Time Scales and Sources of European Temperature Variability

Årthun, Marius; Kolstad, Erik W.; Eldevik, Tor; Keenlyside, Noel

doi:10.1002/2018gl077401

Cited by 30 publications

(13 citation statements)

References 44 publications

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“…The Nordic Seas, comprising the Norwegian, Greenland, and Iceland Seas, are a critical region at the northern extremity of the Atlantic Meridional Overturning Circulation (AMOC). Warm and saline Atlantic Water flowing northward across the Greenland-Scotland Ridge into the Nordic Seas releases heat to the atmosphere and helps maintain the temperate climate of northwest Europe 1 , 2 . Transformation to colder, fresher, and denser water masses occurs both in the interior basins and within the boundary current system around the Nordic Seas 3 – 5 .…”

Section: Introductionmentioning

confidence: 99%

The Iceland-Faroe Slope Jet: a conduit for dense water toward the Faroe Bank Channel overflow

et al. 2020

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Dense water from the Nordic Seas passes through the Faroe Bank Channel and supplies the lower limb of the Atlantic Meridional Overturning Circulation, a critical component of the climate system. Yet, the upstream pathways of this water are not fully known. Here we present evidence of a previously unrecognised deep current following the slope from Iceland toward the Faroe Bank Channel using high-resolution, synoptic shipboard observations and long-term measurements north of the Faroe Islands. The bulk of the volume transport of the current, named the Iceland-Faroe Slope Jet (IFSJ), is relatively uniform in hydrographic properties, very similar to the North Icelandic Jet flowing westward along the slope north of Iceland toward Denmark Strait. This suggests a common source for the two major overflows across the Greenland-Scotland Ridge. The IFSJ can account for approximately half of the total overflow transport through the Faroe Bank Channel, thus constituting a significant component of the overturning circulation in the Nordic Seas.

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Section: Introductionmentioning

confidence: 99%

The Iceland-Faroe Slope Jet: a conduit for dense water toward the Faroe Bank Channel overflow

et al. 2020

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“…In this ;150-yr observational period, North Atlantic SSTs exhibit significant variability on time scales of decades and longer; this is often referred to as the Atlantic multidecadal variability (AMV) or Atlantic multidecadal oscillation (e.g., Kushnir 1994;Enfield et al 2001;Deser et al 2010). The AMV has been shown to influence decadal climate in the surrounding continental regions, including North America (McCabe et al 2004;Sutton and Hodson 2005;Knight et al 2006;Ting et al 2009;Hu and Feng 2012;Ruprich-Robert et al 2018), Europe (Sutton and Hodson 2005;Knight et al 2006;Sutton and Dong 2012;O'Reilly et al 2017;Ruprich-Robert et al 2017;Qasmi et al 2017;Ghosh et al 2017;Årthun et al 2018), and the Sahel (Palmer 1986;Folland et al 1986;Zhang and Delworth 2006;Martin et al 2014;Martin and Thorncroft 2014). The AMV has also been linked to decadal variability in hurricane frequency (e.g., Zhang and Delworth 2006;Yan et al 2017) and Arctic sea ice (e.g., Zhang 2015).…”

Section: Introductionmentioning

confidence: 99%

Assessing External and Internal Sources of Atlantic Multidecadal Variability Using Models, Proxy Data, and Early Instrumental Indices

2019

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Atlantic multidecadal variability (AMV) of sea surface temperature exhibits an important influence on the climate of surrounding continents. It remains unclear, however, the extent to which AMV is due to internal climate variability (e.g., ocean circulation variability) or changes in external forcing (e.g., volcanic/anthropogenic aerosols or greenhouse gases). Here, the sources of AMV are examined over a 340-yr period using proxy indices, instrumental data, and output from the Last Millennium Ensemble (LME) simulation. The proxy AMV closely follows the accumulated atmospheric forcing from the instrumental North Atlantic Oscillation (NAO) reconstruction (r = 0.65)—an “internal” source of AMV. This result provides strong observational evidence that much of the AMV is generated through the oceanic response to atmospheric circulation forcing, as previously demonstrated in targeted modeling studies. In the LME there is a substantial externally forced AMV component, which exhibits a modest but significant correlation with the proxy AMV (i.e., r = 0.37), implying that at least 13% of the AMV is externally forced. In the LME simulations, however, the AMV response to accumulated NAO forcing is weaker than in the proxy/observational datasets. This weak response is possibly related to the decadal NAO variability, which is substantially weaker in the LME than in observations. The externally forced component in the proxy AMV is also related to the accumulated NAO forcing, unlike in the LME. This indicates that the external forcing is likely influencing the AMV through different mechanistic pathways: via changes in radiative forcing in the LME and via changes in atmospheric circulation in the observational/proxy record.

