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.

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

2017 ◽  
Author(s):  
Yan Li ◽  
Birger Tinz ◽  
Hans von Storch ◽  
Qingyuan Wang ◽  
Qingliang Zhou
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Observation Data ◽  
Web Page ◽  
China Meteorological Administration ◽  
Data Set ◽  
Long Term Trends ◽  
The City

Abstract. We present a homogenized time series surface air temperature at 2 meters (SAT) 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 German National Meteorological Service from 1899 to 1913; National observation data of China Meteorological Administration (CMA) from 1961–2014 and a gridded data set of Willmott and Matsuura in Delaware to fill the gap from 1914 to 1959. Based on this new series, long-term trends are described. The SAT in Qingdao has a significant warming trend of 0.11 °C (10 yr)-1 during 1899–2014. The coldest period occurred in 1909–1918 and the warmest period occurred in 1999–2008. For the seasonal mean SAT, the most significant warming can be found in spring, followed by winter. Access to the data is provided in excel and archived by Deutscher Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte (http://www.dwd.de/EN/ourservices/overseas_stations/ueberseedoku/doi_qingdao.html) or be freely available at https://doi.org/10.5676/DWD/Qing_v1.

Long-term trends and extremes in observed daily precipitation and near surface air temperature in the Philippines for the period 1951–2010

2014 ◽  
Vol 145-146 ◽  
pp. 12-26 ◽  
Author(s):  
Thelma A. Cinco ◽  
Rosalina G. de Guzman ◽  
Flaviana D. Hilario ◽  
David M. Wilson
Keyword(s):  
Air Temperature ◽  
Daily Precipitation ◽  
Surface Air Temperature ◽  
The Philippines ◽  
Near Surface ◽  
Long Term Trends

Long-term trends of surface air temperature in india

1985 ◽  
Vol 5 (5) ◽  
pp. 521-528 ◽  
Author(s):  
L. S. Hingane ◽  
K. Rupa Kumar ◽  
Bh. V. Ramana Murty
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Long Term Trends

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 Is the Last Years Abrupt Warming in the National Observatory of Athens Records a Climate Change Manifestation?

2013 ◽  
Vol 9 (2) ◽  
pp. 107-116
Keyword(s):  
Time Series ◽  
Air Temperature ◽  
Eastern Mediterranean ◽  
National Observatory ◽  
Urban Effect ◽  
Long Term Trends ◽  
Temperature Records ◽  
The 19Th Century ◽  
Temperature Time

Instrumental data time series show an average global warming of approximately 0.90C over the last century. Eastern Mediterranean air temperature follows the northern hemisphere (NH) secular trend till 1970s, while the NH warming of the last 30 years is not noticeable in the eastern Mediterranean till 1990s. National Observatory of Athens (NOA) meteorological time series start from the last decades of the 19th Century, therefore are at first suitable for detection of long term trends in the region. In this study we investigate whether the abrupt increase of the NOA air temperature time series, which appears during the last few years, is the finger-print of the broader scale climatic change or it is a discontinuity in the record of urban effect or of station’s problems origin. It is shown that NOA air temperature records display a statistically significant discontinuity attributable to change of the station thermometers on 1995 and therefore NOA records must be treated with caution for long term air temperature trends detection.

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 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).

Correction of Inhomogeneities in Observed Land Surface Temperatures over China

2020 ◽  
Vol 33 (20) ◽  
pp. 8885-8902
Author(s):  
Jizeng Du ◽  
Kaicun Wang ◽  
Baoshan Cui ◽  
Shaojing Jiang
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Cmip5 Models ◽  
Near Surface ◽  
Land Surface Temperatures ◽  
Long Term Trends ◽  
The Difference

