Geophysical Research Letters
RESEARCH LETTER
10.1029/2018GL079682
Key Points:• Links between natural variability andGreenland melt are studied undervarious greenhouse forcings in theCommunity Earth System Model• A negative summer North Atlantic
Oscillation and positive Atlantic
Multidecadal Oscillation promote
extreme melt in all forcing scenarios• The timing of emergence ofwidespread 21st century Greenland
melt is dependent on the phasing ofthese modes of natural variability

Supporting Information:• Supporting Information S1
Correspondence to:
L. Hahn,lchahn@uw.edu

Citation:
Hahn, L., Ummenhofer, C. C.,& Kwon, Y.-O. (2018). North Atlantic
natural variability modulates
emergence of widespread Greenland
melt in a warming climate. GeophysicalResearch Letters, 45. https://doi.org/
10.1029/2018GL079682
Received 18 JUL 2018
Accepted 22 AUG 2018
Accepted article online 27 AUG 2018

North Atlantic Natural Variability Modulates Emergenceof Widespread Greenland Melt in a Warming Climate
L. Hahn1
1, C. C. Ummenhofer1, and Y.-O. Kwon1

Physical Oceanography Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

Abstract Record-breaking melt over Greenland in recent decades is linked not only to climate change but
also to natural variability, including persistent atmospheric high-pressure conditions in the negative
phase of the North Atlantic Oscillation and warm North Atlantic Ocean temperatures during the positive
phase of the Atlantic Multidecadal Oscillation. However, the relative importance of natural variability forGreenland melt under varying degrees of greenhouse forcing is still unclear. Using reanalysis data and a large
ensemble of climate model simulations, we ﬁnd that a negative North Atlantic Oscillation and positive
Atlantic Multidecadal Oscillation consistently promote heightened summer melt under various forcing
conditions. Moreover, timing of widespread 21st century Greenland melt varies considerably between
ensemble members due to different phasing of these modes of natural variability. These results indicate theimportance of natural modes of variability across a range of external forcing conditions for interannual
melt variability and the emergence of widespread Greenland melt.

Plain Language Summary Extreme Greenland Ice Sheet melt is partly driven by climate change, asrising greenhouse gas concentrations promote warmer temperatures, especially for the Arctic region.
However, natural ﬂuctuations in the climate system can also promote Greenland melt. In particular, persistenthigh-pressure systems in the atmosphere over the region and warm North Atlantic Ocean temperatures
have been linked to enhanced Greenland melt in recent decades. Yet the importance of these natural
oscillations for melt in the past and future remains unclear. We use a series of climate model simulations toshow that high-pressure conditions and a warm North Atlantic Ocean consistently promote heightenedsummer melt under various warming scenarios for the past and future. Additionally, the timing of widespread
Greenland melt in the 21st century depends on the timing of these modes of natural variability.
1. Introduction
Greenland Ice Sheet (GrIS) meltwater production is expected to contribute signiﬁcantly to global sea level rise
(Fettweis, Franco, et al., 2013; Shepherd et al., 2012) and may weaken the Atlantic meridional overturning
circulation by suppressing deep convection (Böning et al., 2016; Oltmanns et al., 2018; Rahmstorf et al.,2015), motivating efforts to understand what drives GrIS melt. Recent work highlights the dominant role ofatmospheric and oceanic variability, speciﬁcally the North Atlantic Oscillation (NAO) and the related
Greenland Blocking Index (GBI; Box et al., 2012; Chylek et al., 2004; Ding et al., 2014; Fettweis, Hanna, et al.,2013; Häkkinen et al., 2014; Hanna et al., 2014, 2013; Lim et al., 2016; McLeod & Mote, 2016; Tedesco et al.,2016), as well as the Atlantic Multidecadal Oscillation (AMO; Chylek et al., 2009; Hanna et al., 2013; McLeod& Mote, 2016; Straneo & Heimbach, 2013), in promoting Greenland surface melt.

©2018. American Geophysical Union.
All Rights Reserved.

HAHN ET AL.

