Rapid Change of the Arctic Climate System
and its Global Influences―Synthesis Report of GRENE Arctic Climate Change
Research Project (2011–2016)―
Takashi Yamanouchi, Kumiko Takata

Rapid Change of the Arctic Climate System
and its Global Influences―Synthesis Report of GRENE Arctic Climate Change Research Project (2011–2016)―
Takashi Yamanouchi1), Kumiko Takata2)
1) National Institute of Polar Research and Graduate University for Advanced Studies
2) National Institute of Polar Research, present affiliation: National Institute for Environmental Studies

3

Synthesis Report of GRENE Arctic Climate Change Research Project (2011–2016) “Rapid Change of
the Arctic Climate System and its Global Influences” by Takashi Yamanouchi and Kumiko Takata

Published by Research Organization of Information and Systems,
National Institute of Polar Research
10–3 Midori-cho
Tachikawa-shi
Tokyo 190–8518
Japan
20 March 2020

Cover photo. Melt water stream over a glacier surface darkened by glacier microorganisms (cryoconite) in Greenland (photo by Shin Sugiyama, Hokkaido Univ.)2Preface
What is happening in the Arctic, under global warming? In warming due to anthropogenic increases in atmospheric carbon dioxide concentration, the surface temperature in the Arctic is increasing
with a speed that is more than double the global average. We call this “Arctic warming amplification,”and an understanding of its mechanism is urgently needed.
To grasp these changes in the Arctic, to understand the mechanism, and to contribute to future climate projection, we conducted the Green Network of Excellence Program (GRENE) Arctic Climate
Change Research Project “Rapid Change of the Arctic Climate System and its Global Influences” forfive years between 2011 and 2016. Thirty-nine institutions all over Japan participated in the Project,and more than 360 scientists tackled all aspects of the Arctic climate system. Comprehensive Arctic
research incorporating multidisciplinary work and collaborations between observation and modeling
was realized.
Here, we have compiled more than 100 papers originated from GRENE Arctic Project and published in the international journals. During the Project, already more than 350 original articles were
published; however, after the project term, still many distinguished papers came out. Although it isnearly four years since the Project successfully ended, we intended to synthesize the primary outcomes of the Project, since there was no collection of the papers in the English language (Japanese final report was published on HP in 2016). We hope you could enjoy and notice the significant productsof the GRENE Arctic Project.

March 2020
Takashi Yamanouchi, Project Manager
Kumiko Takata, Project Coordinater

Contents

1. Introduction.......................................................................................................1
References
2. Development and practice of the project...................................................................4
References
3. Arctic warming amplification (AA)..........................................................................8
3. 1. Arctic warming processes and its seasonality in climate models....................................8
3. 2. Heat transport...............................................................................................123. 3. Cloud feedback related to sea ice........................................................................15
3. 4. Other factors contribute to AA...........................................................................19
References
4. Arctic and mid-latitude link..................................................................................31
4. 1. Robustness of Arctic sea ice influence on the Eurasian cold winters..............................31
4. 2. Stratosphere path way .....................................................................................33
4. 3. Future trends ................................................................................................42
4. 4. Is the conclusion solid?. ...................................................................................45
References
5. Sea ice distributions and Arctic sea routes................................................................48
5. 1. Sea ice distribution by ocean heat transport and rafting of sea ice.................................48
5. 2. Estimation of thin ice thickness and sea ice production. .............................................54
5. 3. Heat budget and sea ice processes in polynya.........................................................59
5. 4. Modeling of circulation and physical processes of the Arctic Ocean..............................66
5. 5. Prediction of the Arctic sea ice distributions. ..........................................................70
5. 6. Navigation support systems ..............................................................................75
Refernces16. Evaluation of the impacts of Arctic change on marine ecosystems and fisheries................81
6. 1. Ocean acidification and the maintenance mechanism of primary production....................81
6. 2. Variation of primary production .........................................................................87
6. 3. Changes and shifts in the dominant group due to warming and sea ice retreat in the Arctic Ocean
...............................................................................................................93
6. 4. Distribution patterns of fish species.....................................................................98
6. 5. Examinations of sea birds and mammals.............................................................100
References
7. Global influences..............................................................................................105
7. 1. Greenhouse gas cycle and its climatic response.....................................................105
7. 2. Terrestrial ecosystem and carbone cycle..............................................................120
7. 3. Ice mass change and global sea level..................................................................126
7. 4. Effect on ocean circulation..............................................................................138
References
8. Synthesis of the project......................................................................................147
References2the largest ice mass in the northern hemisphere, is also

1. Introduction

melting markedly, and the flow- out or collapse from its
terminal glacier is serious (Zwally et al., 2002). This will
be a concern in terms of the world sea level rise (IPCC,

Scientific background2013). Moreover, the snow cover area or duration of theWhat is happening now in the Arctic, under global

snow season is decreasing (Robinson et al., 1993). In

warming? In warming due to anthropogenic increases insuch ways, snow and ice have been showing great chang-carbon dioxide concentration, the surface temperature ines, and thus great impacts on atmospheric circulation and

the Arctic is increasing with a speed that is more than dou-

terrestrial biology are foreseen. Changes in terrestrial bi-

ble the global average (e.g., Serrreze and Barry, 2011; Fig.

ological activity will alter the amount of atmospheric

1.1a). We call this “Arctic warming amplification,” and an

carbon dioxide absorption by vegetation, and will affectunderstanding of its mechanism is urgently needed.

global warming as feedback (IPCC, 2013). Although it

Reduction of sea ice extent in the Arctic Ocean is con-is far away from Japan, we cannot turn a blind eye totinuing, and an abrupt decrease since the 2000s is espe-

Arctic climate change, since we are affected by it, too

cially noticeable (e.g., Stroeve et al., 2007; Fig. 1.1b).

(e.g., Honda et al., 2009).

Owing to this change, potential for regular ship naviga-

In order to grasp these abrupt changes in the Arctic, totion through the Arctic Ocean has increased, and they

understand the mechanism, and to contribute to future

have become broadly known as “Arctic sea routes.” Theclimate change projection, we conducted the Green Net-

reduction of the sea ice extent and the warming of thework of Excellence Program (GRENE) Arctic Climate

ocean are expected to lead to changes not only in the

Change Research Project “Rapid Change of the Arctic

physical and chemical ocean environment, but also to

Climate System and its Global Influences” for five years

changes of marine ecosystems.

between 2011 and 2016 (Fig. 1.2). 39 institutions from

Looking at the land area, many glaciers distributed

all over Japan participated in the project, and more thanaround the Arctic are retreating. The Greenland ice sheet,

360 Japanese scientists tackled all aspects of the Arctic
climate system. Comprehensive Arctic research incorporating multidisciplinary work and collaborations between
observation and modeling had been realized.
Here we will report the scientific results achieved. In
the project, four strategic research targets were presented
and the outcomes for each target are described on the following pages. Finally, the mutual relationships between
each target and synthesis of “Rapid Change of the Arctic
Climate System and its Global Influences” are shown in
the flow diagram.

Historical back groundWe have a long history of Arctic research in Japan.
The first scientific activity in the Arctic was made byProfessor Nakaya of Hokkaido University famous for his
studies on ice crystals in the falling snow, who visited

Fig. 1. 1 (top) Surface air temperature change (by T. Nozawa
from the data of UK Met. Office). The mean surface airtemperature in the Arctic has clearly increased twice as rapidly
as that of the global mean, both in the last 100 years and in thelast 40 years. (bottom) Change of sea ice area in September(from IPCC reports; Stroeve et al., 2012). The Arctic sea ice
area in September has decreased faster than what has been
simulated by climate models. Moreover, the decrease is
projected to continue through the 21st century.

Greenland Site 2 to study glacier ice crystal under the
support of US expedition conducted in IGY 1957 (Higashi, 1962). Then he continued to join the Arctic, at T-3
observatory drifting in the Beaufort Sea, from 1959 to
1961, to conduct ice and oceanographic observation. He
sent three young scientists from Hokkaido University tocover wintering observations at T-3.
1

Fig. 1. 2 Schematic diagram of climate systems dealt in the GRENE Arctic Climate Change Research Project.

Before these activities by Nakaya, there was a pioneer-

search Center” and started research activities at Ny-Åle-ing expedition to the Arctic Ocean made by Captain Eii-

sund, Svalbard. Japan Agency for Marine Science andchi Taketomi, not well known to the public yet, who wasTechnology (JAMSTEC) also started the marine observa-

a captain at Bureau of Fisheries, Ministry of Agriculture

tions in the Arctic Ocean in 1992. Including these two

and Commerce. He passed through the Bearing strait,

institutions, Arctic research activities became active by

sailed into the Arctic Ocean to 66° 30’ N first in 1923 by

the Japanese scientists from many universities and insti-“Hakuho-maru” and then sailed up to the northern mosttutes; however, their activities were rather small in theof 71° N, 176° W and to the west end at 70° N, 164° W

scale and limited to the single discipline, not so wellnear the mouth of Kolima river in the eastern Siberia by

known to the international communities. Noticing these

the cruise with “Kaiho-maru”, a kind of ice strengthensituations, efforts were carried out to joint those Japanese

ship about 1000 ton, in 1937. Finally, he started on thediverse activities in the Arctic to concentrate in collabo-

trans world cruise in June 1941, planned to pass throughrated project. Finally, a new initiative of all-Japan activi-

the Arctic sea route, reach Germany, then head south toties have been started as a “GRENE Arctic Climate

the Antarctic and back to Japan; however, unfortunately

Change Research Project”, drawn in detail from the next

due to the start of World War II (attack/ intrusion of Ger-

chapters.

man troupe to the Soviet Union), the expedition was canceled on the way to the Bearing strait (Takahashi and Na-

References

ganobu, 2016).

Higashi, A., 1962. Obituary: Ukichiro Nakaya, 1900–1962. J.
Glaciol., 4 (33), 378–380, https://doi.org/10.3189/S0022
143000027763.
Honda, M., Inoue, J., Yamane, S., 2009. Influence of low Arctic
sea-ice minima on anomalously cold Eurasian winters.
Geophys. Res. Lett., 36, L08707, doi: 10.1029/2008GL
0307079.
IPCC, 2013: Climate Change 2013: The Physical Science Ba-

Since the Nakaya’s expeditions, several small activities were continued by Japanese scientists during 1960s,
70s and 80s, before the Arctic research became so active
even in Japan, following the termination of cold-war at
the end of 1980s. In 1990s, National Institute of Polar
Research (NIPR) established “Arctic Environment Re2

sis. Contribution of Working Group I to the Fifth Assessment Report of the IPCC [Stocker, T.F., D. Qin, G.-K.
Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y.
Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge Univ.
Press, Cambridge, United Kingdom and New York, NY,
USA, 1535p. doi:10.1017/CBO9781107415324.
Robinson, D. A., Dewey, K. F., Heim Jr., R. R., 1993. Global
snow cover monitoring: an update. Bull. Am. Meteorol.
Soc. 74, 1689–1696.
Serreze, M., Barry, R. G., 2011. Processes and impacts of Arctic amplification: A research sysnthesis. Global and Planetary Change, 77, 85–96, doi.org/10.1016/j.gloplacha.2011.
03.004.
Stroeve, J., Holland, M. M., Meier, W., Scambos, T., Serreze,
M. C., 2007. Arctic sea ice decline: faster than forecast.

Geophys. Res. Lett. 34, L09501. doi:10.1029/2007GL
029703.
Stroeve, J. C., Kattsov, V., Barrett, A., Serreze, M., Pavlova, T.,
Holland, M., Meier, W. N., 2012. Trends in Arctic sea ice
extent from CMIP5, CMIP3 and observations Geophys.
Res. Lett. 39 L16502, doi:10.1029/2012GL052676.
Takahashi, S., Naganobu, M., 2016. Uncompleted voyage
across the Arctic Ocean –Challenge of Arctic Ocearn
crossing by Kaihou-maru in 1941–. Proceedings, The 31st
International Symposium on Okhotsk Sea & Sea Ice, February 2016, Mombetsu, Hokkaido, Japan, S-2.
Zwally, H. J., Abdalati, W., Herring, T., Larson, K., Saba, J.,
Steffen, K., 2002. Surface melt-induced acceleration ofGreenland icesheet flow. Science, 297(5579), 218–222,
doi: 10.1126/science.1072708.

3

impacts (PI: Jinro UKITA, Niigata University),

2. Development and practice of the
project4) The role of Arctic cryosphere in global change (PI:
Hiroyuki ENOMOTO, NIPR),
5) Studies on greenhouse gas cycle in the Arctic andtheir response to climate change (PI: Shuji AOKI,Composition of the project

Tohoku University),

A new Arctic research project was assigned by Minis-

6) Ecosystem studies on the Arctic Ocean declining

try of Education, Culture, Sports, Science and Technolo-

sea ice (PI: Takashi KIKUCHI, JAMSTEC),

gy, Japan (MEXT) in June 2011 under the Green Net-

7) Projection of sea ice distribution and Arctic sea

work of Excellence Program (GRENE) to NIPR toroutes (PI: Koji SHIMADA, Tokyo University of

conduct cooperative research on Arctic climate change as

Marine Science and Technology; Sub PIs: HASU-

a core institution among universities and research insti-

MI and Hajime YAMAGUCHI, University of To-tutes. 4 Strategic Research Targets were presented as

kyo),follows:

and 5 year project had been started for FY2011~2015.

1) Understanding the mechanism of warming amplifi-

39 institutions from all over Japan participated in the

cation in the Arctic,

project, and more than 360 Japanese scientists tackled all2) Understanding the Arctic system for global climate

aspects of the Arctic climate system. Comprehensive

and future change,

Arctic research incorporating multidisciplinary work and3) Evaluation of the impacts of Arctic change on

collaborations between observation and modeling had

weather and climate in Japan and on marine ecosys-

been realized. Since the top-down strategic targets were

tem and fisheries,

answered by the research themes in bottom-up manner, it4) Projection of sea ice distribution and Arctic sea

became a unique and idealized construction as a researchroutes,project.

under the Terms of Reference: to a) conduct cooperative

General view of the project organization was as shownresearch projects (themes) with applications, b) improvein Fig. 2.1  NIPR worked as a “core institute” under the

infrastructures and c) support research community such

Project Manager and operated a Project Steering Com-as Japan Consortium for Arctic Environmental Researchmittee composed of senior research scientists most from

(JCAR) and that cooperation among different research

universities and institutes outside of NIPR (members are

fields and especially cooperation between observation

on Table 2.1), which worked to make a selection of Re-

and modeling were desired.

search Themes, discuss and approve total and annual re-

NIPR started the GRENE Arctic Climate Change Re-

search plans and budgets, made an advice for the roadsearch Project with the title, “Rapid Change of the Arctic

mapping to the research targets and controlled the prog-

Climate System and its Global Influences” (Project Man-

ress. In order to promote collaborations among Research

ager: Takashi YAMANOUCHI, NIPR; Deputy Project

Themes, the Coordinator was assigned (Kumiko Takata,

Manager: Masao FUKASAWA, JAMSTEC), and issued

NIES).   JAMSTEC was an “associated institute” in

Research Announcements to research community.
Among 22 applications, 7 Research Themes were selected as follows:
1) Improvement of coupled general circulation models
based on validations of Arctic climate reproducibility and on mechanism analysis of Arctic climatechange and variability (PI: Toru NOZAWA, National Institute for Environmental Studies (NIES) and
now Okayama University).
2) Change in the terrestrial ecosystems of the pan-Arctic and effects on climate (PI: Atsuko SUGIMOTO,
Hokkaido University),3) Atmospheric studies on Arctic change and its global

Fig. 2. 1 Project organization.

4

Table 2. 1 Members of Project Steering Committee

charge of operating R/V “Mirai” as one of the infrastruc-

Table 2. 2  Specifications of cloud radar, FALCON-A

tures under the Deputy Project Manager. NIPR supplied
other infrastructures such as cloud radar, foreign ice
breakers and Arctic Data archive System (ADS), andsupported JCAR through the activities of executive secretary.

Equipment and infrastructures
In order to investigate cloud 3-D profile (particle size,
ice/ water content, vertical speed) following the requirements of the project, cloud radar was installed at Ny-Ålesund, Svalbard in September 2013. The radar “FALCON-A” was 95 GHz FM-CW (Frequency Modulated
Continuous Wave) cloud profiling Doppler radar with 2antennas (specifications shown in Table 2.2 and photo in
Fig. 2.2), developed by Chiba University, following theprototype of FALCON-I developed 10 years ago (Takano
et al., 2010).  The research observatory “Rabben” inNy-Ålesund, Svalbard, was established by NIPR in 1991
under the cooperation with Norwegian Polar Institute(Yamanouchi et al., 1996), and maintained since then andalso supplied for the usage in this project; long term ob-

Fig. 2. 2 Cloud radar, FALCON-A, at NIPR station in NyAlesund, Svalbard. Radar is settled inside the container (leftgreen color, right two antennas and electronics; photo by T.
Takano).

servations of atmospheric science and field observationof terrestrial ecosystems have been conducted.
The Arctic cruise of R/V Mirai in 2012 was operated
as one of the major infrastructures of the project. Other
cruises were also conducted during the project in 2013,

breaker, and could not go into sea ice area. Several

14 and 15 to the open water area of the Arctic Ocean.

cruises of foreign icebreakers, Louis S. St-Laurent,

Though Mirai was already deployed, she was not an ice-Amundsen and Sir Wilfrid Laurier of Canadian Coast
5

Fig. 2. 3  ADS system configuration

Fig. 2. 4 Map of GRENE-Arctic observation activities.

Guard, were prepared partly for the use of this project.

those data obtained through observations and model sim-

Without cost, Korean icebreaker, Araon, was also usedulations in the project. ADS was composed of three

under collaboration and training vessel “Oshoro-maru”

parts, 1) ADS Metadata registration System (AMS) andof School of Fisheries, Hokkaido University was also

Key service for Inter Working Arctic data (KIWA) whichused with collaboration. Several mooring buoys were

was a research data registration system and Metadata

deployed for general use of the project.

search service (Fig. 2.3), 2) online visualization applica-

The third item was Arctic Data archive System (ADS)tion (VISION) for climate, satellite and simulation data,

which intended to collect, manage (control) and open

and 3) Visualization service to view satellite data (VISH6OP) for semi-real-time polar environment observation,

and among 16 applications, 12 were accepted with 8 tomonitoring and sea ice prediction. Also, the ADS joined

visit University of Alaska, Fairbanks, U. S. A., and 4 tothe project to invest doi numbers to dataset to encourage

visit universities in Canada, which belong to ArticNet.

data registration.

For this activity, NIPR-Office was settled in International
Arctic Research Center, University Alaska, Fairbanks
and accommodation was also kept in the city of Fair-

Deployment of Pan Arctic observations

banks.

Pan Arctic observations had been performed within

In order to contribute to the activities of Arctic Com-

GRENE Arctic Project at Ny-Ålesund, Svalbard, Skandi-mittee (AC), expert scientists were dispatched to thosenavia, Russian Siberia, Alaska, U. S. A., Northern Cana-

meetings, such as AC Working Group CAFF (Conserva-da, Greenland and Arctic Ocean (Fig. 2.4). At Ny-Åle-tion of Arctic Flora and Fauna) or its Arctic Biodiversity

sund, cloud profiling radar was installed since September

Congress, AMAP (Arctic Monitoring and Assessment

2013, and long time continuous observation with several

Program) and Arctic Circle.

intense observations was made.
References:
Takano, T., Yamaguchi, J., Abe, H., Futaba, K., Yokote, S.,
Kawamura, Y., Takamura, T., Kumagai, H., Ohno, Y., Nakanishi, Y., Nakajima, T., 2010. Development and performance of the millimeter-wave cloud profiling radar at 95
GHz: sensitivity and spatial resolution. Electron. Commun. Jpn,93 (3), 257–262.
Yamanouchi, T., Aoki, S., Morimoto, S., Wada, M., 1996. Report on atmospheric science observations at Ny-Ålesund,Svalbard. Mem. Natl Inst. Polar Res., Spec. Issue, 51,
153–163.

Promoting international cooperation and capacity
building
In order to promote international cooperation and capacity building which are indispensable to the Arctic climate research, the project provided supports for young
scientists to visit institutions in the Arctic countries, andmade feasible to join the international cooperative projects. This issue was conducted in FY2014, following thecomments from review committee. Public call was made7

3. 1. Arctic warming processes and its seasonality
in climate models

3. Arctic warming amplification (AA)As for the strategic target 1, Modeling Theme, Terres-

Although single process of AA had been discussed intrial Theme, Atmosphere Theme, Glaciology Theme andmany studies made before, systematic evaluation of theGreenhouse Gas Theme joined together to work on therelative importance of each process, interaction of pro-aim of “understanding the mechanism of warming ampli-

cesses and seasonality of each process were not com-

fication in the Arctic”. It is already clear that the Arctic

monly made. Here, quantifying the relative importanceis sensitive to the climate change and global warming isof individual effects was made as one of the essential

greatly amplified in the Arctic. However, since at highsteps toward an understanding of the underlying mecha-

latitudes, global solar radiation has a large seasonal vari-nisms (Yoshimori et al., 2014a, b). Feedbacks wereation, and it is necessary to consider the seasonal varia-

quantified in two different versions of an atmosphere-tion of the climate processes such as ice-albedo feedback

ocean GCM under idealized transient experiments based

and so on. Also, many processes are interrelated compli-

on an energy balance analysis that extends from the sur-catedly, it is needed to analyze not only ice-albedo feed-

face to the top of the atmosphere. To conduct this analy-

back but also each of number of processes overall. First

sis, a “feedback-response analysis method” (CFRAM; Luof all, Fig. 3.1 shows vertical distribution of linear trends

and Cai, 2009) was applied not only to annual mean butof temperature change in recent 33 years (1979–2013)

also to different seasons. The GCM used in the study

from ERA-interim reanalysis data. Except for the large

were the Model for Interdisciplinary Research on Cli-amount in the winter stratosphere, most warming appears

mate version 4, medium resolution and version 5 (MI-in the lower troposphere in the Arctic and contribution of

ROC4m and MIROC5). Fig. 3.1.1 shows the underlying

sea ice decline is pointed out (Serreze et al., 2009; Screen

processes considered in the paper (Yoshimori et al.,and Simmonds, 2010). However, warming in the sum-

2014a, b).

mer Arctic seems to be limited.

Yoshimori (2014), following Yoshimori et al. (2014a),showed seasonality of individual processes of AA in Fig.

Fig. 3. 1 Vertical distribution of linear trends of temperature change in recent 33 years (1979–2013) from ERA-interim reanalysis
data (Yoshimori, 2014).

8

3.1.2. These are the results of experiments for two con-by the jth feedback process and ΔTSIM is simulated total

ditions, preindustrial control and an 1% annually increas-

temperature change. The superscripts denote the domain

ing CO2 experiment (twice the level of initial concentra-of the average. A positive AAI represents the process thattion reached at 70 years and the 20 year average frommaintains anomalous temperature contrast between theyears 61 to 80 was used for the analysis). AA is referred

Arctic and the rest of the globe. Increase in surface tem-to an enhanced warming of the Arctic compared to theperature then AA over the Arctic is the smallest in sum-rest of the world, and the contributing processes to themer but the largest in winter. Over the Arctic Ocean in

AA are quantified by an Arctic amplification index (AAI)

summer, ice-albedo feedback due to sea ice retreat, overdefined as

the Arctic land area, ice-albedo feedback due to snow

melting (retreat), respectively, has the largest tendency to(3.1)

warm the surface; however, the surface warming iswhere ΔTj denotes the partial temperature change induced

smallest because of the large heat uptake by the open
ocean. In autumn and winter, the heat stored in the Arctic Ocean in summer is released into the atmosphere.
Also, cloud feedback enhance the warming in autumn towinter. Moreover, there are a number of other factorsthat contribute to warming the surface: strong verticalstability in the atmosphere traps warming close to thesurface; increased low-level clouds enhance the greenhouse effect by trapping more longwave radiation; surface warming sensitivity is larger in colder regions thanin warmer regions due to the effect (non linearity) of the
Stefan-Boltzmann law; and so on. As a consequence, theFig. 3. 1. 1 Physical processes contribute to AA which were
quantified in Yoshimori et al. (2014a, b) (Yoshimori, 2014).

largest AA occurs in winter.

Fig. 3. 1. 2 Seasonality of Arctic amplification (Yoshimori, 2014) made from Yoshimori et al. (2014a). a) AA based on total ofindividual processes and simulations. b) Contributions of each process on AA.

9Fig. 3. 1. 3 Surface temperature changes averaged (a) over Arctic and tropical oceans, (b) over the GINL Seas and the Arctic Ocean,(c) over Arctic and tropical land areas, and (d) over Greenland and the ice-free Arctic land. Solid lines represent the ensemble-meanvalues; dashed lines indicate the mean dispersion among models (plus or minus one std deviation around ensemble mean) (Laine etal., 2016).

For the similar objective, Laine et al. (2016) conducted

winter, with a peak in November, and minimum during

a multi model analysis. The roles of different factors

summer. In terms of amplitude, the seasonal cycle ofcontribution to local surface warming are quantified us-surface warming is larger over oceanic surfaces than overing the “radiative kernel method" applied at the surfacc

land surfaces in the Arctic region. Over the oceans, theafter 100 years of global warming under a representative

total Arctic mean warming (blue line) is mostly influensd

concentration pathway 4.5 (RCP4.5) scenario simulatedby the Arctic Ocean (orange line) compared to the GINL

by 32 climate models from phase 5 of the Coupled Mod-

Seas (green line) where seasonal variability is muchel Intercomparison Project (CMIP5). The warming fac-smaller, as the former domain is more than 3 times larger

tors and their seasonality for land and oceanic surfaces

in area than the latter. Over land, the total Arctic mean

were investigated separately and for different domains(blue line) is mostly determined by the ice-free Arctic

within each other surface type where mechanisms differ.

land (orange line) compared to Greenland (green line)

AA is defined as the ratio of temperature changes in thewhere seasonality is also smaller, as the former area is

Arctic (latitudes greater than 60°N) relative to the tropics

about 6 times larger than the latter.

(between 30°S and 30°N). The overall oceanic domain

Fig. 3.1.4a shows the decomposition of surface tem-

north of 60°N is called the Arctic oceans in this study,

perature changes over the Arctic Ocean for the different

and it is further separated into the Greenland-Islan-months of year. The sum of positive contributions is the

dic-Norwegian-Labrador (GINL) Seas and the Arctic

largest during winter month with a peak in November,

Ocean. Also, the land surface north of 60°N (domain re-whereas the sum of negative contributions is the largest

ferred to as Arctic land) is separated into Greenland andin summer, explaining most of the seasonality of surface

the rest of the domain, ice-free Arctic land. These sepa-

temperature changes (black line). Most factors exhibit arations of the Arctic ocean and land area were performed

clear seasonal cycle, the largest, seasonal amplitudes be-because mechanisms were found to be very different ining related to the albedo and other surface effects. The

these separate regions.

positive albedo effect is the largest in summer, with a

Fig. 3.1.3 shows seasonality, both over Arctic land andpeak of +5°C in July, linked with a large retreat of theoceans, the mean warming is the largest during boreal

mean sea ice cover and a large solar irradiance, whereas
10Fig. 3. 1. 4 Contributions (colored bars) to surface temperature changes (black line) for Arctic Ocean (a) and for ice-free Arctic land (b)(Laine et al., 2016).

it has no effect in winter when solar irradiance is weak or

feedback peaks in spring over the ice-free Arctic land,nonexistent. Other surface effects have a strong negative

similar to over the GINL Seas, but differently from overcontribution in summer, partly balancing, the albedo

the Arctic Ocean where it peaks in summer. The feed-

feedback, and as strong positive effect in winter. The

back is due to a large snow cover retreat in spring than indifferent terms of the decomposition suggest that thesummer, since snow is fully melted at mid summer. We

main contributor to overall energy absorption in summer

can therefore consider that this difference in the timing

is associated with the larger warming of the mixed layer,of the albedo feedback between the Arctic Ocean and theand to a lesser extent with a larger melting of sea ice in

ice-free Arctic land can be indirectly attributed to a dif-early summer and larger vertical mixing in late summer.

ference in their mean latitude. A large snow cover differ-

The excess release of heat in winter seems mostly associ-ence is also found in fall as snow starts to accumulate

ated with an enhanced cooling of the mixed layer and, tolater in late twenty-first-century runs, but solar irradiancea lesser extent, with a larger sea ice formation balanced

is weaker than in spring and only results in a small sec-

by increased vertical heat mixing as winter progresses.

ondary peak in albedo feedback.

Cloud feedback has the warming effect up to +2.5°C

The timing of the cloud feedback is also partly differ-in winter and a cooling effect in summer. It is consistentent from over the Arctic Ocean, with the weak or nega-with an increased mean cloud cover over the Arctic

tive contributions occurring in spring rather than in sum-

Ocean in the models, leading to both increased green-

mer. The positive cloud contribution during the coldhouse and parasol effects, favoring backward LW radia-

season, consistent with a larger cloud cover, is similar fortion in winter but decreasing incoming SW radiation inboth surface types. Cloud property changes other thansummer. Other changes in cloud properties (e.g., thethe cloud cover itself may also play a role in the total

amount of phase of condensates or the mean altitude or

cloud feeclback. The latent heat flux effect is very differ-depth of clouds) may also play a role in the total cloudent between the ice-free Arctic land and the Arctic

feedback.

Ocean, with increased evaporation occurring mostly inContribution to surface temperature changes over thesummer over the former domain and in winter for the lat-ice-free Arctic land are shown in Fig. 3.1.4b. Contribu-

ter. Finally, other surface effects have much less impact

tion factor amplitudes are smaller than over oceans in theof the surface warming over land than over oceans.

Arctic region. The seasonality of factors is more com-

The largest warming over the ice free Arctic land

plex than over the Arctic Ocean, with different contribu-

during the coldest months is attributed to many positive

tions peaking at different times of the year. The albedo

contributions peaking at this time of the year and to weak11

cooling factors, whereas the minimum found in July-August is associated with weaker warming factors, especially albedo and surface warming sensitivity, nonbarotropic
warming, cloud or synergy effects, and a relatively large
latent cooling.
Arctic amplification in a multi-decadal time scale
Using a simple energy balance model (EBM), Tanaka
and Tamura (2016) discussed the natural variability andlinear warming trend due to the increasing radiative forcing of CO2. Based on the fact that the variation of ice albedo feedback causes the Arctic amplification, a hypothetical experiment was conducted by introducing a
multi-decadal variability of the ice albedo feedback inreference to the observed long-term variation of the global mean temperature. The rapid warming during 19702000 could be explained by the superposition of the natural variability and the background linear trend. It was
shown that the recent AO negative pattern of EOF-1 with

Fig. 3. 2. 1 Illustration of the experiments. “Fix” and “Cal”
stand for fixed and calculated lower boundary conditions of
the atmosphere (i.e., SST and sea ice conditions), respectively(Yoshimori et al., 2017).

the warm Arctic and cold mid-latitude was in good agreement with planetary albedo change, indicating negative
anomaly in high latitudes and positive anomaly in
mid-latitudes. EOF-2 showed the Arctic amplification
pattern, with warming in the Arctic and cooling in Sibe-

atmospheric-slab ocean model and A- means simply an

ria.

atmospheric model without slab ocean model. Two experiments, AS-CNTL2 and AS-RTM2 (Fig. 3.2.1e, f), areconducted to investigate the reason for the asymmetric3. 2. Heat transportpolar warming of Northern and Southern hemisphere.

The role of atmospheric heat transport and regional

Experiments are integrated for 60 years, and the last 30

feedbacks

years are used for the analysis except for Fig. 3.2.1e andWhile the surface albedo feedback is often referred tof; while A- experiments are integrated for 40 years, andas the explanation of the enhanced Arctic warming, thethe last 30 years are used for the analysis. In order to de-importance of the enhanced heat transport from the lower

compose the simulated temperature change to contribu-

latitudes has also been reported in previous studies (e.g.,

tions of individual processes, climate feedback-response

Graversen and Brutu, 2016). Here, Yoshimori et al.

analysis method (CFRAM) is used (Lu and Cai, 2009;(2017) made an attempt to understand how the regional

Yoshimori et al., 2014a, b). The CFRAM solves the en-

feedbacks in the Arctic are induced by the change in at-

ergy with SW and LW denoting shortwave and longwave

mospheric heat transport and vice versa.

radiation, respectively.

The model used in the study is an atmospheric generalFig. 3.2.2 shows the zonal and annual mean atmo-

circulation model (GCM) coupled to a mixed-layer slab

spheric temperature change from the control simulation.

ocean model, identical to that used in Yoshimori et al.

In the CO2 doubling experiment with the global slab

(2014b), and the atmospheric model component is identi-

ocean model, well-known features of enhanced warming

cal to that of the coupled atmosphere-ocean GCM, MI-near the Arctic surface and tropical upper atmosphere are

ROC4m, used in Yoshimori et al. (2014a). The ocean

simulated (Fig. 3.2.2a). The warming is also enhanced

model component has a constant depth of 50 m andnear surface at the Southern Hemisphere (SH) high lati-solves only the thermodynamic equation and does nottudes, though, to a lesser degree compared to the NH

calculate the circulation. Experiments conducted in the

counterpart. The NH mid-high latitudes show, on the

study are illustrated in Fig. 3.2.1, where AS- means theother hand, a limited warming in response to the CO2
12

Fig. 3. 2. 2 Zonal and annual mean air temperature changes in the atmosphere from the control experiments (°C): a) AS-2xCO2; b)
AS-LCL; c) AS-RMT; d) the sum of the changes in AS-LCL and AS-RMT (Yoshimori et al., 2017).

forcing when the sea surface in other latitudes are fixed

2xCO 2, AS-LCL and AS-RMT, the albedo feedback

at 1xCO2 conditions (Fig. 3.2.2b). Much of the full re-which tends to warm the surface in summer is nearlysponse seen in the global slab ocean model experimentcancelled by the surface heat uptake that represents the(Fig. 3.2.2a) is captured by the experiment forced by theenergy consumed for the ocean warming and sea ice

remote surface warming (Fig. 3.2.2c). The addition ofmelting. The seasonal evolution of the Arctic surface

warming in the two experiments isolating the remote andwarming is characterized by the absorption of heat anom-

local effect (Fig. 3.2.2b, c) reproduces the full response

aly in summer and its release in winter, consistent withreasonably well although small differences are discern-

previous studies (Yoshimori et al., 2014a, b; Fig. xx

ible (Fig. 3.2.2a, d). This linearity suggests that the com-above). There are other terms that significantly contrib-

parison of the two sensitivity experiments (AS-LCL andute to winter warming in the AS-RMT, however.

AS-RMT) is meaningful in the context of sensitivity“Other” terms in Fig. 3.2.3 is presented individually inmeasurement of local and remote influence.

Fig. 3.2.4 for A-RMT and AS-RMT. A-RMT reveals

All experiments exhibit least warming in summer

how the atmospheric dynamics induced by the remote(June-July-August). The maximum warming occurs insurface warming tends to warm the Arctic surface while

November except for the experiment with sea ice surface

the difference of the two experiments reveals how theconditions fixed at the NH mid-high latitudes (A-RMT),

NH mid-high latitude surface response feeds back to thewhere the warming occurs unsurprisingly by a similar

Arctic surface warming. Fig. 3.2.4a shows that the in-magnitude throughout the year.

creased poleward transport of DSE (DYN) and LH (LSC)

Fig. 3.2.3 shows the partial surface temperature change

warms the Arctic atmosphere which in turn tends towith respect to the control simulation averaged over thewarm the surface. In addition, the increased water vapor

Arctic region for individual months diagnosed using thetransport tends to warm the Arctic surface through green-

CFRAM technique. Only albedo feedback (ALB) andhouse effect of water vapor (WVP) and clouds (CLDL).

surface heat uptake (RESS) terms are explicitly displayed

The moisture source of increased water vapor and clouds

here with the rest term, the “Others”. The simulated

most likely originates from remote regions and is trans-change (SIM) is well reproduced by the sum of all terms

ported horizontally via the atmosphere. The fractional

diagnosed by the CFRAM technique (SUM). In AS-

contribution of water vapor, clouds and large-scale con13

Fig. 3. 2. 4 Monthly partial surface temperature change of
selected terms (“Others” in Fig. 3.2.3) with respect to thecontrol experiment diagnosed using the CFRAM technique (°C):a) A-RMT; b) AS-RMT; and c) the difference between AS-RMTand A-RMT. WVP water vapor, CLDS shortwave cloud, CLDL
longwave cloud, LSC large scale condensation, DYN advection
and EVAP evaporative cooling. The partial temperature change
in the vertical axis is averaged to the north of 70°N (Yoshimori
et al., 2017)

Fig. 3. 2. 3 Monthly partial surface temperature changes withrespect to the control experiments diagnoses using the CFRAM
technique (°C): a) AS-2xCO2; b) AS-LCL; c) AS-RMT; andd) A-RMT. ALB albedo feedback, RESS snow-ice melting andocean heat uptake, OTHERS other terms, SUM sum of all termsof the partial temperature change (SUM=ALB+RESS+OTHERS),
and SIM simulated skin temperature change. The partial
temperature change in the vertical axis is averaged to the northof 70°N (Yoshimori et al., 2017)

densation to the total Arctic surface temperature changeamounts to about 58% while dynamical heating andevaporative cooling contribute 18 and 17%, respectively,on the October-November-December average. As the increase in the northward atmospheric DSE and LH trans-

Fig. 3. 2. 5 Zonal and annual mean surface temperature changewith respect to the control experiments. The black solid line
represents the simulated surface temperature change in ASRMT2 and other lines represent the partial surface temperaturechange in AS-RMT2 or AS-2xCO2 diagnosed using the CFRAM
technique (°C). SW and LW denote shortwave and longwave
components, respectively (Yoshimori et al., 2017).

port at 70°N are about equal, the LH transport appears to
be much more effective in warming the Arctic surface
through the greenhouse effect of water vapor and clouds
as also found by Graversen and Burtu (2016).
Once the sea surface is allowed to respond, an enhanced greenhouse effect of clouds is further strengthened and contributes positively to the Arctic warming(CLDL in Fig. 3.2.4c). Also the sun-shade effect by
14

clouds become substantial in summer (CLDS in Fig. 3.2.4

3. 3. Cloudsb, c).

Cloud feedback related to sea ice

Given the approximate symmetry in the increased atmospheric heat transport to the NH and SH high latitudes

As shown in the previous section, one of the major

in AS-CO2 and the importance of remote forcing in theprocesses of AA is cloud feedback, which increases

Arctic warming, one may wonder why the Southern

greenhouse effect and surface warming in autumn-winter.

Oceans does not warm as much as the Arctic Ocean (Fig.

Abe et al. (2016) investigated the effect of sea ice reduc-3.2.2a). To seek the answer to this fundamental question,

tion on the increase of cloud cover and enhancement of

two additional experiments (AS-CTRL2 and AS-RMT2)

AA. Historical simulations using a coupled atmo-

are conducted. The black solid line in Fig. 3.2.5 rep-

sphere-ocean general circulation model, MIROC5, which

resents simulated surface temperature changes and thewas used in the Coupled Model Intercomparison Projectasymmetric polar warming between AS-RMT2 and AS-

Phase 5 (CMIP5) were analyzed. Simulations are per-CTRL2. While the albedo, large-scale condensation andformed from 1850 to 2005 using anthropogenic forcing

LW cloud terms contribute to the warming roughly at therecommended by CMIP5 project, having five ensemble

same magnitude between the Arctic and Southern Ocean,members with different initial conditions.

the SW cloud term exhibits a striking difference between

Results are compared between two periods 1976–1985

the two regions. The large cooling effect by clouds in theand 1991–2005. The decrease in the simulated Arctic sea

Southern Ocean is explained by an increase in the cloud

ice area (SIA) in all months and the maximum reduction

amount associated with the solid to liquid phase change

in September, consistent with observations (Comiso etof cloud condensate and consequent increase in cloud lifeal., 2008) and probably due to recent global warming, aretime under warming, as elaborated by Ogura et al.

found (Fig. 3.3.1a). As for the simulated cloud cover av-(2008a, b) and discussed by Yoshimori et al. (2009).

eraged over the Arctic Ocean (Fig. 3.3.1b), low-level

From the observations at Ny-Ålesund, Svalbard, Ya-

cloud cover is at maximum of 50% in summer and con-manouchi (2019) explained the warm-moist air intrusion

tinuously decreases during fall and winter, reaching afrom the lower latitudes to the Arctic in enhancing cloud

minimum in April. The simulated seasonal amplitude ofand water vapor longwave radiation to warm the Arctic.

the total cloud cover was similar to that of the low-level

This mechanism was one of the realizations of the pro-

clouds. The simulated Arctic cloud cover for fall, wintercess proposed by Yoshimori et al. (2017).

and sprig increased between two periods (1976–1985 and1996–2005), although the change was not substantial.

Fig. 3. 3. 1 Seasonal cycle of (a) Arctic mean sea ice area averaged over the periods 1976–1985 and 1996–2005 in MIROC5; (c)identical to (a) except for the total and low cloud covers. The unit of sea ice area is 106 km2 (Abe et al., 2016).

15

The largest increase in simulated cloud cover in Octoberward longwave radiation (ΔCSSDLR) between the periods

agrees with previous studies using satellite data and cli-

1976–1985 and 1996–2005 was examined for the ΔSI-

mate model simulations (Liu et al., 2012; Vavrus et al.,grids with a substantial sea ice reduction (a linear trend

2011; Wu and Lee, 2012).

in sea ice concentration of less than −0.1 decade−1) and

The cloud radiative forcing (CRF) is examined since

ΔSI+ grids without a substantial sea ice reduction (a lin-

cloud cover changes could affect the energy balance

ear trend in sea ice concentration exceeding −0.1 de-through the CRF. During the fall, the downward long-cade−1) in each month (Fig. 3.3.2a). Positive ΔCSSDLR

wave radiation by clouds may play a more important role

was dominant in the ΔSI- case when compared with thein the surface energy balance than in the lower latitudes

ΔSI+ case, particularly during fall, winter and spring.

because of the reduced or absent incoming shortwave ra-This positive ΔCSSDLR resulted from both warming anddiation. An increase in cloud cover in the Arctic Ocean

moistening due to the increased open ocean and global

should increase the downward longwave radiation at thewarming can largely affect the surface energy balance insurface; a positive change in CRF for the surface down-

the grids with substantially reduced sea ice concentra-

ward longwave radiation could occur with the substantialtion.

reduction in seaice concentration. In addition, an in-

ΔCRFSDLR in the ΔSI- case were also large and positive

crease in the downward longwave radiation because offrom September to April; the changes in the ΔSI+ caseincreased water vapor and air temperature is also an im-

were small. This result indicate that the increased CRF

portant factor contributing to Arctic warming (Rinke et

was not negligible and potentially contributed to the in-al., 2013).

creased radiation energy in the surface in the grids with

The change in CRF for the surface downward long-

substantially reduced sea ice concentration, but the large

wave radiation (ΔCRFSDLR) and clear sky surface down-

positive ΔCSSDLR was more dominant than ΔCRFSDLR.
To evaluate the relative importance of the changes in
CRF to the changes in clear sky downward longwave radiation, an index was defined as the ratio of ΔCRFSDLR to
ΔCSSDLR as shown in Fig. 3.3.2b. The sign of the indexes
was the same as that of ΔCRFSDLR since ΔCSSDLR was
positive in all the months. The indexes for the ΔSI- casewas negative in summer, increased approximately from
0.4 to 0.5 during September-December, reached a maximum (~0.7) in January-March and increased in Spring(Fig. 3.2.2b).
An increase in the cloud cover as a result of reduced
sea ice enhanced the surface downward longwave radiation. The indexes during the period October-December
showed that the all-sky surface downward longwave radiation in the ΔSI- cases increased by approximately 40–
60% compared with the clear sky surface downward

Fig. 3. 3. 2 Annual time series of (a) the change in (crosses) the
CRF in surface downward longwave radiation (ΔCRFSDLR) and(closed circle) clear sky surface downward longwave radiation(ΔCSSDLR) between the averages for 1976–1985 and 1996–2005
in the MIROC5 simulations and (b) the index ((ΔCRF/ΔCS)
SDLR, the ratio of ΔCRFSDLR to ΔCSSDLR). The solid red (broken
black) lines indicate the ΔSI- (ΔSI+) case. See the text for thedefinition of the index. Shading and error bars indicate thestandard deviations for the ensemble members in the ΔSI- andΔSI+ cases, respectively (Abe et al., 2016).

Fig. 3. 3. 3 An integrated time duration in which “in-cloud”
data were obtained at the Zeppelin Observatory during eachmonth. No data were obtained between August and October
2014 due to construction of the observatory (Koike et al., 2019).

16

longwave radiation. The indexes in the ΔSI- cases wereIn-situ measurements of microphysical properties oflarger than those in the ΔSI+ cases, although the index incloudsthe ΔSI- grids in November was not clearly distinguished

Koike et al. (2019) made two years of continuous infrom that in the ΔSI+ grids. Thus, considering the reduc-situ measurements of microphysical properties of Arctic

tion in sea ice in October, the change in the CRF due to

low-level clouds at the Mount Zeppelin Observatory

reduced sea ice was not disregarded as a factor affecting(78°56′N, 11°53′E), in Ny-Ålesund, Spitsbergen. Fre-

Arctic warming, contrary to Rinke et al. (2013). With re-quency of cloud detections was as Fig. 3.3.3, the integra-

gard to the feedback between sea ice and clouds, the ef-tion time durations during which in-cloud data were ob-

fects of cloud cover on sea ice are also considerable.

tained for each month. The monthly median value of theThis study focused on the changes in Arctic cloud cover

cloud particle number concentration (Nc) showed a clear

as a result of reduced sea ice; however, unable to observeseasonal variation as in Fig. 3.3.4: Its maximum appearedan effect of increased cloud cover on sea ice reduction.

in May–July (65±8 cm−3), and it remained low between
October and March (8±7 cm−3). At temperatures warmer
than 0°C, a clear correlation was found between thehourly Nc values and the number concentrations of aero-Fig. 3. 3. 4 (a) A time series of the monthly median values of the Nc and N70 data. Vertical bars indicate the 25th–75th percentiles.
No cloud data were obtained between August and October 2014 due to construction of the observatory. (b) A time series of themonthly median values of the median diameter of aerosol size distribution, where the integrated aerosol number concentrations
greater and smaller than this diameter are equal (open circles) for the DMPS (mountaintop) measurements. The diameters of the
maximum concentration of aerosol size distributions (dNa/dlogD) are also shown (closed circles). Vertical bars indicate the 25th–75th
percentiles. (c) Same as (b) but for the SMPS (mountain base) aerosol measurements. DMPS = differential mobility particle sizer;
SMPS = scanning mobility particle sizer (Koike et al., 2019).

17

Fig. 3. 3. 5 A scatter plot of one-hour data between N70 and
Nc. (a) DMPS-derived N70 (mountaintop) and (b) SMPS-derived
N70 (mountain base) are used. Colors of the data points indicate
the temperature at the Zeppelin Observatory. Black circles andvertical bars indicate median values and 25th–75th percentiles,
respectively, for data obtained with T > 0°C. They arecalculated within the individual data ranges, in which a similar
number of data were obtained. The red line in (a) denotes
the threshold values used to separate the “CCN-controlled”
and “CCN-uncontrolled” data sets (above and below thisline, respectively). DMPS = differential mobility particle
sizer; SMPS = scanning mobility particle sizer; CCN = cloud
condensation nuclei (Koike et al., 2019).

Fig. 3. 3. 6 A histogram of the depolarization ratios at altitudesof 450, 480, and 510 m measured by the micropulse lidar (at the
AWI or Rabben Observatories, before and after March 2015,
respectively; Table 1 and Figure 1b) when (a) CCN-controlledand (b) CCN-uncontrolled clouds were observed at Zeppelin(T < 0°C). Five-minute lidar data were used. The vertical line
(a depolarization ratio of 0.043) indicates the threshold value
used to distinguish spherical (liquid droplets) and nonspherical(ice) particles in this study. The colors denote the number
concentrations of the precipitating particles (r = 25–775 μm)
measured by the MPS. Note that different color scales are used
for a and b. AWI = Alfred Wegener Institute; CCN = cloud
condensation nuclei; MPS = Meteorological Particle Sensor(Koike et al., 2019).

sols with dry diameters larger than 70 nm (N70), which

controlled data persistently appeared throughout the year

are proxies for cloud condensation nuclei (CCN) (Fig.

at Zeppelin. The aerosol-cloud interaction index

3.3.5). When clouds were detected at temperatures cold-(ACI=dlnN c/(3dlnN 70)) for the CCN-controlled data

er than 0°C, some of the data followed the summertime

showed high sensitivities to aerosols both in the summer

Nc to N70relationship, while other data showed systemati-(clean air) and winter–spring (Arctic haze) seasonscally lower Nc values. The lidar-derived depolarization(0.22±0.03 and 0.25±0.02, respectively). The air parcel

ratios (micropulse lidar; Uchiyama et al., 2014) suggest-model calculations generally reproduced these values.

ed that the former (CCN-controlled) and latter (CCN-un-

The threshold diameters of aerosol activation (D act),

controlled) data generally corresponded to clouds con-which account for the Nc of the CCN-controlled data,

sisting of supercooled water droplets and those contain-

were as low as 30–50 nm when N70 was less than 30

ing ice particles, respectively (Fig. 3.3.6). The CCN-

cm−3, suggesting that new particle formation can affect18

Arctic cloud microphysics.

3. 4. Other factors contribute to AA
Black carbon in the atmosphere
In the Arctic, the effects of black carbon (BC) include
both the warming from absorption of solar radiation in
the atmosphere and absorption of radiation from deposition on snow/ice. The Arctic warming from BC is highlyvariable with season of emission, physical transport into
the Arctic, and the deposition to snow and ice. In addition, processes that emit BC also co-emit other particles
and gases that lead to sulphate and organic carbon aerosols. Sand et al. (2016) estimated the total equilibrium
Arctic surface temperature response to all (natural andFig. 3. 4. 1 Scatter plots between daily mean (a) babs (PSAP)
for PM1 and babs (PSAP) for PM10 and between daily meanbabs (COSMOS) and babs (PSAP) for (b) PM1 and (c) PM10 at
Barrow during 2012–2015. (d) Scatter plot between daily meanbabs (COSMOS) and babs (PSAP) for PM10 at Ny-Ålesund during
2012–2014 (Sinha et al., 2017).

anthropogenic) global 2010 emissions of SLCFs (shortlived climate forcers) to be −0.44 K, with a model rangeof −1.02 to −0.04 K, using chemical transport models.
Of this 0.48 (0.33–0.66) K is due to BC in atmosphereand snow, −0.18 K is due to organic carbon, −0.85 K isdue to sulphate and 0.05 K is due to tropospheric ozone.
However, estimates by climate models of the effects ofcoefficients measured by a PSAP, b abs (PSAP), were

BC on Arctic warming are still highly uncertain, in partscaled by comparing with mass concentration of BC, MBC

because measurements of the spatio-temporal distribution(COSMOS), measured by COSMOS. Finally, the scaledof the mass concentration of BC (MBC) in the atmosphere

PSAP measurements were used to derive the long-term

are limited and not sufficiently accurate. In the GRENE

variations of MBC (PSAP) in these sites.

Arctic, new precise measurements were started by Sinha

At first, as shown in Fig. 3.4.1a, babs (PSAP) for PM1

et al. (2017). A continuous soot monitoring system

was highly correlated with babs (PSAP) for PM10 at Bar-called COSMOS was deployed, which is a filter-based

row. At Ny-Ålesund, there was no particle size cutoff for

instrument equipped with an inlet heated at 300°C to re-

the PSAP data. However, since it is unlikely that a sub-move non-refractory components from the aerosol phasestantial number of particles with Dp > 10 μm was includ-(Miyazaki et al., 2008; Kondo et al., 2011). The mea-

ed in the PSAP measurements, these data were referredsurements of absorption coefficients, babs by COSMOS,

to as PM 10 data for comparison with COSMOS data.

babs (COSMOS), and the derived MBC (COSMOS) values

Here comparisons of babs (COSMOS) were made with babs

were made by using a PM1 impactor inlet (i.e., with a(PSAP) (PM 1) at Barrow (Fig. 3.4.1b) and with b abs

size cutoff at an aerodynamic diameter Dp of about 1(PSAP) (PM10) at Ny-Ålesund (Fig. 3.4.1d) and propo-μm). Using a particle soot absorption photometertional coefficient (slope of the correlation) β was derived(PSAP), a long term measurement was conducted at Bar-for each comparison.

row Alaska (71.32°N, 156.61°E) by the National Oceanic

Then it became possible to derive the mass concentra-

and Atmospheric Administration (NOAA) (Hirdman ettion of BC by using the babs (PSAP) data applying theseal., 2010), and at Ny-Ålesund, Svalbard operated by theempirically determined relationships. The scaled MBC

Norwegian Polar Institute (Sharma et al., 2013). Howev-(PSAP) iser, these studies did not perform detailed error analyses,

MBC (PSAP) = β x babs (PSAP) / MAC (COSMOS), (3.2)

and measurements obtained by filter-based absorption
techniques need to be corrected for the effects of co-ex-

where MAC (COSMOS) (m2 g −1) is mass absorption

isting non-BC aerosol particles in the filter medium, be-cross section, and equal to 8.73 m2 g−1 for these cases.

cause ambient aerosols comprise a complex mixture ofTime series of daily mean MBC (COSMOS) and M BC

light-absorbing and non-absorbing particles. Absorption(PSAP) for the entire period (2012–2015) of COSMOS
19

Fig. 3. 4. 2 Time series of daily mean MBC (COSMOS) and MBC (PSAP) at (a) Barrow (2012–2015) and (b) Ny-Ålesund (2012–2015) (Sinha et el., 2017).

