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  <front>
    <journal-meta>
<journal-id journal-id-type="publisher">ESD</journal-id>
<journal-title-group>
<journal-title>Earth System Dynamics</journal-title>
<abbrev-journal-title abbrev-type="publisher">ESD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Earth Syst. Dynam.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2190-4987</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>

    <article-meta>
      <article-id pub-id-type="doi">10.5194/esd-7-611-2016</article-id><title-group><article-title>Soil-frost-enabled soil-moisture–precipitation feedback over northern high latitudes</article-title>
      </title-group><?xmltex \runningtitle{Soil-frost-enabled soil-moisture--precipitation feedback}?><?xmltex \runningauthor{S.~Hagemann et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Hagemann</surname><given-names>Stefan</given-names></name>
          <email>stefan.hagemann@mpimet.mpg.de</email>
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Blome</surname><given-names>Tanja</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff2">
          <name><surname>Ekici</surname><given-names>Altug</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5526-4949</ext-link></contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Beer</surname><given-names>Christian</given-names></name>
          
        <ext-link>https://orcid.org/0000-0002-5377-3344</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Max-Planck-Institut für Meteorologie, Bundesstr. 53, 20146
Hamburg, Germany</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>Earth System Sciences, Laver Building, University of Exeter, Exeter,
UK</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Department of Environmental Science and Analytical Chemistry (ACES)
and Bolin Centre for Climate Research, Stockholm University, Stockholm,
Sweden</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Stefan Hagemann (stefan.hagemann@mpimet.mpg.de)</corresp></author-notes><pub-date><day>25</day><month>July</month><year>2016</year></pub-date>
      
      <volume>7</volume>
      <issue>3</issue>
      <fpage>611</fpage><lpage>625</lpage>
      <history>
        <date date-type="received"><day>17</day><month>February</month><year>2016</year></date>
           <date date-type="rev-request"><day>29</day><month>February</month><year>2016</year></date>
           <date date-type="rev-recd"><day>27</day><month>June</month><year>2016</year></date>
           <date date-type="accepted"><day>4</day><month>July</month><year>2016</year></date>
      </history>
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<self-uri xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016.pdf">The full text article is available as a PDF file from https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016.pdf</self-uri>


      <abstract>
    <p>Permafrost or perennially frozen ground is an important part of the
terrestrial cryosphere; roughly one quarter of Earth's land surface is
underlain by permafrost. The currently observed global warming is most
pronounced in the Arctic region and is projected to persist during the coming
decades due to anthropogenic CO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> input. This warming will certainly have
effects on the ecosystems of the vast permafrost areas of the high northern
latitudes. The quantification of such effects, however, is still an open
question. This is partly due to the complexity of the system, including
several feedback mechanisms between land and atmosphere. In this study we
contribute to increasing our understanding of such land–atmosphere
interactions using an Earth system model (ESM) which includes a
representation of cold-region physical soil processes, especially the effects
of freezing and thawing of soil water on thermal and hydrological states and
processes. The coupled atmosphere–land models of the ESM of the Max Planck
Institute for Meteorology, MPI-ESM, have been driven by prescribed observed
SST and sea ice in an AMIP2-type setup with and without newly implemented
cold-region soil processes. Results show a large improvement in the simulated
discharge. On the one hand this is related to an improved snowmelt peak of runoff
due to frozen soil in spring. On the other hand a subsequent reduction in soil moisture enables a positive feedback to precipitation over the high
latitudes, which reduces the model's wet biases in precipitation and
evapotranspiration during the summer. This is noteworthy as soil-moisture–atmosphere feedbacks have previously not been the focus of research on the
high latitudes. These results point out the importance of high-latitude
physical processes at the land surface for regional climate.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <title>Introduction</title>
      <p>Roughly one quarter of the Northern Hemisphere terrestrial land surface is
underlain by permafrost (Brown et al., 1997; French, 1990), which is defined
as ground that is at or below 0 <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C for more than 2 consecutive
years. Permafrost soils build a globally relevant carbon reservoir as they
store large amounts of deep-frozen organic
material with high carbon contents (Ping et al., 2008) leading to a total
pan-Arctic estimate of 1300 Pg of soil carbon (C) in these areas (Hugelius
et al., 2014), which is twice the amount of the atmosphere's content.
Moreover, the high northern latitudes are one of the critical regions of
anthropogenic climate change, where the observed warming is clearly above
average due to the so-called Arctic amplification (Solomon et al., 2007;
ACIA, 2005). Climate model simulations project this trend to continue
(Serreze and Barry, 2011). The combination of the high C stocks in subarctic
and Arctic soils with the pronounced warming in the affected regions could
thus lead to a positive biogeochemical feedback through the release of
formerly trapped, “deep-frozen” C into the atmosphere, when near-surface
permafrost thaws. For the thawed soils and their biogeochemistry, it is
decisive whether dry or wet conditions predominate: aerobic decomposition is
relatively fast and leads to the release of CO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>, while anaerobic
decomposition is much slower and leads to the release of CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> as the main
product of the combustion of organic soil material. CH<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">4</mml:mn></mml:msub></mml:math></inline-formula> is a much more
potent greenhouse gas but has a shorter lifetime of about 10 years, after
which it is converted to CO<inline-formula><mml:math display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> by oxidation. Therefore, not only the soil's
temperature but also its moisture status are important for the assessment of
the biogeochemical response to climatic conditions and thus should be
represented in climate or Earth system models in a realistic and
process-based manner. Thus, the adequate representation of permafrost
hydrology is a necessary and challenging task in Earth system modelling.</p>
      <p>Hagemann et al. (2013a) described relevant hydrological processes that occur
in permafrost areas and that should preferably be represented in models
simulating interactions of permafrost hydrology with vegetation, climate, and
the carbon cycle. The current state of the representation of processes in
general circulation models (GCMs) or Earth system models (ESMs) can be
obtained by systematic model intercomparison through the various climate
model intercomparison projects (CMIPs; Meehl et al., 2000), which have a long
history within the climate modelling community. Results from CMIPs provide a
good overview of the respective state of ESM accuracy and performance.
