The inﬂuence of North Atlantic atmospheric and...

INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 33: 862–880 (2013)Published online 30 March 2012 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/joc.3475

The influence of North Atlantic atmospheric and oceanicforcing effects on 1900–2010 Greenland summer

climate and ice melt/runoff

Edward Hanna,a* Julie M. Jones,a John Cappelen,b Sebastian H. Mernild,c Len Wood,d

Konrad Steffene and Philippe Huybrechtsf

a Department of Geography, University of Sheffield, Sheffield, UKb Danish Meteorological Institute, Copenhagen, Denmark

c Climate, Ocean and Sea Ice Modelling Group, Computational Physics and Methods, Los Alamos National Laboratory, Los Alamos, NM, USAd School of Earth, Ocean & Environmental Sciences, University of Plymouth, Plymouth, UK

e Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, USAf Earth System Sciences and Departement Geografie, Vrije Universiteit Brussel, Brussel, Belgium

ABSTRACT: Correlation analysis of Greenland coastal weather station temperatures against the North Atlantic Oscillation(NAO) and the Atlantic Multidecadal Oscillation (AMO) indices for the summer season (when Ice Sheet melt and runoffoccur) reveals significant temporal variations over the last 100 years, with periods of strongest correlations in the earlytwentieth century and during recent decades. During the mid-twentieth century, temperature changes at the stations arenot significantly correlated with these circulation indices. Greenland coastal summer temperatures and Greenland IceSheet (GrIS) runoff since the 1970s are more strongly correlated with the Greenland Blocking Index (GBI) than withthe NAO Index (NAOI), making the GBI a potentially useful predictor of ice-sheet mass balance changes. Our resultsshow that the changing strength of NAOI–temperature relationships found in boreal winter also extends to summer overGreenland. Greenland temperatures and GrIS runoff over the last 30–40 years are significantly correlated with AMOvariations, although they are more strongly correlated with GBI changes. GrIS melt extent is less significantly correlatedwith atmospheric and oceanic index changes than runoff, which we attribute to the latter being a more quantitative indexof Ice Sheet response to climate change. Moreover, the four recent warm summers of 2007–2010 are characterised byunprecedented high pressure (since at least 1948 – the start of the NCEP/NCAR reanalysis record) in the troposphericcolumn. Our results suggest complex and changing atmospheric forcing conditions that are not well captured using theNAO alone, and support theories of an oceanic influence on the recent increases in Greenland temperatures and GrISrunoff. Copyright 2012 Royal Meteorological Society

KEY WORDS Atlantic multidecadal oscillation; climate; global warming; Greenland; Greenland Blocking Index; NorthAtlantic Oscillation

Received 26 October 2011; Revised 27 February 2012; Accepted 28 February 2012

1. Introduction

Recent studies of Greenland Ice Sheet (GrIS) mass bal-ance changes have pointed to the possible role of awarming ocean in driving dynamical changes of severalmajor outlet glaciers observed during the last decade (e.g.Luckman et al., 2006; Holland et al., 2008; Hanna et al.,2009; Murray et al., 2010; Straneo et al., 2010, 2011).Luckman et al. (2006) hypothesised that warmer oceanwaters off of south-east Greenland might have causedthe synchronous retreat and acceleration of Helheim andKangerdlussuaq glaciers during the early 2000s. Hol-land et al. (2008) attributed the unprecedented retreat andacceleration of Jackobshavn Isbrae in the late 1990s to a

∗ Correspondence to: E. Hanna, Department of Geography, Universityof Sheffield, Winter Street, Sheffield S10 2TN, UK.E-mail: [email protected]

recently warmer Labrador Sea. Hanna et al. (2009) con-cluded that key outlet glacier changes on both sides ofGreenland were linked with high sea-surface tempera-ture anomalies. Murray et al. (2010) extended the aboveanalyses in their inference of a key interplay betweenthe East Greenland Coastal Current, and Greenland meltand iceberg discharge. Straneo et al. (2010, 2011) foundevident but complex links between the configuration andflow of different ocean water masses and east Greenlandglacier runoff and dynamics. These well-documentedrecent Greenland outlet glacier changes (Howat et al.,2011) may have been influenced not just by anthro-pogenic global warming but by natural climate variabilityin sea-surface temperatures in the North Atlantic, notablya positive phase of the Atlantic Multidecadal Oscilla-tion (AMO) since the 1990s (Howat et al., 2008). TheAMO is the leading low-frequency (multi-decadal) modeof sea-surface temperature (SST) variability across much

Copyright 2012 Royal Meteorological Society

FORCING OF 1900–2010 GREENLAND SUMMER CLIMATE AND ICE MELT/RUNOFF 863

of the North Atlantic (Arguez et al., 2009), apparentlybeing closely related to meridional overturning circula-tion variability (Msadek and Frankignoul, 2008), and hasbeen shown to be a likely influence on North Americanand western European multidecadal summertime climate(Sutton and Hodson, 2005; Knight et al., 2006), includingsouthern Greenland (Arguez et al., 2009).

Other studies have pointed to a key negative correlationbetween the North Atlantic Oscillation (NAO) mode ofatmospheric circulation variability (Hurrell, 1995; Hur-rell et al., 2003) and Greenland temperatures over thepast 50 years (Hanna and Cappelen, 2003), which weak-ened in favour of a hemispheric/global warming influ-ence since the 1990s (Hanna et al., 2008a). Woollingset al. (2010) recently argue that the NAO is a two-stateregime, with distinct physical differences, correspondingto blocked or zonal atmospheric flow over Greenland.They suggest that the frequency of Greenland blockingoccurrence largely determines NAO phase, and this maylimit the usefulness of many existing studies that typicallyassume a linear dependence between NAO and tempera-ture. According to this theory, negative NAO events areGreenland blocking episodes, and only negative NAOis highly related to – and a result of – the frequency ofGreenland blocking. However, there remains a pressingneed to interpret these different strands of evidence of keyforcings of Greenland climate and GrIS mass balance byconsidering more potential climatic forcing influences,in order to improve current understanding and predic-tive capability of ice-sheet response to ongoing climatechange.

Therefore here we carry out an analysis of surfaceair temperature data from coastal Greenland weatherstations, updated from that presented in Hanna et al.(2008a), alongside indices of North Atlantic atmosphericcirculation (NAO and Greenland Blocking Index – GBI,defined in next section) and SST data (AMO = proxyfor oceanic structure/circulation) over the past century.Coastal temperatures are analysed instead of interior ice-sheet temperatures (Greenland Climate Network (Steffen

et al. 2001) because the latter are only available for a rel-atively short time period since 1990. We investigate sta-tionary wave pattern anomalies in atmospheric circulationthat are related to the above climate indices, and relationsof the latter with GrIS simulated melt extent and modelledmeltwater runoff (seasonal net loss of meltwater fromthe ice sheet). Our main aim is to improve understandingof the changing relative influences of atmospheric andocean surface circulation changes on Greenland summerclimate – which is seasonally most important for melt-ing/mass loss of the GrIS (e.g. Hanna et al., 2002, 2005,2008a, 2009, 2011; Rignot et al., 2008), and hence forglobal sea-level change (Steffen et al., 2010).

2. Climate and ice-sheet datasets

Monthly surface air temperature data for four long-running Greenland (World Meteorological Organization,WMO) station series, 04 221 Ilulissat, 04 250 Nuuk,04 270 Narsarsuaq/34 262 Ivittuut, and 04 360 Tasiilaq(Table I, Figure 1), were acquired from Cappelen et al.(2011). The combined 04 270 Narsarsuaq/34 262 Ivittuutrecord represents the merging of two shorter seriesfrom neighbouring far southern Greenland stations, asrecommended by Cappelen et al. (2011). In addition,an improved Composite Greenland Temperature record(CGT2) for the period 1961–2010 based on nine coastalstations (Table I, Figure 1) was formulated based onHanna and Cappelen (2003). CGT2 is an average ofthe temperatures from all nine stations. Several gaps inindividual monthly temperature series were infilled usingmultivariate regression analysis of available nearest-neighbour stations, whose temperature variations werefound to be significantly correlated with those of theprimary station. The main difference from the CGT usedin Hanna and Cappelen (2003) was the entirely consistentuse of all nine stations here via the aforementionedgap filling rather than, as in Hanna and Cappelen(2003), using seven out of eight stations if one station’sdata was missing. Also, here we dropped the inland

Table I. Details of Greenland meteorological stations used in this study (sources: Cappelen et al. 2001, 2011).

World MeteorologicalOrganization code

Name Latitude (°N) Longitude (°W) Available dataperiod

34210/04210/04211 Upernavika 72°47′ −56°08′ Sep 1873–present04220 Aasiaata 68°42′ −52°45′ Jan 1958–present34216/04216/04221 Ilulissata 69°14′ −51°04′ Jul 1873–present04230/04234 Sisimiuta 66°55′ −53°40′ Jan 1961–present34250/04250 Nuuka 64°10′ −51°45′ Jan 1874–present04260 Paamiuta 62°0′ −49°40′ Jan 1958–present34262 Ivittuut 61°12′ −48°11′ Jan 1875–Dec 196604270 Narsarsuaqa 61°10′ −45°25′ Jan 1961–present04272 Qaqortoqa 60°43′ −46°03′ Jan 1961–present34360/04360 Tasiilaqa 65°36′ −37°38′ Oct 1894–present

All stations are within ∼100 m of sea level.a Stations that are used in our new/updated CGT2 series. Neighbouring stations 34262 Ivittuut and 04270 Narsarsuaq are combined to providea long south Greenland temperature time series.