In recent decades, the most extreme melt seasons exhibited signiﬁcant high-pressure blocking anomalies
over Greenland associated with positive GBI values and the negative phase of the NAO (Fettweis, Hanna,
et al., 2013; Tedesco et al., 2016; Figure 1), with signiﬁcant increases in summer Greenland blocking since
1981 (Hanna et al., 2016, Hanna, Hall, et al., 2018). These prolonged atmospheric blocking episodes may promote melt via northward advection of warm air over west Greenland (Fettweis et al., 2011), adiabatic warming of sinking air associated with anticyclonic circulation anomalies (Ding et al., 2017), or radiative impacts ofcloud cover changes resulting from blocking conditions (Hofer et al., 2017; Lim et al., 2016). The positive AMO
phase, deﬁned by anomalously warm North Atlantic sea surface temperatures (SSTs) after the global warming
signal has been removed, is also signiﬁcantly linked to surface melt over the entire GrIS potentially due to astrong connection between the AMO and NAO (Hanna et al., 2013; McLeod & Mote, 2016), although the direction of causality for this connection is debated (Gastineau & Frankignoul, 2015; Häkkinen et al., 2011; Peings &Magnusdottir, 2014). With atmospheric and oceanic variability established as dominant drivers of Greenland1Geophysical Research Letters10.1029/2018GL079682

Figure 1. a) Time series of June–August (JJA) melt (mm water equivalent [mmWE]/month; dashed line) from the Modèle Atmosphérique Régional simulation and
June–August near-surface temperature at 2-m height (T2m; °C; solid line) from ERA-Interim reanalysis averaged over Greenland for 1979–2015, with red dots
indicating summers with extreme melt, deﬁned as those with T2m in the top decile. JJA composite anomalies for top decile melt summers relative to the 1980–2009
mean for (b) melt (mmWE/month) and (c) Z500 (m). Stippling indicates anomalies statistically signiﬁcant at the 95% conﬁdence level.

surface melt particularly for the past few decades, we analyze the extent to which these links are present in
the absence of anthropogenic forcing and whether global warming will change or supersede these links inthe future.

2. Data and Methods
2.1. Model and Reanalysis Data
To investigate links between Greenland melt and natural variability under various greenhouse forcing
scenarios, we primarily use the fully coupled Community Earth System Model version 1 Large Ensemble(CESM1-LE; Kay et al., 2015). All 40 ensemble members are forced by the same time-varying external forcing
but begin with different initial conditions. Therefore, these ensemble members exhibit distinct internal variability and thus different phasing of the NAO and AMO. The ensemble is run with historical forcing from 1920
to 2005 (hereafter CESM1 HIST) and with RCP8.5 forcing from 2006 to 2100 (hereafter CESM1 RCP8.5) following the Coupled Model Intercomparison Project Phase 5 (CMIP5) protocol. We additionally use the
10-member CESM1 Global Ocean-Global Atmosphere experiment from 1920 to 2015 (hereafter CESM1ATM), in which atmosphere and land model components are identical to the fully coupled CESM1, but oceanand sea ice components are inactive, instead using SST from the National Oceanic and Atmospheric
Administration Extended Reconstructed Sea Surface Temperature and sea ice from the Hadley Centre Sea
Ice and Sea Surface Temperature data set (HadISST). CESM1 ATM is run with historical forcing identical to
CESM1 HIST from 1920 to 2005 and with RCP8.5 forcing from 2006 to 2015. Lastly, the fully coupled

