PUBLICATIONSJournal of Geophysical Research: Biogeosciences
REVIEW
10.1002/2015JG003132
Special Section:
Arctic Freshwater Synthesis
Key Points:• Speciﬁc humidity and precipitation
have generally increased in the Arctic• The Arctic water cycle is projected tointensify during this century• Complexity of physical processes
causes challenges in modeling ofwater cycle

Correspondence to:T. Vihma,timo.vihma@fmi.ﬁ

Citation:
Vihma, T., J. Screen, M. Tjernström,
B. Newton, X. Zhang, V. Popova,
C. Deser, M. Holland, and T. Prowse(2016), The atmospheric role in the
Arctic water cycle: A review onprocesses, past and future changes,
and their impacts, J. Geophys. Res.
Biogeosci., 121, 586–620, doi:10.1002/
2015JG003132.
Received 2 JUL 2015
Accepted 30 NOV 2015
Accepted article online 11 DEC 2015
Published online 30 MAR 2016
Corrected 30 JUN 2016
The copyright line for this article waschanged on 30 JUN 2016 after original
online publication.

The atmospheric role in the Arctic water cycle:
A review on processes, past and futurechanges, and their impacts
Timo Vihma1, James Screen2, Michael Tjernström3, Brandi Newton4, Xiangdong Zhang5,
Valeria Popova6, Clara Deser7, Marika Holland7, and Terry Prowse8
1

Finnish Meteorological Institute, Helsinki, Finland, 2University of Exeter, Exeter, UK, 3Stockholm University, Stockholm,
Sweden, 4Water and Climate Impacts Research Centre, University of Victoria, Victoria, British Columbia, Canada,
5International Arctic Research Center and Department of Atmospheric Sciences, University of Alaska Fairbanks, Fairbanks,
Alaska, USA, 6Institute of Geography, Russian Academy of Sciences, Moscow, Russia, 7National Center for AtmosphericResearch, Boulder, Colorado, USA, 8Water and Climate Impacts Research Centre, Environment Canada/University of Victoria,
Victoria, British Columbia, Canada

Abstract Atmospheric humidity, clouds, precipitation, and evapotranspiration are essential componentsof the Arctic climate system. During recent decades, speciﬁc humidity and precipitation have generally
increased in the Arctic, but changes in evapotranspiration are poorly known. Trends in clouds vary dependingon the region and season. Climate model experiments suggest that increases in precipitation are related to
global warming. In turn, feedbacks associated with the increase in atmospheric moisture and decrease in sea
ice and snow cover have contributed to the Arctic ampliﬁcation of global warming. Climate models havecaptured the overall wetting trend but have limited success in reproducing regional details. For the rest of the
21st century, climate models project strong warming and increasing precipitation, but different models yield
different results for changes in cloud cover. The model differences are largest in months of minimum sea ice
cover. Evapotranspiration is projected to increase in winter but in summer to decrease over the oceans andincrease over land. Increasing net precipitation increases river discharge to the Arctic Ocean. Over sea ice insummer, projected increase in rain and decrease in snowfall decrease the surface albedo and, hence, further
amplify snow/ice surface melt. With reducing sea ice, wind forcing on the Arctic Ocean increases with impacts
on ocean currents and freshwater transport out of the Arctic. Improvements in observations, process
understanding, and modeling capabilities are needed to better quantify the atmospheric role in the Arctic water
cycle and its changes.

1. Introduction
The atmosphere contains water in the forms of vapor, liquid, and ice. The total water content in the atmosphere is approximately 13,000 km3 [Gleick, 1996], of which 200 km3 over the Arctic [Serreze et al., 2006].
These numbers are very small compared to the ocean, ice sheets, glaciers, lakes, rivers, and ground.
However, the atmosphere is a fundamental component of the water and energy cycles. Water vapor in the
Arctic atmosphere has a residence time of about a week, compared to a decade for freshwater in the
Arctic Ocean [Carmack et al., 2016], and thousands of years for ice sheets and glaciers. Atmospheric moisture,clouds, and precipitation simultaneously affect and are affected by the recent rapid climate change in
the Arctic.

©2015. The Authors.
This is an open access article under theterms of the Creative Commons
Attribution License, which permits use,
distribution and reproduction in any
medium, provided the original work is
properly cited.

VIHMA ET AL.

Our understanding of the past and present-day Arctic and high-latitude hydrological cycle is based on insitu and remote sensing observations, atmospheric reanalyses, and climate model hindcasts. Longer-term
historical context is provided by proxy (paleo) indicators. Estimates on future hydrological changes arederived from climate model projections with various scenarios for human activities and associated external
forcings. However, understanding of the atmospheric moisture budget in the Arctic is far from complete.
The main challenges are primarily related to sparcity (in space and time) of accurate in situ and remotesensing observations. This is the case for almost all moisture variables, including atmospheric speciﬁcand relative humidity, clouds (coverage, type, layers, phase, and properties), precipitation (amount andphase), and evapotranspiration. In many cases, the uncertainties are largest over sea ice and the open
ocean but considerable also over land areas. Also atmospheric reanalyses and climate model results include

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Figure 1. Schematic presentation of the main freshwater components and processes in the Arctic atmosphere. The numbers
3in black indicate the freshwater transports in km /yr on the basis of Serreze et al. [2006], representing the period of 1979–2001,
whereas the numbers in red are estimated for the period 2000–2010 by Haine et al. [2015].

large errors and uncertainties in the Arctic [Jakobson et al., 2012; Liu et al., 2015; Lique et al., 2016]. In thecase of reanalyses, these are affected by the lack of good observations and limited consideration on homogeneity of the data throughout the entire analysis period using the state-of-the-art assimilation methods[Trenberth, 2016; Zhang et al., 2013]. An important source of errors, for both reanalyses and climate models,is the suite of complex physical processes extant in the Arctic atmosphere and their interaction with the
Earth surface [Vihma et al., 2014]. These processes include, for example, physics of mixed-phase clouds,
radiation-cloud-turbulence interactions, and evaporation from drifting/blowing snow and spray droplets
over the open ocean.
Despite the scarcity of data, efforts were made as early as the 1960s to evaluate the freshwater budget of
the Arctic Ocean [Mosby, 1962; Aagaard and Greisman, 1975; Carmack et al., 2016]. Budyko [1963] presented
estimates on global evaporation, and Palmen and Vuorela [1963] ﬁrst calculated atmospheric water vapor
transport, in the region 40°S to 40°N, soon followed by estimates reaching as far north as 70–75°N[Starr et al., 1965; Rakipova, 1966; Oort, 1971]. The ﬁrst Pan-Arctic moisture budget estimates were madeby Oort [1975]. A recent synthesis of the large-scale freshwater cycle of the Arctic, including its atmospheric,oceanic, and terrestrial components during 1979–2001, was presented by Serreze et al. [2006]. Although thestudy was more focused on the ocean than atmosphere, the results for the latter included important
estimates for the mean values, annual cycles, and interannual variations for the total (i.e., verticallyintegrated) water vapor (TWV), evapotranspiration (i.e., the sum of evaporation from the Earth surface
and transpiration from plants), precipitation, and net precipitation (i.e., precipitation minus evapotranspiration) over the Arctic sea and land areas (Figure 1). A more recent major effort was the assessment “Snow,
Water, Ice, and Permafrost in the Arctic” (SWIPA), which included a review on recent variations in the Arctic
climate [Walsh et al., 2011a]. The important message was that the years since 2000 have been quite wet
according to both precipitation and river discharge data. There are also indications of increases incloudiness over the Arctic, especially in low clouds during the warm season [Walsh et al., 2011a, 2011b].
In the SWIPA assessment, however, atmospheric moisture, clouds, and precipitation were only addressed

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among the many other climate variables without any particular focus on the atmospheric freshwater
system in the Arctic. A recent synthesis on the intensiﬁcation of the Arctic freshwater cycle was presented
by Rawlins et al. [2010] and its feedbacks and impacts on terrestrial, marine, and human life were reviewed
by Francis et al. [2009]. In Figure 1, we schematically illustrate the Arctic freshwater budget with a focus on
the atmosphere; with increases in river runoff and net precipitation, also this ﬁgure demonstrates a recent
intensiﬁcation of the freshwater cycle.
The objectives of this paper are (a) to brieﬂy review the role of Arctic atmospheric moisture, clouds, evaporation, and precipitation in the Earth system, (b) to provide an up-to-date quantiﬁcation of the historical and ongoing changes in atmospheric freshwater in the Arctic and their drivers, (c) to evaluate thenature and causes of changes expected during the 21st century, (d) to provide a synthesis of the impactsof the changes, and (e) to identify the knowledge gaps. This paper is a contribution to the “Arctic
Freshwater Synthesis (AFS)” (http://www.climate-cryosphere.org/activities/targeted/afs and Prowse etal. [2015a] which is a joint effort to review the current knowledge on the Arctic freshwater system andits components: the ocean [Carmack et al., 2016], terrestrial hydrology [Bring et al., 2016], ecosystems[Wrona et al., 2016], and water resources [Instanes et al., 2016], with a separate synthesis on modelingof the system [Lique et al., 2016]. Following the AFS introductory paper [Prowse et al., 2015a], we deﬁne
the Arctic domain as the area consisting of the Arctic Ocean, its marginal seas, and the land areas contributing to river discharge into these seas. Precipitation and evaporation in the river basins are importantcomponents of the atmospheric role in the Arctic water cycle. However, not all literature we cite has used
the same deﬁnition of the Arctic. Hence, we point out the different deﬁnitions, when the differences affectthe conclusions made.

2. System Function and Key Processes
The spatial and temporal distributions of water vapor in the Arctic are closely related to air temperature distributions. The moisture holding capacity of air increases exponentially with air temperature according to
the Clausius-Clapeyron equation. Hence, TWV (or precipitable water) decreases poleward, decreases fromsummer to winter, and is higher over the open ocean than sea ice and cold continents (Figure 2) (seeWebster [1994] for thorough discussion on the climatological importance of the Clausius-Clapeyron
equation). In the Arctic, clouds are very common over the open ocean and sea ice, but less common overthe continents. The cloud fraction reaches its maximum in late summer and early autumn and a minimum
during late winter [Curry et al., 1996; Shupe et al., 2011]. In the central Arctic, typical winter conditions swap
between overcast and clear skies [Makshtas et al., 1999] determined mostly by the large-scale atmosphericcirculation [Morrison et al., 2012]. In summer low stratus or stratocumulus clouds are present for most of thetime [Curry et al., 1996; Tjernström et al., 2012]. The occurrence of water vapor and clouds in the Arctic ispartly due to local evaporation/evapotranspiration and condensation and partly due to transport fromlower latitudes (Figure 1). The transport is mostly due to transient cyclones [Jakobson and Vihma, 2010].
Information on precipitation over the central Arctic is limited and of questionable accuracy, butatmospheric reanalyses suggest that the large-scale spatial and seasonal distributions of precipitation
roughly follow those of TWV (Figure 2), but with added spatial detail due to orography. As an annual mean,
precipitation exceeds evapotranspiration over almost all land and ocean areas in the Arctic and sub-Arctic,
the primary exceptions being parts of the Norwegian and Barents Seas [Jakobson and Vihma, 2010]. In thefollowing subsections we provide more detailed information on the different aspects of atmospheric water
cycle in the Arctic.
2.1. Atmospheric Moisture
Water vapor is the source for cloud and fog formation, but also affects radiative transfer andevaporation/condensation. Relative humidity, together with the atmospheric dynamics and the availability
and properties of condensation nuclei, controls the cloud formation. The level where saturation is reacheddepends on the vertical proﬁles of temperature and speciﬁc humidity. Inversions, i.e., layers were the values
increase with height, are common in the Arctic for air temperature as well as speciﬁc and relative humidity.
The air temperature inversions occur somewhere between the surface and 1–1.5 km altitude throughout theyear but display a strong seasonality [e.g., Serreze et al., 1992; Tjernström and Graversen, 2009]. Also, speciﬁc
humidity inversions have a high importance in the Arctic climate system, especially for cloud formation

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Figure 2. Seasonal means of precipitable water, precipitation, and evaporation for winter (DJF) and summer (JJA) during the period 1980–2013 on the basis of JRA-55
reanalysis. The green lines indicate the boundaries of the Arctic river catchment.

