Analysis Of USHCN Dataset | NOT A LOT OF PEOPLE KNOW THATAs can be seen, there has been a steady climb in the percentage since the 1990’s. For 2013, the total number of estimated months was 5125, representing 35.1% of the annual total.
This is the highest percentage since 1905. The number of estimates is alarmingly high, and appears to be growing.
There are 254 stations, or 21% of the total of 1218, where there are no readings at all for 2013.
estimated does not mean adjusted. the vast majority of raw data are adjusted for one reason or another. estimated data is infilled by algorithm because there was no data deemed acceptible.
Estimating/Infilling
This all brings us round to the question of how temperatures are estimated.
The USHCN V2 website explains how their homogenisation software compares station trends with a number (up to 40) of highly correlated series from nearby stations. This is used not only to adjust for non-climatic biases, such as station moves, but also to estimate temperatures where infilling is necessary, and even to adjust for UHI.
The key point here is that the “nearby stations” will not necessarily be USHCN stations, which are regarded as “high quality”. Indeed, it is extremely likely that the vast majority of such stations will be non-USHCN.
Much of the detail behind the latest “Gridded” system in use for US Climate Divisions, just introduced in February, is contained within an AMS paywalled paper, “Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions”. This shows how thousands of stations have been added to the database, many with very short and/or extremely incomplete records.
These in turn have to be “homogenised” to fill in their gaps. So we find that USHCN stations are homogenised against stations that have themselves been estimated.
The danger is clear – the USHCN sites, which have been selected as high quality, and with long, well documented records, are at risk of being swamped by potentially unsuitable sites. Far from the latter being adjusted to the trend of USHCN stations, the opposite is likely to occur.
bad sites, which have been shown to be warmer on average, influence good sites. every bit of extra warming then influences the homogenization process whereby warming readings are expected and cooling readings are rejected and filled in with (you guessed it) warming readings from other lower quality stations. one would expect to see a slow creep of rising temperatures over the history of the data set with this method, and you do. the reported numbers for 1995-2005 get a little higher every year. the 2007 version is warmer, the 2009 version is warmer still, etc.
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