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Keywords:

  • Arctic Ocean fluxes;
  • ice-ocean interactions

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Ocean/ice interface heat fluxes (F0) are calculated from upper ocean measurements obtained from autonomous systems repeatedly deployed in the Arctic Ocean Transpolar Drift between 2002 and 2010. Average F0 values over the nine summer heating season realizations varied between 4.6 and 10.5 W m−2 with an average summer value of 7.6 W m−2. Between 2002 and 2010, summer-averagedF0passed through a clear minimum, with most inter-annual variability inF0 dominated by differences in ocean heat content, rather than by differences in surface forcing. We test if Transpolar Drift F0 is supported primarily by local, radiative energy flux entering the upper ocean through areas of open water (Frw). Frwis estimated by combining re-analysis solar radiation products with satellite-borne passive microwave ice concentration products and observed divergence of drifting buoys. Inter-annual variability of summer-averaged surface insolation is relatively small (0.04 normalized standard deviation, NSTD), so differences in open water fraction (0.30 NSTD) are the most likely sources of the observedF0variability. Ensemble-averaged over the 2002–2010 summers, the satellite and buoy-divergenceFrw, are equal to 8.1, and 8.0 W m−2, respectively. Therefore, over the course of the summer season, sufficient energy enters the upper ocean through open water to wholly support the observed F0. Reasonable agreement between the two open water fraction estimates further indicates that mechanical processes, rather than lateral melting, are controlling the amount of radiation entering the upper ocean, implying that ocean ice-albedo feedbacks were not strong in the Transpolar Drift in the last decade.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] The ocean boundary layer below sea ice, referred to hereafter as the under-ice ocean boundary layer (IOBL), plays an important role in Arctic air/sea/ice interactions. Solar radiation entering the upper ocean through leads or thin ice,Frw, is rapidly mixed through and stored within the IOBL. Over the course of the summer season, much of this heat is transported to the base of the ice cover by the ocean-to-ice heat flux,F0, where it is converted to latent heat through ice melting. Ice thickness measurements made in Arctic pack ice typically show that summertime surface and bottom ablations are comparable [e.g., Perovich et al., 2003], directly indicating that F0is as large as the net atmosphere-to-ice energy flux. As the area [e.g.,Comiso et al., 2008] and thickness [e.g., Kwok and Rothrock, 2009; Maslanik et al., 2007] of the Arctic sea ice cover decrease, the upper ocean absorbs more solar energy. One result of this change is that the IOBL is playing an increasingly significant role in the enthalpy balance of the sea ice cover through the corresponding increase in F0. And, this significance will be further amplified through the positive reinforcement of the ocean-ice-albedo feedback.

[3] Although a basic understanding of the significance of summer solar heating in supporting F0 has been established, uncertainties remain concerning the perturbations (thermodynamic or mechanical) that lead to potentially rapid and widespread reductions in Arctic sea ice cover. Based on measurements from the 1975 Arctic Ice Dynamics Joint Experiment (AIDJEX), Maykut and McPhee [1995] find that the annual average of F0 was 5.1 W m−2 and that this flux could be accounted for by solar radiation entering the upper ocean through the ice pack, with about 10% trapped below the summer meltwater layer. As summertime melting proceeds, a thin, fresh summer melt layer develops, stratifying and isolating heat below. For the 1997–1998 Surface Heat Budget of the Arctic Ocean (SHEBA) ice camp drift, Shaw et al. [2009] demonstrate that there is good agreement between F0and observed ice-bottom ablation rates and find that the annual averageF0 for the SHEBA drift was 7.6 W m−2. For the SHEBA observations, Perovich [2005] finds that shortwave penetration through thin ice, in addition to that through open water of leads, is necessary to support the observed F0 and bottom ablation rates. Krishfield and Perovich [2005] have made estimates of F0 from a large number of automated buoys drifts in the Western and Central Arctic. They find that variability in upper ocean heat content is explained reasonably well with simply the local solar zenith angle.

[4] Wind-forcing has the potential to accelerate or decelerate summer melting depending on whether the winds cause divergent or convergent ice motion. Oceanic advection of heat from the North Atlantic and North Pacific could accelerate melting if this heat could be vertically mixed from depth to the base of the ice. Vertical mixing rates in the Arctic Basins are generally very weak [e.g.,Fer, 2009; Rainville and Winsor, 2008]. Several studies emphasize that F0 is enhanced over topographic features, such as the Yermak Plateau northwest of Svalbard [McPhee et al., 2003], and over the continental shelf [Steele and Morison, 1993]. In the Western Arctic, where heat-containing, Pacific-origin water masses are located in close proximity to the IOBL, a vertical heat transport from below the IOBL to the underside of the ice has been documented [Shaw et al., 2009; Yang et al., 2001]. Krishfield and Perovich [2005]noted that the upper ocean is often elevated above freezing in the wintertime in the Transpolar Drift, indicative of heat sources in addition to solar heating in this region. The degree to which open water is created by lateral melting or ice divergence and the significance of oceanic heat advection to ice melting are observational questions with direct bearing on understanding of the ocean-ice albedo feedback mechanism. Recent modeling work bySteele et al. [2010] has addressed the significance of oceanic heat advection in the Western Arctic.

[5] In this paper, repeated, time series estimates of F0 and Frwalong drifts tracks from near the North Pole to Fram Strait over the years 2002–2010 are used to: (1) document the inter-annual variability in the ocean-to-ice heat flux (2) test how well this variability can be explained in terms of the amount of solar radiation entering the upper ocean through open water, and (3) quantify the amount of open water attributable to ice divergence. Here we do not consider radiative fluxes directly through the ice cover, testing whether radiative fluxes through open water are sufficient to support the observed ocean-ice fluxes. TheF0estimates are derived from near-interface temperature and salinity measurements and estimates of the interface stress. The stress estimates are made from ice motion observations and direct eddy-correlation measurements. TheFrw estimates are made by combining reanalysis radiation estimates with two types of open water fraction estimates: ice concentration products from satellite passive radiometers, and divergent opening estimates from ‘opportunistic arrays’ of drifting buoys. Although recent record minima in sea ice extent have been dominated by reductions in the Western Arctic, ice thickness in the Transpolar Drift has decreased by about 50% from 2001 to 2007 [Haas et al., 2008], potentially conditioning this area for rapid ice retreat. McPhee et al. [2003] and Inoue and Kikuchi [2006]have addressed aspects of the summertime ocean-to-ice heat flux in the Transpolar Drift, using observations from the same automated drift station.Inoue and Kikuchi [2006]note a correlation between summer-averageF0and mean ice speed over June and July. Here, we present results over the full measurement period to the summer of 2010 and focus on the fundamental comparison between the ocean-to-ice heat transport and the amount of solar energy entering the ocean through open water.

