M-1242 | 2018

Climate in Svalbard 2100
– a knowledge base for climate adaptation
NCCS report no. 1/2019

Photo: Ketil Isaksen, MET Norway

Editors
I.Hanssen-Bauer, E.J.Førland, H.Hisdal, S.Mayer, A.B.Sandø, A.Sorteberg

CLIMATE IN SVALBARD 2100

Commissioned by

CLIMATE IN SVALBARD 2100

Title:

Date

Climate in Svalbard 2100
– a knowledge base for climate adaptation

January 2019ISSN nr.

Rapport nr.

2387-3027

1/2019Authors

Classification

Editors: I.Hanssen-Bauer1,12, E.J.Førland1,12, H.Hisdal2,12,
S.Mayer3,12,13, A.B.Sandø5,13, A.Sorteberg4,13Free

Authors: M.Adakudlu3,13, J.Andresen2, J.Bakke4,13,
S.Beldring2,12, R.Benestad1, W. Bilt4,13, J.Bogen2, C.Borstad6,
K.Breili9, Ø.Breivik1,4, K.Y.Børsheim5,13, H.H.Christiansen6,
A.Dobler1, R.Engeset2, R.Frauenfelder7, S.Gerland10,
H.M.Gjelten1, J.Gundersen2, K.Isaksen1,12, C.Jaedicke7,
H.Kierulf9, J.Kohler10, H.Li2,12, J.Lutz1,12, K.Melvold2,12,A.Mezghani1,12, F.Nilsen4,6, I.B.Nilsen2,12, J.E.Ø.Nilsen5,8,13,
O. Pavlova10, O.Ravndal9, B.Risebrobakken3,13, T.Saloranta2,
S.Sandven6,8,13, T.V.Schuler6,11, M.J.R.Simpson9, M.Skogen5,13,
L.H.Smedsrud4,6,13, M.Sund2, D. Vikhamar-Schuler1,2,12,
S.Westermann11, W.K.Wong2,12Clients
Norwegian Environment Agency (Miljødirektoratet)

Client’s reference
http://www.miljodirektoratet.no/M1242

Affiliations: See Acknowledgements!

Abstract
The Norwegian Centre for Climate Services (NCCS) is collaboration between the Norwegian Meteorological Institute, the Norwegian Water Resources and Energy Directorate, Norwegian Research Centre and the Bjerknes
Centre for Climate Research. The main purpose of NCCS is to provide decision makers in Norway with relevant
information regarding climate change adaptation. In addition to the partners, the Norwegian Environment
Agency is represented on the Board.
The NCCS report series includes reports where one or more authors are affiliated to the Centre, as well asreports initiated by the Centre. All reports in the series have undergone a professional assessment by at least
one expert associated with the Centre. They may also be included in report series from the institutions towhich the authors are affiliated.

This report was commissioned by the Norwegian Environment Agency in order to provide basic information for use
in climate change adaptation in Svalbard. It includes descriptions of historical, as well as projections for the futureclimate development in the atmosphere, hydrosphere, cryosphere and ocean, and it includes effects on the physical nature e.g. on permafrost and various types of landslides and avalanches. The projections for the future climate
are based on results in the IPCCs fifth assessment report. The report is to a large degree an assessment of existing literature and model results. New results from atmosphere, ocean and hydrological models are, however, alsopresented. The report may be downloaded from the Norwegian Centre for Climate Service’s web portal
www.klimaservicesenter.no.

Keywords
Climate in Svalbard, projections, temperature, precipitation, wind, runoff, floods, sediment transport, snow, glaciers,
permafrost, landslides and avalanches, ocean climate, sea ice, sea level

Disciplinary signature2

Responsible signature

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Contents

7. PERMAFROST, AVALANCHES AND LANDSLIDES  . . . . . . . . . . . . . . . . . . 113

ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
SAMMENDRAG  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1. INTRODUCTION  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

8 OCEAN, ACIDIFICATION, SEA ICE AND SEA LEVEL  . . . . . . . . . . . . . . . . . 126

2. THE CLIMATE SYSTEM IN THE SVALBARD AREA,
INCLUDING ADJACENT SEA AREAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

9. ASSESSMENT AND USE OF PROJECTIONS  . . . . . . . . . . . . . . . . . . . . . . . 146

1.1 Rationale and background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
1.2 Contents of the report  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

2.1 Atmospheric circulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2 Ocean circulation and sea ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.3 Arctic amplification and feedbacks  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3. METHODS, DATA, MODELS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
3.1 Methods and data, atmosphere  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.2 Methods and data, hydrology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3 Methods and data, glaciers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.4 Methods and data, permafrost  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.5 Methods and data, ocean  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.6 Methods, data and models, sea level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

4. ATMOSPHERIC CLIMATE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.1 Air temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.2 Variables derived from air temperature  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  60
4.3 Precipitation  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.4 Heavy rainfall  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.5 Wind, cyclonic activity and waves  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

7.1 Permafrost temperatures and active layer thickness  . . . . . . . . . . . . . . . . . . . . .  1137.2 Solifluction  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1187.3 Avalanches and landslides  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  119

8.1 Past, present and future climate around Svalbard . . . . . . . . . . . . . . . . . . . . . . . .  1268.2 Acidification  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
8.3 Sea ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.4 Sea level  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
9.1 Uncertainties related to the climate projections . . . . . . . . . . . . . . . . . . . . . . . . . .  1469.2 Treatment of uncertainties in different variables  . . . . . . . . . . . . . . . . . . . . . . . . .  1489.3 Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

10. LONGYEARBYEN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152

10.1 Atmosphere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
10.2 Hydrology, snow and glaciers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
10.3 Permafrost, landslides and avalanches  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  16410.4 Ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
10.5 Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
APPENDIX  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

A1: Climate model results applied for the atmospheric projections in this report  .  192A2: Climate projections for temperature  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
A3: Climate projections for precipitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

AUTHORS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

5. HYDROLOGY  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.1 Runoff  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2 Snow  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
5.3 Erosion and sediment transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

6. GLACIERS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

6.1 Holocene glacier change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
6.2 Recent glacier front retreat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.3 Glaciological mass balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.4 Geodetic mass balance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
6.5 Mass balance modelling  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.6 Projections for future climate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6.7 Gravity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  112

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Acknowledgements
This report was commissioned by the Norwegian Environment Agency. Forty-eight authors from eleveninstitutions and two research centres have contributed. The numbers below identify the affiliations given in
the list of authors:
1. Norwegian Meteorological Institute (MET Norway)
2. Norwegian Water Resources and Energy Directorate (NVE)
3. Norwegian Research Centre (NORCE)
4. University of Bergen (UiB)
5. Institute of Marine Research (IMR)
6. The University Centre in Svalbard (UNIS)
7. Norwegian Geotechnical Institute (NGI)
8. Nansen Center (NERSC)
9. Norwegian Mapping Authority (Kartverket)
10. Norwegian Polar Institute (NPI)
11. University of Oslo (UiO)
12. Norwegian Centre for Climate Services (NCCS)
13. Bjerknes Centre for Climate Research (BCCR)The Norwegian Environment Agency is the principal source of funding for the report. The above mentioned institutions have, however, contributed with considerable in-kind.
The editors and lead authors are responsible for the scientific content of the report. We want to acknowledge Deborah Lawrence and Howard Parker for correcting the English language in parts of the manuscript.
Thanks also to Magne Velle for report design, and to several photographers who enabled us to brighten up
this report with their excellent photographs.

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Summary
Main findings
Under medium to high scenarios for future climate
gas emissions, the following changes are projected
for Svalbard from 1971–2000 to 2071–2100:●●Increased annual air temperature (ensemble
median about 10 ºC for high and 7 ºC for medium
emissions)●●Increased annual precipitation (ensemble
median about 65% for high and 45% for medium
emissions)●●Events with heavy rainfall will be more intense
and occur more frequently●●River flow will increase, but the magnitude
will strongly depend on the precipitation andtemperature increase and contribution of glaciermeltwater●●In regions where the maximum annual snow
storage will decrease, snowmelt floods will
become smaller●●Increased precipitation, and increasing fraction as
rain, will lead to increased rain-floods andincreased combined snowmelt-, glacier melt- andrain-floods●●The snow season will become shorter●●Erosion and sediment transport will increase●●Near-surface permafrost is projected to thaw incoastal and low altitude areas for the highemission scenario●●Many types of avalanches and landslides will
become more frequent●●The glacier area and net mass balance will beseverely reduced during the 21st century●●The loss of glacier mass and area will change thelandscape and contribute to global sea-level rise●●Because of large land uplift and reduced
gravitational pull, the mean sea level aroundSpitsbergen will probably decrease8

A climate projection for the ocean areas (formedium emissions) gives from 2010–2019 to2060–69:●●An average warming of surface waters around
Svalbard of about 1°C●●A substantially decreased sea ice concentrationin the northern Barents Sea
Inadequate knowledge of the climate system’s
sensitivity and of future natural climate variations,as well as limitations in the climate models, lead tolarge uncertainties in the projections even under agiven emission scenario. It is still clear that reduced
greenhouse gas emissions will lead to reducedchanges in the physical climate.
Background
This report was commissioned by the Norwegian
Environment Agency in order to provide basic
information for climate change effect studies andclimate change adaptation in Svalbard. It includes
descriptions of historical, as well as projections
for future climate development in the atmosphere,hydrosphere, cryosphere and ocean, and it includes
effects on the physical nature, e.g. risks associatedwith landslides and avalanches. The projections for
future climate are based on the global climate
models used in the IPCCs fifth assessment report(IPCC, 2013). Dependent on availability of model
data, three scenarios for emissions of greenhousegases are used: “RCP8.5” (“business as usual”;“high emissions”), “RCP4.5” (reductions after
2040; “medium emissions”) and “RCP2.6” (drastic
cuts from 2020; “low emissions”). Climate change
in the atmosphere and land surface are projected
up to the year 2100 and in the ocean up to the year
2070.

The report is to a large degree an assessment ofexisting literature and model results, e.g. the Arctic
CORDEX regional climate models. In addition,a fine scale atmospheric regional climate model(COSMO-CLM) has been run, and the results wereapplied for estimating changes in e.g. heavy rainfall,
frost days, snow, permafrost and glaciers. Further,a hydrological model has been run for Svalbard forpresent and projected future climate, based on input
data from Arctic CORDEX. Also for the ocean,
new analyses have been performed, based on thebest available model data. Below follows a summary based on a combination of the assessment andresults from new analyses.
Atmospheric variables
Air temperature and derived variables. The
mean observed annual temperature (1971-2000)
for the manned weather stations in Svalbard varies
from -1.7°C (Bjørnøya) to -5.9°C (Svalbard Airport). The estimated average temperature for Svalbard land areas is -8.7 °C. The difference between
this average and the station values may be explainedby all stations being situated at low altitudes and inwestern, coastal environments. From 1971 to 2017,
a warming of 3 – 5°C has been observed (less inthe south, more in the inner fjords), with the largest increase in winter and the smallest in summer.
For RCP8.5, the ensemble median projections
from regional models and statistical downscaling
indicate an increase in annual mean temperaturefor Svalbard of almost 10 °C from 1971-2000 to
2071-2100. The projected increase is highest in thenortheast and lowest in southwest. The uncertainty
is large, and the fine scale simulation projects a 7°C increase under RCP8.5. Under emission scenario
RCP4.5 the projected ensemble median temperature
increase is 6 – 7 °C and for RCP2.6 about 4 °C.
The temperature projections imply a considerable
increase in the number of growing days for grass
and a decrease in the number of frost days. Only amoderate change is projected in the annual numberof days with maximum temperature above and minimum temperature below 0°C. However, a decreasein such zero-crossings is projected in summer andan increase in winter.

Precipitation. Mean annual precipitation (19712000) measured at the weather stations in Svalbard
varied from 196 mm (Svalbard Airport) to 581 mm(Barentsburg). The estimated average annual precipitation for the Svalbard land areas is about 720
mm. Some of the difference between measured andestimated values is due to increasing precipitation
with altitude, and that models often overestimate
precipitation in this area. However, it is also known
that precipitation gauges, especially during snowfall
and strong winds, do not capture all precipitation.
Historical and projected futurechanges are given in percent of the 1971-2000
values. On Bjørnøya and Hopen, significant positive trends in annual precipitation (20 – 30%) occur
from 1971 to 2017, mainly because of increased
precipitation in winter and spring. On Spitsbergen,few trends are significant for this period, but trends
tend to be positive in autumn and winter and negative in spring and summer. For emission scenario
RCP8.5, the ensemble median projections fromregional models show an increase in annual precipitation for Svalbard of about 65% from 1971-2000
to 2071-2100. The uncertainty is large, and thefine scale simulation projects a 35% increase for
RCP8.5. For RCP4.5 the projected ensemble
median precipitation increase is 45%.
On Spitsbergen, episodes with heavy rainfall may
occur even midwinter. At Svalbard Airport andNy-Ålesund, the highest daily rainfalls of 43 and98 mm respectively make up around 25% of theaverage annual rainfall. In the latest decades thefrequency of heavy rainfalls has increased, andtowards the end of the century an increase is
projected for frequency as well as intensity of heavy
rainfalls. For the fine scale simulations underemission scenario RCP8.5, the increase in heavy
daily rainfall is projected to be about 20%, i.e.
lower than for the total annual precipitation for thismodel.
Wind. Svalbard lies in the transit zone between cold
Arctic air in the north and mild maritime air in thesouth. Thus the cyclonic activity is high, especially in
winter. The area is dominated by northeasterly winds,
though the local wind direction will be affected bytopographic effects. The fine scale simulation projects

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increased average wind speeds northeast of Spitsbergen, slightly reduced wind speeds west of Spitsbergen,and only minor changes in the fjord areas.

Glaciers

In regions where the annual maximum snow storage is expected to decrease, snowmelt floods will
become smaller. Increased precipitation and a larger
fraction as rain will increase the magnitude andHydrological variables
frequency of rain floods and combined snowmelt,
glacier melt and rain floods. For the high emission
Runoff. Because runoff observation records are few scenario towards the end of the century, theand short, the runoff development in recent years is glacier area and volume in several catchments willbased on model calculations with re-analysis data
be reduced to the extent that the contribution from(precipitation and temperature: Sval-Imp data) as
glacier meltwater to floods will be negligible.
input to a hydrological model. During the period
1980-2015, the annual runoff has increased by more With continued warming and melting of glaciers,
than 35%. The increased runoff is mainly caused by glacial lake outburst floods (GLOFs) may becomeenhanced glacier melt.
more frequent, but such floods will not necessarily
affect settlements.
The average annual runoff for Svalbard in the reference period 1971–2000 is estimated to be 600 mm.
Snow. The snow season duration for the Svalbard
The projections show a rapid and large increase
land areas has decreased over the period 1958–2017
towards the end of this century. Runoff using the two with approximately 20 days. The future snow
emission scenarios RCP4.5 and RCP8.5 is quite sim- conditions up to 2071–2100 strongly depend on
ilar until the middle of the century (2031-2060) withthe emission scenario. The median simulation withmore than 100% increase relative to the reference pe- RCP4.5 still shows areas with the same, or slightly
riod. The increase is partly due to increased precipita- more, snow storage, while the median simulation
tion, but the main contribution is glacier melt caused with RCP8.5 shows a reduction in maximum snow
by increasing temperatures. Towards the end of thestorage of 50% or more over most of Svalbard. The
century (2071-2100), the projected runoff for RCP8.5 fine scale simulation gives increased maximum
increases further. This is caused by a strong warming snow storage (up to 100% for certain areas) for(and consequently an increase in glacier meltwater)
large areas in the northeast and reduced amounts
and a large increase in precipitation.
in western and southern areas. The number of days
with snow cover will be reduced all over Svalbard
The modelled changes in individual catchments
for both RCP4.5 and RCP8.5. In some areas themay deviate considerably from the projectedsnow cover duration is only slightly reduced, inchanges in runoff for the total Svabard land area,other areas it is drastically reduced. With increasing
mainly depending on the expected changes in
winter temperatures, the snow line will increase andglacier melt. One example is Bayelva, close to Nythe timing of maximum snow storage will shift fromÅlesund, where the fraction of glaciers is 50% andJune (1971–2000) to May (2071–2100) in the finethe altitude ranges from 4 to 742 m a.s.l. For thisscale RCP8.5 simulation.
catchment, the median RCP8.5 projection shows amore moderate increase in runoff towards the
Sediment transport and erosion. A warmer
middle of the century, and a slight decrease thereclimate will affect the erosion intensity and increase
after because the glacier area and volume are so
the water discharge and sediment supply to the
reduced that the glacier meltwater decreases even if rivers from both glaciers with moraines and fromthe temperature increases.
areas without glaciers. The increased sedimentsupply may increase channel splitting and lateral
Floods. The flood estimates for Svalbard are highly activity of channels and expose adjacent slopes to
uncertain, but changes in the frequency anderosion. This accelerated erosion may affect themagnitude of floods are strongly linked to changes
tributaries first, and after some time, the large
in precipitation, snow storage and glacier regimes.
sandurs in the main valleys
10

Following deglaciation under early Holocene, most
local glaciers in Svalbard disappeared during the
Early Holocene around 9700 years before present.
Glaciers reformed during the Late Holocene andreached their historical maxima when the Little Ice
Age culminated in the 19th century.
Presently the glaciers in Svalbard are losing more
ice through melting and calving than they are accumulating through snowfall. All of the well-observed
glaciers are shrinking, and models and satellite
measurements confirm that Svalbard as a whole islosing glacier mass. An average glacier area reduction of 7% was estimated from the period 19611990 to the 2000s. The glacier area was reduced
in all regions. This loss of glacier mass and area ischanging the landscape and contributing to sea-levelrise. Presently more than half of Svalbard’s ice area
terminates in the ocean and are subject to calving,
but future warming will lead to a decline in thenumber of these ”tidewater glaciers” around
Svalbard.
Variability in the glacier mass balance in Svalbard
is primarily driven by summertime melt variations.
Lengthening of the melt season and warmer summers in coming decades are likely to continue
driving increased glacier melt and glacier retreat.
Given the temperature increase projected by thefine scale simulation for emission scenario RCP8.5,
the modelled glacier net mass balance becomes
progressively more negative during the 21st century. The winter balance does not have a clear trend;
the strong negative trend in net balance is driven
entirely by increasingly negative summer balance.
Comparing the two time periods 1971-2000 and
2071-2100 shows that the equilibrium line altitude(ELA) rises by 400 m on average, and that there isa five-fold increase in glacier mass loss. There isconsiderable spatial variability in these changes,
with more pronounced ELA increases in the northwest and south of Svalbard than in the northeasternregion, but due to the larger glacier area in the latterregion, the change in overall mass loss is strongest
there. There are some suggestions that increased
melt hastens the triggering effect on surges (rapid

glacier advance) and a number of large-scale surge
events have been reported in Svalbard. These eventsaffect the total ice discharge and have the potential
to considerably increase the sea level contribution
from land ice within short time.
Permafrost, landslides and avalanches
Permafrost. Svalbard has the warmest permafrost
this far north in the Arctic. Mean permafrost
temperatures at 10-20 m depth vary from about-2.5°C at coastal western sites to about -5°C in
central parts. Since 2009 the permafrost temperatures have increased at rates between 0.06°C and
0.15°C per year at 10 m depth. In central Spitsbergen, at Janssonhaugen, a significant temperature increase can be detected down to 80 m depth
reflecting a multi-decadal permafrost warming. In
western and central parts of Spitsbergen the present
thickness of the active layer is in the range of 100
to 200 cm. Since 2000 the active layer thickness in
Adventdalen (sediments) and Janssonhaugen (rock)has deepened by respectively 0.6 cm and 1.6 cm
per year. At Kapp Linné (rock) the active layer increased by 6.2 cm per year between 2009 and 2017.
Modelled ground temperatures indicate thatnear-surface permafrost in coastal and low altitude
areas is projected to thaw before the end of the century under emission scenario RCP8.5. At some sites
permafrost thaws are projected to reach depths of atleast 5 meters, while permafrost still persists at 10m depth and below. For most of the lowland landscape in western Svalbard, all available modelling
results suggest that the limit between “permafrost
remains (at least partly)” and “the upper metersof the permafrost thaws completely” towards theend of the century will be somewhere between theemission scenarios RCP4.5 and RCP8.5. Increases
in the active layer thickness and permafrost temperature will speed up slope processes controlled
by permafrost, with a marked increase in slope
instability. Present and future permafrost warming
and degradation will affect coastal erosion processes, especially where the coastline consists only of
sediments.

11

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Solifluction. A warmer permafrost and increased
rainfall will speed up solifluction; - i.e. slowly
deforming active layers moving downslope due togravity. Solifluction sheets and lobes are widespreadon the lower slopes in the Svalbard landscape.
Landslides and avalanches. Because meteorological factors are important triggers for landslides andavalanches, future climate change will very likely
increase the frequency of most types of avalanches
and landslides in the coming decades.
If more extreme events with heavy snowfall or
heavy rain on snow occur, an increase in the occurrence of snow avalanches including wet snow
avalanches and slushflows, can be expected. Towards the end of the century, in areas with a shorter snow season and a reduction in the maximum
annual snow amounts, the probability of dry snow
avalanches will decrease. However, the probabilityof wet snow avalanches and slushflows is expected
to increase.
Degradation of permafrost may play an importantrole in the detachment of larger rockslides.
Rising temperatures will in some areas lead to more
freeze-thaw events, and therefore rockfall events
may increase in the future. Increased depth of theactive layer and higher permafrost temperature leadto more active slope processes and significantly
greater instability in the mountain slopes. Combinedwith more precipitation and increased frequency of
episodes of heavy rainfall on sloping terrain, thiswill increase the likelihood of various types of soil
slides, including quick clay-like landslides.
Ocean climate
Oceanography. During the early Holocene, theclimate of Svalbard was considerably warmer thanpresent climate. Between 8200 and 6000 years
ago shallow water temperatures off Svalbard wereabout 4°C warmer than present. Subsequently, watertemperature cooled to present-day values about
4500 years ago. The observed temperatures haveincreased since 1970, and have been particularlyhigh since the late 1990s. Recent changes in thelarge-scale atmospheric circulation patterns havebrought warm Atlantic Water from the West Spits12

bergen Current onto the West Spitsbergen Shelf andfurther into the fjords even during winter. Especiallythe northern Barents Sea has experienced a rapid
climate shift and is described as the “Arctic warming hotspot” where the surface warming and loss of
winter sea ice is the largest in the entire Arctic.
Projections under emission scenario RCP4.5 showthat in fifty years, the surface waters around Svalbard will be about 1°C warmer than present. Thisis generally true for most areas that today have low
concentration of sea ice, but some areas further
south have an even stronger projected warming. A
few areas, like the outer parts of Storfjorden andFram Strait are projected to cool about 1°C. The
Nordic and Barents Seas will probably continue towarm after 2070 unless the thermohaline convection, and thus the northward oceanic heat transport,
is reduced.
Acidification. East of Svalbard the surface pH
values increase northwards and towards the bottom.
Aragonite saturation state is a measure of the ability
for carbonate (aragonite) to form or dissolve. Temperature strongly influences the aragonite saturation
state and the highest surface values are found in
the south, where pH is lower than in colder, fresher
northern water. Fresh water from ice melting plays
an important role in modifying the waters around
Svalbard. This is especially prominent in the waters
close to glaciers and in the fjords. Sea ice acts as abarrier for diffusion of gases between ocean andatmosphere, and the distribution of sea ice in timeand space strongly influences carbon chemistry
dynamics in the waters around Svalbard. For theNordic and Barents Seas, the pH under RCP4.5 isprojected to decline by around 0.12 from year 2005
to 2070.
Sea ice. The ocean warming has halted sea icefrom forming and has opened up large areas of icefree waters around Svalbard. Global warming has
likely lead to more wind-generated upward mixingof warm and saline Atlantic Water from about 200
m depth, preventing formation of sea ice. The ice
export through the Fram Strait is an important partof the ice budget in the Arctic basin and since 1979it has increased by about 6% per decade. In thelast decade, the fjords on the west coast have been

almost ice-free in the winter and the whole Barents
Sea, including the area east of Svalbard, has been
ice-free for several months in the summer andautumn. For Kongsfjorden most years after 2006
had a low ice extent and a shorter season of fast ice
than earlier, and during 1997-2016 the thicknessof sea ice as well as snow cover on ice has becomethinner.
The simulated changes in future sea ice concentration indicate a major reduction in the northern
Barents Sea, a reduction in the western fjords, anda certain increase in the outer and western parts ofStorfjorden. Areas with decreased sea ice concentration reflect increased sea surface temperature andvice versa. None of the global CMIP5 climate
models are capable of simulating sufficient inflow
of relatively warm Atlantic Water through the
Barents Sea Opening, explaining a systematic bias
for the recent Barents Sea ice loss. Because the
Barents Sea also dominates the Arctic Ocean sea ice
variability during winter, this missing warm Atlantic
inflow has importance far beyond the region, possibly through altering the large-scale atmosphericcirculation.
Sea level. Tide gauge records from Spitsbergenshow negative trends in relative sea level from themid-20th century to present. The main reason for
the reduced sea level is high land uplift rates
largely caused by the Earth’s response to presentday glacier changes. The high uplift rates show
large spatial variability; in west Spitsbergen the
uplift rates are between 7 and 10 mm per year.
Projections indicate that Spitsbergen will experiencea fall in relative sea level over the 21st century for
all emission scenarios. This fall is driven by reduced
gravitational pull and by land uplift owing to future
local ice mass losses. Thus increasing global

temperatures lead to more negative changes inrelative sea level on Spitsbergen. This is the opposite of what is projected along the majority of theworld’s coastlines. The future sea level may deviatesignificantly from the latest IPCC main report if acollapse of the marine portions of the Antarctic ice
sheet is triggered.
Uncertainties and use of projections
Traditionally, the major uncertainties in climate
projections are related to 1) future anthropogenic
emissions, 2) natural climate variations and 3)
non-perfect climate models. In regions close to themarginal ice zone, such as Svalbard, systematic
biases in the initial sea ice extent comes in as afourth source of uncertainty, because it can
influence projections far longer than a few decades.
The first type of uncertainty is here taken into
account by using 2-3 emission scenarios whereveravailable. The second and third types of uncertainties are to some degree taken into account bygiving intervals based on ensembles of several
model calculations. However, these span only apart of the total uncertainty. Thus it cannot be ruledout that future climate changes could fall outside
the intervals. Further, as a majority of the global
climate models have too much sea ice for present
day climate in the Svalbard area, the upper part ofthe span in changes in temperature and precipitation
may be unrealistic.
If you plan to use climate projections in research
and management, we therefore recommend that you
contact relevant scientific communities. This mayhelp in the interpretations of the results and to gain
information on new knowledge.

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CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Atmosfæriske variable

SammendragHovedfunn
For middels til høye scenarier for fremtidige
klimagassutslipp beregnes følgende endringer for
Svalbard fra 1971-2000 til 2071-2100:●●Årstemperaturen vil øke (ensemble-median ca. 10ºC for høye og 7 ºC for middels utslipp)●●Årsnedbøren vil øke (ensemble-median ca. 65 %for høye og 45 % for middels utslipp)●●Hendelser med kraftig nedbør vil forekomme
hyppigere og bli mer intense●●Vannføringen i elvene på Svalbard vil øke, menhvor mye vil være svært avhengig av hvor mye
nedbør, temperatur og bidrag fra bresmelting øker●●I områder hvor det beregnes redusertesnømengder, forventes mindre snøsmelteflommer●●Økt nedbør, og en økende andel som regn, vil
gi flere og større regnflommer og kombinertesnøsmelte-/bresmelte- og regnflommer●●Snøsesongen vil bli kortere●●Erosjon og sedimenttransport vil øke●●Permafrosten varmes opp over hele Svalbard
og de øverste meterne av permafrosten vil tine ikyst- og lavereliggende områder (for høyeutslipp)●●Mange typer snøskred og løsmasseskred vil
forekomme hyppigere●●Både massebalanse for breer og breareal forventes å bli betydelig redusert innen 2100●●Økningen i massetap fra isbreer vil gi betydeligøkning i bidrag til havsnivåøkning●●På grunn av endringer i gravitasjon og stor
landheving vil midlere havsnivå ved Spitsbergensannsynligvis bli lavere
En klimaprojeksjon for havområdene (for middels
utslipp) gir fra 2010-2019 til 2060-69:●●En oppvarming av overflatevannet rundt Svalbard
på i gjennomsnitt ca. 1 °C●●En betydelig reduksjon av sjøiskonsentrasjonen
nord i Barentshavet

14

Mangelfull kunnskap om klimasystemets følsomhet
og om fremtidige naturlige klimavariasjoner, samt
utilstrekkelige klimamodeller, fører til stor
usikkerhet i beregningene selv under et gitt utslippsscenario. Det er likevel klart at reduserte
klimagassutslipp vil føre til reduserte forandringer idet fysiske klimaet.
BakgrunnDenne rapporten ble bestilt av Miljødirektoratet
for å gi et kunnskapsgrunnlag for klimatilpasningpå Svalbard. Rapporten inneholder beskrivelse av
historisk klima, samt beregninger av fremtidig
klimautvikling i atmosfæren, hydrosfæren, kryosfæren og havet, og inkluderer også effekter på fysisk
natur, f.eks. knyttet til skredfare. Fremskrivningene
for fremtidig klima er hovedsakelig basert på klimamodeller som ble benyttet i IPCCs femte hovedrapport (IPCC, 2013). Avhengig av tilgjengeligheten
av modelldata benyttes tre scenarier for utslipp av
klimagasser: “RCP8.5” (“business as usual”, “høyeutslipp”), “RCP4.5” (reduksjon etter 2040, “middels utslipp”) og “RCP2.6” (drastiske kutt fra 2020;“lave utslipp”). Klimaendringene er beregnet frem
til år 2100 i atmosfæren og frem til 2070 i havet.
Rapporten er i stor grad en oppsummering av
eksisterende kunnskap fra vitenskapelig litteraturog modellresultater, f.eks. de regionale klimamodellene benyttet i Arctic CORDEX. I tillegg blir detogså presentert nye resultat fra en finskala atmosfærisk regional klimamodell (COSMO-CLM).
Disse resultatene er benyttet til å beregne endringer
i bl.a. kraftig nedbør, frost-dager, snø, permafrost
og isbreer. Det er også kjørt en hydrologisk modell
for Svalbard for både nåtids og forventet fremtidig
klima, basert på inngangsdata fra Arctic CORDEX.
Også for havet er det blitt utført nye analyser basert
på de beste tilgjengelige modelldataene. Nedenfor
følger en oppsummering basert på en kombinasjon
av foreliggende publisert kunnskap og nye
resultater.

Lufttemperatur og avledede variabler. Den
gjennomsnittlige årstemperaturen (1971-2000) forde bemannede værstasjonene på Svalbard varierer fra -1,7 °C (Bjørnøya) til -5,9 °C (Svalbard
lufthavn). Estimert årlig gjennomsnittstemperaturfor Svalbards landområder er -8.7 °C. Forskjellen
mellom denne verdien og stasjonsverdiene kan
forklares ved at alle målestasjonene ligger i lavlandet i vestlige kystområder. Fra 1971 til 2017 var
det en oppvarming på 3 - 5 °C (mindre i sør, mer iindre fjordstrøk), med størst økning om vinteren og
minst om sommeren. For RCP8.5 viser medianprojeksjonene fra både regionale modeller og statistisk
nedskalering en økning på nesten 10 °C i gjennomsnittlig årstemperatur fra 1971-2000 til 2071-2100.
Den beregnede økningen er størst i nordøst og minsti sørvest. Usikkerheten er stor, og med
finskala modellen beregnes det en økning på 7 °C
for RCP8.5. For utslippsscenario RCP4.5 erberegnet medianverdi for økning i årstemperatur
6 - 7 °C, og for RCP2.6 ca. 4 °C.
Den beregnede temperaturøkningen medfører en
betydelig økning i antall vekstdager for gress og en
nedgang i antall frostdager. For antall dager per år
med maksimumstemperatur over 0 °C og
minimumstemperatur under 0 °C forventes det kun
moderate endringer. Imidlertid forventes det en
reduksjon i slike dager om sommeren og en økningom vinteren.
Nedbør. Gjennomsnittlig årsnedbør (1971-2000)
målt på værstasjonene på Svalbard varierte fra 196
mm (Svalbard lufthavn) til 581 mm (Barentsburg).
Estimert gjennomsnittlig årsnedbør for Svalbards
landområder er ca. 720 mm. En del av forskjellen
mellom målte og estimerte verdier skyldes at nedbøren øker med økende høyde over havet, samt at
modellene ofte overestimerer nedbør i detteområdet. Det er imidlertid også kjent at nedbørmålerne, spesielt under snøfall og kraftig vind,ikke fanger opp all nedbør som faller. Historiske
og beregnede fremtidige endringer er gitt i prosent
av verdiene fra 1971-2000. På Bjørnøya og Hopenvar det signifikant økning (20-30 %) i årsnedbør fra
1971 til 2017, hovedsakelig på grunn av økt nedbør
vinter og vår. På Spitsbergen er det for året som

helhet lite tegn til endring i denne perioden, men det
er en tendens til økning høst og vinter og minking
vår og sommer. For utslippsscenario RCP8.5 viser
de beregnede medianverdier fra regionale modelleren økning i årsnedbør for Svalbard på ca. 65 % fra
1971-2000 til 2071-2100. Usikkerheten er stor, og
med finskala modellen beregnes det en økning på
ca. 35 % for RCP8.5. For RCP4.5 er medianverdienfor økning i årsnedbør ca. 45 %.
På Spitsbergen kan episoder med kraftig nedbør
som regn forekomme når som helst på året, til og
med midt på vinteren. For Svalbard lufthavn og
Ny-Ålesund er de høyeste målte døgnsummene
hhv. 43 og 98 mm; - dvs. omtrent 25 % av
gjennomsnittlig årsnedbør kan falle i løpet av ett
døgn. I de siste tiårene har hyppigheten av kraftige
nedbørepisoder økt, og frem mot slutten av århundret er det forventet en økning både i hyppighet og
intensitet av slike kraftige regnskyll. Simuleringer
med finskala modellen for utslippscenario RCP8.5
gir en økning i kraftig 1-døgns nedbør på ca. 20 %,dvs. mindre enn økningen i total årsnedbør for den
samme modellen.
Vind. Svalbard ligger i overgangssonen mellom
kald arktisk luft i nord og mild maritim luft i sør.
Dermed er lavtrykksaktiviteten høy, spesielt om
vinteren. Området domineres av storstilt nordøstlig
vind, men den lokale vindretningen blir påvirket
av topografiske effekter. Med finskala modellen
beregnes det økt gjennomsnittlig vindhastighet nordøst for Spitsbergen, litt redusert vindhastighet vest
for Spitsbergen, og bare små endringer ifjordområdene.
Hydrologi
Vannføring/avrenning. Fordi det er få og korte
vannføringsserier på Svalbard, er utviklingen iavrenningen de siste årene basert på modellberegninger med reanalyse-data (nedbør og temperatur:Sval-Imp data) som inngangsdata i den hydrologiske modellen. I perioden 1980-2015 haravrenningen økt med mer enn 35 %. Trolig skyldesøkningen i hovedsak stor økning i bresmelting.
Midlere årsavrenning for Svalbard i referanseperioden 1971-2000 er estimert til 600 mm. Fremskrivningene viser rask og stor økning fremover i
15

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

dette århundret. Avrenningen ved bruk av RCP4.5
og RCP8.5 er ganske lik frem mot midten av århundret (2031-2060) med over 100 % økning relativt
til referanseperioden. Økningen skyldes delvis
økt nedbør, men det store bidraget kommer fra
nedsmeltning av breene forårsaket av økt temperatur. Mot slutten av århundret (2071-2100), økeravrenningen basert på RCP8.5 ytterligere. Dette erforårsaket av stor temperaturøkning (og følgelig
økt mengde smeltevann fra breer) og stor økning inedbør.

mer for det meste av Svalbard. Finskala simuleringen gir økte snømengder (opp til 100 % økning) for
store områder i nordøst, og reduserte snømengder ivest og sør. Snøsesongens lengde vil i følge simuleringene avta for hele Svalbard med både RCP4.5
og RCP8.5. Noen steder er reduksjonen liten, mens
den andre steder er dramatisk. Med økt vintertemperatur vil snøgrensen ligge høyere over havet og
tidspunktet for maksimalt snømagasin vil forskyves
fra juni i perioden 1971-2000 til mai i perioden
2071-2100 i finskala RCP8.5 simularingen.

Modellerte endringer i avrenning for individuelle
nedbørfelt kan avvike betydelig fra de beregnede
endringene for hele Svalbard, avhengig av bidraget til avrenning fra bresmelting. Ett eksempel er
Bayelva nær Ny-Ålesund hvor nedbørfeltet går fra
4 m o.h. til 742 m o.h., og med 50 % bre. Her viser
fremskrivningene med RCP8.5 en mer moderatøkning i vannføringen mot midten av århundret og
en svak reduksjon etter dette fordi brearealet og
volumet minker så mye at bresmeltingen avtar selvom temperaturen øker.

Sedimenttransport og erosjon. Et varmere klima
vil gi økt vannføring og erosjonsintensitet både fra
breer med morene og fra brefrie områder. Dette vil
øke tilførselen av sedimenter til elvene. Økt
sedimenttransport i vassdragene vil påvirke
elveløpene. I de fleste vassdragene er det utviklet
sandurer som er karakterisert ved mange løpsforgreninger og hyppig sideveis forflytning av elveløpene.
Denne typen løpsendringer vil øke ytterligere når
klimaet endres.

Flom. Flomestimatene for Svalbard er svært usikre,
men endringen i antallet og størrelsen på flom ersterkt knyttet til endringer i nedbør, snøforholdog breer. I områder hvor en forventer redusertesnømengder, vil snøsmelteflommene bli mindre,
mens økt nedbør og mer nedbør som regn vil gi
flere og større regnflommer og kombinerte snø-/bresmelte- og regnflommer. Med det høye utslippsscenariet vil brearealet og volumet i flere nedbørfelt
reduseres så kraftig at bidraget fra bresmelting til
flom blir neglisjerbart mot slutten av århundret.
Med fortsatt kraftig temperaturøkning og nedsmelting av breene, kan forekomsten av jøkullaup øke,men slike flommer vil ikke nødvendigvis berøre
bebodde områder.
Snø. Snøsesongens lengde for Svalbard sett under
ett i perioden 1958-2017, er redusert med omtrent
20 dager. Fremtidige snøforhold for perioden 20712100 avhenger sterkt av utlippsscenario. Simuleringene med median RCP4.5 viser områder som
fortsatt vil ha den samme eller litt økte snømengder,mens simuleringen med median RCP8.5 viser en
reduksjon i maksimalt snømagasin på 50 % eller
16

Isbreer
Etter nedsmeltingen av et større sammenhengde
isdekke under tidlig Holocen, forsvant en stor delav de lokale isbreene på Svalbard for rundt 9 700 år
siden. Isbreer ble dannet påny under den sene
Holocen og nådde sitt historiske maksimum daden lille istid kulminerte i det 19. århundret. For
tiden mister isbreene på Svalbard mer is gjennom
smelting og kalving enn de øker på grunn av nedbør
som snø. Alle de godt observerte isbreene minker,
og modeller og satellittmålinger bekrefter at heleøygruppen mister ismasse. En gjennomsnittlig
isbre-reduksjon på 7 % ble estimert fra perioden
1961-1990 til 2000-tallet. Brearealet ble redusert ialle regioner. Dette tapet av isbre-masse og -areal
endrer landskapet og bidrar til havnivåstigning.
I dag ender mer enn halvparten av Svalbards isbreer
i havet, men fremtidig smelting vil føre til en nedgang i antall slike breer rundt Svalbard.
Variabiliteten i ismassebalansen på Svalbard
skyldes primært variasjoner i avsmelting om
sommeren. Varmere somre og en forlengelse av
smeltesesongen i de kommende tiårene vil trolig
fortsette å drive økt smelting og tilbaketrekning av

isbreer. Med den temperaturøkningen som beregnes
av finskala modellen for utslippsscenario RCP8.5,
blir den modellerte nettobalansen for isbreer stadig
mer negativ utover i dette århundret. Vinterbalansen
har ingen klar trend; - den sterke negative utviklingeni nettobalansen er drevet utelukkende av stadig mer
negativ sommerbalanse. Sammenligning av de totidsperiodene 1971-2000 og 2071-2100 viser at likevektshøyden (ELA) stiger med i gjennomsnitt 400 m,
og at det er en fem ganger økning i masse-tap. Det er
betydelig romlig variabilitet i disse endringene, med
større ELA økninger i nordvest og sør på Svalbard
enn i den nordøstlige regionen. Men på grunn av det
store isbrearealet i sistnevnte region er endringen i
det totale massetapet størst i nordøst. Det er mulig
at økt smelting kan ha en utløsende effekt på rask
fremrykking og kalving av isbreer, og det har blitt
rapportert om en rekke slike hendelser på Svalbard.
Disse hendelsene påvirker den totale nettobalansen
av isbreer, og har potensial til å øke bidraget til
havnivånivåendring fra landis i løpet av kort tid.
Permafrost og skred
Permafrost. Svalbard har den varmeste permafrosten så langt nord i Arktis. Gjennomsnittlig permafrost temperaturer på 10-20 m dybde varierer fra ca-2,5 °C langs kysten i vest til ca -5 °C i sentraledeler av Spitsbergen. Siden 2009 har temperaturen ipermafrosten i gjennomsnitt økt med mellom
0,06 °C og 0,15 °C per år på 10 meters dyp. I
sentrale deler av Spitsbergen (ved Janssonhaugen),kan en signifikant temperaturøkning observeres
ned til 80 m dybde. Dette gjenspeiler en oppvarming som har pågått over mange tiår. I vestlige og
sentrale deler av Spitsbergen er den nåværende
tykkelsen av det aktive laget (som tiner og fryser
hver sommer over permafrosten) på mellom 100
og 200 cm. Siden 2000 har tykkelsen av det aktive
laget i Adventdalen og ved Janssonhaugen økt med
henholdsvis 0,6 cm (sedimenter) og 1,6 cm per år(fjell). På Kapp Linné økte det aktive laget med
6,2 cm per år mellom 2009 og 2017 i fjell.
Modellering av temperatur i bakken indikerer at deøverste metrene av permafrosten i kyst- og lavereliggende områder tiner før slutten av århundret ved
utslippsscenario RCP8.5. På enkelte steder tiner

permafrosten ned til minst 5 meter under bakkeoverflaten, mens det fortsatt vil være permafrost
dypere enn 10 meter. For de fleste lavtliggende- og
vestlige områder på Svalbard antyder alletilgjengelige modelleringsresultater at grensen
mellom at “permafrost forblir (i hvert fall delvis)”
og “de øverste meterne av permafrosten tiner helt”
mot slutten av hundreåret, ligger et sted mellom
utslippsscenarioene RCP4.5 og RCP8.5. Økt dybde
av det aktive laget og høyere permafrosttemperatur
fører til mer aktive skråningsprosesser og betydelig
større ustabilitet i fjellsidene, noe som øker skredfaren. Nåværende og fremtidig oppvarming ogtining av permafrost vil påvirke erosjonsprosesser
langs kysten, spesielt der kystlinjen bare består av
sedimenter.
Solifluksjon. Varmere permafrost og økt nedbør
vil øke hastigheten på solifluksjon; - dvs. langsom
deformering av aktive lag som beveger seg nedoverpå grunn av tyngdekraften. Solifluksjon er utbredt
på nedre skråninger i Svalbard-landskapet.
Skred. Fordi meteorologiske faktorer er viktige
utløsningsårsaker til skred, vil klimaendringer føre
til økt sannsynlighet for de fleste typer skred i dekommende tiårene.
Ved en økning av ekstreme hendelser med kraftig
snøfall eller kraftig regn på snø, kan vi forvente enøkning i antallet snøskred, inkludert våtsnøskredog sørpeskred. Mot slutten av århundret, i områder
hvor snøsesongen blir kortere og snømengdene
reduseres, vil sannsynlighetne for tørrsnøskred
reduseres. Imidlertid øker sannsynligheten forvåtsnøskred og sørpeskred.
Oppvarmingen av permafrost kan spille en rolle i
fremtidig utløsning av større fjellskred. Økt temperatur vil mange steder gi flere fryse-tine-sykluserog kan dermed føre til flere steinsprang. Økt dybde
av det aktive laget og høyere permafrosttemperatur
fører til mer aktive skråningsprosesser og betydelig
større ustabilitet i fjellsidene. Sammen med mer
nedbør og økt hyppighet av episoder med kraftig
nedbør i skrånende terreng, vil dette øke sannsynligheten for ulike typer løsmasseskred, inkludert
kvikkleire-lignende skred i utsatte områder.
17

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Havklima
Oseanografi. Under tidlig Holocen var klimaet på
Svalbard betydelig varmere enn i dagens klima. Fra
ca. 8200 til 6000 år siden var overflatetemperaturen
i havområdene utenfor Svalbard ca. 4 °C høyere enn
i dagens klima. Deretter avtok havtemperaturen tilomtrent dagens nivå for ca. 4500 år siden. Observert
havtemperatur har økt siden 1970, og har værtspesielt høy siden slutten av 1990-tallet. Nylige
endringer i de storstilte atmosfæriske sirkulasjonsmønstrene har ført til at varmt atlanterhavsvann fra
Vestspitsbergenstrømmen også vinterstid strømmer
inn på sokkelen vest for Spitsbergen og videre inn
i fjordene. Spesielt har det nordlige Barentshavet
opplevd et raskt klimaskifte; - dette blir beskrevet
som “the Arctic warming hotspot” fordi
oppvarmingen og tapet av sjøis om vinteren er
større her enn i andre deler av Arktis.
Fremskrivninger under utslippsscenario RCP4.5
viser at om 50 år vil overflatevannet rundt Svalbard
være ca 1 °C varmere enn for dagens klima. Dette
gjelder generelt for de fleste områder som i dag harlav konsentrasjon av sjøis, men noen områder
lenger sør får sterkere oppvarming. Noen fåområder, som de ytre delene av Storfjorden og
Framstredet, avkjøles med rundt 1 °C. Norskehavet
og Barentshavet vil sannsynligvis fortsette å varmes
opp etter 2070, med mindre transporten nordover av
varmt atlanterhavsvann blir redusert.
Havforsuring. Øst for Svalbard øker pH-verdien
nordover og nedover i dypet. Aragonittmetning gir
et mål for muligheten for dannelse eller oppløsningav kalsiumkarbonat (aragonitt).Temperaturen påvirker aragonittmetningen, og de høyeste overflateverdiene er funnet i sør. Her er pH-verdien lavere
enn i nordlige farvann, som er kaldere og som
inneholder mer ferskvann. Ferskvann fra issmelting
spiller en viktig rolle i å modifisere vannmassene
rundt Svalbard. Dette er spesielt fremtredende ivannmasser nær isbreer og i fjordene. Havis
fungerer som en barriere for diffusjon av gasser
mellom hav og atmosfære, og fordeling av sjøis itid og rom påvirker i betydelig grad dynamikken ikarbonkjemien i farvannet rundt Svalbard. For Norskehavet og Barentshavet er pH-verdien for
18

utslippsscenario RCP4.5 antatt å falle med rundt
0,12 fra 2005 til 2070.
Sjøis. Oppvarmingen i havet har hindret dannelsen
av sjøis om vinteren og store areal rundt Svalbard er
blitt isfrie hele året. Den globale oppvarmingen harsannsynligvis medført en mer vindgenerert vertikal
blanding av varmt og salt atlanterhavsvann fra ca.
200 m dybde, noe som har hindret dannelsen av
sjøis. Iseksporten gjennom Framstredet er en viktig
del av isbudsjettet i det Arktiske bassenget, og siden
1979 har denne transporten økt med om lag 6 % pertiår. Det siste tiåret har fjordene på vestkysten værtnesten isfrie om vinteren, og hele Barentshavet,
inkludert området øst for Svalbard, har vært isfritt iflere måneder om sommeren og høsten. Etter 2006
har Kongsfjorden de fleste år hatt lite sjøis og en
kortere fastis sesong enn tidligere. Fra 1997 til 2016
har også tykkelsen av sjøis og snødekke på is blitt
tynnere.
Den beregnede fremtidige sjøis-konsentrasjonen
viser fortsatt stor reduksjon i det nordlige
Barentshavet, noe reduksjon i de vestlige fjordene
og en viss økning i de ytre og vestlige delene av
Storfjorden. Områder med redusert sjøis-konsentrasjon gjenspeiler økt sjøoverflatetemperatur ogomvendt. De globale CMIP5-klimamodellene
simulerer ikke tilstrekkelig tilførsel av relativt
varmt atlanterhavsvann inn i Barentshavet, noe som
forklarer en systematisk feil i det simulerte tapet av
sjøis i Barentshavet om vinteren de senere årene.
Da variasjonene i Barentshavet også dominerer
variasjonene i sjøis i Arktis om vinteren, har denne
manglende innstrømmingen av varmt atlantisk vannstor betydning også utenfor regionen, fordi den
muligens kan påvirke beregningene av den storstilte
atmosfæriske sirkulasjon.

tivt havnivå videre utover i det 21. århundret foralle utslippsscenarioer. Dette fallet skyldes i stor
grad landheving og redusert gravitasjonskraft på
grunn av fremtidige lokale ismassetap. Dermed
fører økende globaltemperatur til økende negative
endringer i relativt havnivå på Spitsbergen; - dvs.
det motsatte av det som beregnes for det meste av
jordens kystlinjer. Utviklingen av fremtidig havnivå
kan avvike vesentlig fra resultatene fra den siste
hovedrapporten fra IPCC dersom det skjer et sammenbrudd av de marine deler av iskappen rundt
Antarktis.

Den første typen usikkerhet er i denne rapporten
tatt i betraktning ved å bruke 2-3 utslippsscenarioer når dette er tilgjengelig. Den andre og tredje typeusikkerhet er til en viss grad tatt i betraktning ved
å gi intervaller basert på grupper av flere modellberegninger (ensembler). Imidlertid spenner dissebare en del av den totale usikkerheten. Det kansåledes ikke utelukkes at fremtidige klimaendringer
kan falle utenfor disse intervallene. Videre, ettersom
et flertall av de globale klimamodellene har for mye
sjøis i Svalbard-området, kan øvre del av spennet i
endringer i temperatur og nedbør være urealistisk.

Usikkerhet og bruk av fremskrivninger.

Hvis du vil bruke klimaberegninger i forskning og
forvaltning, anbefaler vi derfor at du kontakter derelevante vitenskapelige miljøer. Disse kan bidra til
tolking av resultatene og til å gi informasjon om ny
kunnskap.

Tradisjonelt er de store usikkerhetene i klimaprojeksjonene knyttet til 1) fremtidige menneskeskapte
utslipp, 2) naturlige klimavariasjoner og 3)
utilstrekkelige klimamodeller. I regioner nær isgrensen, som Svalbard, kommer systematiske feil
(bias) i starttilstanden av sjøisutbredelse inn som en
fjerde kilde til usikkerhet. Denne biasen kan påvirke
fremskrivninger betydelig lenger frem enn noen fåtiår.

Havnivå. Tidevannsmålinger fra Spitsbergen viser
negative trender i relativt havnivå fra midten av det
20. århundret til i dag. Hovedårsaken til synkende
havnivå er stor landheving som i stor grad skyldes
jordoverflatens respons på minkende isbremasse.
Den kraftige landhevningen har stor romlig
variabilitet; - for vestlige deler av Spitsbergen er
landhevingen mellom 7 og 10 mm / år. Prognoser
indikerer at Spitsbergen vil oppleve et fall i rela19

CLIMATE IN SVALBARD 2100

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1.2 Contents of the report
The report is organized as follows:
Chapter 2 provides general background information about the climate system in the Svalbard area.

1. Introduction

Chapter 3 provides information on datasets, models and methods applied in the report.

1.1 Rationale and background
The air temperature in Svalbard has increased by 3
to 5 °C during the last 4 to 5 decades. In the later
years, there have been episodes of heavy rainfall
during winter. Fjords along the west coast havebecome ice free most of the year. The permafrost
has warmed considerably, and there have been anumber of avalanches in and near Longyearbyen.
These changes affect ecosystems as well associeties in a number of ways. The global warming
is projected to continue in the coming decades. How
will this warming affect the climate, hydrology andphysical nature in and around Svalbard?

effects on the physical nature e.g. permafrost andvarious types of landslides and avalanches. The
projections for future climate are based on resultsfrom the CMIP5 global climate models (IPCC,
2013), and on available regional climate model
results for the atmosphere and ocean. Dependenton availability of model data, three scenarios foremissions of greenhouse gases are used: “RCP8.5”(“business as usual”), “RCP4.5” (reductions after2040) and “RCP2.6” (drastic cuts from 2020) andthe time horizon for the projections varies from 50
to 100 years.

The present report was commissioned by the
Norwegian Environment Agency, to provide information relevant for climate change adaptation andclimate change effect studies in Svalbard.
Svalbard was not included in the similar reports
for the Norwegian mainland, “Climate in Norway
2100” (Hanssen-Bauer et al. 2015, 2017), forseveral reasons. Observational data as well as model
data are more limited in the Svalbard area than for
the Norwegian mainland. Further, discrepancies
between different climate models are especiallylarge in the Arctic, indicating an even larger uncertainty than at lower latitudes. It was thus concluded
that a special report for Svalbard would be neededto achieve a useful knowledge base for climatechange adaptation in this area.