Fig. 3. 4. 3 Time series of monthly mean MBC (PSAP) values at Barrow (January 1998 to July 2012) (closed circles) and NyÅlesund (April 2006 to March 2012) (closed diamonds). The series are extended to December 2015 with MBC (COSMOS) values at
Barrow (open sirces) and Ny-Ålesund (open diamonds). The gap in the PSAP data at Barrow from January 2010 to April 2011 wascaused by an instrument malfunction (Sinha et al., 2017).

measurements at both sites were examined in Fig. 3.4.2

MBC reached a maximum in winter and a minimum into evaluate the consistency of these two instruments. The

summer, as shown by previous studies. At Barrow, mean

temporal variations of MBC (COSMOS) and MBC (PSAP)

MBC (COSMOS) was 38.4±26.0 ng m−3 in winter andwere generally well correlated over wide ranges of val-

9.3±12.0 ng m−3 in summer between 2012 and 2015,

ues, as expected from the high correlation between babs

whereas at Ny-Ålesund during these years, mean MBC(PSAP) and babs (COSMOS) as shown in Fig. 3.4.1. The(COSMOS) was 22.3±21.0 ng m−3 in winter and 6.2±7.9

time series qualitatively shows the degree of the differ-ng m−3 in summer.

ences between individual daily mean values of MBC

It is important to investigate year-to-year variations of(COSMOS) and MBC (PSAP). The difference between

MBC in the Arctic with reliable data sets because varia-

monthly mean MBC (COSMOS) and MBC (PSAP) values

tions in BC emissions and transport pathways can be re-−3

was generally less than 10 ng m and agreed to within

flected in MBC changes. Sinha et al. (2017) examined

5% and 2% at Barrow and Ny-Ålesund, respectively.

time series of monthly mean MBC (PSAP) from January
20

Fig. 3. 4. 4 Time series of monthly mean MBC (PSAP) in
winter (November-April) and summer (May-October) during
1998–2015 at Barrow. The circles (winter) and diamonds(summer) represent seasonally averaged MBC (PSAP) values.
The regression lines were obtained by applying the leastsquares method to the MBC (PSAP) time series (Sinha et al.,2017).

Fig. 3. 4. 5 Latitude distribution of mean BC and ion concentrations obtained during 2012 and 2015 in Alaska. ● showswhole layer, ◆ surface layer 0–2 cm, △ layer 2–10 cm, andvertical bars for 1ϭ (Tsukagawa et al., 2016).

1998 to July 2012 at Barrow and from April 2006 to
March 2012 at Ny-Ålesund. Monthly mean MBC (COS-

BC in snow

MOS) values at these sites also partly overlap these timeseries and extend them up to December 2015 as shown inBlack carbon (BC) particles influence the radiation

Fig. 3.4.3. In winter, MBC showed year-to year variations

budget of the earth’s surface by changing the albedo ofof up to a factor of two with a relative variability of aboutsnow through the deposition of BC on it. The impor-22%. In summer, MBC was much lower than it was intance of BC in snow has long been discussed; however,

winter, and year-to-year variability in MBC was corre-

the accuracy of measurements of BC in snow has re-

spondingly lower, was about 36% in summer. Year-to-

mained low, and measurement data were limited. Mostyear variability can be caused by variations in BC emis-

measurements before used the filter method, which aresions, especially those due to biomass burning, as well as

liable to be uncertain due partly to the capturing efficien-by differences in the transport pathway and the degree ofcy and to the contamination of light absorbing aerosols

the wet deposition of BC during transport.

other than BC. Here, Tsukagawa et al. (2016) measured

Sinha et al. (2017) also examined the long-term trends

BC concentrations in snow using a single particle sootin MBC. The least squares method was applied to time

photometer (SP2), which is based on the laser-induced

series of MBC (PSAP) averaged over winter (November

incandescence technique. Moreover, normal SP2 mea-

to April) and summer seasons (May to October) fromsurement is liable to underestimate the concentration due

1998 to 2015 to obtain regression lines as seen in Fig.

to the limited wavelength region under 800 nm, wide−3

−1

3.4.4. The slopes were −0.56±0.17 ng m yr (−1.3%/
2−3yr) with r =0.10 for winter and −0.53±0.17 ng m yr

range-SP2 (WR-SP2) was used to cover up to 4 μm (Mori−1et al., 2016).

2(−4.7%/yr) with r =0.50 for summer. The lack of statisti-

Seasonal snow-cover samples were collected fromcal reliability is due to the large year-to-year variability

Alaska in late February or early March during 2012–

in MBC (PSAP) especially in winter, which potentially in-

2015. Sampling points distributed from Anchorage tocludes the year-to-year variability of MAC (PSAP). No

Fairbanks along the George Parks Highway, Poker Flat,similar trend analysis of the Ny-Ålesund data was per-Yukon River, Arctic Circle, Cold Foot, Brooks Mountain

formed owing to the lack of MBC (PSAP) data with suffi-

and Toolik Lake along Dalton Highway up to Prudhoe

cient reliability prior to 2006.

Bay, and then Barrow. Snow samples were collected21

in snow still has many uncertainties, and further study isneeded.
Aoki et al. (2014) under the “Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic”(SIGMA) Project (2011–2015), collaborated with the
GRENE Arctic, examined the effect of light-absorbing
snow impurity concentration on northwest Greenland Ice
Sheet (GrIS). The role of light-absorbing snow impurity
concentration to the recent snow melt in GrIS was discussed and concluded that enhancement of the snow surface impurities occurred in the observation period, which
can be explained by the effect of sublimation/ evaporation and snow melt amplification associated with drastic
melting.
Then, Sinha et al. (2018) made accurate measurementsof the concentration and size distributions of BC in snow

Fig. 3. 4. 6 BC concentrations in the surface snow by the present study and comparison with other former studies (Doherty etal., 2010; Doherty et al., 2014; Pedersen et al., 2015), (Tsuka­
gawa et al., 2016).

that could be compared with model calculations reliably.
Glacier microorganisms-cryoconite
Although not in the Arctic but in Himalaya, Koshimafrom the surface to 2 cm depth, 2–10 cm depth and theet al. (1993) found the biological activities would accel-

whole snow layer. From the whole snow layer, BC con-erate the glacier melting. Surface dust contains products−1

centrations were 0.4–13.6 μg L , 0.2–21.4 μg L

−1

and

such as the organisms themselves, their dead remains and

0.2–15.0 μg L−1, for 0–2 cm and 2–10 cm depth, respec-

decomposed organic matter. Since these organic parti-

tively. Fig. 3.4.5 shows the latitudinal distribution of BC

cles have relatively small single scattering albedo, yet areconcentrations, together with the altitude and other ion

large in volume, they are optically effective on the sur-concentrations. South of 69°N, BC concentration wasface albedo. Such biogenic surface dust on glacial ice isthe lowest around Alaska Range. BC concentration was

known as “cryoconite”, which is first named by the arctic

high around Fairbanks, and at the north of Arctic Circle

explorer, A. E. Nordenskjöld (1875), and it exerts a sig-

except Prudhoe Bay, lower than at Fairbanks. BC con-nificant impact on the albedo of the ablation surface on

centration was highest at Prudhoe Bay, by the anthropo-

some glaciers (Takeuchi et al., 2001). In the spectrum

genic emission due possibly to the oil drilling station.

consideration, Takeuchi (2009) presented the reflectances

BC concentrations for the surface snow layers were simi-(350–1050 nm in wavelength) of the glacier surface on

lar or higher than those for whole snow layers and latitu-

Gulkana Clacier, Alaska Range, at six different eleva-

dinal distributions were similar. Except for Barrow andtions from May to September 2001. The temporal andPrudhoe Bay along the transect of Alaska, BC mass con-spatial variations in spectral reflectance are discussed incentrations for whole layer were largest in the middle

terms of physical properties and impurities of snow andpart, three times higher than southern part and two times

ice. The result clearly showed the relationship between

higher than northern part in average. Across Alaska

spectral albedo reduction and surface dust (cryoconite)

Range, BC concentrations change greatly, and middle

increase relative to the change of the surface conditions

part is affected by anthropogenic emission from the cityfrom snow to ice (Fig. 3 and 4 of Takeuchi, 2009). Mean

Fairbanks.

amount of cryoconite increased up to 60 g m−2 in some

BC concentrations derived by Tsukagawa et al. (2016)

altitude range in late summer.

were compared with other former results. As shown inWithin GRENE Arctic, the group studied cryoconite in

the map of Fig. 3.4.6, the present results generally shownorthwest Greenland and Suntar-Khayata Mountainlower values compared to other studies. Since they are

Range in Russian Siberia. The Greenland Ice Sheet, theusing different measurement technique, filter method, it

second largest continuous body of ice in the world, hasis rather difficult to coincide with the present study. BC

been reported to be losing mass. Increased surface melt22

part and upper snow area. These distributions were also
confirmed from satellite data. The Landsat 7 satellite image showed that surface reflectivity varied across the Qaanaaq Ice Cap, and although it was generally higher at
mid-elevations, it was highest about 0.68–0.72 near theterminus, decreased up-glacier to a minimum 0f 0.25–
0.30 at an elevation if 800–900 m, then gradually increased up-glacier to the maximum of 0.60–0.68 m.
The reason impurities were more abundant in the middle area of glaciers is discussed. The distribution may be
explained by supply and removal processes of mineralparticles and/or biological process of cryoconite formation. Dust supply from mineral-rich layers in the ice may
result in such spatial variation. Altitude variations in the
microbial production rate and/or those in algal communities also could affect the variation. Since cryoconite
granules, which are the main components of the impurities on the ice area, are formed by entanglements of filamentous cyanobacteria, their distribution could also result in abundant cryoconite in the area. Smaller amountsof impurities in the upper area may be due to smaller
biomass of snow algae, which is commonly observed on
glaciers and results from physical conditions such as
shorter duration of melt season. To understand which
factors determine the abundance of impurities, further

Fig. 3. 4. 7 (a) Mean amount of impurities (dry weight g m−2)
on the surface and (b) organic matter content (dry weightpercentage) in impurities of all study sites on Qaanaaq Ice Cap
and Tugo Glacier. Error bar is standard deviation (Takeuchi etal., 2014).

studies are necessary. The organic matter contents in the
impurities in the lower snow area were comparable tothose in the ice area, suggesting that red snow algae arelikely to be main constitution of the impurities. The redsnow algae has been commonly observed on polar gla-

ing is a significant factor in the mass loss and is likely

ciers and they often bloom in the area closed to the firncaused by temperature rise and reduction of surface albe-line, which is consistent with the observation in the

do. The surface albedo of the Greenland Ice Sheet variedstudy. Thus, the bloom of red snow algae may largely

spatially, in particular, a part of the bare ice surface ap-

control the spatial distribution of impurities in the snow

peared to be particularly dark. That area is referred to as

surface on the glaciers.

the dark region and is particularly prominent in the mid-

The abundance of impurities on glaciers in the north-dle-west Greenland from MODIS images (Wientjes andwest Greenland revealed in the study was comparable toOrlemans, 2010).

those on the other glaciers in polar or sub-polar regions.

Takeuchi et al. (2014) investigated spatial variations in

For examples, the abundance on the ablation surfaces

impurities (cryoconite) on the glacier surface on Qaanaaq

have been reported to be 1.14±1.60 g m−2 for Canadian

Ice Cap and Tugo Glacier in the northwest Greenland in

Arctic, 23.0±13.3 g m−2 for Alaska and 38.8±25.6 g m−2

the melting season of 2012 (Fig. 3.4.7). The abundancefor Patagonia and they are roughly equal to those for the−2of impurities ranged from 0.36 to 119 g m on the barenorthwest Greenland in the present study, 18.8±21.6 g

ice surface and from 0.01 to 8.7 g m−2 on the snow sur-m−2. On the other hand, the abundance was much smaller

face of the study sites. There wa no significant difference

than those of Asian mountain glaciers (Fig. 3.4.8).

in the abundance between the Qaanaaq Ice Cap and Tigo

Changes in physical conditions of glaciers due to cli-

Glacier. The abundance of impurities on the Qaanaaq

mate warming such as expansion of bare ice area, melt

Glacier was greater in the middle part than in the lower

snow surface and length of melt season, may affect abun23

Fig. 3. 4. 9 Sr-Nd isotopic ratios of silicate minerals in cryoconites and those of moraine and englacial dust on and around the
Qaanaaq Glaciers, and loess and sand reported in the Asian and
African regions (Nagatsuka et al., 2016).

Fig. 3. 4. 8 Comparison of abundance of impurities (cryoconite)on the ablation surface of glaciers in the world. Error bar isstandard deviation. Dry weight fraction of mineral and organic
matter in the impurities were shown in the graph (Takeuchi etal., 2014).

blown sediments and/or englacial melt-out debris. Stable
isotopic rations of strontium (87Sr/86Sr) and neodymium

dance and distribution of impurities on the glaciers.

(143Nd/144Nd or εNd(0)) can reveal the source and trans-

Which means that cryoconite may have a feedback effectport process of mineral dust in cryoconite. Sr and Nd areon the Arctic warming.

commonly contained in trace amounts in natural materi-

In order to better understand the source of minerals on

als such as rocks and water as well as in organisms.

the dark-colored ice, located in the Greenland Ice Sheet

Their isotopic ratios vary greatly, depending on theirablation zone, Nagatsuka et al. (2016) analyzed the Srgeological origin and the ratios rarely change during

and Nd isotopic ratios of minerals in cryoconite. Recent-transportation in the atmosphere or after deposition asly, the area of dark-colored ice covered by cryoconites

sediment. Sd and Nd isotopic ratios and mineral compo-

has expanded on the Greenland Ice Sheet. One of thesitions of cryoconite collected from seven glaciers inpossible causes of dark ice expansion is an increase ingeographically separated regions in northwest and south-

cryoconite abundance on the ice surface. Because miner-

west Greenland are analyzed. The variations in the isoto-al dust is the major constituent of cryoconite (Takeuchi et

pic ratios and compositions are discussed in terms of theal., 2014), it is important to know its sources and the pro-sources of silicate minerals found on the glaciers.

cesses by which it accumulates on the ice sheet. The

According to previous studies, there are three possible

minerals in cryoconite may also affect the microbial pro-sources of minerals in cryoconites on the Greenland Ice

duction on the glacial surface, which is another contribu-

Sheet: (1) dust transported by wind from the local area,

tor to the cryoconite mass. Because glacial microbes areprobably the nearby tundra; (2) englacial dust that was

likely to incorporate nutrients from mineral dust and dis-deposited in the past in the accumulation area, traveled

solved components in snow and ice, the minerals on an

through the ice sheet and outcropped again in the abla-

ice sheet and/or glacier could affect their biomass bytion zone; or (3) long-range transported dust from distant

supplying nutrients.

deserts (Wientjes and Oerlemans, 2010; Takeuchi et al,

There are different possible sources of the mineral dust2014). The minerals in the cryoconite are likely to be ain the cryoconite on the Greenland Ice Sheet. The miner-

mixture of those from the three different sources. Results

al dust in ice cores from Greenland originated from theare shown in Fig. 3.4.9, the Nd isotopic ratios of the

Sahara in Africa. On the other hand, the dust deposited

cryoconites analyzed in this study were significantly low-on the ablation area was locally derived from wind-

er than those of distant deserts but were close to those of24the moraine and englacial dust on and around the glaciers. This suggests that the silicate minerals in the cryoconites are probably not derived from Asia and African
desearts [source (3)] but mainly from the area surrounding the glaciers.
The mineralogical composition of the cryoconites also
supports the predominance of local dust on the glaciers.
Nagatsuka et al. (2014) revealed that cryoconites on the
Qaanaaq, Qaqortaq, Tugo, Bowdoin and Sun Glaciers
contained much lower amounts of clay minerals compared to Asian desert sand (Fig. 3.4.8).
Finally, Takeuchi et al. (2015) investigated characteristics of impurities and their impact on the ablation of Glacier No. 31 in the Suntar-Khayata Mountain Range in
Russian Siberia during summer 2014. Of the glacierised
regions in the Arctic latitudes, those within Russia aresome of the least studied. There are a number of glaciersin the mountain ranges in Arctic and sub-Arctic regionsin eastern Siberia, Russia, and Suntar-Khayata Mountain
Range lies near the coldest core of Oymyakon, east of
Yakutsk. Positive degree-day factors (PDDFs) obtained
from 20 stakes measurements distributed across the glacier’s ablation area varied from 3.00 to 8.55 mm w.e. K−1
day−1. The surface reflectivity measured with a spectrometer as a proxy for albedo, ranged from 0.09 to 0.62
and was negatively correlated with PDDF, suggestingthat glacier ablation (melting) is controlled by surface albedo (absorption of shortwave radiation) on the studied
glacier. Mass of total insoluble impurities on the ice surface varied from 0.1 to 42.5 g m−2 and was not correlated

Fig. 3. 4. 10 Relationships between surface reflectivity andtotal impurities (A) and algal biomass (B) on the ice surfaces of
Glacier No. 31. The correlation was not statistically significant
between surface reflectivity and total impurities (A), but
significant between surface reflectivity and algal biomass (B).
Open and solid marks indicate Sites A and B, respectively.
Error bars indicate standard deviation of measurements ofsurface reflectivity and impurities at each stake (Takeuchi et al.,2015).

with surface reflectivity (Fig. 3.4.10), suggesting that albedo is not directly conditioned by the mass of the impurities. The lack of correlation between surface reflectivity and total mass of impurities is probably due to asmaller density of the organic matter including algal cells
compared with that of inorganic impurities. Some of thedata showing higher reflectivity and greater impurities inFig. 3.4.10 can also be explained by greater contents ofwhite transparent mineral particles, such as quartz anding of ice surface was enhanced by the growth of ice al-feldspar, in the impurities, which have a lower light ab-gae, which increased the melting rate 1.6–2.6 times grat-

sorbency. Microscopy of impurities revealed that theyer than that of the impurity free bare-ice.

comprised mineral particles, cryoconite granules and ice
Snow cover extent

algal cells filled with dark-reddish pigments. As shownin Fig. 3.4.10 (B), there was a significant negative cor-

Snow cover is an essential geophysical parameter for

relation between surface reflectivity and algal biomass orunderstanding the Earth’s climate system. Its high short-organic matter, suggesting that the ice algae and theirwave albedo and longwave emissive properties signifi-

products are the most effective constituents in defining

cantly impact Earth’s climate via the radiation budget.

glacier surface albedo. The results suggest that the melt-

Snow cover also plays an important role as a thermal in25

Fig. 3. 4. 11 Time series plot of Northern Hemisphere snow cover extent (SCE) of JASMES (red color) and NOAA (blue color)from November 1978 to December 2015. Dotted lines indicate primary SCE (PMSCE) and solid lines denote error-adjusted SCE(EASCE) (Hori et al., 2017).

sulator in cold continental areas, and snow cover itself

NOAA (blue) SCE products during the 1978–2015 peri-

provides regional terrestrial drainage and fresh water tood. The SCE from both data sets exhibit typical periodic

downstream oceans during melt seasons. Hori et al.

annual cycles with local maxima and minima in January(2017) developed a long term Northern Hemisphere (NH)and July, respectively. The confidence intervals (CI) also

daily 5-km snow cover extent (SCE) product, released on

exhibit periodic cycles, becoming high in melting andthe website “JAXA Satellite Monitoring for Environmen-

snow-onset seasons when NH SCE changes rapidly.

tal Studies (JASMES)”, by the application of a consistentFig. 3.4.12 shows the time series of seasonally aver-objective snow cover mapping algorithm to data fromaged PMSCEs derived from the JASMES (solid lines

historical optical sensors on polar orbiting satellites fromwith filled circles) and NOAA (broken lines ) products.

1978 to 2015. In this study, radiance data acquired with

Fig. 3.4.12(b) shows the time series of the PMSCE (ex-the AVHRR on TIROS/NOAA series satellites and MO-

actly the same plot as in Fig. 3.4.12(a)) and EASCE from

DIS were used. Of these sensors, MODIS has the ability

JASMES (dotted line with square symbols), and Fig.

to calibrate observed radiance and thus the MODIS data

3.4.12(c) is the same as Fig. 3.4.12(b) but for those de-were used without additional calibration; however,rived from NOAA (similar time series plots of monthly

AVHRR cannot calibrate radiances in the visible andaverages of all the SCEs (PMSCE) and EASCE). In ad-

near-infrared spectral bands in orbit. These sensors are

dition, a linear regression equation with the slope and in-

known to degrade over the course of satellite operational

tercept was calculated for each SCE. The linear regres-life span after being launched. The effect of the AVHRR

sion lines for statistical significant linear trends are

sensor degradations on radiance was simply corrected byplotted in the figures, with a solid line for PMSCE and aobserving a reference target, Dome C on the Antarctic

broken line for EASCE.

Ice Sheet, where the surface reflectance is considered to

NOAA’s seasonally averaged PMSEs exhibit negative

be unchanging. Also compared are the NOAA/NCDC

trends in spring and summer but positiove trends in win-

climate data record (CDR) weekly snow charts from Jan-ter and autumn, whereas the NOAA EASCEs exhibituary 1978 to June 2014 (hereafter, NOAA SCE) in order

negative trends in all seasons (Fig. 3.4.11(a) and (c)) withto explore possible differences in the long-term trends.

the following slopes: PMSCE (EASCE) of +50.4 (−24.7),

Bi-monthly NH SCEs were derived from the weekly−74.5 (−12.5), −82.4 (−21.2) and +59.3 (−37.8) km2/yr in

SCE maps of JASMES and NOAA in two ways. In thewinter, spring, summer and autumn, respectively. Basi-

first, an ordinary direct method was used that sums thecally, the negative trends of the seasonally averagedsnow pixels area in a weekly SCE map to determine the

EASCEs of NOAA are also consistent with those of JAS-

total weekly SCE, primary product of SCE (PMSCE), in

MES, which suggests that the error-adjustment approach

the NH. In addition to this direct method, also an er-that takes into account the area proportion of snow coverror-adjusted SCE (EASCE) was estimated from the SCE

and the accuracy of the SCE maps evaluated with in-situ

map by using the accuracy data of the SCE map derived

snow data succeeds in generating consistent SCEs fromwith in-situ station data. Fig. 3.4.11 shows the long-term

the JASMES and NOAA products.

variations of the NH PMSCE (dotted line) and EASCE

On the other hand, the JASMES SCEs (both the PM-(solid line) derived from the JASMES (red color) andSCE and the EASCE shown in Fig. 3.4.11b) exhibit neg26

Fig. 3. 4. 12 Time series of (a) seasonally averaged Northern Hemisphere snow cover extent (PMSCE) derived from JASMES (solidlines with filled circles) and NOAA (broken lines), (b) seasonally averaged PMSCE and error-adjusted SCE (EASCE; dotted lines
with square symbols) derived from JASMES, and (c) the same plots as in (b) but for those derived from NOAA. The equations for
the PMSCE of JASMES were derived from the data for the 1981–2015 period only (filled circles). We plotted only those regression
lines of statistically significant linear trends (Kendall-Mankind test a > 0.05). Error bars on the EASCE plot denote the confidence
intervals averaged for individual seasons (Hori et al., 2017).

ative trends in all seasons as follows: PMSCE (EASCE)

In another application example, the annual snow coverof −39.1 (−38.8), −25.8 (−17.8), −15.3 (−10.7) and −93.5

duration (SCD) was estimated. The SCD is the sum of

2(−43.5) km /yr in winter, spring, summer and autumn, re-the temporal fraction of snow cover within each bi-

spectively. The trend of autumn EASCE is smaller thanmonthly period, for every year and every pixel. Fig.

that of the PMSCE, which suggests an overestimation of

3.4.13a and b shows the 30-year average and trend, re-the autumn trend by JASMES’s original SCE (i.e., PM-

spectively, of the SCD in the NH (1982–2013, excludingSCE). However, the autumn slope of the error-adjusted

1994 and 1995). Fig. 3.4.13c is the same as Fig. 3.4.13b

SCE (EASCE) is still the largest of all the seasons, which

except that it shows only the area of the statistically sig-

indicates that it is the main driver for the shrinkage ofnificant SCD trend (Kendall-Mankind test with a > 0.05).

snow cover in the NH.

Basically, the satellite-derived SCD trends to shorten sig-

As a result, although there are some systematic posi-nificantly in western Eurasia (shortening one to twotive biases of the JASMES SCE in winter and autumn,

months over three decades) and also trends to shorten

the signs of the trends do not change before and after the

weekly in the center of Siberia, Alaska and eastern Northerror adjustment of the SCE, which indicates that theAmerica. On the other hand, the SCD seems to lengthen

JASMES PMSCE has more stable temporal accuracy

in parts of China, the Himalayas and in North America incompared with the NOAA SCE case. In addition, thethe western mountain region (the Rokies) and a small

EASCE derived from JASMES and NOAA exhibit con-portion of the Canadian Arctic Archipelago. By analyz-

sistent trends, which reveal that the shrinkage of the NH

ing the snow onset and end dates from the JASMES SCE

SCE due to global warming has steadily continued in all

data, it was found that most of significant SCD shorten-

seasons at least since the 1970s.

ing trends observed in western Eurasia occurred due to a27

Fig. 3. 4. 13 Spatial distributions of the (a) 30-year average (1982–2013, excluding 1994 and 1995) of snow cover duration (SCD,
in month), (b) 30-year trend of SCD (day/year) in the Northern Hemisphere derived from the long-term satellite-derived SCE, (c) thesame as (b) except that only the areas with statistically significant SCD trends (Kendall-Mankind test a > 0.05) are shown, and (d)SCD trend derived from in-situ snow data measured at the ground stations that have complete annual snow depth observation records
during the 32- year analysis period (Hori et al., 2017).

trend of later onset dates of the first snow in autumn or

snow. Atmos. Chem. Phys., 10, 11647–11680.
Doherty, S. J., Dang, C., Hegg, D. A., Zhang, R., Warren, S. G.,
2014. Black carbon and other light-absorbing particles insnow of central North America. J. Geophys. Res., 119,12807–12831.
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Kuchiki, K., Niwano, M. and Enomoto, H., 2017: A 38year (1978–2015) Northern Hemisphere daily snow coverextent product derived using consistent objective criteria
from satellite-borne optical sensors. Remote Sens. Environ., 191, 402–418.
Koike, M., Ukita, J., Strom, J., Tunved, P., Shiobara, M., Vitale,
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Fig. 3.4.13d shows plots of the SCDs (filled circles)
derived from the selected in-situ stations. Although themagnitudes of in-situ derived SCD trends tend to besmaller than those derived from satellite data, the spatial
pattern of the SCD trends seem to be quite consistent.
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30

probability of severe winter has more than doubled in

4. Arctic and mid-latitude linkcentral Eurasia. In these simulations, the atmospheric response to sea ice decline is approximately independent ofWithin the recent Arctic abrupt warming, impacts to

the Arctic Oscillation.

the mid-latitude are also noticed. As for these mid-lati-

To clarify the relationship between the sea ice concen-

tude links, there are may discussions such as 6 reviews

tration (SIC)-driven atmospheric response and internalalready exist, Vihma (2014), Walsh (2014), the National

fluctuations of circulation, the dominant modes of vari-

Academy of Sciences (2014), Cohen et al. (2014), Barnes

ability governing winter surface air temperature (SAT)and Screen (2015) and Orverland et al. (2015), but still

anomalies over Eurasia were extracted by applying em-

many questions are left on (1) what is the mechanism topirical orthogonal function (EOF) analysis to DJF-meanconnect Arctic warming or sea ice retreat and mid-lati-

SAT anomalies (1979–2013) in the reanalysis, over lon-tude extreme weather and (2) is the signal from the Arc-

gitude 0°–180°, latitude 20°N–90°N (Fig. 4.1.1). The

tic to mid-latitude meaningful or only the result of natu-

first mode (EOF1), accounting for 31% of total variance,

ral variability (Overland, 2016)? In case of North

represents a pattern of uniform warming over the entire

America, signal to noise ratio is especially small and it isEurasian continent, accompanied by a meridional sea

not clear if the signal overcomes the natural variability.

level pressure (SLP) dipole over the North Atlantic (Fig.

As for the east Eurasia and Japan, impacts of sea ice de-

4.1.1a). The hemispheric circulation pattern resembles

crease on the mid-latitude weather is much solid. Honda

the North Atlantic Oscillation (NAO) or the Arctic Oscil-

et al. (2009) by reanalysis and numerical experiments,

lation (AO), and the associated principal components

clearly showed the stationary Rossby wave response to(PC1) are highly correlated with the AO index (r=0.85)

the increased turbulent heat flux resulting from summer(Fig. 4.1.1c). The second mode (EOF2), accounting for

Arctic sea ice reduction intensifies the winter Siberian

23% of the total variance, is characterized by a “Warmhigh that brings cold air outbreaks. Following this re-

Arctic and Cold Eurasia” (WACE) pattern, straddled bysults, Inoue et al. (2012) explained actual synoptic mech-

positive SLP anomalies, which corresponds to an intensi-

anism in detail that the intensification of Siberian highfied Siberian High on its western edge (Fig. 4.1.1b). A

would occur following the shift of cyclone route to themarkedly linear trend with positive slope (0.45/decade)north in Siberian coast and made this relation very solid.

is shown by the principle components (PC2; Fig. 4.1.1d),

After then, Japanese weather forecaster began to remindstrongly tied to the winter SIC anomaly averaged in thethe relation with the Arctic for the forecast of extreme

BKS. Therefore, it is evident that WACE represents SAT

winter weather in Japan.

variation associated with the SIC anomaly in the BKS
and serves as a distinct mode of variability, independentof the AO.

4. 1. Robustness of Arctic sea ice influence on theEurasian cold winters

The relative importance of these two modes to the occurrence of severe winters over Eurasia was examined by

Over the past decade, severe winters occurred fre-a scatter plot of PC scores (Fig. 4.1.1e), categorized byquently in mid-latitude Eurasia, despite increasing glob-

SAT anomalies over central Eurasia. The plot shows that

al- and annual-mean surface air temperatures. Observa-

almost all severe winters coincided with a negative phasetions suggest that these cold Eurasian winters could haveof the AO and with a positive phase of the WACE.

been instigated by Arctic sea ice decline, through exci-

Therefore, the severe winters in central Eurasia can betation of circulation anomalies similar to the Arctic Os-

explained by combination of these two modes. From

cillation. In climate simulations, however, a robust at-

combined 200-member AGCM ensembles, a comparison

mospheric response to sea ice decline has not been found,of the probability density functions (PDFs) of PCs calcu-

perhaps owing to energetic internal fluctuations in the at-

lated separately for low-ice (LICA) and high-ice (HICE)

mospheric circulation. Here, Mori et al. (2014) used ademonstrates that the polarity of WACE in the model

100-member ensemble of simulations with an atmospher-tends to be more positive in LICE and more negative in

ic general circulation model driven by observation-based

HICE runs (Fig. 4.1.1f).

sea ice concentration anomalies to show that a result of

The dependence of the ensemble-mean response oversea ice reduction in the Barents-Kara Sea (BKS), thecentral Eurasia on ensemble size reveals that the robust31

Fig. 4. 1. 1 Two leading mode governing winter SAT anomalies over Eurasia. a, b, DJF-mean SAT, SLP (contours, 1 hPa interval,
negative contours are dotted) and near-surface wind (vector) anomalies in ERA-interium, regressed on normalized PC1 (a) and PC2(b). Stippling indicates regions exceeding 95% statistical confidence. c, d, PC time series, AO index and SIC anomaly in the BKS(axis reversed). e, f, scatter plot of PCs for reanalysis (e) and simulation (f). Blue and red marks indicate cold and warm winters,
respectively, grey marks in e indicate winters that were warm nor cold. Two-dimensional PDF of 200 members (contours, 3%interval) and differences between LICE and HICE PDFs (shading) are imposed in f (Mori et al., 2014).

SAT response (p≦0.01) to SIC anomalies in the BKS, interms of sign, could not be detected with less than 80 ensemble members (Fig. 4.1.2a). This small signal-to-noise
ratio justifies the production of a large ensemble for signal detection. By contrast, in terms of polarity of PC2,
the robust response to SIC anomalies could be detectedwith just 20 ensemble members (Fig. 4.1.2b), which implies that the effect of the AO on SAT anomalies weaken
the signal-to-noise ratio. This partly explains the weakerensemble-mean anomalies in the AGCM compared to thereanalysis.
Previous modeling studies that investigated possible
impacts of sea-ice changes on the atmosphere suggestthat the circulation response to sea-ice reduction has anegative AO/NAO like structure in winter. This may not
be inconsistent with our results, because our AGCM simulation also shows a slight increase in frequency of theFig. 4. 1. 2 Robustness and dependence of signal detection on
ensemble size. Box-and-whisker plots of the ensemble-mean
SAT difference between LICE and HICE experiments (former
minus latter) averaged over central Eurasia (a) and ensemblemean PC2 scores for LICE (blue) and HICE (red) experiments(b), shown as a function of ensemble size. Boxes and whiskers
indicate ranges of one standard deviation and the 99th percentile,
respectively, and are estimated based on bootstrap random
sampling (100,000 times). Vertical dashed lines indicate theminimum number of ensemble members required for robust
signal detection − that is, at the 99% confidence level (Mori etal., 2014).

negative AO in LICE compared to HICE. However, theSIC-forced signal represented by a shift of the AO PDF
is considerably smaller than for the WACE. Therefore,
SAT anomalies associated with the AO are regarded asnoise rather than signal in this analysis.
Given that the observed sea ice decline largely reflects
the global warming signature, and that it will continue todecline in the future, one might expect the frequency ofcold winters to increase as well, by the mechanism presented above. To examine this possibility, 22 climate
32

simulations performed by the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensembles wereanalyzed for the period 1979–2098. Results indicate thefrequency of severe winters may not increase, but rather
decrease towards the end of the century, despite sea ice
decline in the BKS. The contribution of the positive
WACE trend to the cooling of Eurasia is identified, but apositive AO trend overcomes the cooling effect and results in a gradual SAT increase over central Eurasia.
Therefore, the frequent occurrence of cold winters may
be temporary phenomena in a transitional phase of eventual global warming, although projection uncertainty remains, owing partly to the insufficient number of ensem-

Fig. 4. 2. 1 (Top) 3-month mean geopotential height anomalies
at 500 hPa (Z500) during (from left to right) OND, NDJ, DJF
and JFM. These anomalies are lag regression coefficients
against the normalized SIA index in November. Note that thesign of the coefficients is reversed so that red (blue) corresponds
to positive (negative) anomalies when Arctic sea ice decreases.
The contour interval is 5 m, and the zero line is omitted. Lightand heavy shading indicate statistical significance of over 95%
and 99%, respectively. (Middle) Regression coefficients ofsea level pressure with a contour interval of 0.5 hPa. (Bottom)
Regression coefficients of 2-m temperature with a contour
interval of 0.5 K (Nakamura et al., 2015).

ble members in CMIP5.

4. 2. Stratosphere path way
The paper by Nakamura et al. (2015) examined thepossible linkage between the recent reduction in Arctic
sea ice extent and the wintertime Arctic Oscillation (AO)/
north Atlantic Oscillation (NAO). Observational analyses using the ERA- interim reanalysis and merged Hadley/Optimum Interpolation Sea Surface Temperature data
reveal that a reduced (increased) sea ice area in Novem-

Arctic sea ice area. Most of the regressed anomalies

ber leads to more negative (positive) phases of the AO

show positive anomalies in the Arctic Ocean region but

and NAO in early and late winter, respectively. The at-

negative anomalies at mid-latitudes. This structure of themospheric response to observed sea ice anomalies was

geopotential height anomaly resembles the negative

simulated using a high-top atmospheric general circula-phase of the AO/NAO pattern. Consistent with this re-tion model (AGCM for Earth Simulator, AFES version

sult, regressions of sea level pressure (SLP) anomalies4.1). The results from the simulation revealed that theshown in Fig. 4.2.1 (middle row) have persistent positive

recent Arctic sea ice reduction resulted in cold winters inanomalies in the Arctic Ocean region and negative anom-

mid-latitude continental regions, which linked to thealies at mid-latitudes.

anomalous circulation pattern similar to the negative
phase of AO/NAO phase, and increased cold air advec-

Climatological impact of recent ice reduction fromtion from the Arctic to the mid-latitudes.

model results

First, observational evidence was examined for a rela-

The control run (CNTL) was performed with a 5-year

tionship between wintertime atmospheric and Arctic sea

monthly varying climatology of SST and SIT for the ear-ice variations. Since sea ice has a much longer memory

ly period (1979–1983). A perturbed run (N.ICE) usedthan the atmosphere, the lag-correlations were calculated

the same SST as CNTL but SIT climatology for the late

between the DJF mean AO index and the sea ice area

period (2005–2009). Both runs used the same initial(SIA) index in the preceding months. The maximum

conditions (the January 1979 monthly mean from JRA-correlation, between November SIA and the DJF mean

25/JCDAS). 60-year integrations was carried out after

AO indices, exceeded the 95% confidence level, was11-year spin-up. The differences between the 60-year

seen.

averages of CNTL and N.ICE were examined. By thisFig. 4.2.1 shows anomalies of 3-month mean geopo-

experimental design, only the sea ice difference is re-tential height at 500 hPa (Z500) from October-Novem-sponsible for atmospheric differences without any influ-

ber-December (OND) to January-February-March (JFM)

ences from other external forcings such as SST varia-

regressed on the time series of the November-December

tions. Fig. 4.2.2 shows the NH temperature response in33

Fig. 4. 2. 2 (a) DJF mean temperature anomalies at 850 hPa of N.ICE against CNTL. Hatched and double-hatched areas indicate
statistical significance (t-test) at the 95% and 99% confidence levels, respectively. (b) Zonal mean temperature anomalies. Black andlight blue lines indicate the glaobal mean and land-only mean, respectively. (c and d) As for (a) and (b), but for the regressed field of
ERA-interim in NDJ upon November SIA index. Values corresponding to −1.0ϭ of SIA index are shown (Nakamura et al., 2015).

N. ICE relative to CNTL, and its zonal mean, respectively. Significant cold anomalies are found in eastern Siberia and warm anomalies over the Arctic Ocean and the
Sea of Okhotsk. The zonal mean temperature responseshows large warm anomalies in the polar region. In
mid-latitudes, while the zonal mean temperature responseshows small anomalies, anomalies averaged only overland are clearly negative. The area-weighted temperature
response over the NH mid-latitudes (30–60°N) is nearlyzero, −0.01 K. However, averaging only over the landarea gives −0.10 K, with a minimum of −0.29 K at

Fig. 4. 2. 3 Histogram of the EOF1 score from the combined
EOF (0.2s bins); red and blue bars indicate the CNTL and
N.ICE periods, respectively. The horizontal axis shows scoresfor the center of each bin. The vertical axis on the left handside indicates the number of counts for each bin. Lines indicate
the probability density function (PDF) estimated from the
EOF1 score for the CNTL (red) and N.ICE (blue) periods,
respectively. The vertical axis on the right-hand side indicates
probability density. The mean score and the integral of the PDF
above (below) 1.0ϭ (−1.0ϭ) are shown in the panel in the colors
corresponding to CNTL and N.ICE (red and blue, respectively)(Nakamura et al., 2015).

44.1°N. In comparison, Fig. 4.2.2c presents regressed
fields of observed NDJ mean temperatures at 850 hPa associated with the normalized time series of November
sea ice area. Warm anomalies in the Arctic and cold
anomalies over mid-latitude land are also found. Continental cold anomalies are located in North America, Europe, and eastern Siberia. Simulated spatial pattern ofthe cold anomalies resemble the observed pattern, although for the simulation, there are no significances inNorth America and less in Europe. The area-weighted
temperature response averaged over the NH mid-latitudes

utes to the cooling of the NH continents.

(30–60°N) is −0.09 K, with a minimum of −0.17 K at

It was found that the stationary Rossby wave response

45.0°N; averaged only over land, this becomes −0.18 K.

to the sea ice reduction in the Barents Sea region inducedwith a minimum of −0.30 K at 49.5°N. Those results

this anomalous circulation. It was also found that a posi-strongly suggest that the recent sea ice reduction contrib-

tive feedback mechanism resulting from the anomalous34

Fig. 4. 2. 4 Geopotential height anomalies of N.ICE with respect to CNTL in OND, NDJ, DJF and JFM at 50 hPa (upper row)and 500 hPa (lower row). The contour interval is 5 m, and the zero line is omitted. Light and heavy shading indicate statisticalsignificance (t-test) at the 95% and 99% confidence levels, respectively (Nakamura et al., 2015).

meridional circulation that cooled the mid-latitudes andover the N.ICE period. The probabilities of positive

warmed the Arctic, which added an extra heating to thescores larger than 1.0ϭ were 24.6% and 8.5% for theArctic air column equivalent to about 60% of the direct

CNTL and N.ICE periods, respectively. On the othersurface heat release from the sea ice reduction.

hand, the probabilities for scores less than −1.0ϭ were

The vertical motion induced by ice reduction warms

8.9% and 22.1% for the CNTL and N.ICE periods, re-

the Arctic and cools mid-latitudes. Such an anomalous

spectively. Furthermore, while the PDF of CNTL is

meridional circulation caused by stationary wave drag is

skewed to the right (skewness=0.175), that of N.ICE is

accompanied by a weakening of the polar vortex, whichskewed to the left (skewness=−0.096). This finding sup-shows variability that is strongly related to the AO/NAM.

ports the more frequent appearances of a strong negative

The contribution of Arctic sea ice reduction to the AO/

phase of the AO/NAM during the N.ICE period. The re-

NAM signals was estimated. To do this, an EOF analysis

sults also indicate that strong positive AO/NAM eventswas applied to the 3-month men Z500 data from the 60-

occur less frequently (<50%) in association with a nega-year results of the CNTL and N.ICE runs, and to thetive shift of the AO/NAM due to the Arctic sea ice reduc-

Z500 data from N.ICE combined with those from CNTL.

tion, and vice versa.

Using this combined EOF analysis, the modulation of

Finally, the seasonal evolution of the impact of sea ice

AO/NAM was evaluated as a response to recent sea ice

reduction was estimated on NH climate fields. The pan-reduction.

els of Fig. 4.2.4 compare the Z50 and Z500 anomalies of

For a more quantitative estimate of the modulation of

the N.ICE run with the CNT run from the OND to JFM

the primary mode, the probability density function (PDF)

periods. In the stratosphere, while positive anomalies areof the EOF1 score was examined using the nonparamet-

only evident in far-eastern Russia in the OND and NDJ

ric density estimation technique. Fig. 4.2.3 shows histo-

periods, large positive anomalies are found in the Arctic

grams and associated PDFs of combined EOF1 scores forsurrounded by negative anomalies in the mid-latitudes in

the CNTL and N.ICE periods. The probability density

the DJF and JFM periods. This indicates the weakening

for positive scores is larger over the CNTL period thanof the polar vortex in mid to late winter. In comparison,35

the polar night jet and the polar stratospheric warming,
which later propagates downward to the troposphere.
The simulated behavior is consistent with downward
propagation of the stratospheric signature induced by theplanetary wave modulation (Baldwin and Dunkerton,
1999, 2001). The results from this high-top model experiment suggested a critical role of the stratosphere in
deepening the tropospheric annular mode and modulationof the NAO in mid to late winter through stratospheretroposphere coupling.
Vertical planetary wave propagation
Extending the results of Nakamura et al. (2015), Jaiser
et al. (2016) aim to provide detailed information on vertical planetary wave propagation and thus the coupling between stratosphere and troposphere by means of thecomparison between the model experiment of Nakamura
et al. (2015) and the ERA-Interim reanalysis data withrespect to low and high sea ice conditions in the Arctic.
A particular focus in on timing and regional characteristics of both upward and downward propagation of signals.

Fig. 4. 2. 5 Winter seasonal evolution of anomalies of N.ICE
with respect to CNTL for (a) zonal mean zonal wind at 60°N (ms−1), (b) zonal mean temperature at 80°N (K), and (c) verticalcomponent of EP flux (104 m2 s−2) at 100 hPa. Contour shadingintervals are as in Fig. 4.2.4 (Nakamura et al., 2015).

In winter, planetary waves are allowed to propagate
from the troposphere into the stratosphere due to theprevalence of westerly winds in these layers. The corresponding vertical component of planetary scale EP flux isshown in Fig. 4.2.6 as the difference between low andnegative AO-like anomalies appear throughout the tropo-

high sea ice conditions. This illustrates the change in thesphere and strengthen toward late winter. The simulated

vertical propagation of planetary waves. The polar cap

seasonal evolution in the troposphere resembles that ob-

average (65°N–85°N and 0°E–360°E) of vertical EP flux

served. The simulated weakening of the polar vortex indifference NICE minus CNTL in the AFES experiment

the stratosphere in the mid to late winter implies a role of(Fig. 4.2.6a) is positive starting in November and contin-

the stratosphere in deepening the tropospheric annularues throughout December, at which the positive signal

mode.

enters the stratosphere. Similar anomalies are present in

Figs. 4.2.5a and b show daily anomalies (N.ICE minus

ERA-Interim (Fig. 4.2.6b), but show less significance

CNTL) of zonal mean zonal wind at 60°N and tempera-

and are more disturbed by short periods of negative verti-

ture at 80°N, respectively. Significant deceleration of thecal EP flux differences. Upward propagating planetarypolar night jet and a corresponding stratospheric warm-

waves are generated in the troposphere underpinning theing anomaly are seen at the end of January. The signals

relationship to sea ice changes. Later they reach the

propagate downward and penetrate into the tropospherestratosphere and interact with the polar vortex.

in February. Fig. 4.2.5c shows a time-latitude cross-sec-

In the difference between the N.ICE and CNTL runtion of the vertical component of the EP flux anomaly(Fig. 4.2.6a) strong negative planetary-scale EP flux

through the lower stratosphere (100 hPa). A positive

anomalies start in mid-January. The signal is significant

anomaly exist around 70°N at the end of December andin the troposphere, while it is more disturbed in thearound 50°N in January, indicating an intensification ofstratosphere. This continues into February, when a sig-

the propagation of the planetary wave from the tropo-

nificant negative anomaly becomes visible from the low-sphere to the stratosphere. The intensified upward propa-

er stratosphere down to the troposphere. The negative

gation of the planetary wave causes the deceleration of

anomaly is related to an interruption of upward propagat36

Fig. 4. 2. 6 Vertical component of planetary scale EP flux (m2 s−2) averaged over 65°N–85°N and 0°E–360°E as 21 day runningmean time vs. height plot. (a) AFES N.ICE minus CNTL, (b) ERA-Interium low ice minus high ice period. Significance on 90%(dotted), 95% (dashed) and 99% (solid) level marked in black contours (Jaiser et al., 2017).

ing planetary waves for low ice conditions. While in the

ley-National Oceanic and Atmospheric Administration/

CNTL run, planetary waves still propagate into the

Optimum Interpolation sea surface temperature (SST)

stratosphere in February, this is not the case in N.ICE

and sea ice concentration (SIC) data sets for period

run. The absence of vertical wave propagation is an indi-

1979–2011 were used for the boundary conditions of thecation of a stratospheric polar vortex breakdown. In themodel. The FREE experiment was the same as the sensi-

ERA-Interim data (Fig. 4.2.6b), the negative anomaly

tivity experiment performed in the previous study by Na-

clearly starts in the beginning of February without pre-kamura et al. (2015), in which sea ice conditions duringceding negative anomalies in the troposphere or strato-

the Early and Late periods were used as the boundary

sphere. Again, the vertical propagation of planetaryconditions for high sea ice (HICE) and low sea ice (LICE)

waves into the stratosphere is reduced under low sea iceruns, respectively.
The RS10 experiment differed from FREE in that theconditions with weak indication of actual downward EP
flux.

zonal mean zonal wind above 10 hPa was restored at every time step by relaxation toward the climatology of the

The role of stratosphere in the Arctic midlatitude cli-

daily annual cycle in the HICE run of FREE with a max-

mate linkage

imum relaxation timescale of 1 day. Relaxation forcing

Nakamura et al. (2016a) showed that midlatitude sur-τ−1 was zero (τ=∞) at and below the lowest level of 10

face signals as a response to the Arctic sea ice reduction

hPa and increased linearly up to 1.0 d−1 (τ=1 day) at thedisappeared when artificially suppressing stratospherichigher level of 3.16 hPa, and was 10 d−1 above that level.

wave mean flow interaction, based on numerical experi-

The RS30 experiment was the same as RS10 except thatments using a high-top atmospheric general circulation

the lowest level was 31.6 hPa and the higher level was 10

model that has already shown sea ice impacts on thehPa.

stratosphere highly consistent with observations. The re-

The simulated responses to sea ice reduction in thesults confirm the active role of the stratosphere in the

Arctic region was evaluated by subtracting the HICE re-

Arctic midlatitude climate linkage. Then, from a posteri-sults from the LICE results. In the FREE experiment, theori analysis we argue that an observed reduction in sea

geopotential height anomalies in the upper troposphere at

ice alone can sufficiently affect atmospheric circulation

the 300 hPa averaged over December-February clearly

to influence surface climate via the stratospheric path-showed the negative AO phase pattern, which is charac-way.

terized by positive anomalies over the Arctic and nega-

The atmospheric general circulation model for Earthtive anomalies in surrounding regions (Fig. 4.2.7b). At 2

simulator (AFES) version 4.1 was used with the modelm height, large negative (cold) air temperature anomalies

top about 60 km. Three sensitivity experiments (FREE,

were found over the Siberia, and less significant negative

RS10 and RS30), each consisting of two perpetual model

anomalies were seen over the Europe and northeasternruns (high sea ice (HICE) and low sea ice (LICE) using

North America region (Fig. 4.2.7c). These results aresea ice conditions of Early (5 year average of 1979–highly consistent with observations that following a low

1983) or Late (2005–2009) periods. The Merged Had-

summertime sea ice cover in the Arctic, the wintertime37

Fig. 4. 2. 7 December-February averaged anomalies (LICE minus HICE) of (a) geopotential height at 50 hPa (in m); (b) geopotential
height at 300 hPa (m); and (c) temperature at 2 m height (K) in the (from left to right) FREE, RS10 and RS30 experiments. LICE and
HICE runs indicate perpetual model simulations with annual cycle of low (2005–2009) and high (1979–1983) sea ice conditions,
respectively. In (a) and (b), red (blue) contours indicate amplitudes of the positive (negative) anomaly, with the zero line omitted.
Light and heavy grey shades indicate statistical significance greater than 95% and 99%, respectively. In (c), red (blue) shading
indicates positive (negative) anomalies. Hatching (cross hatching) indicates statistical significance greater than 95% (99%) (Nakamura
et al., 2016a).

AO tends to be in its negative phase, which brings severe

thermore, the surface temperature anomalies differ

winter weather to Europe and the North Atlantic sector.

among three experiments except for warm anomalies

In the stratosphere the signal of the negative AO phaseover ice reduction regions, suggesting large natural fluc-

was marked in the geopotential height anomalies at 50

tuations of the temperature responses that hinder to de-hPa (Fig. 4.2.7a).

tect sea ice impacts on the surface (Mori et al., 2014).

