Introduction

Near surface air temperature over land has a significant diurnal cycle, primarily determined by diurnal variation of surface net radiation, i.e., the sum of the net solar (incident minus reflected) and the net longwave (absorbed downwelling minus emitted upwelling) radiation at the surface. During the nighttime, surface net radiation is negative and near surface air temperature decreases with time, reaching its minimum (Tmin) in the early morning. Surface net radiation becomes positive during the daytime due to the absorption of incident solar radiation at the surface and this surface net radiation is partitioned into latent heat and sensible heat fluxes. As the air above the surface is directly heated by the sensible heat flux, air temperature reaches its maximum (Tmax) in the early afternoon1.

Tmax and Tmin have been operationally observed at weather stations globally since the middle of the 19th century2,3. Their mathematical average T2 = (Tmax + Tmin)/2 has been taken as the standard method for calculating mean air temperature (Tm)4 and has been the backbone of current global analyses of air temperature over land5,6,7. It is well known that T2 is different from the true Tm, i.e., mean air temperature calculated from 24 hourly air temperature observations (T24)8,9.

It has been assumed that T2 can be used to accurately depict annual anomalies and trends in Tm10,11. However, recent studies have shown that T2 trends can be significantly biased. It was found that T2 trends have significant biases of 25% (one standard deviation) at a grid scale size of 5°×5°12. This is because T2 only samples the diurnal cycle of air temperature twice daily and T2−T24 changes with the land surface conditions (e.g., wetness and vegetation coverage). Surface conditions dominate the partitioning of surface net radiation into the latent heat and sensible heat fluxes. Sensible heat fluxes directly heat air above the surface and largely determine the diurnal curve of air temperature above land13.

Air temperature has been measured more frequently at weather stations since the 1950 s, with the establishment of the World Meteorological Organization (WMO). Under the guidelines of the WMO, air temperature at 00:00, 06:00, 12:00 and 18:00 UTC (Coordinated Universal Time) has been measured operationally at weather stations. Tm can also be calculated from these four observations (T4). We have accumulated more than 60 years of T4 data at global weather stations. Since the 1990 s, hourly air temperature has been widely available at global weather stations with the development of automatic weather stations3. However, to remain homogenous, the century-long global analyses of Tm still rely on T24,11.

In this study, we first evaluate and compare the uncertainty of T2 and T4 in depicting the trend in Tm from 2000 to 2013. We use T24 as the true Tm to serve as a comparison8,9. We calculate T2, T4 and T24 from hourly observations from the Integrated Surface Hourly Database (ISD)14 and the Global Historical Climatology Network (GHCN) daily database15 released by the National Climate Data Center (NCDC). In evaluating the trend bias of T2 (or T4), we select the exact same stations and time periods of T2 (or T4) and T24. We found that there are more than 3000 globally distributed stations from which T2, T4 and T24 were available for more than 84 months during 2000 to 2013.

Results

Figures 1 and 2 show that T2 demonstrates significant uncertainty in quantifying the trend in Tm. For the global average, T2 underestimated the trend in global mean Tm by 0.03 °C/decade compared with T24 from 2000 to 2013. This indicates that the recent hiatus of warming was slightly overestimated by using current global analyses of T2. This mean bias of T2 can be reduced to 0.01 °C/decade by using T4. This also partly explains the discrepancy between the observed and modelled warming hiatus during the most recent 15 years16. Current climate models run at a time step of approximately 30 minutes and calculate Tm from these calculations, which is nearly equal to T24, whereas reference data provided by existing global analyses use T2 over land.

Figure 1
figure 1

Trend biases in mean air temperatures (Tm) calculated from different methods: T2, calculated from daily maximum and minimum temperature (T2, left column) and T4, calculated from four observations at 00:00, 06:00, 12:00 and 18:00 UTC (middle column).