and maintenance, across a wide range of spatial and temporal scales [Nygård et al., 2014]. Speciﬁc humidity
inversions are often collocated with temperature inversions, with the base near the temperature inversion
base close to the cloud top. Hence, during entrainment (mixing of the cloud layer air with the warmer inversion layer air above it) the inversion layer acts as a moisture source to the cloud layer [Solomon et al., 2011;2014; Sedlar et al., 2012]. In the case when cloud layers are decoupled from the Earth surface (due to asecondary inversion below the cloud base), moisture inversions may be the only moisture source to theclouds [Solomon et al., 2011; 2014; Savré et al., 2015]. Even a small vertical gradient in speciﬁc humidity
may be important for the occurrence of condensation or for the rate of evaporation from existing cloud
droplets or ice crystals [Nygård et al., 2014].
In addition to effects on clouds, the proﬁle of speciﬁc humidity is important for the longwave and shortwave radiative transfer in the atmosphere. The humidity proﬁle matters particularly for longwave radiationin clear-sky conditions [Devasthale et al., 2011], when the difference in downward longwave radiation at the
Earth surface between dry and humid conditions may reach 10–20 W/m2 in conditions typical for the Arctic[e.g., Prata, 1996]. The effect is enhanced by the common co-occurrence of temperature and humidity
inversions in the Arctic; humid air emits more longwave radiation than dry air because of its humidity
and high temperature.
The loss of sea ice and snow cover, and increases in atmospheric moisture and clouds, amplify warmingin the Arctic region [e.g., Manabe and Stouffer, 1980; Screen and Simmonds, 2010a, 2010b; Pithan and
Mauritsen, 2014]. As a consequence, the Arctic warms faster than the middle latitudes (or global average),a phenomenon known as Arctic ampliﬁcation [Serreze and Francis, 2006; Serreze et al., 2009; Serreze andBarry, 2011]. As water vapor is the strongest greenhouse gas, an increase in its content yields warming

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at all latitudes. By itself it does not cause the Arctic ampliﬁcation, but it has been suggested toamplify the feedbacks due to clouds, surface albedo, and air temperature (the lapse rate and Planck
feedbacks) [Pithan and Mauritsen, 2014]. Warming generated by these feedbacks yields increased water
vapor contents and associated radiative effects, which further enhance the Arctic ampliﬁcation [Langen
et al., 2012].
2.2. Evaporation and Moisture Transport From Lower Latitudes
Atmospheric moisture in the Arctic is a result of local evaporation and moisture transport from lower latitudes(Figure 1). Due to large evaporation from leads and polynyas, the near-surface air humidity over Arctic sea ice
is usually at or close to saturation with respect to ice phase [Andreas et al., 2002], and the sublimation fromsnow/ice surface is therefore weak [e.g., Persson et al., 2002]. Sublimation from drifting/blowing snow may
be more efﬁcient because of the large surface area of snowﬂakes in the air [Déry and Tremblay, 2004]. Partof the drifting/blowing snow is transported to leads, representing a freshwater ﬂux to the ocean [Fichefet
and Morales Maqueda, 1999]. Compared to sea ice, sublimation from drifting/blowing snow is probably more
important over Arctic and sub-Arctic land areas, where the air is drier.
Over the open ocean under strong winds evaporation from spray droplets gives an important addition toevaporation from the ocean surface. According to Andreas et al. [2008], with a wind speed of 11–13 m s 1 evaporation from spray droplets is 10% or greater of the surface evaporation, and with a wind speed of 25 m s 1,
common for Polar lows (intensive mesoscale cyclones in Polar regions) [e.g., Rasmussen and Turner, 2003],
the contribution is already of the order of 70–80% (for sea surface temperature (SST) = 0°C). Under suchwind speeds, however, the numbers include a considerable uncertainty due to lack of data. The process ofevapotranspiration from vegetated surfaces is thoroughly discussed in Bring et al. [2016], and therefore notaddressed here.
Another source for atmospheric moisture in the Arctic is transport from lower latitudes, driven by the northsouth gradient in air speciﬁc humidity and affected by large-scale circulation patterns, such as planetary
waves, the subtropical jet stream, the Polar front jet stream, storm tracks, as well as the Halley, Ferrel, andPolar cells. In a classical approach [Palmen and Vuorela, 1963] the transport is dived into contributions ofmean meridional circulation, stationary eddies, and transient eddies. Stationary eddies represent deviations
from the zonal mean, whereas transient eddies represent deviations from the temporal mean, i.e., transient
cyclones. On the basis of ERA-40 reanalysis, Jakobson and Vihma [2010] calculated that transient eddies dominate the moisture transport across 70°N, contributing to 80–90% of the total northward transport. The contribution of mean meridional circulation is only 1% in summer to 12% in winter. Changes in poleward
moisture transport may originate from two reasons: changes in north-south moisture gradient andatmospheric circulation. The ﬁrst is expected to dominate in time scales of several decades and longer[Skiﬁc et al., 2009], whereas the second dominates in interannual and shorter time scales. According toSorteberg and Walsh [2008], the interannual variability in moisture transport is mainly driven by variability
in cyclone activity over the Greenland Sea and East Siberian Sea. Regional linkages are complex. For example,
if many cyclones enter the central Arctic from the Kara Sea, it reduces the total moisture transport. This isbecause after reaching the central Arctic, the Kara Sea cyclones generate northerly wind anomalies overthe Greenland and Barents Seas, reducing the moisture transport from there [Sorteberg and Walsh, 2008].
Atmospheric rivers (narrow corridors of intensive transport, which occur within the warm conveyor belt of
extratropical cyclones) are a key component of the poleward moisture transport in winter, in particular in
events of extreme transport [Newman et al., 2012; Gimeno et al., 2014; Neff et al., 2014].
The sensitivity of the above mentioned results to the reanalysis applied is not known. Further, the vertical
distribution of moisture transport is poorly known. On the basis of rawinsonde data, Overland and Turet[1994] found out that the poleward transport across 70°N peaks at the 850 hPa level, but Jakobson andVihma [2010] detected the peak at 930 hPa level in winter and at 970–990 hPa level in other seasons.
Reanalyses are liable to errors in moisture variables [e.g., Jakobson et al., 2012], but sea areas are underpresented
in circumpolar estimates based on radiosonde data.
2.3. Clouds
Clouds have a large impact on climate through interaction with radiation but, due to lack of in situ observations and complexities in monitoring clouds from space (section 6), the cloud climatology for the Arctic is far

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from complete. Clouds also remain one of the largest uncertainties for projections of climate change[Lique et al., 2016, section 4]. Several Arctic characteristics contribute to clouds having Arctic-speciﬁc
effects. These include high-reﬂective ice/snow-covered surfaces and the pronounced seasonal cycle insolar radiation, where the Sun is absent for a large part of winter and present all day in summer, but
at large solar zenith angle.
Available data indicate that the Arctic is very cloudy, with a pronounced annual cycle in cloudiness, ranging
from 40–70% in winter to 80–95% in summer and autumn [Intrieri et al., 2002b; Shupe et al., 2011]. Low clouds
and fog dominate [Intrieri et al., 2002a, 2002b; Shupe et al., 2011; Shupe, 2011], predominantly having a warming effect on the surface, especially over the ice-covered Arctic Ocean. In winter, with no solar radiation,
clouds determine the distribution of net thermal radiation (deﬁned positive downward), a distinct bimodal
distribution with one peak at ~ 40–50 W m 2, with the lowest temperatures and associated with clear conditions, and another one near 0 W m 2 [Stramler et al., 2011; Morrison et al., 2011] associated with clouds. Also
through a large part of summer, longwave radiation dominates the cloud radiative effect (CRE), althoughsolar radiation is present; this is due to a high surface albedo and a large solar zenith angle [Sedlar et al.,2011]. Except for, possibly, July and August, when open water and melt ponds are present, clear-sky
conditions reduce surface net longwave radiation more than they increase the net shortwave radiation[Curry et al., 1996; Intrieri et al., 2002a; Shupe and Intrieri, 2004].
Cloudy conditions are often related to a warmer free troposphere, as cloudy episodes are typically associatedwith advection from lower latitudes [Vihma and Pirazzini, 2005; Morrison et al., 2011]. Cloud base temperatures are sometimes higher than the snow/ice surface temperature, enhancing CRE [Persson et al., 2002;
Persson, 2012]. However, if the bases of temperature and humidity inversions are located close to the cloud
top, the higher temperature and speciﬁc humidity aloft do not affect CRE; their radiative effects at the surface
are masked by the presence of optically thick clouds [Tjernström et al., 2012]. Near the marginal ice zone,
strong sustained surface inversions may form in cases of on-ice ﬂow; in these cases speciﬁc humidity may also
increase with altitude, or low clouds or fog may form. The inﬂow of this warm and moist air has a substantial
impact on the radiative ﬂux both at the surface and at the top of the atmosphere [e.g., Tjernström et al., 2015].
Clouds also control the vertical structure of the lower troposphere. Contrary to shear-driven near-surface
turbulence [Persson et al., 2002; Tjernström et al., 2012] and convection over leads, polynyas and the open
ocean in winter [Lüpkes et al., 2012], much of the mixing in the lower troposphere over sea ice is driven by
cloud overturning from cloud top radiative cooling. This mixing sometimes contributes to forming a single
deep well-mixed layer [Vihma et al., 2005], but more often the clouds are decoupled from the surface[Tjernström, 2007; Shupe et al., 2013; Sotiropoulou et al., 2014]. A key to understanding these effects lies in
the cloud microphysics and interactions with aerosols. Measurements from expeditions into the Arctic indicate that aerosol concentrations in the Arctic are low compared to the rest of the Earth, especially in summer[Tjernström et al., 2012, 2014]. Paucity of ice-forming particles favors mixed-phase clouds, common in the
Arctic even at very low temperatures [Prenni et al., 2007]. These have a thin supercooled liquid water layer
at the cloud top, continuously shedding frozen precipitation (see Morrison et al. [2011] for a review). The
net effect is persistent clouds [e.g., Shupe, 2011] with more or less constant but weak frozen precipitation
and with a large impact on the surface energy balance [Persson et al., 2002; Tjernström, 2005; Prenni et al.,2007; Sedlar et al., 2011; Bennartz et al., 2013].
Low concentrations of cloud condensation nuclei (CCN) generate clouds with few but large droplets, andhence a relatively low cloud albedo [Twomey, 1977]. With exceptionally low concentration, droplets growlarge enough to deposit. Since each droplet brings with it the CCN it formed on, this feeds back to an evenlower CCN concentration and generates optically thin clouds [Mauritsen et al., 2011]. Observations from several expeditions indicate that in summer this might occur as often as 30% of time. When CCN < ~10 cm 3,increasing CCN concentrations lead to a large increase in the longwave surface CRE, but a relatively small
effect on solar surface CRE; the latter (the so-called Twomey effect) becomes important only for
CCN > 10 cm 3, as the cloud becomes optically thick and emits as a blackbody. Increasing the aerosol
concentrations may thus have both cooling and warming effect on the surface.
Studies indicate that the strong inﬂuence by clouds on CRE has a strong inﬂuence on the sea ice melt. Kapsch
et al. [2013, 2014] indicate that years with more clouds and water vapor in spring from advection from south are
the years with the lowest sea ice extent in September. Moreover, modeling studies [Kapsch et al., 2015] also