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CESM1 simulation with ﬁxed 1850 preindustrial radiative forcing (hereafter CESM1 PI), which otherwise uses
the same conﬁguration as the CESM1-LE, allows for investigation of preindustrial conditions.
As shown by Vizcaíno et al. (2013), CESM1 realistically reproduces Greenland surface climate conditions incomparison to a regional climate model. In comparison with ERA-Interim and Modern-Era Retrospective
analysis for Research and Applications, Version 2, the two reanalyses most consistent with observations ofsurface air temperatures over Greenland (Eyre & Zeng, 2017; Lindsay et al., 2014), CESM1 is signiﬁcantly colder
for the reference period 1980–2009 (Figures S1a and S1b in the supporting information). This cold bias may
be related to CESM1’s underestimation of Greenland blocking, although the model produces summer
500-hPa geopotential height (Z500) mean and variance patterns similar to the reanalyses (not shown) andshows Greenland blocking anomalies similar to those in ERA-Interim for high melt years from 1980 to 2009
(Figure 2). The temperature trends and number of extremes per decade for the 1980–2009 period in
CESM1 are within range of the reanalysis products, further conﬁrming the utility of CESM1 for this study.
Both the fully coupled CESM1 HIST and atmosphere-only CESM1 ATM runs for the 1980–2009 period show
median summer GrIS temperatures and 15-year temperature trends at or above the 75% quantile of their
1920–1949 counterparts and of the preindustrial experiment (CESM1 PI; Figures S1b and S1c). This warming
signal is also evident in the evolution of the clustering of extreme temperature years, where there are morehigh melt summers and fewer low melt summers per decade for 1980–2009 compared to 1920–1949, a
precursor to the 7 °C summertime warming over Greenland projected by CESM1 RCP8.5 by the end of the
21st century (Figures S1a, S1d, and S1e).
In comparison with the CESM1 simulations, reanalysis and observational data sets as well as a regional
climate model simulation over Greenland are used. Surface melt in recent decades is provided by the
Modèle Atmosphérique Régional version 3.5.2 regional climate model simulation for the GrIS (Fettweis
et al., 2017). In addition, we use atmospheric temperature and geopotential height from the European
Centre for Medium-Range Weather Forecasts Interim Reanalysis (ERA-Interim; Dee et al., 2011) andModern-Era Retrospective analysis for Research and Applications, Version 2 (Molod et al., 2015) reanalysis
data sets. We also use the HadISST data set (Rayner et al., 2003).
2.2. Climate Indices
The summer (June–August) GBI is calculated as the 500-hPa geopotential height area-averaged for 60°N to
80°N and 20°W to 80°W and time averaged for the summer season following Hanna et al. (2013). For thesummer North Atlantic Oscillation (June–August NAO), the Hurrell (1995) station-based index deﬁnition isused, while the AMO is calculated by area averaging North Atlantic SST from 0°N to 60°N, removing thequadratic trend to exclude the climate change signal and computing the yearly average. We found similar
AMO results using alternate methods of excluding the climate change signal, including linear detrending
and removing the global mean SST (not shown).

3. Results
3.1. Atmospheric Variability and Greenland Melt
As established previously (Fettweis, Hanna, et al., 2013) and shown in Figure 1a, Greenland near-surface
temperature and surface melt are highly correlated, motivating our use of near-surface temperature at 2-mheight (T2m) as a proxy for melt. We investigate links between Greenland melt and atmospheric pressure
conditions in different forcing scenarios by calculating geopotential height anomalies during summer melt
seasons with Greenland T2m above the standard deviation for a given period in comparison to the fullperiod (Figure 2).
Similar to the reanalysis for 1980–2009, the CESM1 fully coupled hindcast (HIST) and atmosphere-onlyhindcast (ATM) both exhibit anomalous high pressure over Greenland and low pressure to the southeastof Greenland characteristic of the negative NAO phase during extreme melt seasons. In fact, all CESM1
experiments and time periods show signiﬁcant high-pressure anomalies over central Greenland during highmelt summers, demonstrating an important role for natural variability in the form of the NAO regardless ofexternal forcing. However, greenhouse gas forcing also appears to promote high melt as extreme melt years
tend to occur near the end of the 1980–2009 and 2070–2099 periods, respectively. To isolate links between
natural variability and Greenland melt, we remove the ensemble mean T2m and Z500 before choosing

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Figure 2. June–August Z500 anomalies (m) for extreme melt years with June–August GrIS T2m more than one standard deviation above the mean T2m foreach period. Anomalies for CESM1 HIST, ATM, and RCP8.5 are calculated for each ensemble member before calculating the ensemble mean of anomalies. Stipplingfor CESM1 PI and ERA-Interim indicates anomalies statistically signiﬁcant at the 95% conﬁdence level; stippling for CESM1 HIST, ATM, and RCP8.5 indicatessigniﬁcance in at least 75% of composited ensemble members.

