Top PDF Snow Cover on the Arctic Sea Ice: Model Validation, Sensitivity, and 21st Century Projections

Snow Cover on the Arctic Sea Ice: Model Validation, Sensitivity, and 21st Century Projections

Snow Cover on the Arctic Sea Ice: Model Validation, Sensitivity, and 21st Century Projections

III.5.b. CICE-Reanalysis Bias Sensitivity Simulation Design The design of the CICE-Reanalysis bias experiment closely follows the design of the CCSM bias experiment. Due to the low bias in snow depths, this experiment serves as a counterpoint to the CCSM bias experiment in Section III.4. CAM and CLM remain active, while POP is replaced with the SOM. As a result, the mean ice state will be different than the CICE-Reanalysis simulation. A 60-year integration is performed. However, the purpose of this simulation is to assess the effect of the biases in snow characteristics produced by CICE-Reanalysis. As in Section III.4.b, a modification is applied to the snow conductivity to impose a thermodynamic correction to the bias observed in CICE-Reanalysis snow depth and density. Please refer to Section III.4.b. for a more in depth discussion of this method. As in Section III.4.b., no modifications are made to albedo. This may have a larger effect in this bias sensitivity than that in Section III.4, due the near zero summer snow depths generated in CICE-Reanalysis. This could have a larger effect as the Delta-Eddington multiple scattering parameterization is most sensitive to snow depth changes when snow is thin. If this limitation were also corrected, it may result in higher albedos, lower melts, and hence greater summer ice survival.
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The importance of sea ice area biases in 21st century multimodel projections of Antarctic temperature and precipitation

The importance of sea ice area biases in 21st century multimodel projections of Antarctic temperature and precipitation

At high latitudes uncertainty in climate model projections of regional change is, at many locations, strongly tied to sea ice [Raisanen, 2007; Bracegirdle and Stephenson, 2013]. This is particularly clear at locations where atmospheric warming occurs in response to the transition from ice cover to open ocean. Over continental Antarctica, the nonlocal effect of sea ice change is also important and climate model sensitivity experiments show warming and increased precipitation in response to sea ice retreat [Simmonds and Budd, 1991; Rind et al., 1995; Bromwich et al., 1998; Weatherly, 2004; Krinner et al., 2014]. Many of the current generation of global coupled climate models (speci fically the Coupled Model Intercomparison Project phase 5 (CMIP5) multimodel ensemble (MME) [Taylor et al., 2012]) exhibit large biases in Southern Hemisphere (SH) sea ice extent, with some simulating less than one third of the observed annual mean climatology [Turner et al., 2013]. Therefore, there is an urgent need to assess the impacts of biases in simulated SH sea ice on future projections in MMEs such as CMIP5, which would potentially contribute to reducing uncertainty in climate change estimates derived from existing and future MMEs.
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Information on the early Holocene climate constrains the summer sea ice projections for the 21st century

Information on the early Holocene climate constrains the summer sea ice projections for the 21st century

different forcings changes during the two periods. Indeed, the forcing is slowly varying in the early Holocene and has a very strong seasonal cycle. By contrast, the forcing changes rapidly over the 20th and 21st centuries, the climate system being in a clearly transient state, and the forcing anomaly is more homogenously distributed for the different seasons. However, we use only a limited number of experiments and only one single model. Additional simulations with LOVE- CLIM using different parameter sets as well as experiments with other coupled climate models would thus be required to test the robustness of the nearly perfect correlation described in Fig. 5. Furthermore, with a larger ensemble of simula- tions, it would be possible to assign to each parameter set a quantitative estimate of its ability to simulate the observed changes in summer ice extent and then provide a probabil- ity distribution for the projected changes instead of simply selecting the most reliable projection as proposed here. In any case, our results strongly suggest that information about the state of the climate system during the early Holocene could help us in reducing our uncertainties on the future de- cline of the summer Arctic ice cover. Additional observa- tions over this period, such as the ones planned in the frame- work of the International Polar Year (e.g.,http://classic.ipy. org/development/eoi/details.php?id=786), are thus required in order to obtain more precise and more reliable projec- tions. Following our experiments, proxy-based estimates of the summer ice cover for the central Arctic, off the shelves of the Kara and Laptev Sea as well as in the Beaufort Sea would provide the strongest tests for model results and observations
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Constraining projections of summer Arctic sea ice

