Seaicealgae represent a key energy source for many organisms in polar food webs, but estimating their biomass at ecologically appropriate spatiotemporal scales remains a challenge. Attempts to extend ice-core derived biomass to broader scales using remote sensing approaches has largely focused on the use of under-ice spectral irradiance. Normalized difference index (NDI) based algorithms that relate the attenuation of irradiance by the snow-ice-algal ensemble at specific wavelengths to biomass have been used to explain up to 79% of the biomass of algae in limited areas. Application of these algorithms to datasets collected using tethered remotely operated vehicles (ROVs) has begun, generating methods for spatial sampling at scales and spatial resolution not achievable with ice-core sampling. Successful integration of radiometers with untethered autonomous underwater vehicles (AUVs) offers even greater capability to survey broader regions to explore the spatial heterogeneity of seaice algal communities. This work describes the pilot use of an AUV fitted with a multispectral irradiance sensor to estimate ice-algal biomass along transects beneath land-fast seaice ( ∼ 2 m thick with minimal snow cover) in McMurdo Sound, Antarctica. The AUV obtained continuous, repeatable, multi-band irradiance data, suitable for NDI-type approaches, over transects of 500 m, with an instrument footprint of 4 m in diameter. Algorithms were developed using local measurements of icealgae biomass and spectral attenuation of seaice and were able to explain 40% of biomass variability. Relatively poor performance of the algorithms in predicting biomass limited the confidence that could be placed in biomass estimates from AUV data. This was attributed to the larger footprint size of the optical sensors integrating small-scale biomass variability more effectively than the ice core in the platelet-dominated ice algal habitat. Our results support continued development of remote-sensing of seaice algal biomass at m–km spatial scales using optical methods, but caution that footprint sizes of calibration data (e.g., coring) must be compatible with optical sensors used. AUVs offer autonomous survey techniques that could be applied to better understand the horizontal variability of seaicealgae from nearshore ice out to the marginal ice zone.
technology have now been employed for the in vitro analysis of ice shavings, extracted brines and melted ice cores (e.g., Ryan et al. 2004; Ralph et al. 2007; Kennedy et al. 2012; Martin et al. 2012). I-PAM (Walz, Germany) provides unprecedented two- dimensional in situ imagery which allows physiologi- cal stress to be measured with significantly less dis- ruption of the physical habitat (Ryan et al. 2011; Hawes et al. 2012; Lund-Hansen et al. 2014). This technology has only been employed once in Antarctica and with mixed success (Ryan et al. 2011), but the capacity to generate in situ data war- rants further investigation. In this preliminary study, our objectives were to expose sea-icealgae to changes in pH to determine whether short-term (8-h) expo- sure to reduced pH influences algal photophysiology (Experiment 1) and to quantify changes in pH that occur during the melt process (Experiment 2). We also determine whether imaging fluorescence is a viable technology to resolve changes in algal photo- physiology during state-transition experiments.
The situation in seaice ecosystems, however, is quite different to that in the open sea. During ice formation, internal gas concentrations are heavily modified by exclusion of gases from the ice crystal structure. This results in a steep decline in pH, changes to gas solubility at high salinity and low temperature often resulting in supersaturation and outgassing . Internal liquid seawater temperatures can drop to below 215 u C with salinities fluctuating between 173 in winter and 0 in summer . Photosynthetic activity has been recorded at temperatures below 210 u C . However, in spring and summer this trend is usually overridden by the photosynthetic activity of dense microalgal communities that cause the pH to rise as a result of the depletion of the dissolved CO 2. Values of up to 8.9, for instance, have been
On the other hand, (2) cell-associated material directly interacts with the producer organism, and its associated benefits may be simpler to describe. A minimum quantity of cell-associated material is required by some producer organisms, and this is important for aiding in cell attachment (e.g. chain forming cells and adhering to ice crystals) and motility. However, this material may become increasingly significant during times of adverse physicochemical conditions, where the producer organism becomes wrapped in extracellular material in an attempt to protect or buffer against potentially harmful conditions. This may be particularly important within the seaice habitat, where increased photosynthetic activity within the confines of brine channels, triggered by improved seasonal changes, induces rapid changes in biogeochemical properties (e.g. carbonate chemistry, nutrient availability, salinity). This thesis provided evidence to support this concept, with an increase in COLLOC observed with the onset of spring (Chapter 4). This suggests the producer organisms were preconditioning themselves for increased photosynthetic activity (Chapter 4).
