Melt ponds cover up to 50–60 % oftheArctic summer seaice area (Fetterer and Untersteiner, 1998; Eicken et al., 2004). They result from the accumulation of meltwater on seaice mainly due to melting of snow. Seaicemelt also contributes to themeltpond formation and growth in advanced stages ofmelt (Rösel and Kaleschke, 2012), driven by increased short- wave absorption during summer (Taylor and Feltham, 2004). During meltpond formation, meltwater either drains into the ocean through cracks and other openings intheseaice or is collected on theice surface in depressed areas. This melt- water is nearly free of salt and has a density maximum above the freezing point, resulting in radiative heating favoring con- vection (Fetterer and Untersteiner, 1998). Convection may be further assisted by winds, increasing temperature erosion ofthepond edge and eventually extending thepond area. As seaice warms during spring, its brine volume increases and melt- water ponds located above the freeboard may drain through vertical seepage to the underlying water (brine flushing, e.g., Fetterer and Untersteiner, 1998), thereby freshening the up- per layer ofthe ocean. This mechanism is believed to be the primary cause for seaice desalinisation (Untersteiner, 1968; Cox and Weeks, 1974). The input of freshwater to the sur- face layer ofthe ocean can lead to the formation of under-icemelt ponds, freshwater lenses trapped under thinner ice ar- eas or in depressions inthe bottom of thicker ice (Hanson, 1965; Weeks, 2010). The discharge of meltwater through theice cover is proportional to theice permeability and the hy- draulic pressure gradient inthe brine system (Darcy’s law). In summer Arcticseaice, this gradient is mostly determined by differences in hydraulic head that develop as a result ofmelt over a non-uniform ice surface (Eicken et al., 2002).
The pattern ofmeltpond distribution in early summer (Fig. 4) of areas with an increased relative meltpond frac- tion inthe beginning of June indicates theice free areas later in September. This agrees with the statements of Perovich et al. (2002b, 2011b), that early occurrence ofmelt 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 meltpond fraction up to 70 % at the end of June on the flat firstyeariceintheCanadian Archipelago. The decrease ofmelt ponds inthe 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 inthe Siberian Arctic and the Fram Strait.
adequately parameterized in models due to a lack of large scale observations. In this paper, results from a multi-scale remote sensing program dedicated to the retrieval ofpond fraction from satellite C-band synthetic aperture radar (SAR) are detailed. The study was conducted on first-yearsea (FY) iceintheCanadianArctic Archipelago dur- ing the summer melt period in June 2012. Approaches to retrieve the subscale FY ice
Changes to the composition ofseaiceintheArctic system affect the accuracy of geophysical and thermodynamic prop- erties, which are required for management strategies (Barber, 2005; Warner et al., 2013). An expected increase inthe rate of both early and late season precipitation and melt events intheArctic will add complexity to both snow thermodynamic modeling and interpretation of microwave remote sensing data, as multiple snow and ice conditions can lead to sim- ilar backscatter results (Barber et al., 2009; Warner et al., 2013; Gill and Yackel, 2012; Gill et al., 2014; Fuller et al., 2014). In such cases, a snow thermodynamic model may be used for comparison and inversion of important snow prop- erties (e.g., snow water equivalent (SWE), grain size) for a given backscatter response. Satellite-based remote sensing provides a larger scale of observation; however, errors stem from relating backscatter values to snow and ice structure and dielectrics (Durand, 2007). Potential solutions to these issues are being developed in state-of-the-art data assimilation tech- niques, which may solve issues of spatial and temporal cov- erage, observability, and spatial and temporal resolution (Re- ichle, 2008). These systems update snow physical and radia- tive models with available in situ snow and meteorological observations (Sun et al., 2004; Andreadis and Lettenmaier, 2006; Pulliainen, 2006; Durand, 2007). These are focused to- ward providing estimates for large areas with few in situ ob- servations, such as theCanadianArctic (Matcalfe and Good- ison, 1993; Langlois et al., 2009). Accurate representations of snow density, albedo, and storage and refreezing of liq- uid water inthe snowpack, as inputs to snow models, are re- quired for consistent results (Essery et al., 2013). Inversion or assimilation schemes that focus on C-band backscatter intheCanadianArctic may encounter error, as in situ conditions may not be as they appear inice charts and satellite imagery (e.g., Barber et al., 2009; Warner et al., 2013).
