Some Ends in Alberta – Watts Up With That?

From Dr. Roy Spencer’s International Warming Weblog

by Roy W. Spencer, Ph. D.


Comparability of rural with city temperature monitoring websites throughout Canada in the course of the summers of 1978-2022 exhibits the anticipated common nighttime heat bias in city areas, with a weaker daytime impact. When utilized to the Landsat imagery-based diagnoses of elevated urbanization over time, 20% of the temperature traits in a small area encompassing Calgary and Edmonton are discovered to be attributable to growing urbanization. Calgary leads the record of Canadian cities with elevated urbanization, with an estimated 50% of the nighttime warming traits throughout 10 Canadian mostly-metro areas attributable to elevated urbanization, and 20% of the daytime warming traits.


That is a part of my persevering with investigation of the diploma to which land-based temperature datasets are producing warming traits exaggerated by growing urbanization (the city warmth island impact, UHI). Present “homogenization” methods for thermometer knowledge adjustment don’t explicitly try to appropriate city traits to match rural traits, though I’d count on that they do carry out this perform if many of the stations are rural. As an alternative, they quantity to statistical “consensus-building” workouts the place the bulk wins. So, if many of the stations are affected by growing UHI results, to various levels, these will not be compelled to match the agricultural stations. As an alternative, the reverse happens. For instance, within the U.S. the Watts et al. evaluation of station knowledge confirmed that the U.S. homogenized dataset (USHCN) produced temperature traits as giant as these produced by the stations with the worst siting by way of spurious warmth sources. They additional discovered that use of solely well-sited thermometer areas results in substantial reductions in temperature traits in comparison with the broadly used homogenized dataset.

I think about homogenization to be a black-box strategy that doesn’t tackle the spurious warming in thermometer information ensuing from widespread urbanization over time. My strategy has been completely different: Doc absolutely the temperature variations between station pairs and relate that to some unbiased measure of urbanization distinction. The Landsat-based world dataset of “built-up” areas (which I’ll loosely refer as measures of urbanization) gives the alternative to appropriate for urbanization in thermometer knowledge extending again to the Nineteen Seventies (when the Landsat sequence of satellite tv for pc began).

My fundamental area of focus to begin has been the southeast U.S., partly as a result of my co-researcher, John Christy, is the Alabama state climatologist, and I’m partly funded by way of that workplace. However I’m additionally analyzing different areas. Up to now, I’ve accomplished some preliminary evaluation for the UK, France, Australia, China, and Canada. Right here I’ll present some preliminary outcomes for Canada.

Step one is to quantify, from closely-spaced stations, the distinction in monthly-average temperatures between more-urban and more-rural websites. The temperature dataset I’m utilizing is the International Hourly Built-in Floor Database (ISD), archived on a seamless foundation at NOAA/NCEI. The info are dominated by operational hourly (or 3-hourly) observations made to help aviation at airports world wide. They’re principally (however not fully) unbiased of the utmost and minimal (Tmax and Tmin) measurements that make up different widely-used and homogenized world temperature datasets. Some great benefits of the ISD dataset is the hourly time decision, permitting extra thorough investigation of day vs. night time results, and higher instrumentation and upkeep for aviation security help. An obstacle is that there will not be as many stations within the dataset in comparison with the Tmax/Tmin datasets.

As I mentioned in my final put up on the topic, a vital part to my methodology is the comparatively current high-resolution (1 km) world dataset of urbanization derived from the Landsat satellites since 1975 as a part of the EU’s International Human Settlement (GHS) venture. This permits me to check neighboring stations to quantify how a lot city heat is related to variations in urbanization as identified from Landsat imagery of “built-up” constructions.

City vs. Rural Summertime Temperatures in Canada

Canada is a mostly-rural nation, with broadly scattered temperature monitoring stations. A lot of the inhabitants (the place many of the thermometers are) is clustered alongside the coasts and particularly alongside the U.S. border. There are comparatively few airports in comparison with the dimensions of the nation which limits what number of rural-vs-urban match-ups I could make.

