Home

SearchCollections

Journals

About

Contact us

My IOPscience

Projected changes in regional climate extremes arising from Arctic sea ice loss

This content has been downloaded from IOPscience. Please scroll down to see the full text.
2015 Environ. Res. Lett. 10 084006(http://iopscience.iop.org/1748-9326/10/8/084006)
View the table of contents for this issue, or go to the journal homepage for more

Download details:
IP Address: 128.117.21.140
This content was downloaded on 04/08/2015 at 17:25

Please note that terms and conditions apply.

Environ. Res. Lett. 10 (2015) 084006

doi:10.1088/1748-9326/10/8/084006

LETTER

OPEN ACCESS

Projected changes in regional climate extremes arising from Arctic
sea ice loss

RECEIVED

7 May 2015
REVISED23 June 2015
ACCEPTED FOR PUBLICATION

9 July 2015
PUBLISHED

3 August 2015

James A Screen1, Clara Deser2 and Lantao Sun3
1
2
3

College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon, UK
Climate and Global Dynamics, National Center for Atmospheric Research, Boulder, CO, USA
Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder and National Oceanic, and Atmospheric
Administration Earth System Research Laboratory, Boulder, CO, USA

E-mail: j.screen@exeter.ac.uk
Keywords: Arctic, sea ice, extremes, climate, weather, temperature, precipitation

Content from this work
may be used under theterms of the Creative
Commons Attribution 3.0
licence.
Any further distribution ofthis work must maintainattribution to theauthor(s) and the title of
the work, journal citation
and DOI.

Supplementary material for this article is available online

Abstract
The decline in Arctic sea ice cover has been widely documented and it is clear that this change ishaving profound impacts locally. An emerging and highly uncertain area of scientiﬁc research,however, is whether such Arctic change has a tangible effect on weather and climate at lower
latitudes. Of particular societal relevance is the open question: will continued Arctic sea ice loss
make mid-latitude weather more extreme? Here we analyse idealized atmospheric generalcirculation model simulations, using two independent models, both forced by projected Arctic sea
ice loss in the late twenty-ﬁrst century. We identify robust projected changes in regionaltemperature and precipitation extremes arising solely due to Arctic sea ice loss. The likelihood andduration of cold extremes are projected to decrease over high latitudes and over central and eastern
North America, but to increase over central Asia. Hot extremes are projected to increase infrequency and duration over high latitudes. The likelihood and severity of wet extremes areprojected to increase over high latitudes, the Mediterranean and central Asia; and their intensity is
projected to increase over high latitudes and central and eastern Asia. The number of dry days over
mid-latitude Eurasia and dry spell duration over high latitudes are both projected to decrease.
There is closer model agreement for projected changes in temperature extremes than for
precipitation extremes. Overall, we ﬁnd that extreme weather over central and eastern North
America is more sensitive to Arctic sea ice loss than over other mid-latitude regions. Our results areuseful for constraining the role of Arctic sea ice loss in shifting the odds of extreme weather, butmust not be viewed as deterministic projections, as they do not account for drivers other than
Arctic sea ice loss.

1. Introduction
The decline in Arctic sea ice cover has been widely
documented (e.g., Stroeve et al 2012a, 2012b) and it
is clear that this change is having profound impacts
locally (e.g., Post et al 2013, Bhatt et al 2014). An
emerging and highly uncertain area of scientiﬁc
research, however, is whether such Arctic change
has a tangible effect on weather and climate at lower
latitudes. Of particular societal relevance is the open
question: is Arctic sea ice loss making mid-latitude© 2015 IOP Publishing Ltd

weather more extreme? This question has recently
received a lot of scientiﬁc and media attention (seereviews by Cohen et al 2014, Vihma 2014,
Walsh 2014, Barnes and Screen 2015). It has beensuggested, for example, that Arctic sea ice loss may
have increased the frequency of cold winters over
North America and Eurasia (Liu et al 2012, Tanget al 2013, Mori et al 2014) and increased theseverity of summer heatwaves (Tang et al 2014,
Coumou et al 2014, 2015). Conversely, otherauthors have argued for fewer daily cold extremes

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al(Screen 2014, Screen et al 2015, Schneider
et al 2015).
Regardless of past changes in extremes, which
may be difﬁcult to detect or to attribute to Arctic
inﬂuence (Barnes and Screen 2015), the expectationis that Arctic sea ice loss and warming will continue
over the coming decades (Boe et al 2009, Mahlstein
and Knutti 2012, IPCC 2013). Therefore, a logicalquestion to ask is: will continued Arctic sea ice loss
make mid-latitude weather more extreme in the
future? We focus our attention on this importantquestion.
The answer to the question posed above may
depend on what is meant by ‘extreme weather’. Thereis no single deﬁnition of what constitutes ‘extreme
weather’. However, the world meteorological organisation (WMO) expert team on climate change detection and indices (ETCCDI) recommends the use oftwenty-seven core indices to characterize extreme
weather (Zhang et al 2011). These indices, or subsets
thereof, have previously been applied to observations(Alexander et al 2006, Donat et al 2013), historical climate model simulations (Sillman et al 2013a), andmodel projections forced by greenhouse gas increases(Sillman and Roeckner 2008, Sillman et al 2013b).
These indices have also been used in the intergovernmental panel on climate change (IPCC) assessment
reports (e.g., IPCC 2013).
Brieﬂy, previous work based on projections fromthe ﬁfth coupled model intercomparison project(CMIP5) multi-model ensemble has shown that
increasing greenhouse gas concentrations result in adecrease in cold extremes and an increase in hot
extremes (e.g., IPCC 2013, Kharin et al 2013, Sillman
et al 2013b). In high latitudes, these changes are notsymmetric with larger declines in cold extremes thanincreases in warm extremes (Kharin et al 2007, 2013),
reﬂecting
reducedtemperature
variance(Screen 2014). Such changes in temperature extremes
are highly robust across models. Projected changes inprecipitation extremes are more regionally variable.
The CMIP5 models robustly project increases in precipitation extremes over high latitudes (Sillman
et al 2013b). By the late twenty-ﬁrst century, and if
emissions are unabated, the majority of models project signiﬁcantly more frequent and more severe wet(and very wet) days over the mid-latitudes, except for
the Mediterranean and southern North America
which are projected to become drier (Sillman
et al 2013b).
Here for the ﬁrst time we systemically apply a large
subset (sixteen) of these indices to idealized atmospheric general circulation model (AGCM) simulations forced by projected Arctic sea ice loss, to isolate
and quantify changes in temperature and precipitation
extremes arising solely due to diminished Arctic sea ice
cover.
2