We fit the move persistence model ( mpm ; Eqs. 1 and 2) to the state-space filtered seal tracks. Fitting to fil- tered tracks accounts for some of the uncertainty inher- ent in telemetry data but potential effects of residual location uncertainty should be examined post-analysis. To ascertain whether c t adequately captures changes in the seals ’ movement patterns, we compare the c t -based behavioral index to discrete behavioral states estimated from a switching state-space model (Jonsen 2016) fitted using the bsam R package. Details on how we fit the bsam model are in Appendix S3. We then fit the move persistence mixed effects model ( mpmm ; Eqs. 1 and 4) to the same state-space filtered seal tracks to infer how the seals ’ movement behavior may be influenced by envi- ronmental features encountered during their months- long foraging trips. In both analyses, we fitted separate models to the ice and pelagic foraging trips. For the mpmm s, we specified mixed effects models with random intercept and slopes to account for variability among individual seals. We fit all possible combinations of fixed and random effects and use AIC and likelihood ratios to find the best supported model for each set of tracks.
The objectives of this work were heavily based around satellite altimeter investigation of seaice, specifically, the measurement of seaice freeboard. Under assessment by ICESat utilising one method based on techniques previously presented in the literature and a second novel method, seaice freeboard was assessed over a 7 year period. ICESat-derived freeboards in austral spring 2009 were within one standard deviation of airborne laser measurements of seaice freeboard and show good overall comparison with in situ measurements. First year seaice freeboard in McMurdo Sound underwent no significant change from 2003-2009 which concurs with the findings of Kurtz and Markus (2012) who examined the wider Ross Sea region. However, a statistically significant increase in MY seaice freeboard was documented using both methods. It is suggested that the seaice regime in McMurdo Sound may switch into a deviant mode, when large tabular icebergs ground in its northern extremities. These icebergs act as barriers to oceanic stresses and accommodate the establishment of a MY seaice cover. The growth of this MY ice may then be enhanced by the ISW plume in McMurdo Sound. The area of MY seaice which experienced the greatest growth rate was coincident with this plume of supercooled ISW exiting the McMurdo Ice Shelf cavity as documented by other studies (Robinson et al., 2014; Gough et al., 2012; Mahoney et al., 2011; Leonard et al., 2011; Dempsey et al., 2010). This evidence is further suggestive of a strong link between ocean processes and seaice growth in McMurdo Sound. Chapter 3 also concluded that a higher spatial resolution of in situ measurements was required, in unison with remote sensing techniques, to attribute the influence of ISW and subsequent platelet ice incorporation or attachment on the observed growth in the MY seaice cover.
of the use of different satellite sensors, each of which may differ in their measurement wavelengths, fields-of-view, an- gles of incidence, ascending node times, and calibrations. In a study of the consistency of long-term observations of oceans and ice from space, Zabel and Jezek (1994) found that ocean observations should be matched at the geophysi- cal product level rather than at the sensor radiance level, an approach that was already in use by the seaice community. Details of the approach, including filling data gaps, reduc- ing land-to-ocean spillover effects, reducing weather effects over ice-free ocean, and finally matching seaice extents and areas for each pair of overlapping sensors, are discussed by Cavalieri et al. (1999).
