The global averaged surface temperature for June 2019 was 0.62C using my spherical triangulation method merging GHCNV3 with HadSST3. This is a further drop of 0.04C from May 2018. The discrepancy with GHCNV4 is however growing. V4C calculated in exactly the same way gives a June temperature of 0.75C, a rise of 0.03C, and 0.13C warmer than V3. This difference is statistically significant.
Both the V3c and V4C spatial distributions for June are shown below. Two warm zones are visible across Europe and Eastern Russia sandwiching a cool zone in central Asia
The annual temperatures, including the first 6 months of 2019, looks as follows.
There is a large and growing discrepancy between V3C and V4C, that begins only after 2002. The 6-month 2019 average temperature for V4 is 0.84C, some 0.07C warmer than V3C. This looks suspicious to me. Although V4C nominally has far more stations than V3C (17378 versus 7280), I am discovering that many of those new stations are actually sub-composites of V3 stations. This introduces an element of double and triple counting, which I hope to write this up in a post fairly soon.
In this post I look at the effect of changes in the original set of CRUTEM3 stations.
Shown below are the spatial changes in temperatures between HadCRUT3 (2013) and the latest HadCRUT4.6 (2019) from 1998 to 2010. These are calculated using just the original set of CRUTEM3 stations and their counterparts in H4. Each annual 5×5 deg. cell is averaged over 12 months.
Fig 1: Map showing the effects of CRU adjustments in station temperature between 1998 & 2010
There is essentially no change in SST between H3 (HadSST2) and H4.6 (HadSST3) so the increased warming trend since AR5 is simply caused by changes to the underlying CRU station data. Most of the significant changes occur in Asia and N. America. Here are the static temperature differences shown on the same scale as the temperature anomalies for 1998 and 2010.
Fig2. Annual temperature anomalies calculated using CRUTEM3 (2013) and HadSST2.
and now 2010.
Fig 3. HadCRUT4.6 calculated using only modern versions of the original CRUTEM3 stations combined with HadSST3.
Finally I show the differences between H4 and H3 temperature anomalies plotted on the same scale. In an idea world this plot should be pale blue with zero difference.
The difference between Figure 4 and Figure 5
The yellow to pink areas are roughly 0.5 to 1.0C warmer than HadCRUT3 , demonstrating how after 7-years an apparent increase in ~4000 global land temperatures can explain why the AR5 hiatus essentially evaporated. The original weather station data cannot have changed, so these effects are probably caused by merging of nearby local stations, homogenisation between regional stations, correcting errors, or something else. Probably only Tim Osborne or Phil Jones could explain.
The previous post showed that changes to weather station data between CRUTEM3 and CRUTEM4.6 are responsible for converting the Hiatus into a warming trend. Here I look into some examples of those changes. I first looked for big changes in the monthly ‘normal’ values between 1961 and 1990. This identified some inconsistencies.
WMO identifiers are supposed to be fixed locations but Station 218240 has been changed and moved thousands of miles between CRUTEM3 and CRUTEM4.6. Despite this several temperature measurements remain exactly the same, which is simply impossible!
Same ID but different stations both using exactly the same 3 year temperature record ?
At the other extreme we have stations whose values have changed significantly
Same station but very different monthly temperature and different normals.
136 stations have seasonal monthly normals that have changed by more than 1C between CRUTEM3 and CRUTEM4.6. In some cases eg. Los Angeles, the station has moved in altitude going from downtown to Pasadena. However, others like Miami apparently are the same station but record completely different monthly mean temperatures going from CRUTEM3 to CRUTEM4.
Miami monthly temperatures CUTEM3 compared to CRUTEM4.6
Maybe this is a different station in another location since Miami is flat. However if so then this just demonstrates how great the variability is, even within one city.
What I think this really highlights is the lack of (available) metadata describing CRU station changes and updates, plus at least one data quality control issue. This doesn’t explain why the post 1998 warming trend changed.