When restoration was applied, the stratospheric AO

Central to stratosphere-troposphere coupling is a dy-

signal was not so different (RS10) or was slightly weak-

namical process by which planetary-scale waves propa-

ened (RS30). In contrast, no clear AO signal at 300 hPa

gate upward, followed by weakening of the stratospheric(Fig. 4.2.7b) nor any significant cold anomalies (Fig.

polar vortex and the downward progress of its signal

4.2.7c) was seen in eastern Siberia. Instead, a significant

back to the troposphere (Baldwin and Dunkerton, 2001;

cold anomaly was seen over northwestern North America

Nishii et al., 2011). These components were all clearly

only in RS10. These results show that by damping strato-

identified as responses to sea ice reduction in the FREE

spheric variations, the tropospheric response to the sea

experiment. During December-March, an increase in

ice reduction was modified in such a way that the nega-anomalies in the vertical component of the Eliassen-Palmtive AO-like pattern and its associated cold anomalies in(E-P) flux (Fz) at 100 hPa averaged over the 50–80°N (athe troposphere were much subdued or even absent in themeasure of the upward propagation of planetary-scale

experiments with restored stratospheric circulation. Fur-wave activity) just prior to a period of negative anoma38

Fig. 4. 2. 8 (a) Time-height cross sections of daily mean anomalies (LICE minus HICE) of zonal mean wind at 60°N. Red (blue)
contours indicate amplitudes of the positive (negative) anomaly, with the zero line omitted. Light and heavy grey shades indicate
statistical significance greater than 95% and 99%, respectively; the contour interval is 2.0 m s−1. (b) Time series of daily anomalies ofFZ (vertical component of the Eliassen-Palm flux) at 100 hPa averaged ver 50–80°N. Black dot indicates the statistical significancegreater than 95%. Purple line segments signify periods when the FZ anomaly exceeded 104 m2 s−2. (c) Lead-lag correlation coefficients of polar cap height (PCH) at various pressure levels with PCH at 100 hPa. Red (blue) shading indicates positive (negative)correlations; contour interval is 0.1 (Nakamura et al., 2016a).

lies in the zonal mean winds at 60°N in the upper strato-

er stratosphere and the troposphere.

sphere was followed 1 to 3 weeks later by downward

These signatures were much reduced in RS10. Al-

propagation of the stratospheric signals to the tropo-

though similar negative wind anomalies in the strato-

sphere (Fig. 4.2.8a and b). These temporal characteris-

sphere appeared after some intensification of upward

tics were further captured by a lead-lag correlation map

wave propagation in December and January, their ampli-of polar cap height (PCH) anomalies (Fig. 4.2.8c). Fromtudes were smaller, and the signal was less significant

the reference height of 100 hPa, the upper stratospherecompared with the signatures in FREE. Importantly, thePCH anomalies led by up to 2 weeks, and the tropospher-signal no longer reached the troposphere (Figs. 4.2.8a

ic PCH anomalies lagged on a much shorter timescale;and b). In RS30, there was no significant downward

together they indicate slow downward propagation of the

propagation of the stratospheric signal (Fig. 4.2.8a). One

stratospheric signal and faster coupling between the low-

might think that the stratosphere-troposphere coupling39

Fig. 4. 2. 9 AGCM differences (NICE-CNTL) in (a) a time-height cross section of zonal-mean zonal wind at 60°N (shading; ms−1), (b) a time series of 50–80°N mean poleward eddy heat flux at 100 hPa (K m s−1), and (c) January-mean poleward eddy heat
flux at 100 hPa (shading; K m s−1). NICE reflects the light ice conditions of the 2005–2009 period, whereas CNTL is for the heavy
ice conditions of 1979–1984. The purple contours in c indicate the climatological (CNTL) poleward eddy heat flux. The JRA-55
anomalies of detrended (d) time-height cross section of zonal-mean zonal wind at 60°N (shading; m s−1); (e) time series of 50–80°N
mean poleward eddy heat flux at 100 hPa (K m s−1); and (f) December mean poleward eddy heat flux at 100 hPa (shading; K m s−1)
for 1979–2015, regressed on the normalized December mean Barents-Kara (15–90°E, 70–85°N) SIC index, are also shown. Notethat the sign of the coefficients is reversed. The purple contours in Fig. 4.2.9f indicate the climatological poleward eddy heat flux.
The horizontal axes in Figs. 4.2.9a, b, d and e indicate the months. The solid (dashed) black lines in Figs. 4.2.9a, c, d and f indicate
the statistical significance at the 90% (95%) level. The circles in Fig. 4.2.9b and eindicate the statistical significance at the 95% level(Hoshi et al., 2017).

would be weak in the restored experiments. On the con-

Relationships between increased poleward eddy heattrary, at zero lag stratosphere-troposphere coupling was

fluxes and upward propagationg wave structure

seen in all three experiments (Fig. 4.2.8c). Thus, one of

Details of the characteristics of upward planetary wave

the reasons for the lack of a tropospheric signal in RS10

propagation associated with Arctic sea ice loss underis that the signal in the lower stratosphere was too weak;

present climate conditions were examined by Hoshi et al.

this was caused by the unrealistically weak amplitudes of(2017) using reanalysis data and simulation results. The

the anomalies in the upper stratosphere, which therefore

study used the Hadley Center Sea Ice and Surface Tem-no longer propagated to the lower stratosphere. The fact

perature data set version1, and the Japanese 55-year Re-

that the lead-lag structure in the upper stratosphere wasanalysis (JRA-55) for the analysis period of 1979–2015.

degraded in a stepwise manner with restoration (Fig.

Also, data from the model simulations presented in Na-

4.2.8c) provides strong evidence for an active strato-

kamura et al. (2015) were used, in which Arctic sea ice

spheric dynamic role and at the same time suggests theloss sensitivity experiments were conducted using theimportance of the upper stratosphere.

AGCM for the Earth Simulator (AFES). The experiments consisted of a pair of model runs, each integrated
over the same annual cycle repeatedly for 60 years using40

the same climatological forcings and boundary condi-

as the increased poleward eddy flux in December andtions except for Northern Hemisphere sea ice. One run

January (Fig. 4.2.9d) and weakened stratospheric polar(CNTL) used sea ice concentration (SIC) averaged overvortex and subsequent downward propagating signal

the high sea ice period of 1979–1983, and the other(Fig. 4.2.9d). Kim et al. (2014) and Nakamura et al.

(NICE) used SIC averaged over low sea ice period of(2015) found similar results using slightly different meth-

2005–2009.

odologies on different data sets.

The poleward eddy heat flux is defined as v*T*, where

A question then arises as to whether there are any pre-v and T are the meridional wind and air temperature, re-ferred locations for enhanced poleward eddy heat flux.

spectively, and superscript * denotes the deviation from

There are indeed two centers of action in the poleward

the zonal mean. Poleward eddy heat flux is a key indica-

eddy heat flux climatology at the 100 hPa level (purple

tor of the upward propagation of planetary scale waves,contours in Figs. 4.2.9c and f). One is situated near theas it is proportional to the vertical component of the Eli-Barents-Kara Sea and the other over the Bering region,

assen-Palm flux in the transformed Eulerian mean frame-appearing consistently in both simulation and JRA-55 re-work. Nishii et al. (2009) decomposed the observed

sults. The January-mean poleward eddy heat flux anom-

poleward eddy heat flux into three terms. Following

alies from the simulations (Fig. 4.2.9c) and from the re-their scheme, poleward eddy heat flux anomalies simu-

gression coefficients corresponding to 1 standard

lated in the AGCM experiments are decomposed as fol-

deviation of the December-mean Barents-Kara Sea SIC

lows:

index (Fig. 4.2.9f) are also shown. For both simulations
and reanalysis data, positive poleward eddy heat flux

[v*T*]NICE-CNTL = [vc* Ta*] + [va* Tc*] + [va* Ta*]a,anomalies appear in eastern Eurasia and its vicinity. In

(4.2.1)

addition, there is an area located approximately south of
the Barents-Kara Sea (central Eurasia region) with signif-

where the subscripts c and a denote the climatological

icant positive poleward eddy heat flux anomalies (Figs.

mean and anomaly, respectively, and the square brackets

4.2.8a and f). This is part of a dipole pattern with an area

denote a 60 year temporal average. The first and secondof negative flux anomalies situated to the west. Compar-

terms are referred to as the linear terms in temperature

ing the location of this dipole pattern with respect to theanomaly and in meridional wind anomaly, respectively.

climatological center, it was noted that the reduction in

They represent poleward eddy heat flux anomalies

Arctic sea ice leads to a shift and strengthening of the re-formed by interactions between the climatological plane-gion of positive poleward eddy heat flux in central Eur-tary wavefield and anomalous temperature and meridio-

asia.

nal wind fields, respectively. The last term is referred to

It was investigated how the spatial and temporal char-as the nonlinear term, which is from both anomalousacteristics of poleward heat flux, and therefore of the up-

fields.

ward propagation of planetary waves, are modulated in

Figs. 4.2.9 a and b show the time evolution of AGCM

response to the present-day reduction in Arctic sea ice.

daily-mean anomalies of zonal-mean zonal wind at 60°N

Both the AGCM simulations and the reanalysis data re-and poleward eddy heat flux averaged over 50–80°N atvealed that the increased poleward eddy heat fluxes at the

100 hPa, respectively. Negative zonal wind anomalies

lower stratospheric level in the central Eurasia and east

first appear in the upper stratosphere in early January,

Eurasia regions result from Arctic sea ice loss. These in-and significant signals subsequently propagate downward

creases in two regions are due to constructive coupling ofto the surface level in late January and February (Fig.

the climatological planetary wave structure with anoma-

4.2.9a). An increase in poleward eddy heat flux in latelous meridional wind and temperature fields, respective-

December and January precedes the weakening of thely, which are dynamically linked through the propagation

polar vortex (Fig. 4.2.9b). Quantitatively, the increase inof stationary Rossby waves emanating from the Bar-the poleward eddy heat flux averaged over January is

ents-Kara Sea region. This study provides a detailed

about 19% of its climatological (CNTL) value. When

three-dimensional picture of the way the recent Bar-these results are compared with the coefficients from theents-Kara sea ice loss has modified the poleward eddy

regression of JRA-55 on the December Barentz-Kara Sea

heat flux field in the lower stratosphere and subsequently

SIC index, there is striking resemblance in features such

affects the stratospheric wave structure, which likely
41

Fig. 4. 3. 1 Change in the geopotential height (gpm) at (a–c) 1000 hPa, (d–f) 700 hPa, (g–i) 250 hPa and (j–l) 100 hPa pressurelevels as response to Barents-Kara sea ice transitions from 100% to 80%, 80% to 40% and 40% to 1% for February (Petoukhov andSemenov, 2010).

plays a key role in the Arctic-midlatitude climate linkag-

formed with the ECHAM5 atmospheric general circula-es under present climate conditions.

tion model, developed at the Max Planck Institute for
Meteorology. Sea ice concentration (SIC) in all six simulations was assigned to the 1987–2006 climatological4. 3. Future trends

mean data everywhere except for the B-K sector (30–

A link between reduced Barents-Kara (B-K) sea ice

80°E, 65–80°N). SIC in this sector in May through Oc-

and cold winter extremes over northern continents had

tober was assigned to the 1987–2006 climatology in all

already been discussed by Petoukov and Semenovsix simulations, whereas November through April SIC(2010). They suggested that high-latitude atmospheric

was set to six different constant values, namely, 100, 80,

circulation response to the B-K sea ice decrease is highly60, 40, 20 and 1% in six corresponding simulations.

nonlinear and characterized by transition from anoma-

The patterns of geopotential height anomalies throughlous cyclonic circulation to anticyclonic one and then

the troposphere (Fig. 4.3.1) principally differ from theback again to cyclonic type circulation as the B-K sea ice

corresponding anomalies associated with the negative

concentration gradually reduces from 100% to ice free

NAO, in case of 80% to 40% SIC decrease (Fig. 4.3.1b,conditions. For the discussion, six simulations were per-e and h) and from the positive NAO, in the events of42

Fig. 4. 3. 2 Differences in the 150 year average of the (a) geopotential height at 300 hPa (m) and (b) temperature at 850 hPa (K)in December-January-February. Anomalies of (from left to right) AICE, Im30, Im40 and Im50 runs from the CNTL run are shown.
A positive (negative) anomaly is indicated by red (blue) contours. The zero line is omitted and the contour interval is displayed at
the bottom left corner of the plot. Statistical significance greater than 95% and 99% are indicated by light and heavy gray shading,respectively. CNTL, high ice period, 1979–1983; AICE, low ice period, 2005–2009; Im30, Im40 and Im50: decrease of 30, 40 and 50
cm, respectively, from the CNTL (Nakamura et al., 2016b).

100% to 80% (Fig. 4.3.1a, d and g) and 40% to 1% (Fig.

the impacts of climate on Arctic sea ice changes are mod-4.3.1c, f and i) SIC reduction. Fig. 4.3.1b, e and hified during the transition from present-day conditions todemonstrate, in particular, the counterclockwise turn of

ice-free conditions. They conducted four sensitivity ex-

the anomalous blocking-like structure around the pole as

periments using the idealized assumption that Arctic sea

compared to the canonic negative NAO pattern. The

ice is reduced to the ice-free condition in a stepwise man-positive anomaly expands far deep to the south with in-ner. Then, an atmospheric general circulation model

crease in height. At the same time, both 100% to 80%(AGCM) was adopted that has successfully captures theand 40% to 1% SIC transitions trigger a development ofnegative AO-like responses to a reduction in sea ice (Na-the anomalous atmospheric circulation structures turned,

kamura et al., 2015, 2016a; Jaiser et al., 2016). The com-relative to the positive-NAO structure, counterclockwise

parison of the atmospheric responses to gradual changes

around the pole in the lower and middle tropospherein sea ice improves the understanding of the underlying(compare Figs. 4.3.1a and d and Figs. 4.3.1c and f). Thismechanism of the association between sea ice and theis due to the lower-troposphere heat anomaly in the B-K

AO. 150 year integrations were performed of a control

sector as a source of the indicated anomalous atmospher-run (CNTL), high ice period 1979–1983, and four per-ic circulation structures. In the upper troposphere andturbed runs, referred to as anomalous ice (AICE), low ice

lower stratosphere, a spatial pattern of the geopotential

period, 2005–2009, Im30, decrease of 30 cm from CNTL,

height anomaly still resembles the counterclock-Im40, decrease of 40 cm from CNTL and Im50, decrease

wise-turned positive NAO pattern in the case of 40% toof 50 cm from CNTL, indicating an ice-free condition

1% SIC decrease (Fig. 4.3.1i and l). Meanwhile, unlike

over the Arctic Ocean in all seasons.

the positive NAO structure, the anomalous Z250 and

The winter Northern Hemisphere (NH) atmospheric

Z100 patterns for the 100% to 80% SIC decrease areresponse to a reduction in present-day sea ice clearly

practically in the opposite spatial phase to their counter-showed a negative AO-like pattern in the upper tropo-

parts in the lower- and middle-troposphere over the Bar-

spheric height anomaly (ΔZ300; Fig. 4.3.2a, AICE). The

ents Sea Region (compare Fig. 4.3.1 a and g).

corresponding near-surface temperature response(ΔT850) was indicative of a warm anomaly over the At-

Atmospheric responses to a reduction in Arctic sea ice

lantic side of the Arctic Ocean, due to the reduction in

In this study, Nakamura et al. (2016b) examined how

sea ice in that area, and a cold anomaly over eastern Si43

Fig. 4. 3. 3 (a) Daily evolutions of zonal mean zonal wind anomalies (respective runs minus CNTL) at 60°N. The contours andshading are the same as those in Fig. 4. 3. 2. (b) Daily evolutions of FZ (i.e., vertical component of the Eliassen-Palm (E-P) flux)
anomalies at 50–80°N and 100 hPa. Periods when the FZ anomaly exceeded 5 x 104 m2 s−2 are indicated by a purple line. (c) Anomalies of amplitude (i.e., square root of the power) decomposed into wave 1 to wave 6 components of January geopotential height at
60°N and 100 hPa (nakamura et al, 2016b).

beria (Fig. 4.3.2b, AICE). Even when sea ice thickness

zonal wind were relatively large and significant, sugges-(SIT) was further reduced artificially as described above,

tive of independent tropospheric processes that differed

the tropospheric responses showed negative AO-like pat-from stratosphere-troposphere coupling. This indepen-

terns with amplitudes that increased as more sea ice wasdence between the stratosphere and troposphere was

reduced (Fig. 4.3.2b, Im30, Im40 and Im50). The experi-

most obvious in Im50 (Fig. 4.3.3a), in which the strato-

ments showed that the negative AO-like pattern intensi-

spheric polar vortex strengthened from November tofies with a reduction in Arctic sea ice. This appears to be

February, consistent with the reduced upward wave prop-

consistent with current studies that presented a negative

agation (Fig. 4.3.3b).

phase shift of the AO induced by the recent Arctic sea ice

To examine why the intensity of the stratosphere-tro-

reduction (Nakamura et al., 2015). Some studies haveposphere coupling weakened with increasing sea ice re-emphasized the role of stratosphere-troposphere coupling

duction, a wave number analysis was applied to the Janu-on the association between sea ice and the AO (Jaiser etary mean height field in the lower stratosphere, anal., 2016; Nakamura et al., 2016a). However, strato-

important level that connects the troposphere with thespheric responses vary depending on the location of sea

stratosphere through upward planetary-scale wave propa-

ice anomalies. Therefore, the role of the stratosphere

gation (Nakamura et al., 2016a). January was chosen

was clarified in our experiments.

due to the clear contrast between the AICE and Im50 cas-

The intensification of the upward wave propagation

es. The wave number 1 amplitude of the 100 hPa geopo-and subsequent stratospheric polar vortex weakening oc-

tential height at 60°N was magnified clearly by 18 m (i.e.,curred sporadically in Im30 and Im40 (Fig. 4.3.3a and b).

geopotential meter) in AICE (Fig. 4.3.3c). While theConversely, the negative tropospheric anomalies of the

wave 1 amplitudes were magnified by 16 and 5 m in44

Fig. 4. 3. 4 Schematic diagram of two physical processes connecting Arctic sea ice loss and negative Arctic Oscillation (AO)like response. (left) The stratospheric pathway corresponding to the AICE case in January. (right) The tropospheric pathway
corresponding to NDJFM Im50 case. Typical characteristics of the circumpolar jet stream at 10, 30, 100 and 300 hPa (only 100
and 300 hPa in the right plot) corresponding to the high and low sea ice cases are shown by grey and purple lines, respectively.
Anomalous atmospheric heating due to associated meridional circulation changes and the turbulent heat flux are given in units of W
m−2. SSW, sudden stratospheric warming; LS, lower stratosphere; UT, upper troposphere (Nakamura et al., 2016b).

Im30 and Im40, respectively, no change was observed inheat flux due to a planetary-scale wave response in

Im50. This suggests that the wave 1 structure corre-

the troposphere (Fig. 4.3.4, tropospheric pathway).

sponding to a typical planetary-scale wave was responsi-

Increased meandering of the tropospheric jet stream,

ble for the stratospheric polar vortex weakening. There-

corresponding to the response of the stationary Ross-

fore, it was suggested that the intensification (suppres-by wave to Arctic sea ice reduction (Honda et al.,sion) of the climatological Siberian trough in the lower

2009; Inoue et al., 2012; Nakamura et al., 2015), in-stratosphere could strengthen (weaken) stratosphere-tro-

duces a negative AO-like pattern. Although the issue

posphere coupling, which has a role in the associationof longtitudinal dependency remains, the associatedbetween sea ice and the AO in accordance with features

eddy momentum flux response is consistent with theof the recent Arctic amplification.

conventional understanding of AO dynamics.

Two processes are proposed that control the association between Arctic sea ice changes and the polarity of

4. 4. Is the conclusion solid?

the winter AO, which are described below and drawn in
Fig. 4.3.4.

Even many studies have been done in GRENE Arctic

1. The stratosphere-troposphere coupling process is

Project on “Arctic and mid-latitude link”, the conclusions

dominated by an intensified climatological plane-of series of studies are not clear. Mori et al. (2014) andtary-scale wave structure (Fig. 4.3.4, stratospheric

Nakamura et al. (2015) tackle this topic squarely; howev-

pathway). This is mainly due to the intensification ofer, the both conclusions seem not to be fully consistent.

the lower stratospheric Siberian trough associatedMori et al. stressed that the link was due to two different

with a reduction in Arctic sea ice on the Atlantic side

elements, AO/NAO and WACE, and the latter was due toof the Arctic Ocean. The results are consistent withBarentz-Kara Sea ice retreat but the former was due

recent studies that demonstrated the role of the strato-

mainly to natural variability. On the other hand, Na-sphere in the association between sea ice and climate

kamura et al. (2015) explained the WACE as an expres-observed in the most recent decade (Nakamura et al.,sion of AO/NAO, and focused to the pathway through2016a). The results support that there are different

the stratosphere. Nakamura et al. (2015) refer to Mori etstratospheric responses to different locations of sur-al. (2014) only for the fact and do not make a discussion

face heat sources.

on the difference of the link.

2. The tropospheric process is controlled by the eddy

After GRENE Arctic Project, still many discussions
45

are continued. Mori et al. (2019a, b) made a clear evalu-

the stratospheric polar vortex by Arctic sea-ice loss. Nat.
Commun., doi: 10.1038/ncomms5646.
Mori, M., Watanabe, M., Shiogama, H., Inoue, J. and Kimoto,M., 2014: Robust Arctic sea-ice influence on the frequent
Eurasian cold winters in past decades. Nature Geoscience,
7, 869–873.
Mori, M., Kosaka, Y., Watanabe, M., Nakamura, H., Kimoto,M., 2019a. A reconciled estimate of the influence of Arctic
sea-ice loss on recent Eurasian cooling. Nature Clim.
Change, 9, 123–129, doi.org/10.1038/s41558-018-0379-3.
Mori, M., Kosaka, Y., Watanabe, M., Taguchi, B., Nakamura,
H., Kimoto, M., 2019b. Reply to: Is sea-ice-driven Eurasian cooling too weak in models?. Nature Clim. Change,
9, 937–939, doi.org/10.1038/s41558-019-0636-0.
Nakamura, T., Yamazaki, K., Iwamoto, K., Honda, M., Miyoshi, Y., Ogawa, Y., Ukita, J., 2015. A negative phase shiftof the winter AO/NAO due to the recent Arctic sea-ice reduction in late autumn. J. Geophys. Res. 120, doi:10.1002/
2014JD022848.
Nakamura, T., Yamazaki, K., Iwamoto, K., Honda, M., Miyoshi, Y., Ogawa, tomikawa, Y. and Y., Ukita, J., 2016a.
The stratospheric pathway for Arctic impacts on midlatitude climate. Geophys. Res. Lett., 43, 3494–3501, doi:
10.1002/2016GL068330.
Nakamura, T., Yamazaki, K., Honda, M., Ukita, J., Jaiser, R.,
Handorf, D. and Dethloff, K., 2016b. On the atmospheric
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Res. Lett., 43, 10,394–10,402, doi: 10.1002/ 2016GL
070526.
National Academy of Sciences, 2014. Linkages between Arctic
Warming and Mid-latitude Weather Patterns. The National
Academies Press, Washington, DC.
Nature communications, 2019. A tug-of-war over the mid-latitudes (Editorial). Published online, 09 Decemmber 2019,
1–2, doi.org/10.1038/s41467-019-0.
Nishii, K., Nakamura, H. and Miyasaka, T., 2009. Modulationsin the planetary wave field induced by upward-propagating
Rossby wave packets prior to stratospheric sudden warming events: A case study. Q. J. R. Meteorol. Soc., 135, 39–
52, doi: 10.1002/qj.359.
Nishii, K, Nakamura, H. and Orsolini, Y. J., 2011. Geographicaldependence observed in blocking high influence on thestratospheric variability through enhancement or suppression of upward planetary-wave propagation. J. Climate,24, 6408–6423.
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ation of the weakness of simulations in describing thelinkage compared to the observations; however, question
was raised (Screen and Blackport, 2019). There is a
growing debate about the strength of the influence ofArctic warming versus that of other regions, and those
discussions were listed up as a “Nature Research collection” from 4 Nature related journals (Nature communications, 2019). One from the collection, Cohen et al.
(2019), gave a wide review for the topic as “divergent
consensus”, and this phrase is a good expression of the
topic at present.
References:
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doi: 10.1029/1999JD90445.
Baldwin, M. P. and Dunkerton, T. J., 2001. Stratospheric harbingers of anomalous weather regimes. Science, 294, 581–
584, doi: 10.11126/ science.1063315.
Barnes, E. A., Screen, J. A., 2015. The impact of Arctic warming on the midlatitude jet-stream: Can it? Has it? Will it?
WIREs Clim. Change 6 (3), 277–286, http://dx.doi.
org/10.1002/wcc.377.
Cohen, J., Screen, J. A., Furtado, J. C., Barlow, M., Whittleston,D., Coumou, D., Francis, J., Dethloff, K., Entekhabi, D.,
Overland, J., Jones, J., 2014. Recent Arctic amplificationand extreme midlatitude weather. Nat. Geosci. 7 (9), 627–
637, http://dx.doi.org/10.1038/ngeo2234.
Cohen, J. and 31 others, 2019. Divergent consensuses on Arctic
amplification influence on midlatitude severe winterweather. Nature Clim. Change, 10, 20–29, doi.org/10.
1038/s41558-019-0662-y.
Honda, M., Inoue, J., Yamane, S., 2009. Influence of low Arctic
sea-ice minima on anomalously cold Eurasian winters.
Geophys. Res. Lett. 36, L08707. http:// dx.doi.org/ 10.
1029/ 2008GL037079.
Hoshi, K., Ukita, J., Honda, M., Iwamoto, K., Nakamura, T.,
Yamazaki, K., Dethloff, K., Jaiser, R. and Handorf, D.,2017. Poleward eddy heat flux anomalies associated withrecent Arctic sea ice loss. Geophys. Res. Lett., 44, 446–
454, doi: 10.1002/2016GL071893.
Inoue, J., Hori, M. E., Takaya, K., 2012. The role of Barents sea
ice in the wintertime cyclone track and emergence of awarm-Arctic cold-Siberian anomaly. J. Clim. 25, 2561–
2568. http:// dx.doi.org/ 10.1175/ JCLI-D-11-00449.1.
Jaiser, R., Nakamura, T., Handorf, D., Dethloff, K., Ukita, J.
and Yamazaki, K., 2016. Atmopheric winter response to
Arctic sea ice changes in reanalysis data and model simulations. J. Geophys. Res., 121, 7564–7577.
Kim, B.-M., Son, S.-W., Min, S.-K., Jeong, J.-H., Kim, S.-J.,
Zhang, X., Shim, T. and Yoon, H.-J., 2014. Weakening of46

Screen, J. A., Blackport, R., 2019. Is sea-ice-driven Eurasiancooling too weak in models? Nature Clim. Change, 9,
934–936, doi.org/10.1038/s41558-019-0635-1.
Vihma, T., 2014. Effects of Arctic sea ice decline on weather
and climate: a review. Surv. Geopys. 35, 1175–1214, http:// dx.doi.org/ 10.1007/ s10712-014-9284-0.
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03.003.

47

or not sea ice can survive during the following summer.

5. Sea ice distribution and Arctic sea
routes

The sea ice growth in winter is influenced by thermohaline stratifications in the upper ocean. In the Pacific sector of the Arctic Ocean, one of the major heat inputs to

We have proposed empirical method to estimate tim-

the upper ocean just beneath the surface mixed layer is

ing of disappearance of sea ice, and proposed a simple

primarily controlled by advection of the warm Pacificmodel using just sea ice motion to evaluate volume trans-

Summer Water (PSW). PSW is transported into the Ca-

portation of upper ocean circulation, so called “Beaufort

nadian Basin (Fig. 5.1.1) by the oceanic Beaufort Gyre

Gyre”, that delivers the heat into the central Arctic Ocean(OBG), driven by winds and sea ice motions. Therefore,

and affects fate of sea ice. Analyses using in-situ andthe volume transport of the OBG is a key parameter tosatellite observation data have clarified that volume

determine upper oceanic heat contents in the basin.

transportation of the oceanic Beaufort Gyre is determined

In the case of the upper ocean circulation driven byby past sea ice motion from about four years previously.

only Ekman pumping without any wave propagation andWe have also constructed methods to derive the timing oflateral vorticity inputs, the upper ocean circulation simul-

the disappearance of sea ice associated with rafting ofsea ice from satellite-derived high-accuracy sea ice motion data. We also conducted a study using a high-resolution numerical Arctic Ocean model to reveal the structure of the heat transport system from the Atlantic to the
Arctic, and the impact of meso-scale eddies on the advance/retreat of sea ice cover.
Another important theme of ours is seasonal-scale sea
ice prediction, which is necessary for a decision ofwhether or not to take the Arctic route. We have improved the statistical method of the prediction using satellite remote-sensing data. In 2015, our prediction provided a good forecast of the timing of summer ice retreat
and the spatial pattern of the minimum ice cover. Our activity regarding the Arctic sea route included many otherthemes, such as the optimal routing of ships, engineeringstudies on ship/ice interaction, ship icing, economic evaluations, and a proposal of scenarios for the use of Arctic
sea routes. With these multidisciplinary efforts, studies
for realizing the sustainable use of Arctic sea routes haveadvanced greatly during the GRENE Arctic Climate
Change Research Project.

5. 1. Sea ice distribution by ocean heat transportand rafting of sea ice

Fig. 5. 1. 1 Bottom topography of study area. Grey (brown)
contours indicate seafloor topographies deeper (shallower) than
1000 m (100 m), contour interval 1000 m (50 m). a) The Arctic
Ocean. Area 1-4 show the areas for which time series of theareal sea ice area (SIA) shown in Fig. 5.1.2b were calculated.
b) The Canada Basin. The zonal band in the blue box, 74.4°–
77°N, 130°–160°W, shows the area for which time series of δ x
uW and δ x uI, shown in Fig. 5.1.3b and c, were calculated. The
black hashed box, 74.4°–77°N, 150°–160°W, is the area used
to calculate time series f the spatial average of ocean dynamic
height (ODHc) shown in Fig. 5.1.3d (Yoshizawa et al., 2015).

Sea ice retreat and ocean circulation
In recent years, the anomalous sea ice retreat into thecentral Arctic basin has been mainly observed in the Pacific sector of the Arctic Ocean. First year ice has replaced multi year ice as the dominant sea ice type in the
Arctic Ocean. In this situation, the thickness of first year
ice at the melt onset, which is influenced by sea ice
growth in winter, is an important precondition of whether
48

taneously responds to surface forcing. The observational
evidence, however, showed that the spatial pattern of the
OBG did not correspond to that of the overlying sea ice
gyre. As such, the spatial inconsistency suggests that
there was a time lag in the response of the OBG relative
to surface forcing. Therefore, the time scale of this delayed oceanic response significantly influences oceanic
heat transportation into the basin and the resulting sea ice
variations. As a first step toward understanding oceanic
influences on sea ice variations, Yoshizawa et al. (2015)
examined the relative contributions of past surface forcing to the current state of the OBG. The quantitative result enables to forecast volume transports of the OBG
within a time scale that is shorter than the delayed oceanic response.
The hydrographic data used were collected under the
Joint Western Arctic Climate Studies (JWACS) with Canadian Coast Guard Ship (CCGS) Louis S. St-Laurent

Fig. 5. 1. 2 Time series pf SIA (km2) in September, a) in theentire Arctic Ocean, the area north of 68°N, b) in the individual
four area depicted in Fig. 5.1.1a: area 1 (120°–180°W, red
curve), area 2 (140°–180°W, blue), area 3 (0°–140°E, black),
and area 4 (0°–120°W, grey). Thick and thin curves denote
3-year running mean values and annual values, respectively(Yoshizawa et al., 2015).

(LSSL) 2002–2008 and R/V Mirai 2002, 2004 and 2008,
and under the Korea-Polar Ocean in rapid transition(K-PORT) expedition with the Korean icebreaker ARAON 2011 and 2012. Also data from XCTD stations, collected during CCGS LSSL 2009–2012, were used. Sea
ice concentration data were analyzed derived from theNASA team algorithm (Cavalieri et al., 1984), provided(AO). In this period, in contrast, no significant changeby the NSIDC. Sea ice motion data were calculated by

was observed in the western hemisphere (areas 1 and 4;

the maximum cross correlation method, the so-called

red and grey curves in Fig. 5.1.2b). The second rapid re-particle image velocimetry method (Adrian, 1991), forduction in 1998 was led by a regional reduction in thebrightness temperature images obtained from AMSR-E

Canadian Basin (area 1; red curve in Fig. 5.1.2b), without(Kamoshida and Shimada, 2010), mainly using 89 GHz

significant changes in other areas (blue, black and grey inchannel data gridded onto a polar-stereo projection (2.08

Fig. 5.1.2b). These regional contrasts suggest that the× 2.08 km).

mechanism of sea ice reduction differs from region to re-Fig. 5.1.2a shows time series of the sea ice area (SIA)gion.

in September in the area north of 68°N (hereafter referred

After 1998, the SIA reduction trends in areas 1, 2 andto as the entire Arctic Ocean). SIA did not show a con-

3 (red, blue and black curves in Fig. 5.1.2b), and also instant reduction trend, but accompanied rapid reduction

the entire Arctic Ocean (Fig. 5.1.2a), were much larger

events in 1989–1990, 1998, 2007 and 2012. First, it wasthan those before 1998. In 2012, SIA in the entire Arctic

investigated whether these events occurred in the entire

Ocean hit a new minimum record.

Arctic Ocean or were dominated by specific regions.

During 1998–2006, before the anomalous sea ice re-Fig. 5.1.2b shows time series of SIA in the four distinct

duction in 2007, an embayment-shaped sea ice retreat

areas (area 1–4 depicted in Fig. 5.1.1a). The rapid reduc-into the basin was observed in the western Canada Basin,tion in 1989–1990 was led by the reduction in the easternand in other areas, the ice edges were still located in thehemisphere in the Arctic Ocean (areas 2 and 3), whereshelf regions or on the shelf slopes. During 2007–2012,

the sea ice motion is characterized by the transpolar sea

in the East Siberian and Kara Seas, the ice edge moved

ice drift (blue and black curves in Fig. 5.1.2b). The re-offshore but did not enter into the basin deeper thanduction in this region is interpreted as discharges of sea

500m. In the Canada Basin, however, the open water

ice through the Fram Strait, which correlate with varia-

area was enlarged northward along the Northwind Ridgetions of climatic indices such as the Arctic Oscillationand the Chukchi Plateau, where the major pathway of49

PSW was identified (Shimada et al., 2001; Steele et al.,2004). This retreat pattern suggests that the oceanic heat
carried by the OBG is a key element to understanding theregional sea ice change in the Canada Basin.
Oceanic influences on sea ice reduction in the Canada
Basin
Shimada et al. (2006) explained that the strengtheningof the OBG in the late 1990s was initiated by thestrengthening of basin-scale sea ice motion due to lessfriction of the sea ice cover against the Alaskan Beaufort
coast, and then more oceanic heat, leading to the substantial sea ice reduction, was delivered into the deep Canada
Basin. Here, attempt was made to identify which parameters have controlled the observed temporal variations of
the summer SIA in recent years.
First of all, features of spatial distributions of the OBG
were briefly mentioned, in order to introduce how thevolume transport can be evaluated by surface forcing
data. Based on the Sverdrup relation, the vertically averaged volume transport of the OBG would be given by
Fig. 5. 1. 3 Time series of a) SIA (km 2) in September in
the Canada Basin (area 1 depicted in Fig. 5.1.1a); b) spatial
averages of ∇x uW (s−1) in the ice-covered period (NovemberJune) in the zonal band in the blue box depicted in Fig. 5.1.1b; c)the same as (b), but for ∇x uI (s−1); d) spatial averages of ODH(dynamic cm) at 100 dbar relative to 800 dbar in summer (July–
September) in the black hashed box depicted in Fig. 5.1.1b,
ODHc. The z-axis in (d) is inverted. In (a)–(d), thick and thincurves denote 3-year running mean values and annual values,
respectively. Blue straight lines denote trends of each observed
variable in 1979–1997, 1998–2006 and 2006-2012, calculated
by a least-squares technique (Yoshizawa et al., 2015).

just the surface torque. Fig. 5.1.3b and c show time series of curls of 10 m winds and sea ice velocities, ∇x uW
and ∇x uI, averaged for the ice-covered period from November to June in the zonal band, in the blue box depicted in Fig. 5.1.1b, respectively.
During 1979–1996, ∇x uW increased (Fig. 5.1.3b),while there were no significant changes in both ∇x uI(Fig. 5.1.3c) and SIA in the Canada Basin (Fig. 5.1.3a).
This suggests that vorticities of surface winds did not
penetrate into sea ice under the heavy ice condition
during this period (Shimada et al., 2006). From 1997 to
2004, ∇x uI decreased (Fig. 5.1.3c) coherently with a

5.1.3a). This implies that the observed variations of SIA

decrease in ∇x uW (Fig. 5.1.3b). This suggests that thein the Canada Basin cannot be explained by just the sur-ratio of the sea ice velocities, which are tangential toface forcing, and some oceanic delayed processes, i.e.,

wind vectors, to the wind velocities, which is a proxy of“ocean inertia” effects, may be necessary to interpret thethe kinematic coupling between atmosphere and sea ice

observed SIA variations after 2007–2008. Before exam-(Kimura and Wakatsuchi, 2000), was getting larger thanining the delayed temporal response of the OBG to thethat before 1997. When the anomalous summer sea ice

surface forcing, recent variations of the OBG were re-reduction was observed in 2007–2008 (Fig. 5.1.3a), bothviewed, as well as the heat content in the subsurface∇x uW and ∇x uI decreased substantially, i.e., clockwise
rotation was strengthened (Fig. 5.1.3b, c).

layer (25–150 m) in the western Canada Basin where thewarm PSW is delivered.

After 2009, ∇x uW rebounded to the value observed
around 2004 or in the mid 1980s (Fig. 5.1.3b). ∇x uI

Changes in the OBG and ocean heat content

also rebounded after 2009, but the rebound did not reachFig. 5.1.4 by Yoshizawa et al. (2015) shows ocean dy-

the levels of the 3-year running mean values before 2007

namic height (ODH) distributions at 50, 100 and 150(Fig. 5.1.3c). Even though the curls rebounded, SIA indbar relative to 800 dbar in the summers (July-Septem-

the Canada Basin continued to decrease after 2009 (Fig.

ber) of 2004, 2008, 2011 and 2012 (color). For these50

Fig. 5. 1. 4 Spatial distributions of ODH (color, dynamic cm) at 50, 100 and 150 dbar relative to 800 dbar in summer (July–
September) of a) 2004, b) 2008, c) 2011 and d) 2012, and sea ice motions (vector, cm s−1) in preceding ice-covered period (November–June) (Yoshizawa et al., 2015).

four summers, hydrographic observations covering thenarrow area of the Northwind Ridge and the Chukchi

OBG region are available. Sea ice motions in preceding

Plateau were observed in all 4 years (color in center andice-covered periods (November-June) are also overlaid

right panels of Fig. 5.1.4a-d), while overlying sea ice

as vectors. In the summers of 2004, 2011 and 2012,

motions were westward in thes regions, i.e., sea ice ve-

ODH showed patterns that were almost the same at thelocity vectors were perpendicular t the direction of thethree levels (Fig. 5.1.4a, c, d). In the summer of 2008,

oceanic geostrophic flows (vector in Fig. 5.1.4). Suchhowever, the pattern of ODH at 50 dbar (left panel of

discrepancies in the spatial patterns of the oceanic andFig. 5.1.4b) slightly differed from the patterns at thesea ice gyres support the notion of a significant role of fi-

deeper levels of 100 and 150 dbar (center and right pan-nite amplitude seafloor topographic features, the North-els of Fig. 5.1.4b). The center of the OBG at 50 dbar inwind Ridge and the Chukchi Plateau, in the formation of2008 was not localized just east of the Northwind Ridge,

the OBG, as pointed out by Sumata and Shimada (2007).

but showed a zonally broad pattern extending southeast-

In such cases, the difference in ODH values between theward, toward the Canadian Beaufort Sea (left panel ofcenter and rim of the gyre is proportional to the volume

Fig. 5.1.4b). The freshwater inputs arising from thetransport, based on the Sverdrup relation. Here, the ODH

melting of multi-year ice anomalously increased in thevalue can be assumed near the stable center of the OBG

summers of 2007 and 2008 in the Canadian Beaufort Sea

as a proxy of the volume transport. Here, the spatial av-(Yamamoto-Kawai et al., 2009). These freshwater inputs

erage of ODH was defined in the black hashed box de-would cause the broadening of the pattern of ODH at 50

picted in Fig. 5.1.1b as a proxy of the northward volume

dbar observed in the summer of 2008.

transport. The subscript c denotes the “center” of the

At 100 and 150 dbar, northward currents in the zonally

OBG.
51

duction of sea ice has encouraged interests in developments of the Arctic sea routes. However, robust sea ice
often remained through summer locally in the Arctic
coastal region used for the shipping routes, in spite of thedecline of the total amount of the summer sea ice area
(SIA). The prediction of the persistence of such robust
sea ice during summer is essential information for making decision of utilization of the Arctic sea routes. Thebalance between the winter sea ice growth and the summer sea ice melting is critical to whether sea ice can per-Fig. 5. 1. 5 Hovmoeller diagram of depth-averaged potential
temperature (color, °C) and salinity (black contour), using
spatially averaged values in the region near Northwind Ridge,
the black hashed box depicted in Fig. 5.1.1b (Yoahizawa et al.,2015).

sist or not through summer. The amount of the sea ice
growth during winter can be a reasonable indicator of thepersistence of the sea ice in the subsequent summer.
Ignoring thermodynamic sea ice growth and melting
and focusing on mechanical sea ice growth, temporal

Fig. 5.1.3d shows temporal variations of ODHc at 100

variations of sea ice volume due to divergence/conver-

dbar, which correspond to the lower level of the PSW

gence of sea ice motion was related to the function de-layer, relative to 800 dbar. The z-axis in the figure wasscribing redistributions of the sea ice thickness due toinverted. The annual value of ODHs increased remark-rafting processes by Yoshizawa (2016). When the con-ably from 2007 to 2010 (thin curve in Fig. 5.1.3d). This

vergent sea ice motion increases ice thickness mechani-

shape was similar to those of the 3-year averaged ∇x uW

cally via rafting processes, SIA will be reduced to con-and ∇x uI in 2006–2009 (thick curve in Fig. 5.1.3b, c),serve mass of sea ice. Based on this relationship, a proxy

but ODHc showed time lags of about 2 years relative toof mechanical sea ice growth was introduced using thethe curls. Trends of these observed variables in 2007–

satellite-derived sea ice velocity and coverage data.

2012 indicate that both the loss of SIA (blue line in Fig.

Daily averaged sea ice velocity data was calculated

5.1.3a) and the volume transport of the OBG (blue line infrom satellite-measured brightness temperature images

Fig. 5.1.3d) increased, while clockwise rotations ofobtained by AMSE-E and AMSAR 2. To validate thewinds and sea ice motions decreased (blue lines in Fig.

performance of the method to estimate sea ice trajecto-

5.1.3b, c). This suggests that the OBG rather than theries by using satellite-derived sea ice motion data, loca-

surface forcing has much more immediate impact on thetions of the ice-tethered profiler (ITP) buoy detected bychange in SIA.

GPS are used (http://www.whoi.edu/website/itp). Also

Fig. 5.1.5 shows time series of potential temperature

sea ice concentration (SIC) data were derived using

and salinity near the Northwind Ridge. The thickness ofNASA team algorithm 2. A proxy of sea ice type wasthe PSW layer (29.5<S<32.5; Steele et al., 2004) associ-obtained from Gradient Ratio (GR), introduced by Cava-

ated with the oceanic thermal condition affecting thelieri et al. (1984) by the following equation,

overlying sea ice cover, increased from 2007 to 2010.
GR(a/b)V = {Ta(V) – Tb(V)}/ {Ta(V) + Tb(V)}, 

The thickness was nearly unchanged after 2009, even

(5.1.1)

when the negative vorticity inputs by surface forcing decreased after 2009 (Fig. 5.1.3b, c). This suggests that thewhere Ta(V) and Tb(V) are brightness temperature of thedelayed response of the OBG to surface forcing is a po-vertical polarized frequencies a and b. Although GR

tential candidate for maintaining lateral heat fluxes to thedoes not identify actual sea ice thickness, GR distin-basin. This delayed oceanic response enables us to fore-

guishes multi year ice (MYI) from first year ice (FYI) by

cast the upper ocean state from the surface forcing in pastdifference of their salinity, and relatively small (large)

years.

values represent old (young) ice.
The mean sea ice motion from November to April in

Estimation of sea ice rafting

the AMSR-E period consisted of the clockwise sea ice

The summer sea ice reduction in the Arctic Ocean has

Beaufort Gyre over the Canada Basin and transport drift

been accelerated since the late 1990s, and continuous re-stream (TDS) of sea ice flowing from the Pacific sector
52

(vectors in Fig. 5.1.6). The western branch of the sea ice

northern Canada Basin, to downstream regions, even in

Beaufort Gyre connected to TDS of sea ice in the Chuk-the late 2000s when MYI coverage decreased in thechi Sea. This suggests that MYI was transported fromdownstream region. In the Pacific sector, the convergent

the upstream regions of these sea ice motion, i.e., thefield of sea ice motions is found in the coastal regions
from Pt. Barrow to the East Siberian Sea, where the divergent field is found in the eastern half of Canada Basin(color in Fig. 5.1.6). On the other hand, such characteristic divergence/ convergence fields are not found in thebasin area. This suggests that sea ice rafting caused by
the convergence of sea ice motions is dominant in the
coastal regions rather than the basin area, and sea ice
traveling the coastal regions would likely to experiencemechanical growth due to rafting than traveling the basin
area.
To assess influences of mechanical sea ice growthduring winter on sea ice condition in the subsequent
spring, it should be estimated along sea ice trajectories
by a Lagrangian approach. Without thermodynamic sea
ice growth, SIC is reduced to conserve sea ice mass,
when convergence of sea ice motions increases sea ice

Fig. 5. 1. 6 The spatial distribution of the divergence of sea
ice motions (s−1, color) in winter (November–April) of the
AMSR-E period (2002/2003–2010/2011), with mean sea ice
velocities (cm s−1, vectors). The values are averaged at eachgrid cell in which sea ice velocities are defined (Yoshizawa,
2016).

thickness via rafting processes. It can be interpreted that
SIC (t + Δt) exceeds 100% due to the sea ice convergenceover Δt, thickness increases by {SIC(t + Δt) – 100}%. In
this constraint, sea ice thickness can be estimated. Con-

Fig. 5. 1. 7 Spatial distributions of EV [x100%] in spring, on 1 May, averaged for the AMSR-E period. The values of EV were
calculated by Eq. (5.1.2) in a Case 1 (GRthreshold = −0.010), b Case 2 (GRthreshold = −0.015), c Case 3 (GRthreshold = −0.020) (Yoshizawa,
2016).

53

vergent motions of new ice such as grease ice does not

5. 2. Estimation of thin ice thickness and sea ice
production

contribute to mechanical sea ice growth, only mechanicalgrowth of relatively thick ice was focused on. Influenceof new ice are eliminated from the estimation of mechan-

Sea surface heat flux is the most important factor toical sea ice growth by using GR. Thresholds are set on

determine the sea ice distributions; however, both inter-

GR, then the convergence of sea ice motions are ignored

act together. Since the thick sea ice acts to suppress air-when GR exceeds the thresholds. Since GR(36/06)V wassea heat transfer, open water area and thin sea ice areamuch sensible to differences between MYI and FYI thansuch as polynya and marginal ice zone are the most im-

GR(36/18)V (Krishfield et al., 2014), GR(36/06)V was used as

portant area where large surface heat flux emerges, andthresholds in this estimation. The values of {SIC(t + Δt)large amount of sea ice is produced and then affects the– 100}% are integrated along Lagrangian sea ice trajec-

distributions of sea ice in other regions. Heat fluxes attories as follows,

the surface of thin sea ice depend on the thickness of sea
ice, then it is important to estimate sea ice thickness for

EV (t + Δt) = ∫0t+Δt {SIC(t + Δt) – 100} dt, (5.1.2)

understanding the heat budget and sea ice distributions inwhen SIC(t + Δt) > 100% and GR(t) < GRthreshold.

the Arctic Ocean.

The integrated values from eq. (5.1.2) is introduces as

Sea ice thickness“effective convergence (EV)” that contributes to mechan-

Iwamoto et al. (2013) developed an algorithm for esti-

ical sea ice growth during winter.

mating thin ice thickness in the Chukchi Sea using Ad-

Fig. 5.1.7 shows spatial distributions of EV on 1 May

vanced Microwave Scanning Radiometer - Earth Observ-

averaged for the AMSR-E period, which were calculated

ing System (AMSR-E) data, applicable not only to theusing different thresholds of GR(36/06)V. Hereafter, EV oncoastal polynyas but also to the marginal sea ice zone

1 May is defined as “EV in spring”. The values of EV in(MIZ). The algorithm is based on comparisons between

case 2 and 3 (Fig. 5.1.7b, c) were smaller than that in

the polarization ratio (PR) of AMSR-E brightness tem-case 1 (a) due to decreasing the MYI coverage in theperatures from the 89 and 36 GHz channels (PR89 and

AMSR-E period. In all cases, sea ice with relativelyPR36) and the “thermal ice thickness” which is estimatedlarge EV zonally distributed in the coastal region in thefrom heat budget calculation using the ice surface tem-

Pacific sector (the southern Canada Basin, the Chukchi

perature from clear-sky Moderate-Resolution Imaging

Sea and the East Siberian Sea). These regions are known

Spectrometer (MODIS) infrared data. AMSR-E has

as choke-points of the Arctic sea routes. This suggests

twice the spatial resolution of Special Sensor Microwave/that locations of sea ice with large mechanical growth

Imager (SSM/I) data and can therefore resolve polynyas

just before summer are important to whether these choke-

at a smaller scale. Although the spatial resolution of thepoints can be used as the shipping route or not. In addi-

89 GHz data (6.25 km) is twice that of the 36 GHz datation, the large EV was found in the region around the(12.5 km), the 89 GHz data can be contaminated by at-Franz Josef Land.

mospheric water vapor and clouds.

In fact, in the region from the southern Canada BasinIce thickness was estimated from the conductive heatto the East Siberian Sea, the spring EV in case 3 and theflux through the ice based on the heat budget analysis atsummer (July–September) SIC are correlated with eachthe ice surface with observed atmospheric data fromother with a correlation coefficient > 0.58, which is sig-

ERA-Interim and ice surface temperature from MODIS

nificant at 90% confidence level. This means that me-

thermal infrared data (Drucker et al., 2003), and called

chanical sea ice growth during winter is a dominant pro-“MODIS thermal ice thickness”. To compare the ice

cess in determining the summer SIC variations in thesethickness with the AMSR-E data, the MODIS 1.1 km

choke-points several months ahead of the summer using

gridded thermal ice thickness is mapped onto the 12.5

only satellite-derived data.

and 6.25 km AMSR-E grid. Pixels with false thin ice
signal due to cloud were removed in the analyses.
The relationship between thermal ice thickness and thepolarization ratio,

54

Fig. 5. 2. 1 Scatter plot of horizontal and vertical polarization components of the AMSR-E brightness temperature, with the thermal
ice thickness denoted by the colors for the case of (a) 36 GHz from the 11 polynya cases, (b) 36 GHz from the 9 MIZ cases, (c) 89
GHz from the 11 polynya cases and (d) 89 GHz from the 9 MIZ cases. The contours indicate the isolines of PR (Iwamoto et al.,2013).

PR = (TBV – TBH)/ (TBV + TBH), (5.2.1)

thickness tends to be obscured in the thickness range >0.3 m, probably because of the influence of snow coverwhere TBH and TBV are the horizontally and verticallyand dynamical ice growth on the thick ice regime.

polarized brightness temperatures, respectively. The

Fig. 5.2.2 shows the scatter plot of the PR and thermal

open water area under the cloud corresponds to the area

ice thickness for the 11 polynya cases (blue dots) and 9with PR36 > 0.15 and PR89 > 0.11. On the other hand, theMIZ cases (green dots). From the scatter plot, exponen-area with a PR < 0.04 corresponds to the thicker ice area

tial relationship was proposed by Martin et al. (2004).

with thickness > 0.2 m for both PR36 and PR89. Fig. 5.2.1

Tamura et al. (2007) and Nihashi et al. (2009) instead ad-shows the scatter plots of horizontal versus vertical po-

opted a linear fit. Here, following Tamura and Oshimalarizations of the AMSR-E brightness temperature on a(2011), both an exponential and linear fit for the relation-

pixel-by-pixel basis, with the thermal ice thickness de-ship between PR and ice thickness were adopted. Be-

noted by colors, for 11 polynya cases and 9 MIZ cases.

cause the PR value is not sensitive to the MODIS thermal

As mentioned earlier, the brightness temperature mea-

ice thickness in the thicker ice range (small PR range), an

sured by passive microwave radiometer is thought be re-ice thickness estimation including a small PR range

lated to the ice thickness through the correlation between

would misinterpret thick ice as thin ice. Therefore, thethe brightness temperature and the surface salinity of ice.

fitting was carried out for the plotted data with the thick-Fig. 5.2.1 demonstrates that similar color (thickness) dis-ness of < 0.3 m. These fitted curves and lines were usedtributes parallel to the isolines of PR, particularly in theto estimate thin ice thickness from PR for ice thickness

thickness range < 0.2 m. The thickness has obviously aof < 0.2 m. From the fitting, the following equations formuch stronger correlation with the PR than with the indi-

ice thickness hi were obtained from PR36 and PR89, re-vidual polarization component. Fig. 5.2.1 shows that the

spectively,

use of PR is reasonable for detecting and estimating thin
ice thickness, as has been done in previous studies.

hi = exp [ 1/ (206 PR36 – 5.4) ] – 1.02,

(5.2.2)

However, the relationship between the PR and the ice

hi = –2.32 PR36 + 0.307,

(5.2.3)55

hi = exp [ 1/ (218 PR89 – 3.0) ] – 1.03,

(5.2.4)hi = –3.06 PR36 + 0.306.

(5.2.5)

Eqs. (5.2.2) and (5.2.3) and eqs. (5.2.4) and (5.2.5) aresuperimposed in Figs. 5.2.2a and b, respectively, with thestandard deviation for every 0.02 segment of PR. From
Fig. 5.2.2a and b, the exponential-fitting seems to be
more suitable representation of the plots than the linear-fitting in both PR36 and PR89.
The very thin ice signal covers the open water areathat is indicated from the ice concentration map, suggesting that only equation (5.2.2) or (5.2.4), the open waterarea tends to be judged as a thin ice area. Therefore, an
open water mask was required to determine the ice-freearea. Also, 89 GHz data have a tendency to be influenced by water vapor in the atmosphere. The microwave
emissions from the surface have a tendency to be depolarized by the water vapor and the cloud liquid water in
the atmosphere through absorption and emission processes and the effect is larger for the higher frequency data.
A water vapor correction was applied on a pixel-by-pixel
basis for PR89 using the relationship showing those pixels
influenced by water vapor, and were treated as missing
pixels. A seamless data set without missing pixels could
be obtained by substituting the thickness from the PR36
for the missing PR89 pixels. Generally, a coastal polynya
is covered with relatively uniform ice, whereas an MIZ

Fig. 5. 2. 2 Scatter plot of the PR value and the thermal ice
thickness for (a) PR36 and (b) PR89 from the 11 polynya cases(blue dots) and the 9 MIZ cases (green dots), The red andblack lines show the fitting curves of eq. (5.2.2) and (5.2.4)
and eq. (5.2.3) and (5.2.5), respectively. The vertical lines
with crossbars show the standard deviation of the thermal ice
thickness with respect to the fitting curve and line (Iwamoto etal., 2013).

consist of open water and ice floes with relatively low ice
concentration, particularly in the melting season. The
thin ice algorithm sometimes judges areas of lower ice
concentration as thin ice because the open water presentsa PR value close to that of thin ice. A method of ice

Fig. 5. 2. 3 Spatial distribution of (a) PR36 and (b) PR89, and (c) ice thickness from PR36 with eq. (5.2.2) and (d) PR89 with eq.
(5.2.4), for the Chukchi polynya on 8 February 2007. The white pixels show the thickness of > 0.2 m in (c) and (d) (Iwamoto et al.,2013).