2. Measurements and Analysis

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[6] In situ data were obtained by buoys deployed as part of the North Pole Environmental Observatory (NPEO) automated drifting station, which is a group of autonomous ocean, ice, and atmosphere measurement systems co-located on a single ice floe (seeMorison et al. [2002] and http://psc.apl.washington.edu/northpolefor more information about the NPEO program). On a continuing basis started in 2000, the NPEO drift station has been established near the geographic North Pole. The NPEO observation systems include Naval Postgraduate School Autonomous Ocean Flux Buoys (AOFB), deployed from 2002 onwards, National Oceanic Administration, Pacific Marine Environmental Laboratory (PMEL) surface meteorology stations and radiation buoys, deployed from 2002 through 2008, and Japan Agency for Marine-Earth Science and Technology Compact Arctic Drifters (JCAD), deployed from 2000 to 2005, which measure temperature and salinity at discrete depths between 25- and 250-m in the upper ocean. In this work, we focus on the 2002–2010 drifts (Figure 1). Over the course of a year or so, these stations all traverse the central Arctic in the Transpolar Drift.

image

Figure 1. Map of the 2002–2010 NPEO ice station drift trajectories. The solid lines denote the ‘summer’ portions of the drift (yeardays 122–275) and the dashed lines denote the remainder of the drifts. Gray shaded contours denote the coastline and the 500, 1000, and 3500 m isobaths. The filled circles on each trajectory indicate position on yearday 200.

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[7] The analysis focuses on summertime conditions, from the beginning of May to the end of September (year days 122–275). Although substantial insolation begins in late March near the Pole, the start of the analysis period is constrained by the initial deployment of the buoys in April. This startup constraint is not expected to have any bearing on our comparisons of the shortwave energy entering the ocean and the heat transport from the ocean to the ice because our open water estimates indicate that there was very little open water prior to 01 May, and upper ocean observations indicate that there was very little warming prior to 01 May (see Results section). Consequently the available observations suggest that there was very little radiation entering the ocean before May. We chose to end our analysis period at the end of September to geographically limit the analysis to data from the Eurasian Basins, i.e., to exclude vertical heat fluxes associated with the presence of warm, near-surface Atlantic water in the vicinity of Fram Strait. For each of the ice station drifts, the sun had set for the Arctic winter by early October, at the latest, and upper ocean observations indicate that the IOBL was very close to the freezing point by the end of September. Thus, the chosen analysis period does appear to span the range of significantFrw and F0.

2.1. Ocean-to-Ice Heat Flux

[8] The AOFB is an automated system for measuring turbulent fluxes and velocity profiles in the IOBL. These buoys are designed and fabricated by the Ocean Turbulence Group at the Naval Postgraduate School. (For a full description of the system, see Shaw et al. [2008] and the AOFB program website, http://www.oc.nps.navy.mil/∼stanton/fluxbuoy.) The buoys have two main components: a surface housing that sits on the ice and an instrument frame that hangs from the housing, by a series of torsionally rigid poles, into the IOBL. The surface housing contains processing electronics, Global Positioning System (GPS) electronics, an Iridium satellite modem, GPS and Iridium antennae, and batteries. The instrument frame is outfitted with a downward looking 300 kHz Acoustic Doppler Current Profiler (ADCP, RDI Workhorse) and a custom-built ‘flux package’. An acoustic travel-time current meter (Falmouth Scientific Inc., ACM 3D current meter, 2.5 mm s−1 RMS noise level), an inductive conductivity cell (±0.002 mS cm−1), a platinum resistance thermometer (Falmouth Scientific Inc., OEM C-T Sensor), and a fast-response thermistor (±1 mC) comprise the flux package sensor suite. For the 2010 deployment, a custom digital travel-time current meter replaced the commercial current meter. These fast-response instruments are collocated within a 0.001m3sample volume, and directly measure the turbulent fluxes of momentum, heat, and salt over approximately 25-min long Reynolds averaging periods using the eddy-correlation technique. After field installation, AOFBs maintain twice-daily, two-way communications with a computer running at NPS. During each data transfer, sampling parameters may be updated. The flux package sensors were installed approximately 5.6 m below the base of the sea ice cover for the 2002–2007 deployments and 4.1 m below the bottom of the ice for the 2008–2010 deployments. AOFB data collection ends when the buoys melt out of the ice, which typically occurs south of Fram Strait during the first winter after the buoys have been deployed.

[9] Near-interface thermal gradients produced by solar insolation have complicated the interpretation of the directly measured heat fluxes within the IOBL during the summer months. The vertical structure makes it difficult to translate heat fluxes from the measurement level to the interface. In this work then, an indirect procedure outlined byMcPhee et al. [2003] is used to estimate F0, rather than the direct correlation between fluctuating temperature and vertical velocity described above. The same method has been widely adopted for studies of ocean/ice interaction [e.g., Steele and Morison, 1993; Maykut and McPhee, 1995; McPhee et al., 2003; Krishfield and Perovich, 2005].

[10] Interface heat flux is calculated with the McPhee [1992] bulk transfer law

  • display math

where ρ0 is a reference density, cp is the specific heat of seawater, u*0 is the interface friction speed, δT = TmlTf (Sml) is the departure from freezing of the well-mixed layer, and the transfer coefficient (or Stanton number)cH has a value close to 0.0057 under a variety of conditions [McPhee, 1992, 2002].

[11] In contrast to the studies cited above, here the directly measured Reynolds shear stresses are used to estimate u*0. Stress at the measurement level was translated to the interface by assuming an Ekman-type exponential decay of stress across the boundary layer as described byMcPhee [2008]. As a comparison and to provide estimates of stress over all of the 2002–2010 drifts, the ocean-ice interface shear stress is also calculated using Rossby similarity theory

  • display math

[12] Here, V0 is the interface (ice) velocity, approximately equal and opposite to the relative speed at the boundary layer edge, u*0 is the interface friction velocity vector, equal to the square root of the kinematic interface shear stress, z0 is the hydraulic roughness length of the underside of the ice, and A and B are similarity constants. The geostrophic drag coefficient is a function of the friction Rossby number,

  • display math

the ratio of boundary layer scale height, hE = u*0/f, to the roughness length.