It is difficult to quantify to what extent increases in air spe- cific and relative humidity and cloud cover are due to seaice decline or increased transports from lower latitudes. Re- cent studies have suggested increasing trends inthe air mois- ture intheArctic (Dee et al., 2011; Screen and Simmonds, 2010a, b; Rinke et al., 2009; Serreze et al., 2012). On the ba- sis of three reanalyses (ERA-Interim, NASA-MERRA, and NCEP-CFSR) Serreze et al. (2012) detected significant in- creasing trends in vertically integrated water vapour content inthe period 1979–2010, in particular inthe regions where theseaice cover decreased most and sea surface tempera- ture increased most. Boisvert et al. (2013) studied evapora- tion from theArctic Ocean and adjacent seas by applying a new method (Boisvert et al., 2012); the air specific humid- ity was calculated from satellite data (specifically, the Atmo- spheric Infrared Sounder onboard the EOS Aqua satellite) and the wind speed from the ERA-Interim reanalysis. Statis- tically significant seasonal decreasing trends in evaporation were found for December, January and February, because ofthe dominant effect of an increase inthe 2 m air specific hu- midity that reduced the surface–air specific humidity differ- ence inthe Kara and Barents seas, the east Greenland Sea and the Baffin Bay region, where there is some open water year- round. Simultaneously, the evaporation slightly increased inthe central Arctic, due to decreased seaice concentration. The results of Boisvert et al. (2013) had both similarities and differences with those of Screen and Simmonds (2010a), based on in situ observations and ERA-Interim reanalysis. Screen and Simmonds (2010a) concluded that general in- creases in evaporation over theArctic were occurring, but their study area did not include the Barents Sea, and their study period did not include November and December: ac- cording to Boisvert et al. (2013) this was probably the main reason for the different general trends.
The monthly haze day data for 756 meteorological stations in China during 1960–2013 have been collected by the Na- tional Meteorological Information Center ofthe China Me- teorological Administration. The haze days from this data set are generally determined according to the immediately weather phenomenon. The monthly haze days here are the total numbers of haze day in a month, which has been also used in previous works (e.g., Wang et al., 2015). For the site observation, it was rejected if there are missing values inthe time series. Thus a subset of total 542 stations is selected. We focus our analysis on haze pollution over eastern China (east of 109 ◦ E, south of 40 ◦ N, mainland China) in this study. As has been indicated, more than 40 % haze pollution occurred in boreal winter (current year December and following year January–February); hence we focus on the winter season. We henceforth focus our analysis in two regions, R1 (east of 109 ◦ E in 30–40 ◦ N, including 112 stations) and R2 (east of 109 ◦ E and south of 30 ◦ N, including 104 stations) in main-
Gruber, N., Ishida, A., Joos, F., Key, R. M., Lindsay, K., Maier-Reimer, E., Matear, R., Mon- fray, P., Mouchet, A., Najjar, R. G., Plattner, G.-K., Rodgers, K. B., Sabine, C. L., Sarmiento, J. L., Schlitzer, R., Slater, R. D., Totterdell, I. J., Weirig, M. F., Yamanaka, Y., and Yool, A.: Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms, Nature, 437, 681–686, 2005.
Abstract. Over the past decade, the diminishing Arcticseaice has impacted the wave field, which depends on theice- free ocean and wind. This study characterizes the wave cli- mate intheArctic spanning 1992–2014 from a merged al- timeter data set and a wave hindcast that uses CFSR winds and ice concentrations from satellites as input. The model performs well, verified by the altimeters, and is relatively consistent for climate studies. The wave seasonality and ex- tremes are linked to theice coverage, wind strength, and wind direction, creating distinct features inthe wind seas and swells. The altimeters and model show that the reduc- tion ofseaice coverage causes increasing wave heights in- stead ofthe wind. However, trends are convoluted by inter- annual climate oscillations like the North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation. Inthe Nordic Green- land Seathe NAO influences the decreasing wind speeds and wave heights. Swells are becoming more prevalent and wind- sea steepness is declining. The satellite data show theseaice minimum occurs later in fall when the wind speeds in- crease. This creates more favorable conditions for wave de- velopment. Therefore we expect theice freeze-up in fall to be the most critical season intheArctic and small changes inice cover, wind speeds, and wave heights can have large impacts to the evolution oftheseaice throughout theyear. It is in- conclusive how important wave–ice processes are within the climate system, but selected events suggest the importance of waves within the marginal ice zone.