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show a signiﬁcant feedback; the lower sea ice concentration in autumn increases the cloudiness and causes
thinner ice at lower concentration at the start of winter.
2.4. Precipitation
Quantitative estimates for annual accumulated precipitation and evaporation over the Arctic Ocean andterrestrial Arctic are shown in Figure 1 and their spatial distributions in Figure 2. In winter the spatial
distributions of precipitation and evaporation are dominated by the difference between large values
over the open seas and low values over snow/ice-covered sea and land areas (Figure 2). In summer,
however, both precipitation and evaporation over Arctic land areas, except Greenland, exceed thevalues over sea areas north of 70°N and are comparable to those at lower latitudes. Precipitation andevaporation/evapotranspiration affect the ocean and terrestrial freshwater budgets, the surface albedo
and energy budget, and the mass balance of ice sheets, glaciers, and sea ice. Over the Arctic Ocean,net precipitation is strongly positive (by 2000–2200 km3 yr 1) and, hence, freshens the ocean (Figure 1).
Precipitation exceeds evaporation particularly during winter (Figure 2), when the surplus is stored onsnow on top of sea ice. Hence, part of the Arctic Ocean freshening occurs during the snow and ice meltin summer. An even more important freshening factor is river discharge, which results from a positive
net precipitation over the surrounding continents, especially Eurasia [Serreze et al., 2006; Bring et al.,2016]. Both net precipitation and river discharge have strong seasonal cycles. Over continents, in mostof the Arctic river catchment, net precipitation is stored as snow for half a year or more. Hence, river discharge peaks in late spring during and soon after the snowmelt period [Bring et al., 2016], with 60% of theriver discharge to the Arctic Ocean occurring from April through July [Lammers et al., 2001]. Net precipitation over the Arctic Ocean reaches its maximum later, typically in late summer and early autumn [Walshet al., 1994]. This is partly due to the summer maximum in Arctic cyclone activity [Zhang et al., 2004;
Serreze and Barrett, 2008].
The snow and ice surface albedo depends on several factors, such as the snow wetness, temperature, density,
grain structure, impurities, melt ponds, and surface slope [e.g., Pirazzini, 2004; Perovich et al., 2009; Perovichand Polashenski, 2012]. The effects of precipitation on snow and ice albedo have received relatively little
attention, although Persson [2012] estimated the rain effect of lowering surface albedo by approximately
0.07 at melt onset, and Sedlar et al. [2011] and Persson [2012] partly attributed the onset of the ice surface
freeze in autumn to an albedo change due to new snow at a time when solar radiation is already decreasing.
If precipitation falls as rain instead of snow, it has a dramatic effect on surface albedo and sea ice melt(see section 3.1.3 on detected changes in the phase of precipitation).
Precipitation is the only major source term for the mass balance of the Greenland ice sheet [Bring et al.,2016], ice caps, and glaciers in the Arctic. Most precipitation is generated orographically when moist air
masses, typically related to cyclones, rise over the coastal slopes [Schuenemann et al., 2009]. Hence,roughly 2–3 times more precipitation falls over the coastal regions of Greenland than over the interiorof the ice sheet, where low temperatures do not allow large amounts of precipitable water [Hakuba
et al., 2012]. Precipitation also affects the mass balance of sea ice, but different effects partly compensate
for each other. On the one hand, snowfall on top of sea ice enhances the thermal insulation and increases
the surface albedo, the ﬁrst effect reducing sea ice growth in winter [Leppäranta, 1993] and the latter
reducing melt in spring and summer [Cheng et al., 2008]. On the other hand, snowfall on sea ice enhances
sea ice growth via snow-to-ice transformation, which may take place via snow-ice formation due to ﬂooding of sea water into the snow layer and subsequent refreezing of slush [e.g., Kawamura et al., 2001] andsuperimposed ice formation due to refreezing of meltwater or slush on top of sea ice [Granskog et al.,2006]. Although rain strongly favors sea ice melt, it may also provide a source for superimposed ice
formation, temporally favoring sea ice growth. Although ﬂooding is not yet common in the Arctic, future
perspectives with thinner sea ice and increasing precipitation (section 4.1) suggest a potentially increasing percentage of snow ice and superimposed ice in the Arctic sea ice thickness. The above mentioned
processes are relevant also for lake and river ice [Bring et al., 2016].
2.5. Summary
There is a multitude of complex processes related to air moisture, clouds, precipitation, and evaporation in
the Arctic. The atmospheric processes closely interact with hydrological and oceanographic processes. It is

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a

Table 1. Estimates of Trends for Atmospheric Freshwater Variables Over the Arctic Ocean and Its Drainage Basin
Variable

SeasonTrend

Regions and References

Near-surface speciﬁc
humidity

AnnualI

Pan-Arctic (Dai [2006], Vincent et al. [2007], Willett et al. [2008, 2013],
Serreze et al. [2012], and Hartmann et al. [2013])

Summer andearly autumn

I

Arctic Ocean (Screen and Simmonds [2010a] and Serreze et al. [2012]),
the Norwegian, Barents, and Greenland Seas (Serreze et al. [2012]),eastern Canada (Vincent et al. [2007], Serreze et al. [2012], andGill et al. [2013]), and eastern Eurasia (Willett et al. [2008]
and Gill et al. [2013])

Other seasonsI

Pan-Arctic except for localized decreases in Canada, Siberia,
and the North Greenland Sea (Vincent et al.
[2007] and Willett et al. [2008])

Annual

I and D

I: Pan-Arctic terrestrial regions north of 60°N,(Eastman and Warren [2013]), Siberia(Nahtigalova [2013], see also Eastman
and Warren [2010], and Walsh et al. [2011a, 2011b])

Cloud coverage

WinterI and D

SpringI and D

D: Eurasia north of 70–75°N (Baikova et al. [2002]
and Nahtigalova [2013])
I: Arctic Ocean (Eastman and Warren [2010])
D: Arctic north of 60°N, particularly Arctic Ocean (Liu et al. [2008])
I: Pan-Arctic north of 60°N, particularly eastern Canada
and the Arctic Ocean (Liu et al. [2008])
D: Pan-Arctic north of 70°N (Screen and Simmonds [2010a])and Arctic Ocean (Eastman and Warren [2010])

Precipitation

AnnualI

Pan-Arctic land areas (McBean et al. [2005], Min et al. [2008],
Rawlins et al. [2010], Walsh et al. [2011a, 2011b],
Hartmann et al. [2013]), northern Canada(Zhang et al. [2000, 2011], Mekis and Vincent [2011],
and Linton et al. [2014]), western Eurasia (Hartmann et al. [2013]),and a large part of Russia (Roshydromet Federal
Service for Hydrometeorology and Environmental Monitoring [2014])

WinterI and D

I: Central Arctic Ocean (Radionov et al. [2013]),western Siberia (Roshydromet Federal Service for Hydrometeorology
and Environmental Monitoring [2008]), southeastern Canada(Zhang et al. [2000] and Mekis and Vincent [2011]),
coastal regions of Canadian Arctic (Zhang et al. [2000],
Mekis and Vincent [2011], and Linton et al. [2014]),and Scandinavia (Liston and Hiemstra [2011])
D: Northeastern Russia (Roshydromet Federal Service for
Hydrometeorology and Environmental Monitoring [2008]) andmidlatitude headwater parts of the basin ofMackenzie River (Zhang et al. [2000], Mekis
and Vincent [2011], Yip et al. [2012], and Linton et al. [2014])

Spring and summer

I

Canada, but in summer both I and D in the upper McKenzie Riverbasin (Zhang et al. [2000], Mekis and Vincent [2011],
Zhang et al. [2011], Yip et al. [2012], Linton et al. [2014])

Summer and autumn

I and D

I: East Siberia (Roshydromet Federal Service for
Hydrometeorology and Environmental Monitoring [2008])
D: Central Eurasia (Rawlins et al. [2006] and Bogdanova et al. [2010])

Snowfall fraction

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WinterI

Northern Canada (Zhang et al. [2000], Vincent and Mekis[2006], Liston and Hiemstra [2011], Zhang et al. [2011])
and midlatitude Eurasia (Liston and Hiemstra [2011]),
particularly over the north of Yenisey and Ob Riverbasins (Bogdanova et al. [2010])

Primarily spring
and autumn

DSouthern Canada (Zhang et al. [2000, 2011], Vincent and Mekis [2006],
Linton et al. [2014]) and high-latitude Eurasia south ofSiberia (Bogdanova et al. [2010] and Liston and Hiemstra [2011])

SummerD

Arctic Ocean (Screen and Simmonds [2012])

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Table 1. (continued)

Variable

SeasonTrend

Regions and References

No. of days with heavy
precipitation

Annual

I and D

I: western Eurasia (Alexander et al. [2006] and Donat et al. [2013])
and northern Canada (Zhang et al. [2001] and Vincent and Mekis [2006]).
For medium/heavy snowfall: Eurasia (Borzenkova and Shmakin [2012])
D: western Canada (Alexander et al. [2006])

Daily P intensity

Annual

I and D

I: northern Canada (Vincent and Mekis [2006], Peterson et al. [2008],
and Donat et al. [2013]) and Eurasia (Donat et al. [2013])
D: southern Canada (Vincent and Mekis [2006], Peterson et al. [2008],
and Donat et al. [2013]) and coastal northern Russia (Donat et al. [2013]),

No. of consecutive dry days

AnnualD

western Canada (Alexander et al. [2006] and Vincent and Mekis [2006])
and western Eurasia (Donat et al. [2013]).

Evapotranspiration

Various

I

Annually most of Canada (Burn and Hesch [2007] and Fernandes et al. [2007])and August–October in the Arctic Ocean(Boisvert and Stroeve [2015])
January–October in the Chukchi, Beaufort, Laptev, andEast Siberian Seas (Screen and Simmonds [2010b])

Net precipitation

AnnualD

January–October in the Greenland Sea (Screen and Simmonds [2010b]),
December–February in the Kara, Barents, and Greenland
Seas (Boisvert et al. [2013])

I

Northern continents, especially Eurasia (Richter-Menge
and Overland [2009], Haine et al. [2015], and Bring et al. [2016]).

a

An increasing trend is denoted by I and a decreasing one by D. References are made only to papers with the study period extending to 21st century. For thenumerical values of the trends and the exact study periods, see the references cited.

particularly challenging to understand the nonlinear interactions of different processes, which are acting
simultaneously on different spatial and temporal scales. This challenge in understanding is reﬂected in problems of modeling the processes.

3. Past Changes and Key Drivers
In the following subsections, we summarize changes in the Arctic atmospheric water cycle during the recent
decades. Information on the changes is mostly based on in situ and remote sensing observations andreanalyses, but for many variables and regions the amount of observations is not adequate to make ﬁrm
conclusions on trends (section 3.1). Information on the performance of climate models in historical simulations (section 3.2) is important in evaluating the reliability of future scenarios based on climate models.
Further, historical climate model experiments have been a major approach to understand the drivers of
the past changes (section 3.3). The modeling strategies are discussed in depth in Lique et al. [2016].
3.1. Observed Changes
Trends in key freshwater variables detected over the latest decades are summarized in Table 1 withrespect to the season, region, and sign of the trend. Papers with the study period ending before year
2000 are not included. For the numerical values and exact periods of the trends, see the references citedin Table 1.
3.1.1. Atmospheric Moisture and Clouds
Widespread increases in annual near-surface speciﬁc humidity have occurred over the pan-Arctic region overthe past several decades. The largest increases have occurred during summer and early autumn. Speciﬁc
humidity has increased also during winter, spring, and all autumn, except for localized decreases (Table 1).
Considering the annual mean vertically integrated water vapor (precipitable water), epoch differences between
years 1986–2013 and 1958–1985, based on the Japanese 55-year reanalysis (JRA-55) [Kobayashi et al., 2015],
suggest increases everywhere in the circumpolar Arctic and sub-Arctic (Figure 3). We chose JRA-55 as it is the
only recent reanalysis covering the entire period since 1958 with an advanced data assimilation method(four-dimensional variational data assimilation). Increases in precipitable water have prevailed also in summer

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Figure 3. Epoch differences between 1986–2013 and 1958–1985 for precipitable water, precipitation, and evaporation on the basis of JRA-55 reanalysis for annual
means, winter (DJF), and summer (JJA). The green lines indicate the boundaries of the Arctic river catchment.

(June-July-August, JJA), the main exceptions being decreases in land areas in western Siberia and in partsof the Kara and Barents Seas. Evaporation has increased in the same sea areas. Hence, the decrease inprecipitable water can only be due to a decrease in moisture transport to these sea areas. Increases inprecipitable water dominate also in winter (December-January-February, DJF), but the air column has
become drier in the Labrador Sea, northeastern Canada, and eastern Siberia and surrounding seas
(Figure 3). The seasonal and regional changes are, however, sensitive to the time period studied andreanalysis applied.

