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Keywords:

  • 2007;
  • Arctic;
  • radiation;
  • sea ice;
  • thermodynamics

Abstract

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[1] Monthly clear-sky anomalies of atmospheric temperature and water vapor over the East Siberian and Laptev Sea regions of the Arctic for 2003–2010 are examined here. This region experiences significant interannual variations in sea ice concentration and is also where ice loss was most apparent in the record year 2007. Clear-sky thermodynamic profiles come from the Atmospheric Infrared Sounder (AIRS) sensor onboard the Aqua satellite. Associated longwave (LW) and shortwave (SW) radiation-flux anomalies are estimated through radiative transfer modeling. Anomalies of temperature (±10 K) and water vapor (±1 g kg−1) often positively covary, resulting in distinct signatures in the clear-sky downwelling LW (LWD) anomalies, occasionally larger than ±10 W m−2around the 2003–2010 climatology. Estimates of mean greenhouse anomalies indicate a shift from negative to positive anomalies midway through the 8-year record. Sensitivity tests suggest that temperature anomalies are the strongest contributor to both LWD and greenhouse anomalies, relative to water-vapor anomalies; monthly averaging of column precipitable water yields relatively small anomalies (order 1 mm) that produce a linear response in greenhouse anomalies. Finally the clear-sky contribution to 2007 monthly ice thickness is estimated. Anomalous clear-sky radiation retards the total 2007 ice thickness by 0.3 m (15–30% of ice-thickness climatology), and anomalous LW radiation is most important for preconditioning the ice during the months prior to, and after, the summer melt season. A highly sensitive interaction between cloud fraction, surface albedo and LWD anomalies is found, and we develop a metric for determining clear-sky anomalous ice melt potential.

1. Introduction

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[2] Arctic perennial sea ice extent, thickness and volume have declined since satellite observations of sea ice began, even more rapidly in the recent decade [e.g., Comiso et al., 2008; J. Zhang et al., 2008]. During 2007, ice extent reached a record low and has continued to be low during subsequent summers although not surpassing the 2007 minimum. Recent studies conclude that anomalous atmospheric pressure patterns may have led to significant surface radiative and dynamical forcings over the eastern Siberian and Pacific Arctic and were at least partly responsible for the 2007 record low ice concentration [Maslanik et al., 2007; Overland et al., 2008; J. Zhang et al., 2008; X. Zhang et al., 2008; Kay and Gettelman, 2009; Kay et al., 2008; Graversen et al., 2011]. Atmospheric transport of heat and moisture to the Arctic via synoptic-scale weather disturbances is a common and important process, sustaining the long-term global energy balance [e.g.,Peixoto and Oort, 1992]. Anomalous atmospheric heat and moisture convergence over the Arctic Ocean fluctuates in magnitude and sign in a manner similar to regional modes of large-scale oscillations. Onset and duration of ice melt have been significantly correlated to the Arctic Oscillation index [Belchansky et al., 2004], suggesting atmospheric anomalies over the Arctic impact the lower atmosphere- and surface dynamics, as well as diabatic processes such as cloud formation. The Arctic atmosphere is relatively dry and cold; advection of heat and moisture control its thermodynamic structure and radiative opacity [Curry, 1983]. A case study of water-vapor advection over Eureka, Canada, indicates such events can potentially enhance the surface longwave (LW) forcing upward of 20 W m−2 [Doyle et al., 2011]. In addition, Devasthale et al. [2011a]have shown that transport of water vapor supports water-vapor inversions common during all seasons and over the entire Arctic Ocean, indicating the efficiency of horizontal advection into high latitudes.

[3] Until recently, atmospheric thermodynamic profiling over the Arctic Ocean could only be examined at specific observational stations or through modeling and reanalysis data sets. With the launch of the Atmospheric Infrared Sounder (AIRS) instrument onboard the Aqua satellite in 2002 [Chahine et al., 2006], profiles of temperature and water vapor, derived using hyperspectral thermal measurements (between 3.7 and 15.4 μm), are now available at all significant pressure levels for cloud-free layers. This study exploits the AIRS profiles from 2003 through 2010, estimating the monthly vertical anomalies of temperature and water vapor for a region of the Arctic Ocean that is particularly sensitive to ice melt during the past decade. The region of interest is shown as the area enclosed by black lines inFigure 1, overlaying the September sea ice concentrations for 2003–2010. This region includes portions of the East Siberian and Laptev Seas, and a portion of the central Arctic (74°–82°N, 135°E–165°W) and is the same region examined in Graversen et al. [2011]. Graversen et al. examined the contribution of heat and moisture convergence from reanalysis data over the area during 2007. They find enhanced heat and moisture advection were sufficient to melt an anomalous 1 m of ice. The primary contributors were identified as increased cloud fraction and cloud LW surface forcing in response to increased moisture convergence. These results agree with the model sensitivity study of Schweiger et al. [2008]. Conversely, a model study by J. Zhang et al. [2008] find an increased net surface heat flux strongly dominated by the surface net shortwave (SWN) radiation, and they hypothesize that increased ice export from this region may have decreased the surface albedo, enhancing the SWN flux.

