The McCall, ID, GHCN Station

Introduction

I didn’t know about it when I started looking at temperature data for my area, but McCall, Idaho, has a Global Historic Climate Network (GHCN) station (USC00105708). When I started, I was working with United States Historic Climate Network (USHCN) data and this site was, so far as I can tell now, not recognized as part of the system (at any rate, the GHCN number “00105708” was not in the data archives that I downloaded and does not seem to be included now). At that time, I had just retired from the Payette National Forest and used the long-term weather station associated with the New Meadows Ranger District (USC00106388), which I had used in some environmental reports, for my first investigations. This doesn’t really surprise me because the organization and analysis of data from the diverse networks of temperature monitoring programs was historically only loosely coordinated. For a data professional like myself, the sleuthing has been fun.

I posted two previous pages investigating the history (metadata) for the New Meadows RS monitoring station because I knew about it; my conclusion was that NOAA and Berkeley Earth Systems (BEST) really knew nothing concrete about station histories and locations. When I made statements on X (formerly Twitter) about this, the climate hysterians insisted that I was either wrong or it didn’t matter because they got “the right answer” regarding trends. The Historical Observing Metadata Repository (HOMR) data have improved and some issues with blending data from distinct stations in the GHCN database that I had found have been corrected, but problems remain.

This post discusses the McCall, Idaho, GHCN station (USC00105708), including location, temperature trends, and some comparison with nearby weather monitoring.

Location

NOAA’s HOMR site shows several different locations for this monitoring station over time, some sensible and some unreasonable, and none where the station is actually situated. There are four locations identified, all shown here (there are two close together at the bottom of the image):

Needless to say, I got a good laugh over the position in Payette Lake, which is about 220 feet (67 meters) deep at that point. As I said above, the actual station location matches none of these locations, and none were likely to have ever hosted it. My guess is that it has always been associated with US Forest Service, Payette National Forest, installations, which are shown here:

In fact, the station is currently located at the new airport helicopter base just south of the Smoke Jumper base (UTM 11T 570698.41 m E, 4971266.55 m N). The station is next to a building and two parking lots:

Temperature Trends

GHCN Results

I downloaded the unadjusted (presumably "raw") and homogenized data archives from the NOAA GHCNm ("m" is for "monthly") site, which no longer includes maximum or minimum temperatures, apparently (wouldn't want too much information available /sarc), so I'll use the ((Tmax+min)/2) simple average they publish. I did decide to remove the very earliest points taken from an obviously unknown location (they would turn the "trend" upward, but they could have come from anywhere). Here is what I think is the most reliable chart:

The upward trend is barely significant at the 5% level, but it is not a very reliable linear model with about 93% of the relationship left unexplained by the model. Quite obviously, variation in the data could be due to station moves, airport development, urban growth, and other things that cannot be well evaluated when actual locations and station history are so vague.

Interestingly, the homogenized data lead to a slightly reduced upward trend, and one that is not statistically significant:

BEST Cross Reference

The rate of change in this model (0.85°/century) is similar to that reported by;Berkeley Earth Systems (BEST; 0.75°/century), and the homogenized NOAA data trend is even more similar (0.73°/century)*. Even more interesting, however, is that BEST indicates three station moves that are not well documented by NOAA (the 2016 move may correspond to one of them). At least one of these moves, and maybe both of the most recent ones, may be reasonable because the helicopter base needed new construction before its activation in 2015 and these are shown near the airport. It Seems odd that BEST had no idea that the station was once moved out of the lake (/sarc), but they do identify an important record break at that time. This is BEST's average temperature trend chart (https://data.berkeleyearth.org/stations/40604):

RAWS Cross Reference

In addition to the long-term GHCN station at the McCall Airport, there is a Remote Automated Weather Station (RAWS) in a relatively pristine setting in the forest nearby (about 4.5 air miles):

These installations are often used by wildland fire fighters to monitor fire weather conditions in real time, but they also automatically record local wether conditions, including air temperature. I have discussed some of the RAWS data in this general area previously; this image shows their locations and their proximity to McCall:

Average annual trend at this station is similar to that shown for McCall:

The apparent trend is a little steeper, I think because it's a shorter record with only fairly recent data compared to the long-term record at McCall. On the other hand, the trend is not statistically significant, so "apparent trend" is an apt description. Many people have noted that average temperatures are apparently increasing in many places because the minimum temperatures are increasing and the maximum temperatures not so much; in my opinion, this is one reason so many alarmists seem to dislike looking at the former. I have reported on this myself for a nearby station that has long GHCN and USHCN records. The GHCN archive no longer seems to include maximum or minimum temperatures (which I think may be the bias mentioned above) and McCall is not a USHCN station (which still records maxima), but RAWS stations do record them. The RAWS maximum record trend is shown here:

As if by magic, the maximum temperatures may, in fact, be declining over time.

