Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR
Figure 1. Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR. The signature of ENSO in the eastern equatorial region of the Pacific and the Arctic amplification are both prominent features. The main feature associated with GCR was a cold...
Main Author: | |
---|---|
Format: | Still Image |
Language: | unknown |
Published: |
IOP Publishing
2013
|
Subjects: | |
Online Access: | https://dx.doi.org/10.6084/m9.figshare.1011926.v1 https://iop.figshare.com/articles/figure/_Geographical_distribution_of_the_regression_coefficients_similar_to_spatial_correlation_for_ENSO_GH/1011926/1 |
id |
ftdatacite:10.6084/m9.figshare.1011926.v1 |
---|---|
record_format |
openpolar |
spelling |
ftdatacite:10.6084/m9.figshare.1011926.v1 2023-05-15T15:15:51+02:00 Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR Benestad, Rasmus E 2013 https://dx.doi.org/10.6084/m9.figshare.1011926.v1 https://iop.figshare.com/articles/figure/_Geographical_distribution_of_the_regression_coefficients_similar_to_spatial_correlation_for_ENSO_GH/1011926/1 unknown IOP Publishing https://dx.doi.org/10.6084/m9.figshare.1011926 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Environmental Science Image Figure graphic ImageObject 2013 ftdatacite https://doi.org/10.6084/m9.figshare.1011926.v1 https://doi.org/10.6084/m9.figshare.1011926 2021-11-05T12:55:41Z Figure 1. Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR. The signature of ENSO in the eastern equatorial region of the Pacific and the Arctic amplification are both prominent features. The main feature associated with GCR was a cold anomaly over eastern Europe. The R 2 -value for the total patterns was 0.45, and estimated as the sum of the products between the multiple regressions for each EOF: {\sum }_{i}{R}_{i}^{2}{d}_{i}^{2}/D where D={\sum }_{j}{d}_{j}^{2}. The physical unit of colour scale is K per standard deviation (annual mean). Abstract Variations in the annual mean of the galactic cosmic ray flux (GCR) are compared with annual variations in the most common meteorological variables: temperature, mean sea-level barometric pressure, and precipitation statistics. A multiple regression analysis was used to explore the potential for a GCR response on timescales longer than a year and to identify 'fingerprint' patterns in time and space associated with GCR as well as greenhouse gas (GHG) concentrations and the El Niño–Southern Oscillation (ENSO). The response pattern associated with GCR consisted of a negative temperature anomaly that was limited to parts of eastern Europe, and a weak anomaly in the sea-level pressure (SLP), but coincided with higher pressure over the Norwegian Sea. It had a similarity to the North Atlantic Oscillation (NAO) in the northern hemisphere and a wave train in the southern hemisphere. A set of Monte Carlo simulations nevertheless indicated that the weak amplitude of the global mean temperature response associated with GCR could easily be due to chance ( p -value = 0.6), and there has been no trend in the GCR. Hence, there is little empirical evidence that links GCR to the recent global warming. Still Image Arctic Global warming North Atlantic North Atlantic oscillation Norwegian Sea DataCite Metadata Store (German National Library of Science and Technology) Arctic Norwegian Sea Pacific |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
Environmental Science |
spellingShingle |
Environmental Science Benestad, Rasmus E Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
topic_facet |
Environmental Science |
description |
Figure 1. Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR. The signature of ENSO in the eastern equatorial region of the Pacific and the Arctic amplification are both prominent features. The main feature associated with GCR was a cold anomaly over eastern Europe. The R 2 -value for the total patterns was 0.45, and estimated as the sum of the products between the multiple regressions for each EOF: {\sum }_{i}{R}_{i}^{2}{d}_{i}^{2}/D where D={\sum }_{j}{d}_{j}^{2}. The physical unit of colour scale is K per standard deviation (annual mean). Abstract Variations in the annual mean of the galactic cosmic ray flux (GCR) are compared with annual variations in the most common meteorological variables: temperature, mean sea-level barometric pressure, and precipitation statistics. A multiple regression analysis was used to explore the potential for a GCR response on timescales longer than a year and to identify 'fingerprint' patterns in time and space associated with GCR as well as greenhouse gas (GHG) concentrations and the El Niño–Southern Oscillation (ENSO). The response pattern associated with GCR consisted of a negative temperature anomaly that was limited to parts of eastern Europe, and a weak anomaly in the sea-level pressure (SLP), but coincided with higher pressure over the Norwegian Sea. It had a similarity to the North Atlantic Oscillation (NAO) in the northern hemisphere and a wave train in the southern hemisphere. A set of Monte Carlo simulations nevertheless indicated that the weak amplitude of the global mean temperature response associated with GCR could easily be due to chance ( p -value = 0.6), and there has been no trend in the GCR. Hence, there is little empirical evidence that links GCR to the recent global warming. |
format |
Still Image |
author |
Benestad, Rasmus E |
author_facet |
Benestad, Rasmus E |
author_sort |
Benestad, Rasmus E |
title |
Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
title_short |
Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
title_full |
Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
title_fullStr |
Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
title_full_unstemmed |
Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR |
title_sort |
geographical distribution of the regression coefficients (similar to spatial correlation) for enso, ghgs, and gcr |
publisher |
IOP Publishing |
publishDate |
2013 |
url |
https://dx.doi.org/10.6084/m9.figshare.1011926.v1 https://iop.figshare.com/articles/figure/_Geographical_distribution_of_the_regression_coefficients_similar_to_spatial_correlation_for_ENSO_GH/1011926/1 |
geographic |
Arctic Norwegian Sea Pacific |
geographic_facet |
Arctic Norwegian Sea Pacific |
genre |
Arctic Global warming North Atlantic North Atlantic oscillation Norwegian Sea |
genre_facet |
Arctic Global warming North Atlantic North Atlantic oscillation Norwegian Sea |
op_relation |
https://dx.doi.org/10.6084/m9.figshare.1011926 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.1011926.v1 https://doi.org/10.6084/m9.figshare.1011926 |
_version_ |
1766346186847944704 |