domain: sde chim

text:
Christian Zdanowicz
email: christian.zdanowicz@geo.uu.se

Mats Björkman Jean-Charles Gallet

Catherine Larose

Thomas Schuler

Bartlomiej Luks

Krystyna Koziol

Andrea Spolaor

Mats P Björkman

Bartłomiej Luks

Elena Barbaro

Tõnu Martma

Ward Van Pelt

Ulla Wideqvist

Johan Ström

Spatiotemporal variability of elemental and organic carbon in Svalbard snow during 2007-2018

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Light-absorbing carbonaceous aerosols, such as black carbon (BC) or "brown carbon" (BrC; organic) that are transported to Arctic latitudes can lower the albedo of snow/ice-covered surfaces on which they are deposited, thereby enacting a positive feedback that amplifies climate warming [START_REF] Bond | Bounding the role of black carbon in the climate system: A scientific assessment[END_REF]. The Svalbard archipelago, owing to its proximity to the European and Russian mainland, is particularly affected by BC and BrC emissions from fossil fuel combustion (FF; heating, gas flaring, etc.) and biomass burning (BB; e.g., agricultural or forest fires). Source attribution using carbon isotopes and atmospheric transport modelling indicates that BC associated with pollution haze events at the Zeppelin Observatory on Spitsbergen include both BB and FF contributions, the latter being proportionally more important in winter than summer [START_REF] Winiger | Isotope-based source apportionment of EC aerosol particles during winter high-pollution events at the Zeppelin Observatory, Svalbard[END_REF][START_REF] Winiger | Source apportionment of circum-Arctic atmospheric black carbon from isotopes and modeling[END_REF]. Quantifying the impact of BC deposition on the Arctic surface albedo requires knowledge of its concentrations, spatial distribution and variability in snow and ice. These data may also serve to verify the efficacy of ongoing and future measures to curb emissions of short-lived climate forcing aerosols, such as BC, that impact the Arctic (AMAP, 2015;[START_REF] Stohl | Evaluating the climate and air quality impacts of short-lived pollutants[END_REF].

On Svalbard, reconnaissance surveys of BC in snow were carried out in 1984-85 by Noone and Clark (1988) and in 2007 by [START_REF] Doherty | Light-absorbing impurities in Arctic snow[END_REF]. This was followed in 2007-09 by more detailed investigations of the distribution of BC across the archipelago [START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF][START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF]. Localized studies have also been carried out near Longyearbyen [START_REF] Aamaas | Elemental carbon deposition to Svalbard snow from Norwegian settlements and long-range transport[END_REF][START_REF] Khan | Impacts of coal dust from an active mine on the spectral reflectance of Arctic surface snow in Svalbard, Norway[END_REF] and Ny-Ålesund (Sihna et al., 2018;[START_REF] Jacobi | Deposition of ionic species and black carbon to the Arctic snowpack: combining snow pit observations with modeling[END_REF]. In addition, two ice cores recovered from the Lomonosovfonna and Holtedahlfonna icefields (Spitsbergen) have provided insights into longer-term variations in BC deposition on Svalbard [START_REF] Ruppel | Increase in elemental carbon values between 1970 and 2004 observed in a 300-year ice core from Holtedahlfonna (Svalbard)[END_REF][START_REF] Ruppel | Do contemporary (1980-2015) emissions determine the elemental carbon deposition trend at Holtedahlfonna glacier, Svalbard?[END_REF][START_REF] Osmont | An 800-year highresolution black carbon ice core record from Lomonosovfonna, Svalbard[END_REF].

Here we present and synthesize new observational data which document the variability of BC in snow across Svalbard in terms of latitude, longitude, altitude and time. These data were gathered through field investigations conducted between 2007-18 on both Spitsbergen and Nordaustlandet (Fig. 1). The two main datasets consist of a spatial survey carried out across 22 glacier sites in April 2016, and an 11-year long series of surface snow observations made on central Brøgger Peninsula on northwestern Spitsbergen. The April 2016 survey included some of the sites previously visited in 2007-09 by [START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF][START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF], thus allowing for comparisons after a ~decadal interval. All data presented in this study were obtained by the thermo-optical transmittance method (TOT), which quantifies separately the more refractory and volatile carbon mass fractions present in particulate material filtered from melted snow [START_REF] Chow | Equivalence of elemental carbon by thermal/optical reflectance and transmittance with different temperature protocols[END_REF]; see below). Following [START_REF] Petzold | Recommendations for reporting "black carbon" measurements[END_REF], we designate the more refractory mass fraction as elemental carbon (EC) and the more volatile fraction as organic carbon (OC).

In this paper, we use the new datasets, combined with meteorological and aerosol data and snowpack modelling, to (i) describe the spatial distribution of EC and OC deposited on Svalbard glaciers, (ii) estimate their mass loading in the winter snowpack https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. and how it relates to spatial variations in snow accumulation, (iii) describe the interannual variability of EC and OC concentration in snow on Brøgger Peninsula between 2007 and 2018, and (iv) constrain plausible estimates of the snowfall washout ratio for EC. Lastly, we place our findings in a broader pan-Arctic perspective by comparison with a compilation of published data obtained between 2002 and 2018 by comparable methods.

Material and methods

Field sampling

April 16 survey

Part of the dataset presented here was produced following a comprehensive, coordinated survey of the physical, chemical and microbiological properties of the Svalbard seasonal snowpack carried out at the end of the 2015-16 winter by individuals from multiple institutions (see acknowledgments). In total, 22 sites were sampled between 4 and 29 April 2016 on 7 glaciers of Spitsbergen and Nordaustlandet (Fig. 1). Snowpits were sampled at three different elevations in the upper, middle and lower reaches of each glacier (Table S1), the snow depth increasing with altitude (e.g., [START_REF] Pramanik | Comparison of snow accumulation events on two High-Arctic glaciers to model-derived and observed precipitation[END_REF]. Glacier sites were targeted partly for logistical reasons (ease of access by snowmobile), but also because sampling supraglacial snow avoided the heterogeneities in snow properties that may arise from interactions with vegetation and/or different substrates (e.g., wet vs. dry tundra soils). In addition, the selected glacier sites were at elevations of 102 to 1193 m a.s.l., which span ~65 % of the maximum relief in Svalbard (1713 m; Fig. 2).