Copyright 2012 Royal Meteorological Society Int. J. Climatol. 33: 862–880 (2013)

864 E. HANNA et al.

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Figure 1. Location map of Greenland climate stations used in thestudy. The inset panel shows the Greenland Blocking Index (GBI)area (60–80°N, 20–80 °W), while the main panel shows the Greenland

stations.

station 04 231 Kangerlussuaq (which began recordingrelatively recently in 1973) and instead used longer-running records from stations 04 211 Upernavik and04 221 Ilulissat. These station changes, and the infillingfor missing data months, make for a more self-consistentand representative Greenland temperature series.

We used the principal component-based NAO index(NAOI) of Hurrell (1995), for summer (June, July, andAugust; JJA), spanning 1899–2010, based on 5-degreelatitude/longitude gridded mean sea-level pressure afterTrenberth and Paolino (1980).

However, as the NAOI may be a far from satisfac-tory indicator of northern North Atlantic airflow changes(e.g. Jonsson and Hanna, 2007; Hanna et al., 2008b),we also use a more Greenland-specific measure of air-circulation changes, the Greenland Blocking Index (GBI)(Fang, 2004). This is defined as the mean 500-hPa heightover the Greenland area 60–80°N, 20–80 °W (Figure 1),‘which is equivalent to the NAO/AO index but emphasiz-ing more the northern centre of the NAO dipole pattern’(Fang, 2004, p. 131). Fang (2004) found a correlationof −0.85/−0.89 between the GBI and the NAO/AOindices for the winter (DJF) season. Here we examinethis relationship for the Greenland summer melt seasonand use summer (JJA) mean values of the NCEP/NCARReanalysis (Kalnay et al., 1996) geopotential height

(GPH) at 700- (∼3 km), 500- (∼5.5 km), and 300-hPa(∼9 km) levels to define the vertical structure of atmo-spheric circulation anomalies, with respect to the standardNCEP/NCAR-defined 1981–2010 baseline climatology,in the GBI region. Because large areas of the GrIS liebetween 2- and 3-km elevation, it is inappropriate touse mean sea-level pressure analyses. However, becauseGreenland generates its own mesoclimate through itstopography and albedo forcing, it is appropriate to con-sider GPH anomalies over the region defined by Fang(2004).

AMO data were acquired from the Earth SystemResearch Laboratory Physical Sciences Division Cli-mate Timeseries webpage of the National Oceanographicand Atmospheric Administration (NOAA). We used theirAMO dataset version based on the unsmoothed and unde-trended version of the North Atlantic monthly SST aver-ages from the dataset of Kaplan et al. (1998) (which wereused to define the AMO index used here), again con-centrating on summer (JJA) averages of these values.We detrended the AMO data in our analysis (as men-tioned below). For the station temperature–SST analysesdescribed below (Section 4.2), the use of independentHadISST1 data (Rayner et al., 2003) instead of Kaplanet al. (1998) SST data yielded very similar results (notshown).

GrIS surface melt-extent data, defined as seasonalmean daily values of the May–September area meltedeach year from 1960–2010, were derived from Snow-Model simulations reported in Mernild et al. (2011),which compared well against passive-microwave satel-lite-derived melt data in that study. SnowModel (Lis-ton and Elder, 2006a, 2006b; Mernild et al., 2006) isa spatially distributed system for modelling meteorolog-ical conditions, surface energy and moisture exchanges,including snow and glacier melt, multi-layer heat- andmass-transfer processes in snow. Atmospheric forcingrequired by SnowModel was provided by MicroMet (Lis-ton and Elder, 2006b), which assimilated and interpolatedtime series of air temperature, relative humidity, windspeed and direction and precipitation from 56 surfacemeteorological stations. MicroMet uses known relation-ships between meteorological variables and the surround-ing landscape (e.g. topography and surface characteris-tics) to distribute these variables in physically plausibleand computationally efficient ways. Data are interpolatedhorizontally to a regular grid using a Barnes objec-tive analysis scheme (Barnes, 1964, 1973; Koch et al.,1983) that applies a Gaussian distance-dependent weight-ing function. Interpolation weights are determined objec-tively as a function of data spacing and distribution. TheSnowModel simulated melt extent for each daily time-step was determined by summing the number of grid cellswhere simulated melt occurred.

GrIS runoff data are modelled output from 1958–2010,updated from Hanna et al. (2008a). The positive-degree-day runoff model is based on downscaled EuropeanCentre for Medium-Range Weather Forecasts (ECMWF)meteorological (re)analysis surface-air-temperature, pre-



cipitation and surface latent heat flux data followingmethods explained in detail in Hanna et al. (2005,2008a, 2011); modelled runoff includes an estimate ofmeltwater retained within the snowpack via pore fillingand refreezing (Janssens and Huybrechts, 2000).

3. Methodology

Geopotential height anomalies at the 300-, 500-, and 700-hPa levels for the aforementioned GBI area (Figure 1)are used to discern the vertical extent/configurationof tropospheric high and low pressure systems overGreenland, which in turn may provide valuable cluesconcerning the formation of these systems and theirrelation with the wider atmospheric circulation.

Timeseries of temperature from the four Greenlandstations, Ilulissat, Nuuk, Narsarsuaq/Ivittuut and Tasi-ilaq, as well as the composite Greenland Temperature(CGT2) were correlated with the AMO, 300, 500, and700hPa Greenland Blocking Index (GBI300, GBI500,and GBI700) and the NAOI. All correlations were carriedout with both timeseries detrended.

Linear correlations between all indices and stationtemperatures were calculated. To determine the stabilityof the relationships, running window correlations werecalculated. Windows of 20-years’ length were selectedfrom the temperature and the circulation index timeseries. The windows were then moved on by one year andthe correlations calculated again. Correlations were only

calculated when more than eighteen of the twenty yearsof data were available (as some timeseries are longerthan others so there are missing data for some timestepsof the windows). All correlations were calculated withboth series detrended over the calculation period, andthe degrees of freedom were adjusted to allow fortemporal autocorrelation when assessing significance ofthe calculation results reported in Figure 3 and Tables II,III and VIII. An adjusted number of degrees of freedomwas calculated using the formula:

N = n × 1 − (CD × CF)

1 + (CD × CF)(1)

where n is the number of timesteps correlated, CD is thelag one autocorrelation coefficient for series one, and CFthe lag one autocorrelation coefficient for series two, andN the adjusted degrees of freedom (Santer et al., 2000).

Statistical Z scores were used to depict outlying yearsof exceptional GPH (typically the highest and lowest six

Table IV. The dependence of phase (wave) speed on wave-length (wave propagation is always westward).

Wavelength (km) Period (days) Phase speed (ms−1)

100 284 −0.0041000 28 −0.415000 5.64 −10.2510 000 2.8 −41.0020 000 1.4 −164.00

Table II. Correlations of station temperatures with the NAOI and GBI, and of the TECs of the station temperature/GPH regressionmaps with the NAOI and GBI.

NAOI/temperature GBI/temperature GPH TEC/NAOI GPH TEC/GBI

1966–1985 1987–2006 1966–1985 1987–2006 1966–1985 1987–2006 1966–1985 1987–2006

Tas −0.72 0.16 0.64 0.00 −0.86 0.09 0.66 0.16Ilu −0.53 −0.40 0.67 0.67 −0.89 −0.60 0.85 0.83Nuuk −0.59 −0.60 0.67 0.79 −0.91 −0.68 0.81 0.87Nars −0.71 −0.56 0.81 0.66 −0.93 −0.73 0.83 0.87CGT2 −0.66 −0.52 0.73 0.72 −0.91 −0.66 0.80 0.85

All timeseries were detrended prior to analysis. Values in italics are significant at p < 0.05, values in bold at p < 0.01, having allowed forautocorrelation.

Table III. Correlations of station temperatures with the AMO, and of the TECs of the station temperature/SST regression mapswith the AMO, NAOI and GBI.

AMO/temp SST TEC/AMO SST TEC/NAOI SST TEC/GBI

1966–1985 1987–2006 1966–1985 1987–2006 1966–1985 1987–2006 1966–1985 1987–2006

Tas −0.19 0.11 −0.32 0.0 −0.57 0.38 0.82 0.77Ilu 0.24 0.64 0.20 0.90 −0.48 −0.30 0.68 0.58Nuuk −0.02 0.56 −0.12 0.73 −0.37 −0.52 0.60 0.72Nars 0.06 0.23 0.07 0.29 −0.52 −0.56 0.72 0.63CGT2 0.08 0.53 0.06 0.76 −0.49 −0.46 0.68 0.68

All timeseries were detrended prior to analysis. Values in italics are significant at p < 0.05, values in bold at p < 0.01, having allowed forautocorrelation.


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Table V. Z scores and ranks (1st = highest) of 700, 500 and 300 hPa geopotential heights (GBI700, 500, 300) over the GreenlandBlocking Index (GBI) area for exceptional (highest combined rank) GBI summers (JJA) during the NCEP/NCAR Reanalysis

period (1948–2010).