56

Fig. 5. 2. 5 Annual cumulative sea ice production, represented
by the ice thickness, averaged over the nine winters (September–May) of 2002/2003–2010/2011. The areas with the ice production of < 1.2 m yr−1 are not colored (Iwamoto et al., 2014).

(5.2.4), the open water area tends to be judged as a thin
ice area. Therefore, an open water mask was required todetermine the ice-free area.
Sea ice production
Persistent areas of thin sea ice in the form of coastal
Fig. 5. 2. 4 Maps of sea ice for the MIZ in the Chukchi Sea
on 7 November 2007. Spacial distribution of (a) PR36 and(b) PR89, (c) ice thickness from PR36 with eq. (5.2.2), (d)ice thickness from PR89 with eq. (5.2.4), (e) Bootstrap Based
Algorithm (BBA) ice concentration and (f) ice thickness from
PR89 with both open water mask and water vapor correction.
The white pixels indicate an ice thickness of > 0.2 m. The blue
and thick black lines show the ice edge defined by the BBA
ice concentration of 30% and by the threshold PR89 of 0.11,
respectively in (c), (d) and (f). The solid lines show the contour
lines of the ice thickness of 0.01 m in (c) and (d) (Iwamoto etal., 2013).

polynyas and marginal ice zones (MIZs) are crucially
important as regions of enhanced ice production andbrine rejection in the polar oceans during freezing periods. New and improved estimates of sea ice productionin the Arctic Ocean were derived from AMSR-E satellite
and atmospheric reanalysis (ERA-Interim) data for theperiod 2002–2011. Following the ice thickness data obtained in the foregoing paragraphs (Iwamoto et al.,2013), ice production over thin ice areas was estimatedby Iwamoto et al. (2014) from heat budget analysis, assuming that all of the heat loss goes into ice formation.

thickness estimation was proposed that excludes the ef-

Under the assumption that there is no oceanic heat flux

fect of the open water fraction in an AMSR-E grid cell,from below, the volume of ice production V in given byin the combination with the ice concentration algorithm.
V = H/ (ρi Lf), 

Fig. 5.2.3 shows the spatial distribution of ice thick-(5.2.6)

ness from PR36 with eq. (5.2.2) and PR89 with eq. (5.2.4),
respectively for the polynya case. The coastal polynya iswhere H is the heat loss at a thin ice surface, ρi (= 920 kg

well represented, particularly from PR89 (Fig. 5.2.3d).

m−3) is the ice density and Lf (= 0.334 MJ kg−1) is the la-Fig. 5.2.4c and d show the thickness distribution for thetent heat of fusion of sea ice. If there is oceanic heat flux

MIZ case from PR36 and PR89, respectively. However,from below, calculated ice production will be overesti-

the very thin ice signal covers the open water area that ismated. Therefore, this mapping would provide the upper

indicated from the ice concentration map (black area inbound of the ice production. For the longwave radiation,

Fig. 5.2.4e), suggesting that, only from eq. (5.2.2) or

turbulent and conductive flux calculations, the same em57

Fig. 5. 2. 6 Time series of sum of the annual cumulative sea
ice production in the 10 major Arctic coastal polynyas for
2002/2003–2010/2011 (Iwamoto et al., 2014).

Fig. 5. 2. 7 Time series of sea ice area on 30 September (dashedline) and cumulative sea ice production integrated over theareas from October to November (solid line) for 2002–2010(Iwamoto et al., 2014).

pirical and bulk formulae as those used in the estimationof MODIS thermal ice thickness. Shortwave radiation iscally map ice production. The land fast ice along Alas-assumed to be completely absorbed in the thin surface

kan coast in the Chukchi polynya is detected in the pres-layer.

ent algorithm, while all of the landfast ice area is judged

Here, the annual cumulative ice production in a polyn-

as thin ice area in Tamura and Oshima algorithm. Suchya was defined as production in the region in which themisclassification in Tamura and Oshima partly causes thepolynya regularly forms, including ice production due tooverestimation of ice production in the nearshore area infall freeze-up. Fig. 5.2.5 shows the resultant annual cu-

the Chukchi polynya and Laptev polynya. The open wa-

mulative ice production in the Arctic Ocean, averaged

ter mask for PR89 thickness in the present algorithm also

over the winter months (September–May) of 2002/2003

contributes to better resolve the open water area in the–2010/2011. High ice production is largely confined to

NOW polynya, because the area tends to be judged as

the major Arctic coastal polynyas, e.g., the North Water

thin ice area in Tamura and Oshima algorithm, resulting(NOW) polynya, Chukchi polynya and along the coast of

in the overestimation of ice production.

Novaya Zemlya, Frantz Josef Land and Svalbard. Among

The recent reduction in summer Arctic ice extent is re-the major polynyas, the NOW polynya has by far themarkable in the Pacific sector, which would affect the ice

highest production rate with the maximum rate reachingproduction in MIZ in this region, especially in the early−1~ 13 m yr in Smith Sound..

stage of freezing period. The area from the western

Fig. 5.2.6 compares total cumulative ice production in

Beaufort Sea to the East Siberian Sea was focused on,

the 10 major polynyas with annual minimum sea ice ex-

away from the continents and large islands by 100 km.

tent in the Arctic Ocean during the period 2002–2010.

Fig. 5.2.7 shows the relationship between the ice produc-3

The average total ice production was about 1180±70 km .

tion in the MIZ of this area from October to November

The magnitude of minimum sea ice extent anomaly may

and the sea ice area on 30 September for each year. The

result in either a positive or negative annual ice produc-

production in the MIZ has clear negative correlation withtion anomaly, depending on which mechanism is domi-

the summer ice extent. Particularly in 2007, when thenant; delay in the onset of freezing could reduce the pro-summer ice extent in the Arctic Ocean showed the mini-

duction. On the other hand, loosened pack ice offshore

mum during this analysis period, ice production reachedcould promote polynya opening and thus production.

about 600 km3, which corresponds to about twice as large

Based on the present mapping, there is no obvious rela-as the ice production in other years. This amount also

tionship between total ice production and summer ice ex-corresponds to about half of the total annual ice produc-

tent, at least during this period, probably because bothtion in the major 10 coastal polynyas, suggesting that theeffects act oppositely.

retreat of summer ice extent results in the larger amountThe ice production in this study is smaller in all coast-of the sea ice production in the central Arctic Ocean. In

al polynyas than that from Tamura and Oshima (2011),

accordance with the drastic northward shift of the ice

who conducted quite similar study from SSM/I data withedge location at the beginning of freezing period in 2007,

lower resolution. Since the AMSR-E thin ice algorithm

significant ice production occurred at much higher lati-with its finer resolution and the landfast ice detection

tudes in 2007. Particularly from the Beaufort Sea tomask can better resolve the polynyas and the ice produc-

Chukchi Sea, the area within which the production ex-tion compared with SSM/I data (Tamura and Oshima),

ceeds 0.6 m yr−1 occurs in the deep basin north of thethe estimation in this study is expected to more realisti-

shelf break, whith production > 1.0 m yr−1 occurring over58

the Canada Basin. These results demonstrate that the reduction of summer ice extents shift the high ice production area from the continental shelf near the coast to thedeeper basin. The direct supply of dense water to theBasin due to ice production in the MIZ may change salinity and density structures in the ocean. This suggeststhat the retreat of summer ice extent might change theocean structure, specifically deepening the winter mixed
layer through brine rejection by strong ice production.

5. 3. Heat budget and sea ice processes in polynya
With ongoing retreat and thinning of Arctic sea ice andgrowing commercial interest in resource extraction andmarine navigation, there is an increasing demand for observational data of ice thickness and velocity. In the paper by Mahoney et al. (2015), data collected as part of

Fig. 5. 3. 1 Configulation of SIZONet moorings deployednear Barrow in 2009/10. Distances indicate approximate rope
lengths netween mooring components (Mahoney et al., 2015).

the Seasonal Ice Zone Observing Network (SIZONet)acquired near Barrow, Alaska, during the 2009/10 ice
season, were combined to make novel comparisons between coincident and co-located observations of sea iceof the overall ice volume.

from above and below, measurements of ice thickness

Here, the comparisons of EM and IPS measurements

and velocity.

were made by calculating their probability distributions

Two moorings (B1 and B2) were deployed, B1 atusing all data that fall within 10 km of mooring B2. Fig.

71.23°N, 156.88°W on 5 August 2009 and retrieved on

5.3.2 shows the distributions of EM-derived ice and snow

29 July 2010, B2 at 71.23°N, 157.65°W on 7 August

thickness and IPS-derived ice draft, binned into 0.05 m

2009 and retrieved on the same day as B1. These moor-intervals. Both distributions have pronounced modes,

ings each comprised an Ice Profiler Ice Profiling Sonar

which represent the thickness and draft of level unde-(IPS) and Acoustic Doppler Current Profiler (ADCP) as

formed ice. The EM data indicate a modal combined

well as a conductivity-temperature (C-T) recorder and a

thickness of ice and snow of 1.6±0.025 m, while the IPS

temperature-pressure (T-P) recorder (Fig. 5.3.1). The

data show a modal ice draft of 1.35±0.025 m. To better

IPSs are used to measure the draft of the sea ice passing

match the footprints of the two instruments, a 70 m

overhead while the ADCPs measure current velocity pro-smoothing filter was applied to the IPS data. This

file of the overlying water column and, of particular rele-smoothing changes the shape of the tail of the IPS draft

vance here, the velocity of the ice through bottom track-

distribution (Fig. 5.3.2) to more closely resemble that ofing.

the EM data. Finally, a conversion factor from ice draft

Sea ice thickness was measured from above using air-to total thickness was determined as 1.20±0.01 m (Fig.

borne electromagnetic sounding by electromagnetic (EM)5.3.3). Then the ice thickness of level sea ice near moor-

induction sensor to determine the distance from theing B2 at 1.48±0.09 m, with a snow depth of 0.12±0.09

towed instrument, known as an EM-bird, to the waterm.

surface. On 12 April 2010, airborne electromagnetic
Supercooled water and frazil ice in polynya

flight was made over the sea ice near Barrow over mooring B2 with helicopter. The EM-bird was flown at an al-

Due to the lack of an insulating ice cover when open

titude of ~15 m, giving an effective sampling footprint ofwater is maintained, heat loss and thus ice production~70 m. Each EM measurement is thus a mean value ofrate are at a maximum during episodes of supercooling

ice and snow thickness over the area and will therefore

and associated frazil ice formation. Under cold andtend to underestimate the maximum thickness of ice ridg-

windy conditions, sea ice is initially generated as small

es, though it can be expected to give an accurate measure

mm- or μm-scale or dendritic crystals, called frazil ice.
59

Fig. 5. 3. 2 Probability distribution of combined ice andsnow thickness (EM) and ice draft (IPS) derived from allmeasurements within 10 km of mooring B2 (Mahoney et al.,2015).

Fig. 5. 3. 3 Probability distribution of combined ice and snow
thickness (EM) and 70 m smoothed, isostatically adjusted ice
draft (IPS*1.20) derived from all measurements within 10 km
of mooring B2 (Mahoney et al., 2015).

Laboratory experiments have shown that underwater fra-

2010 and the other during the period 10–25 February.

zil ice formation occurs in association with supercooled

On 20–21 and 23–26 December, during the former event,water resulting from large surface heat loss under turbu-

episodic potential supercooling occurred coincidentallylent conditions with strong wind (Ushio and Wakatsuchi,

at 30 m depth for B1 and at 40 m depth at B2 (Fig. 5.3.4

1993). However, in situ observations of supercoolinga and b). These supercooling events occurred at bothand underwater frazil ice formation have been hamperedsites at the same time, reducing the likelihood of the ob-

by logistical challenges in polar oceans.

served supercooling being caused by contamination ofFormation of supercooled water and frazil ice wasthe conductivity cell by ice. During the latter persistentstudied by Ito et al. (2015) in the Chukchi Sea coastal

polynya event, potential supercooling of ~ 10 mK waspolynya off Barrow, Alaska, in winter 2009/10, using

observed on 8 February at B1 and on 19–20 February atmoored salinity/temperature sensors and Ice ProfilingB2. All these episodic supercooling events occurred

Sonar (IPS) data already discussed in the previous para-

when the heat loss was 200–600 W m−2 to the stmospheregraph (Mahoney et al., 2015) along with satellite data.

(Fig. 5.3.4c). Therefore, it is likely that these supercool-This study covers the sea ice production from the in-situ

ing events are caused by strong direct surface cooling.

data, which was already shown from remote sensing data

On the other hand, even when the polynya was not

such as explained in the previous section (Iwamoto et al.,open, specifically for 10–18 December and 4–28 January,2014). This study might provide the first IPS observation

coincident potential supercooling of ~ 15 mK persistedof underwater frazil ice in the ocean, using a newly de-for 1–3 weeks at the two mooring sites. Under the sea

veloped profile mode option. The IPS is capable of much

ice cover of thick ice, the heat loss is small and thus in-higher temporal observation than the upward-looking so-

sufficient to create supercooled water at depth. Accord-

nar. On the basis of these observations, the formation

ing to the ADCP data collected at 40 m depth at B2 (Fig.

processes of supercooled water and frazil ice were dis-

5.3.4e), a northeastward current of 0.35–0.45 m s−1 (30–cussed to improve our understanding of the processes

40 km d−1) started to be dominant from ~1 week before

governing high ice production rates in a coastal polynya.

the events and persisted for the first half of the events.

Fig. 5.3.4 shows the time series of the mooring data

Although the polynya was closed at the mooring loca-

with sea ice and atmospheric conditions from AMSR-E

tions, the AMSR-E ice thickness data show that otherand ERA-Interim during December 2009 to February

coastal polynyas had formed ~400–600 km to the south-

2010. During this period, two persistent coastal polynya

west in these periods. Thus, the potentially supercooled

events occurred at or in the immediate vicinity of thewater masses were likely advected from these polynyas.

mooring sites (Fig. 5.3.4a and d). These polynya events

According to temperature and salinity data from the con-lasted for 10–15 days and were associated with strong

ductivity and temperature (C-T) recorders (the data at B2−1

offshore winds (> 10 m s ; Fig. 5.3.4g); one event took

are shown in Fig. 5.3.4f), all the supercooling events

place during the period 20 December 2009 to 1 January

ended with the advection of saltier and warmer Atlantic60

Fig. 5. 3. 4 Time series of the mooring data with AMSR-E and meteorological data, during 1 December 2009 to 28 February 2010. (a)The water temperature relative to in situ freezing point (blue) and the potential temperature relative to the surface freezing point (red)at 30 m depth at B1 from C-T recorder. The light blue line is the freezing point. (b) Same as (a) at 41 m depth at B2. (c) AMSR-E
ice thickness (green) and calculated heat loss (orange) at B2. (d) Polynya extents expressed as the sum of thin-ice (< 0.2 m) area forregions I (red), II (blue), III (green) and IV (grey), normalized by the maximum extent in winter. (e) The ocean current (3 hour mean)from the ADCP measurement at 40 m depth at B2. (f) The potential temperature (red) and salinity (blue) at 40 m depth at B2. (g) 2 m
air temperature (pink) and the 10 m wind (green) from the ERA-Interim data at the gridpoint closest to B2. Thick marks correspond
to 0:00 UTC (Ito et al., 2015).

Water from the northeast.

these facts, the observed targets in the IPS profile data at

Fig. 5.3.5 a shows the time series of range and profile

B2 are considered to be frazil ice. This is likely the firstdata at B2 on 20 February when potential supercoolingdetection of underwater frazil ice in sea water by an IPS.

was ovserved in association with a coastal polynya at theOn the other hand, on 20–21 and 23–26 December

mooring locations. These IPS signals were interpreted as

during periods of in situ and/or potential supercooling at

frazil ice based on several lines of evidence presented

both moorings, underwater targets were not detected ex-

below. According to the Advanced Synthetic Aperture

cept fairly close to the ocean surface. Fig. 5.3.5b shows

Radar (ASAR) image, the region around the mooring

the data obtained by the IPS at B1. According to thesites show relatively high backscatter, which indicates

AMSR-E ice thickness data on this day, the ice thick-rough surfaces corresponding to a wind-roughened sea

nesses around the mooring sites are ~0.02–0.05 m. Thus,

surface with capillary waves and wind waves. According

the ice detected by the IPSs is probably thin sheet ice

to the AMSR-E data, the region around the mooring site

such as nilas.

shows polarization ratio close to that of the open-water
Hybrid latent and sensible heat polynya

area. Further, the ERA-Interim data show strong offshore wind on that day (Fig. 5.3.4a). While the most

Following the detection of signals of frazil ice forma-prominent signals occurred on 20 Febryary with deep po-tion accompanied by supercooled water and vanishingtential supercooling (Fig. 5.3.4b), such underwater sig-

the signals with the arrival of warm Atlantic Water (AW)nals were observed at B2 during the period of 12–22

in the Barrow Coastal Polynya (BCP) by Ito et al. (2015),

February, which corresponds exactly to the active polyn-

it was suggested that the BCP, which is known as a latentya period (Fig. 5.3.4c) with strong offshore winds pre-heat polynya, may have characteristics of a sensible heatvailing (Fig. 5.3.4g). During the entire mooring data re-polynya as well. Hirano et al. (2016) examined the na-

cord for 2009/2010, such long-term underwater signals

ture of BCP using data from year-long under-ice moor-

were observed only in this period. Considering all ofings (Mahoney et al., 2015) already used in above para61

graph, satellite-derived products (sea ice concentration

In addition, the Chukchi shelf-Canada Basin interaction

and production rate), atmospheric reanalysis data andoccasionally occurs through upwelling of warm AW

tracer experiment results from a high-resolution pan-Arc-

around Barrow Canyon. Therefore, various water masses

tic ice-ocean model.

are expected to be present on the shelf region around

The BCP is located within the pathway of Pacific-ori-

Barrow Canyon due to the seasonal variability of Pacif-

gin water entering the Canada Basin via Barrow Canyon.

ic-origin water and/or the upwelling of AW. Fig. 5.3.6
shows the relationships of temperature (relative to freezing point Tf) and salinity at B2 (41 m) from August 2009
to July 2010. Except for the upwelling events of warm
water described below, the observed temperature and salinity of Pacific Summer Water (PSW) were near thefreezing point and ~32, respectively. The water temperature increased after June, Pacific Summer Water (PSW)
was then dominant in this region. AW signals were frequently found from October to early June as shown in
shifts toward the upper right in Fig. 5.3.6, although they
were less clear in April. Except for summer and early
fall when PSW was dominant, these AW signals resulted
from the upwelling caused by northeasterlies blowing in
the direction of Barrow Canyon.
Time series of wind, SIC and ice production from November to May are shown in Figs. 5.3.7a and b. The SIC
started increasing in mid-November and decreasing inlate April, and then dropped to zero in June. Considering
this seasonal variability, the ice-growth period around
Barrow Canyon in 2009/2010 is defined to be frommid-November to mid-May. During this period, the region around Barrow Canyon was almost entirely coveredby sea ice (i.e., SIC is 100%) except for BCP events defined below. The dominant winds during the BCP events

Fig. 5. 3. 5 Time series of range (gray line) and profile (colordots) data obtained by the IPS (a) at B2 on 20 February 2010
and (b) at B1 on 25 December 2009. The vertical axis shows the
distance between the IPS and each target. Dot color indicates
return strength (in counts) of the profile data (see color bar in (b)for their values). The upper panel of (a) shows the range data inan enlarged vertical scale (Ito et al., 2015).

were commonly northeasterly (toward 240°on average).
These northeasterly winds resulted from a pressure pattern dominated by the Aleutian Low and Beaufort High.
Their direction was almost parallel to the Barrow Canyon
with an offshore component near Barrow. For each BCP

Fig. 5. 3. 6 Relationship between temperature (relative to freezing point) and salinity at 41 m of B2 from August 2009 to July 2010
after applying a tide killer filter. Colors of dots indicate time in each month. Red, light blue and pink-shaded areas represent watermass classifications for Pacific Summer Water (PSW; T > Tf + 1°C with S < 32.5), Pacific Winter Water (PWW; T < Tf + 1°C with S <34) and Atlantic Water (AW; T > Tf + 1°C with S > 34). The gray contours denote potential density surfaces (Hirano et al., 2016).

62

Fig. 5. 3. 7 Time series of (a) wind speed (solid line) and direction (red dots) at Barrow Willey-Post Airport, (b) sea ice concentration(solid line) and production rate (green bar) at the AMSR-E grid point, (c) temperature relative to freezing point (blue) and salinity(red) at 41 m after applying a tide killer filter, (d) along-BC velocity at 11 m (balck, positive values indicate down-canyon flows) andpotential density at 41 m (purple). Blanks in d represent the flagged data. Shadings represent periods of BCP events. Data at B2 areshown in c and d (Hirano et al., 2016).

event, active ice production occurred with rates of 0.005–−1along with prevailing northeasterly winds can result in−10.10 m d and maximum exceeding ~0.15 m d (Fig.

both enhancement and inhibition of ice production in the

5.3.7b, green bars) if the open water mask of AMSR-E is

BCP. Unless the upwelled warm water outcrops, upward

assumed to be correct. This is a remarkable feature ofheat transport accompanied by vertical mixing into thethe BCP events.

surface mixed layer is required to suppress ice produc-

Figs. 5.3.4c and d show time series of temperature, sa-tion and/or to melt sea ice. Such upward heat transport

linity and potential density obtained at B2 (41 m).

transforms the BCP into a sensible heat polynya in which

During the BCP events, warm and saline (dense) AW ap-

heat loss mostly goes into cooling of the water column

peared at the moorings, with the most prominent signals

and/or suppression of ice growth. Fig. 5.3.7d shows aof T > Tf + 2°C, S > 34 and ϭθ > 27 kg m−3 (Figs. 5.3.6,

time series of ocean current along the Barrow Canyon

5.3.7c and d). Whenever the BCP was open, it is reason-(63°; along-BC velocity) at B2 (11 m). Down-canyon

able to consider that the temperature is vertically uniform

flow toward the Canada Basin was up to ~0.5 m s −1.

and near the freezing point over the shallow shelf (Fig.

However, each time the BCP started forming, up-canyon

5.3.7c). In contrast, the upwelling warm water is consid-

flow toward the BCP region gradually strengthened andered to be present below the surface mixed layer, result-reached its maximum of up to ~1 m s−1 nearly coincident

ing in two-layer thermal structure.

with the local SIC minimum (Fig. 5.3.7b). In addition,

Dominant northeasterly winds over the BCP region in-

the up-canyon flow corresponded with higher potential

duce two different phenomena as follows: (1) sea ice di-density, likely caused by the upward displacement ofvergence causing the formation of the BCP and (2) up-

density surfaces due to the upwelling.

welling of warm saline water, originationg from the

The reduction of ice production by the ocean heatmouth of Barrow Canyon and the southern Canada Ba-

transport from below was estimated with and without thesin, compensating surface Ekman flow toward the off-

open water mask (figure not shown). The difference be-

shore. Thus, the combination of the coastline orientation

tween the two estimates considered to be roughly equiva-

around the Barrow and the location of Barrow Canyon

lent to the amount of suppressed ice production by ocean
63heat transform from below. The ocean heat transport had
the largest impact on ice production for Event 3 when the
open water area became vast. Specifically, ice production was suppressed by almost half, compared to thatwithout the open water mask, when the open water area
was largest on 22 February. Throughout the ice-growth
period in 2009/2010, ice production in the BCP is estimated to decrease by ~17% due to ocean heat transport
from the upwelled warm water. Another point to note isthat polynya events end up with ice formation, because
reduced northeasterly wind forcing (Fig. 5.3.4a) weakens
upwelling and subsequent intensive heat loss cools the
upwelled water down to the freezing point.
Heat budget analysis of Arctic sea ice variation
Kashiwase et al. (2017) showed the dominance of heatFig. 5. 3. 8 Map of the Arctic Ocean with September sea ice
concentration averaged from 1979 to 2014. The heat budget
analysis and calculation of ice divergence were completed
for the fan-shaped area. The simplified model was applied forthe rectangular region. The map is drawn by GrADS 2. 0. 2
(Kashiwase et al., 2017).

input through the open water fraction on sea ice loss andits variation, which is a necessary condition for ice-ocean
albedo feedback, based on the relationship between sea
ice retreat and heat budget over the ice-covered area.
Then the specific trigger of the feedback effect was explored and examined whether ice melt is in fact amplified
significantly by this feedback and whether the drastic re-face melt is exclusively used to reduce ice thickness. In

duction in summer ice extent can be explained by thiscalculation of Qm and Adv, a time dependent ice thickness

feedback, based on the combined analysis of satellite ob-

which decreases from 1.4 m to 0.86 m was used throughservations and a simplified ice-ocean model. Fan shaped

surface melt driven by Qi (Fig. 5.3.9a).

area in the Pacific Arctic sector was selected as the main

Estimation from the heat budget analysis and satellite

study area (Fig. 5.3.8). This area experienced the largest

observations show that Qu corresponds well with Qm both

reduction in summer sea ice extent and volume anywhere

for seasonal and interannual variations quantitatively

in the Arctic beginning in the 2000s. Interannual varia-(Fig. 5.3.9b, c and e). Correlation coefficients between

tion of ice retreat in this region explains about 86% of

Qu and Qm were statistically significant as 0.77, 0.85, 0.92

the variance over the Arctic Ocean.

and 0.91 for the monthly mean from May to August, re-

For the ice-covered area defined by ice concentration

spectively (Fig. 5.3.9e). The correlation coefficient be-> 15%, the daily heat budget was analyzed for the water

tween yearly values is also statistically significant as 0.96

and ice surfaces from 1979 to 2014. Since net heat fluxand 0.91 for detrended variations. However, results ofat the water surface (Fw) is much larger than that at thethe heat budget analysis have a relatively large uncertain-

ice surface (Fi), the amount of heat input into the upperty mainly due to the formation of melt ponds. It is noted

ocean was focused on through the open water fractionthat ice export from the fan-shaped area and its interan-(Qu), and this heat was compared with the volume of sea

nual variation (green lines in Figs. 5.3.9a and c) are much

ice melt (Qm) which was calculated from the observed

smaller than Qu and Qm. These results indicate that ice

decrease of ice area, accounting also for the decrease byretreat in the Pacific Arctic is mainly due to the ice melt

ice advection (Adv). In the calculation of Qu, the analysis

caused by heat input through the open water fraction, im-area varies as the ice retreats. It should be noted that,plying that the necessary condition for ice-ocean albedo

since most of the sea ice area exceeds 80% ice concentra-

feedback is satisfied in the study area.

tion even in summer, the total heat input at the ice sur-

The divergence in ice motion of the Pacific Arctic is

face is comparable to that at the water surface and thus

determined mainly by the Transpolar Drift, the Beaufort

contributes significantly to ice melt. In this paper, it was

Gyre and the migration of the ice edge. To deal withassumed heat input at the sea ice surface resulting in sur-these factors simultaneously, the mean ice divergence64Fig. 5. 3. 9 Results of heat and sea ice budget analysis. (a) Seasonal evolution of mean ice thickness calculated from the surface
melt by heat input at the ice surface. (b) Seasonal and (c) interannual variations in heat input through the open water fraction (Qu,red line), ice melt volume (Qm, black line), and the volume of ice export (Adv, green line). The volume of ice is converted to theheat required for the equivalent amount of ice melt. (d) Interannual variations of ice divergence (Div) averaged from mid-May toearly-June (blue line) and Qm. (e) Scatter plot of Qu and Qm. Monthly means (May to August) for each year are plotted. (f) Scatterplot of Div averaged from early-May to mid-June versus Qm. Blue crosses and red circles indicate values before and after 2000,
respectively. Dashed lines indicate the regression line for both periods. Uncertainties due to errors in the satellite observations areshown as shaded envelopes (Kashiwase et al., 2017).

over the analysis area (Div) was calculated using the icetion of sea ice retreat in the Pacific Arctic, particularly

drift velocity derived from satellite observations. Com-

since the early 2000s. In the early melt season, sea ice

parison between Div and ice retreat conditions show thatconcentration sustains nearly 100% for both time peri-

Div during the melt season significantly correlates withods, while, the fraction of multi year ice has decreased

the simultaneous/subsequent ice concentration and icefrom 49 to 31%. This reduction affects sea ice dynamics,

melt volume. Particularly, Div in the earliest stage of thein particular through decrease in ice mechanical strength

melt season (from mid-May to early-June) has the high-and internal forces, and increases in ice deformation

est correlation with sea ice retreat lagged by 1–2 months,

rates. These outcomes in turn increase the momentum

with high correlation persiting through the end of August.

flux from the atmosphere to the ocean, and strengthen

Thus, the early Div is also well correlated with the yearly

anticyclonic circulation in the Beaufort Gyre with avalue of Qm (Fig. 5.3.9d), with a correlation coefficient ofsteeping sea surface height anomaly after the 2000s

0.69 and 0.55 for detrended variations. These suggest(Yoshizawa et al., 2015; shown in Section 5.1). As a re-that the divergent ice motion in the early melt season cansult of such changes, ice drift speed has significantly in-

be a trigger of ice melt acceleration through ice-ocean al-

creased, likely responsible for the increase in early sum-

bedo feedback. After the 2000s, such relationship has

mer D iv from 1.9 to 3.7% mo -1. Although the direct

likely become stronger, suggested by a much higher re-contribution of the increased divergence to reductions ingression coefficient than that prior to 2000s (Fig. 5.3.9f).

ice concentration is quite small, heat absorption by the

Findings from this study show that the feedback effectupper ocean through the end of August has gradually in-triggered by early-season divergent ice motion plays acreased through ice-ocean albedo feedback, with an in-

key role in the seasonal evolution and interannual varia-

crease by a factor of up to 1.5 (from 153 MJ m−2 to 230
65

Fig. 5. 3. 10 Schematic of ice and heat budgets during seasonal ice retreat. Mean ice conditions and heat budgets for the two
periods (a) 1979–1999 and (b) 2000–2014 are shown. Divergent ice motion in the early melt season induces a small reduction in
ice concentration (upper panel). A key finding is that although the direct contribution of doubled divergent ice motion after 2000 to
the ice concentration reduction is small, this trigger accelerates ice melt through the enhanced solar heat input over the open water
fraction (ice-ocean albedo feedback) until the end of August (lower panel). All values in the lower panel are standardized as heat perunit area (MJ m−2 yr−1), and the volume of ice is converted to the equivalent heat required for ice melt (Kashiwase et al., 2017).

MJ m−2; Fig. 5.3.10). This increased heat uptake can ex-

distribution and consequently on the Arctic stratification.

plain about 70% of the observed 2.1-fold increase in sea

The ice-ocean coupled model used in the study is Centerice melt (from 121 MJ m−2 to 257 MJ m−2; Fig. 5.3.10).

for Climate System Research (CCSR) Ocean Component

This contrast in the increase in annual ice melt compared

Model, version 4.5 (COCO4.5; Hasumi, 2015), which

to heat input is also evident in Fig. 5.3.9d, and is partly

has also been employed as the ice-ocean component for

explained by the continuing decline in mean ice thick-the Model Interdisciplinary Research on Climate, version

ness.

5 (MIROC5; Watanabe et al., 2010). The model domain

This study provides a new perspective on the observed

is global, with a horizontal grid size of approximately 60

drastic ice reduction in demonstrating, through modelingkm in the Arctic Ocean, and 17 vertical levels in the up-

and analysis of remote sensing data, that ice divergence

permost 250 m. The sea ice component includes a sub-in the early melt season is a key trigger for amplification

grid-scale sea ice thickness distribution, which greatlyof ice retreat through ice-ocean albedo feedback. This

improves Arctic atmosphere-ocean heat exchange (Komuro

findings also suggest that early-season ice divergence isand Suzuki, 2012).

associated with a substantial skill for seasonal ice predic-

The control experiment (CNTL) with the standard set-tion.

tings generally reproduces the largescale temperature andsalinity structures, flow patterns and horizontal sea ice
distribution. However, the CNTRL results have some bi-

5. 4. Modeling of circulation and physical processes of the Arctic Ocean

ases in the Arctic Ocean. In particular the mixed layer isdeeper than reality, and its improvement is one of the

Basic model

motivations of this study. The horizontal extent of the

Numerical modeling is key to deepening our under-

Beaufort gyre is also too small. In addition, the relative-

standing of the interaction between the Arctic and global

ly coarse resolution puts limitations on the reproducibili-

climates. The study by Komuro (2014) focuses on thety of features such as coast currents. Three sensitivity

representation of the Arctic river water outflow and its

experiments are performed: low vertical diffusivity KVb at

impact on surface stratification. It is found that specify-

all depths (LALL), low KVb at the surface (LSFC), anding weak surface mixing in a global ice-ocean coupled

low K V b at surface with no wave-breaking mixing

model has a considerable impact on the Arctic river water(LSNW). LALL employes a background value of KVb =66

Fig. 5. 4. 1 Salinity for the uppermost 250 m along the section from the Bering Strait (BS) to the Fram Strait (FS). Note that NP
stands for the North Pole. (a) PHC, (b) CTRL, (c) LSFC and (e) LALL (Komuro, 2014).

10−6 m2 s−1 at all depths, which is approximately an orderthese experiments, fresher surface water is also conspicu-

of magnitude smaller than that for CNTL in the surface

ous in the coastal areas, where river water is added to thelayer.

sea surface. The low-salinity water in the central Arctic

Salinity cross sections of the uppermost 250 m acrossOcean seems to be connected to the fresh coastal water.

the Arctic basin are shown in Fig. 5.4.1 for the four runs

These spatial patterns are also found in the PHC data.

and for the Polar Science Center Hydrographic Climatol-Fig. 5.4.2 depicts the annual-mean fraction of the Arc-ogy (PHC; Steele et al., 2001). The climatological data

tic river water tracer along the section indicated in Fig.

show that surface water with low salinity (around 31 psu)

5.4.1. The fraction is the lowest in CTRL, where theextends out from the continental shelves. In CNTL, themaximum appears below the sea surface (Fig. 5.4.2a). In

volume of low salinity water is very small and it is

the other cases, the fraction is higher and the maximum

trapped at the shelf break (Fig. 5.4.1b). In the other three

is at the surface (Fig. 5.4.2b–d). Thus the river water

experiments (LSFC, LSNW and LALL), low salinity wa-

distribution in LSFC, LSNW and LALL is more realistic

ter spreads out from the shelves, although to a smaller

than that in CTRL, with LALL giving the best results.

extent than in the climatology (Figs. 5.4.1c–e). The ver-

The increase in the river water fraction in the surface

tical salinity gradient at around 100 m in LALL is sharp-

layer is largely responsible for the surface freshening in

er and closer to the PHC data than in the other cases be-these three cases. Changes are also found in sea ice

cause of the low backgraound vertical diffusivity and is

thickness among the four cases, although the parameterconsistent with previous modeling studies with low verti-

settings for the sea ice component of the model are thecal diffusion. Sea surface salinity in and around the Arc-

same in all the experiments. Arctic summer (July–Sep-

tic Ocean shows similar behavior (not shown). Low-sa-

tember) sea ice thickness in CTRL averaged over thelinity water spreads out from the shelves in LSFC,

Arctic Ocean is 242 cm. The sea ice thickens over most

LSNW and LALL, especially in the latter two cases. Inof the Arctic ice extent in all the cases. The fresher the67

Fig. 5. 4. 2 Annual mean fraction of the Arctic river water
tracer along the section shown in Fig. 5. 4. 1 for (a) CTRL, (b)LSFC, (c) LSNW and (d) LALL (Komuro, 2014).
Fig. 5. 4. 3 (top) The bathymetry of the Nordic Seas, theBarents Sea and the Nansen Basin. (bottom) A shematic for thecirculation of warm/salty Atlantic water (red arrow) and cold/
fresh East Greenland Current (blue arrow) around the Fram
Strait (Kawasaki and Hasumi, 2015).

surface salinity, the thicker the summer sea ice: the summer thickness averaged over the Arctic Ocean comparedwith that for CTRL is 17, 28 and 39 cm greater for LSFC,
LSNW and LALL, respectively.
In summary, the representation of the surface low-salinity water is improved in the Arctic Ocean in all thepassing through the Fram Strait, many moorings havecases with lower background KVb. LALL is the most re-

been deployed and repeated CTD observations have beenalistic, but LSNW also performs well, particularly in the

conducted along 79°N (Schouer et al., 2008). On thesurface layer. The distribution of the river water tracer

other hand, the ocean circulation and its variability to thesuggests that the concentration of river water in the sur-north of 79°N have not been described so clearly as thoseface layer leads to these improvements. In addition, theto the south due to the observational difficulty caused by

Arctic sea ice thickness is also sensitive to the change in

sea ice (Fig. 5.4.3).

bV

background K .

Kawasaki and Hasumi (2015) tried to reproduce theAtlantic water inflow (the West Spitsbergen Current

Heat transport(WSC) and eddy activity) to the Arctic Ocean as realisti-

Interannual variability of the Arctic sea ice is con-cally as possible by using an ice-ocean model withtrolled not only by heating or cooling by the atmospherehigh-horizontal resolution around the Fram Strait. Then,but also by the heat flux from the ocean underneath as

they examined the heat transport to the Arctic Ocean byshown in the previous paragraph. Cold low salinity wa-

the Atlantic water passing through the Fram Strait with

ter originated in the river runoff covers the sea surface infocusing on: (1) the amount of heat removed by eddies,

the Arctic Ocean. Warm Pacific Water passing throughreturning currents, and sea surface cooling, and (2) fac-

the Bering Strait lies just below it, and a change of its be-tors controlling the interannual variability of heat trans-

havior caused the recent drastic decline of Arctic sea ice

port at the north of the Fram Strait (79°N).

(Shimada et al., 2006). Below Pacific Water, the Atlantic

The ice-ocean general circulation model employed in

Layer Water is found (~150–400 m depth), which is orig-

this study is COCO version 4.5 (Hasumi, 2015). The

inated from the Atlantic water entering through the Frammodel domain is global. As the Rossby’s deformation

Strait and contains a larger amount of heat than Pacificradius is ~10 km around the Fram Strait, effects of meso-

Water. It melts the Arctic sea ice through the significant

scale eddies cannot be explicitly represented unless the

upward heat flux by the enhanced vertical mixing overhorizontal grid size is significantly smaller than 10 km.

the rough topography and double diffusion along the

As in the study of Kawasaki and Hasumi (2014; ex-

Barents and Laptev Slopes. For monitoring the water

plained in Section 7.4), special high resolution is applied68

to the region of interest by placing the poles of the gener-

constrained by the bathymetric structure on the shelf.

al curvilinear horizontal coordinates close to the region,

This water reaches the Nansen Basin by passing between

on the Greenland and Scandinavian Peninsula. The hori-

Frantz-Josef Land and Novaya Zemlya and through thezontal resolution is eddy resolving (2–3 km) in the Fram

St. Anna Trough. The net volume transports through theStrait and the Barents Sea Opening (BSO), and is eddy

Fram Strait, the WSC and the BSO are 2.8, 7.1 and 2.4

permitting (3–10 km) around the Nordic Seas, the Bar-

Sv (Sv = 106 m3 s−1), respectively from the model andents Sea and the Nansen Basin (Fig. 5.4.4). The integrat-

2.0, 6.6 and 2.0 Sv, respectively from observations. Alled period is from 1980 to 2010. The first 10 years (1980–

simulated volume transports are slightly larger than ob-

1990) is for spin-up of sea-ice distribution and mean

served. The net southward volume transport at the Framcirculation in and around the Arctic Ocean.

Strait is comparable with the net eastward volume trans-

The modeled temperature and salinity around the Framport at the BSO, which is consistent with observations.

Strait and the Barents Sea averaged in the shallow layer

The 17 year mean heat transports at the Fram Strait(0–300 m depth) and for 1993–2010 are displayed in Fig.

and the BSO are 58 and 88 TW, respectively, when

5.4.5. The warm, salty Atlantic water is transported−0.1°C is chosen as the reference temperature for thenorthward by the Norwegian Atlantic Current in the Nor-sake of comparison with previous observed estimates. In

wegian Sea. A part of the Norwegian Atlantic Current

comparison to a mooring observation (16–41 TW inflows into the Barents Sea through the Barents Sea Open-

1997–1999 and 40–50 TW in 2004–2006; Schauer et al.,ing (BSO) and the rest flows into the Nansen Basin

2004, 2008), it is slightly higher at the Fram Strait. It isthrough the Fram Strait. The Atlantic water entering the

likely that the heat flux is overestimated in the model.

Barents Sea flows northeastward along several routes

The heat flux at the BSO is in the range of estimates by amooring observation (73 TW) and an inverse model (103TW). As the heat flux into the Nansen Basin through the
St. Anna Trough is −12 TW (the negative value meansthat the temperature of passing water is less than −0.1°C),
it is obvious that the Atlantic water flowing through theFram Strait has larger contribution to the heat supply to
the Nansen Basin than that through the Barents Sea. The
temperature of the Atlantic water entering the Nansen
Basin through the Barents Sea and St. Anna Trough is
low because of the strong sea surface cooling on the way
flowing northeastward on the shelf (Fig. 5.4.5a). On theother hand, the Atlantic water flowing through the Fram
Strait toward the Nansen Basin retains a high-temperature property. As a consequence, the Atlantic watertransported through the Fram Strait provides the Arctic
Ocean with a larger amount of heat than that through the
Barents Sea.

Fig. 5. 4. 4 Horizontal grid size of model (km) (Kawasaki and
Hasumi, 2015).

Mean meridional velocity and potential temperature

Fig. 5. 4. 5 Distribution of (a) potential temperature and (b) salinity averaged in the shallow layer (0–300 m depth) and for 1993–2010 (Kawsaki and Hasumi, 2015).

69

5. 5. Prediction of the Arctic sea ice distributions
Monitoring of sea ice thickness
As for the support of ice navigation, sea ice area is not
only the necessarily information but also sea ice thickness is indispensable. The ice draft algorithm for
AMSR-E data is adapted from ice thickness measurement method using passive microwave radiometer Special Sensor Microwave Imager (SSM/I) data developed
by Tateyama et al. (2002). The moored upward looking
sonar (ULS) measurements are used to calibrate a coarse
algorithm to estimate sea ice thickness using satellite microwave observations by Krishfield et al. (2014). The
sea ice thickness detecting ability of a polarization ration
PR36 was examined, calculated from 36 GHz brightness
temperatures measured by ship mounted microwave radiFig. 5. 4. 6 Distributions across the Fram Strait (79°N) of (a)temperature and (b) meridional velocity averaged for 2002–
2008 (in°C and m s−1) (Kawasaki and Hasumi, 2015).

ometers on RV Shirase in the Antarctic. PR36 is calculat-

distributions at the Fram Strait (along 79°N) between

PR36 = (TB36V – TB36H)/(TB36V + TB36H)(5.5.1)

ed using the following equation (same stile as the eq.
5.2.1):

2002 and 2008 are shown in Fig.5.4.6. This period is selected for comparison with an observational study. The

where TB36V and TB36H are brightness temperatures ofwarm Atlantic water is transported northward by thevertical and horizontal polarization of 36 GHz measured

WSC at the eastern Fram Strait (on the side of the Sval-

microwave radiometers, respectively. Fig. 5.5.1 showsbard). On the other hand, the cold Polar Water is trans-

the relationship between EM thickness and PR36 ob-

ported southward by the EGC at the western Fram Strait

served in the Lutzow-Holm Bay, East Antarctica on the(on the side of the Greenland). This qualitative feature

way to the Syowa Station, extending from early summerof currents is consistent with many observations, andmelt to fall freezeup. A negative correlation between

only possible by the high-resolution experiments.

PR36 and EM thickness is shown by the figure; however,

It was demonstrated that the interannual variability ofheat flux toward the Nansen Basin is related to the Siberian high variability. The strengthen of Siberian high induces the weakening of Atlantic water inflow toward the
Nansen Basin, and finally the sea ice in the Nansen Basin. Several recent studies showed that the Arctic sea ice
retreat induces enhancement of winter Siberian high (Inoue et al., 2012; Tang et al., 2013; Mori et al., 2014).
These studies suggested only the effect of the Arctic sea
ice on the atmosphere (the Siberian high). This study
presented the first scientific evidence of possible effect of
atmospheric (the Siberian high) change on the oceanic
heat flux. The linked variability of the Arctic sea ice andSiberian high should be examined by using a climate

Fig. 5. 5. 1 The relationship between EM thickness andPR36 derived from observation in the Luzow-Holm Bay, East
Antarctica (67–69°S and 38–40°E) in 2006–2007 Antarctic
summer. The blue and pink dots mean the period of December
2006 and February 2007, respectively. The solid curve means
regression curve (Krishfiel et al., 2014).

model.

70

Fig. 5. 5. 3 Seasonal change of thickness difference between
IJIS draft (from PR36 and GR6–36) and ULS draft (Krishfield etal., 2014).

thickness derived from AMSR-E and EM. PR36 showedno relation to thickness of second-year and multiyear ice
which has a fresh ice layer on the surface or deeper layer
formed through summer melting season. Multiyear ice ischaracterized by greater penetration depth than first year
ice because its properties include much lower salinity
and less moisture. Though the physical theory on microwave signal variations with multiyear ic thickness is still
uncertain, decreasing of gradient ratio (GR) may be attributed to volume scattering by deep snow on thicker
ice. Here, GR between vertically polarized 18 and 36
GHz (as eq. 5.1.1):
GR18–36 = (TB36V – TB18V)/(TB36V + TB18V)(5.5.2)
was compared with EM thickness in Fig. 5.5.2b. The
figure shows a better sensitivity for the multiyear ice.
This result suggests that GR18–36 changes with differences
ice temperatures at each penetration depths of 18 and 36
GHz, and snow depth over the sea ice. PR36 and GR18–36
were calculated from AMSR-E brightness temperaturesand compared with daily-averaged ice draft data derived
from the ULS. Although AMSR-E PR36 and GR 18–36
showed agreement with ULS draft in September, GR18–36
thickness showed saturation and significant underestimation in other months. The difference between 6 and 36

Fig. 5. 5. 2 (a) Distribution of sea ice thickness derived from
AMSR-E and EM between 29 September and 12 October
2010, (b) relationship between EM thickness and PR36, (c)relationship between EM thickness and GR18–36 (Krishfield etal., 2014).

GHz is the most sensitive to ULS draft. So, PR36 was
confirmed as the best sea ice thickness parameter for the
first year ice and for older ice, GR6–36 was chosen as theparameter.
Fig. 5.5.3 shows the seasonal change of thickness dif-

only sensitive in the thickness of less than 1 m.

ference between ULS draft and IJIS draft calculated us-

In the Arctic, sea ice thickness estimation using EM

ing PR36 and GR6–26. The thickness difference for all theand microwave radiometers occurred during IJIS (Inter-

years and all mooring points plotted together show sig-

national Arctic Research Center – Japan Aerospace Ex-

nificant seasonal variability with minimum in January

ploration Agency information System) cruise in theand September. Underestimation error increases toward

Beaufort Gyre. Fig. 5.5.2 shows a relationship with EMsummer and causes a maximum in June. Overestimation
71

error increases toward late autumn and causes a maximum in October. This suggests that existence of surface
liquid water and surface salt drainage in summer and refreezing melt water contribute to large signal shifts on
microwave radiation. To minimize the seasonal error
empirically, the equation listed in Fig. 5.5.3 (red line)
was applied.
Short-term sea ice prediction by high resolution regional model
In order to utilize the Arctic Sea Routes (ASR), accurate sea ice observations and predictions are vital to protect ships and offshore/coastal structures. Long-term,
medium-term and short-term predictions are needed forsafe navigation in the ASR. Long-term predictions –looking 20–30 years ahead – inform decisions regarding
the construction of new icebreakers and ports. Medium-term predictions – about three to six months – contribute to deciding on whether to utilize the ASR or theconventional southern sea route in the coming summer

Fig. 5. 5. 4 Seaice extent from the whole-Arctic model withcoarse grid, the Laptev Sea regional model with the fonegrid and Advanced Microwave Scanning Radiometer-Earth
Observing System (AMSR-E) satellite observations during (a)20 July 2004 to 17 August 2004 and (b) 20 July 2005 to 17
August 2005. Area covered with ice concentration more than
15% taken into the comparison (De Silvia et al., 2015).

navigation season. Finally, short-term predictions – one
to two weeks – are used to choose the safest and shortest
path in the Arctic Ocean once a ship enters ice-covered
areas.
This study by De Silva et al. (2015) considers how numerical modeling can be used to make a short-term predictions of sea ice conditions. Numerical modeling ofresolution regional models, respectively. The model do-sea ice has become an important instrument for ice moni-

mains for the high-resolution models are Laptev Sea re-toring, understanding past conditions, explaining recently

gion (LS) including the Laptev Sea and part of the Kara

observed changes and making future predictions regard-

and East Siberian seas, and Chukchi Sea region (CS) in-ing the Arctic Ocean. Here, De Silva et al. (2015) aim tocluding parts of the East Siberian and Chukchi seas. The

reproduce the short-term sea ice conditions in the ASR

European Center for Medium-Range Weather Forecast

using mesoscale eddy-resolving ice-ocean coupled mod-

Re-Analysis Interim (ERA-Interim) six-hourly data fromel, while explicitly treating the ice discrete characteristics

2000 to 2011 are given as the atmospheric forcing.

in marginal ice zones. Ice discrete characteristics are in-

Although the whole-Arctic model was able to capture

troduced to the model using Sagawa’s (2007) floe colli-

sea ice conditions accurately, it cannot be used to accu-

sion rheology. In addition, to minimize sea ice diffusion

rately predict details of ice-edge locations and extents.

and improve the accuracy of locating ice edges, sub-Figs. 5.5.4 and 5.5.5 show a time series of sea ice extent

grid-scale ice motion (semi Lagrangian movements) wasin the LS and CS regions for short-term hindcast simula-incorporated into the sea ice dynamics (Rheem et al.,tions in 2004 (when North-east Passage was closed) and2007). The ice-ocean coupled model developed in this2005 (North-east Passage was open), respectively. In

study is based on a three-dimensional high-resolution re-

both the LS and Cs regions, computations are performed

gional model in the Sea of Okhotsk (Fujisaki et al., 2010)

from 20 July to 17 August in both years. As shown inin which an ocean model is based on generalized coordi-

Fig. 5.5.4a, the sea ice extent simulated in the LS region-nates, the Message Passing Interface version of the Princ-

al model varied from 1.8 × 106 km2 to 1.4 × 106 km2

eton Ocean Model (POM; Mellor et al., 2002). The zon-within four week period in 2004 because of thermody-

al and meridional grid spacings are approximately 25 ×namics and dynamics activities. In the first two weeks,

25 km and 2.5 × 2.5 km for the whole-Arctic and high-the computation shows a similar reduction pattern with72

contrast to the LS region, the whole-Arctic simulation(extracted CS region) showed a similar decreasing trend
in sea ice extent compared to the satellite observations in
the CS region. Considering all the qualitative and quantitative comparisons of the regional model results, it isbelieved that the coarse-grid computation cannot be used
to reproduce sea ice variations in summer accurately.
But the fine-grid computation can reproduce sea ice variations accurately using satellite observations. However,even high-resolution computations are unable to follow
the dramatic reduction of sea ice extents, such as shownin Fig. 5.5.4a.
There are several possible reasons for this discrepancy
in the results between coarser and high-resolution gridcomputations. The first reason is mesoscale eddy-resolving capability. The second is that small-scale sea ice dynamics (converging and diverging process) were morecorrectly captured with the high-resolution models. The
third possible reason is that the high-resolution computaFig. 5. 5. 5 Sea ice extent from the whole-Arctic model withcoarse grid, the Chukchi Sea regional model with the finegrid and Advanced Microwave Scanning Radiometer-Earth
Observing System (AMSR-E) satellite observations during (a)20 July 2004 to 17 August 2004 and (b) 20 July 2005 to 17
August 2005. Area covered with ice concentration more than
15% taken into the comparison (De Silvia et al., 2015).

tion well expresses the ice-albedo feedback process,which accelerates ice melting in the spring and summer.
Medium-range prediction of the summer Arctic sea-ice
area
Recent reduction of summer sea ice cover in the Arctic
has accelerated maritime transport using the Arctic sea

observations. In the third week, the observational sea ice

routes. Sea ice prediction is essential to realize safe andextents show a dramatic reduction, but the model cannot

efficient use of the route. We are trying to predict sum-

capture such a dramatic reduction. At the end of the third

mer Arctic sea ice cover in spring by analyzing satellite

week (11 August 2004), the model sea ice extent is al-observation data. This prediction focuses on the ice

most equal to the observational results. But again in the

thickness in spring as preconditioning of the ice melting

fourth week, the model has drifted away from the obser-

in summer. For this prediction, a daily-ice velocity prod-vation. Fig. 5.5.4b shows the model sea ice extent varia-

uct on a 60-km resolution grid is prepared using data bytion from 1.5 × 106 km2 to 1.1 × 106 km2 in 2005. In con-satellite passive microwave sensors AMSR-E and AMSR2.

trast to 2004, when the North-east Passage was closed,

Kimura et al. (2013) found that the winter divergence/

the high-resolution regional model overestimates the sea

convergence of the ice motion is strongly related to theice extent compared to the observations in 2005. Insteadsummer ice cover in some regions. A daily-ice velocity

of high-resolution computations, the coarse-grid whole-data set has been prepared for winter season between 1

Arctic computation (extracted LS region) cannot repro-

December and 30 April, which is the same one used induce the ice extent variations in both years. Sea ice inthe study on the Southern Ocean (Kimura and Wakatsu-

the whole-Arctic model was almost unchanged in 2004,

chi, 2011), calculated from images of the AMSR-E satel-

while it showed a slightly decreasing trend throughout

lite. Ice velocity is computed from the gridded bright-

the 2005 computations.

ness temperature of 89 GHz horizontal and vertical

As shown in Fig. 5.5.5, the sea ice extent in 2004 and6polarization channels which is binned to square pixels of22005 varied in the CS region from 0.5 × 10 km to 0.05
66.25 × 6.25 km by the National Snow and Ice Data Cen-2

× 10 km within the four weeks. However, the differ-ter (NSIDC). The procedure for detecting ice motion,ence between the observations and the model is muchwhich has been improved in the studies (e.g., Kimura and2

Wakatsuchi, 2000, 2004), is based on the maximum

larger (about 0.1 × 106 km ) in 2004 than in 2005. In
73

Fig. 5. 5. 6 Bold lines indicate the fraction of sea ice existingsince before 1 December and the temporal evolution of thenumber particles within that part of the Beaufort Sea for 2005
and 2008. The unit of the particle number is converted to ice
area (km2) by multiplying by 37.5 × 37.5, under the assumptions
that each particle represents a 37.5 × 37.5 km of area with
100% ice concentration and that ice melting during December
and April is negligible. Thin lines show the corresponding total
sea ice area within the same region calculated from the dailyice concentration (Kimura et al., 2013).