Koven et al. (2012) analysed the performance of ESMs in the most recent
CMIP5 exercise over permafrost areas. They found that the CMIP5 models have
a wide range of behaviours under the current climate, with many failing to
agree with fundamental aspects of the observed soil thermal regime at high
latitudes. This large variety of results originates from a substantial range
in the level of complexity and advancement of permafrost-related processes
implemented in the CMIP5 models (see, e.g., Hagemann et al., 2013a), whereas
most of these models do not include permafrost-specific processes, not even
the most basic process of freezing and thawing of soil water. Due to missing
processes and related deficiencies in their land surface schemes, climate
models often show substantial biases in hydrological variables over high
northern latitudes (Luo et al., 2003; Swenson et al., 2012). Moreover, the
land surface parameterizations used in GCMs usually do not adequately
resolve the soil conditions (Walsh et al., 2005). The parameterizations
often rely on either point measurements or on information derived from
satellite data. Therefore, large efforts are ongoing to extend ESMs in this
respect in order to improve simulated soil moisture profiles and associated
ice contents, river discharge, and surface and sub-surface runoff. The ESM
improvement over permafrost areas, for example, was one of the research objectives
of the European Union Project PAGE21 (<uri>www.page21.org</uri>).</p>
      <p>The most basic process in permafrost areas is the seasonal freezing and
thawing of soil water in the presence of continuously frozen ground below a
certain depth. The response of the soil to freezing leads to specific
variations in the annual cycle of soil hydrology. Frozen ground and snow
cover also influence rainfall–runoff partitioning, the timing and magnitude
of spring runoff, and the amount of soil moisture that is subsequently available for evapotranspiration in spring and summer (Beer et al., 2006, 2007;
Koren et al., 1999). Soil moisture controls the
partitioning of the available energy into latent and sensible heat flux and
conditions the amount of surface runoff. By controlling evapotranspiration,
it links the energy, water, and carbon fluxes (Koster et al., 2004;
Dirmeyer et al., 2006; Seneviratne and Stöckli, 2008). Seneviratne et
al. (2006) stated that a northward shift of climatic regimes in Europe due
to climate change will result in a new transitional climate zone between dry
and wet climates with strong land–atmosphere coupling in central and
eastern Europe. They specifically highlight the importance of
soil-moisture–temperature feedbacks (in addition to
soil-moisture–precipitation feedbacks) for future climate changes over this
region. A comprehensive review of soil moisture feedbacks is given by
Seneviratne et al. (2010).</p>
      <p>Largely, soil moisture feedbacks to the atmosphere are confined to regions
where the evapotranspiration is moisture-limited. These are regions where
the soil moisture is in the transitional regime between the permanent
wilting point (soil moisture content below which the plants can not extract
water from the soil by transpiration as the suction forces of the soil are
larger than the transpiration forces of the plants) and the critical soil
moisture, <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi mathvariant="normal">crit</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>, above which plants transpire at the potential rate
imposed by the atmospheric conditions, i.e. the potential evapotranspiration
(see, e.g., Fig. 5 in Seneviratne et al., 2010). In this respect, the
high latitudes are usually excluded from those regions as they are
considered to be predominantly energy-limited (Teuling et al., 2009) and an area where the coupling between soil moisture and the atmosphere does not play a
role (Koster et al., 2004, 2006).</p>
      <p>Note that in previous studies where an ESM's land surface model (LSM) was
equipped with cold-region soil processes, effects of resulting model
improvements usually have not been directly considered in a coupled
atmosphere–land context. Either simulated changes were only considered in
the LSM standalone mode (e.g. Ekici et al., 2014, 2015; Lawrence and Slater,
2005; Gouttevin et al., 2012; Slater et al., 1998) or changes between
different LSM version were not limited to cold-region processes alone (Cox
et al., 1999). Only Takata and Kimoto (2000) conducted a kind of precursor
to our study, using a very coarse-resolution atmospheric GCM (600 km
resolution), but they neither used large-scale observations to evaluate the
results of their study nor specifically addressed land–atmosphere feedbacks.
