WUWT readers may recall that NOAA did an experiment at Oak Ridge National Laboratory that vindicated my findings about the effects of local urbanization on surface temperature measurements.
The urban heat island (UHI) effect is strongly affected by urban-scale changes to local land surfaces. Basically, the more asphalt, concrete, buildings, etc. that exist near a thermometer, the more the overnight low temperature is biased upwards due to heat storage.
Climate monitoring thermometers are therefore biased upwards. This new UHI database further vindicates my findings in 2015 released at the AGU Fall Meeting. – Anthony
New Surface Urban Heat Island Database for the United States
| A new study published in the ISPRS Journal of Photogrammetry and Remote Sensing presents clear-sky surface UHI (SUHI) intensities for 497 urbanized areas in the United States by combining remotely-sensed data products with multiple US census-defined urban areas. The SUHI intensity is the difference in surface temperature between the built-up and non-built up pixels of an urbanized area. The study reported that the daytime summer SUHI was 1.91 °C higher and the daytime winter SUHI was 0.87 °C higher. The study also reports that the SUHI intensity is lower in census tracts with higher median income and higher proportion of white people. Unfortunately, the study didn’t report on how the UHI effect changes with time. h/t to Friends of Science |
The paper: https://www.sciencedirect.com/science/article/abs/pii/S0924271620302082#!
Abstract
The urban heat island (UHI) effect is strongly modulated by urban-scale changes to the aerodynamic, thermal, and radiative properties of the Earth’s land surfaces. Interest in this phenomenon, both from the climatological and public health perspectives, has led to hundreds of UHI studies, mostly conducted on a city-by-city basis. These studies, however, do not provide a complete picture of the UHI for administrative units using a consistent methodology. To address this gap, we characterize clear-sky surface UHI (SUHI) intensities for all urbanized areas in the United States using a modified Simplified Urban-Extent (SUE) approach by combining a fusion of remotely-sensed data products with multiple US census-defined administrative urban delineations. We find the highest daytime SUHI intensities during summer (1.91 ± 0.97 °C) for 418 of the 497 urbanized areas, while the winter daytime SUHI intensity (0.87 ± 0.45 °C) is the lowest in 439 cases.
Since urban vegetation has been frequently cited as an effective way to mitigate UHI, we use NDVI, a satellite-derived proxy for live green vegetation, and US census tract delineations to characterize how vegetation density modulates inter-urban, intra-urban, and inter-seasonal variability in SUHI intensity. In addition, we also explore how elevation and distance from the coast confound SUHI estimates. To further quantify the uncertainties in our estimates, we analyze and discuss some limitations of these satellite-derived products across climate zones, particularly issues with using remotely sensed radiometric temperature and vegetation indices as proxies for urban heat and vegetation cover. We demonstrate an application of this spatially explicit dataset, showing that for the majority of the urbanized areas, SUHI intensity is lower in census tracts with higher median income and higher proportion of white people. Our analysis also suggests that poor and non-white urban residents may suffer the possible adverse effects of summer SUHI without reaping the potential benefits (e.g., warmer temperatures) during winter, though establishing this result requires future research using more comprehensive heat stress metrics. This study develops new methodological advancements to characterize SUHI and its intra-urban variability at levels of aggregation consistent with sources of other socioeconomic information, which can be relevant in future inter-disciplinary research and as a possible screening tool for policy-making.
The dataset developed in this study is visualized at: https://datadrivenlab.users.earthengine.app/view/usuhiapp
Just to remind everyone that the skeptic funded Berkley Earth project looked at thousands and thousands of sites and found NO urban heat bias.
And had it done so you would be screaming ‘ skeptic funded’ ! Doesn’t make the study correct.
LIAR.
The original funding was CONNED by them pretending they were going to be doing real science.
The funding lasted one year and was then withdrawn when it was realised what an anti-science scam it was.
It is now mostly funded by “anonymous” $500,000 .. ie Soros et al. !!
griff
Just to remind you, “Absence of evidence does not mean evidence of absence.”
SUHI
1. This has been known for a long time
2. Make a prediction How many temperature sites are locates in urban areas?
Go ahead
3. Your guess is wrong
Your employment bias is showing Mosh.
Your credibility is now less than ZERO.
Your employers use all the WORST data they can find, and combine it with what they think are “regional expectation”
Their analysis of urban heat effect consisted of looking at lights at night.. a totally farcical way of determining urban warming.
To try to DENY that a large proportion of sites have large urban effects is to deny facts and reality.
