No! Ah! Part II. The return of the uncertainty monster

Some may have noticed that a new NOAA paper on the global mean temperature has been published in Science (Karl et al., 2015). It is minimally different from the previous one. Why the press is interested, why this is a Science paper, why the mitigation sceptics are not happy at all is that due to these minuscule changes the data no longer shows a "hiatus", no statistical analysis needed any more. That such paltry changes make so much difference shows the overconfidence of people talking about the "hiatus" as if it were a thing.

You can see the minimal changes, mostly less than 0.05°C, both warmer and cooler, in the top panel of the graph below. I made the graph extra large, so that you can see the differences. The thick black line shows the new assessment and the thin red line the previous estimated global temperature signal.



It reminds of the time when a (better) interpolation of the datagap in the Arctic (Cowtan and Way, 2014) made the long-term trend almost imperceptibly larger, but changed the temperature signal enough to double the warming during the "hiatus". Again we see a lot of whining from the people who should not have build their political case on such a fragile feature in the first place. And we will see a lot more. And after that they will continue to act as if the "hiatus" is a thing. At least after a few years of this dishonest climate "debate" I would be very surprised if they would sudden look at all the data and would make a fair assessment of the situation.

The most paradox are the mitigation sceptics who react by claiming that scientists are not allowed to remove biases due to changes in the way temperature was measured. Without accounting for the fact that old sea surface temperature measurements were biased to be too cool, global warming would be larger. Previously I explained the reasons why raw data shows more warming and you can see the effect in the bottom panel of the above graph. The black line shows NOAA's current best estimate for the temperature change, the thin blue (?) line the temperature change in the raw data. Only alarmists would prefer the raw temperature trend.



The trend changes over a number of periods are depicted above; the circles are the old dataset, the squares the new one. You can clearly see differences between the trend for the various short periods. Shifting the period by only 2 years creates large trend difference. Another way to demonstrate that this features is not robust.

The biggest change in the dataset is that NOOA now uses the raw data of the land temperature database of the International Surface Temperature Initiative (ISTI). (Disclosure, I am member of the ISTI.) This dataset contains much more stations than the previously used Global Historical Climate Network (GHCNv3) dataset. (The land temperatures were homogenized with the same Pairwise Homogenization Algorithm (PHA) as before.)

The new trend in the land temperature is a little larger over the full period; see both graphs above. This was to be expected. The ISTI dataset contains much more stations and is now similar to the one of Berkeley Earth, which already had a somewhat stronger temperature trend. Furthermore, we know that there is a cooling bias in the land surface temperatures and with more stations it is easier to see data problems by comparing stations with each other and relative homogenization methods can remove a larger part of this trend bias.

However, the largest trend changes in recent periods are due to the oceans; the Extended Reconstructed Sea Surface Temperature (ERSST v4) dataset. Zeke Hausfather:

They also added a correction for temperatures measured by floating buoys vs. ships. A number of studies have found that buoys tend to measure temperatures that are about 0.12 degrees C (0.22 F) colder than is found by ships at the same time and same location. As the number of automated buoy instruments has dramatically expanded in the past two decades, failing to account for the fact that buoys read colder temperatures ended up adding a negative bias in the resulting ocean record.

It is not my field, but if I understand it correctly other ocean datasets, COBE2 and HadSST3, already took these biases into account. Thus the difference between these datasets needs to have another reason. Understanding these differences would be interesting. And NOAA did not yet interpolate over the data gap in the Arctic, which would be expected to make its recent trends even stronger, just like it did for Cowtan and Way. They are working on that; the triangles in the above graph are with interpolation. Thus the recent trend is currently still understated.

Personally, I would be most interested in understanding the difference that are important for long-term trends, like the differences shown below in two graphs prepared by Zeke Hausfather. That is hard enough and such questions are more likely answerable. The recent differences between the datasets is even tinier than the tiny "hiatus" itself; no idea whether that can be understood.





I need some more synonyms for tiny or minimal, but the changes are really small. They are well within the statistical uncertainty computed from the year to year fluctuations. They are well within the uncertainty due to the fact that we do not have measurements everywhere and need to interpolate. The latter is the typical confidence interval you see in historical temperature plots. For most datasets the confidence interval does not include the uncertainty because biases were not perfectly removed. (HadCRUT does this partially.)

