Surface Air Temperature Datasets Research Articles

Evidence of Urban Blending in Homogenized Temperature Records in Japan and in the United States: Implications for the Reliability of Global Land Surface Air Temperature Data

Abstract To reduce the amount of nonclimatic biases of air temperature in each weather station’s record by comparing it with neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new nonclimatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases.” This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and the United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ∼60% of the long-term warming). The U.S. Historical Climatology Network (USHCN) dataset contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ∼20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been low as a result of urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed. Significance Statement Most weather station–based global land temperature datasets currently use a process called “statistical homogenization” to reduce the amount of nonclimatic biases. However, using temperature data from two countries (Japan and the United States), we show that the homogenization process unintentionally introduces new nonclimatic biases into the data as a result of an “urban blending” problem. Urban blending arises when the homogenization process inadvertently mixes the urbanization (warming) bias of the neighboring stations into the adjustments applied to each station record. As a result, the urbanization biases of the unhomogenized temperature records are spread throughout all of the homogenized data. The net effect tends to artificially add warming to rural stations and subtract warming from urban stations until all stations have about the same amount of urbanization bias.

Newly reconstructed Arctic surface air temperatures for 1979–2021 with deep learning method

A precise Arctic surface air temperature (SAT) dataset, that is regularly updated, has more complete spatial and temporal coverage, and is based on instrumental observations, is critically important for timely monitoring and improving understanding of the rapid change in the Arctic climate. In this study, a new monthly gridded Arctic SAT dataset dated back to 1979 was reconstructed with a deep learning method by combining surface air temperatures from multiple data sources. The source data include the observations from land station of GHCN (Global Historical Climatology Network), ICOADS (International Comprehensive Ocean-Atmosphere Data Set) over the oceans, drifting ice station of Russian NP (North Pole), and buoys of IABP (International Arctic Buoy Programme). The last two are crucial for improving the representation of the in-situ observed temperatures within the Arctic. The newly reconstructed dataset includes monthly Arctic SAT beginning in 1979 and daily Arctic SAT beginning in 2011. This dataset would represent a new improvement in developing observational temperature datasets and can be used for a variety of applications.

The reconstruction for the monthly surface air temperature over the Tibetan Plateau during 1901–2020 by deep learning

The Tibetan Plateau (TP) has experienced a prominent warming in recent decades, and there are many datasets to help people to understand the variations and distributions of climate change over the TP. However, the observed stations, satellite images, simulation models, and reanalysis products have weaknesses of sparse spatial distribution, short-term span, inconvenient access, and coarse resolution, respectively. Therefore, it is scarce for the high-resolution and long-term temperature dataset over the TP. In this study, based on the Climatic Research Unit (CRU) gridded data, the monthly surface air temperature dataset with a spatial resolution of 0.1° over the TP during 1901–2020 was reconstructed by a deep learning method called the Generative adversarial networks (GAN). Compared with the observation, the accuracy of temperature from the GAN (with Root Mean Square Error and Correlation Coefficients of 1.35 °C and 0.97) is improved compared to the CRU (with Root Mean Square Error and Correlation Coefficients of 3.30 °C and 0.82). In comparison with the CRU, Global Land Precipitation and Temperature (GLPT), ERA5, and NCEP/NCAR, the temperature from the GAN have the best robustness, especially over the western TP, suggesting the generated dataset has the best performance. Besides, compared to CRU, the temperature of GAN has an improved resolution and can present a more detailed texture structure of the temperature. Compared to Downscaling CRU with the same resolution, the temperature of GAN inherits the spatial and temporal distribution characteristics of CMFD and therefore better reflects the variation of temperature with longitude. Overall, the reconstruction for the monthly surface air temperature over the TP during 1901–2020 has a long-term temporal span and high spatial resolution, which provides an alternative basis for the temperature change over the TP.

