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.

scholarly journals Spatial and Temporal Distribution of Air Temperature in the Northern Hemisphere

2013 ◽  
Vol 6 (3) ◽  
pp. 177-182
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Temporal Distribution ◽  
Surface Air Temperature ◽  
Temperature Time Series ◽  
Mean Values ◽  
The Mean ◽  
Temperature Time

In the present study, the spatial and temporal surface air temperature variability for the Northern Hemisphere has been examined, for the period 1900-1996. Factor Analysis has been applied to 5o Latitude x 10o Longitude grid box data covering the area from almost the equator to 70o N. These data are anomalies of the mean annual air temperature from the respective mean values of the period 1961- 1990. The analysis showed that, mainly 20 regions were determined in the Northern Hemisphere with significantly covariant air temperature time series. The comparison of the trends of the mean annual surface air temperature time series of these regions, revealed such common characteristics as the minimum of the first decade of the 20th century and the recent years warming. The results of this study are also compared to the respective results of a former study in which data for the last half of the century (1948-1996) have been analyzed. The findings extracted indicate the stability of climate distribution in Northern Hemisphere during the 20th century.

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 Nonlinear Trends, Long-Range Dependence, and Climate Noise Properties of Surface Temperature

2012 ◽  
Vol 25 (12) ◽  
pp. 4172-4183 ◽  
Author(s):  
Christian Franzke
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Null Model ◽  
Warming Trend ◽  
Temperature Time Series ◽  
Trend Detection ◽  
Detection Methods ◽  
Nonlinear Trends ◽  
Temperature Time ◽  
Climate Noise

Abstract This study investigates the significance of trends of four temperature time series—Central England Temperature (CET), Stockholm, Faraday-Vernadsky, and Alert. First the robustness and accuracy of various trend detection methods are examined: ordinary least squares, robust and generalized linear model regression, Ensemble Empirical Mode Decomposition (EEMD), and wavelets. It is found in tests with surrogate data that these trend detection methods are robust for nonlinear trends, superposed autocorrelated fluctuations, and non-Gaussian fluctuations. An analysis of the four temperature time series reveals evidence of long-range dependence (LRD) and nonlinear warming trends. The significance of these trends is tested against climate noise. Three different methods are used to generate climate noise: (i) a short-range-dependent autoregressive process of first order [AR(1)], (ii) an LRD model, and (iii) phase scrambling. It is found that the ability to distinguish the observed warming trend from stochastic trends depends on the model representing the background climate variability. Strong evidence is found of a significant warming trend at Faraday-Vernadsky that cannot be explained by any of the three null models. The authors find moderate evidence of warming trends for the Stockholm and CET time series that are significant against AR(1) and phase scrambling but not the LRD model. This suggests that the degree of significance of climate trends depends on the null model used to represent intrinsic climate variability. This study highlights that in statistical trend tests, more than just one simple null model of intrinsic climate variability should be used. This allows one to better gauge the degree of confidence to have in the significance of trends.

scholarly journals Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity

2010 ◽  
Vol 7 (2) ◽  
pp. 621-640 ◽  
Author(s):  
S. A. Henson ◽  
J. L. Sarmiento ◽  
J. P. Dunne ◽  
L. Bopp ◽  
I. Lima ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Primary Production ◽  
Global Climate Change ◽  
Decadal Variability ◽  
Global Climate ◽  
Warming Trend ◽  
Ocean Colour ◽  
Ocean Productivity ◽  
The Impact

Abstract. Global climate change is predicted to alter the ocean's biological productivity. But how will we recognise the impacts of climate change on ocean productivity? The most comprehensive information available on its global distribution comes from satellite ocean colour data. Now that over ten years of satellite-derived chlorophyll and productivity data have accumulated, can we begin to detect and attribute climate change-driven trends in productivity? Here we compare recent trends in satellite ocean colour data to longer-term time series from three biogeochemical models (GFDL, IPSL and NCAR). We find that detection of climate change-driven trends in the satellite data is confounded by the relatively short time series and large interannual and decadal variability in productivity. Thus, recent observed changes in chlorophyll, primary production and the size of the oligotrophic gyres cannot be unequivocally attributed to the impact of global climate change. Instead, our analyses suggest that a time series of ~40 years length is needed to distinguish a global warming trend from natural variability. In some regions, notably equatorial regions, detection times are predicted to be shorter (~20–30 years). Analysis of modelled chlorophyll and primary production from 2001–2100 suggests that, on average, the climate change-driven trend will not be unambiguously separable from decadal variability until ~2055. Because the magnitude of natural variability in chlorophyll and primary production is larger than, or similar to, the global warming trend, a consistent, decades-long data record must be established if the impact of climate change on ocean productivity is to be definitively detected.

Multiscale Oscillations of the Global Climate System as Revealed by Wavelet Transform of Observational Data Time Series

1999 ◽  
Vol 64 (1-2) ◽  
pp. 131-142 ◽  
Author(s):  
D. M. Sonechkin ◽  
N. M. Astafyeva ◽  
N. M. Datsenko ◽  
N. N. Ivachtchenko ◽  
B. Jakubiak
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Observational Data ◽  
Global Climate ◽  
Climate System

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 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 Impacts of Global Climate Change on Duration of Logging Season in Siberian Boreal Forests

Forests ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 756 ◽  
Author(s):  
Anna V. Chugunkova ◽  
Anton I. Pyzhev
Keyword(s):  
Boreal Forests ◽  
Global Climate ◽  
Moving Average ◽  
Warming Trend ◽  
Climatic Data ◽  
Krasnoyarsk Krai ◽  
Start Date ◽  
Long Run ◽  
Frost Period ◽  
Almost All

In Siberia, most boreal forests are located in an area with relatively moist forest soils, which makes logging activities possible exclusively during the frost period with a permanent snow cover and stable sub-zero temperatures. As the global climate is experiencing a trend towards warming, it is reasonable to suppose that the duration of the logging season might shorten over time, influencing the economic potential of Siberian forests. To test this hypothesis, we created a concept for calculating the duration of the logging season, taking into account the economic and climatic peculiarities of doing forest business in these territories. Using the long-run daily-observed climatic data, we calculated the duration of the logging season for eight representative stations in Krasnoyarsk Krai (Yeniseysk, Boguchany, Achinsk, and Minusinsk) and Irkutsk Oblast (Bratsk, Kirensk, Tulun, and Yerbogachen) in 1966–2018. We found strong evidence of logging season duration shortening for almost all considered stations, with an uneven effect on the start and end boundaries of the season. Climate warming has almost no effect on the start date of the season in winter, but it significantly shifts the boundaries of the season end in spring. Using the autoregressive-integrated-moving average modeling (ARIMA) models, we demonstrated that, in the near future, the trends of the gradual shortening of the logging season will hold for the most part of the considered stations. The most pronounced effect is observed for the Achinsk station, where the logging season will shorten from 148.4 ± 17.3 days during the historical sample (1966–2018) to 136.2 ± 30 days in 2028, which reflects global warming trend patterns. From an economic perspective, a shorter duration of the logging season means fewer wood stocks available for cutting, which would impact the ability of companies to enact their logging plans and lead them to suffer losses in the future. To avoid losses, Siberian forest firms will have to adapt to these changes by redefining their economic strategies in terms of intensifying logging operations.

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