For 150 km most area between station pairs, in addition to a couple of different checks for inclusion (e.g. lower than 300 m elevation distinction between stations), Fig. 1 exhibits the variations in common temperature and area-average Landsat-based urbanization values for (a) 09 UTC (late night time) and (b) 21 UTC (afternoon). These instances have been chosen to approximate the instances of minimal and most temperatures (Tmin and Tmax) which make up different world temperature datasets, so I can do a comparability to them.

Fig. 1 Comparability of closely-spaced Canadian station variations in temperature versus Landsat-based urbanization estimates for (a) nighttime and (b) daytime. Knowledge included are month-to-month common temperatures for June, July, and August for the years 1988-1992, 1998-2002, and 2012-2016, which correspond to the Landsat dataset years of 1990, 2000, and 2014. There weren’t ample thermometer knowledge within the ISD archive to make use of with the 1975 Landsat urbanization estimates. The world-averaging Zone 3 is ~21×21 km in dimension, centered on every station.

As different research have documented, the UHI impact on temperature is bigger at night time, when photo voltaic power absorbed into the bottom by pavement (which has excessive thermal conductivity in comparison with soil or vegetation) is launched into the air and is trapped over town by the steadiness of the nocturnal boundary layer and weaker winds in comparison with daytime. For this restricted set of Canadian station pairs the UHI heat bias is 0.21 deg. C per 10% urbanization in the course of the day, and 0.35 deg. C per 10 % at night time.

Subsequent, if we apply these relationships to the month-to-month temperature and urbanization knowledge at ~70 particular person stations scattered throughout Canada, we get some thought of how a lot growing urbanization has affected temperature traits. (NOTE: the relationships in Fig. 1 solely apply in a median sense, and so it’s not identified how effectively they apply to the person stations within the tables beneath.)

Throughout roughly 70 Canadian stations, the ten stations with the most important identified spurious warming traits (1978-2022) are listed beneath. Observe that the uncooked traits have appreciable variability, a few of which is probably going not weather- or climate-related (adjustments in instrumentation, siting, and so forth.). Desk 1 has the nighttime outcomes, which Desk 2 is for daytime.

TABLE 1: Most Urbanized Nighttime Temperature Traits (1978-2022)

Location Uncooked Temp. Pattern De-urbanized Pattern City Pattern Element
Calgary Intl. Arpt. +0.33 C/decade +0.16 C/decade +0.17 C/decade
Ottawa Intl. Arpt. +0.07 C/decade -0.08 C/decade +0.14 C/decade
Windsor +0.20 C/decade +0.08 C/decade +0.11 C/decade
Montreal/Trudeau Intl. +0.47 C/decade +0.36 C/decade +0.10 C/decade
Edmonton Intl. Arpt. +0.10 C/decade 0.00 C/decade +0.10 C/decade
Saskatoon Intl. Arpt. +0.03 C/decade -0.04 C/decade +0.07 C/decade
Abbotsford +0.48 C/decade +0.41 C/decade +0.07 C/decade
Regina Intl. -0.11 C/decade -0.17 C/decade +0.06 C/decade
Grande Prairie +0.07 C/decade +0.02 C/decade +0.05 C/decade
St. Johns Intl. Arpt. +0.31 C/decade +0.27 C/decade +0.04 C/decade
10-STN AVERAGE +0.19 C/decade +0.10 C/decade +0.09 C/decade

Calgary, Ottawa, Windsor, Montreal, and Edmonton are the 5 station areas with the best charge of elevated urbanization because the Nineteen Seventies as measured by Landsat, and subsequently the best charge of spurious warming since 1978 (the earliest for which I’ve full hourly temperature knowledge). Averaged throughout the ten highest-growth areas, 48% of the typical warming pattern is estimated to be attributable to urbanization alone.

Desk 2 exhibits the corresponding outcomes for summer season afternoon temperatures, which from Fig. 1 we all know have weaker UHI results than nighttime temperatures.