image

Figure 1. September mean sea-ice concentration (fraction, contours) for 2003 to 2010 from AMSR-E [Spreen et al., 2008]. The box region over the East Siberian and Laptev Seas (74–82°N, 135°E–165°W) indicates the geographic region of interest where temporal and spatial averages of variables are computed. September sea-ice area (a × 105 km2) within the boxed region for ice concentrations greater than 15% are provided in the bottom right of each panel.

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[4] In this study, we take a step back to examine the thermodynamic anomalies during clear skies for 2003–2010. Clear-sky LW radiative flux anomalies, based on satellite observations of thermodynamic profiles, are estimated using radiative transfer. This allows both a quantification of the clear-sky radiative forcing over the Arctic, and also provides an observational compliment to other studies that rely on model or reanalysis data. The latter is important because cloud fields are prognostic and observations of clouds are not directly assimilated into reanalyses. Thus, to a first degree, modeled clear- and cloudy fractions are not constrained by reality.

[5] After examining the impacts of clear-sky thermodynamics on the downwelling LW (LWD) and the atmospheric greenhouse effect, we focus on the evolution of the record low ice concentrations within our analysis region (Figure 1) during 2007. We estimate the climatological and 2007 anomalous ice-thickness changes associated with radiation from a clear-sky only perspective. This allows the development of a sensitivity metric by which the competing factors affecting summertime clear-sky ice melt can be quantified.

2. Data and Methods

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

2.1. Observations

[6] AMSR-E September sea ice concentrations [Spreen et al., 2008] in Figure 1indicate a general Arctic-wide decline with substantial interannual variability. The September ice areas within the box region for ice concentrations greater than 15% are included in the bottom right of each panel. Clearly, 2007 was an outlier, with ice area over 2 orders of magnitude lower than the other years.

[7] Clear-sky atmospheric thermodynamic profiles come from AIRS retrievals of temperature and water vapor. We use the AIRS Daily Level 3 (1° × 1°) Version 5 Standard Product to compute monthly mean clear-sky temperature and water-vapor profiles for the box region; ascending and descending satellite overpasses have been merged. This standard product provides thermodynamic retrievals at 12 pressure levels from 1000 hPa up to 100 hPa, including one additional level for the skin temperature. Only clear-sky profiles from the daily product are selected to compute temporal and spatial means within the box region ofFigure 1. AIRS uses a cloud-clearing methodology to obtain all-sky radiances that is dependent on an accurate estimate of the clear-sky radiance signature [Susskind et al., 2003; Nasiri et al., 2011]. Flags indicating clear-sky confidence are thus included in the Level 3 product, and we use strictly cloud-free AIRS observations for our spatially and temporally averaged profiles. In the last 10 years, AIRS retrievals have been validated extensively, including over the high latitudes [e.g.,Divakarla et al., 2006; Fetzer, 2006; Gettelman et al., 2006; Kahn et al., 2008] and are matured enough to investigate climatological features [Devasthale et al., 2010, 2011a]. Thermodynamic retrieval uncertainties are reported as 1 K km−1 and 15% per 2 km for temperature and water vapor, respectively. Monthly averaged cloud fraction is obtained from the MODIS instrument also flying onboard the Aqua satellite [Ackerman et al., 1998]. Cloud fraction from the Level 3 product is used in this study. Despite the complications inherent with passive sensor detection of clouds in the Arctic, MODIS' use of multiple spectral threshold tests for cloud masking [Ackerman et al., 1998] limits the uncertainty in cloud fraction to around 10–20% compared to active remote sensors [Ackerman et al., 2008; Liu et al., 2004, Liu et al., 2010]; the majority of this uncertainty occurs during the polar night [Liu et al., 2010; Chernokulsky and Mokhov, 2012] when cloud masking is limited to infrared-only threshold tests. Monthly averaged surface albedo is needed for estimates of SWN. We take the monthly averaged forecast surface albedo from ERA-Interim [Simmons et al., 2007] reanalysis and average spatially for the box region in Figure 1; this albedo is calculated using a climatological value, with modifications based on forecast realizations of ice concentration, skin temperature and precipitation [e.g., European Centre for Medium-Range Weather Forecasts, 2012].