2. Data and methods
2.1. Simulations
We analyse simulations from two independent
AGCMs, namely, the UK Met Ofﬁce Hadley centre
global atmospheric model version 2 (HadGAM2) andthe National Centre for Atmospheric Research(NCAR) community atmosphere model version 4(CAM4). Full descriptions are these AGCMs can befound in Collins et al (2011) and Gent et al (2011),
respectively. The version of HadGAM2 used here has ahorizontal resolution of 1.875° longitude and 1.25°
latitude (known as N96) and 38 vertical levels. The
utilized conﬁguration of CAM4 has a horizontal
resolution of 1.25° longitude by 0.9° latitude and 26
vertical levels.
We performed two core experiments with both
AGCMs. In the ﬁrst experiment, we prescribed arepeating annual-cycle of sea surface temperatures(sst) and sea ice concentrations (sic) representative ofthe late twentieth century. These sst and sic values
were taken from the CMIP5 ‘historical’ simulations
with the coupled versions of the models (known as
HadGEM2 and CCSM4), averaged for the period
1980–99 and across all available ensemble members.
The late twentieth century simulations with HadGAM2 and CAM4 are referred to hereafter as had20c
and cam20c, respectively.
In the second experiment, we prescribed the same
sst values as just described, but this time with sea ice
conditions representative of the late twenty-ﬁrst century. The sic values were taken from the CMIP5‘rcp8.5’ simulations averaged for the period 2080–99
and across all available ensemble members. The‘rcp8.5’ simulations are forced by a continuous
increase in greenhouse gas concentrations and areoften viewed as a ‘business-as-usual’ scenario, withlimited mitigation strategies applied. This scenario
was chosen to maximize the signal-to-noise ratio. In
grid-boxes that lost ice between the late twentieth andtwenty-ﬁrst centuries, the sst value for the late twentyﬁrst century was used. This procedure accounts for theocean surface warming in regions where ice is reduced.
The late twenty-ﬁrst century simulations with HadGAM2 and CAM4 are referred to hereafter as had21c
and cam21c, respectively. Further details on theexperimental setup can be found in Screen et al (2015).
Additionally in the supplementary material, we present selected results from a third experiment with
mid-twenty-ﬁrst century sea ice conditions (had21c_mid and cam21c_mid).
The mean March (month of annual maximum)
and September (month of annual minimum) sea ice
concentrations, and the annual cycle of Arctic sea ice
area, from each simulation are shown in ﬁgure 1. In
September there are ice-free conditions in both
had21c and cam21c. In fact, had21c is ice-free (or very
nearly) from July to November and cam21c is ice-freefrom August to October. In March, had21c retains ice

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

Figure 1. March sea ice concentrations in (a) had20c and (b) had21c and September sea ice concentrations in (c) had20c and (d)had21c. (e) Annual cycle of Arctic sea ice area (106 km2) in had20c (solid curve), had21c (dotted curve) and had21c_mid (dashedcurve). March sea ice concentrations in (f) cam20c and (g) cam21c and September sea ice concentrations in (h) cam20c and (i)cam21c. (j) Annual cycle of Arctic sea ice area (106 km2) in cam20c (solid curve) and cam21c (dotted curve) and cam21c_mid (dashedcurve). Magenta contours show the sea ice extent (deﬁned as the 15% contour) in (a)–(d) had20c and (f)–(i) cam20c.

along the northern coasts of Greenland, Canada andSiberia, but open water dominates the Atlantic sector.
In comparison, cam21c retains more multi-year ice
with the largest reductions (compared to cam20c) in
the Barents Sea, Sea of Okhotsk and Bering Sea. In
terms of mean March sea ice area, had21c has a 50%
reduction (compared to had20c) and cam21c has a
20% reduction (compared to cam20c). The CMIP5mean loss of March sea ice area over this time period(under rcp8.5) is 32% (15%–63%; 5th–95th percentile range). Thus, the prescribed sea ice area loss in
HadGAM2 is towards the upper-end of the CMIP5
model spread and in CAM4, is towards the lower end.
All simulations (had20c, cam20c, had21c andcam21c) were run for 260 years. In our modelling framework where the boundary forcing repeats annually,each year can be considered a separate ensemble member starting from a different atmospheric initial condition. Thus, we have 260-member ensembles fromwhich to calculate robust statistics of extreme weather.
We note that since these are atmosphere-only simulations, we cannot capture possible changes in weather
extremes related to ocean circulation changes that may
result from Arctic sea ice loss (Deser et al 2015).
In what follows we analyse daily values of maximum near-surface (1.5 m in HadGAM2; 2 m inCAM4) temperature (Tmax), minimum near-surface
temperature (Tmin), daily-mean near-surface temperature (Tave) and total precipitation (Ptot). We
applied the ETCCDI indices (see below) to the full 260
years and then derived long-term means. To approximate the response to Arctic sea ice loss, we subtracted
the long-term mean of the had20c (cam20c)
3

