scholarly journals Restoration of Time-Spatial Scales in Global Temperature Data

2012 ◽  
Vol 01 (03) ◽  
pp. 154-163 ◽  
Author(s):  
Igor Zurbenko ◽  
Ming Luo
Keyword(s):  
Spatial Scales ◽  
Temperature Data ◽  
Global Temperature

scholarly journals Responding to the Need for Better Global Temperature Data

Eos ◽  
2013 ◽  
Vol 94 (6) ◽  
pp. 61-62 ◽  
Author(s):  
Jay Lawrimore ◽  
Jared Rennie ◽  
Peter Thorne
Keyword(s):  
Temperature Data ◽  
Global Temperature

Application of spectra-temporal analysis methods to detect common signals in length of day, global sea level rise, global temperature data, and ENSO indices

2020 ◽  
Author(s):  
Wieslaw Kosek
Keyword(s):  
Time Series ◽  
Sea Level Rise ◽  
Sea Level ◽  
Southern Oscillation ◽  
Temporal Analysis ◽  
Temperature Data ◽  
Global Temperature ◽  
Length Of Day ◽  
Global Sea Level ◽  
Enso Indices

<p>It is already well known that intra-seasonal oscillations in the Earth’s global temperature are driven by ENSO (El Niño Southern Oscillation) events. ENSO signal is also present in length of day and global sea level rise, because during El Niño the increase of the length of day and global sea level rise can be noticed. To detect common oscillations in length of day, global sea level rise, global temperature data and ENSO indices the wavelet-based semblance filtering method was used. This method, however, seeks the signals with a good phase agreement of oscillations in two time series thus, no phase agreement results in very small amplitudes of the common signals. The spectra-temporal semblance functions allow detecting the similarity of two time series in spectral bands in which the amplitudes and phases of the oscillations are consistent with each other. The amplitudes of oscillations in the considered data vary in time and in order to detect the signals with similar amplitude variations between pairs of time series the normalized Morlet wavelet transform (NMWT) and the combination of the Fourier transform bandpass filter with the Hilbert transform (FTBPF+HT) were used. These two methods enable computation of the instantaneous amplitudes and phases of oscillations in two real-valued time series. In order to detect oscillations with similar amplitude variations in two time series correlation coefficients between the amplitude variations as a function of oscillation frequencies were computed.</p>

Inference about trends in global temperature data

1992 ◽  
Vol 22 (3) ◽  
pp. 209-221 ◽  
Author(s):  
John W. Galbraith ◽  
Christopher Green
Keyword(s):  
Temperature Data ◽  
Global Temperature

Recent Climate Observations: Disagreement with Projections

2009 ◽  
Vol 20 (4) ◽  
pp. 595-596 ◽  
Author(s):  
David R.B. Stockwell
Keyword(s):  
Linear Trend ◽  
Temperature Data ◽  
Climate System ◽  
Global Temperature ◽  
Recent Climate ◽  
Non Linear

The non-linear trend in Rahmstorf et al. [2007] is updated with recent global temperature data. The evidence does not support the basis for their claim that the sensitivity of the climate system has been underestimated.

scholarly journals Temperature Anomalies, Long Memory, and Aggregation

Econometrics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 9
Author(s):  
J. Eduardo Vera-Valdés
Keyword(s):  
Long Memory ◽  
Formal Analysis ◽  
Temperature Data ◽  
Global Temperature ◽  
Cross Sectional ◽  
Radius Of Influence ◽  
Bandwidth Parameter ◽  
The Tropics ◽  
Individual Grid ◽  
Global Heating

Econometric studies for global heating have typically used regional or global temperature averages to study its long memory properties. One typical explanation behind the long memory properties of temperature averages is cross-sectional aggregation. Nonetheless, formal analysis regarding the effect that aggregation has on the long memory dynamics of temperature data has been missing. Thus, this paper studies the long memory properties of individual grid temperatures and compares them against the long memory dynamics of global and regional averages. Our results show that the long memory parameters in individual grid observations are smaller than those from regional averages. Global and regional long memory estimates are greatly affected by temperature measurements at the Tropics, where the data is less reliable. Thus, this paper supports the notion that aggregation may be exacerbating the long memory estimated in regional and global temperature data. The results are robust to the bandwidth parameter, limit for station radius of influence, and sampling frequency.

scholarly journals Estimate of climate sensitivity from carbonate microfossils dated near the Eocene-Oligocene global cooling

2012 ◽  
Vol 8 (5) ◽  
pp. 4923-4939
Author(s):  
M. W. Asten
Keyword(s):  
Climate Sensitivity ◽  
Deep Ocean ◽  
Temperature Data ◽  
Global Temperature ◽  
Published Data ◽  
Sea Temperature ◽  
Central Value ◽  
Partial Pressure Of Co2 ◽  
Global Cooling ◽  
Temperature Proxy

Abstract. Climate sensitivity is a crucial parameter in global temperature modelling. An estimate is made at the time 33.4 Ma using published high-resolution deep-sea temperature proxy obtained from foraminiferal δ18O records from DSDP site 744, combined with published data for atmospheric partial pressure of CO2 (pCO2) from carbonate microfossils, where δ11B provides a proxy for pCO2. The pCO2 data shows a pCO2 decrease accompanying the major cooling event of about 4 °C from greenhouse conditions to icecap conditions following the Eocene-Oligocene boundary (33.7 My). During the cooling pCO2 fell from 1150 to 770 ppmv. The cooling event was followed by a rapid and huge increase in pCO2 back to 1130 ppmv in the space of 50 000 yr. The large pCO2 increase was accompanied by a small deep-ocean temperature increase estimated as 0.59 ± 0.063 °C. Climate sensitivity estimated from the latter is 1.1 ± 0.4 °C (66% confidence) compared with the IPCC central value of 3 °C. The post Eocene-Oligocene transition (33.4 Ma) value of 1.1 °C obtained here is lower than those published from Holocene and Pleistocene glaciation-related temperature data (800 Kya to present) but is of similar order to sensitivity estimates published from satellite observations of tropospheric and sea-surface temperature variations. The value of 1.1 °C is grossly different from estimates up to 9 °C published from paleo-temperature studies of Pliocene (3 to 4 Mya) age sediments. The range of apparent climate sensitivity values available from paleo-temperature data suggests that either feedback mechanisms vary widely for the different measurement conditions, or additional factors beyond currently used feedbacks are affecting global temperature-CO2 relationships.

scholarly journals Spatial trend analysis of gridded temperature data at varying spatial scales

2020 ◽  
Vol 6 (1) ◽  
pp. 1-12
Author(s):  
Ola Haug ◽  
Thordis L. Thorarinsdottir ◽  
Sigrunn H. Sørbye ◽  
Christian L. E. Franzke
Keyword(s):  
Spatial Scales ◽  
Gaussian Random Field ◽  
Summer Season ◽  
Temperature Data ◽  
Hierarchical Bayesian Model ◽  
Spatial Smoothing ◽  
Spatial Correlations ◽  
Spatial Trend ◽  
Trend Coefficient

Abstract. Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space–time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modelled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all subregions.

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