“In a recent talk at the World Meteorological Organization, T. Oke (2001, personal communication) stated that there has been considerable advancement in the understanding of urban climatology in the last 15 years. He went on to say that urban heat islands should be considered on three different scales. First, there is the mesoscale of the whole city. The second is the local scale on the order of the size of a park. And the third scale is the microscale of the garden and buildings near the meteorological observing site. Of the three scales, the microscale and local-scale effects generally are larger than mesoscale effects….

Gallo et al. (1996) examined of the effect of land use/ land cover on observed diurnal temperature range and the results support the notion that microscale influences of land use/land cover are stronger than mesoscale. A metadata survey provided land use information in three radii: 100 m, 1 km, and 10 km. The analysis found that the strongest effect of differences in land use/land cover was for the 100-m radius. While the land use/land cover effect ‘‘remains present even at 10,000 m….

Recent research by Spronken-Smith and Oke (1998) also concluded that there was a marked park cool island effect within the UHI. They report that under ideal conditions the park cool island can be greater than 5 C, though in midlatitude cities they are typically 1 –2C. In the cities studied, the nocturnal cooling in parks is often similar to that of rural areas. They reported that the thermal influence of parks on air temperatures appears to be restricted to a distance of about one park width….

Park cool islands are not the only potential mitigating factor for in situ urban temperature observations. Oceans and large lakes can have a significant influence on the temperature of nearby land stations whether the station is rural or urban. The stations used in this analysis that were within 2 km of the shore of a large body of water disproportionally tended to be urban (5.8% of urban were coastal versus 2.4% of rural).

Looking at airports will also help you cement the difference between the micro and the LCZ in your thinking. With that in mind we will turn to airports and look at various pictures to understand the difference between the micro and the local- the nearby city or the nearby ocean or field.

First a few details about airports. In my metadata I have airports classified as small, medium and large

First, the small: some are paved. Pixels (30m) detected as artificial surface are colored orange:

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Some are dirt

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Now large airports

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We will get to medium, but first a few other airports by water, a 10km look, the blue dot is the station, red squares are 30meter urban cover

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Zooming in

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The medium airport I choose was one of Willis’ favorite airports, discussed in this post. Before we get to that visual, I encourage you all to read that post, because it put me on a 6 year journey. Willis is rather rare among those who question climate science. He does his own work, and he raises interesting testable questions. He doesn’t merely speculate; he looks and reads and does actual work. He raised two points I want to highlight:

Many of the siting problems have nothing to do with proximity to an urban area.

Instead, many of them have everything to do with proximity to jet planes, or to air conditioner exhaust, or to the back of a single house in a big field, or to being located over a patch of gravel.

And sadly, even with a map averaged on a 500 metre grid, there’s no way to determine those things.

And that’s why I didn’t expect they would find any difference … because their division into categories has little to do with the actual freedom of the station from human influences on the temperature. Urban vs Rural is not the issue. The real dichotomy is Well Sited vs Poorly Sited.

It is for this reason that I think that the “Urban Heat Island” or UHI is very poorly named. I’ve been agitating for a while to call it the LHI, for the “Local Heat Island”. It’s not essentially urban in nature. It doesn’t matter what’s causing the local heat island, whether it’s shelter from the wind as the trees grow up or proximity to a barbecue pit.

Nor does the local heat island have to be large. A thermometer sitting above a small patch of gravel will show a very different temperature response from one just a short distance away in a grassy field. The local heat island only needs to be big enough to contain the thermometer, one air conditioner exhaust is plenty, as is a jet exhaust

I think we both agree that the micro, what he calls local, is important. However, the area outside of the immediate area cannot be discounted: Hong Kong airport next to a huge city is going to be influenced by that locale, whereas, a large airport ( see above) on an island next to the sea, is arguably not going to be biased as much.

The second point Willis made was about the problems with 500meter data. In particular the MODIS classification system which required multiple adjacent pixels before a pixel was classified as urban. At that time we did not have a world database at 30m; Today we can look at that station and calculate the artificial area using 30m data. The next 4 images show the site at various scales: 500m, 1000m, 5000m and lastly 10000m. At the microscale ( <500meters) it classified as greater than 10% artificial, at 1km greater than 10% artificial, and at 5km and 10km it was less than 10% artificial.

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There were some concerns about the temperature at this station being used. However, there has never been enough data from this station to include in any global series, even Berkeley’s. Nevertheless, it lets us see the kind of improvements that can be made now that higher resolution data is available for the entire world. Also, even when airports are included in the data analysis, the bias can be reduced in some cases. Here a 2C bias is removed.

One last small airport to give you some kind of idea of that data that we can produce today.

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AC Osborn also wanted to know just how many airports were in GHCN v4; and, I think it’s safe to say that many skeptics believe that the record is dominated by airport stations. Well, is it? We can count them and see. For this count I will use 1km as a distance cut off. There are couple ways to “determine” if a station is at an airport. The least accurate way is to look at the names of the stations. This misses a large number of airports. To answer the question I use GPS coordinates compiled for over 55000 airports world wide, including small airports, heliports, balloon ports, and seaplane ports. I then calculate the distance between all 27K stations and the 55K airports and select the closest airport. I then cross check with those stations in GHCN that have a “name” that indicates it is an airport.

For this we consider a 1km distance for being “at an airport”. While this is farther than the microsite boundary, the point of the exercise is to illustrate that not all the stations are at airports.

Using 1km as a cut off, I find there are 1,129 stations by small airports, 1830 by medium airports, and 267 by large airports. That’s from a total of ~27,000 stations.

To assess the ability of the 30m data to detect airport runways and other artificial surfaces we can look at the stations that are within 500 meters of a large airport and ask? Does our 30m data show artificial surface?. There are 131 stations within 500m of an airport. We know that no sensor data/image classification system is perfect, but we can see that in the aggregate the 30m data performs well.

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We can also ask how many large airports are embedded in Local climate Zones that have less than 10% artificial cover out to 10km. As expected large airports are in local areas that are also built up at levels above 10%. You don’t get large airports where there are no people.

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Conversely, you get small airports embedded in local zones that are not heavily built out, a few cases of small airports embedded in Local Climate Zones that are heavily built out.

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Summary

Here are the points that I would like to emphasize.

1. We can discuss or differentiate between at least 2 types/sources of bias: the close and immediate and those sources more distant

2. Bias at the short range (micro) can be more important than bias at the long range.

3. A good site can be embedded in a “bad” area or “good” area, similarly for a bad site.

4. 30m data is better than 500m data

5. Skeptics should not argue that all the sites or a majority are at airports. They are not.

6. There are different types of airports.

7. One way to tell if there is a bias is by comparing Airports with Non airports.

Climate data shows no recent warming in Antarctica, instead a slight cooling

Reblogged from Watts Up With That:

Below is a plot from a resource we have not used before on WUWT, “RIMFROST“. It depicts the average temperatures for all weather stations in Antarctica. Note that there is some recent cooling in contrast to a steady warming since about 1959.

Data and plot provided by http://rimfrost.no 

Contrast that with claims by Michael Mann, Eric Steig, and others who used statistical tricks to make Antarctica warm up. Fortunately, it wasn’t just falsified by climate skeptics, but rebutted in peer review too.

Data provided by http://rimfrost.no 

H/T to Kjell Arne Høyvik‏  on Twitter

ADDED:

No warming has occurred on the South Pole from 1978 to 2019 according to satellite data (UAH V6). The linear trend is flat!