In advance of the field campaign, standardized protocols were developed for the measurement of important snow physical properties (e.g., density, temperature) and the collection of samples for a variety of analyses, including EC, OC, and stable oxygen isotope ratios (δ 18 O). These protocols are documented in [START_REF] Gallet | Protocols and recommendations for the measurement of snow physical properties, and sampling of snow for black carbon, water isotopes, major ions and microorganisms[END_REF], and details relevant for this paper summarized hereafter. Snow sampling was performed in snowpits excavated down to the hard, icy firn surface representing the previous year's late summer ablation surface (in the accumulation zone of glaciers), or to the underlying bare ice surface (in the ablation zone). All snowpits were located well away from point sources of contamination (e.g., field camps), were accessed by foot from at least 100 m, and personnel doing the sampling wore protective, non-particulating suits, gloves, and face masks, and employed pre-cleaned plastic or stainless steel tools. The snow accumulation of each snowpit (hSWE, in water equivalent; w.e.) was calculated from discrete density measurements. After recording the physical properties of snow strata, large volume snow samples (~5 L each) were collected from the top 5 cm of the snowpack, and, where snowpack depth allowed for it, at 50-cm depth intervals beneath. The near-surface samples were collected to quantify EC and OC concentrations in snow (𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ) at depths where light absorption by carbonaceous particles has the largest impact on snow albedo [START_REF] Marks | The effects of additional black carbon on the albedo of Arctic sea ice: variation with sea ice type and snow cover[END_REF]. The deeper samples were used to estimate the total column mass loading of EC and OC (𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , Analysis of downscaled ERA Interim climatological fields [START_REF] Dee | The ERA-Interim reanalysis: configuration and performance of the data assimilation system[END_REF] over Svalbard show that surface temperatures in the 2015-16 winter exceeded the 30-year climatological normals for the 1981-2010 period by 2 to 6 °C, with the largest anomalies observed in the northeastern part of the archipelago (Fig. S1a). Total winter precipitation also exceeded 1981-2010 normals by 0.2 to 0.7 m w.e. over much of central and northern Spitsbergen (Fig. S1b). These unusual conditions arose partly owing to an extreme winter warming and precipitation event associated with a southerly air intrusion over Spitsbergen that occurred in late December 2015 [START_REF] Binder | Exceptional air mass transport and dynamical drivers of an extreme wintertime Arctic warm event[END_REF][START_REF] Kim | Major cause of unprecedented Arctic warming in January 2016: Critical role of an Atlantic windstorm[END_REF][START_REF] Maturilli | Arctic warming, moisture increase and circulation changes observed in the Ny-Ålesund homogenized radiosonde record[END_REF]. The implications of these climatological circumstances for the interpretation of our snow survey data are discussed later.

𝐿𝐿

Surface snow monitoring, Brøgger Peninsula

In S2). These samples were collected irregularly, on an opportunistic basis, by NPI staff during other field research activities, but were handled and analyzed in the same manner as all those previously described. Altogether, the dataset presented here comprises a total of 324 measurements of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 from 49 separate sites across Svalbard.

https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

EC and OC analyses

All snow samples used in this study were processed in the same way: they were first melted at room temperature and the meltwater was filtered through pre-ashed, 47-mm diameter quartz microfiber filters, following the procedure described in [START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF]. The filters were then air-dried at room temperature overnight, stored in sterile petri dishes, and later [START_REF] Wang | The influence of dust on quantitative measurements of black carbon in ice and snow when using a thermal optical method[END_REF][START_REF] Lim | Refractory black carbon mass concentrations in snow and ice: method evaluation and inter-comparison with elemental carbon measurement[END_REF]. A total of 31 snow filters obtained from 7 glaciers surveyed in April 2016 (35 % of samples) were found to have faint to pronounced yellow-pink or grey-brown coloration, likely indicating the presence of k-feldspars and/or oxides which are commonly found in cryoconites, although carbonates may also be present on these filters (see notes in Supplemental dataset). The samples that produced the colored filters were typically found in snow layers near the base of snowpits, suggesting windblown dust dispersion and deposition in the autumn when the ground is only partially snowcovered. In 11 of the colored filters, the measured 𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝐸𝐸𝐸𝐸 were noticeably lower than in filters from snow layers immediately above, and in 6 filters the 𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝐸𝐸𝐸𝐸 was < LOD, possibly due to the effect of dust on TOT measurements. Some surface snow filters obtained from Sverdrup or Gruvebadet near Ny-Ålesund between 2010 and 2018 also showed coloration indicating the probable presence of windblown dust. Correcting for the effects of dust on the OC-EC split point of individual TOT thermograms is feasible, but it requires experimental data which we did not have (e.g., [START_REF] Wang | The influence of dust on quantitative measurements of black carbon in ice and snow when using a thermal optical method[END_REF] (2)

where 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

EC

and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 OC are in mg m -2 , ρi is the mean density of snow layer i, zi its thickness, and n the number of discrete layers. For samples which yielded 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 <1 ng g -1 we assigned a value of 0.5 ng g -1 (half the LOD) in order to compute snowpack loadings (see below). An estimated error on individual density measurements (σρ) of ± 6 % was used [START_REF] Conger | Comparison of density cutters for snow profile observations[END_REF][START_REF] Proksch | Intercomparison of snow density measurements: bias, precision, and vertical resolution[END_REF], and the meter-scale variability of snow layer density at spatial scales of 1 to 100 m 2 was assumed to be on the order of 5 %, after [START_REF] Koenig | Annual Greenland accumulation rates (2009-2012) from airborne snow radar[END_REF] (n = 22 snowpits).

