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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Mighty Greenland glacier slams on brakes

Tallbloke's Talkshop

Jakobshavn glacier, West Greenland [image credit: Wikipedia]
Even the climate alarm oriented BBC has finally had to admit the inconvenient truth about Greenland’s largest glacier. Instead of dropping in height by 20m. a year, it’s now thickening by 20m. a year. This isn’t supposed to happen when one of the stock phrases of the fearmongering media is ‘the rapidly melting Arctic’. Of course logic says that since glaciers can grow naturally they can also retreat naturally, despite attempts to blame humans.

European satellites have detailed the abrupt change in behaviour of one of Greenland’s most important glaciers, says BBC News.

In the 2000s, Jakobshavn Isbrae was the fastest flowing ice stream on the island, travelling at 17km a year.

As it sped to the ocean, its front end also retreated and thinned, dropping in height by as much as 20m year.

But now it’s all change. Jakobshavn is travelling…

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Polar bear habitat update for early spring shows no influence of a CO2 control knob

polarbearscience

The primary feeding period for polar bears is rapidly drawing to a close in much of the Arctic, although it may continue for another few weeks in the farthest north. Mating is pretty much over as well, which means the polar bears’ need for abundant sea ice is declining even more rapidly than the ice does itself at this time of year.

Walking bear shutterstock_329214941_web size

Despite the fact that CO2 levels have now reached 415 ppm (see tweet below), sea ice is still pretty much as it was in 2006 when CO2 was about 385 ppm. In other words, the state of sea ice at this time of year – just over 12 million kilometres squared in 2006 and in 2019 – shows no correlation with rising CO2 levels. There is also not a hint of imminent catastrophe for polar bears anywhere within their range, despite the hand-wringing messages from conservation fear-mongers

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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 :

clip_image004Detroit_lakes_USHCN

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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Continuous observations in the North Atlantic challenges current view about ocean circulation variability

Reblogged from Watts Up With That:

Kevin Kilty

May 10, 2019

[HiFast Note:  Figures A and B added:

osnap_array_schematic_v2_13Nov14

Figure A. OSNAP Array Schematic, source:  https://www.o-snap.org/]

20160329_OSNAP_planeview-1Figure B. OSNAP Array, source:  https://www.o-snap.org/observations/configuration/]

clip_image002Figure 1: Transect of the North Atlantic basins showing color coded salinity, and gray vertical lines showing mooring locations of OSNAP sensor arrays. (Figure from OSNAP Configuration page)

Figure 1: Transect of the North Atlantic basins showing color coded salinity, and gray vertical lines showing mooring locations of OSNAP sensor arrays. (Figure from OSNAP Configuration page)

From Physics Today (April 2019 Issue, p. 19)1:

The overturning of water in the North Atlantic depends on meridional overturning circulation (MOC) wherein warm surface waters in the tropical Atlantic move to higher latitudes losing heat and moisture to the atmosphere along the way. In the North Atlantic and Arctic this water, now saline and cold, sinks to produce north Atlantic Deep water (NADW). It completes its circulation by flowing back toward the tropics or into other ocean basins at depth, and then subsequently upwelling through a variety of mechanisms. The time scale of this overturning is 600 years or so2.

The MOC transports large amounts of heat from the tropics toward the poles, and is thought to be responsible for the relatively mild climate of northern Europe. The heat being transferred from the ocean surface back into the atmosphere at high latitudes is as large as 50W/m2, which is roughly equivalent to solar radiation reaching the surface at high latitudes during winter months2.

In order to evaluate models of ocean overturning oceanographers have relied upon hydrographic research cruises. But the time increment between successive cruises is often long, and infrequent sampling cannot measure long term trends reliably nor gauge current ocean dynamics.

To get a better handle on MOC behavior an array of sensors to continuously monitor temperature, salinity, and velocity measurements known as the Overturning in the Subpolar North Atlantic Program (OSNAP) was recently deployed across the region at multiple depths. Figure 1 shows sensor moorings in relation to the various ocean basins of the North Atlantic. Figure 2 shows data from the first 21 months of operation, and displays a rather large variability of overturning in the eastern North Atlantic between Greenland and Scotland that reaches +/-10 Sverdrup (Sv=one million cubic meters per second) monthly, and amounts to one-half the MOC’s total annual transport. Researchers had thought that such variability was only possible on time scales of decades or longer.