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Quantitative interpretation of visual and satellite-based cloud data is not straightforward, but there areindications of increases in cloudiness over the Arctic, especially in low clouds during the warm season[Walsh et al., 2011a, 2011b]. Annual zonally averaged midlatitude and high-latitude terrestrial cloud coverseems to have generally increased, except in the northernmost parts of Eurasia (Table 1). It should be notedthat many studies have focused on the total cloud cover, but changes in clouds in various altitudes may be
opposite. The increase in terrestrial cloud cover has been primarily due to an increase in low-level clouds[Eastman and Warren, 2013; Nahtigalova, 2013]. On the contrary, on the basis of both satellite observations
and ERA-40 reanalysis, Schweiger et al. [2008] found that over the Arctic Ocean the sea ice decline has been
related to a decrease in low-level cloud coverage and a simultaneous increase in midlevel clouds. Seasonal
trends in total cloud cover have been mixed, the sign depending on the region (Table 1), exact study period,and possibly also on the data set used [Wang and Key, 2005; Liu et al., 2008; Eastman and Warren, 2013;
Khlebnikova and Sall, 2009; Sun and Groisman, 2000].
3.1.2. Precipitation and Evapotranspiration
Changes in precipitation and evapotranspiration over the last 56 years are illustrated in Figure 3 as epoch
differences between 1986–2013 and 1958–1985. The results suggest a general increase in the annual mean
precipitation and evaporation in the Arctic but with large spatial differences. The changes are also sensitive tothe time period and reanalysis applied. Hence, the literature-based results reported in Table 1 suggest
changes that partly differ from those shown in Figure 3. Annual precipitation in circumpolar midlatitudeand high-latitude regions has increased over the latest decades (Table 1); however, considerable spatial
and temporal variability exists [McBean et al., 2005; Min et al., 2008; Hartmann et al., 2013]. In the Arctic, recent
annual precipitation generally exceeds the mean of the 1950s by about 5% [Walsh et al., 2011a, 2011b].
Winter precipitation trends have been mixed with increases and decreases reported for different regions(Table 1). Summer precipitation has generally increased across Canada, but signiﬁcant trends with both signs
have been observed in the upper Mackenzie River basin (Table 1). Spring precipitation has increased in
Canada, while mixed trends have been detected for autumn. Summer and autumn precipitation haveincreased over East Siberia and decreased over central Eurasia (Table 1). Several different variables are used
to quantify precipitation extremes. Signiﬁcant trends have been detected in the number of days with heavy
precipitation, daily precipitation intensity, and the number of consecutive dry days. In the ﬁrst two variables,
the sign of the trend has varied between regions, whereas the number of consecutive dry days has decreased
in western Canada and western Eurasia (Table 1). Evaluation of precipitation trends is naturally hampered bymeasurement errors, which usually depend on the precipitation gauge type. Among the many error sources,
wind is serious particularly in the case of snowfall. Over recent decades, however, corrections accounting for
known precipitation gauge biases have been applied and, according to our evaluation, most of the discrepancies in precipitation trend estimates shown in Table 1 are caused by differences in the study regions
and periods instead of measurement errors.
Changing patterns of winter precipitation and temperature have resulted in changes to the ratio of snow tototal precipitation, with decreasing trends primarily in spring and autumn but increasing trends in winter innorthern Canada and Siberia (Table 1). Increasing winter precipitation in the Ob and Yenisey basins has beenaccompanied by an increase in maximum snow depth and snow water equivalent [Bulygina et al., 2011]. The
decreasing trends have contributed to shortening of the snow cover duration [Liston and Hiemstra, 2011;
Zhang et al., 2011]. Further, summer snowfall has strongly decreased (40% between 1989 and 2009) over theArctic Ocean [Screen and Simmonds, 2012], mostly due to a change from snowfall to rain in a warming climate.
Rising temperatures, particularly in high latitudes, and changing patterns of precipitation have been accompanied by increases in annual evapotranspiration over most of Canada (Table 1). Over sea areas, information on
historical changes in evaporation is mostly based on reanalyses, hence being less reliable than information fromland areas. According to ERA-Interim, evaporation in January through October has had an increasing trend from1989 to 2009 in the Chukchi, Beaufort, Laptev, and East Siberian seas but a decreasing trend in the Greenland
Sea [Screen and Simmonds, 2010b]. Recent methods to estimate evaporation applying air moisture based on
Atmospheric Infrared Sounder (AIRS) satellite data and wind speed based on ERA-Interim have yielded scatteredresults depending on the version of AIRS data applied [Boisvert et al., 2013; Boisvert and Stroeve, 2015], with thelatest version suggesting an increasing evaporation trend in August–October in 2003–2013 in the Arctic Ocean[Boisvert and Stroeve, 2015]. The epoch differences (1986–2013 compared to 1958–1985) based on JRA-55 suggest an increased annual mean evapotranspiration over both land and sea areas in most of the circumpolar

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Arctic and sub-Arctic (Figure 3). In addition to evapotranspiration, transport from lower latitudes is an importantsource for atmospheric moisture in the Arctic. See section 3.3 for changes in the transport.
Net precipitation has increased over northern continents, especially Eurasia (Table 1), seen as an increase in
the river discharge to the Arctic Ocean. The trends, however, depend on the region and period addressed.
Considering the JRA-55-based epoch differences between 1986–2013 and 1958–1985, in a large part ofEurasian midlatitudes as well as in smaller regions in Canada and USA, the annual mean precipitation has
decreased but evapotranspiration has simultaneously increased (Figure 3). Accordingly, these predominantly
midlatitude regions have changed toward “water poor.” An increase in precipitation with a simultaneous
decrease in evapotranspiration has taken place further north, e.g., in the Bafﬁn Island and coasts of the
Kara Sea, which have become more “water rich” (Figure 3). Over longer time periods, since 1930s, the generaltrend in the Arctic river catchments in Eurasia has been an increasing discharge, i.e., toward more water rich[Richter-Menge and Overland, 2009].
Analyses on historical changes in net precipitation over the Arctic Ocean have been limited. According to
Rawlins et al. [2010], net precipitation over the Arctic Ocean shows no trend for the period 1979–2007, estimated on the basis of the JRA-25 reanalysis. According to Haine et al. [2015], however, net precipitation over
the Arctic Ocean has increased from 2000 ± 200 mm yr 1 in 1980–2000 (mostly based on Serreze et al. [2006])
to 2200 ± 220 mm yr 1 in 2000–2010 (Figure 1). See Carmack et al. [2016] for more discussion on precipitationand evaporation over Arctic seas.
3.2. Historical Simulations
3.2.1. Clouds
Analysis of historical simulations on Arctic clouds has focused mainly on their climatology, aiming to understand associated feedback process contributing to Arctic climate change. The Advanced Very High Resolution
Radiometer Polar Pathﬁnder (APP-x) data set [e.g., Wang and Key, 2003] has been widely used in the analysis,
showing maximum climatological cloud cover over the Norwegian and Barents Seas. The cloud cover dataalso demonstrate an obvious seasonal cycle, with peak values occurring in summer. Comparing with theseobserved climatological features, the ensemble mean of Climate Model Intercomparison Project phase 3(CMIP3) models realistically captures average aspects of the amount and spatial distribution of Arctic clouds,
as well as predominant types of low clouds, during summer and fall, but exhibits a large across-model spreadoccurring during winter and spring [Vavrus et al., 2009; Karlsson and Svensson, 2011]. A continuing analysis
indicates that the large across-model spread remains in the follow-up CMIP5 model historical simulations,suggesting no obvious improvement has been made [Karlsson and Svensson, 2013].
Cesana and Chepfer [2012] compared the cloud vertical structure in ﬁve of the Atmospheric Model
Intercomparison Project models against the Cloud-Aerosol Lidar and Infrared Pathﬁnder Satellite
Observation satellite measurements. The results indicated that, during 1979–2008, the models performed
rather well for the low cloud amount (ranged from 37% to 57% between models, compared to the observed44%) but did not capture the observed seasonal cycle. Examination of a stand-alone Community
Atmospheric Model, version 5 (CAM5) simulation exhibited insufﬁcient amount of cloud cover throughout
the year [English et al., 2014]. The across-model spread in cloud variables contributes to the across-model
spread in surface radiative forcing, impacting cloud albedo effects and leading to possibly biased understanding and assessment of Arctic climate change [Karlsson and Svensson, 2013]. In addition, it is also argued
that cloud cover is not a well-deﬁned variable, in general and in particular for the Arctic, as substantial portions of Arctic cover are optically thin [Koenigk et al., 2013]. For more discussion on challenges in historicalsimulations on Arctic clouds, see Lique et al. [2016].
3.2.2. Precipitation and Evapotranspiration
The historical climatology and changes in precipitation and net precipitation in the Arctic have been simulated by various state-of-the-art climate models [Walsh et al., 1998; Holland et al., 2006; Kattsov et al., 2007;
Bengtsson et al., 2011]. Observed climatological precipitation has a distinct seasonal cycle, peaking in summer. It is generally high on the North Atlantic side of the Arctic, in particular over the Greenland andNorwegian Seas, and with an extension to the Eurasian shelf seas. Kattsov et al. [2007] analyzed simulation
results from the 21 models participating in the CMIP3, focusing on comparison of the simulated precipitationand net precipitation against observational or observationally based data sets over the Arctic (deﬁned as
north of 70°N and the four largest Eurasian and American Arctic river basins). Although the observations

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are uncertain (section 3.1.2) and there are large areas with no observations, Kattsov et al. [2007] concluded
that the multimodel ensemble mean overestimates precipitation over the shelf seas and adjacent lands
and underestimates in the central Arctic Ocean during the winter season. When compared with the observationally based estimates of precipitations from 1960 to 1989 by Bryazgin [1976] and Khrol [1996], and with
ERA-40, the CMIP3 simulations show spatially dependent biases with an overestimate over the western
Arctic and an underestimate over the eastern Arctic [Kattsov et al., 2007]. Overall, the multimodel simulated,regionally averaged, annual mean precipitation appears realistic, though substantial differences occur acrossindividual model simulations, with the largest spread in early fall.
Positive trends of annual mean precipitation have been detected at northern high latitudes [Trenberth, 2011].
The ensemble mean of the CMIP3 models, or simulations by individual models, such as Community Climate
System Model version 3 (CCSM3) and ﬁfth-generation European Centre/Hamburg model (ECHAM5), generally captures this long-term change over the Arctic Ocean in the historical simulations, consistent with theresults from an earlier generation of climate models [Kattsov and Walsh, 2002; Holland et al., 2006; Kattsov
et al., 2007; Bengtsson et al., 2011]. The modeled trend in annual mean precipitation is mainly due to positivechanges in the winter season. The model simulations show an increasing trend of evapotranspiration overthe pan-Arctic terrestrial region [Kattsov et al., 2007; Rawlins et al., 2010]. However, large diversity across models exists. It is most challenging to examine model’s performance due to a lack of enough observations.
3.3. Drivers
Human-induced global warming is a prominent climate feature during past decades. This warming trend
is considered as a fundamental driver for the observed long-term changes in water cycle during this time
period. Theoretical estimates based on the Clausius-Clapeyron relation and observations have indicatedan increase in lower tropospheric moisture content (speciﬁc humidity) at a rate of about 7% K 1 [Wentz
and Schabel, 2000; Held and Soden, 2006]. This moistening trend could be ampliﬁed over the Arctic due
to enhancement of poleward moisture transport [Zhang et al., 2013]. In fact, the atmospheric circulation
has experienced shifts with more meridionally oriented wind ﬂow since the late 1990s, strengthening
the poleward moisture transport [Zhang et al., 2008]. This has increased the atmospheric moisture content over the largest Eurasian river basins [Zhang et al., 2013]; the consequences are discussed in Bringet al. [2016].
In addition to human-induced warming, drivers of changes in the Arctic atmospheric water cycle are related
to inherent variability of the polar jet stream, storm tracks, and related synoptic-scale weather. Part of thevariability in the jet stream and storm tracks can be characterized by large-scale circulation modes such as
the Arctic Oscillation (AO), North Atlantic Oscillation (NAO), East Atlantic Pattern [Woollings and Blackburn,2012], Paciﬁc Decadal Oscillation, and Southern Oscillation [Newton et al., 2014a, 2014b], but the relationships
vary regionally [Overland et al., 2015]. When the NAO/AO index is positive, the storm tracks shift northward
and winters are typically mild in northern Eurasia and the eastern United States but cold in Greenland,
Canada, Alaska, and eastern Siberia. When the NAO/AO index is negative, the storm tracks shift southward
and temperature ﬁeld is reversed [Cohen et al., 2014]. Several studies suggest that the number of cyclones
entering the Arctic has increased during recent decades [Zhang et al., 2004; Trigo, 2003; Sorteberg andWalsh, 2008; Sepp and Jaagus, 2011], favoring an increase in the moisture transport from lower latitudes,
but according to Sepp and Jaagus [2011] the number of cyclones formed north of 68°N has not increased.
Storms have a strong effect on the freshwater cycle, e.g., via ocean mixing [Carmack et al., 2016].
Storminess (the occurrence of storms) in the Arctic has varied in decadal time scales [Atkinson, 2005;
Mesquita et al., 2010] and increased in some locations in the North American Arctic, but there are no indications of systematic increases in storminess in the circumpolar Arctic since 1960s [Walsh et al., 2011a, 2011b].
Note that storminess differs from cyclone activity, as most Arctic cyclones are not associated with storm-levelwinds. Due to decadal changes in the availability of in situ and remote sensing data, however, evaluation of
past changes in Arctic cyclone activity and storminess is difﬁcult [Döscher et al., 2014].
Not only cyclones but also anticyclones are essential drivers of the Arctic freshwater cycle. Of particularimportance is the Beaufort High [Serreze and Barrett, 2011], centered over the Beaufort Sea, which is typicallystrong when the AO is negative [Rigor et al., 2002] and has strengthened in summer during the most recent
decades [Wu et al., 2014]. A strong Beaufort High favors clear skies [Kay et al., 2008] and reduced precipitation,but otherwise the direct effects of the Beaufort High on the atmospheric water cycle have not received much