Constraining projections of summer Arctic sea ice

Abstract. We examine the recent (1979–2010) and future (2011–2100) characteristics of the summer Arctic sea ice cover as simulated by 29 Earth system and general cir- culation models from the Coupled Model Intercomparison Project, phase 5 (CMIP5). As was the case with CMIP3, a large intermodel spread persists in the simulated summer sea ice losses over the 21st century for a given forcing sce- nario. The 1979–2010 sea ice extent, thickness distribution and volume characteristics of each CMIP5 model are dis- cussed as potential constraints on the September sea ice ex- tent (SSIE) projections. Our results suggest first that the fu- ture changes in SSIE with respect to the 1979–2010 model SSIE are related in a complicated manner to the initial 1979– 2010 sea ice model characteristics, due to the large diversity of the CMIP5 population: at a given time, some models are in an ice-free state while others are still on the track of ice loss. However, in phase plane plots (that do not consider the time as an independent variable), we show that the transition towards ice-free conditions is actually occurring in a very similar manner for all models. We also find that the year at which SSIE drops below a certain threshold is likely to be constrained by the present-day sea ice properties. In a sec- ond step, using several adequate 1979–2010 sea ice metrics, we effectively reduce the uncertainty as to when the Arc- tic could become nearly ice-free in summertime, the interval [2041, 2060] being our best estimate for a high climate forc- ing scenario.
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Boreal and temperate snow cover variations induced by black carbon emissions in the middle of the 21st century

Boreal and temperate snow cover variations induced by black carbon emissions in the middle of the 21st century

none of the RCP emission inventories used in CMIP5 simu- lations over the 21st century considers variations of “local” emissions in the Arctic, which could be associated with a significant increase in ship traffic in the Arctic or to an in- tensification of biomass burning in boreal and temperate re- gions. For this reason, we performed another simulation – S3 – similar to S2 but replacing the baseline Arctic ship emis- sions in the RCP8.5 2050 by a scenario that includes impor- tant ship traffic over Arctic routes. These larger ship emis- sions are based on the “high-growth” scenario of Corbett et al. (2010), considering a high increase in ship traffic over the current Arctic routes. This scenario takes also into account the diversion routes opened during the summer following the seasonal retreat of sea ice expected in the next decades. Fi- nally, an S4 simulation was also performed, similar to S2, but with enhanced biomass burning activity. Following Flan- nigan (2009a, b), we consider an increase of 50 % of BC and other aerosols emitted by fire during all the year. In addition, we consider also a 1-month extension of the fire season in the Northern Hemisphere (starting 15 days prior and extending 15 days after the fire season of the present-day): from January to June (resp. from August to December), monthly emissions are computed as the average between the emission of the cur- rent month and those of the following (resp. previous) month. S3 and S4 emission variations are applied to sulphate, BC and OC. S2, S3 and S4 experiments consist of a pair of 11 yr simulations, with initial conditions slightly modified in one of them, to be able to analyze 20 yr of model output, as 10 yr would clearly be insufficient to make comparisons statisti- cally robust. In addition, to evaluate in more details the im- pact of the future aerosol emissions changes without consid- ering atmospheric feedbacks, we realised three more experi- ments nudged toward our first 2050–2060 simulation: S2 N, S3 N and S4 N all have winds nudged toward S2, each of them using the same aerosol emissions as, respectively, S2, S3 and S4. Note that S2 N has been nudged toward itself (S2). This has been done to analyze the difference between
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Canadian snow and sea ice: historical trends and projections