Table 2. Mean annual trend (1993–2012) of Palmer-Long Term Ecological Research (PAL- LTER) surface (depth < 5 m) carbonate chemistry and hydrography from the Western Antarc- tic Peninsula. Regression statistics include the mean annual rate (yr − 1 ), standard error (SE), number of measurements (NM), number of years (NY), r-square, and p value for aragonite saturation state (Ω arag ), pH, dissolved inorganiccarbon (DIC, umol kg −1 ), total alkalinity (TA, ueq kg − 1 ), temperature ( ◦ C), and salinity. Trends with a p value < 0.05 are statistically signifi- cant at the 95 % confidence level (values bolded). Points that were outliers at 95 % probability level were excluded (o).
Hegglin, M. I., Gettelman, A., Hoor, P., Krichevsky, R., Man- ney, L. G., Pan, L. L., Son, S.-W., Stiller, G., Tilmes, S., Walker, K. A., Eyring, V., Shepherd, S., Waugh, D., Akiyoshi, H., Anel, J. A., Austin, J., Baumgaertner, A., Bekki, S., Braesicke, P., Br¨uhl, C., Butchart, N., Chipperfield, M., Dameris, M., Dhomse, S., Frith, S., Garny, H., Hardiman, S. C., J¨ockel, P., Kinnison, E. D., Lamarque, J. F., Mancini, E., Michou, M., Mor- genstern, O., Nakamura, T., Olivi, D., Pawson, S., Pitari, G., Plummer, D. A., Pyle, J. A., Rozanov, E., Scinocca, J. F., Shi- bata, K., Smale, D., Teyssdre, H., Tian, W., and Yamashita, Y.: Multimodel assessment ofthe upper troposphere and lower stratosphere: extratropics, J. Geophys. Res., 115D, D00M09, doi:10.1029/2010JD013884, 2010.
This statement remains valid when considering SPM which were sampled at various water depths from 0 to 150 m, including the deep chlorophyll maximum (Fig. 2b). Com- pared with results obtained for surface waters, the relationship between SPM and POC within the water column: (i) is the same inthe river delta (as only surface waters were sampled); (ii) only slightly changes inthe open ocean waters (slope of 0.20 instead
These results demonstrate that there is a separation of length scales inmeltpond structure. Such findings can ultimately lead to more realistic and efficient treatments ofmelt ponds and the melting process in climate models. For example, a large connected pond which spans an ice floe is likely more effective at helping to break apart a floe than many small disconnected ponds. Moreover, a separation of scales inthe microstructure of a composite medium is a necessary con- dition for the implementation of numerous homogenization schemes to calculate its effective properties. “Homogeniza- tion”, also known as “upscaling”, refers to a set of ideas and methods in applied mathematics which address the problem of computing the effective behavior of inhomogeneous me- dia or systems. For example, consider an electrically insu- lating host containing uniformly dispersed conducting inclu- sions. Homogenization theory gives a range of mathemati- cal techniques for obtaining rigorous information about the effective or overall conductivity of this composite (Milton, 2002; Torquato, 2002). Thus, the existence of a scale sep- aration inthepond/ice composite implies that rigorous cal- culations ofthe effective albedo are amenable to powerful homogenization approaches. Similarly, techniques of mathe- matical homogenization can thus be applied to finding the ef- fective behavior of transmitted light through melt ponds and its influence on the heat balance ofthe upper ocean and bio- logical productivity. Our findings for the fractal dimension ofthemelt ponds and its variation are similar to results on the fractal dimensions of connected clusters in percolation and Ising models (Saleur and Duplantier, 1987; Coniglio, 1989). Like melt ponds, clouds strongly influence Earth’s albedo. However, the geometric structure of clouds and rain areas was found through similar calculations (Lovejoy, 1982) to have a fractal dimension of 1.35. This result was constant over the entire range of accessible length scales, which is in stark contrast to what we find here for Arcticmelt ponds. Interestingly, seaice floe size distributions display a similar separation of scales with two fractal dimensions (Toyota et al., 2006).