Final Thoughts

I wouldn't find it surprising to see McCall temperatures increasing, it is an increasingly busy resort town. I suspect the station was moved from a forest service office downtown at some point and out to the airport. This would be consistent with HOMR's 1997 break and it looks like there is a break in the data record at that time, though I doubt that it was ever in the subdivision out by the water treatment plant, but it's not impossible. I would expect it to have been at either the McCall or Krassel Ranger District offices (the locations of these are shown above). It would be nice to know whether the trend is the same for maximum and minimum temperatures, which I may be able to determine at a later date with the daily records from GHCNd ("d" is for "daily"). Urbanization would be expected to retain heat over night better than rural settings, although minima are rising at the nearby Ski Hill RAWS site as well. I think that temperatures can also be affected by Payette Lake, particularly with respect to freeze up and ice out. This may be something worth investigating further if good records have been maintained. At any rate, temperatures aren't changing much.

It is clear that NOAA's HOMR records cannot be taken at face value. While I applaud their efforts to gradually improve its information, I can't see that they're doing a really great job of it. I understand that it is difficult to go back and resurrect information that resides in various places and may not even have been officially recorded. I have had to do this before and it can be difficult and sometimes impossible; but a simple inspection would tell you that the McCall station was most likely never in the lake. I've pointed out issues with the New Meadows Ranger Station site (with which, to be discussed elesehere, BEST has done an even worse job) and I've seen improvements but not real accuracy from my experience.

As a professional data manager, I have always disliked government efforts to consolidate data, several of which occupied my time before I retired. Most of the ones I worked with sought to force field data into standardized formats that may or may not have fit the original data; consequently, some also attempted to reduce data. There is no way in which standardizing and consolidating data with widely varying structures can avoid losing information, and lost information can never be recovered (unless, of course, at least digital copies of field data sheets are archived and someone takes the time and effort to look). It appears that NOAA may be intentionally restricting data by removing maximum and minimum temoeratatures from the GHCNm archive (the "m" is for monthly; as of now, GHCNd still has them). The Climate Hysterian crowd likes to assert that USHCN is "deprecated", that is, replaced officially with GHCN, but USHCN continues to be populated and archives maxima and minima. I will stop short of suggesting that this is part of an effort to intentionally hide (and maybe lose) information, but, coupled with ongoing moves to make the archives less transparent to the non-specialized public (e.g., by removing user-friendly web sites, expecting searches by coordinates or exact names, NetCDF, etc.) can make one suspicious.

Correlation Among Six Western USCRN Stations

Introduction 

One of the arguments used by climate scientists to infill or interpolate data for stations with broken or “suspect” records and areas where no measured data are available is that anomalies (i.e., differences of values form some arbitrary baseline period) are correlated over long distances. I expect this is largely true, though I question this for describing temperatures for an unsampled area or for creating missing measurements. There are many statistical ways of modeling or interpolating data and these are not the subject of this essay. In this essay, I just wanted to take a look at how well temperatures at dispersed sites are, in fact, correlated, whether there are confounding factors of interest, and what their individual trends are (this last will be in a subsequent post). 

Methods 

I decided to use the United States Climate Reference Network (USCRN) for this exercise. The USCRN was established by the National Oceanic and Atmospheric Administration (NOAA) in 2003 to provide a well distributed network of high quality instruments in relatively pristine areas to monitor climate attributes, theoretically eliminating the need to compensate for the limitations of stations already in existence (NOAA 2023), such as station relocations, data gaps, poor instrument siting, etc. These should represent both the best longer term climate monitoring (up to 20 years) available in a spatially well dispersed network.