Changes in the diurnal temperature range over East Asia from 1901 to 2018 and its relationship with precipitation

Since the 1950s, the East Asian diurnal temperature range (DTR), defined as the difference between the daily maximum (Tmax) and minimum temperatures (Tmin), has gradually decreased. Precipitation changes have often been cited as a primary cause of the change. However, the East Asian DTR change before 1950 and its relationship with precipitation remain unclear. Here, we used a newly developed China Meteorological Administration-Land Surface Air Temperature dataset v1.1 to examine the climatological patterns and long-term trends of the DTR in East Asia from 1901 to 2018 and its relationship with precipitation. The mean annual DTR averaged over East Asia for 1951–2018 was approximately 10.0 °C. East Asian DTR changes during 1901–2018 show two distinct characteristics. First, the DTR decreased significantly by approximately 0.60 °C during 1901–2018, and the decrease rate in the second half of the twentieth century (by ~0.53 °C) was significantly larger than that over the rest of the Northern Hemisphere and the global land due to rapid urbanization over East Asia. Second, before the 1950s, the DTR in East Asia showed a significant non-linear increase especially in middle latitude areas, mainly due to the warming rate of Tmax is higher than that of Tmin. Additionally, we found that the spatial pattern of long-term DTR change shows a significant negative correlation with mean precipitation patterns except in arid and semi-arid areas during 1901–2018. The decreasing trend of DTR gradually became smaller from arid regions to humid regions during 1901–2018, mainly because the difference between Tmax and Tmin warming rate gradually became smaller.

Construction of homogenized daily surface air temperature for the city of Tianjin during 1887–2019

Abstract. Century-long continuous daily observations from some stations are important for the study of long-term trends and extreme climate events in the past. In this paper, three daily data sources – (1) the Department of Industry Agency of the British Concession in Tianjin covering 1 September 1890–31 December 1931, (2) the Water Conservancy Commission of North China covering 1 January 1932–31 December 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering 1 January 1951–31 December 2019 – have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from 1 January 1887 (1 September 1890 for minimum) to 31 December 2019 has been constructed and assessed for quality control with an early extension from 1890 back to 1887. Several significant breakpoints are detected by the penalized maximal T test (PMT) for the daily maximum and minimum time series using multiple reference series around Tianjin from monthly Berkeley Earth (BE), Climatic Research Unit Time-Series version 4.03 (CRU TS4.03) and Global Historical Climatology Network (GHCN) v3 data. Using neighboring daily series the record has been homogenized with quantile matching (QM) adjustments. Based on the homogenized dataset, the warming trend in annual mean temperature in Tianjin averaged from the newly constructed daily maximum and minimum temperature is evaluated as 0.154 ± 0.013 ∘C per decade during the last 130 years. Trends of temperature extremes in Tianjin are all significant at the 5 % level and have much more coincident change than those from the raw data, with amplitudes of −1.454, 1.196, −0.140 and 0.975 d per decade for cold nights (TN10p), warm nights (TN90p), cold days (TX10p) and warm days (TX90p) at the annual scale. The adjusted daily maximum, minimum and mean surface air temperature dataset for Tianjin city presented here is publicly available at https://doi.org/10.1594/PANGAEA.924561 (Si and Li, 2020).

Assessment of Temperature Changes on the Tibetan Plateau During 1980–2018

AbstractSurface air temperatures affect a diverse set of physical and biological systems in many parts of the world. For regional‐scale studies, gridded surface air temperature data sets are frequently used as input variables. Here we evaluate 10 commonly used gridded air temperature products with spatial resolutions ranging from 0.1° × 0.1° to 5.0° × 5.0°, relative to observations from in situ weather stations on the Tibetan Plateau. Gridded temperatures are consistently lower, with mean annual air temperature biases ranging from −4.68 °C to −1.72 °C, and root mean square error (RMSE) from 3.24 °C to 6.11 °C. The mean biases of mean seasonal air temperatures for spring, summer, autumn, and winter are −3.21 °C, −2.98 °C, −2.89 °C, and −2.98 °C, respectively, and RMSEs of 4.81 °C, 4.39 °C, 4.3 °C, and 4.7 °C. Of all 10 products, the China meteorological forcing data set compares best with observations. Annual and season temperatures all increase significantly during 1980–2018 on the Tibetan Plateau, with winter dominating the warming rate.