Within the framework of the Russian–German research co- operation Laptev Sea System, two helicopter-based electro- magnetic (HEM) ice thickness surveys were made in the south-eastern Laptev Sea at the end of April 2008 (campaign TD XIII) and 2012 (campaign TD XX, Fig. 1). The measure- ments made over pack ice zones north of the landfast ice edge were used to estimate seaice production in flaw polynyas (Rabenstein et al., 2013; Krumpen et al., 2011) and for vali- dation of ESA’s SMOS (Soil Moisture Ocean Salinity) satel- lite derived ice thickness products (Huntemann et al., 2014; Tian-Kunze et al., 2014, 2017). Flaw polynyas are open wa- ter sites between pack ice and fast ice of high net ice pro- duction sustained by winds. For a detailed description of the HEM principle we refer to (Haas et al., 2009; Krumpen et al., 2016). In short, the instrument that is towed by a helicopter 15 m above the ice surface utilizes the contrast of electrical conductivity between sea water and seaice to determine its distance to the ice–water interface. An additional laser al- timeter yields the distance to the uppermost snow surface. The difference between the laser and HEM derived distance is the ice plus snow thickness. According to Pfaffling et al. (2007), the accuracy over level seaice is of the order of ±10 cm.
et al., 2014; Hamilton and Stroeve, 2016). From a statistical point of view, Drobot et al. (2006) showed that 46 % of the pan-Arctic minimum seaice extent would be predictable as early as February based on monthly seaice concentration, surface albedo, downwelling longwave radiation and surface skin temperature. Lindsay et al. (2008) have shown that their statistical model based on a wide range of predictors (e.g., atmospheric circulation indices, seaice extent and seaice concentration, ocean temperature at different levels) exhib- ited a greater skill in predicting the September seaice ex- tent (SSIE) than those by Drobot et al. (2006). The forecasts based on the state-of-the-art coupled atmosphere–ocean seaice models (Chevallier et al., 2013; Sigmond et al., 2013) do not show better results when compared with the statistical models (Kapsch et al., 2014; Schröder et al., 2014; Zhan and Davies, 2017). These caveats indicate that our understanding regarding the controlling factors of Arctic seaice may still be insufficient. Overall, skillful forecasts extend only 2 to 5 months ahead, for the summer months (Stroeve et al., 2015; Schröder et al., 2014), regardless of the type of the model used for the forecast (dynamical or statistical). The results and error margins based on these different approaches have highlighted how difficult it is to make skillful prediction for the SSIE. This is particular true for the years with extreme low September seaice concentrations (e.g., 2012 or 2007), with both the dynamical and the statistical models showing similar limitations (Stroeve et al., 2014, 2015; Schröder et al., 2014; Hamilton and Stroeve, 2016). Stroeve et al. (2014) have shown that seasonal predictions of the SSIE are most ac- curate in years when the seaice extent is near the long-term trend, but skillful seaice extent prediction appears challeng- ing in years when the weather plays a larger role (Hamilton and Stroeve, 2016).
The SnowModel mean s.w.e. for all areas at the end of the simulation is 2 cm higher than the in situ s.w.e. mean. How- ever, SnowModel clearly presents two very different snow accumulation patterns, one in the west covering area 1 and one in the east covering areas 2 and 3. Mean s.w.e. values in area 1 reach a maximum of 2 cm during the 8-month study period while in areas 2 and 3 they are in excess of 10 cm. This broad spatial distribution produced by SnowModel compares well with in situ measurements and general observations in November 2011, which recorded an increasing gradient in snow depth from west to east (Fig. 4). However, when each fastening area is directly compared to in situ means for those areas, s.w.e. is underestimated in area 1 (2 cm > in situ), slightly overestimated in area 3 (1 cm > in situ) and substan- tially overestimated in area 2 (5 cm > in situ) (Fig. 2). Only modelled s.w.e. in area 3 falls within the standard devia- tion of the in situ mean. In the east, snow depth increases are noted in mid-May, mid-June, early July, early and mid- August, and late September. The snow depth evolution in the west of the sound over