The analysis cited above reveals a small inhomogeneity in monthly CET Tavg, shown in the following two figures:
The small anomalous step down in temperatures is in 2004, which corresponds to a date on which there was a change in the station composition, see the following reference for details of that and other changes:
Philip Eden version
An alternative version was being created by Philip Eden (a distinguished British meteorologist, now deceased), and the following figure shows both HadCET (the official version) and the Eden version, and their difference, all as 12-month moving averages:
Note that the Philip Eden version (in blue) is a bit warmer than HadCET from around 2005, removing some of the 2004 inhomogeneity shown above.
This post is about the current spatial sampling of the stations with monthly average rainfall data in GHCNM version 2, and temperature data in GHCNM versions 3 and 4, the current source data for many “official” reconstructions of the global land rainfall and surface air temperature history.
Preliminary results are included for GHCNM version 4, which has not yet been released officially.
A figure of around 8000 stations is often quoted for GHCNM version 3, which might appear to be an adequate spatial sampling. However, most of those 8000 stations are no longer providing updates, and there are questions about the adequacy of the current spatial coverage in two distinct areas:
Spatial sampling of the varying climate around the globe
Detection and correction of inhomogeneities in the currently reporting stations
These two questions will be discussed on a per-country basis, starting with Australia.
The Australian Bureau of Meteorology has 112 stations in ACORN-SAT(2012), intended to describe the varying temperature histories around the country:
The currently reporting stations in GHCNMv3 (unadjusted), for monthly TAVG (average temperature) data is shown in the following list for Australia, generated by recording only those stations with data in 2018:
These 62 stations in Australia that are currently reporting monthly TAVG to GHCNMv3 are possibly adequate to represent the varying temperatures around the country, but only if those stations remain unchanged in equipment, environment and procedures, and are free of errors.
ACORN-SAT was constructed by homogenisation involving many hundreds of other stations. This is no longer possible in GHCNMv3, which only has current data for 62 stations in Australia.
To confirm that all of the many other Australian stations in GHCNMv3 are currently non-reporting, here is a table of all Australian TAVG data (qcu: “unadjusted”) for 2017:
The 4-digit numbers in the table above, with separate columns for Jan to Dec, are the average temperature in hundredths of a degree C, -9999 means missing data.
GHCNM version 4 (preliminary) results are as follows. A total of 101 Australian stations contributed monthly average TAVG (unadjusted) data for January 2018, shown below in two parts:
101 stations will be a significant improvement on 62, but falls short of the 300 that is my guesstimate for the minimum number required to have a good chance of detecting and correcting inhomogeneities, and infilling missing data.
GHCNM version 2 (now containing only monthly rainfall totals)
Australian stations with rainfall data in 2018 are shown in the following list:
The above list includes the rainfall totals for January 2018, in tenths of a mm.
The 29 stations in the list are insufficient to allow analysis of contemporary rainfall, and detection/correction/infilling of anomalous/missing data.
The entirety of the 2017 rainfall data for Australia is given in the following table, with missing data (-9999) shown in red:
GHCNMv2 is no longer fit-for-purpose, and the Australian BoM has questions to answer about why so much data is missing from these stations, many/most of which are at Meteorological Offices.
You are invited to visit a new website that describes a relatively simple, but nevertheless effective method of reconstructing the regional average history of monthly average surface air temperature variations for a region from its weather station data, aided by any metadata that is available:
Photo above: A recent picture of the weather station at Rutherglen, Australia, from the BoM webpage cited below. Other photos are shown at the end of the post.
Post Summary and Conclusions
This post documents some analysis of changes in minimum temperatures (Tmin) at Rutherglen, a rural weather station in South East Australia. It is found that:
Early 20th century Tmin measurements are around 1.0C higher (annual average) than those that would have been measured if the recording system/location/environment of today had been in place then. There is some variation between months that make up this annual average.