Table 2. Percentage change in: nucleation (Nucl.), condensation (Cond.), aqueous phase ox- idation (Wet Ox.), ageing (Age) and accumulation (acc. wet dep.) and Aitken mode (Ait. wet dep.) wet deposition mass flux between simulations. Also shown is the absolute value of each metric inthe present day (PD) run (column 1). Average is taken over grid-boxes where the CCN change between PD and no-ice simulations is less than −10 % at the surface (sfc) and ∼ 900 hPa (250–350 m) as shown in Fig. 5 (blue). Note: the same grid-boxes are used for all runs, although the CCN change between PD and no-ice [SS] is never less than 10 %. However data from this run is included for comparison.
Little auks (Alle alle) are endemic to theArctic and the most abundant seabird inthe North Atlantic Arctic, with an estimated population of 40–80 million individuals . Recent studies have demonstrated that they are affected by the ecological consequences of higher ocean tem- peratures intheArctic [10–13]. Beyond ocean temperatures, little auks might also be affected by the presence/absence ofseaice. During the breeding season, this planktivorous species is known to use the marginal ice zone (the transition area between pack ice and open water), whenever accessible, to forage and to rest [14–18], a behaviour also suspected to occur outside ofthe breeding season . Moreover, prey availability and species composition are predicted to differ significantly according to seaice concentration (SIC, percentage ofsea surface covered by icein a given area), particularly inthe case ofice-associated species . Such organisms are the preferred prey of little auks, because of their high lipid concentration , and little auks feeding within Atlantic ice-free water masses have been found to forage in less optimal conditions due to smaller, leaner prey [11,12]. Yet marine productivity is also tightly linked to bathymetry . In particular, continental shelves and shelf break slopes modify water fluxes and induce plankton concentration and aggregation of top predators [22,23]. Aggregations of little auks have been observed along the shelf-break outside the breeding season, probably reflecting an area of high prey density [24–26]. Inthe perspective of an ice-free Arctic Ocean in summer, bathymetry is, with light intensity, the environmental parameter that will remain unchanged. Understanding how little auks take advantage of bathymetric features is thus needed to predict climate change impacts on this species.
Inthe Tana River basin, DIC generally increased downstream during all three seasons, a strong indication of DIC build- up possibly contributed by rock weathering. This is partic- ularly evident along the Tana River mainstream, where DIC strongly increased downstream during all seasons (Fig. 4a), which corresponded to an increase in suspended matter (Bouillon et al., 2009; Tamooh et al., 2012). Nyambene Hills tributaries recorded exceptionally high DIC concentrations (Fig. 4a) during all three seasons, a phenomenon we as- sociate with a high rate of rock weathering. The lithology ofthe Nyambene Hills subcatchment mainly includes Qua- ternary volcanic rocks. The high rate of chemical weather- ing could result from the presence of ashes and pyroclas- tic rocks, as these have much higher weathering rates than basalts and trachytes (Hughes et al., 2012). HCO −
The notion that theArctic Ocean could have lost all its summer iceinthe mid-Holocene is of interest inthe light ofthe observed current downward trend intheArcticsea-ice cover. Inthe late 1970s thefirst satellites were launched, en- abling monitoring oftheArcticsea-ice cover. The mean sea- ice extent during the period 1979–2000 ranged from a max- imum of 16 million km 2 in March to a September minimum of 7 million km 2 (Serreze et al., 2007). The extent has de- clined since 1979, with the smallest extent (3.4 million km 2 ) since 1979 observed in September 2012. The changes ob- served inthesea-ice extent since the late 1970s are mainly confined to the coasts of Alaska and Siberia, with smaller changes along the northern coast of Greenland and the Cana- dian Archipelago (Cavalieri et al., 2008). The reduction insea-ice cover observed in recent years is most likely due to anthropogenic climate change (Notz and Marotzke, 2012). A reduction ofthesea-ice thickness has also been observation- ally confirmed. The thinning oftheice is observed over the entire Arctic Ocean (Kwok and Rothrock, 2009; Rothrock et al., 2003), and mainly reflects a reduction ofthe thick multi- yearice. During the same period the increase in surface tem- perature intheArctic region exceeded 2 ◦ C, which is twice as much as the global average temperature increase (Solomon et al., 2007).