[13] The departure from freezing is calculated using 20-min average values from the flux package temperature and conductivity sensors that are recorded about every four hours. Problems with the onboard processing of the velocity measurements onboard the 2002 and 2003 buoys led to contaminations of the stress estimates. For these two drifts, only the Rossby similarity versions of the interface friction speed and presented. For the Rossby similarity calculations, ice velocity is calculated from GPS positions and is low-pass filtered to match the nominal four-hour sampling of temperature and conductivity. Inertial and tidal components are removed from the buoy GPS-derived velocity record because they do not contribute significantly to interfacial shear and stress [McPhee et al., 2003]. The roughness length is assumed to lie in the range 0.05–0.2 m, which is expected to span the potential values for perennial pack ice [e.g., McPhee, 2002; Shaw et al., 2008]. This uncertainty in z0 introduces a factor of about 0.12 uncertainty in u*0 for the ice speeds encountered. For presentation of results, u*0 and F0 values are based on z0 equal to 0.1 m.

2.2. Shortwave Radiation Entering Ocean

[14] The area-averaged flux of shortwave radiation entering the upper ocean is calculated as

  • display math

where Fr is the shortwave flux at the surface of the ice, αw is the albedo of open water and leads (taken as 0.07) [Pegau and Paulson, 2001], and 1 − C is the open water fraction, with C the local ice concentration. This estimate represents a lower bound on the actual shortwave flux entering the ocean because it neglects penetration through the ice cover. It also neglects atmospheric energy flux terms including net longwave radiation [e.g., Persson et al., 2002].

[15] The PMEL radiation buoys (located on the same floe as the AOFBs) provide measurements of the surface shortwave flux Fr. Each of the radiation buoys was outfitted with a pyranometer (Kipp & Zonen CM22 for 2002–2004; Eppley PSP for 2006–2008) and a pyrgeometer (Kipp & Zonen CG4 for 2002–2004; Eppley PIR for 2006–2007). During the 2004 and 2007 NPEO drifts, the stand that supported the radiometers tilted in mid to late-July as the surface of the ice melted. No radiometers we deployed in 2009 or 2010.

[16] Because of the gaps in the in situ radiation measurements, the use of re-analysis products was explored. Surface shortwave radiation grids from the European Center for Median-Range Weather Forecasts were interpolated onto the drift tracks of the NPEO stations and compared to the in situ observations. These comparisons of the in situ and ECMWF shortwave fluxes (not shown) are favorable. The ECMWF estimates are biased slightly low because the re-analysis appears to underestimate the number of clear sky periods with elevated shortwave flux. Averaged over the summer analysis periods, the ECMWF irradiance is about 10% lower than the in situ measurements. Given the favorable comparison, the interpolated ECMWF radiation data are used forFr.

[17] Satellite-borne passive microwave radiometers provide estimates of open water fraction. We used the Special Sensor Microwave Imager (SSM/I) instrument with NASA Team algorithm processing [Cavalieri et al., 1996]. This satellite data set is valuable because it provides nearly continuous coverage of sea ice conditions along the NPEO drift trajectories. There are algorithm ambiguities in converting the radiometer signals to ice concentration, however: weather artifacts, presence of sea ice in areas of open water, and confusion between areas containing melt ponds and areas of actual reduced ice concentration [e.g., Partington, 2000]. For example, in a comparison of SSM/I (using NASA Team and Bootstrap algorithms) and synthetic aperture radar (SAR) open water estimates, Kwok [2002] attributes a poor correlation between the two estimate types to the melt pond problem. Also, Steffen and Schweiger [1991]find mean differences between SSM/I and LandSat ice concentration estimates in the concentration range 0.04 to 0.11 as a function of how surface-type ‘tie-points’ are defined in the algorithms.Markus and Dokken [2002] find very good agreement (high correlation and negligible bias away from the marginal ice zone) between SSM/I data processed with the NASA Team 2 algorithm and ice concentration derived from SAR data. Other practical problems are that the SSM/I data set has a large polar hole that encompasses portions of the NPEO drifts.

[18] From the SSMI data set, ice concentration in the neighborhood of the NPEO drift stations was estimated by averaging concentration values from grid points lying within circles centered on the daily averaged drift station positions. Radii of 15, 30, 50, and 100 km were tried for this spatial averaging. Away from the ice edge, the results did not vary significantly as a function of radius, so the more robust 100-km average value was used in further analysis. The large coverage gap at the North Pole was filled by a two-dimensional, cubic interpolation.

[19] Divergent opening estimates based on drifting buoy data from the International Arctic Buoy Program (IABP) are used to provide direct insight into the role of ice divergence in generating open water as well as to provide an additional estimate of open water fraction to be compared to the remote-sensing estimates. The divergent opening estimate was made by tracking the area of IABP buoy triangles in the vicinity of the NPEO drifts as a function of time. The data source is the IABP ‘C’ data set (available fromftp://iabp.apl.washington.edu/pub/IABP/C/}), which contains twelve hourly positions for all of the IABP buoys, organized by year.

[20] IABP buoy coverage of the Transpolar Drift varies from year to year, and we had to use the available drift tracks opportunistically. Buoy triangles deemed useful for analysis were chosen based on the following three criteria: triangle area did not change wildly due to the vertices becoming collinear, array deformation was not strongly impacted by convergence against landmasses (the NPEO stations were typically in the open ocean during the summer season), and that distances between the triangle vertices was not more than about 500 km. In 2002, 2005, and 2006 only one useful buoy triangle could be defined, whereas in 2003, 2004, 2007, 2008, 2009 and 2010 we were able to define three, four, three, two, three and two triangles, respectively. The triangles typically included the NPEO station itself. There are gaps in triangle coverage at the beginning and end of the 2003 summer period and at the beginning of the 2006 summer period. The early summer gaps were filled by assuming there was no open water fraction and the 2003 late summer gap was filled by continuing the last available open water fraction through the end of the analysis period.

