Surface Air Temperature Series Research Articles

Adjustment of the urbanization bias in surface air temperature series based on urban spatial morphologies and using machine learning

Urbanization bias is one of the main systematic biases in surface air temperature (SAT) in climate change monitoring. The traditional linear approach assumes that the impact of urbanization on SAT sequences increases at a uniform rate, but this contradicts the real urbanization processes that occurred around observation stations. This paper puts forward a new adjustment method by using urban spatial morphologies and a random forest (RF) model. Results showed a warming rate of 26.2% (59.1%) during 1991–2020 (2006–2020) that was caused by urbanization. The RF model achieved better performance between urbanization bias and urban morphologies around 4 km buffer areas of the national meteorological stations than other buffers. It was noted that the importance analysis illustrated the urban horizontal morphologies had a stronger explanation for urbanization bias than vertical morphologies. Finally, the importance values were introduced into the traditional adjustment method. Taking Hefei as an example, the adjustment results obtained using the new method reveal that the urbanization bias maintains dynamic consistency with the observed changes in the surrounding environment of the target station. The new adjustment method might help improve our understanding of the relationship between human activities and regional climate change and also would provide technical references for the protection of the observation environment and the construction of reference climate data.

Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

It is important to quantify changes in the local meteorological observational environment (MOE) around weather stations if we are to obtain accurate assessments of the regional warming of the surface air temperature (SAT) in relation to urbanization bias. Current studies often use two-dimensional parameters (e.g., the land surface temperature, land use/land cover and the normalized difference vegetation index) to characterize the local MOE. Most of the existing models of the relationship between urbanization bias in SAT series and MOE parameters are linear regression models, which ignore the non-linear driving effect of MOE changes on SAT series. By contrast, there is a lack of three-dimensional parameters in the characterization of the morphological features of the MOE. Changes in the MOE related to urbanization lead to uncertainties in the contribution of SAT series on different scales and we need to introduce vertical structure indexes to enrich the three-dimensional spatial morphology of MOE parameters. The non-linear response of urbanization bias in SAT series to three-dimensional changes in the MOE and its scale dependence should be explored by coupling computational fluid dynamics model simulations with machine learning.

A New Method for Correcting Urbanization-Induced Bias in Surface Air Temperature Observations: Insights From Comparative Site-Relocation Data

The effect of urbanization on surface air temperature (SAT) is one of the most important systematic biases in SAT series of urban stations. Correcting this so-called urbanization bias has the potential to provide accurate basic data for long-term climate change monitoring and research. In the western region of the Yangtze River Delta, 42 meteorological stations with site-relocation history from 2009 to 2018 were selected to analyze the statistical characteristics of the differences in comparative site-relocation daily average SAT. The annual average differences in comparative site-relocation SAT series between the old and the new stations (SATDON) were used to characterize the impact of urbanization bias on the air temperature observation series. Using remote sensing technology, spatial datasets of land-use, landscape, and geometric parameters of the underlying surface in the 5-km buffer zone around the station were established as the observed environmental factors of the site, and the differences in these observed environmental factors (DOEFs) between the old and the new stations were calculated to indicate the change induced by urbanization. Next, multiple linear regression models of SATDON and DOEFs were constructed, showing that the error range of the model for simulated SATDON was 3.66–18.21%, and the average error was 10.09%. Finally, this new correction method (NCM) and conventional correction method (CCM) were applied to the correction of the urbanization bias of SAT series at Hefei station. After comparison, it is found that the NCM could reveal clear contributions of the rapid and slow stages of the urbanization process and resultant environmental changes around the stations to the observed SAT. In summary, the NCM based on remote sensing technology can more reasonably and effectively correct the urbanization bias caused by local human activities, as well as reduce the error caused by the selection of reference stations via the conventional correction method.

Re-Assessing Climatic Warming in China since 1900

The regional mean surface air temperature (SAT) in China has risen with a rate of 1.3–1.7°C (100 yr)−1 since 1900, based on the recently developed homogenized observations. This estimate is larger than those [0.5–0.8°C (100 yr)−1] adopted in the early National Reports of Climate Change in China. The present paper reviews the studies of the long-term SAT series of China, highlighting the homogenization of station observations as the key progress. The SAT series of China in early studies showed a prominent warm peak in the 1940s, mainly due to inhomogeneous records associated with site-moves of a number of stations from urban to outskirts in the early 1950s, thus leading to underestimates of the centennial warming trend. Parts of China were relatively warm around the 1940s but with different-phase interdecadal variations, while some parts were even relatively cool. This fact is supported by proxy data and could partly be explained by interdecadal changes in large-scale circulation. The effect of urbanization should have a minor contribution to the observed warming in China, although the estimates of such contributions for individual ur-ban stations remain controversial. Further studies relevant to the present topic are discussed.