δ 18 O analyses

The stable isotope ratio of oxygen ( 16 O: 18 O) in snowpit samples collected in April 2016 was used to detect evidence of warming events associated with large autumn or winter snowfalls, that could help to interpret the 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 data. The measurements were made at the Institute of Geology of Tallinn's University of Technology, Estonia, using a Picarro model L2120-i water isotope analyzer (Picarro Inc., Sunnyvale, USA) [START_REF] Lis | High-precision laser spectroscopy D/H and 18 O/ 16 O measurements of microliter natural water samples[END_REF]. Results are reported in the standard delta notation δ 18 O relative to the Vienna Standard Mean Ocean Water. The analytical precision was ±0.1 ‰.

Supporting data

Surface meteorological observations

Automated weather stations (AWS) were operated on 6 glaciers sampled during the April 2016 survey (Table S3). These stations were situated close to the estimated equilibrium line altitude (ELA) of the glaciers, and provided hourly recordings of air temperature and ultrasonic soundings of snow surface height changes that were used to interpret snowpit stratigraphic data, in particular the timing of snow accumulation and of snowmelt events. Data from the AWSs were supplemented with records from Longyearbyen and the airport in Ny-Ålesund obtained from the Norwegian Meteorological Institute, and from the Polish Polar Station Hornsund (N 77.00°, E 15.11°, 9 m a.s.l.).

Snowpack modeling

Owing to the scarcity of direct precipitation measurements across Svalbard, reconstructing the snowpack accumulation history is challenging, and estimates from snowpits, probing and radar can only fill some of the spatial and temporal gaps. This difficulty can be partly circumvented by using the output of a snowpack model forced with meteorological observations (e.g., [START_REF] Jacobi | Deposition of ionic species and black carbon to the Arctic snowpack: combining snow pit observations with modeling[END_REF]. In this study, we use a coupled energy balance-snow model [START_REF] Van Pelt | Simulating melt, runoff and refreezing on Nordenskiöldbreen, Svalbard, using a coupled snow and energy balance model[END_REF] for the surface energy balance as well as internal snowpack processes (e.g., densification, melt-freeze events) that makes it possible to simulate the evolution of the seasonal snowpack (thickness and internal structure) for individual 1x1 km grid cells over Svalbard. The snow model routine simulates subsurface density, temperature and water content, while accounting for vertical water transport, liquid water storage, refreezing and runoff [START_REF] Marchenko | Parameterizing deep water percolation improves subsurface temperature simulations by a multilayer firn model[END_REF][START_REF] Van Pelt | A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957-2018[END_REF]. Fresh snow density is described by a temperature-and wind-dependent function [START_REF] Van Kampenhout | Improving the representation of polar snow and firn in the Community Earth System Model[END_REF], while snow densification is the sum of destructive metamorphism, compaction by overburden pressure and compaction by drifting snow [START_REF] Vionnet | The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2[END_REF]. Snow scouring and redistribution by wind is not accounted for, however. Layered snow properties are modelled with a vertical resolution of 1 cm.

Here, we used the model to simulate the snowpack evolution at some of the sites sampled during the April 2016 survey. For the April 2016 survey snowpits, simulations were limited to those sites located close to or above the local ELA (Table S1). As in van Pelt et al. ( 2019), the model was forced with downscaled, 3-hourly meteorological fields generated with the High Resolution Limited Area Model (HIRLAM, version 6.4.2; [START_REF] Reistad | A high-resolution hindcast of wind and waves for the North Sea, the Norwegian Sea and the Barents Sea[END_REF]. For all modelled sites, precipitation was locally calibrated (scaled with a factor) to assure matching modelled and observed snow depths at the time of observation

(April 2016). The snowpack model was also used to characterize the spatial variability of the seasonal snow cover over Brøgger Peninsula for the period 2008-18 during which surface snow was sampled for EC and OC (Fig. S3).

Black carbon aerosol measurements

The atmospheric mixing ratio of BC above Svalbard follows a well-defined seasonal cycle, peaking in late winter and early spring [START_REF] Eleftheriadis | Aerosol black carbon in the European Arctic: Measurements at Zeppelin station, Ny-Ålesund, Svalbard from 1998-2007[END_REF]. To establish how the timing of surface snow sample collection on Brøgger Peninsula compares with this seasonal cycle, and also to estimate BC washout ratios, we obtained aerosol data from the Zeppelin Observatory (N 78° 54.43', E 11° 53.20', 474 m a.s.l.), 2 km south of Ny-Ålesund, over the period 2007-18. The data used are filter-based measurements of the hourly mean aerosol light absorption coefficient (σap) made with a Particle Soot Absorption Photometer (PSAP; λ = 525 nm; [START_REF] Bond | Calibration and intercomparison of filter-based measurements of visible light absorption by aerosols[END_REF] or an aethalometer (λ = 880 nm; [START_REF] Eleftheriadis | Aerosol black carbon in the European Arctic: Measurements at Zeppelin station, Ny-Ålesund, Svalbard from 1998-2007[END_REF]. These data were used to calculate the hourly BC mass-equivalent mixing ratio in air (𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 , in ng m -3 ; [START_REF] Petzold | Recommendations for reporting "black carbon" measurements[END_REF], following:

𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 = 𝐶𝐶 𝑓𝑓 × 𝜎𝜎 𝑎𝑎𝑎𝑎 MAC 𝜆𝜆 × 10 9 (3)
where σap is in m -1 , MACλ is the wavelength-specific BC aerosol mass absorption coefficient cross-section (m 2 g -1 ), and Cf = 3.45 is a unitless correction factor accounting for light absorption in the filter matrix [START_REF] Backman | On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic[END_REF]. We used an MAC525 value of 12.5 m 2 g -1 for the PSAP data, and a MAC880 value of 15.9 m 2 g -1 for the aethalometer data, after [START_REF] Eleftheriadis | Aerosol black carbon in the European Arctic: Measurements at Zeppelin station, Ny-Ålesund, Svalbard from 1998-2007[END_REF] and [START_REF] Sinha | Evaluation of ground-based black carbon measurements by filter-based photometers at two Arctic sites[END_REF]. are left-censored by the analytical LOD, the median and mean were estimated by replacing values < 1 ng g -1 with 0.5 x LOD (0.5 ng g -1 ), following [START_REF] Hornung | Estimation of average concentration in the presence of nondetectable values[END_REF], while the geometric mean was estimated by the beta factor method of Ganser in samples from glaciers varied from <0.01 to 0.43, and tended to be higher (max. 1.56) in surface snow collected near Ny-Ålesund than at other sites.

