Application of the Singular Spectrum Analysis Technique to Study the Recent Hiatus on the Global Surface Temperature Record

Land surface temperature (LST) is a basic parameter in energy exchange between the land and the atmosphere, and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. Time series of satellite LST data have usually deficient, missing, and unacceptable data caused by the presence of clouds in images, the presence of dust in the atmosphere, and sensor failure. In this study, the singular spectrum analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of the Moderate Resolution Imaging Spectroradiometer (MODIS) with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran, Turkmenistan, and the Caspian Sea. In this study, MODIS LST products (MOD11A1) were used during 2015 with approximately 1 km × 1 km spatial resolution and day/night LST data (daily temporal resolution). On average, the data have 36.37% gaps in each pixel profile with 730 day/night LST data. The results of the SSA algorithm in the reconstruction of LST images indicated a root mean square error (RMSE) of 2.95 Kelvin (K) between the original and reconstructed LST time series data in the study region. In general, the findings showed that the SSA algorithm using spatio-temporal interpolation can be effectively used to resolve the problem of missing data caused by cloud cover.

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Application of Singular Spectrum Analysis for Investigating Chaos in Sea Surface Temperature

Pure and Applied Geophysics ◽

10.1007/s00024-019-02140-4 ◽

2019 ◽

Vol 176 (8) ◽

pp. 3769-3786 ◽

Cited By ~ 2

Author(s):

Swarnali Majumder ◽

Partha Pratim Kanjilal

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Sea Surface ◽

Singular Spectrum

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A Novel Spectral-Spatial Singular Spectrum Analysis Technique for Near Real-Time In Situ Feature Extraction in Hyperspectral Imaging

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2020.2992230 ◽

2020 ◽

Vol 13 ◽

pp. 2214-2225 ◽

Cited By ~ 1

Author(s):

Hang Fu ◽

Genyun Sun ◽

Jaime Zabalza ◽

Aizhu Zhang ◽

Jinchang Ren ◽

...

Keyword(s):

Feature Extraction ◽

Real Time ◽

Hyperspectral Imaging ◽

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Singular Spectrum ◽

Analysis Technique

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Application of the Singular Spectrum Analysis Technique for Phase Constant Determination of Symmetric Reflecting and Nonreflecting Lines

IEEE Transactions on Electromagnetic Compatibility ◽

10.1109/temc.2019.2945043 ◽

2020 ◽

Vol 62 (2) ◽

pp. 571-579

Author(s):

Ugur Cem Hasar ◽

Taner Ince ◽

Musa Bute

Keyword(s):

Spectrum Analysis ◽

Singular Spectrum Analysis ◽

Singular Spectrum ◽

Constant Determination ◽

Analysis Technique ◽

Phase Constant

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Reconstruction of the Cloud-Free Time Series Satellite Observations of Land Surface Temperature (LST) Using Singular Spectrum Analysis (SSA)

10.20944/preprints201806.0327.v1 ◽

2018 ◽

Author(s):

Hamid Reza Ghafarian Malamiri ◽

Iman Rousta ◽

Haraldur Olafsson ◽

Hadi Zare ◽

Hao Zhang

Keyword(s):

Time Series ◽

Missing Data ◽

Surface Temperature ◽

Land Surface Temperature ◽

Spectrum Analysis ◽

Land Surface ◽

Cloud Cover ◽

Singular Spectrum Analysis ◽

Series Data ◽

Singular Spectrum

Land Surface Temperature (LST) is a basic parameter in energy exchange between the land and atmosphere and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. LST time series data have usually deficient, missing and unacceptable data caused by the presence of clouds in images, presence of dust in atmosphere and sensor failure. In this study, Singular Spectrum Analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of MODIS with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran and Turkmenistan and Caspian Sea. In this study, MODIS LST sensor (MOD11A1) was used during 2015 with 1×1 Km spatial resolution and day/night LST data (daily temporal resolution). The results of the data quality showed that cloud cover caused 36.37% of missing data in the studied time series with 730 day/night LST images. Further, the results of SSA algorithm in reconstruction of LST images indicated the Root Mean Square Error (RMSE) of 2.95 K between the original and reconstructed data in LST time series in the study region. In general, the findings showed that SSA algorithm using spatio-temporal interpolation in LST time series can be effectively used to resolve the problem of missing data caused by cloud cover.

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Reconstruction of the Interannual to Millennial Scale Patterns of the Global Surface Temperature

Atmosphere ◽

10.3390/atmos12020147 ◽

2021 ◽

Vol 12 (2) ◽

pp. 147

Author(s):

Nicola Scafetta

Keyword(s):

Surface Temperature ◽

Climate Sensitivity ◽

Empirical Models ◽

Natural Variability ◽

Global Temperature ◽

Temperature Record ◽

Equilibrium Climate Sensitivity ◽

Global Surface Temperature ◽

Semi Empirical ◽

Global Surface

Climate changes are due to anthropogenic factors, volcano eruptions and the natural variability of the Earth’s system. Herein the natural variability of the global surface temperature is modeled using a set of harmonics spanning from the inter-annual to the millennial scales. The model is supported by the following considerations: (1) power spectrum evaluations show 11 spectral peaks (from the sub-decadal to the multi-decadal scales) above the 99% confidence level of the known temperature uncertainty; (2) spectral coherence analysis between the independent global surface temperature periods 1861–1937 and 1937–2013 highlights at least eight common frequencies between 2- and 20-year periods; (3) paleoclimatic temperature reconstructions during the Holocene present secular to millennial oscillations. The millennial oscillation was responsible for the cooling observed from the Medieval Warm Period (900–1400) to the Little Ice Age (1400–1800) and, on average, could have caused about 50% of the warming observed since 1850. The finding implies an equilibrium climate sensitivity of 1.0–2.3 °C for CO2 doubling likely centered around 1.5 °C. This low sensitivity to radiative forcing agrees with the conclusions of recent studies. Semi-empirical models since 1000 A.D. are developed using 13 identified harmonics (representing the natural variability of the climate system) and a climatic function derived from the Coupled Model Intercomparison Project 5 (CMIP5) model ensemble mean simulation (representing the mean greenhouse gas—GHG, aerosol, and volcano temperature contributions) scaled under the assumption of an equilibrium climate sensitivity of 1.5 °C. The harmonic model is evaluated using temperature data from 1850 to 2013 to test its ability to predict the major temperature patterns observed in the record from 2014 to 2020. In the short, medium, and long time scales the semi-empirical models predict: (1) temperature maxima in 2015–2016 and 2020, which is confirmed by the 2014–2020 global temperature record; (2) a relatively steady global temperature from 2000 to 2030–2040; (3) a 2000–2100 mean projected global warming of about 1 °C. The semi-empirical model reconstructs accurately the historical surface temperature record since 1850 and hindcasts mean surface temperature proxy reconstructions since the medieval period better than the model simulation that is unable to simulate the Medieval Warm Period.

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