This uncertainty becomes relatively more important on short time scales (and for smaller regions); for large time scales are large regions (global) many biases will compensate each other. For land temperatures a 15-year period is especially dangerous, that is about the period between two inhomogeneities (non-climatic changes).

The recent period is in addition especially tricky. We are just in an important transitional period from manual observations with thermometers Stevenson screens to automatic weather stations. Not only the measurement principle is different, but also the siting. It is difficult, on top of this, to find and remove inhomogeneities near the end of the series because the computed mean after the inhomogeneity is based on only a few values and has a large uncertainty.

You can get some idea of how large this uncertainty is be comparing the short-term trend of two independent datasets. Ed Hawkins has compared the new USA NOAA data and the current UK HadCRUT4.3 dataset at Climate Lab Book and presented these graphs:



By request, he kindly computed the difference between these 10-year trends shown below. They suggest that if you are interested in short term trends smaller than 0.1°C per decade (say the "hiatus"), you should study whether your data quality is good enough to be able to interpret the variability as being due to climate system. The variability should be large enough or have a stronger regional pattern (say El Nino).

If the variability you are interested in is somewhat bigger than 0.1°C you probably want to put in work. Both datasets are based on much of the same data and use similar methods. For homogenization of surface stations we know that it can reduce biases, but not fully remove them. Thus part of the bias will be the same for all datasets that use statistical homogenization. The difference shown below is thus an underestimate of the uncertainty and it will need analytic work to compute the real uncertainty due to data quality.



[UPDATE. I thought I had an interesting new angle, but now see that Gavin Schmidt, director of NASA GISS, has been saying this in newspapers since the start: “The fact that such small changes to the analysis make the difference between a hiatus or not merely underlines how fragile a concept it was in the first place.”]

Organisational implications

To reduce the uncertainties due to changes in the way we measure climate we need to make two major organizational changes: we need to share all climate data with each other to better study the past and for the future we need to build up a climate reference network. These are, unfortunately, not things climatologists can do alone, but need actions by politicians and support by their voters.

To quote from my last post on data sharing:

We need [to share all climate data] to see what is happening to the climate. We already had almost a degree of global warming and are likely in for at least another one. This will change the sea level, the circulation, precipitation patterns. This will change extreme and severe weather. We will need to adapt to these climatic changes and to know how to protect our communities we need climate data. ...

To understand climate, we need a global overview. National studies are not enough. To understand changes in circulation, interactions with mountains and vegetation, to understand changes in extremes, we need spatially resolved information and not just a few stations. ...

To reduce the influence of measurement errors and non-climatic changes (inhomogeneities) on our (trend) assessments we need dense networks. These errors are detected and corrected by comparing one station to its neighbours. The closer the neighbours are, the more accurate we can assess the real climatic changes. This is especially important when it comes to changes in severe and extreme weather, where the removal of non-climatic changes is very challenging. ... For the best possible data to protect our communities, we need dense networks, we need all the data there is.

The main governing body of the World Meteorological Organization (WMO) is just meeting until next week Friday (12th of June). They are debating a resolution on climate data exchange. To show your support for the free exchange of climate data please retweet or favourite the tweet below.

Please RT if you want the World Meteorological Organization to share climate data. #meteoworld http://t.co/C8qD0p6YXx pic.twitter.com/X4zymbIYJ6

— Victor Venema (@VariabilityBlog) June 3, 2015

We are conducting a (hopefully) unique experiment with our climate system. Future generations climatologists would not forgive us if we did not observe as well as we can how our climate is changing. To make expensive decisions on climate adaptation, mitigation and burden sharing, we need reliable information on climatic changes: Only piggy-backing on meteorological observations is not good enough. We can improve data using homogenization, but homogenized data will always have much larger uncertainties than truly homogeneous data, especially when it comes to long term trends.

To quote my virtual boss at the ISTI Peter Thorne:

To conclude, worryingly not for the first time (think tropospheric temperatures in late 1990s / early 2000s) we find that potentially some substantial portion of a model-observation discrepancy that has caused a degree of controversy is down to unresolved observational issues. There is still an undue propensity for scientists and public alike to take the observations as a 'given'. As [this study by NOAA] attests, even in the modern era we have imperfect measurements.