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“…Changes to the mean climate are known to project onto patterns of stochastic or episodic variability, such as atmospheric circulation patterns (Hall et al, ; Holmes et al, ; Mahony & Cannon, ; Vogel et al, ; Zanchettin, ). For instance, the North Atlantic Oscillation (NAO) has been identified as a leading mode of observed wintertime temperature trends in Europe (Deser et al, ), pointing to a link between European variability and the NAO as (see also Årthun et al, ; Davini et al, ). Other recent examples include Xu et al (), who investigated variability in four climate factors in different regions of China for 1960–2013, and Hansen and Sato (), who compared changes in probability density functions (PDFs) of observed temperature over the 1950–2015 period between several regions.…”

Section: Introductionmentioning

confidence: 99%

“…One reason for this is that, generally, large amounts of data are needed to separate a forced signal from intrinsic variability. Efforts over recent years (Williams et al, ) have produced a variety of approaches to quantify variability and its influence on means and extremes (Årthun et al, ; Bador et al, ; Bathiany et al, ; Deser et al, ; King et al, ; Lejeune et al, ; Mahlstein et al, ; Schaller et al, ; Wills et al, ; Xu et al, ; Yu & Zhong, ). A tool that has recently become available and that is well‐suited for studying the links between unforced variability and forced changes is large ensemble simulations (LENS) or initial condition ensembles.…”

Section: Introductionmentioning

confidence: 99%

How Daily Temperature and Precipitation Distributions Evolve With Global Surface Temperature.

et al. 2019

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The climate is an aggregate of the mean and variability of a range of meteorological variables, notably temperature (T) and precipitation (P). While the impacts of an increase in global mean surface temperature (GMST) are commonly quantified through changes in regional means and extreme value distributions, a concurrent shift in the shapes of the distributions of daily T and P is arguably equally important. Here, we employ a 30-member ensemble of coupled climate model simulations (CESM1 LENS) to consistently quantify the changes of regionally and seasonally resolved probability density functions of daily T and P as function of GMST. Focusing on aggregate regions covering both populated and rural zones, we identify large regional and seasonal diversity in the probability density functions and quantify where CESM1 projects the most noticeable changes compared to the preindustrial era. As global temperature increases, Europe and the United States are projected to see a rapid reduction in wintertime cold days, and East Asia to experience a strong increase in intense summertime precipitation. Southern Africa may see a shift to a more intrinsically variable climate but with little change in mean properties. The sensitivities of Arctic and African intrinsic variability to GMST are found to be particularly high. Our results highlight the need to further quantify future changes to daily temperature and precipitation distributions as an integral part of preparing for the societal and ecological impacts of climate change and show how large ensemble simulations can be a useful tool for such research.Plain Language Summary The weather varies naturally from day to day and between regions, seasons, and years. Ecosystems and our society are both adapted to the average weather conditions of a given place and to how variable the temperature and rainfall amounts are around that average. As the global surface temperature changes, whether through natural cycles or human interference, so may this variability. In this paper, we investigate changes to the distributions of daily temperature and rainfall for different levels of surface temperature increase. By using a large set of simulations from the same climate model, we estimate their means and shapes, currently and in the near future. We find that in parts of Europe and the United States, wintertime cold days will disappear more rapidly with global warming than hot days increase, leading to a less variable climate state. In Africa and the Arctic, however, climate conditions will rapidly transition out of the range of preindustrial variability and into a climate state not yet experienced by modern society. We emphasize that in order to prepare for the impacts of climate change, we need information about changes to average properties and to extreme events and about the potential changes to daily variability itself.

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Time Scales and Sources of European Temperature Variability

Cited by 30 publications

References 44 publications

The Iceland-Faroe Slope Jet: a conduit for dense water toward the Faroe Bank Channel overflow

The Iceland-Faroe Slope Jet: a conduit for dense water toward the Faroe Bank Channel overflow

Assessing External and Internal Sources of Atlantic Multidecadal Variability Using Models, Proxy Data, and Early Instrumental Indices

How Daily Temperature and Precipitation Distributions Evolve With Global Surface Temperature.

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