The annual average ACORN-SAT(2012) correction of 1.7C for early data is therefore substantially too high
The daily ACORN-SAT(2012) corrections for 1920/21/22 (the only years examined) show a nonphysical discontinuity between the end of November and the start of December
Rutherglen (BoM id 82039) is a rural weather station at a research farm, with no nearby man-made structures, at least from 1975, as revealed by photos and descriptions from the BoM webpage given below:
The RAW Tmin data from Rutherglen, and from many nearby stations, show a net cooling over the last 100 years, as revealed in the following figure:
Questions have been asked about why the raw temperature trend of net cooling has been adjusted in ACORN-SAT to a net warming trend, and the BoM have responded with the webpage cited above.
The dates and sizes (annual average) of Tmin corrections applied by ACORN-SAT(2012) are given in the following extract from its adjustment summary document:
A later (September 2014) summary from the BoM about Rutherglen does not mention the 1928 Tmin correction:
but it is unclear if that correction has been disowned (without saying so) or simply not mentioned. The original 2012 documentation is taken to be definitive, as it matches daily temperature data available in October 2017.
Data prior to the last-listed correction in 1928 is reduced, on average, by 1.7C, the sum of all corrections. The following figure shows the daily corrections for 1920/21/23 (the only years examined):
The corrections appear to change in jumps from month to month, in particular with a very large jump (marked A in the figure above) from November to December, surely an undesirable and erroneous artifact rather than a genuine weather phenomenon.
I have estimated the monthly average corrections that would be needed to be applied to raw Rutherglen Tmin data to remove non-climatic influences relative to those present in recent years. The methodology is being documented in a separate blog:
The following figure shows the annual average correction needed for periods of data (the bold blue lines are the moving averages) deemed to be stable, tracking the regional average (in red) reasonably closely:
The required correction is the temperature difference between the bold blue and dashed red lines, which are respectively the 15-year moving average of raw Rutherglen Tmin data, and the 15-year moving average of the regional average temperature variations. The figure also shows the 12-month moving average of weather-corrected raw Tmin data at Rutherglen.
The key features of the data shown in the figure above are as follows:
1914 to 1926: The average correction needed for Tmin data in this early period of stability is around 1.0C, the ACORN-SAT(2012) correction of 1.7C is too much
1914: There was a step change in temperatures, probably associated with the station move in January 1914 (source: Torok thesis 1997), a move that fails to get a mention or a correction in ACORN-SAT(2012)
1928: There was a step change in temperatures around 1928, but they recovered around 1936. ACORN-SAT (2012) has the step down in 1928, but not the recovery in 1936, an example of errors caused in ACORN-SAT by transient perturbations.
1966: There was a large drop in temperatures
1974: There was another drop in temperatures, but note that this was the date of some heavy rainfall (see below), and the temperature drop looks a bit like the sharp edge of a sawtooth perturbation
1984: This marked the start of a long period of stable temperatures with a trend matching that of the regional average
1998 (29th January): This was the date of a switch to an AWS system, which does not appear to have had a significant impact on measured temperatures
2012: There was a drop in temperatures at that date, possibly associated with a period of heavy rainfall, more on that below
The regional moving average temperature history was derived by averaging periods of stable temperature (such as the ones shown above in bold for Rutherglen) across stations in the region.
The following set of figures show eyeball-estimated corrections for each month, being the average temperature difference between the raw data (in black, red for its average) and the regional average (in blue/mauve):
The figures shown above confirm that the periods 1914-1966 and 1984 to 2012 were roughly stable in terms of non-climatic influence, justifying the use of these periods in obtaining the regional average temperature history. If a corrected (“homogenised”) version of Rutherglen Tmin data is required then early data (before 1966) must be reduced by around 1.0C, with some monthly variation in that figure.
The following figure shows more of the periods of data used to form the regional average temperature history:
The complete set of the data periods used in regional averaging at Rutherglen is shown here:
Finally, the following figure shows a summary of the regional average Tmin and rainfall history back to 1885, indicating the heavy rain that may explain some of the anomalous changes in temperature around 1974 and 2012:
Conclusions: See the start of this post.