Which leads me to a final proposition for a more scientifically sane future ...

This whole train of events does rather speak to the fact that we can and should observe in a more sane, sensible and rational way in the future. There is no need to bequeath onto researchers in 50 years time a similar mess. If we instigate and maintain reference quality networks that are stable SI traceable measures with comprehensive uncertainty chains such as USCRN, GRUAN etc. but for all domains for decades to come we can have the next generation of scientists focus on analyzing what happened and not, depressingly, trying instead to inevitably somewhat ambiguously ascertain what happened.

Building up such a reference network is hard because we will only see the benefits much later. But already now after about 10 years the USCRN provides evidence that the siting of stations is in all likelihood not a large problem in the USA. The US reference network with stations at perfectly sited locations, not affected by urbanization or micro-siting problems, shows about the same trend as the homogenized historical USA temperature data. (The reference network even has a non-significant somewhat larger trend.)

There is a number of scientists working on trying to make this happen. If you are interested please contact me or Peter. We will have to design such reference networks, show how much more accurate they would make climate assessments (together with the existing networks) and then lobby to make it happen.

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Further reading

Metrologist Michael de Podesta sees to agree with the above post and wrote about the overconfidence of the mitigation sceptics in the climate record.

Zeke Hausfather: Whither the pause? NOAA reports no recent slowdown in warming. This post provides a comprehensive, well-readable (I think) overview of the NOAA article.

A similar well-informed article can be found on Ars Technica: Updated NOAA temperature record shows little global warming slowdown.

If you read the HotWhopper post, you will get the most scientific background, apart from reading the NOAA article itself.

Peter Thorne of the ISTI on The Karl et al. Science paper and ISTI. He gives more background on the land temperatures and makes a case for global climate reference networks.

Ed Hawkins compares the new NOAA dataset with HadCRUT4: Global temperature comparisons.

Gavin Schmidt as a climate modeller explains who well the new dataset fits to climate projections: NOAA temperature record updates and the ‘hiatus’.

Chris Merchant found about the same recent trend in his satellite sea surface temperature dataset and writes: No slowdown in global temperature rise?

Hotwhopper discusses the main egregious errors of the first two WUWT posts on Karl et al. and an unfriendly email of Anthony Watts to NOAA. I hope Hotwhopper is not planning any holidays. It will be busy times. Peter Thorne has the real back story.

NOAA press release: Science publishes new NOAA analysis: Data show no recent slowdown in global warming.

Thomas R. Karl, Anthony Arguez, Boyin Huang, Jay H. Lawrimore, James R. McMahon, Matthew J. Menne, Thomas C. Peterson, Russell S. Vose, Huai-Min Zhang, 2015: Possible artifacts of data biases in the recent global surface warming hiatus. Science. doi: 10.1126/science.aaa5632.

Boyin Huang, Viva F. Banzon, Eric Freeman, Jay Lawrimore, Wei Liu, Thomas C. Peterson, Thomas M. Smith, Peter W. Thorne, Scott D. Woodruff, and Huai-Min Zhang, 2015: Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons. Journal Climate, 28, pp. 911–930, doi: 10.1175/JCLI-D-14-00006.1.

Rennie, Jared, Jay Lawrimore, Byron Gleason, Peter Thorne, Colin Morice, Matthew Menne, Claude Williams, Waldenio Gambi de Almeida, John Christy, Meaghan Flannery, Masahito Ishihara, Kenji Kamiguchi, Abert Klein Tank, Albert Mhanda, David Lister, Vyacheslav Razuvaev, Madeleine Renom, Matilde Rusticucci, Jeremy Tandy, Steven Worley, Victor Venema, William Angel, Manola Brunet, Bob Dattore, Howard Diamond, Matthew Lazzara, Frank Le Blancq, Juerg Luterbacher, Hermann Maechel, Jayashree Revadekar, Russell Vose, Xungang Yin, 2014: The International Surface Temperature Initiative global land surface databank: monthly temperature data version 1 release description and methods. Geoscience Data Journal, 1, pp. 75–102, doi: 10.1002/gdj3.8.