Экстремальные метеорологические явления на сезонных и внутрисезонных интервалах времени в контексте изменения климата

The automatic identification of objects associated with various extreme weather events (EWE) on seasonal and intraseasonal timescales is done based on surface air temperature and precipitation datasets (NCEP/NCAR daily reanalysis fields for the Northern Hemisphere). Some features of the spatial and temporal variability of the extreme characteristics of temperature and precipitation regimes are considered in the context of climate change. An inventory of extreme events is carried out for the Northern Hemisphere in 1981–2019 depending on the spatial extent, duration, and intensity of EWEs. The years with the most striking events are noted, and a brief description of their specific features is given. The results will be used to analyze the EWE predictability in the context of the verification of long-range weather forecasts. Keywords: extreme weather events, climate change, identification of extreme events, long-range weather forecasts

Land Surface Air Temperature Variations Across the Globe Updated to 2019: The CRUTEM5 Data Set

AbstractClimatic Research Unit temperature version 5 (CRUTEM5) is an extensive revision of our land surface air temperature data set. We have expanded the underlying compilation of monthly temperature records from 5,583 to 10,639 stations, of which those with sufficient data to be used in the gridded data set has grown from 4,842 to 7,983. Many station records have also been extended or replaced by series that have been homogenized by national meteorological and hydrological services. We have improved the identification of potential outliers in these data to better capture outliers during the reference period; to avoid classifying some real regional temperature extremes as outliers; and to reduce trends in outlier counts arising from climatic warming. Due to these updates, the gridded data set shows some regional increases in station density and regional changes in temperature anomalies. Nonetheless, the global‐mean timeseries of land air temperature is only slightly modified compared with previous versions and previous conclusions are not altered. The standard gridding algorithm and comprehensive error model are the same as for the previous version, but we have explored an alternative gridding algorithm that removes the under‐representation of high latitude stations. The alternative gridding increases estimated global‐mean land warming by about 0.1°C over the course of the whole record. The warming from 1861–1900 to the mean of the last 5 years is 1.6°C using the standard gridding (with a 95% confidence interval for errors on individual annual means of −0.11 to +0.10°C in recent years), while the alternative gridding gives a change of 1.7°C.

A comparison of gridded datasets of precipitation and temperature over the Eastern Nile Basin region

Three gridded datasets of precipitation and surface air temperature over the Eastern Nile Basin (ENB) were compared and evaluated for further use in studies of the basin, such as for the optimization and validation of regional climate models for the basin and for hydrologic studies of the ENB. The Tropical Rainfall Measuring Mission (TRMM), Climate Research Unit (CRU), and Global Precipitation Climatology Center (GPCC) gridded datasets for precipitation were assessed and compared to available ground-based observations of the precipitation in the Ethiopian Highlands, which receive most of the precipitation that falls in the basin. Pearson’s correlation coefficient (COR), the modified index of agreement (MIA), and the mean absolute error (MAE) were used in statistical analyses of the gridded datasets. TRMM and GPCC were found to agree well with each other and with the GHCN station data, although GPCC showed the closest agreement with the ground-based observations. This advantage of GPCC was clearly apparent in the calculated statistical metrics, where GPCC gave the best performance (the highest COR and MIA and the lowest MAE) among the three datasets when they were compared with the ground-based observations. CRU indicated drier conditions than the other two datasets, especially over the highlands, while it also yielded the poorest agreement with ground-based observations. Temperature datasets for the ENB from the University of Delaware (UDel) and CRU were also evaluated. Both datasets performed well, and the bias between them was small. When compared to ground-based observations, both showed high COR and MIA and low MAE for most stations, but UDel presented slightly better calculated statistical metrics than CRU. Based on these results, we recommend the use of GPCC when modeling the precipitation and UDel when modeling the air temperature over the ENB.

Mapping atmospheric waves and unveiling phase coherent structures in a global surface air temperature reanalysis dataset.

In the analysis of empirical signals, detecting correlations that capture genuine interactions between the elements of a complex system is a challenging task with applications across disciplines. Here, we analyze a global dataset of surface air temperature (SAT) with daily resolution. Hilbert analysis is used to obtain phase, instantaneous frequency, and amplitude information of SAT seasonal cycles in different geographical zones. The analysis of the phase dynamics reveals large regions with coherent seasonality. The analysis of the instantaneous frequencies uncovers clean wave patterns formed by alternating regions of negative and positive correlations. In contrast, the analysis of the amplitude dynamics uncovers wave patterns with additional large-scale structures. These structures are interpreted as due to the fact that the amplitude dynamics is affected by processes that act in long and short time scales, while the dynamics of the instantaneous frequency is mainly governed by fast processes. Therefore, Hilbert analysis allows us to disentangle climatic processes and to track planetary atmospheric waves. Our results are relevant for the analysis of complex oscillatory signals because they offer a general strategy for uncovering interactions that act at different time scales.

