Community Climate Model (CCM2, CRM) [Hack et al.,. 1993] it is possible to estimate the total optical thickness for different cloud conditions. The clear sky ...
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. C10, 3173, doi:10.1029/2000JC000723, 2002
Dependence of the Arctic Ocean ice thickness distribution on the poleward energy flux in the atmosphere Go¨ran Bjo¨rk and Johan So¨derkvist Department of Oceanography, Earth Sciences Center, Go¨teborg University, Go¨teborg, Sweden Received 20 November 2000; revised 1 February 2002; accepted 29 May 2002; published 25 October 2002.
[1] The sensitivity of the Arctic Ocean ice cover on the atmospheric poleward energy flux,
D, is studied using a coupled column model of the ocean, ice, and atmosphere. In the model, the ice cover is described by a thickness distribution and the atmosphere is a simple twostream gray body in radiative equilibrium. It is shown that the thickness distribution, in combination with the albedo function, gives a strong nonlinear response to positive perturbations of D. The response on D is sensitive to the albedo parameterization and the shape of the thickness distribution, controlled by ridging and divergence. An increase of about 9 W m2 from a standard value of D = 103 W m2 has a dramatic effect, reducing the ice thickness by more than 2 m, and generates a large open-water fraction during summer. The reduction of ice thickness is characterized by a clear transition between two regimes, going from a regime where first-year ice survives the next summer melt period to a seasonal ice regime where the first-year ice melts completely. It is shown that the existence of seasonal ice regime is dependent on the surface mixed layer thickness. The model enters a completely ice-free state if the thickness of the mixed layer is increased above a threshold value. The adjustment timescale for the ice cover is 6 years for small positive and negative perturbations in D. For larger positive perturbations of about 10 W INDEX TERMS: 1620 Global Change: Climate m2, the adjustment timescale is up to 20 years. dynamics (3309); 3339 Meteorology and Atmospheric Dynamics: Ocean/atmosphere interactions (0312, 4504); 4207 Oceanography: General: Arctic and Antarctic oceanography; 4504 Oceanography: Physical: Air/sea interactions (0312); 4540 Oceanography: Physical: Ice mechanics and air/sea/ice exchange processes; KEYWORDS: Arctic Ocean, air/sea interactions, sea ice, poleward energy flux, ice thickness, distribution climate Citation: Bjo¨rk, G., and J. So¨derkvist, Dependence of the Arctic Ocean ice thickness distribution on the poleward energy flux in the atmosphere, J. Geophys. Res., 107(C10), 3173, doi:10.1029/2000JC000723, 2002.
1. Introduction [2] The Arctic Ocean ice cover together with the overlying atmosphere makes up a complicated dynamic and thermodynamic system. This system includes numerous subprocesses, which are certainly important but hard to describe in detail. Examples of such processes are formation of pressure ridges, melt ponds on the ice floes, cloud formation and precipitation. Although internal subprocesses can play an important role there should also exist a strong and fundamental coupling between the basic state of the system and the energy supply from external sources. The main energy sources for the Arctic atmosphere and ice cover are atmospheric transport of warm and moist air from lower latitudes, solar radiation, and oceanic heat conducted through the ice. When the ice cover is at equilibrium, that is, with no net growth or melt over the year, there is a balance between the total heat supply and the thermal radiation to space. Using a very simplistic approach Thorndike [1992], hereafter TH92, identified these processes and linked them together in a time-dependent and fully coupled ocean-iceCopyright 2002 by the American Geophysical Union. 0148-0227/02/2000JC000723