The following photo of the Rutherglen station is from the ACORN-SAT station catalogue:
Photos of the Rutherglen site from the BoM website cited above (click to enlarge):
I believe that it was James Hansen and NASA/NOAA co-workers who first pointed out in print the problem of historical non-climatic warming in temperature reconstructions, as depicted in the following diagram:
This post shows examples of the problem manifesting itself in station mergers in ACORN-SAT, and quantifies the sizes of the resulting errors.
I construct regional average temperature histories, separately for each month, of monthly average daily maximum (Tmax) and minimum (Tmin) temperatures. Full details of the methodology are documented in this blog:
Normalising station data to the level of the most recent regional average reveals the time-history of non-climatic influence at the stations being merged, indicating the size of corrections that would be needed in the construction of composite station records.
Example 01: Wagga Wagga (NSW) Tmin
The following figure shows 12-month (and longer) moving averages of Tmin for Wagga Wagga Kooringal, and AMO, with consistent normalisation for both stations, i.e. allowing actual measured temperature differences to be seen:
The figure above also indicates typical sizes of temperature corrections that would be needed in the construction of a composite record. The composite corrections needed for early data (1910-25) are much lower than those applied in ACORN-SAT, which are shown in the following extract from its documentation:
Working backwards in time from the present, ACORN-SAT applies the following corrections to Tmin data:
1968 (-0.46C): There was indeed a step change in temperature of around this size at that date
1964 (-0.09C): The data are consistent with this small correction
1948 (-1.62C): It appears that this enormous correction is the consequence of the anomalous warming at Wagga Kooringal between around 1930 and 1950, a period that I mark as being too anomalous to include in the regional averaging process.
1928 (+0.43C): This correction achieves some damage limitation, but the net correction (the sum of all of them) applied to data before 1928 is -0.46 – 0.09 -1.62 + 0.43 = -1.74 C, which results in considerable over-correction
Regional Average Stations
The list of stations used in constructing the regional averages in the analysis above is as follows, indicating which ones were omitted:
Monthly average surface air temperature data in South-East Australia (and probably in other regions) show a relatively sudden increase in maximum temperatures at the end of the 20th century. Unfortunately, this was also the time when the BoM introduced Automatic Weather Stations (AWS) at many of its sites. This post presents some data on temperature and rainfall changes around this “climate shift” and shows graphically that the calibration of the AWS systems in the area examined had close matches to those of the systems they replaced, at least at the level of monthly Tmax averages. The seasonal differences in temperature and rainfall variations may provide clues to the cause(s) of the climate shift.
Regional Average Temperatures and Rainfall
The figure below shows the climate shift in a region of NSW/VIC bounded by lines joining Mildura, Hillston, Wagga Wagga, Rutherglen, Echuca, Nhill and back to Mildura:
The data shown in the figure above represent estimates of the regional average temperature history, in this case for 6-monthly Tmax data. Details of how to estimate regional averages, detecting and correcting inhomogeneities, will be given in later posts.
I have examined the regional average temperature history for each separate month, and find that each month from September to February has a similar upward shift in Tmax near the end of the 20th century, so have averaged over this 6-month period to illustrate the phenomenon (red curves above). The other months all show a similar lack of anything special happening around that time (blue curves for the 6-month average).
There is normally a close association between Tmax fluctuations and rainfall levels, but the following figure shows that there was no particular trend in rainfall around the time of the climate shift:
Are AWS Systems Involved?
Many stations in the region had AWS systems installed in the late 20th century, for example becoming the primary sensors in November 1996 at both Mildura Airport and Wagga Wagga AMO. Fortunately, many nearby stations retained their manual systems, and I have checked their temperature histories against those that switched to AWS.
