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Geographical distribution of the regression coefficients (similar to spatial correlation) for ENSO, GHGs, and GCR

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posted on 23.09.2013 by Rasmus E Benestad

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 R2-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).


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.