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Figure 3. Climate indices for average conditions and for extreme GrIS summer melt years in CESM experiments and reanalysis. (a) June–August (JJA) Greenland
Blocking Index (GBI), (b) JJA North Atlantic Oscillation index (NAO), and (c) Atlantic Multidecadal Oscillation index (AMO) for all years (left, ﬁlled box for eachperiod), extremely low (middle box for each period), and extremely high (right box for each period) JJA Greenland T2m years, where JJA T2m for extreme years ismore than one standard deviation above or below the mean T2m for a given period.

extreme melt years and calculating anomalies and ﬁnd an even stronger NAO signal across the fully coupledlarge ensemble experiments, particularly for RCP8.5 2070–2099 (Figure S2).
This summertime NAO-melt connection in the CESM1 simulations is more symmetrically illustrated in
Figure 3b, with high (low) melt years exhibiting a more negative (positive) NAO phase for all periods. We also
plot the GBI (Figure 3a), a measure of high-pressure conditions over Greenland, as observations show astronger correlation with Greenland melt for the GBI than the NAO (Hanna et al., 2013). High melt summers
experience more frequent blocking conditions than low melt summers, with 75% of summers displaying nooverlap in blocking values between the all, low, and high melt categories for all forcing scenarios. Behaving
similarly to ERA-Interim, the much larger sets of data points for CESM1 experiments (Table S1) lend morestatistical robustness to this result.
3.2. Oceanic Variability and Greenland Melt
In addition to the NAO, we investigate links between the AMO and melt under various greenhouse forcingsby calculating SST anomalies during extreme melt seasons compared to each full period (Figures S3 and S4)
and the AMO index for all, low, and high melt years (Figure 3c). While high melt seasons co-occur withwarmer North Atlantic SSTs, the tripole structure of these anomalies for the fully coupled simulations(Figures S3 and S4) suggests that North Atlantic warming in high melt years is driven by the negative NAO
rather than the AMO (Hurrell & Deser, 2010). Downward (upward) turbulent heat ﬂux anomalies over warmer(colder) ocean regions during high melt seasons further suggest that NAO-related wind and heat ﬂuxes predominantly force ocean temperatures (Figure S5).
However, the ocean inﬂuence on Greenland melt appears stronger in the reanalysis, as the warm SST
anomalies in the subpolar gyre are damped by anomalous upward (negative) turbulent heat ﬂuxes in ERAI
(Figures S3 and S5). Similar heat ﬂux patterns hold for CESM1 ATM for 1980–2009, which uses the observed
SST anomalies as the boundary condition. Consistently, AMO index values are more distinct between low andhigh melt years for the observed SST (i.e., CESM1 ATM and HadISST) than for the fully coupled CESM1 PI and
HIST (Figure 3c). Therefore, the fully coupled models may be underestimating the observed AMO impact on
Greenland melt.
3.3. Impact of Natural Variability on Emergence of Widespread Greenland Melt
Our results so far establish a strong link between interannual Greenland melt variability, the summer NAO
phase, and North Atlantic SSTs for all periods. We now investigate how the timing of NAO and AMO phases
in different ensemble members, overlaid onto identical external forcing, may impact the emergence of widespread Greenland melt in a warming climate. Early and late emergence ensemble members are identiﬁed

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Figure 4. Timing of widespread Greenland Ice Sheet melt under global warming and associated circulation and temperature anomalies over the North Atlantic
sector. (a) Number of grid points over the Greenland Ice Sheet with at least 7 years per decade above 0 °C, accumulated in time beginning in 1920 for each
CESM1 HIST and RCP8.5 ensemble member and averaged for early emergence (red), late emergence (blue), and all (black) ensemble members. (b) As in (a), withindividual early and late emergence ensemble members (dashed), triangle for the average emergence year for early (red) and late (blue) emergence members, andshading for 10-year emergence periods surrounding this year. Anomalies for each extreme emergence ensemble member compared to the ensemble averageduring emergence periods are averaged for early members and for late members to produce (c), (d) June–August (JJA) Z500 (m), and (e, f) JJA T2m (°C) averageanomalies. Stippling for (c)–(f) indicates anomalies statistically signiﬁcant at the 95% conﬁdence level.

based on a decadal-scale sum of potential melt grid points over Greenland for 1920–2100 in CESM1 HIST and
RCP8.5. We calculate the number of grid points over Greenland with at least 7 years per decade with T2mabove 0 °C, then accumulate this quantity in time beginning in 1920 for each CESM1 HIST and RCP8.5
ensemble member. Early and late emergence ensemble members fall outside a standard deviationthreshold on the distribution of the timing when each ensemble member reaches a criterion for
widespread melting, that is, 50% of its 2100 cumulative frequency value. We trialed thresholds of 1, 7, and10 years per decade above 0 °C in addition to 25%, 50%, and 75% of the 2100 cumulative frequency value
but found that the 50% and 75% thresholds produce better separation of cumulative frequency timeseries for early and late members than the 25% threshold (not shown). Variations using 1, 7, and 10 years
per decade combined with 50% and 75% of the 2100 cumulative frequency value all produce similar
patterns for geopotential height and near-surface temperature anomalies for early and late emergenceensembles (not shown).