High‐quality sea surface temperature measurements along coast of the Bohai and Yellow Seas in China and their long‐term trends during 1960–2012

AbstractAs semi‐enclosed and shallow seas, the Bohai and Yellow Seas (hereafter, BYS) marine ecosystems are more easily affected by natural and anthropogenic climate changes than those in the open oceans. An accurate assessment of sea surface temperature (SST) change is crucial for the near‐shore water environment. In this work, new high‐quality monthly mean SST time series from 11 in situ coastal hydrological stations at BYS for the period of 1960–2012 have been generated. Monthly mean SST time series were initially subjected to penalized maximal t (PMT) test, using homogeneous monthly mean surface air temperature (SAT) series as references. Homogenized monthly mean SST series were obtained by adjusting all significant change points which have been supported by historic metadata. Our study shows that there are 29 significant change points in these 11 SST series. The majority of them are caused by instrument changes and station relocations—both account for about 51.7 and 31%, respectively. Then, homogeneous SST series are used to assess the long‐term trends. As the regional average, the annual mean homogeneous SST series shows a rapid warming trend, with the rate of 0.21°C/decade. Compared to the homogeneous SST series, the original SST series underestimate the long‐term trend (only 0.13°C/decade), indicating the artificial change points usually result in a warm‐bias in the early period measurements. Seasonally, the most significant warming occurs in boreal winter during the period analysed. The relationships of the significant warming in boreal winter and atmospheric circulation modes have also been investigated. Results reflect the remarkable and direct influence of the East Asian Trough (EAT) on SST along the coastal BYS.

Vertical groundwater flux estimation from borehole temperature profiles by a numerical model, RFLUX

AbstractWe developed a numerical model, RFLUX, which uses the heat tracer method for vertical groundwater flux estimation, and applied it to the Leizhou Peninsula, South China, to provide information to inform local groundwater resource utilization and management. The temperature–depth (TD) profiles of 24 boreholes, along with the observed ground surface temperature (GST) and surface air temperature (SAT) series in recent decades, were collected in this area. Underground TD data demonstrated the capacity to identify groundwater flow patterns, and local GST and SAT data demonstrated a strong correlation with each other over monthly, seasonal, and annual scales. In the RFLUX model, the average GST and SAT series were applied as an upper boundary condition, and a nonlinear initial condition was set using an analytical solution from the literature. The model results of selected TD profiles demonstrated that the annual vertical groundwater flux was about 0.15 m a−1, which tended to be overestimated if a linear initial condition was used. This model can be easily applied with minor modifications, considering its clear purpose and simplicity.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature (SAT) series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization (MASH) method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924–2016 at all the stations, ranging from 0.48 to 3.57°C (100 yr)−1, with a regional mean trend of 1.65°C (100 yr)−1; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5°C (100 yr)−1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data are available online at https://doi.org/www.dx.doi.org/10.11922/sciencedb.516.

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.

Construction of a surface air temperature series for Qingdao in China for the period 1899 to 2014

Abstract. We present a homogenized surface air temperature (SAT) time series at 2 m height for the city of Qingdao in China from 1899 to 2014. This series is derived from three data sources: newly digitized and homogenized observations of the German National Meteorological Service from 1899 to 1913, homogenized observation data of the China Meteorological Administration (CMA) from 1961 to 2014 and a gridded dataset of Willmott and Matsuura (2012) in Delaware to fill the gap from 1914 to 1960. Based on this new series, long-term trends are described. The SAT in Qingdao has a significant warming trend of 0.11 ± 0.03 ∘C decade−1 during 1899–2014. The coldest period occurred during 1909–1918 and the warmest period occurred during 1999–2008. For the seasonal mean SAT, the most significant warming can be found in spring, followed by winter. The homogenized time series of Qingdao is provided and archived by the Deutscher Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte (http://www.dwd.de/EN/ourservices/overseas_stations/ueberseedoku/doi_qingdao.html) in ASCII format. Users can also freely obtain a short description of the data at https://doi.org/https://dx.doi.org/10.5676/DWD/Qing_v1. And the data can be downloaded at http://dwd.de/EN/ourservices/overseas_stations/ueberseedoku/data_qingdao.txt.