The report is to a large degree an assessment ofexisting literature and model results. However, italso includes results from novel atmospheric andhydrological models. Statistical methods wereapplied to downscale temperature from all CMIP5
models. Further, a fine scale atmospheric regional
climate model was applied for estimating changes
in e.g. heavy rainfall, frost days, snow, permafrost
and glaciers. Emission scenario RCP8.5 waschosen for this model run, as the Norwegian white
paper on climate change adaptation (Meld. St. 33(2012–2013)) says that to be precautionary, thegovernment wants risk assessments of climatechange to be based on the high climate projections.
The hydrological model was run using bias-adjusted
and downscaled input from the regional climate
models made available through the Arctic
CORDEX initiative (http://www.climate-cryosphere.org/activities/targeted/polar-cordex/arctic)
under emission scenarios RCP8.5 and RCP4.5.

This report summarizes updated information on
the past, present and projected future climate in the
Svalbard area. The word “climate” is here used in a
broad sense, including the atmosphere, hydrosphere,
cryosphere and ocean. The report also includes

20

Chapter 4 to Chapter 8 describe the past, present and projected/expected future conditions concerning
atmospheric variables (Chapter 4), hydrological variables (Chapter 5), glaciers (Chapter 6), permafrost,
avalanches and landslides (Chapter 7) and ocean variables, including sea ice and sea level (Chapter 8).
In Chapter 9, sources for uncertainty and application of results from the report are discussed.
Chapter 10 is a crosscutting chapter, where results from all chapters are combined and specificallypresented for Longyearbyen, which is the largest settlement and the administrative centre of Svalbard.

21

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

2. The climate system in the Svalbard area,
including adjacent sea areas
2.1 Atmospheric circulation
Large scale atmospheric circulation
The large scale circulation in the Arctic is in thewinter strongly influenced by the semi-permanent
low and high pressure systems in the mid latitudes
governing the heat and moisture transport into the

Arctic (Figure 2.1.1 a). Over the ocean there are
two distinct low-pressure systems (the Icelandic
low in the north Atlantic and the Aleutian low in
the Pacific) while land areas are dominated by highpressure systems (Siberian high over Russia and theweaker Beaufort high over North America).

In summertime the pressure centers weakens
considerably and the central Arctic is dominated bya weak low pressure (Figure 2.1.1 b). The imprintof the large scale circulation can be seen clearly on
Arctic winter temperatures (Figure 2.1.1 c) with
intrusion of warmer air west of the oceanic low
pressure centers and tongues of cold air east of theSiberian and Beaufort High. In summertime (Figure
2.1.1 d) the temperature is to a large extent following the sea-ice cover and is near 0 °C across large
parts of the central Arctic.
Climate in Svalbard and surrounding areas
Svalbard and the surrounding ocean are considerably milder, wetter and cloudier than the average forthe latitude. This is mainly caused by atmosphericheat and moisture transport associated with the Icelandic low and the warm West Spitsbergen Current(WSC), which runs along the western coastline of
Spitsbergen. The West Spitsbergen Current isparticularly important in the shaping of the island’s
climatic conditions through its release of heat during
wintertime and its influence on the concentration ofsea ice (Walczowski and Piechura, 2011). As aconsequence of the strong influence of atmosphericheat transport and the West Spitsbergen Current
Spitsbergen, cold season temperature variability isvery pronounced at Svalbard.

a)Figure 2.1.1: (a) 1961-1990 average wintertime (DJF) and (b) summertime (JJA) Mean Sea Level Pressure (L indicates low pressure centers and H high pressure centers). Estimated 2m temperatures (°C) in (c) winter (DJF) and(d) summer (JJA) based on reanalysis data. Source: NCEP reanalysis.
22

Averaged over the year cyclonic (counterclockwise)
atmospheric circulation patterns are more frequent
than anticyclonic (clockwise) with the share of
cyclonic patterns increasing in winter and autumn.
The most often frequented air mass is flow from theeast sector (particularly in autumn and winter) as
a consequence of the low pressure systems in the
Norwegian Sea (Przybylak and Maszewski, 2012).
The meteorological stations are to a large extent
situated on the western side of Spitsbergen andclose to the coast. Thus, the climate of Spitsbergeninterior and the surrounding islands is still rather
unknown. Attempts have been made to downscalecoarse resolution atmospheric reanalysis using
detailed topographical information (Østby et al.
2017) in order to get a spatial picture of Svalbard’s
climatology. The downscaled reanalysis dataset,
Sval-Imp (more information can be found in
Chapter 3.1) has been evaluated against observed
temperatures in Vikhamar-Schuler et al. (2019).
The main conclusion was that this reanalysis givesa realistic picture of the temperature conditions in
the Svalbard area with annual biases being less than
±1°C. The lowest temperatures are found in mountain- and glacial areas in the north (in Spitsbergen:
north of Isfjorden; Figure 2.1.2 a). In winter (seeFigure 4.1.2), the coastal areas in the north-east are
particularly cold as a consequence of the cold air
masses from the north east.
b)

Figure 2.1.2. Downscaled annual temperature (oC; a) and precipitation (mm; b) from the Sval-Imp dataset (Østby etal., 2017) averaged over the period 1971-2000. Seasonal maps for summer and winter are found in Chapter 4.
23

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

The highest temperatures are found in the southsouthwestern parts of the archipelago where theWest Spitsbergen Current is playing an important
climatic role.
In situ precipitation observations from Svalbard
are sparse and undercatch in precipitation gauges
during snowfall at high wind speeds reduces thequality of the measurements (Førland and
Hanssen-Bauer, 2000). Additionally, the limited
number of stations makes the observational data
insufficient to provide a representative descriptionof precipitation over the entire Svalbard archipelago(Chapter 3.1). A spatial picture covering Svalbard

was attempted by Østby et al. (2017) by downscaling the reanalysis using a simple orographic
precipitation model (Figure 2.1.2 b). There is a clear
topographic influence on the precipitation, whichis particularly pronounced in autumn and winter(Vikhamar-Schuler et al., 2019). The main featuresin this map are supported by a shorter dynamical
downscaling simulation by Dobler (2019), but theseasonal cycle is stronger in the Dobler (2019)
results with early summer being the driest andautumn the wettest. Highest annual precipitation isseen on the western side of the mountain
ranges, with secondary maxima on the eastern partof Edgeøya and on the southwestern part of
Nordaustlandet.

2.2 Ocean circulation and sea ice
The ocean climate around Svalbard is closely
connected to the large-scale circulation between the
North Atlantic and the Arctic Ocean as sketched in
Figures 2.2.1, where the red arrows indicate relative
warm waters and the blue relative cold. The Atlantic
Water in the Gulf Stream propagates northeastwards as the North Atlantic Current from the North
American coast across the North Atlantic between
the Subpolar Gyre and the Subtropical Gyre andfurther towards the Nordic Seas. The relative
strength of these to gyres contributes to variabilityof the hydrographic properties in the Atlantic Water(Hatun et al, 2005). Similarly, atmospheric pressure
systems and the corresponding variability in theseare essential to the exchange of waters between the
North Atlantic and the Nordic Seas over the Greenland-Scotland Ridge (Sandø et al., 2012).
The branch of the North Atlantic Current that continues into the Nordic Seas, follows the continental
slope along the Norwegian Coast as the NorwegianAtlantic Current and releases heat to the

Figure 2.2.1 Circulation and bathymetry in the Nordic Seas (a) and circulation and bathygraphyaround Svalbard and in the Barents Sea (b).

24atmosphere in terms of air-sea fluxes (Mork et al.,2014), which is decisive for the relative mild climate in the regions surrounding the Nordic Seas.
There is also a continuous mixing between the
Atlantic Water in the Norwegian Atlantic Current
and cold and fresh Arctic Water from the East
Icelandic Current as well as fresh water from theNorwegian Coastal Current. Finally, when the
Atlantic Water reaches the Arctic Ocean around
Svalbard, it submerges below the sea ice andfurther into the Arctic Ocean. Due to a fresh
halocline between the sea ice and the Atlantic Water
deeper below, there is usually limited direct contact
between the sea ice and this water mass, preventing
large scale melting of sea ice along the path of thenorthward flowing Atlantic Water into the Arctic.
Cold and fresher waters enter the Nordic Seas fromthe Arctic through the Fram Strait west of Svalbard
as the East Greenland Current. In addition, there is aquite substantial transport of sea ice along with thecurrent. This current continues along the Greenland
coast in the Labrador Sea and further southward
along the Canadian coast as the Labrador Current.
East of Newfoundland the Labrador Current meetsthe northeastward flowing Gulf Stream and partsof it is deflected eastwards. The mixing of thesecurrents constitutes thereby the North Atlantic
Current which propagates northeastwards along thesouthern limb of the Subpolar Gyre (see also Figure
8.1.1). The strength and width of this Subpolar Gyre
is mainly governed by a complex interplay between
the deep overflow from the Nordic Seas, convective
processes in the Labrador Sea, and the atmosphericphenomenon called the North Atlantic Oscillation(Langehaug et al., 2012). The North Atlantic
Oscillation is defined as fluctuations in the relative
strength of the Icelandic low and the Azores high,and controls the strength and direction of the southwesterly winds as well as storm tracks in the North
Atlantic, and thereby also the climate in the Nordic
Seas region.
As mentioned above, the varying width and strengthof the Subpolar Gyre generates hydrographic
anomalies that propagates northeastwards in the
North Atlantic, and to some extent also into theNordic Seas and further northwards along the

Norwegian coast. At the continental shelf breakclose to the Barents Sea Opening, the NorwegianAtlantic Current splits into two branches where
the western part continues towards Svalbard as
the West Spitsbergen Current and the eastern part
into the Barents Sea. The Atlantic waters enter the
Barents Sea between the Bear Island (Bjørnøya)
and Norway through the Barents Sea Opening andflows northeastwards until it enters the Arctic Ocean
between Franz Josef Land and Novaya Zemlya. In
the Barents Sea the warm and saline Atlantic Water
meets the colder and fresher Arctic Water in the
Polar Front, as indicated by the grey line in
Figure 2.2.1 b. Close to Svalbard this front isstrongly attached to the bathymetry and follows thesouthern continental slopes of Svalbard. Further
east, the front is wider and more variable.
The heat anomalies associated with the propagatinghydrographic anomalies from the North Atlantic
have shown to be important for the regional climate
both in the Barents Sea and in the waters close to
Svalbard. In the Fram Strait, the West Spitsbergen
Current also splits into two branches where one
continues through the Fram Strait and then eastwards along the northern coast of Svalbard, whereas
the other branch turns left in the Fram Strait andthen southwards into the Greenland Sea. Due tothe cold air and relative warm water masses, theair-sea fluxes in the Greenland Sea can be quite
large where the ocean is in direct contact with theatmosphere. During winter, the surface waters may
then be subject to strong cooling, become denser
than the intermediate waters below and sink to great
depths, a process referred to as convection anddeep-water formation. The air temperatures during
winter is strongly influenced by the North Atlantic
Oscillation, and when this index is low, the surface
cooling is particularly high, and large amounts ofdense water can be formed and flow over the Greenland-Scotland Ridge into the North Atlantic as a
deep-water current. These series of events constituteimportant components of the North Atlantic-Arctic
Ocean climate variability which also affects Svalbard. The most important of these is probably thenorthward propagation of salt and heat anomalies
which contribute to major anomalies in the sea ice
extent north of Svalbard (Ivanov et al., 2016),
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CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

but also further downstream at intermediate depths
in the Arctic Ocean and into the Barents Sea
between Svalbard and Franz Josef Land.
Episodes of Atlantic Water intrusion on the West
Spitsbergen Shelf represent an increased oceanic
heat flux into the fjord systems and toward theSvalbard glaciers (Nilsen et al., 2008). Convergence, mixing, and exchange of Atlantic Water,
Arctic water, and freshwater from land characterize
the West Spitsbergen Shelf. Within an annual cycle,
the waters on the shelf and in the adjacent fjords
switch from a state of Arctic dominance (cold andless saline in winter) to one of Atlantic dominance(warm and saline in summer) and back. Hence, thelocal climate in Svalbard is dependent on importantinteraction processes between the fjord/shelf and theAtlantic Water from the West Spitsbergen Current(WSC). The WSC is topographically steered along
the continental slope, and is the major source ofwarm and saline Atlantic Water to the Arctic Ocean.
The WSC is subject to cooling and freshening as it
flows northward, and interactions with West Spitsbergen fjords such as Isfjorden and Kongsfjorden

can make a significant contribution to this modification. Understanding the mechanisms governing theinteraction between the WSC and West Spitsbergenfjords is therefore important, not only for explaining
environmental conditions inside the fjords, but also
for explaining variability in the Arctic Ocean.
The regional effects of climate change can beheavily modulated by internal variability such as
the Atlantic Multidecadal Oscillation which works
on timescales from seasons to decades. In addition,
the effects may be modified by feedbacks in the
climate system such as an increased warming in
the Arctic regions due increased melting of sea ice,less reflection of solar radiation, and thereby moreabsorption of solar heat, the so-called ice-albedo
effect (Screen and Simmonds, 2010). Such changes
in sea ice extent and thickness in combination withpropagating hydrographic anomalies in the Atlantic
Water may lead to even stronger anomalies in thesea ice cover along the path of the Atlantic Water asshown (Ivanov et al., 2016, Polyakov et al., 2017)and illustrated in Figure 7.1.1.

2.3 Arctic amplification and feedbacks
The term Arctic amplification refers to a greatertemperature change near the poles compared to therest of the globe for a given global climate forcing(change in greenhouse gasses, solar insolation etc.).
This can be seen clearly in the observational record
where the warming trend in the Arctic is twice aslarge as the global average in recent decades (Figure
2.3.1).
The cause of this is partly linked to climate feedbacks. A climate feedback is an internal climate
process that amplifies or dampens the initial climate

Figure 2.3.1: 5-years zonal mean temperature anomalies (°C) compared to the 1961-1990 normal (a) and temperature trends (°C) from 1950-2017 (b). Data from NASA Goddard Institute for Space Studies (NASA GISS).
26

response to a specific climate forcing. If a process
amplifies the climate response it is denoted as apositive feedback, if it dampens the response it is anegative feedback.
The direct response of an object to increased radiation is that it heats up and efficiently radiates out(emits) energy to dampen the effect of the increased
incoming radiation. This is done rather efficient as
the amount of emitted radiation goes with the fourth
power of the temperature. This is known as
Stefan-Boltzmann’s law. If no feedbacks were
present in the climate system one would still expect
a weak Arctic amplification as the temperature of theatmospheric layer that efficiently radiates to space islower in the Arctic than the in lower latitudes,
meaning that the Arctic is less efficient in getting rid
of energy (Pithan and Mauritsen, 2014). In the literature this is often termed the Planck feedback, despite
the fact that it refers to the direct response. Anotherdirect effect is caused by dismissing sea ice (notincluding the feedbacks related to change in albedo)
which expose the Arctic atmosphere to a larger areaof relatively warm oceanic temperatures which will
amplify the near surface temperature change.
Other potential candidates for the amplification are
the water vapour feedback, the surface albedo feedback, cloud feedbacks and the lapse rate feedbacks(see Figure 2.3.2 for a description of the different

feedbacks). Studies suggest that the largest feedbackin the Arctic is the lapse rate feedback (Pithan and
Mauritsen, 2014; Graversen et al, 2014). The lapse
rate feedback is the coupling between surface airtemperature changes and changes in the atmosphericlayers that radiates efficiently out to space (around5000 meters above the surface). For the greenhouseeffect to work, the atmospheric temperature has tobe colder in the region that efficiently radiate out tospace than at the surface. If this temperature difference is altered it will change the efficiency of thegreenhouse effect. This is what is called the lapse rate
feedback. In the Arctic the temperature change spacethan at the surface. If this temperature difference isaltered it will change the efficiency of the greenhouseeffect. This is what is called the lapse rate feedback.
In the Arctic the temperature change for a given
climate forcing is largest near the surface. This meansthat temperature difference between the surface andthe atmospheric layers that radiates efficiently out tospace is getting larger and the efficiency of the greenhouse effect increases. The surface albedo feedback
connected to the reduction of snow and ice is thought
to be the second large contributor to the Arctic amplification. However, model simulations where the two
feedbacks explained above are suppressed indicatethat a large portion of the Arctic amplification is
unexplained by these feedbacks. This indicates animportant role of the direct effects and other feedbacks.
It should be noted that feedback
analysis do not give much insight into
which physical processes that are thedriving causes of the Arctic amplification as the feedbacks are the sumof the interplay between a wide rangeof physical processes that are closely
coupled to each other. Among others,changes and variability in the northward atmospheric and oceanic energy
transport (Feldl et al. 2017), clouds andatmospheric moisture content (Cao et
al, 2017) and the stability of theatmospheric boundary layer (Esau andZilitinkevich, 2010) has been shown toplay important roles in theamplification.

Figure 2.3.2: Four important radiative feedbacks.
27

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3. Methods, data, models
This chapter comprises methods, data and models
that have been used to assess the state of the current
climate and future climate change. The chapter isdivided into six subchapters covering methods and

3.1 Methods and data, atmosphere3.1.1 Observations and observation-based
datasets
The first permanent weather station in the Svalbard
region was established in Green Harbour (nearBarentsburg) in 1911. During the past 100 years,
Norway, Russia and Poland have operated regularweather stations for shorter or longer time periods
at several sites in the region. The measurements
include temperature, atmospheric pressure,humidity, wind speed, wind direction, andprecipitation. Manual observations are usually taken
4-8 times daily, while at automatic weather stations(Figure 3.1.1) measurements are taken at least once
an hour. A survey of regular manual and automaticweather stations in the Svalbard area is given in
Vikhamar-Schuler et al. (2019).

data with respect to atmosphere (3.1), hydrology(3.2), glaciers (3.3), permafrost (3.4), ocean (3.5)
and sea level (3.6).

Due to large climate gradients and harsh weather
conditions in the Arctic, even small relocations orchanges in environment at the measuring sites may
cause substantial changes in measuring conditions.
Also, instrumental changes and transition frommanual to automatic observations may influence
the homogeneity of the long-term climate series.
Measuring precipitation in the Arctic is a challenge;- precipitation may be overestimated because ofcontributions from blowing and drifting snow. In
addition, depending on wind exposure, precipitation
gauges do not completely capture precipitation,
resulting sometimes in a considerable “undercatch”.
This is especially significant for snowfall duringstrong winds. Førland and Hanssen-Bauer (2000)
found the “true” precipitation to be almost twice themeasured amount in Ny-Ålesund.

It is not trivial to study long-term changes inobserved weather elements in the Svalbard region.
Just a few stations have long observational series,
and identification and adjustment of inhomogeneities in Arctic climate series is hampered by thesparse station network. By combining series fromseveral measuring sites - including observations
made by hunting and scientific expeditions - ahomogenized, composite temperature time series forLongyearbyen/Svalbard Airport has been extended
back to 1898 (Nordli et al., 2014), and a precipitation time series back to 1912 (Nordli et al., 1996).
Also, for other Svalbard stations (e.g. Ny-Ålesund
and Isfjord Radio) the observational precipitation
and/or temperature series have been extended backin time and homogenized (Nordli et al., 1996;
Førland et al., 1997b). At Hopen, a relocation of thegauge in 1997 led to a severe homogeneity break in
the precipitation series; - and the values before 1997
are adjusted by a factor of 0,60 for annual precipitation and between 0,41 and 0,82 for seasonal values(Vikhamar-Schuler et al., 2019). Original as well
as extended, homogenized series for all weather
stations are available at www.eklima.met.no.

a linear model of orographic precipitation (Smithand Barstad, 2004; Schuler et al., 2008), Østby et al.
(2017) used a statistical model to downscale ERA40 and ERA-Interim reanalyses to spatial resolutionof 1 km with a model output frequency of 6-hours
covering the period 1958-2017. The dataset (“SvalImp” (Svalbard impact assessment forcing dataset,version 1; (Schuler, 2018)) comprises variables toresolve the surface energy balance (downwellingshortwave and longwave radiation, temperature,humidity and windspeed) as well as precipitation.
Also MET Norway carried out a regional climate
model simulation with the COSMO-CLM forced
by ERA-Interim to produce simulations with hourly
output with a spatial resolution of 2.5 km. This
simulation covers the period 2004-2017 and isnamed “CCLM data set” (see details in ch. 3.1.4).
In this report, the Sval-Imp data set is used for thereanalysis-based description of present climate
conditions (see ch. 4).

The Sval-Imp and CCLM data sets were
evaluated against observed temperature and precipitation at Hopen, Svalbard Airport and Ny-Ålesund(Vikhamar-Schuler et al., 2019). For Hopen, theSval-Imp data set has a warm bias of 0.2 – 0.7 °C
Most of the regular weather stations are situatedfor all seasons in the reference period 1971-2000
in western parts of Spitsbergen (Figure 3.1.2), andnone are elevated above 40 m above sea level. Thus, (annual bias 0.5 °C). For the period 1988-2017 there
was no bias. The other stations have cold annual
neither temperature nor precipitation from thesestations is representative for the Svalbard land area. biases (Ny-Ålesund: 0.4 °C; Svalbard Airport:
0.8°C) in the period 1971-2000. These were rather
Temperatures are usually lower in the inland, andbecause of orographic enhancement, precipitation at similar for the last 30 years. The cold biases are at
maximum in summer, while minor warm biases arehigher elevations may be substantially higher thanat the coastal stations. Near Ny-Ålesund an increase seen during winter.
of 20 % per 100 m was found for summer precipitation, at least for elevations up to 300 m a.s.l. (FørIn terms of precipitation amount, the Sval-Imp data
land et al., 1997a). For snow precipitation, Sand et
set shows a positive bias (overestimation) at Hopen,al., (2003); - based on three years of snow surveys
Svalbard Airport and Ny-Ålesund (Vikhamaras well as previous studies, found an average vertical Schuler et al., 2019). The Sval-Imp data set overgradient of snow precipitation of 16 % per 100 m
estimates the annual precipitation by 50-150 % forelevation. Figure 2.1.2b illustrates the strong
the period 1988-2017. For most stations the differtopographical influence on precipitation in theence between modelled and measured precipitation
Svalbard region.
may be explained by undercatch in the precipitation
gauges during events with snowfall and strong
winds.
To illustrate historical climate conditions also inareas without measurements, high-resolution datasets based on reanalysis are applied. Combining the
TOPOscale method (Fiddes and Gruber, 2014) with

Figure 3.1.1 Campaign weather station at Klauva, with view towards Sassendalen. Photo: Ketil Isaksen, MET.
28

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Figure 3.1.3 left) Historical and projected annual greenhouse gas emission pathways in gigaton carbon dioxide per
year from 1950 until 2100 according to four scenarios (IPCC, 2013); right) annual global mean temperature anomalies w.r.t. the 1986-2005 global average (IPCC, 2013).

increase of 4°C is projected (see Figure 3.1.3
right)). For the low GHG emission scenario,
RCP2.6, a warming by 1°C is projected by year
2040 and remains constant until the end of the
century. Note that these temperature changes are
given relative to the period 1986-2005. To find thewarming compared to preindustrial time, about 0.6°C should be added.
3.1.3 Climate models and downscaling
Figure 3.1.2 Regular meteorological and hydrological measurement stations at Svalbard.

Testing for historical trends in observed andmodelled time series was performed with thenon-parametric Mann-Kendall test, which may be
used without knowing the distribution of the timeseries as it is a rank test. In order to study variations on selected time scales, graphs of the timeseries were smoothed by a Gaussian filter. Details
on the use of Mann-Kendall trend test and Gaussiansmoothing on Svalbard temperature series arepresented by Gjelten et al. (2016).
3.1.2 Emission scenarios and climate models
Climate projections depend on different global
emission scenarios. These are given in so-called“Representative Concentration Pathways (RCPs)”
that are based on concentrations of greenhousegases (GHG) in the atmosphere over time (IPCC,

302013). RCPs are given as an additional humaninduced radiative forcing affecting the atmospheresince 2006 until 2100. The unit is Watt per square
meter, e.g., the scenario RCP2.6 gives anadditional warming effect of 2.6 W/m-2. In this report
we mainly address the scenarios RCP8.5, RCP4.5
and RCP2.6 depending if climate model data isavailable. The scenario RCP2.6 implies drastic
reduction of GHG emissions throughout the century,
starting already before 2020 (see Figure 3.1.3 left).
RCP4.5 implies a slow increase of emissions until
2050 followed by emission reductions. RCP8.5
implies that GHG emissions will continue toincrease with a similar rate as before 2005 and reacha rate of approximately 100 Gt/year around 2070
(see Figure 3.1.3a)). For the high GHG emission
scenario, RCP8.5, a mean global temperature

The only approach to calculate a possible futureclimate on Earth is to use data from global climate
models or Earth system models (ESMs) that areforced with different future GHG emission
scenarios (see Figure 3.1.3). These simulations are
performed by several research institutions aroundthe world coordinated by the World Climate
Research Programme. Current experiments
comprise the fifth phase of the Coupled Model
Inter-comparison Project (CMIP5). Since ESMs are
run over the whole globe over a long period of time,such simulations are computationally very expensive and have therefore constrains, e.g. in terms ofhorizontal and vertical resolution.
To project future climate for the archipelago of
Svalbard is a challenge due to two main reasons.
(i) global climate models have a coarse horizontal resolution of typically 100x100 km2, thus they
are not fit-for-purpose to resolve key atmosphericprocesses, such as atmospheric circulation and its

interaction with the ocean and sea ice or the topographical influence on the airflow (Koenigk et al.,2015). (ii) Sea-ice concentration and extent is often
overestimated for the present climate in ESMs. This
may lead to a large, probably unrealistically high,temperature increase when the sea-ice cover around
Svalbard will have been disappeared in a futureclimate. Despite improvements of the sea-ice
representation in ESMs, there is still a large spread
between the models in calculating present andfuture climate conditions (Stroeve et al., 2012;
Koenigk et al., 2014).
Climate models are used to simulate the state of
the Earth’s climate for both the past and the future.
They are able to reproduce observed large-scale
phenomena, such as major wind systems andtemperature patterns, based on many grid boxes.
However, global climate models have a minimum
skilful scale due to the way they are constructed,
which means that individual quite coarsely sized
grid box calculations are subject to substantial
inaccuracies. This means that global climate models
provide only limited information about the characterof local climate (e.g. for Spitsbergen, Bjørnøya, andHopen). Consequently, it is necessary to downscale
results from climate models to a higher horizontal
and temporal resolution. There are two approaches
to perform downscaling, (i) empirical-statistical and(ii) dynamical. Although both methods haveadvantages and disadvantages, they can act
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complementary. Both methods are briefly described
below.
The local climate is affected by geography, thelarge-scale phenomena, and processes over a
wider region. The dependency between the different
spatial and temporal scales can be used to enhance
the information about the local climate.
Empirical-statistical downscaling (ESD) is atechnique that makes use of the dependency
between both the large-scale conditions as well as
systemic effects of the geography to estimate thelocal temperature or precipitation. It makes use ofdifferent sources of information (empirical fromobservations and statistical theory), requires
little computational resources, and can make use oflarge-scale features that are skilfully simulated by
the ESMs and bypass biases associated with local
details.
One of the main strengths of empirical-statistical
downscaling is that it is ideal for downscaling large
multi-model ensembles of ESM simulations tocapture the range of natural variability and accommodate for biases between ESMs. Hence, it
provides a picture of both the likely outlook as wellas information about uncertainties. Drawbacks with
empirical-statistical downscaling differ to those of
dynamical downscaling models and include weakconnections between predictors and predictands
and the requirement of high-quality and long seriesof observations. In this report ESD was applied fordownscaling temperature from all available CMIP5
models (see Appendix 2, Table A.2.2).
The calibration of ESD models was done withdetrended data, and the models were subsequently
evaluated by replacing the detrended with theoriginal data for comparing the predicted trends
with those observed. The models were also tested
through a standard cross-validation. The predictors
used to describe the large-scale conditions wererepresented through common empirical orthogonal
functions (EOFs) (Benestad, 2001) to ensure thesame spatial patterns used for model calibration
were also used in projections. Furthermore, thesecommon EOFs facilitated an evaluation of the32

representation of the large-scale conditions in the
ESM simulations. The downscaling applied to
Svalbard involved multi-model ensembles, and theevaluation of the downscaled ensemble also involved testing whether the downscaled temperatures
were subject to similar statistics as those observed.
Hence, an evaluation was carried out on an ensemble basis in addition to the traditional evaluation ofindividual simulations (Benestad et al., 2016).   
In dynamical downscaling, data from global
climate models are used as input to regional climate
models. Just as ESMs, regional climate models areformulated on the basis of dynamic and thermodynamical laws. They are applied for the region ofinterest and can therefore be run with an increased
horizontal grid resolution. The increased grid
resolution enables regional climate models toresolve regional weather phenomena on a smallerscale and therefore in a more realistic way. In
practice, at the end of such a computationallyexpensive modelling chain, more data points withmore valuable information within a region is available. Due to the huge computational costs,
dynamical downscaling is performed in international
initiatives for chosen regions. Arctic CORDEX is
an international initiative to dynamically downscaleatmospheric data from CMIP5 simulations for theArctic region by using an ensemble of regional
climate models.
The Arctic CORDEX simulations (Appendix A1,
Table A1.1) enable the analysis of future changes intemperature, precipitation and many more variables
often used in impact models. One should be awareof, that a horizontal grid of 0.44° (~50 km) is stillquite coarse with respect to Svalbard’s size,
complex topography and complex processes
between open sea, sea-ice, land and atmosphere.
In this report, the reference period (current climate)is defined as the period 1971-2000. Projected meanannual and seasonal changes in temperature andprecipitation have been calculated for the middle(2031-2060), and the end of the century, 2071-2100.

3.1.4 Fine-scale modelling
As discussed by Prein et al. (2015), climate model
simulations at a convection-permitting scale (i.e., agrid resolution < 4 km) can provide a clear
added-value compared to lower resolution simulations due to improved representation of physicalprocesses. Additionally, a study by Mölg and Kaser(2001) has shown that statistical post-processing(bias-correction or downscaling) and its associateduncertainties can be avoided due to the high grid
resolution. High-resolution regional climate
models have thus the potential to provide local
climate information as well as data suited to directly
drive impact models.
MET Norway has therefore run the COSMO-CLM(Früh et al., 2016) at a convection-permitting scale
focusing over the Svalbard area (Dobler, 2019).
The meteorological fields from the global MPIESM-LR Earth system model (Giorgetta et al.,2013) have been downscaled to 0.22° over northern
Europe, followed by a further dynamical downscaling step to 0.022° (around 2.5 km) overSvalbard. The simulations cover the time periods
1971-2000 and 2071-2100, using the high GHG
emission scenario RCP8.5.
In the COSMO-CLM (CCLM) simulations, the sea
surface temperature and concentration of sea-ice is
specified by the driving ESM. Although the ESMs
include an ocean model with a higher spatial resolution than the atmospheric part, the resolution is stillcoarser than in the regional climate model. This ispartly considered by interpolating the global data to
the regional climate model grid and land-sea mask,
but biases originating from the global model will still
remain.
Evaluation of sea-ice concentration in the MPIESM-LR driven 0.22° CCLM run against HadISST
analyses (Rayner et al., 2003) shows that the modelis capable to reproduce historical sea-ice concentrations (Dobler, 2019), although it simulates too highsea-ice concentrations north of Svalbard and westof Novaya Zemlya. For summer, the MPI-ESM-LR
shows generally too low concentrations of sea-ice
for the high Arctic. Altogether, the MPI-ESM-LR

shows a good representation of sea-ice, but the
shortcomings mentioned above should be kept in
mind, since climate in the Svalbard region isstrongly influenced by the sea surface temperatureand sea-ice conditions.
At a grid resolution of 2.5 km, regional climate
model simulations are extremely computationallyexpensive. Thus, the simulations are limited to thedownscaling of a single ESM projection for twotime slices of 30 years at the end of the 20th and21st century. Although this single simulation does
not provide robust projections, robustness can beachieved by comparison to other regional climate
model projections (e.g. those available by Arctic
CORDEX) and ensembles of statistically downscaled ESMs. Furthermore, Giorgi et al. (2016)
showed that the downscaling signal for precipitation and temperature in all RCMs they looked atover the European Alps, is roughly in line with theensemble average downscaling signal, regardless ofthe sign and intensity of the large-scale change inthe single RCMs.
Thus, assuming the same downscaling signal for theArctic CORDEX ensemble average and the CCLM,
the signal from the CCLM simulation can be added
to the mean Arctic CORDEX signal. Basically, thisis a spatial interpolation, adding the CCLM residuals to the Arctic CORDEX mean to improve thespatial resolution but maintaining the spatial mean.
Note that this method is only applied to create themaps of mean climate changes over Svalbard in
Chapter 4, while for tables and quantitative
measures, unaltered Arctic CORDEX data is used.
Due to the long-term temperature memory inherent
in permafrost and poly-thermal glacier systems, datafrom transient runs are required for glacier massbalance and permafrost modelling, rather than timeslices representative for different states of a long-term
evolution. To fill the simulation gap 2001-2070, theSTatistical Analogue Resampling Scheme (STARS,
Orlowsky et al. 2008) is applied. STARS is a statistical climate model that has already been used togenerate climate projections for different regions of
the world (e.g., Zhu et al. 2013, Feldhoff et al., 2015,33

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Lutz and Gerstengabe, 2015) and is computationally much cheaper than a dynamical regional climate
model. More information on the STARS model isprovided at https://www.pik-potsdam.de/research/climate-impacts-and-vulnerabilities/models/stars/
star-statistical-regional-model.
Usually, STARS is applied on observation or reanalysis data to extend them to the (near) future
following a prescribed temperature trend. Here,
we have applied STARS to the two 30-year timeslices modelled by the CCLM to extend the simulations into the uncovered time period 2001-2070.
The historical simulation (1971-2000) is used tosimulate the near future (2001-2035) while the far
future projection (2071-2100) is used to simulate
the intermediate period (2036-2070). Thus, it ispossible to combine the advantage of the high-resolution dynamical model (well resolved spatial fields,
coherence between different variables) with theadvantage of a statistical model (low computationalcosts) to provide transient climate projections for
impact modelling.

3.2 Methods and data, hydrology
Two important purposes of this report are to: (1)characterize the present state of hydrologicalconditions in the Svalbard Archipelago based on
observed data of the hydrology and the cryosphere
supported by hydrological model results; and (2)describe the impacts of climate change on land
surface water balance and flow processes usinghydrological model simulations. The model has
algorithms for describing hydrological processes

including mass balance of glaciers at a range of
temporal and spatial scales, and for solving problems relevant for climate change impact studies.
3.2.1 The rivers in Svalbard
In Svalbard, large parts of the rivers are braided.
That is, they are composed of a network of river
channels separated by bars. The channel splitting
is caused by deposition of coarse sediment carried

Figure 3.2.1 The valley sandur system and the delta in the lower part of Adventdalen. Left: Todalselva with its oldriver fan is eroded by the river of the main valley. Photo: The Norwegian Polar Institute34

as bed load during high water discharge. Braided
rivers are characterized by relatively large sedimentloads, steep gradients and broad and deep channels
formed in coarse grain sizes. For a given gradientand moderate grain size, there is a threshold levelof sediment load that may transform a meanderingriver into a braided one. In Svalbard, a major partof the sediment budget is supplied from glaciers andin such cases; the braided river systems are named
sandurs. Meandering channel systems are rare in
Svalbard and occur only in smaller streams. At least
two types of sandur systems may be recognized.
The extension and development of the smaller
systems like the one in Bayelva reflect the activity of
the nearby glaciers, Brøggerbreene and the erosion of
their moraines. The large sandurs that are developedin the large open valleys of for example Adventdalen(Figure 3.2.1), are composed of sediment deliveredfrom a number of glacier fed tributaries.
Valley walls confine most of the sandurs in
Svalbard. These type of systems are termed valley
sandurs according to the classification introduced by
Krigstrøm (1962). River fans accumulate where thetributaries enter the main valley and the sandurs growin size downstream as more sediment is supplied tothe system. The lower part of Adventdalen is an
example of delta formation as the sandur systemmeets the sea (Figure 3.2.1).

Anastomosing rivers or streams are similar to
braided rivers in that they consist of a network ofchannels. However the channels are low-gradient,narrow, deep channels with stable banks in contrast
to braided rivers, which form on steeper gradientsand display less bank stability. Anastomosing river
reaches with floodplains covered by fine fractions
seems to be rare in Svalbard, but have been
observed in the lower part of Reindalen.
Because the rivers consist of a network ofconstantly changing channels, it is difficult to obtaina stable river profile and maintain continuous riverlevel and river flow measurements over time. Two
problems arise, the profile itself can undergo a gradual displacement and the “moving river channel”
may move or destroy measurement equipment.
3.2.2 Observations
Hydrological processes in Svalbard have beenmonitored by several institutions, including the
Norwegian Water Resource and Energy Directorate(NVE) that have been running a program for observing streamflow in several catchments since thebeginning of the 1990’s. Today, there are only a few
stations with continuous measurements of hydrological processes in the Svalbard Archipelago. This
report makes use of discharge measurements in thetwo catchments Bayelva and De Geerdalen (Figure
3.1.2).

Figure 3.2.2 Location of thecatchments Bayelva (left) and De
Geerdalen (right), (Haagmans,
2018). (a) Details of Bayelva.
Discharge gauging stations arelocated at BAY, meteorologicalstation in Ny-Ålesund is marked
as MET. (b) Details of De Geerdalen. Discharge gauging stations
are located at DEG.
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The Bayelva station is located close to Ny Ålesund,at 78°55’N, 11°56’E. It has a catchment area of 31
km2 and a mean altitude of 265 m a.s.l. (Haagmans,
2018). With a mean annual temperature of -5.7 °C,
conditions are favourable for glaciers. As much as
50 percent of the catchment is covered by glaciers(Figure 3.2.2a), with a maximum ice thickness of
100 m (Bruland and Hagen 2002; Sund 2008). The
discharge station has been operated since 1989. The
quality of the rating curve is considered poor due toan unstable profile situated on permafrost, and too
few discharge measurements (Stenius, 2016).
The De Geerdalen station is located 20 km northeast of Longyearbyen, at 78°16’N, 11°19’E. At 78
km2, its catchment is about twice the size ofBayelva (Figure 3.2.2b), and the mean altitude is
410 m a.s.l. (Haagmans, 2018). The mean annual
temperature is -5.9 °C and the precipitation isapproximately half of the precipitation received in
Bayelva. Ten percent of the catchment is covered byglaciers. Discharge has been measured since 1991,
but winter measurements are prone to errors due to
ice jamming (Stenius, 2016).
As the gauging stations are encapsulated in ice
during winter and river flow is freezing, river flow
is set to zero in the winter period from approximately October to June (H. Songe, pers. comm.). The
combined effects of a sparse stations network, shortobservation records and measurement errors due
to difficulties with operating stations in the Arctic
environment leads to a high degree of uncertainty
in the discharge observations.
3.2.3 Hydrological modelling
Due to the sparse station network in the Arctic,
hydrological models that describe the water balanceof the land surface are necessary for determining theimpacts of climate change on land surface hydrological processes. The Arctic landscape is characterized by mountains, glaciers, alpine and tundra
vegetation species, a large number of lakes andbogs, and a shallow layer of surface deposits
consisting of glacial tills. In this environment, waterbalance studies must focus on the spatial distribution of landscape elements that are significant
for glacier mass balance, snow cover, subsurface
moisture conditions, runoff and evapotranspiration
36

fluxes. A framework for modelling of land
surface hydrology in Arctic environments is therefore provided by models that integrate thecontributions from several small scale elements.
A spatially distributed version of the HBV hydrological model (Bergström et al., 1995; Beldring etal., 2003) has been adopted to water balance
simulations in Arctic environments with glaciers.
The model performs water balance calculations for
1 by 1 km2 grid cell landscape elements characterized by their elevation and land use. Each grid cell
may be divided into a maximum of four land
surface classes; two land use zones with different
vegetation, a lake area and a glacier area. A regionally applicable set of model parameters wasdetermined by calibrating the model with therestriction that the same parameter values are used
for all computational elements of the model that fall
into the same class for land surface properties. The
model has been modified to take glacier melt into
account. For the historic simulations, 1971–2005,
the glacier area was kept static. For the simulations
2006–2100, the glacier thickness was reduced as afunction of glacier melt. When/if no ice remained in
the grid cell, the glacier covered area was reducedby 1 km2 (the size of one grid cell). A dynamic
treatment of glacier mass balance changes (Huss etal., 2010; Li et al., 2015) was also attempted, but
was discarded because of too rapid melting andretreat of glaciers. The discharge from catchmentsin the Svalbard Archipelago is strongly influenced
by snowmelt and glacier mass balance, as well as
the input temperature and precipitation. The model
parameter sets must take these processes into
account in order to simulate discharge properly.
Data was available for calibration and validationof the model from the Bayelva catchment and the
De Geerdalen catchment. Observed discharge data
were derived from the Norwegian Water Resources
and Energy Directorate, and mass balance and icevolume data were based on results from Hagen andSætrang (1991) and Kohler et al. (2007). Two calibration strategies were applied; (1) calibration of themodel for the two catchments with input precipitation and temperature data from the meteorologicalstations Ny-Ålesund and Svalbard Airport operated

by the Norwegian Meteorological Institute, and(2) calibration of the model with input precipitationand temperature data from 1 by 1 km2 downscaled
reanalysis (Sval-Imp; Chapter 3.1.1). The first calibration strategy allows the model to be run only forthe two catchments Bayelva end De Geerdalen, while
the second calibration strategy allows the model tobe run for the entire Svalbard Archipelago, except theislands Hopen and Bjørnøya that were not includedin the model domain. In both cases, the model
parameters were based on a multi-objective calibration strategy where discharge data from the twocatchments Bayelva and De Geerdalen were used toobtain a regional set of model parameters for 1 by 1
km2 grid cells, with parameter values conditioned onland surface characteristics. Observed meteorological
data are often more trustworthy than atmosphericmodel simulations and the performance of the hydrological model is better for the first calibration strategy. On the other hand, the second calibration strategy
allows hydrological processes for the entire Svalbard
Archipelago to be characterized.
The HBV model was driven by downscaled andbias-adjusted temperature and precipitation from
Arctic CORDEX (Box 5.1). The systematic biases
between the Arctic CORDEX climate model outputs and the observed temperature and precipitation (Sval-Imp reanalysis dataset) in the reference
period, 1971-2000, in addition to the mismatch inscale, necessitate a post-processing of climate model outputs before they can be used as forcing datafor hydrological modelling. The RCM outputs werefirst re-gridded to a 1 x 1 km2 grid using asimple nearest neighbour method. The Sval-Imp
data, which also have a 1 km resolution, weretreated as ‘observed’ data. An empirical quantile
mapping method (EQM) was adopted to biascorrect and bias-adjust the climate projections oftemperature and precipitation (Gudmundsson etal., 2012; Sorteberg et al., 2014; Wang et al. 2016).
The EQM method utilizes the empirical cumulative
distribution functions (ECDFs) for both observed
and modelled variables. A transfer function matching the modelled ECDF in the reference period withthe observed ECDF was applied to adjust values
from the climate projection quantile by quantile sothat they yielded a better match with the observed.

Twelve calendar-month-specific transfer functions
for each grid cell were derived. These derived
transfer functions from the reference period werethen assumed to be valid for use in the projection
period. (Chapter 9).
3.2.4 Snow modelling
To estimate the amounts of snow and the length of
the snow cover season at Svalbard, we have used the
seNorge snow model (Saloranta, 2016). This model
has been applied to produce snow maps for the
Norwegian mainland since 2004 (www.senorge.no).
The seNorge snow model (v.1.1.1) requires thedaily mean air temperature T [°C] and the daily sumof precipitation P [mm] as input forcing. The model
consists of two main sub-models: 1) the SWE (Snow
Water Equivalent) sub-model for snowpack waterbalance and 2) the snow compaction and density
sub-model for converting SWE to snow depth. The
SWE sub-model is independent of the snowpackcompaction and density sub-model, implying that
the SWE is not affected by snow density in themodel. The SWE sub-model uses a threshold airtemperature TS to separate between precipitation as
snow and rain, handles separately the ice and liquidwater fractions of the total SWE and keeps track of
the accumulation and melting of snow. The snowpack can retain liquid water from snowmelt andrain up to a percentage rmax (default 11 %) of its ice
content, while the excess goes to runoff. The liquidwater in the snow pack can also be refrozen to ice.
An extended degree-day algorithm is used to
calculate potential snowmelt rates M*

where b0 and c0 are empirical parameters and S* thepotential incoming daily solar irradiance [J/day]. The
melt parameters b0 and c0 are estimated on the basisof over 3500 daily melt rate observations from theNorwegian mainland snow pillow network.
Incoming solar irradiance is a function of the geographical latitude and the day of the year. Moreover,
the average grid cell snowmelt rates are also affectedby the simulated fraction of snow covered area (SCA)
in the model grid cells. The snow compaction anddensity sub-model calculates changes in snow depth37

CLIMATE IN SVALBARD 2100

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due to snowmelt and new snowfall events, as wellas due to viscous compaction, where the compaction
rate is dependent on the mass, density, temperatureand liquid water content of the snow pack.
For this report, the seNorge snow model was forcedby daily temperature and precipitation values fromall the bias-adjusted Arctic CORDEX simulations
described in Chapter 3.1.3 (Appendix A1, Table
A.1.1) and the COSMO-CLM fine-scale modelling(Chapter 3.1.4). This includes both the reference
period, and RCP4.5 and RCP8.5 projections.

Projected changes in mean annual maximum snow
water equivalent, as well as number of snow coverdays, have been computed for two 30-year periods:
mid-century (2031-2060), and the end of the century (2071-2100). Absolute and relative changes arecomputed by comparison with the reference period(1971-2000). Snow conditions for the current
climate have been computed by running the
seNorge model with daily temperature and precipitation from the Sval-Imp dataset (Chapter 3.1.1).
Snow cover duration is computed by counting thenumber of days per year with snow water equivalent>= 20 mm.

3.3 Methods and data, glaciers
Roughly 60% of the total land area of Svalbard iscovered by glaciers (König, et al. 2014). Svalbard
represents ~10% of the total Arctic glacier area, excluding the Greenland ice sheet (König et al., 2014).
Svalbard’s glaciers range from small valley glaciersto large ice caps and ice fields, whose type depends
on the local precipitation and the underlying bedrock topography. There are roughly 1,100 glaciers
whose area is greater than one square kilometre.
While most glaciers on Svalbard end on land, about
15% are tidewater glaciers, that is, glaciers whose
fronts terminate in seawater. While their number issmall, more than 60% of the total glacier ice area
flows end in a tidewater glacier.
Mass loss from the world’s “small” glaciers, that is,
all ice bodies apart from the Greenland and Antarctica ice sheets, is one of the main contributors tocurrent sea-level rise (Gardner, et al. 2013). Furthermore, freshwater from glacier melt is an importantcomponent in the hydrological cycle, affecting
ecosystems and ocean flow (Lydersen et al. 2014).
Accordingly, glaciologists seek to estimate the massgain or loss in all glaciated regions of the world, theso-called glacier mass balance.
Glacier mass balance is the amount of snow and ice
lost or gained on a glacier over a certain period, andis influenced primarily by winter precipitation andsummer temperature. Svalbard glaciers as a whole
are losing mass (Wouters, et al. 2008; Moholdt, etal. 2010; Nuth et al. 2010); summer temperature38

has the strongest inﬂuence on Svalbard glacier massloss (van Pelt et al. 2012), and decadal-scale Arctic
summer warming has led to increasing rates of massloss (Kohler et al. 2007). With the further warming
predicted (Chapter 4.1.3), Svalbard glaciers can be
expected to continue losing mass.
Glacier mass loss manifests itself most noticably asa retreat of the glacier front. The short-term exceptions on Svalbard are due to the many “surge”
glaciers found there. Surging is a cyclical dynamic
instability in which a relatively slow-moving glaciersuddenly starts flowing at high speeds, with frontal
advances of up to many kilometres occur over thecourse of a few years. Following this advance, theglacier stagnates and its front begins to retreat. The
quiescent phase of a surge-type glacier can last fromdecades to over a century. Surging is not directly
related to climate change, and during a surge mass
is simply transferred from the upper glacier to thefront. Following a surge, in a warming climate,
mass loss and the long-term frontal retreat willcontinue regardless of these short-term advances.
This can be seen on the few glaciers on Svalbard
that have been observed to undergo a surge morethan once since the records start, in the early 1900s.
Nevertheless, it is important to determine the massbalance since changes in front positions can beaffected by internal glacier dynamics as well asclimate.

There are two basic approaches to directly measuring mass balance: the glaciological and the geodetic
method.
3.3.1 Glaciological mass balance
The glaciological mass balance is obtained from
regular field visits to a few selected glaciers, andcomprise end-of-winter winter snow-depth sounding and repeated measurement of heights of an arrayof stakes. Typically, these are installed in the glacierice along its centreline, since much of the variability of a glacier’s balance is explained by elevation,
obviating the need for a dense network of stakes
distributed over the entire glacier.
The winter balance Bw is obtained by soundingsnow-depth over as much of the glacier as is practical, estimating the superimposed ice formed during
the winter at the base of the snowpack from shallow
ice-cores, and measuring snow depths at the stakes.
Snow density measurements are used to convert
snow depth to water equivalent. In Svalbard, thiswork is carried out at the end of the accumulation
period, in April or May. Obtaining the winterbalance strictly from sounding to the previous
year’s summer surface in the spring defines theso-called stratigraphic method. For glaciers with awide range of elevations, the method is time-transgressive; that is, the sounded surface is formedat different times along the glacier’s extent. Thisviolates the assumption that the balance can becalculated uniformly over the whole glacier for asingle period. Therefore, autumn measurements are
preferred for establishing the reference horizon forstake measurements at known dates.
The summer balance is obtained directly fromchanges in exposed stake heights over the courseof the melt season. This is usually done at the endof summer, in September or October. The snow andice facies at each stake must be known since theirdensity varies from 0.5-0.9 g cm-3.
Balance estimates are extrapolated over the entire
glacier basin by determining the balance terms asfunctions of elevation, and averaging them after
applying weights determined from the glacier’s
hypsometry, that is the relative distribution of

glacier area as a function of elevation.
An additional important mass balance component isthe dynamic loss at the front of tidewater glaciers,that is, glaciers that terminate in seawater and lose
ice by calving. About 60% of Svalbard’s total
glacier area drains through tidewater fronts(Błaszczyk, et al. 2009), such that a significant partof the total ice mass loss occurs via calving. Warmer
ocean water around Svalbard, in combination withthe overall atmospheric warming, has led to retreatof Svalbard’s tidewater glaciers (Luckman et al.
2015).
Ice flux through the calving front is typicallyobtained (e.g. Błaszczyk, et al. 2009; Luckman
et al. 2015) as the product of the average glacier
speed along a profile or gate located at or near the
tidewater front and the average ice thickness of thegate, with an additional term for change for frontal
advance or retreat.
At present, there are thirteen glaciers with annual
mass balance measurements currently being made,and that have continuous records 5 years or
longer (Table 3.3.1). For logistical reasons, theseare mostly located along the western coast (Figure
3.3.1), and most are in the Kongsfjorden area. The
longest-term field measurements of mass balance in
Svalbard are on the two small glaciers Austre
Brøggerbreen and Midtre Lovénbreen, with records
that start in the mid-1960s (Table 3.3.1).
3.3.2 Geodetic mass balance
While the glaciological mass balance is accurate indescribing short-term glacier changes on individualglaciers, it becomes less accurate when summed
over time. Furthermore, measuring mass balance inthe field is time-consuming and costly, and therefore
spatially limited. The geodetic mass balance iscomputed by differencing glacier-wide elevation
data from two or more different times. Elevation
data can be from a variety of sources: surface surveys; contours from older maps; digital elevation
models made photogrammetrically from aerial
photographs or satellite imagery; or satellite
altimeters.
39

CLIMATE IN SVALBARD 2100

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Glacier

AgencyRegionArea (km2)

Elevation range(m a.s.l.)YearsAustre Brøggerbreen NPI

Kongsfjord

5.8

80-6801967-present

Midtre Lovénbreen

NPI

Kongsfjord

5.1

50-6901968-present

Kongsvegen

NPI

Kongsfjord

106.9

0-10001987-present

HansbreenPAS

Hornsund

63.4

0-695

1989-present

Waldemarbreen

UNC

Kaffiøyra

2.7

140-590

1996-present

Irenebreen,

UNC

Kaffiøyra

3.4

110-780

2002-present

Kronebreen-

NPI

Kongsfjord

380.0

0-14002003-present

Etonbreen

NPI/UiO

Nordaustland

633.0

0-800

2004-present

Nordenskiöldbreen

UU/UU

Billefjorden

141.2

0-12302006-present

Austre Lovénbreen

PRIC/CAS

Kongsfjord

5.0

95-6502006-present

Pedersenbreen

PRIC/CAS

Kongsfjord

6.1

50-7902006-present

Austre Lovénbreen

CNRS

Kongsfjord

5.0

95-6502008-present

Vestre Brøggerbreen

NCPOR

Kongsfjord

4.6

50-5802012-present

Holtedahlfonna

Figure 3.3.1 Location of mass balance studies in the Ny-Ålesund Kongsfjord area. Map to lower right shows thelocations of the three programs outside of Kongsfjord.