atmosphere model. The model forcing was step functions with winter and summer values but otherwise constant in time during each season, and the processes in the atmosphere and ice were simplified down to the ‘‘bare bones physics.’’ An expression, based on the model, for the equilibrium ice thickness in terms of the poleward energy flux in the atmosphere, solar radiation, total optical thickness of the atmosphere, oceanic heat flux and thermal conductivity was one of the major results. In particular, it was shown that the ice thickness is highly dependent on the poleward energy transport in the atmosphere. This model allows for two steady states. A perennial ice cover and a totally ice-free state, whereas a possible solution with a seasonal ice cover is unsteady and therefore not realized. [3] The coupled ocean-ice-atmosphere system has later been studied by more sophisticated models and with interannual variability of the forcing. Bitz et al. [1996] used an ocean-ice-atmosphere column where the atmospheric part consisted of 18 layers including a full radiative and convection scheme. The ocean was a fixed 50-m mixed layer and the ice cover was modeled as a uniform slab. The natural variability of the ice cover was then addressed by applying stochastic variations to the poleward energy transport and cloudiness. One important result was that high-
37 - 1
37 - 2
¨ RK AND SO ¨ DERKVIST: ICE THICKNESS DISTRIBUTION BJO
frequency variations in the forcing may generate variability of the ice thickness at much lower frequencies. In the model study by Holland and Curry [1999], the ice cover was described by a thickness distribution and the atmospheric part included a radiative transfer scheme. They investigated the variability of the Arctic ice volume, applying stochastic variations to both surface air temperature and ice divergence and found that inclusion of a thickness distribution significantly affects the variability. [4] The main objective with the present investigation is to explore the response of the ice thickness distribution when changing the poleward energy transport in the atmosphere. It may been seen as an extension of TH92 with a more sophisticated model tool. Compared to TH92 the present model includes additional characteristics and processes which we believe are in the next order of importance. These are an ice thickness distribution, a snow layer on the ice, ice divergence, ridging processes, turbulent heat fluxes at the ice/ocean surface, and an active oceanic mixed layer. The forcing consists of monthly mean values instead of step functions over a cold and warm season. The main difference in our model approach to the earlier studies by Holland and Curry [1999] and Bitz et al. [1996] is that we use an ice sheet with a thickness distribution that is fully coupled to the atmosphere and ocean. Another aspect of the model is that it is relatively simple since the aim is not to model the system in detail but instead to focus on the overall function. [5] Many model studies of the Arctic ice-ocean-atmosphere system include much more sophisticated descriptions of subprocesses than is the case here. The dynamic response of the ice cover to wind forcing in combination with the thermodynamic growth/melt has been studied using dynamic-thermodynamic models [e.g., Hibler, 1979], which generates horizontal fields of ice speed and ice thickness as resolved by the grid. The representation of the ice cover can be refined further using a thickness distribution within each grid square [Flato and Hibler, 1995]. Models for thermodynamic processes in the ice may include the effect of melt ponds and have several temperature points in the ice and snow [Ebert and Curry, 1993]. There are detailed models for transmission of the solar radiation within the ice [Ebert et al., 1995], and for the heat fluxes at the ice/ocean interface [Holland et al., 1997]. Models for Arctic atmosphere normally include radiative transfer schemes for shortwave and longwave radiation with separate wavelength bands and the effect of clouds at different levels [e.g., Beesley, 2000]. A full treatment of moisture including a cloud formation scheme was implemented in a specific model study in connection with the SHEBA experiment [Pinto et al., 1999]. [6] A truly quantitative ranking of the impact of different processes and to which level of details these need to be described is of course hard to find but we motivate our level of the modeling effort by comparing with observations and results from other investigations where a quantitative judgement can be made. We also make a direct comparison of the result from our standard simplistic atmospheric model with a more detailed model formulation of the atmospheric radiative transfer. The model is described in section 2 and results in section 3, including verification of the standard case annual cycle (3.1), the steady state sensitivity of the ice cover to changes of different forcing quantities (3.2), and
some aspects of the time-dependent response (3.3). Some of the results are discussed and put in perspective in section 4. Conclusions are given in section 5.