simulation from that in the had21c (cam21c) simulation. Statistical signiﬁcance was calculated using a difference of means test (Student’s T-test) where the null
hypothesis is that the two samples (n = 260) have thesame mean. We report responses where the null
hypothesis can be rejected with 95% conﬁdence. In
addition to testing for statistical signiﬁcance, we also
assess the robustness of the response between the twomodels. Robust responses are considered to be thosefor which there is model agreement on the sign andsigniﬁcance (i.e., the response is signiﬁcant at the 95%
conﬁdence level in both models).
2.2. Extreme indicesWe analyse sixteen of the ETCCDI core indices, which
are only brieﬂy introduced here; full deﬁnitions can bein Zhang et al (2011). Speciﬁcally these are (ofﬁcial
identiﬁer in parentheses): frost days (FD; annual countof days when Tmin < 0 °C), icing days (ID; annual
count of days when Tmax < 0 °C), cold nights(TN10p; % of days when Tmin < 10th percentile), cold
days (TX10p; % of days when Tmax < 10th percentile),
cold spell duration (CSDI; annual count of days withat least 6 consecutive days when Tmin < 10th percentile), warm nights (TN90p; % of days when
Tmin > 90th percentile), warm days (TX90p; % of days
when Tmax > 90th percentile), warm spell duration(WSDI; annual count of days with at least 6 consecutive days when Tmax > 90th percentile), wet days
(R10mm; annual count of days when Ptot > = 10 mm),very wet days (R20mm; annual count of days when Ptot
> = 20 mm), wet day precipitation (R95pTOT; annual
total precipitation on days when Ptot > 95th

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

percentile), very wet day precipitation (R99pTOT;
annual total precipitation on days when Ptot > 99th
percentile), precipitation intensity (SDII; average precipitation on days when Ptot > = 1 mm), longest wet
spell (CWD; annual maximum number of consecutive
days when Ptot > = 1 mm), dry days (Rnnmm; annual
count of days when Ptot = 0 mm; see note below) andlongest dry spell (CDD; annual maximum number ofconsecutive days when Ptot < 1 mm). We note that one
ETCCDI index is user-deﬁned (Rnnmm; annual
count of days when Ptot is above a user-chosen threshold); here we apply a threshold of 0 mm and refer tothis index as dry days. For the percentile-based indices,the percentile thresholds were determined from thelate twentieth century simulations (had20c andcam20c) and kept the same for the evaluations of thechanges in these indices in the late twenty-ﬁrst
century.
The indices were calculated at each model gridpoint before regional averaging, using twelve regional
domains (shown in ﬁgure 3(a)) based on those deﬁned
in the IPCC special report on managing the risks ofextreme events and disasters to advance climatechange adaptation (SREX; Seneviratne et al 2012) andlater used in the IPCC ﬁfth assessment report(IPCC 2013). These are: Alaska and western Canada(AWC; 105–168 °W 50–75 °N), eastern Canada andGreenland (ECG; 10–105 °W 50–85 °N), Scandinavia(SCA; 0–40 °E 58–85 °N), Siberia (SIB; 40–180 °E
50–85 °N), western United States (WUS; 105–130 °W
30–50 °N), central US (CUS; 85–105 °W 30–50 °N),
eastern US (EUS; 60–85 °W 30–50 °N), central Europe(CEU; 10 °W–40 °E 45–58 °N), Mediterranean (MED;
10 °W–40 °E 30–45 °N, western Asia (WAS; 40–75 °E
30–50 °N), central Asia (CAS; 75–100 °E 30–50 °N)
and eastern Asia (EAS; 100–145 °E 30–50 °N). We
hereafter refer to these regions by the three letter
abbreviations just provided.
2.3. Model evaluation
To evaluate the models’ ability to simulate realistic
weather extremes, we compared climatological valuesof the ETCCDI indices (for the non-percentile-based
indices) in the late twentieth century simulations toestimates of these quantities in the real world. Since
there is large observational uncertainty, we utilize four
different reference data sets: HadEX2 (Donat et al2013), based solely on in situ observations, and threecommonly used reanalysis products, ERA-Interim(Dee et al 2011), NCEP/DOE (Kanamitsu et al 2002)
and NCEP/NCAR (Kalnay et al 1996). The two models
simulate well the main spatial features of the observed
climatologies of the ETCCDI indices (supplementaryﬁgure 1), for example, the latitude and altitudedependence of cold extremes and enhanced precipitation extremes in coastal and mountainous regions.
Figure 2 shows the mean biases in the models,compared to each reference data set, averaged within
4

