Evaluating USCRN and nClimDiv to USCHN

Comparing USCRN and nClimDiv to USCHN

By Andy May

Steven Mosher complained about my previous post on the difference between the final and raw temperatures in the conterminous 48 states (CONUS) as measured by NOAA’s USCHN. That post can be found here. Mosher’s comment is here. Mosher said the USHCN is no longer the official record of the CONUS temperatures. This is correct as far as NOAA/NCEI is concerned. They switched to a dataset they call nClimDiv in March 2014. Where USHCN had a maximum of 1218 stations, the new nClimDiv network has over 10,000 stations and is gridded to a much finer grid, called nClimGrid. The nClimGrid gridding algorithm is new, it is called “climatological aided interpolation” (Willmott & Robeson, 1995). The new grid has 5 km resolution, much better than the USCHN grid.

While the gridding method is different, the corrections to the raw measurements recorded by the nClimDiv weather stations are the same as those used for the USCHN station measurements. This is discussed here and here. As a result, the nClimDiv and USHCN CONUS yearly averages are nearly the same as seen in Figure 1. The data used to build the nClimDiv dataset is drawn from the GHCN (Global Historical Climate Network) dataset (Vose, et al., 2014).

Figure 1. The USCRN record only goes back to 2005, it is shown in blue. nClimDiv and USHCN go back to the 19th century and lie on top of one another, with very minor differences. In this plot both datasets are gridded with the new nClimGrid gridding algorithm.

In Figure 2, USCRN, nClimDiv and USCHN, gridded with nClimGrid, are shown overlain with the average USCHN station data used in my previous post. The station average is plotted with a yellow dashed line.

Figure 2. Same as Figure 1, but the final USCHN non-gridded final temperature anomalies have been moved to a common reference (1981-2010 average) and plotted on top of the grid averages. The difference between the gridded and non-gridded averages is most noticeable in the peaks and valleys.

In Figure 2 we can see that the nClimDiv yearly gridded average anomalies are similar to the older, non-gridded USHCN yearly averages. The difference is not in the final data, but in the gridding process. The USCRN reference network station data is also similar to nClimDiv and USHCN, but it only goes from 2005 to the present.

As explained here, by NOAA:

“The switch (from USCHN) to nClimDiv has little effect on the average national temperature trend or on relative rankings for individual years, because the new dataset uses the same set of algorithms and corrections applied in the production of the USHCN v2.5 dataset. However, although both the USHCN v2.5 and nClimDiv yield comparable trends, the finer resolution dataset more explicitly accounts for variations in topography (e.g., mountainous areas). Therefore, the baseline temperature, to which the national temperature anomaly is applied, is cooler for nClimDiv than for USHCN v2.5. This new baseline affects anomalies for all years equally, and thus does not alter our understanding of trends.”

Prior to making Figure 2, we adjusted the USCHN station average, from our previous post, to the new baseline. The difference is approximately -0.33°C. This shift is legitimate and results from the new gridding algorithm that explicitly accounts for elevation changes, especially in mountainous areas.


While NOAA/NCEI has dropped USCHN in favor of a combination of USCRN and nClimDiv, the anomaly record from 1900 to today hasn’t changed in any significant way. The baseline (or reference) changed slightly, but we are plotting anomalies and the baseline is not important, it just moves the graph up and down, the trends and year-to-year changes stay the same. More importantly the adjustments made to the raw data, including the important time-of-day bias corrections and the pairwise homogenization (PHA) changes that looked so suspicious in my previous post have not changed at all and are still used.

The nClimDiv dataset uses a lot more stations than USCHN and if the stations are well sited and well taken care of this is a good change. The USCRN dataset is from a smaller set of weather stations, but these are highly accurate and carefully located. I do not think the USCRN stations are part of the nClimDiv set but are used as an independent check on them. The two systems of stations are operated independently.

My previous post dealt with the corrections, that is final minus raw temperatures, used in the USCHN dataset. They looked very anomalous from 2015 through 2019. The same set of corrections are used in the GHCN dataset, which is the source of the data fed into nClimDiv. So, the problem may still exist in the GHCN dataset. I’ll try and check that out and report on it in a future post.

You can purchase my latest book, Politics and Climate Change: A History, here. The content in this post is not from the book.

Vose, R., Applequist, S., Squires, M., Durre, I., Menne, M., Williams, C., . . . Arndt, D. (2014, May 9). Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions. Journal of Applied Meteorology and Climatology, 53(5), 1232-1251. Retrieved from https://journals.ametsoc.org/jamc/article/53/5/1232/13722

Willmott, C., & Robeson, S. (1995). Climatologically Aided Interpolation (CAI) of Terrestrial Air Temperature. International Journal of Climatology, 15, 221-229. Retrieved from https://rmets.onlinelibrary.wiley.com/doi/abs/10.1002/joc.3370150207

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