2. Model Description and Forcing [7] The most serious simplification made in the model by TH92 is probably that the ice cover was described as a slab with only one ice thickness. No leads exist then where solar radiation can penetrate, heat up the water column, and generate basal melting. The single thickness does not permit a description of the large turbulent heat fluxes and ice growth during winter in areas with open water or thin ice. Another important characteristic, omitted in TH92, is that the Arctic Ocean ice cover is not in a thermodynamic steady state since large amounts of ice are exported out of the area each year resulting in a net ice growth. The export occurs at a rate of about 12% of the basin area each year [Kwok and Rothrock, 1999] and represents an average heat flux of about 4 W m2. An active mixed layer having a thickness dependent on the buoyancy flux and mechanical forcing, instead of a layer with fixed thickness as in TH92, is likely needed in order to distribute and store the oceanic heat in a realistic way. [8] The model we use here is a column model representing the horizontally average properties across the Arctic Ocean, excluding the Barents Sea. The ice cover is described by a thickness distribution and the model is fully coupled between ocean, ice, and atmosphere regarding the heat fluxes but there is no explicit interaction concerning moisture fluxes and precipitation (see Figure 1 for a schematic sketch). [9] The ice and ocean models are nearly identical with that of Bjo¨rk [1997] with some modifications to handle the differences in surface stress between ice and open water and also some slight changes of parameter values (see Table 2). The ocean model is a column with a dynamic mixed layer on top, which deepens during winter by entrainment forced by the ice/ocean stress and negative buoyancy flux. During summer when the buoyancy flux is positive, due to melting and river water input, it becomes thinner again. The column below the mixed layer is maintained by inflow from Bering Strait and a geostrophically controlled outflow. The ocean model also includes a procedure that redistributes the properties in the column in order to mimic the circulation due to the large ice production and salinity increase in coastal polynyas. This process is handled by circulating mixed layer water through a hypothetical shelf, where the salinity is enhanced, from where it flows back in the column and interleaves at the level of matching density. The properties at the lowest level in the model are prescribed to have a typical temperature and salinity of the Atlantic water. The ocean model has shown to be able to reproduce the T-S structure in the upper layers [Bjo¨rk, 1989] as well as timedependent tracers such as bomb tritium and Freons [Becker and Bjo¨rk, 1996]. [10] The model ice cover is partitioned in thickness categories, which develop independently in a Lagrangian fashion in time. New categories are created when new ice is formed in the leads during winter and by ridging which transform thin ice to piles of thicker ice. Categories disappear when thin ice melts completely during summer. The
¨ RK AND SO ¨ DERKVIST: ICE THICKNESS DISTRIBUTION BJO
Figure 1. Schematic sketch of the atmosphere-ice-ocean model. FSW, FUP, and FDN, are the shortwave, upward longwave, and downward longwave radiations, respectively. a is the surface albedo, D is the poleward energy flux in the atmosphere, R is the reradiation, FTURB is the turbulent heat flux from/to the surface, FT determines the distribution of FTURB in the atmosphere, h is the optical height, N is the total optical thickness at the top of the atmosphere. Ta is the atmospheric temperature, T the ocean temperature, and S the ocean salinity. The funnel-like structure in the ocean visualizes the shelf circulation, Qs, which transforms the water in the mixed layer to a number of more saline water types incorporating brine (the droplets) from the ice when it is growing. The shelf water sink down in the water column and interleave at the appropriate level. divergence and ridging are the processes that create open water during winter where new ice can form. The rate of open-water formation due to divergence, e, is connected to the ice export while the rate of open-water formation due to ridging, r, is prescribed directly. The redistribution of ice by the ridging process is parameterized using a participation parameter, go, and a thickness multiplier, M. Categories within the thinnest go fraction of the ice distribution are ridged to M times the original ice thickness. Each ice category has one internal temperature point and a snow layer on top. The snow albedo is a prescribed annual cycle based on observations (Table 1), and the surface albedo parameterization for bare ice, which play a central role in this analyze, is given by aice ¼ min 0:08 þ 0:44H 0:28 ; 0:64