Canadian snow and sea ice: historical trends and projections

Abstract. The Canadian Sea Ice and Snow Evolution (Can- SISE) Network is a climate research network focused on de- veloping and applying state of the art observational data to advance dynamical prediction, projections, and understand- ing of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. Here, we present an assessment from the CanSISE Network on trends in the historical record of snow cover (fraction, water equivalent) and sea ice (area, concen- tration, type, and thickness) across Canada. We also assess projected changes in snow cover and sea ice likely to oc- cur by mid-century, as simulated by the Coupled Model In- tercomparison Project Phase 5 (CMIP5) suite of Earth sys- tem models. The historical datasets show that the fraction of Canadian land and marine areas covered by snow and ice is decreasing over time, with seasonal and regional variability in the trends consistent with regional differences in surface temperature trends. In particular, summer sea ice cover has decreased significantly across nearly all Canadian marine re- gions, and the rate of multi-year ice loss in the Beaufort Sea and Canadian Arctic Archipelago has nearly doubled over the last 8 years. The multi-model consensus over the 2020– 2050 period shows reductions in fall and spring snow cover fraction and sea ice concentration of 5–10 % per decade (or 15–30 % in total), with similar reductions in winter sea ice concentration in both Hudson Bay and eastern Canadian wa- ters. Peak pre-melt terrestrial snow water equivalent reduc- tions of up to 10 % per decade (30 % in total) are projected across southern Canada.
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Sensitivity of Greenland Ice Sheet surface mass balance to perturbations in sea surface temperature and sea ice cover: a study with the regional climate model MAR

Sensitivity of Greenland Ice Sheet surface mass balance to perturbations in sea surface temperature and sea ice cover: a study with the regional climate model MAR

Abstract. During recent summers (2007–2012), several sur- face melt records were broken over the Greenland Ice Sheet (GrIS). The extreme summer melt resulted in part from a persistent negative phase of the North Atlantic Oscillation (NAO), favoring warmer atmospheric conditions than nor- mal over the GrIS. Simultaneously, large anomalies in sea ice cover (SIC) and sea surface temperature (SST) were ob- served in the North Atlantic, suggesting a possible connec- tion. To assess the direct impact of 2007–2012 SIC and SST anomalies on GrIS surface mass balance (SMB), a set of sen- sitivity experiments was carried out with the regional climate model MAR forced by ERA-Interim. These simulations sug- gest that perturbations in SST and SIC in the seas surround- ing Greenland do not considerably impact GrIS SMB, as a result of the katabatic wind blocking effect. These offshore- directed winds prevent oceanic near-surface air, influenced by SIC and SST anomalies, from penetrating far inland. Therefore, the ice sheet SMB response is restricted to coastal regions, where katabatic winds cease. A topic for further in- vestigation is how anomalies in SIC and SST might have indirectly affected the surface melt by changing the general circulation in the North Atlantic region, hence favoring more frequent warm air advection towards the GrIS.
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Snow in the changing sea ice systems

Snow in the changing sea ice systems

Ice and snow depth variability and change in the high Arctic Ocean observed by in situ measurements.. Arctic climate change as manifest in cyclone behavior.[r]

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Multimodel evidence for an atmospheric circulation response to Arctic sea ice loss in the CMIP5 future projections

Multimodel evidence for an atmospheric circulation response to Arctic sea ice loss in the CMIP5 future projections

Plain Language Summary How the atmospheric circulation will respond to climate change in the coming decades remains uncertain. The loss of Arctic sea ice has been identified as one of the factors that can influence atmospheric circulation, and a better understanding of this connection is important to improve our confidence in the regional impacts of climate change. To do this, we have analyzed future climate projections from computer simulations based on a large set of different climate models. Using a novel approach, we were able to demonstrate that Arctic sea ice loss exerts a consistent and nonnegligible impact on the atmospheric circulation response. In particular, in late winter and in the North Atlantic and Euro-Asian sector, Arctic sea ice loss tends to oppose the poleward shift of the midlatitude westerly winds, which is a common feature of the future projections of atmospheric circulation change. These results are important as they provide the first assessment that Arctic sea ice loss is important for the atmospheric circulation response to climate change based on a large number of climate models.
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Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network