The following figure shows the temperature history (12-month and 15-year moving averages, after subtraction of regional average temperature fluctuations) for 3 stations that switched to AWS, together with the regional average temperature history (black curves):
Note that there are no substantial deviations from the regional average when the AWS systems became the primary sensors. For comparison, the following two figures show the same data for 6 stations that did not get converted to AWS:
There may be calibration differences of a tenth or two degrees C at the level of monthly Tmax averages between the AWS and manual systems employed in the region, but not more than that. This conclusion is consistent with the absence of corrections for AWS installations in ACORN-SAT, the early one at Cape Otway being the only one that has a correction.
Later posts will look at how the climate shift varied around Australia, which may shed some light on cause(s).
This is the first in a series of posts with the general theme of “Do it yourself temperature homogenisation“. The full series of posts will outline a simple but effective procedure for turning raw instrumental temperature data (aided by any available station history and data on rainfall) into reconstructions of the background temperature history for any area with a “sufficient” number of weather stations, the sufficient number depending mostly on the quality and extent of the data.
The overall procedure involves visual detection of inhomogeneities, followed by averaging and integration of interannual temperature differences in which the inhomogeneous data is omitted. Effectively, large inhomogeneities allow visual detection, followed by removal from the data, small ones are suppressed by the averaging process used to obtain regional histories.
Averaging Multiple Records
This first post in the series deals with the final step of the procedure, the method by which multiple temperature records are combined to give a regional average temperature history. The following figure gives a schematic picture of what typical temperature data looks like:
The figure above illustrates the following:
Black data: Historical urban warming, followed by a station move (typically to an airport or other out-of-town site), followed by a switch to automatic sensors
Red Data: A good rural station, but with some missing data
Blue data: A station with a transient perturbation, possibly of non-climatic origin, or possibly due to a period of localised heavy cloud/rainfall
It is assumed for the purposes of this post that the previous stage of the overall procedure has identified the inhomogeneities, marked all periods of “transition” within a computer program, and that the analyst has corrected the data for any large residual inhomogeneities at transition boundaries and at all ends (more on that last issue below and in subsequent posts). The computer program, under analyst control, then does the following:
Either infills missing raw data or leaves gaps if there is doubt about the station history within a gap. Infilling can also be done manually.
Computes all valid differences in temperature, separately for each month, between years N and (N-1). Valid differences are those that do not cross, or lie within transitions
Extrapolate temperature differences for all stations with missing years using the average of valid temperature differences
The following figure illustrates the resulting full coverage of temperature differences:
The temperature differences can now be averaged again with the important feature that each station has a constant weight in the averaging process (1/3 for each station in the example shown in the figure above). Finally, the average temperature differences can be integrated forwards and backwards in time from any desired reference year/temperature to obtain average temperature histories for each month.
The reason for extrapolating temperature differences for all stations can be seen from the example of the blue data in the figure above. In some cases the dip in the blue data will be deemed to be associated with a period of heavy rainfall, i.e. a genuine climatic effect. This genuine climatic effect will only produce the correct impact on the regional average (i.e. only within the period of rainfall) if the weighting of the blue data is constant in time, which is what results from the extrapolation of temperature differences for the red data.
Using interannual temperature differences avoids the need to estimate and correct most inhomogeneities, but there is a small price to pay for that substantial benefit, the problem of residual inhomogeneities at boundaries, illustrated in the following figure:
The figure above shows an example of a temperature record with perturbations from the regional average. The perturbation within the record is not really a problem, because the downward shift in temperature is matched by the exact reverse in later data, with the constant weighting of each record ensuring that the perturbation will not influence the end-to-end shift in average temperature.
The problem illustrated in the figure above arises from the inhomogeneous ends of the record (more generally from any boundary, including ones created by defining “transitions”), which can distort the end-to-end variation of average temperature if the inhomogeneities are not detected and corrected, which can be done manually (see a later post).
The paper has generated some hype and fake news, such as this from “energylivenews”:
“Wind turbines produce more power on the coldest days than the average winter day.”