Thus, soil moisture feedbacks to the atmosphere related to cold-region soil
processes have generally been neglected so far.</p>
      <p>In the present study, we show that the implementation of cold-region soil
processes in the ESM of the Max Planck Institute for Meteorology, MPI-ESM,
has a pronounced impact on the simulated terrestrial climate over the
northern high latitudes and that this is mainly related to a positive soil-moisture–precipitation feedback. Section 2 introduces the ESM
version used and the setup of the associated simulations; Sect. 3 discusses the main
results over several high-latitude river catchments, followed by a summary
and conclusions in Sect. 4.</p>
</sec>
<sec id="Ch1.S2">
  <title>Model, data, and methods</title>
<sec id="Ch1.S2.SS1">
  <title>Model description</title>
      <p>In this study, the atmosphere and land components of the ESM of the Max
Planck Institute for Meteorology (MPI-M), MPI-ESM 1.1, are utilized, which consist of the atmospheric GCM ECHAM6.3 (Stevens et al., 2013) and its land
surface scheme JSBACH 3.0 (Raddatz et al., 2007; Brovkin et al., 2009). Both
models have undergone several further developments since the version
(ECHAM6.1/JSBACH 2.0) used for the Coupled Model Intercomparison Project 5
(CMIP5; Taylor et al., 2012). Several bug fixes in the ECHAM physical
parameterizations led to energy conservation in the total parameterized
physics, and a recalibration of the cloud processes resulted in a medium-range climate sensitivity of about 3 K. JSBACH 3.0 comprises several bug
fixes, and a new soil carbon model (Goll et al., 2015) and a five-layer soil
hydrology scheme (Hagemann and Stacke, 2015) replaced the previous bucket
scheme. These five layers correspond directly to the structure used for soil
temperatures, and they are defined with increasing thickness (0.065, 0.254,
0.913, 2.902, and 5.7 m) down to a lower boundary at almost 10 m depth. In
addition, a permafrost-ready version of JSBACH is considered (JSBACH-PF), in
which physical processes relevant in high-latitude land regions have been
implemented by Ekici et al. (2014). Most importantly, these processes
comprise the freezing and thawing of soil moisture. Consequently, the latent
heat of fusion dampens the amplitude of soil temperature, infiltration is
decreased when the uppermost soil layer is frozen, and soil moisture is bound in
solid phase when frozen and, hence, cannot be transported vertically or
horizontally. Dynamic soil thermal properties now depend on soil texture as
well as on soil water and ice contents. Dynamic soil hydraulic properties
that depend on soil texture and soil water content may decrease when soil
moisture freezes (such as the hydraulic conductivity). Moreover, a
snow scheme has been implemented in which snow can develop in up to five
layers while the current scheme only represents up to two layers. In the
original snow scheme, the snow is thermally growing down inside the soil; i.e. the snow cover becomes part of the soil temperature layers so that soil
temperatures are mixed with snow temperatures. In the new scheme, snow is
accumulated on top of the soil using snow thermal properties. Further, a
homogeneous organic top layer is added with a constant depth and specific
thermal and hydraulic properties. Note that in the following the term soil
moisture generally refers to the liquid soil moisture if not mentioned
otherwise. In this respect, total soil moisture refers to the sum of liquid
and frozen soil moisture.</p>
</sec>
<sec id="Ch1.S2.SS2">
  <title>Experimental setup</title>
      <p>Two ECHAM6.3/JSBACH simulations (Hagemann, 2016) were conducted at T63
horizontal resolution (about 200 km) with 47 vertical layers in the
atmosphere. They were forced by observed sea surface temperature (SST) and
sea ice from the AMIP2 (Atmospheric Model Intercomparison Project 2) dataset
during 1970–2009 (Taylor et al., 2000). 1970–1988 are regarded as spin-up
phase; only the period 1989–2009 is considered for the analyses. The two
simulations are
<list list-type="bullet"><list-item>
      <p>ECH6-REF: simulation with the standard version of JSBACH 3.0 with a fixed
vegetation distribution and using a separate upper-layer reservoir for bare-soil evaporation as described in Hagemann and Stacke (2015). Note that the
latter is switched off by default in JSBACH 3.0 to achieve a better
performance of simulated primary productivity, which is not of interest in
the present study;</p></list-item><list-item>
      <p>ECH6-PF: as ECH6-REF but using JSBACH-PF.</p></list-item></list></p>
      <p>Note that both simulations used initial values of soil moisture, soil
temperature, and snowpack that were obtained from an offline simulation (land
only) using JSBACH (as in ECH6-REF) forced with WFDEI data (Weedon et al.,
2014).</p>
</sec>
<sec id="Ch1.S2.SS3">
  <title>Calculation of internal model climate variability</title>
      <p>The internal climate variability of ECHAM6/JSBACH with respect to 20-year
mean values has been estimated from results of three 20-year, five-member
ensembles, in which the ensembles used different land–atmosphere coupling
setups (de Vrese and Hagemann, 2016). Within each ensemble, the model setup
is identical but the simulations were started using slightly differing
initial conditions. Following the approach of Hagemann et al. (2009), we
first calculated the standard deviation of 20-year means for each ensemble,
and then the spread for each model grid box is defined as the maximum of the
three ensemble standard deviations. This spread is then used as an estimate
of the model's internal climate variability. Thus, if simulated differences
between ECH6-PF and ECH6-REF are larger than this spread, they are considered
robust and directly related to the introduction of cold-region soil processes
into JSBACH.</p>
</sec>
<sec id="Ch1.S2.SS4">
  <title>Observational data</title>
      <p>We use climatological observed river discharges from the station network of
the Global Runoff Data Centre (Dümenil Gates et al., 2000). Near-surface
air (2 m) temperature and precipitation are taken from the recent global
WATCH dataset of hydrological forcing data (WFDEI; Weedon et al., 2014). The
WFDEI combine the daily statistics of the Interim reanalysis of the
European Centre for Medium-Range Weather Forecasts (ERA-Interim; Dee et al.,
2011) with the monthly mean observed characteristics of temperature from the
Climate Research Unit dataset TS2.1 (CRU; Mitchell and Jones, 2005) and
precipitation from the Global Precipitation Climatology Centre full dataset
version 4 (GPCC; Fuchs et al., 2007). For the latter, a gauge-undercatch
correction following Adam and Lettenmaier (2003) was used, which takes into
account the systematic underestimation of precipitation measurements that
have an error of up to 10–50 % (see, e.g., Rudolf and Rubel, 2005).</p>
      <p>For an estimate of observed evapotranspiration (ET), we are using data from
the LandFlux-EVAL dataset. This new product was generated to compile
multi-year global merged benchmark synthesis products based on the analyses
