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Torben Schmith

Abstract

The performance of a statistical downscaling model is usually evaluated for its ability to explain a large fraction of predictand variance. In this note, it is shown that although this fraction may be high, the longest time scales, including trends, may not be explained by the model. This implies that the model is nonstationary over the training period of the model, and it questions the basic stationarity assumption of statistical downscaling. This is exemplified by using a simple regression model for downscaling European precipitation and surface temperature where appropriate Monte Carlo–based field significance tests are developed, taking into account the intercorrelation between predictand series. Based on this test, it is concluded that care is needed in selecting predictors to avoid this form of nonstationarity. Even though this is illustrated for a simple regression-type statistical downscaling model, the main conclusions may also be valid for more complicated models.

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Torben Schmith and Carsten Hansen

Abstract

Historical observations of multiyear ice, called “storis,” in the southwest Greenland waters exist from the period 1820–2000, obtained from ship logbooks and ice charts. It is argued that this ice originates in the Arctic Ocean and has traveled via the Fram Strait, southward along the Greenland coast in the East Greenland Current, and around the southern tip of Greenland. Therefore, it is hypothesized that these observations can be used as “proxies” for reconstructing the Fram Strait ice export on an annual basis. An index describing the storis extent is extracted from the observations and a linear statistical model formulated relating this index to the Fram Strait ice export. The model is calibrated using ice export values from a hindcast study with a coupled ocean–ice model over the period 1949–98. Subsequently, the model is used to reconstruct the Fram Strait annual ice export in the period 1820–2000. The model has significant skill, calculated on independent data.

Based on this reconstruction, it is discussed how time periods with large and small ice export on multidecadal timescales coincide with time periods of cold and warm North Atlantic sea surface temperatures reported by others. This implies that trend studies based on satellite observations should be regarded with some care, since the time period of satellite observations, the last decades, where a particularly strong negative trend is observed in the ice export, is preceded by a time period with a positive trend. The occurrence of “great salinity anomalies” (GSAs) is also connected to the multidecadal variability. The GSAs observed in Greenland waters around 1968–70 and 1980–82 both occurred when the general level of ice export was high. Prior to these there was a long period with generally low ice export and no GSAs, but during an epoch around the turn of the nineteenth century several GSAs occurred. Finally, it is found that the correlation between the Fram Strait ice export and the North Atlantic Oscillation (NAO) index has alternating intervals of significant and nonsignificant correlation throughout the period.

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Torben Schmith, Søren Johansen, and Peter Thejll

Abstract

Global sea level rise is widely understood as a consequence of thermal expansion and the melting of glaciers and land-based ice caps. Because of the lack of representation of ice-sheet dynamics in present-day physically based climate models, semiempirical models have been applied as an alternative for projecting future sea levels. There are, however, potential pitfalls in this because of the trending nature of the time series. A statistical method called cointegration analysis that is capable of handling such peculiarities is applied to observed global sea level and land–ocean surface temperature. The authors find a relationship between sea level and temperature and find that temperature causally depends on the sea level, which can be understood as a consequence of the large heat capacity of the ocean. They further find that the warming episode in the 1940s is exceptional in the sense that sea level and warming deviate from the expected relationship. This suggests that this warming episode is mainly due to internal dynamics of the ocean rather than external radiative forcing. On the other hand, the present warming follows the expected relationship, suggesting that it is mainly due to radiative forcing. In a second step, the total radiative forcing is used as an explanatory variable, but it is unexpectedly found that the sea level does not depend on the forcing. The authors hypothesize that this is due to a long adjustment time scale of the ocean and show that the number of years of data needed to build statistical models that have the relationship expected from physics exceeds what is currently available by a factor of almost 10.

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Torben Schmith, Shuting Yang, Emily Gleeson, and Tido Semmler

Abstract

The surface of the world’s oceans has been warming since the beginning of industrialization. In addition to this, multidecadal sea surface temperature (SST) variations of internal origin exist. Evidence suggests that the North Atlantic Ocean exhibits the strongest multidecadal SST variations and that these variations are connected to the overturning circulation.

This work investigates the extent to which these internal multidecadal variations have contributed to enhancing or diminishing the trend induced by the external radiative forcing, globally and in the North Atlantic. A model study is carried out wherein the analyses of a long control simulation with constant radiative forcing at preindustrial level and of an ensemble of simulations with historical forcing from 1850 until 2005 are combined. First, it is noted that global SST trends calculated from the different historical simulations are similar, while there is a large disagreement between the North Atlantic SST trends. Then the control simulation is analyzed, where a relationship between SST anomalies and anomalies in the Atlantic meridional overturning circulation (AMOC) for multidecadal and longer time scales is identified. This relationship enables the extraction of the AMOC-related SST variability from each individual member of the ensemble of historical simulations and then the calculation of the SST trends with the AMOC-related variability excluded. For the global SST trends this causes only a little difference while SST trends with AMOC-related variability excluded for the North Atlantic show closer agreement than with the AMOC-related variability included. From this it is concluded that AMOC variability has contributed significantly to North Atlantic SST trends since the mid nineteenth century.

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EC-Earth

A Seamless Earth-System Prediction Approach in Action

Wilco Hazeleger, Camiel Severijns, Tido Semmler, Simona Ştefănescu, Shuting Yang, Xueli Wang, Klaus Wyser, Emanuel Dutra, José M. Baldasano, Richard Bintanja, Philippe Bougeault, Rodrigo Caballero, Annica M. L. Ekman, Jens H. Christensen, Bart van den Hurk, Pedro Jimenez, Colin Jones, Per Kållberg, Torben Koenigk, Ray McGrath, Pedro Miranda, Twan van Noije, Tim Palmer, José A. Parodi, Torben Schmith, Frank Selten, Trude Storelvmo, Andreas Sterl, Honoré Tapamo, Martin Vancoppenolle, Pedro Viterbo, and Ulrika Willén
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