Rostosky et al. (2018) follow a similar approach as Markus and Cavalieri (1998) using a gradient ratio and two linear regression coefficients. However, instead of using the gradi- ent ratio between 18.7 and 36.5 GHz, they apply the gradi- ent ratio of 6.9 to 18.7 GHz. The lower frequencies enable a determination of snow depths exceeding 50 cm (due to mi- crowave emissions emanating from deeper within the snow at this frequency), where the 36.5 GHz channel becomes sat- urated. Furthermore a simulation by Markus et al. (2006) and a correlation analysis by Rostosky et al. (2018) suggest a stronger relation of snow depth to this gradient ratio. To use this gradient ratio, they determined a new set of regression coefficients by fitting AMSR-E and AMSR2 brightness tem- peratures to OIB snow depth. They exclude single years for verification and validation work. Furthermore they extend the approach to be applicable over both FYI and MYI, while the approach of Markus and Cavalieri (1998) was found to de- liver reasonable results only over FYI. The extension to MYI is achieved by fitting a second set of parameters to the MYI- covered part of the OIB data. We use the coefficients deter- mined with OIB data from 2009 to 2014 since our test data are from 2015. When applying this algorithm, the ice type (FYI or MYI) must be known with confidence. They use the ice type product from OSISAF – derived by a combination of microwave radiometry and scatterometer data (Aaboe et al., 2016) – and discard areas where the ice type is not known with high confidence (confidence level < 4). On FYI snow depth is calculated from
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Biogeochemical impact of snow cover and cyclonic intrusions on the winter Weddell Sea ice pack

Biogeochemical impact of snow cover and cyclonic intrusions on the winter Weddell Sea ice pack

A direct consequence of these temperature contrasts is the unusual occurrence of elevated Rayleigh numbers in the upper half of the ice cover at some stations (Figure 5a). Indeed, elevated Rayleigh numbers are usually encountered within the bottom layers of thermodynamically growing sea ice (Carnat et al., 2013; Notz & Wor- ster, 2009; Zhou et al., 2014b), which is actually the case in none of the AWECS stations. High Rayleigh num- bers in the upper sea ice cover are rather typical of warming spring sea ice, leading to episodic brine convective events across the whole ice cover (e.g., Zhou et al., 2013). At the SIMBA spring stations, ‘‘brine tubes,’’ initiating close to the ice cover surface, and sometimes extending all the way to the bottom were reported (e.g., Lewis et al., 2011, Figures 8 and 11). Brine tubes morphology differs from that of brine channels. While the latter usually show a funnel-like geometry, with adventitious secondary channels converging toward a central main drainage channel, brine tubes are generally broader linear single features. Brine tubes were observed in several of the AWECS stations (493, 496, 500, 506b—Figures 3 and 7), but generally limited to the upper 0.30-0.40 m of the ice cover. We suggest that they result (as it was the case at the SIMBA process sta- tions) from the combination of flooding and temperature cycling in the ice. During the cooling phases, surface sea water and brines partly refreeze which increases brine salinity. During the warming phases, these high salinity brines thermodynamically readjust, progressively dissolving the snow/ice as they move downward under gravity, forming the brine tubes. The latter materialize salts transport downward, a movement which is sometimes witnessed by a local maximum in salinity at those depth (see inserts of Figure 4b). At the SIMBA Spring stations, the process was intense enough for brine tubes to reach all the way to the bottom of the sea ice cover, with visible plumes
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On the influence of model physics on simulations of Arctic and Antarctic sea ice