The geodetic mass balance is more accurate overlonger periods and data can be acquired over larger
areas. However, only recently has it been possible
to collect Svalbard-wide elevation data within asingle year, mostly due to advances made in satellite
remote sensing.
3.3.3 Glacier mass balance modelling
Data from the few glaciers where it is measured canbe directly extrapolated to larger areas or regions,but major assumptions must be made (e.g. Hagen etal. 2003). Field measurements of mass balance are
more profitably extrapolated to larger scales whencombined with mass balance models.
Climatic mass balance models (CMBs), forced
either by meteorological observations or output
from regional climate models, evaluate the surface
40

energy balance to determine surface temperatureand melt production. The most complete models
couple the surface module to subsurface routines toaccount for the impact of water storage andrefreezing on the mass and energy budgets. Field
data are used to calibrate model parameters and tovalidate model output.

1957−2014, using a different CMB model forced bydownscaled ERA-40 and ERA-Interim data. Bothmodels are parameterized in part using the fieldderived mass balance data described above, from asubset of the glaciers in Table 3.3.1.

There are at present only a few studies modelling
historical Svalbard-wide mass balance using fully
physical surface models, as opposed to those that
calculate the surface energy balance using statistical(Day et al., 2012) or temperature-index (Möller etal., 2016) approaches. Aas et al. (2016) perform amass balance simulation for all of Svalbard overthe period 2003-2013, using output from the WRF
RCM forced by ERA-Interim data. Østby et al.
(2017) simulate mass balance over the longer period

For future prognoses we use a modified version of
the CMB model of Mölg et al. (2008, 2009), whichtakes as input the fields of near-surface temperature,humidity, pressure, winds, incoming radiation, andprecipitation from the regional climate model
COSMO-CLM (Chapter 3.1.4). The modelcomputes the column-specific mass balance fromsolid precipitation, surface and subsurface melt,
refreezing, and liquid water storage in the snowpack, and surface vapor fluxes. The model solves

Table 3.3.1: Long-term
ongoing glacier massbalance measurements
around Svalbard. NPI: Norwegian Polar Institute; PAS:
Polish Academy of Science;
UNC: University of Nicolaus
Copernicus, Poland; UiO:
University of Oslo; UUS:
Uppsala University,
Sweden; UUN; Utrecht
University, Netherlands;
PRIC: Polar ResearchInstitute of China; CAS:
Chinese Academy of
Science; CNRS: French
National Center for
Scientific Research;
NCPOR: National Center for
Polar and Oceanographic
Research, Goa India. Areasand elevation ranges aretaken from most recent
version of the Svalbard
Glacier Database, covering
the 2000s(Nuth, et al., 2013).

the surface energy balance to determine the energy
available for surface melt, and resolves the glaciersubsurface down to a “user defined” depth (here, 20
m divided into 17 vertical layers). This is anoffline-application of a model previously used by
Aas et al. (2016). Further information about theinteractive coupling between the CMB model andWRF is given by Collier et al. (2013, 2015). For thepresent simulation, we use the parameter settings of
Aas et al. (2016).
Using this CMB model, two simulations have been
conducted, one using the historically-driven downscaled dataset from COSMO-CLM, 2004-2017 andthe second one using the pseudo-transient scenario
RCP8.5 (see Chapter 3.1.4).

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CLIMATE IN SVALBARD 2100

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The first dataset evaluates the quality of the productof this model chain, compared to direct fieldmeasurements of glacier mass balance, and compared to similar, Svalbard-wide CMB simulationsby Østby et al. (2017) and Aas et al. (2016).
3.3.4 Glacier mass balance from remotesensing
Remote sensing visible imagery can be used todetermine spatial patterns of winter precipitation, as
well as to determine losses through frontal retreat
and calving. However, the most useful remote sensing product for estimating mass change comes fromthe GRACE (Gravity Recovery and Climate

Experiment) satellite, which mapped the timevarying gravity field of the Earth over theperiod 2002-2017 (Tapley et al. 2004). GRACE data
have been used to estimate changes in ice mass for
glaciated areas around the world (e.g. Gardner,
et al., 2013). The GRACE footprint is large; in aregion the size of Svalbard, elevation change iscomputed for a handful of pixels. Furthermore,corrections must be made for ongoing and longterm isostatic rebound. However, satellite gravimetry also sidesteps problems with the other methods,
such as the need to account separately for temporally varying calving or frontal retreat terms, or to
extrapolate point measurements to larger areas.

3.4 Methods and data, permafrost
Areas with permafrost consist of sediment or rockand included ice and organic material, which
remain at or below 0°C for at least two consecutive
years (French, 2018). The most direct indicators ofchanges related to permafrost, are the two essential
climate variables (ECVs) for permafrost; permafrost
temperature and active-layer thickness (ALT - thethickness of the layer that freezes and thaws
annually over the permafrost). Permafrost temperature measured at a depth where there is practicallyno annual fluctuation in the ground temperature (thedepth of zero annual amplitude (ZAA)) is the bestindicator of long-term change. This depth varies
from a few meters in warm, ice-rich permafrost to
20 m or more in cold permafrost and in bedrock(Romanovsky et al., 2010; Smith et al., 2010). The
monitoring of the ECVs for permafrost is coordinated through the Global Terrestrial Network on
Permafrost (GTN-P) by the International Permafrost
Association and it consists of two specific monitoring programmes: The Thermal State of Permafrost
and the Circumpolar Active Layer Monitoring(CALM) (Romanovsky et al., 2010; Shiklomanov etal., 2012). In addition, satellite-based ECV monitoring has recently been initiated in the “GlobPermafrost” (www.globpermafrost.info) and “Permafrost
CCI” (http://cci.esa.int/Permafrost) projects by theEuropean Space Agency (ESA). These techniques
can complement terrestrial monitoring by providing

42(Isaksen et al., 2001), which allows for studies ofchanges in the permafrost temperatures since then.
3.4.2 Permafrost modelling
A new land surface scheme designed for permafrost
applications, CryoGrid 3 (Westermann et al., 2016),
is employed to simulate the present and future
ground thermal regime for selected sites on Svalbard where active permafrost observations and/or

meteorological stations exist. The CryoGrid 3
model framework simulate the permafrost temperatures based on input data sets of meteorologicalvariables, such as incoming radiation, air temperature, wind speed and precipitation obtained from the
COSMO-CLM (CCLM) datasets (Chapter 3.1.4).
The properties of the applied future projection
naturally is a main source of uncertainty for theobtained permafrost model results, which must be
considered when comparing to existing studies.

3.5 Methods and data, ocean

variability at Svalbard. The Nordic and Barents seas
are relatively well sampled when it comes to hydrographic observations, with some time series going
Except from observations in the waters close to Sval- back more than 100 years (Korablev et al., 2014).
bard, also areas upstream in the Nordic Seas are relevant for describing the climate and the corresponding
3.5.1 Observations

spatially distributed estimates of ground temperatures and ground temperature changes over time(Westermann et al., 2016; Obu et al., 2019).
3.4.1 Observations
Systematic monitoring of active-layer thickness
and permafrost temperature profiles are presently
limited to sites in western and central parts of Spitsbergen (see Figure 7.1.1). Data from active permafrost monitoring sites have been examined as partof the international collaborative permafrost monitoring network in Svalbard Integrated Arctic Earth
Observing System (SIOS) in Svalbard(Christiansen et al., 2019a). Very recently
systematic permafrost observations were started in
Barentsburg and Hornsund
As part of the State of Environmental Science in
Svalbard (SESS) reporting a first overview of permafrost thermal conditions and active-layer thickness (ALT) at all the permafrost monitoring sites in
Svalbard during the 2016/2017 hydrological year
from 1 September 2016 to 31 August 2017 havebeen compiled (Christiansen et al., 2019b). Permafrost thermal monitoring at some sites in the Longyearbyen and Kapp Linne areas extend back to theInternational Polar Year (IPY) 2008-2009 (Christiansen et al., 2010) and for Janssonhaugen to 1998

Figure 3.5.1 Map offixed stations andsections with regularobservations of hydrographic variables. Red
dots show coastal
stations with regularobservations, andthe blue cross shows
the location of Ocean
Weather Ship Station
Mike (OWS M) at (66°N, 02° E), and the
new buoy to observe
currents, hydrography
and biogeochemical
properties.

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Annual observations of hydrographic and biogeochemical properties have been carried out by theInstitute of Marine Research (IMR) in many of thesections (see Figure 3.5.1) for more than 50 years
using ship-borne conductivity temperature density(CTD) measurements. In the coastal waters around
Svalbard, especially on the northern and easternsides, such observations are less regular, but are alsoavailable some decades back. Today, it is additionally a continuous activity using Acoustic Doppler
Current Profilers on anchored moorings, gliders
and, temperature-salinity profiling floats, so-called
ARGO buoys (Riser et al., 2016).
Figure 3.5.1 shows the fixed stations operated by
IMR. Sections in the eastern Norwegian Sea illustrate how the hydrographic properties develop along

the path of the Atlantic water inflow at Svinøy-NW
and Gimsøy-NW. Temperature and salinity havebeen sampled annually since 1950 in Svinøy and1963 in Gimsøy, and 2-5 times every year since1977 (Blindheim et al., 2000). Bjørnøya-W is also
one of the fixed sections operated 2-5 times a year,
while annual samples of hydrography from the
Sørkapp section in the Fram Strait west of Svalbard
have been taken since 1977.
Data from the Barents Sea Opening, FugløyaBjørnøya, and in the southern Barents Sea, VardøNorth (Figure 3.5.1), have been sampled annually
since 1953, and 4-6 times each year since 1977.
The variability of the Atlantic water inflow to the
Barents Sea has been observed by moored current
meters in the Barents Sea Opening since 1997

Figure 3.5.2 Fixedstations and sections
where observations ofhydrographic variables
are collected regularly
by UNIS. Dots show
CTD stations and UNIS
mooring positions
observing current, hydrography and oxygen
over different periods.
The longest mooringtime series is situatedon the southern side ofIsfjorden mouth area
(2006-present, UNIS)
and in Kongsfjorden(2002-2014, UoB/
UNIS). Crosses indicate
stations east of Svalbard sampled for ocean
acidification variables
throughout the watercolumn.

44(Ingvaldsen et al., 2004). Since 1995, also the
Atlantic water inflow to the Nordic Seas in the
Svinøy section has been monitored continuously
by moored current meters (Orvik et al., 2001).
At the OWS M (Figure 3.5.1) daily meteorologicaland oceanographic measurements have been performed since 1948 by MET Norway and the University of Bergen (UoB). In 2009, the ship at OWS M
was replaced by the operational Polar buoy, an ocean
surveillance buoy transferring meteorological andoceanographic data directly to land via satellite. In
general, the observational data shows an increase intemperature and salinity since the mid-1990s. However, this does not include the most recent years.
Since 1994 the University Centre in Svalbard(UNIS) has collected hydrodynamic data on thewest Coast of Spitsbergen, north of Svalbard and in
the fjords indenting the west coast (Figure 3.5.2).
The longest hydrographic time series are thesections from the inner part of Isfjorden and Kongsfjorden across the West Spitsbergen shelf and theWest Spitsbergen Current confined to the slopein eastern Fram Strait. Most of the CTD profiles
have been collected in summer and autumn, but the
Isfjorden and Kongsfjorden sections have also beencollected during some winter/spring seasons since
1994. The UNIS data is organised and calibrated ina UNIS hydrographic database (UNIS HD) withadditional data from the Norwegian Marine Database (IMR), PANGEA (Alfred Wegener Institute),
ICES data and from the NISE database (Norwegian
Polar Institute). A subset of the data in the UNIS
HD is synthesised for the Kongsfjorden section(Tverberg et al., 2018) and Isfjorden section(Skogseth et al., pers. comm.). In addition, theInstitute of Oceanology Polish Academy of
Sciences (IOPAS) in Sopot, Poland, started theirmonitoring program in 1936 with yearly summer
cruises to the west coast of Spitsbergen, Kongsfjorden and Hornsund, undertaking both physicaland biological sampling (not shown).
On the east coast of Svalbard, no regular hydrographic observations are currently in operation. A
non-regular time series exist from Storfjorden(Figure 3.5.2) between 1997 and 2007 (Skogseth etal., 2008, 2013) documenting effective ice growthand brine release in the Storfjorden polynya.
In order to evaluate future climate model projections, historical climate variability reconstructedfrom proxy data prior to 1850 is important. Suchmarine reference data sets are based on temperaturereconstructions using alkenones and planktonic
foraminifers from sediment cores acquired along theAtlantic water inflow pathway and are available in
the Faroe-Shetland Channel area, at the Vøring Plateau, and south of Svalbard (Eldevik et al., 2014).
The “Flagship programme for Ocean Acidificationand effects in northern waters” within the FramCentre, has surveyed waters around Svalbard since2012. NPI and IMR have annually sampled Svalbard fjords and the Fram Strait at the hydrographic“UNIS stations” (Figure 3.5.2).
The water column section at 33°E shows that thewater masses surrounding Svalbard have a large
span of ocean carbon chemistry characteristics likealkalinity, total inorganic carbon, pH and aragonite
saturation state. There are clear differences between
the water in the Arctic Ocean, and the Atlantic
water. East of Svalbard there is, in addition, a component of recirculated Atlantic water from the Fram
Strait at 100 m depth between 77°N and 80°N.
Generally, fresh water from ice melting plays animportant role in modifying the waters around
Svalbard. This is especially prominent in the waters
close to glaciers and in the fjords (Fransson et al.,2015).
3.5.2 Ocean modelling
Regional numerical models are an important tool todescribe the large-scale ocean circulation upstreamof Svalbard and thus provide valuable information
for the area around Svalbard. These models provide
results for the present and future ocean climate andare mainly based on dynamical downscaling of
ESMs from the CMIP5 archive (Taylor et al., 2012).
The simulations utilised here are performed withthe regional ocean model ROMS (Shchepetkin
and McWilliams, 2005) for the periods 1958-2007
and 2006-2070. The future climate simulation
45

CLIMATE IN SVALBARD 2100

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uses initial conditions, boundary conditions, andatmospheric forcing from the Norwegian Climate
Model (NorESM, Bentsen et al., 2013) following
the RCP4.5 scenario. The horizontal resolution of
the model setup used here is around 10 km in the

3.6 Methods, data and models, sea level
3.6.1 Instrument record
There are two tide gauges on Svalbard, Barentsburg
and Ny-Ålesund, which provide observations ofRelative Sea Level (RSL) change. Data from thesetide gauges are available for download from thePermanent Service for Mean Sea Level (PSMSL,
Holgate et al. 2013). For Barentsburg, we focus on
BAR1 (PSML-id 541), as this tide gauge provides
the longest record, i.e. it starts in 1948 and is still in
operation. (A second Barentsburg tide gauge record
is available but stops in the early 1990s).
The Barentsburg tide gauge is operated by the
Russian Federal Service for Hydrometeorology
and Environmental Monitoring. The Ny-Ålesund

Norwegian and Barents Seas. The choice of global
CMIP5 model for downscaling is based on evaluation of different models with respect to poleward
ocean heat transport and sea ice extent (Sandø et al.,2014).

in sea level. Two study periods were analysed, i.e.,
the entire length of each record and from 1992 to2017. The latter interval was chosen as it enables acomparison to estimates from satellite altimetry andbecause for both tide gauges it is a period with nomajor data gaps.

tide gauge is operated by the Norwegian Mapping
Authority. It was established in 1976 and is still in
operation. Unfortunately, there is a gap in the record
from 1988 to 1992.

While tide gauges observe RSL and are influenced
by vertical land motion, altimetry satellites
measure sea level heights in a global geodetic
reference frame. In addition, altimetry is primarily a technique for observing sea level in the open
ocean and does not provide direct a measurement
at the coast. There is therefore a significant conceptual difference between these two techniques, andmeasurements from altimetry and tide gauges may
differ significantly, especially in areas characterisedby vertical land motion. It is important to be awarethat the waters around Svalbard are not sampled
by several altimetry missions, i.e., Topex/Poseidon
and the Jason-satellites observe the oceans between±66 degrees latitude only. In addition, altimetry
measurements at high latitudes are occasionallydisrupted by the presence of sea ice, no calibration/
validation facilities exist at high latitudes, andseveral range corrections are less accurate in the
Arctic Ocean. As a consequence, the altimetry
record of the waters around Svalbard is less reliable
than at lower latitudes.

The Svalbard tide gauge records were analysedusing the same method as Breili et al. (2017). We
use records with a monthly sampling interval andthe revised local reference datasets, as recommended by the PSMSL. A linear regression model witha first order autoregressive (AR(1)) covariancestructure was fitted to the observations (McKinney
et al., 2011). We included terms accounting for the
sea-level trend and the initial height of the sea-levelin the regression model, as well as periodic terms
accounting for annual and semi-annual variation

Figure 3.6.1 Left panel shows the locations of the permanent tide gauges (circles) and measurements from temporary tide gauges (blue dots). Right panel shows the locations of the existing GNSS stations (red triangles) andplanned GNSS stations through the EPOS-N project (orange triangles). The red star marks the Earth observatory at
Ny-Ålesund which is a core geodetic site.
46

3.6.2 Observed contributions to regional
sea-level change
Changes in the distribution of mass on the Earth’s
surface produce a non-uniform sea level pattern due
to gravitational changes (Farrell and Clark, 1976).
This sea level response is often referred to as a“fingerprint”. Estimates of regional sea-level change
owing to land ice and terrestrial water storagechanges can be calculated by solving the elastic sea
level equation. In a recent study, Frederikse et al.
(2017) use the sea level equation to model regional
sea-level changes for different mass contributions.
We discuss their results for Svalbard in Chapter 8.4.
Steric trends in sea level can be calculated fromin-situ observations of ocean temperature andsalinity. Here we make use of the EN4 data (Goodet al., 2013) to calculate local steric trends for theperiod 1993-2013.
3.6.3 Land motion and gravity measurements
Glacial isostatic adjustment plays an importantrole in determining RSL changes for Svalbard. The
primary method for measuring present-day uplift isfrom Global Navigation Satellite System (GNSS)
stations. The Norwegian Mapping Authority operates a network of five continuous GNSS stations on
Svalbard (Ny-Ålesund, Longyearbyen, Svea, andon the islands of Hopen and Bjørnøya). The Polish
Department of Polar and Marine Research operates
a station in Hornsund. There is also a smaller
campaign network in the Kongsfjorden area which
has been measured regularly since 1998.

Figure 3.6.2 The Earth observatory at Ny-Ålesund which is a core geodetic site, i.e. a number of different geodetic
instruments are collocated. The site is part of a global network that is of fundamental importance for determining
accurate measurements of sea level from satellite altimetry. The twin VLBI telescope is pictured. Photo: Bjørn-Owe
Holmberg47

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

There are currently no observations in the northernand eastern parts of Svalbard. However, provided thenecessary permissions from the local authorities are
granted, the EPOS-N project is planning to install six
new GNSS stations collocated with seismologicalstations in these areas. Better geographical coveragewith GNSS is crucial for constraining ice mass changes, projections of sea level, and improving our understanding of GIA and other geophysical processes.
The observatory in Ny-Ålesund is a core geodeticsite and, in addition to GNSS, hosts several other
instruments; an existing Very Long Baseline Interferometry (VLBI) telescope, a new twin VLBI telescope, a satellite laser ranging instrument, absolutegravity points, and two superconducting gravimeters. Among other things the instruments provide
information about land motion and gravity changes.
The GNSS stations give daily coordinates at an
accuracy level of around five millimetres. Time seriesof the daily coordinates give information on crustal
deformation processes and land uplift rates with anaccuracy of a few tens of a millimetre per year. The
method applied here to derive daily solutions for
the Svalbard stations is the same as in Kierulf et al.
(2014) and employs the GAMIT/GLOBK software(Herring et al., 2011). We use data up until 2018
and the velocities presented here are realised in the
ITRF2008 reference frame (Altamimi et al., 2011).
Visual inspection of the time series from Svea andHopen (both grounded in permafrost) indicates thatthese sites are unfortunately not sufficiently stablefor glacial isostatic adjustment and sea level studies.
These stations are therefore omitted from our results.
3.6.4 Observed sea level extremes
Storm surges occur when a low-pressure weather
system and increased surface wind stress simultaneously contribute to increase sea level and pushwater up against the coast. Depending on the astronomical tide, extreme water levels can occur. The
surge observed on Svalbard is generally smaller
than on most of mainland Norway. As mentioned,
the Norwegian Mapping Authority operates the tidegauge in Ny-Ålesund; here there is data available toallow a statistical analysis of the extremes. For the
Barentsburg tide gauge only the monthly and annual
48

averages are available from PSMSL and, therefore,
no analysis of the extremes is possible.
The average conditional exceedance rate method isused for estimating the return periods of the extreme
sea levels at the Ny-Ålesund tide gauge. For adetailed description of the method see Næss andGaidai (2009) and Skjong et al. (2013). This is thesame approach used for calculating the official return
periods for Norway; the results are given in Ravndal
and Sande (2016). Data up until 2015 is used to estimate the extremes at the Ny-Ålesund tide gauge.
There are also a number of shorter time seriesof water level data from Svalbard, Bjørnøya andHopen dating from the beginning of the 20th century
to present day and thus of varying quality. These
series are too short to be analysed with respect tosea level extremes but can be used to analysevariations of the astronomical tide in the area.
Based on the shorter tide gauge records from acrossthe region it is known that the amplitude and time of
the tide varies significantly. Using this information,
the return heights from Ny-Ålesund can be extrapolated to other parts of the coast e.g. Longyearbyen.
This approach assumes that the meteorologicalconditions affecting sea level are large-scale phenomena that have a similar effect on sea level over alarge area and vary smoothly.

4. Atmospheric climate
This chapter deals with observed and modelledatmospheric climate in Svalbard during the period
1900 – 2100. A short summary of paleo climatic

evidence for climate variability during Holocene(the last 10 000 years) based upon data fromglaciers is given in Chapter 6.

4.1 Air temperature
4.1.1. Temperature climatology
The temperature climatology of Svalbard has earlier
been described in a number of scientific publications(e.g. Førland et al., 2011; Nordli et al., 2014; Gjelten etal., 2016). The following description is mainly basedon observations from Svalbard weather stations withmeasurements or composite series covering at least
the period from 1971 to present (see Figure 3.1.2 forstation locations). These stations are all situated at
low altitudes, and close to the coast. To get a moreconsistent picture of the climate, covering all partsof Svalbard, we have also applied a 1x1 km2 dataset
derived from downscaled ERA40/ERA-Interim
reanalysis for the period 1958 to 2017 (Østby et al.,2017). More information on this “Sval-Imp dataset”
is given in Chapter 3.1.1.
Figure 4.1.1 shows the temperature climatology forselected weather stations at Svalbard for the period
1971-2000, which is used as reference period in thisreport.

3.6.5 Sea level modelling
Our regional sea level projections are based on
findings from IPCC (2013) and CMIP5-output(Chapter 3.1.3). An important component of
sea-level change in Svalbard is glacial isostatic
adjustment. This contribution to future sea-levelchange in this region is dependent on both thelong-term response of the solid Earth to past ice
mass changes and the present and future response toglacier changes on Svalbard. We therefore pay
special attention to how glacial isostatic adjustment
is treated in our sea level projections. The projections presented here show changes to mean sea
level, i.e., the average over some period of time.
We do not consider future changes to sea level
extremes (e.g. storm surges) and/or waves. Thereis generally low confidence in our ability to project
regional changes of these effects, so we generally assume no future changes in their amplitude or
frequency.

Figure 4.1.1. Seasonal cycle (averages for 1971-2000) for temperature [°C] for selected meteorological stations at
Svalbard. Positions of the stations are given in Figure 3.1.2.
49

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

February is on average the coldest month at the
Svalbard meteorological stations, while the highestmonthly mean temperatures usually are measured
in July or – for the most maritime stations Bjørnøya
and Hopen – in August. Bjørnøya is considerably
milder than the Spitsbergen stations and Hopen
during winter, while the summer temperatures at
Bjørnøya are similar to the most maritime of theSpitsbergen stations (Isfjord Radio and Ny-Ålesund).
The highest summer temperatures and the lowest
winter temperatures are measured at the stations
situated furthest into the fjords of Spitsbergen.
As all the above stations are situated close to thecoast and fjords, the reanalysis Sval-Imp dataset isapplied to give a more spatially consistent pictureof the local climatology. Average summer (Jun-JulAug) and winter (Dec-Jan-Feb) temperatures for thereference period 1971-2000 are presented in Figure
4.1.2. (Figure 2.1.2 shows corresponding annual
temperatures). The downscaled reanalysis dataset
has been evaluated against observed temperatures(see Chapter 3.1.1 and Vikhamar-Schuler et al.,2019). The main conclusion is that the downscaled
ERA40/ERA-Interim reanalysis gives a realistic
picture of the temperature conditions in the Svalbard area, though at the coastal stations in Spitsbergen, the Sval-Imp dataset has a cold bias of approximately -0.5 °C.

Figure 4.1.2 shows that though the winter temperatures are 10 to 20 °C lower than the summer
temperatures, the spatial temperature variation israther similar in winter and in summer. The highest
temperatures are found in the south-southwesternparts of the archipelago, while the lowest temperatures occur in mountainous and glacial areas in thenorth and east. In winter, also coastal areas in thenortheast are among the coldest sites in the region.
To simplify the description of historic and futureclimate, Svalbard, with the exception of Bjørnøya,
is divided into three regions, roughly based upon
the temperature map: (1) A south-western region
including Spitsbergen south of Isfjorden, (2) anorthwestern region including Spitsbergen northof Isfjorden and west of Widjefjorden, and (3) aneastern region including the rest of Spitsbergen
and the islands further east. These regions are usedin addition to the climate stations to describe thepresent climate at Svalbard. They are also used
for describing the recent climate development andprojections for future climate.
Average annual and seasonal temperatures for
Svalbard weather stations and regions are listed
in Table 4.1.1. The eastern region is coldest in allseasons. This is the region with the highest sea ice
concentration (cf. Chapter 8.3.1), which affects
the local air temperatures considerably (Isaksen

1961 – 1990 average
Station name Annual Winter Spring Summer Autumn Annual
Bjørnøya
-2.3
-7.5
-4.8
3.6
-0.5
-1.7
Hopen
-6.4
-13.3
-9.9
1.3
-3.7
-5.6
Hornsund
-5.4
-12.2
-8.8
3.0
-3.6
-4.7
Isfjord Radio
-5.0
-11.4
-8.6
3.6
-3.6
-4.4
Barentsburg
-6.1
-13.7
-10.0
3.9
-4.6
-5.5
Svalbard Air-6.7
-15.0
-10.8
4.1
-5.2
-5.9
port
Ny-Ålesund
-6.3
-13.7
-9.7
3.5
-5.3
-5.7
Svalbard total
-9.3
-17.2
-12.4
0.3
-8.1
-8.7
Svalbard E
-10.1
-18.6
-13.2
-0.1
-8.8
-9.5
Svalbard NW
-9.1
-16.6
-12.0
0.2
-8.1
-8.4
Svalbard SW
-7.2
-14.3
-10.3
1.4
-7.4
-6.7

50

-13.9

-9.6

4.5

-4.7

-12.9
-16.0
-17.2
-15.4
-13.4

-8.8
-11.8
-12.7
-11.3
-9.8

3.7
0.5
0.1
0.5
1.8

-4.7
-7.4
-8.1
-7.6
-5.4

Table 4.1.1 Average annual and seasonal temperature [°C] for weather stations (observations) and Svalbard regions(Sval-Imp dataset).

et al., 2016). The difference between summer andwinter temperatures is highest in this region. The
southern region is warmest in all seasons, and thetemperature difference between winter and summer
is smaller than in the other regions. The station
temperatures are in general higher than the averagetemperatures for the corresponding regions. This isdue to the stations’ locations at low altitudes andin coastal areas. For stations as well as for regions,both the north-south and the east-west temperature
gradient can be seen (cf. Gjelten et al., 2016).

Station nameFigure 4.1.2. Winter (Dec-Jan-Feb; left) and summer (Jun-Jul-Aug; right) temperature [°C] averaged over 1971-2000
based upon the Sval-Imp dataset. Note that the colour scales are different!

1971 – 2000 average
Winter Spring Summer Autumn
-6.6
-4.0
3.9
-0.2
-12.3
-8.8
1.7
-3.1
-11.2
-7.7
3.3
-3.2
-10.7
-7.7
4.0
-3.1
-12.8
-9.1
4.1
-4.2

Bjørnøya
Hopen
HornsundIsfjord Radio
Barentsburg
Svalbard Airport
Ny-Ålesund
Svalbard total
Svalbard E
Svalbard NW
Svalbard SW

4.1.2 Temperature development at Svalbard in
the instrumental period
The 1990s were significantly warmer than the
1960s, thus the average annual temperatures over
1971-2000 are between 0.5 and 1 °C higher than the
1961-1990 values, for regions as well as stations.
Temperatures during winter increased considerably
more than during summer, thus, the difference
between winter and summer temperatures has
decreased slightly.

1961-19901981-20101988-2017
-0.6
-0.8
-0.7
-0.6
-0.6
-0.6
-0.6
-0.6
-0.7
-0.7
-0.6

0.5
0.8
0.6
0.5
0.6
0.8
0.5
0.7
0.8
0.6
0.5

1.1
1.7
1.4
1.2
1.4
1.7
1.2
1.5
1.6
1.3
1.2

Table 4.1.2 Difference in average annual temperature [°C] between different 30-year periods and thereference period 1971-2000 for weather stations(observations) and Svalbard regions (Sval-Imp).
51

CLIMATE IN SVALBARD 2100

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The temperatures have continued to increase
after year 2000. Temperature differences between
the reference period 1971-2000 and other 30year periods for regions and weather stations
are summarised in Table 4.1.2. Note that thetemperature average for Svalbard for the latest 30
years is already 1.5 °C higher than it was during
the reference period 1971-2000.
The annual mean temperatures for all weather
stations in Svalbard show a similar long-term
pattern (Figure 4.1.3). The smoothed time series
indicate multi-decadal variability, leading to a
general warming before the 1930s, a relatively
warm period during the subsequent two decades, atemperature decrease from the 1950s to the 1960s,
and thereafter a general temperature increase. Thesefeatures are discussed by Hanssen-Bauer (2002),
and can also be seen for other parts of the Arctic(e.g. Polyakov et al. 2003). The warming of the
Arctic that started in the 1910s and lasted for two
decades is often referred to as the “early 20th century
warming”, and is one of the most spectacular climate events of the twentieth century (Bengtsson etal., 2004). The data coverage was extremely limitedin the Arctic in the first half of the 20th century,

but it is still clear that the spatial pattern of theearlier warm period in the 1930’s and 40’s wasdifferent from that of the current warm anomaly. In
particular, the current warm period is partly linked
to the Northern Annular Mode and affects a broader
region (Polyakov et al. 2003).
Regional time series based on reanalysis are available from 1958. They show similar trends as theindividual stations. The eastern region, which includes Hopen, shows a stronger warming during thelatest decades than the other regions.
Linear trends are used to quantify the temperaturedevelopment in Svalbard (Table 4.1.3), even though
there is certain scepticism against using linear
trends as a measure for climate change, becausesuch changes not necessarily occur linearly (e.g.
Benestad, 2003). Earlier studies (e.g. ACIA, 2005;
Førland et al., 2011) have shown that the longest
Arctic series can be divided into three periods
where the first and the last show statistically significant warming (“early 20th century warming” andthe present period of warming), while the middle
period shows statistically significant cooling.

In this report, we present the trends only for the fulllength of the series, and for the period 1971 topresent. The last period is chosen because 1971 is
the first year of the reference period in this study.
Trends for the full series (Table 4.1.3, left), showthat the annual mean temperature has increased
significantly for all stations and regions. For theseries starting in the warm period in the 1930s, thisis new compared to earlier studies. The temperature
has increased in all seasons – with the strongest increase in winter and/or spring. For Svalbard Airport/
Longyearbyen the linear trend indicates an increase
in mean annual temperature of 3.7 °C during thelatest 118 years, which is about three times theestimated global warming during the same period.
The trend is statistically significant (5% level) forall seasons. For the period 1971-2017, trends arecalculated for all weather stations as well as for theregions. The linear trends (Table 4.1.3, right part)
indicate that the annual temperature at all stations

Station name52

While the reasons for the Svalbard warming in the
1930s are still unclear, Hanssen-Bauer and Førland(1998) argue that the warming from the 1960s to themid-1990s can be mainly linked to changes in thelarge-scale atmospheric circulation. From a study ofradiosonde measurements from Ny-Ålesund,Maturilli and Kaiser (2017) also conclude thatchanges in atmospheric circulation lead to an
enhanced advection of warm and moist air fromlower latitudes during the period 1993 to 2014,
causing warming and moistening of the atmosphereparticularly during winter. However, Dahlke andMaturilli, (2017) estimate that only about 25% of
the winter warming from 1996 to 2015 could be

Full time series (start – 2017)

1971 – 2017

AnnualWinterSpringSummerAutumnAnnualWinterSpringSummerAutumn

0,17

0,15

0,32

0,1

0,13

0,64

1,04

0,52

0,29

0,74

0,47

0,76

0,53

0,25

0,38

0,99

1,76

0,86

0,36

1,02

0,29

0,39

0,45

0,15

0,18

0,81

1,45

0,69

0,29

0,86

Isfjord Radio(start 1935)

0,23

0,24

0,38

0,16

0,17

0,71

1,21

0,68

0,27

0,72

Barentsburg(start 1934)

0,26

0,32

0,41

0,16

0,16

0,81

1,4

0,78

0,33

0,76

0,31

0,41

0,43

0,13

0,31

1,01

1,67

0,95

0,47

1,01

0,24

0,26

0,36

0,14

0,21

0,71

1,35

0,57

0,24

0,74

Bjørnøya
(start 1920)
Hopen(start 1946)
Hornsund(start 1935)

Svalbard Airp.
(start 1899)
Ny-Ålesund(start 1935)

Figure 4.1.3. Annual mean temperature [˚C] for weather stations (observations) and regions (Sval-Imp dataset).
The series are smoothed by a Gaussian filter to show decadal scale variability.

and regions has increased by between 3 and 5 °C
during this 47-year period. The temperature increase
was largest in winter, where it adds up to 5 to 8 °C
for all stations and regions. All seasonal and annual
temperature trends are statistically significant at the
5% level during this period.

Full time series (1958 – 2017)

1971 – 2017

Regions(start 1958)

AnnualWinterSpringSummerAutumnAnnualWinterSpringSummerAutumnSvalbard total
Svalbard E
Svalbard NW
Svalbard SW

0,81
0,88
0,78
0,67

1,61
1,79
1,47
1,25

0,54
0,55
0,56
0,47

0,35
0,3
0,43
0,42

0,78
0,9
0,67
0,57

0,87
0,95
0,8
0,75

1,58
1,78
1,38
1,27

0,57
0,61
0,51
0,51

0,35
0,31
0,42
0,39

1,01
1,11
0,91
0,84

Table 4.1.3 Linear trends in temperature [°C/decade] for weather stations (observations) and regions (Sval-Imp
dataset). Bold types: Significant trends (5% level)53

CLIMATE IN SVALBARD 2100

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attributed to changes in circulation. Isaksen et al.
(2016) conclude that the warming at Svalbard was
mainly related to changes in air mass properties
during the period 2001 to 2015. The air is now
milder for all seasons, regardless of large-scalewind direction. The warming of air masses appears
to be primarily caused by heat exchange fromincreasingly ice-free sea areas north of Svalbard andthe Barents Sea. This is in agreement with conclusions drawn by Kohnemann et al. (2017). In addition, reduced sea ice cover in the fjords contributes
to increased temperature locally, especially during
the winter months. This effect has been particularly clear after 2006 (Muckenhuber et al., 2016). In
summary, the increase in temperature after 2000 is
not primarily driven by more “southerly winds”, butrather from the retreat of sea ice, along with higher
sea temperatures and a general background warming (Isaksen et al., 2016). It is not possible, yet, tosay how much of the warming in Svalbard is due
to natural climate variation, and how much is due
to increased man-made greenhouse gas emissions.
However, for the entire Arctic the image is clearer:
Increasing concentrations of greenhouse gases in
the atmosphere are the primary underlying causeof the warming observed over the last 50 years(AMAP, 2017).

4.1.3 Projected temperature development towards 2100
4.1.3.1 Projections from global models
Sea ice reduction has influenced the warming in the
Svalbard region in the later decades, and it will also
be an important factor in the future. Applying projections directly from global climate models (without involving downscaling) results, for the Svalbard
area, on average in an increase in annual mean temperature of about 3 °C, 6 °C, and 10 °C for emission
scenarios RCP2.6, RCP4.5 and RCP8.5, respectively, from 1971-2000 to 2071-2100 (Figure 4.1.4).
This corresponds roughly to two to three times theestimated global warming under the same scenarios(IPCC 2013).
Large contrasts are, however, expected within the
Svalbard area, due to regional patterns in the sea
ice retreat. The withdrawal of sea ice in the northern and eastern Svalbard areas will continue, whilesouthern and western areas are already ice-free mostof the year in present climate. A generally large
heating of the surface and near surface atmospherein areas becoming ice-free may also affect weather
systems, as low pressures may move further into the
Arctic (e.g. Moore et al. 2018). Thus, downscaled
climate projections are necessary to investigate howlarge scale changes might affect the climate locally.

Figure 4.1.4. Annual mean temperature anomalies[°C] as simulated by the CMIP5 global climate modelruns (without downscaling) following RCP2.6 (blue),
RCP4.5 (orange) and RCP8.5 (red) emission scenarios. The envelopes are based on the 5th and 95th percentiles of the multi-model simulations, hence, display
the 90 % spread over the number of runs (indicatedin the legend between parentheses) used for eachensemble simulations. The black line shows the SvalImp dataset, which is applied as reference dataset. All
the simulations have been subject to a spline smoother to extract the inter-annual climate change signal.

54

4.1.3.2 Downscaled projections
The following downscaled projections of futuretemperature development for Svalbard are based
upon (a) empirical statistical downscaled (ESD)
projections from all available global CMIP5 models and for the emission scenarios RCP2.6, RCP4.5
and RCP8.5, (b) results from the Arctic CORDEX
ensemble of regional climate models (RCMs) for
RCP4.5 and RCP8.5, and (c) the fine scale climate
model COSMO-CLM (CCLM) for RCP8.5. See
Chapter 3.1 for information on the methods, andAppendix 1 for information on global models thatare applied in the different approaches. For allmodels except CCLM, annual and seasonaltemperature changes from 1971-2000 towards
2031-2060 and 2071-2100 are calculated. For
CCLM, only temperature changes from 1971-2000
towards 2071-2100 are available. Calculations are
performed for the Svalbard land area, as well as
for the regions “E”, “NW” and “SW” defined in
Chapter 4.1.1. Some projections are also given for
Svalbard Airport. All results are tabled in Appendix
2. The main results are summarised in the following
paragraphs.
a) ESD results; large ensembles
Table 4.1.4 shows projected temperature changes
towards the end of the century for the Svalbard landarea, based on ESD of the available CMIP5 ensemble under the emission scenarios RCP2.6, RCP4.5
and RCP8.5, respectively. The ensembles are shownas median values and the 5th and the 95th percentiles. The span between the 5th and 95th percentile

RegionSvalbard
total(land area)

Scenario:RCP2.6 (65 runs)
Season Med
Low
Annual
3.8
0.6
DJF
5.8
-0.4
MAM
3.2
0.5
JJA
1.4
-0.3
SON
4.8
0.6

High
6.5
10.6
5.4
3.2
8.0

may be interpreted as a measure for the uncertainty(cf. Chapter 9). The annual values correspond wellwith the values deduced directly from the global
models (Figure 4.1.4).
A majority of the global models have too much sea
ice in the “present climate” (cf. Chapter 8.1.2). This
may lead to unrealistic local temperature trends,
as these trends are sensitive to if, and when, thelocal sea ice disappears. Thus, a sub-ensemble wasselected, where the GCMs with most sea ice inthe “present climate” were excluded. The differences between the temperature projections in the
sub-ensemble and the full ensemble are moderate.
Thus, the results from the ensemble of “selectedmodels” are shown only in Appendix 2 (Table A2.2
and A.2.3), and the full ensemble will be applied
to represent the results from ESD. This does notnecessarily imply that using the full ensemble isadvisable. A closer investigation would, however,be necessary to remove all models with unrealistic
control climate.
b) Arctic CORDEX results, and comparison with
ESD results
Table 4.1.5 shows projected temperature changes
towards the end of the century for the Svalbard
land area, based on Arctic CORDEX projections
under emission scenarios RCP4.5 and RCP8.5.
The ensembles are shown as median values and thefull span of projections due to the small number ofensemble members.

RCP4.5 (108 runs)
Med
Low
6.2
3.6
9.9
4.4
5.7
2.8
2.4
0.2
7.1
4.1

High
10.0
16.6
9.6
4.8
11.2

RCP8.5 (81 runs)
Med
Low
9.5
6.4
15.1
9.0
8.3
5.1
4.2
1.5
10
7.0

High
13.3
23.9
12.6
7.2
15.6

Table 4.1.4. ESD results for changes in temperature [°C] from 1971-2000 to 2071-2100 over the Svalbard land area
following the scenarios RCP2.6, RCP4.5 and RCP8.5. “Low” corresponds to the 5th percentile, “High” to the 95th
percentile and “Med” to the median projection based on all available CMIP5 runs (“large ESD ensemble”). Tables forregions can be found in Appendix 2.3.

55

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100
In spite of a considerable difference in number ofmodels included in the full ESD and the RCM ensembles, the median estimated temperature changes
for the Svalbard land area towards the end of the
21st century are quite similar, both for RCP4.5
and for RCP 8.5 (Tables 4.1.4 vs. 4.1.5 and Figure
4.1.5). The “high” and “low” projections from the
ESD and RCM ensembles differ more than the median values. The full ESD ensemble is more likely
to give a realistic measure of the uncertainty, as the
RCM ensemble is too small to represent the total
natural climate variability in the area (Deser et al.,2012). Further, outliers may have a large impact on
small ensembles.
In order to distinguish between discrepancies
caused by different methods and by different
ensemble sizes, Table 4.1.6 shows annual and sea-

RegionScenario:RCP4.5 (5 runs)
SeasonMed
Low
Annual6.9
4.9

sonal results at the end of the century for ESD-ensembles where only the CMIP5 models applied in
Arctic CORDEX are included. The model results
are further weighted to be directly comparable
to Table 4.1.5. The results confirm that the small
ensembles do not completely represent the spreadof the results from the full ensemble, especiallyfor RCP4.5 where only 5 runs are considered. For
RCP8.5, the 90% spread in the full ESD ensemble
is nevertheless nearly covered when considering thecomplete spread in the Arctic CORDEX projections.

c) CCLM projection, and comparison with theensembles
The fine-scale CCLM temperature projection isclose to the “low” ensemble projections (Figure
4.1.5 and Table 4.1.5, last column). It is also clearlylower than the ESD projections based on the same
global model (Table 4.1.6, last column). It has
earlier been documented that the improved spatial
resolution in CCLM leads to reduced warming ratesin Svalbard land grid boxes (Isaksen et al. 2017).
Though we are not ready to conclude on the reason

Tables 4.1.4 to 4.1.6 also indicate that ESD projected temperature changes are slightly smaller thancomparable Arctic CORDEX projected changes.
For both methods, seasonal projections indicate thatthe temperature increase in winter will be aroundthree times as large as in summer in these areas.

High
9.4

RCP8.5 (8 runs)
Med
Low
9.8
6.7

High
15.6

RCP8.5
CCLM
7.2

Svalbard DJF

10.3

6.8

13.2

14.4

10.6

21.9

9.1

total
MAM
(land area)JJA
SON

6.5
3.3
8.2

4.1
2.3
6.0

8.4
4.7
11.4

9.2
5.8
10.3

7.7
2.0
6.5

16.1
8.4
16.2

7.2
4.9
7.4

Table 4.1.5. RCM results for changes in temperature [°C] from 1971-2000 to 2071-2100 over the Svalbard land area
following scenarios RCP4.5 and RCP8.5. “Low” corresponds to the lowest, “High” the highest and “Med” the median projection from Arctic CORDEX. “CCLM” corresponds to the 2.5 km CCLM projection. Tables for regions can befound in the Appendix 2.1.

Scenario:RCP4.5 (5 runs)
RegionSeasonAnnual
Svalbard DJF
total
MAM
(land area)JJA
SON

Med
6.2
8.5
5.7
2.8
7.2

Low
4.7
6.5
4.6
2.0
5.7

RCP8.5 (5 runs. weighted)
High
8.2
12.7
6.5
3.8
10.7

Med
8.8
14
8.5
4.1
9.5

Low
8.4
12.6
7.3
3.8
7.8

High
11.6
18.5
9.3
6.6
13.2

RCP8.5MPIESM-LR
8.4
12.8
8.3
3.9
8.8

Table 4.1.6. As Table 4.1.5, but for an ESD-ensemble based on the global models applied in Arctic CORDEX(“common models”). “MPI-ESM-LR”: ESD results from the model used for the CCLM-run.

56

Figure 4.1.5. Annual mean temperature for the Svalbard land area given as deviation [°C] from the reference period
1971-2000. The points and black curve show historical values based on reanalysis from 1958 to 2017 (Chapter
4.1.2). Future projections are given for RCP4.5 (a) and RCP 8.5 (b). ESD projections are based upon 108 CMIP5
models for RCP4.5, and 81 models for RCP8.5. RCM projections are based upon the 5 and 8 Arctic CORDEX
simulations respectively. Changes are projected to the periods 2031-2060 and 2071-2100. Median, “high” and “low”
projections are shown. The result for the CCLM RCP8.5 projection towards the end of the century is indicated by adiamond.
57

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

for this, we assume that the improved spatial
resolution leads to more realistic results. Further,
the CCLM is downscaled from a global model withreasonable sea ice climatology in the control period.
However, it is important to keep in mind that resultsfrom only one model run must be used with caution
and always placed in conjunction with otherprojections.

the century when adding them to the 1971-2000
temperatures (Figure 4.1.6). This is not realistic, andis probably caused by a control climate that is toocold (see Box 4.1). The “high” ESD projection is
also unreasonably warm, even though application of
the 95 percentile as the “high” projection excludes
the highest model runs (Figure 4.1.6).

4.1.3.3 Application of the downscaled
projections

The increased spatial resolution of the CCLM
projection can definitely add value to local climatechange assessments. The fact that the CCLM
control period temperatures are rather realistic in the
Svalbard area (Box 4.1) also enables us to derive
climate indices without adding to the uncertainty by
application of bias adjustment (cf. Box 5.1). Hence,
results from CCLM will in this and the following
chapters be applied both for improving the spatial
resolution of temperature and precipitation
projections, and for estimating changes intemperature indices (Chapter 4.2), snow conditions(Chapter 5.2), glaciers (Chapter 6) and permafrost(Chapter 7.1). When assessing the results fromthese analyses, one should take into account that the
CCLM projection is close to the low temperature
projections in the RCP8.5 ensembles except during

Our preliminary advice is to apply the medians of
the temperature ensembles as a main guideline forassessing consequences of climate change. This is
supported by the similarity of the medians from the
ESD and RCM projections, which indicate a robust
result. The spread of the model results is often used
as a measure for the uncertainty of the results. In the
Svalbard area one should, however, consider the realism, particularly of the “high” projections, before
applying them for assessing consequencesof climate change. Under emission scenario RCP8.5
for example, the regional models with the highest
warming rates give average winter temperatures
for Svalbard well above 0 °C towards the end of

Figure 4.1.6. Winter (Dec-Jan-Feb), spring (Mar-Apr-May), summer (Jun-Jul-Aug) and autumn (Sep-Oct-Nov) temperature [°C] for the period 1971-2000 based on the Sval-Imp dataset (black curve), and projections for the period
2071-2100 under the emission scenario RCP8.5 (coloured curves) for the Svalbard land grid points. Winter is plottedin both ends to show the whole seasonal cycle. Future projections are given for the fine scale CCLM (yellow), andfor the Arctic CORDEX (pink) and ESD (red) ensembles. Ensembles are represented by the “low”, “median” and“high” ensemble members.
58

BOX 4.1 Temperature projections from regional climate models (RCMs)
The Arctic CORDEX ensemble of RCM projections includes five projections for emission scenario RCP4.5,
and eight for RCP8.5. These are applied to project changes in temperature from the control period 1971-2000
towards 2031-2060 and 2071-2100 for Svalbard (Figure 4.1.5).
The projected changes will depend not only on the general climate sensitivity of the models, but also on temperature conditions in the “present climate”. A majority of the Arctic CORDEX ensemble members are colder
in the control period compared to the observed climate (Figure B4.1). In such models, melting of the surplus
ice may through feed-back mechanisms lead to locally enhanced warming. The modelled temperature change
may then be larger than it would have been for the same model under a more realistic control climate. When
adding this temperature change to the observed temperature during the period 1971-2000, the results may beunrealistic. E.g. the EC-EARTH-based projections (Figure B4.1, green curves) have 1971-2000 annual temperatures for Svalbard land areas about 5 °C below those estimated from the Sval-Imp dataset. This “bias” is evenlarger in winter (Figure B5.1), and the mean winter temperatures would end up well above 0 °C (Figure 4.1.6).
This is not likely during the “Polar night”. One should thus be aware that the upper end of the uncertainty span
indicated by adding the ensemble to the observed temperature is unrealistically warm, and should therefore
be applied with great caution. Throughout this report, all available Arctic-CORDEX models are still applied.
Only a small model ensemble is available: eight members for RCP8.5 and five memebers for RCP4.5, whereas
an ensemble consisting of at least 30 models would be preferable to obtain robust statistics. Excluding models
from the ensemble is therefore not a viable solution.
Figure B4.1 Arctic CORDEX and CCLM projectionsof annual mean temperatures for Svalbard underemission scenario RCP8.5.
The black curve shows results for the Sval-Imp dataset. The curves are smoothened to show decadal scalevariability.

The fine scale CCLM projection (purple curves in Figure B4.1) is based upon the same global model (MPIESM-LR) as two Arctic CORDEX projections (yellow and orange curves) with rather realistic temperaturescompared to the Sval-Imp dataset. However, the control period 1971-2000 happens to be in the warmer part ofa multi-decadal variation in this model, and thus the control period has a warm bias. Though the CCLM model
projects an average annual temperature of around -1°C for the last 30 years of the century, which is in the middle of the span of the Arctic CORDEX models (Figure B4.1), the temperature change projected by this modelis in the low end of the RCP8.5 ensemble and close to the median of the RCP4.5 ensemble (Figure 4.1.5). The
results from the CCLM model may thus be regarded as an example of a possible future climate under either
scenario. It will be applied for projecting changes in indices where an improved spatial resolution is ofimportance.

59

CLIMATE IN SVALBARD 2100

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summer (Figure 4.1.6), and actually closer to themedian of the RCP4.5 scenario (Box 4.1). Anyway,these results serve the purpose of pointing out
the direction and spatial patterns of the projectedclimate changes.
4.1.3.4 Projections of temperature development
through the 21st century
Figure 4.1.5 summarises the temperature
projections under RCP4.5 (a) and RCP8.5 (b)towards the end of the century. For the period 1971
to present, the observed trend is slightly above themedian projections. This may be because the loss of
sea ice in the area has been larger in reality
compared to the model medians.

next (Figure 4.1.5). Also decadal variability is
considerable, and inter-annual as well as decadalscale variability is in the future expected to besuperposed on the linear trends depicted in Figure
4.1.5. Thus shorter periods with decreasing temperatures are expected to occur also in the future.
4.1.3.5 Projections of the geographical variationof warming towards the end of the 21st century

The geographical variation within the Svalbard landarea for the median of the Arctic CORDEX projections under RCP8.5 towards the end of the century,is illustrated in Figure 4.1.6. In order to improve thespatial resolution, the results have been scaled using
the CCLM projection (see Chapter 3.1.4 for details
on the scaling method). Figure 4.1.7 shows large
gradients in projected temperature change, with the
The projections for RCP4.5 (a) and RCP8.5 (b) arelargest warming in the northeastern part. This is true
rather similar until the mid-century (medians: + 4.0
for all seasons except summer (Figure 4.1.7, lower
to +5.5 °C) , while they differ considerably towards
left panel). The pattern is especially pronounced in
the end of the century (medians: +6 to +10 °C). As
winter (Figure 4.1.7, lower right panel): While thepointed out earlier, the CCLM result towards thewestern part of Svalbard shows an increase of about
end of the century is low in the RCP8.5 ensembles,
12-14 °C, the northeastern part shows changes ofand may be regarded as more representative for the
up to almost 20 °C for the time period 2071-2100
RCP4.5 ensembles. The median increase of the ESD
following the RCP8.5 scenario. For the RCP4.5
projections under RCP2.6 is 3.6 °C for the mid-censcenario (not shown), the changes are about 4 °C
tury (Appendix 2, Table A.2.3), and 3.8 °C for thesmaller, and the gradient is less pronounced.
end of the century (Table 4.1.4). The difference
between the RCP2.6 and RCP8.5 medians is thus
Average temperature changes on annual and sealess than 2 °C for the mid-century, and more than 6
sonalbasis for the regions defined in Chapter 4.1.1°C towards the end of the century.
are calculated based upon RCMs (Table 4.1.7) andESD (Table 4.1.8). The results for the southwesternInter annual-temperature variations are in generaland northwestern regions are rather similar, whilelarge at high latitudes. Even averaged over the total
the warming of the eastern region according to theSvalbard land area, the annual mean temperaturemodels is expected to be larger than in the othercan vary with several degrees from one year to theregions, except for the summer.

