Search

Identifying and separating the signal of urbanization effects in current temperature data series is essential for accurately detecting, attributing, and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based on the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951–2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the mean and extreme temperature indices series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual mean and extreme temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades. [ABSTRACT FROM AUTHOR]

A dataset from 763 national Reference Climate and Basic Meteorological Stations (RCBMS) was used to analyze surface air temperature (SAT) change in mainland China. The monthly historical observational records had been adjusted for urbanization bias existing in the data series of size-varied urban stations, after they were corrected for data inhomogeneities mainly caused by relocation and instrumentation. The standard procedures for creating area-averaged temperature time series and for calculating linear trend were used. Analyses were made for annual and seasonal mean temperature. Annual mean SAT in mainland China as a whole rose by 1.24°C for the last 55 years, with a warming rate of 0.23°C decade−1. This was close to the warming of 1.09°C observed in global mean land SAT over the period 1951–2010. Compared to the SAT before correction, after-corrected data showed that the urbanization bias had caused an overestimate of the annual warming rate of more than 19.6% during 1961–2015. The winter, autumn, spring, and summer mean warming rates were 0.28°, 0.23°, 0.23°, and 0.15°C decade−1, respectively. The spatial patterns of the annual and seasonal mean SAT trends also exhibited an obvious difference from those of the previous analyses. The largest contrast was a weak warming area appearing in central parts of mainland China, which included a small part of southwestern North China, the northwestern Yangtze River, and the eastern part of Southwest China. The annual mean warming trends in Northeast and North China obviously decreased compared to the previous analyses, which caused a relatively more significant cooling in Northeast China after 1998 under the background of global warming slowdown. [ABSTRACT FROM AUTHOR]

Correlations of the urban heat island intensity (UHII) and key surface variables with the short-duration intense rainfall (SDIR) events are examined for the Beijing urban areas by applying hourly data of a high-density automatic weather station (AWS) network. Higher frequencies (amounts) of the SDIR events are found in or near the central urban area, and most of the SDIR events begin to appear in late evening and nighttime, but tend to end in late night and early morning. Correlations of the UHII with the SDIR frequency (amount) are all highly significant for more than 3 h ahead of the beginning of the SDIR events. Although the UHII at immediate hours (<3 h) before the SDIR occurrence is more indicative of SDIR events, their occurrence more depends on the magnitude of the UHII at earlier hours. The UHII before the beginning of the SDIR events also shows high-value centers in the central urban area, which is generally consistent with the distribution of the SDIR events. The spatial and temporal patterns of regional SDIR events exhibit similar characteristics to the site-based SDIR events and also show a good relationship with the UHII in the urban areas. In addition to the UHII over the urban areas, surface air temperature, surface air pressure, relative humidity, and near-surface wind directions at the Beijing station experience large changes before and after the beginning time of regional SDIR events, and have the potential to indicate the occurrence of SDIR events in the studied area. [ABSTRACT FROM AUTHOR]

Understanding the long-term change of extreme temperature events is important to the detection and attribution of climate change. It is unclear, however, how much effect urbanization has had on trends of the extreme temperature indices series constructed based on the commonly used datasets on a subcontinental scale. Applying a homogenized daily temperature dataset of the national reference climate stations and basic meteorological stations, and a rural station network previously developed, urbanization effects on trends of extreme temperature indices in mainland China for the time period 1961-2008 are evaluated. It is found that 1) the country-averaged annual- and seasonal-mean extreme temperature indices series generally experience statistically significant trends; 2) annual-mean urbanization effects in the country as a whole are statistically significant for daily minimum temperature (Tmin), maximum temperature (Tmax), and mean temperature of Tmin and Tmax (Tavg), reaching 0.070°, 0.023°, and 0.047°C (10 yr)−1, respectively, with the largest values for annual-mean Tmin occurring in north China; 3) annual- and seasonal-mean urbanization effects for the declining diurnal temperature range (DTR) are highly significant, and the largest seasonal-mean DTR decline because of urbanization occurs in winter and spring; 4) annual-mean urbanization effects for the lowest Tmin, summer days, tropical nights, and frost days series are significant, but an insignificant urbanization effect is detected for icing days series; 5) urbanization has led to a highly significant decline of annual cold nights at a rate of −1.485 days (10 yr)−1 and a highly significant increase of annual warm nights at a rate of 2.264 days (10 yr)−1. Although urbanization effects are also significant for cold days and warm days, they are relatively smaller, and 6) the smallest absolute values of annual-mean urbanization effects for most of the indices series are found to dominantly appear during 1966-76, a well-known deurbanization period resulting from the Cultural Revolution. [ABSTRACT FROM AUTHOR]