Year GBI700 Z GBI700 Z rank GBI500 Z GBI500 Z rank GBI300 GBI300 Z rank

1957 1.5 5th 1.6 5th 1.4 5th1958 1.8 3rd 1.5 7th 1.0 10th1998 1.2 9th 1.6 5th 1.8 4th2007 1.8 3rd 2.2 1st 2.5 1st2008 1.9 2nd 2.0 2nd 1.9 3rd2009 2.1 1st 1.8 4th 1.3 7th2010 1.4 6th 1.9 3rd 2.3 2nd

1955 −1.7 61st −1.5 59th −1.3 55th1967 −1.4 58th −1.4 57th −1.5 59th1972 −1.7 61st −2.0 63rd −2.2 63rd1976 −1.5 59th −1.6 60th −1.6 60th1983 −1.9 62nd −1.8 61st −1.4 58th1992 −1.0 52nd −1.5 59th −1.9 62nd1994 −2.0 63rd −1.9 62nd −1.7 61st

Table VI. The highest and lowest normalised NAOI sum-mer (JJA) values during the NCEP/NCAR Reanalysis period

(1948–2010).

Rank/year NAOI

1/1958 −2.392/2009 −2.013/2008 −1.264/1993 −1.175/2007 −1.106/1987 −1.067/1980 −0.978/1956 −0.919/1960 −0.9010/1948 −0.79

54/2006 1.2455/1961 1.3356/1996 1.3657/1972 1.3958/1976 1.5459/1989 1.6260/1967 1.7061/1955 1.9762/1994 2.0963/1983 2.21

Note the reversal of rank orders from lowest to highest compared withTable V, to make easier comparability, given the inverse correlationbetween NAOI and GBI GPH (see main text). Years with NAOI values≥2.0 (≤ −2.0) are highlighted in bold.

GPH values in the 63 years of NCEP/NCAR Reanalysisrecord), which were then analysed in more detailed usingmonthly tropospheric pressure charts to establish theanomalous nature of the airflow.

To determine the patterns of atmospheric circulationand SST that are most strongly linked to tempera-ture at each of the stations, we calculate regressionmaps between station temperatures and NCEP/NCAR

Table VII. The highest and lowest AMO (° C) and normalisedZ scores for summer (JJA) during the NCEP/NCAR Reanalysis

period (1948–2010).

Rank/year AMO (Z)

1/2010 22.92 (2.3)2/1998 22.91 (2.2)3/2005 22.84 (1.9)4/2006 22.81 (1.8)5/2003 22.73 (1.5)6/1995 22.69 (1.3)7/2004 22.67 (1.3)8/1952 22.66 (1.2)9/2008 22.66 (1.2)10/1999 22.65 (1.2)

54/1986 22.12 (−1.0)55/1967 22.10 (−1.0)56/1984 22.10 (−1.1)57/1975 22.09 (−1.1)58/1968 22.09 (−1.1)59/1978 22.07 (−1.2)60/1976 22.02 (−1.4)61/1971 21.95 (−1.7)62/1972 21.92 (−1.8)63/1974 21.86 (−2.0)

Years with Z scores ≥2.0 (≤ −2.0) are highlighted in bold.

500-hPa GPH, and station temperatures and SSTs, overthe Northern Hemisphere. The time expansion coef-ficients (TECs) of each regression map were calcu-lated, by projecting the standardised GPH/SST anomaliesfor the domain onto each regression map (Widmann,2005). This provides a timeseries for each regressionmap, allowing quantification, through correlation coef-ficients, of the regression maps with the atmospheric andoceanic indices, and with the original station tempera-ture data. We do this for two 20-year periods, chosen



Table VIII. Correlation coefficients between Greenland Ice Sheet modelled runoff (Hanna et al., 2008a, updated) or mean meltextent (Mernild et al., 2011) (rows) and NAOI, mean 300/500/700 hPa geopotential height for Greenland Blocking Index region

and AMO (columns), for summer (JJA).

Period NAOI GBI300 GBI500 GBI700 AMO

Runoff 1958–2010 −0.34 0.67∗ 0.60 0.50 0.44Runoff 1961–1990 −0.44 0.52 0.56 0.53 0.04Runoff 1971–2000 −0.46 0.75∗ 0.70 0.59 0.49

Runoff 1981–2010 −0.20 0.70∗ 0.57 0.40 0.51Runoff 1990–2010 0.05 0.68 0.48 0.25 0.59Melt extent 1960–2010 −0.18 0.25 0.26 0.24 0.28Melt extent 1961–1990 −0.38 0.30 0.35 0.35 0.29Melt extent 1971–2000 −0.29 0.30 0.32 0.30 0.07Melt extent 1981–2010 −0.06 0.20 0.16 0.11 0.11Melt extent 1990–2010 0.20 0.10 −0.01 −0.11 0.13

The r values in italics (bold) are significant at p ≤ 0.05 (p ≤ 0.01), having allowed for autocorrelation. All data were detrended prior to analysis.∗ For the GBIXXX and AMO columns, correlation coefficients significantly greater than those for the NAOI with runoff/melt for the same timeperiod.

as the period of strongest NAOI/temperature correla-tions (1966–1985), and of weaker NAOI, stronger GBI,and stronger AMO correlations with station temperatures(1987–2006) (Figure 3, Section 4.2).

We also examine stationary wave patterns in hemi-spheric atmospheric flow, which are clearly important forgiving a sustained weather type for a given region suchas Greenland. Certain wavelengths are more likely thanothers to give stationary patterns when zonal mean flowmatches the phase speed of the westward-propagatingRossby wave. This has been derived from analysis suchas that of Holton (1992), where for a barotropic atmo-sphere, absolute vorticity is conserved in the wave motiondue to the variation of Coriolis force with latitude.

Zonal phase speed is given by:

c = u − β

k2 (2)

where u is the zonal mean wind speed, k2 is the sumof the squares of the wave numbers in the zonal andmeridional directions, and β is the Rossby parameter.

The analysis is for sinusoidal waves and shows that thephase speed of Rossby waves increases the smaller thewave number, i.e. the longer the wavelength. Table IVhas been formulated from this analysis and gives thecorresponding phase speeds related to five wavelengths.Given that zonal mean wind speeds are realisticallybetween 10 and 50 ms−1, the most likely wavelength forstationary Rossby waves is between 5000 and 10 000 km.The wavelength in a five-wave pattern is about 5000 kmat 45° latitude. It is assumed here that the flow is two-dimensional and non-divergent at the 500-hPa level.

4. Results

As mentioned above, all analysis was undertaken for thesummer (JJA) period.

4.1. Ranking of recent Greenland summers

It is important to rank Greenland temperature summeraverages and meltwater runoff yearly (i.e. summer)totals, as individual extreme years will referred to whencomparing with atmospheric circulation/oceanic changeslater on in the results. Our new/updated CGT2 givesthe six warmest summers (since 1961 incl.) as 2010(8.1 °C), 2003 (7.8 °C), 2007 (7.6 °C), 2005 and 2008(both 7.4 °C), and 2009 (7.3 °C); nine of the ten warmestyears have occurred since 2000 inclusive; the coldest yearwas 1972 at 4.1 °C, with the ‘volcanic’ years of 1983and 1992 (both 4.6 °C) ranking as, respectively, third andfourth coldest. The recent Greenland warming is evidentfrom the 11-year running means shown in Figure 2,which clearly shows that recent (last five years’) summertemperatures have exceeded the earlier 1930s warm peakin southern coastal Greenland. For a similar (1958–2010)period, the six highest GrIS runoff years (highest first)are: 2010, 1998, 2007, 2006, 2003, and 2008, andthe lowest runoff year is 1992. Summer 2009 runoffwas unexceptional (ranked only 32nd out of 52 years).Therefore summers 2007 and 2008 had comparativelyhigh, although not unprecedented or record, Greenlandsurface air temperature and runoff. The same is also truefor summer 2009 Greenland surface air temperature. Theyear 2010 had record summer temperature and ice-sheetmelt and runoff, as already noted elsewhere (Box et al.,2010; Mernild et al., 2011; Tedesco et al., 2011).

4.2. The relationships between Greenland temperatureand atmospheric circulation and sea surfacetemperatures

4.2.1. Running window correlations

The strength of the NAOI/station temperature rela-tionship is not stable: for windows starting at thebeginning of the twentieth century, and from ∼1960onwards (Figure 3(a)) correlations are significantly neg-ative (p < 0.05), whereas correlations are insignificant


868 E. HANNA et al.

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Figure 2. Greenland temperature series, including Composite Green-land Temperature 2 [updated/improved from Hanna and Cappelen(2003) – see main text], Atlantic Multidecadal Oscillation temperatureindex minus 22 °C (for easy comparison purposes) and North AtlanticOscillation Index data, all based on summer (JJA) averages and plottedas 11-year running means. See main text for discussion and comparison

of these data series.

(p > 0.05) for windows starting between 1925 and1945 for all stations – indeed changing sign for Tasi-ilaq, which has the least stable correlations. Inter-estingly the first period of insignificant correlationscovers windows over the 1930s–1940s warm period(Figure 2). The regression map of the NAOI on 500hPaGPH for two selected 20-year windows (Figure 4(e)and (f)) indicates a more southerly flow componentto the east coast of Greenland (where Tasiilaq islocated) during 1987–2006 (with insignificant NAOI cor-relations) than during 1966–1985 (significant negativecorrelations).

The significant correlations between station tempera-tures and the AMO are positive (Figure 3(b)), as duringthe positive AMO phase warm water is present in theNorth Atlantic (Sutton and Hodson, 2005). Correlationsare also not stable through time (indeed relatively little ofmost series is significant), and the only period when allstations have significant correlations is for windows start-ing between 1905 and 1915. Ilulissat, on the west coast,has the longest periods of significant correlations with theAMO (for windows beginning in the recent period, andaround 1960). Periods of insignificant correlations for allstation temperatures with AMO include windows startingfrom ∼1918 to the 1940s, around the same time as theaforementioned insignificant NAOI correlations.

