Erlend M. Knudsen 1,2 , Yvan J. Orsolini 2,3 , Tore Furevik 1,2 , and Kevin I. Hodges 4
1 Geophysical Institute, University of Bergen, Bergen, Norway, 2 Bjerknes Centre for Climate Research, Bergen, Norway, 3 Norwegian Institute for Air Research, Kjeller, Norway, 4 Department of Meteorology, University of Reading, Reading, UK
Abstract The Arcticseaice retreat has accelerated over the last decade. The negative trend is largest in summer, but substantial interannual variability still remains. Here we explore observedatmospheric conditions and feedback mechanisms during summer months of anomalousseaicemelt in the Arctic. Compositing months of anomalous low and high seaicemelt over 1979–2013, we ﬁnd distinct patterns in atmospheric circulation, precipitation, radiation, and temperature. Compared to summer months of anomalous low seaicemelt, high melt months are characterized by anomalous high sea level pressure in the Arctic (up to 7 hPa), with a corresponding tendency of storms to track on a more zonal path. As a result, the Arctic receives less precipitation overall and 39% less snowfall. This lowers the albedo of the region and reduces the negative feedback the snowfall provides for the seaice. With an anticyclonic tendency, 12 W/m 2 more incoming shortwave radiation reaches the surface in the start of the season. The melting seaice in turn promotes cloud development in the marginal ice zones and enhances downwelling longwave radiation at the surface toward the end of the season. A positive cloud feedback emerges. In midlatitudes, the more zonally tracking cyclones give stormier, cloudier, wetter, and cooler summers in most of northern Europe and around the Sea of Okhotsk. Farther south, the region from the Mediterranean Sea to East Asia experiences signiﬁcant surface warming (up to 2 .4 ◦ C), possibly linked to changes in the jet stream.
With a more zonal jet stream, more cyclones track across the North Atlantic Ocean into northern Europe instead of the Arctic (Figure 6a). Screen et al.  discovered that fewer cyclones tracking into the Arctic in MJJ favored lower September SIE. Using an empirical orthogonal function analysis, Dong et al.  found a signiﬁcant anticorrelation between cyclones entering the Arctic and those reaching northwestern Europe of −0 .61. They linked the two distinct North Atlantic paths to the summer NAO and blocking frequency over the United Kingdom and northwestern Europe. Here we ﬁnd that only 5% of the anomalousseaicemelt underlying the cyclonic tendency can be explained by the NAO. In fact, not more than 10% of our seaicemelt index is due to any of the climate patterns analyzed (NAO, AO, and BSH). Our Figures 3a, 3b, 3d, and 10 bear some resemblance to the results dominated by the Arctic Dipole in Overland et al. . This is expected since half of their data (June 2007–2012) are included in our HMR months. However, for the rest of our time period (1979–2013), we ﬁnd low coincidence between their Figure 2 and our LMR and HMR months in Figure 1. Finally, although there are some similarities to the features in Figures 3b, 3c, and 4 here, we do not ﬁnd the negative summer Northern Annular Mode (NAM) in Tachibana et al.  to demonstrate the observedatmosphericpatterns in our study.
in terms of the strong mismatch between modeled and observed trends over the last 11 years, comes from impacts of the strong positive state of the winter NAM during 1989 – 1995 (highest in over 100 years). Altered wind patterns flushed much of the Arctic Ocean’s store of thick ice into the Atlantic via Fram Strait. While the NAM has subsequently regressed back to a more neutral phase, this episode left the Arctic with thinner ice, more apt to melt in summer, contributing to sharply lower September ice extent in recent years [Rigor and Wallace, 2004]. Atmospheric vari- ability in the post-positive NAM era has also favored ice loss [Maslanik et al., 2007] as have changes in Atlantic heat inflow [Polyakov et al., 2005] and the transport of Pacific-derived waters [Shimada et al., 2006]. Assuming these processes reflect natural variability, it is likely that in their absence, the September trend would be smaller than observed.