ð1Þ
according to Maykut [1982], where H is the ice thickness (see also Figure 2). The maximum value for aice is reached when H = 2.3 m. The reason to use this albedo parameterization is that it is simple but still includes the
37 - 3
well established characteristics of rapidly decreasing albedo with thickness for thin ice [Ebert and Curry, 1993]. [11] The atmospheric model is similar to TH92 and is a two stream, gray body, radiative equilibrium, atmosphere which is transparent to solar radiation. The leading idea behind the use of this, admittedly very simplified, atmospheric model is to get a reasonable downward longwave radiation to the ice surface for a given poleward heat flux and cloudiness in the atmosphere. An attractive property of this model is that the rather complicated state of water in the atmosphere, as determined by the water vapor distribution and different kinds of clouds at different levels, is collectively described by one single parameter; the total optical thickness, N. The full equations for the atmospheric model and the coupling procedure with the heterogeneous ice/ ocean surface are given in the Appendix. There are of course also drawbacks with this simplification. It has been shown that the thermal equilibrium of the atmosphere and ice is sensitive not only to the amount of clouds but also to the level occupied by clouds [Beesley, 2000]. Increasing the amount of low-level clouds give an equilibrium with thicker ice while more high level clouds give thinner ice. The atmospheric model differs from TH92 by including effects of the turbulent heat flux which is needed in connection with the thickness distribution since the turbulent heat fluxes can be very large over thin ice and open water. The heat supply/removal to/from the atmosphere by the turbulent flux is treated as a heat source/sink, which is distributed close to the surface using an exponentially decaying function with optical height. The decay scale is set by a parameter, z, with a value much smaller than N. [12] The model is forced by monthly means of poleward energy flux at the vertical boundary, solar radiation at the surface, snow precipitation, river runoff, Bering Strait inflow, ice export, and wind. The total optical thickness is used to tune the model ice thickness and is given as a prescribed time-dependent parameter with an annual cycle. Since N is an integrated measure of all absorbers of thermal radiation in the atmosphere, of which cloud particles play a dominate role, it is prescribed to follow the annual cycle of cloudiness (Figure 3). It is hard to evaluate directly how well the tuned optical thickness in Figure 3 describes the actual conditions, since no data of total optical thickness exists, to our knowledge. Using model result from a more sophisticated radiative convective column model, NCAR Community Climate Model (CCM2, CRM) [Hack et al., 1993] it is possible to estimate the total optical thickness for different cloud conditions. The clear sky optical thickness from this model, using the 1980 – 1987 January mean temperature and humidity profile from the Historical Arctic Rawinsonde Archive (HARA) data set, obtained National Snow and Ice Data Center (NSIDC), Boulder, Colorado, is about 0.3. A realistic summer Arctic cloud coverage with 70% of low clouds, 25% of middle and high clouds, respectively, corresponds to an optical thickness of about 4.9. For the winter profile with low and middle cloud fraction at 50%, and 25% high cloud coverage gives an optical thickness of about 2.3. The low-level cloud fraction is about a factor 2 above climatological mean, taken from the Russian North Pole drift stations (NP stations). We motivate this high value by the fact that the CRM model does not include ice crystals, which would increase the
37 - 4
¨ RK AND SO ¨ DERKVIST: ICE THICKNESS DISTRIBUTION BJO
Table 1. Model Forcing and Seasonal Dependent Parametersa Parameter
Jan.
Feb.
March
April
May
June
July
Aug.