In the global lands, the bias of urbanization effects still exits in the surface air temperature series of many city weather stations to a certain extent. Reliable reference climate stations need to be selected for the detection and correction of the local manmade warming bias. The underlying image data of remote sensing retrieval is adopted in this study to obtain the spatial distribution of surface brightness temperature, and the surface air temperature reference stations are determined based on the locations of the weather stations in the remote sensing surface thermal fields. Among the 672 national reference climate stations and national basic weather stations of mainland China, for instance, 113 surface air temperature reference stations are selected for applying this method. Compared with the average surface air temperature series of the reference stations obtained by a more sophisticated method developed in China, this method is proven to be robust and applicable, and can be adopted for the evaluation and adjustment study on the urbanization bias of the currently used air temperature records of surface climate stations in the global lands. [ABSTRACT FROM AUTHOR]

Precipitation accumulations, integrated over rainfall events, are investigated using hourly data across continental China during the warm season (May–October) from 1980 to 2015. Physically, the probability of precipitation accumulations drops slowly with event size up to an approximately exponential cutoff scale sL where probability drops much faster. Hence sL can be used as an indicator of high accumulation percentiles (i.e., extreme precipitation accumulations). Overall, the climatology of sL over continental China is about 54 mm. In terms of cutoff changes, the current warming stage (1980–2015) is divided into two periods, 1980–97 and 1998–2015. We find that the cutoff in 1998–2015 increases about 5.6% compared with that of 1980–97, with an average station increase of 4.7%. Regionally, sL increases are observed over East China (10.9% ± 1.5%), Northwest China (9.7% ± 2.5%), South China (9.4% ± 1.4%), southern Southwest China (5.6% ± 1.2%), and Central China (5.3% ± 1.0%), with decreases over North China (−10.3% ± 1.3%), Northeast China (−4.9% ± 1.5%), and northern Southwest China (−3.9% ± 1.8%). The conditional risk ratios for five subregions with increased cutoff sL are all greater than 1.0, indicating an increased risk of large precipitation accumulations in the most recent period. For high precipitation accumulations larger than the 99th percentile of accumulation s99, the risk of extreme precipitation over these regions can increase above 20% except for South China. These increases of extreme accumulations can be largely explained by the extended duration of extreme accumulation events, especially for "extremely extreme" precipitation greater than s99. [ABSTRACT FROM AUTHOR]

The relative roles of buoy and Argo observations in two sea surface temperature (SST) analyses are studied in the global ocean and tropical Pacific Ocean over 2000–16 using monthly Extended Reconstructed SST version 5 (ERSSTv5) and Daily Optimum Interpolation SST version 2 (DOISST). Experiments show an overall higher impact by buoys than Argo floats over the global oceans and an increasing impact by Argo floats. The impact by Argo floats is generally larger in the Southern Hemisphere than in the Northern Hemisphere. The impact on trends and anomalies of globally averaged SST by either one is small when the other is used. The warming trend over 2000–16 remains significant by including either buoys or Argo floats or both. In the tropical Pacific, the impact by buoys was large over 2000–05 when the number of Argo floats was low, and became smaller over 2010–16 when the number and area coverage of Argo floats increased. The magnitude of El Niño and La Niña events decreases when the observations from buoys, Argo floats, or both are excluded. The impact by the Tropical Atmosphere Ocean (TAO) and Triangle Trans-Ocean Buoy Network (TRITON) is small in normal years and during El Niño events. The impact by TAO/TRITON buoys on La Niña events is small when Argo floats are included in the analysis systems, and large when Argo floats are not included. The reason for the different impact on El Niño and La Niña events is that the drifting buoys are more dispersed from the equatorial Pacific region by stronger trade winds during La Niña events. [ABSTRACT FROM AUTHOR]