Abstract. In contrast to the Arctic, where total seaice extent (SIE) has been decreasing for the last three decades, Antarc- tic SIE has shown a small, but significant, increase during the same time period. However, in 2016, an unusually early onset of the melt season was observed; the maximum Antarc- tic SIE was already reached as early as August rather than the end of September, and was followed by a rapid decrease. The decay was particularly strong in November, when Antarctic SIE exhibited a negative anomaly (compared to the 1979– 2015 average) of approximately 2 million km 2 . ECMWF In- terim reanalysis data showed that the early onset of the melt and the rapid decrease in seaice area (SIA) and SIE were associated with atmospheric flow patterns related to a posi- tive zonal wave number three (ZW3) index, i.e., synoptic sit- uations leading to strong meridional flow and anomalously strong southward heat advection in the regions of strongest seaice decline. A persistently positive ZW3 index from May to August suggests that SIE decrease was preconditioned by SIA decrease. In particular, in the first third of Novem- ber northerly flow conditions in the Weddell Sea and the Western Pacific triggered accelerated seaice decay, which was continued in the following weeks due to positive feed- back effects, leading to the unusually low November SIE. In 2016, the monthly mean Southern Annular Mode (SAM) index reached its second lowest November value since the beginning of the satellite observations. A better spatial and temporal coverage of reliable ice thickness data is needed to assess the change in ice mass rather than ice area.
lous geostrophic winds; for example, that anomalous southerly winds east of Greenland would contribute to dynamic ice retreat and would also be anomalously warm, contribut- ing to icemelt. However, the paper did not quantify the effects of these mechanisms, and in winter melt is less important than growth which is heavily moderated by ocean as well as atmospheric temperature. This suggests that the thermodynamic component may be more complicated than the simple picture of atmospheric warm advection causing melt. Later, Rigor et al. (2002) found evidence that the AO had a large effect on observedseaice in the period 1979–1998. Regressions suggested that during a positive AO winter, there was increased ice divergence in the eastern Arctic. Consistent with this divergence there was more open ocean, allowing for more growth of thin seaice and associated strong heat fluxes to the atmosphere. These in turn provided a preconditioning for less seaice in the following summer. However the interpretation of such results is challeng- ing due to the short record and to the strong trends in the AO, SIC, and temperature. The divergence and growth effect was also found in the coupled seaice–ocean model used by Zhang et al. (2000), who highlighted the role of coupling between dynamic and ther- modynamic processes (e.g. ice advection exposing the warm, low albedo ocean surface). Day et al. (2012) estimated, based on relationships obtained in long climate model sim- ulations, that the AMO could explain approximately 30% of the 1979–2010 negative SIE trend. However, they found no relationship with the AO.
The pattern of melt pond distribution in early summer (Fig. 4) of areas with an increased relative melt pond frac- tion in the beginning of June indicates the ice free areas later in September. This agrees with the statements of Perovich et al. (2002b, 2011b), that early occurrence of melt ponds has a strong influence on the formation of open water areas. Clearly visible is also the appearance of homogeneous ar- eas with a very high relative melt pond fraction up to 70 % at the end of June on the flat first year ice in the Canadian Archipelago. The decrease of melt ponds in the week start- ing from August 28, 2008 was caused by a cold air advec- tion from Greenland with temperatures far below the freezing point. The weather situation changed on 5 September 2008, as warm continental air masses from Siberia caused further melting in the Siberian Arctic and the Fram Strait.
However, as discussed in Sect. 4.1.3, there are numerous atmospheric and oceanic factors that influence the simulation of Arcticseaice. As highlighted by Massonnet et al. (2012), a model can simulate the “right” results for the wrong rea- sons, perhaps due to error compensation. This does not mean that the analysis of seaice simulations for past climates, such as the mid-Pliocene, is not valuable and justified, but that it is important to highlight that the forcings behind the seaice simulation have to be better understood. Variability modes, such as NAO or AMO, whilst shown to have influence on seaice extent from an annual viewpoint, do not appear to exert significant influence over the mean seaice state on a decadal timescale. The models’ representation of seaice motion, and by extension ocean currents and surface winds, is an impor- tant influence on the distribution of seaice, and worthy of a more detailed study. Future studies must particularly aim at quantifying the contribution of the various forcings on the seaice in warmer climates.