The correlations for the GBI at all levels are verysimilar: hence only the GBI500 results are presented(Figure 3(c)). At Ilulissat and Nuuk, correlations aresignificant for all 20-year periods. The positive corre-lations correspond to the warm-air advection and pos-sibly reduced cloudiness associated with high pressure

over Greenland with a positive GBI. Tasiilaq (south-east Greenland) however shows strongly declining cor-relations (becoming insignificant) towards present. Theseresults corroborate those with the NAOI discussed above,that the relationship between temperatures and atmo-spheric circulation at Tasiilaq has weakened in recentdecades, which the NAOI results indicate happened alsoearlier in the twentieth century. This earlier period corre-sponds to a period of the strongest positive correlationswith the AMO and hence influence of SSTs at this station(Figure 3(b)).

In addition to the above analysis, we carried out amultiple regression based on detrended datasets for thefull 1961–2010 period of common data. The resultsshow a correlation coefficient r = 0.730 for CompositeGreenland air temperature (CGT2)-v.-NAOI, GBI500 andAMO (the most comprehensive regression model), r =0.730 for CGT2-v.-GBI500 and AMO, r = 0.698 forCGT2-v.-NAOI and GBI500, and r = 0.652 for CGT2-v.-NAOI and AMO. None of these correlations is muchhigher (and indeed the latter one is lower) than for CGT2-v.-GBI500 (r = 0.695 for the same period), althoughthey are substantially higher than for CGT2-v.-AMO(r = 0.432) and CGT2=v.-NAOI (r = −0.546). Theseresults indicate that GBI500 has the greatest associationwith CGT2, and the multiple regression model skill isincreased by adding AMO rather than NAOI to GBI500.Hence, although the primary link of Greenland coastalair temperature is with GBI500, there is a discernible(albeit much lesser) signal of the SST (AMO) variationsin CGT2.

4.2.2. Regression maps

We present here the regression maps for Illulissat andTasiilaq, as contrasting stations respectively on the westand east coasts of Greenland (Figure 1). Results ofcorrelations for the other stations are shown in Tables IIand III.

The 500hPa Geopotential Height. For the 1966–1985window, the regression maps for station temperatureswith 500hPa GPH show in much of the hemisphere aninverse NAO-type pattern (as would be expected fromFigure 3(a) and Table II), with positive regression coef-ficients over Greenland and the North Pole and nega-tive coefficients at mid-latitudes (Figure 4(a) and (c)).The maximum positive coefficients are ∼20 geopoten-tial metres (gpm) per °C change in temperature, whichis about what would be expected based on the hypo-sometric relationship. The correlations of between −0.86(Tasiilaq) and −0.93 (Narsarsuaq) between the detrendedTEC of the GPH regression map for each station and thedetrended NAOI (Table II) indicates that the timeseries ofthe anomaly pattern most strongly related to temperaturesat these stations is very closely related to the NAOI.

There is a stronger influence of high-latitude pres-sure anomalies during the window 1987–2006, and aweaker influence of mid-latitude anomalies at Ilullisaat(Figure 4(b)), but not at Tasiilaq (Figure 4(d)). Maxi-mum positive coefficients are 36 gpm per °C temperature



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window. The horizontal lines depict correlation thresholds for p < 0.05.

change (CGT2, not shown), which is considerably greaterthan expected based on the hypsometric relationship(therefore there may well be some additional, unidenti-fied mechanism affecting lower tropospheric thickness).Correlations of both station temperatures and the regres-sion map TECs with the NAOI are weaker than in theearlier period (except for Nuuk) (Table II), confirming aweaker influence of the NAO on station temperatures.

4.2.3. Sea surface temperatures

The regression maps between station temperatures andSSTs (Figure 5) show for both stations and for both peri-ods, significant positive regression coefficients south ofGreenland, i.e. higher station temperatures with higherSSTs in this region. The pattern over the whole NorthAtlantic during 1966–1985 (Figure 5(a) and (c)) resem-bles an inverse of the so-called SST dipole that has been

found to be related to the NAO in winter (Visbeck et al.,2003), A tripole pattern is evident in the regression mapof the NAOI with SSTs (Figure 5(g)) for this period, butnot during 1987–2006 (Figure 5(h)), perhaps reflectingthe abovementioned reduced NAO signal in station tem-peratures during the latter period.

The tripole pattern is weakened in the Ilulissat SSTregression maps in the later period (Figure 5(b)), whoseSST regression maps look more similar to that of theAMO (Figure 5(e) and (f)), and temperatures at this westcoast station are significantly correlated with the AMO(Figure 3(b), Table III). The stronger AMO influenceat this station for this period is shown by the signifi-cant (p < 0.01) correlations between the detrended TECof the SST regression maps for each station and thedetrended AMO, compared to the lack of significant cor-relations in the earlier period (Table III), with a striking


870 E. HANNA et al.

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(e–h). Solid (dashed) contours show positive (negative) coefficients; regions that are statistically significant at p < 0.05 are shaded.

correlation of 0.90 for Ilulissat. The AMO regressioncoefficients in the North Atlantic are stronger during thelater period, reaching 0.48 °C/standard deviation south ofGreenland, compared to 0.40 °C/standard deviation ear-lier, perhaps explaining this stronger relationship.

The TECs of the SST regression maps for all stationsare significantly positively correlated with the GBI forall stations for both periods, which can be seen in theGBI/SST regression maps (Figure 5(i) and (j)). Thiscould be due to the strong negative relationship betweenthe GBI and the NAO. This analysis shows that theSST patterns most strongly linked to station temperatures

in Greenland during the two chosen periods are notindependent of the atmospheric circulation patterns thatare linked to station temperatures (Table III). During thelater period when the NAO is more weakly related tostation temperatures, both the regression maps and TECcorrelations indicate that the SST anomalies are lesssimilar to the SST anomalies associated with the NAO,and resemble more strongly those of the AMO at thewest coast stations of Nuuk (not shown) and Ilulissat.The focus of this paper is not the links between the oceanand the NAO: hence we do not investigate lagged or low-frequency relationships between the summer NAO and



SSTs. The relevance of dependence of summer Greenlandtemperatures on earlier (e.g. spring) season’s conditionsshould also be assessed as part of future work.

4.3. Anomalous geopotential height/NAOI/AMOsummers and relation with Greenland melt/runoff

It is useful to analyse the relation between GBI/GPH,NAOI and AMO, and not just Greenland temperaturechanges but also – as one of the primary impacts – meltand runoff from its ice sheet. We do this using bothcorrelation analysis, documentation and comparison ofrelative timings of extreme high and low summer valuesin all these datasets, and analysis of GPH maps for highlyclimatically anomalous summers (including the recentfour warm summers 2007–2010) with abnormal GBIvalues.

Table V shows the seven highest and seven lowestcombined rank GPH summers and their statistical Zscores over the GBI area for the 300-, 500- and 700-hPalevels, and Tables VI and VII show the ten highest andlowest NAOI (AMO) summers, during the NCEP/NCAR(1948–2010) reanalysis period. Summer 2007 (2009) wasranked as having the highest GPH at 300 and 500 (700)hPa, while summer 2008 had the second (third)-highestGPH at 700 and 500 (300) hPa. Summer 2010 had

the second (third)-highest GPH at 300 (500) hPa. Otherrecent warm Greenland summers – including 2003, thesecond warmest of the last 50 years – feature far lessprominently in Table V.

Correlations are highly significant between GBI GPHand GrIS runoff for 1958–2010 and sub-periods(Table VIII, Figure 6), and are strongest for the 300 hPaGPH level and least strong (although still significant) forthe 700 hPa GPH level. At 300 hPa, these correlationsare typically significantly higher than those between theNAOI and runoff (Table VIII), as revealed through useof Fisher’s Z transformation (e.g. Spiegel and Stephens,1999, p. 317). In contrast to those for runoff, correlationsbetween the various climatic indices and GrIS melt extentare generally insignificant except for NAOI and GBI500,which are significant only for 1961–1990 at the 5% level(Table VIII).

Concentrating now on the recent warm summers,Figure 7(a) and (b) illustrates an exceptional mid-tropospheric high pressure over Greenland during sum-mer 2007 that was unusual in the context of the wholeperiod of available NCEP/NCAR reanalysis data, and thatsummer (JJA) also had a significantly negative NAOI,being ranked fifth out of 62 years (Table VI). The GPHanomalies in summer 2007 at 700, 500, and 300 hPa

(a) Reg Illu/KAPLAN SST 1966-1985 (b) Reg Illu/KAPLAN SST 1987-2006 (c) Reg Tas/KAPLAN SST 1966-1985

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Solid (dashed) contours show positive (negative) coefficients; gridboxes that are statistically significant at p < 0.05 are shaded.


