Clear-sky thermodynamic anomalies over a sea-ice sensitive region of the Arctic

A reoccurring conclusion from recent studies of the atmospheric impact on sea-ice reduction is the anomalously large convergence of atmospheric heat and moisture and the enhancement of downwelling longwave radiation from enhanced cloudiness. Here, the clear-sky atmospheric greenhouse effect is examined by analyzing the anomalies in thermodynamic profiles from instruments onboard the Aqua satellite.

Arctic sea-ice extent reduction records continue to be set. Recent studies of the atmospheric impact on sea-ice reduction have used reanalysis data and satellite observations. A reoccurring conclusion from these studies is the anomalously large convergence of atmospheric heat and moisture and the enhancement of downwelling longwave radiation from enhanced cloudiness, the cloud greenhouse effect (Kay et al. 2008; Schweiger et al. 2008; Graversen et al. 2011). Here, the clear-sky atmospheric greenhouse effect is examined by analyzing the anomalies in thermodynamic profiles from the AIRS instrument onboard the Aqua satellite, a constellation within the A-TRAIN.

Monthly-averaged thermodynamic profiles are ingested into the Rapid Radiative Transfer Model (Mlawer et al. 1997), producing profiles of longwave radiation, and thus allowing a comparison of the clear-sky radiative forcing potential with studies that have concluded that cloud forcing was the primary cause for maximum sea-ice reduction. Results are displayed for the East Siberian Sea region (74 deg-82 deg N, 135 deg E-165 deg W, as in Graversen et al. 2011) where the largest sea-ice melt was observed during 2007.

Distinct variability in heat and moisture transport

Vertical thermodynamic monthly anomalies from 2003-2010 are shown in Fig. 1, with temperature [K] anomalies in the top panel and water-vapor anomalies in the lower panel [g kg-1]. Distinct annual and inter-annual variability in heat and moisture transport over the region are evident, and 2007 is clearly anomalous relative to the monthly averages. Often heat and moisture display a positive co-variability, although this is not always the case. Heat and moisture anomalies occur throughout the depth of the troposphere, and temperature anomalies span a larger vertical extent, even observed within stratosphere.

Time-height anomalies
Figure 1. Time-height anomalies of temperature (top panel) and water vapor (lower panel) relative to the monthly average from 2003-2010.
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Large contribution of the heat convergence to the total LWD anomalies

The clear-sky longwave downwelling (LWD) radiation anomalies associated with the monthly-averaged thermodynamic profiles are shown in Figure 2 (solid black). Although there is large variability in the LW anomalies, a clear correlation to the heat and moisture anomalies is found (see Figure 1).

Linear regression of the LWD anomalies suggest a positive anomaly trend of 0.05 W m-2 per month (dashed black). Although the correlation of the linear model to the observations is small (r = 0.21), it is statistically significant and positive, representing an increased LWD flux of 4.5 W m-2 relative to the averages for 2003-2010.

Sensitivity tests, fixing either the temperature (blue) or water vapor (green) profiles to the average monthly values while allowing the other to vary, indicate the large contribution of the heat convergence to the total LWD anomalies. Water-vapor increases contribute less to the clear-sky LWD anomalies relative to temperature increases for the observed water vapor anomalies during 2003-2010.

Downwelling longwave anomalies
Figure 2. Downwelling longwave (LWD) anomalies relative to the monthly average LWD flux at the surface (black). Sensitivity of LWD anomalies with water vapor varying while temperature fixed to the 2003-2010 monthly average (blue) and temperature varying with water vapor fixed to the 2003-2010 monthly average (green). Dashed lines are the linear regression to the monthly anomalies.
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Enhanced ice-albedo feedback likely increase sea-ice melt

Four of the 5 months from April to August in 2007 show positive LWD anomalies greater than 1 standard deviation above the 2003-2010 average anomalies; August 2007, with a 16 W m-2 LWD anomaly was over 2 standard deviations larger. The average LWD anomalies for these 4 months was greater than 10 Wm-2, a value that is over 60% of the LWD anomaly estimated from all-sky conditions by Graversen et al. (2011).

Assuming this surplus of heat is consumed through melting sea ice, the total melt contribution from LWD anomalies is on the order of 0.34 m. Considering these calculations are for clear-sky conditions, an enhanced ice-albedo feedback would likely further increase shortwave absorption resulting in further increases in sea-ice melt. Thus, an increased cloud fraction to yield an enhanced LW surface radiative forcing would not have been necessary for producing a large portion of the observed sea-ice melt in the East Siberian Sea sector of the Arctic in 2007.

Monthly clear-sky greenhouse anomalies, defined as upwelling LW at 1000 hPa minus upwelling LW at 100 hPa for the same region are shown in Figure. 3.

Although these estimates do not consider divergence of greenhouse energy out of the Siberian Sea region, nor is the record length long enough for a climate metric, the results do agree with changing thermodynamics and LWD anomalies above.

Sensitivity tests indicate that the total clear-sky greenhouse anomalies are almost entirely influenced by the lapse-rate changes rather than the increased water-vapor loading (not shown).

Largest positive anomalies are found during 2007, and there is a distinct increase in positive greenhouse anomalies after 2007 compared to pre-2007. Thus, increasing heat and moisture into the Arctic, even without enhancing cloudiness and cloud surface LW forcing, can lead to significant energy increases at the surface, potentially manifesting into enhanced sea-ice melt.

monthly clear-sky greenhouse anomalies
Figure 3. Monthly clear-sky greenhouse anomalies as a function of year. Greenhouse anomalies are calculated by the difference between upwelling LW at 1000 hPa minus upwelling LW at 100 hPa.
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References

Graversen, R.G., T. Maurtisen, S. Drijfhout, M. Tjernström and S. Mårtensson, 2011: Warm winds from the Pacific caused extensive Arctic sea-ice melt in summer 2007, Clim. Dyn., 36, 11-12, 2103-2112.

Kay, J.E., T. L’Ecuyer, A. Gettelman, G. Stephens and C. O’Dell, 2008: Ther contribution of cloud and radiation anomalies to the 2007 Arctic sea ice exent minimum, Geophys. Res. Lett., 35, L08503, doi:10.1029/2008GL033451.

Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono and S.A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102, D14, 16663-16682.

Schweiger, A.J., J. Zhang, R.W. Lindsay and M. Steele, 2008: Did unusually sunny skies help drive the record sea ice minimum of 2007?, Geophys. Res. Lett., 35, L10503, doi:10.1029/2008GL033463.