To help understand this issue, we show in Fig. 3 the run- ning trend in SSIE for all CMIP5 models for RCP8.5. As sug- gested in the figure, the trends, and thus the seaice changes, become increasingly large sometime during the 21st century, and then go to zero. The timing of the most negative trend is marked with a vertical bar in the figure, and is clearly model-dependent. To gain further insight into this, we dis- play in Fig. 4 the evolution of SSIE trends as a function of the mean SSIE, in order to visualize the dynamics of the sys- tem. In these “phase-plane” plots (a variable versus its time derivative), clear similarities come to light. All models fol- low a similar trajectory: they start from the right, with rel- atively high mean SSIE at the beginning of the simulation. Then they move leftwards as the mean SSIE decreases and all experience a U-shaped trajectory as the mean SSIE de- creases further to ice-free conditions (the 2030–2061 posi- tion of each model is marked with a colored dot). In Fig. 4, the spread in the CMIP5 population is thus represented by the different 1979–2010 positions of the CMIP5 models on their trajectories (colored crosses): for example, BCC-CSM1.1, CanESM2 and GISS-E2-R are already near the minimum, while EC-EARTH and CCSM4 have not reached it yet. Un- der RCP4.5, similar trajectories exist (supplement figure) for the subset of models that reach ice-free conditions in September by ∼ 2060 – the approximate year at which the RCP4.5 forcing stabilizes – suggesting that, as long as the SSIE reaches (near) ice-free conditions under the effect of increased radiative forcing, the U-shaped trajectory occurs.
Although there are large differences in terms of sampling frequency and area between the half-hourly visual observa- tions (2 km diameter) and the one-second interval of EM31 thickness measurements (footprint 13 m, 3.7 times the instru- ment altitude 3.5 m, Reid et al., 2006), the information pro- vided shows general agreement. Based on literature (Hass et al., 2010), the EM31 are most accurate with respect to their modal thickness, which is consistent with that seen from our visual observations, i.e., the thickness of the dominant ice type. As shown in Fig. 6, the highest modal thicknesses (160 cm for northward leg and 140 cm for the southward leg) match well with the corresponding visual observations (150 cm and 130 cm, respectively). The differences between the two are partly due to the snow depth that was part of the EM31 thickness but was not added into the ice thickness of visual observations, as well as errors from both the instru- ment and visual observations. For most of the ice observa- tions, there was no snow on the ice, except for the period 1–8 August when there were new snowfall events (6–10 cm snow) and both EM31 data and ice observations were avail- able. Adding the 6–10 cm snow into the observed snow and ice thickness would only change the statistics a few centime-
The patternsobserved in Fig. 9 are, at least qualitatively, consistent with the observed distribution of the seaice thick- ness (Kwok and Rothrock, 2009) and concentration (http: //nsidc.org/data/seaice/index.html) (Comiso, 1990, updated 2012). In summer, large M values are observed at the edge of the basin in the Beaufort, Chukchi and Laptev seas, i.e. in re- gions where the multi-year ice cover has been progressively disappearing during the last decade (Maslanik et al., 2011). On the contrary, small values of M can be observed along the Canadian coasts, where the average ice thickness is at its maximum (Kwok and Rothrock, 2009). Seaice behaves more as a strongly cohesive plate in this region, unsensitive to the Coriolis force. This zone corresponds to the multi-year ice still remaining nowadays. Large values of M are also ob- served south of Fram Strait, which is consistent with the buoy trajectory plotted in Fig. 4a, which we discussed in Sect. 3.1. In contrast, the winter pattern does not reveal any particu- lar structure within the basin. The values of M are small over the whole basin, except relatively larger M values com- puted north of Canadian coasts. These larger M values could be attributed to buoys drifting along the “Circumpolar flaw lead” that consists of a sheared, thus highly fragmented, zone (Lukovich et al., 2011).