Independent Aridity and Drought Pieces of Evidence Based on Meteorological Data and Tree Ring Data in Southeast Banat, Vojvodina, Serbia

In this study, aridity data and tree ring data were collected in Northern Serbia, in Southeast (SE) Banat, a subregion within Vojvodina, and Vojvodina at large. They were each investigated independently. The De Martonne Aridity Index and the Forestry Aridity Index are derived from examining the relationship between precipitation and surface air temperature data sets sourced from seven meteorological stations in SE Banat, and from 10 meteorological stations located in Vojvodina as a whole. Vojvodina is a large territory and used as the control area, for the period 1949–2017. The Palmer Drought Severity Index was derived for the period 1927–2016, for both SE Banat and the totality of Vojvodina. The results of the Tree Ring Width Index were obtained from samples collected in or around the villages of Vlajkovac and Šušara, both located in SE Banat, for the period 1927–2017. These tree ring records were compared with three previous aridity and drought indices, and the meteorological data on the surface air temperature and the precipitation, with the objective being to evaluate the response of tree growth to climate dynamics in the SE Banat subregion. It was noted that the significant positive temperature trends recorded in both areas were too insufficient to trigger any trends in aridity or the Tree Ring Width Index, as neither displayed any change. Instead, it appears that these climatic parameters only changed in response to the precipitation trend, which remained unchanged during the investigated period, rather than in response to the temperature trend. It appears that the forest vegetation in the investigated areas was not affected significantly by climate change in response to the dominant temperature increase.

Variation of global diurnal temperature range associated with the Madden-Julian Oscillation

This study examines the impact of the intraseasonal Madden-Julian Oscillation (MJO) on global land surface diurnal temperature range (DTR) using two daily surface air temperature datasets. The results show that the MJO significantly affects DTR over all the continents. The two datasets are highly consistent with each other, suggesting that the results are robust. Significant correlations exist in all seasons over the tropical continents, including Africa, India, southeast Asia, Australia, central America and Amazon. In the extratropics, the correlations change with season. The highest correlations occur in winter for Europe, in winter and spring for China, in autumn for United States, and in summer and winter for Canada. The surface DTR anomalies coincide well with the outgoing longwave radiation (OLR) anomalies at the top of the atmosphere, suggesting that variations of DTR are associated with variations of clouds, which is consistent with the results of previous studies on DTR trend and variability at seasonal to multi-decadal time-scales. The DTR variations are also associated with changes of deep convection, soil moisture and snow cover.

Evaluation of CMIP5 ability to reproduce twentieth century regional trends in surface air temperature and precipitation over CONUS

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability.

Uncovering temporal regularity in atmospheric dynamics through Hilbert phase analysis.

Uncovering meaningful regularities in complex oscillatory signals is a challenging problem with applications across a wide range of disciplines. Here, we present a novel approach, based on the Hilbert transform (HT). We show that temporal periodicity can be uncovered by averaging the signal in a moving window of appropriated length, τ, before applying the HT. As a case study, we investigate global gridded surface air temperature (SAT) datasets. By analyzing the variation of the mean rotation period, T¯, of the Hilbert phase as a function of τ, we discover well-defined plateaus. In many geographical regions, the plateau corresponds to the expected 1-yr solar cycle; however, in regions where SAT dynamics is highly irregular, the plateaus reveal non-trivial periodicities, which can be interpreted in terms of climatic phenomena such as El Niño. In these regions, we also find that Fourier analysis is unable to detect the periodicity that emerges when τ increases and gradually washes out SAT variability. The values of T¯ obtained for different τs are then given to a standard machine learning algorithm. The results demonstrate that these features are informative and constitute a new approach for SAT time series classification. To support these results, we analyze the synthetic time series generated with a simple model and confirm that our method extracts information that is fully consistent with our knowledge of the model that generates the data. Remarkably, the variation of T¯ with τ in the synthetic data is similar to that observed in the real SAT data. This suggests that our model contains the basic mechanisms underlying the unveiled periodicities. Our results demonstrate that Hilbert analysis combined with temporal averaging is a powerful new tool for discovering hidden temporal regularity in complex oscillatory signals.