Figure 2: Twenty-one months of observational data showing large month to month variation in MOC flows.

Figure 2: Twenty-one months of observational data showing large month to month variation in MOC flows.

The original experimental design for sensor placement in OSNAP was predicated on much smaller variability of a few Sv per month3. The report does not address what impact this surprising level of transport variability has on validity of the experiment design; but the surprisingly large variations in flow challenge expectations derived from climate models regarding the relative amount of overturning between the Labrador Sea and the gateway to the Arctic between Greenland and Scotland.

As one oceanographer put it, the process of deep water formation and sinking of the MOC is more complex than people believed, and these results should prepare people to modify their ideas about how the oceans work. This improved data should not only help test and improve climate models, but also produce more realistic estimates of CO2 uptake and storage.

References:

1. Alex Lopatka, Altantic water carried northward sinks farther east of previous estimates, Physics Today, 72, 4, 19(2019).

2. J. Robert Toggweiler, The Ocean’s Overturning Circulation, Physics Today, 47, 11, 45(1994).

3. Susan Lozier, Bill Johns, Fiamma Straneo, and Amy Bower, Workshop for the Design of a Subpolar North Atlantic Observing System, URL= https://www.whoi.edu/fileserver.do?id=163724&pt=2&p=175489, accessed 05/10/2019.

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.

Comparison of global climatologies confirms warming of the global ocean

Reblogged from Watts Up With That:

Institute of Atmospheric Physics, Chinese Academy of Sciences

200635_web

IMAGE: Deployment of an APEX float from a German research ship.

Credit: Argo

The global ocean represents the most important component of the Earth climate system. The oceans accumulate heat energy and transport heat from the tropics to higher latitudes, responding very slowly to changes in the atmosphere. Digital gridded climatologies of the global ocean provide helpful background information for many oceanographic, geochemical and biological applications. Because both the global ocean and the observational basis are changing, periodic updates of ocean climatologies are needed, which is in line with the World Meteorological Organization’s recommendations to provide decadal updates of atmospheric climatologies.

“Constructing ocean climatologies consists of several steps, including data quality control, adjustments for instrumental biases, and filling the data gaps by means of a suitable interpolation method”, says Professor Viktor Gouretski of the University of Hamburg and a scholarship holder of the Chinese Academy of Sciences’ President’s International Fellowship Initiative (PIFI) at the Institute of Atmospheric Physics, Chinese Academy of Sciences, and the author of a report recently published in Atmospheric and Oceanic Science Letters.

“Sea water is essentially a two-component system, with a nonlinear dependency of density on temperature and salinity, with the mixing in the ocean interior taking place predominantly along isopycnal surfaces. Therefore, interpolation of oceanic parameters should be performed on isopycnals rather than on isobaric levels, to minimize production of artificial water masses. The differences between these two methods of data interpolation are most pronounced in the high-gradient regions like the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current,” continues Professor Gouretski.

In his recent report, Professor Gouretski presents a new World Ocean Circulation Experiment/ARGO Global Hydrographic Climatology (WAGHC), with temperature and salinity averaged on local isopycnal surfaces. Based on high-quality ship-board data and temperature and salinity profiles from ARGO floats, the new climatology has a monthly resolution and is available on a 1/4° latitude-longitude grid.

“We have compared the WAGHC climatology with NOAA’s WOA13 gridded climatology. These climatologies represent alternative digital products, but the WAGHC has benefited from the addition of new ARGO float data and hydrographic data from the North Polar regions”, says Professor Gourteski. “The two climatologies characterize mean ocean states that are 25 years apart, and the zonally averaged section of the WAGHC-minus-WOA13 temperature difference clearly shows the ocean warming signal, with a mean temperature increase of 0.05°C for the upper 1500-m layer since 1984”.

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:

clip_image004

Some are dirt

clip_image006

Now large airports

clip_image008

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

clip_image010

Zooming in

clip_image012

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.

clip_image014clip_image016clip_image018clip_image020

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.

clip_image022

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.

clip_image024

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.

clip_image026

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.

clip_image028

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!