Fig. 5. 5. 7 Correlation coefficient between the number ofparticles on 30 April migrated from 1 December (NP4) and thetotal ice area on 10 September (IA9) of the same year for 108
domains, based on the nine years’ data (Kimura et al., 2013).

becomes lower compared with convergence areas. Summer ice cover has a relation to the ice motion before

cross-correlation method described by Ninnis et al.

spring probably through the dynamic change of ice thick-(1986) and Emery et al. (1991). After these processes, aness.

daily-ice velocity data set was constructed without miss-For example, in the Beaufort Sea, sea ice cover retreats

ing data over the sea ice area on a 37.5 × 37.5 km grid (6between May and September in both 2005 and 2008 (Fig.

× 6 of original 6.25 km square grid) for the nine year pe-

5.5.6). In 2008, more than 60% of the particles were ex-riod 2003–2011.

ported from this region between 1 December and 30

To visualize the dynamic redistribution of sea ice,

April, that is, the ice area at the end of April consists ofmovement of particles spread over the ice covered area ismore than 60% thin ice produced after December. In re-calculated. About 10000 particles are arranged at an in-sponse to the thinning, ice area in 2008 decreased rapidly

terval of 37.5 km over the ice area on 1 December ofafter May. In contrast, in 2005 there was little export ofeach year. Daily displacement of particles released on

ice during winter and the resulting predominantly thickthe day is calculated from AMSR-E derived ice velocity

ice resulted in the slow decrease of total sea ice area

on one-day time steps. Since sea ice concentration with-during the melting season.

in the Arctic is near 100% during winter, ice divergenceThis paragraph examines the relationship between theand convergence yield new ice production and dynamical

winter ice redistribution and summer ice area based on

thickening, respectively. The spatial inhomogeneity of

the nine years of 2003–2011 for 108 domains defined by

the particles distribution becomes significant with time.

375 × 375 km2 (10 × 10 pixels, 1.4 × 105 km2). Here, twoThough there are obvious interannual differences, parti-

quantities are focused on, total ice area on 10 September

cles generally become sparse in the Kara, Laptev and(IA9) and the number of particles converted into the area

Beaufort seas in contrast to the convergence of ice parti-

on 30 April moved from 1 December (NP4): the unit ofcles in the East Siberian Sea. In the convergence area,

both is square kilometers. IA9 is calculated by adding up

the density of the particles becomes higher than initial

the ice concentration within each domain. Base on nine

distribution. This means that dynamic deformation anddata points of the nine years’ data, Fig. 5.5.7 shows theaccompanied thickening of sea ice through rafting or

correlation coefficient between NP4 and IA9 of sameridging of ice floes is occurring.

In contrast, ice diver-year, for each domain. There are high correlation coeffi-

gence promotes the formation of leads and polynyas,

cients between the winter ice redistribution and summer

which is associated with new ice production; it results in

ice area in several domains. The relationship is signifi-a higher fraction of thin ice compared with the conver-cant in 18 domains at a 95% confidence level, that is, do-gence areas. As a result, ice thickness in divergence area

main with a correlation coefficient of more than 0.67.
74

Fig. 5. 5. 8 Observed ice cover on 11 September 2013, 2014 1nd 2015, overlapped by the predicted ice edge line for the same days.
Ice edge location is defined by the 30% of ice concentration. By considering the initial ice thickness, accuracy of the prediction in2015 becomes higher compared with 2013 and 2014 (Kimura and Yamaguchi, 2016).

The strong relationship in these domains supports thethe initial ice thickness only in the thick-ice (> 1.5 m)

scenario that a greater ice divergence or outflow in winterarea and the highest correlation between the spring ice

and resulting thinner ice cover in spring contributes tothickness and summer ice cover was acquired. By con-the smaller ice area in summer.

sidering the initial ice thickness, accuracy of the predic-

There are other factors controlling the spring ice thick-tion becomes higher compared with the prediction based

ness. Small scale ice process such as ice thickening

on the particle density only (Fig. 5.5.8). Based on thethrough rafting/ridging of ice floes and new ice produc-

analysis, first report of the summer ice prediction show-tion in leads, thermodynamic ice thickening/thinning, ice

ing the ice concentration map for 1 July to 11 September

movement and melting attributed to the summer weather

is released in May on the website*1 and Arctic Data ar-condition and so on are ignored in this study. The point

chive System*2 (ADS). After then, updated predictions

is that even though these processes were not taken into

are released at the end of June and July. Information of

consideration, the linear relation between the winter ice

the predicted ice area was also sent to Sea Ice Outlook*3.

motion and summer ice cover is still significant in many
regions with large interannual variability. Based on the

5. 6. Navigation support systems

linear relationship between NP4 and IA9, we can potentially improve the prediction of the summer ice area in

In order to support ship navigation in the Arctic sea

spring by analyzing the satellite derived winter ice mo-routes, it is necessary to investigate the effects of sea icetion. This medium-term forecast looking several months

and cold oceans on ships.

ahead is useful for human activity in the Arctic, foe exShip icing

ample to determine whether or not the shipping route
through the Arctic – the Northern Sea Route – will be

Marine disasters by ship icing occur frequently in coldnavigable.

regions. In recent years, the summer condition of the
Arctic Ocean has been changing and expansion of thePrediction capability

open water area on the Arctic sea routes increases theFollowing this study by Kimura et al. (2013), addition-

frequency of sea spray generation and marine icing, be-al information of ice thickness from Krishfield et al.

cause the air temperature remains cold enough and con-(2014) improved the forecast of summer ice extent.

*1 http://www.1.k.u-tokyo.ac.jp/YKWP/ 2015article_e.html*2 https://ads.nipr.ac.jp/vishop/*3 https://www.arcus.org/sipn/sea-ice-outlook

Kimura and Yamaguchi (2016) estimated provisional ice
thickness on 30 April by particle density multiplied by75

ditions are suitable for sea spray icing. To address icing
on the ship, liquid water content (LWC) of sea water is
one of the important parameter. In order to address icing
on ships, Ozeki et al. (2016) developed sea spray meters,
the sea spray particle counter (SPC) and the marine rain
gauge type spray gauge (MRS), for measuring the sprayparticles and the amount of spray. The SPC was originally developed for the measurement of drifting snow
(Nishimura and Nemoto, 2005). The flux distribution
and the transport rate could be calculated as a function ofFig. 5. 6. 1 Correlation diagram of the amount of seawater
spray measured by the SPC and relative wind velocity (Ozeki
et al., 2016).

particle size. Based on the previous observation case(Ozeki and Adachi, 2013), the SPC was improved to be aseawater spray particle counter. The SPC measures particle size range from 100 to 1000 μm in diameter.
The marine rain gauge type spray gauge (MRS) consists of marine rain gauge and cylindrical spray trap. The
marine rain gauge was developed to measure rainfall on
ships that pitch and roll. It collects seawater spray or
precipitation in a catchment funnel which has a crosssectional area of 100 cm2, and the inside diameter of thefunnel is 112 mm. The cylindrical spray trap was attached above the marine rain gauge to capture the seawater spray from the horizontal direction instead of precipitation. The amount of seawater spray is measured every

Fig. 5. 6. 2 Spray particle size distribution by each windvelocity (Ozeki et al., 2016).

minute and the smallest measurable unit is 0.1 mm.
The field tests were conducted on the r/v Shirase (icebreaker), when the vessel sailed in open water. The
amount of seawater spray and the size and number ofwind velocity. It indicates that the particle size distribu-

seawater particles were measured by an SPC and twotion becomes braod and the rate of large particle increase

MRSs during Japanese Antarctic Research Expedition

with increases in relative wind velocity. The large parti-(JARE) -55 and 56. To analyze the seawater spray char-cles affect the increase of seawater spray volume strong-acteristics, a correlation analysis was performed between

ly, because the volume of droplet is in proportion to thethe amount of seawater spray, the size and number ofthird power of the diameter of droplet. Therefore, theseawater particles and the wind velocity. According towind velocity dependence of the amount of seawater

the well known equation of the vertical distribution ofspray might include the effect of the shift of particle size

liquid water content (e. g., Zakrzewski, 1987) in the sea-

distribution. On the other hand, when the relative wind

water spray, it is expected that the amount of seawatervelocity was lower than 19 m s−1, the amount of seawater

spray depends on the relative wind velocity under a fixedspray was very small, because the compass deck of largeheight and a constant wave height condition. As shownvessel was higher enough to reduce the drifted seawaterin Fig. 5.6.1, the amount of seawater spray measured byspray under the low wind velocity condition. From these

the SPC was strongly related to the relative wind velocity

results, it may be concluded that the SPC measurement is

at every observation point, although the fitting curve wassuperb way to analyze the particle size distribution of im-

not a quadratic expression.

pinging seawater spray.

To clarify the characteristics of the size of seawater
Optimal routing of ships

particles on the deck, the wind velocity dependence ofthe particle size distribution was analyzed using the SPC

Takagi et al. (2014) formulated the selection of the sea

data obtained in JARE-55 and -56. Fig. 5.6.2 shows theroute in the ice sea as the finding-path problem. Sea ice

average spray particle size distributions by each relative

has caused a significant damage to vessels in the Arctic.
76

Fig. 5. 6. 3 Milestone of ship of circle (Takagi et al., 2014).

There have been over 200 reported damage events overthe past 25 years. It is important to avoid the collision
with sea ice, and to select the route to save the fuel andFig. 5. 6. 4 Roadmap graph in ice cover modeling (Takagi etal., 2014).

time safely. However, it is not easy to select the best sea
route promptly and safely because the shape and distribution of sea ice are very complex. Probablistic roadmap
method (PRM) is a popular path planning scheme that

Ordinary milestones are given by the point. The pro-can find a collision-free path by connecting the start andposed method uses a circles as a milestone. The radiusgoal through a roadmap constructed by drawing randomof the milestone is set in the distance where the vessel

node in the free configuration space. The PRM consists

can navigate safely as in Fig. 5.6.3. Fig. 5.6.4 shows aof two phases: a construction and a query phase. The

roadmap graph with milestones in the sea ice that is con-construction phase have two processing before construct-

structed with the ice cover modeling. Each milestone

ing the roadmap graph; construction free space anddoes not overlap as shown in Fig. 5.6.3. It can be imple-

checking collision with the obstacles for all local paths.

mented using the circle-circle collision detection. And

These processing needs a lot of calculation time. The

all paths between nodes don’t come in contact with thepaper presented new method that constructed a roadmap

sea ice.

graph without these processings. The path between theFig. 5.6.5 shows a binary image of vessel radar by thestart and goal was decided by using Dijkstra’s Algorithm

image processing. The range of the observation of this(Dijkstra, 1959) as a query. Experimental results showedvessel radar is 3 NM. This image was obtained by Louisthe effectiveness of the proposed method.

S. St-Laurent in the Arctic Ocean on August 26 of 2012.

To examine the proposed method by various concen-

White parts show the sea ice, and black parts show thetration of sea ice, a sea ice in the algorithm described isopen water. It is not possible to obtain information of thepresented as an irregular polygon inscribed into an el-height of the sea ice like ice ridge or ice berg from the ra-lipse. A sea ice is defined as a ratio of the minimum di-dar image of the vessel. However, it is possible to get theameter of the circumscribed ellipse dmin to its maximum

distribution and size of the sea ice. Fig. 5.6.6 shows adiameter dmax. To detect collision (intersection or one be-roadmap graph of this radar image. All routes do noting inside another), it can be implemented using the po-come in contact with the sea ice. The center of the circle

lygonal ellipse-circle detection after stretching of the el-

is a point of the present position of the vessel. That is, it

lipses so that the other becomes a circle. Fig. 5.6.3

is a start point.

shows that the sea ice of various densities is generated.

Ice in the Arctic is a very complex and dynamic mate-

Dijkstra’s algorithm is a graph search algorithm thatrial. It has a wide range of thickness, concentration, age

solves the single-source shortest path problem for a graphand roughness. Moreover, ice conditions in the Arctic

with non-negative edge path costs, producing a shortest

change throughout the year. Transport Canada has im-path tree. This algorithm is often used in routing and as

plemented a Regulatory Standard that is intended to min-a subroutine in other graph algorithms.

imize the risk of pollution in the Arctic due to damage of77

Fig. 5. 6. 7 Connected distance and total distance of sea route(Takagi et al., 2014).

Fig. 5. 6. 5 Binary image of vessel radar (Takagi et al., 2014).

Fig. 5.6.7 shows the relation between the connecteddistance between milestones and the total distance of thesea route. When the connected distance becomes long,the sea route shortens. Here, a method has been proposed that minimizes the distance to the goal point fromthe starting point. In the future, it is considered as anevaluation function such as time and fuel, depending on
the size of the ship.
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2016, 67p.
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amd Chung, K-H., 2015. Delayed responses of the oceanic
Beaufort Gyre to winds and sea ice motions: influences
on variations of sea ice cover in the Pacific sector of the
Arctic Ocean. J. Oceanogr., 71, 187–197, DOI 10.1007?
s10872-015-0276-6.
Zakrzewski, W. P., 1987. Splashing a ship with collision-generated spray. Cold Regions Sci. Technol., 14, 65–83.

80and to < 30% at the beginning of June. Therefore, obser-

6. Evaluation of the impacts of Arctic
change on marine ecosystems and fisheries

vations were carried out about 3 months and 1 month after the ice retreat in 2012 and 2013, respectively. Because of the difference in season as well as interannual
variability in hydrographic conditions, distribution of Ω

The Arctic Ocean has experienced significant warm-

was largely different between two observations. Bying, freshening and ocean acidification mainly caused bycomparing with distributions of physical and biogeo-

rapid reduction of sea ice. In the project Ecosystemchemical parameters, factors controlling Ω will be dis-

Studies on the Arctic Ocean with Declining Sea Ice

cussed. Based on these results, reconstruction of season-(ECOARCS) under GRENE Arctic as strategic target 3b,

al variations of Ω in the bottom water was attempted in

we investigated the impact of sea ice retreats on marine

the southern Chukchi Sea by using data from a 2-year-

ecosystems.

round mooring observation between July 2012 and July
2014. Hydrographic data were collected in the Chukchi
and Bering seas during the cruises of R/V Mirai of the

6. 1. Ocean acidification and the maintenancemechanism of primary production

Japan Agency for Marine-Earth Science and Technology(JAMSTEC) from 13 September to 14 October in 2012

Ocean acidification(Kikuchi, 2012) and T/S Oshoro-Maru of Hokaido Uni-

During the last decade, ocean acidification due to up-

versity from 3 July to 18 July in 2013 (Hirawake, 2013).

take of anthropogenic carbon dioxide (CO2) has emerged

Here, data from stations north of 66°N with a bottom

as an urgent issue in ocean research (Orr et al., 2005).

depth shallower than 70 m were used. A mooring system

Increasing acidity and consequent changes in seawater

was deployed in Hope Valley from 16 July 2012 to 19

chemistry are expected to impact the marine ecosystem

July 2014. The system was first deployed at 67°42’ N,and may threaten some organisms. Of particular concern

168°50’ W from 16 July 2012 to 2 October 2012, andis the impact on calcifying organisms, such as coralline

then moved slightly to the north at 68°02’4 N, 168°50’ W

algae, pteropods, bivalves and corals, because acidifica-on 3 October 2012. On 20 July 2013, the system was re-tion lowers the saturation state (Ω) of calcium carbonate

covered for maintenance and redeployed at the latter lo-(CaCO3) in seawater, which affects the ability to thesecation until 19 July 2014. Bottom depth was 52, 59 andorganisms to produce and maintain their shells or skele-

60 m, respectively, for each deployment. Sensors for T,

tons. In fact, a decrease in Ω of water can cause en-S, dissolved oxygen (DO), chlorophyll a and turbidity

hanced mortality of juvenile shellfish and decreased cal-were equipped on the mooring at 7 m above the bottom.

cification, growth, development and abundance ofDistribuitons of Ω in bottom waters of Chukchi Sea

calcifiers.

(Fig. 6.1.1) were significantly different between Septem-

The shallow shelf seas of the Arctic Ocean are known

ber–October 2012 (hereafter autumn 2012) and July 2013

to be especially vulnerable to ocean acidification. Cold

(hereafter summer 2013). In autumn 2012, Ω in bottom

water dissolves more CO2, large fresh water inputs fromwater ranged from 0.3 to 2.0 for aragonite (Ωar) and fromrivers and sea ice melt reduce calcium ion concentrations

0.5 to 3.2 for calcite (Ωca). Aragonite undersaturation (Ωarand alkalinity, the buffering capacity of seawater to add-

< 1; black circles in Fig. 6.1.1) was observed at stations

ed CO2 (Yamamoto-Kawai et al., 2011), and respiration

off Pt. Barrow, in Hope Valley, in the Bering Strait andat the bottom of a salt stratified water column accumu-

near the northern continental slope. The lowest Ωar waslates CO2 in bottom water (Bates et al., 2009). Because

observed on 3 October 2012 in Hope Valley at 68°N, inof these characteristics both surface and bottom waters ofthe dome-like structure of bottom water with low T, highArctic shelf seas exhibit naturally low Ω compared to

S and low dissolved oxygen (DO) (Nishino et al., 2016)

other ocean waters (Yamamoto-Kawai et al., 2013). The

with high total alkalinity (TA), high dissolved inorganic

Chukchi Sea is one of these seas.

carbon (DIC) and high fCO2 (fugacity of CO2). Calcite

Yamamoto-Kawai et al. (2016) show results of ship-

undersaturation was also found in this dome. [Nishino etbased observations in September–October of 2012 andal. (2016) described the dome-like structure as a common

early July of 2013 in the Chukchi Sea. In both years, sea

feature found in this region associated with Hope Valley

ice concentration decreased to < 80% at the end of May

topographic depression, although water properties can
81

Fig. 6. 1. 1 Distribution of Ωar (a, c) and Ωca (b, d) in bottom water in September–October 2012 (a, b) and July 2013 (c, d). Circled
stations were undersaturated with CaCO3 minerals. White arrows indicate mooring sites. An insert photo shows bivalves collected by
dredge trawl at a station marked with a star in July 2013 (Yamamoto-Kawai et al., 2016).

differ between years and seasons.]

DO and apparent oxygen utilization (AOU, the difference

In 2013, Ω in bottom water was 1.1–2.8 for aragonite

between saturated and observed concentrations of DO),and 1.4–4.4 for calcite: all of the observed waters werewith a range of −3.6 and +0.45 for aragonite and −0.57

oversaturated with respect to aragonite and calcite (Fig.

and +0.71 for calcite. For the whole period, Ω ranged

6.1.1). Ω in bottom waters at these stations was slightly

from 0.2 to 2.1 for aragonite and from 0.3 to 3.4 for cal-lower than stations north or south but still very oversatu-

cite (Fig. 6.1.2). It was shown that the ship-based obser-

rated with respect to CaCO 3 (Ω ar = 1.7−1.9; Ω ca =vations in autumn 2012 and summer 2013 captured low

2.8−3.0). Waters with lower Ωar of 1.1−1.2 and Ωca ofand high Ω periods, respectively. Seasonal variation of

1.8−1.9 were found in northern stations at around 71°N

Ω mirrors that of DO, low in autumn due to stratification

where T and DO were lower and fCO2 was higher than in

and respiration and high in spring and early summer due

the south. These results show that variations in Ω in theto photosynthesis. In the unusually stratified autumn

Chukchi Sea bottom water are controlled largely by or-

2012, bottom water Ω was ~0.3 for aragonite and ~0.5

ganic matter remineralization with minor contributions offor calcite. In 2013, Ω decreased after the ship-based ob-

T and S. In fact, low Ω waters were observed in regions

servation in July, and intermittent aragonite undersatura-

off Pt. Barrow and Hope Valley, known as biologicaltion was predicted in August, September and October, al-“hotspots” in the Chukchi Sea, characterized by high pri-though Ω was higher than in the same months in 2012.

mary productivity, high export flux of organic matter toAt the beginning of the cooling and convection periods

depth, a high respiration rate in sediment community andin November–December 2012 and October–November

high benthic biomass (Nishino et al., 2016). Strong strat-

2013, ventilation of bottom water increased DO and Ω.

ification prevents ventilation and accumulates more CO2Then, Ω in bottom water remained low during winter un-

in the bottom water.

til the initiation of photosynthesis in May. Low Ω in

Based on the analysis from the comparison of mooring

winter is likely due to continued respiration by benthic

and ship-based observations, the regression equationsorganisms, as suggested by positive AOU. From springwere applied to the mooring data in Hope Valley. The

to autumn, predicted large temporal variation in Ω sug-reconstructed Ω is shown in Fig. 6.1.2 together with S, T,

gests inhomogeneous distribution of undersaturated wa82

Fig. 6. 1. 2 Time series of salinity (S, top), temperature (T, second; °C) and dissoleved oxygen (DO, third; m mol kg−1). Ωar (fourth)and Ωca (bottom) reconstructed from mooring data of T, S and AOU shown above. Gray lines in the fourth and bottom panels indicate
ranges of −3.6 and +0.45 for aragonite and −0.57 and +0.71 for calcite. Red symbols indicate ship-based observations. In the third
panel, red squares are ship-based data of AOU (Yamamoto-Kawai., 2016).

ters during this period. In winter, in contrast, variability

Ω ranged from 0.2 to 2.6 for aragonite and from 0.4 to 4.1

in Ω is relatively small. This suggests that low Ω is afor calcite. For the future case, Ω ranged from 0.2 to 1.5

widespread feature during winter.

for aragonite and from 0.2 to 2.4 for calcite. This means

The reconstructed Ω suggests the frequent occurrence

that CaCO3 undersaturation might have occurred, at leastof aragonite undersaturation in the bottom water of thefor aragonite, even with the productivity much lower

Chukchi Sea, not only in summer/autumn, but also in thethan that occurring today and without perturbation by an-

winter months. In previous studies, continuous aragonite

thropogenic CO2.

undersaturation has been observed in bottom waters in

In terms of duration, the period of aragonite (calcite)

the Bering and Chukchi seas, but in limited seasons. The

undersaturation was estimated to be 3.9 (2.6) and 1.7

present study is the first to estimate year-round variabili-(0.3) months in the first and second year, respectively, in

ty of Ω in the bottom water of the Chukchi Sea.

the preindustrial period case. By comparing with the

In order to roughly quantify the effect of anthropogen-original estimate of 7.5–8.6 months, it was suggested that

ic CO2 on the timing and duration of CaCO3 undersatura-

the period of aragonite undersaturation has been signifi-tion in the 2-year time series of Ω, estimation has been

cantly extended by an input of anthropogenic CO2 bymade on Ω in two cases: (1) the preindustrial period casenow. In the future case with an atmospheric pCO2 of 650

with an atmospheric partial pressure of CO2 (pCO2) ofppm, the period of undersaturation is estimated to in-

280 ppm, and (2) the future case with pCO2 of 650 ppm

crease further to > 11 months for aragonite and half year

(50 years later in the high CO 2 emission scenario

for calcite. This analysis indicates that anthropogenic(RCP8.5), and the end of the century in a moderate sce-

CO2 has a significant impact on the duration of CaCO3

nario (RCP6; IPCC, 2013)). For the preindustrial case,undersaturation in the bottom water even though season83

al and interannual variations of Ω are mainly controlled
by biological processes.
Biological hotspot
The southern Chukchi Sea is one of the most biologically productive regions of the world’s ocean because ofnutrients supplied by northward flow of Pacific-originating water advected over the shelves from the northern
Bering Sea into the Arctic Ocean. Due to high primary
productivity, a large quantity of organic matter descends
to the seafloor as potential food for benthic communities,
resulting in high benthic biomass. Consequently, large
benthic feeders at high trophic levels, such as grey
whales and walruses, also congregate there. Such a re-Fig. 6. 1. 3 Map showing the bathymetric features of the study
area and the hydrographic stations for the R/V Mirai cruises in2012 (red dots) and 2013 (blue dots). Green diamonds represent
the SCH-12 (southern site) and SCH-12-2/SCH-13 (northernsite) mooring sites. Data from the stations enclosed by black
dotted lines were used for the illustrations of vertical sections
shown in Fig. 6.1.5. The area enclosed by the red dotted circle
is the southern Chukchi Sea biological hotspot, where themoorings were installed and detailed hydrographic surveys
were conducted (Nishino et al., 2016).

gion of high biological activity is called a biologicalhotspot. Here Nishino et al. (2016) analyzed mooringand ship-based data obtained from a biological hotspot in
the southern Chukchi Sea to understand the water mass
characteristic (and temporal changes thereof) that influence phytoplankton biomass and productivity. Mooring
data, including temperature (T), salinity (S), dissolved
oxygen (DO), Chl a and turbidity near the bottom of thebiological hotspot in the southern Chukchi Sea, were collected from July 2012 to July 2014 for the first time. The

site. DO concentration (blue line in Fig. 6.1.4) varied indata were used to examine changes in water mass charac-

response to the change in water masses. The BWW andteristics and phytoplankton biomass associated with

HSW had high DO concentrations (> 300 μ mol kg−1) be-

spring and autumn blooms in this biological hotspot.

cause the waters undergo cooling and convection in win-

Three temporally sequenced moorings (named SCH-12,

ter with oxygen supplied from the atmosphere. On the

SCH-12-2 and SCH-13) from 16 July 2012 to 19 Julyother hand, there was a wide range of DO concentrations

2014 to aquire T, S, DO, Chl a and turbidity time series

in BSAW. DO concentration was high (~ 300 μ mol

near the bottom of a biological hotspot located in Hopekg−1) in the beginning when the BSAW occupied theValley of the southern Chukchi Sea (Fig. 6.1.3). Ship-mooring site in July. Then it decreased gradually overbased hydrographic surveys were conducted in the Chuk-time and had minimum values between September andchi Sea and Canada Basin from 13 September to 4 Octo-

November 2012 and between August and October 2013.

ber 2012 and from 31 August to 4 October 2013 on board

Turbidity (red line in Fig. 6.1.4) was lowest in an annual

the R/V Mirai of the japan Agency for Marine-Earth Sci-

cycle during winter and spring when BWW or HSW oc-

ence and Technology, JAMSTEC (Fig. 6.1.3; Kikuchi,

cupied the site. Then it increased sharply in May 20132012 and Nishino, 2013).

and 2014, when the DO concentration also increased. In

The bottom waters in the Chukchi Sea are classified

July 2012 and 2013, when the BSAW began to occupy

into Alaskan coastal water (ACW; S < 31.8, T = 0–4°C),

the site, turbidity became relatively lower and subse-

Bering shelf – Anadyr water (BSAW; S > 31.8, T = −1.0–

quently reached an annual maximum (10–15 FTU (For-4°C), Bering winter water (BWW; S = 32.4–34.0, T <mazin Turbidity Units)) between September and Novem-−1.6°C) and hypersaline water (HSW; S > 34.0 withber 2012 and between August and October 2013. The

freezing temperature of seawater, T ~ −1.8°C). The T

period of annual maximum turbidity corresponded withand S characteristics from the mooring data (Fig. 6.1.4)

the period of annual minimum DO.

indicate seasonal changes between these waters. The

Chl a concentration (Fig. 6.1.4c) increased sharply in

BSAW (BWW) was generally presented in summer andMay, when sea ice still remained in the area, and the highautumn (winter and spring) at the bottom of the mooring

concentration continued until July. The shrap increase in84

ing Strait, Hope Valley and Barrow Canyon, where primary productivity in the water column was also highcompared to that in the central Chukchi Sea and the Canada Basin. The high productivity regions are thought tobe the biological hotspots. The quantity of Chl a in thewater column in 2013 was higher everywhere compared
to 2012, and the highest quantity was detected in Hope
Valley. Despite being downstream from nutrient-richwater from the Bering Sea, the algal biomass and primary productivity in Hope Valley were comparable to or
higher than those in the Bering Strait during both years.
A hydrographic section was obtained from Bering
Strait to the shelf slope of the Chukchi Sea along 168°45’
W across the biological hotspot of the southern Chukchi
Sea at ~68°N on 13–17 September 2012 (Fig. 6.1.5). T
and S (Fig. 6.1.5a) characterize the water mass distribution in this section. In the shelf area (from the Bering
Strait to 72°N), ACW (S < 31.8, T > 4°C) was found at~67 and 69–70°N in the upper layer (< ~20 m) and
BSAW occupied the lower layer. Over the shelf slope

Fig. 6. 1. 4 Time series of (a) temperature (°C; red) andsalinity (blue), (b) dissolved oxygen (DO, m mol kg−1; blue)
and turbidity (in Formazin Turbidity Units, FTSs; red) and (c)chrolophyll a (Chl a, mg m−3; green). The data were obtained
from the SCH-12, SCH-12-2 and SCH-13 moorings during 16
July 2012–19 July 2014. The vertical axis scale in (c) below
the dotted line is exaggerated where the concentration is < 3
mg m−3. Periods when sea ice concentration was > 50% at themooring site are indicated by blue bars (Nishino et al., 2016).

(north of 72°N), BWW with near-freezing temperature
was found at 73–74°N below a depth of ~40 m. The saline bottom water (> 33) around 72°N was classified as
BSAW, but the water temperature was relatively low indicating that it was likely influenced by the adjacent
BWW to the north. A dome-like structure of bottom water was found characterized by an uplifted isohaline (isopycnal) surface at ~68°N with lower T and higher S than

Chl a in May was synchrony with the sharp increase in

those of the surroundings. This bottom water at ~68°N

DO concentration and turbidity (blue and red lines in Fig.

was also characterized by the lowest light transmission in

6.1.4b, respectively). In addition, relatively high Chl athis section (Fig. 6.1.5b). The light transmission was rel-−3

concentration ( > 1 mg m ) were found in September–atively low in the bottom water around 72°N, but it in-

October 2012 and August–October 2013, although thecreased sharply in the BWW (73–74°N).

concentrations were much lower than those in late spring

fSIM was calculated wether the water was influenced byto early summer (May–July). The time series of the tur-sea ice melt or brine rejection (Fig. 6.1.5c). The surface

bidity data showed two peaks in accordance with annual

water was influenced largely by se ice melt (fSIM > 0), es-variation in Chl a concentration, i.e. high turbidity in late

pecially at 67–69°N and over the shelf slope. On thespring and early summer and in autumn. However, tur-other hand, the bottom waters at ~68 and 72°N and thebidity was higher in autumn than in late spring and early

BWW (73–74°N) were associated with brine rejection

summer, despite Chl a concentrations being lower in au-(fSIM < 0).

tumn.

The DO distribution (Fig. 6.1.5d) showed a subsurface

The ship-based hydrographic and biogeochemical sur-

DO maximum over the shelf slope, which was almost co-veys were conducted in the Chukchi Sea and the Canada

incident with a subsurface Chl a maximum and associat-

Basin during September to early October 2012 and 2013,

ed with the photosynthesis in this maximum layers as de-when the mooring data indicated high Chl a and turbidity

scribed in previous studies (e.g., Martin et al., 2010). Awith low DO concentrations. The spatial distribution of

notable feature in this section was the lowest DO in the

Chl a integrated over the water column in 2012 showedbottom water at ~68°N. Nitrate (Fig. 6.1.5e) was deplet-that the quantity of Chl a was relatively high in the Ber-ed at the surface, except for the Bering Strait, and high85

Fig. 6. 1. 5 Vertical sections of (a) temperature (°C), (b) light transmission (%), (c) fraction of sea ice meltwater (fSIM), (d)dissolved oxygen (DO, m mol kg−1), (e) nitrate (m mol kg−1) and (f) ammonium (m mol kg−1) along the 168°45’ W meridian near the
US-Russia border obtained during the 13–17 September 2012 R/V Mirai cruise. The water sampling level at each station is indicatedby a black dot. Salinity contours are superiposed on each section with a 0.5 contour interval. The thick contour in each section
indicates a salinity of 33 (Nishino et al., 2016).

concentrations (~20 μ mol kg−1) were found in the bot-transmission there was extremely low compared to the

tom water of the strait and BWW. The nitrate concentra-

surroundings, but higher than that in 2012.

tion in the bottom water at ~68°N was relatively low (~7The Chl a mooring data captured phytoplankton

μ mol kg−1). Ammonium (Fig. 6.1.5f) was also depleted

blooms, as indicated by the high Chl a concentration inat the surface, and in contrast to the nitrate, the concen-

spring to early summer and in autumn (Fig. 6.1.4c). The

trations were low in the bottom water of the Bering Strait

first bloom in May was likely a spring bloom including a−1−1(~2 μ mol kg ) and BWW (< 0.5 μ mol kg ) and highest

bloom of ice algae. At the onset of the spring bloom in−1in the bottom water at ~68°N (~12 μ mol kg ).

May, both the DO concentration and the turbidity in-

The biological hotspot in the southern Chukchi Sea

creased sharply (blue and red lines in Fig. 6.1.4b, respec-

was revisited and hydrographic and biogeochemical sur-

tively), which is consistent with the oxygen production

veys were conducted on 3–4 October 2012. Similar toaccompanying phytoplankton photosynthetic activity andthe previous survey in mid-September, a dome like struc-the resultant increase in phytoplankton particles.

ture of bottom water was found at ~68°N with lower T,

The second bloom (Chl a > 1 mg m−3), which occurred

higher S, and lower light transmission than those of thein September-October 2012 and August-October 2013,

surrounding water. However, bottom water T was higher,

was an autumn bloom. Before the autumn bloom, theS was lower and light transmission was lower than the

DO concentration decreased and the turbidity increased

values from the previous survey. In 2013, hydrographic

from the end of July to the beginning of August in 2012and biogeochemical surveys was conducted again fromand 2013. The annual DO minimum and turbidity maxi-

the Bering Strait to the shelf slope of the Chukchi Sea

mum occurred during the bloom. The high turbisity inalong 168°45’ W from 27 September to 4 October. The

autumn suggests that the turbid water contained not only

T and S distribution indicated that BSAW was dominantphytoplankton particles but also other biogenic and litho-in this region, except for the upper layer (< ~20 m) where

genic particles. The DO minimum in this period suggests

ACW was found at around 67 and 69°N. The cold water

decomposition of organic matter that was transported tonorth of 72°N below a depth of ~40 m was a mixture of

the bottom with the particles, the amount of which were

BSAW and BWW, as was the case in 2012. Although alargest in autumn in the annual cycle.

dome-like structure of bottom water was found again at

The above mentioned mooring data revealed two nov-

~68°N with higher S than surroundings, T was similar toel results regarding the annual cycle of water characteris-the surroundings and higher than that in 2012. Light

tics related to the autumn bloom. A large decrease in86

bottom water DO occurred just before the autumn bloom

trieved from in situ Rrs using ABPM satisfied a factor of

but not during the spring bloom (Fig. 6.1.4b and c). The

2 measured values. PPeu estimated from the Moderatedecreas in DO was accompanied by an increase in bot-

Resolution Imaging Spectroradiometer (MODIS) Rrs data

tom water turbidity, and DO (turbidity) had minimum

were within the range of historical values. These esti-(maximum) values during the autumn bloom.

mated PPeu values were less than half of those of theDome-like structure of dense and turbid bottom watermodel based on Chl a, and the difference between thein the biological hotspot of the southern Chukchi Sea was

two models reflected the influence of CDOM and NAP

found based on hydrographic surveys during autumn

absorptions.

blooms. The dome-like structure would have been asso-

Estimated daily primary productivity using the ABPM

ciated with the Hope Valley topographic depression(Fig. 6.2.1a) represents quite different features from thewhere dense water may converge and particles likely ac-

result using the vertically generalized production model

cumulate. The bottom water characteristics there (at(VGPM) (Fig. 6.2.1b). The level of primary productivity~68°N) depend on the influences of the BSAW andobtained using the new approach is less than half that of

BWW. The BWW, which is generally influenced by

the VGPM estimation. Extremely high productivity wasbrine rejection in winter, has negative and low fSIM val-

not widely distributed around the coastal region of Alas-ues.

ka in the ABPM estimation (Fig. 6.2.1a). Although the

Although the mooring in this study was deployed only

primary productivity > 10,000 mg C m −2 d−1 was fre-at the biological hotspot site in the southern Chukchi Sea,

quently found in the VGPM, productivity > 1,500 mg C

the data show a temporal change in phytoplankton bio-m−2 d−1 using the new model was found only at the Yukon

mass and related parameters for the first time. Spring

River mouth, and in the Kotzebue and Mackenzie Riverand autumn blooms were observed associated with highestuaries, where an effect of high CDOM and NAO was

Chl a concentrations. At the onset of the spring bloom,

likely to remain because the in situ spectroradiometerboth DO and turbidity increased sharply, which is consis-

and the quantitative filter technique (QFT) data used totent with the oxygen production accompanying phyto-

validate aph (443, 0-) were not collected in these veryplankton photosynthetic activity and the resultant in-high CDOM and NAP regions.

crease in phytoplankton particles. On the other hand,

The large difference in the PPeu estimations between

before the autumn bloom, turbidity increased but DO de-

the ABPM (Fig. 6.2.1a) and the VGPM (Fig. 6.2.1b)

cresed, suggesting accumulation and decomposition ofmight be caused by the overestimation of the Chl a con-particulate organic matter (POM; nutrient regeneration)

centration attributed to high absorption by CDOM andon the bottom. This may have been a trigger for the au-

NAP at the blue wavelengths. To investigate the effect oftumn bloom at this site.

CDOM and NAP on the differences in the PPeu estimations, the difference between the two models (VGPM/
ABPM; Fig. 6.2.1c) was compared with the relative

6. 2. Variation of primary production

abundance of absorption by CDOM and NAP to phytoplankton absorption at 443 nm [adg (433, 0-)/ aph (443,

Sea ice retreat in the Arctic Ocean affects the bloom-0-); Fig. 6.2.1d]. The relationship adg (443, 0-)/ aph (443,

ing at the ice edge during spring and summer.
Ocean color remote sensing has been utilized for0-) was used as an index of interference by CDOM andstudying primary productivity in the Arctic Ocean. How-NAP absrptions. The VGPM estimation was 1.5–5 times

ever, phytoplankton chlorophyll a (Chl a) is not predict-larger than ABPM retrieval (Fig. 6.2.1c), and overall, theed accurately because of the interference of colored dis-high VGPM/ ABPM region corresponded to a high adg

solved organic matter (CDOM) and non-algal particles(443, 0-)/ aph (443, 0-). Note that the Russian side of the(NAP). To enhance the estimation accuracy, Hirawake etBering Strait and western Bering Sea (55–60°N, 170°E–

al. (2012) applied a phytoplankton absorption-based pri-

180°E) had relatively low adg (443, 0-)/ aph (443, 0-) andmary productivity model (ABPM) to the Bering and

VGPM/ ABPM. Althoug the Chukchi Shelf region had

Chukchi Seas. The phytoplankton absorption coefficienthigher values of adg (443, 0-)/ aph (443, 0-) > 4.0, thewas determined correctly from sea surface remote sens-

VGPM/ ABPM ratio was ~2.5–3.5 and not as high as theing reflectance (Rrs) and reduced the effect of CDOM andcoastal region of the Bering Sea.

NAP in primary productivity (PPeu) estimates. PPeu re-Interannual variation in PPeu between 2002 and 201087

Fig. 6. 2. 1 Distribution of daily primary productivity (PPeu; mg C m−2 d−1) from MODIS data for Bering and Chukchi Seas. For
July 2002, estimation using (a) the absorption-based productivity model (ABPM, this study) and (b) the VGPM. The ration of the
VGPM estimation to the ABPM estimation in (c). The ratio of the absorption coefficient of CDOM + NAP, adg (443), to that ofphytoplankton, aph (443) in July 2001 is shown in (d) (Hirawake et al., 2012).

derived from satellite data is shown in Fig. 6.2.2. The

was revealed.

PPeu derived using the new approach (Fig. 6.2.2a and b)
Phytoplankton in coastal and RIKUDANA area

increased significantly over the 9-year period in all cate2

gories in both August (r = 0.65–0.85, p < 0.01) and Sep-

Typical examples in the GRENE Arctic using this re-2

tember (r = 0.74–0.79, p < 0.005). The increments ofmote sensing technique was made by Fujiwara et al.

PPeu from 2006 to 2007 in the Bering Strait and over the(2016). They focused on the timing of the sea ice retreat

Chukchi Shelf increased by a factor of 1.51–1.80 in Au-(TSR) and the phytoplankton size composition in thegust and 1.66–2.46 in September. In addition, productiv-

marginal ice zone (MIZ), which is the area where ice

ity for 2006–2007 increased by 2.71 times in September

melt has just recently occurred. They used satellite dataat the shelf break. Peaks of primary productivity werefrom SeaWiFS (reflectance Rrs (λ), λ = 412, 443, 490, 555

found in 2009 and 2010, and the value in the Beringand 670 nm) and Aqua-MODIS (Rrs (λ), λ = 412, 443,

Strait reached 983.9 mg C m−2 d−1. These trends are quite

488, 555 and 667 nm). Also euphotic depth Zeu and pho-different from the results of VGPM (Fig. 6.2.2c and d).

tosynthetic available radiation (PAR) were derived. The

Productivity estimated by the VGPM in the northern Ber-Rrs (λ) values from SeaWiFS were converted to Rrs (λ)

ing Sea, Bering Strait and Chukuchi shelf had a higher

values for MODIS using conversion factors that removed

range, from 500 to 1900 mg C m−2 d−1, an increasing

the biases between them.

trend was not found, and productivity in the northern

An index for the phytoplankton community size composition (FL) was defiend as the ratio of the Chl a at-

Bering Sea declined in August.

tributed to cells larger than 5 μm (Chl a>5μm) to the toatal

The new productivity model using a phytoplankton

Chl a (Chl atotal):absorption coefficient developed in this study reduced
the effect of CDOM in the estimation of Arctic produc-

FL = Chl a>5μm / Chl atotal * 100 (%). 

tivity. The reliability of absorption estimation from in(6.2.1)situ remote sensing reflectance data was also confirmed.
Using the new model, an increasing trend in primary pro-

The satellite FL values was estimated using the phyto-

ductivity during August and September of 2002–2010

plankton size derivation model (SDM) proposed in Fuji88

Fig. 6. 2. 2 Interannual variation of column-integrated daily
primary productivity (PPeu) from 2002 to 2010 n the northern
Bering Sea, the Bering Strait, the Chukchi shelf break and theCanada Basin. Monthly averaged data during (a) August and(b) September were input to the absorption-based productivity
model (ABPM, this study). (c and d) Results for August and
September using the VGPM (Hirawake et al., 2012).

Fig. 6. 2. 3 Location of in situ sampling statins for the IPY
cruises (red), GRENE cruises (violet) and BASIS cruises (blue).
Note that the stations of the daily matches between the in situ
FL measurements and MODIS level-2-derived FL values areshown in large circles, and crosses represent the sites where
primary productivity measurements were conducted. Contours
indicate the 100 m, 200 m and 1000 m bathymetry lines, basedon ETOPO-2 (Fujiwara et al., 2016).

wara et al. (2011), which was optimized for the phytoplankton communities and optical properties in the
Bering and Chukchi seas. Then, FL could be derived using Eq. (6.2.2):

summer of 2013 (June to July) on the Bering and Chukchi shelves (Fig. 6.2.3). The in situ primary productivity

FL = 1 /(PP) data, as well as the Chl a data, were also used to as-{1 + exp[ −(X0 + X1 aph(λ1)/ aph(λ2) + X2 γ)]} * 100 (%),

sess the influence of the subsurface Chl a maximum(SCM) on the remotely estimated PPeu. PP samples were

(6.2.2)collected during the TR/S Oshoro-maru (Hokkaido Uniwhere X0 = 3.175, X1 = −0.570 and X2 = −0.565, λ1 = 488

versity, IPY and GRENE cruise) and R/V Mirai (JAMS-

and λ2 = 555, and γ (spectral slope of the particle back-

TEC, GRENE cruise) cruises in the same region, along

scattering coefficient) was empirically quantified usingwith Chl a samples (Fig. 6.2.3).

the Rrs (γ) ratio (Fujiwara et al., 2011). PPeu was derived

The accuracy of the SDM derived FL was evaluated byusing the absorption-based productivity model (ABPM)

comparing the FL values from the in situ measurements

proposed in Hirawake et al. (2011, 2012). Then, the an-and daily matched MODIS level-2 data set. The SDM

nual net primary production (APP) was regressed using

successfully retrieved the FL values for 17 of the 25 data

the annual median FL, SST, and length of the open water

points (68% of the data) within a ±20% FL range. The

period (OWP) for every one-by-one pixel:

RMSE was 25%. However, it should be taken into account that there was a slight overestimation in the low FL

APP = A1 * OWP + A2 * FL + A3 * SST,

range and relatively large underestimation in the high FL(6.2.3)

range (slope = 0.48 and intercept = 0.18). We found that
where A1, A2 and A3 indicate the partial regression coeffi-

the MODIS significantly underestimates Rrs(λ) at every

cients.

wavelength (the slopes ranged from ~0.34 to ~0.46). It

The in situ total and size-fractionated Chl a samples

can be said that the underestimations of Rrs(λ) significant-were obtained during the cruises of the Bering Arctic

ly caused the underestimation of FL especially in the

Subarctic Integrated Study (BASIS) program conducted

middle to high range of FL.

in late summer to autumn (August to October) of 2005,

To confirm how the satellite derived FL and PPeu repre-

2006 and 2007, and the cruises of the GRENE Arctic Cli-

sent a water column’s phytoplankton size structure andmate Change Research Project conducted in late summer

productivity, the surface abd vertically integrated FL val-

to autumn of 2012 (September to October) and earlyues (calculated using Eq. (6.2.1) with water-column-inte89

Fig. 6. 2. 4 Distribution of 16-year median of (a) TSR, (b) date of CMAX and (c) date difference between CMAX and TSR. Notethat the color scales of (a) and (b) are indicated on the upper tick marks of the color bar, and the scale of (c) is indicated on the lower
tick marks. The arrow on the color bar denotes the date of the summer solstice (day of year: 174). Contours indicate 50 m and 100 mbathymetry lines (Fujiwara et al., 2016).

Fig. 6. 2. 5 Spatial distributions of Sperman’s rank correlation (ρ) between (a) FL during MIZ period and TSR, (b) SST during MIZ
period and TSR, (c) ΔOHC during MIZ period and TSR, and (d) PAR during MIZ period and TSR. Yellow indicates a significantly
positive ρ (p < 0.05). Contours indicate 50 m and 100 m bathymetry lines (Fujiwara et al., 2016).

grated Chl atotal and a>5μm) was compared using in situ

for every one-by-one pixel between FL and TSR, SST

data set. The vertically integrated FL showed a signifi-

and TSR, ΔOHC (change in ocean heat content) andcant relationship with the surface FL in spite of the pres-

TSR, and PAR and TSR (Fig. 6.2.5a-d). It was foundence of the subsurface FL maximum at the many of thethat during the MIZ period, FL was negatively associatedsampling sites, especially in low and middle range of thewith TSR in 68% of the shelf area, with a significant cor-surface FL were observed.

relation in 15% of the area (p < 0.05). A significantly posi-

In order to determine the timing of the spring phyto-

tive (p < 0.05) relationship was found for the westernplankton bloom, the relationship between the timing ofside of the Bering Strait and western coast of Alaska; thisthe sea ice retreat (TSR) and date of occurrence of thearea accounted for 2% of the whole study area. During

yearly Chl a maximum (CMAX) was investigated. Fig.

the MIZ period, SST was tightly and positively correlat-

6.2.4a shows the climatological median of TSR fromed with TSR over most (92%) of the region (Fig. 6.2.5b),

1998 to 2013. Then influence of TSR on phytoplanktonwith a significant correlation in 65% of the area. Thissize composition during MIZ blooms was investigated.

spatial pattern of ΔOHC was also similar to the SST pat-

Sperman’s rank correlation coefficient (ρ) was calculated

tern though it was inverse, with a statistically significant
90

negative relationship for more than 61% of the area (Fig.

production is basically weak.

6.2.5c). These results reveal that an earlier sea ice retreat

The study by Fujiwara et al. (2014) assesses the re-is associated with a cold surface temperature and rela-sponse of phytoplankton assemblages to recent climate

tively large amount of heat release from the sea surfacechange, especially with regard to the shrinking of sea ice

during the MIZ period. However, on the northwesternin the northern Chukchi Sea of the western Arctic Ocean.

edge of the Chukchi Sea, the opposite correlations were

It is important to understand the influence of sea ice re-found, with a significantly positive ρ between SST and

duction on phytoplankton community composition be-

TSR, and significantly negative ρ between ΔOHC andcause different phytoplankton functional types such as

TSR (Fig. 6.2.5b and c). PAR was strongly and positive-

large diatoms and small flagellates play important but

ly correlated with TSR in the northern Bering Sea up todifferent roles in biogeochemical cycles and ecosystems.

and through the Bering Strait, but became negative in the

Therefore, surface distribution patterns of phytoplankton

northern part of the Chukchi Sea shelf (Fig. 6.2.3d). The

assemblages were examined as derived from algal pig-

highest positive ρ between PAR and TSR was found inment data collected from western Arctic Ocean during

the area where the sea ice normally retreats before thethe late summers of 2008–2010. Data were collectedsummer solstice, at the time when the latest daily PAR

during cruises of the R/V Mirai, primarily in September

would be observed (Fig. 6.2.4a).

of 2008 (28 August–6 October), 2009 (11 September–10

Factors controlling annual net primary production

October) and 2010 (4 September–13 October).

(APP) were examined. A standardized multiple regres-Multiple regression analysis (MRA) was applied to thesion analysis was used to determine the contribution ofphytoplankton pigment data to determine the most pre-the controlling factors for the APP. This analysis re-dominant phytoplankton group at each sampling station.

vealed that longer growing season, larger proportions of

MRA can access the contributions of accessory pigment

large-sized phytoplankton assemblages, and higher SSTs

to chl a levels and does not require any assumptions

generally enhanced the APP. However, the magnitude ofabout the pigment ratios of each algal group, as does

the contribution changed regionally. Larger partial re-

Chemical Taxonomy (CHEMTAX). On the other hand,gression coefficients for the length of the open water pe-cluster analysis (CA) was performed to divide samplingriod (> 0.7) were found mainly in the northern shelf area

sites into groups that have similar pigment composition,of the Chukchi Sea. Larger partial regression coefficientswith Ward’s linkage method using Euclidean distance.

for FL (> 0.7) were mainly found on the Bering Sea shelf

The same pigments used for CHEMTAX analysis forand part of the central Chukchi Sea shelf (67–70°N,

Baffin Bay, in the Arctic Ocean, by Vidussei et al. (2004)

170–175°W), where other variables showed relatively

were chosem for CA because similar algal groups are ex-lower contributions. Conversely, the partial regression

pected to appear in the study region – chl c3, peri, but,coefficients for the SST were negative on the southernfuco, prasi, hex, allo, zea, lut and chl b. Surface chl aedge of the Bering Sea shelf study area. Based on the re-concentration of the three cruises were successfully ex-sults of the standardized multiple regression analysis, inpressed from MRA (Eq. 6.2.4), which included six acces-

regions with seasonal ice cover, a large amount of phyto-sory pigments:

plankton positively contributed to the APP on the northchl a = 1.49 [peri] + 1.85 [fuco] + 1.74 [hex] + 5.88 [allo]

ern Bering Sea shelf, and a longer open water period was+ 3.54 [zea] + 1.31 [chl b] + 0.02,

a major factor for the APP in the Chukchi Sea shelf.