April 2016 survey

The and16.2 mg m -2 with a median of 0.8 mg m -2 (mean 2.0 mg m -2 ), while 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 were between 1.7 and 320.1 mg m -2 , with a median of 20.5 mg m -2 (mean 49.3 mg m -2 ). The median EC/OC was only marginally higher (0.06) at glacier sites <200 m a.s.l., compared to higher elevations (range: 0.03-0.04) (Fig. S7). was between 1 and 100 ng g -1 , and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 between 10 and 1000 ng g -1 . For years in which snow samples from both areas were obtained, the range of variations on Austre Brøggerbreen (𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 : <1-45.1 ng g - 1 ; 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 : <1-1076.1 ng g -1 ) overlapped with that near Ny-Ålesund (𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 : <1-266.5 ng g -1 ; 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 : <1-7250.3 ng g -1 ; 2 outliers excluded; Fig. 5a,b). However on average (all years and months considered), 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 near Ny-Ålesund was 3 times higher than on Austre Brøggerbreen, while 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 was 7 times higher, but as much as 30 times higher in 2016. There were significant interannual variations in springtime 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 (range: 0.4-8.2 ng g -1 ) and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 (range: 1.8-691.4 ng g -1 ) between 2007 and 2018 (Fig. 5c). These variations were evident near Ny-Ålesund as well as on Austre Brøggerbreen, which are separated by ~5.5 km and ~400 m in elevation. [START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF][START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF]. Based on data from sites where direct comparisons with the 2007-09 surveys are possible, 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in the seasonal snowpack varies, from year to year, by at least one order of magnitude, and sometimes more (Fig. 3). The snowpack on glacier sites that are highest and/or further inland (e.g., Lomonosovfonna across the land grid provides estimates of the total aerosol mass that accumulated in the snowpack. The area-averaged loads were 1.8 mg EC m -2 and 71.5 mg OC m -2 . These figures translate to monthly mean accumulation rates of ~0.2 mg EC m -2 mo -1 , and ~8.2 mg OC m -2 mo -1 , respectively, over the period of snow accumulation from 1 Sept. 2015 and 30 April 2016. Averaged on a daily basis, the wintertime EC accumulation rate were ≤0.01 mg EC m -2 d -1 , at the low end of the range of estimated wintertime wet deposition rates for BC aerosols in rural sites elsewhere (~0.1-0.2 mg EC m -2 d -1 ; [START_REF] Barrett | Wet deposition of black carbon: A synthesis[END_REF].

Using the snowpack model, we also estimated the relative contributions to 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 made by each of the snowpack layers sampled in the accumulation zone of the 7 glaciers surveyed in April 2016. The number of layers sampled varied from 4 on Austre Lovénbreen and Lomonosovfonna, to 8 on Werenskioldbreen. On-site surface height soundings by AWSs at several glaciers (Fig. S10) indicate that snow accumulation in the 2015-16 winter was more or less equally divided between the autumn period leading to the late December 2015 snowstorm, and the months that followed up to mid-/late April 2016, when the snowpits were sampled. The snowpack model simulations, forced with downscaled HIRLAM precipitation data, gave similar results (Fig. 8). The AWSs also show that the December 2015 storm saw winter temperatures on nearly all glaciers rise above 0°C for several days, the warming being largest in southern Spitsbergen. Clear evidence for this was found in a >0.2 m thick icy snow layer at mid-depth in the snowpack on Hansbreen (site HB3). The depth of the layer is in good agreement with that predicted by the snowpack model at this site, showing that the simulation provides a reasonable estimate of local surface conditions. Icy layers also occurred in the lower half of the seasonal snowpack on other glaciers, but none of these could be unambiguously ascribed to the late December 2015 storm period.

The timing of EC and OC accumulation, inferred from the snowpack model chronology, varied considerably between glaciers (Fig. 9). On Austfonna in Nordaustlandet, ~80 % of the EC and OC was found in snow layers estimated to have been deposited in or after December 2015. On glaciers of northern and central Spitsbergen (Austre Lovénbreen, Kongsvegen, Holtedahlfonna and Lomonosovfonna), the accumulation sequence was more variable and differed between EC and OC. On Hansbreen and Werenskioldbreen in southern Spitsbergen, most of the EC and OC was contained in the deeper layers of the seasonal snowpack, estimated to have been deposited prior to January 2016. Surface meteorological records from the Polish Polar Station Hornsund and from an AWS on Werenskioldbreen show that several large snowfall events occurred in this area during the autumn of 2015, as well as some thaw events (Fig. S10). The stratigraphy of snowpits excavated on Hansbreen and

Werenskioldbreen in April 2016 also showed clear evidence of melt-freeze events in the early part of the 2015-16 winter (e.g., site HB3 on Hansbreen; Fig. 8). Also visible in these snowpits were multiple positive excursions in δ 18 O (i.e. shifts to less negative values) indicative of snowfall events presumably associated with relatively moist and warm southerly air intrusions over Spitsbergen.