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attention. As a driver of the Beaufort Gyre, the Beaufort High is, however, very important for the oceanic freshwater budget [Haine et al., 2015; Carmack et al., 2016] and variability in the strength and location of theBeaufort High has played a role in the rapid loss of Arctic sea ice [Rigor and Wallace, 2004; Ogi and Wallace,2007; Wang et al., 2009].
As mild winters in high latitudes are associated with increased atmospheric moisture, a positive relationshiphas been generally identiﬁed between the total cloud cover, Arctic Oscillation index, and a poleward shift ofsynoptic-scale storm tracks [Eastman and Warren, 2010; Serreze et al., 1993; Zhang et al., 2004]. However, lowlevel clouds seem less correlated with the large-scale atmospheric circulation or synoptic-scale weather
regimes [Shupe et al., 2011]. An analysis of CMIP3 model simulations suggests that local evaporation would
be a leading candidate for the Arctic cloud response due to their high correlation [Vavrus et al., 2009]. In addition to air temperatures and moisture variables, variations in the jet stream and storm tracks have strong
effects on the ocean [Carmack et al., 2016].
Drivers for the observed changes in Arctic precipitation and evapotranspiration may also be inferred from climate model simulations under the observed forcing in the twentieth century. Increases in precipitation or
precipitation minus evaporation are identiﬁable in present-day climate simulations [Holland et al., 2006;
Kattsov et al., 2007; Bengtsson et al., 2011; Koenigk et al., 2013; Bintanja and Selten, 2014], suggesting the forcing role of the anthropogenic effects and resulting Arctic sea ice retreat. However, evaporation exhibits an
inconsistent response across models. For example, it shows a decrease in ECHAM5 [Bengtsson et al., 2011] andan increase in CCSM3 [Holland et al., 2006] and CMIP5 models [Bintanja and Selten, 2014]. Evaluation of modeled evaporation is also subject to a lack of direct observations, in particular over the Arctic region.
Nevertheless, according to the increase in the net poleward moisture transport detected from reanalysis data
[Zhang et al., 2013] and simulated by climate models [Bengtsson et al., 2011], the rate of evaporation increase
must be less than that of precipitation increase.
To attribute the long-term increasing trend of annual mean precipitation, Min et al. [2008] employed a standard optimal detection approach to compare observed and simulated Arctic land precipitation. They foundthat the increase in anthropogenic forcing has dominated the observed upward trend of high-latitude precipitation in the second half of the twentieth century (Figure 4). Meanwhile, they also indicated that themodel simulated precipitation response to the anthropogenic forcing is weaker than that in the observations,which is consistent with the ﬁndings in other studies [Holland et al., 2006; Kattsov et al., 2007; Bengtsson et al.,2011; Koenigk et al., 2013; Lique et al., 2016]. Note that AO has generally been used to represent intrinsic
atmospheric variability in detection and attribution analysis. However, anthropogenic forcing may also affect
AO, which further affects atmospheric moisture transport from lower latitudes, precipitation, and riverdischarge into the Arctic Ocean [Zhang et al., 2008, 2013]. But the interactions between intrinsic atmosphericvariability and anthropogenically forced changes in the atmospheric circulation have not been well simulated
and understood. These problems may have contributed to the weaker precipitation in models thanin observations.
3.4. Summary
Over longer periods and large areas, there seems to be a trend toward wetter conditions, but when smaller
regions and shorter time periods are analyzed, variable trends are detected. Models capture the overall wetting trend but have problems in reproducing the regional details [Lique et al., 2016]. The overall increase inprecipitation is linked to the general warming trend, partly driven by anthropogenic forcing, and ampliﬁed
in the Arctic via various positive feedbacks.

4. Projected Changes and Key Drivers
In the following subsections, we brieﬂy summarize atmospheric freshwater projections based on model
simulations performed as part of CMIP5. More information on the various challenges, error sources, and strategic aspects in modeling of the future freshwater in the Arctic are discussed in Lique et al. [2016]. Here allstatements are based on the CMIP5 multimodel mean (or median), rather than individual models, unless stated otherwise. The CMIP5 projections are broadly consistent with those from the CMIP3 models analyzed by
Kattsov et al. [2007]. We focus on multidecadal projections by the end of the 21st century, although near-term
projections (up to 2050) are broadly similar in sign, but with reduced magnitude and greater uncertainty due

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1

Figure 4. Large-scale precipitation trends (0.01 mm d per 50 years) during 1950 to 1999 based on observations (OBS)
and CMIP3 model simulations driven by different forcings: anthropogenic (ANT; greenhouse gases and sulfate aerosols),
natural (NAT; solar and volcanic), and both of them (ALL). Areas with less than 40 years of observations are marked withwhite space. Reproduced with permission from Min et al. [2008].

to natural variability. Beyond 2050, the projected changes are generally of the same sign in all emission
scenarios but are larger in magnitude for higher greenhouse gas concentration pathways. To ﬁrst order,
the magnitude of projected changes in the atmospheric freshwater system scales with projected
temperature changes.
4.1. Model Projections
4.1.1. Humidity
Speciﬁc humidity (SH) is projected to increase with warming, according to the Clausius-Clapeyron relation.
Simulated SH increases in the Arctic are sustained by increased local evaporation and enhanced moisture ﬂux
convergence [Skiﬁc and Francis, 2013]. Since warming is ampliﬁed at the surface, SH rises are also expected tobe largest in the lowermost atmosphere. The CMIP5 model ensemble suggests that relative humidity (RH) will
decrease [Collins et al., 2013]. Over northern land regions, RH is projected to decrease in the annual, winter,and summer mean. This decrease arises because land regions warm faster than open ocean regions. When
maritime air masses are advected over land, the air is warmed and its relative humidity drops, as any further
moistening of the air over land is insufﬁcient to maintain constant RH [Collins et al., 2013]. Over the Arctic
Ocean, the near-surface RH is very sensitive to lead occurrence, which depends on sea ice dynamics over abroad range of spatial scales. Climate models can hardly simulate it reliably enough to allow solid conclusionsof future evolution of RH.

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4.1.2. Clouds
The CMIP5 models project a general increase in annual mean total cloud fraction by a few percent over theArctic Ocean and high-latitude land regions by the late 21st century [Collins et al., 2013]. Further south in the
midlatitudes, cloud cover is projected to decrease consistent with reduced RH. However, these projectedchanges are not robust across the CMIP5 models, reﬂecting model differences in cloud physics and cloud parameterizations. These discrepancies appear to arise primarily due to differences in simulated low clouds [Collins
et al., 2013]. In the Arctic, the cloud discrepancies are seasonally largest in autumn. For example, while one
CMIP5 model (EC-Earth) projects a decrease in autumn cloud cover [Koenigk et al., 2013], another (CCSM4)
model projects a large increase [Vavrus et al., 2012]. In both these models, the largest cloud cover changes occur
in the months and regions of greatest Arctic sea ice loss, but the responses are of opposite sign. Thus, the cloud
cover response to reduced sea ice cover is a key area of uncertainty in projected cloud cover trends.
4.1.3. Precipitation
The CMIP5 models project a robust increase in precipitation (P) over the Arctic and midlatitudes, related towarming and increased available moisture [Collins et al., 2013; Laine et al., 2014; see also Lique et al., 2016](Figure 5). A robust increase in precipitation with scattered results for trends in cloudiness (section 4.1.2) andseasonally and regionally varying trends for relative humidity (section 4.1.1) is an interesting combination. It
can probably be explained by the fact that if the clouds contain more water, precipitation may increase evenin regions where relative humidity and cloud coverage decrease, i.e., precipitation intensity increases. In thewinter half-year (October–March), Arctic mean P is projected to increase by 35% and 60%, under medium
and high greenhouse gas representative concentration pathways (RCP), respectively (RCP4.5 and 8.5), relative
to the period 1986–2005 [Intergovernmental Panel on Climate Change (IPCC), 2013]. The projected P changes
in the warmer months (April–September) are weaker: 15% and 30%. Spatially, the largest relative changes areprojected over the Arctic Ocean [IPCC, 2013; Laine et al., 2014].
An annual mean decrease in snowfall is projected over North America, Northern Europe, and midlatitude Asia
[Krasting et al., 2013]. These decreases are driven by reductions in the snowfall-to-precipitation ratio (i.e., ashift from snow to rain), which is temperature dependent (see section 5.4 and Wrona et al. [2016], on ecological impacts). Seasonally, the changes are largest in spring and autumn, associated with an earlier snow-raintransition in spring and later rain-snow transition in autumn [Krasting et al., 2013]. In high latitudes (e.g.,
northern Siberia) and at high altitudes (e.g., central Greenland), where air temperatures remain sufﬁciently
cold, snowfall is projected to increase, especially in winter.
In addition to projected increases in mean P, there is increasing evidence that P extremes will also increase infrequency and/or intensity. According to Kharin et al. [2013], relative changes in the intensity of precipitation
extremes generally exceed relative changes in annual mean precipitation. On the basis of eight CMIP5 models,Toreti et al. [2013] found out that the 1 in 50 year return level of daily precipitation is projected to increase in allseasons over the middle and high latitudes, being reliable and consistent between models particularly overnorthern Eurasia in winter and the North Paciﬁc and northwestern Atlantic/Arctic Ocean in summer. Due tothe Arctic ampliﬁcation (section 2.1), in consistence with the Clausius-Clapeyron constraint, the largest increases
are projected poleward of 60°N. The 20 year return level of annual extremes of daily precipitation is also projected to increase over the Arctic and Arctic River catchments, by around 2–6% per degree of local warming[Kharin et al., 2013]. Other indices of extreme P—namely total wet day precipitation, very wet day precipitation,
maximum 5 day precipitation, and heavy precipitation days—are all projected to increase over the midlatitude
to high-latitude northern landmasses, especially north of 60°N [Sillmann et al., 2013]. Increases of 20–30% in the
maximum 5 day precipitation are projected for Alaska, northern and eastern Canada, Greenland, and northernEurasia. The increases are less pronounced in summer than in winter with the strongest increases in northern
Asia of about 40% in winter and 20% in summer [Sillmann et al., 2013]. In northern Europe, a CMIP5 ensemble
shows a consistent increase only for winter. In Alaska, northern and eastern Canada, Greenland, and northern
Asia these increases in precipitation are accompanied by a decrease in the number of consecutive dry days,
whereas in northern Europe no consistent change is projected for dry days [Sillmann et al., 2013].
4.1.4. Evaporation
Evaporation (E) is projected to increase in winter over the Arctic and northern continents, except for in the North
Atlantic region and Greenland [Laine et al., 2014; see also Lique et al., 2016] (Figure 5). These increases are consistent with increases in humidity and P, and overall warming that increases potential E. The projected decreaseover the North Atlantic is related to a reduced air-sea humidity contrast. In summer, E is projected to decrease

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Figure 5. (a and d) Multimodel mean changes in precipitation (P), (b and e) precipitation evaporation (P E ), and (c and f)
evaporation (E) for the winter (DJF; Figures 5a–5c) and summer (JJA; Figures 5d–5f) seasons. Regions are dotted in which thesign of the changes is robust among the models at the 95% conﬁdence level. In the Figures 5b and 5e and Figures 5c–5f,
regions are hatched in which the particular contribution dominates the precipitation changes indicated in Figures 5a and 5d.
Reproduced with permission from Laine et al. [2014].