2.2. Radiative Transfer Modeling

[8] Monthly upwelling and downwelling SW and LW radiation are computed using the Rapid Radiative Transfer Model (RRTM) [see Mlawer et al., 1997]. Averaged profiles of temperature and water vapor from AIRS are input for RRTM. For greenhouse gases, we use a constant CO2concentration of 380 ppm and ignore additional gases such as ozone and methane. Monthly mean solar zenith angle (SZA) is estimated from hourly SZAs for the mean box latitude band (78°N) for RRTM SW calculations. The impact of aerosols in the RRTM calculations is ignored. Monthly clear-sky surface radiation anomalies are computed by subtracting off the monthly average from 2003 to 2010 (hereafter referred to as climatology). We stress that the 8-year record is not long enough to be considered a climatological data set and refer to it as such only to distinguish from the monthly averaged time series.

3. Results

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

3.1. Clear-Sky Thermodynamic Anomalies

[9] Annual arctic cloud fractions are high [e.g., Shupe et al., 2011; Devasthale et al., 2011b], however clear-sky conditions do occur (Figure 2a). Mean monthly clear-sky fraction for the box region peaks near 65% during the end of winter and approaches a summer minimum near 10% in August. Summer melting months often experience clear-sky conditions on average for 10 to 30% of the season (Figure 2a).

image

Figure 2. (a) Monthly mean climatology of clear-sky fraction (line and circles) and standard deviation (bars) from MODIS from 2003 to 2010 for the box region inFigure 1. Monthly time-pressure anomalies (contours) of (b) temperature (K) and (c) water-vapor mixing ratio (g kg−1) from AIRS clear-sky profiles. Monthly anomalies are computed relative to the 2003–2010 monthly climatological values.

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[10] Vertical temperature (Figure 2b) and water-vapor (Figure 2c) anomalies relative to the 2003–2010 clear-sky climatology reveal distinct annual and interannual variability in regional heat and moisture transport. Anomalies are generally larger than the absolute AIRS retrieval uncertainties indicating robustness in thermodynamic anomalies. Temperature anomalies span a larger vertical extent and are observed even within the stratosphere; water-vapor anomalies are generally found for pressures larger than 500 hPa. Often heat and moisture show a positive covariability, although this is not always the case; for example the anomalies around midyear for 2003 and 2004. Thermodynamics anomalies impacting 2007 are striking, with temperatures and mixing ratios throughout much of the troposphere approaching 5 K and 1 g kg−1 above climatology, respectively.

3.2. Radiative Anomalies

[11] Clear-sky near-surface (1000 hPa) LWD radiation anomalies are shown in black inFigure 3. LWD anomalies generally range within ±7 W m−2(approximately 1-σ about the mean anomaly value). Larger anomalies are present and correspond to extreme anomalous thermodynamic events (Figures 2b and 2c). Negative LWD anomalies tend to occur during the end of the years, followed by positive anomalies in the beginning of succeeding year. This interannual feature does not hold for all years, for example in 2007, and seems to decrease in occurrence, or become reversed from 2008 to 2010. A linear regression of LWD anomalies indicates an increasing trend (0.42 W m−2 yr−1) in LWD surface flux with a total increase of 3.4 W m−2over the 8 years examined. However, the trend is not statistically significant because the linear model is unable to represent the month-to-month and interannual LWD variations. Applying a 3-month running average to the LWD anomalies reduces a portion of this variability (not shown). A linear regression to this running-averaged time series reveals a statistically significant increasing trend (0.45 W m−2 yr−1) in clear-sky LWD flux at the 99% confidence interval corresponding to an increased flux of 3.6 W m−2 from 2003 to 2010. Quantitatively, the September sea ice areas (a × 105 km2, Figure 1) generally follow the cumulative monthly near-surface LWD anomalies from winter through autumn. Years with negative or variable LWD anomalies from January through September correspond with larger sea ice areas (2003, 2004, 2006, 2009), and vice versa when anomalies were mainly large and positive (2007, 2010).

image

Figure 3. Monthly clear-sky downwelling longwave (LWD) flux anomalies (W m−2, solid lines) relative to the 2003–2010 climatological values. The black line represents the total clear-sky LWD anomalies, the blue line represents the LWD anomalies when holding the temperature profile to the monthly climatological value while allowing the water-vapor profile to vary as observed, and the red line holding the water-vapor profile to the monthly climatological value while allowing the temperature profile to vary as observed; the blue and red sensitivity time series sum to the black line. Monthly time series of total-column precipitable water (PW) is shown in gray.

