Comparing GHCN V1 and V3

Much Ado About Very Little

Guest post by Zeke Hausfather and Steve Mosher

E.M. Smith has claimed (see full post here: Summary Report on v1 vs v3 GHCN ) to find numerous differences between GHCN version 1 and version 3, differences that, in his words, constitute “a degree of shift of the input data of roughly the same order of scale as the reputed Global Warming”. His analysis is flawed, however, as the raw data in GHCN v1 and v3 are nearly identical, and trends in the globally gridded raw data for both are effectively the same as those found in the published NCDC and GISTemp land records.

Figure 1: Comparison of station-months of data over time between GHCN v1 and GHCN v3.

First, a little background on the Global Historical Climatology Network (GHCN). GHCN was created in the late 1980s after a large effort by the World Meteorological Organization (WMO) to collect all available temperature data from member countries. Many of these were in the form of logbooks or other non-digital records (this being the 1980s), and many man-hours were required to process them into a digital form.

Meanwhile, the WMO set up a process to automate the submission of data going forward, setting up a network of around 1,200 geographically distributed stations that would provide monthly updates via CLIMAT reports. Periodically NCDC undertakes efforts to collect more historical monthly data not submitted via CLIMAT reports, and more recently has set up a daily product with automated updates from tens of thousands of stations (GHCN-Daily). This structure of GHCN as a periodically updated retroactive compilation with a subset of automatically reporting stations has in the past led to some confusion over “station die-offs”.

GHCN has gone through three major iterations. V1 was released in 1992 and included around 6,000 stations with only mean temperatures available and no adjustments or homogenization. Version 2 was released in 1997 and added in a number of new stations, minimum and maximum temperatures, and manually homogenized data. V3 was released last year and added many new stations (both in the distant past and post-1992, where Version 2 showed a sharp drop-off in available records), and switched the homogenization process to the Menne and Williams Pairwise Homogenization Algorithm (PHA) previously used in USHCN. Figure 1, above, shows the number of stations records available for each month in GHCN v1 and v3.

We can perform a number of tests to see if GHCN v1 and 3 differ. The simplest one is to compare the observations in both data files for the same stations. This is somewhat complicated by the fact that station identity numbers have changed since v1 and v3, and we have been unable to locate translation between the two. We can, however, match stations between the two sets using their latitude and longitude coordinates. This gives us 1,267,763 station-months of data whose stations match between the two sets with a precision of two decimal places.

When we calculate the difference between the two sets and plot the distribution, we get Figure 2, below:

Figure 2: Difference between GHCN v1 and GHCN v3 records matched by station lat/lon.

The vast majority of observations are identical between GHCN v1 and v3. If we exclude identical observations and just look at the distribution of non-zero differences, we get Figure 3:

Figure 3: Difference between GHCN v1 and GHCN v3 records matched by station lat/lon, excluding cases of zero difference.

This shows that while the raw data in GHCN v1 and v3 is not identical (at least via this method of station matching), there is little bias in the mean. Differences between the two might be explained by the resolution of duplicate measurements in the same location (called imods in GHCN version 2), by updates to the data from various national MET offices, or by refinements in station lat/lon over time.

Another way to test if GHCN v1 and GHCN v3 differ is to convert the data of each into anomalies (with baseline years of 1960-1989 chosen to maximize overlap in the common anomaly period), assign each to a 5 by 5 lat/lon grid cell, average anomalies in each grid cell, and create a land-area weighted global temperature estimate. This is similar to the method that NCDC uses in their reconstruction.

Figure 4: Comparison of GHCN v1 and GHCN v3 spatially gridded anomalies. Note that GHCN v1 ends in 1990 because that is the last year of available data.

When we do this for both GHCN v1 and GHCN v3 raw data, we get the figure above. While we would expect some differences simply because GHCN v3 includes a number of stations not included in GHCN v1, the similarities are pretty remarkable. Over the century scale the trends in the two are nearly identical. This differs significantly from the picture painted by E.M. Smith; indeed, instead of the shift in input data being equivalent to 50% of the trend, as he suggests, we see that differences amount to a mere 1.5% difference in trend.

Now, astute skeptics might agree with me that the raw data files are, if not identical, overwhelmingly similar but point out that there is one difference I did not address: GHCN v1 had only raw data with no adjustments, while GHCN v3 has both adjusted and raw versions. Perhaps the warming the E.M. Smith attributed to changes in input data might in fact be due to changes in adjustment method?

This is not the case, as GHCN v3 adjustments have little impact on the global-scale trend vis-à-vis the raw data. We can see this in Figure 5 below, where both GHCN v1 and GHCN v3 are compared to published NCDC and GISTemp land records:

Figure 5: Comparison of GHCN v1 and GHCN v3 spatially gridded anomalies with NCDC and GISTemp published land reconstructions.

If we look at the trends over the 1880-1990 period, we find that both GHCN v1 and GHCN v3 are quite similar, and lie between the trends shown in GISTemp and NCDC records.

1880-1990 trends

GHCN v1 raw: 0.04845 C (0.03661 to 0.06024)

GHCN v3 raw: 0.04919 C (0.03737 to 0.06100)

NCDC adjusted: 0.05394 C (0.04418 to 0.06370)

GISTemp adjusted: 0.04676 C (0.03620 to 0.05731)

This analysis should make it abundantly clear that the change in raw input data (if any) between GHCN version 1 and GHCN version 3 had little to no effect on global temperature trends. The exact cause of Smith’s mistaken conclusion is unknown; however, a review of his code does indicate a few areas that seem problematic. They are:

1. An apparent reliance on station Ids to match stations. Station Ids can differ between versions of GHCN.

2. Use of First Differences. Smith uses first differences, however he has made idiosyncratic changes to the method, especially in cases where there are temporal lacuna in the data. The method which used to be used by NCDC has known issues and biases – detailed by Jeff Id. Smith’s implementation and his method of handling gaps in the data is unproven and may be the cause.

3. It’s unclear from the code which version of GHCN V3 that Smith used.

STATA code and data used in creating the figures in this post can be found here: https://www.dropbox.com/sh/b9rz83cu7ds9lq8/IKUGoHk5qc

Playing around with it is strongly encouraged for those interested.