Why raw temperatures show too little global warming

In the last few amonths I have written several posts why raw temperature observations may show too little global warming. Let's put it all in perspective.

People who have followed the climate "debate" have probably heard of two potential reasons why raw data shows too much global warming: urbanization and the quality of the siting. These are the two non-climatic changes that mitigation sceptics promote claiming that they are responsible for a large part of the observed warming in the global mean temperature records.

If you only know of biases producing a trend that is artificially too strong, it may come as a surprise that the raw measurements actually have too small a trend and that removing non-climatic changes increases the trend. For example, in the Global Historical Climate Network (GHCNv3) of NOAA, the land temperature change since 1880 is increased by about 0.2°C by the homogenization method that removes non-climatic changes. See figure below.

(If you also consider the adjustments made to ocean temperatures, the net effect of the adjustments is that they make the global temperature increase smaller.)


The global mean temperature estimates from the Global Historical Climate Network (GHCNv3) of NOAA, USA. The red curve shows the global average temperature in the raw data. The blue curve is the global mean temperature after removing non-climatic changes. (Figure by Zeke Hausfather.)

The adjustments are not always that "large". The Berkeley Earth group may much smaller adjustments. The global mean temperature of Berkeley Earth is shown below. However, as noted by Zeke Hausfather in the comments below, also the curve where the method did not explicitly detect breakpoints does homogenize the data partially because it penalises stations that have a very different trend than their neighbours. After removal of non-climatic changes BEST come to a similar climatic trend as seen in GHCNv3.


The global mean temperature estimates from the Berkeley Earth project (previously known as BEST), USA. The blue curve is computed without using their method to detect breakpoints, the red curve the temperature after adjusting for non-climatic changes. (Figure by Steven Mosher.)

Let's go over the reasons why the temperature trend may show too little warming.

Urbanization and siting

Urbanization warms the location of a station, but these stations also tend to move away from the centre to better locations. What matters is where the stations were in the beginning of the observation and where they are now. How much too warm the origin was and how much too warm the ending. This effect has been studied a lot and urban stations seem to have about the same trend as their surrounding (more) rural stations.

 

A recent study for two villages showed that the current location of the weather station is half a degree centigrade cooler than the centre of the village. Many stations started in villages (or cities), thermometers used to be expensive scientific instruments operated by highly educated people and they had to be read daily. Thus the siting of many stations may have improved, which would lead to a cooling bias.

 

When a city station moves to an airport, which happened a lot around WWII, this takes the station (largely) out of the urban heat island. Furthermore, cities are often located near the coast and in valleys. Airports may thus often be located at a higher altitude. Both reasons could lead to a considerable cooling for the fraction of stations that moved to airports.

 

Changes in thermometer screens

During the 20th century the Stevenson screen was established as the dominant thermometer screen. This screen protected the thermometer much better against radiation (solar and heat) than earlier designs. Deficits of earlier measurement methods have artificially warmed the temperatures in the 19th century.

 

Some claim that earlier Stevenson screens were painted with inferior paints. The sun consequently heats up the screen more, which again heats the incoming air. The introduction of modern durable white paints may thus have produced a cooling bias.

 

Currently we are in a transition to Automatic Weather Stations. This can show large changes in either direction for the network they are introduced in. What the net global effect is, is not clear at this moment.

 

Irrigation

Irrigation on average decreases the 2m-temperature by about 1 degree centigrade. At the same time, irrigation has spread enormously during the last century. People preferentially live in irrigated areas and weather stations serve agriculture. Thus it is possible that there is a higher likelihood that weather stations are erected in irrigated areas than elsewhere. In this case irrigation could lead to a spurious cooling trend. For suburban stations an increase of watering gardens could also produce a spurious cooling trend.

It is understandable that in the past the focus was on urbanization as a non-climatic change that could make the warming in the climate records too strong. Then the focus was on whether climate change was happening (detection). To make a strong case, science had to show that even the minimum climatic trend was too large to be due to chance.