of existing land evapotranspiration datasets (monthly timescale, time
periods 1989–1995 and 1989–2005). The calculation and analyses of the
products are described in Mueller et al. (2013). In our study we are using
the diagnostic products available for the period 1989–2005 that are based on
various observations, i.e. from remote sensing, diagnostic estimates
(atmospheric water-balance estimates), and ground observations (flux
measurements). Here, we considered the mean, minimum, and maximum of the
respective diagnostic ensemble.</p>
      <p>Surface solar irradiance (SSI; 2000–2010) is taken from the Clouds and Earth
Radiation Energy System (CERES; Kato et al., 2013), which provides surface
solar radiation fluxes on a global scale derived from measurements on board the EOS Terra and Aqua satellites (Loeb et al., 2012). We used surface
albedo data from MODIS (MCD43C3, ver5; 2000–2011; Cescatti et al., 2012),
CERES (2000–2010), and the GlobAlbedo project (1998–2011; Muller et al.,
2012) of the European Space Agency (ESA). With regard to the accumulated
snowpack, we compared model data to snow water equivalent data from the ESA
GlobSnow project (Takala et al., 2011), NASA's Modern-Era Retrospective
Analysis for Research and Applications (MERRA; 1979–2013; Rienecker et al.,
2011), and the snow data climatology (SDC) of Foster and Davy (1988).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1"><caption><p>Boreal summer (JJA) precipitation differences (%) relative to
WFDEI data for <bold>(a)</bold> ECH6-REF, <bold>(b)</bold> ECH6-PF, and <bold>(c)</bold>
difference between ECH6-PF and ECH6-REF (in % of WFDEI precipitation).</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f01.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S3">
  <title>Results</title>
      <p>The simulations ECH6-REF and ECH6-PF are evaluated over the northern high
latitudes analogously to how the evaluation of surface water and energy
fluxes of the CMIP5 version of MPI-ESM was conducted by Hagemann et
al. (2013b). The main differences in precipitation and 2 m temperature
between both simulations occur in boreal summer. In ECH6-PF, precipitation is
generally reduced compared to ECH6-REF over the northern high latitudes
(Fig. 1). On the one hand, this leads to a general reduction in the wet bias
compared to WFDEI data over the more continental areas north of about
60<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, especially over Canada and Russia. On the other hand, it
enhances the dry bias over the adjacent midlatitudes. Note that this summer
dry bias of MPI-ESM 1.1 over midlatitudes is more pronounced and widespread
than in the CMIP5 version of MPI-ESM (cf. Fig. 4, middle row, in Hagemann et
al., 2013b), which is likely associated with bug fixes or the recalibration
of cloud processes in ECHAM6.3 (cf. Sect. 2.1). The same is also the case for
Northern Hemisphere summer warm biases in ECH6-REF (Fig. 2). These warm
biases are enhanced in ECH6-PF. This enhancement is partly related to the
fact that the reduced precipitation is accompanied by a reduced cloud cover
and, hence an increased incoming solar radiation at the land surface
(Fig. 3). Compared to CERES data, the low bias in SSI over the high latitudes
is largely removed, while the overestimation over the midlatitudes is
slightly increased. The reason for the warmer air temperatures can partly be
found in a decreased evapotranspiration (ET) when permafrost-relevant
physical soil processes are switched on. A detailed analysis of their effects
was carried out to elucidate the specific influence of these processes and is
shown for two large example catchments (Fig. 4). (1) The Arctic catchment is
represented by the six largest rivers flowing into the Arctic Ocean: Kolyma,
Lena, Mackenzie, Northern Dvina, Ob' and Yenisey. The associated catchments
comprise a large fraction of permafrost-covered areas. (2) The Baltic Sea
catchment includes only a low amount of permafrost-covered areas, but soil
moisture freezing still plays a role over large parts of the catchment during
the winter.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F2"><caption><p>Boreal summer (JJA) 2 m temperature differences (K) to WFDEI data
for <bold>(a)</bold> ECH6-REF, <bold>(b)</bold> ECH6-PF, and <bold>(c)</bold> difference
between ECH6-PF and ECH6-REF.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f02.jpg"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F3"><caption><p>Boreal summer (JJA) surface solar incoming radiation differences
(W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula>) to CERES data for <bold>(a)</bold> ECH6-REF, <bold>(b)</bold> ECH6-PF, and
<bold>(c)</bold> difference between ECH6-PF and ECH6-REF.</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f03.jpg"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><caption><p>Catchments of the Baltic Sea and of the six largest Arctic rivers
(from left to right: Mackenzie, Baltic Sea, Northern Dvina, Ob', Yenisey, Lena, Kolyma).</p></caption>
        <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f04.png"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><caption><p>Mean monthly climatology (1989–2009) of discharge (upper panels)
and precipitation (lower panels) over the six largest Arctic river catchments
(left column) and the Baltic Sea catchment (land only, right column).
Observations comprise climatological observed discharge and WFDEI
precipitation, respectively.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f05.png"/>

      </fig>

<sec id="Ch1.S3.SS1">
  <title>Arctic river catchments</title>
      <p>ECH6-PF simulates the discharge of the six largest Arctic rivers more
reliably than ECH6-REF, especially with regard to the timing and size of the
snowmelt-induced discharge peak in spring (Fig. 5a). This is largely
related to the fact that in ECH6-PF, a major part of the snowmelt turns
into surface runoff as it cannot infiltrate into the ground when this is
still frozen in the beginning of spring. This is the opposite of ECH6-REF where
larger parts of the snowmelt infiltrates into the soil due to the
missing freezing processes such that the observed discharge peak is largely
underestimated.</p>
      <p>Consistent with Fig. 1, the large wet bias in the summer precipitation of
ECH6-REF is strongly reduced in ECH6-PF (Fig. 5c). This reduction in summer
precipitation is accompanied by a reduction in summer evapotranspiration
(Fig. 6a) that is now much closer to the mean of diagnostic estimates from
the LandFlux dataset, while it is likely overestimated in ECH6-REF as the
simulated evapotranspiration is close to the upper limit of the LandFlux
diagnostic estimates. This ET reduction in ECH6-PF is directly related to a
completely changed seasonal cycle of liquid relative soil moisture (actual
soil moisture divided by the maximum soil water holding capacity) in the
root zone (Fig. 6c). In ECH6-REF, the soil is very wet throughout the whole
year with somewhat lower values in summer that are related to the summer ET.