Large cyclonic storms that reach Svalbard in December typically track from the south-southeast [START_REF] Rinke | Extreme cyclone events in the Arctic: Wintertime variability and trends[END_REF], and often make their landfall on southern Spitsbergen, bringing heavy snowfall and relatively warm, moist air, as for example on 19 December 2015 [START_REF] Hancock | Combining high spatial resolution snow mapping and meteorological analyses to improve forecasting of destructive avalanches in Longyearbyen,Svalbard[END_REF]. Such events may enhance wet deposition of EC and OC aerosols on local glaciers such as Hansbreen and Werenskioldbreen, relative to more northerly sectors of Spitsbergen that are located further along the storm track. Previous observations of acidic snowfall deposition on Hansbreen during periods of southerly polluted air advection support this interpretation [START_REF] Nawrot | Chemistry of snow cover and acidic snowfall during a season with a high level of air pollution on the Hans Glacier[END_REF]. Furthermore, meltwater percolation during surface thaws (or rainon-snow) can redistribute or concentrate some of the more hydrophilic EC and OC into icy layers near the base of the snowpack [START_REF] Aamaas | Elemental carbon deposition to Svalbard snow from Norwegian settlements and long-range transport[END_REF][START_REF] Xu | Postdepositional enrichment of black soot in snow-pack and accelerated melting of Tibetan glaciers[END_REF]. These circumstances could explain why the 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 across Svalbard, as was previously observed for wet deposition of SO4 2-, NO3 -and OC on glaciers near Hornsund [START_REF] Kühnel | Reactive nitrogen and sulphate wet deposition at Zeppelin Station, Ny-Ålesund, Svalbard[END_REF][START_REF] Kozioł | Organic carbon fluxes of a glacier surface: A case study of Foxfonna, a small Arctic glacier[END_REF]. 6), as well as previous studies [START_REF] Bourgeois | Pollution transport efficiency toward the Arctic: Sensitivity to aerosol scavenging and source regions[END_REF][START_REF] Browse | The scavenging processes controlling the seasonal cycle in Arctic sulphate and black carbon aerosol[END_REF] suggest that wet deposition is the predominant mode of EC deposition in Arctic snow. To control for the possible role of snowfall rate, we compared 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in surface layers for March, April and May (MAM) 2008-18 with simulated monthly snowfall anomalies over central Brøgger Peninsula over the same period. However, as mentioned earlier, no significant correlation was found, (Fig. 5f).

EC and OC in

A simple metric used to quantify wintertime wet deposition of BC is the snow washout ratio W (sometimes called scavenging ratio), and defined as 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝐸𝐸 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝐸𝐸 ⁄ , which accounts for atmospheric BC removal by snowfall through both withinand below-cloud nucleation scavenging (Noone and Clark, 1988). Previous observations made during springtime snowfall events on Svalbard have yielded a wide range of estimates for W of ~13 to 2000, with a median of ~380 at Zeppelin Observatory, and of ~60 to 600 in Ny-Ålesund [START_REF] Hegg | Measurements of black carbon aerosol washout ratio on Svalbard[END_REF][START_REF] Gogoi | Scavenging ratio of black carbon in the Arctic and the Antarctic[END_REF]. Here, we used our data on 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in spring snow at Austre Brøggerbreen, combined with 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 measured at Zeppelin Observatory, to constrain plausible estimates of W (Fig. 10). Specifically, we compared the probability distribution of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in surface layers at Austre Brøggerbreen with that calculated from 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝐸𝐸 at Zeppelin Observatory during the same time periods, in order to identify values of W that produced an optimal match. The calculation was: where 𝐶𝐶 ̅ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 is the estimated mean concentration of eBC in the top 5 cm of the snowpack for the dates on which snow samples were actually obtained, t is the number of days prior to the snow sampling date over which the computed values of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 were averaged, W is the washout ratio, ρair the air density and f is a precipitation weighing factor. We performed this calculation for 54 snow sampling days during the spring months of 2011-18, which are the months (years) in which the 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 data had the fewest missing values. The simulated snowpack at Austre Brøggerbreen summit was used to estimate, for each day, the value of t (averaging interval) based on prior changes in the snow surface height. This varied between 3 and 24 days. The weighing factor f was calculated from changes in hSWE for days on which the simulated snowpack surface rose by ≥ 1 mm (i.e., on days without snowfall, f was 0). Monthly mean values of ρair were used, calculated from Ny-Ålesund radiosonde data (Maturilli, 2011 and seq.).

𝐶𝐶 ̅ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 = 𝑊𝑊 × ∑ � 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 × 𝑓𝑓 𝜌𝜌 𝑎𝑎𝑓𝑓𝑎𝑎 � 𝑓𝑓 𝑓𝑓 𝑓𝑓=1 𝑡𝑡 (
An overlap between the calculated 𝐶𝐶 ̅ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 and measured 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 was obtained for 10 ≤ W ≤ 300 (Fig. 10). These values agree well with those reported by [START_REF] Gogoi | Scavenging ratio of black carbon in the Arctic and the Antarctic[END_REF] for Ny-Ålesund (~13-270), but are low compared to those of Hegg et al.

(2011) for Zeppelin Observatory (~250-2000). There are, however, several sources of uncertainty in the calculated 𝐶𝐶 ̅ 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 .

First, the calculation uses 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 measured at Zeppelin Observatory (474 m a.s.l.), whereas aerosols are likely scavenged by snowfall over a range of altitudes with variable BC mixing ratios. Second, the calculation neglects dry deposition contributions.

Third, the simulated snow surface changes at Austre Brøggerbreen that were used to define the averaging period t for 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 do not account for the possible effect of snow drifting and mixing. Consequently, the comparison presented on Fig. 10 should be considered with some caution. What the results imply is simply that it is possible to forecast a realistic range of eBC (or EC) concentrations in surface snow at Austre Brøggerbreen using 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 at Zeppelin Observatory and W values of 10 to 300.

Placing results in geographic perspective

Figure 11 shows the range of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in winter/spring Svalbard snow measured in this study, compared with data from other circum-Arctic or subarctic sites sampled between 2002 and 2018 (see figure caption for data sources). All EC data in this comparison were obtained by thermo-optical measurements on snow filters, but variations in the thermal protocol used between studies may account for some of the inter-site differences (Table S4). The 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 measurements made by Aamas et al. (2011) near Longyearbyen were excluded because of local pollution of the snow cover by coal dust [START_REF] Khan | Impacts of coal dust from an active mine on the spectral reflectance of Arctic surface snow in Svalbard, Norway[END_REF]. The pattern of EC distribution in winter/spring snow across the circum-Arctic shows the lowest 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in central Greenland (<1 ng g -1 ), followed by the southwestern Yukon (3-5 ng g -1 ), northern and central Alaska (3-5 ng g -1 ), Svalbard (2-7 ng g -1 outside Ny-Ålesund), northern Scandinavia (~15-30 ng g -1 depending on site), and reaching maximum values of 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in eastern and central Siberia (35-66 ng g -1 ). This geographic pattern is in broad agreement with that observed in pan-Arctic surveys conducted in 2007-09 [START_REF] Doherty | Light-absorbing impurities in Arctic snow[END_REF] and between 2012 and 2016 [START_REF] Mori | Black carbon and inorganic aerosols in Arctic snowpack[END_REF] using other analytical methods.