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[12] The contributions of thermodynamic anomalies to the total LWD anomalies are included in Figure 3. These sensitivities are estimated by holding either temperature (blue line) or water vapor (red line) to the monthly climatological value, while allowing the other to vary as observed. These results indicate temperature anomalies have the most contribution to the total LWD anomaly for nearly all months, while water vapor yields an anomalous contribution ranging between ±3 W m−2. These results differ from those of Doyle et al. [2011], who show water-vapor contributions to LWD are initially greater than, or the same magnitude, as temperature contributions for their advective-intrusion case. Monthly averaged precipitable water (PW) time series (Figure 3, gray) reveals interannual differences, however monthly averaging effectively reduces larger PW intrusions associated with individual advective events. Therefore, changing atmospheric opacity due to water-vapor variations are of 2nd order importance for monthly averaged calculations relative to total-column temperature variations, and are likely more restricted to the layer between the near-surface and 800 hPa [Ohmura, 2001] where temperature anomalies are already large, ±5 K (Figure 2b).

[13] Total changes in clear-sky LWD fluxes are also important as they impact the relative modification of radiation forcing during all-sky conditions. As clear-sky atmospheric greenhouse forcing changes, so does the effective magnitude of surface forcing from clouds since cloud radiative forcing is estimated as the difference between all-sky and clear-sky fluxes [Ramanathan et al., 1989]. Therefore an enhanced clear-sky flux, through either increased water-vapor loading or increased emitting temperature, inherently results in a decreased effective cloud forcing for a cloud with the same microphysical characteristics and base temperature. For example, the maximum clear-sky LWD anomaly of 12 W m−2occurs during April, August and September 2007. Accordingly, cloud forcing estimates, relative to those without enhanced clear-sky greenhouse forcing, for these months would be reduced by the same magnitude. Following the cloud-emissivity parameterization ofStephens [1978], change in cloud emissivity (ε) as liquid water path (LWP) changes is:

  • display math

where a is LWD mass absorption coefficient (0.158 m2g−1, Stephens [1978]). A typical low-level Arctic blackbody cloud with LWP = 50 g m−2 [e.g., Shupe and Intrieri, 2004; Sedlar et al., 2011] in the presence of an enhanced clear-sky atmospheric greenhouse forcing of 12 W m−2 effectively reduces the cloud radiative forcing to that of a gray body cloud with LWP equal to ≈19 g m−2 following equation (1). These effective changes to cloud LW forcing have the potential to alter both the magnitude and sign of all-sky surface forcing.

3.4. Greenhouse Anomalies

[14] An estimate of the clear-sky greenhouse is made by subtracting the upwelling LW (LWU) at top of the atmosphere (100 hPa) from near-surface (1000 hPa) LWU followingRaval and Ramanathan [1989] and Webb et al. [1993]. Monthly greenhouse anomalies relative to climatology are shown as contours in Figure 4a. Although these estimates do not consider divergence of greenhouse energy from the box region, nor is the record length long enough for a climate metric, the results do agree with the variable thermodynamics and LWD anomalies described above. Anomalies generally range between ±5 W m−2, with no clear annual or interannual trend. Prior to 2007, greenhouse anomalies were often small and negative, with a 2003–2006 mean anomaly of −1.20 W m−2. Anomalies from 2007 and onward are suggestive of increased energy residuals within the atmospheric column; the mean for 2007–2010 increased to +1.18 W m−2. A double-sidedt-test with the null hypothesis that these means are equal was disproved at the 99% confidence interval. Therefore, in large part to the extreme greenhouse anomalies during 2007, a shift in atmospheric greenhouse energy occurred during the latter half of the data record.

image

Figure 4. (a) Time series of monthly clear-sky greenhouse anomalies (W m−2, contours) estimated as described in section 3.4. The sensitivity of greenhouse anomalies to (b) water-vapor variations (water-vapor greenhouse anomalies), and (c) temperature variations (lapse-rate greenhouse anomalies) are estimated the same as described forFigure 3.

