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

2021 ◽  
Vol 13 (5) ◽  
pp. 2211-2226
Author(s):  
Peng Si ◽  
Qingxiang Li ◽  
Phil Jones
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Minimum Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Tianjin City ◽  
Monthly Journal

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).

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

2021 ◽  
Author(s):  
Peng Si ◽  
Qingxiang Li ◽  
Phil Jones
Keyword(s):  
Air Temperature ◽  
Minimum Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Daily Maximum ◽  
Daily Data ◽  
Long Term Trends ◽  
Tianjin City ◽  
Monthly Journal ◽  
Annual Scale

Abstract. The 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) Department of Industry Agency of British Concession in Tianjin covering Sep 1 1890–Dec 31 1931 (2) Water Conservancy Commission of North China covering Jan 1 1932–Dec 31 1950 and (3) monthly journal sheets for Tianjin surface meteorological observation records covering Jan 1 1951–Dec 31 2019 have been collected from the Tianjin Meteorological Archive. The completed daily maximum and minimum temperature series for Tianjin from Jan 1 1887 (Sep 1 1890 for minimum) to Dec 31 2019 has been constructed and assessed for quality control and an early extension from 1890 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, CRUTS4.03 and GHCNV3 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 decade-1 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, with amplitudes of −1.454 d decade−1, 1.196 d decade−1, −0.140 d decade−1 and 0.975 d decade−1 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.pangaea.de/10.1594/PANGAEA.924561 (Si and Li, 2020).

scholarly journals The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth

2014 ◽  
Vol 6 (1) ◽  
pp. 61-68 ◽  
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Temperature Data ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Data Set ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature, version 4) land-surface air temperature data set is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this data set: online access to monthly, seasonal and annual data values and time series graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 data set. A mathematical description of the construction of the CRUTEM4 data set (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the data set used in a particular study. The CRUTEM4 data set used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

scholarly journals Global air temperature variability independent of sea-surface temperature influences

2012 ◽  
Vol 37 (1) ◽  
pp. 29-35
Author(s):  
Andrew C. Comrie ◽  
Gregory J. McCabe
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Air Temperature ◽  
Recent Literature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Sea Surface ◽  
Sst Variability ◽  
Global Surface

Mean global surface air temperature (SAT) and sea surface temperature (SST) display substantial variability on timescales ranging from annual to multi-decadal. We review the key recent literature on connections between global SAT and SST variability. Although individual ocean influences on SAT have been recognized, the combined contributions of worldwide SST variability on the global SAT signal have not been clearly identified in observed data. We analyze these relations using principal components of detrended SST, and find that removing the underlying combined annual, decadal, and multi-decadal SST variability from the SAT time series reveals a nearly monotonic global warming trend in SAT since about 1900.

scholarly journals The CRUTEM4 land-surface air temperature dataset: construction, previous versions and dissemination via Google Earth

2013 ◽  
Vol 6 (2) ◽  
pp. 597-619
Author(s):  
T. J. Osborn ◽  
P. D. Jones
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Research Unit ◽  
Google Earth ◽  
Climatic Research Unit ◽  
Annual Data ◽  
Markup Language ◽  
Online Access ◽  
Precise Version

Abstract. The CRUTEM4 (Climatic Research Unit Temperature version 4) land-surface air temperature dataset is one of the most widely used records of the climate system. Here we provide an important additional dissemination route for this dataset: online access to monthly, seasonal and annual data values and timeseries graphs via Google Earth. This is achieved via an interface written in Keyhole Markup Language (KML) and also provides access to the underlying weather station data used to construct the CRUTEM4 dataset. A mathematical description of the construction of the CRUTEM4 dataset (and its predecessor versions) is also provided, together with an archive of some previous versions and a recommendation for identifying the precise version of the dataset used in a particular study. The CRUTEM4 dataset used here is available from doi:10.5285/EECBA94F-62F9-4B7C-88D3-482F2C93C468.

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

2018 ◽  
Vol 10 (1) ◽  
pp. 643-652
Author(s):  
Yan Li ◽  
Birger Tinz ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Observation Data ◽  
China Meteorological Administration ◽  
Ascii Format ◽  
Long Term Trends ◽  
The City

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.

Diurnal Cycle of Surface Air Temperature within China in Current Reanalyses: Evaluation and Diagnostics

2018 ◽  
Vol 31 (11) ◽  
pp. 4585-4603 ◽  
Author(s):  
Jizeng Du ◽  
Kaicun Wang ◽  
Jiankai Wang ◽  
Shaojing Jiang ◽  
Chunlüe Zhou
Keyword(s):  
Air Temperature ◽  
Diurnal Cycle ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Positive Trend ◽  
Southeastern China