The EC/OC (inset, et al., 2013). The only site where a significantly lower mean EC/OC in snow has been measured is on the Eclipse Icefield in the southern Yukon (0.01±0.01; Table S5). This may be explained by the remoteness of this high elevation site (3020 m a.s.l.), which is mostly exposed in winter to relatively clean air advected from the Gulf Alaska.

Summary and conclusions

We have presented a large dataset of observations of atmospheric EC and OC deposited in snow on the archipelago of Svalbard, made between 2007 and 2018. The spatial snow survey conducted across 22 glacier sites in April 2016 was one of the most extensive and detailed carried out on Svalbard, and allows direct comparisons with the surveys by [START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF][START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF], made nearly 10 years earlier. Across all glacier sites, 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in the snowpack ranged from <1.0 to 45.2 ng g -1 (median 2.0 ng g -1 ), while 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ranged from 11.9 to 3448.9 ng g -1 (median 48.6 ng g -1 ). The calculated 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 EC were between 0.1 and 16.2 mg m -2 (median 0.8 mg m -2 ), while 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 were between 1.7 and 320.1 mg m -2 (median 20.5 mg m -2 ). The 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Using these relationships, we estimated the area-averaged, monthly mean EC and OC accumulation rates over the whole of Svalbard to be ~0.2 mg EC m -2 mo -1 and ~8.9 mg OC m -2 mo -1 for the winter 2015-16 (September to April). The relationship between 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 S4). The box-whisker plots are as defined in Fig. 3

  𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ) in the seasonal snowpack. Quantification of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in layers from discrete snowfall events was not feasible, owing to the large snow volume required to achieve a sufficient particulate carbon mass for TOT analysis. All snow samples https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. were double-bagged in sterile low-density polyethylene bags and returned frozen to a location where they were subsequently melted and filtered. Depending on logistics, this was done either at the Polish Polar Station Hornsund operated by the Polish Academy of Sciences, or at the Norwegian Polar Institute (NPI) facilities in Ny-Ålesund (Sverdrup station), Longyearbyen (UNIS) or Tromsø. A total of 89 samples were obtained from all 22 sites.

  addition to the April 2016 survey, we report measurements of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in surface layers sampled by NPI staff from three sites on Brøgger Peninsula between 2007 and 2018 (Fig.S2). The first of these sites is in the accumulation zone of Austre Brøggerbreen (78.87° N, 11.92° E, 456 m a.s.l.), which was accessed by snowmobile from Ny-Ålesund. The other sites are in the outskirts of Ny-Ålesund, one ~80 m southeast of NPI's Sverdrup station (78.92° N, 11.93° E, ~50 m a.s.l.), the other near the Gruvebadet Atmospheric Laboratory (78.92° N, 11.89° E, ~50 m a.s.l.). Sampling was carried out at approximately weekly intervals by the NPI permanent staff at Sverdrup station, whenever their work schedule made it possible, and when safe snowmobile driving conditions (e.g., proper visibility, firm surface) allowed access to Austre Brøggerbreen. Because of these restrictions, the snow samples could not always be collected immediately after snow fall events. Over the ~11-year period considered, a total of 201 samples were collected between February and June, 86 % of which were taken in the spring months (March-May), April being the most represented month (n = 44). Methods for field sample collection were the same as those described above for the April 2016 survey. Sample collection was limited to the top 5 cm of the snowpack (occasionally deeper). These data provide long-term estimates of the interannual variability of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸

  , which has recently been employed to investigate glacier and snow conditions across Svalbard (van Pelt et al., 2019). The model includes subroutines https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

  https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. 3 Results Descriptive statistics of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 for all samples analyzed in this study are summarized inTable 2. The probability distributions of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 both have skewness >4, therefore we use medians (𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ) as measures of their central tendency, but also report arithmetic and geometric means for comparisons with other published data. As both 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸

  spatial variations of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 across the glacier sites surveyed in April 2016 are summarized on Fig. 3 and 4, with additional details from snowpits shown in Fig. S4 and S5. In the seasonal snowpack (n = 22 sites), 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 ranged from <1.0 to 45.2 ng g -1 , with 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 = 2.0 ng g -1 , while 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ranged from 11.9 to 901.4 ng g -1 , with 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 = 48.6 ng g -1 . These values fall well within the range of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 observed in surface layers on Austre Brøggerbreen between 2007 and 2018 (see also section 3.2). At nearly all of the snowpit sites surveyed in 2016, the 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in surface layers (top 5-10 cm) was larger than the weighted mean for the whole snowpack (Fig. S6). This was not the case for 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 , which showed no systematic enrichment in surface layers relative to the bulk of the snowpack. In the April 2016 snowpack, 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 >10 ng g -1 were only found on Hansbreen and Werenskioldbreen on southern Spitsbergen (max. 33.0 ng g -1 ). However, comparable 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 were measured in surface layers of Kongsvegen and Midtre Lovénbreen, northeastern Spitsbergen, in 2017 (max. 25.4 ng g -1 ), and on Lomonosovfonna, central Spitsbergen, in 2008 (Forsström et al., 2013; interquartile range: 7.3-20.5 ng g -1 ). The snowpack on Hansbreen and Werenskioldbreen also contained the most layers with 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 >100 ng g -1 . No meaningful differences in 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 were found in the April 2016 snowpack across different sectors of Spitsbergen (Fig. 3, range: 1.1-2.4 ng g -1 ; Kruskal-Wallis test, p >0.1). However the snowpack on Austfonna (Nordaustlandet) had a significantly lower 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 (1.1 ng g -1 ) than on glaciers of southern or northwestern Spitsbergen. This held true even if values of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 <1 ng g -1 were excluded. The 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in April 2016 were generally lowest on Lomonosovfonna (central Spitsbergen) and on Austfonna, and highest on southern Spitsbergen glaciers (Hansbreen and Werenskioldbreen). Binning the 2016 snowpit data by elevation (Fig. 4) showed no meaningful differences in 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 (2.2-2.8 ng g -1 ) or 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 (50.3-103.0 ng g -1 ) over the https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. ~1100 m altitude range of the 22 glacier sampling sites (Kruskal-Wallis test, p >0.1). The calculated 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 were between 0.1