area 1 follows a separate pattern with negligible increases in mid- to late April, mid-May, mid-July, late September and early November. When coincident pixels are directly compared to in situ data with coincident pixels SnowModel overestimates s.w.e. in the study area and there- fore the model has better agreement with in situ maximum values (r 2 = 0.56) than with the mean (r 2 = 0.53) or mini- mum (r 2 = 0.30) values (Fig. 3). It is important to note the importance of redistribution by wind which is provided by SnowModel. The consequences of neglecting this influence on snow accumulation in the study region are clearly demon- strated in Fig. 4. Figure 4a displays the accumulated precip- itation from MicroMet, while this is built on in Fig. 4b with the inclusion of the other SnowModel components. Over eastern areas of the study region, the MicroMet precipitation output as a standalone product provides s.w.e. values double that of the highest s.w.e. measured in situ. Although vastly improved, the general overestimation of s.w.e. by Snow- Model is clearly visible in Fig. 4b. Values in the eastern-most section of the seaice cover in McMurdo Sound, adjacent to Ross Island, are of the order of 20 to 35 cm s.w.e. These val- ues are all larger than the highest in situ measured s.w.e. of 17.7 cm and, for large areas, they still remain over double the measured value. In the central area of the sound, modelled s.w.e. decreases in agreement with measured s.w.e., with 5 in situ sites agreeing within ± 0.5 cm of SnowModel s.w.e. (Figs. 3 and 4b). The western region of seaice in fastening area 1 has far less measured snow. The model produces this well but values are too low. The extremes, where there is a lot of snow and where there is very little snow, both seem to be exaggerated by the model.
where C is the confidence and TU is the total uncertainty, a = 0.06 and b = 0.1 are estimated based on the relation- ship between confidence and uncertainty in the more re- cent OSISAF observations. Observations flagged with a con- fidence of 0 or 1 are not used in our study. For verifica- tion of the modelled SIC, the ESA SeaIce Climate Change Initiative, SeaIce Concentration Climate Data Record from the AMSR-E and AMSR-2 Instruments at 25 km Grid Spac- ing, version 2.0 (Toudal Pedersen et al., 2017). The data set consists of satellite observations from the National Space Agency’s Advanced Microwave Scanning Radiometer in- struments (AMSR-E/AMSR-2). The AMSR-E/2 observa- tions are, like the OSISAF SIC observations, also based on measurements from a passive microwave measuring the brightness temperature. The observations are structured on a 25 km grid. The OSISAF and AMSR-E/2 data sets are differ- ent data products, but are in many cases tuned to give similar results and cannot be viewed as true independent data sets. The AMSR-E/2 product has a gap from October 2011, when AMSR-E failed, to July 2012, when AMSR-2 became oper- ational. This is in the middle of our analysis period, resulting in less data for verification. The AMSR-E/2 SIC observation product includes individual uncertainty estimates for all grid points. This uncertainty is based on the sum of the algorithm uncertainty and smearing uncertainty. Smearing uncertainty is related to the location of the observation compared to the grid.
Abstract. Satellite seaice concentrations (SICs), together with several ocean parameters, are assimilated into a regional Arctic coupled ocean–seaice model covering the period of 2000–2008 using the adjoint method. There is substantial im- provement in the representation of the SIC spatial distribu- tion, in particular with respect to the position of the ice edge and to the concentrations in the central parts of the Arctic Ocean during summer months. Seasonal cycles of total Arc- tic seaice area show an overall improvement. During sum- mer months, values of seaice extent (SIE) integrated over the model domain become underestimated compared to observa- tions, but absolute differences of mean SIE to the data are reduced in nearly all months and years. Along with the SICs, the seaice thickness fields also become closer to observa- tions, providing added value by the assimilation. Very sparse ocean data in the Arctic, corresponding to a very small con- tribution to the cost function, prevent sizable improvements of assimilated ocean variables, with the exception of the sea surface temperature.