In ECH6-PF, the soil is rather dry in winter as larger parts of the total
soil moisture are frozen (Fig. 7) and, hence, not accessible for ET. With
infiltration of snowmelt in the spring when the soil water of the upper
layer has thawed, the soil moisture increases and reaches its maximum in
summer. The total amount of liquid soil moisture in ECH6-PF is much lower
than in ECH6-REF. On the one hand large parts of the soil are frozen in
winter and adjacent months (Fig. 7), and on the other hand this is related
to the much lower infiltration in spring, so that less soil moisture is
available throughout the whole year. In the autumn and winter, the amount of
total soil moisture increases somewhat (Fig. 6c) as due to freezing, it
is locally bound and can neither flow off laterally nor evaporate. If
compared to the model's internal climate variability (Fig. 8), we note that
the differences between ECH6-PF and ECH6-REF are robust for ET and
precipitation from April–October and April–August, respectively.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><caption><p>Mean monthly climatology (1989–2009) of evapotranspiration (upper
panels) and relative root zone soil moisture (lower panels) over the six largest Arctic river catchments (left column) and the Baltic Sea catchment
(land only, right column). Evapotranspiration data comprise the mean,
minimum, and maximum diagnostic estimates from the LandFlux Eval (LF) dataset. The
dashed blue line (PF-total) denotes the total root zone moisture content
(liquid <inline-formula><mml:math display="inline"><mml:mo>+</mml:mo></mml:math></inline-formula> frozen) for ECH6-PF.</p></caption>
          <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f06.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7"><caption><p>Mean frozen fraction of total root zone soil moisture (1989–2009)
in ECH6-PF over the six largest Arctic river catchments (solid curve) and the
Baltic Sea catchment (land only, dashed curve).</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f07.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8"><caption><p>Mean monthly climatological differences (1989–2009) between ECH6-PF
and ECH6-REF for precipitation (<inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>P</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> and evapotranspiration (<inline-formula><mml:math display="inline"><mml:mi mathvariant="normal">Δ</mml:mi></mml:math></inline-formula>ET) over the six largest Arctic rivers (upper panel) and the Baltic Sea
catchment (lower panel). The dashed lines indicate the corresponding spreads
obtained from MPI-ESM simulations of de Vrese and Hagemann (2016).</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f08.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F9" specific-use="star"><caption><p>Mean monthly climatology (1989–2009) of 2 m temperature
differences to WFDEI data (upper panels) and surface solar irradiance (SSI;
lower panels) over the six largest Arctic river catchments (left column) and
the Baltic Sea catchment (land only, right column). SSI observations comprise
CERES data for 2000–2010.</p></caption>
          <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f09.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F10" specific-use="star"><caption><p>Mean monthly climatology (1989–2009) of surface albedo (upper
panels) and snowpack snow water equivalent (SWE; lower panels) over the six largest Arctic river catchments (left column) and the Baltic Sea catchment
(land only, right column). Albedo observations data from MODIS (2000–2011),
CERES (2000–2010), and GlobAlbedo (1998–2011); SWE observations comprise
data from GlobSnow (1989–2009), MERRA (1979–2013), and SDC climatology.</p></caption>
          <?xmltex \igopts{width=398.338583pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f10.png"/>

        </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F11"><caption><p>Chain of processes involved in the soil-moisture–precipitation
feedback over high latitudes. Red arrows indicate the initiation of the
positive feedback loop by the presence of frozen soil; blue arrows indicate
the loop itself.</p></caption>
          <?xmltex \igopts{width=236.157874pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f11.pdf"/>

        </fig>

      <p>The decreased ET during warm months, however, brings about less evaporative
cooling of the land surface and a reduced upward moisture flux into the
atmosphere, which in turn seems to reduce cloud cover, and, hence SSI is
increased in ECH6-PF (Fig. 9c, see also Fig. 3). Both of these effects
result in a further increase in the summer warm bias in 2 m air temperature
(Fig. 9a, see also Fig. 2).</p>
      <p>The surface albedo is rather similar in both experiments (Fig. 10a) but shows
some distinct biases if compared to various observational datasets. During
the winter JSBACH seems to overestimate the mainly snow-related albedo,
indicating that it may have difficulties to adequately represent the
snow-masking effect of boreal forests. (Note that a version of MODIS albedo
data was used where low-quality data over the very high northern latitudes
were filtered out in the boreal winter due to too low an available radiation
(A. Löw, personal communication, 2016). Due to these missing data over
mainly snow-covered areas, MODIS albedo averaged over the six largest Arctic
rivers is biased low in the winter.) During the summer, there is a larger
uncertainty in the observations. While the simulated albedo is close to MODIS
and CERES data, it is lower than GlobAlbedo data. As too low an albedo would
lead to a warm bias, this may indicate a better reliability of the GlobAlbedo
data for this region in summer. Note that a sensitivity test where surface
albedo was increased by 0.05 north of 60<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N led to a reduction in
the warm bias by about 1–2 K (not shown). As already indicated by the
surface albedo, the simulated snow cover does not significantly differ
between the experiments, either (Fig. 10c). It is lower than various
observational estimates, which should impose a low-albedo bias in winter. As
this bias is in the opposite direction, it can be concluded that the low
snowpack compensates for part of the snow-masking problem mentioned above.</p>
</sec>
<sec id="Ch1.S3.SS2">
  <title>Baltic Sea catchment</title>
      <p>A similar effect of the frozen ground is found over the Baltic Sea catchment,
although this is less strong than for the Arctic rivers. The frozen ground
leads to an enhanced snowmelt runoff in spring (Fig. 5b) and a less strong
replenishment of the soil water during the
winter as is the case for ECH6-REF (Fig. 6d). Consequently the average level
of liquid soil moisture is lower in ECH6-PF compared to ECH6-REF. This leads
to more infiltration of water and less drainage and, hence, less runoff in
the summer, which in turns leads to an improved simulation of discharge
(Fig. 5b). The impact on the atmosphere is much less pronounced than for the
Arctic rivers. On the one hand there is less frozen ground in the Baltic Sea
catchment (Fig. 7), and on the other hand the average soil moisture content
is larger than for the Arctic rivers (Fig. 6d). In ECH6-REF, the soil
moisture is generally above <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi mathvariant="normal">crit</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (cf. Sect. 1) in the Baltic Sea
catchment so that ET is largely energy limited and mostly occurs at its
potential rate. Even though the ECH6-PF soil moisture is lower, it is
generally still close to <inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mi mathvariant="normal">crit</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> so that ET is only slightly
reduced, especially in the second half of the year (Fig. 6b). Precipitation
is also somewhat reduced (Fig. 5d), but this seems to be mostly related to
the internal climate variability except for September and October when a
somewhat stronger and robust reduction in ET leads to a robust precipitation
decrease (Fig. 8).</p>
</sec>
</sec>
<sec id="Ch1.S4">
  <title>Discussion and conclusions</title>
      <p>The results described in the previous section show that soil freezing and
thawing processes enable the positive soil-moisture–precipitation feedback
(e.g. Dirmeyer et al., 2006; Seneviratne et al., 2010) over large parts of
northern mid- and high latitudes during the boreal summer. The chain of
processes leading to and influencing this feedback is sketched in Fig. 11.