[21] For each triangular buoy array, open water fraction, 1–C, was calculated as

  • display math

where A(t) is triangle area as a function of time and A0(t) is the minimum triangle area that has previously occurred. This formulation allows for some compaction of the ice cover to occur, but neglects any change in open water fraction that results from lateral melting. The method assumes that C(t) is equal to one at the beginning (01 May) of the calculation. For each drift, the divergent opening estimate was taken as the average of the individual triangles available that season.

[22] Divergence calculations using the trajectories of drifting buoys from the 2004 summer season are illustrated in Figure 2. In this case, the areas of four sets of triangles of buoys (Figure 2, top) are calculated through time, allowing a set of 4 local divergence rates and hence 4 open water fractions to be estimated over time (Figure 2, bottom). Limitations of this method arise from the sparseness of buoys within a distance in which their trajectories were coherent – a distance of approximately 100–300 km in this study. In this example 6 buoys were available (including the AOFB), making up 4 triads of buoys, which is the maximum number we found for the 2002–2010 time series, with 1, 2 or 3 sets of buoy triads more typical. In addition to the spatial decorrelation problem, as the triangles formed become shallow, the resulting area estimates error became large, again limiting the number of area changes available to the divergence estimates [Hutchings et al., 2012]. A comparison of the passive microwave and the four buoy divergence–based open water estimates (Figure 2, bottom) shows the short term variability between them.

image

Figure 2. Example divergence and ice concentration calculations, AOFB 3. (top) Ice divergence was estimated from temporal changes in the area of triangles formed by the vertices of the AOFB and two other local buoys. In this case, four buoy triangles were available for the calculations. (bottom) The ice concentration time series resulting from the four buoy divergence estimates (colored lines), and the corresponding SSMI passive microwave estimate of ice concentration.

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[23] Finally, the radiometer data and the two versions of open water fraction (referred to hereafter as SSMI, and IABP) were combined to produce daily estimates of Frw according to (2).

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[24] The 2002–2010 NPEO drift stations were established during April, within about 175 km of the geographic North Pole (Figure 1 and Table 1). Through the summer months, the stations drifted across the Eurasian Basins toward Fram Strait, with inter-annual variability in speed and degree of wandering along the drift tracks. The 2008 station was deployed earlier than the others, and by the start of the analysis period it had drifted further to the south than the others. It was 232 km from the North Pole by 01 May.

Table 1. Drift Station Trajectory Statistics
YearDeployment1 Mayb30 Sepc
DateLat (deg)Lon (deg)Distancea (km)Lat (deg)Lon (deg)Distancea (km)Lat (deg)Lon (deg)Distancea (km)
  • a

    Distance from the geographic North Pole.

  • b

    1 May is the start of the summer analysis period.

  • c

    30 Sep is the end of the summer analysis period.

200229 Apr88.571.616388.465.517284.417.3612
200326 Apr89.182.310488.971.711985.340.8515
200427 Apr89.3123.37289.3112.57588.029.0220
200527 Apr89.4145.26289.3122.97784.5−5.7608
200627 Apr88.9175.512688.5−169.016184.836.0577
200728 Apr88.8−12.213388.7−12.514781.9−2.6885
200808 Apr88.416.117687.9−6.423282.92.9779
200912 Apr89.632.24187.94.622982.0−5.1876
201019 Apr88.7145.214489.1145.010384.1367.4653

[25] For the most part, the ice station drifts followed one of two distinct trajectories through the summer analysis period. During the analysis period, the 2002, 2003, and 2006 stations drifted across the long axis of the Amundsen Basin, over the Gakkel Ridge, and into the Nansen Basin (Figure 1). The latitude of these stations at the end of the summer period ranged from 84.4° to 85.3° N, 515 to 612 km from the North Pole (Table 1). The 2006 station was deployed on the western slope of the Lomonosov Ridge, the only one deployed to the west of the Lomonosov Ridge. The 2005, 2007, 2008, 2009 and 2010 stations followed a trajectory along the axis of the Amundsen Basin (approximately the Prime Meridian), directly toward the entrance to Fram Strait. These stations drifted relatively far south over the summer period, to the range 84.5° to 81.9°N latitude, 608 to 885 km from the North Pole. The 2007 station nearly reached the entrance to Fram Strait by day 275, the most southerly displacement any of the stations during the summer period. The trajectory of the 2004 station was anomalous. Ice motion in the Transpolar Drift was very slow during the 2004 summer, with the result that this station remained above 88°N, within the central part of the Nansen Basin throughout the summer season. By a wide margin, the 2004 station remained closest to the Pole (Table 1).

[26] In the remainder of this section, we describe the variability of the ocean-to-ice heat fluxF0 and the shortwave flux entering the upper ocean Frwfor the 2002–2010 NPEO drift stations and make a comparison between the amount of heat entering the upper ocean from the shortwave flux and the amount of heat transported to the ice cover by the ocean-to ice heat flux. Owing to storage of incoming radiation in the upper ocean,F0 is not expected to equal Frw instantaneously. Given that the analysis period spans the times of significantly nonzero Frw and F0, as described above, averages of these fluxes over the analysis period should be nearly equal, if insolation is the dominant heat source supporting the ocean to ice heat flux. If the summer average of F0 were greater than Frw, it would imply that heat sources in addition to the shortwave flux entering the ocean were significant. To quantify the observed inter-annual variability and to make a meaningful comparison betweenF0 and Frw, we introduce two averaging procedures. Temporal averages over individual summer analysis periods are denoted using an overline notation, e.g., inline imagerefers to the summer average (over days 122–275) of the ocean-to-ice heat flux. Ensemble averages over the nine realizations of the repeated drifts are denoted using an angle-bracket notation, e.g., 〈F0(t)〉 refers to the nine-year ensemble average of summer-averaged ocean-to-ice heat flux as a function of year day. It is also possible to combine the two averages to produce an ensemble summer average of the flux, inline image. Summer averages and ensemble summer averages are listed in Table 2.

Table 2. Summer Period Statisticsa
Year inline image (m s−1) inline image (K)ρ0cpcH × inline image (W m−2) inline image (W m−2) inline image (W m−2)
inline image (W m−2) inline image (W m−2) inline image (W m−2)u*0δT′ (W m−2) inline image (W m−2)SSMIIABP
  • a

    Summary statistics of the surface friction velocity, departure from freezing, intra and inter-annual components of the ocean-to-ice heat flux, ocean-to-ice heat flux, and incoming solar radiative flux, using the averaging procedures described in the text. In particular, the components of the ocean-to-ice heat flux are described byequation (3), so that entries in the inline image column are equal to the sum of entries in the preceding five columns.