2
127.0 109.8 119.7 106.20 72.6 78.4 87.4 88.4 Energy flux, W m 0.0 5.1 32.9 142.4 256.8 302.0 232.6 132.9 Solar radiation, W m2 Optical thickness 2.4 2.4 2.4 2.4 4.6 5.5 5.5 5.5 0.17 0.17 0.16 0.16 0.09 0.13 0.12 0.12 Divergence, yr1 Snow albedo 0.85 0.84 0.83 0.81 0.82 0.78 0.64 0.69 3.52 3.52 3.52 3.52 21.2 0.0 0.0 0.0 Precipitation, 109 m s1 Rivers 6 3 1 0.026 0.021 0.022 0.023 0.11 0.29 0.16 0.12 Runoff, 10 m s Temperature, C 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Bering Strait 1.02 0.95 0.34 0.78 1.13 1.26 1.47 1.07 Flow, 106 m3 s1 Salinity, psu 32.2 32.6 32.7 32.6 32.3 32.2 32.4 32.1 Temperature, C 1.7 1.8 1.8 1.8 1.2 0.8 3.8 4.3 5.6 5.7 5.3 5.1 5.0 5.2 5.2 5.4 Wind velocity, m s1 3.3 3.3 3.0 3.0 2.7 2.9 3.1 3.2 Wind SDA, m s1 Ice/wind speed ratio 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02
Sep.
Oct.
Nov.
Dec.
Mean
93.5 47.6 5.5 0.13 0.84 54.6
108.1 9.6 4.6 0.13 0.85 54.6
121.4 0.0 3.0 0.16 0.85 3.52
122.7 0.0 2.4 0.18 0.85 3.52
103.0 96.8 3.8 0.14 0.80 12.6
0.094 0.0 0.66 32.0 4.2 6.2 3.8 0.01
0.063 0.0 0.87 31.6 3.1 6.2 3.5 0.01
0.031 0.0 0.90 31.5 1.2 5.8 3.5 0.01
0.026 0.0
0.08 0.0
0.34 31.7 1.7 5.5 3.2 0.01
0.90 32.2 0.42 5.5 3.2 0.01
a
The first parameter, the poleward energy flux, is based on rawinsonde data and the GFDL model [Overland and Turet, 1994]; the incoming solar radiation is from Russian North Pole drift stations, obtained from the National Snow and Ice Data Center (NSIDC). The divergence is based on an 18-year-long time series, 1978 to 1996, of ice motion in the Fram Strait from satellite passive microwave data [Kwok and Rothrock, 1999]. Some ice is also exported into the Barents Sea and through the Canadian Archipelago. We estimate this export to about 20% of the Fram Strait export, and the total export is then 1.2 times the Fram Strait value. The divergence is computed by dividing the ice export with the ice-covered area, assumed equal to the total area, except during the summer months, when it is decreased by 12% in order to account for the open-water fraction. The annual cycle of the river runoff is from Becker and Bjo¨rk [996]. Volume flow, salinity, and temperature in the Bering Strait are based on 4-year-long time record [Roach et al., 1995]. Here the flow is increased by 10%. Snow albedo and precipitation are from Maykut [1982]. Wind speed, wind standard deviation (SDA), and ice/wind speed ratio are from Bjo¨rk [1989].
3. Results
0.6
Albedo
3.1. Standard Case, Annual Cycle [15] In this section we show that the model can reproduce the present day characteristics of the upper ocean, ice and lower atmosphere. The standard case gives an ice cover with an annual mean area averaged thickness of 3.3 m and
Total optical thickness, N
[14] In the light of observed thinning of the ice cover from about 3 m in the 1970s to about 2 m in the 1990s [Rothrock et al., 1999] there is actually some ambiguity in specifying an observed ice thickness. We have conservatively chosen the commonly used 3 m [McLaren et al., 1994] as the observed annual mean thickness and tuned the
model towards that by adjusting the total optical thickness. The model is run using interpolated values between the monthly means of the forcing quantities and with a time step of one day.
0.4
6
95
5
80
4
65
3
50
2
0.2
0
0
1.0
2.0 Ice thickness (m)
3.0
4.0
Figure 2. Dependence of the bare ice albedo on ice thickness as used in the standard case.
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Cloud cover (%)
optical thickness. The values of the total optical thickness computed from the CRM model in this way are relatively close to the values used in the present model (Figure 3). [13] The standard forcing is shown in Table 1 and the standard values of some selected model parameters are presented in Table 2.