The frozen soil during the cold season (late autumn to early spring) leads
to less infiltration of rainfall and snowmelt during this season and,
hence, to more surface runoff especially during the snowmelt period. On the one hand this leads to a large improvement in simulated discharge, mainly due to
the improved snowmelt peak. This improved discharge due to the
representation of frozen ground has been also reported for other models
(Beer et al., 2006, 2007; Ekici et al., 2014; Gouttevin et al., 2012). On
the other hand, this leads to a decrease in soil moisture. This spring soil
moisture deficit from the increased discharge extends into the boreal summer
due to the soil moisture memory (e.g. Koster and Suarez, 2001; Orth and
Seneviratne, 2012), when it actually causes more infiltration and less
runoff and, hence, less discharge. The latter strongly improves the
simulated discharge in the Baltic Sea catchment from summer to early winter.
The decreased soil moisture leads to a reduced ET in regions where the soil
moisture is in the transitional regime. Here, there is less recycling of
moisture into the atmosphere, and the lower atmospheric moisture causes a
reduction in precipitation that in turn leads to a further reduction in soil
moisture.</p>
      <p>Our new finding of the importance of the positive
soil-moisture–precipitation feedback in northern high latitudes has been
supported by correlations between soil moisture and precipitation using
monthly values from 1989 to 2009. While there are higher correlations between
soil moisture and precipitation in the midlatitudes for ECH6-REF (Fig. 12a),
the high latitudes are mostly characterized by rather low correlations using
the reference version of JSBACH. Figure 13b and c show that the correlation
between soil moisture and precipitation is strongly increased in ECH6-PF over
large parts of the northern high latitudes, especially over North America and
eastern Siberia. This confirms an increased coupling of soil moisture and
precipitation and, hence, also indicates that the
soil-moisture–precipitation feedback is highly enabled in these areas. This
positive soil-moisture–precipitation feedback improves the simulated
hydrological cycle, especially over the Arctic rivers where the wet biases in
summer precipitation and ET are reduced. Less ET and, hence, less evaporative
cooling cause an increase in summer 2 m air temperatures. This, in
combination with more incoming surface solar radiation due to fewer clouds,
increases and extends the existing summer warm bias of MPI-ESM north of about
50<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N. Since air temperature is a main driver of soil freezing and
thawing processes, there are more indirect interactions between energy and
water balances which call for even more advanced factorial model experiments in the future.</p>
      <p>Changes in the simulated hydrological cycle induced by the utilization of
the improved soil scheme are mostly confined to areas where freezing and
thawing of water play a role. To illustrate this, Fig. 13 shows the number
of months where in the climatological average of 1989–2009, the upper soil
layer is below 0 <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in ECH6-PF. Changes in precipitation (Fig. 1)
and surface solar irradiance (Fig. 3), indicating changes in cloud cover,
are mostly located in regions where the upper layer is frozen for at least
3 months within the climatological average. Changes outside of regions
with soil frost may be imposed by changed atmospheric humidity and heat
transport from soil-frost-affected regions on the one hand. On the other
hand, Ekici et al. (2014) also introduced a permanent, static organic top
layer as part of the new JSBACH-PF soil scheme. If switched on, as in the
current ECH6-PF simulation, it is considered globally uniform, thus
introducing a soil-isolating effect also outside permafrost regions. As a
consequence, the partitioning of the surface heat balance is altered during
snow-free months towards a decreased ground heat flux, which needs to be
compensated for by the turbulent heat fluxes, in particular by the sensible
heat flux. This in turn contributes to the warming of the 2 m air temperature, which can also be seen in areas without any soil frost (Fig. 2). Even though
the uniform organic insulation layer was implemented globally, Fig. 12 shows
that the correlation between soil moisture and precipitation advances
strongly in northern high latitudes only while this correlation has hardly changed in the temperate zone and in particular in drought-dominated
areas in south-east Europe or mid-west USA. Note that, currently, the land
surface scheme has been further advanced by a mechanistic model of moss and lichen dynamics (Porada et al., 2016) which will replace the actual
static organic top layer for soil insulation. This will enable a more
realistic representation of the temporal and spatial variation of the soil
insulation.</p>
      <p>A positive soil-moisture–precipitation feedback has not been pointed out for
the northern high latitudes so far, even though in their coarse-resolution
GCM study, Takata and Kimoto (2000) found similar impacts to those shown in
Fig. 11 induced by soil water freezing. Previously, the northern high
latitudes have generally been considered energy-limited regimes where
land–atmosphere coupling due to soil moisture does not play a role (e.g.
Teuling et al., 2009). But this principal feedback loop has been found for
drier regions where the soil moisture is generally in the transitional
regime and land–atmosphere coupling plays a role. Koster et al. (2004)
considered the strength of coupling between soil moisture and precipitation
in an ensemble of atmospheric GCMs. The resulting map is very similar to the
map regarding the strength of coupling between soil moisture and temperature
in the same GCMs (Koster et al., 2006). This suggests that in these models,
the same process controls both couplings, namely the ET sensitivity to soil
moisture that leads to a positive feedback (Seneviratne et al., 2010). But
on the one hand it can be assumed that many models participating in those
earlier studies did not include the freezing and thawing of soil water.
