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1. Introduction The adequate simulation of internal climate variability is vital for efforts involving historical attribution ( Santer et al. 2009 ; Stott et al. 2010 ; Schurer et al. 2013 ; Imbers et al. 2014 ; Deser et al. 2016 ; Wallace et al. 2016 ; McKinnon and Deser 2018 ), seasonal and decadal prediction ( Robertson et al. 2015 ; Thoma et al. 2015 ; Meehl et al. 2016 ; Vitart et al. 2017 ; Simpson et al. 2019 ), and multidecadal projection ( Deser et al. 2012a , 2014 , 2017b
1. Introduction The adequate simulation of internal climate variability is vital for efforts involving historical attribution ( Santer et al. 2009 ; Stott et al. 2010 ; Schurer et al. 2013 ; Imbers et al. 2014 ; Deser et al. 2016 ; Wallace et al. 2016 ; McKinnon and Deser 2018 ), seasonal and decadal prediction ( Robertson et al. 2015 ; Thoma et al. 2015 ; Meehl et al. 2016 ; Vitart et al. 2017 ; Simpson et al. 2019 ), and multidecadal projection ( Deser et al. 2012a , 2014 , 2017b
observed tropical widening. However, the ensemble mean, which represents the forced signal, is significantly smaller than observed at 0.1°–0.2° decade −1 ( Johanson and Fu 2009 ; Hu et al. 2013 ; Allen et al. 2014 ; Quan et al. 2014 ; Nguyen et al. 2015 ; Tao et al. 2016 ). Recent papers suggest that this underestimation is not necessarily due to model shortcomings but may be related to the real-world timing of unforced, natural climate variability (which models will only reproduce by chance
observed tropical widening. However, the ensemble mean, which represents the forced signal, is significantly smaller than observed at 0.1°–0.2° decade −1 ( Johanson and Fu 2009 ; Hu et al. 2013 ; Allen et al. 2014 ; Quan et al. 2014 ; Nguyen et al. 2015 ; Tao et al. 2016 ). Recent papers suggest that this underestimation is not necessarily due to model shortcomings but may be related to the real-world timing of unforced, natural climate variability (which models will only reproduce by chance
1. Introduction Extreme weather events, such as floods or heat waves, have great societal impact. However, changes in the probability of extreme events are more difficult to diagnose and understand than changes in mean climate. Apart from the greater statistical sampling variability in rare events, other difficulties are that high-resolution temporal data and more sophisticated methods are needed to analyze changes. A need for data that reside on the moderately extreme side of climate phenomena
1. Introduction Extreme weather events, such as floods or heat waves, have great societal impact. However, changes in the probability of extreme events are more difficult to diagnose and understand than changes in mean climate. Apart from the greater statistical sampling variability in rare events, other difficulties are that high-resolution temporal data and more sophisticated methods are needed to analyze changes. A need for data that reside on the moderately extreme side of climate phenomena
and the Aleutian low, dominate weather and climate patterns across California over the course of the seasonal cycle. In addition, the diverse geography of the state—complex topography, maritime influence, and time-varying land use patterns—contributes to an additional set of spatial–temporal physical controls on regional climate variability ( Fig. 1 ). The climatic complexity within the state provides a suitable test bed for characterizing regional- and state-scale climate variability. California
and the Aleutian low, dominate weather and climate patterns across California over the course of the seasonal cycle. In addition, the diverse geography of the state—complex topography, maritime influence, and time-varying land use patterns—contributes to an additional set of spatial–temporal physical controls on regional climate variability ( Fig. 1 ). The climatic complexity within the state provides a suitable test bed for characterizing regional- and state-scale climate variability. California
1. Introduction On submillennial time scales, internal natural variability is a substantial component of Earth's climate variability. The nature (amplitude, frequency) of unforced climate variability is tied to the mean state of the climate. This conclusion is supported, for example, by modern observational evidence that El Niño events have intensified in the central Pacific as a result of climate change ( Lee and McPhaden 2010 ), paleoclimate evidence suggesting that the magnitude and
1. Introduction On submillennial time scales, internal natural variability is a substantial component of Earth's climate variability. The nature (amplitude, frequency) of unforced climate variability is tied to the mean state of the climate. This conclusion is supported, for example, by modern observational evidence that El Niño events have intensified in the central Pacific as a result of climate change ( Lee and McPhaden 2010 ), paleoclimate evidence suggesting that the magnitude and
