Why Adjust Temperatures?
There are a number of folks who question the need for adjustments at all. Why not just use raw temperatures, they ask, since those are pure and unadulterated? The problem is that (with the exception of the newly created Climate Reference Network), there is really no such thing as a pure and unadulterated temperature record. Temperature stations in the U.S. are mainly operated by volunteer observers (the Cooperative Observer Network, or co-op stations for short). Many of these stations were set up in the late 1800s and early 1900s as part of a national network of weather stations, focused on measuring day-to-day changes in the weather rather than decadal-scale changes in the climate.
Nearly every single station in the network in the network has been moved at least once over the last century, with many having 3 or more distinct moves. Most of the stations have changed from using liquid in glass thermometers (LiG) in Stevenson screens to electronic Minimum Maximum Temperature Systems (MMTS) or Automated Surface Observing Systems (ASOS). Observation times have shifted from afternoon to morning at most stations since 1960, as part of an effort by the National Weather Service to improve precipitation measurements.
All of these changes introduce (non-random) systemic biases into the network. For example, MMTS sensors tend to read maximum daily temperatures about 0.5 C colder than LiG thermometers at the same location. There is a very obvious cooling bias in the record associated with the conversion of most co-op stations from LiG to MMTS in the 1980s, and even folks deeply skeptical of the temperature network like Anthony Watts and his coauthors add an explicit correction for this in their paper.
Time of observation changes from afternoon to morning also can add a cooling bias of up to 0.5 C, affecting maximum and minimum temperatures similarly. The reasons why this occurs, how it is tested, and how we know that documented time of observations are correct (or not) will be discussed in detail in the subsequent post. There are also significant positive minimum temperature biases from urban heat islands that add a trend bias up to 0.2 C nationwide to raw readings.
Because the biases are large and systemic, ignoring them is not a viable option. If some corrections to the data are necessary, there is a need for systems to make these corrections in a way that does not introduce more bias than they remove.
What are the Adjustments?
Two independent groups, the National Climate Data Center (NCDC) and Berkeley Earth (hereafter Berkeley) start with raw data and use differing methods to create a best estimate of global (and U.S.) temperatures. Other groups like NASA Goddard Institute for Space Studies (GISS) and the Climate Research Unit at the University of East Anglia (CRU) take data from NCDC and other sources and perform additional adjustments, like GISS’s nightlight-based urban heat island corrections.
Time of Observation (TOBs) Adjustments
Temperature data is adjusted based on its reported time of observation. Each observer is supposed to report the time at which observations were taken. While some variance of this is expected, as observers won’t reset the instrument at the same time every day, these departures should be mostly random and won’t necessarily introduce systemic bias. The major sources of bias are introduced by system-wide decisions to change observing times, as shown in Figure 3. The gradual network-wide switch from afternoon to morning observation times after 1950 has introduced a CONUS-wide cooling bias of about 0.2 to 0.25 C. The TOBs adjustments are outlined and tested in Karl et al 1986 and Vose et al 2003, and will be explored in more detail in the subsequent post. The impact of TOBs adjustments is shown in Figure 6, below.
Pairwise Homogenization Algorithm (PHA) Adjustments
The Pairwise Homogenization Algorithm was designed as an automated method of detecting and correcting localized temperature biases due to station moves, instrument changes, microsite changes, and meso-scale changes like urban heat islands.
The algorithm (whose code can be downloaded here) is conceptually simple: it assumes that climate change forced by external factors tends to happen regionally rather than locally. If one station is warming rapidly over a period of a decade a few kilometers from a number of stations that are cooling over the same period, the warming station is likely responding to localized effects (instrument changes, station moves, microsite changes, etc.) rather than a real climate signal.
To detect localized biases, the PHA iteratively goes through all the stations in the network and compares each of them to their surrounding neighbors. It calculates difference series between each station and their neighbors (separately for min and max) and looks for breakpoints that show up in the record of one station but none of the surrounding stations. These breakpoints can take the form of both abrupt step-changes and gradual trend-inhomogenities that move a station’s record further away from its neighbors. The figures below show histograms of all the detected breakpoints (and their magnitudes) for both minimum and maximum temperatures.
While fairly symmetric in aggregate, there are distinct temporal patterns in the PHA adjustments. The single largest of these are positive adjustments in maximum temperatures to account for transitions from LiG instruments to MMTS and ASOS instruments in the 1980s, 1990s, and 2000s. Other notable PHA-detected adjustments are minimum (and more modest maximum) temperature shifts associated with a widespread move of stations from inner city rooftops to newly-constructed airports or wastewater treatment plants after 1940, as well as gradual corrections of urbanizing sites like Reno, Nevada. The net effect of PHA adjustments is shown in Figure 8, below.
The PHA has a large impact on max temperatures post-1980, corresponding to the period of transition to MMTS and ASOS instruments. Max adjustments are fairly modest pre-1980s, and are presumably responding mostly to the effects of station moves. Minimum temperature adjustments are more mixed, with no real century-scale trend impact. These minimum temperature adjustments do seem to remove much of the urban-correlated warming bias in minimum temperatures, even if only rural stations are used in the homogenization process to avoid any incidental aliasing in of urban warming, as discussed in Hausfather et al. 2013.
The PHA can also effectively detect and deal with breakpoints associated with Time of Observation changes. When NCDC’s PHA is run without doing the explicit TOBs adjustment described previously, the results are largely the same (see the discussion of this in Williams et al 2012). Berkeley uses a somewhat analogous relative difference approach to homogenization that also picks up and removes TOBs biases without the need for an explicit adjustment.
With any automated homogenization approach, it is critically important that the algorithm be tested with synthetic data with various types of biases introduced (step changes, trend inhomogenities, sawtooth patterns, etc.), to ensure that the algorithm will identically deal with biases in both directions and not create any new systemic biases when correcting inhomogenities in the record. This was done initially in Williams et al 2012 and Venema et al 2012. There are ongoing efforts to create a standardized set of tests that various groups around the world can submit homogenization algorithms to be evaluated by, as discussed in our recently submitted paper. This process, and other detailed discussion of automated homogenization, will be discussed in more detail in part three of this series of posts.