872 E. HANNA et al.

(g) Reg NAOI/KAPLAN SST 1966-1985 (h) Reg NAOI/KAPLAN SST 1987-2006 (i) Reg GBI/KAPLAN SST 1966-1985

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surface meltwater runoff, all summer (JJA) values, 1958–2010.

were respectively 1.8, 2.2, and 2.5 standard deviationsabove the 1948–2010 mean (i.e. the entire NCEP/NCARReanalysis period), and this north–south-aligned highpressure was therefore particularly deep and most anoma-lous in the high troposphere, having (as we have seen)record values at the 300- and 500-hPa levels (Table V).Summer 2008 experienced an almost equally intense mid-tropospheric high, displaced further northwestwards overthe Greenland region and with its main axis orientatedmore northeast–southwest (Figure 7(c) and (d)), with

GPH respectively 1.9, 2.0, and 1.9 standard deviationsabove the mean 1948–2010 values (Table V). The sum-mer 2008 NAOI was even more negative than in 2007,being the third most negative summer NAOI value since1958 (Table VI). Summer 2009 had the most intense700 hPa high pressure, and was the fourth highest pres-sure summer at 500 hPa but was statistically less excep-tional higher up at the 300 hPa level (Table V). Themid-tropospheric high in summer 2009 was focused tothe north of Greenland, stretching westwards over NorthAmerica (Figure 7(e) and (f)). NCEP/NCAR Reanaly-sis shows an intense (virtual) mean-sea-level pressureanomaly of ∼4–6 hPa (30–50 m in 850 hPa heights)over most of the ablation zone of Greenland, whichgenerally exceed corresponding anomalies (respectively∼2–4 hPa and 20–40 m) in the other three recent sum-mers highlighted (2007, 2008, and 2010). Summer 2009had a remarkably high negative NAOI anomaly – muchmore negative than 2007 and 2008 – and is rated thesecond lowest NAOI summer since 1948 (1958 holdsthe record; Table VI). Summer 2010 had the second(third) highest GPH values at the 300 (500) hPa levels(Table V), with a broad geopotential high focused overcentral southern Greenland (Figure 7(g) and (h)), but hadonly a modestly low NAOI anomaly of −0.3. However,the above results collectively show that unusual summerblocking over Greenland, effectively measured by means



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of the GBI, is mirrored in rather lower-than-normal – ifnot unprecedently low – summer NAOI.

The second warmest (1961–2010) coastal Greenlandsummer, 2003, and the second highest (1958–2010)GrIS runoff year, 1998, had weaker and broader mid-tropospheric high pressures (related to the high geopo-tential height anomalies at that level) over Greenlandthan summers 2007–2010 (Figure 8(b) and (d)), although1998 ranks as the fourth and fifth highest geopotentialheights at the 300- and the 500-hPa levels, respectively,averaged across the GBI area (Table V). However, 2010and 1998 were the years with the first and second highest

AMO values (Table VII). The post-volcanic years 1983(El Chichon) and 1994 (Mt. Pinatubo) feature unusu-ally low mid-tropospheric pressure over Greenland, asdid 1972 (Table V, Figure 6). Moreover, 1992 (imme-diately following Pinatubo) witnessed the second (fifth)lowest summer pressure at 300 (500) hPa (Table V,Figure 6) but was not such an unusually low-pressuresummer lower in the troposphere at 700 hPa (Table V).However, conditions evolved so that the 1994 low-pressure anomaly was most pronounced nearest the sur-face (Table V). These are also strikingly positive NAOIyears (Table VI).


874 E. HANNA et al.


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Figure 8. The 500-hPa geopotential heights and their anomalies relative to 1981-2010 mean for summer (JJA) (a,b) 1998, (c,d) 2003, (e,f) 1992.Composites of six highest (g) and six lowest (h) GBI summers (JJA) are also shown.

Regarding possible links with ocean forcing, AMO andrunoff/melt extent are significantly correlated for mostof the periods considered (Table VIII), although severalrecent summers 2007–2009 feature far less prominentlyin a rank order of high AMO years: the highest ofthese years being 2008 in only ninth place (Table VII).On the other hand as we have already seen above,the warmest AMO year 2010 (Table VII) was also theyear of record melt extent (Mernild et al., 2011) andgreatest GrIS runoff, and moreover the fourth warmestAMO year 2006 was also the fourth highest runoffyear.

4.4. Anomalous atmospheric hemispheric wavepatterns during high GBI summers

The stationary nature of the hemispheric wave patternsduring recent warm, high-GBI-index Greenland summers2007–2010 is noteworthy. Both summers 2007 and2008 show a five-wave pattern in the 5625-m contour(representative of mid-latitude flow) of the mean 500 hPahemispheric flow (Figure 7(a) and (c)), with a wavelengthof ∼5000 km and a phase speed of −10.25 ms−1 at45°N according to theory (Table IV). The NCEP/NCARreanalysis zonal flow over the North Atlantic at 45°N iscorrespondingly ∼10 ms−1, which represents a very good



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Figure 9. The 500-hPa vector wind plots from NCEP/NCAR reanalysis for summer (JJA), 2007–2010.

agreement of observations with theoretical considerationsof this stationary wave pattern despite its asymmetry. Thetrough over northern Canada was most pronounced insummer 2007, which left Greenland under the influenceof mild south-westerly winds (Figure 9(a)). This was thethird highest runoff year in the GrIS SMB 1958–2010records (Section 4.1). The trough is still present insummer 2008, although the airflow into Greenland thenhad a more westerly component (Figure 9(b)).

In contrast to 2007 and 2008, summer 2009 showsmore of a four-wave pattern in the hemispheric circula-tion, which is very asymmetric (Figure 7(e)). The majorcircumpolar axis seems to have changed from 90 to 270degree long. in 2007, to 0 to 180 degree long. in 2009.The intensity of the block in 2009 increases towards the

surface as shown by GBI in Table V. So 2009 has thehighest GPH anomaly at 700 hPa (GBI700). On the otherhand, the 500 hPa flow in summer 2009 was weaker overa larger area of Greenland compared with 2007 and 2008(Figure 9(c)). The weaker mild westerly wind in summer2009 coincides with unexceptional runoff during that sea-son (2009 does not appear in the list of high runoff yearsin Section 4.1).

Summer 2010 also shows a four-wave pattern, withhighly meridional (south–south westerly) airflow oversouthern and western Greenland (Figures 7(g) and 9(d)).This was the highest runoff year, and the 500 hPahemispheric chart contrasts with the lowest GrIS runoffyear 1992 (Figure 8(e) and (f)), which showed a three-wave pattern. The longer wavelength associated with this


876 E. HANNA et al.

wave number gives higher wave speeds (Table IV) andless likelihood of a stationary wave pattern. There wasa weak zonal flow over Greenland in summer 1992.Similar wave pattern differences are also reflected in thecomposite six highest and lowest GBI summer GPH plotsshown in Figure 8(g) and (h), with a 4-wave pattern forhighest GBI, and a 3-wave pattern for lowest GBI.

5. Discussion

Most previous studies have concentrated on the tempera-ture-/melt- related volcanic aspects of Greenland sum-mer climate forcing (which is understandable consideringthe timing and magnitude of the Mt. Pinatubo erup-tion); our results show a clear relationship between recenthigh-pressure anomalies over Greenland and Green-land summer climate and meltwater runoff prevail-ing over the GrIS, and expand substantially on previ-ous related findings by Mote (1998a, 1998b) and Fet-tweis et al. (2011). Apart from influential atmospheric-circulation changes/anomalies, there is evidence of asso-ciation of record/high Greenland runoff years in the 1990sand 2000s with oceanic temperature forcing as gaugedthrough the AMO. The record two high AMO summers1998 and 2010, which are also the two highest runoffseasons (Section 4.1), suggest that record-warm Atlanticwaters may have amplified the GrIS melt/runoff signalduring those years.

In common with Mote (1998a, 1998b) but building onhis findings, our results suggest that GPH anomalies arean important factor associated with Greenland summerwarmth: a higher GPH over the Greenland region tends tobe related to greater summer warmth and the latter can bemore strongly related to GPH (GBI) than NAO changes.In line with our results, Tsukernik et al. (2009) foundthat Fram Strait sea-ice motion, i.e. Arctic sea-ice exporteast of Greenland, is more closely related to the Barents-Greenland sea-level pressure dipole pattern – featuringa Greenland high pressure – than the NAOI. Also inline with our results, Simmonds et al. (2008) note thebreakdown of the relationship between the NAOI andhigh-latitude climate/sea-ice during the last decade.

Successive highly anomalous GPH conditions overGreenland in the four recent summers 2007–2010 (pre-liminary data for 2011 follow the same pattern) haveeffectively enhanced the sensitivity of GrIS melting andmass loss to recent rapid Greenland regional climatewarming (Hanna et al., 2008a; Box et al., 2010; Fet-tweis et al., 2011), although it is uncertain whether theserecent high GPH summer anomalies will be maintained.Notably, however, this recent observed change, althoughit may just be natural variability, is opposite to a morepositive Northern Annular Mode/NAO shift and decreasein Arctic surface pressure highlighted in most recentGCM simulations (e.g. Meehl et al., 2007), and indeedsummers 2007–2010 all had notably negative NAOI(Table VI, although 2010 was only modestly negative sois not shown in that table). As noted above, there were

substantial differences in the vertical extent, orientationand spatial focus of the high pressures in these four years,which must have consequences for GrIS melt anomaliesbeing concentrated at high elevations in west Greenlandin 2007 (Mote, 2007; Box et al., 2008), at intermediateelevations on the western flank of the GrIS in 2010 (Boxet al., 2010) but at lower elevations all around the GrIS,especially in north Greenland, in 2008 (Box et al., 2009).