The Global Historical Climatology Network Monthly Temperature Dataset, Version 4

We describe a fourth version of the Global Historical Climatology Network (GHCN)-monthly (GHCNm) temperature dataset. Version 4 (v4) fulfills the goal of aligning GHCNm temperature values with the GHCN-daily dataset and makes use of data from previous versions of GHCNm as well as data collated under the auspices of the International Surface Temperature Initiative. GHCNm v4 has many thousands of additional stations compared to version 3 (v3) both historically and with short time-delay updates. The greater number of stations as well as the use of records with incomplete data during the base period provides for greater global coverage throughout the record compared to earlier versions. Like v3, the monthly averages are screened for random errors and homogenized to address systematic errors. New to v4, uncertainties are calculated for each station series, and regional uncertainties scale directly from the station uncertainties. Correlated errors in the station series are quantified by running the homogenization algorithm as an ensemble. Additional uncertainties associated with incomplete homogenization and use of anomalies are then incorporated into the station ensemble. Further uncertainties are quantified at the regional level, the most important of which is for incomplete spatial coverage. Overall, homogenization has a smaller impact on the v4 global trend compared to v3, though adjustments lead to much greater consistency than between the unadjusted versions. The adjusted v3 global mean therefore falls within the range of uncertainty for v4 adjusted data. Likewise, annual anomaly uncertainties for the other major independent land surface air temperature datasets overlap with GHCNm v4 uncertainties.

Optimal average annual mean surface air temperature for East Asia since 1901

In this paper, the authors use Climate Research Unit (CRU) land surface air temperature (SAT) and empirical orthogonal function (EOF)-based spectral optimal averaging (SOA) method to estimate the regional average annual mean SAT series for East Asia since 1901 with a comparison of arithmetic averaging (AA). The SOA method minimizes the first-M-mode mean square error (MSE) between the estimates and true SAT anomalies series. In the optimization process, the errors of CRU SAT dataset are used to test their contribution to the uncertainties of regional average annual mean SAT series estimated by SOA method. Furthermore, the MSE of SOA estimates is also calculated. Our results show that (i) the EOF-based SOA method can accurately estimate the regional average SAT anomalies series than AA method with smaller error, particularly when the sampling is poor, (ii) the SAT of East Asia has also experienced pronounced interdecadal cycles during last century, with the linear trend of being 0.91 °C(100a)−1, (iii) the errors of SOA estimates are large and close to 0.2 °C at the beginning of the twentieth century and then declining to 0.1 °C by 1960s, and (iv) the SOA estimates are non-sensitive to the values of error variance in the range of 0.001 to 1.0, but tends to zero as error variance tends infinity or extremely small values near or equal to zero.

Global diurnal temperature range (DTR) changes since 1901

Previous observational analyses show that the land-surface diurnal temperature range (DTR) has decreased in the past 6 decades worldwide. Based on a newly developed China Meteorological Administration–Land Surface Air Temperature (CMA-LSAT) dataset, we analyzed the DTR changes between 1901 and 2014. Results indicate that the global land surface DTR significantly decreased at a rate of − 0.036 °C decade− 1 over the 1901–2014 period, mainly due to the large decrease in DTR from 1951 to 2014. For the first half of the twentieth century, most grid boxes (spatial resolution 5° × 5°) show a positive DTR trend, with the positive trends of 32.4% grid boxes being statistically significant, leading to a large and significant increase of 0.048 °C decade− 1 in DTR. However, a dramatic reversal in DTR change occurred in early 1950s, with most parts of global lands exhibiting a shift from increasing to decreasing trends. The global land average DTR decrease during 1951–2014 was − 0.054 °C decade− 1, with 45.0% grid boxes showing significant negative trends. The reverse phenomenon is more obvious in the Northern Hemisphere than that in the Southern Hemisphere. For the periods 1979–2014 and 1998–2014, the decreasing trends in DTR mainly occur in the Northern Hemisphere. The DTR in the Southern Hemisphere experienced much larger increases during the two recent periods than during the period 1951–2014. Asia, Eastern North America, and Australia exhibited widespread decreases in DTR, although the trend pattern for global DTR is generally mixed during 1979–2014 and 1998–2014. There is a good negative correlation between DTR and precipitation in the Northern Hemisphere from 1901 to 2014, with a correlation coefficient of − 0.61. The change in precipitation and number of volcanic eruptions, and the “early brightening” of Europe (Stockholm) all benefit the increase of DTR at global and regional scales in the first half of the twentieth century.