Now that we know that the Earth is warming, we no longer just need a minimum estimate of the temperature trend, but the best estimate of the trend. For a realistic assessment of models and impacts we need the best estimate of the trend, not just the minimum possible trend. Thus we need to understand the reasons why raw records may show too little warming and quantify these effects.

Just because the mitigation skeptics are talking nonsense about the temperature record does not mean that there are no real issues with the data and it does not mean that statistical homogenization can remove trend errors sufficiently well. This is a strange blind spot in climate science. As Neville Nicholls, one of the heroes of the homogenization community, writes:

When this work began 25 years or more ago, not even our scientist colleagues were very interested. At the first seminar I presented about our attempts to identify the biases in Australian weather data, one colleague told me I was wasting my time. He reckoned that the raw weather data were sufficiently accurate for any possible use people might make of them.

One wonders how this colleague knew this without studying it.

The reasons for a cooling bias have been studied much too little. At this time we cannot tell which reason is how important. Any of these reasons is potentially important enough to be able to explain the 0.2°C per century trend bias found in GHNv3. Especially in the light of the large range of possible values, a range that we can often not even estimate at the moment. In fact, all the above mentioned reasons could together explain a much larger trend bias, which could dramatically change our assessment of the progress of global warming.

The fact is that we cannot quantify the various cooling biases at the moment and it is a travesty that we can't.

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Other posts in this series

Irrigation and paint as reasons for a cooling bias

Temperature trend biases due to urbanization and siting quality changes

Changes in screen design leading to temperature trend biases

Temperature bias from the village heat island

Temperature bias from the village heat island

The most direct way to study how alterations in the way we measure temperature affect the registered temperatures is to make simultaneous measurements the old way and the current way. New technological developments have now made it much easier to study the influence of location. Modern batteries have made it possible to just install an automatically recording weather station anywhere and obtain several years of data. It used to be necessary to have nearby electricity access, permissions to use it and dig cables in most cases.

Jenny Linden used this technology to study the influence of the siting of weather stations on the measured temperature for two villages. One village was in North Sweden, one in the West of Germany. In both cases the center of the village was about half a degree Centigrade (one degree Fahrenheit) warmer than the current location of the weather station on grassland just outside the villages. This is small compared to the urban heat island found in large cities, but it is comparable in size to the warming we have seen since 1900 and thus important for the understanding of global warming. In urban areas, the heat island can be multiple degrees and is studied much because of the additional heat stress it produces. This new study may be the first for villages.

Her presentation (together with Jan Esper and Sue Grimmond) at EMS2014 (abstract) was my biggest discovery in the field of data quality in 2014. Two locations is naturally not not enough for strong conclusions, but I hope that this study will be the start of many more, now that the technology has been shown to work and the effects to be significant for climate change studies.

The experiments

A small map of Haparanda, Sweden, with all measurement locations indicated by a pin. Mentioned in the text are Center and SMHI current met-station.

The Swedish case is easiest to interpret. The village [[Haparanda]] with 5 thousand inhabitants is in the North of Sweden, on the border with Finland. It has a beautiful long record, measurements started in 1859. Observations started on a North wall in the center of the village and were continued there until 1942. Currently the station is on the edge of the village. It is thought that the center did not change much any more since 1942. Thus the difference could be interpreted as the cooling bias due to the relocation from the center to its current location in the historical observations. The modern measurement was not at the original North wall, but free standing. Thus only the difference of the location can be studied.

As so often, the minimum temperature at night is affected most. It has a difference of 0.7°C between the center and the current location. The maximum temperature only shows a difference of 0.1°C. The average temperature has a difference of 0.4°C.

The village [[Geisenheim]] is close to Mainz, Germany, and was the first testing location for the equipment. It has 11.5 thousand inhabitants and is on the right bank of the Rhine. Also this station has a quite long history and started in 1884 in a park and stayed there until 1915. Now it is well-sited outside of the village in the meadows. A lot has changed in Geisenheim between 1915 and now. So we cannot make any historical interpretation of the changes, but it is interesting to compare the measurements in the center with the current ones to compare with Haparanda and to get an idea how large the maximum effect would theoretically be.

A small map of Geisenheim, Germany. Compared in the text are Center and DWD current met-station. The station started in Park.