Tms calculated from 24 hourly air temperatures (T24) were used as reference data. Trend differences between T2 and T4 were also shown in the right column. The cold season is defined as November to April over the Northern Hemisphere and May to October over the Southern Hemisphere and the warm season is defined as May to October over the Northern Hemisphere and November to April over the Southern Hemisphere. T2 and T4 at 6000 stations and T24 at 3000 stations from 2000 to 2013 from the Global Historical Climatology Network (GHCN) daily database and the Integrated hourly Surface Data (ISD) were used here. T2 and T24 at identical time periods at a station were selected to calculate monthly anomalies. Monthly anomalies at the stations were aggregated into a 1°×1° grid and then into 5°×5° grid monthly anomalies, from which trends were calculated and shown in the left column. This is the same for the middle and right columns. The Figures were produced by MATLAB software.

Full size image
Figure 2
figure 2

Fitted histograms of annual, cold season and warm season trend differences: T2−T24 (black), T4−T24 (red) and T2−T4 (blue) at 5°×5° grid as shown in Fig. 1.

Statistical parameters of the trend differences, including mean and standard deviation (STD), are shown here. The data used here are the same as in Fig. 1. From 2000 to 2013, the global averaged trend in the mean air temperature over land is slightly underestimated by 0.03 °C/decade by T2. At a 5°×5° grid scale, the trend in T2 has a significant standard deviation error of 0.23 °C/decade. This error can be substantially reduced by more than two-thirds by using T4.

Full size image

At a 5°×5° grid scale, T2 has a standard deviation error of 0.23 °C/decade in quantifying the trend in Tm (Fig. 2). This error is 0.31 °C/decade during cold seasons and is 0.26 °C/decade for warm seasons. This is because the diurnal cycle of air temperature changes with the surface conditions12. The error is higher during cold seasons because the impact of land-atmosphere interactions on diurnal variation in the surface air temperature is stronger during cold seasons. The surface is drier with lower vegetation coverage during cold seasons and therefore, a higher fraction of surface net radiation is partitioned into the sensible heat flux, which directly heats the air above the surface.

This standard deviation error of the trend in T2 can be substantially reduced by two-thirds using T4 (Fig. 2). The primary reason for this is that T4 samples the diurnal cycle of air temperatures four times a day, whereas T2 only samples twice. Figures 1 and 2 indicate that T4 is more suitable to study the trend in Tm than T2.

We therefore compare the trends in Tm over land on a global scale from 1973 to 1997, the enhanced warming period. We select 1973 as the start year of the period because the T4 data released by ISD have much better spatial coverage since 1973. On average, compared with T4, T2 overestimated the global mean warming rate by 0.02 °C/decade from 1973 to 1997, the enhanced warming period (Table 1).

Table 1 This table summarizes the linear trends from 1973 to 1997 of mean air temperature (Tm) calculated from daily maximum and daily minimum temperature (T2) and from four observations of temperature at 00:00, 06:00, 12:00, 18:00 UTC time (>T4).
Full size table

Compared with T4, T2 overestimates the warming rate over the northern high latitudes, which is more obvious in cold seasons. In particular, the trend of T2 is 0.06 °C/decade (27%) higher than that of T4 over Northern Siberia (Table 1). The overestimation is stronger during cold seasons. The enhanced warming rate over these regions has been identified in the IPCC reports17,18. Different mechanisms have been proposed to explain the enhanced warming rate over these regions19,20. It still cannot be fully explained and the state-of-the-art global climate models have not been able to fully reproduce the enhanced warming rate21. Figure 3 and Table 1 show that the use of T2 at least partially explains the reported enhanced cold season warming over the northern high latitudes.

Figure 3
figure 3

Differences in mean air temperature trends calculated using different methods (left column): T2 was calculated from daily maximum and minimum temperature and T4 was calculated from four observations at 00:00, 06:00, 12:00 and 18:00 UTC.

Definitions of the cold season and warm season can be found in the caption of Fig. 1. Hourly observations of air temperatures from 1973 to 1997 at 3300 stations were used here. T2 and T4 at identical time periods at a station were selected to calculate monthly anomalies. Monthly anomalies at the stations were aggregated into a 1°×1° grid and then into 5°×5° grid monthly anomalies, from which trends were calculated and shown. Compared with T4, T2 significantly underestimated the warming rate over the central U.S. and overestimated warming over the northern high latitudes and mainland China. For comparison, the differences in trends calculated from monthly raw T2 (Tqcu) and monthly homogenized adjusted T2 (Tqca) are shown in the right column. The Figures were produced by MATLAB software.