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.

Climatic warming in China during 1901–2015 based on an extended dataset of instrumental temperature records

Monthly mean instrumental surface air temperature (SAT) observations back to the nineteenth century in China are synthesized from different sources via specific quality-control, interpolation, and homogenization. Compared with the first homogenized long-term SAT dataset for China by Cao et al (), which contained 18 stations mainly located in the middle and eastern part of China, the present dataset includes homogenized monthly SAT series at 32 stations, with an extended coverage especially towards western China. Missing values are interpolated by using observations at nearby stations, including those from neighboring countries. Cross validation shows that the mean bias error (MBE) is generally small and falls between 0.45 °C and −0.35 °C. Multiple homogenization methods and available metadata are applied to assess the consistency of the time series and to adjust inhomogeneity biases. The homogenized annual mean SAT series shows a range of trends between 1.1 °C and 4.0 °C/century in northeastern China, between 0.4 °C and 1.9 °C/century in southeastern China, and between 1.4 °C and 3.7 °C/century in western China to the west of 105 E (from the initial years of the stations to 2015). The unadjusted data include unusually warm records during the 1940s and hence tend to underestimate the warming trends at a number of stations. The mean SAT series for China based on the climate anomaly method shows a warming trend of 1.56 °C/century during 1901–2015, larger than those based on other currently available datasets.

Comparisons of Time Series of Annual Mean Surface Air Temperature for China since the 1900s: Observations, Model Simulations, and Extended Reanalysis

Abstract Time series of global or regional average surface air temperature (SAT) are fundamental to climate change studies. A number of studies have developed several national and regional SAT series for China, but because of the diversity of the meteorological observational sites, the different quality control routines for processing the data, and the inconsistency of the statistical methods used, they differ in their long-term trends. This paper assesses the similarities and differences of the existing time series of the annual average SAT for China that are based upon historical meteorological observations since the 1900s. The results indicate that the China average is similar to the series for the Northern Hemisphere (NH) landmass, except that the initial warming of the NH series derived from the CRUTEM3/4 datasets, which represent global historical land surface air temperatures and near-surface air temperature anomalies over land, respectively, ends earlier (before the early 1940s) than in China’s series. A major difference among the existing China average time series is the 1940s warmth, a period when there were very few observations across the country because of World War II. The SAT anomalies for China during the 1930s to 1940s have been reduced by improved homogeneity assessment compared to previous estimates. The new improved time series is in better agreement with both the historical twentieth-century reanalysis data and the historical climate simulation of phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. The new time series also shows the slowdown of the warming trend during the past 18 yr (1998–2015). The best estimate of a linear trend for increases in temperature with a 95% uncertainty range is 0.121° ± 0.009°C decade–1 for 1900–2015, indicating that the improved homogeneity assessment for China leads to a slightly greater trend than that based on raw data (0.107° ± 0.009°C decade–1).

Estimation of sampling error uncertainties in observed surface air temperature change in China

This study examines the sampling error uncertainties in the monthly surface air temperature (SAT) change in China over recent decades, focusing on the uncertainties of gridded data, national averages, and linear trends. Results indicate that large sampling error variances appear at the station-sparse area of northern and western China with the maximum value exceeding 2.0 K2 while small sampling error variances are found at the station-dense area of southern and eastern China with most grid values being less than 0.05 K2. In general, the negative temperature existed in each month prior to the 1980s, and a warming in temperature began thereafter, which accelerated in the early and mid-1990s. The increasing trend in the SAT series was observed for each month of the year with the largest temperature increase and highest uncertainty of 0.51 ± 0.29 K (10 year)−1 occurring in February and the weakest trend and smallest uncertainty of 0.13 ± 0.07 K (10 year)−1 in August. The sampling error uncertainties in the national average annual mean SAT series are not sufficiently large to alter the conclusion of the persistent warming in China. In addition, the sampling error uncertainties in the SAT series show a clear variation compared with other uncertainty estimation methods, which is a plausible reason for the inconsistent variations between our estimate and other studies during this period.