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Geopotential height and near-surface temperature anomalies are calculated for early (late) emergencemembers compared to the ensemble mean of all members for the same period. With all ensembles
experiencing the same external forcing, early emergence members exhibit widespread melting conditions(when 50% of the cumulative melt area from 1920 to 2100 is reached) up to 16 years sooner than lateemergence members (Figures 4a–4b). In the decade surrounding the average emergence year, early (late)members show a more negative (positive) NAO phase and warmer (colder) North Atlantic temperatures thanthe ensemble mean for the same period (Figures 4c–4f), suggesting an important role for natural variability inimpacting the timing of widespread Greenland melt.

4. Conclusions
We ﬁnd strong links between summer Greenland melt and the NAO and AMO under all greenhouse forcing
scenarios. Although ocean temperatures generally appear to be driven by the NAO signal during high melt
summers, experiments with observational SSTs show a potentially stronger ocean inﬂuence on Greenland
melt than the fully coupled experiments. Lastly, we ﬁnd that under identical external forcing, ensemble
members’ disparate timing of emergence of widespread melt depends on the timing of modes ofnatural variability.

Acknowledgments
This research was supported by the
Woods Hole Oceanographic Institution
Summer Student Fellow program andby the U.S. National Science Foundation
under AGS-1355339 to C. C. U. andY.-O. K. and ANS-1736738 to Y.-O. K. Useof the following data sets is gratefully
acknowledged: CESM1-LE simulation
output provided by the CESM Large
Ensemble Community Project, available
on the NCAR Earth System Grid, andsupercomputing resources provided by
NSF/CISL/Yellowstone; CMIP5 model
output data provided by the WHOI
CMIP5 Community Storage Server,
Woods Hole Oceanographic Institution,
Woods Hole, MA, USA from theirwebsite at http://cmip5.whoi.edu/; ERAInterim data provided by the European
Centre for Medium-Range Weather
Forecasts Data Archive; MERRA-2 datafrom the NASA Goddard Earth Sciences(GES) Data and Information Services
Center (DISC); HadISST data from the
Met Ofﬁce; and MAR data provided at
ftp://ftp.climato.be/fettweis/MARv3.5.2.
The authors also thank the Editor, Noah
Diffenbaugh, and two anonymous
reviewers for their comments.

HAHN ET AL.

As natural variability appears important for Greenland melt in the past, present, and future, more accurate
prediction of future Greenland melt hinges on understanding how modes of natural variability like the
NAO and AMO may respond to greenhouse gas forcing. While the sign of the summer NAO does not looksigniﬁcantly different for 2070–2099 in comparison to past periods in CESM1-LE (Figure 3b), the CMIP5
ensemble mean projects a slightly more positive summer NAO under future greenhouse gas forcing(Fettweis, Hanna, et al., 2013). However, the CMIP5 models are known to generally underestimate observed
blocking in the high-latitude North Atlantic and Europe (Anstey et al., 2013; Dunn-Sigouin & Son, 2013;
Masato et al., 2013; Hanna, Fettweis, & Hall, 2018), calling into question their ability to project future NAO
changes and highlighting a need for better model representation of blocking and further study into theimpacts of climate change on natural variability. An additional open question concerns the relative
importance of different mechanisms for connecting high-pressure conditions to extreme Greenland melt
in different periods, as even for recent decades the mechanisms of warm air advection, cloud and radiative
impacts, and large-scale sinking and adiabatic warming have all been implicated (Ding et al., 2017;
Fettweis et al., 2011; Hofer et al., 2017; Lim et al., 2016). Furthermore, determining the extent to which the
AMO impacts Greenland blocking and melt will be particularly important as the AMO phase and strength
evolve in the future. Further study of natural variability links to Greenland melt is essential for predicting
the climate response to greenhouse gas forcing.

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