On the influence of model physics on simulations of Arctic and Antarctic sea ice

A careful look at Table 2 suggests that the performances of LIM2 and LIM3 in the SH are comparable for ice thickness and drift, and that none of the models is systematically out- performing the other for ice concentration and extent. We advance 3 possible reasons for explaining this observation. First and foremost, the quality of the atmospheric reanalyses (NCEP/NCAR) in the SH is lower on average than in the NH, essentially due to the sparse spatial coverage of records in Antarctica and the Southern Ocean (Bromwich et al., 2007). Substantial biases in the surface energy budget due to errors in the reanalysis have been suggested (Vancoppenolle et al., 2011; Vihma et al., 2002). It is also worth mentioning that the poor representation of the Antarctic Peninsula in the re- analysis land-sea mask introduces a bias in the representation of winds, with an overestimation of westerlies (Timmerman et al., 2004). Accordingly, the simulated ice accumulates (is drifted away) immediately west (east) of the peninsula, and the simulated ice thickness is thus overestimated (underesti- mated) at these locations (Fig. 3). The bias in winds are also potentially responsible for the unrealistic magnitude of the drift as depicted in Fig. 4. Second, one has to bear in mind that both simulations have been carried out at a coarse (1 ◦ ) resolution. Important ocean small-scale processes (e.g. ed- dies) are not represented in the models, although they trans- port considerable amounts of heat and momentum (Rintoul
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Future Arctic marine access: analysis and evaluation of observations, models, and projections of sea ice

Future Arctic marine access: analysis and evaluation of observations, models, and projections of sea ice

The region and routes evaluated in this study include the Bering Strait, Northeast Passage, Northwest Passage, and Arctic Bridge (Fig. 7). The Bering Strait region separates the Pacific and Arctic Oceans between Russia and Alaska. This region has particular significance to the Arctic, because it provides access to the Arctic Ocean from the Pacific region. It freezes every winter and thaws every summer, a condition which is expected to continue through 2099 in all model out- puts. For the purposes of this study, a route can be considered open if sea ice does not block the route in question. Visually, this means the existence of a corridor of open water along the entire route. However, in the early stages of ice freeze-up or during late stages of thaw, a Polar Class 7 vessel is capable of traversing thin ice; some leeway was required when interpret- ing the results. In some stretches of both the Northwest and Northeast Passages, where the data have only a single pixel of sea ice, the ice may be capable of blocking the entire route. In these cases, we considered a route closed if at least two consecutive pixels of sea ice blocked the route. While trans- port ships can contract icebreakers for escort through these corridors, for the purposes of this study, we considered the route only open if a PC-7 could traverse the route unaided.
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The land-ice contribution to 21st-century dynamic sea level rise

The land-ice contribution to 21st-century dynamic sea level rise

In this study we develop projections of DSL change asso- ciated with new plausible scenarios of land-based ice melt. We assess two ice melt scenarios developed under the aus- pices of the European Union ice2sea project which include updated projections of the Glacier and Ice Cap (G&IC) contribution and Greenland and Antarctic ice sheet fresh- water contributions. The ice sheet components are derived from simplified simulations which include information about likely regions of glacial dynamic instability. The spatially and temporally varying glacial freshwater fluxes are applied in simulations with the HadCM3-coupled climate model (Gordon et al., 2000). The objective is to determine the de- tectability of DSL changes from the addition of these rel- atively small freshwater flux anomalies. We consider the role of this additional freshwater under both pre-industrial radiative forcing and under the Special Report on Emis- sions Scenarios (SRES) A1B greenhouse gas warming sce- nario (IPCC, 2000), which is usually regarded as a medium business-as-usual emissions scenario.
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Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations

Improved Arctic sea ice thickness projections using bias-corrected CMIP5 simulations