over the Arctic Ocean and subpolar ocean [Laine et al., 2014] (Figure 5). This is because near-surface air temperature and humidity rise, reducing the air-sea temperature and moisture contrasts and therefore, reducing E. Overthe northern continents, summer E increases in line with warming.
4.1.5. Precipitation Evaporation
Wintertime P E is projected to increase over the central Arctic Ocean but decrease over the coastal seas where
the winter sea ice concentration is reduced, allowing more evaporation [Laine et al., 2014; see also Lique et al.,2016] (Figure 5). The projected sea ice loss leads to large local increases in E that exceed the P increases. Overthe central Arctic Ocean, where winter sea ice cover remains limiting air-sea exchange, the warming-driven P
increases exceed the E increases. Over northern landmasses P E increases during winter, as P increases fasterthan E in response to warming. Conversely in summer, P E decreases over northern continents due to larger
increases in E than P [Laine et al., 2014] (Figure 7). Over the Arctic Ocean and subpolar oceans, summer P E
increases in response to increased P and decreased E. For the high latitudes, the physical consistency and the

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similar behavior across multiple generations of models and forcing scenarios indicate that the projected P, E, andP E changes discussed above are likely with high conﬁdence [Collins et al., 2013].
4.2. Drivers of Projected Change
4.2.1. Surface Moisture Budget
An analysis of the CMIP5 models found that over 50% of the projected Arctic precipitation increase by
the late 21st century is related to local evaporation changes, closely tied to the retreat of sea ice[Bintanja and Selten, 2014] (Figure 6). Seasonally, the contribution due to local evaporation is largest in
winter, and the contribution due to moisture transport is largest in late summer and autumn. Several
studies have sought to isolate the inﬂuence of projected Arctic sea ice loss by running atmosphericmodel simulations prescribed with future sea ice conditions but unchanged sea surface temperaturesor radiative forcing. Such simulations project an intensiﬁed Arctic hydrological cycle in response to futuresea ice loss, with the largest changes near regions of sea ice loss and in the cold season [Deser et al.,2010; Peings and Magnusdottir, 2014; Deser et al., 2014]. The sea ice retreat will lead to more open waterand for more of the year, giving rise to new near-coastal hydrological regimes with implications for marine
and freshwater ecosystems [Carmack et al., 2016; Wrona et al., 2016]. Over land, projected reductions
in snow cover enhance lower tropospheric warming [Alexander et al., 2010; Henderson et al., 2013].
However, the impact of projected snow cover declines on the atmospheric moisture budget has notbeen explored.
4.2.2. Poleward Moisture Transport
Atmospheric transport of moisture into the Arctic region is projected to increase in response to risinggreenhouse gas (GHG) concentrations. This increase can be partially explained by simple thermodynamics(Held and Soden [2006] and see also discussion in Lique et al. [2016]. As the atmosphere warms, it can hold moremoisture. Humidity increases more rapidly at lower latitudes for a given temperature increase, leading to a larger poleward moisture gradient. Both increased atmospheric moisture content and a larger poleward moisturegradient lead to a larger transport of poleward moisture into the Arctic. Approximately 75–80% of the total projected annual change in moisture transport across 70°N, between the late 20th and 21st centuries, is thermodynamically driven, i.e., due to change in the meridional gradient of speciﬁc humidity [Skiﬁc et al., 2009; Skiﬁc andFrancis, 2013]. Although smaller, the dynamic term is also positive and related to an increase in low-pressure
systems over the central Arctic that transport substantial moisture into the Arctic [Skiﬁc et al., 2009; Skiﬁc andFrancis, 2013].
North Atlantic atmospheric rivers are projected to become stronger and more frequent [Lavers et al., 2013],
transporting increased moisture into the North Atlantic and Eurasian sectors of the Arctic, which via increasing precipitation contributes to freshening of the seas [Carmack et al., 2016]. This projected increase is predominantly a thermodynamic response to warming and moistening [Lavers et al., 2013], rather than due todynamical changes in storms and their associated frontal features.
Haine et al. [2015] reviewed climate model projections for the end of the 21st century and concluded that theatmospheric moisture ﬂux convergence over the Arctic Ocean and Canadian Arctic archipelago will beapproximately equally large as the oceanic freshwater transport through the Bering Strait (~2500 km3/yr,
the oceanic transport calculated with respect to a reference salinity of 34.80 in the practical salinity scale),whereas the river runoff will be roughly double as large (5500 km3/yr). These inﬂows will be balanced by
the oceanic outﬂow, mostly via the Fram Strait and Davis Strait.
4.2.3. Storm Tracks
Projected upper tropospheric warming is larger in the tropics than in the extratropics, leading to an
enhanced poleward temperature gradient at upper levels. This is expected to induce a poleward shift of
the jet streams and accompanying storm tracks in middle latitudes [Held, 1993]. However, at the same time,
projected Arctic sea ice loss greatly ampliﬁes lower tropospheric warming in the Arctic relative to the
midlatitudes. This low-level warming produces a weakening of the equator-to-pole near-surface temperature
gradient, opposite to that at higher altitudes. A reduced lower tropospheric temperature gradient isassociated with an equatorward shifted jet stream and storm tracks [Hoskins and Woollings, 2015; Hallet al., 2015]. The net effect of these two opposing effects in winter is close to cancelation, but in some models
one inﬂuence is larger than the other. This results in weak and nonrobust winter storm track shifts across theCMIP5 models [Barnes and Polvani, 2013; Harvey et al., 2014]. Efforts to understand model spread in the atmospheric circulation responses to a rise in GHG have highlighted the competing effects of tropical upper

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Figure 6. Simulated annual and monthly 21st century changes in Arctic mean precipitation, poleward moisture transport
across 70°N (remote origin), and surface evaporation components (local origin). (a) Annual mean changes, where each bar
denotes one CMIP5 model sorted according to the magnitude of the simulated precipitation change (horizontal black lines
represent 50% of each column). (b) Seasonal changes, where each bar represents the monthly and multimodel mean. Changes
are given for the strong (RCP8.5) and intermediate (insets, RCP4.5) forcing scenarios. The total Arctic surface evaporation(the sum of the red and orange bars) is separated into its ice retreat (red) and non-ice retreat (orange) components. The
attribution of sea ice retreat to the total surface evaporation was evaluated by calculating monthly evaporative ﬂux differences
over only the ice retreat regions, which were then summed over the respective 10 year periods. Reproduced with permission
from Bintanja and Selten [2014].

tropospheric warming and Arctic sea ice loss [Cattiaux and Cassou, 2013; Haarsma et al., 2013; Harvey et al.,2014; Barnes and Polvani, 2015; Deser et al., 2015]. Stratospheric polar temperature trends have also been
implicated as a source of spread in future tropospheric circulation trends [Manzini et al., 2014]. In other seasons, the impact of Arctic sea ice loss on the atmosphere is weaker than in winter [e.g., Deser et al., 2010],
resulting in a robust poleward shift of the storm track in the warm part of the year [Grise and Polvani, 2014;
Barnes and Polvani, 2015; Deser et al., 2015].

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4.3. Summary
The latest state-of-the-art climate model projections suggest a robust future intensiﬁcation of the Arctichydrological cycle. Mean precipitation and daily precipitation extremes are projected to increase overmidlatitude and high latitude, largely in response to warming-driven increases in the moisture holding
capacity of the air. The relative increases precipitation extremes are expected to exceed relative increases
in mean values. This increased moisture comes from increased evaporation (itself largely due to the loss of
sea ice cover) and enhanced poleward moisture transport. There is however, signiﬁcant model divergencein future cloud cover trends, especially those arising from sea ice loss, and in the wintertime storm
track changes.

5. Cross-System Effects
A schematic summary of cross-system impacts is presented in Figure 7, with focus on the effects of intensiﬁcation of the Arctic atmospheric water cycle. The discussion is closely tied with the papers by Bring et al.
[2016], Carmack et al. [2016], Instanes et al. [2016], and Wrona et al. [2016].
5.1. Remote Atmospheric Impacts
So far we have focussed attention on changes in the Arctic. However, the large changes in the Arctic
freshwater storages may have implications for regions beyond the Arctic. Numerous recent studies haveaddressed the effects of Arctic sea ice loss and changes in Siberian snow cover on midlatitude weather
and climate (see Vihma [2014] and Cohen et al. [2014] for reviews). Several linkages have been found,but many of them are regional and episodic and result from a combination of internal variability, lower
tropospheric temperature anomalies [Screen, 2014], and midlatitude teleconnections [Overland et al.,2015]. There are physical mechanisms, such as the weakening of midtropospheric westerly winds dueto the Arctic ampliﬁcation, via which changes in the Arctic can inﬂuence midlatitude weather, but it
is often difﬁcult or impossible to prove if changes in the Arctic have been the main causal factor for
observed anomalies in midlatitude weather [Barnes and Screen, 2015]. Some studies have suggested
that the Arctic ampliﬁcation of climate warming favors a weaker and more meandering jet stream,
and this may have contributed to the extreme snowfall events in recent winters in the U. S. East
Coast [e.g., Francis and Skiﬁc, 2015]. Also, model simulations by Screen [2013] suggested that Arctic
sea ice loss induces a southward shift of the summer jet stream over Europe and increased northern
European precipitation. This has probably contributed to the observed six consecutive wet summers
from 2007 to 2012 in northern Europe.
Arctic ampliﬁcation is robustly projected to continue in the future in response to increasing GHG concentrations [IPCC, 2013], further reducing the near-surface north-south atmospheric temperature gradient. Recent
work has shown that future midlatitude climate change is moderated by the magnitude of projected Arctic
ampliﬁcation. For example, models that simulate larger Arctic ampliﬁcation depict smaller poleward shifts of
the winter jet streams and storm tracks [Barnes and Polvani, 2015; Harvey et al., 2014], with implications for
precipitation over the Arctic river catchments [Bring et al., 2016].
5.2. Oceanic Impacts
Precipitation minus evaporation changes have direct impacts on the salinity of the Arctic Ocean above
the halocline [Carmack et al., 2016, Figure 7]. However, the freshening effect of increased P E is small
compared to freshwater input from increased river discharge (see section 5.3) as well as melting sea ice,
glaciers, and ice sheets. Continents and islands play an important role in generating orographic precipitation; part of the precipitated water ends up in the Arctic and sub-Arctic Seas via river and meltwater
discharge, iceberg calving, and glacier ﬂow into the sea. Another important atmospheric inﬂuence on
the ocean comes from wind forcing (Figure 7); changes in large-scale atmospheric circulation and storm
tracks are summarized in section 3.3. A recent freshening of the western Arctic Ocean has been
linked to a wind-driven spin-up of the Beaufort Gyre [Giles et al., 2012; Carmack et al., 2016], which isdue to strengthening of the Beaufort High (section 3.3). Wind forcing is also vital in determining
freshwater export via the Fram Strait [Kwok et al., 2013]. Considering sea ice export, there is no increasing trend [Spreen et al., 2009]. A small increase in the atmospheric pressure gradient across the Fram
Strait would favor it, but the effect is compensated by a decrease in sea ice concentration [Kwok

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Figure 7. Schematic summarizing some of the important impacts of an intensiﬁed Arctic atmospheric water cycle. Impacts are colored depending on whether they
are primarily a result of a warmer (orange), wetter (blue), windier (purple), and cloudier (grey) atmospheric state. This is not intended to be comprehensive, but rather
to illustrate the multifaceted nature of change in the Arctic freshwater system.

et al., 2009; Polyakov et al., 2012]. In coastal regions, the reduction of sea ice cover and related increasein wind forcing on the open ocean have increased wave activity and erosion rates of permafrost coasts[Overeem et al., 2011].
Irrespective of changes in wind, the loss of sea ice (or more speciﬁcally, the increase in leads and open water)
has allowed for greater wind forcing of the upper ocean and sea ice, with important implications on shelfbreak upwelling [e.g., Carmack and Chapman, 2003]. The sea ice cover is becoming more mobile, mostly
due to thinning and mechanical weakening of ice [Rampal et al., 2009; Vihma et al., 2012], although
increase in wind forcing has played a role in the Central Arctic but not in the entire basin [Spreen et al., 2011].
The more mobile sea ice cover affects the freshwater redistribution within the Arctic and export from the
Arctic [Carmack et al., 2016].