  • b

    This is the ensemble average over the 2002–2010 drifts, so that entries in this row are equal to averages.

20020.81568.12.00.30.1−0.99.71535.45.8
20030.80438.1−0.30.30.0−0.47.61499.55.0
20040.58358.1−1.9−2.10.50.04.61548.53.7
20050.71378.1−1.5−0.70.1−0.75.41465.110.6
20060.83288.1−3.00.6−0.2−0.64.81436.06.6
20070.72518.11.1−0.6−0.1−0.68.01506.911.1
20080.86578.12.10.90.2−0.710.515213.910.3
20090.80508.10.80.30.0−1.67.61546.94.7
20100.87488.10.61.00.10.710.513611.014.1
Ensembleb0.78458.1000.1−0.67.61498.18.0

3.1. Ocean-to-Ice Heat Flux

[27] Records of temperature, salinity, and ice velocity from the AOFBs provide highly resolved time series estimates of δT and u*0 from 2002 to 2010 (Figure 3). These time series document the intra and inter-annual evolution of near-surface heat content and surface mechanical forcing over this period.

image

Figure 3. Summer heating season departure from freezing δT (gray) and interface friction speed u*0 (eddy correlation, red, and Rossby similarity model, blue) for the 2002–2010 drift stations. Ensemble averaged quantities, 〈δT(t)〉 and 〈u*0(t)〉, are in the tenth panel.

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[28] The ensemble-averaged time series ofδT (Figure 3, tenth panel) provides a view of the typical intra-annual evolution of upper ocean heat content. At the start of the analysis period on 01 May,δTwas close to zero. Through mid-June (about year day 165),δTincreased slowly to a value of about 15 mK. This period of slow heating was followed by more rapid heating through early August (about year day 216), at which point the ensemble-averaged time series reached a maximum value of 128 mK. For the remainder of August and through September,δT decreased back toward zero. The ensemble summer average was inline image = 45 mK (Table 2).

[29] In terms of inter-annual variability, summer-averaged departure from freezing, inline image, was relatively large in 2002 and 2008, moderate in 2003, 2007, 2009 and 2010, and relatively small is 2004, 2005, and 2006 (Table 2). That is, summertime upper ocean heat content passed through a minimum during the period 2002–2010. Most of this variability is associated with the magnitude of δTduring the upper ocean heat content peak of mid-summer rather than the timing of the onset or cessation of elevatedδT at the beginning and end of summer (Figure 3). Or, in other words, most of the inter-annual variability can be explained by modulating the magnitude of the typical heat content time series as represented by the ensemble average time series. There are several exceptions to this general statement, though. The 2007 drift time series has a large maximal value ofδT but only an average value of inline image, because of a relatively late onset of warming. The upper ocean started to warm relatively early during the 2006 drift, but small values of δT through the remainder of summer caused the 2006 drift to have the smallest value of inline image, 29 mK, of the observational period. The large inline image of the 2002 drift was sustained in part by a persistence of elevated temperatures later in the season than was typical. The 2008 drift contained an anomalous period of elevated temperature early in the summer season.

[30] Within the summer analysis period, variability in u*0 arises as a result of the passage of atmospheric storm systems at a multiday, synoptic time scale (Figure 3). Large storms forced peak u*0 in excess of 1 cm s−1. The ensemble-averagedu*0 time series (Figure 3, tenth panel) indicates that surface forcing tended to increase moderately during August and September (yeardays 215–275) in comparison to June and July. Typical values of inline image were about 0.8 cm s−1 (Table 2). There is good correlation between the eddy-correlation and Rossby-similarity model versions ofu*0. The magnitudes of the eddy-correlation estimates are about 80% of the Rossby similarity estimates. Summer surface forcing was relatively weak in 2004 ( inline image = 0.58 cm s−1) and was relatively strong in 2008 and 2010 ( inline image = 0.86 and 0.87 cm s−1, respectively). A cumulative distribution of u*0 over each summer time series (Figure 4) shows the dominance of low wind, low surface stress conditions for 2004, in contrast to the dominance of higher surface stress for years 2008 and 2010. Through most of the summers, u*0 was greater than 1 cm s−120–30% of the time, but for 2004 this occurred less than 5% of the time. Thus, the summer of 2004 was characterized by weak synoptic-scale surface forcing in addition to slow, season-scale drift. The 2002, 2003 and 2008 drifts had largeu*0 values during June (year days 153 to 182). The average June values of u*0 were 0.9, 0.7 and 1.0 cm s−1 for 2002, 2003, and 2008, respectively. The average June values for the other years were less than 0.6 cm s−1.

image

Figure 4. A cumulative sum of friction velocity u*0 for each summer time series for 2002 to 2010. This summarizes the distribution of stress values during each summer.

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[31] The typical summertime evolution of F0is illustrated with the ensemble-averaged time series (Figure 5, tenth panel). Significant heat transports to the base of the ice began in early June (close to year day 160) at about the same time that the rate of increase of δT accelerated (Figure 3, tenth panel). Over the time span of these observations, then, both IOBL warming and ocean-to-ice flux typically began in early June. The two versions of theF0 estimates compare well, the significant differences result only from the u*0 differences described above. Because of the lack of eddy correlation estimates for the 2002 and 2003 drifts, quantitative descriptions of F0will be made using the version based on Rossby-similarityu*0.

image

Figure 5. Ocean-to-ice heat fluxF0 for the 2002–2010 drift stations. Ensemble average in the tenth panel. Heat fluxes calculated with Rossby similarity based friction velocities are plotted in blue, and heat fluxes calculated with eddy correlation based friction velocities are plotted in red.

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[32] From mid-July to near the end of August (yeardays 185 to 230),F0 reached a plateau value of about 15 W m−2. The summer average of the ensemble-averaged time series was 7.6 W m−2 (Table 2). There was significant inter-annual variability inF0 (Figure 5). As expected, inline image was correlated with inline image (Table 2). inline image was large in 2002, 2008 and 2010 (9.7, 10.5 and 10.5 W m−2, respectively), moderate in 2003, 2007 and 2009 (7.6, 8.0 and 7.6 W m−2, respectively), and small in 2004, 2005, and 2006 (4.6, 5.4, and 4.8 W m−2, respectively). The onset of F0 was earlier for 2002 and 2008 than for other years, which is at least partially attributable to the strong June surface forcing of those two years that was noted above. The largest heat flux events, with instantaneous F0 > 60 W m−2, occurred during the two, large, mid-summer storms of the 2008 drift and a storm near the end of August during the 2010 drift.