complicate the deployment of instruments and limit their life- time. Because of these difficulties the temperature observa- tion coverage intheArctic Ocean is very sparse. We iden- tified 30 valid drifters with real-time data transmission dur- ing 2011, resulting in a density ofin situ buoys inthe Arc- tic ocean of approximately 1 per 500 000 km 2 . Furthermore, temperature measurements from drifters may be dubious, be- cause the drifters nesting on theice may be buried in snow or even be solar heated. Satellite observations ofthe snow and ice surface temperatures can complement thein situ observa- tions in order to increase the coverage of surface temperature observations. The IST data analysed here are estimated us- ing Thermal Infra-Red sensors (TIR) from the polar orbiting METOP satellite under clear sky conditions. The 6 GHz mi- crowave radiometer data have elsewhere been used for IST estimation during all sky conditions, but these data provide an integrated snow pack temperature rather than the surface temperature, because ofthe microwave’s penetration in to the snow and ice (Tonboe et al., 2011; Hwang and Barber, 2008). There are other satellite infra-red IST products like those based on MODIS data (Hall, 2004b) and the AVHRR Po- lar Pathfinder data by Fowler et al. (2012). These products have been validated and described by, for example, Scam- bos et al. (2006) and Hall et al. (2004b) and used for cli- mate and case studies. The Pathfinder dataset is well suited for climatologically studies, but can not be used for recent or real-time ice surface temperature analysis, due to irreg- ular dataset updates. Furthermore, the Polar Pathfinder spa- tial resolution is 5 km, which makes it less suitable for fine scale mapping and analysis. The MODIS IST product has very similar characteristics to the METOP IST product (see Sect. 6), with product timeliness and sensor continuity as the main differences. Timeliness and data continuity are essen- tial issues for the model communities to setup data valida- tion and assimilation schemes (Stammer et al., 2007). The MODIS seaice products have a time lag of days, from obser- vation to product availability, and timeliness ofthe present IST product is a couple of hours. The METOP AVHRR data stream that is used for this IST production is guarantied con- tinuity and is scheduled until at least 2020, in contrast to the MODIS data stream that will end with the current Aqua and Terra missions. No satellite IST products are, to our knowl- edge, used in Numerical Weather Prediction (NWP) models or inseaice models despite a potential for improving the model predictions. We think that this may be due to the lack of highest quality, future proofed, fully validated operational IST products, in near real time. The objectives of this study is to present and validate a new high resolution (1 km) IST product for theArctic, that meets these requirements.
modelling point of view a systematic year round 5 W energy flux anomaly can be suf- ficient to change theseaice regime from seasonal to perennial seaice, or vice versa (Bj ¨ork and S ¨oderkvist, 2002). Hence, the determination ofthe IST quality with respect to both error and bias is crucial for the applicability of satellite based IST fields in mod- els. The extreme conditions intheArctic complicate the deployment of instruments and
Methodology/Principal findings: Cholera-associated morbidity and mortality data were prospectively collected at the commune level according to the World Health Organization standard definition. Attack and mortality rates were estimated and mapped to assess epidemic clusters and trends. The relationships between environmental factors were assessed at the commune level using multivariate analysis. The global attack and mortality rates were 488.9 cases/10,000 inhabitants and 6.24 deaths/10,000 inhabitants, respectively. Attack rates displayed a significantly high level of spatial heterogeneity (varying from 64.7 to 3070.9 per 10,000 inhabitants), thereby suggesting disparate outbreak processes. The epidemic course exhibited two principal outbreaks. Thefirst outbreak (October 16, 2010–January 30, 2011) displayed a centrifugal spread of a damping wave that suddenly emerged from Mirebalais. The second outbreak began at the end of May 2011, concomitant with the onset ofthe rainy season, and displayed a highly fragmented epidemic pattern. Environmental factors (river and rice fields: p,0.003) played a role in disease dynamics exclusively during the early phases ofthe epidemic.
Table 1. Summary of cruise dates, start location, measured carbonate system parameters, and the main study area for each year. All expeditions ended in McMurdo Sound, Ross Sea. Continuous surface water measurements of chlorophyll a, sea surface temperature (SST), and salinity (S) were performed during all four cruises. AmS denote the Amundsen Sea and SIZ refers to the seasonal ice zone.