(6.2.4)Hence, the ocean color remote sensing is believed tobe applicable to discuss the temporal and spatial relation-

where the adjusted R2 was 0.99, and all regression coeffi-

ships between the distribution of sea ice and phytoplank-

cients of Eq. (6.2.4) were statistically significant. Sur-ton variables (i.e., FL, PPeu, and Chl a), at least in theface phytoplankton groups were divided into 4 clusters

study area.

using CA and their pigment compositions. Using CA,
45, 10, 13 and 8 samples were grouped into cluster 1, 2,

Phytoplankton in basin area

3 and 4, respectively. To determine the dominant phyto-

Since we have seen the enrichment of phytoplankton

plankton group in each cluster, the average pigment/chl ain the shelf region in the above section (Fujiwara et al.,ratio and the average contribution to chl a of the major

2016), we would like to look at basin area where primary

pigments were confirmed, which was derived by MRA
91

(Fig. 6.2.6).

cept the area of 170–175°W (Fig. 6.2.7a). However, inFig. 6.2.7a-c indicate the distributions of sea ice con-2009 and 2010, the northernmost ice edge was located at

centration (SIC) and sea surface temperature (SST) asa similar latitude as in 2008, though the sea ice retreat atmonitored by a satellite on 1 September 2008–2010,

such a high latitude was observed only in the narrow ar-along with the dominant phytoplankton groups at the sea

eas of 150–155°W in 2009 and 160–165°W in 2010 (Fig.

surface during the cruises in each year. The ice edge re-6.2.7b and c). The distribution of sea surface tempera-

treated to ~78°N throughout the study area in 2008, ex-ture (SST) also showed spatial variation. A relatively
high SST (> 3°C) was common in the southern Chukchi
Sea (~72°N), where open water area commonly spread
during the three years, and extended to ~75°N in theeastern Cuukchi Sea in 2008 (Fig. 6.2.7a), where ice cover was observed in 2009 and 2010 (Fig. 6.2.7b and c).
The interannual variability of SIC from 2008 to 2010
was analyzed, assuming that sea ice distribution was oneof the determining factors of algal taxonomic distribution. Fig. 6.2.8a-c show the distribution of sea ice on theFig. 6. 2. 6 Average percent contribution to chl a of major algal
accessory pigments for each cluster. These contributions were
determined by multiple regression analysis (Fujiwara et al.,2014).

dates that SIC became < 10%, the criteria that defined theonset date of open water area. In the study area, sea ice
retreat generally begins in May or June in the shelf, andFig. 6. 2. 7 Distribution of dominant phytoplankton groups at the surface layer in (a) 2008, (b) 2009 and (c) 2010. Clusters wereidentified by cluster analysis. SIC data were collected by AMSR-E on September 1: SST data were collected by MODIS andcomposited to 9-day average centered on 1 September of each year. Depth contours indicate 100-, 200-, 500-, 1500- and 2000-m
intervals, respectively (Fujiwara et al., 2014).

Fig. 6. 2. 8 Distribution of the clusters across Julian day when the area became open water in (a) 2008, (b) 2009 and (c) 2010.
Clusters were identified by cluster analysis. White indicates area where no open water appeared during the entire year (Fujiwara etal., 2014).

92

the shrinking continues until August or September, when

the spatiotemporal patterns and changes in phytoplankton

the ice edge reaches the basin. However, there was tem-community structure should be taken into account whenporal and spatial variability in the onset date of sea ice

assessing biogeochemical cycles and food webs in theretreat, especially in the eastern Chukchi Sea (140–western Arctic Ocean.

160°W), where the algal assemblages of cluster 3 wereobserved. The onset date in that area occurred in July in

6. 3. Changes and shifts in the dominant groupdue to warming and sea ice retreat in the Arctic
Ocean2008 (Fig. 6.2.8a); however, sea ice retreat occurred inJuly and August in 2009 and 2010, respectively (Fig.
6.2.8b and c). In short, 1 to 2 months earlier sea ice retreat observed in 2008 than in 2009 and 2010 in the deep

To evaluate the effect of sea ice reduction on zoo-basin (Fig. 6.2.8b and c).

plankton, Matsuno et al. (2011) studied year-to-year

Cluster 3 dominated by haptophytes was observedchanges of zooplankton community structure in the

only in 2008 in the eastern Chukchi Sea, where prasino-

Chukchi Sea during summers of 1991, 1992 (when sea

phytes were dominant in the other two years. To under-

ice extended), 2007 and 2008 (when sea ice reduced).

stand the reason for this difference among the years, theZooplanktons are secondary producers of the marine eco-interannual variability of open water area and the tempo-

system and comprise a vital link between primary pro-ral variability of the onset of sea ice melt were focusedduction (explained in Section 6.2) and fishes (Section

on. As indicated in Fig. 6.2.8a-c, there were significant

6.4) or marine mammals (6.5) in the western Arctic

differences in the onset date among the three years. In

Ocean. In the southern Chukchi Sea, since Pacific waterparticular, an earlier onset of 1 to 2 months was observed

flows into the sea through the Bering Strait, not only Arc-in the eastern Chukchi Sea in 2008. It is hypothesizedtic copepods but also large-sized Pacific copepods arethat this earlier sea ice melt and longer ice free periods

dominant taxa. The zooplankton community in the west-

reduced the ice albedo in 2008, and warm water (~5°C)

ern Arctic Ocean is expected to be changed after 2007

consequently distributed in the eastern Chukchi Sea. The

when the drastic decrease of sea ice area was observed,

locations of haptophyte dominated samples showed good

but no study has contrasted zooplankton community

agreement with early open water and warm water (Fig.

structure between 1990s and recent years.

6.2.7a and Fig. 6.2.8a). Such relatively warm and oligo-

A total of 120 zooplankton samplings were conductedtrophic water conditions should be favorable for hapto-

by T/S. Oshoro-Maru in the Chukchi Sea (66°00’–71°11’

phytes. In addition, an earlier sea ice retreat might re-N, 162°02’–168°58’ W) during 24–31 July 1991 (n =lease phytoplankton from light limitation. Under light27), 24–31 July 1992 (n = 34), 5–13 August 2007 (n =limited conditions, phytoplankton tends to synthesize chl31) and 7–13 July 2008 (n = 28). Zooplankton samples

b and chl a to increase their photosynthesis efficiency. In

were collected at day and night by vertical tows with a

the Arctic Ocean, phytoplankton often experiences light

NORPAC net from 5 m above the surface.

limitations due to the presence of sea ice. Therefore, pra-

Zooplankton abundance ranged from 4,000 to 316,000

sinophytes, which contain more chl b due to low light ac-

ind. m−2 (mean: 70,000) (Fig. 6.3.1a). Abundance inclimation, are distributed widely in the Arctic. However,

1991, 1992 and 2008 was greater north of Lisburne Pen-the longer ice free period of 2008 may have triggered theinsula, while in 2007 abundance was greater south ofappearance of unusual phytoplankton community that

Lisbrune Peninsula (Fig. 6.3.1a). Zooplankton biomass

dominated by haptophytes due to improved light condi-

ranged from 0.07 to 286 g WM m−2 (mean: 36). The

tions. Thus, it is suggested that the dominance of hapto-

geographic distribution of biomass was not parallel withphytes observed only in 2008 was due to the spread ofabundance except for the south of Lisbrune Peninsula inwarm, nutrient-depleted water and/or changing light con-2007 (Fig. 6.3.1b).

ditions, all of which likely follow early sea ice retreat.

Copepods composed 2–86% (mean: 31) of zooplank-

Although the impact of phytoplankton community

ton abundance, and barnacle nauplii and cypris larvae

shift on biogeochemical cycles and food webs might be(Balanus crenatus Brugiere) were also dominant in thesmall, the longer ice free periods and larger open water

northern area (Fig. 6.3.2). Within the copepods, propor-

areas predicted for the future may amplify the spatial andtions of C. glacialis and Pseudocalanus spp. were rela-temporal influence of algal community shifts. Therefore,

tively high, especially in 1991 and 1992. In 2007, pro93

cluster analysis were latitude, longtitude, sea surface
temperature, sea surface salinity and bottom salinity, with
24–37% coefficient of determinations (r2). Mean abundances of groups A, B and D ranged from 76,800 to
101,030 ind. m−2, but those of groups C, E and F werelower and ranged from 5,744 to 30,283 ind. m−2. For
groups B, C and E, barnacle larvae dominated and species diversities were low. Groups A, D and F were characterized by few barnacle larvae and dominance of Pacific copepods (groups A, D) or Arctic copepods (group F).
Species diversity of groups A and D was high because of
the occurrence of both Pacific and Arctic copepods. Distribution of each group was well separated both geographically and interannually (Fig. 6.3.3). Group B wasbserved offshore north of Lisburne Peninsula in 1991 and
1992, and north of Lisbrune Peninsula in 2007 and 2008.
Group D, characterized by the high abundance and dominance of Pacific copepods, was observed only south of
Lisburne Peninsla in 2007. Concerning interannual variability in the zooplankton community, geographic distribution was similar for 1991 and 1992 (Fig. 6.3.3). In
2007, the whole distribution area was shifted northward
compared to 1991 and 1992 and group D, characterized
by dominance of Pacific copepods, was observed southof Lisburne Peninsula. In 2008, geographic distributionof groups was similar to 1991 and 1992, but distributionof group A, which was observed south of Lisburne Peninsula in 1991 and 1992, also penetrated to the north ofPeninsula (Fig. 6.3.3).
Comparison between 1991/1992 (sea ice extent) and2007/2008 (sea ice reduced) showed a remarkable north-Fig. 6. 3. 1 Geographic distribution of the zooplankton
abundance (a) and biomass (b) in the Chukchi Sea during July–
August of 1991, 1992, 2007 and 2008. Values in the parenthese
indicate mean values of each year (Matsuno et al., 2011).

ward shift in geographic distribution of zooplankton
communities (Fig. 6.3.3). This northward shift in zooplankton community in 2007/2008 may be related to thedifferences in formation of water masses. The greatest

portions of Pacific copepods (E. bungii and M. pacifica)differences in the hydrographic environment between

were high, while the proportion of barnacle larvae was

1991/1992 and 2007/2008 were in the presence (1991/

low (Fig. 6.3.2). Barnacle larvae predominated in 2008.

1992) or absence (2007/2008) of sea ice meltwater andThrough the 4 years of analysis, 22 species of calanoid

were also evident in the salinity of the surface layer,

copepods belonging to 13 genera were identified. Within

which was low for 1991/1992 and high for 2007/2008.

these species, 6 species belonging to 4 genera were Pa-

Concerning the zooplankton community, the Arctic

cific copepods. Since Pacific copepods were dominant incopepod C. glacialis was relatively abundant in 1991/

2007, total copepod abundance was the greatest in 2007.

1992, and less so in 2007/2008. The zooplankton com-

When barnacle larvae were dominant in 2008, total zoo-

munity in 2007 was characterized by the dominance ofplankton abundance was the highest in 2008.

Pacific copepods, and in 2008 was dominated by barna-

Based on the taxon abundance, zooplankton communi-

cle larvae (Fig. 6.3.2). The dominance of barnacle larvae

ties were classified into six groups (A-F) by cluster anal-

in 2008 suggests an earlier release of the larvae fromysis. Environmental parameters significantly affecting

their benthic adult form than in 1991/1992. Concerning94

Fig. 6. 3. 3 Geographic distributions of the six groups (A–F)
identified from Bray-Curtis similarity based on zooplankton
abundances in the Chukchi Sea during July-August of 1991,
1992, 2007 and 2008 (Matsuno et al., 2011).

large sized Pacific copepods, and thus may have positively affected productivity. However, increased inflow of
PSW caused changes in the marine ecosystem structure

Fig. 6. 3. 2 Taxonomic composition in total zooplankton abundance of the Chukchi Sea during July–August of 1991, 1992,
2007 and 2008 (Matsuno et al., 2011).

in the Chukchi Sea (loss of characteristic Arctic species
in part of the region), which is considered as a negative
effect of global warming.

the release timing of barnacle larvae from benthic adults,
Larger zooplankton, copepods

laboratory rearing studies show that the amount of foodconcentration (phytoplankton) is a key factor for release

Matsuno et al. (2014) investigated seasonal changes intiming. Field studies also indicate that onset of the phy-mesozooplankton swimmers, copepods. Zooplankton

toplankton bloom is a key factor stimulating the releaseswimmers collected by sediment trap sampling at 13–15

of barnacle larvae. Because the sea surface salinity was

day intervals moored at 184 m on the Northwind Abyssal

higher in 2007/2008 than in 1991/1992, the timing of sea

Plain (75°00’ N, 162°00’ W, bottom depth 1975 m;

ice reduction was considered to be earlier in 2007/2008.

NAPt) during October 2010–September 2011 were ana-This earlier sea ice reduction in 2007/2008 may induce

lyzed (Fig. 6.3.4). This station is seasonally affected bythat earlier phytoplankton bloom for these years because

inflow of Bering Shelf Water from the Bering Strait.

of the earlier release from the light limitation on the pho-Through this analysis, seasonal changes in swimmer

tosynthesis. The earlier onset of the phytoplankton

community structure and population structure of the four

bloom in 2008 is considered to be a cause of the domi-

dominant copepods (Calanus hyperboreus, Metridia lon-nance of the barnacle larvae in that year. The earlier sea

ga, Paraeucaeta glacialis and Heterorhabdus norvegi-

ice reduction and onset of phytoplankton bloom was alsocus) were evaluated. For zooplankton swimmer samples

the case of 2007. However, the greater inflow of Pacific(> 1 mm), identification and enumeration of zooplankton

summer water (PSW) in 2007 forced the barnacle distri-were made under a dissecting microscope. In this study,

bution area north (Fig. 6.3.3), leading to a lower abun-

swimmers were defined as zooplankton > 1 mm samples

dance of barnacle larvae in 2007 than 2008.

that swam actively into the trap.

From the viewpoint of zooplankton production, in-

The trap depth varied between 181 and 218 m, andcreased inflow of PSW induced the dominance of thewas mostly stable around 184 m then temporally deep95

Fig. 6. 3. 4 Location of St NAPt (Northwind Abyssal Plaintrap) in the western Arctic Ocean where the sediment trap wasmoored at 184 m during October 2010–September 2011. ACW,
Alaskan Coastal Water; AW, Anadyr Water; BSW, Bering Shelf
Water (Matsuno et al., 2014).

Fig. 6. 3. 5 Seasonal changes in flux and copepodid stage
composition of the four dominant copepods: Calanus hyperboreus (a), Metridia longa (b), Paraeuchaeta glacialis (c),Heterorhabdus norvegicus (d), at 184 m at St NAPt during 4
October 2010–28 September 2011(Matsuno et al., 2014).

ened within a short period. Temperature at the sediment
trap ranged from −1.6 to −0.6°C. The 5-day mean ofcurrent velocity showed a slow subsurface current (<11.3 cm s−1 at 45 m and < 2.7 cm s−1 at 188 m water

individuals dominated during October–December, theirdepths) at St NAPt. Sea ice around the sediment trap site

lipid levels gradually decreased during March–June andshowed clear seasonal changes, with a decrease from ear-

all specimens had no oil sacs in July. The lipid accumu-

ly July, complete melting (sea ice concentration: 0%) in

lation of P. glacialis showed fluctuating seasonal change.

September, a rapid increase during October and 100%

Gonad maturation of C6Fs of the dominant copepods

coverage during November to June. The total mass flux

(excluding H. norvegicus) showed more distinct season-(< 1 mm size range) ranged from 19.3 to 215.9 mg DM

ality. For C. hyperboreus, mature individuals were ob-

m

−2

day

−1

and peaked during November–December.

served only during February–April. Mature M. longa

High chl a was observed during August–September. At

were seen during March–July, although only one speci-

St NAPt, the midnight sun occurred during early May to

men was observed in September, and lipid accumulation

early August, and the polar night was from early Novem-

decreased. Most P. glacialis (47%) matured during Au-

ber to early February.

gust–January, when egg carrying adult females also seen.

The population structure of the four dominant cope-

The Pacific copepod Neocalanus cristatus occurred

pods varied with species. Throughout the year, C. hyper-(0–0.92 ind. m −2 day−1) throughout the year, and was

bpreus was predominated by C6F. The population struc-more abundant during August–September when the sea

ture of M. longa and P. glacialis showed seasonal

ice was reduced. All N. cristatus were stage C5, and lip-change; both were dominated by C6F from January to

id accumulation varied.

May, and early copepodic stages (C1–C4) occurred during

Throughout the previous studies, copepods are theJune–October. Heterorhabdus norvegicus showed a dif-most dominant taxa, followed by amphipods or ptero-ferent seasonal pattern; it was dominated by C5 during

pods consistent with the present study. Dominant cope-

November–February, the contribution of C6F/M in-

pods in this study were H. norvegicus followed by M.

creased during March–October and C1 and C2 stages oc-

longa, unique because of the dominance of mesopelagic

curred in June–July.

copepods (Yamaguchi and Ikeda, 2000). Heterorhabdus

Lipid accumulation in C6Fs of the dominant copepods

norvegicus is reported to be the second most dominant(excluding H. norvegicus) also showed species-specificcopepod in the zooplankton swimmer fauna of the east-

seasonal patterns. Most C. hyperboreus contained more

ern Greenland Sea (Seiler and Brandt, 1997); thus thislipids (stage III) during December–January (83%) thanspecies may be a dominant copepod throughout the me-during February–October (56%). For M. longa, stage III

sopelagic layer of the Arctic Ocean. Additional to these96Fig. 6. 3. 6 Schematic diagram of seasonal changes in daylight hours, sea ice concentration, chl a, total mass flux (upper panel),swimmer community, population structure and reproduction period of copepods (C. hyperboreus, M. longa, P. glacialis and H.
norvegicus), and occurrences of the Pacific N. cristatus collected by sediment trap at 184 m of St NAPt during 4 October 2010–28
September 2011 (lower panel). For C. hyperboreus, M. longa and P. glacialis, the reproduction period was evaluated by C6F gonad
maturation. For H. norvegicus, their reproduction period was estimated by the occurrence of early copepodid stages (Fig. 5d) (Matsuno
et al., 2014).

copepods, the occurrence of a small number of Pacifichyperboreus. Since daylight hours changed greatly from

copepods is a special feature of this study. Since the zoo-

0 (polar night) to 24 h (midnight sun) during February toplankton swimmer community showed clear seasonal

April, this change in day–night cycle may affect thechanges (groups A, B-1 and B-2), in the following, thearousal from diapause for C. hyperboreus. As a special-characteristic of each swimmer group were discussed.

ized seasonal event in this study, the occurrence of Pacif-

The group A, corresponded with the open water period

ic copepods was observed. The Pacific copepod N. cri-

(July–October), dominated by the carnivorous P. glacial-status C5 was abundant during August–September whenis. Thus, if P. glacialis exhibits similar DVM to P. nor-sea ice coverage decreased (Fig. 6.3.6). This seasonalvegica, the dominance of P. glacialis in group A (July–pattern suggests that the amount of Pacific Water inflow

October) was caused by P. glacialis diel vertical migra-

may increase when the sea ice coverage decreases. Whiletion (DVM) during this period. Changes in the zoo-

N. cristatus C5 are known to perform SVM to deep lay-plankton swimmer community from groups A to B-1

ers to diapause during late summer (Miller et al., 1984),

corresponded to the timing of ice coverage and entry into

the greater flux of N. cristatus C5 in August–September

polar night. The group B-1 was characterized by the

may also be caused by their SVM. Since their reproduc-

dominance of mesopelagic M. longa and H. norvegicus.

tion occurs > 1000 m depth (Miller et al., 1984), the pos-

While their flux showed little seasonal change (Fig.

sibility of their reproduction in the Arctic Ocean could

6.3.5b and d), the other two species showed clear season-

not be evaluated in this study.

ality: peaked in May–June for C. hyperboreus (Fig.

Thus, the reproduction seasons of the dominant four

6.3.5a) and July–October for P. glacialis (Fig. 6.3.5c).

copepods differed (Fig. 6.3.6), and possible causes ofThus, group B-1 was composed mainly of mesopelagicthese differences include species-specific feeding modes,

copepods (M. longa and H. norvegicus) in the mesope-seasonal changes in food availability and the presence/lagic layer. For the other two species (C. hyperboreus

absence of ontogenetic vertical migration. In summary,and P. glacialis), seasonal vertical migration (SVM) andwhile the limitation of sampling design, through the anal-

presence/absence of DVM is considered to be a key

ysis of zooplankton swimmers collected by the sedimentmechanism governing the seasonal changes in the swim-

trap, zooplankton communities were clearly separated

mer community.

into three seasons (Fig. 6.3.6). The season when each

During the ice coverage period (November–June), thecopepod dominated corresponded with the active verticalswimmer community changed from groups B-1 to B-2 inmigration timing of each species. For the four dominantFebruary due to the drastic increase (ca. 10 times) of C.

copepods, their reproduction timings varied with species,
97which may be related to the feeding modes. In addition,
the occurrence of Pacific copepods was observed. Since
their peak period (August–September) corresponded withtiming of their SVM, they may have entered the sediment
trap while performing descent SVM.
Reproductive success
The species composition of Arctic zooplankton differs
greatly from that of the zooplankton of the North Pacificand Bering Sea. Particularly with greater warming from
Fig. 6. 4. 1 Sampling stations (St) and abundance of polar cod(catch per unit effort as number of fish per km2 of area swept
by bottom trawl) in the northern Bering Sea (NB), southern
Chukchi Sea (SC) and central Chukchi Sea (CC). No fish werecollected at St03 (Nakano et al., 2016).

sea-ice retreat, the reproduction of North Pacific species
transported into the Chukchi Sea and beyond may lead tochanges in the Arctic pelagic ecosystem. Matsuno et al.
(2015) reported the egg production and hatching of the
Pacific copepod Neocalanus flemingeri in the Chukchi
Sea based on shipboard experiments performed in Sep-

condition of polar cod were compared between three re-tember 2013. The reproductive capability of N. flemin-gions with different environments: the northern Bering

geri observed in the Chukchi Sea resembled that reported

Sea (NB), southern Chukchi Sea (SC) and central Chuk-in the Pacific, with the exception of a lower hatching

chi Sea (CC).

success. Only 7.5% of N. flemingeri eggs hatched com-

Sampling was conducted aboard the T/S Oshoro-Marupared with 93% in Pacific experiments (Saito and Tsuda,

at 12 stations (St; Fig. 6. 4. 1) at depths between 34 and2000). This large discrepancy may be the results of dif-

68 m during July 4–17, 2013. The density of polar cod

ferences in the fraction of eggs successfully fertilized.

was calsulated as the number of fish per unit survey area.

Adult males of N. flemingeri were not observed in thisPolar cod were most abundant at St05 (Fig. 6.4.1). No

study. The extremely low hatching success in the Arctic

fish were collected at St03, and a single fish was collect-

Ocean (7.5%) is considered to be caused by failures of

ed at St04. Polar Cod collected in the NB and the SC

fertilization. The potential recruitment number for N.

were larger than those collected in the CC. The stomach

flemingeri suggests that it is unlikely to establish expatri-fullness indices were higher in the NB and SC than in theate Arctic populations in the near future.

CC, while body condition indices did not differ between
the regions.
Prey items were found in 211 stomachs out of 238 fish

6. 4. Distribution patterns of fish species

collected. Although the percentage index of relative im-

From the long term survey by T/S Oshoro-Maru fromportance (IRI) of each prey type varied between the sta-

Hokkaido University, polar cod was found to be a domi-

tions (Fig. 6.4.2), the average IRI across stations withinnant species in northern Bering Sea and Chukchi Sea,

the region showed that appendicularians were the mostwhile suketo cod was in shelf region of Bering Sea.

dominant prey found in the polar cod stomachs collectedin the NB (47%) and SC (50%), while in the CC appen-

Diets and body conditions

dicularians were completely absent (Fig. 6. 4. 2).

Polar cod, Boreogadus saida, is an abundant epipelag-

The stomach fullness index of polar cod collected in

ic fish found throughout the Arctic Ocean, an important

the NB and SC was greater than in the CC, possibly re-food for other fish, marine mammals and seabirds, andflecting the higher biomass of zooplankton in the water

thus a key species in the Arctic Ocean ecosystem. To un-column in the SC and NB than CC. Inflow of nutri-

derstand how changes in Arctic marine food webs may

ent-rich water from the Pacific induces higher primary

impact polar cod, a study of their trophic responses

production in the SC than CC, which presumably ex-(changes in diet and energy stores) to differences in zoo-

plains higher zooplankton abundance. This high primaryplankton and benthic invertebrate communities across re-production, seasonal ice cover, and shallow water depth

gions with different environments was conducted (Na-may also support a large biomass of benthic communities

kano et al., 2016). The stomach contents and bodyin the NB and SC. In addition to the availability of prey,98

Fig. 6. 4. 2 The percentage composition of prey taxa, shown by
the index of relative importance (IRI), found in the stomachs ofpolar cod collected in the northern Bering Sea (NB), southern
Chukchi Sea (SC) and central Chukchi Sea (CC). Sample sizes(number of stomachs) are shown at the top of bars (Nakano etal., 2016).
Fig. 6. 4. 3 The distribution and abundance of Boreogadus
saida in (a) 2008 and (b) 2013 (Kono et al., 2016).

water temperature might influence foraging activities andhence stomach fullness. Water tempearature at the seafloor in our study, however, were highest in the SC (0.8–northern Bering Sea and the Chukchi Sea, including wa-

1.3°C), followed by the CC (−1.7 to −0.8°C) and then the

ter temperatures, timing of phytoplankton blooms and

NB (−1.7 to −1.6°C); indicating that ambient temperaturezooplankton reproduction in spring and summer, as wellmight not explain observed differences in stomach full-as fish and broader ecological system (Grebmeier et al.,ness.

2006). The composition and distribution of fish assem-

The reduced sea ice coverage has been proposed to fa-

blages also would be affected by sea ice reduction via

vor a phytoplankton-/zooplankton-dominated ecosystems

environmental change because demersal fish assemblages

over a sea ice algae/benthos ecosystem (Grebmeier,are known to be governed by water temperature and hab-2012), while the expansion of warmer Pacific water into

itats are structured by water masses. Because the fish

the southern Arctic Ocean induced the dominance oflarvae studied here have a lower swimming ability andwarm water copepod species (Matsuno et al., 2011). Cli-

are smaller than adult fish, they are easily preyed uponmate change may also influence the distribution andby other carnivorous fish and cannot escape from unfa-

abundance of gelatinous zooplankton. These expectedvorable environmental conditions. Therefore, the analy-

climate-induced changes in pelagic zooplankton and ben-sis of fish larval assemblages may facilitate the prediction

thic invertebrate communities either to the gelatinousof ecosystem responses to a changing climate.

zooplankton or to warm water living copepods may in-

Fish larvae were collected at 57 sampling stations influence the diet and body condition of polar cod, and2008 and 2013. The total number of fish larvae collected

hence, their recruitment has been found in Atlantic cod.

by bongo net was 1186, and the average abundances offish larvae was 23.53 inds. m−2 comprised of 7 families

Distribution patterns

and 17 taxa in 2 years. Abundances were numerically

Kono et al. (2016) examined the distribution pattern of

dominated by B. saida (35%), A. hexapterus (27%) andPolar cod (Boreogadus saida) and other fish larvae in re-

Pleuronectidate (17%). Results of the analysis showedlation to oceanographic parameters, including sea surface

that the main indicator species for group A, B, C and D

temperature and salinity (SST and SSS), the mode ofwere Pleuronectidate (87.95% of total abundance intemperature and salinity within the water column (Ftemp

group A), B. saida (85.33% in group B), A. hexapterus

and Fsal), and the temporal duration between the date of(51.12% in group C) and A. hexapterus (32.53% in groupsea ice retreat and the date of field surveys (dSRT) in theD), respectively.

northern Bering and Chukchi Seas during the summers

Kono et al. focused on the marine environment where
B. saida larvae were distributed. The abundance of B.

of 2008 and 2013.
Changes in sea ice extent and timing of sea ice retreat

saida varied greatly across sampling stations with a me-

affect the physical and biological environment in thedian of 4.9 inds. m−2. The high abundance regions were99

Fig. 6. 4. 4 Scatter plots of the duration from sea ice retreat timing (dSRT) against (a) abundance of Boreogadus saida and (b) sizeof B. saida (Kono et al., 2016).

predominantly in the northeastern Chukchi Sea (Fig. 6. 4.

formed during the previous winter as a result of a polyn-

3) where SST ranged from 0.31 to 5.89°C, Ftemp ranged

ya. North of 70°N, these water properties reflect thefrom −1.75 to 5.75°C, Fsal ranged from 31.3 to 32.8, SSS

presence of Winter Water, which formed during the pre-ranged from 29.0 to 32.5 and dSRT ranged from 6 to 66

vious winter under the sea ice, with temperature <days. The low abundance regions were predominantly in−1.4°C and salinities between 32.5 and 33.5. Conditions

the northern Bering Sea and in the southern Chukchi Sea

in both of these areas, characterized by polynya wate and(Fig. 6.4. 3), where SST ranged from 0.66 to 9.7°C, Ftemp

WW, respectively, appear to provide a suitable environ-ranged from −1.75 to 6.25°C, Fsal ranged from 30.8 toment for the growth of B. saida and supported high abun-

32.8, SSS ranged from 26.3 to 32.6 and dSRT ranged

dance of this species.

from 11 to 74 days. Sea surface environment (SST and

In the present study, B. saida were more abundant andSSS) and dSRT were significantly different between sta-smaller in the Chukchi Sea, where the sea ice retreatedtion with low and high abundance. Although not statisti-

more recently (shorter dSRT). B. saida typically spawn

cally significant, Ftemp of high abundance stations wasunder the sea ice, beginning under the ice cover and end-

lower than that of low abundance station. Further, theing near the surface in ice-free areas after the ice coverabundance of B. saida was weakly and negatively cor-has melted, and larvae hatch in areas recently cleared ofrelated with dSRT (r = −0.49, p < 0.01, Fig. 6. 4. 4).

ice. We conclude that spatial differences observed across

The total length of B. saida varied among areas. Lar-the study region reflect the temporal changes in abun-val B. saida captured south of St. Lawrence Island weredance and size at a given location following ice retreat.

larger (range 6.6–14.2 mm, mean 10.6 mm) compared

Also, the potential presence of two separate spawningwith those captured in the Chukchi Sea (4.4–18.5 mm,area in the northern Bering and Chukchi Seas were sug-

8.4 mm). The size of B. saida was positively and nega-

gested.

tively correlated with Ftemp and Fsal, respectively, but was
not correlated with SST and SSS. Finally, smaller

6. 5. Examinations of sea birds and sea mammals

lengths of B. saida were associated with stations with ashorter dSRT and vice versa (Fig. 6. 4. 4).

Among the Arctic ecosystems, examinations of sea

The northern Bering Sea and Chukchi Sea are an area

birds and mammals clarify not only their own ecosystemof high productivity with a short food web that supports

but also could assume distributions of feeding species

a high benthic biomass, fishes, sea birds and marine

such as zooplankton, bottom livings and fishes. The ma-

mammals. Among the four groups of stations identified

rine ecosystems of the Bering Sea and adjacent southernin this study, B. saida was the dominant indicator species

Chukchi Sea are experiencing rapid changes due to re-only in group B, which was characterizes by cold/saline

cent reduction in sea ice.

waters and occurred both south of St. Lawrence Island
Migrating sea birds

and to the north of 70°N. South of St. Lawrence Island,these properties reflect the presence of Anadyr waters

Short-tailed shearwaters Puffinus tenuirostris is awith temperatures between −1.0 and 1.5°C and subsur-

trans-equatorial migrant, which breeds in southern Aus-

face salinities between 32.5 and 33.5, which likely

tralia from October to March and visit this region in huge
100fore provide a useful indicator of wider ecosystem changes, especially in krill availability, in this region, unlessother environmental changes associated with reduced sea
ice result in a shift in diet or short-tailed shearwatersfrom krill to other types of prey. Given the huge numbers of shearwaters that migrate to this region, changes
in the size or species composition of their diet, as well as
in their foraging locations, may have a top-down influence on the abundance or distribution of their prey, withpotentially major effects on energy transfer pathways inlocal marine ecosystems and on food web structure in

Fig. 6. 5. 1 Latitudinal movements of short-tailed shearwaters
between the breeding colony (40°15’S) and the North Pacificduring the non-breeding period (April–October) (Yamamoto etal., 2015).

general.
Diving sea birds
Subarctic environmental changes are expected to affectnumber between boreal summer and autumn during

the foraging ecology of marine top predators, but the re-non-breeding season, and represent one of the dominantsponse to such changes may vary among species if they

top predators. To understand the implications for thisuse food resources differently. Kokubun et al. (2016) ex-

species of ongoing environmental change in the Pacificamined the characteristics of foraging behavior of two

sub-Arctic and Arctic seas, Yamamoto et al. (2015)

sympatric congeneric diving sea bird: common murres

tracked the migratory movements of 19 and 24 birds in(Uria aalge: hereafter COMUs) and thick-billed murres2010 and 2011, respectively, using light-level geoloca-(U. lomvia: hereafter TBMUs) breeding on St. George

tors. Fieldwork was carried out on Great Dog Island

Island, located in the seasonal sea ice region of the Ber-(40°15’S, 148°15’E), Tasmania, Australia. 50 and 46 in-

ing Sea. They investigated their foraging trip and flight

cubating short-tailed shearwaters in early December 2009

durations, diel patterns of dive depth, and underwaterand 2010, respectively were captured and fitted Mk15

wing strokes, along with wing morphology and blood

geolocation-immersion loggers (British Antarctic Survey,

stable isotopic signatures and stress hormones.

Cambridge, UK).

Acceleration-temperature-depth loggers (ORI-

In both years, tracked birds occupied North Pacific,

380D3GT: housed in a cylindrical container, 12 mm di-southeast Bering Sea, from May to July (Fig. 6. 5. 1). In

ameter, 45 mm length, mass 10 g, Little Leonard, Tokyo,

August-September of 2010, but not 2011, a substantial

Japan) were attached to chick-guarding birds, and data

portion (68% of the tracked individuals in 2010 com-were obtained from 7 COMUs and 12 TBMUs. Stable

pared to 38% in 2011) moved through the Bering Strait

isotopic analyses, observation of prey delivered to chicks,

to feed in the Chukchi Sea. Based on the correlationand stress hormone analyses were used to examine inter-with oceanographic variables, the probability of shearwa-

specific differences in diet and consequent nutritionalter occurrence was highest in waters with sea surface

stress. We combine detailed foraging behavior, diet, andtemperatures (SSTs) of 8–10°C over shallow depths.

morphology to discuss how interspecific differences inFurthermore, shearwaters spent more time flying when

the foraging behavior may affect the responses of two

SST was warmer than 9°C, suggesting increased searchmurre species to environmental change in the southeast-

effort for prey.

ern Bering Sea.

We hypothesized that the northward shift in the distri-The data covered 14 and 21 foraging trips that includ-

bution of shearwaters might have been related to tem-ed 64 and 79 dive bouts for COMUs and TBMUs, re-perature-driven changes in the abundance of their domi-

spectively. COMUs had smaller body mass (COMUs:nant prey, krill (Euphasusacea), as the timing of krill

946±45 g; TBMUs: 1023±64 g), smaller wind area (CO-spawning coincides with the seasonal increase in water

MUs: 0.053±0.007 m2; TBMUs: 0.067±0.007 m2), andtemperature. Hence, the expected future reduction in sea

greater wing loading than TBMUs (COMUs: 176±26 N

ice will possibly shift the distribution of shearwatersm−2; TBMUs: 151±20 N m−2). There were no significant

northwards. The movements of this species may there-

differences in these morphological parameters between
101

Fig. 6. 5. 3 Carbon (d13C) and nitrogen (d15N) stable isotope
ration values of common murres (COMU: open circles) andthick-billed murres (TBMU: closed circles) measured in red
blood cells. Smaller circles show individual data, and larger
circles with error bars show means±standard deviation (SD)(Kokubun et al., 2016).

Fig. 6. 5. 2 (a, b) Frequency distribution and (c, d) depth distribution pattern of dives in relation to time of day. Left panels
represent data for common murres (COMU) and right panels
represent data of thick-billed murres (TNMU). Mean±standard
deviation (SD) are shown in (a) and (b), calculated byindividual bird data. The timing of sunrise and sunset is shownby marks on the top horizontal axis (Kokubun et al., 2016).

was higher in COMUs than TBMUs. The number ofwing strokes during the dive descent phase did not differ

the sexes in either COMUs or TBMUs. Foraging dura-

between the species either in the daytime or the night-tion, total flight duration and dive bout duration did nottime.

differ between COMUs and TBMUs. The maximum dis-

20 and 39 prey items were observed delivered bytance from the colony during foraging trips estimated byparent COMUs and TBMUs, respectively, to feed their

total flight duration was 42.6±21.1 km for COMUs andchicks. The proportion of fishes was higher for COMUs

38.1±21.9 km for TBMUs, respectively. With thesecompared to TBMUs. Conversely, the proportion of in-small foraging ranges, both COMUs and TBMUs proba-

vertebrates for COMUs, was higher for TBMUs com-

bly foraged on the continental shelf. The sea surface

pared to COMUs.

temperature (SST), where the dive bouts occurred, did

The stable isotope analysis for red blood cells showed

not differ between COMUs and TBMUs. The tempera-

differences in the potential adult diet between the spe-ture at depth (> 40 m) did not differ between COMUs

cies. δ15N was higher in COMUs than TBMUs (Fig. 6. 5.

and TBMUs. The thermocline depth and thermocline in-3). δ13C was also slightly higher for COMUs comparedtensity did not differ between the species.

to TBMUs (Fig. 6. 5. 3). Based on the Bayesian mixing

Both COMUs and TBMUs showed a diel diving pat-analysis for estimating potential food sources, COMUs

tern that indicated more dives with divergent depths inwere inferred to have fed on more fishes such as age-1

the daytime and fewer dives with shallow depths in thewalleye Pollock and age-0 flounder, whereas TBMUs

nighttime (Fig. 6. 5. 2). The portion of the daytime andwere inferred to have fed on more invertebrates such asnighttime dives did not differ between the species.

euphausiids and squids.

During the daytime, birds dove to both shallow (< 40 m)

The study investigated the fine-scale differences inand deep (> 40 m) depths in regard to the maximum ther-

foraging behavior between two closely related seabirds:

mocline depth. In the nighttime, both COMUs and TB-

common and thick-billed murres. Both species showed

MUs dove almost exclusively to shallow (< 40 m) depths.

similar foraging ranges and diel patterns of diving (Fig. 6.

During the daytime, the shallow diving depth did not dif-

5. 3). Both species used similar thermal environments at

fer between the species. However, the deep diving depthsea, with no significant interspecific differences in SST,

was deeper for COMUs (74.2±8.7 m) compared to TB-

temperature at depth, thermocline depth and intensity.

MUs (59.7±7.9 m). In the nighttime, the depth of shal-Thus the two species appeared to forage in similar strati-

low dives did not differ between the species. There were

fied water masses, presumably in the middle- or out-

no significant differences between the sexes in either

er-shelf domains around ST. George Island. However,

COMUs or TBMUs dive depths. The number of wing

despite similarities in geographic location, COMUs dovestrokes during the bottom phase of day and night divesto deeper depths in the daytime and showed more fre102

quent underwater wing strokes during dive bottom time,

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compared to TBMUs. In addition, COMUs used higher-trophic-level prey, presumably consisting of larger
fishes such as age-1 walley Pollock, as estimated from
SIAR models, whereas TBMUs used lower-trophic-level
prey, which possibly includes squids and meso-zooplankton. Red blood cells reflect adult diet during incubationand early chick rearing.
This study was conducted during the chick-rearing period of both species, when the energy demands of parents
are highest. High energy demand may force both COMUs and TBMUs to forage closer to the colony, compared to during incubation and post or pre-breeding periods. Potential foraging range and the diel patterns of
diving were similar between COMUs and TBMUs at the
study colony, which may reflect the necessity to guard
chicks, along with the similar nest attendance patterns.
Reproductive success was similar between the species
at the study colony in 2014 and it was higher than longterm averages (Mudge et al., 2015). This is supported by
the relatively low level of stress hormones measured inour study birds, which suggests that the behavioral data
shown in this study represent a year with favorable feeding conditions for both COMUs and TBMUs. In order
for a clear prediction to be made regarding how these
two species will respond to environmental change itwould be necessary to determine whether the segregation
patterns observed in this study persist in years with relatively unfavorable foraging conditions.
In conclusion, interspecific comparison of foraging behavior between closely related common and thick-billedmurres in the Bering Sea showed both species foraged insimilar foraging ranges with similar diel pattern of diving
frequency. However, common murres dove to deeper
depths below the thermocline (> 40 m) in the daytime,
showed more frequent underwater wing strokes during
the bottom phase of dives, and used higher-trophic-level
prey, compared to thick-billed murres. Common murres
have smaller wings, which potentially enable the pursuitof more mobile prey. These results suggest that common
and thick-billed murres segregated prey species in relation to differences in their morphology. These differences in food resource use may lead to the differential responses of the two murre species to marine environmental changes in the Bering Sea.
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104

source of GHG and climatic response of GHG sources

7. Global Influenceswere discovered from the shallow ice core and firn air
analysis.

As for the strategic target 2, Modeling Theme, TerresSurface based observations of CO2

trial Theme, Atmosphere Theme, Glaciology Theme,
Greenhouse Gas Theme and Sea Ice Theme joined to-

Several species of GHGs already have been measured

gether to work on the aim of “understanding the Arctic

for a long term. At the main Japanese Arctic observatory,

system for global climate and future change”.

Ny-Ålesund, Svalbard, atmospheric concentrations ofCO2, CH4 and stable isotopes have been continuously
measured by flask sampling since early 1990s (Yamanou-

7. 1. Greenhouse gas cycle and its climatic response

chi et al., 1996; Morimot et al., 2006). During the
GRENE Arctic, also the continuous measurements ofIn order to clarify global influences of Arctic change,

CO2 and oxygen (O2), δ(O2/N2), has been started.

it is indispensable to improve the prediction of futureO2 and CO2 exchange at photosynthesis or respiration,

greenhouse gas (GHG) concentrations. In this direction,

but they are exchanged independently at the sea surface.

it is needed to understand variation of sink and source ofFrom these relations, we could obtain information as forgreenhouse gases depending on climate and environmen-

the ocean and terrestrial biosphere sinks, respectively.

tal change. To this objective, behaviors of GHGs were

From the concentration changes of CO2 (ΔCO2) and O2

observed in the network shown in Fig. 7.1.1, through(ΔO2), absorption of CO2 by terrestrial biosphere (B) andground surface, airborne and ship based measurements.

ocean uptake (O) are shown with exhaust from fossil

Based on these observation data, source and sink of GHG

fuel, F,in the Arctic were analyzed using high resolution atmospheric chemistry transport models. Discussions of car-

ΔCO2 = F − B − O

(7.1.1)bon cycles together with biospheric changes are reported

ΔO2 = −1.4 F + 1.1 B − Z,

(7.1.2)in Section a. Additionally, temporal variation of sink andhere Z is a net release of O2 from the ocean. From both
equations, absorption by terrestrial biosphere B andocean uptake O could be calculated as follows,
O = (−0.3F − ΔO2 − 1.1ΔCO2 − Z)

(7.1.3)

B = F − ΔCO2 − O.(7.1.4)
Manning and Keeling (2006) defined the atmosphericpotential oxygen APO as eq. (7.1.5), then O could beshown as eq. (7.1.6).
APO = [O2] + 1.1[CO2],(7.1.5)
O = (−1.3F − ΔAPO − Z)/ 1.1

(7.1.6)

From the observations at Ny-Ålesund and Syowa station, Antarctica, Ishidoya et al. (2012) estimated the average CO2 uptake during the period 2001–2009, as shownin Fig. 7.1.2, to be 2.9±0.7 and 0.8±0.9 Gt C yr−1 for the

Fig. 7. 1. 1 Pan Arctic observation network for GHGs constructed in this project. Red dots are surface based stations,yellow dots tower site, and blue dots small airplane sites. Blueline shows line of commercial flight with GHG observations,green line and green area is of Mirai cruise, and purple dots forice core drilling and firn air sampling sites, all referred in thissection.

ocean and terrestrial biosphere, respectively. By excluding the influence of El Nino around 2002–2003, the terrestrial biospheric uptake for the period 2004–2009 increased to 1.5±0.9 Gt C yr−1, while the oceanic uptake
decreased slightly to 2.8±0.8 Gt C yr−1.
105Fig. 7. 1. 2 Interannual variations of the oceanic and terrestrial
biospheric CO2 uptake estimated using the average increase rateof APO observed at Ny-Ålesund and Syowa. Shades aroundsolid lines with Ocean and Land represent uncertainties of theirvalues (Ishidoya et al., 2012).

In order to contribute to a better understanding of the
global carbon cycle, Goto et al. (2013a) developed a highprecision continuous measurement system for atmospheric O2/N2 ratio using a fuel cell oxygen analyzer. To obtain highly precise values of the atmospheric O2/N2 ratio,pressure fluctuations of the sample and standard air werereduced to within ±0.005 Pa, with temperatures stabilized
Fig. 7. 1. 3 Temporal variations in (a) δ(O2/N2), (b) CO2 and (c)APO observed at Ny-Ålesund from November 2012 to January
2015. Cots, solid lines and dashed lines represent hourly meanvalues, the best fit curve, and the long-term trend, respectively.
The results obtained from discrete flasksampling measurements(Ishidoya et al., 2012) are shown by an open circles, Thevertical axis of APO is scaled 1.5 times compared to that ofδ(O2/N2) (Goto et al., 2017a).

to 32±0.1°C. The analytical precision of the system wasestimated to be 1.4 per meg for 24-minutes measurement
as the standard deviation of replicate analyses of thesame sample air. Using the new system, they initiated asystematic observation of the atmospheric O2/N2 ratio asa test case, and the measurements clearly showed seasonal and diurnal cycles, along with short-term variations ontime scales of several hours to several days, caused byterrestrial biospheric and human activities.

values reported by Ishidoya et al. (2012), and found that

Goto et al. (2013b) installed a similar system with acontinuous measured data were slightly higher than flask

consideration of remote measurements, at Ny-Ålesund,sampling data by 0.27±14.48 per meg, and both were

Svalbard and started systematic observation on Novem-

correlated (correlation coefficient was 0.95).

ber 2012. From the continuous observations of CO2 andThe continuous measurements revealed short-termδ(O2/N2) conducted using the system, two year data werefluctuations, which were not detected by discrete flask

obtained and discussed by Goto et al. (2017a). Fig. 7.1.3

sampling (Goto et al., 2017a). During May–June 2013,shows hourly mean values of atmospheric δ(O2/N2), CO2large irregular variation in APO was seen. If these fluc-mole fraction and APO. The best-fit curves and long-tuations occurred in association with terrestrial biospher-term trends were obtained by applying digital filtering

ic activity, the amplitudes of δ(O2/N2) and CO2 fluctua-

technique to the daily mean values. The δ(O2/N2) andtions were expected to be nearly equal, since theCO2 mole fraction decreased and increased secularly, andexchange ratio, –O2/CO2, is 1.1 (Keeling and Manning,varied seasonally in opposite phase each other. The APO2014). However, the observational results indicated thatshowed not only a secular decrease, but also a seasonal

the CO2 mole fraction showed much smaller amplitudes

cycle that was almost in phase with that of δ(O2/N2). The(Fig. 7.1.3), suggesting that the fluctuations were more

results reported by Ishidoya et al. (2012) from mass spec-

likely to be affected by oceanic process than by terrestrial

trometry analyses of weekly flask air samples are also

biosphere process. From trajectory analysis to determine

presented in Fig. 7.1.3. The daily mean values of δ(O2/the origin of short-term APO variations, it was shownN2) calculated from this observation were compared withthat the high APO values are observed when air masses
106Fig. 7. 1. 4 Temporal variations of the atmospheric (a) CO2 mole fraction, (b) d13C, (c) δ(O2/N2), and (d) APO observed at NyÅlesund, Svalbard. The dots and solid lines represent the observed values and their best fit curves, respectively (Goto et al., 2017b).

arrived at Ny-Ålesund after passing over the seas on thecycle superimposed on a secular increase with an averagesouth side of the Svalbard Islands, while most of low orrate of 2.0 ppm yr−1 for the period of 1996–2013. On thestable APO values are observed when the air arrived

other hand, δ13C and δ(O2/N2) decrease secularly at anfrom the ice-covered Arctic Ocean. Moreover, the south

average rate of –0.020‰ yr−1 for 1996–2013 and –19.9

side ocean area was of high marine net primary produc-

per meg yr−1 for 2001–2013, respectively. Secular trends

tion (NPP). These continuous observations of atmo-

and rates of increase of the CO2 mole fraction, δ13C, δ(O2/spheric δ(O2/N2) and CO2 mole fraction could play anN2), and APO are shown in Fig. 7.1.5. The CO2 mole

important role in detecting possible changes in the carbon

fraction and d13C were also observed at Alert (82.45°N,

cycle in the near future.

62.51°E), Mauna Loa (19.54°N, 155.58°W), and the

Systematic observations of CO2 mole fraction (Yama-

South Pole (89.98°S, 24.80°W) by NOAA/ESRL GMD

13nouchi et al., 1996), the isotopic ratio δ C of CO2 (Mori­(Dulgokencky et al., 2016; White et al., 2015). Measure-

moto et al., 2001), and oxygen to nitrogen ratio (δ(O2/ments of δ(O2/N2) and CO2 were also conducted by theN2)) in the atmosphere (Ishidoya et al., 2012) have been

Scripps O2 program (http://scrippso2.ucsd.edu) (Keeling

carried out at Ny-Ålesund, Svalbard since 1991, 1996

and Manning, 2014) at the three sites. The rates of in-and 2001, respectively. Fig. 7.1.4 shows temporal varia-

crease obtained from these observations are compared in

13

tions of the observed CO2 mole fraction, δ C, δ(O2/N2)

Table 7.1.1 with these results. The rates of increase ofand APO. The CO2 mole fraction shows a clear seasonal

CO2 and APO are very close to the values at Ny-Ålesund.
107Fig. 7. 1. 5 Secular trends and rates of increase of (a) the atmospheric CO2 mole fraction, (b) d13C, (c) δ(O2/N2), and (d) APO
observed at Ny-Ålesund. The dots, blue solid lines, and red solid lines represent seasonally adjusted observed values, long-term
trends, and rates of increase, respectively. Four-year mean values of the respective rates of increase are also shown as gray solid
horizontal bars (Goto et al., 2017b).
Table 7. 1. 1 Average rates of increase of the atmospheric CO2 mole fraction, δ13C, δ(O2/N2), and APO observed at Ny-Ålesund,
Alert, Mauna Loa, and the South Pole for the respective periods (Goto et al., 2017b).

This suggests that their secular trends observed at

why such a discrepancy exists is not clear at the moment.

Ny-Ålesund for the respective variables are representa-

Goto et al. (2017b) also estimated globally averaged

tive of the global averages. On the other hand, the rate of

terrestrial biospheric and oceanic CO2 uptakes employing

13

increase in δ C observed at Ny-Ålesund is clearly larger

two kinds of approach, one using APO and the CO2 molethan those obtained by NOAA/ESRL GMD. The reason

fraction (the APO method) and the other using δ13C and108

Fig. 7. 1. 6 (top) Terrestrial biospheric and oceanic CO2 uptakes estimated using δ13C method and the APO method for the respective
periods of 1996–2013 and 2001–13. The yellow shaded periods represent El Nino years. (bottom) Annual CO2 emissions from 1996
to 2013, taken from the statistical data of the Carbon Dioxide Information Analysis Center (CDIAC) (Boden et al., 2016), are slso
shown (Goto et al., 2017b).