[33] In an idealized, one-dimensional scenario, the IOBL heat budget represents a balance between tendency of heat content, incoming solar radiation, ocean-to-ice heat flux, and heat flux entering the IOBL from below. TheδT, u*0, and F0 time series (Figures 2 and 4) reveal aspects of this simple budget: near-surface temperature tends to increase during lulls in surface forcing and tends to decrease when the forcing is strong. Examples of a warming upper ocean during calm conditions include the periods 2005 year day 209–229 and 2007 year day 207–215. During these periods, apparently, the incoming radiative flux exceededF0, and hence the IOBL warmed. There are numerous examples of the opposite case in each of the years. The two most dramatic examples occurred in 2008, when large storms beginning on year days 191 and 217 led to large decreases in δT. During these periods, apparently, F0 was greater than the incoming radiative flux, and the IOBL cooled, releasing the stored heat to ice melting.

[34] As a quantification of the relative roles of δT and u*0in producing inter-annual variability of inline image, we introduce two perturbation quantities using the temporal and ensemble averaging procedures introduced above. With u*0 as an example, we define

  • display math

where the prime and double prime notations denote inter- and intra-annual perturbations, respectively. The prime variables have a value for each year, while the double prime variables have values for each day of the summer. Introducing this notation intoequation (1), the summer averaged heat flux may be expressed as

  • display math

[35] Within this framework, then, inter-annual variability of the summer average heat flux, inline image, arises from inter-annual, first-order perturbations/variability inδT (second term on right hand side) and in u*0(third term), inter-annual, second-order combined perturbations ofδT and u*0(fourth term), and intra-annual correlation ofδT and u*0 perturbations (fifth term). The values of these terms for each of the drifts are listed in Table 2.

[36] For seven of the nine summers studied, differences in the summer average heat flux from the ensemble summer average could be attributed to inter-annual variability of inline image through the first order term inline image. For the high heat flux years of 2002 and 2008, inline image had a value of 2.2 W m−2 (Table 2). For the low heat flux years of 2004 through 2006, the same term reduced inline image through negative values of δT′.

[37] For the years of strongest (2008 and 2010) and weakest (2004) surface forcing, F0was also significantly affected by inter-annual variability of inline image through the term inline image. As a result of the above-average surface forcing, inline image had values of 0.9 and 1.0 W m−2 for the 2008 and 2010 drifts. And for the weak surface forcing of the 2004 drift, inline image had a value of −1.9 W m−2. 2004 and 2010 were the only summers for which the mechanical forcing contribution was larger than the ocean warming contribution. The second-order, inter-annual combination term,u*0δT′, was small compared to the first-order terms and was nearly as likely to be negative as it was positive.

[38] As expected, the intra-annual correlation ofδT and u*0was always negative, because the surface layer tends to cool during strong surface forcing events. Interestingly, there was not very much inter-annual variation in this term, indicating thatF0could have been reasonably well estimated using only season-averaged quantities and the ensemble-averaged value for the term inline image.

3.2. Penetrating Radiation

[39] The ECMWF re-analysis surface shortwave radiationFr (Figure 6) did not vary greatly from year to year. The ensemble summer average value of Fr was inline image = 149 W m−2 (Table 2). Values of inline image over the 2002–2010 summers ranged from 136 to 154 W m−2, with a normalized standard deviation of 0.04. In contrast, the normalized standard deviation of inline image was 0.30, immediately indicating that differences in Frarising from inter-annual variability in cloud cover and from the different geographic trajectories of the drift are not capable of accounting for the observed variability ofF0.

image

Figure 6. Summer heating season surface shortwave radiation Fr(gray) and open water fraction 1 -C(red lines are SSMI-based estimates and green lines are divergence-based estimates) for the 2002–2010 drift stations. Ensemble averages in the tenth panel.

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[40] The two instantaneous estimates of open water fraction (SSMI, and IABP) do not agree very well (Figure 6). The SSMI open water fraction estimates vary between 0 and 0.2 over the summer seasons. The divergent opening IABP estimates have magnitudes comparable to the SSMI estimates, but correlate poorly with the passive microwave estimates. An exception is the open water estimates obtained for the 2010 drift. During summer 2010, the SSMI and divergent-opening estimates are consistent, with both dominated by a low-concentration event during August and September associated with a persistent low pressure system over the gyre [Kawaguchi et al., 2012].

[41] Although the two instantaneous open water fraction estimates do not provide a consistent view of the conditions along the individual drift station trajectories, the ensemble-average SSMI and IABP estimates are reasonably consistent (Figure 6, tenth panel). In the ensemble mean, both time series start the summer season at near-zero open water fraction. The May averages of the SSMI and IABP open water fraction estimates are 0.02 and 0.01 respectively. Both of these open water fraction estimates increase through the summer analysis period, reaching September–averaged values of 0.16.

[42] As noted above, the divergent opening estimate assumes that the there is no open water at the beginning of the analysis period. For the most part, the passive microwave data confirm that this is a reasonable assumption. Except for 2004 and, to a lesser extent 2008, the passive microwave estimates indicate that there was little open water at the start of the summer analysis period. The reasonable agreement between the ensemble average SSMI and IABP open water fraction time series suggests that, at least typically, open water in the Transpolar Drift is formed primarily by divergence of ice motion rather than by thermodynamic processes such as lateral melting.

[43] Ensemble average time series of the shortwave radiation entering the upper ocean, surface shortwave radiation modulated by open water fraction (equation (2)), illustrate the typical seasonal evolution of Frw (Figure 7, tenth panel). Multiplication of Frby open water fraction produces near-zero values ofFrw at the beginning and end of the analysis period. Early in the period, although there was abundant surface flux, there was little open water through which it could enter the ocean. Late in the period, although there was ample open water, the surface flux had decreased with the angle of the sun. Maximum values of Frwtypically occur between early to mid-July (between approximately year days 190 to 200). The peak, ensemble averaged SSMIFrw estimates are nearly 25 W m−2, and the corresponding IABP estimates are about 20 W m−2. The July peak in Frwled the late-July/August plateau inF0 by a few weeks, consistent with the idea from the simple IOBL heat budget that heat from incoming radiation is temporarily stored in the upper ocean and then transported to the bottom of the ice cover.

image

Figure 7. Estimates of shortwave flux entering the ocean Frw = Fr (1−αw) (1−C) for the 2002–2010 drift stations (red lines are SSMI-based estimates and green lines are divergence-based estimates). Ensemble averages in the tenth panel.