This post attempts to provide a more accurate description of what the paper says, and what it does not (but should) say.
The authors of the paper are all meteorologists or climatologists. The meteorological aspects of the paper are excellent, especially the insights provided into the particular weather patterns that lead to most cold spells and associated high demands for electricity. The absence of electrical engineering input is apparent in the incomplete analysis of the contribution of wind power to meeting future peak demands.
The paper quantifies how well current wind power deals with an old problem (high demand during cold spells on a system without wind power) but fails to quantify how additional wind power would contribute to solving current problems. Current GB wind power already has more than sufficient capacity to deal with the relatively small excess demands that appear to occur during some windy cold spells, so windy cold spells are no longer a problem. In fact, the current nameplate capacity of wind power of around 15GW (metered plus embedded generators) is so large that it has shifted the current problem (the peak demands placed on the rapidly diminishing conventional sources of supply) to times of such low wind conditions that additional wind power capacity will have negligible effect on the current capacity problem for the foreseeable future.
The following figure shows how the analysis could be improved to draw appropriate conclusions about additional wind power:
The figure above has been copied and pasted from the paper, with the addition by me of the red line, which provides a rough estimation of where the current problem lies, the peak demands that conventional sources might be expected to have to meet when cold spells fall on working days. The slope of the red line follows from the current total nameplate capacity of GB wind power. I have assumed that the conventional sources can supply 1040 GWh per day, so the red curve starts at that level. As total wind power increases higher total demands can be met thanks to the wind contribution. The current problem is events below the red line, several of which had very low wind power. Those very low wind power events had a BIT less demand than the highest but a LOT less wind power than the average, and that is the current peak capacity problem, and additional wind power will not solve it.
The figure above can be used to see the outcome of several what-ifs. If demands increase (such as via increased electrification of heating and transport) then many more events will move to the right into the danger region. If more conventional supply is lost then the red line will move to the left, bringing many more events into the danger region.
What-if more wind power capacity is added? Suppose that an extra 1.5 GW (nameplate) is added in the next few years, will that improve the security of the GB electricity system? The answer can be seen from the effect on the red line, whose slope will merely decrease by 10% (since total nameplate capacity has risen by 10%), making very little difference to the problem area below the red line.
Wind power enthusiasts may be tempted to argue that there is very little in the way of events below the red line so there is not much of a capacity problem, especially when more wind power is added. There are two problems with that argument, firstly that the temporal resolution (daily wind averages) used in the paper underestimates the number of events below the red line (more on that below), but even if that issue is minor the capacity problem includes the large number of events that are poised to enter the danger region via a rise in demand and/or a fall in conventional supply. Wind power has changed the statistics of the supply/demand balance, but that change in statistics has now all but stopped, and somehow the rapidly falling conventional supply has to be reconciled with the expected rapid rise in demand.
The reanalysis data used, from 1979, includes long periods of relatively mild (and presumably windy) winters in the UK, and this is likely to have biased the statistics in the over-optimistic direction. The following figures show HadCET data for daily winter maximum and minimum temperatures from 1878, with exceptionally cold days shown with blue markers.
Finally, the paper uses daily average wind power, when it should have made an attempt to estimate wind power at the critical early evening period, when peak demands occur. Critical events with wind power lulls in the early evening will have been biased towards higher apparent wind power by the use of daily averages. There would be many more dots below the red line in Figure 6 of the paper (shown above) if the analysis had been done at a finer resolution, with Roger Andrews showing example data at 5-minute resolution from a particular cold spell in his blog post cited above.
Many people suspect that there are inaccuracies in the major homogenisations of instrumental temperature records. This article asserts that there are substantial errors resulting from the homogenisation procedures commonly employed, and provides a general explanation for them. In short, there are many transient perturbations in temperature records, and the homogenisation procedure over-corrects for many of them. I am currently quantifying this over-correction in the ACORN-SAT version of Australian surface air temperatures, and hope that this article will inspire others to help, or to look at data from other countries. The article is based on knowledge mainly of ACORN-SAT, but there is no reason to suppose that the conclusions do not apply generally.