our regional domains. More speciﬁcally, we areaaveraged each index over our twelve regions andcompared the regional averages derived from had20c
and cam20c to those from the reference data sets. The
biases were simply calculated by subtracting thereference value (area and time mean) from thesimulated value, such that a positive bias implies themodel, on average (in space and time), overestimates
the quantity of interest. For most indices and regions,the magnitudes of the biases vary considerably
depending on the reference data set used (in somecases the biases are of opposite sign). This prevents ameaningful quantitative assessment of the model
biases; however, there are some consistent features ofthe biases (corroborated by multiple reference datasets) that enable qualitative statements on the ability ofthe models to simulate particular types, or characteristics, of extreme weather. The models have generally
smaller biases for the ‘event frequency’ indices, forexample frost days or wet days (R10mm), than for the‘event duration’ indices. Most notably, both models
substantially overestimate the mean cold spell duration (CSDI). They also tend to underestimate dry spell
duration (CDD), but these biases are smaller than forcold spells. Relatively large biases are also identiﬁed forwarm spell duration (WSDI), but the sign of these vary
between the reference data sets, complicating theirinterpretation. Given the large observational uncertainty, biases in all the indices likely reﬂect inadequacies in the observations as well as in the models.
Sillman et al (2013b) assessed the ability of theCMIP5 models (including HadGEM2-ES andCCSM4) to simulate observed global climatologies of
the ETCCDI indices. HadGEM2-ES and CCSM4 wereshown to perform comparatively well in this regard
with generally small biases globally (see their ﬁgure10), and to out-perform most other CMIP5 models(especially for the precipitation indices). Thus despite
the substantial biases shown in ﬁgure 2, we consider
these models to be two of the best available for studying future changes in extremes in response to sea ice
loss. However, the projected changes in the ETCCDI
indices arising from sea ice loss revealed in the following sections need to be viewed with a degree of caution
in light of the model climatological biases in these
indices.

3. Results
3.1. Cold extremes
Both models project signiﬁcant reductions in frostdays (ﬁgure 3(b)), icing days (ﬁgure 3(c)), coldnights (ﬁgure 3(d)) and cold days (ﬁgure 3(e)) overthe high-latitude regions; namely, AWC, ECG, SCA
and SIB. Both models also project signiﬁcantly
fewer frost days, icing days, and cold nights overcertain mid-latitude regions; namely, CUS and
EUS. Also, cold days decrease in both models over

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

Figure 2. Mean biases in (a) had20c and (b) cam20c relative to observed estimates, expressed as percentages of the reference
climatological values. Biases are calculated for 9 ETCCDI indices (x axes) each averaged over the twelve regions (y axes). For eachindex and region, the coloured quadrants show biases calculated relative to four different reference data sets: HadEX2 (top), ERAInterim (right), NCEP-NCAR (bottom) and NCEP-DOE (left). The reference climatologies are based on the 1981–2010 period(except for HadEX2, which uses 1961–1990). Grey shading indicates insufﬁcient reference data (deﬁned here as >20% of grid-boxes
within the regional domain with <30 years of data).

Figure 3. Projected changes in regional temperature extremes in response to Arctic sea ice loss. Each bar graph (b)–(i) corresponds toa different extreme index. Within each bar graph the pairs of coloured bars correspond to averages over the coloured regions in themap (a), and in each pair the left and right bars correspond to HadGAM2 and CAM4, respectively. Bars are only shown for responses
that are statistically signiﬁcant at the 95% conﬁdence level. Robust regional responses (i.e., models agree on the sign and signiﬁcanceof the response) are listed at the bottom of each graph. Note that the vertical axes have different units (provided in each sub-title) andscales.

EUS and a small, but statistically signiﬁcant, reduction in frost days is projected over MED. CAS is the
only region for which both models project anincrease in any of the aforementioned indices, asmall, but signiﬁcant, increase in cold nights.
Projected changes in these indices over WUS, CEU,
WAS and EAS are either statistically insigniﬁcant in5

one or both of the models, or are of opposite sign inthe two models and thus, cannot be considered to
be robust. Regarding the latter, frost days and icing
days over WAS both decrease in HadGAM2 but
increase in CAM4, and cold nights and cold days
over WUS both increase in HadGAM2 but decrease
in CAM4.

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

Figure 4. Projected changes in regional precipitation extremes in response to Arctic sea ice loss. The bar graphs are analogous to thosein ﬁgure 2, but for indices of precipitation extremes.