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[15] The sensitivity of total greenhouse anomalies associated with water vapor (Figure 4b) and temperature (Figure 4c) anomalies are estimated as described in section 3.2. The contribution from water-vapor variations ranged between ±2 W m−2, while temperature variation contributions were larger, by as much as ±10 W m−2. These results indicate the importance of temperature, or lapse-rate, structure as LW emission is sensitive to temperature to the 4th power following the Stefan-Boltzmann relationship. These results, however, donotindicate that water-vapor variations are negligible to the total greenhouse effect.Figure 5ashows climatological (red circles) and monthly (black dots) clear-sky greenhouse magnitudes as a function of total-column PW. The greenhouse effect exhibits a distinct exponential increase with increasing PW in agreement withRaval and Ramanathan [1989] and with central Arctic estimates made by Curry et al. [1995]. However, monthly averaged water-vapor profiles limit PW estimates to within ±1 mm of the climatological means (Figure 5b); potentially larger increases/decreases in water vapor through individual advection events on timescales shorter than 1 month are essentially damped by the temporal averaging. These relatively small changes in PW result in a linear greenhouse anomaly response (Figure 5b) dominated by temperature changes (Figure 3b). Little change is necessary in atmospheric opacity (water vapor) and yet substantial greenhouse anomalies will emerge following equation (2) of Raval and Ramanathan [1989]as a result of large temperature anomalies. Due to thermodynamic constraints controlled by the Clausius-Clapyeron relationship over the Arctic, water-vapor increases (decreases) must be accompanied by increases (decreases) in temperature since the relative humidity of the Arctic atmosphere is at or near ice saturation for much of the year, except for the summer months when relative humidity is saturated with respect to liquid [Curry et al., 1995]. Therefore in terms of monthly greenhouse anomalies, the lapse-rate contribution is dominant via the constraints on water-vapor anomalies and generally small PW deviations from climatology.

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Figure 5. (a) Clear-sky greenhouse effect (W m−2) as a function of column-integrated preciptable water (PW, mm) for all monthly profiles (black dots) and climatological monthly means (red circles). (b) Clear-sky greenhouse anomalies (W m−2) as a function of PW anomalies (mm).

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4. Evolution of the 2007 Ice Minimum

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

4.1. Impact of Clear-Sky Radiative Fluxes

[16] The impact of anomalous thermodynamics, clear-sky fraction and surface albedo on the radiative fluxes at the surface during the anomalous 2007 sea ice minimum year within the box region are examined in this section.Figure 6ashows the monthly (solid) and climatology (dashed) evolution of MODIS cloud fraction (black) and ERA-Interim surface albedo (blue). In general, cloud fraction was anomalously high, by as much as 20% during late autumn, in agreement withSchweiger et al. [2008] and Graversen et al. [2011]. Surface albedo decreased from climatology during July and was anomalously low for the remainder of the year. Net surface clear-sky LW (blue), SW (red) and total flux (black) are shown inFigure 6b. Clear-sky SWN is calculated using the monthly averaged SZA and albedo for the region, neglecting contributions from aerosol forcing. Without clouds to reflect SW to space, climatological SWN (red dashed) increases rapidly after the end of the polar night and peaks in July near 230 W m−2, before declining to zero as both SZA and surface albedo increase. The reduction in surface albedo during 2007 resulted in an anomalous increase of SWN ranging between +20–40 W m−2. Net LW (LWN) during clear-skies (blue) is always negative due to the lack of cloud greenhouse trapping surface emission to space [e.g.,Stramler et al., 2011]. The deficit in LWN during 2007 was slightly greater than climatology during all months except during midsummer. However, the total clear-sky net radiation (black) during the melt period is strongly controlled by the annual cycle in SW. Both climatology and 2007 averages indicate that a clear-sky surplus of energy is available at the surface from May through August, peaking between 150 and 180 W m−2 in July.

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Figure 6. The monthly (solid lines) evolution of 2007 and the monthly climatology of 2003–2010 (dashed lines) of (a) MODIS cloud fraction (black) and ERA-Interim surface albedo fraction (blue); (b) clear-sky net shortwave (red), net longwave (blue) and total net (black) radiation at the surface (W m−2); (c) net radiative anomalies (black) relative to climatology, including the net radiative anomalies estimated using the climatological upwelling surface longwave radiation (green); all in W m−2; (d) surface temperature (K); (e) monthly ice melt (positive) or freeze (negative) thickness (black) (m) due to the net clear-sky surface radiative flux estimated usingequations (2)(3); the green line is the monthly clear-sky melt/freeze thickness estimated using the climatological upwelling surface longwave radiation.

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[17] Total clear-sky net radiative anomalies for 2007 are shown in black inFigure 6c, where an additional 20 and 40 W m−2is available to melt ice during July and August, respectively, in strong agreement with all-sky model estimates reported inJ. Zhang et al. [2008]. During summer, more energy is absorbed and melting is enhanced via the ice-albedo feedback [e.g.,Sellers, 1969], causing the surface temperature (Figure 6d, black) to increase above the melting temperature of saline water. Surface temperatures remain above climatology into autumn and winter via the anomalous heat absorption in the open ocean during the melt season. Perovich et al. [2008] have shown the ocean absorption can enhance ice melt from the bottom up on the order of 500% over the Beaufort Sea region. Even with an albedo 20% lower than climatology in September, the increased SZA and a LWN deficit of 15 W m−2 more than climatology (Figure 6b) prevented the total net flux anomaly from being positive, marking the end of the radiative melt season.

