The Parallel Observations Science Team (POST) is looking across the world for climate records which simultaneously measure temperature, precipitation and other climate variables with a conventional sensor (for example, a thermometer) and modern automatic equipment. You may wonder why we take the painstaking effort of locating and studying these records. The answer is easy: the transition from manual to automated records has an effect on climate series and the analysis we do over them.
In the last decades we have seen a major transition of the climate monitoring networks from conventional manual observations to automatic weather stations. It is recommended to compare these instruments before the substitution is effective with side by side measurements, which we call parallel measurements. Climatologists have also set up many longer experimental parallel measurements. They tell us that in most cases both sensors do not measure the same temperature or collect the same amount of precipitation. A different temperature is not only due to the change of the sensor itself, but automatic weather stations also often use a different, much smaller, screen to protect the sensor from the sun and the weather. Often the introduction of automatic weather stations is accompanied by a change in location and siting quality.
From studies of single temperature networks that made such a transition we know that it can cause large jumps; the observed temperatures at a station can go up or down by as much as 1°C. Thus potentially this transition can bias temperature trends considerably. We are now trying to build a global dataset with parallel measurements to be able to quantify how much the transition to automatic weather stations influences the global mean temperature estimates used to study global warming.
TemperatureThis study is led by Enric Aguilar and the preliminary results below were presented at the Data Management Workshop in Saint Gallen, Switzerland last November. We are still in the process of building up our dataset. Up to now we have data from 10 countries: Argentina (9 pairs), Australia (13), Brazil (4), Israel (5), Kyrgyzstan (1), Peru (31), Slovenia (3), Spain (46), Sweden (8), USA (6); see map below.
Global map in which we only display the 10 countries for which we have data. The left map is for the maximum temperature (TX) and the right for the minimum temperature (TN). Blue dots mean that the automatic weather station (AWS) measures cooler temperatures than the conventional observation, red dots mean the AWS is warmer. The size indicates how large the difference is, open circles are for statistically not significant differences.
The impact of the automation can be better assessed in the box plots below.
The bias of the individual pairs are shown as dots and summarized per country with box plots. For countries with only a few pairs the boxplots should be taken with a grain of salt. Negative values mean that the automatic weather stations are cooler. We have data for Argentina (AR), Australia (AU), Brazil (BR), Spain (ES), Israel (IL), Kyrgyzstan (KG), Peru (PE), Sweden (SE), Slovenia (SI) and the USA (US). Panels show the maximum temperature (TX), minimum temperature (TN), mean temperature (TM) and Diurnal temperature range (DTR, TX-TN).
On average there are no real biases in this dataset. However, if you remove Peru (PE) the differences in the mean temperature are either small or negative. That one country is so important shows that our dataset is currently too small.
To interpret the results we need to look at the main causes for the differences. Important reasons are that Stevenson screens can heat up in the sun on calm days, while automatic sensors are sometimes ventilated. The automatic sensors are, furthermore, typically smaller and thus less affected by direct radiation hitting them than thermometers. On the other hand, in case of conventional observation, the maintenance of the Stevenson screens—cleaning and painting—and detection of other problems may be easier because they have to be visited daily. There are concerns that plastic screens get more grey and heat more in the sun. Stevenson screens have more thermal inertia, they smooth fast temperature fluctuations, and will thus show lower highs and higher lows.
Also the location often changes with the installation of automatic weather stations. America was one of the early adopters. The US National Weather Service installed analogue semi-automatic equipment (MMTS) that did not allow for long cables between the sensor and the display inside a building. Furthermore, the technicians only had one day per station and as a consequence many of the MMTS systems were badly sited. Nowadays technology has advanced a lot and made it easier to find good sites for weather stations. This is maybe even easier now than it used to be for manual observations because modern communication is digital and if necessary uses radio making distance much less a concern. The instruments can be powered by batteries, solar or wind, which frees them from the electricity grid. Some instruments store years of data and need just batteries.