As well as altering the frequency of cold extremes,
Arctic sea ice loss may effect the duration of cold
extremes. Indeed, a shift towards more prolonged cold
extremes has been hypothesized (Francis and Vavrus 2012, 2015). Turning then to cold spell duration(ﬁgure 3(f)), both models project signiﬁcant decreases
over AWC, ECG, SCA, SIB, CUS and EUS, but a signiﬁcant increase over CAS. The shift towards shorter
cold spells, especially over the central and eastern US,
is opposite to that hypothesized by some in response to
Arctic warming.
In summary, both models depict the largest reduction in cold extremes and their duration over the highlatitudes. Over mid-latitudes, there are consistent
reductions in cold extremes and their duration overcentral and eastern US, but little change over Europe.
Central Asia is unique in being the only region wherecold extremes are projected to increase in frequency
and length.
3.2. Hot extremes
Both models depict signiﬁcant increases in warmdays (ﬁgure 3(g)) and warm nights (ﬁgure 3(h)) overthe high latitude regions of AWC, ECG, SCA and SIB.
Also, warm nights increase signiﬁcantly over EUS in
both models. In general over mid-latitudes, thechanges in warm extremes are less robust than thosein cold extremes. For example over WUS, HadGEM2
projects signiﬁcant decreases in warm nights andwarm days, whereas both indices signiﬁcantly
increase in CAM4. Similar model divergence is foundfor warm nights over CAS and warm days over CUS
and EUS.
Next we consider warm spell duration(ﬁgure 3(i)), which like cold spell duration, has also
been hypothesized to increase in response to Arctic
warming (Tang et al 2014, Coumou et al 2015). Warmspells are projected to lengthen over AWC, ECG, SCA
and SIB in both models in response to Arctic sea ice
6loss. However, no robust changes in this index are projected over the mid-latitudes.
In summary, robust increases in warm extremes
and their duration are projected over the high-latitudes, but the mid-latitude changes are weak and nonrobust.
3.3. Wet extremes
Signiﬁcant increases in wet days (ﬁgure 4(b)) and invery wet days (ﬁgure 4(c)) are projected by HadGAM2
for all regions considered. Fewer of the regionalchanges are signiﬁcant in CAM4, but those that are,are predominantly positive. CAM4 depicts signiﬁcant
increases in wet days over AWC, ECG, SIB, MED and
CAS; and very wet days over the same regions (except
AWC, where the change is insigniﬁcant). In contrast
to HadGAM2, CAM4 projects a signiﬁcant decrease in
wet days and very wet days over CUS, and in wet days
over SCA.
Total wet day precipitation (ﬁgure 4(d)) andtotal very wet day precipitation (ﬁgure 4(e)) also
increase signiﬁcantly across the majority of regionsin HadGEM2. Again the changes in these indices are
weaker in CAM4. CAM4 projects signiﬁcant increases in one or both these indices over ECG, SIB, MED
and CAS; but a decrease in the former index
over CUS.
The precipitation intensity index (ﬁgure 4(f))
documents changes in the average amount of precipitation on wet days. Thus, this metric is a measure of the severity of wet extremes rather than theirfrequency. Precipitation intensity increases signiﬁcantly in all regions in HadGAM2. Signiﬁcantchanges are also projected in some regions in
CAM4, but they are of mixed sign. Precipitation
intensity increases signiﬁcantly over CAS, but
decreases signiﬁcantly over AWC, SCA, SIB, EUS
and EAS.

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

Figure 5. Projected changes in annual-mean temperature (a), daily temperature variance (b), annual total precipitation (c) and daily
precipitation variance (d) in response to Arctic sea ice loss. The bar graphs are analogous to those in ﬁgure 2.

Next, the longest wet spell index (ﬁgure 4(g)) provides a measure of the annual average maximum duration of wet spells. The longest wet spell increases
signiﬁcantly in HadGAM2 for all regions except SCA.
In CAM4, this index increases signiﬁcantly for ﬁve ofthe twelve regions, speciﬁcally, AWC, ECG, SIB, CAS
and WAS; and decreases signiﬁcantly for CEU. The
latter change mirrors decreases in wet day (and very
wet day) precipitation and precipitation intensity overthis region in CAM4, but is in stark contrast to theincreases projected by HadGAM2.
In summary, the two models project contrastingchanges in wet extremes. HadGAM2 depicts signiﬁcant increases in the frequency, severity and duration of wet extremes over all or most regions. In
contrast, the changes in CAM4 are of mixed sign andsigniﬁcance.
3.4. Dry extremes
Dry days (ﬁgure 4(h)), i.e. days with no precipitation,
are generally projected to decrease, especially over
mid-latitude Europe and Asia. Both models depict
signiﬁcant reductions in dry days over SIB, EUS,
CEU, WAS, CAS and EAS. The models diverge
signiﬁcantly over ECG (HadGAM2 increases; CAM4
decreases) and CUS (HadGAM2 decreases; CAM4
increases).
Lastly, the longest dry spell (ﬁgure 4(i)) is projected by HadGAM2 to decrease in all regions, except
SCA. Signiﬁcant decreases are corroborated by CAM4
over AWC, ECG, SIB and EAS. The only region with asigniﬁcant increase in dry spell length, in either model,
is SCA in HadGAM2.
In summary, there is an overall tendency for fewer
dry days and shorter dry spells in both models,although these changes are signiﬁcant over moreregions in HadGAM2 than they are in CAM4.
7

4. Discussion
The regions with projected reductions in cold
extremes coincide with regions of annual-mean
warming (ﬁgure 5(a)). The loss of sea ice induces local
warming (Screen et al 2012, 2013, 2014), which isadvected to neighbouring regions by the mean atmospheric circulation and transient eddies (Deseret al 2010), causing mean warming in mid-latitudes.
Accompanying this mean warming is a decrease inhigh- and mid-latitude daily temperature variance(ﬁgure 5(b)), arising from the reduced north-southtemperature gradient (Screen 2014, Schneider
et al 2015, Screen et al 2015). Both the mean warming
and reduced variance favour fewer cold extremes. It isnotable that the warming and reduced variance are oflarger magnitude over EUS than for any of the othermid-latitude regions (ﬁgure 5). The mean tropospheric circulation over North America, which displays a southward dip east of the Rocky Mountains,appears conducive for propagating the Arctic warming
signal to mid-latitudes over central and especially,eastern North America compared to other midlatitude regions. Also, reductions in Hudson Bay sea
ice, which occur at relatively low latitudes compared toother longitudes, may enhance the response overeastern North America. Hence, the decline in cold
extremes is more pronounced and robust here than inother mid-latitude regions.
The CAS region is unique in displaying a robust
cooling response (ﬁgure 5), which helps explain therobust increase in cold nights and cold spell duration
for this region only (ﬁgure 3). The cooling response
appears to be dynamically driven and associated with astrengthened Siberian High (supplementary ﬁgure 2),
which enhances cold air advection to the region,