AbstractLand surface temperature Ts and near-surface air temperature Ta are two main metrics that reflect climate change. Recently, based on in situ observations, several studies found that Ts warmed much faster than Ta in China, especially after 2000. However, we found abnormal jumps in the Ts time series during 2003–05, mainly caused by the transformation from manual to automatic measurements due to snow cover. We explore the physical mechanism of the differences between automatic and manual observations and develop a model to correct the automatic observations on snowy days in the observed records of Ts. Furthermore, the nonclimatic shifts in the observed Ts were detected and corrected using the RHtest method. After corrections, the warming rates for Ts-max, Ts-min, and Ts-mean were 0.21°, 0.34°, and 0.25°C decade−1, respectively, during the 1960–2014 period. The abnormal jump in the difference between Ts and Ta over China after 2003, which was mentioned in existing studies, was mainly caused by inhomogeneities rather than climate change. Through a combined analysis using reanalyses and CMIP5 models, we found that Ts was consistent with Ta both in terms of interannual variability and long-term trends over China during 1960–2014. The Ts minus Ta (Ts − Ta) trend is from −0.004° to 0.009°C decade−1, accounting for from −3.19% to 5.93% (from −3.09% to 6.39%) of the absolute warming trend of Ts (Ta).

scholarly journals Prediction of terrestrial and extraterrestrial parameters by modelling and extrapolating their natural regularities

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 117-132
Author(s):  
NITYANAND SINGH ◽  
S. K. PATWARDHAN
Keyword(s):  
Time Series ◽  
Harmonic Analysis ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Zonal Wind ◽  
Surface Air Temperature ◽  
Fundamental Period ◽  
Short Term ◽  
Quasi Biennial Oscillation

Extrapolation of dominant modes of fluctuations after fitting suitable mathematical function to the observed long period time series is one of the approaches to long-term weather or short-term climate prediction. Experiences suggest that reliable predictions can be made from such approaches provided the time series being modeled possesses adequate regularity. Choice of the suitable function is also an important task of the time series modelling-extrapolation-prediction, or TS-MEP, process. Perhaps equally important component of this method is the development of effective filtering module. The filtering mechanism should be such that it effectively suppresses the high frequency, or unpredictable, variations and carves out the low frequency mode, or predictable, variation of the given series. By incorporating a possible solution to these propositions a new TS-MEP method has been developed in this paper. A Variable Harmonic Analysis (VHA) has been developed to decompose the time series into sine and cosine waveforms for any desired wavelength resolution within the data length (or fundamental period). In the Classical Harmonic Analysis (CHA) the wavelength is strictly an integer multiple of the fundamental period. For smoothing the singular spectrum analysis (SSA) has been applied. The SSA provides the mechanism to decompose the series into certain number of principal components (PCs) and then recombine the first few PCs, representing the dominant modes of variation, to get the smoothed version of the actual series.   Twenty-four time series of terrestrial and extraterrestrial parameters, which visibly show strong regularity, are considered in the study. They can be broadly grouped into five categories: (i) inter-annual series of number of storms/depressions over the Indian region, seasonal and annual mean northern hemisphere land-area surface air temperature and the annual mean sunspot number (chosen cases of long term/short term trends or oscillation); (ii) monthly sequence of zonal wind at 50- hPa, 30-hPa levels over Balboa (representative of quasi-biennial oscillation); (iii) monthly sequence of surface air temperature (SAT) over the India region (strongly dominated by seasonality); (iv) monthly sequence of sea surface temperature (SST) of tropical Indian and Pacific Oceans (aperiodic oscillations related to El Nino/La Nina); and (v) sequence of monthly sea level pressure (SLP) of selected places over ENSO region (seasonality and oscillation). Best predictions are obtained for the SLP followed by SAT and SST due to strong domination of seasonality and/or aperiodic oscillations. The predictions are found satisfactory for the lower stratospheric zonal wind over Balboa, which displays quasi-periodic oscillations. Because of a steep declining trend a reliable prediction of number of storms/depressions over India is possible by the method. Prediction of northern hemisphere surface air temperature anomaly is not found satisfactory.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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