CO2 (the δ13C method), under the assumption that theQuere et al., 2016). To better understand the impact of

secular trends and the interannual variations of the relat-

climate change on the global carbon cycle, it is crucial to

ed variables at Ny-Ålesund are representative of theirmonitor changes in the terrestrial biospheric and oceanic

global averages, as indicated in the previous section. The

uptake of anthropogenic CO2 for a long time.

average terrestrial biospheric and oceanic CO2 uptakes
were estimated to be 1.6±0.8 and 2.3±0.5 GtC yr−1 for theSurface based observations of methane

period of 2001–2013. As seen in Fig. 7.1.6, the terrestri-

Atmospheric CH4 is one of the most important gases

al biosphere shows larger year-to-year variations in thefor the atmospheric greenhouse effect and the atmospher-

CO2 uptake than the ocean does. For example, the bio-

ic chemistry. To predict future climate change more pre-spheric CO2 uptake is close to zero for the periods ofcisely, characterizing variations in the CH4 sources and

1997–98 and 2002–03 when the El Nino event occurred.

sinks and their response to climate variability is indis-

However, the terrestrial biospheric CO2 uptake in 2009/

pensable. Systematic observations of the CH4 mole fac-

10 is not so different from the values in adjacent years,

tion and its carbon isotope ratio (δ13C) has been conduct-

though the El Nino event occurred in this period. To bet-

ed at Ny-Ålesund, Svalbard using air samples collected

ter understand the relationship between climate change

weekly since 1991 and 1996, respectively (Morimoto et

and the CO2 budget on the Earth’s surface, further studiesal., 2006, 2009). Here, Morimoto et al. (2017) presented

are needed. As seen in Fig. 7.1.6, the interannual varia-

long-term variations of the CH4 mole fraction and δ13C attion in the oceanic CO2 uptake is relatively small, but the

Ny-Ålesund until the end of 2013 and discussed the caus-

CO2 uptake increases gradually with time for the period

es of the atmospheric CH4 variations based on the δ13C

covered by tis study. Since a rapid growth of the atmo-

data. Fig. 7.1.7a and b show the CH4 mole fractions andspheric CO2 mole fraction by anthropogenic CO2 emis-

δ13C values observed at Ny-Ålesund, respectively, alongsions increases the CO2 partial pressure difference (pCO2)

with their best-fit curves and long-term trends, obtained

between the atmosphere and the ocean, it is expected thatby using a digital filtering technique. In the curve fitting

the oceanic CO2 uptake is steadily enhanced. Some pre-procedure, the cut-off period of the low-pass filter was

cious studies also reported that the oceanic CO2 uptake

set to four months to derive the best-fit curves to the data

had been increasing in recent years (Iida et al., 2015; Leand to five years to extract the long-term trends. As seen109

Fig. 7. 1. 7 Temporal variations of the CH4 mole fraction (a) and δ13C of CH4 (b) observed at Ny-Ålesund, Svalbard. Also shown are
their best-fit curves (solid lines) and long-term trends (broken lines) (Morimoto et al., 2017).

in the figures, the CH4 mole fraction and δ13C showed

tope ratios (δ13C and δD) of atmospheric methane (CH4)

clear seasonal cycles superimposed on the long-term

at Churchill (58°44’N, 93°49’W) in the northern part oftrends. Average peak-to-peak amplitudes of the CH4 andthe Hudson Bay Lowlands (HBL), Canada, since 2007,

δ13C seasonal cycles were 45 ppb and 0.44‰, respective-

under the cooperation between National Institute of Polarly. The amplitudes and seasonal phase are similar to

Research (NIPR), Tohoku University (TU) and Environ-those previously reported (Morimoto et al., 2006). The

ment and Climate Change Canada (ECCC). HBL, theCH4 mole fraction increased from 1991 to 2000, stagnat-

second largest continuous wetland in the world, is an im-

ed until around 2006, and then resumed increasing at a

portant natural CH4 source region in northern latitudes.

similar rate as the 1990s after 2006. Such a stepwise

Nevertheless, there still remains a large uncertainty in

CH4 increase has also been observed around the world

magnitude, seasonality, and spatial distribution of CH4(Rigby et al., 2008; Dlugokencky et al., 2009). On theemissions in the HBL. In addition to the regional influ-

13

other hand, δ C observed at Ny-Ålesund showed a con-ence, the HBL area is also affected to some extent by an-tinuous increase from 1996 to around 2006 and a de-thropogenic CH4 released in Europe and boreal Asia due

crease afterwards, with smaller temporal variability thanto long-range air transport, especially in winter. Thereat Alert (White et al., 2015). By comparing the rates of

may also be large anthropogenic CH4 sources in Alberta

13change in the CH4 mole fraction and δ C under the as-

located to the west of the HBL in association with natural

sumption that the atmospheric CH4 lifetime is constant, it

gas production. Churchill is a small port city on theis suggested that the temporal pause of the CH4 molewestern shore of Hudson Bay with a population of about

fraction observed at Ny-Ålesund is attributed to reduc-900. The land cover around Churchill is mainly charac-

tions of CH4 release from the microbial and fossil fuelterized by the Arctic tundra and the boreal forest. Air

sectors. On the other hand, the increase in CH4 after

samples were taken from an intake mounted at the top of

2006 could be ascribed to an increase in microbial CH4

a 60-m high tower in the Churchill Northern Studies

13

release. The CH4 and δ C data presented in this paper

Centre (https://www.churchillscience.ca/), located 23 km

would be useful for clarifying their temporal variations

east of the town of Churchill. Each air sample was auto-in the Arctic atmosphere, as well as providing additional

matically collected twice a week into a 2-L Pyrex glass

constraints on the global CH4 budget.

flask. The collected samples with a dew point of around

Fujita et al. (2018) conducted simultaneous measure-−60°C were first analyzed at ECCC for mole fractions ofments of the mole fraction and carbon and hydrogen iso-various trace gases such as CO2, CH4, CO, N2O, and SF6110and then transported to NIPR, Japan, for isotope analysesof atmospheric CH4. At NIPR, each sample was divided
into four glass flasks, two for the analysis of δ13C at
NIPR and two for δD at TU. To interpret temporal variations of CH4 in the atmosphere at Churchill and to estimate CH4 emissions from the HBL, forward simulationsof atmospheric CH4 mole fraction were conducted for
2007–2013 using the CCSR/NIES/FRCGC (Center for
Climate System Research/National Institute for Environmental Studies/Frontier Research Center for Global
Change) AGCM-based Chemistry Transport Model(ACTM) developed at JAMSTEC (Japan Agency for
Marine-Earth Science and Technology), with the setup
described in Patra et al. (2016).
Compared with the measurements at an Arctic baseline
monitoring station, Ny-Ålesund, Svalbard, CH4 mole
fraction is generally higher and δ13C and δD are lower at
Churchill due to regional biogenic CH4 emissions. As
seen in Fig. 7.1.8, the CH4 mole fraction at Churchillshows a clear seasonal cycle with a prominent minimumin June–July and a broad maximum in late winter. Similar characteristics are also observed at Ny-Ålesund.
However, there are noticeable differences between theCH4 variations at Churchill and Ny-Ålesund: (1) the an-

Fig. 7. 1. 8 Average seasonal cycles of (a) the CH4 mole fraction, (b) δ13C, and (c) δD observed at Churchill (red lines) andNy-Ålesund (blue lines) for 2007–2013. Dotted lines represent
the 95 percentile bootstrap confidence intervals (see text). Eachaverage seasonal cycle is plotted after adding its average value
for 2007–2013 (Fujita et al., 2018).

nual mean CH4 mole fraction is higher by 3–16 ppb at
Churchill than at Ny-Ålesund for 2007–2013, and (2) thetiming of the seasonal CH4 minimum is earlier by about
1 week, on average, at Churchill than at Ny-Ålesund(Fig. 7.1.8a). A clear seasonal cycle is also observed inδ13C and δD at Churchill and Ny-Ålesund, showing the
maximum in early summer and the minimum in autumn.

−317±33, −197±51, and −211±15‰ for δD (Sherwood et

From inspection of the observation data at the two sites,al., 2017). Fig. 7.1.9a and b show the calculated monthly

it is obvious that (1) the annual means are lower by 0.1–

contributions of individual CH4 sources (SBIO, SFF, SBB)

0.2‰ for δ13C and 1–4‰ for δD at Churchill than atfor Churchill and Ny-Ålesund, respectively, together

Ny-Ålesund, and (2) the average seasonal maxima ofwith those of CH4 destruction by OH. As seen in Fig.

13

δ C and δD at Churchill precede those at Ny-Ålesund by

7.1.9, biogenic sources of CH4 are the most dominant

about 2–3 weeks (Fig. 7.1.8b and c). The differences inones for the seasonal cycle of atmospheric CH4 observed

13

annual mean CH4, δ C, and δD between the two sites

at Churchill and NyÅlesund, with large contributions insuggest that Churchill is more strongly affected by bio-

summer. This source would be boreal wetlands, since

13

genic CH4 sources with low δ C and δD than Ny-Åle-there is a vast amount of wetlands (e.g., bogs, fens, andsund.

tundra) in northern high latitudes from which a large

To examine the contributions of biogenic, fossil fuel,quantity of CH4 is released, showing a strong seasonaland biomass burning CH4 sources to the observed sea-variation unlike anthropogenic biogenic CH4 (e.g., rumi-

sonal CH4 cycle at Churchill and NyÅlesund, a simple

nants, landfills, and waste). The biogenic CH4 contribu-

one-box model was employed. The respective isotopic

tion at Churchill begins in May, reaches a maximum insignatures of biogenic (BIO), fossil fuel (FF), and bio-

July, and then ceases in November (Fig. 7.1.9a). This

mass burning (BB) sources were assumed to be −61.7

seasonality is probably associated with soil temperature

13±6.2 (±1σ), −44.8±10.7, and −26.2±4.8‰ for δ C andrise and snow melting, the highest soil temperature, and111

Fig. 7. 1. 10 Seasonal cycle in mean CO2 concentrations in thePBL (solid line) and LFT (dashed line) from aircraft observations (Sasakawa et al., 2013).

tude-dependent seasonal temperature pattern.
Airborne observations of GHGs in the atmosphere
Siberia, in northern Eurasia, contains large quantitiesof plant biomass and soil organic carbon, making it oneof the largest carbon reservoirs in the world. Accurate

Fig.7. 1. 9 Monthly contributions of biogenic (BIO), fossil fuel(FF), and biomass burning (BB) CH4 sources to the seasonalCH4 cycle estimated using the one-box model for (a) Churchilland (b) Ny-Ålesund. Error bars denote the 68 percentile
confidence intervals derived by the Monte Carlo method with
5000 pseudo time series. Open circles connected with line are
the monthly values of CH4 destruction by OH (Fujita et al.,2018).

estimates of carbon fluxes in Siberia are therefore essential both for understanding global and regional carbon
cycles and for predicting future changes in the Siberiancarbon cycle. Inverse modeling using atmospheric transport models and atmospheric CO2 observations can be effective for estimating regional and global carbon fluxes
from limited atmospheric observations, and this approachhas been successful in deriving reasonable carbon fluxes

low surface temperatures and snow cover in the respec-

for most land and ocean areas. However, the available

tive months. Seasonal variations of the contribution of

CO2 observations remain too sparse to fully constrain

biogenic CH4 estimated for Churchill and Ny-Ålesund

carbon fluxes in Siberia with inversion modeling. To

are slightly different from each other (Fig. 7.1.9a and b).

overcome this problem, the National Institute for Envi-

For example, the biogenic CH4 is discernible at Churchillronmental Studies (NIES), with the cooperation of thein May, but there is no appearance of such a contribution

Russian Academy of Science (RAS), began periodic flask

at Ny-Ålesund. Moreover, the seasonal maximum of thesampling for GHG measurements from aircraft over threebiogenic CH4 contribution appears in July at Churchillsites in Siberia in 1993. In addition, in 2001, NIES andand in August at Ny-Ålesund. This difference is presum-

RAS began establishing the Siberian tower network,

ably attributable to the influence of local/regional wet-which at present consist of nine towers, to obtain region-

land CH4 emissions on Churchill and to different lati-

al and short-term variations of GHGs and to produce data

tudes of the two sites. Churchill is located on thefor inverse modeling to obtain regional carbon estimates.

northern perimeter of the HBL; thus, CH4 emitted from

Sasakawa et al. (2013) evaluated the temporal variation

HBL wetlands could directly affect the CH4 mole fractionof CO2 concentration in the planetary boundary layer

at Churchill. On the other hand, since Ny-Ålesund is far(PBL) and the lower free troposphere (LFT) using mea-

from strong CH4 sources, seasonal signals of CH4 emis-

surements carried out by light aircraft on and above thesions from boreal wetlands may reach the site with a time

tower at Berezorechka (56°08’45”N, 84°19’49”E) in thelag. It is also noteworthy that the onset of wetland CH4

taiga region of West Siberia from 2001 to 2011. Fig.

emissions is earlier at lower latitudes due to the lati-

7.1.10 shows the average seasonal cycle for the PBL
112

characterized by a minimum in late July and a maximumin November–December, yielding a seasonal amplitudeof 29 ppm. This amplitude was about twice the minimum in the LFT (14 ppm), and the seasonal minimum in
the PBL occurred about half a month earlier than theminimum in the LFT, demonstrating the strong CO 2
source-sink forcing by the tiga ecosystem.
Under the NIES program, collection of air samples
over western Siberia had been carried out vertically at altitudinal levels ranging from 0.5 to 7.9 km upwind of
Surgut (61°N, 73°E) within about 100-km distance oncea month since July 1993 using a chartered aircraft (AN24). Umezawa et al. (2012) analyzed the time series of
the CH4 concentration, δ13CH4 and δD-CH4 observed at 1
and 2 km altitudes over Surgut during 2006–09. δ13CH4
showed a clear seasonal minimum in the late summer,
while seasonality of CH4 and δD-CH4 was ambiguous
due to the local disturbances. By inspecting the relationships between the CH4 concentration and isotopes, wefound that isotopic source signatures in the winter (December–April) are −41.2±1.8 and −187±18‰ for δ13CH4and δD-CH4, respectively, and the corresponding values

Fig. 7. 1. 11 (a) Monthly CH4 emissions from an area including
Surgut employed by Patra et al. (2009). Abbreviations are as
follows: ff, fossil fuel; ani, animals; swmp, swamps; bogs,
bogs; and tund, tundra. Also shown is the monthly fractionof biogenic CH 4 emissions (=(ani + swmp + bogs + tund)/all). (b) Monthly flux-weighted δ 13CH4 of the source basedon the CH4 emissions shown in Fig. 7.1.11a. (c) Same as Fig.
7.1.11b but for δD-CH4. δ13CH4 and δD-CH4 for fossil andbiogenic emissions are assumed to have three cases (S1, S2and S3) shown in the respective panels. In Figs 7.1.11b and c,
shaded bands show the source values estimated from Miller/
Tans plot for each season: wintertime raw data estimates (red);summertime raw data estimates (dark green); estimates byresiduals from the trends (light blue), from the best fit curves(blue) and from the data at 2 km (light green) (Umezawa et al.,2012).

in the summer (June–October) are −65.0±2.5 and −282±25‰. These values indicate predominant CH4 emissions
from fossil fuel facilities in the winter and wetlands inthe summer. It was also found that the shorter-term CH4
variations are more influenced by fossil CH4 than thatfrom wetlands. The finding presumably reflects the factthat the former is released from limited areas such asleakage from fossil fuel facilities, while the latter is released from a vast expanse of wetland. By employing a
CH4 emission data set used in an atmospheric chemistrytransport model (Patra et al., 2009), they calculated seasonal isotopic changes of CH4 sources in western Siberia
and compared them to the estimates obtained in thisstudy. The results, Fig. 7.1.11 indicated that the seasonalchange in the CH4 emission data set is reasonable at least

Inoue, 1996; Machida et al., 2008). Sawa et al. (2015)

in terms of a ratio of fossil to biogenic emissions.

reported a new program launched in April 2012 to con-

To increase the spatiotemporal data coverage, aircraft

duct a flask sampling on commercial aircraft flights be-observation campaigns have been conducted; however,tween Europe and Japan, as part of the GRENE Arctic.

up to now regular aircraft observations are still limited

Objective of these midlatitude and Arctic observations

over Siberia. Commercial airlines constitute a very pow-are (i) to reveal seasonal changes and differences in GHG

erful platform for observing spatiotemporal variations of

distributions on a wider latitudinal band from the equator

trace gases, and the commercial airliner project named

to 70°N with the CONTRAIL project and (ii) to derive

Comprihensive Observation Network for Trace gases by

the seasonal transport/mixing of GHGs inferred from ob-

AirLiners (CONTRAIL) with Japan Airlines (JAL) has

served north-south or vertical gradients in the upper tro-

been conducting flask sampling at low latitudes, mainly

posphere (UT)/lower stratosphere (LS). Air samples

between Australia and Japan, since 1993 (Matsueda andhave been collected once a month by the Automatic air
113

Fig. 7. 1. 12 Time series of greenhouse gases observed in the upper troposphere since April 2012. Monthly means and standard
deviations for (a) CO2, (b) CH4, (c) N2O, and (d) SF6. Blue color represents the mole fractions observed over the Eurasian continent
in the 50–70°N latitudinal band. Red, orange, and green colors represent those observed over the western North Pacific in the
0–10°N, 10–20°N, and 20–30°N latitudinal bands, respectively. The lines are the fitted curves to the data, each composed of a linear
trend with or without harmonics based on Akaike information criteria (AIC) (Sawa et al., 2015).

Sampling Equipment (ASE) between Europe and Japan

Sawa et al. (2012), suggesting strong influence of photo-

aboard a commercial airliner JAL, Boeing 777-200ER at

synthesis activity by land biosphere at higher latitudes.

cruising altitudes of 9–12 km, since April 2012 (Machida

CH4 showed ~20 ppb higher mole fractions over Siberia

et al., 2008). GHG distributions at about 9–12 km alti-(50–70°N) than those in the low latitudes (0–30°N) overtudes in the etratropics are largerly determined by thethe western North Pacific, and the difference was foundtropopause height. In order to derive vertical profiles andto be statistically significant at 95% confidence level us-

their variations relative to the tropopause, they used theing the Student t test. The higher mixing ratios of CH4 atdifference in the potential temperature at flight level (Θ)

higher latitudes suggest larger emissions at high latitudes

and the dynamical tropopause (ΘTP indicated by the 2and a transport and mixing from the surface to the upper

2

−1

−1

PVU (potential vorticity unit, 1 PVU = 106 m s K kg )troposphere. CH4 showed opposite phase in the seasonal

surface, ΔΘTP = Θ − ΘTP.

cycle between 0–10°N and 20–30°N. While CH4 showed

Fig. 7.1.12 shows the time series of CO2, CH4, N2O,

lower mole fractions and a summer minimum in 0–10°N

and SF6 in the upper troposphere between 50° and 70°N

due to reactions with OH as observed at surface stations,over Siberia, as well as those observed between the equa-

the summer maximum of CH4 in 20–30°N could be influ-

tor and 30°N over the western North Pacific between

enced by emissions in South and East Asia and the sub-May 2011 and March 2014. The depicted monthly

sequent transport to the upper troposphere over the west-means are based on the average values observed between

ern Pacific. In addition, suppression of vertical transport

7 km altitude and the tropopause (ΘTP) in each latitudinalin winter could contribute to the CH4 minimum aroundband. N2O did not show a significant latitudinal gradientMarch in 20–30°N. Slightly higher SF6 in the high lati-

from the equator to 70°N (Fig. 7.1.12c). Compared totudes was found than that in the low latitudes, suggesting

low latitudes, the seasonal variation of CO2 at northernstronger anthropogenic emissions at high latitudes.

high latitudes showed somewhat large seasonal peak-to-

However, the differences in SF6 between 50–70°N andpeak amplitude (with ~9 ppm) with a phase shift of ~2

20–30°N bands were not statistically significant due tomonths. Earlier decrease in CO2 in the UT over Siberia

large variability.

compared to regions in the North Pacific was reported by

The observed seasonal cycles of CO2, CH4, N2O, and114

Fig. 7. 1. 13 Time series of greenhouse gases observed in the upper troposphere and the lower stratosphere between April 2012
and March 2014. Monthly means and standard deviations for (a) CO2, (b) CH4, (c) N2O, and (d) SF6. The colors represent the mole
fractions observed at every 12.5 K bin from the local tropopause along the flight route over the Eurasian continent. The lines are thefitted curves to the data, each composed of a linear trend with or without harmonics based on AIC (Sawa et al., 2015).

SF6 in the lower stratosphere and the upper tropospherespheric N2O has a long life time in the troposphere, andin the high latitudes are shown in Fig. 7.1.13. The ob-shows only small seasonal variation. On the other hand,served values are binned into layers of 12.5 K thickness

stratospheric CH 4 and N2O showed a strong seasonaleach, beginning with 0 < ΔΘ < 12.5 K up to 37.5 < ΔΘ <variation. The major mechanisms to derive this seasonal

50 K. The tropospheric data, the same values for 50–

variation is, as similar to CO2, intrusion of tropospheric

70°N shown in Fig. 7.1.12, are also shown in Fig. 7.1.13

air in summer and subsidence of upper atmosphere infor comparison. Stratospheric CO2 showed a weak sea-

winter. Due to a large difference between LS and UT

sonal cycle with amplitudes of about 1–2 ppm, with aand larger vertical gradients in the stratosphere comparedslow decrease from autumn to spring, followed by a rap-

to CO2 make this large seasonal variations in LS. SF6 has

id increase from June to September. On the contrary, tro-

a long life time in the UT as N2O, shows little seasonal

pospheric CO2 showed a large opposite seasonal varia-

variation, but in LS, clear seasonal variation is made bytions. Stratospheric CH4 and N2O, due to chemical de-

the transport in summer and winter due to the difference

struction in the LS, have a large difference to their con-in concentration in the UT and LS, and large gradient.

centrations in the troposphere, and also have a large ver-Since SF6 is stable in the atmosphere, we could derive

tical gradient. Atmospheric concentrations of CH4 andan average time (age) of the air parcel in the stratosphereN2O in the UT does not show distinct seasonal variation,

after its leaving from the troposphere. At potential tem-but showed a gradual secular increases similar to the sur-peratures of 37.5–50 K above the tropopause, SF 6 age

face values. Tropospheric CH4 near the ground surface

was estimated to be about 22 months in May and 9shows a clear seasonal variation with a low value in sum-months in November. This strong seasonal variation ismer due to destruction by OH radicals; however, in the

explained by the subsidence of high-stratospheric air in

low pressure layer such as UT, reaction with OH radicals

spring and the effective flushing of the lowermost strato-

become weak and less seasonal variation. Also, as in thespheric air with tropospheric air in autumn.

continental inland area such as Siberia, vertical transport
Shipborne observationsof high concentration CH4 becomes active in summer andthis also makes the seasonal variation smaller. Tropo-

Atmospheric potential oxygen (APO) is a useful pa115

Fig. 7. 1. 14 Locations where air samples were collected(small stars) onboard MIRAI for the period 5 September–15
October 2012 (red) 29 August–6 October 2013 (blue), and 1
September–9 October 2014 (green). Locations of NyÅlesund,Svalbard, and Sendai, Japan, are also shown (large purple stars)(Ishidoya et a., 2016).
Fig. 7. 1. 15 Temporal variations in δ(O2/N2), CO2 concentration and δ13C of CO2 observed onboard MIRAI for the period
1 September–9 October 2014 (black filled circles). Temporalvariation in CO concentration (grey crosses), latitude and longitude of the cruise are also shown (Ishidoya et a., 2016).

rameter to discuss the marine biological and physicalprocesses. In order to increase our understanding of therelationship between the local air-sea O2 flux variation
and APO, we need to observe APO just above the sea
surface. Simultaneous observations of atmospheric po-in δ(O2/N2) are also affected significantly by the air-sea

tential oxygen (APO = O2 + 1.1 × CO2) and air-sea O2

exchanges of O2 and N2, similar variations in CO2 are

flux, derived from dissolved oxygen in surface seawater,

less affected by the air-sea exchange of CO2 because of

were carried out by Ishidoya et al. (2016) onboard the re-the bicarbonate equilibrium (e.g. Keeling et al., 1993).

search vessel Mirai in the northern North Pacific and theFollowing the discussion in last paragraph, we can ex-

Arctic Ocean in the autumns of 2012–2014 as shown intract the oceanic component from the measured values ofFig. 7.1.14. A simulation of the APO was also carriedδ(O2/N2) by using APO [defined by eq. (3)]. The APO

out using a three-dimensional atmospheric transport

values for the period 1 September–9 October 2014 aremodel that incorporated a monthly air-sea O2 flux clima-

shown in Fig. 7.1.16 (black filled circles). The APO val-

tology. Fig. 7.1.15 shows the temporal variations in

ues are also plotted, calculated from the STAG model

13

δ(O2/N2), CO2 concentration and δ C of CO2 observed

driven by FO2_cli and FN2_cli (blue crosses). The average

onboard MIRAI for the period 1 September–9 Octobervalue of the simulated APO for each cruise was adjusted

2014. Sampling locations (in latitude and longitude) are

as a function of the differences between FO2_obs and FO2_cli.

also shown at the bottom of the graph. As seen in Figs.

It is seen from Fig. 7.1.16 that the observed APO values

13

7.1.15, δ(O2/N2) and δ C show a gradual decrease withdecrease gradually with time for each cruise. This de-time, while CO2 shows a gradual increase. Negative cor-crease from September to October was widely observed

13

relations of δ(O2/N2) and δ C with CO2 were also ob-in the Northern Hemisphere in other studies (e.g. Keelingserved in the day-to-day variations. It is known thatet al., 1998; Tohjima et al., 2012), and reflected seasonal13

δ(O2/N2) and δ C are negatively correlated with CO2 duechanges in the air-sea O2 flux. While the simulated APO

to fossil fuel combustion and terrestrial biospheric pro-values showed similar seasonal decreasing trend in APO

cesses (e.g. Keeling et al., 1993; Nakazawa et al., 1997).

for each cruise, the observed day-to-day variation was

Furthermore, while seasonal and subseasonal variations

significantly under estimated by the model. This under116

al APO cycles widely seen at various northern hemispheric observational sites in the fall season, and the importance of knowing the variation of air-sea O2 fluxes
due to the marine biological productivities.
The extent and thickness of sea ice in the Arctic Ocean
have been declining in recent decades, and because ofthis decline in the extent of sea ice, the air-sea CO2 fluxis also thought to be dramatically changing.
In late summer 2013, Kosugi et al. (2017) made shipboard observations in the Chukchi Sea and in the Canadian Basin of the western Arctic Ocean. As shown in Fig.
7.1.17, an expanse of surface water was observed with
low CO2 partial pressure (pCO2sea) (< 200 μatm) in the
Chukchi Sea of the western Arctic Ocean. The large undersaturation of CO2 in this region was the result of massive primary production after the sea-ice retreat in June
and July. In the surface of the Canada Basin, salinity waslow (< 27) and pCO2sea was closer to the air–sea CO2
equilibrium (~360 μatm). From the relationships between salinity and total alkalinity, it was confirmed thatthe low salinity in the Canada Basin was due to the larger
fraction of meltwater input (~0.16) rather than the riverine discharge (~0.1). Such an increase in pCO2sea was not
so clear in the coastal region near Point Barrow, where
the fraction of riverine discharge was larger than that ofsea-ice melt. Low pCO2sea (< 250 μatm) in the depth ofFig. 7. 1. 16 Temporal variations in APO (filled black circles)
and air-sea O2 flux observed onboard MIRAI (FO2_obs; green
dots, positive value denotes flux emitted from the ocean tothe atmosphere) for the period 1 September–9 October 2014.
Corresponding APO values simulated using the STAG modelthat incorporated the monthly air-sea O2 (N2) flux climatology(blue crosses), and the climatological air-sea O2 flux along theobservation route (FO2_cli; blue dots) are also plotted. Simulated
APO values calculated by including the contribution of airsea CO2 flux are also shown by the blue dotted line (see text).
Green crosses denote temporal variations in APO calculated
by using FO2_obs (see text). Chlorophyll-a concentration, partialpressure of O2 and CO2 in surface water, heights of planetary
boundary layer (PBLH), wind speed at 25 m heights, latitude
and longitude of the cruise are also shown (Ishidoya et a.,2016).

30–50 m was also identified under the halocline of theCanada Basin. This subsurface low pCO2sea was attributed to the advection of Pacific-origin water, in which dissolved inorganic carbon was relatively low, through the
Chukchi Sea where net primary production was high.
Oxygen supersaturation (> 20 μmol kg−1) in the subsurface low pCO2sea layer in the Canada Basin indicated significant net primary production undersea and/or in preformed condition. If these low pCO2sea layers surface bywind mixing, they will act as additional CO2 sinks; however, this is unlikely because intensification of stratification by sea-ice melt inhibits mixing across the halocline.
Yasunaka et al. (2016) produced 204 monthly maps ofthe air-sea CO2 flux in the Arctic north of 60°N, includ-

estimation could be due to the difference between FO2_cli

ing the Arctic Ocean and its adjacent seas, from January

used to drive the model and the actual air-sea O2 flux

1997 to December 2013 by using a self-organizing map

used to calculate the observed APO, and the average val-

(SOM) technique. The partial pressure of CO2 (pCO2) in

ue of the observed air-sea O2 fluxes was systematicallysurface water data were obtained by shipboard underway

higher than that of the climatological O2 flux. In order tomeasurements or calculated from alkalinity and total in-show this to be the case, we also plotted in Fig. 7.1.16,

organic carbon of surface water samples. Subsequently,

FO2_obs and FO2_cli for each cruise. This could explain the

we investigated the basin-wide distribution and seasonal

discrepancy between the observed and simulated season-

to interannual variability of the CO2 fluxes. They used117

Fig. 7. 1. 18 Seventeen-year annual means of (a) CO2 flux (mmol m−2 day−1) (negative values indicate influx into the ocean),(b) SIC (%), (c) wind speed (m s−1), and (d) ΔpCO2 (= pCO2w– pCO2a) (m atm). Darker shades show values in grids where
values were smaller than the uncertainty (Yasunaka et al.,2016).

also used shipboard pCO2w measurements obtained during cruises of the R/V Mirai that have not yet been archived in the SOCAT and LDEO databases (cruises
MR09_03, MR10_05, MR12_E03, and MR13_06; http://
www.godac.jamstec.go.jp/darwin/e).
pCO2w was estimated by a SOM technique similar tothat used by Telszewski et al. (2009) and Nakaoka et al.
(2013). In their estimation, they used SST, SSS, SIC,
and pCO2a as training parameters. Among those variables representing the spatial distribution and temporalvariation of surface water properties in the Arctic, SST,
SSS, and SIC are basic parameters and their gridded
Fig. 7. 1. 17 Surface water properties along the track of
cruise MR13-06 from 4 to 11 September 2013. (a) Sea surface
temperature (SST); (b) sea surface salinity (SSS); (c) BCW(Barrow coastal water), CSW (Chukchi Sea water), and CBW(Canada Basin water) water types according to SST and SSS.
The numbers over triangles indicate distance sailed from Dutch
harbor, Alaska, USA (km). (d) Fraction of sea ice melt (fSIM), (e)fraction of riverine outflow (fRRO) and (f) pCO2sea (Kosugi et al.,2017).

products are readily available. Monthly air-sea CO2 flux(F) values was calculated from the pCO2w values estimated as described above by using the bulk formula of Wanninkhof (1992):
F = kL (pCO2w − pCO2a), (7.1.7)

where k is the gas transfer velocity (calculated from windspeed) and L is the solubility of CO2. The 17-year annual

pCO2w observations (converted from the fugacity of CO2mean CO2 flux distribution shows that all areas of thevalues) from the Surface Ocean CO2 Atlas (SOCAT) ver-Arctic Ocean and its adjacent seas were net CO2 sinks

sion 3 (Pfeil et al., 2013; Bakker et al., 2014, 2016; http://(Fig. 7.1.18a). The annual CO2 influx to the ocean waswww.socat.info/), and the Global surface pCO2 (LDEO)

strong in the Greenland/Norwegian Seas (>10 m mol m−2

database version 2014 (Takahashi et al., 2015; http://cdiac.

day−1), the Barents Sea (~10 m mol m−2 day−1), and theornl.gov/oceans/LDEO_ Underway_Database/). They

Chukchi Sea (~4 m mol m−2 day−1). In contrast, it was118

Fig. 7. 1. 19 Area-mean seasonal variations in (a) the Greenland/Norwegian Seas (65°–80°N, 45°W–15°E), (b) the Barents Sea(65°–80°N, 15°–50°E), and (c) the Chukchi Sea (65°–75°N, 180°–160°W). The upper panels show seasonal variations in the CO 2
flux (black, standard; blue, SIC constant; green, wind constant; red, ΔpCO2 constant; gray, from Takahashi et al., 2009) in eachregion (m mol m−2 day−1), and the lower panels show seasonal variations in SIC (%) (blue), wind speed (m s−1) (green), and ΔpCO2 (matm) (red). Error bars indicate the uncertainty (Yasunaka et al., 2016).

weak within the uncertainty in the Eurasian and Canadalate summer. The amplitudes of the interannual variation

Basins, as well as in the Laptev and East Siberian Seasin these regions were about 10 m mol m−2 day−1. The(<4 m mol m−2 day−1). Our annual CO2 flux estimates av-

ΔpCO2 contribution to the interannual variation was thus

eraged in the Greenland/Norwegian Seas and the Barents

much larger than its contribution to the seasonal variation

Sea are consistent with those reported by most previousof the CO2 fluxes. About 80% of the ΔpCO2 interannual

studies (e.g., Aoki et al., 1996; Nakaoka et al., 2006).

variation arose from the interannual variation of pCO2w.

Next, they examined the relationship between the CO2

The 17-year trend in the CO2 flux was negative (i.e., up-

flux and SIC, wind speed, and ΔpCO2, parameters thattake by the ocean was increasing) in the Greenland/Nor-

directly relate to the CO2 flux. The annual mean CO2

wegian Seas and northern Barents Sea, and positive (i.e.,flux distribution showed strong similarities to the SIC

uptake was decreasing) in the southern Barents Sea. The

and wind distributions (Fig. 7.1.18b and c). The 75%

17-year trends were larger than the uncertainty in these−2

regions. The ΔpCO2 trend in all of these regions corre-

day contour of the CO2 flux, indicating that in the area

sponded well to the SST trend. This result showed thatwith an average sea ice cover of more than 75%, the CO2

whether pCO2w increased faster or slower than the in-

SIC contour mostly corresponds to the −4 m mol mol m
−1

−2

−1

influx was less than 4 m mol mol m day . Strong flux-

creasing pCO2a depended on the thermodynamic relation-

es into the Greenland/Norwegian Seas, the Barents Sea,

ships. Cai et al. (2010) also reported a rise in pCO2w withand the Chukchi Sea were associated with high winds

increasing SST in the Canada Basin. Simultaneousand large areas of open water in those regions. In thetrends toward positive ΔpCO2 and decreased SIC suggest

Eurasian Basin, the CO2 influx was relatively small be-that the CO2 uptake can be expected to decrease once thecause of the large SIC (>75%), even though ΔpCO2 wasocean becomes free of sea ice, as suggested by Cai et al.

highly negative (Fig. 7.1.18d). Here they defined the(2010). Retreating sea ice has two opposite effects on

Arctic Ocean as the ocean area north of 65°N excluding

pCO2w and the CO2 flux; on the one hand, it leads to in-

the Greenland/Norwegian Seas and Baffin Bay, the esti-

creased pCO2w due to increased SST, while on the othermated 17-year average influx to the Arctic Ocean was 4

hand, it leads to decreased pCO2w due to active biologicalm mol m−2

−1

day , equivalent to an uptake of 180 TgC−1production.

−1

yr . The formal uncertainty is large (210 TgC yr ),After GRENE Arctic, Yasunaka et al. (2018) again es-

however, mainly because of the poor coverage of pCO2

timated monthly air–sea CO2 fluxes in the Arctic Ocean

observations and the uncertainty of the SIC effect on gas

and its adjacent seas north of 60°N from 1997 to 2014,exchange. 17-year monthly mean CO2 fluxes and relatedusing similar SOM technique, but with incorporating

variables were shown in Fig. 7.1.19.

chlorophyll a (Chl a) concentration. This was because

Interannual variation of the CO2 flux was larger than

one possible reason for the large uncertainties of the esti-

uncertainty in the Greenland/Norwegian Seas and themated CO2 uptake by Yasunaka et al. (2016) (180±210

Barents Sea in early winter, and in the Chukchi Sea in

Tg C y−1) was that no direct proxies for the effect of bio119

logical processes on pCO2w were used in that study, leading to an underestimation of the seasonal amplitude of
pCO2w. The addition of Chl a as a parameter in the SOM
process enabled us to improve the estimate of pCO2w,
particularly via better representation of its decline inspring, which resulted from biologically mediated pCO2w
reduction. As a result of the inclusion of Chl a, the uncertainty in the CO2 flux estimate was reduced, with a net
annual Arctic Ocean CO2 uptake of 180±130 Tg C yr−1.

7. 2. Terrestrial ecosystem and carbon cycle
Carbon cycle has a crucial role in the earth system andits changes have substantial effects on climate change.

Fig. 7. 2. 1 Time series of annual water use efficiency inSpasskaya Pad during 2004–2010. Water use efficiency in
2004–2007 was increased by evapotranspiration, and that in2008–2010 was increased by gross primary production (Ohta etal., 2014).

Terrestrial ecosystems have both positive and negative
feedback to the carbon cycle. It is urgent to better understand mechanisms of the carbon cycles and their interactions with the earth system.
The boreal forests in the Northern Hemisphere currently fix CO2 in the atmosphere, but their changes asso-

and the global database has been established that enableciated with climate change due to increased CO 2 are

us to mutually use the data.

highly uncertain (IPCC, 2013). This is because boreal

In the pan-Arctic region, observations of energy, water

forests have the potential to enhance CO 2 fixation

and CO2 fluxes have been conducted for more than 10

through fertilization effects of increased CO2 and to ex-

years at a few sites (e.g., Yakutsk, Fairbanks, etc.). Ohta

pand its coverage northward due to global warming (i.e.,

et al. (2014) analyzed the observation data at larch for-negative feedback), while they also have the potential toests from 1998 to 2011 at Yakutsk, in an Eastern Siberi-increase CO 2 emission due to increased respiration

an, to understand the relationship of the interannual vari-caused by the warming and due to declining their bio-

ations of climate changes and forest changes. They foundmass resulted from their vulnerability to climate changethat in the wet years from 2005 to 2008 the larch forest(i.e., positive feedback).

had been damaged by the wet stress, resulted in the de-It has been confirmed, from ground-based and atmo-

crease of gross primary production and water use effi-

spheric observations of CO2 concentration, that the north-ciency (Fig. 7.2.1).

ern terrestrial ecosystems have been functioning as sinks

It is widely known that the below-ground biomass isfor CO2 and that seasonal amplitude of CO2 absorption

large in comparison to the above-ground biomass in per-over land has been increasing in the recent years (Gvaven

mafrost regions. The field measurements of above-et al., 2013). The current conditions and future projec-

ground and below-ground biomass of trees and understo-

tions of the CO2-flux balances are described, in this sec-ry plants in the black spruce forests in Fairbanks, Alaska,tion, from various observations and modeling on the ter-showed that the ratio of below-ground biomass was high-restrial area and comparison of the estimated values by

er especially for fine roots on shallower permafrost table.

multiple methods.

It is also revealed that understory plants would have significant contribution to below-ground carbon dynamics

Changes in CO2 balance observed on landin forest on permafrost (Noguchi et al, 2016; Fig. 7.2.2).

Observations of energy, water and CO2 fluxes on land

In addition, the relationships between tree growth and theused to be performed in various ways in various places.

effects of mycorrhizae on N uptake using isotope ratios

Since the 1990s, they have been carried out by the com-of nitrogen (N) in foliage and root were investigatedmon methods for the common variables. A global obser-

along a slope. It has shown that tree growth depends lessvation network FLUXNET (see https://fluxnet.fluxdata.

on mycorrhizae for N uptake on the N-rich upper slope,org/, viewed on 25 January 2020) was started in 1997,

while it depends more on the lower slope with the shal120

Fig. 7. 2. 3 Tree-ring width index and Ci/Ca in the six circumArctic forest sites studied. Ring-width chronologies (gray line)at (a) Kalina (KAL, Estonia), (b) Yakutsk (YAK), (c) Ust'Maya(UST). The smooth curve (bold line) shows the 5 year mean of
the chronologies. Trends in chronologies after 1901 (green dottedline) and 1961 (red dotted line) are also shown. We used therank-based nonparametric Mann Kendall test (Mann Kendall
coefficient τ) to evaluate trends' significance. Significant trends
are indicated by * (p < 0.05), ** (p < 0.01), and *** (p < 0.001)(Tei et al., 2017).

Fig. 7. 2. 2 Fine root biomass of black spruce (Picea mariana;
top) and understory plants (bottom) at each sampling date.
Figures show results on fine roots 0.5–2.0 mm in diameter.
Gray and black columns represent data from DP and SP plots,
respectively. Data shown are mean + SE (N = 9). Different
letters above the columns indicate significant difference
between the values (Tukey’s HSD test, α = 0.05) (Noguchi etal., 2016).

cle, and compared it with climate change (Tei and Sugilow permafrost table where tree growth is small (Tana-moto, 2018). As a result, it was found that the change

ka-Oda et al., 2016).

tendency of RWI and NDVI3g differs depending on theTree ring analysis and carbon isotope analysis are use-region (Fig. 7.2.5), and the relationship with climate

ful for investigating longer-term changes in boreal for-change showed different tendencies between the Arctic

ests. According to the tree-ring analysis results at Yakutsk

ecosystems north of 67°N and the boreal forests of 50–

and Ustmaya, in the latter half of the 20th century, the67°N (Fig. 7.2.6). After the GRENE Arctic Project term,

annual ring width decreased with increasing temperature,

still some papers are originated related to this topic, suchand the carbon isotope ratio showed that the stomatal re-as by Tei et al. (2017b) and Tei et al. (2018a, b).

sistance increased due to water stress and slowed the CO2

Many estimates of forest biomass have been made us-

fixation then the tree growth as shown in Fig. 7.2.3 (Teiing satellite data. In order to predict future changes in

et al., 2017a). On the other hand, the estimated value ofbiomass, it is important to understand not only the

NPP by the dynamic vegetation model (DGVM) showedamount of biomass and its change until now, but also the

an increasing trend (Fig. 7.2.4). This is because DGVM

physical quantities that might be the changing factors.

is too sensitive to increased precipitation and does not

Traditionally, forest-wide biomass and leaf area index

adequately reflect the effects of water stress. This sug-

(LAI) had been estimated using multiple satellite data

gests that the future prediction of CO2 fixation rate by the(e.g., Myneni et al., 2002; Nagai et al., 2019). Combinedmodel may be overestimated (Tei et al., 2017a). In addi-

with the vegetation canopy radiative transfer model, LAI

tion, we analyzed long-term and wide-ranging spa-of canopy could be derived as Fig. 7.2.7 (Kobayashi ettio-temporal variability of vegetation using annual tree-al., 2010). In addition, by combining a high-resolution

ring width index (RWI) and normalized difference

multi-channel visible sensor with in-situ forest phenolo-

vegetation index (NDVI3g) by satellite in the Arctic Cir-gy observation, it became possible to estimate a wide121

in NEP values among the three plots owing to their variable cover of S. polaris, we found that net primary production for all plants (vascular plants and cryptogams; Pv+ Pc) and Rh had strong sensitivities to temperature increase as shown in Fig. 7.2.10. The value of Pv + Pc decreased with increasing temperature because respirationof both S. polaris and S. uncinata increased markedly
under warmer temperatures. In contrast, Rh rose with increasing temperature. As a result, NEP (Pv + Pc − Rh) responded significantly to temperature (Fig. 7.2.10c). Although all plots were shown to be CO2 sinks under thepresent temperature condition, two of the three plots became CO2 sources for the atmosphere (with a negative
NEP) under a 2°C temperature rise. All three plots became carbon sources, under a 4°C temperature increase.
In contrast, gross ecosystem production (GEP), calculated as the difference between NEP and ecosystem respiration, gradually decreased with increasing temperature(Fig. 7.2.10d).
Estimation of CO2 balances by land surface models

Fig. 7. 2. 4 Yearly variations in intrinsic water use efficiency(iWUE) for the six circum-Arctic forest sites studied. The
iWUE (gray line) was calculated from tree-ring δ13C registeredfrom 1901 to 2012 at (a) Kalina (KAL, Estonia), (b) Yakutsk(YAK), (c) Ust’Maya (UST). The 90% confidence interval isindicated by the gray shade. The smooth curve (black boldline) shows the 5 year mean of the chronologies. Trends inchronologies after 1901 (green dotted line) and 1961 (red dottedline) are also shown. We used the rank-based nonparametricMann Kendall test (Mann Kendall coefficient τ) to evaluate
trends' significance. Significant trends are indicated by * (p <0.05), ** (p < 0.01), and *** (p < 0.001) (Tei et al., 2017).

There are many studies that estimate the CO2 balanceusing a land surface hydrological model. The development of terrestrial hydrological models for climate research was accelerated in the 1980s, with intercomparisons for various purposes (e.g., Henderson-Sellers,
1996), which is extended to the Coupled Climate Carbon
Cycle Model Intercomparison (C4MIP) (Arora and Matthews, 2009). However, the Arctic region has not been
included in the target area due to the lack of observation
data.

distribution of the timing of leaf and leaf litter, start of

Saito, K. et al. (2018) conducted a research project for

the growing seaon and end of the growing season (SGS

estimation and comparison (GTMIP) of the developed& EGS), throughout East Siberia (Fig. 7.2.8, Nagai et al.,land model in various fields such as physical processes,

2019).

biogeochemical processes, and ecosystem processes us-

A method for estimating gross primary production

ing the observation data at multiple sites described in the(GPP) and net ecosystem CO2 exchange (NEE) has beenprevious section as input values to determine the CO2

developed by a machine learning algorithm, the supportbalance in the Arctic land (Fig. 7.2.11). First, we con-vector regression (SVR), using satellite data (Ichii et al.,structed an observation network in which observation

2017). This development has enabled data-driven estima-

items and methods were arranged at multiple points, andtion of CO2 fluxes over a wide area (Fig. 7.2.9).

conducted intercomparison studies using the observation

In High Arctic tundra, Uchida et al. (2016) made sen-data as input values. (Miyazaki et al., 2015). Four points,

sitivity analysis of ecosystem CO2 exchange to climate

Yakutsk (2005–2011), Fairbanks (2011–2013), Kevochange using an ecological process-based model. Al-(1995–2012) and Tiksi (1997–2012), from which long-though in situ net ecosystem production (NEP) values

term continuous observation data can be obtained forover the growing season varied widely among the three

items required as model inputs are selected and entered.

study plots, all NEP values were positive (CO2 sink)

The output data has been released by Sueyoshi et al.

−2−1within the range 17–110 mgC m h . Despite the range(2016). Using these data, we analyzed the energy and122

Fig. 7. 2. 5 Trends in NDVI3g and tree radial growth (RWI) over the northern high latitude region. Left panels (a–b): Spatial
distribution in linear trends for NDVI3g and RWI. These trends were estimated for the period from 1982 to the last year of each RWIchronology (1990s or 2000s). Green and red circles show significant positive and negative trends (p < .10), respectively, and the graycircles show a nonsignificant trend. Right panels (c–d): Percentage of sites showing positive, negative, and nonsignificant trends for
NDVI3g and RWI over the circumarctic and circumboreal forest ecosystems (Tei and Sugimoto, 2018).

water balance among the experimental results for 12

between model researchers have been established. The

models of physical systems and 7 models of ecosystems.

foundation for building a next-generation cold-land mod-The difference between the model systems (physical sys-

el was developed by quantifying the uncertainty of thetems and ecosystems) was not remarkable, and the model

land model and the variation due to model implementa-

median was observed. It was shown that it might be re-tion (Saito, K. et al., 2018).

garded as a representative. It was also suggested that it
was important to sufficiently capture the snow and frozen

Inter-comparison of terrestrial CO2 flux estimated bysoil peculiar to the cold region as in Fig. 7.2.12 (Saito, K.

multi-models with multi-methods

et al., 2018). In addition to estimating the carbon budget

The changes in the processes involved in the CO2 bud-based on site observation data, we are also working on an

get to climate change vary by region and time scale. As

inter-comparison using global climate model prediction

mentioned at the beginning of this section, the Arctic ter-results of future warming. From the preliminary experi-

restrial ecosystem plays an important role in the global

ment, it was shown the soil respiration to increase overall

carbon budget. It is known that the change in the CO2

the Arctic following the soil temperature increase (ref).

balance during warming is highly uncertain especially on

This suggests that the terrestrial ecosystem in the pan-land (IPCC, 2013, Chap.6).

Arctic could be a source, albeit a weak sink at present. In

Estimation of land-based CO2 balance is generally dif-

addition, through this initiative, a coordination systemficult to estimate with high accuracy over a wide area due

between observation and model research in the Arctic

to uneven surface conditions (land cover, topography,

land and an information exchange/ cooperation systemsurface conditions, etc.). Among them, the area around123Fig. 7. 2. 6 Optimal multiregression model, with monthly climate variables for NDVI3g and tree radial growth (RWI) over thenorthern high latitude region. Left panels (a–b): Spatial distribution in the optimal NDVI3g and RWI models. We used the monthly
temperature and precipitation in the following months as candidates of independent variables for each seasonal model, namely, (i)June, July, and August in the previous year for the PS (previous summer) model (red circles); (ii) September, October, and Novemberin the previous year for AW (previous autumn and winter) model (orange circles); (iii) December in the previous year and January
and February in the current year for the MW (mid-winter) model (blue circles); (iv) March, April, and May in the current year for
the WS (winter and spring) model (green circles), and (v) June, July, and August in the current year for the CS (current summer)
model (black circles). Optimal models were selected according to the lowest Akaike information criterion (AIC). Right panels (c–d): Percentage of sites showing these optimal models over the circumarctic and circumboreal forest ecosystems (Tei and Sugimoto,2018).

Fig. 7. 2. 7 Larch overstory LAI in eastern Siberia estimatedfrom SPOT-VGT data sets (Nagai et al., 2019).

Fig. 7. 2. 8 Spatial distribution of the first date on which
GRVIMODIS was more than zero in spring (i.e., SGS; top)
and the first date on which GRVIMODIS was less than zero inautumn (i.e., EGS; bottom) in 2015 in eastern Siberia between
70_N and 50_N and 105_E and 165_E. The color scales show
day of year (DOY) (Nagai et al., 2019).

Yakutsk, Russia with flat terrain and uniform vegetationis considered to be highly representative of a wide area.
Various methods have been used at the area to estimate
the CO2 balance as described above. As bottom-up esti124

Fig. 7. 2. 9 Spatial distribution of (a) estimated annual gross primary productivity (GPP), (b) estimated annual net ecosystem
exchange (NEE). Annual mean values were calculated for the 2000 to 2015 period (Ichii et al., 2017).

Fig. 7. 2. 10 Effect of temperature increase on a net primary
production (Pv + Pc), b heterotrophic respiration (Rh), and c net
ecosystem production (NEP) and d gross ecosystem production(GEP) for the growing season (73 days) at three study plots (A,B, and C) (Uchida et al., 2016).

Fig. 7. 2. 11 The habitat of models participating in the GTMIP.
The vertical and horizontal axes show the ratio of the incorporation of biogeochemical processes and physical processes,
respectively (Miyazaki et al., 2015).

mation methods, there are flux measurements using theeddy correlation method (Ohta et al., 2014), estimation
based on terrestrial ecosystem models (Saito, K. et al.,ations, the top-down estimated values were close to the2018), and the statistical method using satellite data.

tower observations, but the bottom-up estimated values

There is an estimate by (Ichii et al., 2017). As a top-down

varied in individual models, and the model mean fluctua-

estimation method, there is an estimation value by the at-tions had become quite small. This might be due to dif-mospheric inversion method (e.g., Niwa et al., 2012;

ferences in the processes considered. Care should be tak-

Saeki et al., 2013). Takata et al. (2017) compared the es-

en in this point when considering the model average as atimated values of CO2 flux in Yakutsk by these multidis-

representative value (Fig. 7.2.14).

ciplinary and multi-methods, and clarified the current

Comparing the multi-method, multi-disciplinary esti-

state of seasonal and interannual variability. The annual

mates suggested some characteristics of the estimated re-balance of CO2 was net sink by all methods. Seasonal

sults by these methods: bottom-up model estimates couldchanges were absorbed in summer and released in winternot adequately represent the inter-annual variability char-by all methods, and were consistent within the range ofacteristic. It was confirmed that the results obtained bymodel variation (Fig. 7.2.13). For the year-to-year fluctu-

statistical methods using satellite data differed depending125

Fig. 7. 2. 12 Heat budget (left) and water budget (right) analyzed for the all term 1980–2013. But annual average of heatbudget and water budget for calendar year and hydrology year,
respectively. From top to bottom, Fairbanks (FB), Kevo (KV),
Tiksi (TK) and Yakutsk (YK). Black dots for observation at thesite (Saito, K. et al., 2018).

Fig. 7. 2. 14 The interannual variations of atmosphere-to-land
CO2 fluxes in July. (a) GTMand TWR, (b) INVand TWR. Units
are kg Cm−2 month−1 (Takata et al., 2017).

7. 3. Ice mass change and the global sea level
Under the strong warming in the Arctic, small glaciers
are going to disappear, and Greenland Ice Sheet will lose
its ice mass through melting and discharge at the ice edge
and then contribute to the global sea level rise. The
Greenland ice sheet was losing mass at a rate accelerating from −121 Gt a−1 in 1993–2010 to −229 Gt a−1 in
2005–2010 (IPCC, 2013). The mass loss was most significant in southeastern Greenland at the beginning, andsince 2005 mass loss has been spreading particularly tonorthwestern coast. Here, in the GRENE Arctic Climate

Fig. 7. 2. 13 The annual, seasonal and monthly mean atmosphereto-land CO2 fluxes averaged for 2004–2007 by TWR (black),
GTM (red), SVR (light and dark green), and INV (blue). Error
bars denote the ranges of the inter-model variations. Units are
kg Cm−2 month−1 (Takata et al., 2017).

Change Project, main activities were concentrated tonorthwestern area of Greenland, where the investigations
were limited before hands. Some activities were alsodone at the mountain glaciers, particularly in the East Siberia glaciers.

on the target time scale and horizontal scale. In the future, by conducting a systematic investigation, such as

Field observations to monitor mass balance and clarify

conducting a comparative experiment with a common

the mechanism of mass balance, Greenlandtime scale and horizontal scale, the way to understand the

Coastal regions of Greenland contain a number of gla-

CO2 balance was shown.

ciers and ice caps (GICs), which are physically separated
126

Fig. 7. 3. 2 Mean melt rate at Q1201–Q1207 measured overthe periods indicated in Table 1. The dashed line is the extrapolation of the line connecting the data at Q1201 and Q1202(Sugiyama et al., 2014).

of the initial glaciological investigation on this ice cap innorthwestern Greenland. Qaanaaq ice cap (77°28’ N,
69°14’ W) is located in northwestern Greenland, covering an area of 289 km2 and an elevation range of 30–1110
m a. s. l. over the central part of a peninsula (Fig. 7.3.1).
A number of outlet glaciers protrude from the ice cap for
distances of 1–10 km. The study site in the 2012 summer
field campaign was Qaanaaq Gletscher, which flows
southwest from the southern part of the ice cap (Fig.
7.3.1c). The field observations were performed along asurvey route, which follows a glacier flow line from one

Fig. 7. 3. 1 (a) Satellite image (Landsat, 5 February 1999)
showing northwestern Greenland including Qaanaaq. The
inset shows the location of the region in Greenland, and thebox indicates the area shown in (b). (b) Satellite image of QIC(ALOS PRISM, 25 August 2009). The locations of Qaanaaq
Airport and the temperature measurement site on the ice cap(SIGMA-B) are indicated by asterisks. The box indicatesthe area covered by (c). (c) Satellite image (ALOS PRISM,
25 August 2009) of the study site, showing the locations of
the measurement sites for surface melt (+), ice velocity (*),
ice radar (*) and the GPS reference station (*). The arrows
are horizontal surface flow vectors from 18 to 29 July 2012
(Sugiyama et al., 2014).

of the highest peaks of the ice cap to the terminus of Qaanaaq Gletscher. Melt rates measured ranged from 7 mm
w. e. d−1 at the ice-cap summit to 46 mm w. e. d−1 at thelowermost site. The mean rate of the seven sites was 30mm w. e. d−1. The rates were greater down-glacier ingeneral, but the relationship to elevation was not simple
as seen in Fig. 7.3.2. The melt rate obtained near the terminus of Qaanaaq Glettscher (46 mm d−1) is comparable
to the values reported for mid-latitude glaciers. The observed melt rates represented midsummer melt conditions
on QIC over the last several years. This ice surface overfrom the Greenland ice sheet. The total area of these ice5

the region including Q1203–Q1205 was covered by dark

2

bodies (1.3 × 10 km ) accounts for 7% of the entire icematerials, and thus absorbed more solar radiation as a re-cover over Greenland, so their volume change has sub-sult of low albedo. The spatial variation in surface re-stantial influence on sea-level rise. Because GICs are lo-

flectance was clearly observed by in situ visual observa-

cated at relatively lower elevations, they are susceptible

tions (Fig. 7.3.3). Darkening of the glacier surface hadto the recent strong warming trend in Greenland.

been reported in many glaciers in the world, including

Sugiyama et al. (2014) reported melt rates, ice velocity

marginal regions of the Greenland ice sheet as alreadyand ice thickness of Qaanaaq ice cap (QIC) as the results

shown in Section 3. 4. Such changes in ice surface con127

Fig. 7. 3. 4 Horizontal ice velocity variations at Q1203 anddaily mean temperature with its variation range measured at
Qaanaaq Airport (Sugiyama et al., 2014).

frozen ice temperature computed by the numerical model. Melt water should had been routed through ice to thebed, although surface annual mean air temperature wasfar below the melting point (below −8°C at the terminus).
To better understand the processes controlling recent
mass loss of peripheral glaciers and ice caps in northwestern Greenland, Tsutaki et al. (2017a) measured surface mass balance (SMB), ice velocity and near- surface
ice temperature on Qaanaaq Ice Cap in the summers of

Fig. 7. 3. 3 Photographs showing (a) clean and (b) dark ice
surface conditions on QIC. Photographs were taken (a) at
Q1201 viewing up-glacier on 18 July 2012 and (b) at the
middle reach of Q1203 and Q1204 viewing down-glacier on 22
July 2012 (Sugiyama et al., 2014).