Environ. Res. Lett. 10 (2015) 084006

J A Screen et alconsistent with previous work (e.g., Mori et al 2014,
Screen et al 2015).
The increase in hot extremes over high latitudes(ﬁgure 3) is consistent with the overall warming
response (ﬁgure 5). Over mid-latitudes, where mean
temperature changes are weaker and less robust(ﬁgure 5), the changes in hot extremes are also weakerand less robust (ﬁgure 3). The robust reductions invariance over many mid-latitude regions do not all
coincide with fewer hot extremes (although there aresome such cases, e.g., for WUS, CUS and CEU in HadGAM2 where warm days and warm spell duration
decrease). This is consistent with the interpretationthat projected variance reductions primarily arise due
to warming at the cold (left-hand) tail of the temperature distribution (Screen 2014).
The projected changes in temperature extremes in
response to sea ice loss by the mid-twenty-ﬁrst century
are, in general, of the same sign as those found for thelate twenty-ﬁrst century, but with reduced magnitude
and statistical signiﬁcance (supplementary ﬁgure 3).
The mid-century robust responses are largely conﬁned
to the high-latitude regions, although robust decreases
in icing days and increases in warm nights are projected for EUS.
Turning now to the projected precipitation
response to late twenty-ﬁrst century sea ice loss, theregions with robust increases in the frequency of precipitation extremes (i.e., wet days or very wet days) allshow robust increases in both mean annual total precipitation and daily precipitation variance(ﬁgures 5(c) and (d)).
The model divergence between the projectedchanges in precipitation extremes—speciﬁcally, thelarger and more spatially coherent changes in HadGAM2 than in CAM4—warrants further explorationand explanation. On closer inspection, the changes in
the annual precipitation extreme indices arise predominantly from large increases in summer extremes
in HadGAM2 (e.g., monthly responses of maximum
1-day and 5-day precipitation [rx1day, rx5day] peak insummer in all regions; not shown). In turn, the differences in extreme precipitation responses can be traced
to differences in the annual cycle of the mean precipitation response in the two models, predominantly
over land regions. Whilst both models depict a broadly
consistent annual cycle of precipitation response overthe high-latitude ocean (a maximum in fall/winter andminimum in summer; albeit with differing magnitude
owning to larger sea ice losses in HadGAM2 than inCAM4), they depict contrasting annual cycles over thehigh-latitude land regions (ﬁgure 6). Here, CAM4
shows small changes that peak in the fall and winter,
whereas HadGAM2 shows a large peak in late springand early summer. This summer maximum is also
projected over the mid-latitude landmasses in HadGAM2, whereas in CAM4 there is little seasonality inthe response.
8

One possible explanation for the varied seasonal
precipitation responses in the two models is the differing sea ice forcing: recall, had21c has considerably
more open water in summer than cam21c. However,an additional experiment using HadGAM2 but with
mid-twenty-ﬁrst century sea ice conditions (had21c_mid), in which the summer sea ice area closely matches that in cam21c (ﬁgure 1), also projects a marked
increase in summer land precipitation relative tohad20c (ﬁgure 6; orange lines) and annual precipitation extremes (supplementary ﬁgure 4). Therefore, itappears that the enhanced summer land precipitation
response in HadGAM2 compared to CAM4 is not primarily due to differences in sea ice forcing between themodels. We further note that the two models have very
similar climatological annual cycles of precipitation(ﬁgure 6; dashed lines) and hence, the differences inforced response do not appear to arise because of differences in mean precipitation.
Spatial maps of the summer precipitation and sea
level pressure (SLP) responses provide additional
insight into the causes of the differing summer precipitation responses in the two models. As mentioned
above, the summer precipitation increase in HadGAM2 is enhanced over land regions and, as revealed
in ﬁgure 7(a), especially over Eurasia. This local intensiﬁcation of the precipitation response may partly arise
from the circulation response, with HadGAM2 projecting an anomalous anticyclone over Eurasia(ﬁgure 7(c)). Also, a precipitation response dipole
over the western Paciﬁc is co-located with a SLP
dipole. None of these features are identiﬁable in the
CAM4 precipitation (ﬁgure 7(b)) or SLP (ﬁgure 7(d))
responses. Thus, model differences in the atmosphericcirculation response likely contribute to the differing
precipitation responses in the two models. However, it
is unclear whether the circulation changes can fully
explain the ubiquitous summer precipitation increaseover land in HadGAM2. It is possible that land-surface
feedbacks and/or convective processes may also play arole, and that the differing summer precipitation
responses in the two models may further reﬂect differing model representation of these processes.

5. Conclusions
We have applied the ETCCDI extreme indices tosimulations from two independent AGCMs, bothforced solely by projected Arctic sea ice loss in the latetwenty-ﬁrst century. On the basis of these model
simulations and considering only those projectedchanges that are robust (i.e., models agree on the signand signiﬁcance of the response), we conclude that
future Arctic sea ice loss is most likely to:(1)Decrease the likelihood and duration of cold
extremes over high latitudes and over central andEnviron. Res. Lett. 10 (2015) 084006

J A Screen et al

Figure 6. Annual cycle of the monthly-mean precipitation (mm d–1) response to Arctic sea ice loss averaged over the (a) high-latitude
ocean, (b) high-latitude land, (c) mid-latitude ocean and (d) mid-latitude land. The black and red lines show projected responses
from HadGAM2 and CAM4, respectively. The orange lines show the responses in HadGAM2 mid-twenty-ﬁrst century experiment.
The dashed green and blue lines show the climatological annual cycle of precipitation in had20c and cam20c, respectively (note theseclimatological values have been divided by a factor of 10 so that they plot on the same vertical axis as the responses).