[18] To emphasize the surface temperature impact on clear-sky LW anomalies, included inFigure 6cis the monthly net clear-sky radiative anomalies (green line) calculated using climatological LWU; these calculations are not biased by large surface temperature changes observed during 2007 (Figure 6d). The results indicate: (1) an increase in net radiative anomalies of 10 to 20 W m−2relative to the observed evolution (black line); (2) a clear signature of the thermodynamic anomalies on the LWD through April; and (3) positive radiative anomalies through years end from a combination of positive LWD anomalies and a reduction in LWU based on cooler surface temperatures. Anomaly calculations including LWU climatology are shown to highlight the importance of thermodynamic anomalies on total net radiation and to remove the positive surface temperature feedback on LWN. The actual radiative anomalies available for surface-melt or temperature modification will lie between the two curves inFigure 6c.

[19] Clear-sky ice melt associated with the monthly radiative fluxes are estimated following:

  • display math

where M is total melt (m), Fcsis net clear-sky radiative flux (W m−2), Ls is latent heat of fusion (3.34 × 105 J kg−1), ρi is density of pure ice (917 kg m−3), and fis monthly clear-sky fraction (converted to total seconds of month with clear-sky conditions). Net clear-sky fluxes (W m−2) are calculated from

  • display math

indicating the dependence on surface albedo (αs), SZA (included in SWD) and thermodynamic anomalies on the LWN; the impact of water-vapor anomalies on monthly averaged SWD was found to be small (order of 2 W m−2) relative to the downwelling radiance and is thus ignored, although recently Di Biagio et al. [2012]have shown that SW water-vapor forcing can be exceptionally large (−20 to −80 W m−2). Figure 6eshows monthly clear-sky ice melt (positive thickness, (m)) and freeze (negative thickness) for 2007 (black line) and climatology (black dashed). According to climatology, total ice thickness continually increases during the polar night and stops increasing with decreasing SZA between March and April. The onset of melt begins in May and continues through August. The annual cycle of ice thickness for 2007 follows that of climatology, however absolute values differ. Enhanced clear-sky LWD during the 1st four months inhibits the total ice growth, even with larger MODIS cloud fractions (reduced clear-sky fraction) during these months. Surface temperature and LWD anomalies for 2007 are shown together inFigure 7. They exhibit a distinct positive correlation with a high correlation coefficient (R = 0.92). The change in LWD flux required for a certain temperature change can be estimated by ∂LW/∂T = 4σT3. Between January and May, the anomalous clear-sky LWD is large enough to account for more than 80% of the observed surface temperature changes, highlighting the rapid adjustment of the surface to radiative forcing.Table 1shows cumulative ice-thickness anomalies split into seasons and for the full year. For January to April, large LWD anomalies resulted in a reduction of ice growth of just over 6 cm.

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Figure 7. Monthly clear-sky surface temperature anomalies (K, black line) and clear-sky downwelling longwave radiation anomalies (W m−2, blue line) for 2007. The anomalies are positively correlated with an R-value of 0.92.

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Table 1. Cumulative Seasonal and Annual Anomalies in 2007 Ice Thickness Relative to Climatological Ice Thicknessa
 Ice Thickness Δz (m)
Jan–AprMay–AugSep–DecJan–Dec
  • a

    Positive anomalies indicate a reduction in ice thickness relative to climatology. The 2007 LWU CLIM estimates the ice-thickness anomalies using climatological upwelling surface longwave radiation.

20070.0670.0070.2120.286
2007 LWU CLIM0.1050.0440.3210.470

[20] Total clear-sky ice melt for May to August for 2007 was 0.713 m, compared to a climatological melt amount of 0.706 m (+0.7 cm anomalous melt,Table 1). Using submarine and satellite measurements of ice thickness from 1955 to 2008, Kwok and Rothrock [2009]report the mean thickness for this region to be between 1 and 2 m in early spring, and 0.5 and 1 m by the end of the melt season. Thus, climatological clear-sky ice melt of 0.7 m represents a substantial fraction of the total ice thickness of the region. Of the melting months, June is the only month during 2007 where melt was larger than climatology despite the relatively large radiative flux anomalies during July and August (Figure 6c). During these months, surface albedo was significantly lower due to increased open-water fraction, but the MODIS effective clear-sky fraction for these months was only 15 and 10%, respectively. Had all the clear-sky anomalous energy for July and August (Figure 6c) been consumed in melting ice, over 1 m of anomalous melt would have occurred. This anomaly agrees with the estimate made by Graversen et al. [2011], where there it was attributed mainly to increased LWD from enhanced cloudiness [see also Schweiger et al., 2008].