Our analysis confirms that the temporal pattern of seaice variation indeed differs significantly between the Barents– Kara seas and the Laptev and Chukchi seas. Seaice refreezes and the sea surface exposed to air is closed up in late fall in the Laptev and Chukchi seas. As a result, significant ab- sorption of solar radiation in summer does not lead to in- creased turbulent heat flux in winter. However, sea surface does not freeze up completely in the Barents–Kara seas. Con- sequently, turbulent heat flux becomes available in winter in the Barents–Kara seas for heating the atmospheric column (Fig. 9f), which in turn increases downward longwave radia- tion (Fig. 9d). The delayed warming from summer energy ab- sorption via albedo feedback (Screen and Simmonds, 2010a; Serreze and Barry, 2011) does not appear to be a necessary and sufficient condition for the feedback process; it appears that the delayed warming is not uniquely responsible for pro- longed seaice melting in the Barents–Kara seas; for example, increased ocean heat transport into the western Barents Sea may have provided a favorable condition for the sustenance of ice-free sea surface in winter. Wind may also be partially responsible for seaice reduction (Ogi and Wallace, 2012).
Abstract. The spatial and temporal dynamics of melt ponds and seaice albedo contain information on the current state and the trend of the climate of the Arctic region. This pub- lication presents a study on melt pond fraction (MPF) and seaice albedo spatial and temporal dynamics obtained with the Melt Pond Detection (MPD) retrieval scheme for the Medium Resolution Imaging Spectrometer (MERIS) satel- lite data. This study compares seaice albedo and MPF to sur- face air temperature reanalysis data, compares MPF retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS), and examines albedo and MPF trends. Weekly av- erages of MPF for 2007 and 2011 showed different MPF dy- namics while summer seaice minimum was similar for both years. The gridded MPF and albedo products compare well to independent reanalysis temperature data and show melt onset when the temperature gets above zero; however MPD shows an offset at low MPFs of about 10 % most probably due to unscreened high clouds. Weekly averaged trends show pronounced dynamics of both, MPF and albedo: a negative MPF trend in the East Siberian Sea and a positive MPF trend around the Queen Elizabeth Islands. The negative MPF trend appears due to a change of the absolute MPF value in its peak, whereas the positive MPF trend is created by the earlier melt onset, with the peak MPF values unchanged. The MPF dynamics in the East Siberian Sea could indicate a temporal change of ice type prevailing in the region, as opposed to the Queen Elizabeth Islands, where MPF dynamics react to an earlier seasonal onset of melt.
Kumar, A., Perlwitz, J., Eischeid, J., Quan, X., Xu, T., Zhang, T., Hoerling, M., Jha, B., Wang, W., 2010. Contribution of seaice loss to Arctic amplification. Geophysical Research Letters 37. Laskar, J., Robutel, P., Joutel, F., Gastineau, M., Correia, A., Levrard, B., 2004. A long-term
numerical solution for the insolation quantities of the Earth. Astronomy and Astrophysics 428, 261–285.
The diﬀerences between the RCP8.5 and AMIPsst+co2 responses in JFM resemble the circulation response to seaice loss found in previous single-model experiments (Blackport & Kushner, 2017; Cvijanovic & Caldeira, 2015; Deser et al., 2010, 2016; Harvey et al., 2015; Oudar et al., 2017; Peings & Magnusdottir, 2014; Sun et al., 2015). In particular, the increase in surface pressure in the northern North Atlantic and northern Siberia, the pressure reduction over North America, the increase in Arctic geopotential height, and the weakening of the high-latitude westerly ﬂow, including the North Atlantic sector, are common features of these studies. This gives us high conﬁdence in interpreting the diﬀerence between RCP8.5 and AMIPsst+co2 as evidence for robust impacts of seaice loss on the atmospheric circulation response in the CMIP5 models. Interestingly, other aspects of the identiﬁed response are present in some, but not all, of the single-model experiments. In particular, a large pressure reduction in the Mediterranean area in response to seaice loss is only present in Blackport and Kushner (2017). Many studies have found a stronger Aleutian pressure deepening in response to seaice loss, although this might result from the indirect eﬀects of seaice loss on global SST changes (Deser et al., 2016), which cannot be separated using this approach.