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[44] Comparison between SSMI and IABP Frw is favorable only when ensemble averaged (Figure 7, tenth panel). Poor intra-annual agreement between SSMI and IABP open water fraction estimates is evident in the twoFrw time series estimates from each year's drift (Figure 7), while the nine year ensemble average shows close agreement through the summer season (Figure 7, tenth panel). Examples include the first half of the 2008 summer during which the SSMI open water fraction was much larger than the IABP fraction, and the beginning and end of the 2005 summer during which the IABP open water fraction was much larger than the SSMI fraction.

3.3. Comparison of F0 and Frw

[45] We begin by comparing the ensemble-averaged summer averages. The ensemble average over all of the drifts of the ocean-to-ice heat flux was inline image = 7.6 W m−2. This value compares reasonably well to SSMI- and IABP-based inline image, which equals 8.1 and 8.0 W m−2, respectively. These results indicate that, averaged over multiple summer seasons, about 95% of the solar radiation entering the upper ocean was used to melt the bottom of the ice, while the remaining 5% was used for lateral melting or was stored in the upper ocean, and possibly transported to the ice cover after the summer analysis period.

[46] Although the ensemble-averaged results present a consistent picture of ‘typical’ radiative heat input to the upper ocean, storage in the IOBL, and transport to the ice, neither of the two sets ofFrwestimates explains the inter-annual variability observed in theF0 records (Table 2 and Figure 8), specifically the passage through a clear minima (summer of 2004–2006) over the years of observation. Although the SSMI and IABP inline image estimates are generally greater than or equal to inline image, as would be expected if F0 were primarily supported by Fr, the summer-averaged values are not significantly correlated over the years 2002–2010. For the SSMI estimates, inline image exceeds inline image for all of the drifts except 2002, inline image was 3.2 W m−2, smaller than the inline image for the 2002 summer. For the IABP estimates, inline image is smaller than inline image for the first three years of the record and larger than inline image for the last four years of the record. The IABP Frwrecord is unusually large in 2005. There was only one IABP triangle available this year for the divergent opening estimate, and the apparent over-estimation of open water fraction is probably due to the area of the single triangle providing a poor representation of ice deformation in the immediate neighborhood of the NPEO station. In summary, the summer-averagedFrwestimates are not consistent with each other or with summer-averagedF0; only when ensemble-averaged over the nine summer realizations is a consistent picture formed.

image

Figure 8. Summer-averaged fluxes for the 2002–2010 drift stations. The three solid blue lines represent inline image calculated with the Rossby similarity estimates of u*0, using z0 equal to 20, 10 and 5 cm (top to bottom). The dashed blue line represents inline imagecalculated with the eddy-correlation measurement of u*0. The red and green lines represent, respectively, the SSMI- and divergence-based estimates of inline image.

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4. Discussion

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[47] For the Transpolar Drift over the summers of 2002–2010, the results (Table 2 and Figure 8) indicate that (1) the ocean-to-ice heat fluxes in the Transpolar Drift are supported by solar insolation entering the ocean through open water areas, and (2) that the amount of open water is controlled by divergence.

[48] We believe that the observational challenge of accurately resolving the relatively small open water fractions encountered in the Transpolar Drift is the primary reason that the summer-averagedFrwestimates do not explain the inter-annual variability contained in the summer-averagedF0 estimates. Observational uncertainties in the F0estimates or a breakdown in the summer-averaged balance betweenF0 and Frware secondary factors. The apparent uncertainties and biases in the open water fraction preclude firm conclusions regarding the sources of heat supporting the observed inter-annual variability ofF0.

[49] The primary uncertainties in the F0 estimates are what is the value of the roughness length z0that is applicable to the floes in the neighborhood of each drift station and how spatially representative the point-measurement-derivedF0 values are compared to the open water estimates, which have a spatial scale on the order of about 100 km. As described in Section 2, we have performed the u*0 calculations with a range of z0 values that is expected to span the possible bottom morphology conditions that would exist from year to year in the Transpolar drift as a result, primarily, of differences in mean ice age. This exercise indicates the range of possible z0 produces only a 10% change in F0 (see Figure 8). This is much less than the observed inter-annual variability of summer-averagedF0, indicating that uncertainty in bottom roughness in the averaging area does not have a qualitative impact on the results.

[50] We have investigated the spatial representativeness of the point measurements of δT by considering F0 estimates derived from observations from the uppermost temperature and conductivity sensors of the JCAD buoys introduced in Section 2. The same method used with the AOFB GPS records to estimate u*0was also applied the JCAD position data. Normally, the JCAD buoys were deployed on the same ice floe as the other NPEO systems. In 2003 and 2004, however, JCADs were deployed upstream of the NPEO station, on the western side of the Lomonosov Ridge. For the 2003 summer, the JCAD was displaced from the NPEO station by about 190 km and the summer-averaged value ofF0 was 5.5 W m−2. For the 2004 summer, the JCAD buoy was separated from the NPEO station by about 300 km and the average value of F0 was 4.0 W m−2. Thus, the upstream measurements were 2.1 and 0.6 W m−2 less than the corresponding estimates from the AOFBs at the NPEO stations (compare to values in Table 2), from which we tentatively conclude that there could be as much as about 2 W m−2 of variability in point estimates of F0over spatial scales of several hundred kilometers. Still, this is less than the amount of observed inter-annual variability inF0. We believe then, that the F0 estimates calculated hare, and similarly in earlier studies, are representative of spatial areas on the order of hundreds of kilometers. Presumably, localized inhomogeneities in heating arising from complex patterns of ice deformation are smoothed out by effective lateral mixing.

[51] The estimation of the shortwave flux entering the upper ocean appears to be more problematic. The favorable agreement between the in situ and re-analysis product surface shortwave flux (Section 3.2) suggests that the Fr estimates employed are reasonably accurate. For open water fraction, obtaining accurate satellite radiometer concentration estimates and making spatially representative divergent opening calculations appear to be the primary obstacles.