[21] Despite the lack of anomalous contribution to melt during summer, positive thermodynamic anomalies during autumn and early winter inhibited the growth of first-year ice (Figure 6e), cumulatively by as much as 21 cm (Table 1). This clear-sky contribution to ice-growth retardation occurs even as the clear-sky fractions are anomalously low during this time of year (Figure 6a). Such a reduction on ice growth is considered important for the total melt amount of the following year, however such an examination is beyond the scope of this paper. Nevertheless, quantitatively we do note that the ice area for 2008 for the box region was the second lowest of the 8 years examined (Figure 1). Summing the clear-sky contributions for 2007, we find the yearly cumulative ice thickness to be nearly 0.3 m less than climatology (Table 1), a reduction that is approximately 15–30% of the annual climatological ice thickness of this region [Kwok and Rothrock, 2009]. Using the climatological value of LWU (Figure 6e, green line), thickness changes are even larger for the majority of months, and the total cumulative contribution to ice thickness is nearly 0.5 m less than climatology (Table 1).

4.2. Clear-Sky Melt Sensitivity

[22] Monthly clear-sky ice melt is highly sensitive to the cloud fraction and surface albedo as shown above, and here we develop a metric to quantify potential ice melt with the competing factors. The temporal evolution of anomalous ice melt associated with anomalies in radiatively important variables is shown inFigure 8(climatological mean values are given in the bottom left of each panel). Contours of anomalous clear-sky ice-melt (m) are shown for an anomalous LWD of 9 W m−2(the average LWD anomaly for June–August 2007). Additionally, solid lines represent the sensitivity of the zero-line contour (no change from climatology) for LWD anomalies of −20 (blue), 0 (black) and +20 (red) W m−2.

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Figure 8. Temporal evolution of the monthly clear-sky sea-ice melt (positive contours) and freeze (negative contours) anomalies (m) for hypothetical cloud fraction (ordinate) and surface albedo (abscissa) anomalies for (a) June, (b) July, and (c) August. The melt (freeze) anomalies are calculated for a clear-sky LWD anomaly of +9 W m−2, the average clear-sky LWD anomaly for these three months during 2007. Cloud fraction and albedo anomalies are applied relative to the monthly climatological mean values provided in the bottom left of each panel. Solid lines indicate the cloud fraction and surface albedo anomaly combinations where there is no change in ice melt (freeze) relative to climatology for LWD anomalies of −20 (blue), 0 (black) and +20 W m−2.

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[23] Clearly, the largest clear-sky melt anomalies occur when both albedo and cloud fraction are anomalously low, and there is a distinct nonlinearlity to the anomalies especially during June and July (Figures 8a and 8b). During August, nonlinearity decreases as mean values restrict the permitted anomalies of cloud fraction and albedo and as SZAs increase. For example, a decrease in both mean cloud fraction and albedo by 10% results in an increased melt of 0.39, 0.40, and 0.28 m for June, July, and August, respectively. However, this increased melt occurs when both cloud fraction and albedo decrease together, a covariability that was not observed from 2003 to 2010, suggesting a potential feedback between cloud fraction and diminishing sea ice [Kay and Gettelman, 2009; Vavrus et al., 2011]. However, as shown in Figure 8, increased melt can occur for increased cloud fraction (decrease in clear-sky frequency) if albedo decreases sufficiently; this is what occurred during June 2007. For July and August 2007, the cloud fraction increases were slightly too large for the observed surface albedo decreases, and thus the climatological melt could not be exceeded regardless of the magnitude of LWD flux anomaly; the zero-line contours for the LWD anomaly ranges tested nearly collapse onto each other (Figure 8). The temporal evolutions of enhanced melt anomalies shown in Figure 8 can be applied anywhere in the high latitudes where similar SZA and surface albedo conditions are encountered. Simple adjustments must be made for the mean cloud fraction and albedo values (baseline anomaly adjustments), as well as applying the appropriate LWN fluxes based on surface temperature changes and changes to LWD.

5. Discussion and Conclusions

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[24] Using retrievals of temperature and water vapor from the AIRS instrument, we present an overview of the clear-sky atmospheric thermodynamic anomalies over the East Siberian Sea and Laptev Sea regions of Arctic where considerable interannual variations in ice concentration and area are found. Monthly anomalies in temperature and water vapor relative to the monthly means from 2003 to 2010 indicate both positive and negative temperature variations as large as 10 K, and mixing ratio changes on the order of 1 g kg−1. Temperature anomalies span the entire atmospheric column (up to 100 hPa level) while water-vapor anomalies were primarily below the 500 hPa level, highlighting the importance of horizontal advection of thermodynamic properties into the Arctic [X. Zhang et al., 2008; Devasthale et al., 2010; 2011a; Graversen et al., 2011].