In the analysis we thus need to consider whether the automatic sensors are placed in Stevenson screens and whether the automatic weather station is at the same location. Where the screen and the location did not change (Israel and Slovenia), the temperature jumps are small. Whether the automatic weather station reduces radiation errors by mechanical ventilation is likely also important. Because of these different categories, the number of datasets needed to get a good global estimate becomes larger. Up to now, these factors seem to be more important than the climate.
PrecipitationFor most of these countries we also have parallel measurements for precipitation. The figure below was made by Petr Stepanek, who leads this part of the study.
Boxplots for the differences in monthly precipitation sums due to automation. Positive values mean that the manual observations record more precipitation. Countries are: Argentina (AG), Brazil (BR), The Check Republic (CZ), Israel (IS), Kyrgyzstan (KG), Peru (PE), Sweden (SN), Spain (SP) and the USA (US). The width of the boxplots corresponds to the size of the given dataset.
For most countries the automatic weather stations record less precipitation. This is mainly due to smaller amounts of snow during the winter. Observers often put a snow cross in the gauge in winter to make it harder for snow to blow out of it again. Observers simply melt the snow gathered in a pot to measure precipitation, while early automatic weather stations did not work well with snow and sticky snow piling up in the gauge may not be noticed. These problems can be solved by heating the gauge, but unfortunately the heater can also increase the amount of precipitation that evaporates before it could be registered. Such problems are known and more modern rain gauges use different designs and likely have a smaller bias again.
Database with parallel dataThe above results are very preliminary, but we wanted to show the promise of a global dataset with parallel data to study biases in the climate record due to changes in the observing practises. To proceed we need more datasets and better information on how the measurements were performed to make this study more solid.
In future we also want to look more at how the variability around the mean is changing. We expect that changes in monitoring practices have a strong influence on the tails of the distribution and thus on estimates of changes in extreme weather. Parallel data offer a unique opportunity to study this otherwise hard problem.
Most of the current data comes from Europe and South America. If you know of any parallel datasets especially from Africa or Asia, please let us know. Up to now, the main difficulty for this study is to find the persons who know where the data is. Fortunately, data policies do not seem to be a problem. Parallel data is mostly seen as experimental data. In some cases we “only” got a few years of data from a longer dataset, which would otherwise be seen as operational data.
We would like to publish the dataset after publishing our papers about it. Again this does not seem to lead to larger problems; sometimes people prefer to first publish an article themselves, which causes some delays, and sometimes we cannot publish the daily data itself, but “only” monthly averages and extreme value indices, this makes the results less transparent, but these summary values contain most of the information.
Knowledge of the observing practices is very important in the analysis. Thus everyone who contributes data is invited to help in the analysis of the data and co-author our first paper(s). Our studies are focused on global results, but we will also provide everyone with results for their own dataset to gain a better insight into their data.
Most climate scientists would agree that it is important to understand the impact of automation on our records. So does the World Meteorological Organization. In case it helps you to convince your boss: the Parallel Observations Science Team is part of the International Surface Temperature Initiative (ISTI). It is endorsed by the Task Team on Homogenization (TT-HOM) of the World Meteorological Organization (WMO).
We expect that this endorsement and our efforts to raise awareness about our goals and their importance will help us to locate and study parallel observations from other parts of the world, especially Africa and Asia. We also expect to be able to get more data from Europe; the regional association for Europe of the WMO has designated the transition to automatic weather stations as one of its priorities and is helping us to get access to more data. We want to have datasets for all over the world to be able to assess whether the station settings (sensors, screens, data quality, etc.) have an impact, but also to understand if different climates produce different biases.
If you would like to collaborate or have information, please contact me.
Related readingThe ISTI has made a series of brochures on POST in English, Spanish, French and German. If anyone is able to make further translations, that would be highly appreciated.
Parallel Observations Science Team of the International Surface Temperature Initiative.
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