The uncertainty in climate projections can be partitioned into three distinct sources: (1) model uncertainty: for the same radiative forcing, different models simulate different mean distributions and temporal changes; (2) internal variability: the natural fluctuations of the climate present with or with- out any anthropogenic induced changes to radiative forcing; (3) scenario uncertainty: uncertainty in future radiative forc- ing resulting from unknown future emissions. Hawkins and Sutton (2009, 2011) assessed these sources of uncertainty in global and regional temperature and precipitation projec- tions, and here we quantify the sources of uncertainty in SIT, utilising the CMIP5 subset multi-model ensemble. Crucially we use the absolute values of SIT rather than considering anomalies as is often done for other climate variables. The methodology for partitioning these sources of uncertainty is detailed in Appendix B. An additional source of uncer- tainty that we neglect here is the PIOMAS calibration un- certainty emerging from the choice of atmospheric reanaly- sis and model tuning. This could be assessed by sampling the different versions of the PIOMAS reanalysis described in Lindsay et al. (2014). They find the different versions are broadly similar and can be accounted for by appropriate tun- ing of the ice model component. This bias in PIOMAS itself will introduce systematic biases to the MAVRIC projections. This bias is not a flaw in MAVRIC however but a limitation intrinsic to the observational data set one is correcting to.
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Improved Arctic sea-ice thickness projections using bias corrected CMIP5 simulations

Improved Arctic sea-ice thickness projections using bias corrected CMIP5 simulations

The uncertainty in climate projections can be partitioned into three distinct sources: (1) model uncertainty: for the same radiative forcing, different models simulate different mean distributions and temporal changes; (2) internal variability: the natural fluctuations of the climate present with or with- out any anthropogenic induced changes to radiative forcing; (3) scenario uncertainty: uncertainty in future radiative forc- ing resulting from unknown future emissions. Hawkins and Sutton (2009, 2011) assessed these sources of uncertainty in global and regional temperature and precipitation projec- tions, and here we quantify the sources of uncertainty in SIT, utilising the CMIP5 subset multi-model ensemble. Crucially we use the absolute values of SIT rather than considering anomalies as is often done for other climate variables. The methodology for partitioning these sources of uncertainty is detailed in Appendix B. An additional source of uncer- tainty that we neglect here is the PIOMAS calibration un- certainty emerging from the choice of atmospheric reanaly- sis and model tuning. This could be assessed by sampling the different versions of the PIOMAS reanalysis described in Lindsay et al. (2014). They find the different versions are broadly similar and can be accounted for by appropriate tun- ing of the ice model component. This bias in PIOMAS itself will introduce systematic biases to the MAVRIC projections. This bias is not a flaw in MAVRIC however but a limitation intrinsic to the observational data set one is correcting to.
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Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

Because the emission model by Burke et al. (1979) is based on the radiative transfer equation, the model does not converge to the correct solution for layer thicknesses ap- proaching zero (Menashi et al., 1993). Thus, we see a jump in the brightness temperature from open water to a very thin ice layer, as well as from bare sea ice to sea ice that is cov- ered by a very thin snow layer (Fig. 1). Moreover, in fur- ther studies we found that the emission model after Burke et al. (1979) is not suitable for considering multiple lay- ers within ice, because the model neglects higher order re- flection terms (Maaß, 2013). Therefore, the emission model after Burke et al. (1979) has been compared to a coherent model, described in Ulaby et al. (1981), that is based on the Maxwell equations and accounts for higher order reflec- tion terms (Maaß, 2013). Except for the first few centimetres of ice and snow layer thickness the brightness temperatures from these two models agreed well. Thus, we think that al- though the emission model after Burke et al. (1979) neglects higher order reflection terms and does not describe the transi- tion from a non-existing layer to a very thin layer (a few cen- timetres), our emission model is able to capture the bright- ness temperature changes caused by a layer of snow on top of sea ice.
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Predictability of the Arctic sea ice edge