Urbanization-related warming in local temperature records: a review

AbstractThere have long been arguments about the impact of urbanization on local meteorological observations. This letter reviews up-to-date studies of the urbanization-related warming in the observed land surface air temperature series in China. Many previous studies have suggested that, over the past few decades, the local warming due to urbanization could have been about 0.1 °C/10 yr, or even larger. However, based on recently developed homogenized temperature records, the estimated urban bias is smaller. Major uncertainties arise from either the data quality or the techniques used to estimate the urbanization effect. A key example is the ‘observation-minus-reanalysis’ method, which tends to overestimate the urban signal in this region, partly due to systematic bias in the multi-decadal variability of surface air temperature in the reanalysis data. It is expected that improved numerical modeling with high-resolution information regarding the changing land surface in the region will help to further unde...

Air-Ground Temperature Coupling: Analysis by Means of Thermal Orbits

Long-term measurements of air, near-surface (soil) and ground temperatures that were collected between 1994 and 2013 at the drill site of the Geothermal Climate Change Observatory (Prague) were analyzed to understand the relationship between these variables and to reveal the mechanisms of heat transport at the land-atmosphere boundary layer. The 2D Thermal Orbit (TO) method was applied to detect regularities that were hidden in noisy and highly variable temperature time series. The results showed that the temperatures at shallow depths were affected by surface air temperature (SAT) variations on seasonal and annual time scales and could be regarded as an accurate proxy for low frequency temperature variations at the Earth’s surface. Only low-frequency/ high-amplitude surface temperature variations penetrate into the subsurface because of strong damping and the filtering effect of the ground surface. The borehole temperatures have good potential to capture temperature variations (periodicities) over long time scales that cannot be detected in the SAT series themselves because of the interference of higher frequency noise. The TO technique is a useful and powerful tool to quickly obtain diagnostics of the presence of long periodicities in borehole temperature time series.

Trend of Surface Air Temperature in Eastern China and Associated Large-Scale Climate Variability over the Last 100 Years

Abstract Using the reconstructed continuous and homogenized surface air temperature (SAT) series for 16 cities across eastern China (where the greatest industrial developments in China have taken place) back to the nineteenth century, the authors examine linear trends of SAT. The regional-mean SAT over eastern China shows a warming trend of 1.52°C (100 yr)−1 during 1909–2010. It mainly occurred in the past 4 decades and this agrees well with the variability in another SAT series developed from a much denser station network (over 400 sites) across this part of China since 1951. This study collects population data for 245 sites (from these 400+ locations) and split these into five equally sized groups based on population size. Comparison of these five groups across different durations from 30 to 60 yr in length indicates that differences in population only account for between 9% and 24% of the warming since 1951. To show that a larger urbanization impact is very unlikely, the study additionally determines how much can be explained by some large-scale climate indices. Anomalies of large-scale climate indices such as the tropical Indian Ocean SST and the Siberian atmospheric circulation systems account for at least 80% of the total warming trends.

Instrumental temperature series in eastern and central China back to the nineteenth century

AbstractIn this study, we bring together different source data sets and use quality control, interpolation, and homogeneity methods to construct a set of homogenized monthly mean surface air temperature (SAT) series for 18 stations in eastern and central China from the late nineteenth century. Missing values are statistically interpolated, and cross validation is used to assess the accuracy of the interpolation approaches. Results show that the errors of interpolation are small, and the interpolated values are statistically acceptable. Multiple homogeneity methods and all available metadata are used to assess the consistency of the time series and then to develop adjustments when necessary. Thirty‐three homogeneity breakpoints are detected in the 18 stations, and the time series is adjusted to the latest segment of the data series. The adjusted annual mean SAT generally shows a range of trends of 1.0° to 4.2°C/100 years in northeastern and southeastern China and a range of trends of −0.3° to 1.0°C/100 years in central China near 30°N. Compared to the adjusted time series, the unadjusted time series underestimates the warming trend during the past 100 years. The regional and annual mean SAT over eastern and central China agrees well with estimates from a much denser station network over this region of China since 1951 and shows a warming trend of 1.52°C/100 years during 1909–2010.