Figure 7. Projected changes in summer (June–July–August) precipitation (a), (b) and sea level pressure (c), (d) in response to Arctic
sea ice loss. Projected responses from HadGAM2 are shown in (a) and (c), and from CAM4 in (b) and (d).

eastern United States, but increase the likelihoodand duration of cold extremes over central Asia.
(2)Increase the likelihood and duration of hot
extremes over high latitudes.
(3)Increase the likelihood and severity of wet extremes
over high latitudes (excluding Scandinavia), theMediterranean and central Asia, and increase theirduration over high latitudes, central and eastern Asia.
(4)Decrease the likelihood of dry days over midlatitude Eurasia and reduce dry spell duration overhigh latitudes.
The projected changes in temperature extremes
are largest over the high latitudes, consistent with alarger mean climate response (e.g., Screen et al 2014).
Considering the heavily populated mid-latitudes,
where changes in extreme weather may have9

signiﬁcant societal implications, the largest sea ice
induced changes in temperature extremes are foundover central and eastern North America. Here, Arctic
sea ice loss appears to be an important driver of
future change in hot and cold extremes. Sea ice loss
reduces the likelihood of North American cold
extremes, consistent with Screen et al (2015), but
contrary to recent speculation (e.g., see discussion in
Wallace et al 2014). We found no clear change intemperature extremes over Europe in response to sea
ice loss and only very small changes over mid-latitude
Asia. Our results suggest therefore, that Arctic sea ice
loss is unlikely to be a major driver of future changes
in temperature extremes over these regions. With
regards to precipitation extremes, the regions mostaffected appear model dependent, based on the analysis of the two models used here. Coordinatedexperiments with a greater diversity of models would
be valuable for assessing the robustness of future

Environ. Res. Lett. 10 (2015) 084006

J A Screen et alchanges in precipitation extremes arising from sea
ice loss.
Lastly, it is important to emphasize that we have
only considered the changing risk of extreme weather
due to projected Arctic sea ice loss. In reality, manyother factors will inﬂuence the future climate andassociated extreme weather. Our results are useful for
constraining the role of Arctic sea ice loss in shifting
the odds of extreme weather, but must not be viewed
as deterministic projections (i.e., our best guess of thechanges that will ultimately occur). For example, related to point (1) above, whilst Arctic sea ice loss may
increase the odds of cold extremes over central Asia,
the net effect of increasing greenhouse gas concentrations is to reduce the frequency of cold extremes (Moriet al 2014).

Acknowledgments
We thank Robert Tomas for conducting some of the
CAM4 simulations. The HadGAM2 simulations were
performed on the ARCHER UK National Supercomputing Service. James Screen is supported by
National Environmental Research Council grant NE/
J019585/1. The National Science Foundation (NSF)
Ofﬁce of Polar Programs supported Lantao Sun. The
NSF sponsors NCAR. Two anonymous reviewers arethanked for their time and expert feedback.

References
Alexander L V et al 2006 Global observed changes in daily climate
extremes of temperature and precipitation J. Geophys. Res.
111 D05109
Barnes E A and Screen J A 2015 The impact of Arctic warming on the
midlatitude jetstream: can it? has it? will it? WIREs Clim.
Change 6 277–86
Bhatt U S et al 2014 Implications of Arctic sea ice decline for the
Earth system Ann. Rev. Environ. Res. 39 57–89
Boe J, Hall A and Qu X 2009 September sea-ice cover in the Arctic
Ocean projected to vanish by 2100 Nat. Geosci. 2 341–3
Cohen J et al 2014 Recent Arctic ampliﬁcation and extreme
midlatitude weather Nat. Geosci. 7 627–37
Collins W J et al 2011 Development and evaluation of an Earthsystem model—HadGEM2 Geophys. Model. Dev. 4 1051–75
Coumou D, Lehmann J and Beckmann J 2015 The weakening
summer circulation in the Northern Hemisphere midlatitudes Science 348 324–7
Coumou D, Petoukhov V, Rahmstorf S, Petri S andSchellnhuber H J 2014 Quasi-resonant circulation regimes
and hemispheric synchronization of extreme weather inboreal summer Proc. Natl Acad. Sci. USA 111 12331–6
Dee D P et al 2011 The ERA-Interim reanalysis: conﬁgurations andperformance of the data assimilation system Q. J. R. Meteorol.
Soc. 137 553–97
Deser C, Tomas R, Alexander M and Lawrence D 2010 The seasonal
atmospheric response to projected Arctic sea ice loss in thelate 21st century J. Clim. 23 333–51
Deser C, Tomas R A and Sun L 2015 The role of ocean-atmosphere
coupling in the zonal-mean atmospheric response to Arctic
sea ice loss J. Clim. 28 2168–86
Donat M G et al 2013 Updated analyses of temperature andprecipitation extreme indices since the beginning of thetwentieth century: the HadEX2 dataset J. Geophys. Res. 118
2098–118