We have shown that summers 2007–2010 troposphericpressure patterns reflect an unusually (for 1948–2010)blocked meridional circulation over Greenland. This kindof meridional pattern, differing from the NAO/ArcticOscillation or Pacific North American-like pressure pat-terns, has been noted to have emerged more generally forthe pan-Arctic atmospheric circulation during 2000–2007compared with the preceding few decades and may besymptomatic of natural climate variability as well asanthropogenic global warming (Overland et al., 2008).Indeed, Wood and Overland (2010) found a strong influ-ence of meridional circulation patterns during the earlyTwentieth Century Arctic warm period during winter,although their meridional indices are based on pres-sure differences between Iceland and Russia, and Ice-land and Scotland/Norway (so a different region of theArctic). Moreover, high-latitude blocking episodes areassociated with upper-level Rossby wave-breaking eventsover the North Atlantic that may well be implicated inlow-frequency variability of the NAO (Woollings et al.,2008), perhaps providing a dynamical as well as the sta-tistical link between the GBI and NAOI noted by Fang(2004). Despite this strong statistical – and likely phys-ical – relationship, both coastal Greenland summer tem-peratures and GrIS melt extent and runoff follow GBIvariations more closely than they do NAOI variability.It would be interesting to extend our GBI GPH analy-sis back before 1948, using recently available and self-consistent Twentieth Century Reanalysis data (Compoet al., 2006, 2011), to include the 1930s/1940s Greenlandwarm period.

This latter period falls within a relatively quiet periodvolcanically, which has been pointed out in a previousanalysis of Greenland temperatures (Box, 2002), andmoreover was a relatively active and rising period ofsolar activity (Hanna, 1996), that together with Arcticamplification of climate forcings (including potentiallyanthropogenic greenhouse warming) might be invokedto explain the pronounced regional Greenland summerwarmth during this period, given that NAOI and AMOchanges during this period apparently do not (Figure 3).

Considering the relationship between coastal Green-land temperatures and atmospheric circulation over alonger time period, we identified a non-stationarity of theGreenland temperature–NAOI relationship. In commonwith our findings, Jones et al. (2003) found the strongestlinks between the winter NAO and European surfacetemperature and precipitation during the late TwentiethCentury, and the late Nineteenth and early TwentiethCenturies. Haylock et al. (2007) furthered this work, alsofor boreal winter, and also found weaker correlations



between the NAOI and Northern Hemisphere extratropi-cal temperatures during the (early) mid-twentieth century,which they suggest are real, and related to reduced SLPvariance in the Atlantic Ocean, and increased variancein the Pacific, during this period. Our work suggeststhat the changing strength of NAOI–temperature rela-tionships found in boreal winter also extends to summerover Greenland.

We also identified variation in the strength of the rela-tionship between Greenland coastal temperatures and theAMO. During the 1920s and early 1930s and again fromthe early 1990s, there were two well-established majorphase shifts/warmings of the AMO (Figure 2), contempo-raneous with the two major Twentieth Century warmingepisodes around coastal southern Greenland (e.g. Hannaet al. 2007, 2008a, 2011), which are periods of significanttemperature/AMO correlations (Figure 3). These decadalAMO phase changes are lower frequency variations thanthose typical of the NAOI (Figure 2) but appear to belinked with temperature changes over Greenland (e.g.Chylek et al., 2009). AMO correlations at Ilulissat onGreenland’s west coast show the strongest link to theAMO, suggesting an influence of this mode since theearly 1990s in this region of Greenland (also seen atNuuk: Table III). Arguez et al. (2009) also found tem-perature anomalies associated with their PC1 (AMO-equivalent) to be stronger on the west coast in the regionof Ilulissat. This may be linked with the increasinglydominant recent correlation of southwest coastal Green-land temperatures with Northern Hemisphere tempera-tures, and weaker correlation with the NAOI, as noted byHanna et al. (2008a) in their analysis of Greenland tem-perature, Northern Hemisphere temperature and NAOIsummer records spanning 1958–2006, and corroboratedby the present study.

The southern node of the summer NAO is further norththan in winter, being centred in July/August (Follandet al., 2009) over western Scandinavia, and in JJA (ourFigure 4(e) and (f)) over the North Sea. The NAOreflects changes in the strength and location of the NorthAtlantic storm tracks (e.g. Thompson et al., 2002; Vallisand Gerber, 2008). The mainly negative correlationsbetween the NAOI and Greenland station temperatures(Figure 3(a)) result from the more southerly (northerly)storm track in the negative (positive) phase of theNAO. This influences the degree of warm (or cold) airadvection, as indicated by the results in Section 4.4,although Folland et al. (2009) suggest that incomingshortwave radiation may also play a role. High pressureover Greenland in a negative NAO situation often resultsin advection of warm air from the south to stationson the south and western sides of Greenland. Changingadvection patterns at Tasiilaq (on the south-east coast, sotherefore more on the other side/periphery of Greenlandhigh pressures from the other stations on the west coast)may contribute towards the different correlation patternsand more fleeting significant correlations there.

Our findings fit with the positive NAO in years fol-lowing volcanic eruptions (e.g. Shindell et al., 2004;

Christiansen, 2008), although these papers consider onlythe DJF NAO. Our results emphasize the importanceof studying pressure changes at several different repre-sentative height levels in order to discriminate betweendifferent atmospheric circulation regimes and potentiallytheir causal physical mechanisms. We have shown thatvolcanically induced stratospheric aerosol cooling (of thetroposphere) seems to have had a more pronounced effecton the prevailing airflow in the mid to high troposphere,rather than near the Greenland surface, for the summer(1992) immediately following the eruption (Section 4.3);this has already previously been noted to have coincidedwith record-low GrIS melt extent and runoff (Abdalatiand Steffen, 2001; Hanna et al. 2005), which is readilyapparent in Figure 6.

The higher GBI300–runoff than NAOI–runoff correla-tions partly reflect the physical interdependence betweenGPH, surface air temperature and runoff, compared withmore general atmospheric circulation forcing expressedthrough the NAOI index. This striking relationship,which largely reflects the GBI GPH-Greenland temper-ature observed above, raises the possibility of using theupper air fields of the Greenland region from GCM simu-lations of future climate change, for example in statisticaldownscaling studies, to help refine predictions of changesin GrIS mass balance.

However, clearly, to improve future estimates of theGrIS mass balance the dynamics behind the large-scaleatmospheric and oceanic forcings that are important forGreenland melt need to be correctly simulated in coupledice sheet-AOGCMs, especially as there currently remainslarge uncertainty in the magnitude and spatial/temporalpattern of predicted warming over Greenland (e.g. Over-land, 2006). Indeed Woollings (2010) identifies simula-tion of processes such as Rossby waves, jet streams andrelated blocking and the NAO over the North Atlanticas being uncertain in the current generation of climatemodels.

6. Conclusions

Multidecadal variations in Greenland coastal surfaceair temperatures (especially in west Greenland) duringsummer are significantly related to the AMO on aninterannual timescale during two periods of the twentiethcentury, coinciding with the 1920s–1930s warming, andthe more recent warming – especially in west/southwestGreenland. There are signs that this relationship may havebeen strengthening over the last 30–40 years, suggestinga more dominant influence of recent SST variabilityon Greenland climate and GrIS surface mass balance.The NAOI also shows short-term (sub-multidecadal)association with Greenland summer temperatures, againwith significant correlations during the early TwentiethCentury and recent decades, with signs of a weakeningof this relationship, particularly at the east coast stationof Tasiilaq.

However, GPH for the GBI area generally correlatesmore highly than the NAOI with both Greenland summer


878 E. HANNA et al.

temperatures and GrIS runoff, making GBI GPH apotentially useful predictor for changes in GrIS surfacemass balance. The GBI (GPH) index appears to be a lessuseful proxy of mean melt extent over Greenland, whichwe attribute to runoff being a more quantitative indexof climate forcing than melt extent, as the latter is notdirectly related to the amount of melt/runoff generated.However, given the strong statistical relation betweenGBI and GrIS runoff, future work should explore theprocesses responsible for the temporal variability of theGBI index.

The period between ∼1920 and 1960, encompassingthe 1930s/1940s warm period in Greenland, is a periodof dominantly insignificant correlations with both theNAOI and AMO, indicating a predominant influence ofdifferent atmospheric/oceanic circulation patterns and/orclimatic forcing factors such as solar forcing and relativelack of volcanic activity during this period; however,this aspect remains poorly understood, so merits furtherinvestigation. Further work could, for example, use theTwentieth Century Reanalysis to investigate the changinginfluence of meridional circulation patterns in summer.

The recent warm Greenland summers of 2007–2010were characterised by unusually high mid-troposphericpressure over Greenland (focused further northwest in2008 and 2009), which is likely to have enhanced meltingof the GrIS, with differences in spatial melt patternsbetween these summers partly attributable to regionaldisplacement of the high. Analogous results were recentlyreported by Fettweis et al. (2011) who developed a‘circulation type classification’ of 500 hPa GPH basedon reanalysis and attributed record surface melt extentduring summers 2007–2009 to a persistent circulationtype that favoured warm-air advection. However, thehigh GBI anomaly of summer 2009 was linked moreto exceptionally high near-surface pressure than it wasto particularly high Greenland margin air temperaturesand meltwater runoff, highlighting the complexity of theGBI/pressure/temperature relation.