1. Introduction Nonuniformity in the global warming trend is usually attributed to corresponding nonuniformities in the external forcing ( Crowley 2000 ). A complementary hypothesis involves multidecadal climate oscillations affecting the rate of global temperature change ( Folland et al. 1986 ; Mann and Park 1994 ; Schlesinger and Ramankutty 1994 ); a possible mechanism for this variability is associated with intrinsic dynamics of the oceanic thermohaline circulation (THC; Delworth and Mann
1. Introduction Nonuniformity in the global warming trend is usually attributed to corresponding nonuniformities in the external forcing ( Crowley 2000 ). A complementary hypothesis involves multidecadal climate oscillations affecting the rate of global temperature change ( Folland et al. 1986 ; Mann and Park 1994 ; Schlesinger and Ramankutty 1994 ); a possible mechanism for this variability is associated with intrinsic dynamics of the oceanic thermohaline circulation (THC; Delworth and Mann
century a more systematic search for relations between physical conditions and different aspects of fish stocks was initiated in Norway by Helland-Hansen and Nansen (1909) . It is now understood that marine ecosystems change on a variety of time scales, from seasonal to centennial and longer. Many of these time scales are forced by atmospheric and climate-related processes, and therefore it is well understood by marine scientists that climate variability is a strong driver of changes in fish
century a more systematic search for relations between physical conditions and different aspects of fish stocks was initiated in Norway by Helland-Hansen and Nansen (1909) . It is now understood that marine ecosystems change on a variety of time scales, from seasonal to centennial and longer. Many of these time scales are forced by atmospheric and climate-related processes, and therefore it is well understood by marine scientists that climate variability is a strong driver of changes in fish
centennial variability among the temperature reconstructions ( Jones et al. 2009 ). Additionally, the geographic coverage of available proxy records decreases back in time and is less extensive for the first half of the last millennium and earlier. Numerical climate models provide a means of separating the changes associated with the external forcings of the last millennium from those reflecting internal variability. Models of varying complexity, from energy balance models ( Crowley et al. 2003 ) to
centennial variability among the temperature reconstructions ( Jones et al. 2009 ). Additionally, the geographic coverage of available proxy records decreases back in time and is less extensive for the first half of the last millennium and earlier. Numerical climate models provide a means of separating the changes associated with the external forcings of the last millennium from those reflecting internal variability. Models of varying complexity, from energy balance models ( Crowley et al. 2003 ) to
within the climate division implies that the climate division values properly represent the temporal precipitation variability within the climate division. For comparison, CDP values were recomputed using the standard post-1930 NCDC method but withholding single USHCN stations. The FNEP method had a higher overall correlation for each period examined ( Table 5 ) when averaged among the 337 USHCN stations (1 in each climate division, if available) used in this test. The performance of the standard CDP
within the climate division implies that the climate division values properly represent the temporal precipitation variability within the climate division. For comparison, CDP values were recomputed using the standard post-1930 NCDC method but withholding single USHCN stations. The FNEP method had a higher overall correlation for each period examined ( Table 5 ) when averaged among the 337 USHCN stations (1 in each climate division, if available) used in this test. The performance of the standard CDP
averaged over the period 1991–2010 presented as anomalies relative to the period 1971–90. (b) The corresponding zonal-mean temperature anomalies. While the Northern Hemisphere experienced a strong warming during the recent decades, the Southern Hemisphere warmed only little. The global-average SST difference between the two time periods amounts to 0.2°C. Climate variability can be either generated internally by interactions within or between the individual climate system components (e.g., atmosphere
averaged over the period 1991–2010 presented as anomalies relative to the period 1971–90. (b) The corresponding zonal-mean temperature anomalies. While the Northern Hemisphere experienced a strong warming during the recent decades, the Southern Hemisphere warmed only little. The global-average SST difference between the two time periods amounts to 0.2°C. Climate variability can be either generated internally by interactions within or between the individual climate system components (e.g., atmosphere