Monitoring Stations 

I selected five USCRN stations in roughly a circle around my area, that has a United States Historical Climate Network (USHCN) station nearby in New Meadows, Idaho (below), that I have discussed previously; I also selected one a bit farther away (Moose, Wyoming) to get one that was in a similar ecological setting to my area because the closest five sites were more like desert than the forest river valley of my area. (Note: USHCN is technically “deprecated” and replaced for official purposes by a Global Historic Climate Network [GHCN] analysis. The GHCN system is analyzed differently than the homogenization employed by USHCN, but NOAA continues to populate the USHCN database and it includes a more complete record than GHCN.)

The analysis is based around the Murphy, Idaho, site because it is the closest to my area. This site is shown in dark red on the map. I have chosen not to display photos of all of the stations on this page because that would not be a good use of page space; I have included these on a separate page here.

The relevant metadata for these stations and my local GHCN station are listed in the table below. I will be placing most emphasis on Tmax because it is less smoothed than Tavg (annual average temperature) and because Tmax is closer to actually indicating warming if it is happening than Tmin.

Analysis

The obvious working hypothesis is that the correlation coefficients (R) should decrease with distance from the Murphy station. I used the latest USCRN data where the stations have differring record lengths, but for the correlation analyses they were restricted to the 15 years where every station had complete, paired record. I looked at actual temperatures rather than anomalies because I wanted data as unprocessed as possible, so I used Average Annual Mean Maximum Temperature (Tmax), Average Annual Mean Minimum Temperature (Tmin), and Average Annual True Mean Temperature (Tavg)*. I created program code in R Studio and used the "cor" routine to create a correlation matrix; ggplot2 was used to chart the trend of correlaton with distance and the ggpairs extension to ggplot2 was used to plot the correlation matrices. 

I will briefly discuss the results, but my intent is mainly to display the results of the analyses without any effort to provide any in-depth analysis. I want to leave most interpretation to the reader because everyone may come to different conclusions as to how they think correlation over distance affects the ability to interpolate temperatures for areas with no or missing data. I intend to take a specific look at trends at these stations individually and in reference to the New Meadows Ranger Station GHCN station in a later post.

Results 

The correlations of of all temperature metrics among these USCRN stations was a somewhat surprising. The Tmax correlation matrix is shown graphically here:

The Arco, Idaho station is the closest to Murphy and is the most stongly correlated as expected, but the farthest site from Murphy (Moose, Wyoming) was almost as strongly correlated; not only are they relatively far apart, they occupy very different ecosystms (see photos here). On the basis of distance alone, one would expect the Dillon, Montana, site to be approximately as correlated with Murphy and Spokane, Washington, to be similar in correlation coefficient to Moose, Wyoming. Interestingly, the two sites farthest from each other (John Day, Oregon and Moose, Wyoming, at 488 miles) are not the least correlated; the least correlated are John Day, Oregon, and Dillon, Montana, 330 miles apart.

We have a similar situation with Tmin (below). Again, Murphy, Idaho, is most correlated with the Arco, Idaho, station and is least correlated with the Dillon, Montana, station; the latter being rather poorly correlated with all other stations except, oddly, Arco, Idaho. Both the Murphy and Arco, Idaho, stations are well correlated with the Moose, Wyoming, station; the Arco station is approximately equidistant from Moose and Dillon, yet the correlations are extremely different.

With Tavg (the true mean temperature), we see a pattern generally similar to Tmax and quite different that that for Tmin. I think this can be explained by the fact that the variability in minimum temperatures is much higher than for Tmax. This is evident by looking at the standard deviations (SD) for these three temperature metrics (second below): SD is typically quite a bit larger for maximum temperatures than for minimums.

Since one of the issues here was distance and we showed that there was no consistent correlation of stations based on distance, it seemed useful to graph the relationship (below): There is a non-significant relationship of Murphy, Idaho maximum temperatures with distance from that station.

Summary 

Distance between stations did not seem to be a reliable indicator of similarity for any temperature metric. This was shown by inconsistency in correlation coefficients of measured temperatures (calculated for true average, obviously) and by a downsloping but non-significant linear model of correlation with distance. The working hypothesis is therefore falsified, and some other factor must be involved. I suspect that factor is ecological setting, as these sites vary quite a bit in elevation and relationship to orographic features, both of which affect prevailing climate. Some "official" temperature data (I think GHCN, for example) contain some ecological metadata, but I haven't seen any analysis of these (but I haven't really looked, either, as of yet).

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* This is different than the normal (Tmax+Tmin)/2 method for calculating average temperature (simple average): USCRN reports the simple average, but it also reports a true mean based on all obervations.