First of all the following figure illustrates why raw data has to be adjusted to reveal the true background temperature variations. The objective is to obtain the temperatures that would have been recorded in the past if the weather station had been at its current location, and with its current equipment. The figure below shows a typical history of the difference in effective temperature calibration between the past and the present:
Station moves and equipment changes are the typical causes of sudden and persistent changes in temperature relative to neighbours, events that computer algorithms are good at detecting and correcting. If that were the whole story then everything would be fine, but things go rapidly downhill from this point on.
The main problem for large-scale homogenisations is that there are many “transient” (rather than persistent) perturbations of temperature. The computer detection algorithms still work to some extent with transient perturbations (though it would be better if they failed to work), but they cannot do the correction part of the procedure without adult supervision. The outcome is illustrated in the following figure, showing two typical transient perturbations, and the erroneous corrections that they generate:
The problem arises when only one end of transient perturbations get detected, the procedure assumes that the transients are persistent, and therefore over-corrects the data before the transient.
It is likely that the most common transient perturbations of temperature involve sudden cooling, for example from the following mechanisms:
Removal of thermometers from an urban-warmed location
Replacement of damaged or degraded screens
Onset of a rainy period after a drought
There can be sudden warmings, for example when thermometers are removed from a shaded location, there is sudden screen damage, or a building is erected nearby, but the other end of those transients are probably more likely to be detected than in the case of sudden coolings. It seems likely that poorly corrected transient perturbations give a bias towards cooling of the early part of temperature records.
There is always scope for improving computer algorithms, but I think that the problem lies with the functional design of the homogenisation procedure, which needs more involvement of expert analysts and less blind faith in what the computer says. The analysts need to examine rainfall data to remove false detections from that source, and they need to look over long periods of time to find both ends of transient perturbations. As the main interest in long temperature records is the end-to-end variation of temperature it may be OK to leave transient perturbations in place, but note that mid-20th century urban warming can convert cyclic variations of temperature into hockey sticks.
I am continuing a review of ACORN-SAT data, trying to separate its step change detections into two groups, those resulting from persistent changes (which need correction), and those resulting from transient perturbations, which don’t need correction. I hope that this article will encourage people to examine data from other regions, to determine the extent of climate distortion introduced by the homogenisation process.
ACORN-SAT is the outcome of a “system” for detection and correction of non-climatic influences on surface air temperature data recorded in Australia. Previous posts have dealt with errors in the correction part of the process, and with false detections. This post deals with failure to detect what should be detected.
Many non-climatic influences on temperature measurements are transient in nature, so if an attempt is made to detect and correct, then both ends of the transient influence must be found. That fact alone makes the process rather risky, and this post shows examples of the risk being realised, with one end of transient perturbations not being detected, resulting in invalid correction of the data before the onset of the transient influence.
The following figure shows a transient warming influence on daily maximum temperatures at Kerang in Victoria. To make the transient warming easier to see the data from a nearby reference station (Echuca Aerodrome) has been subtracted from the Kerang data (black curve), removing most of the natural background variation in temperature. The figure also shows matching results for nearby Deniliquin (red) and Rutherglen (blue), for which transient perturbations (detected via ACORN-SAT, and verified visually by me) have been corrected.
ACORN-SAT has a detection for Kerang in 1957 at the end of the transient influence, but no detection for the start, therefore it falsely corrects the data all the way back to 1910. The right answer is to correct the data only back to 1943, the onset of the transient, or to make no correction at all.
This post will be updated with any further detection failures that are found.
NOTE: Missing months of data have been infilled by interpolation, following the temperature variations of neighbouring stations, and partial quality control adjustments have been made for anomalous spikes and dips, in particular at Rutherglen in 1925.