[52] The relatively high ice concentration conditions encountered in the Transpolar Drift requires that the satellite radiometer data sets have small ice concentration biases. For example, typical summer open water fractions in the region appear to be about 0.05 to 0.10 (see tenth panel in Figure 6, which includes radiometer and divergence-based estimates). This required level of accuracy is a challenge for the satellite passive microwave products, which have biases in open water fraction on the order of 0.01 to 0.1 (Section 2.2).

[53] Spatial representativeness appears to be the limiting problem of the IABP divergent opening calculations. We had to opportunistically choose buoy arrays for area calculations, and often the geometry of the triangles was not ideal. For example, for three of the years there was only a single triangle available, with the NPEO station as one of the vertices, for calculation. The divergent opening calculations will benefit greatly from the deployments of purposed, small-scale GPS-buoy arrays, which are currently being undertaken at NPEO.

[54] During summer, conductive flux through the ice cover is typically small, making it possible to directly convert the summer-average heat flux into a bottom ablation,hmelt, according to

  • display math

where Lice = Lfresh (1–0.03 Sice) and Lfresh = 333.5 KJ kg−1 are the latent heats for salty and fresh ice, respectively, Sice= 6 psu is the salinity of sea ice, and Δt is the duration of the summer analysis period, 153 days. Although a factor of two range in summer-averagedF0 from 4.6 to 10.5 W m−2 may not seem particularly dramatic, it corresponds to a range of summertime bottom ablation of 0.22 to 0.50 m. Given that the modal ice thickness in the Transpolar Drift was 0.9 m in 2007 [Haas et al., 2008], summertime bottom melting during the high heat flux years must have significantly affected the extent and thickness of sea ice in the region.

[55] Although we cannot make firm conclusions from the these observations on the sources of heat contributing to the inter-annual variability in ocean-to-ice heat flux, it of interest to note some possibilities that can be examined in greater detail in future studies. The observed inter-annual heat flux variability may simply be a result of inter-annual variability in divergent opening that is not reliably estimated with the opportunistic buoys that we available from 2002 to 2010. It is possible that significant divergent opening early in the summer season could set up a positive feedback that would have continued through the summer. The start of significant ocean-to-ice heat fluxes was relatively early in the high flux years of 2002 and 2008. A factor that we have not considered is penetration of solar radiation through thin ice. The observed inter-annual cycle of ocean-to-ice heat flux may also be influenced by ice age and thickness or the onset of snow melting, both of which would affect the amount of radiation entering the upper ocean.

[56] Even given perfect open water estimates, other mechanisms including radiative penetration through ice and melt ponds and lateral melting are contributing to heat storage and transport in the upper ocean.

5. Summary and Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[57] Ocean/ice interface heat fluxes (F0) are calculated from upper ocean measurements obtained from autonomous, ice-based observing systems repeatedly deployed in the Transpolar Drift of the Central Arctic for 2002 through 2010. Average values ofF0 over the nine summer heating season realizations varied between 4.6 and 10.5 W m−2. The ensemble-averaged, over the nine years, summer-averaged value was 7.6 W m−2. Between 2002 and 2010, the summer-averaged value ofF0passed through a clear minimum. With the exception of 2004 and 2010 summers, which had unusually weak and strong surface forcing, respectively, inter-annual variability in summer averagedF0 was dominated by differences in upper ocean heat content, rather than by differences in surface forcing.

[58] We tested whether F0 in the Transpolar Drift is supported primarily by local, solar radiative energy flux entering the upper ocean through areas of open water (Frw). If F0is supported primarily by local insolation, then summer-averagedFrwshould be equal to or slightly greater than summer-averagedF0, i.e., enough heat should enter the upper ocean to supply the heat used to melt the bottom of the ice cover. Radiation entering the upper ocean is estimated by combining in situ radiometer measurements and two types of open water estimates: satellite-borne passive microwave ice concentration products and observed divergence of arrays of drifting buoys. Inter-annual variability of summer-averaged surface insolation is relatively small, the normalized standard deviation is only 0.04, so differences in open water fraction are the most likely sources of the observedF0variability, which has an annual normalized standard deviation of 0.30. Incoming radiation estimates based on Special Sensor Microwave Imager (SSM/I) instrument ice concentration and buoy divergence indicate that local insolation is sufficient to support the observed ocean-to-ice heat flux. Ensemble-averaged over the 2002–2008 summers the SSM/I and buoy-divergenceFrw, are equal to 8.1, and 8.0 W m−2, respectively. Typically then, about 95% of the solar radiation entering the upper ocean is transported to the base of the ice during summer, confirming the hypothesis that F0is primarily supported by local insolation. The reasonable agreement between the 9 year ensemble IABP and SSMI open water fraction time series further indicates that mechanical processes in the ice cover rather than melting are controlling the amount of radiation entering the upper ocean. This implies that the ocean-ice albedo feedback was not particularly active in the Transpolar Drift in the last decade. Uncertainty in the estimation of open water fraction hinders inter-annual comparisons and precludes firm conclusions about sources of the inter-annual variability observed inF0. There are two primary reasons that the agreement is not holding at seasonal to daily timescales. We believe that that much of the high frequency discrepancy between the SMMI and IABP based open water fraction estimates arise from the low number of buoy positions available and potential weak triangle geometry used in the IABP divergence method. Second, there are likely other mechanisms including radiative penetration through ice and melt ponds and lateral melting that are contributing to heat storage and transport in the upper ocean.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[59] Jim Stockel, Rob Wyland, Ron Cowen, Keith Wyckoff, and Jim Lambert all made significant contributions to the development and construction of the AOFBs. Rick Krishfield, Jamie Morison, Andy Heiberg, and Dean Stewart contributed logistical support and assisted with the field deployment of the buoys at the NPEO camps. Jim Overland provided the shortwave radiation data sets and Don Perovich provided the ECMWF radiation data along the drift tracks. We appreciate the constructive reviewer comments that simplified and strengthened the paper. This work was supported by NSF grants ARC-0856868, ARC-0632041, OPP-0084858, ARC-0520328 and ARC-1023662.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Measurements and Analysis
  5. 3. Results
  6. 4. Discussion
  7. 5. Summary and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
FilenameFormatSizeDescription
jgrc12462-sup-0001-t01.txtplain text document1KTab-delimited Table 1.
jgrc12462-sup-0002-t02.txtplain text document1KTab-delimited Table 2.

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