TABLE 2: Most Urbanized Afternoon Temperature Traits (1978-2022)

Location Uncooked Temp. Pattern De-urbanized Pattern City Pattern Element
Calgary Intl. Arpt. +0.26 C/decade +0.16 C/decade +0.11 C/decade
Ottawa Intl. Arpt. +0.27 C/decade +0.19 C/decade +0.09 C/decade
Windsor +0.27 C/decade +0.20 C/decade +0.07 C/decade
Montreal/Trudeau Intl. +0.35 C/decade +0.28 C/decade +0.06 C/decade
Edmonton Intl. Arpt. +0.42 C/decade 0.36 C/decade +0.06 C/decade
Saskatoon Intl. Arpt. +0.18 C/decade +0.13 C/decade +0.04 C/decade
Abbotsford +0.45 C/decade +0.40 C/decade +0.04 C/decade
Regina Intl. +0.08 C/decade +0.04 C/decade +0.04 C/decade
Grande Prairie +0.19 C/decade +0.16 C/decade +0.03 C/decade
St. Johns Intl. Arpt. +0.31 C/decade +0.28 C/decade +0.03 C/decade
10-STN AVERAGE +0.28 C/decade +0.22 C/decade +0.06 C/decade

For the highest 10 most more and more urbanized stations in Desk 2, the typical discount within the noticed afternoon warming traits is 20%, in comparison with 48% for the nighttime traits.

Comparability to the CRUTem5 Knowledge in SE Alberta

How do the leads to Desk 1 have an effect on widely-reported warming traits averaged throughout Canada? On condition that Canada is generally rural with solely sparse measurements, that may be troublesome to find out from the obtainable knowledge. However there isn’t a query that the general public’s consciousness relating to local weather change points is closely influenced by circumstances the place they stay, and most of the people stay in urbanized areas.

As a single sanity check of using these principally airport-based measurements of temperature for local weather monitoring, I examined the area of southeast Alberta bounded by the latitude/longitudes of 50-55N and 110-115W, which incorporates Calgary and Edmonton. The comparability space is set by the IPCC-sanctioned CRUTem5 temperature dataset, which studies common knowledge on a 5 deg. latitude/longitude grid.

There are 4 stations in my dataset on this area, and averaging the 4 stations’ uncooked temperature knowledge produces a pattern (Fig. 2) primarily equivalent to that produced by the CRUTem5 dataset, which has in depth homogenization strategies and (presumably) many extra stations (which are sometimes restricted of their durations of report, and so should be pieced collectively). This excessive degree of settlement is not less than partly fortuitous.

Fig. 2. Month-to-month common summer season (June-July-August) temperatures, 1978-2022, for southeast Alberta, from the IPCC CRUTem5 dataset (inexperienced), uncooked temperatures from 4 stations (purple) and de-urbanized 4-station common temperatures (blue). A temperature offset is utilized to the CRUTem5 anomalies so the pattern strains intersect in 1978.

Making use of the urbanization corrections from Fig. 1 (giant for Calgary and Edmonton, tiny for Chilly Lake and Crimson Deer) result in a median discount of 20% within the area-average temperature pattern. This helps my declare that homogenization procedures utilized to world Tmax/Tmin datasets haven’t adjusted city traits to rural traits, however as a substitute symbolize a “voting” adjustment the place a dataset dominated by stations with growing urbanization will principally retain the pattern traits of the UHI-contaminated areas.


Canadian cities present a considerable city warmth island impact in the summertime, particularly at night time, and Landsat-based estimates of elevated urbanization counsel that this has induced a spurious warming part of reported temperature traits, not less than for areas experiencing elevated urbanization. A restricted comparability in Alberta suggests there stays an city warming bias within the CRUTem5 dataset, in keeping with my earlier postings on the topic and work accomplished by others.

The difficulty is essential as a result of rational power coverage ought to be primarily based upon actuality, not notion. To the extent that world warming estimates are exaggerated, so shall be power coverage selections. As it’s, there may be proof (e.g. right here) that the local weather fashions used to information coverage produce extra warming than noticed, particularly in the summertime when extra warmth is of concern. If that noticed warming is even lower than being reported, then the local weather fashions change into more and more irrelevant to power coverage selections.

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