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Changes in phase of precipitation have implications for the sea ice albedo and hence, mass balance. Screen
and Simmonds [2012] estimated that summer snowfall has declined over the Arctic, lowering the ice albedo
during the melt season (snow-covered ice is more reﬂective than bare ice). This albedo decline due to lesssnow on ice was estimated to be comparable to that related to the decline in sea ice cover [Screen andSimmonds, 2012]. Accordingly, the decrease in summer snowfall and increase in rain have probably had
a large contribution to the Arctic sea ice decline. Models project future declines in snow on ice both insummer and spring, the latter due to later autumn freeze-up allowing less time for snow accumulation
on ice [Hezel et al., 2012]. The declines reduce the surface albedo, and a lower albedo enhances surface melt
leading to ponding, which further lowers the albedo. On the other hand, if winter snowfall increases, thesnowpack gets thicker, which causes delay in the spring-summer decrease of albedo, reducing theaccumulated melt.
The interactions between the oceanic and atmospheric components of the Arctic freshwater system are reciprocal. Changes in sea ice and sea surface temperature affect ocean-atmosphere moisture and energy ﬂuxes, withlocal and remote atmospheric impacts [e.g., Deser et al., 2010; Screen et al., 2013]. Changes in river discharge andnet precipitation alter salinity, and hence buoyancy, affecting ocean circulation and SSTs [Carmack et al., 2016]
and thereby, the atmosphere. An example is the projected cooling of the atmosphere over eastern Greenland
and the northern North Atlantic during the 21st century due to increased freshwater in the North Atlantic andweakening of the thermohaline circulation [e.g., Drijfhout et al., 2012].
5.3. Hydrological Impacts
The impacts of changes in atmospheric water cycle on terrestrial hydrology include considerable spatial
variability between and within river basins [Bring et al., 2016]. Many impacts are also sensitive to the exact
time period studied. Here we only summarize the major large-scale impacts during the recent decades andsome projections for the future. Increased precipitation over the Arctic river catchments (Figure 7) has
been associated with greater discharge from the large Arctic rivers [Peterson et al., 2002; Richter-Menge
and Overland, 2009]. This increase in river ﬂow is mostly due to increased net precipitation.
Precipitation and its division between snow and rain affect the extent and thickness of terrestrial snow
cover (Figure 7), which are important for ecology [Wrona et al., 2016]. According to Brown and Derksen[2013], who compared four independent sources of snow cover data, Eurasian autumnal snow cover extent
has decreased over 1982–2011. Also, autumn precipitation has increased in East Siberia and the rain-toprecipitation ratio has increased in autumn in southern Canada and Eurasia south of Siberia (section 3.1.3).
These changes affect autumn soil moisture and may have implications for spring runoff response andextreme ﬂood events. In winter, atmospheric moisture, precipitation, and snow accumulation have increased
over large parts of Russia [Bulygina et al., 2009; Borzenkova and Shmakin, 2012; Callaghan et al., 2011; Parket al., 2012]. Higher temperatures in spring and early summer have resulted in earlier melt, and the reductionof Northern Hemisphere snow cover in June has been even faster than the reduction of Arctic sea ice cover in
September [Derksen and Brown, 2012].
Projected changes in precipitation phase can cause a shift from nival (snow-fed) to pluvial (rain-fed)
regimes and with important consequences for the terrestrial freshwater system [see Bring et al., 2016]
and ecosystems [Wrona et al., 2016]. Projected changes in precipitation and evapotranspiration also inﬂuence soil moisture and runoff; however, nonatmospheric factors also play a role. Warming contributes topermafrost thaw and a deepening of the active layer [Bring et al., 2016], with implications for infrastructure[Instanes et al., 2016].
The Greenland ice sheet is an indicator of long-term changes in the regional freshwater cycle. For estimateson the mass loss and runoff from Greenland and their effects on the sea level rise, see Bring et al. [2016].
Snowfall is the main source term for the mass balance of the ice sheet. Also, rain acts as a source term,as the rain water mostly freezes in the ice sheet, but the mass gain from rain is negligible compared tosnowfall. The export of blowing snow and the sum of evaporation, sublimation, deposition, and condensation are small terms [Box et al., 2004; Lenaerts et al., 2012]. The main sinks of mass are the surface meltwaterand glacier discharges, the latter including iceberg calving and glacier ﬂow into the sea. Clouds are
essential for the surface melt, as the cloud radiative forcing is positive (i.e., cloud have a net warming effectat the surface) over most of Greenland even in summer [e.g., Bennartz et al., 2013] due to the high surface

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albedo. This differs from conditions elsewhere in the same latitudes, where cloud radiative forcing is
negative for several months during the warm season, and also from conditions over sea ice in the central
Arctic (section 2.2).
During the past decade, the Greenland ice sheet mass balance has been negative due to increasing glacierdischarge and meltwater runoff [Box et al., 2012; Franco et al., 2013]. Increasing air temperatures are
responsible for the increasing trend in meltwater discharge. Increasing surface melt also affects glacier
dynamics and subglacial sliding. In addition to the trend, Greenland melt has a substantial interannual
variation. The variations are related to large-scale atmospheric circulation (characterized by NAO andthe Greenland Blocking Index) [Hanna et al., 2014], associated transport of moisture over Greenland[Neff et al., 2014], and changes in cloud cover and properties [Bennartz et al., 2013]. Climate model experiments suggest that in a warmer climate there will be more winter snowfall, but the mass gain will notcompensate the increasing mass loss via increased summer meltwater runoff [Fettweis et al., 2013]. Also,
smaller Arctic glaciers and ice caps have continued to lose area and mass during the latest decades[Vaughan et al., 2013].
5.4. Ecological Impacts
Ecological impacts of changes in clouds, precipitation, and evaporation can be divided into impacts on
marine [Carmack et al., 2016] and freshwater ecosystems [Wrona et al., 2016]. For the terrestrial freshwater
ecology (Figure 7), for example, changes in temperature and precipitation may partially drive greening(e.g., poleward expansion of shrubs) or browning; and increased winter rainfall in a warmer climate(and associated ice layers in snow) affects the ecosystem [Wrona et al., 2016]. The ﬂuxes of carbon dioxideand methane between the Earth surface and atmosphere are mostly controlled by processes at or below
the Earth surface, such as changes in water and sediment temperatures, thawing of subsea and terrestrial
permafrost, and changes in water budgets and levels [Wrona et al., 2016]. These processes are, however,strongly affected by accumulated changes in the air temperature and precipitation.
For both marine and freshwater ecosystems, potential changes in cloud cover are important as theyaffect the intensity of solar radiation, including UV radiation. The effects of UV radiation on Arctic
freshwater ecosystems have been reviewed by Wrona et al. [2006]. The potential harmful effects include
biomolecular, cellular, and physiological damage to organisms, and alterations in species composition.
An increase in cloud-to-ground lightning strikes over the Arctic region is projected in a warming climate.
Correspondingly, the burned area is expected to increase, especially over high latitudes [Krause et al.,2014].
5.5. Implications for Freshwater Resources and Human Activities
The atmospheric water cycle has a large direct (e.g., ﬂooding) and indirect effect on human activities in the
Arctic (Figure 7), as precipitation and evaporation affect the soil water budget and the thickness andextent of snowpack, and clouds affect the net radiation and, hence, the Earth surface temperature. The
status and changes in soil water, snowpack, and surface temperature are essential for numerous economic
activities, such as agriculture, forestry, hydropower, reindeer herding, road, and rail transport, and in particular in the permafrost zone, for construction work and infrastructure maintenance. Of particular challenge are the effects of extreme weather events. Recent changes in changes in the frequency andintensity of precipitation extremes have been spatially variable (section 3.1.3), but precipitation extremes
are projected to increase over midlatitude and high latitude (section 4.1.3) and rain events in winter will
become more common [McAfee et al., 2013], causing challenges, among others, to hydropower production[Instanes et al., 2016]. Hence, regional downscaling models have been developed at least for Norway,
Iceland, Sweden, and Greenland to capture the extreme events [Instanes et al., 2016]. Another important
aspect for hydropower production are the latitudinal differences in changes in net precipitation, the highlatitudes becoming more water rich while the more southerly latitudes are becoming more “water poor”[Prowse et al., 2015b].
Precipitation over the sea also affects the snowpack on sea ice and, via snow-to-ice transformation, the thickness and structure of sea ice itself, with impacts on navigation. The effects of atmospheric water cycle on
human activities via impacts on the Earth surface conditions are more discussed in Bring et al. [2016],
Instanes et al. [2016], and Wrona et al. [2016].

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More direct effects of atmospheric water cycle include effects of changes in cloud conditions on aviation (lowclouds and fog are the main problems in the Arctic), atmospheric icing of structures (wind mills [Makkonen
et al., 2001], towers, and power lines), and on solar shortwave and thermal longwave radiation. A reduced
cloud cover results in an increase in solar radiation, which has a strong positive impact on vitamin D status[Andersen et al., 2013] and mental health of people [Grimaldi et al., 2009] but simultaneously increases theoccurrence of skin cancer [De Fabo and Noonan, 2002]. On the other hand, in the Arctic and sub-Arctic thecloud radiative forcing is positive for a large part of the year, so that an increase in cloudiness yields higher
near-surface air temperatures, which reduce mortality [Kue Young and Mäkinen, 2010] and improve wellbeing and physical performance of people [Rintamäki, 2007].

6. Major Knowledge Gaps and Future Research Directions
Our knowledge of the Arctic freshwater cycle has gaps related to limited understanding of physicalprocesses, insufﬁciency and inaccuracy of observations, and deﬁciencies in modeling. These generate uncertainty about the past and future changes in the water cycle.
6.1. Process Understanding
The Arctic freshwater cycle is affected by numerous small-scale processes in the atmosphere, snow, sea ice,and the ocean. Many of these processes and their parameterization in weather and climate models wererecently reviewed by Vihma et al. [2014]. Hence, we only provide a very brief summary here. Insufﬁciently
understood atmospheric processes include interactions of aerosols, cloud droplets, and ice particles inmixed-phase clouds as well as in the coupling of clouds, radiative transfer, and turbulence [Shupe et al.,2013; Treffeisen et al., 2007; Morrison et al., 2012]. In addition, there are major knowledge gaps incircumpolar-scale feedbacks in the Arctic, above all related to the interaction of water vapor and cloud feedbacks with the other feedbacks contributing to the Arctic ampliﬁcation of climate warming [Serreze and Barry,
2011; Pithan and Mauritsen, 2014]. Lack of process understanding is due to complexity of the processes andlimited amount and accuracy of observations available.
6.2. Insufﬁcient Observations
The main reason for insufﬁcient knowledge on the past changes and present status of the atmospheric water
cycle in the Arctic is the lack of spatially well-covered and accurate data. This is most serious over the oceans,but problems also remain over land areas, with a coarse station network. Considering different atmosphericwater variables, the lack of in situ observations is most evident for evaporation and precipitation [Yang et al.,2005; Pavelsky and Smith, 2006]. Further, there are very few data on cloud properties (such as cloud water andice contents and droplet size distribution) and air moisture above the atmospheric surface layer. Except forlimited short-term campaigns, there are no in situ observations of the above mentioned variables over theArctic and sub-Arctic seas. Over land areas, vertical proﬁles of air moisture are routinely measured at only
some 36 radiosonde sounding stations north of 65°N [Nygård et al., 2014]. Important advances have been
made in ground-based remote sensing of air moisture and cloud properties, but measurements are only
made at a few stations in the circumpolar Arctic and the records are still short [Shupe, 2011; Shupe et al.,2011]. During the last decades, satellite remote sensing has started to provide important information on
air moisture and clouds, in particular from microwave sensors [Bobylev et al., 2010], but optical data on clouds
are missing from the Arctic night.
A large part of the existing data is very uncertain. In situ observations of snowfall may include errors of up
to 200%, mostly due to the effect of wind [Aleksandrov et al., 2005], and new satellite-borne sensors of precipitation (e.g., the Global Precipitation Monitoring satellite) provide coverage only up to about 65°N. Observations
and estimates of evapotranspiration over terrestrial surfaces suffer from various errors and uncertainties[Bring et al., 2016]. Accuracy of observations of air humidity is poor in low temperatures; unheated and unventilated hygrometers often become coated with ice [Déry and Stieglitz, 2002; Makkonen and Laakso, 2005] and theaccuracy of radiosonde data decreases as the water vapor concentration and pressure decrease [Elliot andGaffen, 1991]. Cloud observations are mostly visual and liable to large errors during the Arctic night [Hahnet al., 1995]. The accuracy of satellite remote sensing observations on cloud water and ice content is reduced
by problems in distinguishing the signals originating from the snow/ice-covered Earth surface and from theatmosphere, in particular in the case of low clouds. The advent of active satellite sensors (e.g., the A-Train)