[25] Radiative transfer modeling is used to estimate the impacts of temperature and water-vapor changes on both upwelling and downwelling fluxes of clear-sky LW radiation, as well as estimating the SWD fluxes. LWD anomalies mainly range between ±7 W m−2, with significant outliers outside of 2-σ about the mean. These estimates of enhanced LWD agree with those reported for a thermodynamic advection event over Eureka, Canada [Doyle et al., 2011]. An estimate of the clear-sky greenhouse effect associated with these thermodynamic anomalies is calculated. Although no apparent annual or interannual variability is observed, 2007 had distinctly large greenhouse anomalies, and there is a statistically significant shift in the average greenhouse anomaly, from negative for 2003–2006, to positive for 2007–2010. A statistically significant linear trend increase of 0.45 W m−2 yr−1in LWD anomalies over 2003–2010 is also found for a 3-month running mean time series of LWD anomalies, suggesting the shift in greenhouse anomaly is at least partially manifested onto the surface downwelling fluxes.

[26] Sensitivity tests indicate temperature changes considerably outweigh water-vapor changes in their contributions toward LWD and greenhouse anomalies. Lapse-rate changes (temperature) are of higher magnitude for a series of reasons: (1) temperature anomalies are found throughout the atmospheric column; (2) these temperature anomalies are relatively large and result in larger fluxes due to the temperature-dependence of LW flux; (3) monthly averaged precipitable water (vertically integrated water vapor) anomalies are relatively small (less than ±1.5 mm). These PW anomalies result in linear rather than exponential response in greenhouse anomalies; (4) increases in water vapor must be accompanied by increases in temperature due to the nearly constant saturation of relative humidity with respect to ice over the Arctic for much of the year [Curry et al., 1995]. Thus, in terms of monthly clear-sky LW anomaly calculations, the variations in temperature associated with advection are significantly larger than the variations in atmospheric opacity from water vapor. On timescales considerably shorter than 1 month, these results should differ, and be in better agreement withDoyle et al. [2011].

[27] Net surface radiation associated with clear-sky thermodynamic anomalies during 2007 is estimated, using mean and anomaly values of cloud fraction (MODIS) and surface albedo (ERA-Interim). During the 2007 melt season (May–August), the melt contribution associated with clear-sky radiative anomalies (0.713 m) was not significantly different than the climatological clear-sky melt (0.706 cm) for 2003–2010; clear-sky thermodynamic anomalies did not directly cause the anomalously low ice concentrations during the active melt period in this region. Instead the importance of clear-sky radiative anomalies is shown to emerge during the winter, spring and late autumn seasons where enhanced downwelling LW fluxes are shown to impact the surface temperature. These seasons correspond to the lowest annual cloud fractions [e.g.,Curry et al., 1996; Shupe et al., 2011; Chernokulsky and Mokhov, 2012], thus clear-sky radiative anomalies are an important mechanism in terms of preconditioning the snow and ice pack for the upcoming melt period. This preconditioning has been identified as a key mechanism for determination of the seasonal melt onset and total melt amount [Persson, 2012]. J. Zhang et al. [2008]conclude that the all-sky preconditioning during early 2007 was important for the low ice concentration of that year. However, their model study showed the preconditioning was due to increased SWN, where we instead find enhanced LW to be the primary preconditioner. In combination, the anomalies of surface albedo, cloud fraction, surface temperature and downwelling LW are shown to have potentially limited the annual total clear-sky ice growth by nearly 0.3 m. This, combined with a climatological melt of 0.7 m in a region where ice thickness ranges between 0.5 and 2 m [Kwok and Rothrock, 2009], suggests the clear-sky contribution to ice thickness is significant and must be considered together with the enhanced LWD from clouds [Schweiger et al., 2008; Graversen et al., 2011]. However, we must note that a portion of the surface albedo anomalies observed during 2007 are directly related to the anomalous amount of ice export out of the Arctic [J. Zhang et al., 2008] as well as the all-sky net surface radiative contribution. An ice melt anomaly metric is developed, highlighting a nonlinear relationship between radiative fluxes, cloud fraction and surface albedo.

Acknowledgments

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

[28] The authors wish to thank the anonymous reviewers for their comments. We thank the AIRS and MODIS Science Teams for making their data available for research.

References

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. 1. Introduction
  4. 2. Data and Methods
  5. 3. Results
  6. 4. Evolution of the 2007 Ice Minimum
  7. 5. Discussion and Conclusions
  8. Acknowledgments
  9. References
  10. Supporting Information
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