Abstract
Global mean surface air temperature (Ta) has been reported to have risen by 0.74°C over the last 100 years. However, the definition of mean Ta is still a subject of debate. The most defensible definition might be the integral of the continuous temperature measurements over a day (Td0). However, for technological and historical reasons, mean Ta over land have been taken to be the average of the daily maximum and minimum temperature measurements (Td1). All existing principal global temperature analyses over land rely heavily on Td1. Here, I make a first quantitative assessment of the bias in the use of Td1 to estimate trends of mean Ta using hourly Ta observations at 5600 globally distributed weather stations from the 1970s to 2013. I find that the use of Td1 has a negligible impact on the global mean warming rate. However, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% at 5° × 5° grids. Therefore, caution should be taken when using mean Ta datasets based on Td1 to examine high resolution details of warming trends.
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Introduction
The measurements of the daily maximum and minimum temperatures (Tmax and Tmin) were developed in English-speaking countries once the maximum/minimum thermometers became widely used in approximately 18601. The maximum (or minimum) thermometer is a unique thermometer in that its reading does not change after the air temperature (Ta) reaches Tmax (or Tmin). Therefore, Tmax and Tmin can be easily measured by checking and resetting the thermometers once a day. The measurements of Tmax and Tmin have been accepted globally and Td1 = (Tmin + Tmax)/2 has already become the most common way to calculate mean Ta2. In many weather stations, the measurements of Tmax and Tmin may be the only data source for historical mean Ta and the only choice for a homogenous century-long analysis of mean Ta2,3,4.
Analyses of global mean Ta and its changes are performed operationally by several groups, including the NOAA National Climatic Data Center (NCDC) Global Historical Climatology Network (GHCN)5,6,7, the Goddard Institute for Space Studies (GISS)8 and a joint effort between the Met Office Hadley Center and the University of East Anglia Climate Research Unit (CRUTEM4)9,10. All of the global temperature analyses over land performed by the aforementioned groups rely heavily on Td111,12. These century-duration analyses have provided basic datasets for global and regional climate change detection and attribution13.
However, the use of Tmin and Tmax to calculate mean Ta has been criticised because Tmin is the response of a shallow nocturnal stable boundary layer12,14 and its variation is sensitive to local land use/land cover15, surface wind speed and humidity and downward long-wave radiation from greenhouse gases16. Ta reaches Tmin in the early morning because of long-wave cooling and reaches Tmax in the early afternoon because of solar short-wave radiation heating (Fig. 1). However, Ta does not change linearly and symmetrically with time (Fig. 1); its diurnal curve depends on the proportion of surface absorbed energy partitioned into sensible and latent heat fluxes (or evapotranspiration)17, which is determined by the surface incident solar radiation, atmospheric downward long-wave radiation16 and surface aridity18. Therefore, Td1 may deviate from real daily mean Ta (i.e., Td0), for both climatology and long-term trends.
In this study, I make a first quantitative assessment of the bias of Td1 in quantifying trends of mean Ta using hourly Ta observations collected by the NCDC Integrated Surface Database (ISD)19, which has provided long-term hourly Ta measurements at approximately 5600 globally distributed weather stations since the 1970s (see Figs. S1 and S2 for detailed information).
Results
The estimation of mean Ta by Td1 has two primary sources of bias: (1) Ta has an asymmetric diurnal curve and (2) Td1 collects only two samples of Ta from the early morning to the early afternoon and leaves roughly two thirds of a day without monitoring. The former introduces systematic bias to Td1 and the later primarily introduces random bias. These two sources of bias are evaluated separately in Figs. 2 and 3, respectively.
The 24-hour Ta from multi-year observations at each station, as shown for example in Fig. 1, are comprised of hourly Ta observations. Td0 and Td1 are calculated from these composite values. Their differences therefore reflect the impact of the shape of the diurnal variation of Ta on the climatology of Td0 and Td1. The mean surface air temperature Td1 calculated from Tmax and Tmin has a substantially different climatology from that of the real mean surface air temperature Td0 (Fig. 2), with a root mean square error of Td1 − Td0 of approximately 0.3 °C for global land measurements (Table 1). These differences in the climatologies between Td1 and Td0 are related to the asymmetric diurnal variation of Ta (Fig. 1), which is determined by the surface energy balance. During cold seasons, Td1 overestimates the mean surface air temperature Td0 almost everywhere. This overestimation is higher in arid or semi-arid regions.