RCP4.5 (5 runs)
Region
Total
E
NW
SW

RCP8.5 (8 runs)

RCP8.5 (CCLM)

Ann

DJF

MAM

JJA

SON

Ann

DJF

MAM

JJA

SON

Ann

DJF

MAM

JJA

SON

6.9

10.3

6.5

3.3

8.2

9.8

14.4

9.2

5.8

10.3

7.2

9.1

7.2

4.9

7.4

7.4

11.1

6.8

3.0

9.0

10.5

16.1

9.8

5.8

11.2

7.6

10.2

7.5

4.9

8.0

6.2

8.4

6.2

3.2

7.1

8.9

12.1

8.7

6.0

9.6

6.7

8.0

6.8

5.0

6.8

6.1

9.1

6.3

4.3

6.5

9.3

13.0

8.7

5.5

9.8

6.4

7.5

6.6

5.0

6.4

Table 4.1.7 RCM results for changes in annual and seasonal temperatures [°C] from 1971-2000 to 2071-2100 over
the Svalbard land area and the regions defined in Chapter 4.1.1. The displayed values correspond to median values
from the ensemble of Arctic CORDEX results for the RCP4.5 and RCP8.5 emission scenarios and the CCLM results
for the RCP8.5 emission scenario.
RCP4.5 (108 runs)
Region
Total
E
NW
SW

RCP8.5 (81 runs)

RCP8.5 (MPI-ESM-LR)

Ann

DJF

MAM

JJA

SON

Ann

DJF

MAM

JJA

SON

Ann

DJF

MAM

JJA

SON

6.2

9.9

5.7

2.4

7.1

9.5

15.1

8.3

4.2

10.0

8.4

12.8

8.3

3.9

8.8

6.3

9.8

5.8

2.3

7.3

10.0

16.3

8.6

4.2

10.4

9.0

13.9

8.9

4.1

9.2

5.9

8.5

5.3

2.3

7.3

9.0

13.6

7.4

4.0

10.1

7.7

11.2

7.1

4.0

8.4

6.3

10.1

5.7

2.7

6.7

9.4

15.0

8.1

4.4

9.3

7.8

11.7

8.0

3.4

8.0

Table 4.1.8 ESD results for changes in temperature [°C] from 1971-2000 to 2071-2100 over the whole Svalbard landarea and the regions defined in Chapter 4.1.1 following the scenarios RCP4.5 and RCP8.5. The displayed values
correspond to the median (50th) from the ensemble of ESD results for the RCP4.5 and RCP8.5 emission scenarios
and the MPI-ESM-LR results for the RCP8.5 emission scenario. Similar tables for the RCP2.6 can be found in the
Appendix 2.3.
Figure 4.1.7: Ensemble mean
temperature changes [°C] from1971-2000 to 2071-2100 in the
Arctic CORDEX simulations
assuming the RCP8.5 emission
scenario. The spatial resolution
is improved by scaling with thesignal from the CCLM run. Upper
panel: Annual temperatures.
Lower panels: Summer (left) andwinter (right).

4.2 Variables derived from air temperature
In this section the RCP8.5 temperature projections
from Arctic CORDEX-models and from CCLM
are used to calculate present and future numberof days per year with average temperature above
5°C (growing days), number of days with averagetemperature below 0 °C (frost days), and number ofdays when the temperature crosses 0 °C

60(zero-crossings). These indicators, and their changes, depend not only on temperature changes, butalso on the absolute temperature in the control
period. The median for the Arctic CORDEX models
is slightly too cold over the major part of Svalbard
in this period (Box 4.1), while CCLM is slightly too
warm. This explains why the numbers of growing

61

CLIMATE IN SVALBARD 2100

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days per year during 1971-2000 are higher and thenumbers of frost days lower in the CCLM than in
the Arctic CORDEX median (Table 4.2.1). On theother hand, the Sval-Imp dataset is slightly too coldin the coastal zone of Spitsbergen, including theSpitsbergen stations (Cf. Chapter 3.1). Bjørnøya is
not covered by the Sval-Imp dataset. The
climate models tend to have too high temperaturesat Bjørnøya in the control period.
4.2.1. Number of days pr. year with T > 5 °C
(Growing season)
The average number of growing days (Førland etal., 2004) for the Svalbard land area is very low inthe control period (Table 4.2.1), as considerable
parts of the archipelago are covered with glaciersand have no such days (Figure 4.2.1 left). The highest values (about one month) occur on Bjørnøya andalong the western coast of Spitsbergen – including
the fjords. The spatial pattern is similar in all datasets, though the CCLM dataset shows the highest
values. For the weather stations at the west coast of
Spitsbergen the observed number of growing days
are somewhere between the CCLM estimates andthe Sval-Imp dataset.

Station name

Svalbard average

Bjørnøya

Observed
Sval-Imp
CORDEX
CCLM

–
3
1
14

–
–
86
64

37
–
60
106

–
–
230
227

6
7
7
25

Observed
Sval-Imp
CORDEX
CCLM

–
294
306
288

–
–
153
195

194
–
148
139

–
–
18
21

245
240
254
216

Observed
Sval-Imp
CORDEX
CCLM

–
–
52
50

–
–
67
60

86
–
68
73

–
–
30
32

78
45
66
66

Hopen

The projected changes in number of growing days
towards the end of this century under RCP8.5(Figure 4.2.1 b) indicate a considerable increase insouthern coastal areas, while northeastern inland
areas still will have very few growing days evenunder this high emission scenario. The CCLM andthe Arctic CORDEX median agree rather well on
the projected number of growing days towards theend of the century (Table 4.2.1, blue columns). For
the coastal Spitsbergen stations, the projections
show approximately 120 to 145 growing days. This
corresponds to a growing season of three to fourmonths, or three to four times more than observed
during 1971-2000.
4.2.2. Number of days with T < 0 °C (Frost days)
All datasets agree that the average number of frostdays in Svalbard was about 300 in the period 19712000 (Table 4.2.1). The highest values occurred in
the northern and northeastern glacial areas (Figure
4.2.2 left). The lowest values occurred in Bjørnøya
and along the west coast of Spitsbergen. The spatial
pattern is similar in all datasets, though the CCLM
simulation shows slightly lower values than theothers. For the west coast weather stations, the observed values are about 240 days, which is between
the CCLM and the median Arctic CORDEX estimates (Table 4.2.1).
HornsundSvalbard AirportNyÅlesund

–
–
123
147

40
6
2
55

–
–
123
128

31
4
1
51

–
–
118
122

–
–
120
110

241
273
253
230

–
–
120
134

242
272
264
239

–
–
117
142

–
–
65
58

60
34
63
50

–
–
65
59

63
34
63
53

–
–
66
61

Isfjord Radio

Growing days per year
–
–
–
30
–
19
–
14
162
5
144
2
155
45
142
66
Frost days per year
–
–
–
238
–
248
–
248
33
246
84
253
56
220 109 218
Zero-crossing days per year
–
69
–
59
–
36
–
35
35
61
59
63
51
63
63
52

Figure 4.2.1: Average number of growing days per year in the control period 1971-2000 (left), and projected change
from 1971-2000 to 2071-2100 (right) according to CCLM under the RCP8.5 emission scenario.

The projected changes in number of frost days
under RCP8.5 indicate a considerable decrease insouthern coastal areas, while the reduction is limited
in northern and northeastern inland areas (Figure
4.2.2 right). For the Spitsbergen stations, the projected number of frost days towards the end of the
century varies from 84 to 142, implying roughly a
50% reduction compared to the control period.
4.2.3. Number of days with Tmax > 0 °C and
Tmin < 0 °C
Annual number of days with zero crossings (alsocalled frost-change days; Geiger, 2012) for thecontrol period is rather similar in the CCLM and the
median CORDEX dataset (Table 4.2.1, whitecolumns). The values also correspond well withobservations at the Spitsbergen stations. In
Bjørnøya and Hopen, the modelled values are too
low, because the small islands are not well resolvedin the models and they have a warm bias there.

The largest number of days with zero crossings in
the control period is located in southern andcoastal areas, while the lowest occurrence of suchdays can be found in the cold glacial inland areasin the north and northeast (Figure 4.2.3 left). The
projected change of the annual values shows mainly
an increase, except for Bjørnøya and Hopen (Table
4.2.1, blue columns; Figure 4.2.3 right). The largest increase is projected in the colder areas, while
only minor changes in annual number of days withzero crossings are projected along the west coast of
Spitsbergen.
The seasonal changes (not shown) are larger than
the annual changes, as the warming leads to more
frequent zero crossings in winter (DJF) and spring(MAM), especially along the coast, and lessfrequent crossings in summer (JJA).

Table 4.2.1: Observed and simulated number of growing days, frost days and zero-crossing days in the reference
period 1971-2000 (white columns) and in 2071-2100 (blue columns) for Svalbard land areas and for different stations and surrounding model grid-boxes.
62

63

CLIMATE IN SVALBARD 2100

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Figure 4.2.2: Average number of frost days per year in the control period 1971-2000 (left), and projected change
from 1971-2000 to 2071-2100 (right) according to CCLM under the RCP8.5 emission scenario.

Figure 4.2.3: Average number of zero crossing days per year in the control period 1971-2000 (left), and projectedchange from 1971-2000 to 2071-2100 (right) according to CCLM under the RCP8.5 emission scenario.

4.3 Precipitation(Vikhamar-Schuler, 2019). The reason is probablythat Longyearbyen is lying in a “rain shadow area”
that is not resolved by the spatial resolution in themodels (Vikhamar-Schuler et al., 2019). At moststations the seasonal differences are rather small,but in general May-June are the driest months,
and September-October the wettest (Figure 4.3.1).
Except for Bjørnøya, all stations have a secondary
maximum in March.

4.3.1 Precipitation climatology
In situ precipitation observations from the Arctic
are sparse and are subject to many site and gauge
issues (e.g. gauge undercatch, station/instrumental
shifts, changes in measurement protocols, automation), while atmospheric reanalyses often containwet biases linked to inadequate representation ofsea ice as well as inhomogeneities from changes
in data streams over time (AMAP 2017). Despite
several relocations, composite homogenised precipitation series are established (cf. Chapter 3.1.1)
for Bjørnøya (1920-present), Hopen (1946-present),
Svalbard Airport/Longyearbyen (1912-present) andNy-Ålesund (1968-present) (Vikhamar-Schuler etal., 2019). The observation-based precipitation results in this report are not corrected for undercatch.
Average annual and seasonal precipitation for
Svalbard for the standard normal period 1961-1990
and the reference period 1971-2000 is presented in
64

Table 4.3.1. The table includes values from measuring stations as well as for modelled regional values
from downscaled reanalysis data (Chapter 3.1.1).
The model data indicates an average annual precipitation for Svalbard around 700 mm, with highest
values for the southwestern region. The modelled
precipitation is higher than the measured, but
considering effects of gauge undercatch, the downscaled reanalysis in general seems to give a realistic
picture of precipitation totals (Vikhamar-Schuler
et al., 2019). Further, water balance assessments
indicate that areal precipitation for two catchments
is twice the measured precipitation at the closest
meteorological stations (Chapter 3.2.1).
The measured annual precipitation at Svalbard
Airport is less than half of what is measured at
Ny-Ålesund and Barentsburg (Table 4.3.1), andalso the model datasets indicate values more than
twice the measured amount at Svalbard Airport

Annual precipitation from the downscaled reanalysis is shown in Figure 2.1.2. The maps of summer
and winter precipitation (Figure 4.3.2) demonstrate
highest amounts in western mountain areas andlowest values in sheltered valleys and low-elevation
areas in the centre and the northeast of Svalbard.
More details on the model data, as well as maps forall seasons are presented in Vikhamar-Schuler et al.
(2019).

4.3.2 Precipitation development in the instrumental period
4.3.2.1 Linear trends
Obtaining reliable estimates of solid precipitation
trends over the Arctic is a challenge (Yang et al.,2005; Groisman et al., 2014; AMAP, 2017).
According to ACIA (2005) the Arctic precipitation
increased by about 2% per decade during the Arctic
warming in the first half of the 20th century (19001945), with significant trends in the Nordic region.
During the two decades of Arctic cooling (19461965), the high latitude precipitation increase wasroughly 1% per decade. Since 1966, annual
precipitation in the Arctic has again increased byabout 2% per decade.
Linear precipitation trends for the Svalbard stations
are outlined in Table 4.3.2. The left part presents
trends from the start of the series, while the rightpart shows trends onwards from 1971; - the starting
year for the reference period used in this report.
65

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

1961-1990 average
Station name1971-2000 average

AnnualWinterSpringSummerAutumnAnnualWinterSpringSummerAutumnBjørnøya

371

93

67

89

121

396

114

82

78

122

Hopen*

250

47

41

84

77

276

58

55

83

79

Hornsund**

405

88

70

122

125

428

90

72

124

142

Isfjord Radio

480

120

99

121

140

NA

NA

NA

NA

NA

Barentsburg

565

173

127

93

173

581

172

134

100

175

Svalbard Airport

189

50

40

51

48

196

51

41

52

52

Ny-Ålesund

385

99

86

84

116

409

110

95

82

122

Svalbard total

694

164

143

162

224

723

181

153

164

225

Svalbard E

659

148

131

158

220

690

170

142

156

222

Svalbard NW

714

171

155

173

214

745

181

165

181

219

Svalbard SW

770

197

162

163

245

791

213

172

167

240

* Adjusted for a severe homogeneity break in 1997, see Chapter 3.1.1** Start August 1978. Values for 1961-1990 from Førland (1993). Values for 1971-2000 based on 1978-2000
Table 4.3.1. Annual and seasonal precipitation [mm] for weather stations (observations) and Svalbard regions (SvalImp dataset)

For Svalbard Airport and Bjørnøya, the (statisticalsignificant) centennial trends for annual precipitation show a linear increase of 3 - 4% per decade.
For Svalbard Airport the seasonal trends are highest
for autumn, while for Bjørnøya the increase is largest for winter and spring. For the latest fifty years(1971-present) just Bjørnøya, Hopen andNy-Ålesund have significant (positive) trends forannual precipitation. The stations on the Svalbard
mainland have experienced the largest seasonalincrease in autumn. For spring and summer, there isa (non-significant) tendency of negative trends for
all the Spitsbergen stations.

Figure 4.3.2. Downscaled precipitation [mm] for the reference period 1971-2000.Left: Winter (Dec-Jan-Feb), right:
Summer (Jun-Jul-Aug)

For the period 1971-2017 with mostly complete
records for observational as well as reanalysis-based
datasets, the annual regional trends from Sval-Imp
dataset are negative in the western region, while theobservation-based trends are positive. These differences may be fictitious as the trends are small andnot statistically significant, except for Ny-Ålesund.
However, as more precipitation is falling as rain
(Figure 4.3.4), the undercatch in the precipitation
gauges is reduced (Førland and Hanssen-Bauer,
2000). Thus the gauges catch a larger fraction of
the “true” (and modelled) precipitation. It is therefore possible that while the measurements show
increased precipitation during 1971-2017, the “trueprecipitation” has decreased slightly, though notstatistically significant (Vikhamar-Schuler, 2019).

partly by changes in atmospheric circulation patterns (e.g. Isaksen et al., 2016; Dahlke andMaturilli, 2017).
4.3.2.2 Decadal variability

Annual and decadal variations are superimposedon the linear trends depicted in Table 4.3.2. Figure
4.3.3 shows long-term variability on a decadal timescale. In contrast to temperature (Figure 4.1.3), theprecipitation series show quite different individual
long-term patterns. The main reason is that precipitation varies locally on a smaller spatial scale than
air temperature. However, most series have somecommon features: Secondary maxima in the warm
periods in the 1930s and late 1950s (cf. Figure
4.1.3); and secondary minima in the cold period in
the late 1940s and 1960s. Unlike most of the observational series, the model data indicates a maxThe statistically insignificant precipitation trends
during the period 1971-2017 may still seem surpris- imum in the 1970s. Most series indicate increasing
ing, taking into account the strong temperature trend precipitation in the warming period after year 2000.
Hanssen-Bauer and Førland (1998) showed that thein this period, and thus the increased potential ofprecipitation trend at Svalbard Airport from 1912 toprecipitable water in the airmasses (Box 4.2).
the 1990s to a large degree could be explained by
Hanssen-Bauer and Førland (1998), however,variations in the atmospheric circulation.
concluded that for Svalbard the local precipitation
trends to a larger degree than temperature trends
are governed by atmospheric circulation patterns.
Several studies have shown that the temperature
trend during the later decades can be explained only

Figure 4.3.1: Seasonal cycle (averages from 1971-2000) in precipitation [mm/month] for selected meteorologicalstations in the Svalbard region. (* see legend for Table 4.3.1)
66

67

CLIMATE IN SVALBARD 2100

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Start - 2017

1971-2017

StationStart

AnnualWinterSpringSummerAutumnAnnualWinterSpringSummerAutumnBjørnøya

1920

3,2

4,9

5,2

0,1

2,3

6,4

12,1

11,7

-4,7

4,3

Hopen*

1946

6,8

12,2

13,2

-0,1

5,7

5

12,5

11,8

-7,2

7,4

Hornsund**

1979

6,7

9,4

-5,7

-7,3

23,6

6,7

9,4

-5,7

-7,3

23,6

Barentsburg

1948

3,9

3,7

4,3

1,2

5,6

0,8

-2,3

-4,7

-6,6

6,6

Svalbard Airp.

1912

3,7

2,4

2,7

4,2

5,2

4,1

8,4

-8,5

-1,2

14,8

Ny-Ålesund

1969

7,8

13,3

-0,4

-1,1

15,2

7,1

14,4

-3,3

-2,3

15,5

Svalbard total

1958

1,9

6

1,6

-1,3

1,7

-1

4,2

-2,5

-6,2

0,6

Svalbard SW

1958

0,5

3,2

1

-1,2

0

-2,3

1,8

-3,7

-7,1

0

Svalbard NW

1958

1,6

4,9

-0,2

0,1

2,7

-2,1

2,7

-6,2

-6,4

2,4

Svalbard E

1958

2,5

7,7

2,7

-2,1

1,9

0

5,8

-0,3

-5,8

0,1

* Adjusted for a homogeneity break in 1997, see Chapter 3.1.1** Data from Hornsund from 1979

Table 4.3.2. Linear trends [% per decade] in precipitation for weather stations (observations) and regions (Sval-Imp
dataset). Bold types: Significant trends (5% level)

Table 4.3.3 indicates that, except for Barentsburg, themeasured precipitation is higher for the most recent
30-year period than for the reference period. Thesefeatures are in line with the graphs in Figure 4.3.3.
The modelled data show small differences between
the various periods, but a tendency to highest values
during the reference period because of the highvalues in the 1970s (Figure 4.3.3). One of the reasons why the downscaled reanalyses show reduced
values while observations show increased values forthe latest period, may be reduced undercatch in the
precipitation gauges during the recent mild winters.
Higher winter temperatures increase the likelihoodthat precipitation will fall as rain instead of snow,
resulting in increased catch efficiency of the
precipitation gauges (Førland and Hanssen-Bauer,
2000). Figure 4.3.4 demonstrates that the annual
fraction of solid precipitation during 1975-2017indeed has decreased at Bjørnøya, Svalbard Airport and Ny-Ålesund. Hansen et al. (2014) and
Vikhamar-Schuler et al. (2016) found a clear
tendency for increase in wintertime rainfall events:
During November-April 1995-96, 2009-10, 2011-12
and 2016-17; more than 50 % of the total precipitation was falling as rain. A large proportion of therainfall occurred in just a few events (cf. Chapter
4.4).

68

4.3.2.3 Rain-on-snow and freezing rain
By studying 37 state-of-the-art climate models,Bintanja and Andry (2017) stated that rain is
projected to become the dominant form of precipitation in the Arctic region at the end of this century.
Rain on snow causes a crust on existing snow, with
implications for wildlife, infrastructure, transportation and other outdoor activities. It may also cause
more unstable snow layers with implications foravalanches throughout the winter (Chapter 7.3.3).
Figure 4.3.5 shows that the frequency of rain on
snow had a minimum in the late 1960s, but has
increased during the last fifty years. Isaksen et al.
(2017) found that in the winter months, there may
be a threefold in the number of mild weather episodes with precipitation as rain up to the end of
the 21st century compared to the current situation.
Hansen et al. (2014) concluded that the frequencyof rain on frozen ground is likely to increase in the
future.
Vikhamar-Schuler et al. (2016) found an increasein number of “mild weather days” (i.e. rainfall andaverage temperature >0°C) as well as for the precipitation sum on rainy “mild weather days” in thewinter months (October-April) in the Longyearbyen
area during the period 1961-2100.

Figure 4.3.3. Annual precipitation [mm] for weather stations (observations) and regions (Sval-Imp dataset).
The series are smoothed by a Gaussian filter to show decadal scale variability.

PeriodeBjørnøya
Hopen*
Hornsund*Barentsburg
Svalbard Airport
Ny-Ålesund
Region-Southwest
Region-Northwest
Region-East
Svalbard-Total

1961-1990
0.93
0.91
0.95
0.97
0.97
0.94
0.97
0.96
0.96
0.96

1981-2010
1.06
1.08
1.04
0.95
0.96
1.01
0.95
0.95
0.99
0.97

1988-2017
1.12
1.08
1.09
0.97
1.02
1.12
0.96
0.97
1.00
0.99

* Hopen and Hornsund: See legend for Table 4.3.1
Table 4.3.3 Ratios between annual precipitation for different 30-year periods and the reference period 1971-2000 for
weather stations (observations) and regions (Sval-Imp dataset).

Groisman et al. (2016) found that freezing rain
occurrences had increased substantially in the Norwegian Arctic during 1975-2014. Freezing rain andfreezing drizzle events may even at low

intensities result in natural hazards that cause
damage to housing, communication lines, and otherman-made infrastructure.

69

CLIMATE IN SVALBARD 2100

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Figure 4.3.4 Fraction of annual precipitation amount falling as snow at Bjørnøya, Svalbard Airport and Ny-Ålesund.

4.3.3 Projected precipitation development towards 2100
The Arctic water cycle will intensify in the future.
Higher temperatures and thus increased moisture
content are expected to lead to more precipitationin the area around Svalbard. Climate models project increases in cold-season precipitation of 20-50% over the Arctic Ocean towards the end of thiscentury, with an increasing portion of that precipitation falling as rain instead of snow (AMAP, 2017).
Previous simulations with a regional climate model
used in the NorACIA project (Førland et al., 2011)
showed an increase of about 10% in annual rainfall
at Svalbard Airport from 1961-90 to 2071-2100. For
autumn and winter the increase was more than 20%.

Figure 4.3.5 Number of days per year, relative to the 1971-2000 average, with observed precipitation and a dailymean temperature above 0°C during the winter season (NDJFMA). The dotted blue line shows annual values for
Svalbard Airport, while the full drawn curves show decadal scale variability for several stations.
70

4.3.3.1 Limitations using empirical-statistical
downscaling (ESD) for precipitation

CORDEX runs indicate largest increases for the
winter season; smallest for the summer season.

Benestad et al. (2016) found that for Svalbard theskill of empirical-statistical downscaling (ESD)models was moderate for the wet-day frequency,
but the projections indicated both increases anddecreases within the range of -5 to +10 % by 2099.
The ESD-results for wet-day mean precipitation
were poor, and consequently the precipitation
projections in this report are concentrated on resultsfrom regional climate models (cf. Chapter 3.1).

The change in annual precipitation is highest in thenortheastern parts (Figure 4.3.7) and smallest in
the southwestern parts. This is also reflected in theregions (Table 4.3.5), where the annual increase for
the Arctic CORDEX runs under RCP8.5 is 70 % for
the eastern region and 40 % for the southwesternregion. Seasonal projections for all regions and bothscenarios are listed in Appendix 3 (Table A.3.1).

4.3.3.2 Arctic CORDEX results

4.3.3.3 CCLM projection and comparison with
the Arctic CORDEX ensembles

Up to the end of the century, the regional Arctic
CORDEX simulations show an increase in annual
precipitation for the Svalbard land area of approximately 40 % for emission scenario RCP4.5 and 60% for RCP8.5 (Table 4.3.4). For RCP8.5 the spread
between low and high projections is large (Figure
4.3.6), with the results from the COSMO-CLM(CCLM)-simulations in the lower end. The Arctic

Table 4.3.4 and 4.3.5 reveal a substantial lower
precipitation increase for the CCLM simulation thanfor the Arctic CORDEX simulations. For the summer season, the CCLM simulation gives an increaseof approximately 15 %, while the lowest Arctic
CORDEX simulation gives ca. 30 % increase.

Figure 4.3.6 Annual mean precipitation for the Svalbard land area as deviation [%] from the reference period 19712000. The points and black curve show single year and smoothed historical values based on downscaled reanalysis
data from 1958 to 2017 (Ch. 3.1.1). Future projections are given for Arctic CORDEX RCM simulations. Changes areprojected to the periods 2031-2060 and 2071-2100, and for the scenarios RCP4.5 and RCP8.5. For RCP8.5, theresult for the COSMO-CLM (CCLM) projection is indicated.
71

CLIMATE IN SVALBARD 2100

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Scenario:
RegionRCP4.5 (5 runs)

RCP8.5 (8 runs)

RCP8.5

Season

Med

Low

High

Med

Low

High

CCLM

Annual

43

22

53

63

27

106

34

Svalbard

DJF

49

22

73

78

22

147

39

(land area)

MAM

44

14

47

55

22

110

40

JJA

26

22

32

48

29

67

16

SON

46

27

66

59

30

110

40

The potential amount of precipitable water in the atmosphere depends on the temperature (Clausius-Clapeyron equation). The temperature considerations outlined in Box 4.1 accordingly influencealso the precipitation simulations; models with the largest (and probably unrealistic) future warming also will tend to have the largest precipitation increase. As the temperature increase projectedby the fine-scale CCLM model is in the lower end of the ensembles (Box 4.1), this may contributeto the lower precipitation increase for CCLM compared to the Arctic CORDEX runs.

Table 4.3.4 RCM results for changes in precipitation [%] from 1971-2000 to 2071-2100 over the whole Svalbard landarea following the scenarios RCP4.5 and RCP8.5. “Low” corresponds to the lowest, “High” to the highest and “Med”
to the median projection from Arctic CORDEX. CCLM corresponds to the COSMO-CLM projection. Similar tables forregions are given in the Appendix 3.

Also the regional CCLM projections (Table 4.3.5)
indicate lower precipitation increase than the median projections from the Arctic CORDEX simulations, e.g. ca. 5 % vs. 50 % for summer for the NW
region. Possible reasons for these discrepancies are
outlined in Box 4.2.
4.3.3.4 Application of the downscaled
precipitation projections
As for temperature (cf. Chapter 4.1.3), we assume
that the improved spatial resolution in CCLM
leads to a more realistic description of precipitation
conditions than the coarser resolution used in the
Arctic CORDEX simulations. However, results

Region
Total
SW
NW
E

Ann
43
32
41
49

RCP4.5 (5 runs)
DJF MAM JJA
49
44
26
41
38
25
44
40
26
54
46
27

SON
46
32
50
55

Ann
63
39
61
70

from only one model run must be used with caution
and always placed in conjunction with an ensembleof other projections. Our preliminary advice is thus,
as for temperature, to apply the Arctic CORDEX
ensemble medians as a main guideline for assessing consequences of climate change. However, it isimportant to keep in mind that the CCLM projections indicate that the Arctic CORDEX simulations
may give a high estimate for the increase in future
precipitation. As for temperature, large inter-annualand decadal variability will also in the future be superposed on the linear precipitation trends depicted
in Figure 4.3.6.

RCP8.5 (8 runs)
DJF MAM JJA
78
55
48
43
40
41
78
51
50
90
62
48

SON
59
33
59
66

Ann
34
17
24
44

RCP8.5 (CCLM)
DJF MAM JJA
39
40
16
13
27
10
32
25
6
52
51
23

SON
40
20
33
50

Table 4.3.5. RCM results for changes in annual and seasonal precipitation [%] from 1971-2000 to 2071-2100 over
the Svalbard land area and the regions defined in Chapter 4.1.1. Arctic CORDEX (median) results are given for the
scenarios RCP4.5 and RCP8.5, and CCLM results for RCP8.5.

72

Box 4.2 Precipitation projections from regional climate models

The CCLM run gives a realistic level of present annual precipitation compared to the Sval-Imp
dataset, but is in the upper end of the model ensemble for present day precipitation (Figure B4.2).
Thus, the same absolute increase (in millimetre) would give a smaller relative increase (in percent) than for the other Arctic CORDEX simulations. Further, the three Arctic CORDEX runs
using the same global forcing as the CCLM (i.e., the MPI-ESM-LR RCP8.5 run) are showing thesmallest precipitation changes of all Arctic CORDEX RCP8.5 projections (Figure B4.2). This
indicates a major influence of the driving global model on the projected changes, for instance due
to changes in humidity transportation towards Svalbard.
It is notable that the present-day annual precipitation for CCLM is higher than the two Arctic
CORDEX runs (yellow and orange curves) with realistic temperatures (see Box 4.1), although thepresent-day annual temperatures are quite similar (Figure B4.1). Thus, also enhanced orographic
effects of the finer terrain model used in the CCLM simulations, as well as differences in parametrisation of precipitation processes, may contribute to the differences between the CCLM and
Arctic CORDEX results in Tables 4.3.4 and 4.3.5.
However, a deeper analysis of this is out of the scope of this report, and would need further investigations and benefit from a larger RCM ensemble. The CCLM results for precipitation are still
regarded as a realistic example of a possible future under RCP8.5, and will be used for projectingchanges in precipitation indices (e.g. heavy rainfall (Chapter 4.4) and snow (Chapter 5.3.2)) wherean improved spatial resolution is of importance.

Figure B4.2 Arctic CORDEX and CCLM projections of annual precipitation for Svalbard underemission scenario RCP8.5.
The black curve shows
results for the Sval-Imp
dataset. The curves aresmoothened to show decadal scale variability

73

CLIMATE IN SVALBARD 2100

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Figure 4.3.7. ‘Ensemble mean
precipitation changes [%] from1971-2000 to 2071-2100 in
the Artic CORDEX simulations
assuming the RCP8.5 emission
scenario. The spatial resolution
is improves by scaling with thesignal from the CCLM simulation(cf. Chapter 3.1.4). Upper panel: Annual values. Lower panel:
Summer (left) and winter (right).

4.4.1 demonstrates the large local differences inheavy rainfalls: The return period estimates fordaily rainfalls for Ny-Ålesund and Hornsund are
more than twice the values for Svalbard Airport. For
Svalbard Airport as well as Ny-Ålesund theobserved daily maximum rainfalls exceed theestimated 100-year return period value. Table 4.4.1
shows that the 100-year estimates for Bjørnøya,
Hopen and Svalbard Airport are quite similar forboth periods, while for Ny-Ålesund they are substantially higher in the most recent period.
The general weather situation favouring heavy
rainfalls is a strong south-southwesterly flow withadvection of water vapour from warmer areas. For
16 out of 21 events where 12-hour precipitation at
Svalbard Airport exceeded 10 mm, Dobler et al.
(2019) found that the large-scale wind direction
was from southwest. In several of the heavy rainfall
events there is a link with “atmospheric river”-likefeatures in the precipitable water anomaly field(Serreze et al., 2015). A heavy rainfall event with“atmospheric river”-like features for precipitablewater anomalies is demonstrated in Figure 4.4.2.
For this kind of events, Spitsbergen is atypical of
most of the Arctic: Lying towards the northern endof the North Atlantic cyclonic track, it can readilydraw from more southerly Atlantic moisturesources.
Serreze et al. (2015) stated that each heavy rainfall
event at Spitsbergen has its own characteristics, andthat strong influences of local topography imply that

4.4 Heavy rainfall
4.4.1 Present conditions
Similar to annual precipitation, large local differences are found for observed large daily rainfalls(Table 4.4.1). The highest measured daily rainfall
in Ny-Ålesund (98 mm) is thus more than twice
the highest value recorded at Svalbard Airport. (InNovember 2016 a mini-logger in Longyearbyen
recorded a rainstorm of 75 mm during 26 hours,
see Chapter 10.3.2). It is notable that the highest
observed daily values at Ny-Ålesund and Svalbard
Airport make up around 25 % of the stations’ total
74

average annual precipitation. High daily rainfalls may occur even mid-winter: 40 % of the fivehighest rainfall events at the five stations in Table
4.4.1 occurred in December-February and 28 % in
September-November (Dobler et al., 2019). Figure
4.4.1 indicates that in the latest decades, the numberof days with heavy precipitation has increased at
Svalbard Airport and Ny-Ålesund.
Heavy rainfall events may cause flooding (Chapter
5.1.5), landslides (mainly debris flows) and slush
avalanches on Spitsbergen (Chapter 7.3.2). Table

extreme events recorded in Ny-Ålesund are
typically not well represented at other Spitsbergenstations (and vice versa). Strong influences of
topography in the Ny-Ålesund area were also
documented by Førland et al. (1997a).
4.4.2 Projected changes in heavy rainfall
According to AMAP (2017), future daily precipitation extremes will increase over mid- and high latitudes, with implications for the management of water
resources, flow of freshwater into the Arctic Ocean,changes in sea ice temperature, and amplification ofregional warming (through reduced surface reflectivity caused by shift from snow to more rain). Towards
the end of the century, a marked increase is projected
for frequency and intensity of heavy rainfalls in the
Svalbard region (Isaksen et al., 2017).
An evaluation of the high-resolution simulations
with COSMO-CLM (CCLM), indicates that observation-based extreme rainfall statistics (Table 4.4.1)
are reasonably represented by the model for present
day climate (Table 4.4.2, left columns). The CCLM
model is therefore used for projections of heavy
rainfall in the Svalbard region (Dobler et al., 2019).
Future changes in 1-day rainfall deduced from the
CCLM runs with emission scenario RCP8.5 areoutlined in Table 4.4.2. For the 99.5 percentile, i.e.
the daily value exceeded approximately twice per
year, an increase of approximately 20 % is projected for most stations. This is well in line withthe projected increase in the 99.5 percentile for the

1976-2005

1988-2017

Bjørn Hopen* Horn* Sv. Ap
q99.5
M10
M50
M100
Max

14
30
41
46
42

17
38
52
58
49

25
NA
NA
NA
NA

10
23
32
36
43

Ny-Ål

Bjørn

21
46
60
66
57

15
33
45
50
42

Hopen Horn*
15
33
45
50
47

24
54
79
89
74

Sv. Ap

Ny-Ål

11
25
34
37
42

23
59
73
82
98

Table 4.4.1. Extreme daily rainfall [mm] at the weather stations Bjørnøya, Hopen, Hornsund, Svalbard Airport andNy-Ålesund. For each station values are presented for the 99.5 percentile (q99.5) and maximum observed (Max) as
well as estimates for return periods (M) 10, 50 and 100 years*Hornsund data from 1995; Hopen: Not adjusted for homogeneity break in 199775

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Norwegian mainland (Hanssen-Bauer et al., 2017).
For return periods 10, 50 and 100 years the picture
is more complex; the 100-year estimates vary froma decrease at Hornsund to an increase of more than
30 percent for Svalbard Airport and Hopen.
Return period estimates based on short, single series
are vulnerable for outliers, but even analyses ofchanges for all Arctic CORDEX models for all grid
points in the Svalbard region did not show any
Fig 4.4.1. Number of days
per year, relative to the
1971-2000 mean, withobserved precipitation
above the 95th percentile.
The dotted blue line shows
annual values for Svalbard
Airport, while the full drawn
curves show decadalscale variability for several
stations.

4.5 Wind, cyclonic activity and waves
4.5.1 Present conditions

As the Norwegian Arctic lies in the transit zone
between cold Arctic air in the north, and mild
maritime air in the south, the cyclonic activity ishigh. Unstable and stormy weather is therefore
common, especially in winter. The area of atmospheric low pressure from Iceland towards the
Barents Sea (Chapter 2.1) reflects the major tracks
of low pressure systems. The air pressure is lowest
in winter and autumn, and the strongest pressure
gradients and the highest wind speeds usually occur
during winter (Førland et al., 2009).
Extreme cyclone events often occur during Arctic
winters, and are of concern as they are associatedwith anomalous warming events. Rinke et al. (2017)
found that typically 20-40 extreme cyclone events(sometimes called “weather bombs”) occur in the
Arctic North Atlantic per winter season, with anincreasing trend of 6 events per decade over 19792015, according to 6-hourly observations from Ny-

Figure 4.4.2 Left: Sea level pressure (left) and precipitable water (right) during the heavy rainfall event on 13.January 2018. (From NOAA)

common pattern, except that the results mostly indicated an increase in future heavy rainfalls (Dobler
et al., 2019). The most robust projections for future
heavy daily rainfalls are those deduced for q99.5.
For emission scenario RCP8.5 this means a 20 %increase in heavy daily rainfall totals from 19712000 to 2071-2100 at Bjørnøya, Hopen and
Svalbard Airport. More results on present andfuture heavy rainfall statistics in the Svalbard region
are outlined by Dobler et al. (2019).

Ålesund. Rinke et al. (2017) related this regional
pattern of trends in extreme cyclones to anomalously low sea-ice extent in recent years, together withassociated large-scale circulation patterns. This also
affects the wave climate, as receding sea ice
cover opens up new regions to both locally generated wind waves and intrusion of remote swell.
The southern coast of Svalbard is exposed to swellpropagating large distances across the NorwegianSea, as well as local waves associated with cyclones
in the vicinity of Svalbard. Aarnes et al. (2012) and
Breivik et al. (2013) estimated the 100-year returnlevels for significant wave height from NORA10(Reistad et al., 2011) to be 12-14 m south of Svalbard. The northern coast of Svalbard is far less exposed, but is sensitive to the ice extent since significant wave height scales with the fetch (the lengthof open ocean over which the wind blows (Hasselmann et al., 1973)). Thomson and Rogers (2014)
showed that the Arctic Ocean and the Beaufort Sea
has experienced an increase in fetch in recent years

Simulated (mm) for 1971-2000
Bjørn Hopen Horn
Sv.Ap Ny-Ål
q99.5
M10
M50
M100
Max

18
36
47
52
38

16
28
36
40
35

23
40
52
58
49

16
30
39
44
39

24
50
69
78
72

Projected changes (%) (RCP8.5, CCLM)
Bjørn Hopen Horn
Sv.Ap Ny-Ål
22
15
12
11
27

20
33
48
55
142

8
4
-5
-9
-2

21
26
32
35
75

14
14
11
9
-11

Table 4.4.2 Extreme heavy daily rainfall: Modelled (CCLM) present day values [mm] and changes (%) from 19712000 to 2071-2100 under RCP8.5. For legend details, see Table 4.4.1

76

77

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as a consequence of the receding ice cover. Since
waves help break up the ice, this could also accelerate the receding ice cover.
Svalbard Airport, Ny-Ålesund and Hopen have50-60 days per year where the maximum (10 minutes) wind speed is strong breeze or stronger (Table
4.5.1). At Bjørnøya and Isfjord Radio, the frequency of strong winds is twice as high. The easterly
winds at Isfjord Radio are strengthened locallyby Isfjorden, which is narrower at the mouth thanfurther in. The mean wind speeds (Figure 4.5.1) andfrequencies of high wind speeds (Table 4.5.1) arelowest during the summer season.
4.5.2 Projected changes
The NORACIA-RCM simulations of average daily
maximum wind speed for the period 1980-2050 indicated small changes during summer, but an increase
north and east of Svalbard during the other seasons(Førland et al., 2009). Also up to the end of the 21st
century rather small changes were projected, but for
sea areas north and east of Svalbard an increase larger
than 10 % was found for average maximum daily

wind speed during winter. For the Barents Sea, Benestad et al. (2016), by use of ESD (see Chapter 3.1.3),
found an increase in number of deep cyclones andsynoptic storms associated with a warming Arctic.
The simulations by the high-resolution COSMO-CLM (CCLM) model indicate that the main
wind directions remain fairly unchanged towards
the end of the century throughout Svalbard, while
the wind force is estimated to increase over thesea in the northeastern part, and decrease somewhat along the northeastern coastal and fjord areas(Figure 4.5.2). The changes in wind conditions arelargest during winter.
For maximum daily wind speed the projections generally indicate an increase in northeastern areas anda decrease in the southwest (Figure 4.5.3), i.e. thesame pattern as found by Førland et al. (2009). For
December-February the daily maximum wind speed
is projected to increase by more than 2 m/s in thenorthernmost areas. The larger increase in this area
is probably linked to diminishing sea ice.

Aarnes et al. (2017) calculated the future wave
climate (2071-2100) with a regional wave model
forced with winds from six CMIP5 models under
the RCP4.5 and RCP8.5 scenarios. The largest
increase was found in the area south and southeastof Svalbard. As the wind field showed a reduction
in a majority of the six CMIP5 models, the primarycause was the receding ice cover. The average impact was a 10 % increase in annual mean significant
wave height, but individual CMIP5 models exhibited considerable variability in both wind speed andice cover.

1971-2000

1988-2017

AnnualAnnualWinterSpringSummerAutumnBjørnøya

119

120

45

30

12

33

Hopen

47

54

22

11

5

16

Hornsund*

66

87

32

22

10

23

Isfjord Radio*

133

NA

NA

NA

NA

NA

Svalbard Airport*

51

61

26

15

5

15

Ny-Ålesund*

35

51

21

13

3

14

Table 4.5.1 Number of days with wind force “strong breeze” or stronger (>10.8 m/s).
*Data: Hornsund from 1995; Isfjord Radio 1947-1975; Svalbard Airport from 1975; Ny-Ålesund from 1974

Figure 4.5.1 Simulated average windspeed and direction
in winter (November-April) and summer(May-October) for thereference period 19712000, based on the
CCLM 2.5 km model.

78

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5. Hydrology
Figure 4.5.2 Projectedchanges in average windspeed and direction from1971-2000 to 2071-2100
for winter (November-April)and summer (May-October). The simulations are
based on the CCLM 2.5
km model run for emission
scenario RCP8.5

Hydrological processes in the Arctic have a large
impact on the water and energy balances. Runoff,
snow cover, glacier mass balance, erosion andsediment transport in Svalbard influence the naturalenvironment through their impacts on for instancebiological activity and water chemistry. Glacierscover a majority of the land area in Svalbard(Chapter 6). Changes in precipitation and temperature will substantially alter glacier mass balanceand snow conditions – both influencing river flow.
Furthermore, water is a primary weathering agent

for rocks and soils, breaking them down, dissolving
them, and transporting the resulting sediments anddissolved solids to the sea. Figure 5.1 shows partsof the Adventdalen catchment near the settlement
Longyearbyen. Mountainous areas with plateaus
and steep slopes intersected by wide valleys occupied by river plains are characteristic landforms of
the Svalbard Archipelago. Glaciers are frequent,while lakes are rare compared to mainland Norway.

Figure 5.1 Adventdalen. Photo: Stein Beldring
Figure 4.5.3. Annual and seasonal changes of daily maximum wind speed from 1971-2000 to 2071-2100. The estimates are based on the CCLM 2.5 km run for emission scenario RCP8.5.

5.1 Runoff
5.1.1 Water balance estimates
Killingtveit et al. (2003) reported the water balance
for the three catchments (Figure 3.1.2) Bayelva at
Ny-Ålesund, De Geerdalen and Endalen/Isdammennear Longyearbyen (Table 5.1.1). They report anannual runoff of 545 mm in Endalen/Isdammen, 539
mm/year in De Geerdalen and 1050 mm in Bayelva
for the period 1990–2001, which is much higher

80

than the observed precipitation at the closest observation stations, Svalbard Airport and Ny-Ålesund.
Observed annual runoff refers to the period within ayear with reliable observations, normally the icefree period from June to October. Glacier melt contributes about 450 mm/year. Average annual evaporation was estimated to approximately 80 mm per
year from glacier-free areas. Comparing with the81CLIMATE IN SVALBARD 2100

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Bayelva

De Geerdalen

Longyearelva

All of Svalbard

Bayelva

De Geerdalen

Longyearelva

Observation record

1989–2017

1991–2017w––

Evapotranspiration1

mm/year

-40

-70

-

Catchment area [km2]

31.1

77.7

22

60909

Net glacier contribution1

mm/year

450

450

-

Fraction of glaciers [%]

50

10

30

57

Simulated areal precipitation1

mm/year

890

550

-

Mean altitude [m a.s.l.]

265*

409*

495

375

Measured point precipitation2

mm/year

385

189

189

Altitude range [m a.s.l.]

4–742*

41–987*

40–1000

25–1676

Mean annual runoff3

mm/year

1100

530

–

Mean annual flood. QM2mm/day33

2039

Table 5.1.2 Characteristics of the discharge stations Bayelva and De Geerdalen. Longyearelva and all of Svalbard
are included for reference. Information for all of Svalbard are taken from the 1x1 km2 grid (the lowest cell is locatedat 25 m a.s.l.). Information taken from Stenius (2016), except data marked with a *, taken from Haagmans (2018).

Mean annual flood. QM2

m3/s

12

17.6

9.9

Table 5.1.1 Water balance estimates at Bayelva, De Geerdalen and Longyearelva. Information taken from Killingtveit, 2003 (1, reference period: 1990–2001), Stenius, 2016 (2, reference period: 1961–1990) and this report (3,reference period: 1989–2017 for Bayelva and 1991–2015 for De Geerdalen). The mean annual flood is the mean of
the maximum daily runoff each year.

results reported by Jania and Pulina (1994) from theHornsund area in southern Spitsbergen, Killingtveit
et al. (2003) found that both the measured runoff
and the precipitation estimates in their own study
in central and north-western parts of the island wasconsiderably lower. Sand et al. (2003) did a studyof regional snow distribution on Spitsbergen, whichindicates that the Hornsund area receives approximately twice as much precipitation during the snow
accumulation period (October–May) as the central region. This corresponds well with the resultspresented in Chapter 5.2 (Figure 5.2.2). Several
studies in Svalbard have documented that discharge
from groundwater springs, e.g. the water supply
reservoirs for Ny-Ålesund, may provide a contribution to runoff and surface water (e.g. Haldorsen and
Heim, 1999). The permafrost forms an impermeable barrier for subsurface water flow, but artesiangroundwater may develop in lower areas and form
pingos (Humlum et al., 2003) which traditionallyhave been used as a source of water supply in Polarregions.
Killingtveit et al. (2003) concluded that areal precipitation for these catchments is twice the measured precipitation at the closest meteorologicalstations (Table 5.1.1). This difference is partly due

82

to undercatch in the precipitation gauges (Chapter
4.3.1), and partly that the precipitation stations are
not representative for the whole catchment area,
which extends up to higher elevations. Precipitation
increases with elevation (Figure 2.1.2b, Killingtveit
et al., 2003), and thus areal precipitation including
high altitudes will tend to be higher than at measuring sites near sea level.
5.1.2 Annual and seasonal runoff in the present
climatethe months from June to September. In the autumn,
all rivers freeze completely, except short reachesof rivers fed by springs or in front of some glaciers (Petterson, 1994). A major part of the runoff
is caused by snowmelt in early summer. After this,runoff is fed by rainfall and melting of glacier ice(Sund, 2008).
From October to June, discharge values are set tozero because the rivers freeze and the loggers may
freeze. Runoff caused by heavy rainfall events inthis part of the year are therefore not captured, andthe number of rain-on-frozen-ground events has
increased (Chapter 4.3.2).
Hydrological model simulations show that thelargest annual runoff rates are located in the west-

ern lowland regions, particularly in the southwest(Figure 5.1.2). This pattern resembles the precipitation pattern shown in Figure 2.1.2b, with the highest
runoff rates in the western part of the archipelago.
However, the largest runoff rates are found at lower
elevations than the maximum precipitation, because melting is more efficient at lower elevations.
The lowest runoff rates are located in the easternhigh-altitude regions where temperatures are too
low for melting and in sheltered valleys receiving
the smallest precipitation amounts (Figure 4.3.2).
A majority of the annual runoff is generated in thesummer, whereas a smaller fraction of the annual runoff occurs in the autumn (especially in thesouth), and almost no runoff is generated in thewinter and spring, except at the coast (not shown).

The following analyses are based on observations
from two active discharge stations in Svalbard:
Bayelva and De Geerdalen. Both stations have shortobservation records (1989–2017 for Bayelva and1991–2015 for De Geerdalen). Key characteristicsof these stations are summarised in Table 5.1.2 andfurther elaborated in Chapter 3.2. Bayelva receives
about twice as much precipitation as De Geerdalen.
Note, however, that the observed annual precipitation is less than the observed annual runoff as pointed out in Chapter 3.1.1 and by Stenius (2016).
The average seasonal runoff in Bayelva and De
Geerdalen for the observation periods is presented
in Figure 5.1.1. River runoff mostly occurs during

Figure 5.1.1 Seasonal cycle at Bayelva (1989–2017) and De Geerdalen (1991–2015).

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Box 5.1 Downscaled and bias-adjusted temperature - effects on runoff estimation.

The primary meteorological variables from the Arctic CORDEX regional climate models
were downscaled and bias-adjusted prior to the hydrological modelling. Original climate
model outputs often contain biases (Koenigk et al., 2015). If the biased data are useddirectly in the hydrological modelling, the model responses will inevitably be different
from what has been observed in the past and present climates. Altered precipitation andtemperature patterns induced by model biases can for example affect snow accumulation and snowmelt patterns and consequently runoff generation processes. The simulatedhydrological regime and seasonal flow patterns may differ significantly from observed
patterns, and this mismatch will also have an effect on the simulated hydrological response to climate change.
As explained in Box 4.1, a majority of the Arctic CORDEX temperature projections arecolder than observed temperatures in the control period. This characteristic is usuallymost pronounced in the winter. Some of the projections therefore most likely overestimate the temperature change signal towards the end of the century and this overestimation is more or less preserved after the bias-adjustment. Figure B5.1 shows how the coldest models before bias-adjustment end up being the warmest models after the procedure
with winter temperatures well above zero. Figure 4.1.5b) illustrates that the high Arctic
CORDEX ensemble with substantially fewer members are warmer than the more robustand larger empirical-statistical downscaling ensemble.
This overestimation of the temperature increase leads to for example too few snow days
and too much glacier melt in the hydrological model. In particular, glacier melt is overestimated in the simulations driven by two of the EC-EARTH-based projections, which inturn results in too much runoff. The way glaciers are represented in the model also tends
to give more melting. With a spatial resolution of 1 km, glaciers in the model melt at
the same rate for the whole grid cell. In reality, however, most of the glacier melt would
occur in a limited region at lower elevations.

Figure B5.1 The Arctic CORDEX and CCLM projections of winter (DJF) temperatures for Svalbard underemission scenario RCP8.5. The solid black line shows results for the Sval-Imp. The dashed brown line indicates the median of the Arctic CORDEX ensemble. Upper panel: original Arctic CORDEX results. Lower panel:bias-adjusted Arctic CORDEX results.

84

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5.1.3 Runoff development in Svalbard in the
instrumental period
The mean annual runoff simulated for the whole of
Svalbard is 700 mm/year for the period 1980–2015(Figure 5.1.3). Annual runoff has increased in thehistorical period, as shown in Figure 5.1.3, bothaccording to the observations at Bayelva and the
HBV simulations for the Svalbard archipelago
driven by the Sval-Imp data (Chapter 3.1). Measurements at De Geerdalen do not show any increase.
Over the longer period 1971–2017, precipitation
increased significantly in Ny-Ålesund near Bayelva,
while there was no significant change at Svalbard
Airport near De Geerdalen (Table 4.3.2). Due tosparse measurements, the runoff development in
Svalbard is inferred from the HBV simulations forthe period 1980–2015 (Table 5.1.3). Linear trends
for all of Svalbard show a statistically significant
increase in the annual runoff, and for all seasons
except spring. Precipitation measurements tend toshow negative trends in spring and summer andpositive in autumn and winter though few trends
are significant (Table 4.3.2). Further, the fraction of
precipitation as rain has increased (Figure 4.3.4).
The increased runoff in winter and autumn are thus

partly attributed to increased precipitation as rainover the period. In the summer and annually, the increased runoff is likely mainly caused by enhanced
glacier melt.
5.1.4 Projected runoff towards 2100
The hydrological projections show that the runoff
in Svalbard will increase during the 21st century(Figure 5.1.4). For all of Svalbard, the future annual
runoff is projected to gradually increase, relative
to the present climate (1971–2000). Runoff in thetwo scenarios follow each other closely towards
the middle of the century, after which the projected runoff in RCP8.5 increases more steeply thanin RCP4.5. These results are expected, because
RCP8.5 represents a stronger warming (Figure
4.1.4, 4.1.5) and a stronger increase in precipitation(Figure 4.3.6) than RCP4.5.
The maximum projected changes in annual runoff
are likely overestimated because the glacier melt
is overestimated in the hydrological model (Box5.1). Runoff simulations are highly sensitive to thetemperature input and the representation of glaciersin the hydrological model, because they control

Figure 5.1.3 Observed annual runoff at the discharge stations and for the whole of Svalbard, based on the gridded
HBV simulations forced with the Sval-Imp data.

1980–2015

Svalbard [mm/decade]AnnualWinterSpringSummerAutumn

83

4

0,8

57

21

Table 5.1.3 Linear trends [mm per decade] in annual and seasonal runoff, as a median of the HBV simulations driven by the Sval-Imp data. Bold types: Significant trends (5% level).

Figure 5.1.2 Annual runoff (absolute values in mm/year) averaged
over 1971–2000 based on theensemble median of eight HBVsimulations driven with downscaled Arctic CORDEX RCPs forthe control period.