Thus, our reference simulation ECH6-REF is in line with results reported in
the literature, generally not showing a strong coupling between
precipitation and soil moisture in permafrost regions, such as indicated by
the rather low correlation values in Fig. 12a. Only the ECH6-PF simulation
using advanced soil physics shows that such strong coupling indeed is
present (Fig. 12b). On the other hand, only annual mean diagnostics were
considered in some of those earlier studies (e.g. Teuling et al., 2009). In
other land–atmosphere coupling studies, which followed, e.g., the GLACE
protocol, such as Koster et al. (2004), prescribed soil moisture conditions
were used that were similar to the average soil moisture climatology. Here,
it seems that the differences between the simulations with free and
prescribed soil moisture in GLACE-type simulations may not be large enough
to reveal a large-scale feedback over the high latitudes. This may only be
possible by an experimental design where more pronounced summer soil
moisture changes are introduced. Note that in the present study, these
pronounced changes were introduced not due to an artificial design, but they
were caused by the implementation of previously missing frozen soil physics
into the model. Our study has shown that spring moisture deficits can lead
to soil moisture conditions during the boreal summer that allow for an
advanced land–atmosphere coupling and a positive soil-moisture–precipitation
feedback over the northern high latitudes.</p>
      <p>Even though our results are obtained with a modelling study, their physical
consistency suggests that cold-region soil processes, especially freezing
and thawing of soil water, may lead to a positive soil-moisture–precipitation feedback during the summer in reality, too. A prerequisite for
the occurrence of a soil-moisture–precipitation feedback is that soil
moisture is in the transitional regime. Thus, the strength of the feedback
depends on the wetness of the soil and, hence, is likely model dependent.
Models with wetter/drier soils over the considered regions may simulate a
weaker/stronger feedback.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><caption><p>Summer (JJA) biases over the six largest Arctic rivers for 2 m
temperature (<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>, to WFDEI), and radiative flux (<inline-formula><mml:math display="inline"><mml:mrow><mml:mi>R</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> into the
surface due to biases in SSI (to CERES), albedo (<inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula>, to GlobAlbedo), and
their combined effect (comb.) as well as the estimated related impact on
surface temperature (<inline-formula><mml:math display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub><mml:mo>)</mml:mo></mml:mrow></mml:math></inline-formula> and the contribution of the SSI bias to
this impact.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="7">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="left"/>
     <oasis:colspec colnum="3" colname="col3" align="left"/>
     <oasis:colspec colnum="4" colname="col4" align="left"/>
     <oasis:colspec colnum="5" colname="col5" align="left"/>
     <oasis:colspec colnum="6" colname="col6" align="left"/>
     <oasis:colspec colnum="7" colname="col7" align="left"/>
     <oasis:thead>
       <oasis:row rowsep="1">  
         <oasis:entry colname="col1">Experiment</oasis:entry>  
         <oasis:entry colname="col2"><inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col3"><inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula> SSI</oasis:entry>  
         <oasis:entry colname="col4"><inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula><inline-formula><mml:math display="inline"><mml:mi mathvariant="italic">α</mml:mi></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5"><inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:mi>R</mml:mi></mml:mrow></mml:math></inline-formula> comb.</oasis:entry>  
         <oasis:entry colname="col6"><inline-formula><mml:math display="inline"><mml:mrow><mml:mi mathvariant="normal">Δ</mml:mi><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">s</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> comb.</oasis:entry>  
         <oasis:entry colname="col7">SSI cont.</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>  
         <oasis:entry colname="col1">ECH6-REF</oasis:entry>  
         <oasis:entry colname="col2">2.1 K</oasis:entry>  
         <oasis:entry colname="col3">5.0 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col4">4.1 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5">9.0 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col6">1.7 K</oasis:entry>  
         <oasis:entry colname="col7">55 %</oasis:entry>
       </oasis:row>
       <oasis:row>  
         <oasis:entry colname="col1">ECH6-PF</oasis:entry>  
         <oasis:entry colname="col2">5.0 K</oasis:entry>  
         <oasis:entry colname="col3">15.8 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col4">4.3 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col5">19.8 W m<inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula></oasis:entry>  
         <oasis:entry colname="col6">3.6 K</oasis:entry>  
         <oasis:entry colname="col7">78 %</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

      <p>Several modelling studies pointed out that there are not only positive
feedback loops between soil moisture and precipitation but also negative
ones that, under specific conditions, such as convective instability and/or
cloud formation, may be stronger over dry soils (e.g. Hohenegger et al.,
2009; Froidevaux et al., 2014). However, to date, the latter results appear
mostly confined to single-column, cloud-resolving, and some high-resolution
regional climate simulations (Seneviratne et al., 2010) and may also depend
on the choice of the convective parameterizations (e.g. Giorgi et al.,
1996). Guillod et al. (2015) noted that precipitation events tend to be
located over drier patches, but they generally need to be surrounded by wet
conditions; positive temporal soil-moisture–precipitation relationships are
thus driven by large-scale soil moisture. Thus, negative feedbacks seem to
have a greater impact on a high resolution and thus on the local scale
(Ho-Hagemann et al., 2015), where the effects of land surface heterogeneity
for the inferred feedbacks also need to be taken into account (Chen and
Avissar, 1994; Pielke et al., 1998; Taylor et al., 2013). Consequently most
GCMs may not be able to represent negative feedbacks between soil moisture
and precipitation via ET. As in the present study, we considered the effect
of large-scale soil moisture changes due to soil freezing processes, the
identification of potential negative feedbacks on the local scale is beyond
the scope of the present study.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F12"><caption><p>Correlation of soil moisture and precipitation for <bold>(a)</bold> ECH6-REF,
<bold>(b)</bold> ECH6-PF, and <bold>(c)</bold> difference between ECH6-PF
and ECH6-REF.</p></caption>