Two factors explain why Td1 − Td0 is much higher in cold seasons than in warm seasons in arid/semi-arid regions. First, in early morning, a larger fraction of the surface absorbed energy is partitioned into sensible heat flux during the cold seasons17 because the surfaces are covered by less vegetation and because it is drier in cold seasons than in warm seasons. These conditions result in a faster increase of Ta in the morning under drier conditions because the sensible heat flux directly heats the air above the surface18. Second, in the afternoon, the surface long-wave cooling effect is more efficient (Fig. 1) in cold seasons because the compensating effect of atmospheric downward long-wave radiation is lower in the cold seasons due to their lower relative humidity16. The combination of these two factors results in higher values of Td1 − Td0 during the dry cold seasons in arid/semi-arid regions.
Td1 samples Ta only twice, during the early morning and the early afternoon, leaving approximately two-thirds of the day unmonitored. The unmonitored times may miss important information about the impact of weather events, such as fronts, on Ta that are important during the cold seasons in the northern high latitudes. The deviation of Td1 from Td0 may be strong, but it is likely to be randomly distributed. The deviation may be quantified by calculating the standard deviation of the daily Td1 − Td0. Fig. 3 confirms that the standard deviations of the daily Td1 − Td0 are higher at the northern high latitudes in the cold seasons and in the global arid/semi-arid regions in both cold and warm seasons. In general, the standard deviations of the daily Td1 − Td0 in the cold seasons are slightly larger than those in the warm seasons, with a mean value of approximately 0.6 °C (Table 1).
Figs. 2 and 3 show that the deviation of Td1 from Td0 depends on the asymmetric diurnal variation of Ta, which is related to (a) the surface aridity and vegetation coverage and (b) the timing and frequency of weather events, i.e., front activity. Both aspects may change significantly under climate change conditions20,21,22, which may introduce a substantial bias to the trend of the mean surface air temperature through the use of Td1. Fig. 2 implies that the bias of Td1 in quantifying the trend of the mean air temperature may be substantial in arid or semi-arid regions if the surface aridity or vegetation coverage changes. I, therefore, further assess the differences in the trends of Td1 versus Td0 using the NCDC ISD data. Fig. 4 shows the relative biases of the trends and the statistical results are presented in Table 1. Td1 has a negligible impact on the long-term trends of the mean surface air temperature, i.e., the global mean warming rate (Table 1).
The trends calculated from the monthly anomalies of Td1, however, have significant biases relative to Td0 at the regional or local scales, with a root mean square error higher than 25% at a 5° × 5° grid scale (Table 1). The spatial coherence of biases in trends of the mean air temperature shown in Fig. 4 is minimal, in contrast with the results in Figs. 2 and 3. Three possible reasons may account for this minimal coherence: (1) the variable changes of the surface conditions occur in both direction and amount at different grids, (2) the measurement bias of the hourly Ta that were used to calculate Tmax, Tmin, Td1 and Td0 and (3) the different time periods of data at different grids in Fig. 4, as shown in Fig. S2.
Discussion
In this study, the maximum and minimum hourly Ta measurements are selected to represent Tmax and Tmin. However, the response time of the Tmax and Tmin thermometer is several minutes. This may introduce some bias to the conclusions of this study. Here, I use the Ta data of five-minute resolution collected by the U.S. Climate Reference Network (USCRN) to demonstrate that it is reliable to use hourly Ta measurements to represent Tmax and Tmin.
USCRN has operated since the year 2000 at a few sites and was officially and nationally commissioned by the National Oceanic and Atmospheric Administration (NOAA) in 200423,24. The primary goal of USCRN is to provide long-term high-quality homogeneous observations, in particular, for Ta and precipitation25; its temperature system consists of a platinum resistance thermometer and an aspirated radiation shield24,25. At each site, the USCRN uses three such thermometers for inter-comparison and quality assurance25. These redundant high-level thermometers at each USCRN site guarantee their high-quality continuous observations of Ta (the missing rate of its Ta data is near zero). The data sampling rate of the USCRN thermometers is 5 seconds and temperature signals averaged over 5-minute intervals are output.
USCRN provides a method for coupling its continuous measurements to the past observations24,26. Tmax and Tmin can be directly determined from its 5-minute averages24,26, from which Td1 is calculated. We processed all of the data in local solar time. Monthly averages are calculated only if the daily means are available on no less than 24 days in a month and if there are no gaps of four or more consecutive days. The trends of the Td0 and Td1 are calculated from the monthly anomalies after removing their seasonal cycles. Fig. 5 shows that the biases in the mean values of Td1 using the USCRN data are similar to those from the ISD, both in amount and spatial variability. The biases in the relative trend of Td1 using the USCRN data are of similar magnitude to those using the ISD data, but with different spatial pattern because the time durations of the two datasets are different. As data availability of the USCRN is temporally and spatially less than that of ISD, the main results in this study are reported using the ISD data.
