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.

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.

Quantifying heating biases in marine air temperature observations, ~1790 - present

2021 ◽  
Author(s):  
Thomas Cropper ◽  
Elizabeth Kent ◽  
David Berry ◽  
Richard Cornes ◽  
Beatriz Recinos-Rivas
Keyword(s):  
Air Temperature ◽  
Diurnal Cycle ◽  
Surface Air Temperature ◽  
Night Time ◽  
Data Set ◽  
Near Surface ◽  
Marine Surface ◽  
Heating Model ◽  
Marine Air

<p>Accurate, long-term time series of near-surface air temperature (AT) are the fundamental datasets on which the magnitude of anthropogenic climate change is scientifically and societally addressed. Across the ocean, these (near-surface) climate records use Sea Surface Temperature (SST) instead of Marine Air Temperature (MAT) and blend the SST and AT over land to create datasets. MAT has often been overlooked as a data choice as daytime MAT observations from ships are known to contain warm biases due to the storage of accumulated solar energy. Two recent MAT datasets, CLASSnmat (1881 – 2019) and UAHNMAT (1900 – 2018), both use night-time MAT observations only. Daytime MAT observations in the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) account for over half of the MAT observations in ICOADS, and this proportion increases further back in time (i.e. pre-1850s). If long-term MAT records over the ocean are to be extended, the use of daytime MAT is vital.</p><p> </p><p>To adjust for the daytime MAT heating bias, and apply it to ICOADS, we present the application of a physics-based model, which accounts for the accumulated energy storage throughout the day. As the ‘true’ diurnal cycle of MAT over the ocean has not been, to-date, adequately quantified, our approach also removes the diurnal cycle from ICOADS observations and generates a night-time equivalent MAT for all observations. We fit this model to MAT observations from groups of ships in ICOADS that share similar heating biases and metadata characteristics. This enables us to use the empirically derived coefficients (representing the physical energy transfer terms of the heating model) obtained from the fit for use in removal of the heating bias and diurnal cycle from ship-based MAT observations throughout ICOADS which share similar characteristics (i.e. we can remove the diurnal cycle from a ship which only reports once daily at noon). This adjustment will create an MAT record of night-time-equivalent temperatures that will enable an extension of the marine surface AT record back into the 18<sup>th</sup> century.</p>

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 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).

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).

Long-term variations in the geomagnetic activity level Part II: Ascending phases of sunspot cycles

1994 ◽  
Vol 12 (10/11) ◽  
pp. 1065-1070 ◽  
Author(s):  
V. Mussino ◽  
O. Borello Filisetti ◽  
M. Storini ◽  
H. Nevanlinna
Keyword(s):  
Correlation Coefficient ◽  
Geomagnetic Activity ◽  
Activity Level ◽  
Activity Levels ◽  
Data Set ◽  
The Past ◽  
Long Term Trends ◽  
A Minor ◽  
Long Term Variations

Abstract. Monthly averages of the Helsinki Ak-values have been reduced to the equivalent aa-indices to extend the aa-data set back to 1844. A periodicity of about five cycles was found for the correlation coefficient (r) between geomagnetic indices and sunspot numbers for the ascending phases of sunspot cycles 9 to 22, confirming previous findings based on a minor number of sunspot cycles. The result is useful to researchers in topics related to solar-terrestrial physics, particularly for the interpretation of long-term trends in geomagnetic activity during the past, and to forecast geomagnetic activity levels in the future.

Unnatural trend of global land long‐term surface air temperature change

2020 ◽  
Author(s):  
Yu Wang ◽  
Pengcheng Yan ◽  
Fei Ji ◽  
Shankai Tang ◽  
Liu Yang ◽  
...  
Keyword(s):  
Temperature Change ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Global Land

scholarly journals Influence of radiative forcing factors on ground–air temperature coupling during the last millennium: implications for borehole climatology

2018 ◽  
Vol 14 (11) ◽  
pp. 1583-1606 ◽  
Author(s):  
Camilo Melo-Aguilar ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
Jorge Navarro-Montesinos ◽  
Norman Steinert
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Radiative Forcing ◽  
Spatial Scales ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Small Variation ◽  
Last Millennium ◽  
Reconstruction Methods

Abstract. Past climate variations may be uncovered via reconstruction methods that use proxy data as predictors. Among them, borehole reconstruction is a well-established technique to recover the long-term past surface air temperature (SAT) evolution. It is based on the assumption that SAT changes are strongly coupled to ground surface temperature (GST) changes and transferred to the subsurface by thermal conduction. We evaluate the SAT–GST coupling during the last millennium (LM) using simulations from the Community Earth System Model LM Ensemble (CESM-LME). The validity of such a premise is explored by analyzing the structure of the SAT–GST covariance during the LM and also by investigating the evolution of the long-term SAT–GST relationship. The multiple and single-forcing simulations in the CESM-LME are used to analyze the SAT–GST relationship within different regions and spatial scales and to derive the influence of the different forcing factors on producing feedback mechanisms that alter the energy balance at the surface. The results indicate that SAT–GST coupling is strong at global and above multi-decadal timescales in CESM-LME, although a relatively small variation in the long-term SAT–GST relationship is also represented. However, at a global scale such variation does not significantly impact the SAT–GST coupling, at local to regional scales this relationship experiences considerable long-term changes mostly after the end of the 19th century. Land use land cover changes are the main driver for locally and regionally decoupling SAT and GST, as they modify the land surface properties such as albedo, surface roughness and hydrology, which in turn modifies the energy fluxes at the surface. Snow cover feedbacks due to the influence of other external forcing are also important for corrupting the long-term SAT–GST coupling. Our findings suggest that such local and regional SAT–GST decoupling processes may represent a source of bias for SAT reconstructions from borehole measurement, since the thermal signature imprinted in the subsurface over the affected regions is not fully representative of the long-term SAT variations.

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