Challenges to the US Temperature Record

Social media and political propaganda sites are filled with accusations that the temperature record is unreliable. I've responded to many of these claims over the years, but I thought it might be helpful to collect links to them in one place, for easy access.

Summary of 2024 CONUS Temperature Data and Trends

In January I collected a bunch of data from various datasets on CONUS temperatures, including NOAA's two surface thermometer datasets (nClimDiv and USCRN), reanalyses (ERA5) and satellite data (UAH) to compare how well they agree with each other. There is broad general agreement between them for the dates they overlap. NOAA started the USCRN project in 2005 with a network of 114 pristine stations at ideally-sited, rural locations, and USCRN shows marginally more warming than all the other datasets. This fact alone is relevant to most of the challenges I respond to below.

Here is a list of the most common challenges to CONUS temperatures with links to my responses and short summaries of what I say. Note that each of these posts I link to cite the relevant literature and evidence, so you don't have to take my word for anything. You can verify everything I say, and if I ever get something wrong, feel free to let me know in the comments, and I'll make corrections.

NOAA Manipulates US Temperatures

1. Accusations that NOAA Manipulates Data Depend on Data Tampering

Tony Heller and John Shewchuk have for years made accusations that NOAA manipulates the  US temperature record to cool the past and warm the present. I show in this post that both Heller and Shewchuk rely on irresponsible and dishonest handling of data to make their case. In fact, they tamper with the data to make their accusations that NOAA tampers with data. Heller has a long history of this, and I've collected resources from others making similar points here.

2. How Bias Correction and Homogenization Actually Works

NOAA (and every scientific institution on the planet) corrects biases the affect raw data. For temperature data, scientists have published numerous studies that can identify these biases, quantify them, and develop methodologies to correct them. The results of this work can be found in the scientific literature and in the documentation associated with the published datasets. As a general rule, bias correction to land temperatures increases warming trends, but adjustments to sea surface temperatures reduces warming trends by a larger amount. The net effect of all adjustments is to lower the amount of global warming. We can evaluate whether bias correction adds any spurious warming to CONUS temperatures by comparing nClimDiv (with homogenization/bias correction) to USCRN (raw temperatures). For the years they overlap, they are nearly identical, and if anything the raw USCRN data shows marginally more warming.

3. Accusations that NOAA Reports Data from "Ghost" Stations

The old USHCN dataset would use interpolation (based on averaging data from surrounding stations in a grid cell) to aid in area weighting when stations either close or don't report data. The interpolated values are clearly marked with an "E" and do not count as recorded data. They also demonstrably have no effect on CONUS temperatures. And USHCN was fully deprecated in 2014. The two replacements, USCRN and nClimDiv, do not use this form of interpolation. Shewchuk is dishonest to refer to "ghost" stations in the old USHCN dataset, but he's doubly dishonest for continuing to make this accusation when NOAA doesn't even use USHCN anymore.

Graph from "Dale Gribble" on X

The graph above shows CONUS temperatures with or without what Shewchuk wrongly calls fake data from "ghost stations."

Warming is Due to Urban Heat Islands (UHIs)

Some argue that the US is warming because of the UHI effect; that is, as the US population grows, more people live in cities, and it's the growth of urban areas that are responsible for much of the warming trends in the US. It's absolutely clear that cities are significantly more warm than rural areas, putting those living in cities at greater risk to the effects of AGW than those in rural areas, but scientists can evaluate whether the UHI effect is having a significant effect on CONUS and global temperature trends.

1. The Effect of UHIs on both Global and CONUS Temperatures is Quantified

Wickham et al 2013 compared a "very rural" subset of station data and compared this subset to "all stations" and found that the very rural subset is warming at least as rapidly as all stations. This confirms that UHI is not having any demonstrable effect on either global or CONUS temperature trends. This is for several reasons:

  1. Cities warm at the same rate as rural areas.
  2. Bias correction removes biases associated with urbanization.
  3. Urban areas are only ~1% of global surface area and ~3% of CONUS surface area (cities and suburbs).

2. A Sniff Test for the UHI effect on CONUS and Global Temperatures

If UHIs were responsible for a significant fraction of warming in the CONUS temperature record, then temperature anomalies would be much higher at cities than in rural areas, but they aren't. You can pick out cities in maps of population density, but they are invisible on maps of temperature anomalies.


3. A Review of Spencer's Paper on the UHI effect on CONUS Temperatures

A recent paper by Roy Spencer argues that UHI is responsible for ~22% of the warming in the raw data at US temperature stations. However, this figure is not weighted by the fraction of surface area covered by rural vs urban lands, and it's also based entirely on the raw data, before urbanization biases are removed from published CONUS datasets.  Even Spencer admits that his paper does not demonstrate that UHI has any impact on CONUS temperature trends in either USCRN or nClimDiv.

Warming is Due to Poor Station Siting

1. Station Siting has a Negligible Effect on CONUS Temperatures

A common refrain is that warming in the US is due to poor station siting - stations located on asphalt, at airports, too close to buildings, etc. A majority of stations are "poorly sited." And yet if you isolate only the well-sited and poorly sited stations and compare them before and after bias correction, the data from poorly-sited stations agrees with well-sited stations. And USCRN (where all stations are well-sited) shows marginally more warming than nClimDiv (which has some poorly-sited stations). So we can rule out the possibility that station siting is adding any spurious warming to CONUS temperature trends.

The links above give more context for these summaries as well as citations to the literature. Please don't take my word for anything. See the evidence I cite and check up on me.

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