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10.1002/2015JG003132[L’Ecuyer and Jiang, 2010] improved this situation, but the time series are very short and only cover the southern
Arctic (< ~82°N). The remote sensing problems are smaller for air humidity than for cloud condensate content,
but the data suffer from a limited vertical resolution [Boisvert et al., 2015].
6.3. Deﬁciencies in Models and Reanalyses
Our knowledge on air moisture, clouds, precipitation, and evaporation in the Arctic is also limited by errors
and inaccuracies in modeling of these variables, due to imperfect representation of complex physical processes (e.g., cloud microphysics) and coarse model resolution. The deﬁciencies are discussed more thoroughly in Lique et al. [2016], and here we focus on some of those related to the results presented in
sections 3 and 4.
Considering numerical weather prediction (NWP) results and atmospheric reanalyses, the problems inmodeling and observations are tied via data assimilation. The observational input to model analyses
and reanalyses varies in time and has limited spatial coverage. Comparisons against in situ observations
have demonstrated that atmospheric reanalyses have large errors in the vertical proﬁles of air temperature
as well as speciﬁc and relative humidity over the Arctic Ocean, with predominantly warm and moist biases
in the lowermost 0.5–1 km [Lüpkes et al., 2010; Jakobson et al., 2012; Wesslén et al., 2014]. The observed
biases in humidity are in many cases comparable to or even larger than the climatological trends during
the latest decades. Reanalyses also have problems in reproducing the observed cloud characteristics
and cloud radiative forcing in the Arctic [Walsh and Chapman, 1998; Wesslén et al., 2014]. Further, reanalyses are not in hydrological balance, i.e., the annual mean vertically integrated water vapor ﬂux convergence does not equal net precipitation in the Arctic. For the National Centers for Environmental
Prediction (NCEP)/National Center for Atmospheric Research (NCAR) and NCEP/DOE reanalyses the imbalance was as large as 60% [Cullather et al., 2000]. There is also a considerable inaccuracy in the vertical proﬁle of water vapor transport to the Arctic; radiosonde sounding data suggest that the transport peaks at
850 hPa level [Overland and Turet, 1994], but ERA-40 suggests the peak much closer to the Earth surface[Jakobson and Vihma, 2010]. We do not know which is closer to the truth; reanalyses are inaccurate but
radiosonde data are biased to land areas.
Reanalyses as well as NWP and climate models also suffer from problems in understanding and parameterization of subgrid-scale physical processes related to moisture variables. Although there have been recent
advances in understanding the physics of Arctic clouds [Morrison et al., 2012], these have not yet yielded
remarkable improvements in model parameterizations [Vihma et al., 2014] and performance (section 3.2.1).
Models still rely on parameterizations based on more well understood, lower latitude clouds. This may havecontributed to the recent ﬁnding that radiation and air temperature errors of climate models in the Arctic arestrongly related to errors in cloud occurrence and heights, as well as to the vertical distribution of cloud waterand ice content [Tjernström et al., 2008; Kay et al., 2011; Cesana et al., 2012; Liu et al., 2012; Wesslén et al., 2014;de Boer et al., 2014]. Further, CMIP3 models suffer from a large intermodel spread, especially in winter, andsome of the models even have an annual cycle with fewer clouds in summer than in winter, i.e., opposite
to the observations [Karlsson and Svensson, 2011].
Considering Earth surface processes, sea ice [Cheng et al., 2008, 2013] and terrestrial hydrology models[Lique et al., 2016; Bring et al., 2016] suffer from considerable inaccuracy in precipitation forcing. In particular, there is a strong need for improvement in modeling the distribution of precipitation between
rain and snow. Further, stand-alone ocean models sometimes simply mimic the surface freshwater
budget by relaxing the upper ocean salinity to climatology [e.g., Stössel et al., 2011]. There is also needfor better parameterizations for evaporation from spray droplets [e.g., Andreas et al., 2008] anddrifting/blowing snow [e.g., Gordon et al., 2006].
Due to the above mentioned problems, the past climatology on all moisture variables in the Arctic is highly
uncertain, particularly over the ocean prior to 1979, when the assimilation of satellite data into reanalyses
was started. Evidence for trends in Arctic precipitation is complicated by inadequacies and inaccuracies
both in in situ and remote sensing measurements [Walsh et al., 2011b], and in the case of evaporation
the situation is even worse. A common assumption is that evaporation has increased in a warmer Arctic,
but also regional and seasonal decreasing trends have been reported [Screen and Simmonds, 2010b;
Boisvert et al., 2013]. See Carmack et al. [2016] for more discussion on evaporation over Arctic Seas.

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6.4. Uncertainty in Future Projections
Uncertainties in model projections arise from three sources: scenario uncertainty, model uncertainty, andinternal variability [Hawkins and Sutton, 2009; Lique et al., 2016]. For near-term trends (out to approximately
2050), internal variability is a major source of uncertainty for regional climate projections [Deser et al., 2012,2014]. Across-model differences are the other major source of uncertainty prior to about 2050 [Hodson
et al., 2013]. This is especially the case for the Arctic region because of large natural variability of highlatitude climate [Screen et al., 2014]. Such large dependence of future trends on internal variability (forexample, Arctic sea ice extent) [Wettstein and Deser, 2014] means that large ensembles of climate simulations, with each ensemble starting from a different initial condition, are required to accurately constrain
forced trends. Model archives such as the CMIP5 often include only one, or a small number of realizations
per model, and thus, it is difﬁcult, if not impossible, to quantify the extent to which spread in near-term
projections is due to internal variability or model uncertainty. Efforts to produce large ensembles of a single model are an important step forward in this regard [e.g., Deser et al., 2012, 2014; Kay et al., 2014] andhave revealed the importance of accounting for internal variability. They have demonstrated that a large
component of the CMIP5 model spread can be explained by internal variability. More such large ensembles
with a greater diversity of models are required to better understand the sources of uncertainty in projections of the atmospheric water cycle.
On multidecadal to centennial time scales, internal variability is less important and model uncertainty
dominates [Hawkins and Sutton, 2011]. For variables such as precipitation and evaporation, the latest models
generally agree on the sign of the response to increasing GHG concentrations. However, there is a large
model divergence of magnitudes of the projected changes and regional differences in sign and magnitude.
For variables such as cloud cover and cloud properties, the models often disagree on the sign, seasonality,
and spatial pattern of the response. This is not surprising, as the cloud response to sea ice loss is a sensitivebalance. Sea ice loss results in increases in the surface ﬂuxes of both sensible and latent heat, the former
restraining and the latter favoring cloud formation. As a rule of thumb, there is greater conﬁdence in projected changes that are closely tied to thermodynamics (e.g., warming) and less conﬁdence in those related
to dynamical changes (e.g., changes in atmospheric circulation, turbulence, and mixing). The latter is a topic
ripe for future research [Shepherd, 2014].
6.5. Future Research Directions
To better quantify the Arctic atmospheric water cycle and its changes, there is a need for more extensive
and accurate observations, better process understanding, better models, and more extensive and systematic utilization of existing models. Considering observations, recommendations for future research
are naturally closely tied to logistical and economic resources available. The possibilities for a large
increase in the number of radiosonde sounding stations in the Arctic are not high, but there may be potential for sustainable cost-effective vertical proﬁling of the lower troposphere applying small Unmanned
Aerial Vehicles (UAVs) [e.g., Jonassen et al., 2012] and for at least occasional missions applying long-range
UAVs and dropsondes [Intrieri et al., 2014]. In addition, ground- or ship-based remote sensing of cloudproperties and air humidity has potential to yield signiﬁcant further advances in process understanding[e.g., Tjernström et al., 2014] and, when the time series will become long enough, cloud climatology[Shupe et al., 2011; Uttal et al., 2015]. Further research should also address the observational and parameterization challenges of evaporation from drifting/blowing snow and spray droplets.
Analogous to the situation in which sea ice loss can produce local and remote effects on weather and climate[e.g., Vihma, 2014], there is the potential for similar effects to be produced by the signiﬁcant reductions thathave occurred, and are projected to continue, in lake and river ice coverage, as reviewed by Bring et al.
[2016]. Of particular interest is the effect of ice loss and associated heating of freshwater bodies on the local evaporative ﬂux and overall contribution to atmospheric moisture transport to and from the Arctic Freshwater
System. The magnitude of such could be signiﬁcant given the large spatial extent of ponds and lakes foundat the midlatitude to high latitude [Prowse et al., 2011] areas that are comparable to those of sea ice loss thathave occurred over recent decades [e.g., Swart et al., 2009].
Modeling issues that deserve more attention also include the representation of unresolved orographic effects
on precipitation [Cullather et al., 2014], storm tracks, and atmospheric rivers [Newman et al., 2012], as well aslarge-scale effects of sea ice on evaporation and precipitation [Zhang and Walsh, 2006], and especially on

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P E which is the key to land surface drying/wetting and impacts ocean salinity through runoff and directly.
Model results for the recent climate and changes have shown various discrepancies, but identiﬁcation of their
exact causes is still difﬁcult. Systematic evaluations on different aspects of the model simulations, such as in
Kattsov et al. [2007], may yield more information. Considering future projections of the atmospheric water
cycle, there is need for more model experiments with large ensembles and a great diversity of models.
Systematic comparisons of the results should allow a better understanding of the sources of uncertainty.
Further, there is need for updated information on the contributions of anthropogenic and natural forcing
to changes in the Arctic freshwater cycle (Figure 4).

7. Conclusions
The physical processes related to atmospheric moisture, clouds, precipitation, and evaporation in the Arctic
are complex. Although our understanding of individual processes has increased, challenges remain in theunderstanding of nonlinear interactions of various processes, partly acting at different spatial and temporalscales. This challenge in understanding is reﬂected in problems of modeling the processes, in particular the
subgrid-scale processes that need to be parameterized in climate and numerical weather prediction models.
Concrete advances in parameterizations have not been fast.
Past trends in atmospheric water variables in the Arctic include large spatial variations and are sensitive to the time
period and source of data. This is the case particularly for precipitation, snowfall, evaporation/evapotranspiration,and cloud variables. Over longer periods and large areas, there seems, however, to be a trend toward wetter conditions, but when smaller regions and shorter time periods are analyzed, different trends are detected. Models
capture the overall wetting trend but have problems in reproducing the regional details. The latter may partly
result from the larger inﬂuence of interannual variability at regional scales and partly (e.g., in regions of complex
orography) from the insufﬁcient spatial resolution. The overall wetting is linked to the general warming trend,
partly driven by anthropogenic forcing, and ampliﬁed in the Arctic due to several feedback effects. The wetter conditions reﬂect an intensiﬁcation of the atmospheric water cycle in the Arctic, seen as increases in the moisturetransport from lower latitudes and precipitation in the pan-Arctic scale, with a considerable uncertainty in thechanges of evaporation in the Arctic.

Acknowledgments
We thank all colleagues involved in the
Arctic Freshwater Synthesis (AFS) for
fruitful discussions. In particular, John
Walsh is acknowledged for his constructive comments on the manuscript. AFS
has been sponsored by the World
Climate Research Programme’s Climate
and the Cryosphere project (WCRP-CliC),
the International Arctic Science
Committee (IASC), and the Arctic
Monitoring and Assessment Programme(AMAP). The work for this paper has been
supported by the Academy of Finland(contracts 259537 and 283101), the UK
Natural Environment Research Council(grant NE/J019585/1), the US National
Science Foundation grant ARC-1023592
and the Program “Arctic” and the Basic
Research Program of the Presidium
Russian Academy of Sciences. NCAR issupported by the U.S. National Science
Foundation. We gratefully acknowledge
the project coordination and meeting
support of Jenny Baeseman and
Gwenaelle Hamon at the CliC
International Project Ofﬁce. No new data
were applied in the manuscript. Data
applied for Figures 2 and 3 are available
from the JRA-55 archive at http://jra.
kishou.go.jp/JRA-55/index_en.
html#usage.

VIHMA ET AL.

Over approximately the next 100 years, climate model simulations indicate robust increases in precipitationand speciﬁc humidity. In the case of evaporation, the projected trends vary between regions and seasons,and for clouds the projections based on different climate models are diverse. A robust increase in precipitation with scattered results for trends in cloudiness seems possible if the clouds contain more water and theprecipitation intensity accordingly increases. Projections for time scales of the order of years to a few decades
have considerable uncertainty due to the large natural interannual and decadal variability. New and sustained observations and process-level observations, especially in data sparse regions such as the Arctic
Ocean, are needed to better understand ongoing changes and to better evaluate and improve reanalyses
and climate models. To narrow uncertainty in future projections, climate models need to be improved inclose collaboration with observational community and more model experiments with large multimodel
ensembles are needed.

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