Land Surface Air Temperature Data Are Considerably Different Among BEST‐LAND, CRU‐TEM4v, NASA‐GISS, and NOAA‐NCEI

AbstractSeveral groups routinely produce gridded land surface air temperature (LSAT) data sets using station measurements to assess the status and impact of climate change. The Intergovernmental Panel on Climate Change Fifth Assessment Report suggests that estimated global and hemispheric mean LSAT trends of different data sets are consistent. However, less attention has been paid to the intercomparison at local/regional scales, which is important for local/regional studies. In this study we comprehensively compare four data sets at different spatial and temporal scales, including Berkley Earth Surface Temperature land surface air temperature data set (BEST‐LAND), Climate Research Unit Temperature Data Set version 4 (CRU‐TEM4v), National Aeronautics and Space Administration Goddard Institute for Space Studies data (NASA‐GISS), and data provided by National Oceanic and Atmospheric Administration National Center for Environmental Information (NOAA‐NCEI). The mean LSAT anomalies are remarkably different because of the data coverage differences, with the magnitude nearly 0.4°C for the global and Northern Hemisphere and 0.6°C for the Southern Hemisphere. This study additionally finds that on the regional scale, northern high latitudes, southern middle‐to‐high latitudes, and the equator show the largest differences nearly 0.8°C. These differences cause notable differences for the trend calculation at regional scales. At the local scale, four data sets show significant variations over South America, Africa, Maritime Continent, central Australia, and Antarctica, which leads to remarkable differences in the local trend analysis. For some areas, different data sets produce conflicting results of whether warming exists. Our analysis shows that the differences across scales are associated with the availability of stations and the use of infilling techniques. Our results suggest that conventional LSAT data sets using only station observations have large uncertainties across scales, especially over station‐sparse areas. In developing future LSAT data sets, the data uncertainty caused by limited and unevenly distributed station observations must be reduced.

Recently amplified arctic warming has contributed to a continual global warming trend

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1–3. Although various physical processes4–8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area 9 . As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs 2 and a drifting buoys based Arctic SAT dataset 11 through employing the ‘data interpolating empirical orthogonal functions’ method 12 . Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR5 1 , for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown.

Reassessment of urbanization effect on surface air temperature trends at an urban station of North China

Based on a homogenized data set of monthly mean temperature, minimum temperature, and maximum temperature at Shijiazhuang City Meteorological Station (Shijiazhuang station) and four rural meteorological stations selected applying a more sophisticated methodology, we reanalyzed the urbanization effects on annual, seasonal, and monthly mean surface air temperature (SAT) trends for updated time period 1960–2012 at the typical urban station in North China. The results showed that (1) urbanization effects on the long-term trends of annual mean SAT, minimum SAT, and diurnal temperature range (DTR) in the last 53 years reached 0.25, 0.47, and − 0.50 °C/decade, respectively, all statistically significant at the 0.001 confidence level, with the contributions from urbanization effects to the overall long-term trends reaching 67.8, 78.6, and 100%, respectively; (2) the urbanization effects on the trends of seasonal mean SAT, minimum SAT, and DTR were also large and statistically highly significant. Except for November and December, the urbanization effects on monthly mean SAT, minimum SAT, and DTR were also all statistically significant at the 0.05 confidence level; and (3) the annual, seasonal, and monthly mean maximum SAT series at the urban station registered a generally weaker and non-significant urbanization effect. The updated analysis evidenced that our previous work for this same urban station had underestimated the urbanization effect and its contribution to the overall changes in the SAT series. Many similar urban stations were being included in the current national and regional SAT data sets, and the results of this paper further indicated the importance and urgency for paying more attention to the urbanization bias in the monitoring and detection of global and regional SAT change based on the data sets.