Full size image

The regional warming rate over land significantly depends on the calculation methods of Tm. Compared with T4, T2 also substantially overestimates the warming rate by 0.06 °C/decade (25%) over China from 1973 to 1997. As a result, the ratio of the warming rate in China to that in the U.S. is 2.3 for T2 and 1.4 for T4. In the central U.S., T2 has a negative trend of −0.05 °C/decade, whereas T4 is 0.03 °C/decade. This partially explains the reported “warming hole” over the central U.S. during this period22. It is known that the variation in recording time has introduced a spurious negative trend in Tmax, Tmin and T223. A homogenization method has been developed to homogenize monthly T224,25. Figure 3 shows that the homogenization has a negligible impact on the T2 trend over the northern high latitudes. The homogenization only partly corrects the underestimation of Tm trend in T2 over the U.S.

Discussion

This study shows that, compared with T4, current widely used datasets based on T2 overestimate the global mean warming rate by 0.02 °C/decade during the enhanced warming period (1973–1997) and underestimate the warming rate by 0.03 °C/decade during the hiatus period of warming (2000–2013). T2 has difficulty in accurately reflecting the impact of land-atmosphere interactions on Tm26,27. The use of T2 also partly explains the reported enhanced cold season warming rates in the northern high latitudes and the “warming hole” over the central U.S. The regional contrast in the warming rate between the U.S. and China is therefore substantially decreased if T4 is used rather than T2. The bias of T2 also partly explains the discrepancy between the observed and modelled recent warming hiatus.

Because we have accumulated more than 60 years of T4 data, it is essential to use T4 to conduct a global analysis of Tm, which will substantially improve upon previous studies of regional climate change detection and attribution. This can be performed by calculating Tm using the recently available Integrated Surface Hourly Database14 and the GHCN daily database15, which are available at ftp://ftp.ncdc.noaa.gov/pub/data/. Both projects have collected hourly or daily data at tens of thousands of weather stations. However, these data sources have not been fully used in current global Tm analyses5,6,7,11. The new analyses of global mean Tm will substantially improve our understanding of spatial warming patterns and regional climate change.

Data and Method

This paper investigates Tm calculated by different methods. Mean air temperature (T2) calculated from daily maximum (Tmax) and minimum (Tmin) air temperatures has been widely accepted. To calculate T2, we use Tmax and Tmin from the Global Historical Climatology Network (GHCN) daily database15, which has the best spatial and temporal coverage. Air temperature observations at specific times, i.e., 00:00, 06:00, 12:00 and 18:00, from the Integrated Surface Hourly Database (ISD)14 are used to calculate T4. Hourly air temperatures from ISD are used to calculate T24, which is regarded as the true Tm8,9 and to evaluate the uncertainty of T2 and T4.

Raw data of Tmax and Tmin have been known to be impaired by changes in recording time3. This is particularly important for the U.S.23. To address this issue, the pairwise comparison method has been used to homogenize the monthly Tm25, which has been demonstrated to perform well over the U.S.24. However, homogenized and adjusted T2 (Tqca) are available at fewer stations than the daily base5,15. To make comparisons between T2, T4 and T24 at exactly the same time and stations, we calculate these Tms from raw hourly and daily data and then calculate their monthly values. In Fig. 3, we show the impact of homogenization is much less than that in different calculation methods.

Because T24 has only been widely available since 2000, we first evaluate the uncertainty of T2 and T4 in depicting Tm trend by comparison with T24 from 2000 to 2013. Our results show that T4 can substantially reduce the uncertainty of T2 in quantifying the Tm trend. We then compare the trends in T2 and T4 from 1973 to 1997 (the enhanced warming period), which shows that spatial contrasts in the warming rate over land significantly depends on the definitions of Tm, i.e., T2 vs. T4.

Additional Information

How to cite this article: Wang, K. C. and Zhou, C. Regional Contrasts of the Warming Rate over Land Significantly Depend on the Calculation Methods of Mean Air Temperature. Sci. Rep. 5, 12324; doi: 10.1038/srep12324 (2015).