86

meltwater contribution during the period of meltdown of glaciers. The glacier mass balance in
Svalbard is expected to decrease towards the end of
this century (Möller et al., 2016; Chapter 6). Someof the bias-adjusted Arctic CORDEX ensemble
members (input to the HBV hydrological model) are
assumed to have too high temperatures (Box 5.1),
which likely leads to highly overestimated glaciermelt. The result is therefore a too high contributionof glacier melt to the projected annual runoff estimates for these ensemble members (Figure 5.1.4).
A clear change in seasonality compared to the present climate is visible for all of Svalbard in Figure5.1.5. The winter runoff increases for RCP8.5 in

the period 2071–2100 (red curve; due to higher
winter temperatures and more rainfall events in thewinter) and the frost-free season extends by several
weeks (runoff starts earlier and lasts longer). The
combined effect of higher summer runoff and higher
winter runoff explains the strong increase in annual
runoff towards the end of the century.
For Bayelva (Figure 5.1.6) and De Geerdalen (notshown), the projected runoff peaks around the middle of the century, and decreases thereafter becauseof a reduced contribution from glacier meltwater.
These two catchments lie at lower elevations than
Svalbard as a whole, so glaciers disappear in themodel and the modelled contribution from glacier87

CLIMATE IN SVALBARD 2100

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melt reduces considerably towards the end of the
century. Because of too high temperature input inparticular for the high-end ensemble members, themodelled runoff is likely too high and likely peaks
too fast.
For Bayelva, the seasonal runoff cycle reveals thatthe runoff increase towards the middle of the century is caused by increased runoff in the winter, spring
and autumn (Figure 5.1.7, brown dotted curve). Towards the end of the century (red curve), the runoff
peak is much reduced, the peak flow occurs earlier
in the spring, and the winter runoff is sustained at

a higher level. The small peak in the beginning of
the summer is likely caused by snowmelt from asmaller snow reservoir that melts completely at the
beginning of the summer. Under RCP8.5, the snow
storage decreases and the length of the season withsnow covered ground will shorten (Chapter 5.2.2.
and Figure 5.2.4). By the end of the century, summer runoff is to a lesser degree caused by glaciermelt, as seen as a runoff reduction throughout
summer, when temperatures are at their highest andthe potential for glacier melt is highest. A similar
pattern is visible for De Geerdalen (not shown).
Figure 5.1.5 The seasonal runoff distribution for all of Svalbard for the period 1971–
2000 versus 2031–2060 and 2071–2100.
The future projections are given for theintermediate and high emission scenarios
RCP4.5 and RCP8.5.

Figure 5.1.4 Mean annual modelled runoff for the total land area in Svalbard, simulated using the HBV model forced
with downscaled and bias-adjusted Arctic CORDEX data (top). The solid red line shows the ensemble median ofeight models; the band outlines the 25th and 75th percentile, whereas the dotted lines indicate the minimum andmaximum. The pink colour refers to RCP8.5 and the blue colour refers to RCP4.5. The runoff values are given as thedeviation (%) from the reference period 1971–2000. The low-pass filtered series are smoothed by Gaussian weighting coefficients, and show variability on a decadal time scale. For the period 1971–2000, the simulated mean annual
runoff for Svalbard is 595 mm/year.

88

Figure 5.1.6 The mean annual modelled runoff for Bayelva given as the deviation (%) from the reference period
1971–2000. The 25th and 75th percentile of the RCP8.5 projections are given as the shaded area. The maximum
and minimum projections are shown as dotted lines. The observed (1989-2017) mean annual runoff for Bayelva is1100 mm/year.
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Figure 5.1.8 Projected mean annual runoff changes[mm] in the HBV simulations (RCP8.5) for the time
period 2071–2100 compared to 1971–2000. The reason
why large-scale squares are visible in the figure is explained in Box 5.2.
Figure 5.1.7 The seasonal distribution of
Bayelva for the period 1971–2000 versus
2031–2060 and 2071–2100 for the median
RCP8.5 projection.

Across Svalbard, the hydrological model simulations show an increase in the annual runoff, particularly at high elevations (Figure 5.1.8). This increase
is attributed to increased precipitation (Table 4.3.4)
and enhanced glacier melt. The future projections
indicate that the glaciers will gradually decrease inmost areas (negative mass balance; Figures 6.6.2
and 6.6.3). The maximum annual snow storage(snow water equivalent) will decrease, but not in thehigh elevation areas in the northeast (Figure 5.2.5).
Although the annual runoff will increase acrossSvalbard, the seasonal runoff changes vary (Figure
5.1.9). The smallest increases occur in the winterand spring and the largest increases in the summer.
A decrease is seen in the lowlands because thesnowmelt peak is shifted from June to May (Figure
5.3.3) and evapotranspiration increases. Glacierised
regions will experience a strong increase in thesummer runoff, whereas glacier-free areas will experience a decease in the runoff. The autumn runoff
will increase due to increased precipitation andglacier melt, except in the lowlands.

90

Too much glacier melt may give too high values ofrunoff in Figures 5.1.8 and 5.1.9, but the spatial pattern is reasonable. Runoff changes are very sensitive
to glacier changes, and the limited knowledge about
how temperature and glaciers will develop throughout this century fosters a large uncertainty.
5.1.5 Floods
The flood estimates for Svalbard are highly uncertain, due to the factors described in Chapter 9.2.
Time series of the mean annual flood (mean of
the maximum daily runoff each year) in Bayelva
and De Geerdalen are shown in Figure 5.1.10. The
largest observed flood in Bayelva was recorded 11
September 1990, with a daily mean of 32 m3/s (91mm/day). The largest observed flood in De Geerdalen was recorded 20 July 2000, with a daily meanof 30 m3/s (33 mm/day). Estimated flood values forthese catchments are given in Table 5.1.1 (Stenius,
2016). The relatively short periods of discharge
observations (23 and 29 years, respectively), render
the analysis uncertain.

Box 5.2 Why are large-scale squares visible in the model figures?
Although the hydrological model results are produced using fine model grids, some of the hydrological results are poorly resolved due to poorly resolved input data. For example, in Figures 5.1.8
and 5.1.9, large-scale squares showing less runoff are visible in central Svalbard. As explained in
Chapter 3.2, temperature and precipitation were regridded from Arctic CORDEX with a spatial
resolution of about 50x50 km2 to a spatial resolution of 1x1 km2. Figure B5.2 shows how Svalbard is represented at this coarse-scale in three different RCMs. The RCM data are re-gridded
to 1x1 km2 resolution simply allocating the 50x50 km2 grid cell value to the 1x1 km2 grid cell.
The bias-adjustment is then performed cell-by-cell on the 1 km scale using the Sval-Imp data as
reference. Some grid cells take information from a RCM land cell, others take information from amodel cell that represents ocean in the RCM. For example, the grid cell in central Svalbard (covering Pyramiden in its lower left corner), is generally colder than the surrounding land cells inmost RCMs, resulting in less runoff for that coarse-scale grid cell. The resulting adjusted data set
therefore outlines the land mask of the coarse-scale RCMs.
This adds to an already large uncertainty caused by a range of factors (Chapter 9).

Figure B5.2 Land masks for Svalbard in SMHI-RCA4 (left), DMI-HIRHAM5 (middle) and MGO-RRCM(right). Red marks land gridcells in the RCM and blue marks ocean gridcells in the RCM.

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Figure 5.1.10 Historical annual maximum streamflow at the two runoff stations in Svalbard.

glaciers and reduced glacier ice volumes will reducethe contribution from glacier ice melt to runoff andfloods. In Bayelva, De Geerdalen and Longyearelva, the simulated runoff peaks in the middle of the
century whereafter the contribution from glaciermelt is reduced. For Svalbard as a whole, the glaciermass is so large that simulated annual maximum
runoff continues to increase towards the end of the
century.
Figure 5.1.9 Projected mean seasonal runoff changes [mm] in the HBV simulations (RCP8.5) for the time period
2071–2100 compared to 1971–2000. Absolute values in mm/year. Note the different scales. The reason why largescale squares are visible in the figure is explained in Box 5.2.

Changes in the frequency and magnitude of floodsin Svalbard are strongly linked to changes in precipitation, snow storage and glacier regimes. In general, a reduced snow cover leads to smaller snowmelt floods, while increased precipitation and anincreased fraction as rain, will increase rain floods
and possibly also combined snowmelt and rain
floods. The snow storage is projected to decrease,especially at low altitudes, under RCP8.5 and by92

the end of the century. This will cause snowmelt
floods to become less frequent and rainfall floods tobecome more frequent. In addition, the maximum
snow storage is shifted several weeks earlier in thespring, leading to an earlier snowmelt runoff maxima (Figure 5.2.3). High runoff and floods may
occur when heavy rainfall coincides with strong
glacier melt in the summer, particularly in periodsof negative mass balance. Eventually the retreat ofThis section is based on observed runoff in the
frost-free period because icing makes winter measurements unfeasible. In recent winters, periods ofmelting and intense rainfall have been reported, forexample 26 January–9 February 2012 (Hansen etal., 2014), 13–14 January 2018 and 27–28 February
2018 (Isaksen, K. pers. comm.). If the rainfall intensity is small relative to the snow storage, the snow
will absorb the rain, however, if the rainfall intensity is large relative to the snow storage, this rain water will drain and will subsequently freeze (Peeters
et al., 2018). With continued winter warming, run-off is expected to increase, but it is not possible toquantify this increase without reliable observations.
In addition to inundation, intense rainfall in wintercauses other consequences, such as slush flows andground icing (Hansen et al., 2014).
These results are highly sensitive to the temperature
input and glacier melt. Due to an increase in glaciermelt, rainfall (including the fraction of precipitation
as rain) and more intense snowmelt, an increase in
the mean annual flood is expected in many rivers,
until the contribution from glacier melt becomes
negligible. However, it is difficult to quantify the
expected increase because of sparse data and uncertain model results.
In a glacial landscape, meltwater may temporarily
be dammed by glacier ice or glacier debris (ice orsoil and rock). When the dam fails, the glacial lakedrains in a so-called glacial lake outburst flood(GLOF). Glacier-dammed lakes are common in
Svalbard (Liestøl, 1975), many of them drain

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periodically (T. Schuler, pers. comm.). Today,none of these glacial lakes are known to be locatedupstream of settlements or infrastructure (T. Schuler, pers. comm.). With continued warming, there isreason to believe that glacial lake outburst floods

5.2 Snow
5.2.1 Historical snow conditions
Snow cover is an important indicator of climatechange in Arctic areas, since it is controlled by bothtemperature and precipitation. Changes in the snow
cover are important for both the climate system andhydrological processes. At Svalbard, the snow isa dominant land cover for more than 7–8 months
in the non-glacierized areas in the present climate(Figure 10.2.4, observed snow cover duration in
Longyearbyen). Changes in the snow cover is aconsequence of multiple environmental drivers andfeedbacks (Brown et al. 2017 and references therein). These drivers include changing atmosphericcirculation, warming, increased moisture availability, increased frequency of rain-on-snow events andwinter thaws. Interaction between these drivers andfeedbacks result in changing spatial, temporal andseasonal snow cover (extent, snow amounts andsnow properties). Both the observed and projectedchanges in the snow cover, described in this report,
support the findings described for the entire Arctic
regions (Brown et al. 2017).
The modelled snow season duration (Chapter
3.2.3) for the entire Svalbard has decreased overthe period from 1958–2017 with approximately 20
days, from 340 to 320 days (Figure 5.2.1, right).
Low altitude and coastal areas have the shortest
snow cover duration with less than 300 days per
year (Figure 5.2.2, right). Northwestern and easternparts are snow covered more than 300 days per year,
particularly higher altitudes and glaciated areas. In
Van Pelt et. al (2016) snow onset and disappearance dates were computed for the entire Svalbard
archipelago between 1961 and 2012. They foundthat the snow onset date shifted to a later start due
to autumn warming. The snow onset was delayed
by up to 5 days per decade. However, they did not
find significant trends for changes in snow disap-

94

will become more frequent, but GLOFs will notnecessarily affect settlements.

pearance dates. Particularly, in the low-lying valleys
in central Svalbard, the snow melted on averagein the beginning of June. These areas are typically
low-precipitation and high-temperature areas. Van
Pelt et al. (2016) also found a general trend of earlier snow disappearance in central Svalbard (minimum −1 day per decade) and later disappearance inthe east and north of Svalbard (maximum 4 days per
decade). Because of the similarities to the precipitation maps, Van Pelt et al. (2016) argue that trends insnow disappearance are to a larger extent controlled
by trends in winter snowfall rather than trends inspring and summer melt. Still, the combined effectof changes in onset and disappearance dates resulted in a slightly shorter snow season duration in the
period from 1961 to 2012. In the simulations withthe seNorge model (Chapter 3.2.3), the definitionof a snow day is a day with more than 20 mm snow
water equivalent. This is a conservative definition,and is a better representation of good skiing conditions than the meteorological definition of snow
available on the ground. There are often strong
winds at Svalbard. Therefore, the modeled snow
water equivalent is larger than the observed local
snow amounts at weather stations. The wind blows
the snow away, partly to the sea, and partly as redistribution on land. However, this effect is not taken
into account with the seNorge snow model. We
assume the snow to be redistributed within the 1x1
km2 gridcells. The model probably overestimates
the precipitation, which also contributes to too highsnow storage estimates.
The maximum annual snow storage for Svalbard
has slightly increased over the period from 1958 to2017 (Figure 5.2.1, left). However, there are verylarge annual variabilities. Figure 5.2.2 (left) shows
the mean annual maximum snow storage for thereference period 1971–2000 for the entire Svalbard, and the three subregions. The snow storageis largest in western, high-altitude regions with up

to 1000-1200 mm mean annual maximum snow
water equivalent. The snow storage is continuously
accumulating in autumn, throughout the winter andspring with maximum snow amounts in June (Figure 5.2.3, left). This pattern is very similar for all
regions. The mean annual maximum snow storage
map shows a strong similarity to the mean annual
precipitation map (Figure 2.1.2b), because it
represents the cumulative snow accumulation foreach snow season. The presented results are in linewith the results published by Van Pelt et al. (2016),
who also found that the number of days with snow
cover was decreasing, while the maximum annual
snow storage was increasing in the period from1961 to 2012.

5.2.2 Future snow conditions
Maximum annual snow storage and mean annual
snow cover duration, have been computed for thetwo periods 2031-2060 and 2071-2100. The simulations are carried out using the seNorge snow
model forced with the Arctic CORDEX simulations,
RCP4.5 (5 simulations) and RCP8.5 (8 simulations)(described in Chapter 3.2.4). Additionally, the snow
simulations are computed using the CCLM data,
RCP8.5 for the period 2071–2100. For the Arctic
CORDEX snow simulations, the ensemble medianfor the two 30 year periods are presented.

Figure 5.2.1 Trends in maximum annual snow storage (mm snow water equivalent) (left), and trends in snow cover
duration (number of days with swe>20 mm) (right), for the period from 1958 to 2017.

Figure 5.2.2 Mean annual maximum snow storage (mm snow water equivalent) (left) and mean annual snow cover
duration (number of days with snow per year) (right) during the reference period 1971-2000 based upon the
Sval-Imp dataset.

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is shown in Figure 5.2.5. The CCLM simulation ismore similar to the Arctic CORDEX RCP4.5 thanthe Arctic CORDEX RCP8.5. The CCLM simulation shows increased amounts of snow storage(up to 100% for certain areas) for large areas in
the northeast, and reduced amounts in western andsouthern areas. The reason for this is probably that
the CCLM simulation is predicting a temperaturechange by the end of the century of approximately 7°C, which is similar to the Arctic CORDEX RCP4.5
median (Table 4.1.5). The Arctic CORDEX RCP8.5
median predicts an annual temperature increase ofalmost 10 °C, with up to more than 14 °C (median)temperature increase in the winter (Table 4.1.5).
Figure 5.2.3 Mean monthly maximum snow storage (mm snow water equivalent) aggregated for Svalbard and thesubregions during the reference period 1971-2000 (left) and the future period 2071-2100 (right). The reference
period is simulated using the Sval-Imp as input, while the future period is simulated with forcing data from the 2.5 km
CCLM model, following emission scenario RCP8.5.

2031–2060
Results for the period 2031–2060 show that maximum snow storage for RCP4.5 resembles RCP8.5,
but RCP4.5 shows slightly more areas with increasing snow amounts than RCP8.5. This is expected
as RCP8.5 is a warmer emission scenario. For bothemission scenarios, higher altitudes in the northwestern and eastern areas will probably get moresnow amounts compared to present climate, whilemost other lower located and coastal areas will get
less snow amounts.
For the snow cover duration, the RCP4.5 also
resembles RCP8.5. Both emission scenarios show
a decrease in the annual number of snow days. The
smallest reductions are expected in the northeasternand northwestern regions, and particularly at thehigh elevation sites. The largest decrease in snow
cover duration is predicted for the low-elevatedsites, and along the western coast.
The predicted changes in snow storage and snow
cover duration is an effect of changes in predicted
temperature and precipitation (Chapter 4 and Box

96

For the snow cover duration, the simulations showthat the number of days with snow will change
from slightly reduced to drastically reduced all overSvalbard, for all the simulations, including both the

Arctic CORDEX runs (RC4.5, 8.5, median) and
CCLM. The main difference is that the Arctic CORDEX RCP8.5 simulations show a much stronger
decrease in the snow season duration than RCP4.5.
With increasing winter temperatures, the snow line
will increase, and precipitation as rain will be more
frequent below the snow line.
Another change is the timing of the maximum snow
storage (Figure 5.2.3, right). The present climate
maximum snow storage is in June, while the CCLM
simulation shows that the maximum period is shifted to May (one month earlier), for Svalbard and its
subregions, except the southern subregion which
will have maximum storage in April (two months
earlier).

5.1). From the present climate to the future period
2031–2060, the precipitation is expected to increase
with 28% for RCP4.5 and 34% for RCP85. In thesame period the temperature is expected to increase
with 4–5 °C (Arctic CORDEX median, RCP4.5 and
8.5). A temperature increase in the winter season
leads to a shorter snow season (later start and earlierend) in areas that have the highest winter temperatures in the present climate. However, the increased
amounts of precipitation that are predicted are probably the reason for an increased snow storage at thehigher elevated sites.
2071–2000
The results for the simulations of snow storage andsnow cover duration for the period 2071–2100 are
shown in the Figures 5.2.4–5.2.7. The maximum
snow storage is strongly different comparing the
RCP4.5 and 8.5 (median for the Arctic CORDEX
simulations), as a contrast to the period 2031–2060.
The simulations with RCP4.5 still show areas withslightly the same or more snow storage, while thesimulations with RCP8.5 show less snow amounts
over most of Svalbard, with 50–100% less snow inmany places. An estimation with CCLM RCP8.5

Figure 5.2.4 Relative changes in maximum annual snow storage from 1971–2000 to 2071–2100 (%), for RCP4.5(left) and RCP8.5 (right). The simulations show the ensemble median for the bias-adjusted Arctic CORDEX runs (1km spatial resolution).

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5.3 Erosion and sediment transport

Figure 5.2.5 Changes in maximum annual snow storage
from 1971–2000 to 2071–2000 (%). The simulations use
the 2.5 km CCLM model as forcing data, following theemission scenario RCP8.5.

Figure 5.2.6. Changes in snow season duration (annual
number of snow days) from 1971–2000 to 2071–2100, for
RCP4.5 (left) and RCP8.5 (right). Simulations show theensemble median for the bias-adjusted Arctic CORDEX
runs (1 km spatial resolution).

Figure 5.2.7 Changes in annual snow season duration(mean annual number of snow days) from 1971–2000 to
2071–2000. The simulations use the 2.5 km CCLM model
as forcing data, following the emission scenario RCP8.5.
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types of erosion. More short-term measurements
in other parts of Svalbard have provided estimatesof specific sediment yields ranging from 28 to 38 t
km-2 year-1 in non-glacierized catchments. In glacierized catchments, much higher specific sedimentyields, in the range of 303 t km-2 year-1 to 2900 t
km-2 year-1, have been reported; Kostrzewski etal., (1989); Krawczyk & Opolka-Gadek (1994);
Barsch et al., (1994); Hodson & Ferguson, (1999),
Hodgkins et al. (2003) Lepkowska and Stachnik(2018). From seismic stratigraphy and sedimentcores, Elverhøy et al. (1995) did estimate the Late
Quaternary sediment yield of the glaciated areain the catchment of Isfjorden in Svalbard to 800 t
km-2 year-1.The sediment yield from the non-glacial
part of the catchment was estimated to 35 t km-2
year-1 for the Holocene. The measurements thus
indicate that the major part of the sediment in the
In Svalbard, sediments derived from glaciers andtheir moraine deposits are the most important source rivers of Svalbard is derived from the glaciers andtheir moraine areas. However, erosion rates may
contributing to the sediment load carried by therivers. The results from long-term measurements of increase considerably during large flood events
caused by extreme rainfall. Such events may trigger
sediment transport of various rivers based on water
discharge and sediment samples have been reported debris slides and debris flows that may erode andsupply to the rivers sediment that have accumulated
by Bogen and Bønsnes (2003), Table 5.3.1.
from weathering processes over a long time period.
Other processes like bank erosion are more rare in
The sediment yield was measured to 586 t km-2
Svalbard, but known to be relatively intensive in
year-1 for the glaciers and their moraines in theareas of frost (Gatto 1995, Sidorchuk and Matveev,
catchment of river Bayelva and a relatively smaller
1994). Kanevskiy (2016) did measure extremely
volume of 82.5 t km-2 year-1 from weathering andhigh rates of riverbank erosion, up to 19 m/year infrost shattering of bedrock in the unglaciated river
ice rich permafrost in northern Alaska. In Svalbard,
Londonelva. Measurements from other rivers in
Svalbard are within the same order of range for both at the lower part of the river fan where Todalen
About 40 % of Svalbards area is outside the glaciated area and is drained by rivers. The stability of thedifferent river reaches are dependent on hydraulicvariables like water discharge, sediment discharge,
grain size and slope and the supply from upstream
erosion in the catchment. To analyse the impact
of climate change on the sediment flux in rivers in
Svalbard, a sediment budget equation may be used.
This equation expresses the sediment transport on aparticular reach as a sum of the delivery from various sources plus or minus the possible storage in
upstream channel reaches (e.g. Jordan and Slaymaker, 1991). The sediment budget may be evaluatedover a long term or short-term period. The latter one
is probably the most important in the management
plan of rivers affecting settlements.

Specific annual sediment yield
Glac.

Grain size/concentration

Spec yield
Mean annu% al transport Spec. Yield glacier (t/ Mean grain Max (mg/l)
size(t/year)(t/km2year) km2year)

Mean(mg/l)

Monitoring
stationArea (km2)

Bayelva

30.9

55

11104

359

586

0.014

11446

305

Londonelva

0.7

0

58

83

0

0.04

5343

162

Endalselva

28.8

20

8102

281

1077

0.021

9043

554

Table 5.3.1 Suspended sediment yield in selected rivers in Svalbard

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meets Adventdalen, Matsuoka et al. (2018)carried out long-term studies of ice wedge
formation. Heavy bank erosion of the terrace occur
in this area due to reduced shear strength caused byhigh water content from melting ice wedges.
Water and sediment discharge from the catchments
is affecting the equlibrium form of alluvial channels, and the channel forming bankful discharge
event has an average of 1.5 years in a wide rangeof rivers. Longer return periods for bankful events,
up to 20 years, have been found in arctic rivers. As
the water discharge is expected to increase in the
future, increased channel erosion will occur as thechannels adapt to a new climate. This effect has
been observed downstream from culverts associatedwith road constructions in Svalbard and elsewhere(Husebye et al., 1993). The lack of data from higharctic alluvial streams, however, limits a complete
understanding of how arctic geomorphic systems

might change in a shrinking cryosphere(Mc Namara and Kane, 2009).
A warmer climate will affect the erosion intensity and increase the water discharge and sedimentsupply to the rivers. Measurements indicate that thelargest volumes of sediments are delivered fromthe glaciers and their moraine areas. The presenceof permafrost limits the erosion. However, as thetemperature of the active layer rise, more sediments are available for transport (Etzelmuller andFrauenfelder 2009). The expected increase in thefrequency of debris flows and debris slides will also
contribute to the volumes of the sediment loads. The
increased sediment supply may increase channel
splitting and lateral activity of channels and expose
adjacent slopes to erosion. This accelerated erosion
may affect the tributaries first, and after some time,
the large sandurs in the main valleys.

6. Glaciers
6.1 Holocene glacier change
We can use the ubiquitous traces left by glaciers in
the landscape to study glacier-climate interactions
on Svalbard prior to the observational period -which
didn`t begin until the second half of the 19th century. We are largely restricted to the Holocene epoch(11,700 years BP until present), as Late Weichselian
ice sheets removed most evidence from previous
glacial periods (Landvik et al., 2013). Researchers
have primarily relied on three lines of evidence tounravel the terrestrial Holocene glacier history of
Svalbard: moraines, vegetation kill dates and lake
sediments. Moraine ridges, thrusted up by advanc-ing ice, provide snapshots of past glacier dimensions when dated. The same holds true for fossil
vegetation, killed and entombed in situ by overriding non-erosive (cold-based) ice. Radiocarbon
dates on such plant remains constrain the timing ofglacier margin advances. Sediments in glacier-fed
lakes may yield continuous records of Holocene
glacier change. The rationale behind this approachbuilds on the link between size and erosion rates
in alpine (erosive) glaciers: variations in evacuatedglaciogenic lacustrine sedimentation capture relative variations in glacier size (e.g. Karlén, 1981).

Figure 6.1.1. A compilation of Holocene glacier reconstructions from Svalbard, modified after De Wet et al. (2017).
Blue fields indicate prolonged periods of glacier activity. Included records derive from (north) west Spitsbergen:
Karlbreen and Hajeren from the Mitra peninsula (79°N, 11°E) (Røthe et al., 2015; van der Bilt et al., 2015), Longyearbreen (78°N, 15°E) (Humlum et al., 2005), Linnébreen and the west-basin cirque in Linnédalen (78°N, 14°E),
Annabreen on Amsterdamøya (79°N, 11°E)(De Wet et al., 2017), and Vårfluesjøen (79°N, 15°E)(Røthe et al., 2018).
100

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Svalbard’s Holocene glacier history was first established by analysing lake deposits in the glaciatedcatchment Linnédalen, in western Spitsbergen.
Using radiocarbon-dated sediments from glacier-fed
Lake Linnévatnet, Svendsen and Mangerud (1997)
identified a three-phase Holocene glacier history.
Following deglaciation after 12,000 years BP, local
glaciers disappeared during the Early Holocene
around 9,700 years BP (Fig. 6.1.1). Conditions subsequently remained unfavourable for glacier growthduring the Middle Holocene period (~8-4 ka BP).
Finally, glaciers reformed during the Late Holocene(Neoglaciation) and reached their historical maxima when the Little Ice Age (LIA) culminated in
the 19th century. Werner (1993) used lichonometricdating of up-valley moraines to provide independent
evidence for a LIA glacier maximum in Linnédalen.
The outlined pattern of change is consistent with reconstructions of glacier change from other parts ofEurope, and attributed to progressive cooling stemming from a gradual decrease in summer insolation
during the Holocene (Wanner et al., 2008).
Reduced sample size requirements for radiocarbon
dating, the advent of cosmogenic nuclide dating,and high-resolution scanning techniques such as
X-Ray Fluorescence greatly improved the level of(chronological) detail of dated (lake) sediments andmoraines over the last decades. This has enabled us
to refine the Holocene glacial history of Svalbard(Fig. 6.1.1), resolving change over human-relevant(decadal to centennial) timescales. For example,

recent studies (Farnsworth et al., 2017; Henriksen etal., 2014; van der Bilt et al., 2015) have identified aphase of widespread glacier growth in the Early Holocene (~10,000-7,000 years BP), a period marked
by high radiative forcing. In addition, Reusche et al.
(2014), Røthe et al. (2015), van der Bilt et al. (2015)
and Miller et al. (2017) show that the onset ofNeoglaciation was characterized by episodic phasesof glacier growth, instead of a gradual response to
declining summer insolation.
These refinements have not only greatly expanded our knowledge of Holocene glacier variations
on Svalbard, but also sparked interest in resolving
their underlying climatic causes. For example, Van
der Bilt et al. (2016), reconcile the coincidence ofglacier growth and maximum summer insolation
during the Early Holocene by inferring a phase ofterrestrial cooling linked to freshwater forcing frommelting ice sheets (Zhang et al., 2016). Moreover,the findings of e.g. Müller et al. (2012) and Werner
et al. (2015) indicate that sea-ice and surface ocean
feedbacks intermittently amplified radiation-driven Late Holocene cooling, explaining the stepwise
pattern of Neoglacial glacier change. In conclusion,
investigations of past (Holocene) glacier change on
Svalbard yield critical insights about the sensitivity
and drivers of the Arctic climate system, contextualizing the present and preparing us for a rapidlychanging future.

et al (1993), whose dates vary regionally from the
1960s to 1990, with the 2000s inventory (Nuth etal., 2013), where change is averaged for all glaciersin the main drainage basins defined by Hagen et al(1993). The mean area change is -7% for the average 32-year time interval between the inventories,
or ca. -0.2 % per year. While this change is spatiallyvariable, it is notable that there are no regions withan increase in glacier area.
While tidewater glacier fronts are not reliable asclimate indicators, they are nevertheless importantto fjord circulation and ecosystems. Since more thanhalf of Svalbard’s ice area terminates in tidewater
glacier fronts, a significant part of the glacier melt
and rainfall on Svalbard enters the ocean at thesefronts; this has a significant influence on the ocean
circulation, particularly in constricted bays or fjords(Lydersen et al., 2014). In summer, glacier meltwater flows on and within the glacier towards the front,
typically entering the ocean below the seawater

surface, usually at the base of the tidewater front.
The relatively low density of the freshwater forces it
to rise rapidly, entraining large volumes of ambient
fjord water. These freshwater “plumes” breach thesurface, then ﬂow outward towards the mouth of
the fjord, further entraining subsurface water. The
steeper the calving face, the more vigorous is thepotential circulation.
Plumes have two distinct effects on their local
environment. First, organisms that are entrained in
the rising plume will be brought towards the surface, where they will be more easily accessible forsurface-feeding animals. Second, as the plume rises
it will entrain large volumes of ambient water, from
10-100 times the original discharge volume. Thiswater will be drawn from all depths that the plume
passes through, leading to more thorough mixing.
Entrainment ensures a continuous resupply of intermediate depth waters, including zooplankton andnutrients, to the glacier front area.

6.2 Recent glacier front retreat
As stated in Chapter 3, front positions are notnecessarily direct proxies for changes in glaciermass on annual to decadal time-scales. This can beseen in Kongsfjord, one of the best-mapped areasof Svalbard (Figure 6.2.1). The glaciers here were
the first mapped in the late 1800s, and thereafter
at irregular intervals, until after the mid-1970s,
when satellite images became increasingly available. The frontal record shows the clearly episodic
tidewater glacier retreat, with long periods of small
fluctuations or minor retreat from stable positions,punctuated by rapid retreat to new stable positions.
Occasionally there are front advances: following the102

surge of Kongsvegen in 1948, the combined front of
Kronebreen and Kongsvegen re-advanced several
kilometres into the fjord (Figure 6.2.1). Blomstrandbreen has surged three times since the late 1800s(ca. 1910-1930; 1956-1966; 2006-2013; Burton etal. 2016), but as can be seen (Figure 6.2.1), once asurge is over, glacier retreat continues.
Nevertheless, changes in front position, or equivalently, glacier area, can give an indication of thelong-term changes around the archipelago. Figure
6.2.2 (Nuth et al., 2013) shows the relative change
in glacier area comparing the inventory of Hagen

Figure 6.2.1 (a) Front positions of tidewater glaciers in Kongsfjord. (b) Front position along glacier centerlines ofthree of the glaciers, relative to their earliest measurements.

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their submarine bed length since 1860; if the retreat
rate were to continue at the same rate, both glaciers
would terminate on land sometime after 2100. On
the other hand, Blomstrandbreen and Conwaybreen
are both near their submarine limits; they likely willterminate on land much sooner, perhaps within a
few years to a decade.

research on Svalbard takes place near the Polish Polar Station at Hornsund and at the Austfonna ice cap
on Nordaustlandet. Other than the local significancefor hydrology and ecology, the primary, global interest in glacier research is to assess the response of
glaciers to climate change and the associated releaseof water. Change in the land-based ice volume is amajor contributor to ongoing global sea-level rise.

Besides the well-investigated Ny-Ålesund/ Kongsfjord area, long-term (>10 years) glaciological

6.3 Glaciological mass balance
Figure 6.2.2: Relative changes in glacier area, comparing areas from Hagen et al (1993), whose dates vary
regionally from the 1960s to 1990, and glaciers areas forthe 2000s Drainage basins are defined in Hagen et al(1993). Total glacier area shown as size (area) proportional symbols.

Glacier fronts thereby are important feeding areas
for seabirds and marine mammals (Lydersen etal. 2014). In Svalbard, several bird species can befound in large numbers, up to thousands of individuals, at tidewater glacier fronts. The birds are often
found in the centre of the meltwater plume, which
is ice-free due to currents and muddy due to upwelling suspended sediments. Plumes are also foraging hotspots for Svalbard’s ringed seals and white
whales. Additionally, prime breeding habitat for
ringed seals in Svalbard occurs deep in the fjords
where ice pieces calved from the glacier fronts
become frozen into land-fast sea-ice, promoting
accumulation of sufficient snow for female seals to
dig out birth lairs above breathing holes. These seal
pupping areas then become important hunting areasfor polar bears and arctic foxes.
With further warming in the Arctic, we can expect continued retreat of Svalbard’s glaciers, and a
decline in the number of tidewater glaciers around
Svalbard. This will impact fjord circulation once
tidewater glaciers retreat to the point that theyterminate on dry land, with implications for fjord
ecosystems. Outﬂow into the fjord would then occur
only via surface drainage, just as with any unglaciated fjord, with a cap of fresh river water flowing
104

over the denser ocean water. There will be lessnutrients and plankton brought to surface, which
will impact bird, seal, and other animal populations.
There will also be more limited entrainment of fjord
water, resulting in less mixing and retention of lesssaline surface waters, which will in turn expedite
sea ice formation. Furthermore, as glaciers retreat
into narrower and more constricted geometries, withincreasing numbers of subaqueous topographic barriers between the glacier front and the outer fjord,
chances improve for preserving fjord ice throughout the winter season, even as outer fjord waters
become warmer. An increase in inner fjord sea ice
could counteract the negative effects of decreasedfood supply, at least for seal populations that dependon annually formed sea-ice for breeding.
It is not yet possible to forecast accurately tidewater
front retreat in response to changes in the climatic forcing. However, using historic observationsof front positions (Figure 6.2.1), and glacier bed
mapping data which define the extent of the glacier
bed below sea level (Lindbäck et al. 2018), we canestimate that full retreat onto land could occur overtime-scales of up to decades or a century or two. For
example, both Kongsbreen North and Kronebreen(Figure 6.2.1) have retreated through about half ofHere we present and discuss data from the threeglaciers with the longest continuous time-series
records, all in Kongsfjord. Data from the otherSvalbard glaciers with mass balance measurements(Chapter 3) show similar patterns and trends, although they have shorter records.
Austre Brøggerbreen (BRG) and Midtre Lovénbreen (MLB) are both small glaciers, less than 10
km2 in area, and neither is higher than about 650

m a.s.l. Both have had consistently negative netbalances since measurements started in the 1960s(Figure 6.3.1), due to their relatively low-lying andsmall accumulation areas. MLB is less negative thanneighbouring BRG, in part due to its slightly higher
elevation and steeper valley sides, both of which
contribute to more accumulation on MLB. The timeseries for Kongsvegen (KNG) is shorter, starting in
198 (Figure 6.3.1). KNG is a larger glacier, with anarea of 100 km2, and whose elevations extend up to

Figure 6.3.1: Winter, summer and net balance for a) Brøggerbreen, b) Midtre Lovénbreen, and c) Kongsvegen. Bluelines show the total accumulation on the glacier during the winter, converted to an area average thickness of water(metres of water equivalent, m w.eq.); red lines similarly show the net melt over the summer, while black lines are
the sum of the two, or the total mass gained or lost through the glacier surface processes over the year. Trends areshown for the three balance terms. The summed net balance (d) is roughly equivalent to the volume change calculated in the geodetic mass balance.
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850 m a.s.l. Because of the higher elevation, it has asignificantly larger accumulation area, and therefore
its net balance record is more positive than the lower-lying small glaciers BRG and MLB.
On Svalbard glaciers, winter accumulation is lessvariable than summer ablation, the latter of which
provides more of the variability in the net balance,
as can be seen in the longest time-series. (Figure
6.3.1). Finally, while trends for both winter andsummer balances are not statistically significant,the overall tendency over the measurement periods
is for decreased winter accumulation and increased
summer melting. The cumulative sum of the annual
net mass balance, which is roughly equivalent to thevolume change, shows that over the long term, these
glaciers are losing mass (Figure 6.3.1), particularly
the two smaller glaciers.

6.4 Geodetic mass balance
Nuth et al. (2010) compared satellite altimetry datafrom the Ice, Cloud, and Land Elevation Satellite(ICESat) mission for the period 2003–2007 to older
topographic maps and digital elevation models fordifferent epochs (1965–1990). Because the ICESat tracks are relatively sparsely distributed, they
extrapolate along-track changes to the larger regions
using the glacier hypsometry. Nuth et al. (2010) findsignificant thinning at the lower elevations of mostglaciers, and either slight thinning or thickening
in the accumulation areas, except for glaciers thatsurged during the observation period; these glaciersshow thickening in the ablation area and thinning in
the accumulation areas. However, the overallbalance is very negative at -0.36 m w.eq./year,
corresponding to -9.7 Gt/year (Table 6.4.1). Thisestimate excludes Austfonna and Kvitøya, and is averaged over different periods for different geographic regions. The most negative geodetic balances arefound in the south and the least negative balances in
the northeast.
Moholdt et al. (2010) determined elevation changes
along the ICESat tracks for the period 2003-2008,
extrapolating these changes to the remaining glacier area using the same hypsometric approach as
Nuth et al (2010) to yield a Svalbard-wide estimateof -0.12 m w.eq./year, or -4.3 Gt/year. Similarly to

Nuth et al (2010), they find that most regions experienced low-elevation thinning and high-elevation
balance or thickening, and that the largest ice losses
occurred in the west and south, while northeastern
Spitsbergen and the Austfonna ice cap gained mass.
Analysis of older maps and modern DEMs time
(Kohler et al., 2007) of a few selected glaciers inwestern Svalbard shows that the rate at which these
glaciers lost mass appears to have accelerated. For
example, the average thinning rate for MLB, theglacier with the best cartographic data, increased
steadily since 1936. Thinning rates for 2003−2005,
the most recent period analysed, were more thanfour times the average for the first measurement period 1936−1962. On Slakbreen, south of Longyearbyen, thinning rates for the period 1990−2003 weremore than four times that of the period 1961−1977(Kohler et al., 2007). James et al. (2012) found asimilar increase in thinning rates for other glaciersaround Svalbard. This increasingly negative massbalance trend is consistent with both worldwide
glacier trends as well as developments in the Arctic(Kaser, et al. 2006).

6.5 Mass balance modelling
Aas et al. (2016) simulate mass balance for all of
Svalbard over the period 2003-2013 (Figure 6.5.1),
and find a strongly negative average mass balanceof −0.26 m w.eq./year, or -8.7 Gt/year. When the
only available estimate of ice discharge from Svalbard glacier calving (Blaszczyk et al., 2009) is included, the overall Svalbard mass balance becomeseven more negative (Table 6.4.1, Figure 6.6.1).
Østby et al. (2017) simulate mass balance over the
longer period 1957−2014 using a different modeland forcing (Figure 6.5.1) to find that while the total
net balance was slightly positive over the wholemodel period, whereas the trend was significantly negative, with a shift from positive balance tonegative occurring around 1980. The average forthe most recent model period 2004−2013 is strongly
negative at −0.2 m w.eq./year; when the Blaszczyk
et al. (2009) calving estimate is included, overall
Svalbard mass balance is even more negative, −0.39
m w.eq./year (Table 6.4.1, Figure 6.6.1). Further,Østby et al. (2017) find a strong correlation between
mass balance and summer temperature, as observedin the long in situ time-series (e.g. BRG and MLB,see above). This relation is explained by the strong
correlation between mass balance and surface albedo, the latter of which largely controls the amountof available energy for melt.
Finally, Möller and Kohler (2018) use a parameterized mass balance model forced by statisticallydownscaled ERA-20C data to model climatic massbalance for the glacier areas on Svalbard for theperiod 1900–2010; the spatially averaged meanannual climatic mass balance for all Svalbard overthis relatively long period is approximately zeroat −0.002 m w.eq. However, this estimate does notinclude the calving flux, which we cannot estimate
on the century scale, given the significant changes
in glacier geometry that have occurred since 1900.
Möller and Kohler (2018) also find comparable
balances to those reported by Aas et al (2016) andØstby et al (2017) for the shorter periods 2003-2010
and 1957-2010, respectively.

Figure 6.5.1: Cumulative monthly mass balances of Svalbard glaciated area, comparing results from three models:Aas et al (2016), Østby et al (2017), and Schuler (COSMO-CLM model, this report).
106

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CLIMATE IN SVALBARD 2100

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Reference

Period

6.6 Projections for future climate

Specific Bn

Bn(m w.eq./year)(Gt/year)-0.36 ± 0.02

-9.7 ± 0.55

Geodetic-0.12 ± 0.04

-4.1 ± 1.4

Geodetic

-0.26

-8.7

Model

-0.46

-15.4

Model + calving

-0.21

-7.1

Model

-0.41

-13.8

Model + calving

0.08

2.7

Model

-0.12

-4

Model + calving

Method(1965-1990) to (2003-2007)

Nuth et al. 2010

(period varies geographically. and excludes
Austfonna. Kvitøya)

Moholdt et al. 20102003-2008

Aas et al.. 2016

2003-2013Østby et al.. 2017Østby et al.. 2017

2004−2013

1957−2014

Möller and Kohler.
2018

1900-2010-0.002Model

1957-2010

0.03

Model

Wouters et al.. 2008

2003-2008-0.26 ± 0.09

-8.8 ± 3

GRACE

Jacob et al.. 2012

2003-2010-0.09 ± 0.06

-3 ± 2

GRACE

2003-2009-0.27 ± 0.03

-9.1 ± 1.0

GRACE method 1

2003-2009-0.46 ± 0.07

-15.5 ± 2.4

GRACE method 2

2004-2012-0.11 ± 0.09

-3.7 ± 3.0

2004-2008-0.20 ± 0.11

-6.8 ± 3.7

2003-2009-0.20 ± 0.06

-6.8 ± 2.0

GRACE Method A

2003-2009-0.13 ± 0.12

-4.4 ± 4.1

GRACE. IceSat

Mémin et al. 2011

Matsuo and Heki. 2013

Gardner et al. 2013

To simulate future changes in mass balance, we
dynamically downscale projected climate evolution (MPI-ESM-LR, RCP 8.5) for two time slices(1970-2000 and 2070-2100) using the regional
climate model COSMO-CLM, with the gap bridged
using the STARS method (see Chapter 4). A control simulation, forced by ERA interim re-analysis
2004-2017, is conducted to evaluate the qualityof the COSMO-CLM model product; we find thatmass balance evolution in good agreement to thecarefully field-validated results of Aas et al. (2016)and Østby et al. (2017) (Figure 6.5.1). To assess theimpact on glacier mass balance, we determine thechange in mean mass balance of each time slice,
separated by 100 years.
Given the projected temperature increase in the 21st
century, it is not surprising that modelled glacier net
mass balance becomes progressively more negative.
The winter balance does not have a clear trend; thestrong negative trend in net balance is driven entirely by increasingly negative summer balance. That
winter balance is largely unchanged in the future
simulation indicates that any increase in precipitation in a warming climate is compensated for by adecreasing snowfall fraction.
Comparing the two time periods 1971-2000(Figure 6.6.2a) and 2071-2100 (Figure 6.6.2b)shows that the equilibrium line altitude (ELA) rises
by on average 400 m, and that there is a 5-fold increase in mass loss (Figure 6.6.3). There isconsiderable spatial variability in these changes,
with more pronounced ELA increases in the northwest and south of Svalbard than in the northeasternregion (Table 6.6.1, Figure 6.6.4), but due to thelarger glacier area in the latter region, the change
in overall mass loss is strongest there. These dramatic findings are derived from a so-called reference mass balance, which assumes no changes in
the glacierized area or surface elevation. The net
effect of this assumption is difficult to assess sincechanges in glacier area and surface elevation have
opposite effect on the mass balance. The effect ofa smaller area is that mass loss would be smaller
than in the reference scenario, whereas a lower
surface elevation would enhance mass loss, due tothe mass balance-elevation feedback. To overcome
this limitation, the evolution of glacier geometry
changes in response to mass balance changes should
be assessed with a glacier dynamics model. While
such models exist for temperate mountain glaciers,a realistic application to Svalbard glaciers would
require considerable model development to accountfor the complex behaviour of marine-terminating
glaciers as well as for the polythermal structure,both of which are common and abundant features of
Svalbard glaciers.

GRACE

Table 6.4.1. Svalbard-wide long-term net balance using different methods.

Figure 6.6.1. Svalbard-wide long-term net balance 1950-2010s, using different methods (see Table 6.4.1).

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Svalbard

NE

NW

S

(Gt year-1)-14.23

-7.45

-3.43

-3.35

(m w.eq. year-1)

-0.39

-0.32

-0.5

-0.5

(Gt year-1)-87.33-46.77-16.89-23.67(m w.eq. year-1)

-2.38

-2.02

-2.47

-3.52

(Gt year-1)-73.01-39.32-13.46-20.3(m w.eq. year-1)

-1.99

-1.7

-1.97

-3.02

(%)

513

528

392

607

ELA 1971-2000 (m a.s.l.)

653

598

854

731

ELA 2071-2100 (m a.s.l.)

1067

976

Above
terrain

1337

ELA change (m a.s.l.)

414

378

>414

606

CMB 1971-2000

CMB 2071-2100

CMB change
Figure 6.6.2: Mean mass balance (m w.eq. yr-1) simulated for the time periods a) 1971-2000 and b) 2071-2100.

Table 6.6.1: Changes in glaciermass balance between the twotime periods 1971-2000 and
2071-2100, for all of Svalbard,
and for the three regions NE, NW
and S (Figure 6.6.4).

Figure 6.6.3: Change in mass balance (m w.eq. yr-1) between the two time periods 1971-2000 and 2071-2100. a)
Spatial change. b) Change as a function of elevation. ELA denotes the mean equilibrium line altitude determinedfrom linear regression.

Furthermore, this simulated mass balance represents only the climatic mass balance and neglects
the ice discharge component. The only available
estimate of current ice discharge from Svalbard
glaciers (Blaszczyk et al., 2009) is a composite ofsnapshots in the period 2000-2006 and amounts to
5.0-8.4 Gt yr-1 (mean 6.75 Gt yr-1), hence, ice discharge is roughly equivalent to the mass loss by
climatic mass balance in the same period. This workurgently needs to be updated, especially in light ofseveral large glacier surges (see Chapter 3) which
discharged large volumes of ice into the ocean overshort periods. One single event, the surge of Ba110

sin-3 from Austfonna, NE Svalbard, has contributed
to sea-level change equivalent to about 7 Gt yr-1
since 2012 (Dunse et al. 2015), hence doubling theice discharge from Svalbard. There are some suggestions that increased melt hastens the triggering
effect on surges and a number of other large-scale
surges events have been reported in Svalbard (Sundet al, 2014) as well as other Arctic regions (e.g.
Willis et al. 2018). These events affect the total ice
discharge and have the potential to considerablyincrease the sea-level contribution from land ice
within short time.

Figure 6.6.4: Svalbard regions
used in Table 6.6.1.

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The dramatically more negative mass balance is afunction of the climate projection; the change inclimatic forcing between the two simulation periods
is discussed in comparison to the other analysedprojections (e.g. Arctic CORDEX, see Chapter
4). Consistent with the observation that the climate simulation for 1970-2000 is on the warm end
compared to other members of the ensemble, wefind that the simulated 1970-2000 mass balance for
Svalbard is more negative compared to other estimates using a variety of methods (Table 6.4.1). For
the period 2070-2100, the projected temperature
is on the colder end of the ensemble, such that the
projected change between the two slices is on theconservative side within the ensemble of considered
models (see Box 4.1). Therefore, the mass balancechanges projected here most likely are underesti-

mated rather than exaggerated. Internal feedback
mechanisms in the glacier system, such as water
retention by refreezing or the influence of increased
melt on glacier sliding, have considerable impact
on the response of glaciers to climate change. In thesimulated projection, the baseline period 1970-2000
is too warm, the mass balance too negative, and, asa result, the internal retention capacity is smaller
than more realistic representations (e.g. Østby et al.,2017). The projected change of the retention capacity is therefore smaller than that expected, sincea good part of the available retention capacity has
been reduced; this would lead to even more negative mass balance.

6.7 Gravity
While satellite gravimetry provides an absolutemeasure of the total mass change in a region, thevalues obtained still need to be disentangled fromcrustal changes due to ongoing and long-term isostatic rebound. Regional gravitation change is also
prone to “leakage” from regions outside of the areaof interest. Furthermore, there are certain technicalchallenges to the data analysis. A number of groups(Wouters et al., 2008; Jacob et al., 2012; Mémin etal. 2011; Matsuo and Heki, 2013; Gardner et al.,2013) working with the same dataset covering only
slightly different periods, but using different datafiltering methods, obtain a range of values for the

total mass loss (Table 6.4.1, Figure 6.6.1). However,
the main conclusion one can reach from the bodyof GRACE analysis is that all find a negative massbalance from Svalbard archipelago, with values
ranging from -0.46 to -0.09 m w.eq./year, or -15.6
to -3.1 Gt/year (Table 6.4.1), even if the error range
for some of the estimates extends into the positive
territory (Figure 6.6.1). Nevertheless, the results are
consistent with geodetic and modelling estimates(Table 6.4.1, Figure 6.6.1).

7. Permafrost, avalanches and landslides
7.1 Permafrost temperatures and active layer thickness
7.1.1. Background
Areas with permafrost consist of sediment or rockand included ice and organic material, which remains at or below 0°C for at least two consecutive
years (French, 2018).
In recent years record-high annual mean groundtemperatures (MGT) have been observed at many
Arctic permafrost observatories with the largest increases in permafrost temperature since 2000 in thecold permafrost of the Alaskan Arctic, the Canadianhigh Arctic, and in Svalbard (Romanovsky et al.,2017). Svalbard has the warmest permafrost thisfar north in the Arctic (Romanovsky et al., 2010).
It is typically about 100 m thick in the valleys and
400–500 m thick in the mountains (Liestøl, 1977).

7.1.2 Observed permafrost temperatures andchanges since the International Polar Year (IPY)
In general, permafrost temperatures are highest at
coastal sites (Kapp Linné and Bayelva), or wherea thicker snow cover during the winter occur (Endalen). During the hydrological year 2016-2017
mean temperatures at the ground surface (MGST)
ranged between -1.3°C and -4.1°C, while permafrost temperatures, at or close to the depth of zero-annual amplitude (MGT), varied from -2.6°C to-5.2°C, in the monitoring boreholes in the six permafrost observation sites in Svalbard (Table 7.1.1).
Differences are attributed to variations in snow
cover, landforms, the degree of continentality andground ice contents. The results clearly show thatrather warm permafrost occur in extensive parts ofparticularly the lowland Svalbard landscape, which

Figure 7.1.1. Location of the active permafrost observationsin Svalbard (red dots, Table 7.1.1) and location of additionalsites where meteorological stations exist and where permafrost modelling have been performed (blue dots). Figure fromChristiansen et al., (2019a).
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CLIMATE IN SVALBARD 2100

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detected down to 80 m depth reflecting a multi-decadal permafrost warming, with 2017 clearly as thewarmest year in the observational record (Romanovsky et al. 2017).

is especially sensitive to warming, and which iswhere the population is living. Along the west coast
there is a clear gradient from the warmest permafrost in the south at Hornsund -1.1°C (measured atonly 2 m depth in the summer), to the coldest in thenorth at Ny-Ålesund with -2.8°C (Christiansen etal., 2019a).

7.1.3 Active layer thickness (ALT) and changes

The permafrost temperature records (Figure 7.1.2)
generally show a continuous increase of permafrost
temperature in the upper 10-20 m of the ground.
Since IPY ground temperatures have increased with
rates between 0.06°C (Bayelva and Old Aurora Station 2) and 0.15°C (Breinosa) per year at 10m depth(Figure 7.1.2a) and between 0.07°C (Janssonhaugen
P10 and Kapp Linne 1) and 0.08°C (Endalen) per
year at 20 m depth (Figure 7.1.2b). On Janssonhaugen a significant temperature increase can be

The thickness of the active layer (ALT), as measured in the autumn 2017 in the two standard
CALM grids (see Chapter 3.4) in Adventdalen andat Barentsburg, and in the permafrost boreholes,
varied from 49 to 300 cm, but generally fell within the range of 100 cm to 200 cm (Figure 7.1.3;
Christiansen et al., 2019a). Thinnest ALT are reported from blockfields at higher elevations and in
sediments. Thicker ALT is encountered in bedrock
settings.