Impacts of Urbanization and Station-relocation on Surface Air Temperature Series in Anhui Province, China

Since the 1990s, many meteorological stations in China have passively “entered” cities, which has led to frequent relocation and discontinuity in observational records at many stations. To study the impacts of urbanization on surface air temperature series, 52 meteorological stations in Anhui Province were chosen based firstly on a homogeneity test of the time series, and then their surrounding underlying surfaces during different decades were identified utilizing Landsat Multispectral Scanner images from the 1970s, Landsat Thematic Mapper images from 1980s and 1990s, and Enhanced Thematic Mapper images after 2000, to determine whether or not the station “entered” city, and then these stations were categorized into three groups: urban, suburban, and rural using Landsat-measured land use/land cover (LULC) around the station. Finally, variations in annual mean air temperature (T mean), maximum air temperature (T max), and minimum air temperature (T min) were analyzed in urban-type stations and compared to their surrounding rural-type stations. The results showed that, in Anhui Province over the past two decades, many rural stations experienced urbanization and changed into urban or suburban locations. This process is referred as the “city-entering” phenomena of stations. Consequently, many of the latest stations were relocated and moved to currently rural and suburban areas, which significantly influenced the continuity of observational records and the homogeneity of long-term trends. Based on homogeneous data series, the averaged annual T mean, T max, and T min over Anhui Province increased at a rate of 0.407, 0.383 and 0.432 °C decade−1 from 1970 to 2008. The strongest effect of urbanization on annual T mean, T max, and T min trends occurred at urban stations, with corresponding contributions of 35.824, 14.286, and 45.161 % to total warming, respectively. This work provides convincing evidences that (1) urban expansion has important impacts on the evaluation of regional climate change, (2) high spatial resolution images of Landsat are very useful for selecting reference climate stations for evaluating the potential urban bias in the surface air temperature data in certain regions of the continents, and (3) meteorological observation adjustments of station-relocation-induced inhomogeneities are essential for the study of regional or global climate change.

Dynamic downscaling of near-surface air temperature at the basin scale using WRF-a case study in the Heihe River Basin, China

The spatial resolution of general circulation models (GCMs) is too coarse to represent regional climate variations at the regional, basin, and local scales required for many environmental modeling and impact assessments. Weather research and forecasting model (WRF) is a next- generation, fully compressible, Euler non-hydrostatic mesoscale forecast model with a run-time hydrostatic option. This model is useful for downscaling weather and climate at the scales from one kilometer to thousands of kilometers, and is useful for deriving meteorological parameters required for hydrological simulation too. The objective of this paper is to validate WRF simulating 5 km/ 1 h air temperatures by daily observed data of China Meteorological Administration (CMA) stations, and by hourly in-situ data of the Watershed Allied Telemetry Experimental Research Project. The daily validation shows that the WRF simulation has good agreement with the observed data; the R 2 between the WRF simulation and each station is more than 0.93; the absolute of meanbias error (MBE) for each station is less than 2°C; and the MBEs of Ejina, Mazongshan and Alxa stations are near zero, with R 2 is more than 0.98, which can be taken as an unbiased estimation. The hourly validation shows that the WRF simulation can capture the basic trend of observed data, the MBE of each site is approximately 2°C, the R 2 of each site is more than 0.80, with the highest at 0.95, and the computed and observed surface air temperature series show a significantly similar trend.

Variability of Wintertime Surface Air Temperature over the Kingdom of Saudi Arabia

Variability of wintertime surface air temperature (SAT) in the Kingdom of Saudi Arabia (KSA) is studied. The study is based on time series over thirty one years in length (1978-2008). For the analysis, we use the coefficient of variability (COV) Mann-Kendal statistical test, running mean and cumulative annual mean (CAM). The coefficient of variability (COV) for wintertime SAT decreases gradually from the north to the south of KSA. The higher values for COV occur in northern and northeastern KSA; there are due to the effect of the traveling Mediterranean depressions and their interaction with the inverted-V shape trough of the Sudan low. The relationship between COV and latitude is highly significant, while with longitude it is not significant. The Mann-Kendal statistical test illustrates that positive trends (warming) in wintertime SAT series occurs over the all stations, and that the trends are significant at middle and southern regions of KSA. Recent warming has only occurred during the last two decades at most stations. While cooling in the wintertime SAT appears for the short period of about 5 years, 1978-1983 and 1988-1992. These trends are consistence with trends in the global mean SAT. The results obtained from CAW lead to the conclusion that the thermic regime is modifying in the KSA. This dramatic enhancement, occurred at the beginning of the year 1993, is reflected in net modification in the SAT time series. The analysis of the SAT also shows a significant warming trend after the year 1997 with a rate of 0.03?C/year.