The results presented here show that more integratedstudies of Greenland’s climate in the context of large-scale atmospheric and oceanic forcing are essential inorder to identify how such forcings may change in thefuture, and how well the mechanisms behind them arerepresented in GCMs, to determine the level of con-fidence that can be had in predictions of future cli-mate change on the GrIS. Regarding atmospheric forcingof GrIS mass change, further studies need to explainthe GBI dynamically and separate out to what extentit is an index purely of blocking or also incorpo-rates/represents the NAO (which it does as they arerelatively highly correlated). More process studies arerequired to enhance understanding of AMO variabil-ity and its regional impact on Greenland: a subjectthat has recently attained great interest but is still rel-atively under-researched compared with its potentialimportance.

Acknowledgements

DMI provided updated Greenland climate data. NAOIndex Data were provided by the Climate AnalysisSection, NCAR, Boulder, USA, Hurrell (1995).

Geopotential height, Kaplan SST V2 data and theNCEP/NCAR reanalysis data were provided by theNOAA/ESRL Physical Sciences Division, Boulder, Col-orado, USA, from their Web site at http://www.cdc.noaa.gov/.

Paul Coles helped (re)draw the figures.

References

Abdalati W, Steffen K. 2001. Greenland ice sheet melt extent:1979–1999. Journal of Geophysical Research 106: 33983–33988.

Arguez A, O’Brien JJ, Smith SR. 2009. Air temperature impacts overEastern North America and Europe associated with low-frequencyNorth Atlantic SST variability. International Journal of Climatology29: 1–10.

Barnes SL. 1964. A technique for maximizing details in numericalweather map analysis. Journal of Applied Meteorology 3(4):396–409.

Barnes SL. 1973. Mesoscale objective map analysis using weightedtime-series observations. NOAA Tech. Mem. ERL NSSL-62.

Box JE. 2002. Survey of Greenland instrumental temperature records:1873–2001. International Journal of Climatology 22: 1829–1847.

Box J, Cappelen J, Bromwich D, Bai L-S, Mote T, Veenhuis B,Mikkelsen N, Weidick A. 2008. Greenland. In State of Climate in2007, Levinson DH, Lawrimore JH (eds). Bulletin of AmericanMeteorological Society 89: S1–S179, DOI: 10.1175/BAMS-89-7-StateoftheClimate.

Box JE, Bai L-S, Benson R, Bhattacharya I, Bromwich DH, Cap-pelen J, Decker D, DiGirolamo N, Fettweis X, Hall D, Hanna E,Mote T, Tedesco M, van de Wal R. 2009. Greenland climate in 2008.Bulletin of American Meteorological Society 90(8): S108–S111(State of the Climate in 2008/NOAA Arctic Report Card 2009 ).

Box JE, Cappelen J, Decker D, Fettweis X, Mote T, Tedesco M, Vande Wal RSW. 2010. Greenland in Arctic Report Card 2010 ,http://www.arctic.noaa.gov/reportcard.

Cappelen J (ed.). 2011. DMI monthly Climate Data Collec-tion 1768–2010, Denmark, The Faroe Islands and Green-land. Danish Meteorological Institute, Technical Report 11-05, DMI: Copenhagen. http://www.dmi.dk/dmi/tr11-05.pdf (report)http://www.dmi.dk/dmi/tr11-05.zip (data).

Cappelen J, Jorgensen BV, Laursen EV, Stannius LS, Thomsen RS.2001. The Observed Climate of Greenland, 1958–1999 – with Cli-matological Standard Normals, 1961–1990. Danish MeteorologicalInstitute Technical Report 00–18, DMI: Copenhagen.

Christiansen B. 2008. Volcanic eruptions, large-scael modes in theNorthern Hemisphere, and the El Nino-Southern Oscillation. Journalof Climate 21: 910–922.

Chylek P, Folland CK, Lesins G, Dubey MK, Wang M. 2009. Arcticair temperature change amplification and the Atlantic MultidecadalOscillation. Geophysical Research Letters 36: L14801.

Compo GP, Whitaker JS, Sardeshmukh PD. 2006. Feasibility of a100 year reanalysis using only surface pressure data. Bulletin ofAmerican Meteorological Society 87: 175–190.

Compo GP, Whitaker JS, Sardeshmukh PD, Matsui N, Allan RJ,Yin X, Gleason BE, Vose RS, Rutledge G, Bessemoulin P,Bronnimann S, Brunet M, Crouthamel RI, Grant AN, Groisman PY,Jones PD, Kruk MC, Kruger AC, Marshall GJ, Maugeri M, MokHY, Nordli Ø, Ross TF, Trigo RM, Wang XL, Woodruff SD, Wor-ley SJ. 2011. The twentieth century reanalysis project. Quar-terly Journal of Royal Meteorological Society 137: 1–28, DOI:10.1002/qj.776.

Fang Z-F. 2004. Statistical relationship between the northernhemisphere sea ice and atmospheric circulation during wintertime.In Observation, Theory and Modeling of Atmospheric Variability.Zhu X (ed.). World Scientific Series on Meteorology of East Asia.World Scientific Publishing Company: Singapore, 131–141.

Fettweis X, Mabille G, Erpicum M, Nicolay S, van den Broeke M.2011. The 1958–2009 Greenland ice sheet surface melt and themid-tropospheric atmospheric circulation. Climate Dynamics 36:139–159.



Folland CK, Knight J, Linderholm HW, Fereday D, Ineson S, Hur-rell JW. 2009. The summer North Atlantic oscillation: past, presentand future. Journal of Climate 22: 1082–1103.

Hanna E. 1996. Have long-term solar minima, such as the MaunderMinimum, any recognisable climatic effect? Part 2: Evidence for asolar-climatic link. Weather 51: 304–312.

Hanna E, Cappelen J. 2003. Recent cooling in coastal southern Green-land and relation with the North Atlantic Oscillation. GeophysicalResearch Letters 30: 1132, DOI: 10.1029/2002GL015797.

Hanna E, Huybrechts P, Mote T, Thomas L. 2002. Surface massbalance of the Greenland Ice Sheet from climate analysis data andaccumulation/runoff models. Annalls of Glaciology 35: 67–72.

Hanna E, Huybrechts P, Janssens I, Cappelen J, Steffen K, StephensA. 2005. Runoff and mass balance of the Greenland Ice Sheet:1958–2003. Journal of Geophysical Research 110: D13108, DOI:10.1029/2004JD005641.

Hanna E, Box J, Huybrechts P. 2007. Recent changes in Greenland icesheet mass balance. Invited MS for State of Arctic Report 2007/ACIAupdate, http://www.arctic.noaa.gov/reportcard/.

Hanna E, Huybrechts P, Steffen K, Cappelen J, Huff R, Shuman C,Irvine-Fynn T, Wise S, Griffiths M. 2008a. Increased runoff frommelt from the Greenland Ice Sheet: a response to global warming.Journal of Climate 21: 331–341, DOI:10.1175/2007JCLI1964.1.

Hanna E, Cappelen J, Allan R, Jonsson T, le Blancq F, Lillington T,Hickey K. 2008b. New insights into North European and NorthAtlantic surface pressure variability, storminess and related climaticchange since 1830. Journal of Climate 21: 6739–6766, DOI:10.1175/2008JCLI2296.1.

Hanna E, Cappelen J, Fettweis X, Huybrechts P, Luckman A, Riber-gaard MH. 2009. Hydrologic response of the Greenland Ice Sheet:the role of oceanographic forcing. Hydrological Processes (SpecialIssue: Hydrologic Effects of a Shrinking Cryosphere) 23: 7–30, DOI:10.1002/hyp.7090.

Hanna E, Huybrechts P, Cappelen J, Steffen K, Bales RC, Burgess EW,McConnell JR, Steffensen JP, van den Broeke M, Wake L, BiggGR, Griffiths M, Savas D. 2011. Greenland Ice Sheet surface massbalance 1870 to 2010 based on Twentieth Century Reanalysis, andlinks with global climate forcing. Journal of Geophysical Research,DOI: 10.1029/2011JD016387.

Haylock MR, Jones PD, Allan RJ, Ansell TJ. 2007. Decadal changesin 1870–2004 Northern Hemisphere winter sea level pressurevariability and its relationship with surface temperature. Journal ofGeophysical Research 112: D11103, DOI: 10.1029/2006JD007291.

Holland DM, Thomas RH, De Young B, Ribergaard MH, Lyberth B.2008. Acceleration of Jackobshavn Isbrae triggered by warmsubsurface ocean waters. Nature Geoscience 1: 659–664.

Holton JR. 1992. An Introduction to Dynamical Meteorology, 3rd edn.Academic Press: New York, 216–222.

Howat IM, Joughin I, Fahnestock M, Smith BE, Scambos TA. 2008.Synchronous reteat and acceleration of southeast Greenland outletglaciers 2000–2006: ice dynamics and coupling to climate. Journalof Glaciology 54: 646–660.

Howat IM, Ahn Y, Joughin I, van den Broeke M, Lenaerts J, Smith B.2011. Mass balance of Greenland’s three largest outlet glaciers,2000–2010. Geophysical Research Letters. 38: L12501, DOI:10.1029/2011GL047565.

Hurrell JW. 1995. Decadal trends in the North Atlantic Oscillation:regional temperatures and precipitation. Science 269: 676–679.

Hurrell JW, Kushnir Y, Ottersen G, Visbeck M (eds.). 2003. The NorthAtlantic Oscillation: climatic significance and environmental impact.Amercan Geophysical Union: Washington, DC.

Janssens I, Huybrechts P. 2000. The treatment of meltwater retention inmass-balance parameterizations of the Greenland ice sheet. Annallsof Glaciology 31: 133–140.