What are the implications of the biases of Td1 and of its trend uncovered in this analysis? It was reported that the warming rate was stronger in the cold-season in semi-arid regions using observations based on Td127. Observations of global mean temperature that are primarily based on Td1, including CRUTEM3, GISS and NCDC, were used to evaluate Td0 from the simulation of the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) climate models. It was found that the model simulations of Td0 did not simulate the full extent of the observed winter time warming of Td1 at the high-latitude Northern Hemisphere28. As shown in Fig. 2, Td1 will overestimate the trend of mean Ta if the area gets drier in the arid/semi-arid regions. Observed evidences show it is the case as middle latitude arid or semi-arid regions have been drier in recent decades29,30. This partly explains the enhanced warming in semi-arid regions in cold seasons.
Although mean temperature, Td1 = (Tmax + Tmin)/2, is perhaps the most common method used to calculate mean air temperature, it is the not the only option. Scandinavian countries developed a special formula to estimate mean Ta31. For example, Sweden still uses the Ekholm–Modén formula from 1916, in which the mean Ta is a linear combination of the Tmax, Tmin and measurements of Ta at 6, 12 and 18 h UTC31. Another option is to calculate the mean surface air temperature from Ta measurements at 00, 06, 12 and 18 h UTC (Td2), which are recently available through the World Meteorological Organization's (WMO) global telecommunication system32. These data have been used in climate-related research33.
The significant differences in the climatology of Td1 and Td0 (or Td2), as shown in Fig. 2 (or Fig. S3), preclude switching from the use of Td1 to Td0 (or Td2) for estimating the long term variability of the mean surface air temperature, although Td0 or Td2 is already globally available (Figs. 2 and S2). To produce a century-long homogenous dataset of mean Ta, it is essential to continue to use the measurement methods currently used at the weather stations3,4. This study indicates that the use of Td1 has a negligible impact on the global mean warming rate. However, Td1 cannot accurately reflect the impact of the changes in the surface conditions on the variability of Ta. These changing surface conditions cause the diurnal curve of Ta to vary with time, thereby resulting in the deviation of Td1 from Td0. Therefore, the trend of Td1 has a substantial bias at regional and local scales, with a root mean square error of over 25% for 5° × 5° grids. Careful attention should be paid when using mean surface air temperature Td1 on studies regarding the spatial patterns of warming. For such studies, recently available hourly measurements19 are recommended.
Factors that can introduce inhomogeneity to the mean Ta have been reviewed by Jones et al.34. In particular, by assuming that a departure from differently derived mean values are comparable, Bradley and Jones35 inferred that the monthly temperature values and the hemispherical estimates from different definitions were the same if calculated as anomalies from a selected period. This study confirms this assumption by demonstrating that the global mean trend calculated from the monthly anomalies of Td1 is the same as that of Td0. However, the trends calculated from the monthly anomalies of Td1 at 5° × 5° grids have a significant error, with a standard deviation of 25%. This large error is caused by the variation of Td1 − Td0 with changing surface conditions (Fig. 2) as a result of the changing diurnal curve of Ta (Fig. 1).
The use of Tmax and Tmin to calculate the mean Ta over land is a result of the fact that many places have used inexpensive instruments that only measure those two temperatures (and could only be checked by an observer once a day) for a long time. Tmin is more sensitive to changes in land cover15 and atmospheric downward long-wave radiation12, while the diurnal temperature range (Tmax − Tmin) is more sensitive to changes in land-atmosphere turbulent fluxes17, which are driven by variations in the surface incident solar radiation18 caused by the changes in the clouds and aerosols36,37. Observations from Tmax and Tmin are therefore very important because of their relationship to climate change impacts and their connection to the global energy balance.
Methods
The hourly observations of Ta over global land collected by the NCDC ISD project19 were used in this study. The ISD compiles data from over 100 original data sources that archive hundreds of meteorological variables. The ISD has archived 2 billion surface weather observations from over 20,000 stations worldwide from 1900 to the present. Currently, the ISD database is updated with observations from over 11,000 active stations on a daily basis.
The ISD provides consistent and standardised quality control of the global hourly meteorological observations19. The ISD contains 54 quality control (QC) algorithms that serve to process each data observation through a series of validity checks, extreme value checks, internal consistency checks and external continuity checks. Among all of the parameters, temperatures are among the most extensively validated parameters. The ISD data can be freely downloaded from www.ncdc.noaa.gov/oa/climate/isd/index.php.