2012–16. The measurements were performed along asurvey route spanning the terminus of an outlet glacier tothe upper reaches (243–968 m a.s.l.). The most negative
SMB was observed in 2014/15, whereas the most positive was in 2012/13 (Fig. 7.3.5a). For example, SMB atditions resulted in melt increase due to the reduction in

Q1201 in 2014/15 was 1.8 times more negative than inalbedo, and thus accelerate mass loss of glaciers and ice

2012/13. The ice-cap-wide SMB ranged from −1.10±

sheets.

0.29 to −0.13±0.26 m w.e. a−1 for the years from 2012/13

The flow vectors of ice motion are shown in Fig.

to 2015/16, and the mean glacier-wide SMB from 2012

7.3.1c. Ice flowed faster in the middle part of the survey

to 2016 was −0.22±0.30 m w.e. a−1. Mass balance showedroute, where ice was expected to be thick. The greatest

substantially large fluctuations over the study period un-

horizontal velocities were observed at Q1203 (67.5 mm

der the influence of summer temperature and snow accu-d−1) and Q1204 (69.0 mm d−1), and from that region themulation. The equilibrium line altitude was 862±8,

velocity progressively decreased down- and up-glacier.

868±19, 1001±12 and 932±9 m a.s.l. for the balance years−1

Flow velocity was only 4.0 mm d at the lowermost sitefrom 2012/13 to 2015/16. Ice velocity (Fig. 7.3.6) showed

Q1201 ~500 m from the terminus. Vertical ice motion

seasonal speedup only in the summer of 2012, suggesting

was downward except for Q1201, where ice emerged at aan extraordinary amount of meltwater penetrated to therate of 0.9 mm d−1. The continuous GPS measurement at

bed and enhanced basal ice motion. Ice temperature at a

Q1203 had shown significant ice velocity variations

depth of 13 m was −8.0°C at 944 m a.s.l., which was−1within the range 36–94 mm d , equivalent to 50–140%

2.5°C warmer than that at 243 m a.s.l., suggesting thatof the mean velocity (Fig. 7.3.4). The variations were di-ice temperature in the upper reaches was elevated by re-

urnal, with minimum and maximum velocities in earlyfreezing and percolation of meltwater. Our study provid-morning and late afternoon, respectively. Diurnal flow

ed in situ data from a relatively unstudied region invelocity variations at Q1203 imply that surface meltwater

Greenland, and demonstrated the importance of contin-

drained to the bed through cold ice and enhanced basal

ued monitoring of these processes for longer timespans

motion. Thus, at least a part of the glacier bed is melting

in the future.

in this region. This observation contradicted the wholly

Kinematic GPS measurements provide in-situ data
128

Fig. 7. 3. 6 (a) Summer ice flow velocity measured at Q1201–
Q1206 in 2012–16. (b) Annual mean ice flow velocity at
Q1201–Q1206 in 2012/ 13–2015/16 (Tsutaki et al., 2017a).

Fig. 7. 3. 5 (a) Measured SMB at Q1201–Q1206 (cross) andthe SMB gradient every 100 m bin for the balance years
2012/13–2015/16. (b) Hypsometry of QG (blue) and QIC(purple) with altitude bands of 100 m (Tsutaki et al., 2017a).

crucial for measuring the surface elevation change ofglaciers. Owing to their accuracy, which is generally
better than half meter, surface elevations derived fromkinematic GPS surveys are useful and essential for generating precise digital elevation models (DEMs) andevaluating geometry changes of glaciers. Tsutaki et al.
(2017b) presented a surface elevation data set derived
from kinematic GPS measurements covering the lower 5
km of Qaanaaq and Bowdoin Glaciers in northwestern
Greenland. The data included elevations over ice-freeterrain nearby the glaciers, important for calibrating re-Fig. 7. 3. 7 The mean rate of elevation change on HIC analyzed
with a grid resolution of 500 m (○), generated DEMs of 100m (*). The blue line indicates the hypsometry of the ice cap
surface shown in 100 m altitude bins (Saito, J. et al., 2016).

mote sensing data. More than 600,000 GPS survey data
points were processed to produce a 1-m resolution meshgrid of elevation data in a CSV file format. Based on ourerror analysis, the accuracies of the elevation data were
better than 0.2 and 0.3 m in horizontal and vertical direc-

from the ablation area of the ice sheet and local glaciers.

tions, respectively. This dataset can be utilized to investi-

The database spans the 123 a from 1892 to 2015, con-gate glacier surface elevation changes by making com-tains a total of ~3000 measurements from 46 sites, and isparison with DEMs obtained in the past, and from future

openly accessible through the PROMICE web portal

remote sensing and in-situ observations.

(http://www.promice.dk).

These data were also provided to the International
Ice loss of Greenland by satellite measurements

communities (Machguth et al., 2016). Glacier surface
mass-balance measurements on Greenland started more

Saito, J. et al. (2016) presented the surface elevation

than a century ago, but no compilation exists of the ob-changes of six ice caps near Qaanaaq in northwesternservations from the ablation area of the ice sheet and lo-

Greenland based on DEMs generated from the Advanced

cal glaciers. Such data could be used in the evaluation ofLand Observing Satellite, Panchromatic Remote-sensing

modeled surface mass balance, or to document changes

Instrument for Stereo Mapping (ALOS PRISM) stereo

in glacier melt independently from model output. Here,pair images. They quantified volume changes of the ice

Machguth et al. (2016) present a comprehensive databasecaps during periods between 2006 and 2010. The magni-

of Greenland glacier surface mass-balance observations

tude of the elevation change generally decreased from129

Fig. 7. 3. 8 The rate of elevation change averaged in each elevation band (red and black) and hypsometry (blue) of the studied ice
caps as shown in 100 m altitude bins. Results are shown for each individual ice cap (a–f) and the entire study area (g). Elevation
change in snow covered high elevation area was assumed as zero (black) or extrapolated from the data in lower elevation (red line).
The red dashed line indicates the mean of these two estimates (Saito, J. et al., 2016).

high to lower elevation, and the rates of the change ob-

2003–2008 period (−0.6±0.1 m a−1) (“North-west sector”

tained at every 100 m elevation range agreed each otherin Bolch et al., 2013). Because the mean elevation waswithin 4–25% (Fig. 7.3.7). Based on these results, theyhigher in this study, the obtained greater thinning rate

assumed that their analysis with 500-m-resolution DEMs

was not due to enhanced thinning in lower elevation, but

was sufficient to quantify total mass loss of the ice caps

likely represented a change in time from 2003–2008 to

and its dependence on surface elevation. All of the ice

2006–2010. This result indicated that the mass loss ofcaps had lost ice mass during this period, with a mean el-

GICs in northwestern Greenland was increasing in recent

evation change rate averaged all over the analyzed ice

years. This was consistent with the increase in ice mass

cap surface area (1215 km 2) of −1.1±0.1 m a −1. The

loss in this region since 2005 (Khan et al., 2010). More-mean rate over each individual ice cap ranged from −1.8

over, thinning rates obtained in this study were signifi-−1

to −0.8 m a , with the greatest thinning rate observed on

cantly greater than those observed on GICs in other re-

Qaanaaq Ice Cap (QIC). Compared to the other five ice

gions at similar latitudes (74°–82°N), in Svalbard (0.12

caps, the elevation change on Steensby Land Ice Capm a−1 for 2003–2008; Moholdt et al., 2012) (0.27 m a−1(SLIC) was relatively small. The ice caps had thinned infor 2001–2010; Zemp et al., 2015) and the Canadian Arc-the entire elevation range, but more significantly in lower

tic Archipelago (0.27 m a−1 for 2003–2009; Nilsson et al.,elevation areas (Fig. 7.3.8). For example, at QIC, the2015). The recent increase in mass loss agreed with amean elevation change rate in the area below 100 m a.s.l.

warming trend in the region studied, which was repre-−1was −3.9 m a , whereas between 1000 and 1100 m a.s.l.

sented by 10% increase in the summer positive de-it was −1.7 m a−1. This reduction in surface elevation in-gree-days (PDD) from 2003–2008 to 2006–2010. More-dicated significant ice cap thinning and mass loss. The

over, reduction in ice and snow surface albedo shouldrate of the total volume change of the six ice caps washave enhanced melt rates and negative surface mass bal-

6

3

−1

−1369 × 10 m a , equivalent to a mass loss of 1.2±0.1

ance. The ice caps in this study showed significantly dif-−1

Gt a , amounting to a sea-level-rise of 0.0032±0.0002

ferent rates of surface elevation change, which was prob-

mm a−1.

ably affected by spatially varying precipitation rates as

The mean rate of elevation change over the six ice
2

well as ice cap surface albedo, but this was not fully in-−1

caps (1215 km ) was −1.1±0.1 m a for the 2006–2010

terpreted in this study.

period. This rate was 1.8 times greater than that reported

Satellite images were analyzed by Sakakibara and2

for GICs in northwestern Greenland (4340 km ) for theSugiyama (2018) to measure the frontal positions and ice
130Fig. 7. 3. 9 Cumulative frontal displacements of the studied
glaciers from the 1980s to 2014. Upward change in the ordinate
represents glacier advance (Sakakibara and Sugiyama, 2018).

Fig. 7. 3. 10 (a) Cumulative frontal displacement (red) andannual ice speed of Heilprin, (b) Tracy, (c) Farquhar, (d)Bowdoin and (e) Diebitsch Glaciers. The speed was measured
near the front (distance approximately equal to a half glacierwidth) along the glacier centerline (Sakakibara and Sugiyama,2018).

speeds of 19 marine- terminating outlet glaciers along thecoast of Prudhoe Land, northwestern Greenland from1987 to 2014. To measure glacier front positions and ice
flow speed, they used satellite images of the Landsat 8
Operational Land Imager (OLI) (level 1 T, with 15/30 mthat experienced greater acceleration, as represented byresolutions, 2013/14), the Landsat 7 Enhanced ThematicTracy Glacier, which experienced a retreat of 200 m a−1

Mapper Plus (ETM+) (level 1T, with 15/30 m resolu-

and a velocity increase of 930 m a−1 during the study pe-tions, 1999–2014), and the Landsat 4 and 5 Thematic

riod. A possible interpretation of this observation was

Mapper (TM) (level 1T, with 30 m resolution, 1987–98)

that flow acceleration induced dynamic thinning near the

distributed by the US Geological Survey. All the studied

termini, resulting in enhanced calving and rapid retreat ofglaciers retreated over the study period at a rate of be-

the studied glaciers. It was hypothesized that atmospher-

tween 12 and 200 m a−1, with a median (mean) retreat

ic warming conditions in the late 1990s triggered glacier−1

rate of 30 (40) m a (Fig. 7.3.9). The glacier retreat be-

retreat in northwestern Greenland since 2000.

gan in the year ~2000, which coincided with an increase

Fig. 7.3.10 compares the timing of frontal retreat within summer mean air temperature from 1.4 to 5.5°C be-

the flow acceleration of selected glaciers. Tracy Glacier

tween 1996 and 2000 in this region. Tracy Glacier re-accelerated twice in 1999–2005 (from 770 to 1230 m a−1

treated most rapidly during the study period (Fig. 7.3.9).

at 2.0 km from the most retreated front) and in 2010–14

The glacier advanced by 0.7 km between 1987 and 1999,(from 1250 to 1740 m a−1) (Fig. 7.3.10b). The onset ofand then retreated by 4.8 km from 1999 to 2007. The re-the first speedup coincided with the disintegration of thetreat rate slightly decreased after 2007, but it increased

glacier tongue shared with Farquhar Glacier, which oc-

again in 2011 and the glacier retreated by 1.0 km fromcurred in the summer of 2001 (Fig. 3.4.10c). This disin-2011 to 2014. The total retreat distance from 1999 totegration affected ice speed of Farquhar Glacier as well.

2014 was 5.7 km (Fig. 7.3.9). Ice speed near the front ofThis interpretation assumed the frontal retreat as the trig-−1

the studied glaciers ranged between 10 and 1740 m a ingering mechanism of the flow acceleration. The second

2014, and many of them accelerated in the early 2000s.

proposed mechanism of flow acceleration was basal ice

In general, the faster retreat was observed at the glaciers

motion enhanced by increased meltwater input to the gla131

cier bed. These data also suggested that glacier bed topography was important for understanding the spatial extent of flow acceleration. Flow acceleration of these
glaciers was greater near the front, thus it enhanced
stretching flow regimes along the glaciers and induced
dynamic thinning. The termini of Heilprin, Tracy, Farquhar and Bowdoin Glaciers were close to the hydrostatic
equilibrium condition during the retreat, suggesting thatthinning of the glaciers resulted in ice flotation and rapid
retreat. It was hypothesized that the general retreat trend
in Prudhoe Land after 2000 was triggered by atmosphericwarming, with five glaciers retreating more rapidly thanthe others under the influence of dynamic thinning induced by flow acceleration. Because it affects the spatial
extent of flow acceleration as well as the glacier front
stability, ocean and glacier bed geometry is an important
controlling factor of future change in outlet glaciers inGreenland.
Mechanism of calving glacierFig. 7. 3. 11 (a) Mean displacement of the Bowdoin Glacierfront position relative to 28 September 1987 (crosses), negativechange in the ordinate represents glacier retreat, and annual
mean ice speed at B1301 (empty circles). (b) Annual PDD(empty circles) at Thule Air Base, July and August mean SST
at 77–78°N, 66–72°W (triangles) and sea- ice opening datein Bowdoin Fjord (filled circles). The sea-ice opening date isdefined as the midpoint of the transition from 100% to 0% seaice cover, indicated by the vertical ranges associated with thedata points (Sugiyama et al., 2015).

To better understand recent rapid recession of marine-terminating glaciers in Greenland, Sugiyama et al.
(2015) performed satellite and field observations near thecalving front of Bowdoin Glacier, a 3 km wide outlet glacier in northwestern Greenland. Former observations
suggested that ice mass loss was spreading from thesouth to the northwest, so collecting data in this region
was of crucial importance.
To investigate the recent frontal variations of Bowdoin
Glacier, they analyzed 44 satellite images acquired in

400 ±10 m at 5.3 km from the terminus. The transverse

May–September between 1987 and 2013. These images

cross sections show typical glacier valley shapes, with awere acquired by the Landsat 5 Thematic Mapper (TM)relatively flat bed in the glacier center and steeply in-sensor, the Landsat 7 Enhanced Thematic Mapper Plusclined side-walls (Fig. 7.3.12b–d). The bed geometries(ETM+) sensor and Landsat 8 Operational Land Imager

were asymmetric along profiles T2 and T3, indicating the(OLI) and were distributed by the United States Geologi-

valley was deeper in the western half of the glacier (Fig.

cal Survey (http://landsat.usgs.gov/). Satellite data re-7.3.12c and d). The radar measurements revealed thatvealed a clear transition to a rapidly retreating phase in

the glacier ice was deeply submerged below sea level2008 from a relatively stable glacier condition that lasted(Fig. 7.3.12). Along the central flowline, ice below sea

for >20 years (Fig. 7.3.11). The terminus position had

level accounted for 60–90% of the full ice thickness (Fig.

been fairly stable from 1987 to 2008, except for a 230 m

7.3.12a). Sea level at the glacier front was nearly equal

retreat from 1999 to 2001. Rapid retreat then began be-to the ice flotation level, suggesting that the glacier ter-

tween July 2008 and June 2009. The retreat rate de-minus was grounded (Fig. 7.3.12a and b).

creased in 2011, but increased again in 2012. The mean

The lack of tidal vertical ice motion also indicated aretreat distance from July 2008 to September 2013 wasgrounded condition. Nevertheless, sea level was very

1.15 km (0.22 km a−1).

close to the flotation level, suggesting that calving trig-

Ice radar measurements showed that the glacier front

gered by ice flotation regulated ice front position. Thiswas grounded, but very close to the floating condition.

hypothesis was consistent with the previously proposed

Ice thickness gradually increased up-glacier and reachedidea that the front position was controlled by the ice
132

Fig. 7. 3. 13 Altitudinal distribution of surface mass balance(SMB) for Tugto Glacier in 2012/13 (blue diamonds) andBowdoin Glacier in 2014/15 (red squares). Red and blue circles
are mean rates of surface elevation change (dh/dt) of Bowdoin
and Tugto Glaciers for 2007–10 for 20 m bins. The error bars
indicate the standard deviations within the bins. Dashed lineis the linear regression of SMB data on Tugto and Bowdoin
Glaciers (Tsutaki et al., 2016).
Fig. 7. 3. 12 Ice surface and bed elevations along the ice
radar profiles, (a) profile L, (b) profile T1, (c) profile T2 and(d) profile T3. The dashed lines are the ice flotation levels
computed with ice and sea-water densities of 910 and 1025
kgm–3. The shaded regions are used to compute the fraction of
ice thickness below sea level (Sugiyama et al., 2015).

strate that the dynamics of Bowdoin Glacier are sensitive
to small perturbations occurring near the calving front.
To quantify recent thinning of marine-terminating outlet glaciers (tidewater glaciers) in northwestern Greenland, Tsutaki et al. (2016) carried out field and satellite
observations near the terminus of Bowdoin Glacier. Sat-

thickness above flotation and rapid retreat of a calving

ellite images were used for the photogrammetric analy-

glacier was initiated by thinning of ice (Van der Veen,sis. Panchromatic Remote-sensing Instrument for Stereo

1996). Near the calving front, ice progressively thinned

Mapping (PRISM) images from the Advanced Land Ob-

as it flew down-glacier under the influences of stretching

serving Satellite (ALOS) have a spatial resolution of 2.5

ice flow and surface melting. Presumably, calving oc-

m, which is sufficient to measure several meters of gla-

curred when ice became thinner than the threshold andcial elevation change. These data were used to computesea level reached the flotation level. These results, inthe change in surface elevation from 2007 to 2013 andcombination with the results of ocean depth soundings,

this rate of thinning was then compared with that of thesuggested bed geometry in front of the glacier was theadjacent land-terminating Tugto Glacier, as shown in Fig.

primary control on the rate and pattern of recent rapid re-7.3.13. Comparing DEMs of 2007 and 2010 shows thattreat.

Bowdoin Glacier is thinning more rapidly (4.1±0.3 m a−1)

Presumably, glacier thinning due to atmospheric and/than Tugto Glacier (2.8±0.3 m a−1). The thinning rate ofor ocean warming triggered the initial retreat. In situ

Bowdoin Glacier was 1.5 times greater than that ofmeasurements showed complex short-term ice speed

land-terminating Tugto Glacier. Because Bowdoin andvariations, which were correlated with air temperature,

Tugto Glaciers are only ~3 km apart and at a similar alti-

precipitation and ocean tides. Ice speed quickly respond-

tude range, it would be expected that the surface melt in-

ed to temperature rise and a heavy rain event, indicating

duced thinning to be similar. Moreover, the negative

rapid drainage of surface water to the bed. Semi-diurnal

SMB observed in this study accounts for only <40% of

speed peaks coincided with low tides, suggesting the ma-

the surface elevation change on Bowdoin Glacier forjor role of the hydrostatic pressure acting on the calving

2007–10. This implies that the rapid thinning of Bowdoin

face in the force balance. These observations demon-

Glacier is affected by ice dynamics. To quantify the con133

these contrasting flow regimes of marine- and land-terminating glaciers on ice thinning, they calculated longitudinal strain rate along the flowline of Bowdoin andTugto Glaciers (Fig. 7.3.14b). At Bowdoin Glacier, thestrain rate increased down-glacier and reaches ~0.043 a−1~4000 m from the terminus. It is likely that this dynamically-controlled thinning has been enhanced by the acceleration of the glacier since 2000. The measurements indicated that ice dynamics indeed play a predominant rolein the rapid thinning of Bowdoin Glacier.
From the ice speed measurements carried out on Bowdoin Glacier by Sugiyama et al. (2015), short term speed
variations were found, correlating with air temperatureand precipitation on the synoptic time scale, and with thesemi-diurnal ocean tides. These observations led to theassumption that the glacier flow is sensitive to (1) water
input at the surface rapidly drained to the bed, resulting
in basal lubrication, and (2) changes in the force balanceat the calving front. In order to simulate these processes,
Seddik et al. (2019) set up two series of numerical experiments, in which the sensitivity of the glacier flow wastested to increased basal lubrication (reduced drag) as
well as varying sea level in the range of the tidal amplitude. It was found that the tested range of reduced basal
drag (10–40%) approximately covers the strength of theobserved episodic speed-ups at the site. The simulated
response of the glacier flow to ocean tides is most pro-

Fig. 7. 3. 14 (a) Surface ice speed and (b) longitudinal strain
rate averaged over 2007–13 along the flowlines on Bowdoin
and Tugto Glaciers. The strain rate was filtered with a local
regression smoothing routine with a bandwidth of 700 m(Tsutaki et al., 2016).

nounced near the calving front and decays to almost zeroa few kilometers upstream. It was also found that, inagreement with the observations, the tidal forcing and thesurface speed are in anti-phase: High tide corresponds to
low speed, and low tide corresponds to high speed.

tribution of ice dynamics to the observed thinning, theHowever, the mean speed was underpredicted by ~ 20%,

ice flow speed of Bowdoin and Tugto Glaciers were ana-

and, more severely, the semi-diurnal speed amplitude

lyzed.

was underpredicted by a factor ~ 3. This study investi-

Fig. 7.3.14a shows the mean flow speed for 2007–13

gated only the present dynamics of Bowdoin Glacier byalong the flowline of Bowdoin and Tugto Glaciers. The

means of diagnostic simulations. Further modeling work

ice speed of Bowdoin Glacier increased towards the gla-

would be desirable to improve the understanding of thecier front, reaching 457 m a−1 near the calving front, indi-changes of the glacier in the recent past (Tsutaki et al.,cating a stretching ice flow regime in the region. Within2016) and, ultimately, to predict the future evolution of~3500 m of the front of Bowdoin Glacier, crevasses wereformed perpendicular to the ice flow direction as ob-

the glacier under warming scenarios for the atmosphereand the ocean.

served in satellite images. Formation of the transverse
Cryoseismology

crevasses was an indication of a longitudinal stretching
ice flow regime, which was typically observed near the

Glacier microseismicity is a promising tool to study

front of calving glaciers. On the other hand, a decrease

glacier dynamics. However, physical processes connect-

in speed toward the terminus of Tugto Glacier indicated a

ing seismic signals and ice dynamics are not clearly un-compressive flow regime. To examine the influence of

derstood at present. Particularly, the relationship be134

tween tide-modulated seismicity and dynamics of calving
glaciers remains elusive. Here, Podolskiy et al. (2016)
analyzed records from an on-ice seismometer placed 250
m from the calving front of Bowdoin Glacier, Greenland.
Using high-frequency glacier flow speed measurements,
they showed that the microseismic activity was related tostrain rate variations. The seismic activity correlatedwith longitudinal stretching measured at the glacier surface. Both higher melt rates and falling tides accelerated
glacier motion and increased longitudinal stretching.

Fig. 7. 3. 15 Rrs555 values averaged from 2002 to 2014 (Ohashi
et al., 2016).

Long-term microseismic monitoring could therefore provide insights on how a calving glacier’s force balance
and flow regime react to changes at the ice-ocean interface.
Kanao et al. (2012) also discussed the relation ofGreenland Ice Sheet dynamics and glacial earthquake activities as a Cryoseismology, and contributed to theGreenland Ice Monitoring Network (GLISN).
Glacier change affecting ocean
Glacial meltwater discharge from the Greenland ice
sheet and ice caps forms high turbidity water in the
proglacial ocean off the Greenland coast. Although thetiming and magnitude of high turbidity water export affect the coastal marine environment, for example,
through impacts on biological productivity, little is
known about the characteristics of this high turbidity water. Ohashi et al. (2016) reported on the spatial and temporal variations in high turbidity water off the Thule region in northwestern Greenland, based on remote sensing
reflectance data at a wavelength of 555 nm (Rrs555).

Fig. 7. 3. 16 Scatter plots of annual maximum extent of thehigh turbidity area and (a) summer mean temperature, (b) windstress, and (c) wind stress direction from 1 July to the timing ofannual maximum extent. Solid lines show linear regression of
the data (Ohashi et al., 2016).

Rrs555 data used for this study were obtained from theNASA Moderate Resolution Imaging Spectroradiometer(MODIS) on the Aqua multispectral platform. They used
the level three data with a spatial resolution of about
0.0417° and temporal resolution of eight days. The period of analysis was from July to August (the period corre-

The extent of the high turbidity area exhibited substan-sponding to open water in summer) during 2002–2014.

tial seasonal and interannual variability, and its annual

These data products were downloaded from NASA’s

maximum extent was significantly correlated with sum-

Ocean ColorWeb (http://oceancolor.gsfc.nasa.gov). The

mer air temperature (Fig. 7.3.16). This positive correla-high turbidity area, identified on the basis of high reflec-tion suggests that the amount of turbid glacial meltwater−1

tivity (Rrs555 >= 0.0070 sr ), was generally distributed

input increased because of increasing amount of surface

near the coast, where many outlet glaciers terminated inmelting. On the other hand, no clear correlation was ob-

the ocean and on land (Fig. 7.3.15). Particularly in Wol-

served between the turbid area and the strength/ direction

stenholme Fjord, Rrs555 values were notably highof wind stress (|R| < 0.4, p > 0.3) (Fig. 7.3.16b and c).

−1(0.007–0.01 sr ) near the coast, but were relatively low

There was no significant correlation with zonal or merid-−1(less than 0.004 sr ) in the offshore region away fromional components of wind stress. These results suggested

the ice sheet.

that sediment transport resulting from wind-induced up135

welling had only minor influence on high turbidity water

governmental Panel on Climate Change’s RCP8.5 sce-near the ocean surface. To summarize, the source of thenario produced additional first-century contributions toturbid water observed off the Thule region was most like-sea level of 22.3 and 8.1 cm from Greenland and Antarc-

ly the discharge of glacial meltwater, rather than re-sus-

tica, respectively, with a range among models of 62 andpension of sediments. Assuming a linear relationship be-

14 cm, respectively. By 200 years, projections increased

tween the high turbidity area and summer temperature,

to 53.2 and 26.7 cm, respectively, with ranges of 79 andannual maximum extent increased under the influence of

43 cm. Linear interpolation of the sensitivity results

increasing glacial meltwater discharge, as could be in-closely approximated these projections, revealing the rel-ferred from present and predicted future warming trends.

ative contributions of the individual forcings on the combined volume change and suggesting that total ice-sheet

Ice sheet modelingresponse to complicated forcings over 200 years could be

In the Fourth Assessment Report (AR4) from the Inter-linearized.

governmental Panel on Climate Change (IPCC), sea-lev-

The SeaRISE effort explored the sensitivity of the cur-el projections for the year 2100 ranged from 0.18 to 0.59

rent generation of ice sheet models to external forcing tom, but these values excluded ‘future rapid dynamical

gain insight into the potential future contribution to sea

changes in ice flow’ (IPCC, 2007). The additional caveat

level from the Greenland and Antarctic ice sheets. All

that these projections do not include ‘the full effects ofparticipating models simulated the ice sheet response tochanges in ice-sheet flow, therefore the upper values ofthree types of external forcings: a change in oceanic con-the ranges are not to be considered upper bounds for sea

dition, a warmer atmospheric environment, and enhanced

level rise’ further weakened the utility of the projectedbasal lubrication. Here, Nowicki et al. (2013) presented

ranges to drive policy decisions related to climate

an analysis of the spatial response of the Greenland icechange. This situation resulted from the fact that no ice-

sheet, and explored the impact of model physics andsheet model could reproduce recent observed rapid

spin-up on the projections. Although the modeled re-changes in ice-sheet elevation and velocity, so there wassponses were not always homogeneous, consistent spatial

no means to include the possible future evolution of thesetrends emerged from the ensemble analysis, indicatingchanges in a deterministic way. The IPCC AR4 conclu-

distinct vulnerabilities of the Greenland ice sheet. Theresions regarding the difficulties in credibly projecting fu-

were clear response patterns associated with each forc-

ture sea level had focused the glaciological community’sing, and a similar mass loss at the full ice sheet scale re-efforts to understand the cause of the observed changes

sulted in different mass losses at the regional scale, as

in a deterministic way so that the causal processes could

well as distinct thickness changes over the ice sheet. All

be included in ice-sheet numerical models.

forcings led to an increased mass loss for the coming

One strategy to deal with this situation led to the proj-

centuries, with increased basal lubrication and warmer

ect SeaRISE (Sea-level Response to Ice Sheet Evolu-

ocean conditions affecting mainly outlet glaciers, whiletion). Ten ice-sheet models were used to study sensitivi-

the impacts of atmospheric forcings affected the whole

ty of the Greenland and Antarctic ice sheets to prescribed

ice sheet.

changes of surface mass balance, sub-ice-shelf melting

Saito, F. et al. (2016) revisited the future surface cli-

and basal sliding (Bindschadler et al., 2013). Results ex-

mate experiments on the Greenland ice sheet proposed

hibited a large range in projected contributions to sea-by the SeaRISE study (Bindschadler et al., 2013). The

level change. In most cases, the ice volume above flota-

projections of the different SeaRISE participants showedtion lost was linearly dependent on the strength of thedispersion, which has not been examined in detail toforcing. Combinations of forcings could be closely ap-

date. A series of sensitivity experiments were conducted

proximated by linearly summing the contributions fromand analyzed using the ice-sheet model for integrated

single forcing experiments, suggesting that nonlinear

Earth-system studies (IcIES) by replacing one or more

feedbacks were modest. The models indicated that

formulations of the model parameters with those adopted

Greenland is more sensitive than Antarctica to likely at-

in other model(s). The results showed that large potentialmospheric changes in temperature and precipitation,

sources of the dispersion among the projections of thewhile Antarctica is more sensitive to increased ice-shelfdifferent SeaRISE participants were differences in thebasal melting. An experiment approximating the Inter-

initialization methods and in the surface mass balance136methods, and both aspects had almost equal impact on

ent accuracies, ranging from poor (20 km grid) to reason-

the results. The treatment of ice-sheet margins in theably good (5 km grid). In the future-climate experiments,

simulation had a secondary impact on the dispersion. We

the simulated absolute ice volumes depended significant-concluded that spinning up the model using fixed topog-

ly on the resolution, while the sensitivities (ice volumes

raphy through the spin-up period while the temperature

relative to the constant-climate control run) varied only

was allowed to evolve according to the surface tempera-

by a few centimeters of sea-level equivalent.

ture history was the preferred representation, at least for
Observations of Mountain Glaciers

the experiment configuration examined in the present paper. A benchmark model experimental setup that most of

Mountain glaciers are small in volume compared to

the numerical models could perform was proposed for

the Greenland ice sheet; therefore or however, they show

future intercomparison projects, in order to evaluate therapid response to climate change compared to ice sheets

uncertainties relating to pure ice-sheet model flow char-and known as sensitive indicators of climate change. The

acteristics.

glacial and mountain environments of the Arctic are

Greve and Herzfeld (2013) applied the dynamic/ther-changing drastically due to the recent intense warming;

modynamic shallow-ice model SICOPOLIS to theyet the Siberian glaciated region is one of the least stud-Greenland ice sheet. Paleoclimatic spin-ups from 125 ka

ied areas in the whole Arctic. Shirakawa et al. (2016)

BP until today, as well as future-climate experiments 500

outlined the meteorological and glaciological observa-

years into the future, were carried out with three different

tions of Glcier No. 31 in the Suntar-Khayata Range, east

grid spacings, namely 20, 10 and 5 km. The scenarios

Siberia, during 2012 and 2014 (Fig. 7.3.17). This region,

were a subset of those specified by the SeaRISE commu-

located between 62° to 63°N and 140° to 142°E, forms anity effort. The bed topography included improved

watershed between the Arctic Ocean and the Sea of Ok-

troughs for Jakobshavn Isbræ, Helheim, Kangerdlugs-

hotsk. In the IGY (International Geophysical Year,suaq and Petermann glaciers, processed by an algorithm

1957–58) period, the Soviet Union set up a weather sta-that preserved shape, orientation and continuity of thetion in Suntar-Khayata mountains, where observations

troughs on the 5 km scale. Comparison of simulated andcontinued for several winters (Koreisha, 1963). Some ofobserved present-day surface velocities showed that thesethe findings from the GRENE Arctic Project by Takeuchi

ice streams and outlet glaciers were resolved with differ-

et al. (2015) were already mentioned in Chapter 3. The

Fig. 7. 3. 17 Map showing the locations of observation sites of the GRENE Project in the Suntar-Khayata Range. On Glacier No. 31,
the contour interval is 5 m. Open circles indicate glaciological observation sites in 2012, 2013 and 2014 (Shirakawa et al., 2016).

137

Fig. 7. 3. 19 Contour map of Glacier No. 31 derived from
DEM data from August 2013. The arrow indicates the surface
flow velocity from August 24, 2012 to August 16, 2013. The
spatial distribution of ice thickness of Glacier No. 31 is also
shown, measured by ice radar in August 2013 (Shirakawa et al.,2016).

Fig. 7. 3. 18 Change with altitude of the surface mass balanceof Glacier No. 31 from August 24, 2012 to August 16, 2013
(Shirakawa et al., 2016).

mean air temperature between July 2012 and August
2013 was −13.9°C at site 31–2 (2240 m a.s.l.; Fig.
7.3.17) and the minimum temperature was −46.0°C. The

km and 2.27 km2 in 2012/13, respectively, showing a

air temperature on the glacier from November to April

slight decrease over the last 54 years.

was approximately 10°C higher than that at Oymyakon
village, where the lowest temperature in the northern

7. 4. Effect on ocean circulation

hemisphere was recorded, suggesting a temperature inversion, which typically occurs during winter in this re-

The Labrador Sea is one of the most prominent regions

gion. The snow depth records show that the snow in-in the World Ocean where open ocean convection takes

creased at the beginning and end of winter, with almost

place. The open ocean convection reaches 1000–2300 m

no change from the beginning of October until the end of

depth and forms Labrador Sea Water (LSW). Many stud-

April. The maximum snow depth from the previous

ies have suggested the LSW influences the global cli-summer was 158 cm at site 31–2 on May 28, 2013.

mate, as it is one of the source waters for the North At-

The average annual surface mass balance for the 6

lantic Deep Water (NADW) and thus affects the Atlanticsites was −1256 mm water equivalent during the period

meridional overturning circulation. The winter time deep

from August 2012 to August 2013, indicating that abla-

convection is preconditioned by weak stratification in thetion proceeded rapidly in all areas of the glacier. In the

Labrador Current (LC) and the West Greenland Current

IGY period, there was an area of ice accumulation and an(WGC). The smaller scale (~100–200 km) localized cy-

equilibrium-line altitude in the vicinity of 2350 m a.s.l.

clonic circulation, which exposes water to wintertime(Koreisha, 1963). However, in this study, all areas of theheat loss for a longer period of time, also preconditions

glacier were ablation areas. As shown in Fig. 7.3.18,

the deep convection in the western Labrador Sea.

there was a high correlation between the elevation and

The large scale cyclonic boundary current system

the mass balance of each site. From the regression equa-(WGC and LC) transports buoyancy into the Labradortion obtained, the equilibrium-line altitude of this glacierSea. The Irminger Water (IW), which originates in thewas estimated to be 2799 m, significantly higher than in

Irminger Sea, is relatively warm and salty and transport-

the IGY period. This result indicated that ablation was

ed by WGC and LC in the subsurface along the continen-

proceeding in all areas of the glacier.

tal slope. On the other hand, cold/fresh water, which is

The surface flow velocity in 2013/2014 was 1.57 m a−1

originated in the Greenland and Arctic Seas, is transport-

at the approximate midpoint of the glacier (Fig. 7.3.19),

ed by WGC along the vicinity of the Greenland coast in

and was much slower than that measured during the IGY

the surface layer. This layered structure is also seen in−1

period (−4.5 m a ). The length and areal extent of thethe central Labrador Sea and formed by the eddy-induced

2

glacier were 3.85 km and 3.2 km in 1958/59 and 3.38

lateral transport from the shelf region. Chanut et al.
138

(2008) classified the eddies into convective eddies (CEs),

In order to investigate the role of eddies in the deepboundary current eddies (BCEs) and Irminger Rings

convection, the model’s buoyancy budget is analyzed(IRs). The CEs are generated by baroclinic instability ofhere. Since the transported buoyancy inhibits the deep

the rim current encompassing the convection area andconvection in winter and restratifies the homogenized

more rapidly (less than two months) restratify the ho-water coloumns in summer, the year-round buoyancy

mogenized water columns than air-sea flux (~half year)

transport should be analyzed. Fig. 7.4.1 shows the 5-year

does in the shallow layer.

mean heat budget integrated vertically from the sea sur-

The mesoscale eddies on the deep convection in theface to the bottom. Heat is lost over the whole Labrador

Labrador Sea are examined by Kawasaki and Hasumi

Sea through the sea surface (Fig. 7.4.1a). This is essen-(2014) using a realistically configured eddy-resolving

tially due to the sea surface cooling in winter. In theice-ocean model. The ice-ocean general circulation mod-boundary current region, heat is gained by the mean cur-el employed in the study is CCO version 4 (Hasumi,

rent (the WGC) and lost by eddies (Fig. 7.4.1b and c).

2015). The model domain is global. The model is for-On the other hand, the interior Labrador Sea gains heat

mulated on the general curvilinear horizontal coordinates

by mesoscale eddies (Fig. 7.4.1c). The small residual(see Fig. 5.4.4). Its poles are placed on the Labrador(Fig. 7.4.1d) indicates that the model is well thermally

Peninsula and Greenland, and the horizontal resolution isequilibrated during the analyzed period.

eddy resolving (4–5 km) in the Labrador Sea, eddy per-The fresh water content (FW) is defined here as

mitting around the Cape Hatteras, Irminger and GreenFW = ∫(Sref − S)/Sref dV, land Seas (10–50 km), and coarse (> 50 km) in other re-(7.4.1)

gions. The sea surface heat, freshwater and momentum
fluxes are calculated using a daily climatology of surface

where V indicates the volume under consideration, S is

air properties based on the ECMWF reanalysis.

salinity and Sref is the reference salinity. The reference

The cyclonic circulation including the WGC and LC,

salinity is set to 34.8 g kg−1, which is the average in thewhich are part of the North Atlantic subpolar gyre, isLabrador Sea. The budget of the vertically integrated

qualitatively well reproduced in the Labrador Sea. The

fresh water content is shown in Fig. 7.4.2. Because of

three types of eddies (IR, BCE, CE) proposed by Chanut

the melting of sea ice in early spring, the net sea surface

et al. (2008) are also well represented in the model.

freshwater flux is downward around the WGC and LC

Fig. 7. 4. 1 Components in 5-year mean vertically integrated heat budget. (a) Sea surface flux, (b) mean advection, (c) eddy, and (d)residual. The positive and negative values mean the local heat gain and loss, respectively. The four boxes (WGC: West Greenland
Current, NL: North Labrador Sea, INT: Labrador Sea interior, and LC: Labrador Current) are defined for quatitative analysis(Kawasaki and Hasumi, 2014).

139

Fig. 7. 4. 2 Same as in Fig. 7. 4. 1 but for freshwater budget (Kawasaki and Hasumi, 2014).

(Fig. 7.4.2a). Several patches of upward freshwater flux

Labrador Sea. The LC (Labrador Current) box is thealong the coast are due to the freezing at coastal polyn-

western boundary of the Labrador Sea, where EKE is notyas. The large downward sea surface freshwater fluxes

significantly large (less than 0.02 m2 s−2) except for thealong the coasts of Greenland and the Labrador Peninsu-

noise accompanied by sea ice formation or melting nearla correspond to river runoff. Since the surface fresh wa-

the Labrador coast

ter flux caused by sea ice does not extend to the interior

Buoyancy relative to reference state B, is decomposed

Labrador Sea, the role of freshwater transport in con-into thermal and haline parts, Bq and BS, respectively. In

trolling the deep convection is not affected by the biased

the buoyancy budget of the whole water column, the total

distribution of sea ice along the western coast of Green-

advection (mean eddy) provides buoyancy by heat in al-

land (Fig. 7.4.2a). The WGC transports low salinity wa-

most all regions. Even considering mean and eddy ad-

ter on the west Greenland shelf, and high salinity watervection separately, freshwater does not contribute to the(IW) from the Irminger Sea on the continental slope (Fig.

buoyancy budget in the Labrador Sea for the whole-col-7.4.2b). Mesoscale eddies transport high salinity waterumn view. However, buoyancy budget in the near sur-from the WGC toward the coast (Fig. 7.4.2c). The ed-

face layer (above 120 m depth) shows the different fea-

dy-induced transfer of high of low salinity water does not

ture. In WGC box, both heat and freshwater comparably

extend to the interior Labrador Sea when vertically inte-(57 and 46 × 104 N s−1, respectively) contribute to thegrated over the whole depth, since the transport of freshbuoyancy injection in the total advection (mean plus

water in the surface layer and that of saltier water in theeddy) (Fig. 7.4.3a and b). In the NL and LC boxes the

subsurface layer cancel each other (Fig. 7.4.2c). The

freshwater contribution to the buoyancy (100 and 98 ×small residual (Fig. 7.4.2d) means that freshwater content

104 N s−1, respectively) is larger than that induced by heatis well equilibrated during the analyzed period in the(69 and 42 × 104 N s−1, respectively). In the north Labra-

Labrador Sea. The heat and freshwater budgets in four

dor Sea (NL box), the freshwater and heat by mean ad-

regions (the boxes labeled as WGC, NL, INT and LC in

vection mainly causes the buoyancy gain in the surface

Fig. 7.4.1) are calculated. In the WGC (West Greenland

layer (Fig. 7.4.3d and e). Quantitative comparison shows

Current) box, the WGC imports seawater from thethat the buoyancy injection by mean advection of fresh-

Irminger Sea. In the NL (North Labrador) box, the WGC

water (91 × 104 N s−1) is twice as large as that by mean

and large eddy kinetic energy (EKE), which indicates ac-

advection of heat (42 × 104 N s−1). In the LC box, thetive IRs, exist. The INT (interior) box corresponds to thesurface layer buoyancy gain is mainly contributed fromarea whose bottom is deeper than 3000 m in the central

the eddy0induced freshwater injection (162 × 104 N s−1).
140Fig. 7. 4. 3 Distribution of thermal contribution to vertically integrated gain or loss of buoyancy above 120 m depth. (a) Total ofmean advection and eddy, (c) mean advection , and (e) eddy. (b), (d) and (f) are same as (a), (c) and (e), respectively, but for haline
contribution. Three box (INTN: northern INT, INTE: eastern INT and INTSW: southwestern INT) are defined for the qualitative
analysis (Kawasaki and Hasumi, 2014).

In the INT box, the thermal buoyancy gain (199 × 104

water fluxes in the interannual variability of formation of−1

LSW, and they concluded that the buoyancy induced byN s ) is about three times larger than the haline buoyan−1

cy gain (73 × 104 N s ) for the total advection. Howev-

freshwater is amaller than heat. Kawasaki and Hasumi

er, while the eddy-induced thermal buoyancy transport(2014) studied the effect of both lateral transport and sea

spreads over the whole Labrador Sea interior, the ed-surface flux of heat and freshwater on the buoyancy in

dy-induced haline buoyancy transport is confined to thethe central Labrador Sea, and the role of freshwater is notnorthern Labrador Sea interior (Fig. 7.4.3e and f). The

small than heat near the sea surface in the northern Lab-

results show that the fraction of haline contribution isrador Sea. The freshwater provides in the upper 100 m

further significant in the northern part of the interior thanof the interior Labrador Sea from March to September isthat in the eastern and southwestern parts. This study di-~30 cm in their result, which is half of observational esti-

rectly points out that the contribution of freshwater to in-mates (Khatiwala et al., 2002). Existence of low salinity

hibiting the convection is important in the northern Lab-water near the sea surface would reduce oceanic heat low

rador Sea for the first time.

under cold air because strong neat surface stratification

A previous modeling study (McGeehan and Maslows-

prevents heat uptake from depth and the sea surface iski, 2011) failed to represent the low salinity water alongcooled immediately, as suggested by Geldeloos et al.

the western coast of Greenland, and winter deep convec-(2012). Then, it is naturally expected that winter convec-

tion drastically and unrealistically developed in thetion does not develop in the northern Labrador Sea evennorthern Labrador Sea therein. Myers and Donnelly

under the influence of the strong cold winds from the(2008) examined the roles of sea surface heat and fresh-

continent. If the model’s underestimation of lateral141

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sphere, ocean, cryosphere and terrestrial environment

8. Synthesis of the project

have respective contributions to the AA; however, in the
opposite direction, AA affects each component, that

The processes we have conducted in our GRENE Arc-means the both directions are seen. AA (ST1) affects thetic Project, “Rapid Change of the Arctic Climate System

global issues (ST2) and also mid-latitude weather (ST3a)and its Global Influences”, are as shown in the schematicor marine ecosystems and fisheries (ST3b). Sea ice re-of Fig. 8.1. In order to tackle these abrupt changes in thetreat and possibilities of Arctic Sea Routes (ST4) also

Arctic, to understand the mechanism, and to contribute tobrought by AA. Thus all individual Strategic Research

future climate change projection, we have conducted

Targets have a systematic connection, and composed

GRENE Arctic Project under 4 Strategic Research Tar-

“Arctic climate system” totally.

gets (STs) as already shown in Chapters 3 to 7. Here, weFuture direction

have summarized outcomes and their interactions withineach Targets. In order to synthesize the final goals, we

Outcomes contributing to four Strategic Research Tar-

had taken steps of “Pyramidal construction” to promote

gets have been greatly achieved through 7 Researchcoordination among each research out put, and then to

Themes under the GRENE Arctic Project. However, wecreate research outcomes.

still have many scientific issues to be accomplished in
the future.

Synthesis of the whole strategic targets.

We have laid a great emphasis on the collaborations

We have seen that each research study contributed to

between observation and modeling, and many efforts

the respective strategic targets. However, four strategic

have been conducted to merge observations with model-

targets themselves do not stand alone, and they haveing. However, these modelings were limited in process

close connection each other and are closely contributing

or single system, respectively, and integrating into theto the others. Now, we have synthesized the relation of

up-to-date global climate/ system model is still behind.

each strategic target together with their research themes

It will need a long lasting study.

as shown in Fig. 8.1.

Arctic and mid-latitude link was one of the most stud-

Among 4 strategic targets, “Arctic warming amplifica-

ied topics in the recent; however, still many discussions

tion (AA)”, ST1 was in the center and key issue ofare on-going, especially how to predict the link. For the

GRENE Arctic Project. Many phenomena in the atmo-

short range prediction, extensive international initiative,

Fig. 8. 1 Schematic of the GRENE Arctic Project, showing “warming amplification in the Arctic and its global impact”. The key toArctic climate change is Arctic warming amplification (AA). AA leads to the change in sea ice extent and then to the realization of
the Arctic sea routes. It also affects the global climate, such as atmospheric circulation, cryosphere, and the carbon cycle. Moreover,AA will alter marine chemical components and the living environment for plankton, and hence have impacts on the major fish
species and fisheries. AA will also influence the mid-latitude weather and climate, and bring cold and heavy snow to Japan in winter147

Year of Polar Prediction (YOPP), has been conductedtinued, especially under strong international cooperation.

under WMO/PPP (Polar Prediction Project), and some

After the GRENE Arctic, the next Arctic project, Arctic

new insights are expected (Inoue et al., 2015, Jung et al.,Challenge for Sustainability (ArCS) Project, is already2016).

ongoing, and its five year term is coming to the success-Observations of ocean, sea ice and atmosphere in the

ful end in March 2020. After then, still, a new Arctic re-broad area of Arctic Ocean are still limited. Long term

search project is anticipated.

effort was continued till the recent, using drifting ice staFinal words

tions, such as North Pole 1 (NP 1) to NP 41, by former
Soviet Union and then by Russia (Frolov, 2005). How-

We tried to synthesize and review the major outcomes

ever, these efforts were ceased partly due to the difficultyof the GRENE Arctic here. However, the progress of ourin the sea ice conditions. A new international project

result is not thoroughly enough. Initially, we intended to

MOSAiC (Multidisciplinary drifting Observatory for thecollect the synthesis of each Strategic Targets compiled

Study of Arctic Climate: 2019–2020; https://mosaic-ex-by each PIs; yet, the idea was not realized, and we, twopedition.org/) was just started to study ocean, sea ice, at-authors, needed to tackle this work. It was so hard tomosphere, marine ecosystems, biogeochemistry and theirhandle all the vast areas of Arctic research by only twointeractions, using the frozen research vessel, Polartern,authors, and the result is so limited, not deeply enough,in the deep Arctic Ocean. It is recommended to join andand covers only a limited area (papers).

contribute the project. However, since the project is justStill, papers reviewed here are of a significant amountlimited to one year or so, it is still needed to continue

and in high scientific quality. This is showing the level ofsome basic observations for long term in the central Arc-

GRENE Arctic, and we are hoping international researchtic Ocean, even not so comprehensive as the project. To

communities would widely recognize these outcomes.

maintain the observation system is the most important is-

Furthermore, we expect the Arctic research will progresssue, and we need to develop a reliable and feasible sys-in the future based on these outcomes brought by

tem such as automatic drifting stations, buoys, AUVs and

GRENE Arctic.

so on.
Many research topics still need to be studied further.

Acknowledgments

One of the main issues in the atmospheric dynamics controls the weather and climate in the Arctic is the polar

The authors are very grateful to all the members ofvortex/jet stream, and “blocking” is a key phenomenon

GRENE Arctic, especially to PIs of Research Themes,

not understood yet. Studies on clouds and aerosols (in-Dr. Masao Fukasawa, Deputy Project Manager, and Proj-

cluding black carbon) have been made in the broad area

ect Steering Committee members. The project could notin the Arctic; however, they are still the largest uncertain-

be accomplished without the kind effort of Prof. Yoshi-

ties in the atmospheric processes even/especially in theyuki Fujii, the former Director-General of the National

Arctic. The extensive study of glacier and ice sheet was

Institute of Polar Research, who kindly took the role ofconducted in the Greenland ice sheet and confirmed that

Acting Project Manager during a problematic health con-the melting and discharge at the ice edge are contributing

dition of Project Manager, TY. The GRENE Arctic wasto the sea level rise. However, to make a quantitative es-

the project funded and supported by the Ministry of Edu-

timation of sea level rise, many more studies need to becation, Culture, Sports, Science and Technology, Japan

done on the interaction between seawater and glacial(MEXT). The finalizing of this report was financiallyfront and the surface mass balance depending on the im-supported by the Arctic Environment Research Center,purities and snow particles. The terrestrial system is an-

National Institute of Polar Research.

other urgent target to be quantified. CH4 emissions fromthe melting of the permafrost on the ground, under theReferenceslake and seawater, or ecosystem responses together with

Frolov, I. E., 2005. The Arctic basin: results from Russian drifting stations. Springer, Berln, 272p.
Inoue, J., Yamazaki, A., Ono, J., Dethloff, K., Maturilli, M.,
Neuber, R., Edwards, P., amaguchi, H., 2015. Aditional
Arctic observations improves weathe and sea-ice forecasts

CO2 exchange depending on warming are still to be clarified.
We still have many items to be tackled in the Arctic
climate systems and hope the research projects to be con148

for the Northern Sea Route. Sci, Rep., 5, 16868, doi:https://doi.org/10.1038/srep16868.
Jung, T. et al., 2016. Advancing polar prediction capabilities on

daily to seasonal time scales. Bull. Amer. Meteorol. Soc.,
97, 1631–1647, https://doi.org/10.1175/BAMS-D-14-00246.1

149

Map of GRENE Arctic Project observation activities

National Institute of Polar Research