Jones PD, Osborn TJ, Briffa KR. 2003. Pressure-based measures of theNorth Atlantic Oscillation (NAO): a comparison and an assessmentof changes in the strength of the NAO and in its influence on surfaceclimate parameters. In The North Atlantic Oscillation: Climaticsignificance and environmental impact. Geophysical Mongraph 134,AGU.

Jonsson T, Hanna E. 2007. A new day-to-day pressure variability indexas a proxy of Icelandic storminess and complement to the NorthAtlantic Oscillation index 1823–2005. Meteorologische Zeitschrift16: 25–36.

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L,Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A,Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J,Mo KC, Ropelewski C, Wang J, Roy J, Dennis J. 1996. The

NCEP/NCAR Reanalysis 40-year project. Bulletin of AmericanMeteorological Society 77: 437–471.

Kaplan A, Cane M, Kushnir Y, Clement A, Blumenthal M, RajagopalanB. 1998. Analyses of global sea surface temperature 1856–1991.Journal of Geophysical Research 103: 18567–18589.

Knight JR, Folland CK, Scaife AA. 2006. Climate impacts of theAtlantic Multidecadal Oscillation. Geophysical Research Letters 33:L17706, DOI: 10.1029/2006GL026242.

Koch SE, desJardins M, Kocin PJ. 1983. An interactive Barnesobjective map analysis scheme for use with satellite and conventionaldata. Journal of Climate and Applied Meteorology 22(9): 1487–1503.

Liston GE, Elder K. 2006a. A distributed snow-evolution model-ing system (SnowModel). Journal of Hydrometeorology 7(6):1259–1276.

Liston GE, Elder K. 2006b. A meteorological distribution systemfor high-resolution terrestrial modeling (MicroMet). Journal ofHydrometeorology 7(2): 217–234.

Luckman A, Murray T, de Lange R, Hanna E. 2006. Rapid andsynchronous ice-dynamic changes in East Greenland. GeophsicalResearch Letters 33: L03503, DOI: 10.1029/2005GL025428.

Marshall J, Johnson H, Goodman J. 2001. A study of the interactionof the North Atlantic Oscillation with the ocean circulation. Journalof Climate 14: 1399–1421.

Meehl GA, Stocker TF, Collins WD, Friedlingstein P, Gaye AT,Gregory JM, Kitoh A, Knutti R, Murphy JM, Noda A, Raper SCB,Watterson IG, Weaver AJ, Zhao Z-C. 2007. Global climateprojections. In Climate Change 2007: The Physical Science Basis.Contribution of Working Group I to the Fourth Assessment Reportof the Intergovernmental Panel on Climate Change. Solomon S,Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M,Miller HL(eds.). Cambridge University Press: Cambridge, NewYork.

Mernild SH, Liston GE, Hasholt B, Knudsen NT. 2006. Snowdistribution and melt modeling for Mittivakkat Glacier, AmmassalikIsland, Southeast Greenland. Journal of Hydrometeorology 7(4):808–824.

Mernild SH, Mote TL, Liston G. 2011. Greenland Ice Sheet surfacemelt extent and trends: 1960–2011. Journal of Glaciology 67:621–628.

Mote TL. 1998a. Mid-tropospheric circulation and surface melt onthe Greenland Ice Sheet. Part I: atmospheric teleconnections.International Journal of Climatology 18: 111–129.

Mote TL. 1998b. Mid-tropospheric circulation and surface melt on theGreenland Ice Sheet. Part II: synoptic climatology. InternationalJournal of Climatology 18: 131–145.

Mote TL. 2007. Greenland surface melt trends, 1973–2007: evidenceof a large increase in 2007. Geophysical Research Letters 34:L22507, DOI: 10.1029/2007GL031976.

Msadek R, Frankignoul C. 2008. Atlantic multidecadal oceanicvariability and its influence on the atmosphere in a climate model.Climate Dynamics. 33: 45–62, DOI: 10.1007/s00382-008-0452-0.

Murray T, Scharrer K, James TD, Dye SR, Hanna E, Booth AD,Selmes N, Luckman A, Hughes ALC, Cook S, Huybrechts P. 2010.Ocean-regulation hypothesis for glacier dynamics in south-eastGreenland and implications for ice-sheet mass changes. Journal ofGeophysical Research 115: F03026, DOI: 10.1029/2009JF001522.

Overland JE. 2006. Arctic change: multiple observations and recentunderstanding. Weather 61: 78–83.

Overland JE, Wang M, Salo S. 2008. The recent Arctic warm period.Tellus 60: 589–597.

Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV,Rowell DP, Kent EC, Kaplan A. 2003. Global analyses of seasurface temperature, sea ice, and night marine air temperature sincethe late nineteenth century. Journal of Geophysical Research. 108:4407, DOI: 10.1029/2002JD002670.

Rignot E, Box JE, Burgess E, Hanna E. 2008. Mass balance of theGreenland ice Sheet from 1958 to 2007. Geophysical ResearchLetters 35: L20502, DOI: 10.1029/2008GL035417.

Santer BD, Wigley TML, Boyle JS, Gaffen DJ, Hnilo JJ, Nychka D,Parker DE, Taylor KE. 2000. Statistical significance of trends andtrend differences in layer-average atmospheric temperature timeseries. Journal of Geophysical Research. 105: 7337–7356.

Shindell DT, Schmidt G, Mann ME, Faluvegi G. 2004. Dynamicwinter climate response to large tropical volcanic eruptions since1600. Journal of Geophysical Research. 109: D05104, DOI:10.1029/2003JD004151.

Simmonds I, Burke C, Keay K. 2008. Arctic climate change asmanifest in cyclone behaviour. Journal of Climate 21: 5777–5796.


880 E. HANNA et al.

Spiegel MR, Stephens LJ. 1999. Schaum’s Outline of Theory andProblems of Statistics, 3rd edn. McGraw-Hill: New York.

Straneo F, Hamilton G, Sutherland D, Stearns L, Davidson F, Ham-mill M, Stenson G, Rosing-Asvid A. 2010. Rapid Circulation ofwarm subtropical waters in a major glacial fjord off East Greenland.Nature Geoscience. 3: 182–186, DOI: 10.1038/NGEO764.

Straneo F, Curry RG, Sutherland DA, Hamilton GS, Cenedese C,Vage K, Stearns LA. 2011. Impact of fjord dynamics and glacialrunoff on the circulation near Helheim Glacier. Nature Geoscience4: 322–327.

Steffen K, Box J. 2001. Surface climatology of the GreenlandIce Sheet: Greenland Climate Network 1995–1999. Journal ofGeophysical Research. 106: 33951–33964.

Steffen K, Thomas R, Rignot E, Cogley G, Dyurgerov M, Raper S,Huybrechts P, Hanna E. 2010. Cryospheric contributions to sea-level rise and variability. In Church J, Woodworth P, Aarup T,Wilson S (eds.) Understanding Sea-level Rise and Variability, Wiley-Blackwell: Oxford, 177–225, DOI: 10.1002/9781444323276.ch7.

Sutton R, Hodson DLR. 2005. Atlantic Ocean forcing of NorthAmerican and European summer climate. Science 309: 115–118,DOI: 10.1126/science.110949.

Tedesco M, Fettweis X, Van den Broeke MR, Van de Wal RSW,Smeets CJPP, Van de Berg WJ, Serreze MC, Box JE. 2011. The roleof albedo and accumulation in the 2010 melting record in Greenland.Environmental Research Letters 6: 014005, DOI: 10.1088/1748-9326/6/1/014005.

Thompson DWJ, Lee S, Baldwin MP. 2002. Atmospheric processesgoverning the Northern Hemisphere Annular Mode/North AtlanticOscillation. In The North Atlantic Oscillation – Climate Significance

and Environmental Impact, Geophys. Mongr., Vol 134, Amer.Geophys. Union, 81–112.

Trenberth KE, Paolino DA. 1980. The Northern Hemisphere sea levelpressure data set: trends, errors, and discontinuities. MonsoonWeather Review 108: 855–872.

Tsukernik M, Deser C, Alexander M, Tomas T. 2009. Atmosphericforcing of Fram Strait sea ice export: a closer look. ClimateDynamics. 35: 1349–1360, DOI 10.1007/s00382-009-0647-z.

Vallis GK, EP Gerber. 2008. Local and hemispheric dynamics of theNorth Atlantic oscillation, annular patterns and the zonal index.Dynamics of Atmospheres and Oceans 44: 184–212.

Visbeck M, Chassigner EP, Curry RG, Delworth TL, Dickson RR,Krahmann G. 2003. The ocean’s response to North AtlanticOscillation Variability, In The North Atlantic Oscillation: ClimaticSignificance and Environmental Impact. Geophysical Monograph134, American Geophysical Union.

Widmann M. 2005. One-Dimensional CCA and SVD, and theirrelationship to regression maps. Journal of Climate 18: 2785–2792.

Wood KR, Overland JE. 2010. Early 20th century Arctic warming inretrospect. International Journal of Climatology 30: 1269–1279.

Woollings T. 2010. Dynamical influences on European climate: anuncertain future. Philosophical Transactions of the Royal Society ofLondon. Series A 368: 3733–3756.

Woollings T, Hoskins B, Blackburn M, Berrisford P. 2008. A newRossby wave-breaking interpretation of the North AtlanticOscillation. Journal of the Atmospheric Sciences 65: 609–626.

Woollings TA, Hannachi A, Hoskins B, Turner A. 2010. A regimeview of the North Atlantic Oscillation and its response toatmospheric forcing. Journal of Climate 23: 1291–1307.


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