2016-17

2008-2009

MAT

MGST

MGT

ALT

MGT

ALT

(°C)

(°C)

(°C)

(cm)

(°C)

(cm)

Old Aurora Station 2

-1.9

-1.3

-5.2(9.9 m)

94-5.6 (9.9 m)

90

Endalen-1.9-2.7 (19 m)

190-3.2 (15 m)

120

Adventdalen Breinosa

-3.8

-4.1

-5.1 (10 m)

49

n/a

n/a

-3.8

n/a

-5.0 (20 m)

n/a-5.5 (20m)µ170

Location

Borehole name/ ID

Janssonhaugen/ P10

Ny-Ålesund
Kapp Linné

Janssonhaugen/ P11-3.8-3.7(0.2 m)

n/a

185

n/a

170

Bayelva

-2.3

-3.6

-2.8 (9 m)

200-2.9 (9 m)π180

Kapp Linné 1

-1.2

-1.6

-2.6 (20 m)

300-3.1 (15 m)

250

Kapp Linné 2

-1.2

-1.6

-2.8 (20 m)

190-3.2 (15 m)

180

Table 7.1.1. Permafrost monitoring sites with mean ground temperature near or at the depth of zero annual amplitude during the hydrological year 2016-2017 (Christiansen et al., 2019a), and during the International Polar Year
(IPY) 2008–2009 (Christiansen et al., 2010) in Spitsbergen.
MAT = mean air temperature of the closest meteorological station); MGST = mean ground surface temperature;
MGT = mean ground temperature at the depth of zero annual amplitude (ZAA) if different from total borehole depth
or at lower most sensor; ALT = active layer thickness as estimated from interpolating the depth of the 0 °C isotherm.
*Only recorded during July and August 2017; µ From Romanovsky et al. 2017; π Recorded in 2010 (Boike, et al.
2018)

114

Figure 7.1.2 Annual mean ground temperatures (MGT) during hydrological years at selected permafrost monitoringsites on Svalbard. a) 10 m below the surface and b) at 20 m depth, near or at the depth of zero annual amplitude(ZAA), only available from Janssonhaugen, Kapp Linné and Endalen. Data series are updated from Isaksen et al.
2007a, Christiansen et al. 2010 and Boike et al. 2018.

ALT records from Janssonhaugen indicate a generalincrease in thickness of 1.6 cm/year between 1999
and 2018. In the sediments of the UNISCALM gridin lower Adventdalen, the average of the 121 point
observations, show an increase of on average 0.6

cm/year in ALT from year 2000 to 2017. At Kapp
Linne in the bedrock borehole ALT increased by 6.2
cm/year since IPY (2008-2009) to 2017.

Figure 7.1.3:
Active-layer thicknesses recorded at
the end of August,2017 from CALM
grids and interpolated from thereported boreholes.
The active-layer
at the Bayelva
site in Ny Ålesund
was estimatedusing the Stephanequation. Noteshading denotes
substrate type (fromChristiansen et al.
2019a).

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7.1.4 Projected changes in permafrost
temperature
For the period 1971-2000, the COSMO-CLM(CCLM, see Chapter 3.1.4) modelled mean annual 2 m air temperature is about 1.5 °C higher thanthe observed temperature, and 2.5 °C higher thanthe Arctic CORDEX model median. Following theemission scenario RCP8.5, the CCLM projects anincrease in air temperature of 6.5 °C for the period
2071-2100, while the Arctic CORDEX runs show
a median increase of 8.7 °C. Thus, while CCLM
shows a smaller increase, both end up at around
2 °C in 2100. This is roughly two degrees higher
than the Arctic CORDEX RCP4.5 runs and theforcing data used in previous modelling efforts byEtzelmüller et al. (2011; see below), which is from amulti- General Circulation Model (GCM) ensemble
based on Coupled Model Intercomparison Project
CMIP3 (Meehl et al., 2007), using the A1b Special
Report on Emissions Scenarios (SRES). We emphasize that the modelling procedure is in line withprevious modelling efforts, (e.g. Etzelmüller et al.,2011), but does not take a range of crucial processes
into account, such as wind redistribution of snow,or spatial patterns of soil moisture caused by lateralflow of water or excess ground ice layers. Furthermore, the model runs employ a standard soil stratigraphy typical for moraine materials (Westermann etal., 2013), which cannot do justice to site-specificvariations in organic and ground ice contents.

Modelled ground temperatures (Figure 7.1.4) are
generally in the range of in-situ observations for the
IPY-period (Table 7.1.1), although the coarse model
set-up does not allow representing the site-specific ground thermal regime. At all modelled sites,
near-surface permafrost is projected to thaw before
the end of the present century, with some sites inwestern Svalbard developing a talik to a depth ofseveral meters, while permafrost still persists at 10m depth and below. The modelling suggests that thisdevelopment will be initiated first at sites, which today already feature high ground temperatures, suchas Kapp Linné and Hornsund. While northern sites
like Verlegenhuken and Rijpfjorden are projected
to have permafrost until the 2080s, a general degradation trend and talik development is underway in2100 as well.
Previous modelling results for the end of the century (Etzelmüller et al. (2011)) for some of the permafrost monitoring sites in Svalbard (Kapp Linné,
Janssonhaugen and Endalen) revealed the following
major effects towards 2100: 1) A signiﬁcant warming (ca. +4°C) in the near-surface layers (<10mdepth). 2) A varying ground temperature increasedepending on the ice/water content and the present permafrost thermal state. 3) Warming rates are
efﬁciently reduced where the temperature is close
to 0°C, and where ice is present due to the consumption of latent heat for melting. 4) The medianground temperatures at the depth of zero annual
amplitude (ZAA) is suggested to increase by 2–4°C
through the period 2000-2100. 5) Over the same
period, ALT increases at all sites. Model results
show degradation of permafrost in bedrock sites
at low elevations. Contrarily, in sedimentary landforms with a relatively high water/ice content,
permafrost conditions continue to exist until 2100.

Figure 7.1.4. Permafrost temperature modelling results for 1 m (light blue), 5 m (red) and 10 m (dark blue) depth
for selected sites on Svalbard where active permafrost observations exists and/or meteorological stations exist (seeFigure 7.1.1). Note that while the CCLM data are only available for two time slices, 1971-2000 and 2071-2100, the
CryoGrid 3 simulations are run by a continuous forcing from 1971-2100, with data synthesized from the two timeslices used to fill the gap in between. Since this does not correspond to a physically-based projection driven by an
Earth System Model, we only show results until IPY-period (2008-2009) and between 2071-2100. Furthermore, themodel results in the first 20 years are strongly influenced by the selected initial condition, so that only results after
1990 (approximately the period for which measurements for validation are available) are shown.

116

Recent ground thermal calculations by Instanes
and Rongved (2017) for three different locationsin Longyeardalen (the Svalbard Science Centre,
the town center and Skjæringa) based on recent
regional climate model runs using the RCP4.5
scenario suggest an increase in ALT, from about 1.5
m today to 2.5 m by the end of the century. Due tohigh ice content in the fluvial material and silt andclay-dominated marine sediments, warming rates

are efﬁciently reduced due to the consumption oflatent heat for melting. By 2100, the calculations
suggest that the temperature at 20 m will still be at-2 to -3°C.
In general, all modelling results are consistent,
taking into account the different forcing data,
geographical locations and ground properties. The
limit between “permafrost remains (at least partly)”and “near-surface permafrost disappears entirelyby 2100” is suggested to be somewhere between
the RCP4.5 and RCP8.5 scenarios for most of thelowland landscape in western Svalbard.
7.1.5 Permafrost responses to climate changes
Although the modelling results suggest that there
will still be permafrost below 5 m depth in mostsites after 2100, increases in the active layer thickness and permafrost temperature, will for buildings
and structures, in general, lead to strength deterioration and deformation, potential accelerated settlements and possible foundation failure (Instanes,2003). Therefore, an evaluation of these effects
must be taken into account in future planning,
design, construction and maintenance of buildings
and structures on Svalbard (Instanes and Rongved, 2017). For the landscape and in particular forthe slopes on Svalbard the observed and projected
near-surface permafrost warming and increase inactive-layer thickness may in certain areas be associated with large-scale thaw settlement as ice-richsoils in the upper permafrost layer thaw. One consequence is a marked increase in slope instability (e.g.
Harris et al. 2001; Haeberli et al. 2010; Chapter
7.3). While permafrost in steep bedrock slopes has
not systematically been investigated in Svalbard,
permafrost warming and thawing may also strongly
affect rock stability and can trigger potentially hazardous rockslides (e.g. Haeberli et al. 2010; Chapter
7.3).
Recent measurements of temperatures in vertical
rockwalls near Ny-Ålesund conducted from 20162018 (unpublished data, University of Oslo) suggestthat ground temperatures in coastal cliffs can beeven higher than in adjacent inland boreholes, most
likely also influenced by the lack of sea ice at the117

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investigated sites. Such warming and thawing permafrost can affect coastal dynamics (e.g. Romanovsky et al., 2017). Along the coastal mixed cliffs
at Vestpynten, close to Svalbard Airport, coastal
erosion of the cliffs due to permafrost degradation
have been identified as largely controlled by nivation processes (primarily erosion by meltwater fromsnow patches located at the lee side of the coastal

cliffs) (Guégan and Christiansen 2016). Coastal erosion is largest where the coastline consists only of
sediments, whereas the mixed coasts (sediment on
top of bedrock) and the bedrock coasts are less exposed to coastal erosion. Coastal erosion is locallyalso controlled by both wave action and the durationof the sea ice cover.

7.2 Solifluction
Slowly deforming active layers moving downslope
due to gravity, in the form of solifluction sheets
and lobes, are widespread on the lower slopes in
the Svalbard landscape (Figure 7.2.1; Harris et al.,2011; Matsuoka et al., 2004; Sørbel and Tolgensbakk, 2002). Harris et al. (2011) used several yearsof detailed solifluction process observations in thelower slopes of the Endalen area next to Longyearbyen to show how active layer thickening causes
increased solifluction movement. Matsuoka et al.
(2004) expected active layer thickening to trigger
temporary, but episodic ground movements, causing
extensive solifluction, in areas where the top permafrost is ice-rich. In areas with one-way freezingof the active layer, future temperature increases areseen as unlikely to significantly affect solifluction(Matsuoka, 2001) when temperature effects are
considered isolated from other changing parameters,such as precipitation. However, winter warm events(including “rain-on-snow”) were found to lead toincreased ground temperatures in a solifluction
lobe in the Endalen area down to two metres depth(Strand, 2016). Increased numbers of such warm
winter precipitation events (Vikhamar-Schuler et al.,2016) might therefore affect solifluction rates. Consequently, it is important to take also solifluction
processes properly into account during the designand construction phases of new infrastructure.

Figure 7.2.1 Solifluction is evident in Longyearbyen where posts formerly used as foundations of houses have tilted
downslope (Photo J. Alean, Swisseduc.ch)118

7.3 Avalanches and landslides
7.3.1 Classification and triggering mechanisms
Different types of snow avalanches and landslides
are the dominant slope processes in cold climatic
landscapes of moderate to steep terrain consistingof various types of sediments and rock types. These
parts of the landscape are typically snow covered
for most of the year.
Avalanches and landslides can be classified in various ways depending on both the type of movement(fall, slide, flow) and the type of material. We use
here a classification based on Norem & Sandersen(2012) which differentiates processes dependingon the relative proportions of rock and soil, water,snow and ice (Figure 7.3.1).
Snow avalanches are – depending on the watercontent of the snow – classified into dry snow
avalanches, wet snow avalanches and slushflows.

As opposed to dry and wet snow avalanches, which
normally occur in steep slopes, slushflows can occur
on slopes from 20 to 200, i.e. they can be initiatedin relatively flat terrain and can have long runouts.
Landslides range from rockfalls, debris slides(coarse material), debris flows (coarse material andhigh water content) and earth slides (fine material)
to mudflows (fine material and high water content)
and quick clay slides. Another typical landslide
phenomenon in arctic environments with permafrost
is active layer detachment. Quick clay slides typically occur in relatively flat terrain in lower lying
areas below the upper marine limit (former seabed).
Such slides are not common in permafrost areas as
the low temperatures in the ground give increased
strength and significantly increase the remoulded
shear strength. However, slides resembling quick
clay phenomena can occur in the upper active layer
during thawing.

Figure 7.3.1: Classification of avalanches and landslides (Modified from Norem and Sandersen, 2012)
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Snow avalanches (Figure 7.3.2) can also be classified
according to their morphology/form into loose snow
avalanches, slab avalanches, cornice fall avalanches
and glide avalanches. Loose snow and slab avalanches both may consist of dry or wet snow. A loose
snow avalanche is a mass of snow that originates at asingle point on a slope and gathers more loose snow
as it descends. In Svalbard, slab avalanches often
form from deposited fresh snow in combination withwind (Eckerstorfer & Christiansen, 2011a). They are
released when the stress of a cohesive slab of snow
exceeds the strength of an underlying weak snow
layer. To trigger a slab avalanche, either an additionalload, such as new or wind accumulated snow or aweakening of the underlying snow, is needed. A skier,snowmobile or other mechanical loading can also
trigger slab avalanches. Break-off of cornices is anadditional documented threat.

Cornices form by wind blowing snow over, forexample, the crest of a mountain. They are often
overhanging, can break off unexpectedly and, once
released, may trigger larger avalanches in the slope
underneath (Hestnes & Lied 1991; Hestnes &Bakkehøi 2003; Eckerstorfer & Christiansen, 2011a;
Vogel et al., 2012). The Svalbard topography, withlarge plateau-like mountains, provide ideal accumulation areas for wind-drifted snow, which is deposited at the edge of the plateaus as cornices. Manyof the cornices are thus located above steep slopes,
where secondary avalanches could be triggeredby the additional load of the cornice falling or thecornices themselves may run far into valleys. The
formation of cornices depends on the terrain, thequality and amount of snow in catch areas and the
direction and strength of the wind fields. Glide avalanches are released after a period of gliding along

the ground, typically after developing glide cracks
in the release area and on ground surfaces with low
friction (bare bedrock or smooth grass).

speeds have triggered both avalanches, slush flows
and landslides (e.g. Eckerstorfer & Christiansen,
2011b).

In Svalbard, small amounts of snow in combination
with relatively low temperatures typically give ashallow snowpack with a high vertical temperature
gradient. This combination is ideal for the development of persistent weak layers in the snowpack
and, consequently, the formation of slab avalanches,
which can occur when these weak layers are buried
under new snow and/or under accumulating snowdrifts. Snow in Svalbard is particularly exposed toredistribution by wind because of the sparse vegetation and relatively smooth surfaces; hence, cornices
and wind packed slabs form easily. In addition,occasional passages of low-pressure atmospheric systems associated with positive temperaturesin the winter, heavy precipitation and high wind

The terms rockfall and rockslide are often usedinterchangeably, but can be distinguished according
to size. According to NGI (2018) rockfalls consistof smaller, individual blocks (up to 100 m3), androckslides have a larger volume (up to 100 000m3). Both types have source areas in steep terrain,
generally with a slope greater than 300-400. Smaller
rockfalls can be triggered by freezing and thawing.
Large rockslides typically result from long-term
geological processes, but weather can be involvedin the triggering of such events. In Svalbard, shear
stress induced by ground freezing are important insplitting bedrock as ice lenses grow in fractures.
Rock and stones thus undergo erosion and fall down
mountainsides due to gravity as rockfalls,

Figure 7.3.2 Avalanche at Fritham March 2015, Photo: Odd Magne Kvålshagen
Figure 7.3.3 Landslide west of Carolinedalen north of Longyearbyen, august 2016 Photo: Lufttransport
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building up large colluvial fans or talus slopes (Hisdal, 1998). Avalanches can also transport rocks thatare plucked from bedrock free faces in their sourceareas (Vogel et al., 2012; Eckerstorfer et al., 2012).
Deposits from avalanches, slushflows and landslides
typically in the form of avalanche and debris fansprovide direct evidence of such processes being
active on mountainsides.

well known (Chapter 10.3.2). In 1953, a slushflow
killed three people and many were injured. Both in1989 and 2012 slushflows damaged infrastructure.
In 2012, slushflows occurred in the mid-winterafter a warm spell and heavy rainfall (Hansen et al.
2014). At another location, Liedfjorden, in 1992,
several persons were hit by a slushflow and one
person died.

Landslides (Figure 7.3.3), primarily in the form ofactive layer detachment slides in sloping terrain canbe triggered by heavy rain in permafrost landscapes(Christiansen et al., 2019b). Smaller more shallow
landslides are also triggered by heavy rain. Permafrost lowers the precipitation threshold for debris
flow initiation, but limits debris flow volumes (deHaas et al., 2015).

Snow avalanches are frequent and have caused several fatalities in Svalbard (Chapter 10.3.2). Many
fatal avalanches have been snowmobile triggered,but in December 2015, two people died when alarge slab avalanche from the ridge north of Sukkertoppen destroyed ten houses in Longyearbyen.
In 2017, another avalanche from Sukkertoppen
triggered a secondary avalanche that reached thesettlement. Several buildings were damaged, butthere were no fatalities.

Debris flows and slushflows have occurred infrequently, but regularly, over the last 2000 years,
based on evidence from lichenometry in Svalbard(André, 1990). In a new study, Berhardt et al.
(2017) analysed a debris fan located close to Longyearbyen and found that the recurrence period fordebris flows is a few decades. This agrees with the
expected accelerated mass wasting in an increasingly warmer Arctic.
7.3.2 Avalanche and landslide events
From the previous sections, we can conclude that
there are “favourable” conditions for avalanches
and landslides in Svalbard. In addition, increases in the commercial, research, educational andrecreational activities during the past fifty years,
particularly in the Longyearbyen area, have had
an impact on peoples’ exposure to natural hazards,
especially to snow avalanches. Population andtourism have grown considerably since the 1960s
and, consequently, the number of people involved inbackcountry activities has strongly increased. Human triggered slab avalanches seem to cause mostfatalities among recreational backcountry skiers andsnowmobilers, while naturally triggered avalanches
are the main threat to infrastructure, transport routes
and residential areas.
The slushflows from Vannledningsdalen in Longyearbyen often occurring during the spring thaw are122

As a consequence of large rainstorms, debris flows
and slides have caused damage to the local infrastructure in the lower Longyeardalen valley (in July1972) and in Longyearbyen (October and November 2016). The November 2016 event also triggered
numerous snow avalanches, which were mappedfrom Sentinel-1 satellite radar images (Figure 7.3.4;
https://titan.uio.no/node/2009).
7.3.3 Climate change effects
Because meteorological factors are important triggers for landslides and avalanches, future climatechange will affect their frequency and type. As described in Chapter 4.1, the temperature in Svalbard
has increased and the winters have warmed morethan the summers. Precipitation trends are less clear,and this may partly be caused by an increased fraction of precipitation falling as rain at the observation stations. In particular, wintertime rainfall events
have increased (Chapter 4.3 and 4.4), and rain on
snow may cause more unstable snow layers with
implications for avalanches throughout the winter.
Regional projections for the Nordic Arctic for the
21st century show continued increase in temperatureand precipitation. They also indicate a significant
enhancement of the frequency and intensity of winter warming events (Vikhamar-Schuler et al., 2016),
with an increase in the frequency of autumn and

Figure 7.3.4 Snow avalanches in November 2016 (yellow stars) mapped from Sentinel-1 satellite radar images.
The background is an optical satellite image from August 2016 taken by Sentinel-2. No optical satellite images areavailable during the winter (polar night), but radar images enable year-round mapping of avalanches and landslides.
(Processing and mapping: Andreas Kääb, University of Oslo; Satellite data source: Copernicus Sentinel data 2016).

winter rainstorms. Snow conditions are describedin Chapter 5.2, and show that for the period 19582017, snow-cover duration has decreased, whereas
the maximum annual snow storage has increased.
The computed future snow conditions show acontinued decrease in the mean annual snow-cover
duration for all the regional projections. The results
for maximum annual snow storage vary in timeand space and depending on emission scenario. In

general, an increase is expected at higher altitudes
in northwestern and eastern regions, whereas adecrease is seen at lower altitudes and in coastal
areas. An exception is the high emission scenario(RCP8.5) towards the end of the century, where lesssnow is modelled for most of Svalbard. No significant changes in wind conditions are expected in
the future, neither magnitude nor direction (Chapter
4.5).
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Ongoing snow avalanche research has a focus on
measurement techniques, process understanding
and improved regional and local avalanche forecasting. It encompasses studies of snow accumulation, snowdrift including spatial variability of snowheight and the mechanical properties of the snow
pack in Svalbard. Fewer publications assess theeffects of future climate change on various types ofavalanches and landslides in Svalbard. Based on our
understanding of processes, however, it is generally
assumed that increasing temperature and precipitation, both as snow and rain, will increase thefrequency of all types of avalanches and landslides
in the coming decades. Given that wind direction
and magnitude will undergo minor changes undera future climate (Chapter 4.5.2), the present wind
controls on the location of snow avalanches will
also be valid in the future.
In a changing climate where, in particular, theshoulder seasons are changing towards warmer
conditions and increasing the length of the potential
period for increased slope activity in the autumn,
an improved understanding of slope processes isnecessary for developing appropriate observation
systems to assist local authorities in handling futurerain- and snowstorm-geohazards in permafrost
landscapes.
In a warming climate, the permafrost can warm
(Chapter 7.1) and lead to a thicker active layer,
both of which can cause a decrease in slope stability. Such changes cause a longer period of the year
in which the ground is thawed and hence longer
periods for active layer detachment sliding. It canalso cause increased sliding due to the melting of
ice in the top permafrost at the end of warm summers and into the autumn before ground freezing
starts (Christiansen et al., 2019a). The control ofpermafrost on large-scale rockslides is complicated, and direct climate change effects depend on thevariation in air temperature, precipitation and wind
through the year (Gruber & Haeberli, 2007; Blikra& Christiansen, 2014).
The following evaluation of the effect of climatechange on avalanches and landslides in Svalbard

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is qualitative. It is based on the climate projections
described in Chapter 4.
Snow avalanches: Svalbard, with its cold, dry andwindy climate has favourable conditions for densesnow packs with frequent occurrences of corniceand slab snow avalanches. It is much debatedwhether increasing temperatures and precipitation
would lead to a more stable or less stable snowpack
and, hence, to an increased or decreased probability of dry snow avalanches by the end of the
century. The climate projections indicate increased
precipitation. In particular, if more extreme eventswith heavy snowfall or heavy rain on snow occur,
an increase in the occurrence of snow avalanches(including wet snow avalanches and slushflows, likein 2012) can be expected. Towards the end of the
century, gradually increasing temperatures may leadto a substantially shorter snow season and reductionin the maximum annual snow amounts in particularin coastal low altitude areas (Chapter 5.2.2), and thesnow line will gradually shift to higher altitudes.
Over time, these factors are expected to decrease theprobability of dry snow avalanches. However, theprobability of wet snow avalanches and slushflows
is expected to increase. Glide avalanches are notcommon in Svalbard in the present day climate, but
may become a problem at some locations in a future
warmer and wetter climate.

Landslides: Increased air and permafrost temperatures will increase the length that the active layer
will be thawed annually and the thickness of theactive layer will increase. This can increase theprobability of various types of landslides in theactive layer. In addition, increased precipitation andextreme rainfall in sloping terrain will increase thelikelihood of all types of landslides. Another very
important effect is the thawing of the icy transientlayer (top of the permafrost) in a future climate,in combination with strong precipitation events as
rain, instead of snow, in late fall. The transient layer
can be very ice-rich. It takes much energy to thaw
it, and therefore it acts as a “buffer” against perma-

frost thaw. However, when parts of it start to thaw,
the soil in these layers will have a very high watercontent and, consequently, the friction at the interface with the soil layers below the transient layer
will decrease. An increasing frequency of strong
precipitation events in the autumn, when the active
layer depths are at their maximum, would then inevitably lead to a rise in landslide activity.
Quick clay: Increasing active layer depths andthawing permafrost will significantly reduce the undisturbed and remoulded soil shear strengths, which
can lead to more quick clay-like slides in the future.

Rockfalls: The outermost meters of free rock faces
are subject to annual freeze-thaw cycles and plucking by snow cornices, which adversely impacts rockwall stability. Smaller rockfalls are often triggered
from these areas. Rising temperatures can lead tomore freeze-thaw events (Chapter 4.2.3), and therefore rockfall events may increase in the future.
Rockslides: Rockslide stability is partly permafrost
dependant and a warming climate may adversely
affect the stability. Studies from Northern Norway(Blikra et al. 2015; Frauenfelder et al., 2018) showthat increasing temperatures leading to degradationof permafrost may play an important role in the
detachment of larger rockslides. There are no published studies on the effect of permafrost on rockslides in Svalbard.

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8 Ocean, acidification, sea ice and sea leveltion was comparable throughout the Holocene. A
reduced seasonal sea ice cover compared to presentcharacterized the early Holocene. Sea ice increased
towards the late Holocene, when the sea ice margin
shifted south of Svalbard (Berben et al. 2017). In
Storfjorden, near-permanent sea ice prevailed between 2800–500 years ago (Knies et al. 2017), but
generally a coastal polynya with seasonally variable
sea ice conditions have occurred since 6,500 years
ago.

Knowledge about the marine climate around Svalbard is based on combinations of observations andmodel studies for the past, present and future climate. For the past and present, the historical climate
is reconstructed from paleoclimate proxy data andinstrumental records, while the future climate isbased on simulations from climate models forced by
known external forcing and different projections ofgreenhouse gas emissions.

8.1 Past, present and future climate around Svalbard
An introduction to the role of the large-scale ocean
circulation for the ocean climate and sea ice variability around Svalbard was briefly given in Chapter
2 and is summarized in Figure 8.1.1.

8.1.1 Sea-ice around Svalbard throughgeological time

Sea ice has been present in the Arctic Ocean atleast since the mid Eocene, 47.5 million years ago,from when sea ice associated diatoms and ice rafted
During the past few decades, the Arctic has warmed debris are found (Stickley et al., 2009). During
approximately twice as rapidly as the entire Norththe last interglacial (Eemian, ~125,000 years ago)ern Hemisphere (Serreze and Barry, 2011; Francis
when the Arctic was warmer than today, summer
and Vavrus, 2012; Stroeve et al., 2012). In Svalbard, sea ice existed in the central Arctic Ocean, but sea
the warming during winter has been particularly
ice was significantly reduced North of Svalbard
strong over recent decades, with an increase indue to inflow of Atlantic Water (Stein et al., 2017;
winter temperature of 2– 3°C per decade at SvalRisebrobakken et al., 2007). During the last glacial
bard Airport (Førland et al., 2011). The essential(30,000–19,000 years ago), sea ice advanced andatmospheric processes controlling the climate inretreated in the Fram Strait west of Svalbard, where
Svalbard are the large-scale transfer of heat anda perennial sea ice cover existed only from 19,000
moisture from lower latitudes, which is strongly
to 17,500 years ago (Müller et al. 2009; Müller andcontrolled by cyclones (Førland et al., 2011). Recent Stein, 2014).
changes in these large-scale atmospheric circulation
patterns have brought warm Atlantic Water from theWest Spitsbergen Current onto the West Spitsbergen Around the beginning of the Holocene (~12 000–
10,000 years ago), seasonal sea ice occurred west of
Shelf and further into the fjords even during winterSvalbard and the sea ice margin was likely located(Cottier et al., 2007; Nilsen et al., 2008; Pavlov etal., 2013; Nilsen et al., 2016, Tverberg et al., 2018). north of Svalbard (Müller et al. 2012, Müller andStein, 2014). There was thus less sea ice than at
This has halted sea ice from forming and opened
preindustrial times in this region largely due to aup large areas of ice-free waters west and north of
Svalbard (Cottier et al., 2007; Tverberg et al., 2014; warmer ocean (e.g. Mangerud and Svendsen, 2017).
Throughout the Holocene (10,000 years ago to presOnarheim et al., 2014) with a potential impact on
the Arctic ecosystem (Hegseth and Tverberg, 2013). ent), the (spring) sea ice cover gradually increased
along the continental slope west of Svalbard due toMore frequent episodes of Atlantic Water intrua lowering of Northern Hemisphere insolation andsion in Arctic regions support the establishment ofdecreasing temperatures (Müller et al. 2012). Easta number of boreal species, for example, the blueof Svalbard, in the Barents Sea, the sea ice evolumussel (Berge et al., 2005).
126

During the early Holocene, the climate of Svalbard
was considerably warmer than at present. This
warm period is well-documented in both terrestrial
and marine geological records. The early Holocene
climate was largely driven by higher summer insolation and changes in the influx of Atlantic waterand feedback processes that amplified the warmingin the Arctic. The mollusc fauna of Svalbard has
been studied quite extensively as it contains clues topast warm climates on Svalbard. A larger number ofmollusc species are found in early Holocene raisedbeached deposits. The majority of these species
are still living along the Svalbard coast and in thefjords of Spitsbergen. However, a few, so-called
thermophilic species, that are found in deposits in
Svalbard are now extinct (Feyling-Hanssen, 1955).
The geographical distribution of these molluscs
through time provides useful information on theregional paleoclimate (Mangerud et al. 2017). The
distribution of thermophilic molluscs trough timefurther suggest that the climate cooled somewhat
after the first warm period which lasted from 11 000
to 9000 years ago. Still, between 8 200-6 000 years
ago shallow water temperatures off Svalbard where
about 4°C warmer than today.
Subsequently, water temperature cooled to present-day values at about 4500 years ago. Beierlein etal. (2015) reconstructed the early Holocene seasonal
water temperature cycle in Dicksonfjorden based onstable oxygen isotope profiles in shells of Arctica
islandica, another thermophilic species found in theearly Holocene raised beach deposits on Svalbard.
Their results suggest that the early Holocene seasonality was about 12.4 °C, which is about twicethat of the modern seasonal amplitude 6°C. Interestingly, one of the previously extinct molluscs

re-appeared at the west coast of Spitsbergen inaround the year 2000 suggesting that an unusuallyhigh northward mass transport of warm Atlantic
water resulted in elevated sea-surface temperatures
along the west coast of Svalbard at that time (Bergeet al., 2005).
8.1.2 Present climate
As described in Chapter 2, the internal variability isexpressed on decadal timescales in the Nordic andBarents Sea. This implies that the waters around
Svalbard is governed by different, but not necessarily independent, mechanisms and processes such as
the Atlantic Multidecadal Oscillation (AMO) andthe North Atlantic Oscillation (NAO). This again
may give rise to propagating heat and freshwater
anomalies between the Atlantic and Arctic basins.
On top of this natural variability comes the anthropogenic warming, which implies that future warm
periods will on average be increasingly warmer andthat future cold periods on average will be less cold
than today.
The temperature and salinity of the inflowing waters
to the Nordic Seas and to the Svalbard region havebeen particularly high after around 2000 (Figure
8.1.2, Onarheim et al., 2014; González-Pola et al.,2018; Tverberg et al., 2018). High recent Atlantic
Water temperatures around Svalbard reflect those in
the North Atlantic recorded as positive values of
the Atlantic Multidecadal Oscillation (Sutton andHodson, 2005). Over the last 100 years the wind
forcing (NAO) also explains a large part of the temperature variability along the Atlantic inflow pathway (Muilwijk et al., 2018). Årthun et al. (2017)and Langehaug et al. (2018) show how anomalies
from the North Atlantic propagate with the Atlantic
Water into the Nordic Seas, along the Norwegiancoast into the Barents Sea through the Barents Sea
Opening and into the Arctic through the Fram Strait
with the West Spitsbergen Current. Reduction andthinning of sea ice north and east of Svalbard inrecent years is consistent with global warming andhave likely lead to more wind-generated upwardmixing of warm and saline Atlantic Water fromabout 200 m depth, preventing formation of sea ice(Ivanov et al., 2016). As this warm and saline water
is cooled by the atmosphere, it gets heavier and127

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sinks in a convection process that brings up moreAtlantic Water to the surface. In 2011, 2012 and2013 such a process likely prevented sea ice formation during autumn and winter in large parts of thewestern Nansen Basin north of Svalbard. Such big
anomalies in the sea ice extent create large air-sea
fluxes between the relative warm ocean and the cold
atmosphere during winter and have together withanomalous weather conditions (Binder et al., 2017)
given many extreme high temperature events recent
years. The different large-scale processes contributing to this anomalous situation is illustrated in
Figure 8.1.1.
Episodes of Atlantic Water intrusion on the West
Spitsbergen Shelf represent an increased oceanic heat
flux into the fjord systems and toward the glaciers.
Currently there is enormous interest and activity ininvestigating the role of ocean heat on the stabilityof ocean terminating glaciers (Luckman et al. 2015).
Propagation of warm oceanic waters into fjords, withthe potential to increase the melt rates of glaciers, hasbeen identified as a likely mechanism leading to theacceleration, thinning, and retreat of glaciers (Straneo
et al. 2010; Inall et al. 2015; Luckman et al. 2015). Recently, increased understanding of important exchange
processes between the oceanic heat source in the West
Spitsbergen Current and the Svalbard Shelf have beenachieved. Characteristics of the water masses in theWest Spitsbergen Current are transferred to the coastal
shelf and fjord systems by wind generated cross-shelfexchange (Cottier et al., 2007, Inall et al., 2015, Nilsen
et al., 2016, Tverberg et al., 2018). Ekman transportand pumping together with variable horizontal density gradients on the continental shelf, caused by thesurface heat flux, are likely to give rise to frontal
instabilities (Saloranta and Svendsen, 2001) that starta residual overturning circulation across the shelf edge
front (Tverberg and Nøst, 2009).
Eddy activity along the shelf edge front is essential
for residual overturning to take place (Tverberg
and Nøst, 2009), and moreover, there must be somedegree of topographic steering to bring the Atlantic
Water from the shelf edge to the inner shelf (Nilsen
et al., 2016). The continental shelf adjoining thewest coast of Spitsbergen is complex, with alternating shallow banks (50–100 m depths) and deep128

troughs (200–400 m depths) cutting across the shelf.
Model results, compared with observations haveshown that the deep Isfjorden Trough (Isfjordrenna)
have a significant signature on the mean circulation (Nilsen et al., 2016) owing to the tendency toconserve potential vorticity. The Atlantic Water
flooding events on the West Spitsbergen Shelf havebecome more frequent during the recent decades
because of changes in the atmosphere pressure
field and the winter cyclone tracks around Svalbard(Rogers et al., 2005; Francis and Vavrus, 2012;
Barnes et al., 2014). Hence, there is a possible link
between the wind-forced circulation on the West
Spitsbergen Shelf (Ekman transport and pumping),
the observed wintertime Atlantic Water intrusion on
the West Spitsbergen Shelf and in the fjords, and thelack of sea ice around Svalbard (Nilsen et al., 2016,
Muckenhuber et al., 2016, Tverberg et al., 2018).
Trends and variability of the future climate issimulated by global coupled climate models as
described in Chapter 3. Arctic sea ice loss is in
general strongly affected by global warming. Thisis also the case in the Barents Sea, which has large
observed recent trends for both atmospheric andoceanic warming. Especially the northern Barents
Sea has experienced a rapid climate shift and is
described as the Arctic warming hotspot where thesurface warming and loss of winter sea ice is thelargest in the entire Arctic (Lind et al., 2018). Theirresults indicate that less sea ice inflow from theinterior Arctic has caused a 40 % freshwater loss inthe northern Barents Sea, leading to weaker stratification and increased vertical mixing with the deep
Atlantic layer. More heat is then brought up fromthe deep Atlantic layer, resulting in a dramaticallywarm Arctic layer.
How well these changes in Arctic sea ice are captured
by global climate models is among other things afunction of northward transport of heat into the Arctic(Sandø et al., 2014b; Langehaug et al., 2018), which
again is very much dependent on the horizontal grid
resolution in the models. Most global models underestimate this ocean heat transport, because the topographic details and corresponding circulation is notproperly resolved. The regional simulation presented
below is done for the period 2006-2070 where thefocus is on the difference between two decades; the2010s and the 2060s under a moderate global warming (RCP4.5) scenario using the best choice global
coupled model available (Sandø et al. 2014a).
Figure 8.1.3 shows the simulated sea surface temperature in March around Svalbard from the first
decade of the downscaled future simulation. The
Atlantic Water is clearly seen as a tongue of warm
water entering the western coast of Svalbard fromsouth as the West Spitsbergen Current. West of
Svalbard the Atlantic Water high temperature is
gradually lost in the surface layer, but is present along way into the Arctic deeper beneath the sea ice.
Weak contours of warm Atlantic waters can also
be seen at the surface as it propagates eastwards
north of Svalbard in the West Nansen Basin and
the Barents Sea between Svalbard and Frantz Josef
Land. The observed temperatures in the northwestern parts of the Barents Sea have increased
considerably since 1970, and particularly since thelate 1990s. This is in part due to warming of the
upstream Atlantic Water, but also to variable wind
patterns and corresponding inflow of Atlantic Waterfrom the north (Lind and Ingvaldsen, 2012). In
addition, there is growing evidence for increased
atmospheric heat transport by increased number ofcyclones north of Svalbard (Chapter 4.5.1;Graham
et al 2017), and a longer duration of cyclones in the
Barents Sea (Wickstöm, 2019).
Arctic water masses dominate the banks right eastof Svalbard in the Barents Sea. Here, the Atlantic
Water is separated from the Arctic Water by the
relative stable Arctic Front (grey line in Figure
2.2.1), which is closely linked to the bathymetry
and follows the continental shelf break around theSvalbard Bank towards the Central Banks. The
largest sea ice re-treat in the winter (March) is thus
further east in the Barents Sea between Franz-Josef
Land and Novaya Zemlya (Onarheim et al 2018).
Figure 8.1.3 shows that the modelled sea ice
concentration north and east of Svalbard highly
correlates with the sea surface temperature. Sea
ice concentrations are highest between Edgeøya,
Spitsbergen, Nordaustlandet and Kong Karl’s Land,
around Kvitøya, and around Franz Josef Land. Con-

centrations are smallest along the path of the warm
Atlantic Water that effectively melt sea ice given
sufficient vertical mixing (Peterson et al., 2017,
Sirevåg and Fer, 2009).
8.1.3 Future climate in Svalbard waters
Fifty years from now, the downscaled RCP4.5 simulation shows that the surface waters around Svalbard
will be about 1oC warmer (Figure 8.1.3). This is generally true for most areas that today have low concentration of sea ice, but some areas further south havea stronger warming. A few areas cool about 1oC likethe outer parts of Storfjorden and Fram Strait. Consistently, simulated sea ice concentration decreases
where temperature increases, mainly in the Barents
Sea, and increases in the outer and western parts ofStorfjorden (Figure 8.1.3). The observed temperatures at Sørøya in Figure 8.1.2 show quite strong
interannual and decadal variability compared to themultidecdal trend. This is also the case for the futurescenario, and the changes due to natural variability
may therefore be comparable in size to the anthropogenic signal over the six decades studied here. Nevertheless, there is a big difference between these twocomponents. While the natural variability is strong,
its contributions are both positive and negative, andabout zero in mean. On the other side, the anthropogenic signal is small, but always positive, and will
therefore accumulate as time passes. It is therefore
reason to believe that the temperatures in the Nordic
and Barents Seas will continue to rise after 2070,
unless the thermohaline convection and thereby thenorthward oceanic heat transport is reduced.
A comparison of two different CMIP3 models thatwere downscaled with the same regional model(ROMS) for the future emission scenario (A1B)
showed relatively good agreement with respect tofuture temperature increase but revealed large differences with respect to changes in salinity (Sandøet al., 2014b). These dissimilarities were attributed
to deviations in the respective global models andwere transferred to the regional models through initial and boundary conditions. CMIP models generally differ extensively on the future changes in salinity, probably due to differences in the representationof the global hydrological cycle (Haine et al 2015).
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Such large differences cannot be fixed by a regional
downscaling, the ocean is too inert and the impactof the global results from the initial and boundary
conditions is too large. So, despite improvements

due to increased resolution in regional models,unrealistic biases in the global model projections
influence the final results.

Figure 8.1.2 Sea surface temperature of Atlantic Water in the Sørkapp section in the northern Norwegian Sea (Institute of Marine Research, ICES data, www.imr.no).

8.2 Acidification

Figure 8.1.1 Variable atmospheric forcing and oceanographic circulation contributing to changes in temperaturesand sea ice extent around Svalbard.

130

The average pH of the surface waters of the global oceans has decreased from approximately 8.2
before the onset of the industrial revolution to apresent average of approximately 8.1 (Caldeira andWickett, 2003; Orr et al., 2005). This ocean acidification is a result of the dissolution of CO2 corresponding to approximately 1/3 of the CO2 released
to the atmosphere from combustion of fossil fuels,
industrial cement production and by changes in landuse (Canadell et al., 2007; Sabine et al., 2004). The
surface water from the North Atlantic entering theNordic Seas is presently equilibrated with atmospheric CO2 and carries small or zero capacity forfurther uptake in the Nordic Sea region (Olsen et al.,2006). The observed pH of ocean surface waters has
decreased by a rate of ~ 0.0018 year−1 over the lastquarter century at several open-ocean time-series
sites (Feely et al., 2009). Lauvset et al. (2015) reporton a decrease in the surface pH in the North Atlantic subpolar seasonally biome of -0.0020±0.0004
year−1 between 1991 and 2011 using data from SOCAT collection (www.socat.info). A time course ofcarbon chemistry from Ocean Weather Station Mike(OWSM, 66°N, 2°E) demonstrated an annual pHchange of -0.001 pH-units year−1 in surface water
between 2001 and 2005 (Skjelvan et al., 2008).

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Figure 8.2.2. Alkalinity (AT) andand total inorganic carbon (CT) ina section (Figure 3.3.4) east of
Svalbard August/September 2016
(From Chierici et al. 2017).

Figure 8.1.3 Simulated sea surface temperature (oC, left) and sea ice concentration (fraction, right) from March inthe present climate (upper) and change in the future (lower) of the RCP4.5 scenario. The present and future periods
are averages from the years 2010-19 and 2060-69, respectively.

Figure 8.2.1. Salinity and temperature in section east of Svalbard(see Fig.3.3.4) August/September
2016 (From Chierici et al. 2017).
132

Figure 8.2.3. pH and aragonite
saturation state (ΩAr) in section(Figure 3.3.4) east of Svalbard
August/September 2016 (FromChierici et al. 2017).

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8.2.1 Present ocean acidification close to
Svalbard
The section east of Svalbard (Figure 3.3.4) in 2016
contains Atlantic water in the south and water fromthe Arctic Ocean in the north. Salinity decreases
northwards throughout the water column (Figure
8.2.1). The southern part of the section carries thehighest alkalinity, AT (~2325µmol kg-1) and inorganic carbon, CT (2200 µmol kg-1) as seen in Figure 8.2.2. North of 80⁰N AT and CT is lower at all
depths. The fresher and colder polar water contains
less inorganic carbon than the Atlantic water. The
pattern was similar in samples from 2015.
Surface pH values increase northwards and towards
the bottom in the section (Figure 8.2.3). Temperature strongly influences aragonite saturation state
and the highest surface values are found in thesouth, where pH is lower than in colder, fresher
northern water. Below 100 m depth saturation statevalues are lower, down to 1.2 at 250 m depth. It ishypothesized that the lowest saturation states are
more influenced by CO2 from microbial degradationof organic material, than from atmospheric anthropogenic CO2.
The water column section at 33°E (see Figure 3.3.4)
shows that the water masses surrounding Svalbard
have a large span of ocean carbon chemistry characteristics. There are clear differences between the water in the Arctic Ocean and the Atlantic water, andeast of Svalbard there is in addition a componentof recirculated Atlantic water from the Fram Strait
at 100m depth between 77°N and 80°N (Figure
8.2.1). Generally fresh water from ice melting plays
an important role in modifying the waters around
Svalbard. This is especially prominent in the waters
close to glaciers and in the fjords (Fransson et al.
2015). In addition, it should be noted that annual
cycles in primary production and biological activity
in general highly modify the carbon chemistry, andthese fluctuations through the years will be largest
in the less saline regions such as fjords surrounded by glaciers (Fransson et al. 2016). The annual
cycles connected to variation in primary production
rates are larger in polar waters than in temperateregions of the ocean, both because of the more

134

extreme light/dark conditions, and the differencein buffering capacity related to alkalinity, which islower in the Polar Ocean than in the Atlantic waters.
Another important factor adding to local variability
is the regional and annual variability in the presenceof sea ice. Sea ice acts as a barrier for diffusionof gases between ocean and atmosphere, and the
distribution of sea ice in time and space strongly
influence carbon chemistry dynamics in the waters
around Svalbard (Fransson et al. 2017).
8.2.2 Future acidification and primary
production
To investigate the effect of further increase inatmospheric CO2 in a future climate, numericalmodels have become an important tool, and using
downscaled physics from a global climate model(NorESM) to force the ecosystem model NORWECOM.E2E, Skogen et al. (2018) have compared
the simulated carbonate system in 2005 and 2070
under the RCP4.5 emission scenario in the Nordic
and Barents seas. On average the decline in pH isaround 0.12 (0.002 year-1) from the first to the last
decade for the whole area. Around Spitsbergen themodeled pH decline is largest to the west, and lowest north of the islands.
There are large uncertainties on the possible
changes in future primary production, and no
general agreement on how this will be affected bothon a global and regional scale. The same simulation
with NORWECOM.E2E predicts lower primary
production in the Norwegian Sea, and no change
in the Barents Sea, except for the ice-covered areas
to the north where the primary production will
increase as the ice retreats. The situation is similar
for the Spitsbergen area, with a predicted decrease
to the west and south and an increase in production
to the east and north. Slagstad et al. (2015) predicts
a general decrease in primary production in boththe Barents and Nordic seas except for areas where
ice retreats in 2100 under emission scenario A1B
using the SINMOD model and climate forcing from
MPI-ECHAM5. However, around Spitsbergen thepatterns are similar to that for NORWECOM.E2E.

8.3 Sea ice

The thickness of the sea ice has also decreased
significantly in the last decades. The mean ice
thickness in the Arctic Basin was 3.59 m in 1975,
Sea ice is a sensitive component of the climate
measured mainly by submarine sonar, while in 2012
system in the Arctic, where freezing, melting andthe mean thickness was 1.25 m, measured by varimotion of the sea ice is governed by thermodynamic ous methods (Lindsay and Schweiger, 2015). Fromand dynamic forcing from atmosphere and ocean.
2000-2012 the thinning of the ice was about 0.58 m
Sea ice has also important impact on the climate
per decade. This reduction in thickness is connected
system through its high albedo and insulating effect, to the change from predominantly multi-year ice
reducing the heat exchange from ocean to atmosto first-year covering the largest part of the Central
phere. In areas where the sea ice has retreated in
Arctic Basin. In March 2016, the ice-covered area
recent years, the albedo is reduced leading to higher comprised of about 78 % first-year ice, while in theabsorption of solar radiation during summer . Less
1980s the firstyear ice fraction for the same monthsea ice during winter implies that the heat flux from was about 55 % (Perovic et al, 2016). The observed
the ocean to the the atmosphere increases, contrib- increase of ice drift speed and increase in the length
uting to winter atmospheric warming.
of the melt season are also connected to the thinningof the ice cover (Vaughan et al., 2013).
The Arctic sea ice area is characterized by a large
seasonal cycle with maximum in March or April and The ice export through the Fram Strait is an imminimum in September, but there is significant inportant part of the ice budget in the Arctic basin.
terannual variability. Systematic data collected from
Estimation of ice area flux from satellite data frompassive microwave data since 1979 shows a decline
1979 shows that the ice export through the Framin the Arctic ice extent of nearly 12 % per decade
Strait has increased by about 6% per decade. The
for September and about 3 % per decade for March
increase is a result of faster southward ice drift due(Figure 8.3.1; se also http://osisaf.met.no/quicklooks/ to stronger southward geostrophic winds (Figure
sie_graphs/nh/en/osisaf_nh_iceextent_seasonal.png). 8.3.2) (Figure 2, Smedsrud et al., 2017).
The most pronounced winter reduction inside theArctic Ocean is observed in the Svalbard and Barents
Sea area (Onarheim et al 2018).
8.3.1 Introduction

Figure 8.3.1 (a) March (blue), September (red), and annual mean (black) Northern Hemisphere sea ice extent,
1979–2016. Shaded regions indicate plus and minus one standard deviation. Linear sea ice concentration trends (%per decade) in (b) September and (c) March. (Fig 1, Onarheim et al 2018).

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Figure 8.3.2. Southward ice area export in Fram Strait. Ice export from 1935 to 2003 is based on the relationshipbetween observed mean sea level pressure and observed ice export by Synthetic Aperture Radar (SAR). Ice export
from 2004 to 2014 is manually tracked based solely on SAR. Annual values (black) are averaged for 1 September through 31 August. Winter export is 1 September–28 February (blue) and Spring is 1 March–31 August (red).
Dash-dotted lines show smoothed values, while thin dashed lines indicate uncertainties due to variations in ice
concentration.

Figure 8.3.3. Surface coverage of Isfjorden between 2000 and 2014 divided into “fast ice”, “drift ice”, and “openwater” by a sea ice expert. White gaps occur when no satellite data wereavailable. The red dots display the “days of fast ice” (DFI) values of the short season (from 01 March to end of ice
season).

Sea ice thickness in the western Fram Strait is monitored by the Norwegian Polar Institute with upwardlooking sonars, installed on moorings, since theearly 1990s. The measurements show that ice thickness has decreased, with a 32% reduction of old andthick ice over the period 1990-2011 (Hansen et al.,2013).
8.3.2 Trends and variability of sea ice in the
Svalbard region
The large-scale trends of sea ice concentration in
the Svalbard area have been documented bysatellite observations over the last four decades,
showing significant winter decrease (Onarheim etal., 2018). The local sea ice conditions in the fjords
and coasts of Svalbard are mainly documented byin situ observations from various monitoring initiatives showing trends towards less ice extent, thinnersea ice, shorter seasons with sea ice cover, earlier
onset of melt, and a reduced snow cover compared
to a few decades ago. At Hopen, ice thickness
monitoring started in the 1960s (Gerland et al.
1362008), while other observing programmes havebeen ongoing for less than two decades. In some locations sea ice conditions have only been observed
a few times in connection with process studies,
which is not sufficient to make any statements on
multiyear means and trends.
The sea ice conditions vary from region to region
and from year to year. West of Svalbard, the properties of the West Spitsbergen Current play an important role for the sea ice in the western fjords. The
inflow of warm Atlantic water to the fjords havestrong impact on the local ice conditions. In thelast decade, the fjords on the west coast have been
almost ice-free in the winter (Muckenhuber et al.,2016). East of Svalbard, the East Spitsbergen
Current transports Polar water and sea ice southwards, causing this region to be ice-covered most of
the year. In the last decade, the whole Barents Sea,
including the area east of Svalbard, has been icefree for several months in the summer and autumn.

8.3.3 Sea ice in fjords on the Svalbard west
coast
Muckenhuber et al. (2016) have analysed the sea ice
in Isfjorden and Hornsund from 2000 to 2014 using
high-resolution Synthetic Aperture Radar (SAR)
and optical images. The analysis showed a significant decrease in the fast ice cover for both fjords inthat period (Figure 8.3.3).
Grønfjorden is a fjord arm of the larger Isfjorden
west of Barentsburg. Zhuravskiy et al. (2012)
analysed long-term ice variability based on visualand instrumental observations, as well as main
oceanographic and meteorological characteristics
here from 1974 to 2008. The beginning of stable
ice formation varied significantly from year toyear, but generally occurred from mid-December toearly January. Their results also indicate a tendency
toward a response of landfast sea ice to a milder
climate for Grønfjorden during the last decades ofthe 20th and the beginning of 21st century.

In Kongsfjorden north of Ny-Ålesund sea ice observations started in 2003 as a part of a long-term
project at the Norwegian Polar Institute and show
substantial interannual variability in fast ice thickness and extent (Gerland and Hall 2006; Gerland
and Renner 2007). Observations include visualobservations and quantification of sea-ice extent andin situ measurements of ice and snow thickness thatare both needed for quantitative sea-ice mass-balance estimates and for characterization of the fjord
ice situation.
Pavlova et al. (2019) summarize published results
and present updated ice-extent and ice and snow
thickness up to 2016. During this period the fast ice
evolution in Kongsfjorden varied interannually withintervals (2-3 years or more) of relatively high andlow sea-ice cover (Figure 8.3.4). After 2006 mostyears had low ice extent and shorter season of fast
ice than earlier. Both fast ice and snow thickness
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around 1 m (Renner et al. 2013, Rösel et al. 2018).
Snow thickness has been observed in winter andspring to be substantial, with mean levels around
0.5 m (Rösel et al. 2018), and formation of snow ice
has been observed.
8.3.5 Sea ice on the east coast of Svalbard

Figure 8.3.4. Maximum fast ice coverage (%) in Kongsfjorden for each month from February to June in the period of
2003-2017, modified after Pavlova et al. (2019).

observation period (1997-2016), towards thinner
ice and snow cover, from levels around 70 cm inthe early 2000s to 40 cm in the years around 2015,
combined with substantial interannual variation.
Snow contributes to the sea ice mass balance inKongsfjorden and other Svalbard fjords (Gerland etal. 1999, Nicolaus et al. 2003, Wang et al. 2015).

at Rijpfjorden based on in situ measurements andan ice mass balance buoy frozen into the fast ice inspring 2011. Seasonal maximum mean ice thickness was observed in June with 81 cm, while snow
was thickest in April with a mean of 19 cm. During
spring, parts of the snow layer melted and re-frozeagain as sea ice (superimposed ice).