To the extent that there are in fact influences on pack physics, they probably exert themselves through multiphase processing not captured in the current version. High molecular weight material implies interfacial activity for a subset of surfactants, adhesion for some (and this is potentially irreversible), colloid formation, and much more [5-7]. Our mechanism deals only with first order, homogenous solid and brine. Natural ice is considered to be a pure crystal, with residual channels and pockets interspersed containing a highly idealized salt solution. Strong simplifications were made in order to handle omitted components. We tacitly assume that all algal biomacromolecules enter our brine quickly and initially in solute form [7,14], and their later interactions along phase boundaries, with each other and with the original producer organisms are duly ignored [19,22,25]. This simple picture serves us well as a startup expedient, but it should be viewed mainly as a convenience. Next generation research could include specific families of proteins, polysaccharides and related re-condensed hybrids known to adsorb during freezing -for which inhibition of phase transition kinetics has sometimes been documented [3,8,84]. Total interfacial areas available for biomacromolecular activity are extreme, with values approaching one square meter per kilogram [85-87]. A single monolayer of polymeric adsorbate corresponds to roughly tens of micromolar dissolved carbon, and it could well appear as solute in the laboratory after a typical (analytical) core-slice-melt sequence [60,62,74]. Adhesive macromolecules or clusters thereof may resist outward flushing pressure from drainage, even in the porous low-ice regime. For example, detrital substance exuded by the pennate diatoms is sometimes retained together with its source cells, inside micro-niches of the skeletal layer [17,25].
Table 2. Change in nucleation (Nucl.), condensation (Cond.), aqueous phase oxidation (Wet ox.), ageing (Age), accumulation mode scaveng- ing (Acc. wet dep.) and Aitken mode scavenging (Ait. wet dep.) flux between the present day and our ice-loss scenarios (no-ice, no-ice[SS] and no-ice[DMS]). We also show the present-day absolute value of each metric (column 1). Our average flux is calculated over grid boxes where the CCN response to sea-ice loss is less than − 10 % (Fig. 5). Note: the same grid boxes are used to derive an average flux in all runs. Thus, the response of modelled microphysical processes from the no-ice[SS] and no-ice[DMS] runs does not necessarily reflect a CCN response of less than − 10 %.
Figure 2 shows the evolution of the daily total area of seaice leads in the Arctic Ocean from 1 January to 30 April averaged for the period of 2003–2015. Superimposed on large year-to-year variation for each single day as shown by the grey shading in Fig. 2, the climatology of the to- tal seaice lead area exhibits a gradually decrease from ∼ 0.8 million km 2 in early January to ∼ 0.5 million km 2 in late April. As shown in Fig. 3a, overall, there is no significant trend in the total area of seaice leads averaged for January– April during 2003–2015, although it shows an increasing ten- dency from 2003 to 2013. The year 2013 had the largest area of seaice leads (0.91 million km 2 ) followed by the small- est area in the year 2014 (0.45 million km 2 ). We also cal- culate the correlation coefficients between July, August and September seaice extent and the area of seaice leads av- eraged from January to April during 2003–2015, which are − 0.51, − 0.30 and − 0.23, respectively. It appears that July seaice extent is more closely related to the area of seaice leads than August and September. Figure 3b shows the spa- tial distribution of the trend of the seaice lead area in each in- dividual 25 km grid box. The area of seaice leads has exhib- ited an increasing trend extending from the Greenland Sea, through the northern Barents Sea, to the Laptev and Kara seas, and a decreasing trend in the southern Barents Sea, be- tween the eastern Siberian Sea and Chukchi Sea, and along the coast of Alaska. In particular, the strong out-of-phase trend between the northern and southern Barents Sea is per- sistent for each individual month. However, most of these trends are not significant at the 95 % confidence level, except the southern Barents Sea.