The difference in the minimum temperature between the center and the current location is 0.8°C. In this case also the maximum temperature has a clear difference of 0.4°C. The average temperature has a difference of 0.6°C.

The next village on the list is [[Cazorla]] in Spain. I hope the list will become much longer. If you have any good suggestions please comment below or write Jenny Linden. Especially locations where the center is still mostly like it used to be are of interest. And as much different climate regions should be sampled as possible.

The temperature record

Naturally not all stations started in villages and even less exactly in the center. But this is still a quite common scenario, especially for long series. In the 19th century thermometers were expensive scientific instruments. The people making the measurements were often the few well-educated people in the village or town, priests, apothecaries, teachers and so on.

Erik Engström, climate communicator of the Swedish weather service (SMHI) wrote:

In Sweden we have many stations that have moved from a central location out to a location outside the village. ... We have several stations located in small towns and villages that have been relocated from the centre to a more rural location, such as Haparanda. In many cases the station was also relocated from the city centre to the airport outside the city. But we also have many stations that have been rural and are still rural today.

Improvements in siting may be even more interesting for urban stations. Stations in cities have often been relocated (multiple times) to better sited locations, if only because meteorological offices cannot afford the rents in the center. Because the Urban Heat Island is stronger, this could lead to even larger cooling biases. What counts is not how much the city is warming due to its growth, but the siting of the first station location versus its current one.

More specifically, it would be interesting to study how much improvements in siting have contributed to a possible temperature trend bias in the recent decades. The move to the current locations took place in 2010 in Haparanda and in 2006 in Geisenheim. Where it should be noted that the cooling bias did not take place in one jump: decent measurements are likely to have been recorded since 1977 in Haparanda, and since 1946 in Geisenheim; For Geisenheim the information is not very reliable).

It would make sense to me that the more people started thinking about climate change, the more the weather services realized that even small biases due to imperfect siting are important and should be avoided. Also modern technology, automatic weather stations, batteries and solar panels, have made it easier to install stations in remote locations.

An exception here is likely the United States of America. The Surface Stations project has shown many badly sited stations in the USA and the transition to automatic weather stations is thought to have contributed to this. Explanations could be that America started early with automation, the cables were short and the technician had only one day to install the instruments.

When also villages have a small urban effect, it is also possible that this gradually increases while the village is growing. Such a gradual increase can also be removed by statistical homogenization by comparison with its neighboring stations. However, if too many stations have a such a gradual inhomogeneity, the homogenization methods will no longer be able to remove this non-climatic increase (well). Thus this finding makes it more important to make sure that sufficient really rural stations are used for comparison.

On the other hand, because a village is smaller, one may expect that the "gradual" increases are actually somewhat jumpy. Rather than being due to many changes in a large area around the station, in case of a village the changes may be expected to be more often nearer to the station and produce a small jump. Jumps are easier to remove by statistical homogenization than smooth gradual inhomogeneities, because the probability of something happening simultaneously in the neighboring station is smaller.

A parallel measurement in Basel, Switzerland. A historical Wild screen, which is open to the bottom and to the North and has single Louvres to reduce radiation errors, measures in parallel with a Stevenson screen (Cotton Region Shelter), which is close to all sides and has double Louvres.

Parallel measurements

These measurements at multiple locations are an example of parallel measurements. The standard case is that an old instrument is compared to a new one while measuring side by side. This helps us to understand the reasons for biases in the climate record.

From parallel measurements we, for example, also know that the way temperature was measured before the introduction of Stevenson Screens has caused a bias in the old measurements of up to a few tenth of a degree. With differences of 0.5°C being found for two locations Spain and two tropical countries, while the differences in North West Europe are typically small.

To be able to study these historical changes and their influence on the global datasets, we have started an initiative to build a database with parallel measurements under the umbrella of the International Surface Temperature Initiative (ISTI), the Parallel Observations Science Team (POST). We have just started and are looking for members and parallel datasets. Please contact us if you are interested.

[UPDATE. The above study is now published as. Lindén, J., C.S.B. Grimmond, and J. Esper: Urban warming in villages, Advances in Science and Research, 12, pp. 157-162, doi: 10.5194/asr-12-157-2015, 2015.]

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