Gerland and Hall (2006) compared fast ice thickness between Kongsfjorden, Van Mijenfjorden andHornsund from thickness transects in one year. The
major question addressed was how much the thickness of fast ice varied among the different fjords ,and, thus, to what degree one can extend findings
from one fjord to the entire western coast. The
three fjords did show differences in ice thickness,
although the thickness did not differ substantiallyamong the three fjords before melt onset.

Drifting ice in and beyond the marginal ice zone
(MIZ) is different from fast sea ice that remain in
the same position throughout the winter. Different types of drifting ice can be observed north of
Svalbard, including second and multiyear ice, andfeatures such as surface melt ponds created by themelting of snow on top of the drifting ice during
summer (e.g. Divine et al. 2015) . Ice and snow
thickness levels were investigated from surveys
in 2010 and 2011 (Renner et al. 2013), and during the N-ICE2015 drifts with RV Lance in 2015
(Granskog et al. 2017; Rösel et al. 2018). First-andsecond-year ice were observed to be thicker than thestationary fast ice on Svalbard . Modal thicknesses (the most often thickness measured) for springsea ice have in recent years been between 1.5 and2 m, which is less than earlier observations, whilein summer modal ice thickness was observed to be

8.3.4 Sea ice north of Svalbard
Fast ice in fjords at the northern coasts of Svalbard
have been less intensively observed than those in
the west (see Chapter 8.3.3), but some observations have been made, mainly at Rijpfjorden at thenorthern part of Nordaustlandet. Wang et al. (2013)
describe parts of the seasonal evolution of fast ice
138

Storfjorden covers the southern half along theeastern side of Spitsbergen. Sea ice forms seasonally and locally here, between December and April(Skogseth et al 2004). Dependent on the atmospheric forcing, a polynya can open with a large area ofopen water and intense heat loss to the atmosphere,
and subsequent sea ice formation. First year ice thatform locally reach about 1.0 m in thickness over
winter in “normal” years using 1960-1990 mean
atmospheric forcing (Hendricks et al 2011). Strong
winds (~ 10 m/s) from the north is the primarypolynya driver, creating typical heat loss of 200300 W/m2 in an air temperature of around -10°C.
During winter, sea ice is formed inside the polynya,
creating high salinity and density water that helps
to ventilate the deep ocean (Skogseth et al 2004).
The polynya length downwind is typically 20-80
km, and down-wind ice advection close to 0.5 m/s(Smedsrud et al 2006). When the winds are southerly the polynya closes, and the related convergencecreates dynamically ridged sea ice up to 5 m inthickness (Hendricks et al 2011). The polynya activity in Storfjorden is atmospherically forced by cold
northerly winds and varies annually and on shortertime scales. Storfjorden is an important location for
production of Arctic dense cold-water (Skogsethat al., 2008), forming on the order of 10% of thisimportant water mass.
Ice-thickness monitoring at the shore of Hopenin the northwestern Barents Sea shows negative
trend between 1966 and 2007 (Gerland et al. 2008).
This decrease in thickness is consistent with thedecreasing sea ice thickness in the Arctic in generaland also the decreasing extent in the Barents Sea.
Gerland et al (2008) also declare that sea-ice thickness variability as a climate indicator provides morequantitative information on the state of the ice coverthan solely sea ice extent.

Pavlova et al. (2014) investigated how atmosphericcirculation and ocean temperature influence the inter-annual variability of sea-ice extent in the Barents
Sea using sea-ice concentrations obtained frompassive microwave satellite imagery, sea-surface
temperatures, and NCEP-NCAR sea-level pressure
data for the period 1979-2010. The results lead tothe conclusion that the contributions of wind andsea-surface temperatures to the variability of sea-ice
extent in the Barents Sea in winter are about equal.
King et al. (2017) shows that sea ice thickness inthe northwestern Barents Sea east of Svalbard canvary significantly between years. In 2003 the ice
regime was dominated by sea ice forming otherplaces and then advected into Barents Sea. Most ofthat ice was multiyear ice, on average 1,4 m thick.
In contrast, sea ice in 2014 was seasonal ice, formedin the northwestern Barents Sea, and the average
thickness was only 0,7 m. The two examples show
how different sea ice characteristics can be,depending on the specific environmental conditions
and forcings.
8.3.6 Future projections for sea ice in the
Svalbard region
The simulated change in future sea ice concentration (Figure 8.1.3) indicate a major reduction thenorthern Barents Sea, a reduction in the western
fjords, and a certain increase in the outer and western parts of Storfjorden. Areas with decreased sea
ice concentration reflect increased sea surface
temperature and vice versa.
The interannual variability of sea ice extent in the
Barents Sea is largely determined by inflow of
relatively warm Atlantic Water through the Barents
Sea Opening (BSO) (Sandø et al., 2010; Årthun etal., 2012, Smedsrud et al, 2013, Sandø et al., 2014a,b). None of the global CMIP5 climate models arecapable of simulating sufficient inflow of heatthrough the BSO (Sandø et al., 2014a; Langehaug
et al., 2013), explaining a systematic bias for the
recent Barents Sea ice loss. Because the Barents Sea
also dominates the Arctic Ocean sea ice variability
during winter (Onarheim et al, 2018), this missing
warm Atlantic inflow has importance far beyond
the region, possibly through altering the large-scaleatmospheric circulation.
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8.4 Sea level

Kaufmann and Wolf, 1996). The pattern of RSLchange suggests the presence of an ice dome(s) in
the northwest Barents Sea during the past glacial(see Forman et al., (2004) for a review of all the
RSL data). RSL data from west Svalbard show a decrease of some tens of meters since the last glacial,while in the east RSL has fallen by >100 m. Thisoverall fall in RSL indicates that it is vertical upliftof the Earth due to GIA that has dominated past
sea-level changes across Svalbard.

Global sea level is rising owing to increasing ocean
temperatures (thermal expansion) and mass loss
from glaciers and the Greenland and Antarctic ice
sheets (e.g. Church et al., 2013). Regional differences in sea level arise due to spatial differences inocean density change, ocean mass redistribution andcirculation, atmospheric pressure and winds, as well
as changes to the gravity field. When considering
sea-level changes at the coastline, vertical land motion must be taken into account. Relative sea level(RSL) is a measure of the distance of the sea surface
height relative to the seafloor, i.e., the shore. For
Svalbard, land motion due to Glacial Isostatic Adjustment (GIA) plays an important role in determining RSL changes. GIA is the ongoing response of
the solid Earth to past and present ice mass changes

8.4.1 Paleo sea level data
During the past glacial (~20,000 years ago) many
parts of the northern hemisphere, including Scandinavia, were ice covered. Global sea level was ~120m below present-day levels (Peltier and Fairbanks,
2006). While it is well recognized that the Svalbard-Barents Sea region was covered by a major ice
sheet, the configuration of the ice during the pastglacial and nature of the retreat remain uncertain.
This is because there is a paucity of direct marine
and terrestrial evidence constraining past ice heightand extent (Ingólfsson and Landvik, 2012).
Paleo data on relative sea level from Svalbard show
a spatial pattern of change that largely reflects landmotion over the past ~10,000 years. These data
have been used to both constrain the history of the
Svalbard-Barents ice sheet (e.g. Lambeck, 1996)
and infer details of regional Earth structure (e.g.

Figure 8.4.1: Relative sea level observed by the tide gauge in Barentsburg (upper panel) and Ny-Ålesund (lower
panel). The black markers indicate the tide gauge observations, the red line indicate the linear trend of the entire
period covered by the tide gauge observations, and the blue line the trend for the period 1992-2017.
140

8.4.2 Instrument record
For the entire tide gauge records, we calculate atrend of -2.7 ± 0.2 mm/year for Barentsburg and-4.5 ± 0.4 mm/year for Ny-Ålesund (Fig 8.4.1). The
trend is significantly less than zero at both sites. For
the shorter, more recent period 1992 to 2017, therates are calculated as -0.3 ± 1.5 mm/year for Barentsburg and -7.1 ± 0.7 mm/year for Ny-Ålesund.
These results suggest that, for both stations and bothstudy periods, the rate of vertical land uplift has
been larger than the rate of sea surface height rise.
If we assume that the change in sea surface height(measured in a geodetic reference frame) is similar
in Barentsburg and Ny-Ålesund, the differences inrates indicate that vertical land motion at the twosites is different. Furthermore, while the rate in NyÅlesund for the most recent period is less than for
the entire study period, the rate over the most recent
period in Barentsburg is larger. This suggests that
temporal variation of vertical land motion at the twosites is different and/or that the tide gauges are notconnected to a stable reference benchmark.

Despite the challenges associated with its high latitude location (Chapter 3.6) there are several studies
on observations of the Arctic Ocean from altimetry.
Most of these studies rely on gridded multi-mission
altimetry products that include the waters around
Svalbard. Volkov et al. (2012) found that the sea
level has risen by 0 to 2 mm/year around Svalbard
for the period 1992 to 2010. They found highest
rates on the west coast of Svalbard and with lower
rates on the east. In Prandi et al. (2012a, 2012b), theaverage sea-level rise for the Arctic Ocean was estimated to 3.6 ± 1.3 mm/year (90% confidence) forthe period 1993 to 2009. They also reported higher
rates of sea-level rise on the west coast and, furthermore, a shift in the sea-level trend after 2003, i.e., asea-level fall of -4.3 mm/year. This fall is supported
by independent measurements of the mass and steric components of sea-level rise.
The pause in sea-level rise is also evident in aregional time series of sea-level anomalies first presented in Cheng et al. (2015). Andersen and Piccioni (2016) present an update of this series extended
to 2015. The extended series shows how regional
sea-level change is characterized by strong naturalvariability, and that the flattening in sea level in the
period after 2003 is followed by sea-level rise after
2010, and a new fall after 2013. For the entire study
period, Andersen and Piccioni (2016) estimate theregional trend of the Arctic Ocean to 2.2 ± 1.1 mm/
year. As expected, sea-level rates estimated from altimetry are significantly larger than RSL rates fromthe tide gauges owing to the influence of verticalland motion.

GNSS station

PeriodUplift Rate (mm/year)

Longyearbyen

2008-2018

7.2 ± 0.6

Ny-Ålesund

1999-2018

8.0 ± 0.3

Hornsund2009-2018

9.4 ± 0.7

Bjørnøya

2012-2018-0.0 ± 0.6

Table 8.4.1. Observed uplift rates
from permanent GNSS stations
across Svalbard

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200

Trend: 8.0 +/- 0.3 mm/year
Observations
Regression line

height (mm)

150

100

Station20-year return height

200-year return height

1000-year return height50

Ny-Ålesund

1.23 (1.16, 1.28)

1.35 (1.24, 1.41)

1.42 (1.29, 1.49)

Longyearbyen

1.33 (1.26, 1.37)

1.44 (1.34, 1.50)

1.51 (1.39, 1.58)

0

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Time (year)

Figure 8.4.2. Observed time series of land height from the permanent GNSS station at Ny-Ålesund

The Global Navigation Satellite System (GNSS)
measurements (Chapter 3.6.1) from Svalbard indicate high land uplift rates with large inter and intra
annual signals (see Table 8.4.1 and Figure 8.4.2).
Observed uplift rates along the west of Spitsbergen are between 7 and 10 mm/year, whereas, at the
Bjørnøya station there is almost no vertical landmotion and rates are not significantly different fromzero. Previous work indicates that the high uplift
rates are largely caused by the Earth’s response topresent-day glacier changes and, to a lesser extent,
GIA due to past ice mass changes (e.g. Kierulf etal., 2009; Omang and Kierulf, 2011; Memin et al.,2011). However, there remain some data-model
discrepancies and other geophysical processes (e.g.
tectonics) might play a role here. The sparse GNSS
network, with no observations in central, north, andeastern Svalbard means we only have partial understanding of land motion across the archipelago.
Annual land motion variations can essentially be
explained by annual mass balance changes of the
nearby glaciers (Kierulf et al., 2009).
In more recent studies, there has been increased
focus on identifying the individual contributions to
sea-level change. Frederikse et al. (2017), for example, use the sea level equation to model regional
sea-level changes for different mass contributions.
Changes in the distribution of mass on the Earth’s
surface produce a non-uniform sea level pattern due
to gravitational changes. Using data from Frederikse (pers. comm.) we estimate the following: From
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1993 to 2014 the Antarctic ice sheet is estimated tohave contributed ~0.25 mm/year to the RSL trend
over the Svalbard region. Similarly, changes interrestrial water storage have contributed 0.25 mm/
year. Ice mass loss from the Greenland ice sheet
contributed to -0.4 mm/year on the west coast of
Spitsbergen and -0.2 mm/year along the easternislands of the Svalbard archipelago over 1993 to2014. This is due to the west–east gradient in theGreenland gravitational fingerprint. Note that as
mass loss from Greenland has increased, the corresponding trends for the more recent period 2003 to2014 are -0.6 and -0.4 mm/year over Svalbard.
The largest contribution to contemporary RSLchange is from glaciers, and more specifically, thelocal glaciers on Svalbard. As glaciers have retreated and lost mass, their gravitational pull has been
reduced and the Earth has uplifted, leading to a
local RSL fall. Mass loss from Svalbard glaciers is
estimated to have contributed a negative RSL trendof -4 to -8 mm/year around the margins of Svalbard
over 1993 to 2014. In total, we calculate that contemporary mass changes contributed an RSL changeof -8 mm/year for both Ny-Ålesund and Barentsburg over the same period.
Regional steric sea-level changes indicate a slightly negative RSL trend for the period 1993 to 2014
(Good et al., 2013). For comparison, the global
mean thermal expansion trend for 1993 to 2015 is
1.1 mm/year (data from Dieng et al., 2017).

Table 8.4.2. Return heights for extreme high waters from Ny-Ålesund and Longyearbyen given in meters above
mean sea level (1996-2014). The 5% and 95% confidence levels are given in parentheses.

8.4.3 Extreme sea level
A selection of the estimated return heights from
Longyearbyen and Ny-Ålesund with corresponding
confidence intervals are given in Table 8.4.2, additional return heights for other periods can be foundon www.kartverket.no/sehavniva.
8.4.4 Modelled land motion as a contribution to
future sea-level change
The mean Glacial Isostatic Adjustment (GIA) field
in IPCC AR5 is evaluated as the mean of the ICE5G model (Peltier, 2004) and the ANU-ICE model(Lambeck et al. (1998) and subsequent improve-ments). These GIA models are based upon global
ice sheet reconstructions where the loading historyof the ice sheets is essentially inferred from paleo
sea level observations. The one standard error of the
GIA field is taken as the difference between the separate models. Figure 8.4.3a shows the RSL change
due to GIA and associated gravitational effects
over the period 1986–2005 to 2081–2100. The GIA
models predict continued land uplift in the Barents
Sea region and, therefore, RSL is projected to fallin this area. Along the west coast of Svalbard GIA
uplift rates are predicted to be stable; this area is thehinge line between vertical uplift and subsidence ofthe surrounding peripheral bulge.

Figure 8.4.3.
Relative sea-level change (m)due to GIA (panela) and future glacier changes for
RCP4.5 (panel b)over the period 1986–2005
to 2081–2100.
Results are from
IPCC AR5 andboth show themean field.

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Figure 8.4.3b shows the glacier contribution to
future RSL change over the period 1986–2005 to
2081–2100. Projected global glacier changes in
IPCC AR5 and used here are based on modelled
Surface Mass Balance (SMB) changes (Slangen andvan de Wal, 2011; Marzeion et al., 2012; Giesen
and Oerlemans, 2013; Radić et al., 2013). Svalbard
is projected to have a negative SMB and, therefore,
the Earth will uplift and the gravitational pull ofglaciers will be reduced causing a local RSL fall.

given on a coarse 1 x 1 degree grid and, therefore,
will not capture the sea level response at smallerscales. Observations from GNSS show that there
can be large spatial variations in uplift rates owing
to local glacier changes (e.g. see the results from
campaign measurements across Ny-Ålesund area
presented by Kierulf et al. (2009)). Furthermore,
we note that the SMB glacier projections do notinclude any ice dynamic changes, or the response ofmarine-terminating glaciers to changes in oceanic
conditions.

Future RSL projections from glacier changes are

8.4.5 Projected 21st century sea level changes
for Svalbard
Here we present regional sea-level projections for
Svalbard using model output from the CMIP5 andfor the emission scenarios RCP2.6, RCP4.5 and
RCP8.5 (Figures 8.4.4 and 8.4.5). The projections
are given with corresponding 5% to 95% ensemble
spread, these ranges are defined as the likely ranges
in IPCC AR5 (P > 66%).
The IPCC AR5 results indicate a projected RSL fall
over the 21st century for all emission scenarios.
The projected RSL fall is larger for scenarios withincreasing greenhouse gas emissions. That is, forincreasing global temperatures we see a more negative RSL change on Svalbard, which is the oppositeof what we expect along the majority of the world’s
coastlines. This is because with increasing future
warming there will be larger ice mass losses on
Svalbard, which will cause larger land uplift, and alarger local RSL fall. For Longyearbyen, the ensemble mean projected RSL change is -0.39 m (ensemble spread -0.84 to 0.13 m) for RCP8.5 and over theperiod 1986–2005 to 2081–2100.

There are only small differences between the RCPs
up until 2050 (Figure 8.4.5). Going towards 2100
the separate projections from the RCPs begin to diverge but there are still large overlaps between theirrespective uncertainties. In fact, inspection of thevertical bars in Figure 8.4.5 shows that differences
between the ensemble means for the different RCPs
are somewhat smaller than the projections ensemble
spread (5% to 95%).
The largest projected contribution to RSL change
on Svalbard, and largest uncertainty, is from future glaciers changes (Figure 8.4.6). As discussed,
future mass loss from local glaciers will cause alocal RSL fall. Other notable contributions to future
RSL are from steric/dynamic sea-level changes andfrom ice sheet changes. Of particular concern is thecontribution from Antarctica, which could potentially exceed the numbers given here, especially if acollapse of the marine portions of the ice sheet were
to be triggered (e.g. DeConto and Pollard, 2016). As
Svalbard sits in the near field of Greenland, projected mass loss there causes a sea-level fall owing to
ice sheet’s gravitational fingerprint.

Figure 8.4.4. Projected ensemble mean relative sea-level change (m) over the period 1986–2005 to 2081–2100 and
for RCP4.5.

Figure 8.4.6. Contributions to projected relative sea-level change for RCP4.5 over the period 1986–2005 to 2081–
2100 for (a) Ny-Ålesund (b) Barentsburg. The ensemble mean and spread (5% to 95%) are shown by the circles andvertical bars, respectively.
Figure 8.4.5. Relative sea-level projections for RCP2.6 (green), RCP4.5 (blue) and RCP8.5 (red) for (a) Ny-Ålesundand (b) Barentsburg. The vertical bars on the right side of the panels represent the ensemble mean and ensemble
spread (5% to 95%) for RSL change for 2081–2100. Annual mean tide gauge observations are shown in yellow.

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9. Assessment and use of projections
9.1 Uncertainties related to the climate projections
Uncertainties can be defined as incomplete knowledge because of lack of certain information ordisagreement on what is known and what we haveknowledge about. Many of the uncertainties inclimate-change projections are different from uncertainties in weather forecasting or seasonal prediction. Weather forecasting and seasonal prediction
are initial value problems where the lack of exact
knowledge of the initial state of the atmosphereand ocean poses major uncertainties on the weather forecast a few days ahead. The uncertainties inclimate projections that goes beyond a few decades
stems from lack of complete knowledge about theboundary conditions (uncertainties related to historical and future changes in greenhouse gases, solar
radiation etc.) and little from the initial conditions.
Traditionally, the major uncertainties in climate projections can be categorized in three main categories:●●Incomplete knowledge related to future man
made emissions●●Incomplete knowledge related to natural variations●●Model uncertainties
However, this notion has to be modified for theregions close to the marginal ice zone as biases in
the initial sea ice extent can influence projections
far longer than a few decades in these regions. Thus,
there is an additional source of uncertainty that has
to be accounted for:●●The initial state of the sea ice.
Incomplete knowledge related to future anthropogenic emissions
Uncertainties related to future anthropogenic emissions are connected to incomplete knowledge about
future economic activity, demographic changes

146such as population growth, future choices of energy
sources, changes in energy efficiencies and othersocioeconomic factors. This lack of certainty about
the future translates into:
Uncertainties in greenhouse gas emissions●●This is primarily related to emissions of carbon
dioxide (CO2), methane (CH4), dinitrogen monoxide (N2O) and chlorofluorocarbon (CFC) gases.
Uncertainties in short lived gases and aerosols●●Uncertainties in man-made emissions from gasesthat are precursors to greenhouse gasses (e.g.
carbon monoxide (CO) and volatile organic
compounds (VOC))●●Uncertainties in man-made emissions of aerosols (small liquid droplets or solid particles thatremain suspended in the air for some time) suchas soot and emissions of gases that can generate
aerosols such as sulphur dioxide (SO2) andnitrogen dioxide (NO2).
Incomplete knowledge related to naturalvariations
Uncertainties connected to natural variations are
partly connected to the fact that the climate system
is non-linear and chaotic (internal variations) andpartly to lack of knowledge about natural forcings
such as future changes in solar radiation and volcanic eruptions.
Uncertainties related to internal variations●●Due to the non-linear and chaotic nature of theclimate system it is not possible to simulate
climate change from one year to the next or fromone decade to the next with the types ofmodels used in this report. On these timescales
the non-predictable internal variations can belarger than the predictable changes that comefrom changes in the external climate forcings(such as changes in greenhouse gases). Internal
climate variations redistribute energy between
different parts of the climate system (e.g. ocean
and atmosphere) or geographically which will influence the regional and global climate. Examplesof this are variations in the amount of heat that istransported from the upper to the deeper ocean,natural climate oscillations such as El Nino andnatural variations in the meridional transport ofenergy by atmosphere and ocean.
●●Many of the internal variations redistributeenergy from one place to another geographicallywithout necessarily changing the global energy
content and will therefore have a larger effectregionally than globally.
●●Global coupled climate models simulate many of thewell-known internal variations (e.g. El Nino), but asthey are not predictable they will not be synchronizedin time with the observed internal variability.
Incomplete knowledge connected to natural external forcings●●With the exception of the well-known 11-year
cycle in solar insolation, the possibilities of predicting future changes in solar radiation are small.
The same holds for the frequency and intensity of
future volcanic eruptions.
●●If solar variations over the next 100 years are ofsimilar magnitude as we have seen the last 100
years, the uncertainties related to solar radiationis smaller than many of the other uncertainties
listed in this chapter. This is partly true for volcanic eruptions as well, but such eruptions may
have pronounced effect on climate on timescalesof a few years and therefore on climatic extremes.
Model uncertainties
Model uncertainty is related to lack of knowledge
about the climate system, our ability to implement
our understanding into a numerical mathematicalframework and limited supercomputing capabilities.
One way to divide this type of uncertainty is in aparametric and structural part.
Parametric uncertainty●●Is due to uncertainty in several parameters withinstate of the art climate models. The most common
way of spanning this type of uncertainty is to do
an ensemble of simulations with the same model but different parameter settings (this is often
called a Perturbed Physics Ensemble, PPE).
Structural uncertainty●●Uncertainties in the structure of the model systems. Which processes are accounted for, to what
extent are they simplified, and are they basedon correct knowledge? It is possible to get an
estimate of this type of uncertainty by runningseveral models with the same climate forcing(often called a Multi Model Ensemble, MME).
However, this will not capture deficiencies thatare shared among the models.
●●Another structural uncertainty is related to processes that we are presently unaware of.
●●The last part of the structural uncertainty is connected to the choice of post processing method(e.g. bias corrections etc.) for the primary climatic variables (such as temperature and precipitation) that are used as input to impact models.
One example is uncertainties introduced due todistortion of the original climate change signal asa result of bias corrections.
The initial state of the sea ice
If the climate projection starts out with a too smallor large sea ice extent this may influence the projections. For models having extensive ice cover,melting of the surplus ice may, through feedback
mechanisms, lead to locally enhanced warming andvice versa for models starting with too small ice
extent. This is discussed in some detail in Box 4.1
and how it influences the bias adjusted variables inBox 5.1.

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9.2 Treatment of uncertainties in different variables

Uncertainties in the oceanic variables

The Arctic is a region with a strong climate change
signal, however it is also a region with large naturalinternal variations related to decadal and multidecadal variations in the large scale oceanic and atmospheric circulation (Chapter 2). These are variations
that are enhanced by strong regional feedbacks(Chapter 2) and results in the Arctic being a region
with a very low signal-to-noise ratio when it comesto climate change. Together with the uncertainties
in the regional feedbacks this makes the Arctic aregion with large uncertainty in the climate change
signal. Due to the different amount of available
information, uncertainty is treated differently for
different climatic variables. The below sections
gives a brief overview:

Due to the lack of oceanic downscaled climate
scenarios, results from only one emission scenario– RCP4.5 and one model are shown in the report.
Thus, there are no estimates of uncertainty linked
to the hydrographic and sea ice related variables.
Analysis and justification for using the NorESM as
boundary condition for the regional ocean model
can be found in Sandø et. al. (2014b) and Langehaug et al. (2013).

Uncertainties in the primary meteorologicalvariables
Results from two possible future emission scenarios
RCP4.5 (medium) and RCP8.5 (high) have been
analyzed. Five regional models have been used for
RCP4.5 and nine for RCP8.5. Many of them ran
with the same global model as boundary condition.
In the time series of temporal development, theuncertainty is illustrated by showing the ensemble
median, 25th and 75th percentiles and the minimum
and maximum of the ensemble. The relative low
numbers of models and their dependence in terms
of using either the same regional model or the same
boundary conditions, implies that the uncertainties

given in the report does not span the full uncertainties as outlined in Chapter 9.1. An investigation
into the spread in Arctic climate response among
the different regional models participating in Arctic
CORDEX can be found in Koenigk et. al. (2015).
Additionally, the fine scale climate model COSMO-CLM (CCLM) has been used for the RCP8.5
scenario (Chapter 3.1.4) in order to investigate
possible effects of fine scale topographical featureson the climate change signal. Finally, for temperature, a suite of global models has been empiricallydownscaled and provide an alternative approach torunning regional models (Chapter 3.1.3).
A majority of the Arctic CORDEX ensemble members have too much sea-ice in the Svalbard region
in the control period, and thus are colder than theobserved climate. The modelled temperature change
therefore tends to be larger than it would have been
for the same models under a more realistic control
climate. Consequently the upper end of the uncertainty span indicated by the ensembles probably is
unrealistically warm (Figure B5.1). As the amountof water vapour the atmosphere can hold before
saturation depends on the temperature, models withthe largest (and possibly unrealistic) future warming
will tend to show the largest precipitation increase(Figure 9.2.1). Further considerations on realism
in the temperature and precipitation projections for
Svalbard are outlined in Box 4.1 and Box 4.2.

Figure 9.2.1: Temperature and precipitation changes for the Arctic CORDEX
ensemble and the high resolution CCLM
simulation for the end of the century with
the RCP8.5 emission scenario. Regional models forced with the same global
model as the high resolution CCLM are
underscored. The blue diamond is the
CCLM model run with coarser resolution (25*25 km2 gridsquares insteadof 2.5*2.5 km2) to indicate the effect ofresolution on the result.

148

Regional sea-level projections for Svalbard wereconducted using model output from the global
CMIP5 models for the emission scenarios RCP2.6,
RCP4.5 and RCP8.5. The projections are given withcorresponding 5% to 95% ensemble spread (fordetails see Chapters 8.4.4 and 8.4.5). The largest uncertainty in the estimated relative sea-level change
in Svalbard is from future glaciers changes as future
mass loss from local glaciers will cause a notablelocal relative sea-level fall which strongly depend
on future glacier loss.
Uncertainties in the primary cryosphericvariables
Simulations of future changes in glacier mass balance was conducted using a climatic mass balance
model (Chapter 3.2.4.3) forced with near-surface
climatic parameters from the fine scale climate
model COSMO-CLM. The simulated mass balance
represents only the climatic mass balance and neglects the ice discharge component which discharges large volumes of ice into the ocean over shortperiods (surges). As only one model was used toforce the mass balance model there are no quantitative estimates of uncertainty. However, a discussion
on the quality of the conducted simulations can befound in Chapter 3.2.4. Other cryospheric variables
such as seasonal snow amounts were simulated withthe hydrological model (Chapter 3.2.3) and theiruncertainties are discussed in the Uncertainties in
the hydrological variables section below.
Uncertainties in the bias adjusted meteorological variables
In this report the empirical quantile mapping(EQM) method used for the climate projections for

Norway (Wong et al., 2016), was applied to bias-adjust temperature and precipitation with the Sval-Imp
data set at 1x1 km2 resolution for Svalbard (Chapter
3.1.1 and Box 5.1). Chapter 3.2.2 provides a brief
description of the methodology. The bias-correction
method uses the Sval-Imp data set as reality without
any observational uncertainties accounted for. It
bias-adjusts one meteorological variable at the time
and the co-variation between different variables
can be altered from the original data. As the EQM
method is applied to temperature and precipitation
separately and on each grid cell individually, theinter-variable, spatial and temporal dependencies
are not corrected relative to the observations. It is
important to bear in mind these limitations wheninterpreting the results of climate impact studies
based on the bias-adjusted climate data. Despite its
limitation, the method is capable of correcting systematic errors in the regional climate model output.
However, new errors may be introduced due to thebias-adjustment (Sorteberg et al., 2014; Maraun,2016). For the Arctic CORDEX simulations with
their coarse resolution of 50x50 km2, the applied
re-gridding to 1x1 km2 using a nearest neighbormethod may have caused unexpected bias-adjustment results, especially along the coastline (see Box
5.2 for details).
Uncertainties in the hydrological variables
As described in the previous section, the bias-adjustment introduces additional uncertainty in the climate change signal that is not present in the original
temperature and precipitation from the RCMs. On
the other hand, the bias-adjustment reduces systematic errors in the simulation of the present day climate and is required to obtain realistic simulationsof present day hydrology.
To estimate the hydrological projections, a hydrological model is applied. Only two river flow gauging stations with rather short records and difficult
measurement conditions implies a poor basis for thehydrological model calibration. The parametrizationof the hydrological model adds to the uncertainty(Lawrence and Haddeland, 2011). The snow modelis calibrated based on observations in Norway and
it is assumed that the model parameters are valid for
Svalbard.
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In Svalbard changes is runoff are very sensitive tothe influence of temperature on glacier mass balance. The modelling results are therefore sensitive
to the representation of glaciers in the model andthe projected temperature changes. That permafrost
and frozen ground is not included in the model,
adds to the uncertainty.
Projected changes in runoff, mean annual floods
and snow are shown as maps (1x1 km2), plots ofthe seasonal cycle, or as time series of the temporaldevelopment. The maps and the plots of the seasonal cycle do not visualize the uncertainty. In the timeseries of temporal development, the uncertainty isillustrated by showing the ensemble median, 25thand 75th percentiles and the minimum and maximum of the ensemble. The very large spread in

the modelling results illustrates the uncertainty. In
particular the bias adjusted temperature for someof the ensemble members leads to unrealisticallyhigh future temperatures resulting in extreme runoff
increases and an extreme reduction in maximum annual snow amounts and snow cover duration. Thereis consistency, however in the direction of change.
Because of the high uncertainty in the hydrologicalmodelling results, expected changes in flood magnitudes are only estimated for the mean annual floodand not for more extreme floods with higher returnlevels.

9.3 Recommendations
Future emissions of greenhouse gases and aerosols
are to a large extent a political and societal question.
It is too early to judge which of the emission scenarios are the most realistic. Since the base year for
the RCP emission scenarios 18 years ago, the real
accumulated greenhouse gas emissions are higher
than all three scenarios (Figure 9.3.1), but this maychange as a consequence of the political commitment embedded in the Paris agreement.
Recommendation on which emission scenarios andwhich model estimates to use cannot be generalized.
It depends on the purpose of the investigation andthe questions posed.
The Arctic CORDEX ensemble medians in thisreport may act as a first guideline for assessing thestrength of climate change in Svalbard. However, it
is important to keep in mind that both the fine-scale
CCLM projections and the excessive sea ice foundin some of the Arctic CORDEX simulations indi-cate that some of the Arctic CORDEX simulations
may give too high estimates for future temperature
increase (Box 4.1). This translates into large changes in the variables derived from air temperature aswell as precipitation (Box 4.2) and hydrologicalchanges (Box 5.1).
For impact and risk analysis we emphasize methods
that take into account both the large uncertainty inthe climate change signal as well as the large naturalinternal variability in the Artic climate. This is ofparticular importance when dealing with low probability-high impact events.
We strongly recommend the users of information inthis report to contact the institutions that have contributed to the analysis and assessment of the resultsthat will be used. This may help in the interpretations of the results and to gain information on new
knowledge.

Figure 9.3.1: a) Global emissions of CO2 (from fossil fuel and cement production ) given in billion tons of carbon
per year from 1959 to 2017 (dots) and the three emission scenarios RCP2.6 (green), RCP4.5 (blue) and RCP8.5(red) from 2000 to 2020. b) Accumulated emissions of CO2 over the 17 years the emission scenarios have existed.
Observationally based data taken from the Global Carbon Budget project (Le Quéré et al 2018).

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10. Longyearbyen
Longyearbyen, the administrative centre of the
Svalbard archipelago, is located in the deeply eroded Longyeardalen valley, with extensive mountainplateaus on both sides (Figure 10.1, Figure 10.3.1).
Longyearelva, a braided river system draining
two glaciers, runs down the central axis of Longyeardalen. Due to a lack of more suitable sites,
most infrastructure in Longyeardalen is confined toareas where potentially hazardous processes, suchas avalanches, slushflows, debris flows, rockfalls,

solifluction and floods, can impact buildings andinfrastructure (Hestnes et al., 2016). In addition,
the permafrost in Longyeardalen is ice-rich, with
frost-susceptible marine sediments, which must
be taken into account in design, construction andmaintenance of buildings and structures (Instanes
and Rongved, 2017).
During the first 50 years of Longyearbyen’s history beginning in the early 1900s, houses were built

Figure 10.1 Longyearbyen located in lower Longyeardalen, with extensive plateaus on both sides, and with the glaciers Larsbreen and Longyearbreen in the end of the valley, 1 May 2016. Photo: Hanne H. Christiansen

152

on locations close to the workplace in the mines.
Longyearbyen then gradually expanded from thewestern side of the valley Longyeardalen, to theLongyearelva river mouth and further to the easternside of the valley. The settlement on the westernside of the valley built prior to the Second World
War was almost completely destroyed in an attackby the German Navy on the 8th of August 1943.
Nybyen, was built after the war, followed by further
building at Haugen, and in approximately1970, thesettlement Lia on the eastern side of the valley wasestablished. The buildings at Blåmyra came in 1985,
and following this, Longyearbyen expanded towards the valley Gruvedalen and the flood plain. In
1989, the process of splitting the coal mining company Store Norske Spitsbergen Kulkompani AS into
three separate entities, including community administration and business development as independent
organisations, was initiated. This led to a change inthe settlement from a pure ‘company town’ to an
open community. The town has grown further insubsequent years, and several buildings and otherinfrastructure are found in hazard-prone areas. The
river Longyearelva is continuously managed so that
it runs in a single channel to gain space for settlement in the valley bottom, where most of the recent
housing development has taken place. As of 2018,river management measures are taken primarily toprevent the river from eroding the river bed so as tosecure the flood plain and allow for further development. Other areas of recent expansion include theharbour area and the area called Gruvedalen. Since
1990, the population in Longyearbyen has grownfrom approximately 1000 to well above 2000 in2018 (https://www.ssb.no/en/befsvalbard/; https://
snl.no/Longyearbyen).

Figure 10.1.1 Annual mean temperature for Svalbard Airport/Longyearbyen [oC]. The points and black curve show
single years and smoothed decadal variability based on observations from 1900 to 2017. Future projections are given for RCP8.5. ESD projections are based on 81 CMIP5 models. RCM projections are based on the 8 Arctic CORDEX simulations. Changes are projected to the periods 2031-2060 and 2071-2100. Median, “high” and “low” projections are shown. A diamond indicates the result for the CCLM RCP8.5 projection towards the end of the century.
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10.1 Atmosphere

The average number of days per year with maximum temperature above and minimum temperature
below 0 oC (“zero crossings”) was 60 in the control
period. Zero crossings occurred in all seasons, but
most frequently in the spring and autumn (Table
10.1.1). CCLM and the Arctic CORDEX mediansuggest a small increase in the number of such days.
According to CCLM, there will be a reduction inzero crossings in the summer and an increase in thewinter.

10.1.1 Temperature
The annual mean temperature at Svalbard Airport/
Longyearbyen during the period 1971-2000 was
approximately -6 °C. Under the emission scenario RCP8.5, the median values for the ensembles
from Empirical-Statistical Downscaling (ESD) andregional climate models (Arctic CORDEX) suggest
a warming of respectively 7.4 and 8.7 °C during the
21st century, while the fine-scale CCLM-model projects a warming of 6.5 °C (Figure 10.1.1 and Table
10.1.1). This means that according to these medianprojections, the annual mean temperature for Svalbard Airport/Longyearbyen will increase to above 0°C at the end of the century.
For the emission scenario RCP4.5, the projected(ensemble median) warming from ESD and Arctic CORDEX are respectively 4.6 and 6.2 °C (notshown). The CCLM projection, which is used toproject changes in temperature-based indices, isthus close to the low RCP8.5 projection (Figure10.1.1) and the medium RCP4.5 projection. This
also holds for seasonal temperatures (Figure 10.1.2).

The average number of days per year with a mean
temperature above 5 oC (“growing days”) was 40 in
the control period 1971-2000, and more than 90%of them occurred during summer (Table 10.1.1).
The CCLM and the Arctic CORDEX median suggest that the number of growing days increases by
a factor of 2 to 3. The CCLM indicates that half theincrease will occur in the summer, and the otherhalf will be distributed between the spring and theautumn.
The average number of days per year with anaverage temperature below 0 oC (“frost days”) wasapproximately 240 in the control period, including
more than 90% of the winter- and spring-days aswell as 2 of 3 days in the autumn (Table 10.1.1).
The CCLM and the Arctic CORDEX medianindicate a reduction of around 95 and 135 days,
respectively. According to the CCLM, there will
be a substantial reduction in frost days during thewinter, though the largest reduction is expected in
the spring and the autumn.

10.1.2 Precipitation
The mean annual precipitation measured in the
Longyearbyen area is about 200 mm (Table 10.1.1);
i.e. lower than the driest areas on the Norwegian
mainland. The precipitation in Longyearbyen is alsoless than half the values measured at Barentsburg,
Isfjord Radio and Ny-Ålesund (Table 4.3.1); dis-a)Observed 1971-2000

Element

ANN

DJF

MAM

JJA

SON

Mean air temperature (°C)

-5.9

-13.9

-9.6

4.5

-4.7

No. of Growing days

40

0

0

38

3

No. of Frost days

241

87

84

5

64

No. of Zero-crossing days

60

9

18

9

23

Mean precipitation (mm)

196

41

52

52

51

b)
Element

Figure 10.1.2 Observed present and projected future seasonal temperature cycle at Svalbard Airport/Longyearbyen[oC]. Winter (Dec-Jan-Feb), spring (Mar-Apr-May), summer (Jun-Jul-Aug) and autumn (Sep-Oct-Nov) temperatures
for the period 1971-2000 (black curve) are based on observations. Projections for the period 2071-2100: Arctic
CORDEX median simulation under emission scenario RCP4.5 (light blue) and RCP8.5 (pink); ESD median simulations under RCP2.6 (green), RCP4.5 (dark blue) and RCP8.5 (red); CCLM RCP8.5 projection (yellow) .
154

charge measurements from De Geerdalen (Chapter
10.2.1) and those modelled from the Sval-Imp dataset (Chapter 3.1.1, Vikhamar-Schuler et al., 2019).
The precipitation measurements are hampered byundercatch in the gauges (Chapter 3.1.1), but themain reason why the Longyearbyen values arelower than the surroundings, is probably that Longyearbyen lies in a “rain shadow” on the leeward
side of the mountain ranges. The historical variability of annual precipitation is shown in Figure 10.1.3.
The observed annual precipitation has increased byapproximately 4% per decade since measurements
started in 1912, and the largest increase is found
for the summer and autumn (Table 4.3.2). As statedin Chapter 4.3.2 the trends in the measured precipitation may be influenced by reduced undercatchbecause of an increasing fraction of the precipitation as rain.

Changes to 2071-2100. CCLM (Arctic CORDEX)
ANN

DJF

MAM

JJA

SON

Air temperature change (°C)

+6.5 (+8.7)+8.2(+11.6)+7.1 (+9.2)+4.3 (+5.8)+6.5 (+8.7)

Growing days, change+73 (+121)

1

14

38

20

Frost days, change-96 (-133)

-18

-35

-2

-40

Zero-crossing days, change+9 (+2)

13

4

-7

-1

Precipitation change (%)+19 (+39)+21 (+26)+29 (+36)+6 (+37)+21 (+40)

Table 10.1.1 a) Present climatology for Svalbard Airport/Longyearbyen and b) Projected changes from 1971-2000 to
2071-2100 under emission scenario RCP8.5. The projections are based on CCLM-simulations; median values from
Arctic CORDEX are given in parenthesis.

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Climate simulations show that both the annual andseasonal precipitation are estimated to increase in
the future. For the RCP8.5 emission scenario, themedian projection for the Arctic CORDEX ensemble gives an increase in annual precipitation of
about 40% towards the end of the century, and for
the CCLM the increase is approximately 20% (Table 10.1.1 and Figure 10.1.3). The CCLM projection indicates that the largest precipitation increase(around 30%) occurs in the spring and the lowest(around 5%) in the summer, while the medianArctic CORDEX results show an increase of 3540% for the spring, summer and autumn and around
25% for the winter season. For emission scenario
RCP4.5 the median increase for annual precipitationin the Arctic CORDEX ensemble is approximately
30%.

Precipitation intensities are usually rather low inLongyearbyen: Daily precipitation exceeding 1 mm
occurs on average 50 days/year and exceeding 5mm just 8 days/year (Isaksen et al., 2017). Despite
the majority of low intensities, events with highrainfalls are not unusual. The highest observed daily
rainfall at Svalbard Airport is 43 mm, and rainfalls
exceeding 25 mm have a return period of around 10
years (Table 4.4.1).
The CCLM simulations for future heavy rainfalls
indicate an increase of 20% for low return periods and 35% for the 100-year return period (Table
4.4.2). In recent years there has been a clear tendency for more precipitation as rain in the wintermonths (Figure 4.3.4). During the winter months,
up to a threefold increase in the number of mild
weather episodes with rain is calculated, as compared to the current situation (Isaksen et al., 2017).

Figure 10.1.3 Annual mean precipitation for Svalbard Airport as a deviation [%] from the reference period 19712000. The points and black curve show single year and smoothed decadal variability based on observations from1912 to 2017. Future projections are given for Arctic CORDEX and COSMO-CLM (CCLM) projections under emission scenario RCP8.5
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10.1.3 WindAt Svalbard Airport, the wind blows with force“strong breeze” (6 Beaufort) or more on 60 daysa year on average and with highest frequencies in
the winter (DJF). In the most recent 40 years, there
has been a slight decline in the frequency of strong
winds at Svalbard Airport (Isaksen et al., 2017).
The dominant wind direction at Svalbard Airport
is southeast, and this is particularly prominent (i.e.
occurs more than 50% of the time) in the winterperiod November – April.

10.2 Hydrology, snow and glaciers
10.2.1 River flow
Discharge measurements from De Geerdalen, approximately 20 km away from the river Longyearelva catchment, were used to set up a hydrologicalmodel for the river Longyearelva. The Longyearelva catchment is rather small, approximately 23
km2, with approximately 30% covered by glaciers(Stenius, 2016). The river drains into Isfjorden, and

The CCLM simulations indicate that the dominantwind direction in the Isfjorden area remains fairly
unchanged towards the end of the century. However, a slight decrease in wind speed is simulated for
Isfjorden sea areas, as is a slight increase for mountain areas east of Longyearbyen. More information
about present and future local wind conditions ispresented in Dobler (2019).

the highest altitudes are more than 1000 m a.s.l. The
modelled and observed runoff in the Longyearbyen
area is more than twice the observed precipitation(Chapter 10.1.2). One reason for the high runoff
values relative to precipitation is likely glacier melt,
as the mass balance of the glaciers in the catchment
has been negative for the previous 30-40 years(Chapter 10.2.4). Other contributing factors areundercatch of precipitation, and that the location of

Figure 10.2.1. Mean annual runoff for the river Longyearelva, simulated using the HBV-model forced with downscaled and bias-adjusted Arctic CORDEX data, for the high emission scenario, RCP8.5. The solid red line shows theensemble median of eight models; the pink band outlines the 25th and 75th percentiles, whereas the dotted lines indicate the minimum and maximum. Runoff values are given as a deviation (%) from the reference period 1971–2000.
The low-pass filtered series are smoothed by Gaussian weighting coefficients and show variability on a decadal timescale.
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precipitation stations (i.e., in low-laying areas) does
not represent the precipitation amounts at higher
elevations (Chapter 4.3.1; Killingtveit et al., 2003).
Continued warming under the medium emission
scenario (RCP4.5, not shown) and the high emission scenario (RCP8.5) leads to an increase in runoff towards the middle of the century, and a weakerincrease towards the end of the century (Figure10.2.1). The increase until approximately 2070 is
caused by a combination of increased precipitationand glacier melt. After this, much of the glacier volume has melted and the contribution from glaciermelt to the river Longyearelva is reduced. As discussed in Chapter 5, glacier melt is likely overestimated by the hydrological model, giving an increasein runoff which is both too rapid and too large, anda maximum runoff which occurs possibly too earlyin the 21st century.

The projected seasonal cycle of runoff shows anincrease in the peak flow towards the middle of the
century, mainly due to more efficient glacier melt
and snowmelt (Figure 10.2.2). Because of increasing temperature, the snowmelt will start somewhat
earlier during the year, and the autumn and winterrunoff will increase slightly towards the end of the
century, snowmelt starts much earlier and the peakrunoff occurs one month earlier than in the present
day climate. Because of reduced glacier volumes,
glacier melt contributes less to the peak flow. Higher winter runoff is expected and, in particular, thelate summer and autumn runoff increases because ofincreased precipitation and high temperatures causing glacier melt. These results are highly uncertain
due to limited observational data, uncertainties inprojected temperature and precipitation (Box 4.1
and 5.1), limited information about the glacier geometry and volume in the current climate and a too
crude representation of the glaciers in the hydrological model.

Figure 10.2.2. Modelled seasonal distribution of the Longyearelva runoff for theperiods 1971–2000,
2031–2060 and
2071–2100.

158

10.2.2 Floods
A flood estimation is available for Longyearelva(Stenius, 2016), based, amongst other things, on theavailable discharge measurements from the river
Bayelva (near Ny-Ålesund) and De Geerdalen. As
described in Chapter 3.2 and Chapter 5.1.1, therecords are short, constrained to the period Juneto September/October, and the observations arevery uncertain. The majority of floods are observed
during the snowmelt period in June and July, but
floods caused by a combination of glacier melt andrain in August, September and October also occur,as do the winter floods generated by rainfall, e.g. in
December 2015/January 2016.

Projected floods in a warmer climate can be assessed by looking at the modelled changes in themean annual flood for the river Longyearelva. The
mean annual flood in Longyearelva is expected toincrease as long as the contribution from glaciermelt increases. The contribution from snowmelt
will gradually decrease as snow amounts are reduced, whereas increases in events with extreme
rainfall can increase the frequency and magnitudeof rain-dominated floods.
The development of the mean annual flood under
RCP8.5 is shown in Figure 10.2.3 for Longyearelva.
Similar to the mean annual runoff, the mean annual
flood increases towards the middle of the century
due to increased glacier melt and increased rainfall.

Figure 10.2.3. Mean annual flood for the river Longyearelva, simulated using the HBV-model forced with downscaled and bias-adjusted Arctic CORDEX data for the high emission scenario RCP8.5. The solid red line shows theensemble median of eight models; the pink band outlines the 25th and 75th percentiles, whereas the dotted lines
indicate the minimum and maximum. Flood values are given as deviation (%) from the reference period 1971–2000.
The low-pass filtered series are smoothed by Gaussian weighting coefficients, and show variability on a decadal
time scale.

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10.2.3 Snow
The number of days with snow cover on the groundat low altitudes in and around Longyearbyen has
decreased since the 1970s (Figure 10.2.4, Svalbard
Airport) and will continue to decrease under bothemission scenarios (Table 10.2.1). Even at higher
altitudes (e.g. Platåberget 445 m a.s.l.), a reductionin the number of days with snow cover is projected
when comparing the reference period 1971-2000 withthe median scenario for both RCP4.5 and RCP8.5 forthe future periods 2031-2060 and 2071-2100. For the
RCP8.5 median projection, the reduction is almost
six months at Svalbard Airport and three and a halfmonths at Platåberget. The more moderate COSMO-CLM (CCLM) projection also shows a reduction of the snow cover season of almost four months
towards the end of the century.

Note that the criteria for snow cover in the model estimates is snow water equivalent larger than 20 mm(i.e. around 20 cm dry snow), while the observedsnow cover just indicates that more than half theground is covered by snow. The main reason why
Table 10.2.1 despite the stronger snow cover criteria
still shows a longer snow season than Figure 10.2.4
is that the precipitation (and thus also snow) is overestimated in the model results (Chapter 5.2.1).
The maximum annual snow storage will also
decrease as temperature and the fraction of precipitation as rain increases. The modelled annual
maximum snow storage for Longyearbyen andPlatåberget are given in Table 10.2.2 The modelled
gradual reduction in maximum annual snow storage
can be seen in Figure 10.2.5. The fastest reduction
takes place under RCP8.5. It should be noted that

From 1971From 19712000 to 2031- 2000 to 20712060
2100

1971-2000

336 mm
494 mm(Sval-Imp)

Arctic CORDEX median,-48% (-15%)
RCP4.5
Arctic CORDEX median,-48% (-19%)
RCP8.5COSMO-CLM RCP8.5-

1971-2000(Sval-Imp)
284 days
328 days

From 1971-2000 to From 1971-2000 to
2031-2060
2071-2100
Arctic CORDEX
RCP4.5
Arctic CORDEX
RCP8.5COSMO-CLM RCP8.5-80 days-53 days-90 days-55 days--128 days-76 days-177 days-108 days-118 days-113 days

Table 10.2.1 Modelled reduction of snow cover duration in the Longyearbyen area, 2 m a.s.l. and Platåberget, 445,
m a.s.l. using different models. (Number of days per year with more than 20 mm snow water equivalent are shownas 30-year mean values.) Italic: Longyearbyen, Bold: Platåberget.
160-90% (-67%)-69% (-66%)

Table 10.2.2 Modelled maximum annual snow storage in the Longyearbyen
area, 2 m a.s.l. and Platåberget 445 m a.s.l. Historic period: mm snow waterequivalent. Future periods: relative change (%) shown as 30-year mean values. Italic: Longyearbyen, Bold: Platåberget.

for Platåberget under RCP4.5, the reduction is notlarge. It should also be noted that the interannualand decadal variability can be large, and in the near
future, years with larger than average snow amounts
can be expected even at lower altitudes.
10.2.4 Glaciers

Figure 10.2.4 Snow cover duration observed at Svalbard Airport, 28
m a.s.l. (Plot generated based on data from http://www.mosj.no/en/
climate/land/duration-snow-cover.html.)-66% (-22%)

Central Spitsbergen is the least glaciated region of
Svalbard (Nuth et al, 2013). Only ~30% of its landarea is covered by glaciers (Figure 10.2.6), due tothe lower precipitation in the interior, comparedwith the coastal areas. The glaciers near Longyearbyen are in general higher in the terrain than thosealong the western coast, and largely comprise valley
glaciers. Similarly to glacier all around Svalbard,
glacier area has been decreasing with time (Figure10.2.6).
Only one glacier near Longyearbyen has had massbalance measurements of any duration: Bogerbreen(Figure 10.2.6) was measured by Soviet Union scientists from 1975-1985, and by University of Oslo,
UNIS and NPI for three years in the early 2000s(Neumann, 2006; J. Kohler, unpublished data )(Figure 10.2.7). The mean net balance of all measurements is -0.5 m w.eq. year-1, roughly the same as
the long-term average for the small valley glaciers
Austre Brøggerbreen and Midtre Lovénbreen nearNy-Ålesund, even though the latter are at lower elevations. A regression of the balance terms between
the Austre Brøggerbreen and Bogerbreen allows

an estimation of the long-term net balance of theformer. The trend is negative, but not statisticallysignificant, as for the Austre Brøggerbreen record.
The mean equilibrium line altitude (ELA) for the
2000s measurements was 824 m a.s.l. No ELA wasavailable for the older Soviet-era measurements.
The long-term outlook for these glaciers is the sameas elsewhere in Svalbard (Chapter 6), namely they
are expected to lose mass at an increasing rate.
Using the future simulations presented in Chapter 6,
we extracted grid points for the Longyearbreen area
to calculate the change in mass balance between the
two time periods 1970-2000 and 2070-2100. Thereis no clear pattern to the change as a function ofelevation, meaning that the mass balance gradientremains close to that of the present, but the stepchange is significant at 1.8 m w.eq. year-1, and thesimulated future ELA is significantly higher thanthe present (around 825 m a.s.l. for Bogerbreen)at 1400 m a.s.l. Since this is much higher than thehighest elevations of the glaciers in the area, we can
expect a rapid wasting away of these glaciers.
10.2.5 Erosion and sediment transport
In its natural state, the entire length of the river
Longyearelva from the glaciers to the sea was a
braided river system. Along with the developmentof Longyearbyen, roads, bridges and housing haveconfined the river. Channel stability has been maintained by removing the flood deposits by bulldozers.