As of August 2013, there were approximately 5600 stations reporting hourly Ta measurements for more than five years (Fig. S1). The ISD data were reported in UTC time and converted into local solar time for the purpose of my analysis. To obtain the bias and to reduce the impact of missing data, I produced a composite of the 24 hourly values at each site, i.e., all of the observations were averaged into hourly values and the 24-hour values of the Ta were obtained for each site. Tmin and Tmax were selected from the 24-hour values, from which Td1 was calculated. Td0 was integrated from the 24-hour values. In this study, I split a year into cold seasons (November to April in the Northern Hemisphere, or May to October in the Southern Hemisphere) and warm seasons (May to October in the Northern Hemisphere, or November to April in the Southern Hemisphere). The climatological differences of Td1 and Td0 were aggregated into a 5° × 5° grid and are shown in Fig. 2. The composite method used here substantially reduces the impact of missing data on the results in Figs. 1 and 2. If there are no missing data, the results shown in Figs. 1 and 2 should be equal to those based on the daily basis, provided that the day is defined as being from midnight to midnight.
The trends shown in Fig. 4 were calculated differently. Tmin and Tmax were first selected from the 24-hour observations for each day at every site. The data were regarded as reliable only if the hourly temperatures were available for more than 22 h a day, from which the daily and monthly Td0 and Td1 were calculated. The monthly values were regarded as reliable only if the daily values were available for more than 15 days a month. The requirement for hourly air temperature measurements is stricter than that for daily values because this study focuses on the difference of Td0 and Td1, which is dominated by the diurnal curve of air temperature. The monthly anomalies of Td1 and Td0 were calculated at each weather station by removing their averaged seasonal cycle. The monthly anomalies were then aggregated into 5° × 5° grid values. The grid averaged-monthly anomalies were regarded as reliable if the data for each month was available at more than 50% of the stations within the grid. The trends of Td0 and Td1 − Td0 calculated from the grid monthly anomalies are presented in Fig. 4 and Table 1. The data duration of Td1 and Td0 at the 5° × 5° grid can be found in Fig. S2.
The measurements of Tmin and Tmax were developed in English-speaking countries where the maximum/minimum thermometers were widely used since approximately 18601. A maximum thermometer is a unique mercury thermometer that functions by having a constriction in the neck close to the bulb. The mercury is forced up through the constriction by the force of expansion as the temperature increases. When there is a decrease in the temperature, the volume of mercury contracts but cannot return to the bulb because of the narrow of the bulb neck. As a result, the column of mercury breaks at the constriction and remains stationary in the tube. The minimum thermometer works similarly, but it does so with steel pin immersed in clear liquid (i.e., ethyl alcohol) in glass.
The measurements of Tmax and Tmin can be made by one visit to the weather station a day. Because of its low cost, the measurements of Tmax and Tmin have been accepted globally and Td1 = (Tmin + Tmax)/2 has become the most common method to calculate the mean surface temperature. For most weather stations, the measurements using this method may be the only data source for historical temperature.
The weather stations of ISD directly measured Tmax and Tmin, in addition to the hourly temperature. The Tmax and Tmin temperatures were defined as the highest and lowest temperatures to have occurred during the past 24 hours. However, the Tmax or Tmin measurements may depend on the observation schedule, which may be different from country to country. The definition of a day is therefore different, such as from midnight to midnight or from noon to noon. In Europe, Tmin and Tmax are usually reported for 12-hour intervals ending at 6 UTC and 18 UTC and are not necessarily the true Tmin and Tmax in many regions, especially during the winter months38. This discrepancy occurs because 6 UTC is before the climatologically coldest hour sunrise in the winter in some regions (i.e., Europe)33 and is also partly a result of the synoptic weather variability. This discrepancy introduces a significant error in the estimations of daily Tmax and Tmin33. The changes of the observation schedules may also introduce inhomogeneity of the climatology of the surface mean air temperature38. These problems can be avoided either by maintaining an unchanged observation schedule at a station or by using hourly observations, as I did here.
The bias caused by changes to the observation schedules of Tmin and Tmax may be important15,38, but are not discussed here because the information on observation schedules is not yet publicly available. Two primary sources of bias of Td1 are discussed in this study (Figs. 2 and 3). They have physical meanings and may introduce substantial bias to trends of mean surface air temperature Td1. The differences among the trends of Td1 and Td0 (Fig. 4), which are much less sensitive to the definition of the day, are calculated using the definition of day as midnight to midnight.
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Acknowledgements
This study was funded by the National Basic Research Program of China (2012CB955302) and the National Natural Science Foundation of China (41175126 and 91337111). Dr. Robert E. Dickinson provided insight and helpful comments in preparing a draft of this paper and Dr. Qian Ma processed some data for this study.
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Wang, K. Sampling Biases in Datasets of Historical Mean Air Temperature over Land. Sci Rep 4, 4637 (2014). https://doi.org/10.1038/srep04637
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DOI: https://doi.org/10.1038/srep04637
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