Predictability of the Arctic sea ice edge

The seasonal cycle of the fractional AEE is less coherent between the models, except for a tendency toward higher values in early autumn (Figure 2, right). A possible explanation is that during this time of the year, the ice edge is contiguous in the Arctic Ocean rather than made up of separate segments in the Pacific, the Labrador Sea, the Fram Strait, and the Barents Sea, resulting in fewer degrees of freedom of ice-edge variations. Our estimate for the DOF of Arctic sea ice edge variability ( ≳4–15) is similar to the estimate for the DOF of Arctic sea ice thickness variability by Blanchard-Wrigglesworth and Bitz [2014] (3–14). The relation between these two measures is, however, not straightforward, one reason being that the former should scale with the perimeter whereas the latter should scale with the area of the sea ice cover (assuming constant spatial correlation lengths).
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Probabilistic projections for 21st century European climate

Probabilistic projections for 21st century European climate

radiation at the top of the atmosphere, shortwave and long- wave cloud radiative forcing, total cloud amount, surface fluxes of sensible and latent heat, and latitude-height distri- butions of zonally averaged atmospheric relative humidity. The emulated equilibrium responses for the 48 observed cli- mate fields (12 variables for 4 seasons) are used in the likeli- hood expression to estimate relative weights associated with the different parameter combinations. Our expression for likelihood, Eq. (3.9) in Murphy et al. (2007), results from a Bayes linear analysis (Goldstein and Wooff, 2007) where uncertain quantities are represented in terms of means and a covariance matrix, thus taking into account relationships between variables. Likelihood weights are calculated in a re- duced dimension space, with a single weight being assigned for each model variant for all predicted variables (Murphy et al., 2009). Discrepancy (Box 6 below) between struc- turally different GCMs implies an additional modelling un- certainty. This is included in the likelihood weighting as an additional variance, and helps prevent poorly modelled vari- ables from overly constraining the distribution. Weights are also readjusted according to the ability of the scaled tran- sient projections to reproduce historical trends in four large- scale temperature variables (Braganza et al., 2003), which together explain much of the variance in spatiotemporal re- sponse (Stott et al., 2006). The historical trends used are: (i) global mean temperature, (ii) the land-ocean temperature difference, (iii) the inter-hemispheric temperature difference, (iv) the north-south temperature gradient in Northern Hemi- sphere mid-latitudes. Uncertainty in the magnitude of past climate forcing is accounted for in this step through the SCM (Box 4).
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Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation

Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation

snow. Hence the mean CS-2 retrievals from the different thresholds are elevated to 0.46 m (40 %), 0.39 m (50 %) and 0.22 m (80 %). With regard to the snow freeboard from ALS, this indicates a location of the main scattering horizon of 0.16 m (40 %), 0.24 m (50 %) and 0.4 m (80 %) below the snow surface. Another airborne survey in the framework of Operation IceBridge (OIB) took place in the same area on 15 April (green dotted line in Fig. 7a). The operating airplane carried a snow-depth radar that is able to map the snow depth along the flight track. The data reveal a mean snow depth of 0.31 m along the track (Kurtz et al., 2012, updated 2014). Furthermore from simultaneous in situ measurements on the ground we additionally know that the mean snow depth ex- ceeded 0.3 m (Willatt and Haas, 2011). Thus if we assume this value as representative for this area, the 40 % threshold does not track the ice surface. Also the 50 % threshold seems to be too low, which is consistent with the conclusions in Kurtz et al. (2014). On the other hand the 80 % threshold seems to be too high considering the estimated snow depths. We acknowledge that the approach of Kurtz et al. (2014) is significantly different and therefore our approach of using an 80 % threshold can yield different results. We also note that this comparison might be only valid for the multiyear ice re- gion north of Alert in spring. This implies that in the case of the 40 and 50 % threshold we need to apply a geometric correction before converting freeboard to thickness (Eq. 6). This has been done for the AWI CS-2 sea-ice product where a 40 % threshold was used. Nevertheless the spatial and tempo- ral variation of such a geometric correction term is unknown. The narrow probability density function of the 80 % threshold indicates less variation in the upper part of the lead- ing edge. We can speculate that the shallow probability den- sity function for the 40 and 50 % thresholds (Fig. 8) originate from volume scattering through the snow layer which affects the lower part of the leading edge and leads to increased scat- tering in the range retrieval.
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