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Figure 10.2.6 Glaciers near Longyearyen, with Bogerbreen outlined in red.

Figure 10.2.5 Maximum annual snow storage (deviation from 1971-2000, %) at the valley bottom (Svalbard Airport,left) and at the mountain plateau area (Platåberget, right). Simulations show the ensemble median, 25-75 percentiles(shaded area), minimum and maximum from the snow model run with the bias-adjusted Arctic CORDEX projections
RCP4.5 (upper panel) and RCP 8.5 (lower panel). The time series are derived from the nearest 1 by 1 km2 grid-cellto the weather station locations.
Figure 10.2.7 Winter, summer and net balance for Bogerbreen, near Longearbyen. Blue lines show the total accumulation on the glacier during the winter, converted to an area average thickness of water (metres of water equivalent, m w.eq.); red lines similarly show the net melt over the summer, while black lines are the sum of the two, or
the total mass gained or lost through the glacier surface processes over the year. The observations (symbols) areextended by performing a linear fit of the Bogerbreen to the long-term Austre Brøggerbreen record. The trend isshown for the regressed net balance.

162

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To secure areas of the floodplain for construction ofhouses and further erosion protection, a sedimentation dam will be built. It is anticipated that climatechange with increasing temperatures, glacier melt
and heavy rainfall will lead to increased river flows
and floods. This will increase the intensity of erosion and the sediment transport and thus sedimentation that are delivered from the sources. Erosionof the moraines of the Larsbreen appear to be an
important sediment source that will be subject to afuture increase. The presence of permafrost limits
the erosion. However, as the temperature of theactive layer rises, more sediments are available fortransport (Etzelmuller and Frauenfelder, 2009).

More sediment delivery to the main river may cause
aggradation, channel changes and lateral activity
in the area upstream from the sedimentation dam.
Debris slides and debris flows need special attention
as they may deliver large amounts of material to thechannel system during single events. To assess thevolumes involved and the possible aggradation at
the glacier outwash fans that may trigger channelchanges, monitoring in the upper part of the catchment, e.g. using red laser scans (Bogen et al., 2016),
is a possibility.

10.3 Permafrost, landslides and avalanches
10.3.1 Permafrost and solifluction
Relatively warm permafrost occur in the Longyearbyen area, which is especially sensitive to warming.
The permafrost temperature records nearby Longyearbyen generally show a continuous temperature
increase in the upper 10-20 m of the ground thelast 20 years. Since 2008-2009 ground temperatures have increased with rates between 0.06°C and
0.15°C per year at 10 m depth (Chapter 7.1).
Recent permafrost modelling by Instanes andRongved (2017) for three different locations inLongyeardalen (the Svalbard Science Centre, thetown centre and Skjæringa) based on recent regional climate model runs using the RCP4.5 scenario
suggest an increase in the active-layer thickness,from about 1.5 m today to 2.5 m by the end of the
century. Using the RCP8.5 scenario to simulate theground temperatures in the upper 10 m in Longyearbyen suggest a substantial thickening of the active
layer and an increase in permafrost temperatures at
5 m and 10 m depth that will be close to 0 °C before
the end of the present century (Chapter 7.1). Due
to high ice content in the fluvial material and silt
and clay-dominated marine sediments in Longyeardalen, warming rates are efﬁciently reduced due tothe consumption of latent heat for melting of ice.
However, as warmer permafrost is more deformable

164

than cold permafrost, this will cause increases increep rates of existing foundations such as piles andfootings (Instanes, 2003) and a speed up of slope
processes controlled by permafrost such as rockglacier deformation and solifluction.
Solifluction is a slope process primarily occurringin the lower slopes of the valley Longyeardalen thatwill also be affected by climate change. Permafrost
warming and increase in active-layer thickness inthe higher rock and debris slopes around Longyearbyen may also lead to increased instability,
manifested as an increased number and magnitudeof rock falls, debris flows, and related phenomena.
This needs to be considered when planning andbuilding new infrastructure (Chapter 7.2).

Snow avalanches have caused a number of fatalities. The first known fatalities concern two teenagers, who were killed by an avalanche on Longyearbreen during Easter 1978. Since February 2001
seven additional fatalities and numerous accidents
due to avalanches have occurred in Longyearbyen,or in the general vicinity of Longyearbyen: twosnowmobilers were caught by an avalanche in February 2001 (Fardalen); further accidents happened
in April 2004 (Mälardalen), March 2009 (Hiorthfjellet) and January 2015 (Fardalen), all involving
snowmobile-triggered avalanches. In December
2015, two people died when a large slab avalanche
from the ridge north of Sukkertoppen destroyed ten
houses in Longyearbyen. In February 2017, anotherslab avalanche released from the top of the mountain Sukkertoppen, triggering a secondary avalanchethat reached the settlement. Several buildings weredamaged, but luckily, this time with no fatalities.
These accidents have led to a higher focus on a safe
infrastructure, especially restructuring of the housing
areas, building avalanche protective measures andfocussing on avalanche observations, prediction, preparedness, mitigation, education and research.

A detailed mapping of avalanches between 2006
and 2009 according to type can be seen in Figure10.3.1 (Eckerstorfer and Christiansen, 2011c),
illustrating avalanche activity on the most usedsnowmobile track “Little Round” close to Longyearbyen. Almost 50% of 423 avalanches werecornice fall avalanches due to the plateau mountaintopography and prevailing south-easterly winds(Chapter 10.1.3). Slab avalanches were also frequent (~30%). Most avalanches were released inspring after low-pressure systems with rising temperatures, high wind speeds and precipitation reaching Svalbard. Hancock et al. (2018) found strong
easterly winds and moderate to heavy snowfall as
precursors to the two devastating naturally triggeredavalanches on the west-facing slope of Sukkertoppen in 2015 and 2017.
Due to favourable topographic and climatic conditions, the valley Vannledningsdalen in Longyearbyen (Figure 10.3.2) has a history of slushflows,
often occurring during the spring thaw. In the summer of 1953, for example, a slushflow occurred on

10.3.2 Avalanches and landslides
Evidence of avalanches and landslides are seen in
the landscape in and around Longyearbyen. Examples are larger rocks from rockfalls in the valley
bottom of Longyeardalen and debris flow tracks
with levees on the mountain slopes. Avalanches
and debris flows occur on all mountainsides. Snowavalanche release in Longyearbyen is primarily aconsequence of drifting snow, embedded weak layers and cornice collapses triggering also larger slab
avalanches (Chapter 7.3). Hestnes et al. (2016) givean overview of avalanches and landslides that havecaused damages to infrastructure and buildings, aswell as fatalities and injuries.

Figure 10.3.1 The furthest runout distance of all
423 avalanches indicated by points along “The
Little Round” snowmobile track for the period 2006
to 2009. The colour of the points distinguishes
avalanche types. (Reprinted from Eckerstorfer andChristiansen, 2011c; reproduced by permission of
Elsevier)
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the large depositional fan called the Haugen area ofLongyearbyen at the mouth of Vannledningsdalen,
destroying several houses, killing three people andinjuring another 30 (Ramsli, 1953). Following thisevent, protective measures were built and routines
were established for drainage measures prior tosnowmelt. The routines were not followed in 1989,
resulting in a slushflow from Vannledningsdalen,
which hit the Haugen area again on 14 June, damaging one house and destroying the main water andheating pipes. In the midwinter of 2012, anotherslushflow caused by a rain-on-snow event destroyed
the pedestrian bridge across the river from Vannledningsdalen (Hansen et al. 2014). Smaller slushflows
have also reached the new harbour-area and havebeen observed from Larsbreen and Tverrdalen following rain events. Hestnes et al. (2016) state thatslushflows from Larsbreen may reach Nybyen.

Rockfalls from steep cliffs and protruding rocknoses along the plateau edges are frequent, but haveso far only led to damages of infrastructure. Debris
flows and slides have also caused damage to infrastructure in Longyearbyen. In July 1972, 80 debris
flows and slides caused damage to the local infrastructure in the lower Longyeardalen valley whenapproximately 30 mm of rain fell in the Longyearbyen area in 12 hours (Larsson, 1982). Two relatively large rainstorms reached Svalbard in Octoberand November 2016, triggering 26 larger hillslope
mass movements mapped in the areas just around
Longyearbyen. The events are described in detail by
Christiansen et al. (2019b). The majority of slope
mass movement occurred during the October rainstorm (e.g. Figure 10.3.3), which had the lowest rain
intensity, with only in total around 20 mm of rain
recorded. The November rainstorm had much morerain, around 75 mm (recorded by a mini-logger inFigure 10.3.2 The valley Vannledningsdalen and the Haugen fan with protective measures, 18 August 2012. Notethe summer-firn, a result of considerable snow accumulation. Photo: Hanne H. Christiansen
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Longyearbyen), and with a much higher intensity.
This storm, however, led to only very limited slope
mass movement locally in the Longyearbyen area.
During the October storm, the still unfrozen active
layer likely became rapidly water saturated with nopercolation beyond the permafrost table. As thaw
was near the annual maximum, the melting of ice
lenses at the top of the permafrost likely contributed
to increased pore water pressure at the active layer/
permafrost interface, contributing to the initiation ofmany slope failures. In contrast, before the second
storm, the upper active layer had frozen, impeding
infiltration of rainfall and thus limiting slope failures despite much more precipitation.
The projected changes in climate with increasing
temperatures and precipitation, although there are
only insignificant changes in wind conditions, may
increase the frequency of all types of avalanches

and landslides that already occur in the Longyearbyen area (Chapter 7.3.3). Climate change andincreased uncertainty were addressed as one of themain challenges for avalanche forecasting and risk
management in Longyearbyen in the evaluation
report after the February 2017 event (Landrø et al.,2017). Indeed, avalanche accidents and avalanche
related problems, along with increased backcountry
activities, led to the establishment of a regional avalanche warning region, Nordenskjöld Land. Since
February 2016 the Norwegian Avalanche Warning
Service at the Norwegian Water Resources and Energy Directorate (NVE, Varsom.no) has issued regional avalanche warnings. After the fatal avalanche
in December 2015, local avalanche danger warnings
for avalanche paths threatening houses in Longyearbyen were also issued by NVE. In addition,
protection measures to prevent avalanches fromhitting buildings have been built after 2015. This

Figure 10.3.3 Slope failures at the Longyearbyen cemetery 16 October 2016 causing a slump and associated debris flow crossing the road. Photo: Hanne H. Christiansen.
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includes snow fences, supporting structures and asmaller melt water drainage. After the avalanche
event in 2017, new hazard maps were made forthe area exposed to avalanches from the west-facing slopes of Sukkertoppen and further protection
measures are recommended (Hoseth et al., 2018;
Gundersen et al., 2018). Regarding the slushflows

in Vannledningsdalen, a warmer and wetter climate
may lead to such slides in the mid-winter without
the possibility for prior draining measures. Hence,
further protective measures are considered also in
Vannledningsdalen.

10.4 Ocean
Atlantic Water (AW) from the West Spitsbergen
Current can reach the upper shelf along western
Spitsbergen and eventually flood into the fjords(Nilsen et al., 2008; 2016). These warm water
intrusions have a strong effect on the seasonal sea
ice cover inside the fjords. A distinct shift to a lower
sea ice cover occurred in 2006, as seen from remotesensing data from 2000 to 2014 (Muckenhuber
et al., 2016). Before 2006 the mean was 50 daysof land-fast ice for Isfjorden, while after 2006 themean dropped to 22 (Figure 8.3.4). These observations also show that Isfjorden had two periods withrelatively high sea ice cover (2000–2005 and 2009–2011) and two periods with relatively low sea ice
cover (2006–2008 and 2012–2014). All periods last
3 years or more, which suggests the involvementof an oceanic mechanism because the atmosphericconditions vary on shorter time scales. However,wind forcing on the West Spitsbergen Shelf ontimescales from days to months is one of the mechanisms starting a circulation of warm AW towards
the fjords (Cottier et al., 2007; Nilsen et al., 2016).
Ongoing hydrographic measurement programs andthe construction of longer time series (e.g. Pavlov
et al., 2013; Tverberg et al., 2018) show that thefjords along west Spitsbergen shifted from an Arctic
state to a more Atlantic state after the winter 2006
(Cottier et al., 2007). This has large implications forthe local climate and the winter air temperatures inLongyearbyen and other settlements along the west
coast of Svalbard.
Figure 10.4.1 shows the simulated winter sea surface temperature and ice concentration today and

Figure 10.4.1 Simulated sea surface
temperature (oC,left) and sea ice
concentration (fraction, right) aroundIsfjorden on thewest coast of Svalbard. Fields shown
are monthly means
for March in thepresent climate (upper, 2010-2019) andthe future change(lower, 2060-2069- 2010-2019) of the
RCP4.5 scenario.

168

change into the future in different fjords close to
Longyearbyen. In general, temperatures will riseand sea ice diminish. The temperatures in the fjords
are much lower than in the ocean outside which isstrongly influenced by AW. The change in futuresea surface temperature appears most affected along
the AW path and where it penetrates into the fjords(Figure 10.4.1 lower left). Ice concentrations are
generally higher inside the sheltered fjords thathave longer pathways for the AW flow. The largest
sea ice loss, of about 20%, occurs in Van Mijenfjorden outside Svea and in Forlandsundet between
Isfjorden and Ny Ålesund, but there is also a clear
loss of sea ice inside Isfjorden itself.

Ny-Ålesund, Barentsburg, and Longyearbyen would
have to come from land uplift. The land uplift due
to Glacial Isostatic Adjustment is similar to Barentsburg (Figure 8.4.3a). Differences that are moredetailed would have to come from individual glaciermass changes, for which projections are currently
not available. Existing AR5 projections of surface
mass balance show no spatial difference between
these locations (Figure 8.4.3b). For Longyearbyen,
the ensemble mean projected relative sea levelchange is -0.39 m (ensemble spread -0.84 to 0.13m) for RCP8.5 and over the period from 1986–2005
to 2081–2100, i.e. the sea level will sink. Differences between emission scenarios are minute and far
smaller than the ensemble spreads (Figure 8.4.5).

Sea level rise is a large-scale phenomenon; hence,
the assessment in Chapter 8.4 is representative forLongyearbyen. Any spatial differences between

10.5 Synthesis
The projections show that the temperature andprecipitation in Longyearbyen will increase towards
the end of the century. Under the emission scenario
RCP8.5 the median value indicates that the annual
temperature will increase by 8.5 °C and the CCLM
indicates an increase of 6.5°C; - i.e. the annual
mean temperature for Longyearbyen will increase to
above 0 °C. The winter temperatures will increase
more than the summer temperatures. The median
RCP8.5 projection indicates an increase of 40% in
the annual precipitation, whereas the CCLM projection indicates an increase of 20%. In addition, thefrequency and intensity of heavy rainfall events will
increase. The wind simulations for a future climate
do not indicate significant changes, either in magnitude or in direction.
These changes will intensify glacier melt, and river
flow including floods will likely increase, although
the results concerning floods are very uncertain.
The percentage of the catchment area covered byglaciers is decisive for the changes in river flow.
The seasonal distribution of runoff will change withan earlier snowmelt and higher winter and autumn
runoff. Increases in rain, glacier melt and river flows
will increase erosion and sediment transport. The
number of days with snow cover will continue todecrease and even the moderate CCLM-projection
shows a reduction of the snow cover season of almost four months towards the end of the century.
The projected changes in climate with increasing
temperatures and precipitation, and increasing
permafrost temperatures, will likely increase thefrequency of all types of avalanches and landslides
that already occur in the Longyearbyen area.
In the ocean, the temperatures will rise, the sea ice
will diminish, and the sea-level will most likely
sink.

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APPENDIX
A1: Climate model results applied for the atmospheric projections in this report
Table A1.1 The following ArcticCORDEX runs (available on the Earth System Grid Federation) were applied for
calculating the RCM-based projections of temperature and precipitation. The MPI-ESM-LR (r1i1p1) is also applied
for the COSMO projections. Symbols - and YES denote non-available and available simulations, respectively.

GCM (run)

RIP

RCM

HIST (1971-2000)

RCP2.6

RCP4.5

NCAR (6)

CCSM4 (6)

1

YES

YES

YES

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

r4i1p1

4

YES

YES

YES

YES

r5i1p1

5

YES

YES

YES

YES

r6i1p1

6

YES

YES

YES

YES

CESM1-BGC (1)r1i1p1

1

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

CESM1-CAM5 (3)r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

CMCC-CM (1)r1i1p1

1

-

YES

YES

YES

CMCC-CMS (1)r1i1p1

1

-

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

-

-

YES

YES

r3i1p1

3

-

-

YES

YES

r4i1p1

4

-

-

YES

YES

NSF-DOE-NCAR (4)RCP8.5r1i1p1YES

CMCC (2)CCCma-CanESM2 (1)r1i1p1ICHEC-EC-EARTH (2)r12i1p1

ICHEC-EC-EARTH (1)r3i1p1

MPI-M-MPI-ESM-LR (3)NCC-NorESM1-M (1)r1i1p1

r1i1p1

SMHI-RCA4

YES

-

YES

YES

SMHI-RCA4-SN

YES

-

-

YES

SMHI-RCA4

YES

-

YES

YES

DMI-HIRHAM5

YES

-

YES

YES

CNRM-CERFACS (5)CNRM-CM5 (5)Climate Model ID

192

YES

YES

YES

YES

YES

-

YES

SMHI-RCA4-SN

YES

-

-

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

SMHI-RCA4

YES

-

YES

YES

r4i1p1

4

YES

YES

YES

YES

r5i1p1

5

YES

YES

YES

YES

r6i1p1

6

YES

YES

YES

YES

r7i1p1

7

YES

YES

YES

YES

SMHI-RCA4

YES

-

YES

YES

RIP | Realization

RCP2.6(65)

RCP4.5(108)

RCP8.5(81)

CSIRO-QCCCECSIRO-Mk3-6-0 (10)Selected

ACCESS1-0 (1)r1i1p1

1

-

YES

YES

YES

ACCESS1-3 (1)r1i1p1

1

-

YES

YES

YES

bcc-csm1-1 (1)r1i1p1

1

YES

YES

YES

YES

bcc-csm1-1-m (1)r1i1p1

1

YES

YES

-

YES

BNU-ESM (1)r1i1p1

1

YES

YES

YES

YES

CanESM2 (5)

-

YES

-

ICHEC (9)EC-EARTH (9)BCC (1)

CCCma (5)

-

1

YES

CSIRO-BOM (2)BNU (1)

5

MGO-RRCM

Table A1.2 All available CMIP5 runs used for statistical downscaling. An overview over available projections assuming the RCP2.6, RCP4.5, and RCP8.5 emission scenarios is given here. Value between brackets show the total
number of simulations in each of the cases. Simulations shown in red are left out in the “selected” ensemble in Table
A.2.2 and A.2.3.
Institute ID

r5i1p1
r1i1p1

r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

r4i1p1

4

YES

YES

YES

YES

r5i1p1

5

YES

YES

YES

YES

LASG-CESS (3)FIO (3)

FGOALS_g2 (1)FIO-ESM (3)r8i1p1

8

YES

YES

YES

YES

r9i1p1

9

YES

YES

YES

YES

r10i1p1

10

YES

YES

YES

YES

r1i1p1

1

-

YES

YES

YES

r2i1p1

2

-

YES

YES

YES

r3i1p1

3

-

YES

-

YES

r6i1p1

6

-

YES

-

YES

r8i1p1

8

YES

YES

YES

YES

r9i1p1

9

-

YES

YES

YES

r11i1p1

11

-

-

YES

YES

r12i1p1

12

YES

YES

YES

YES

r13i1p1

13

-

-

YES

YES

r1i1p1

1

YES

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

193

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

NOAA GFDL (3)GFDL-CM3 (1)r1i1p1

1

YES

YES

YES

YES

GFDL-ESM2G (1)r1i1p1

1

YES

YES

YES

YES

GISS-E2-H (15)

GISS-E2-H-CC (1)NASA-GISS (34)

GISS-E2-R (15)

GISS-E2-R-CC (1)

194

YES

YES

YES

YES

YES

YES

YES

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

r4i1p1

4

YES

YES

YES

YES

1

-

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

r4i1p1

4

YES

YES

YES

YES

IPSL-CM5A-MR (1)r1i1p1

1

YES

YES

YES

YES

IPSL-CM5B-LR (1)r1i1p1

1

-

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

MIROC5 (3)r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

MIROC-ESM (1)r1i1p1

1

YES

YES

YES

YES

MIROC-ESM-CHEM(1)

r1i1p1

1

YES

YES

YES

YES

r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

YES

YES

YES

YES

r3i1p1

3

YES

YES

YES

YES

MOHC (5)HadGEM2-ES (4)GFDL-ESM2M (1)

NIMR-KMA (1)HadGEM2-CC (1)HadGEM2-AO (1)r1i1p1

1

YES

YES

YES

YES

r1i1p1

1

YES

YES

YES

-

r2i1p1

2

-

YES

YES

-

r3i1p1

3

-

YES

YES

-

r4i1p1

4

-

YES

-

-

r5i1p1

5

-

YES

-

-

r1i1p2

1

YES

YES

YES

-

r2i1p2

2

-

YES

YES

-

r3i1p2

3

-

YES

YES

-

r4i1p2

4

-

YES

-

-

r5i1p2

5

-

YES

-

-

r1i1p3

1

YES

YES

-

-

r2i1p3

2

-

YES

-

-

r3i1p3

3

-

YES

-

-

r4i1p3

4

-

YES

-

-

r5i1p3

5

-

YES

-

-

r1i1p1

1

-

YES

-

-

r1i1p1

1

YES

YES

-

-

r2i1p1

2

-

YES

-

-

r3i1p1

3

-

YES

-

-

r4i1p1

4

-

YES

-

-

r5i1p1

5

-

YES

-

-

r1i1p1

6

-

YES

-

-

r1i1p2

1

YES

YES

-

-

r2i1p2

2

-

YES

-

-

r3i1p2

3

-

YES

-

-

r4i1p2

4

-

YES

-

-

r5i1p2

5

-

YES

-

-

r1i1p3

1

YES

YES

-

-

r2i1p3

2

-

YES

-

-

r3i1p3

3

-

YES

-

-

r4i1p3

4

-

YES

-

-

r5i1p3

5

-

YES

-

-

r6i1p3

6

-

YES

-

-

r1i1p1

1

-

YES

-

-

1

YES

YES

YES

YES

INM (1)

r1i1p11

r1i1p11

r2i1p1

inmcm4 (1)IPSL-CM5A-LR (4)IPSL (6)

MIROC (5)MPI-ESM-LR (3)MPI-M (6)MPI-ESM-MR (3)r1i1p1

1

YES

YES

YES

YES

r2i1p1

2

-

YES

-

YES

r3i1p1

3

-

YES

-

YES

MRI

MRI-CGCM3 (1)r1i1p1

1

YES

YES

YES

YES

NCC

NorESM1-M (1)r1i1p1

1

YES

YES

YES

YES

NCC

NorESM1-ME (1)r1i1p1

1

YES

YES

YES

YES

195

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

A2: Climate projections for temperature

Table A.2.2 ESD changes of annual and seasonal temperatures over all regions and Longyearbyen by 2071-2100
assuming the RCP2.6, RCP4.5 and RCP8.5 emission scenarios respectively. Displayed values show the medium,low (5%), and high (95%) percentiles of absolute changes with regards to 1971-2000. Results obtained when using
the MPI-ESM-LR, all, selected (skipping the red model runs in Table A.1.2), and common global climate models
to downscale temperature are also shown in the table for comparison with the CCLM simulation (Table A.2.1. Last
column)Table A.2.1 Annual and seasonal temperature changes calculated from RCM under the emission scenarios RCP4.5
and RCP8.5 for the middle and the end of the century

Temp.
change[°C]

1971-2000 to 2031-2060
RCP4.5Med

1971-2000 to 2071-2100

RCP8.5

RCP4.5

RCP26

RCP8.5

Low

High

Med

Low

High

Med

Low

High

Med

Low

High CCLM

Svalbard land area
Annual

4.9

4.3

7.1

5.5

4.4

8.7

6.9

4.9

9.4

9.8

6.7

15.6

7.2

DJF

7.1

5.7

10.3

7.9

6.8

13.3

10.3

6.8

13.2

14.4

10.6

21.9

9.1

MAM

4.4

3.1

6.9

4.9

4

7.7

6.5

4.1

8.4

9.2

7.7

16.1

7.2

JJA

2.3

1.6

3.6

3.1

1.2

3.9

3.3

2.3

4.7

5.8

2

8.4

4.9

SON

5.5

5.3

8.2

6.5

4.5

10.2

8.2

6

11.4

10.3

6.5

16.2

7.4

Northeast Svalbard area

ModelsMPI-ESM-LR

All models

Selected models

Common models

Perc. Name

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Value

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Svalbard-land area
Annual

4

3.8

0.6

6.5

3.9

-0.5

6.6

4

3.8

4.3

DJF

5.3

5.8

-0.4

10.8

6.2

-2.6

10.9

5.3

4.9

7.4

MAM

4.5

3.2

0.5

5.4

3.2

-0.3

5.5

3.6

3

4.5

JJA

1.6

1.4

-0.3

3.2

1.5

-0.6

3.3

1.6

1

1.9

4.8

4.8

0.6

8

4.6

-0.2

7.8

4.8

4.6

5.4

Annual

4.3

3.9

0.7

6.8

4

-0.3

6.8

4.3

4

4.8

DJF

5.6

5.9

-0.1

11.8

6.2

-2.2

11.9

5.6

5.2

8.7

MAM

4.8

3.4

0.8

5.8

3.5

0.1

5.8

3.7

3.3

4.8

JJA

1.6

1.3

-0.3

3.4

1.4

-0.5

3.5

1.6

0.9

2

SON

5.2

4.9

0.8

7.8

4.9

0

7.7

5.2

4.9

5.7

Annual

5.3

4.5

7.3

5.9

4.4

9

7.4

5.2

9.7

10.5

7

16.4

7.6

SON

DJF

7.2

6.2

10.6

8.8

7.1

13.8

11.1

7.3

13.8

16.1

11.1

23.5

10.2

Svalbard/East

MAM

4.6

3

6.9

4.9

3.9

7.8

6.8

4.1

8.5

9.8

7.8

16.7

7.5

JJA

2.3

1.5

3.5

3

1.1

3.8

3

2.3

4.6

5.8

1.9

8.4

4.9

SON

6.1

5.9

8.8

7.1

4.9

11

9

6.7

12.1

11.2

7.1

17.3

8

Northwest Svalbard area

Temperature changes by 2071-2100

Annual

4.5

4

6.4

4.9

4.3

8

6.2

4.5

8.8

8.9

6.6

14.9

6.7

Svalbard/North-West

DJF

6.5

4.9

9.1

6.8

6

12.1

8.4

5.9

11.8

12.1

10

20.4

8

Annual

3.5

3.4

0.1

6.3

3.5

-1

6.4

3.5

3.2

3.6

MAM

4.1

2.9

6.6

4.7

3.9

7.5

6.2

3.9

8

8.7

7.8

15.7

6.8

DJF

4.7

5

-1.2

9.8

5.1

-3.4

9.9

4.7

3.8

5.7

JJA

2.4

1.5

3.7

3.1

1.5

3.9

3.2

2.3

4.8

6

2.3

8.2

5

MAM

3.7

2.9

-0.1

5.6

2.9

-1.1

5.7

3.3

2.1

3.7

SON

4.9

4.7

7.4

6

4.3

9.4

7.1

5.4

10.8

9.6

6.2

15.8

6.8

JJA

1.4

1.4

-0.3

3

1.5

-0.5

3.1

1.4

0.9

1.7

SON

4.4

4.6

0.2

8.3

4.4

-0.6

8

4.4

4.4

5.2

South Svalbard area

Svalbard/South

Annual

4.4

3.9

7.5

5

4.1

8.8

6.1

4.5

9.3

8.7

6.1

14.3

6.4

Annual

3.9

3.8

0.5

6.3

3.8

-0.6

6.4

3.8

3.6

3.9

DJF

7.1

5.2

11

7

6.1

13

9.1

6.3

13

11.8

9.7

19.2

7.5

DJF

5.1

6.1

-0.5

10.7

6.4

-2.6

11

5.1

5.1

7

MAM

4.3

3.7

7.2

4.8

4.1

8.1

6.3

4.2

8.7

8.7

7.4

15.1

6.6

MAM

4.9

3.4

0.3

5.5

3.4

-0.9

5.5

3.6

2.9

4.9

JJA

3.1

1.7

4.1

3.3

1.3

5.2

4.3

2.3

5.5

5.8

1.9

9.1

5

JJA

1.6

1.6

-0.5

3.4

1.6

-0.8

3.4

1.6

1.3

1.7

SON

4.6

4.2

7.6

5.3

3.8

9.1

6.5

4.6

9.8

8.5

5.5

13.7

6.4

SON

4

4.3

0.6

7.8

4

-0.2

7.7

4

3.9

4.8

Annual

3.1

3

0.3

5

3.1

-0.3

5.1

3.1

2.7

3.5

Longyearbyen grid-point

Svalbard/ Airport

Annual

4.6

4

7.1

5

4.6

8.3

6.3

4.4

8.9

8.7

7.3

14.4

6.5

DJF

4

4.5

-2.1

8

4.6

-1.6

8.1

4

3.3

5.8

DJF

6.4

5

9.8

6.9

5.9

12

8.1

6

11.9

11.6

9.8

18.9

8.2

MAM

3.2

2.3

-1.4

5.5

2.3

-2.1

5.5

3

1.7

3.2

MAM

4.4

3.5

7.3

5

4.2

8.1

6.6

4.4

8.6

9.2

8.2

15.9

7.1

JJA

1

1

-0.3

2.3

1.1

-0.4

2.3

1

0.9

1.3

JJA

2.8

2.4

3.7

3.2

2.7

4.4

3.8

2.8

5.1

5.8

4.3

8.7

4.3

SON

4.1

4.2

0.8

7.4

4.1

0.1

7.4

4.1

3.3

5.5

SON

4.8

4.3

7.7

5.5

4.4

9

6.7

4.6

10.1

8.7

6.2

14.1

6.5

196

197

CLIMATE IN SVALBARD 2100

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RCP45
Models

RCP85

Temperature changes by 2071-2100

MPI-ESM-LR

All models

Selected models

Common models

ModelsTemperature changes by 2071-2100

MPI-ESM-LR

All models

Selected models

Common models

Perc. Name

-

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Name

-

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Value

-

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Perc. Value

-

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Svalbard-land area

Svalbard-land area

Annual

4.7

6.2

3.6

10

6.2

3.5

9.5

6.2

4.7

8.2

Annual

8.4

9.5

6.4

13.3

9.3

6.4

13.2

8.8

8.4

11.6

DJF

6.5

9.9

4.4

16.6

9.9

4.1

16.2

8.5

6.5

12.7

DJF

12.8

15.1

9

23.9

15

8.5

23.1

14

12.6

18.5

MAM

4.6

5.7

2.8

9.6

5.6

2.7

9.1

5.7

4.6

6.5

MAM

8.3

8.3

5.1

12.6

8.2

5

12.5

8.5

7.3

9.3

JJA

2

2.4

0.2

4.8

2.5

0.6

4.8

2.8

2

3.8

JJA

3.9

4.2

1.5

7.2

4.4

1.5

7.3

4.1

3.8

6.6

SON

5.7

7.1

4.1

11.2

7

3.9

10.1

7.2

5.7

10.7

SON

8.8

10

7

15.6

9.8

6.9

14.9

9.5

7.8

13.2

Svalbard/East

Svalbard/East

Annual

5

6.3

3.6

10.1

6.3

3.5

10

6.7

5

8.8

Annual

9

10

6.7

13.8

9.9

6.7

13.6

9.4

9

12.2

DJF

6.9

9.8

4.1

17.6

9.7

3.7

16.8

9.6

6.9

13.4

DJF

13.9

16.3

9.2

25

16.2

8.9

23.8

15.5

13.5

19.8

MAM

4.8

5.8

2.6

9.7

5.8

2.6

9.5

6

4.8

7

MAM

8.9

8.6

5.2

12.9

8.5

5.1

12.6

8.9

7.6

9.9

JJA

2.1

2.3

0.2

5

2.3

0.6

5

2.9

2.1

3.8

JJA

4.1

4.2

1.2

7.4

4.5

1.7

7.5

4.3

3.8

6.6

SON

6.1

7.3

4.1

11.2

7.2

4

10.4

7.7

6.1

11.5

SON

9.2

10.4

7.3

15.8

10

7.1

15.3

10

7.9

13.8

Svalbard/North-West

Svalbard/North-West

Annual

4.2

5.9

3.1

9.2

5.8

3

8.9

5.4

4.2

7.4

Annual

7.7

9

5.7

13.1

8.6

5.6

12.7

7.9

7.6

10.7

DJF

5.6

8.5

3.6

14.6

8.2

3.5

13

6.5

5.6

10.9

DJF

11.2

13.6

6.7

21.6

13.1

6.2

20.3

11.3

10.2

16.1

MAM

4

5.3

2.4

10.3

5.3

2.4

9.2

5

4

5.7

MAM

7.1

7.4

3.7

12.1

7.2

3.7

11.8

7.3

6.6

7.9

JJA

1.8

2.3

0.3

4.6

2.4

0.5

4.6

2.7

1.8

3.9

JJA

4

4

1.6

7.1

4.2

1.7

7.1

4

3.7

6.9

SON

5.3

7.3

3.8

11.3

7

3.7

10.1

7

5.3

10.2

SON

8.4

10.1

7

16.3

9.7

6.9

14.9

9.2

8.3

12.9

Svalbard/South

Svalbard/South

Annual

4.5

6.3

3.2

10.4

6.4

3.2

9.8

5.7

4.5

7.7

Annual

7.8

9.4

6.3

13.3

9.2

6.1

13.1

8.4

7.7

10.9

DJF

6.3

10.1

4.3

17.6

10

4.1

17.5

7.7

6.3

12.5

DJF

11.7

15

8.3

23.2

14.7

8.1

23.1

12.9

11.7

17.3

MAM

4.7

5.7

2.9

10

5.6

2.8

9.6

5.5

4.7

5.8

MAM

8

8.1

5.2

13.1

8.1

5.1

12.7

8.3

7.2

9.4

JJA

2

2.7

0.1

4.9

2.7

0.5

4.9

2.8

2

3.5

JJA

3.4

4.4

1.4

6.9

4.6

1.3

7.1

3.7

3.4

6.2

SON

4.9

6.7

3.6

10.4

6.6

3.5

9.3

6.3

4.9

9.2

SON

8

9.3

6.4

15

8.9

6.3

13.6

8.6

6.8

12

Svalbard/ Airport

Svalbard/ Airport

Annual

3.7

4.6

2.2

7.7

4.6

2.4

7.4

4.4

3.6

7.4

Annual

6.4

7.4

4.6

11.1

7.3

4.3

11.1

6.8

6.1

9.5

DJF

5.1

6.2

-1.9

12.7

6.3

-1.8

12.5

5.5

3.4

11.4

DJF

9.4

9.7

2.6

18

9.8

2.6

18.1

10

8.8

13.5

MAM

3.2

4.4

1.1

8.4

4.4

1.1

8.4

4.1

3.2

6.2

MAM

6.2

6.6

3

11.5

6.3

2.9

11.3

6.4

5

8.9

JJA

1.5

1.8

0.5

3.8

1.8

0.6

3.9

2.1

1.5

2.6

JJA

2.1

3.2

1.5

5.5

3.3

1.5

5.5

3.2

2.1

5

SON

5.1

6.8

3.6

10.1

6.7

3.6

9.5

6.5

5.1

9.7

SON

7.8

9.8

5.7

14.7

9.4

5.6

14.2

8.5

5.5

12.5

198

199

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Table A.2.3 ESD changes of annual and seasonal temperatures over all regions and Longyearbyen by 2031-2060
assuming the RCP2.6, RCP4.5 and RCP8.5 emission scenarios respectively. Displayed values show the medium,low (5%), and high (95%) percentiles of absolute changes with regards to 1971-2000. Results obtained when using
the MPI-ESM-LR, all, selected (skipping the red model runs in Table A.1.2), and common global climate models
to downscale temperature are also shown in the table for comparison with the CCLM simulation (Table A.2.1. Last
column)RCP26
Models

RCP45
ModelsTemperature changes by 2031-2060

MPI-ESM-LR

All models

Selected models

Common models

Perc. Name

-

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Value

-

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Annual

3.8

4.5

2

6.9

4.5

1.8

6.8

4.4

3.8

5.8

DJF

5.3

7

1.5

11.7

6.9

0.9

11.2

5.9

5.3

8.7

MAM

3.9

3.8

1.3

7.3

3.7

1.2

7

4.2

3.9

4.3

JJA

1.6

1.6

0.2

2.8

1.7

0.4

2.7

1.8

1.6

2.3

SON

4.5

5.4

2.9

8.1

5.3

2.8

7.9

5.6

4.5

7.9

Temperature changes by 2031-2060

MPI-ESM-LR

All models

Selected models

Common models

Perc. Name

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Value

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Svalbard-land area

Svalbard-land area

Annual

3.6

3.6

1.4

5.2

3.6

1.1

5.2

3.6

3.6

4.3

DJF

5.4

5.3

-0.1

8.9

5.3

-0.9

8.9

5.4

4.3

6.9

MAM

3.1

3

1.3

5.1

3

1

5.1

3.1

3

3.9

JJA

1.4

1.3

0.2

2.4

1.3

0.1

2.5

1.4

1.1

1.5

Annual

4

4.6

2.2

6.9

4.6

2

6.7

4.8

4

6.2

SON

4.6

4.1

1.6

6.4

4

1.5

6.1

4.6

4.3

5.2

DJF

5.6

7.2

1.8

12.4

7.1

1.2

12.1

6.6

5.6

9.2

MAM

4.1

3.8

1.5

7.2

3.7

1.4

7.2

4.4

4.1

4.6

JJA

1.6

1.6

0.2

2.7

1.6

0.5

2.7

1.9

1.6

2.3

SON

4.9

5.5

3

8.3

5.4

2.9

8

6

4.9

8.5

Annual

3.5

4.2

1.6

6.6

4.2

1.4

6.4

3.8

3.5

5.1

DJF

4.6

5.5

0.5

10.1

5.5

0.4

9.9

5

3.8

7.3

Svalbard/East

Svalbard/East

Annual

3.8

3.5

1.5

5.7

3.5

1.2

5.7

3.8

3.8

4.6

DJF

5.7

5.4

0.3

9.6

5.5

-0.5

9.7

5.7

4.5

7.3

MAM

3.2

3.2

1.1

5

3.2

0.8

5

3.3

3.2

4.1

JJA

1.4

1.2

0.2

2.5

1.3

0.1

2.5

1.4

1.1

1.7

SON

5

4.3

1.7

6.4

4.3

1.6

6

5

4.5

5.7

Annual

3.2

3.2

1

5.3

3.2

0.7

5.1

3.2

3

3.7

MAM

3.5

3.6

0.9

7.3

3.6

0.9

7.2

3.6

3.3

3.8

DJF

5

4.5

-0.9

8.9

4.3

-1.5

7.5

5

3.3

5.6

JJA

1.6

1.7

0.2

2.8

1.7

0.6

2.8

1.8

1.4

2.5

MAM

2.5

2.6

0.8

5.2

2.6

0.5

5.4

2.5

2.2

3.5

SON

4.2

5.3

2.6

8.6

5.3

2.5

8.2

5.4

4.2

7.2

JJA

1.3

1.3

0.3

2.2

1.3

0.2

2.2

1.3

1.1

1.4

SON

4.3

4.2

1.3

6.6

4.1

1.1

6.4

4.3

4.2

4.9

Svalbard/South
Annual

3.6

4.5

1.8

7.7

4.5

1.5

7.2

4

3.6

5.5

Annual

3.5

3.4

1.1

5.6

3.4

0.7

5.6

3.5

3.1

4.2

DJF

5.1

7.2

1.8

13.1

7.2

1

13

5.9

4

8.7

DJF

5.2

5.2

-0.5

8.8

5.2

-1.3

8.9

5.2

4.4

7.3

MAM

3.8

4

1.2

7.7

3.9

1.1

7.2

4

3.8

4.3

MAM

3.4

2.9

1.1

5.1

2.9

0.8

5.1

3.4

2.9

3.8

JJA

1.6

1.8

0.1

2.9

1.9

0.4

2.9

1.9

1.6

2.5

JJA

1.5

1.4

0.1

2.5

1.4

0.1

2.5

1.5

1.4

1.5

SON

3.8

5.1

2.6

8.1

4.8

2.4

7.2

4.9

3.8

6.8

SON

4

3.8

1.6

6

3.7

1.4

5.9

4

3.6

4.4

Svalbard/ Airport
Annual

3.3

3.5

1

5.4

3.5

1.1

5.4

3.5

2.2

5.4

DJF

4.8

4.8

-1.3

9.2

4.8

-1.2

9

4.9

1

8.6

MAM

3

3

-0.6

5.5

2.9

-0.9

5.3

3.1

2.1

4.6

JJA

1.1

1.2

0.1

2.1

1.3

0.1

2.1

1.3

1.1

1.7

SON

4.2

5

2.5

7.7

5

2.8

7.2

5

4.2

7

Svalbard/North-West

Svalbard/South

Svalbard/ Airport
Annual

2.9

2.7

0.7

4.1

2.7

0.7

4.1

2.9

2.2

3.9

DJF

4.3

3.3

-2.1

6.6

3.6

-1.9

6.6

4.3

2.8

6.4

MAM

2.1

2.1

0

4.2

2.1

-0.1

4.2

2.1

1.3

3.9

JJA

0.9

0.9

0.1

1.8

0.9

0.1

1.8

0.9

0.9

1.2

SON

4.2

3.9

1.9

6.1

3.9

1.7

5.9

4.2

3

4.7

200

Svalbard/North-West

201

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

RCP85
Models

A3: Climate projections for precipitation

Temperature changes by 2031-2060

MPI-ESM-LR

All models

Selected Models

Common models

Perc. Name

-

Med

Low

High

Med

Low

High

Med

Low

High

Perc. Value

-

50%

5%

95%

50%

5%

95%

50%

Min.

Max.

Svalbard-land area
Annual

4.9

5.1

2.2

8.4

5.1

1.9

8.2

5.2

4.8

6.6

DJF

8.1

8

1.4

13.2

7.8

1.3

13

8.1

7.6

10.7

MAM

4.1

4.3

1.4

7.8

4.1

1.2

7.8

4.6

4.1

5.8

JJA

1.7

1.9

0.5

3.7

2

0.4

3.7

2.1

1.7

2.8

SON

5.8

5.9

2.9

10.3

5.8

2.8

10.1

6.5

4.7

8.2

Annual

5.3

5.3

2.5

8.8

5.3

2.1

8.2

5.5

5.3

7.2

DJF

8.6

8.5

1.5

13.6

8.4

1.5

13.4

8.9

8.1

11.6

MAM

4.4

4.5

1.9

7.9

4.4

1.6

7.8

4.8

4.4

6.3

JJA

1.8

1.9

0.5

3.6

2

0.5

3.7

2.2

1.8

2.8

SON

6.2

6.2

2.9

10.6

6.2

2.9

10.5

7

5

8.7

Svalbard/East

Svalbard/North-West

Table A3.1 RCM rel. precipitation changes, near and far future, rcp4.5 & rcp8.5, annual and seasonal, all regions +Longyearbyen, med/low/hi CORDEX +, CCLM

Precip.
change[%]1971-2000 to 2031-2060
RCP4.5Med

1971-2000 to 2071-2100

RCP8.5

RCP4.5

RCP8.5

Low

High

Med

Low

High

Med

Low

High

Med

Low

High CCLM

Svalbard land area
Annual

27

20

44

33

16

49

43

22

53

63

27

106

34

DJF

33

15

62

40

19

72

49

22

73

78

22

147

39

MAM

30

16

44

34

12

44

44

14

47

55

22

110

40

JJA

21

13

25

26

8

35

26

22

32

48

29

67

16

SON

26

25

44

35

15

53

46

27

66

59

30

110

40

Northeast Svalbard area
Annual

29

19

46

37

20

54

49

23

57

70

36

112

44

DJF

36

14

69

43

24

83

54

21

84

90

33

162

52

MAM

31

15

43

37

13

44

46

18

49

62

28

112

51

JJA

22

13

25

27

8

36

27

23

33

48

30

74

23

SON

29

25

48

38

22

56

55

27

70

66

39

115

50

Annual

4.5

4.6

1.6

7.7

4.4

1.4

7.6

4.6

4

5.8

Northwest Svalbard area

DJF

7.1

7

1.1

11.8

6.5

0.7

10.8

7.1

5.7

8.8

Annual

26

22

41

30

10

45

41

23

56

61

18

116

24

MAM

3.6

3.9

0.7

7.3

3.6

0.5

6.9

3.9

3.3

4.9

DJF

32

21

52

39

10

57

44

28

76

78

8

157

32

JJA

1.7

1.9

0.5

3.5

2

0.5

3.6

2

1.7

2.9

MAM

26

17

48

28

12

47

40

11

45

51

12

123

25

SON

5.5

5.8

2.8

10.4

5.5

2.6

10.2

6

4.9

7.7

JJA

20

13

27

24

9

32

26

21

35

50

29

56

6

SON

28

25

44

35

7

54

50

30

71

59

21

128

33

Annual

4.6

4.8

1.8

8.6

4.8

1.6

8.4

5

4.2

6.2

DJF

7.6

7.8

1.6

14.7

7.8

1.4

14.5

7.7

6.7

9.9

MAM

4

4.3

1

8

4

1

7.8

4.5

4

5.6

JJA

1.6

2.1

0.2

3.7

2.1

0.2

3.9

2

1.6

3.1

SON

5

5.4

2.6

9.7

5.2

2.4

9.1

5.6

3.9

7.1

Svalbard/South

Svalbard/ Airport
Annual

3.9

3.9

1.1

7.3

3.9

1.1

7.1

4.2

3.1

5.8

DJF

7

5.6

-0.7

10.5

5.7

-0.7

10.7

7

4.9

9.1

MAM

3.2

3.5

-0.1

6.9

3.4

-0.1

6.7

3.9

1.7

5.6

JJA

0.5

1.5

0.2

2.6

1.5

0.1

2.6

1.5

0.5

SON

5

5.5

2.6

9.8

5.3

2.8

9.6

5.7

3.3

202

South Svalbard area
Annual

25

17

40

24

13

41

32

17

41

39

13

78

17

DJF

27

9

54

26

14

55

41

17

52

43

6

94

13

MAM

29

13

42

31

6

43

38

7

45

40

18

88

27

JJA

21

12

32

25

8

38

25

23

29

41

13

63

10

SON

22

14

36

25

5

45

32

17

48

33

6

79

20

Longyearbyen grid-point
Annual

22

14

32

21

14

33

29

14

30

39

22

55

19

DJF

19

3

31

20

10

29

21

10

28

26

7

49

21

MAM

31

9

45

27

-3

39

31

-2

41

36

19

66

29

2.4

JJA

21

18

39

26

8

39

29

23

39

37

17

70

6

7.4

SON

20

10

29

24

4

40

20

5

38

40

-3

68

21
203

CLIMATE IN SVALBARD 2100

CLIMATE IN SVALBARD 2100

Authors

Gundersen, Jeanette

NVE

Contrib. author Ch. 7.3 and 10.3.2

Isaksen, Ketil

MET Norway, KSS

Lead author 3.4, 7.1, 10.3.1;
Contrib. author Ch. 4; Internal review

Jaedicke, ChristianNGI

Contrib. author Ch. 7.3, 10.3.2

Kierulf, Halfdan

Kartverket

Contrib. author Ch. 8.4

Kohler, Jack

NPI

Lead author Ch. 6 and Ch. 10.2.4;
Contrib. author Ch. 9

Li, Hong

NVE, KSS

Hydrological modelling; Contrib. author Ch. 5.1

Lutz, Julia

MET Norway, KSS

Contrib. author Ch. 3.1, 4.1, 4.2, 4.3, 4.4, 4.5

Melvold, Kjetil

NVE, KSS

Contrib. author, Ch. 5

Mezghani, Abdelkader

MET Norway, KSS

Responsible for ESD modelling; Contrib. author
Ch. 3.1, 4.1, Appendix

Nilsen, Frank

UNIS, UiB

Contrib. author Ch. 2.2, 3.5, 8.1, 8.2, 9 and 10.4

Nilsen, Irene Brox

NVE, KSS

Lead author Ch. 5.1, 10.2.1,10.2.2; Contrib.
author Ch. 3.2

Nilsen, Jan Even Øie

IMR, NERSC, Bjerknes Centre

Contrib. author Ch. 3.4, 8.4, 10.4

Pavlova, Olga

NPI

Contrib. author Ch. 8.3

Ravndal, Oda

Kartverket

Contrib. author Ch. 8.4

Risebrobakken, Bjørg

NORCE, Bjerknes Centre

Contrib. author Ch. 8.1

Saloranta, Tuomo

NVE

Contrib. author Ch. 3.2, 5.2

Sandven, SteinNERSC, UNIS, Bjerknes Centre

Lead author Ch. 8.3; Contrib. author Ch. 3.5 and 9

Schuler, Thomas Vikhamar

UiO, UNIS

Responsible for glacier modelling; Contributing
author Ch. 3.3, 6, 10.2.4

Simpson, Matthew

Statens kartverk

Lead author Ch. 3.4, 8.4; Contrib. author Ch.9,
10.4

Contributors

Affiliation(s)Responsibilities

Hanssen-Bauer, Inger

MET Norway, KSS

Coordinating editor; Editor Ch. 1 and 4; Lead: Ch.
4.1, 4.2, 10.1; Contrib. author: Ch. 3.1, 4.3, 4.4,
4.5 and 9

Førland, Eirik

MET Norway, KSS

Editor Summary; Lead: Ch. 4.3, 4.4, 4.5; Contrib.
author: Ch. 2, 3, 4, 9, 10.1

NVE, KSS

Editor Ch. 5, 7, 10; Lead: Ch. 7.3, 10.3.2,10.5;
Contrib. author: Ch. 3.2, 5, 9, 10.2.1, 10.2.2,
10.2.3

EDITORS:

Hisdal, Hege

Mayer, Stephanie

NORCE, Bjerknes Centre, KSS

Editor Ch. 3; Lead author: Ch. 3.1;
Contrib. author: Ch. 4, 9 and 10.1

Sandø, Anne Britt

IMR, Bjerknes Centre

Editor Ch. 8; Lead author: Ch. 2.2, 3.5, 8.1, 8.2
and 10.4; Contrib. author: Ch. 9

Sorteberg, Asgeir

UiB, Bjerknes Centre

Editor Ch. 2 and 9; Contributing author: Ch. 4

AUTHORS:
Adakudlu, Muralidhar

NORCE, Bjerknes Centre

Contrib. author Ch. 3.1, 4.1 and 4.2

Andersen, Jess

NVE

Contrib. author Ch. 5.2

Bakke, Jostein

UiB, Bjerknes Centre

Contrib. author Ch. 6

Beldring, Stein

NVE, KSS

Responsible for hydrological modelling; Leadauthor 3.2; Contrib. author Ch. 5.1

Benestad, Rasmus

MET Norway

Contrib. author 3.1

Bilt, Willem van der

UiB, Bjerknes Centre

Lead author 6.1

Skogen, Morten

IMR, Bjerknes Centre

Contrib. author, Ch. 8.1 and 8.2

Bogen, Jim

NVE

Lead author Ch. 5.3, 10.2.5;
Contrib. author Ch.3.2

Smedsrud, Lars Henrik

UiB, UNIS, Bjerknes Centre

Contrib. author Ch. 2.2, 3.5, 8.1, 8.2, 8.3 and 10.4

Borstad, Christian

UNIS

Contrib. author Ch. 6

Sund, Monica

NVE

Contrib. author Ch. 7.3 and 10.3.2

Breili, Kristian

Kartverket

Contrib. author Ch. 8.4

Breivik, Øyvind

MET Norway, UiB

Contrib. author Ch. 4.5

Vikhamar-Schuler, Dagrun

MET Norway,NVE, KSS

Responsible for snow modelling; Lead author Ch.
5.2, 10.2.3; Contrib. author Ch. 3.2

Børsheim, Knut Yngve

IMR, Bjerknes Centre

Contrib. author Ch. 8

UiO

Christiansen, Hanne H.

UNIS

Lead author Ch. 7.2; Contrib. author Ch. 7.1, 7.3,
10.3

Westermann, SebastianResponsible for permafrost modelling;
Contrib. author Ch. 7.1

NVE, KSS

Dobler, Andreas

MET Norway

Responsible for the CCLM modelling; Contrib. author Ch. 3.1, 4.1, 4.2, 4.3, 4.4, 4.5,10.1, Appendix

Wong, Wai Kwok

Responsible for bias adjustment; Contrib.
author Ch. 3.2, 5.1

Engeset, Rune

NVE

Contrib. author Ch. 7.3 and 10.3.2

Frauenfelder, Regula

NGI

Contrib. author Ch. 7.2, 7.3 and 10.3.2

Gerland, SebastianNP

Contrib. author Ch. 8.3

Gjelten, Herdis Motrøen

MET Norway

Contrib. author Ch. 4.1 and 4.3

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NansensenteretPhoto, visual elements: Ketil Isaksen