10

Francis J A and Vavrus S J 2012 Evidence linking Arctic
ampliﬁcation to extreme weather in mid-latitudes Geophys.
Res. Lett. 39 L06801
Francis J A and Vavrus S J 2015 Evidence for a wavier jet stream in
response to rapid Arctic warming Environ. Res. Lett. 10
014005
Gent P R et al 2011 The community climate system model version 4
J. Clim. 24 4973–91
IPCC 2013 Climate Change 2013: The Physical Science Basis ed
T F Stocker, D Qin, G-K Plattner, M Tignor, S K Allen,
J Boschung, A Nauels, Y Xia, V Bex and P M Midgley(Cambridge: Cambridge University Press) pp 1535
Kalnay E et al 1996 The NCEP/NCAR 40-year reanalysis project
Bull. Am. Meteorol. Soc. 77 437–71
Kanamitsu M et al 2002 NCEP-DOE AMIP-II reanalysis (R-2) Bull.
Am. Meteorol. Soc. 83 1631–43
Kharin V V, Zwiers F W, Zhang X and Wehner M 2013 Changes intemperature and precipitation extremes in the CMIP5
ensemble Clim. Change 81 249–65
Kharin V V, Zwiers F W, Zkang X and Hegerl G C 2007 Changes intemperature and precipitation extremes in the IPCC
ensemble of global coupled model simulations J. Clim. 20
1419–44
Liu J, Curry J A, Wang H, Song M and Horton R M 2012 Impact of
declining Arctic sea ice on winter snowfall Proc. Natl Acad.
Sci. USA 109 4074–9
Mahlstein I and Knutti R 2012 September Arctic sea ice predicted todisappear near 2 °C global warming above present J. Geophys.
Res. 117 D06104
Mori M, Watanabe M, Shiogama H, Inoue J and Kimoto M 2014
Robust Arctic sea-ice inﬂuence on the frequent Eurasian cold
winters in past decades Nat. Geosci. 7 869–73
Post E et al 2013 Ecological consequences of sea-ice decline Science341 519–24
Schneider T, Bischoff T and Plotka H 2015 Physics of changes insynoptic midlatitude temperature variability J. Clim. 28
2312–31
Screen J A 2014 Arctic ampliﬁcation decreases temperature
variance in northern mid- to high-latitudes Nat. Clim.
Change 4 704–9
Screen J A, Deser C and Simmonds I 2012 Local and remotecontrols on observed Arctic warming Geophys. Res. Lett. 39
L10709
Screen J A, Deser C, Simmonds I and Tomas R 2014 Atmospheric
impacts of Arctic sea ice loss: separating forced change fromatmospheric internal variability Clim. Dyn. 43 333–44
Screen J A, Deser C and Sun L 2015 Reduced risk of North America
cold extremes due to continued Arctic sea ice loss Bull. Am.
Meteorol. Soc. in press (doi:10.1175/BAMS-D-14-00185.1)
Screen J A, Simmonds I, Deser C and Tomas R 2013 The
atmospheric response to three decades of observed Arctic sea
ice loss J. Clim. 26 1230–48
Seneviratne S I et al 2012 Changes in climate extremes and theirimpacts on the natural physical environment Managing theRisks of Extreme Events and Disasters to Advance Climate
Change Adaptation ed C B Field et al (Cambridge: Cambridge
University Press) pp 109–230
Sillmann J and Roeckner E 2008 Indices for extreme events inprojections of anthropogenic climate change Clim. Change 86
83–104
Sillmann J, Kharin V V, Zwiers F W, Zhang X and Bronaugh D
2013b Climate extremes indices in the CMIP5 multimodel
ensemble: II. Future climate projections J. Geophys. Res. 118
2473–93
Sillmann J, Kharin V V, Zhang X, Zwiers F W and Bronaugh D
2013a Climate extremes indices in the CMIP5 multimodel
ensemble: I. Model evaluation in the present climate
J. Geophys. Res. 118 1716–33
Stroeve J C, Serreze M C, Holland M M, Kay J E, Maslanik J and
Barrett A P 2012b The Arctic’s rapidly shrinking sea ice cover:a research synthesis Clim. Change 110 1005–27
Stroeve J C, Kattsov V, Barrett A, Serreze M, Pavlova T,
Holland M and Meier W N 2012a Trends in Arctic sea ice

Environ. Res. Lett. 10 (2015) 084006

J A Screen et al

extent from CMIP5, CMIP3 and observations Geophys. Res.
Lett. 39 L16502
Tang Q, Zhang X and Francis J A 2014 Extreme summer weather innorthern mid-latitudes linked to a vanishing cryosphere Nat.
Clim. Change 4 45–50
Tang Q, Zhang X, Yang X and Francis J A 2013 Cold winter extremes
in northern continents linked to Arctic sea ice loss Environ.
Res. Lett. 8 014036
Vihma T 2014 Effects of Arctic sea ice decline on weather andclimate: a review Surv. Geophys. 35 1175–214

11

Wallace J M, Held I M, Thompson D W J, Trenberth K E andWalsh J E 2014 Global warming and winter weather Science343 729–30
Walsh J E 2014 Intensiﬁed warming of the Arctic: causes andimpacts on middle latitudes Glob. Planet. Change 117
52–63
Zhang X, Alexander L, Hegerl G C, Jones P, Tank A K, Peterson T C,
Trewin B and Zwiers F W 2011 Indices for monitoringchanges in extremes based on daily temperature andprecipitation data WIREs Clim. Change 2 851–70