Abstract Diurnal cycle of surface air temperature T is an important metric indicating the feedback of land–atmospheric interaction to global warming, whereas the ability of current reanalyses to reproduce its variation had not been assessed adequately. Here, we evaluate the daily maximum temperature Tmax, daily minimum temperature Tmin, and diurnal temperature range (DTR) in five reanalyses based on observations collected at 2253 weather stations over China. Our results show that the reanalyses reproduce Tmin very well; however, except for Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2), they substantially underestimate Tmax and DTR by 1.21°–6.84°C over China during the period of 1980–2014. MERRA-2 overestimates Tmax and DTR by 0.35° and 0.81°C, which are closest with observation. The reanalyses are skillful in reproducing the interannual variability of Tmax and Tmin but relatively poor for DTR. All reanalyses underestimate the warming trend of Tmin by 0.13°–0.17°C (10 yr)−1 throughout China during 1980–2014, and underestimate the warming trend of Tmax by 0.24°–0.40°C (10 yr)−1 in northwestern China while overestimating this quantity by 0.18°–0.33°C (10 yr)−1 in southeastern China. These trend biases in Tmax and Tmin introduce a positive trend bias in DTR of 0.01°–0.26°C (10 yr)−1 within China, especially in the north China plain and southeastern China. In the five reanalyses, owing to the sensitivity discrepancies and trend biases, the surface solar radiation Rs and precipitation frequency (PF) are notable deviation sources of the diurnal cycle of air temperature, which explain 31.0%–38.7% (31.9%–37.8%) and 9.8%–22.2% (7.4%–15.3%) of the trend bias in Tmax (DTR) over China, respectively.

scholarly journals Discontinuous Daily Temperatures in the WATCH Forcing Datasets

2015 ◽  
Vol 16 (1) ◽  
pp. 465-472 ◽  
Author(s):  
Henning W. Rust ◽  
Tim Kruschke ◽  
Andreas Dobler ◽  
Madlen Fischer ◽  
Uwe Ulbrich
Keyword(s):  
Land Surface ◽  
Water Cycle ◽  
Research Unit ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Monthly Means ◽  
Tropical Africa ◽  
Average Temperature ◽  
Surface Models ◽  
The Tropics

Abstract The Water and Global Change (WATCH) forcing datasets have been created to support the use of hydrological and land surface models for the assessment of the water cycle within climate change studies. They are based on 40-yr ECMWF Re-Analysis (ERA-40) or ECMWF interim reanalysis (ERA-Interim) with temperatures (among other variables) adjusted such that their monthly means match the monthly temperature dataset from the Climatic Research Unit. To this end, daily minimum, maximum, and mean temperatures within one calendar month have been subjected to a correction involving monthly means of the respective month. As these corrections can be largely different for adjacent months, this procedure potentially leads to implausible differences in daily temperatures across the boundaries of calendar months. We analyze day-to-day temperature fluctuations within and across months and find that across-months differences are significantly larger, mostly in the tropics and frigid zones. Average across-months differences in daily mean temperature are typically between 10% and 40% larger than their corresponding within-months average temperature differences. However, regions with differences up to 200% can be found in tropical Africa. Particularly in regions where snowmelt is a relevant player for hydrology, a few degrees Celsius difference can be decisive for triggering this process. Daily maximum and minimum temperatures are affected in the same regions, but in a less severe way.

Climate Dynamics as a Nonlinear Brownian Motion

1998 ◽  
Vol 08 (04) ◽  
pp. 799-803 ◽  
Author(s):  
D. M. Sonechkin
Keyword(s):  
Time Series ◽  
Brownian Motion ◽  
Global Climate ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Climate System ◽  
Temperature Oscillations ◽  
Almost All ◽  
Temperature Time ◽  
The Heat Balance

Based on the heat balance equation of the global climate system the well-known surface air temperature time series of the Northern and Southern hemispheres were analyzed as realizations of a fractional Brownian motion. The technique of the so-called wavelet transform was used for this purpose. The technique easily admits splitting time series of interest to statistically stationary oscillations and a trend. Such temperature oscillations were extracted which include within themselves almost all differences between both hemispheric time series. As a result of subtraction of the oscillations from the primary hemispheric series a residual trend-like component was evaluated. The latter evidences a single warming trend of the global climate system that was started from the early 20th century.

Assessment of urbanization effects in time series of surface air temperature over land

Nature ◽  
1990 ◽  
Vol 347 (6289) ◽  
pp. 169-172 ◽  
Author(s):  
P. D. Jones ◽  
P. Ya. Groisman ◽  
M. Coughlan ◽  
N. Plummer ◽  
W-C. Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Surface Air Temperature

scholarly journals Assessment of MERRA-2 Land Surface Energy Flux Estimates

2018 ◽  
Vol 31 (2) ◽  
pp. 671-691 ◽  
Author(s):  
Clara S. Draper ◽  
Rolf H. Reichle ◽  
Randal D. Koster
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Longwave Radiation ◽  
Research Unit ◽  
Surface Energy Budget ◽  
Shortwave Radiation ◽  
Boreal Summer ◽  
Climatic Research Unit ◽  
Daily Maximum ◽  
Global Land

In the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here aspects of the MERRA-2 land surface energy budget and 2-m air temperatures [Formula: see text] are assessed. For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W m−2), the sensible heating (by 6 W m−2), and the downwelling shortwave radiation (by 14 W m−2) while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10–15 W m−2 each). These results differ only slightly from those for NASA’s previous reanalysis, MERRA. Comparison to various gridded reference datasets over boreal summer (June–August) suggests that MERRA-2 has particularly large positive biases (>20 W m−2) where LH is energy limited and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during boreal summer, the globally averaged anomaly correlations [Formula: see text] with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs Global Land Evaporation Amsterdam Model data) and the daily maximum T2m (from 0.69 to 0.75 vs Climatic Research Unit data). In regions where [Formula: see text] is particularly sensitive to the precipitation corrections (including the central United States, the Sahel, and parts of South Asia), the changes in the [Formula: see text] [Formula: see text] are relatively large, suggesting that the observed precipitation influenced the [Formula: see text] performance.

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