not explain the warming in Eastern Europe and western Siberia. By checking the coincidence between occurrences of European cold winter months and sea-ice re- duction over the Barents–Kara Seas in 13 CMIP5 models simulations for the 21st century (2006–2100) under two Rep- resentative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5), Yang and Christensen (2012) suggested that a moderate reduction of SIC in the Barents–Kara Seas between 2006 and 2050 will likely provide favorable conditions for the occurrence of cold winters in Europe. Earlier studies also suggested that the winter atmospheric circulation response to autumn Arcticsea-ice reduction contains large uncertainties. For example, some studies show that a negative-AO-like pat- tern could persist into winter (Francis et al., 2009; Liu et al., 2012, 2013; Li and Wang, 2013b), while other studies (Bl¨uthgen et al., 2012; Screen et al., 2014) argue that autumn atmospheric circulation anomalies cannot continue into win- ter. In addition, Screen et al. (2013) and Liu et al. (2012) reported contrasting winter AO tendencies in response to the autumn Arctic SIC trend using CAM3. This contrast could have been caused by either the different sizes of ensemble members or the difference in boundary conditions. For exam- ple, the Arctic SIC and associated SST during the whole year were used in Screen et al. (2014), whereas only the autumn
Abstract The choice between two common ice strength parameterizations can have a large effect on the reproduction of satellite observations of Arcticseaice concentration, thickness and drift in viscous-plastic seaice models. One parameter- ization calculates the ice strength from a multi-category ice thickness distribution and the other uses a two-category thickness model. With the latter parameter- ization the ice strength depend linearly on mean thickness, but with the multi- category model this dependence is quadratic on average. The aim of this study is to determine which of the differences between the two parameterizations are cru- cial for their impact on basin-scale models. A rederivation of the multi-category strength in the limiting case of only two thickness categories allows to perform Arctic model simulations that allow to distinguish effects of mean dependences on thickness and concentration from effects of the choice of thickness representa- tion. The results show that a two-category strength is better suited for Arcticseaice simulations than a multi-category strength and that the mean dependence of strength on thickness is only second order. In the original derivation of the multi-category strength, energy stored and dissipated during ridging is assumed to determine the large-scale ice strength. This assumption emphasizes the role of the thin ice fraction computing the ice strength, which we find to be detrimental to model performance. When calculating the ice strength, a larger role of energy dissipated in shear can explain both that the mean ice thickness determines the ice strength and that the ice strength is linear in the ice thickness.
Although there is an overall shift towards earlier MO for much of the Arcticseaice cover, the high variability from year to year in individual sub-regions, gives evidence that the timing and magnitude of MO area accumulations are still highly dependent on the atmospheric conditions present at the time of MO in a particular region. Anderson and Drobot  have shown that the MO dates in one region of the Arctic are largely independent of the MO dates in other regions. This is most likely due to the regional nature of cyclonic activity and the associated warm air anomalies [13,19] and increased longwave radiation flux at the surface due to enhanced cloud cover (e.g., [36,37]). Therefore, an extreme MO area accumulation (one that is at either the early or late ends of the 50% MO area range) in a region one year may not appear to be an abnormal year for other regions. For example, the MO area accumulation is extremely early for 1990 in the East Siberian Sea (Figure 11b) and also shows up as an early MO accumulation year for the Kara, Laptev, Chukchi, and for at least a portion of the Central Arctic region (Figure 9b,10b,12b,15b). However, 1990 MO area accumulation occurs later in the year than the mean for the Beaufort Sea and the Canadian Arctic Archipelago (Figure 13b,14b). 1990 is an anomalous year in terms of MO area because this year is associated with a strongly positive AO Index in the winter and the early spring . The positive AO Index was found to be associated with earlier snow melt onset in the Laptev, East Siberian, and Chukchi Sea regions. Since MO is so closely tied to the cyclonic-scale weather conditions, variations in the MO area accumulations from year to year reflect the variability of spring weather. Although out of the scope of this paper, it is clear from the significant changes in MO area accumulations that the weather conditions and timing of melt forcing have changed over the study period (1979–2012).
ice import from the Arctic Ocean. McLaren et al.  report a mean draft of 4 – 5 m of mainly first year ice within M’Clure Strait; this is from an under-ice profiling sonar operated during two submarine surveys in February and August of 1960. Contemporary ice thickness estimates are not available. Taking the seaice to be 4 m thick, the annual mean ice volume export into the Arctic Ocean would be 80 km 3 . The uncertainty in this estimate is high: we suspect that this may not be representative of current ice thickness in the Strait considering the observed thinning in Arcticseaice during the past 40 years [Rothrock et al., 1999]. We speculate that half of that volume seems more reasonable but would indicate significantly thinning here.