35
Figure 3. Annual cycle of the total optical thickness used in the standard case (solid line) and observed monthly mean cloud cover from Russian North Pole drift stations, NP stations (squares). The cloud cover was obtained from National Snow and Ice Data Center (NSIDC), Boulder, Colorado, and originates from the Arctic and Antarctic research Institute in St. Petersburg, processed by Marshunova and Mishin [1994].
37 - 5
¨ RK AND SO ¨ DERKVIST: ICE THICKNESS DISTRIBUTION BJO Table 2. Values of Constants and Standard Case Parameters Definition
Ab n r go
7.8 1012 m2 40 0.40 yr1 0.07
M A
6 320 W m2
B
4.6 W m2 K1
z Cs l k ps
0.03 1.3 103 0.7 103 0.15
Basin area Number of ice categories Ridging activity Thinnest area fraction participating in ridging Ridge thickness multiplier Constant in the linearization of the Stefan- Bolzman law Constant in the linearization of the Stefan- Bolzman law Vertical e-folding scale for turbulent heat Transfer coefficient for turbulent heat flux Parameter for the geostrophical outflowa Vertical diffusivitya Salt flux fraction to shelf circulationa
a
0
Model NP Stations
5
10
o
Value
Ta(0) ( C)
Parameter
15
20
25
30
Different values compared to Bjo¨rk [1989] and Bjo¨rk [1997]. Jan
with a seasonal thickness variation of 0.5 m, similar to that of Bjo¨rk [1997]. In late winter, the modeled ice thickness distribution agrees well with ice draft data from submarine cruises from 1991, 1992, and 1994 (Figure 4). The melting season is about 90 days with a start in the beginning of June and ends in late August which agrees well with data [Rigor et al., 2000]. At the end of the melting season, about 12% of the area is ice free, less than 1% is covered by ice thinner than 1 m, 65% is in the interval 1 m to 4 m, and 25% of the total area is covered by ice thicker than 4 m (Figure 9b). The annual cycle of the surface air temperature is close to the observations at the NP stations (Figure 5). The temperature profile aloft (not shown) is hard to compare directly
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 5. Annual cycle of the surface air temperature, Ta(0), from the model (solid line) and observed monthly mean from the NP stations (squares), see Figure 3 for details about the data set. with observations, by the use of optical height as vertical coordinate. Using the CRM model with the winter profile of humidity and clouds described in section 2, it is possible to roughly estimate how the optical thickness is related to height. The present model does not capture the large scale inversion of the lowest 3000 m in the 80– 87 January mean profile from the HARA data set and is about 10C colder than data at 2000 m, but is closer to the observed profile at higher levels. The main reason for the discrepancy is probably that the model distributes D evenly throughout the atmosphere. The lowest 200 m agree with the data within 2C. The annual cycle of the downward longwave radiation at the surface follows close to the observed at the NP stations (Figure 6). Modeled upward longwave radiation at the top of the atmosphere during winter is 140 W m2, which is about 25 W m2 below the observed mean value 280
Model NP Stations
260
2
FDN(0) (W m )
240
220
200
Figure 4. Late winter cumulative thickness distribution from the model standard case (solid line) and from observations (dashed line). The observed ice distribution is a composite from three late-winter observations (April – May) by submarines from 1991, 1992, and 1994. All data >87N are used which gives a total cruise length of about 1600 km. The distribution is computed from raw data stored in 0.1 m bins consisting of under-ice thicknesses (draft), which are converted to ice thickness by multiplying with a factor 1.12. Draft data were obtained from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado.
180
160
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Figure 6. Annual cycle of the downward longwave radiation at the bottom of the atmosphere, FDN(0), from the model (solid line) and observed monthly mean from NP stations (squares), see Figure 3 for details about the data set.
¨ RK AND SO ¨ DERKVIST: ICE THICKNESS DISTRIBUTION BJO
0 a
1
0
2
(W m )
100
F
TURB
200
300
400
H>4 1