        <?xmltex \igopts{width=207.705118pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f12.png"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="Ch1.F13"><caption><p>Number of months when in the climatological average of 1989–2009,
the upper soil layer is below 0 <inline-formula><mml:math display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C in ECH6-PF.</p></caption>
        <?xmltex \igopts{width=207.705118pt}?><graphic xlink:href="https://esd.copernicus.org/articles/7/611/2016/esd-7-611-2016-f13.png"/>

      </fig>

      <p>In MPI-ESM, an unwelcome effect of implementing cold-region soil processes
is the increase in the existing warm bias over the high latitudes during
summer. In order to estimate the contribution of biases in SSI and surface
albedo to this warm bias, we calculated an upper limit for the temperature
change that may be imposed by a radiation difference in the related energy
flux into the ground [SSI <inline-formula><mml:math display="inline"><mml:mo>×</mml:mo></mml:math></inline-formula> (1 <inline-formula><mml:math display="inline"><mml:mo>-</mml:mo></mml:math></inline-formula> albedo)]. For this estimation we
assume that the surface temperature adjusts in a way that this
radiation difference is compensated for by thermal radiation following the
Stefan–Boltzmann law. Here, any change in the turbulent surface heat fluxes
is neglected so that the resulting temperature change is an upper limit for
the temperature bias that may be explained by a radiation bias.</p>
      <p>Considering the mean summer biases over the six largest Arctic rivers
(Table 1) indicates that a part of the warm bias may be attributed to the
overestimation in SSI. For ECH6-PF (ECH6-REF), the SSI bias may cause a warm
bias of up to 2.9 K (0.9 K). The surface albedo may contribute another
0.7 K (0.8 K) to the warm bias if compared to GlobAlbedo data, but this is
a rather vague estimation due to the large uncertainty regarding surface
albedo observations (see Fig. 10). Nevertheless biases in both of these
variables cannot explain the full bias of 5 K (2.1 K) in 2 m temperature.
Further contributions to this warm bias may be related to too weak a vertical mixing of heat within the boundary
layer or too much advection of warm air. The latter may also influence the
recycling ratio of water within and outside regions of soil frost. A deeper
investigation of this is beyond the scope of the present study and should be
dealt with in future model improvements.</p>
      <p>We have shown that soil physical processes such as thawing and freezing have
an impact on the regional climate over the high-latitude permafrost areas.
Flato et al. (2013) reported that CMIP5 GCMs tend to overestimate
precipitation over northern high latitudes except for Europe and western
Siberia. As many of these GCMs are still missing basic cold-region
processes, a missing interaction between soil moisture and precipitation in
those GCMs is likely to contribute to this wet bias. An adequate
implementation of physical soil processes in an ESM is only the first
necessary step to yield an adequate representation of land–atmosphere
interactions over the high latitudes. This also includes the incorporation
of wetland dynamics, which will be the next step in the JSBACH development
with regard to high latitudes, thereby following an approach of Stacke and
Hagemann (2012). In addition, a reliable hydrological scheme for permafrost
regions will allow investigations of related climate–carbon-cycle feedback
mechanisms (McGuire et al., 2006; Beer, 2008; Heimann and Reichstein, 2008).</p>
      <p>Our findings demonstrate that soil freezing and thawing induce a much
stronger coupling of land and atmosphere in northern high latitudes than
previously thought. The additional importance of the positive soil-moisture–precipitation feedback in high latitudes will have a strong impact on future
climate projections in addition to other biophysical (e.g. albedo) or
biogeochemical (e.g. climate–carbon-cycle) feedback mechanisms. Therefore,
the findings of this study additionally highlight the importance of
permafrost ecosystem functions in relation to climate.</p>
</sec>
<sec id="Ch1.S5">
  <title>Data availability</title>
      <p>The data used in this paper are available in Hagemann (2016).</p>
</sec>

      
      </body>
    <back><ack><title>Acknowledgements</title><p>The authors acknowledge the financial support to T. Blome by the European
Union FP7-ENV project PAGE21 under contract number GA282700. S. Hagemann is
supported by funding from the European Union within the Horizon 2020 project
CRESCENDO (grant no. 641816).<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?>The article
processing charges for this open-access <?xmltex \hack{\newline}?> publication were
covered by the Max Planck Society.<?xmltex \hack{\newline}?><?xmltex \hack{\newline}?> Edited by:
C. Heinze<?xmltex \hack{\newline}?> Reviewed by: R. Orth and one anonymous referee</p></ack><ref-list>
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    <!--<article-title-html>Soil-frost-enabled soil-moisture–precipitation feedback over northern high latitudes</article-title-html>
<abstract-html><p class="p">Permafrost or perennially frozen ground is an important part of the
terrestrial cryosphere; roughly one quarter of Earth's land surface is
underlain by permafrost. The currently observed global warming is most
pronounced in the Arctic region and is projected to persist during the coming
decades due to anthropogenic CO<sub>2</sub> input. This warming will certainly have
effects on the ecosystems of the vast permafrost areas of the high northern
latitudes. The quantification of such effects, however, is still an open
question. This is partly due to the complexity of the system, including
several feedback mechanisms between land and atmosphere. In this study we
contribute to increasing our understanding of such land–atmosphere
interactions using an Earth system model (ESM) which includes a
representation of cold-region physical soil processes, especially the effects
of freezing and thawing of soil water on thermal and hydrological states and
processes. The coupled atmosphere–land models of the ESM of the Max Planck
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SST and sea ice in an AMIP2-type setup with and without newly implemented
cold-region soil processes. Results show a large improvement in the simulated
discharge. On the one hand this is related to an improved snowmelt peak of runoff
due to frozen soil in spring. On the other hand a subsequent reduction in soil moisture enables a positive feedback to precipitation over the high
latitudes, which reduces the model's wet biases in precipitation and
evapotranspiration during the summer. This is noteworthy as soil-moisture–atmosphere feedbacks have previously not been the focus of research on the
high latitudes. These results point out the importance of high-latitude
physical processes at the land surface for regional climate.</p></abstract-html>
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