Three daily SIC data sets are used in this study. The SICCI fields from AMSR-E (Lavergne and Rinne, 2014) are used in the data assimilation. This product consists of daily fields provided on a 25 km polar-centered EASE2 grid (Brodzick et al., 2012). In the SICCI data set, the North Pole data gap is filled by interpolation, and daily maps of total standard error (the sum of algorithm uncertainties and smear uncertainties that refers to the representation error on a different grid reso- lution) are provided. If the uncertainties contain the smearing error the data assimilative system will account for this. The ice concentration data used for comparison are from the Na- tional Snow and Ice Data Center (NSIDC; Cavalieri et al., 1984). This product also consists of daily fields with 25 km grid spacing on a polar stereographic projection. For sum- mer 2010, the NSIDC ice concentration fields are derived from a different passive microwave instrument (SSMI/S on- board DMSP F-17) and with a different algorithm (NASA- Team). AMSR-E has a finer native spatial resolution than SSMI/S so that, although both products are provided on a 25 km grid, the SICCI (AMSR-E-based) fields have more details and appear less smoothed than the NSIDC (SSMI/S- based) fields, especially in the seaice edge area (Fig. 1). Strictly speaking, the differences between the SICCI and NSIDC products – different Earth grids (polar stereographic vs. EASE2) and finer native spatial resolution of AMSR-E – do not make them independent data, because both are de- rived from passive microwave instruments, but we may as- sume that they are sufficiently different for to be treated as independent. As a third data set for comparison and discus- sion, we use the MODIS-based SIC and melt pond fraction (MPF) data from University of Hamburg. These data are ob- tained from surface reflectance in several MODIS frequency bands and a method that is based on the fact that different sur- face types (melt ponds, seaice, snow, and open water) have different reflectance spectra (Rösel et al., 2012, and Rösel and Kaleschke, 2012). Thus, the MODIS-derived melt pond and open water fractions (OWFs), which are related to SIC by 1 − OWF, are completely independent observations and as such we can use them for the forecasting system’s as- sessment. Because of the strong influence of cloud cover on MODIS, these data are provided as composites over 8 days on a 12.5 km resolution grid. The absolute MPF that has not been weighed over the SIC is used in this study. In order to account for a possible bias in MODIS-derived MPF and SIC data product (Mäkynen et al., 2014) and other uncertain- ties (Rösel et al., 2012), we followed Kern et al. (2016) and decreased the MPF estimates by 0.08 and replaced negative values of the MPF by 0. MODIS SIC was increased by 0.03 and limited to a maximum of 1.0.
We have calculated AD and PD values from SMOS bright- ness temperature and used the MLE approach to obtain SIC estimates over the Arctic Ocean for the year 2014. We have estimated SIC using different tie points, characterized by their central value and dispersion. For seawater, we have used a single year-round median value and the associated standard deviation for each index. For ice tie points, we have used two sets of values, as suggested by the results in Fig. 7. For the first set, we have computed for all years the median of the tie points between December and May (Table 1), i.e. the winter– spring months, when Arctic seaice extent is close to its an- nual maximum. For the second set, we have used those same winter–spring values for the months of October through May but the average of the summer values for the months between June and September (Table 1). We have used neither the Oc- tober nor the November data to compute ice tie point values because these are months of maximum extension of thin ice, and underlying emission through thin ice could cause some errors on the SIC estimates (Fig. 5 and Table 2).
Abstract. Advances in remote sensing of seaice over the past two decades have resulted in a wide variety of satellite- derived seaice thickness data products becoming publicly available. Selecting the most appropriate product is chal- lenging given end user objectives range from incorporating satellite-derived thickness information in operational activi- ties, including seaice forecasting, routing of maritime traffic and search and rescue, to climate change analysis, longer- term modelling, prediction and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current seaice thickness data products, collating key details of primary interest to end users. We assess 8 years of seaice thickness observations derived from sensors on board the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salin- ity (SMOS) satellites. We evaluate the satellite-only obser- vations with independent ice draft and thickness measure- ments obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonar (ULS) instruments and Oper- ation IceBridge (OIB), respectively. We find a number of key differences among data products but find that products utiliz- ing CS2-only measurements are reliable for seaice thickness, particularly between ∼ 0.5 and 4 m. Among data compared, a blended CS2-SMOS product was the most reliable for thin ice. Ice thickness distributions at the end of winter appeared realistic when compared with independent ice draft measure- ments, with the exception of those derived from AVHRR. There is disagreement among the products in terms of the