A site doesn’t have to be in an urban setting per se to experience UHI. KRDU is located between three metro areas next to a 6000 acre forest and is always a couple of degrees higher than surrounding reporting stations. Why? It’s a major airport. Compare the temperatures of KRDU and KTTA, located 30 miles south in a very rural area. I’ve seen an 8 degree F spread many a nights.
“WUWT readers may recall that NOAA did an experiment at Oak Ridge National Laboratory that vindicated my findings about the effects of local urbanization on surface temperature measurements.”
NOPE
1. you never published your findings and data
2. Check their paper again.. read carefully
“1. you never published your findings and data”
Taking straight out LYING now, hey mosh !
No wonder you now have zero credibility.
https://wattsupwiththat.files.wordpress.com/2009/05/surfacestationsreport_spring09.pdf
Psst
I wrote to Chakraborty a couple years back when he first published his dataset
Why?
To see how many stations fell in the areas he designated as Urban
Guess what?
Stop p1ssing about and just tell us or go back under your stone.
The Emotional Adolescence is strong with this one.
Dunno’ Mosher. Guess what, wot?
If you have something to say, be like every other intelligent person here and say it. In clear English in proper sentences with punctuation.
This constant knowing, wink wink, I know something but I bet you don’t nonsense simply demonstrates what a plonker you are. That’s why no-one takes you seriously anymore. Either grow up and post rational comments or don’t waste your time and ours posting your incoherent, condescending, patronising, garbled nonsense.
While this may well be true, how does one then explain the dramatic and sustained warming being recorded by the satellite data, which is not corrupted by urban heat island effect?
The satellite data isn’t dramatic, in fact it is far less than the surface warming.
0.13C per decade trend is “… dramatic and sustained warming …,” Leon?
And the satellite data clearly shows it is NOT sustained warming.
There is very minimal warming, if any, between strong El Nino events.
The stated value “(1.91 ± 0.97 °C)” implies a lot of urban heat islands are 3°C or more. Now Mr. Mosher needs to correct the averaging algorithm for the USA to restrict the extrapolation and area-weighting of these temperatures to only the urban areas that are affected, and use the cooler suburban temperatures to calculate the average temperatures between the stations unaffected by UHI. or we could just use the well-sited stations (1 and 2 ratings) and get a more accurate result.
i) Estimates are only valid for clear-sky conditions and influenced by the scale of temporal aggregation;
ii) NDVI is not a perfect proxy for all types of urban vegetation, particularly with reference to their local cooling potential; and
iii) Discrepancies between satellite-derived LST, near-surface Ta, and heat stress.
Years of this analysis 2013-2017 = 5-years of so-called satellite derived UHI data.
Other then the bleeding obvious, that UHI exists, 5=tears does note determine trends accurately at all.
Still waiting, since 2012 for the actual list of stations used in non-peer reviewed Watts (2012).
The surfacestations project is what introduced me to this site. Thanks, Anthony.
I hope that the SurfaceStations database will reappear. It’s just not the same without that info.
http://gallery.surfacestations.org/main.php
The UHI impact on temperature data logically suggests that modern temperatures should be adjusted down ( or older temperatures adjusted up) to allow apples with apples comparisons.Yet bureaus like BOM in Australia make random adjustments the other way which helps confirm their biased narrative of global warming. The extreme heat conditions of the 1930s have all but disappeared with these adjustments.
Without theses adjustments and with proper adjustments for UHI I suspect that the real Trend over the last 150 + years woils be opposite of what is claimed by the Warmists.
Anthony Watts. The OG Social Justice Warrior.
Who knew?
Weird that their Conclusion is weather is racist and not that NOAA’s data proving AGW sucks.
Steven Mosher September 29, 2020 at 2:12 am
Huh? A link to someone else using satellite data looking at trees/lawns/parks and their effect on the temperatures of “urban” areas would go a long way towards supporting that claim.
Sorry, not answerable without a definition of “urban areas”.
No, your question is poorly framed.
Steve, you know a lot about this question. I know that I’ve learned things from you about temperatures. But your drive-by commenting style is not working to either your or our advantage.
Me, I’ve gone to the trouble of reading the study and posting my comments on it as coherently as I can.
Near as I can tell, whoever wrote the article didn’t know what was measured. The article says:
===
“The SUHI intensity is the difference in surface temperature between the built-up and non-built up pixels of an urbanized area.”
===
In fact, the SUHI method doesn’t distinguish “built-up” from “non-built up”. Their method doesn’t distinguish a parking lot from a thirty-story building.
They distinguished high chlorophyll from low chlorophyll. Interesting, but I’m not sure what that proves. I would expect that the cooling from transpiration alone, neglecting the thermal storage differences, would be measurably large.
I would be interested in your comments.
w.