2017
DOI: 10.1038/sdata.2017.76
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A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations

Abstract: Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations… Show more

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Cited by 96 publications
(130 citation statements)
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“…Spatially extrapolated instrumental datasets show that the summer of 1816 was characterised by an anomalous low pressure system centred over Northern Europe (figure 2(a)), with Central European mean values among the most extreme since at least 1780 (figure S1d and Luterbacher et al 2002, Küttel et al 2010, Franke et al 2017. The anomalously cold and wet conditions are accordingly centred over western and central Europe and southern Scandinavia.…”
Section: Resultsmentioning
confidence: 96%
“…Spatially extrapolated instrumental datasets show that the summer of 1816 was characterised by an anomalous low pressure system centred over Northern Europe (figure 2(a)), with Central European mean values among the most extreme since at least 1780 (figure S1d and Luterbacher et al 2002, Küttel et al 2010, Franke et al 2017. The anomalously cold and wet conditions are accordingly centred over western and central Europe and southern Scandinavia.…”
Section: Resultsmentioning
confidence: 96%
“…December–February 1783–1784 European surface temperature anomalies (K) for (a) 400‐year Ensemble Kalman Filter reanalysis (Franke et al, ), (b) Laki ensemble average, and (c) noLaki ensemble average. Anomalies are calculated with respect to the 5 years before the eruption.…”
Section: Resultsmentioning
confidence: 99%
“…Difference in standard deviation (%) of temperature for (a) June–August (JJA) and (b) December–February (DJF) for the 400‐year Ensemble Kalman Filter (EKF400) reanalysis (Franke et al, ) between 1751–1800 and 1951–2000 for HadCRUT4 (Morice et al, ). Blue shading indicates that variability in the 1751–1799 period is underestimated compared to the 1951–1999 period.…”
Section: Resultsmentioning
confidence: 99%
“…For the analysis of atmospheric circulation during the 19th and 20th century, we use the reconstruction EKF400 (Reconstruction by Ensemble Kalman Fitting over 400 years; Franke et al, 2017a). This global, three-dimensional reconstruction is based on an off-line data assimilation approach of early instrumental, documentary, and proxy data into an ensemble of climate model simulations.…”
Section: Climate Model Simulations and Reconstructionsmentioning
confidence: 99%