Homogenization in climate research means the removal of non-climatic changes. Next to changes in the climate itself, raw climate records also contain non-climatic jumps and changes, for example due to relocations or changes in instrumentation. The most used principle to remove these inhomogeneities is the relative homogenization approach in which a candidate station is compared to a reference time series based on one or more neighboring stations. The candidate and reference station(s) experience about the same climate, non-climatic changes that happen only in one station can thus be identified and removed.
Climate observations To study climate change and variability, long instrumental climate records are essential, but are best not used directly. These datasets are essential since they are the basis for assessing century-scale trends or for studying the natural (long-term) variability of climate, amongst others. The value of these datasets, however, strongly depends on the homogeneity of the underlying time series. A homogeneous climate record is one where variations are caused only by variations in weather and climate. Long instrumental records are rarely, if ever homogeneous. Results from the homogenization of instrumental western climate records indicate that detected inhomogeneities in mean temperature series occur at a frequency of roughly 15 to 20 years. It should be kept in mind that most measurements have not been specifically made for climatic purposes, but rather to meet the needs of weather forecasting, agriculture and hydrology. Moreover, the typical size of the breaks is often of the same order as the climatic change signal during the 20th century. Inhomogeneities are thus a significant source of uncertainty for the estimation of secular trends and decadal-scale variability. If all inhomogeneities would be purely random perturbations of the climate records, collectively their effect on the mean global climate signal would be negligible. However, certain changes are typical for certain periods and occurred in many stations, these are the most important causes as they can collectively lead to artificial biases in climate trends across large regions.
Causes of inhomogeneities
The best known inhomogeneity is the urban heat island effect. The temperature in cities can be warmer than in the surrounding country side, especially at night. Thus as cities grow, one may expect that temperatures measured in cities become higher. On the other hand, with the advent of aviation, many meteorological offices and thus their stations have often been relocated from cities to nearby, typically cooler, airports.
Other non-climatic changes can be caused by changes in measurement methods. Meteorological instruments are typically installed in a screen to protect them from direct sun and wetting. In the 19th century it was common to use a metal screen in front of a window on a North facing wall. However, the building may warm the screen leading to higher temperature measurements. When this problem was realized the Stevenson screen was introduced, typically installed in gardens, away from buildings. This is still the most typical weather screen with its characteristic double-louvre door and walls for ventilation. The historical Montsouri and Wilds screens were used around 1900 and are open to the North and to the bottom. This improves ventilation, but it was found that infra-red radiation from the ground can influence the measurement on sunny calm days. Therefore, they are no longer used. Nowadays automatic weather stations, which reduce labor costs, are becoming more common; they protect the thermometer by a number of white plastic cones. This necessitated changes from manually recorded liquid and glass thermometers to automated electrical resistance thermometers, which reduced the recorded temperature values in the USA. Also other climate elements suffer from inhomogeneities. The precipitation amounts observed in the early instrumental period, roughly before 1900, are biased and are 10% lower than nowadays because the precipitation measurements were often made on a roof. At the time, instruments were installed on rooftops to ensure that the instrument is never shielded from the rain, but it was found later that due to the turbulent flow of the wind on roofs, some rain droplets and especially snow flakes did not fall into the opening. Consequently, measurements are nowadays performed closer to the ground. Other typical causes of inhomogeneities are a change in measurement location; many observations, especially of precipitation are performed by volunteers in their garden or at their work place. Changes in the surrounding can often not be avoided, e.g., changes in the vegetation, the sealing of the land surface, and warm and sheltering buildings in the vicinity. There are also changes in measurement procedures such as the way the daily mean temperature is computed (by means of the minimum and maximum temperatures, or by averaging over 3 or 4 readings per day, or based on 10-minute data). Also changes in the observation times can lead to inhomogeneities. A recent review by Trewin focused on the causes of inhomogeneities. The inhomogeneities are not always errors. This is seen most clear for stations affected by warming due to the urban heat island effect. From the perspective of global warming, such local effects are undesirable, but to study the influence of climate on health such measurements are fine. Other inhomogeneities are due to compromises that have to be made between ventilation and protection against the sun and wetting in the design of a weather shelter. Trying to reduce one type of error (for a certain weather condition) in the design will often lead to the more errors from the other factors. Meteorological measurements are not made in the laboratory. Small errors are inevitable and may not be relevant for meteorological purposes, but if such an error changes, it may well be an inhomogeneity for climatology.
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