  https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. summit) had generally lower 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 than at low-elevation, near-coastal sites. Ice-free open water areas or frost flowers on seaice are potential sources of particulate OC aerosols (e.g., microbes, diatoms, plankton, exopolymers from biofilms) during autumn and winter, some of which are likely deposited in snow by settling or through ice nucleation[START_REF] Bowman | Elevated bacterial abundance and exopolymers in saline frost flowers and implications for atmospheric chemistry and microbial dispersal[END_REF] Campbell et al., 2018;[START_REF] Karl | New insights in sources of the submicrometre aerosol at Mt. Zeppelin observatory (Spitsbergen) in the year 2015[END_REF]. The quantity of these aerosols deposited in Svalbard snow might be expected to decrease with inland distance and altitude, which is consistent with our observations of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 .Comparing results of the April 2016 survey with the 2007-09 data from[START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF] also shows that, on an interannual basis, the 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in the late winter snowpack across Svalbard can vary by at least two orders of magnitude (Fig. S8 and S9). For the winter 2015-16, our estimates of 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 were generally lower than in 2007-09. For example, 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 at the summit of Holtedahlfonna (site HDF3; elev. 1119 m a.s.l.) was 1.1 mg m -2 in April 2016, which is 70% lower than the 3.7 mg m -2 calculated in April 2008 at the same location (Forsström et al., 2013). For their part, Ruppel et al. (2017) estimated an annual mean 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 of 10 mg m -2 using snow samples and a firn core from Holtedahlfonna spanning ~8 years (2006-14). The corresponding mean 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸in the late winter (end April) snowpack could be less than half of this value (~5 mg m -2 ), but the high interannual variability in net snow accumulation at this site(Pramanik et al., 2014;[START_REF] Van Pelt | Modelling the long-term mass balance and firn evolution of glaciers around Kongsfjorden, Svalbard[END_REF], and the uncertainty in the chronology of the firn core makes such an estimate tentative at best. Differences between our estimates of 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and those from the 2007-09 surveys probably reflect, to a large extent, the variability of atmospheric EC transport and deposition between years and seasons, but also in space (local scale; Svensson et al., 2013). The estimated 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in the April 2016 snowpack were generally largest at higher elevations on glaciers, where snow accumulation is greater (Fig. S8 and S9). From equations (1) and (2) above, it is expected that 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 should increase non-linearly with hSWE, since the mass of EC in OC in the snowpack is the cumulative sum of the product of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 (or 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ) by some fraction of hSWE in discrete snow layers. For the winter 2015-16 snowpack, we modelled the relationship between 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and hSWE across all snowpits with an exponential function (Fig. 6a; R 2 = 0.86; RMSE = 0.59). The goodness-of-fit was confirmed through a Kolmogorov-Smirnov (KS) test of normality on the standardized model residuals (α = 0.05, p = 0.2). A similar model applied to 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 against hSWE gave a poorer fit (Fig. 6b; R 2 = 0.73; RMSE = 7.26; KS test: p = 0.32), owing to greater scatter in the 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 data. The 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 estimated by Forsström et al. (2009, 2013) from glacier surveys in 2007-09 showed a much poorer correlation with hSWE than the April 2016 estimates (Fig. 6a). This may partly reflect methodological differences in the estimation of both 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and hSWE between these studies, and the fact that the estimates from Forsström et al. (2009, 2013) span three different months, whereas the 2016 estimates are based on measurements over a limited time period of ~3 weeks in April. From the intercept of our exponential model for 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , we estimated that the contribution of dry deposition to 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 to the winter 2015-16 EC mass accumulation in the glacier snowpits to be ~0.18 mg m -2 . This represents >50 % of 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 at windswept glacier sites with low snow accumulation, but <5 % of 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 at sites with hSWE >1000 mm, such as Hansbreen and Werenskioldbreen in southern Spitsbergen. At the summit of Kongsvegen (site KVG3, elev. 672 m a.s.l.; hSWE = 825 mm) the estimated dry-deposited EC accounted for only ~11 % of 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 . This is much less than the ~50 % estimated by Jacobi et al. https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. (2019) at the same site in March 2012 (hSWE = 943 mm) using calculations of wet and dry deposition fluxes constrained by refractory black carbon (rBC) measurements in melted snow (SP2 method; Stephens et al., 2003). Our calculated 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 for sites with low hSWE, such as those on the lower reaches of glaciers exposed to wintertime katabatic winds, likely underestimate both dry-and wet-deposited EC owing to wind scouring of the snowpack. Nevertheless, our results suggest that the contribution of wintertime dry deposition to 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 on Svalbard is likely minor, consistent with findings from recent springtime observations of BC deposition near Ny-Ålesund (Sihna et al., 2018). The estimated monthly mean EC accumulation rates (by wet and dry deposition) at Kongsvegen summit (672 m a.s.l.) and at site ALB2 on Austre Lovénbreen (

  https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

𝐸𝐸𝐸𝐸

  of Hansbreen and Werenskioldbreen in April 2016, and also why >65 % of the accumulated EC was found in the deepest part of the snowpack at these sites. It also suggests that a few large precipitation events could explain a large part of the interannual variability in 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸

  surface snow, Brøgger Peninsula. Two features of the ~11-year record of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 from Brøgger Peninsula (Fig. 5) are of particular interest: the difference in 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 between Ny-Ålesund and Austre Brøggerbreen, and the synchronous interannual variations at both sites. The generally higher 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 near Ny-Ålesund compared to Austre Brøggerbreen point to the probable existence of a wintertime gradient in atmospheric EC and OC deposition from sea level up to at least 456 m a.s.l. on this part of Brøgger Peninsula. Evidence for such a gradient was observed during experimental snowmobile-based surveys of nearsurface 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 over Edithbreen and Kongsvegen (see Fig. 1 and S2 for locations) made in April 2016 (Spolaor et al., 2017). Such a situation may not, however, necessarily persist in all winter months. For example, Aamaas et al. (2011) measured a 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 of 6.6 ng g -1 near Ny-Ålesund in March 2008, which was very close to the mean of 6.3 ng g -1 on Austre Brøggerbreen snow during the same month. Variations in the frequency and strength of near-surface thermal inversions could account for fluctuations in 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 gradients between the coast and the accumulation area of Austre Brøggerbreen, as was shown to be the case for atmospheric SO4 2-aerosols (Dekhtyareva et al., 2018). Stable inversion layers established by strong surface https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. radiative cooling could trap aerosols emitted from Ny-Ålesund (EC) or from nearby open waters or sea ice (OC), leading to enhanced concentrations of these aerosols in coastal surface snow during these periods. Aamaas et al. (2011) could not detect local EC pollution in coastal snow within 20 km of Ny-Ålesund, but as these authors pointed out, this could have been due tounfavourable wind conditions at the time of their sampling. Our data, however, suggest that that winter/spring surface snow near Ny-Ålesund is commonly enriched in EC relative to OC when compared to snow deposited higher up on Austre Brøggerbreen, as shown by ~4-fold differences in the median EC/OC between these sites (

  4) https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

Fig. 11 )

 11 in the Svalbard snowpack outside Ny-Ålesund averages 0.07±0.08 (1 s.d.), which is similar, within https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. errors, to that reported in snowpacks in central Greenland (0.08±0.05; Hagler et al., 2007), northern Alaska (0.09±0.03; Dou et al., 2017), northeastern Russia and Siberia (0.07±0.03; Evangeliou et al., 2018), and northern Finland (0.05±0.06; Meinander

  comparable or lower than those found in spring 2007-09 glacier snow, but no clear spatial gradients could be identified across the archipelago. Both 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 were found to increase exponentially with elevation and hSWE.

𝐸𝐸𝐸𝐸

  and hSWE also point to dry EC deposition in snow being minor compared to wet deposition. The accumulation of EC and OC in the snowpack was inferred to be equally distributed over the winter 2015-16 at most sites. Relatively high 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 were found in the accumulation zones of glaciers on southern Spitsbergen, which we attribute to enhanced wet aerosol deposition when large Atlantic cyclonic storms made landfall in this area during the autumn and mid-winter.The set of EC and OC measurements made in surface snow on Brøgger Peninsula in 2007-18 is one of the longest such datasets available from the Arctic. During this period, the range of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 near Ny-Ålesund (50 m a.s.l.) overlapped with that at Austre Brøggerbreen (456 m a.s.l.). However, 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 near Ny-Ålesund was, on average, 3 times higher than on Austre Brøggerbreen, while 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 was 7 times higher, pointing to an elevation gradient in EC and OC deposition between these sites. While no long-term trends were detected over the period 2007-18, the snow sampling sites, the largest ones occurring near Ny-Ålesund. The EC/OC in snow also showed interannual variations with large differences between Ny-Ålesund and Austre Brøggerbreen, which are likely controlled by changes in the frequency and strength of wintertime near-surface thermal inversions in the area. Further investigations of winter/spring micro-to mesoscale meteorological conditions are needed to clarify what these variations might imply about the dynamics of atmospheric EC and OC deposition in snow at these sites. We used the measured 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 on Austre Brøggerbreen summit combined with 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 data from Zeppelin Observatory and snow modelling to constrain plausible https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License. estimates of the snowfall washout ratio W for EC, and found that values of 10 to 300 produce a realistic range of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑒𝑒𝑒𝑒𝐸𝐸 in spring surface snow. Extending the surface snow monitoring program for EC and OC on Austre Brøggerbreen would allow to test the robustness of these findings. Finally, comparing results from this study to those from other surveys confirms the existence of a broad longitudinal gradient in EC deposition across the Arctic and sub-Arctic, with the lowest 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 found in the western Arctic (Alaska, Yukon) and central Greenland, and the highest in northwestern Russia and Siberia.

Fig. 1 .

 1 Fig. 1. Location map of the Svalbard archipelago, showing the glaciers where snow was sampled during the April 2016 survey. See Fig. S2 for sampling sites near Ny-Ålesund on Brøgger Peninsula.

Fig. 2 .Fig. 3 .Fig. 4 .

 234 Fig. 2. Distribution of the Svalbard snow sampling sites with respect to elevation and surface hypsometry. Open circles are glacier sites sampled duringduring the April 2016 survey, while full circles are sites where surface snow was collected on the Brøgger Peninsula between 2007 and 2018. The thicker black line is the total area distribution over the archipelago (upper scale; from James et al., 2012), while the colored lines show the glacier hypsometry (lower scale) over southern (S), central (C), northeastern (NE) and northwestern (NW) Svalbard, as defined inKönig et al., 2014 (inset). Also shown are estimated ranges of the long-term mean Equilibrium Line Altitude(ELA; 1957-2018) for glaciers in each of the aforementioned sectors[START_REF] Van Pelt | A long-term dataset of climatic mass balance, snow conditions, and runoff in Svalbard (1957-2018[END_REF], and the minimum (winter) and maximum (summer) thickness of the Planetary Boundary Layer (PBL) in the maritime sector of the European Arctic, based onERA-40 reanalysis over 1969-2001[START_REF] Esau | Climatology of the Arctic planetary boundary layer[END_REF].

Fig. 5 .

 5 Fig. 5. (a) 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and (b) 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in surface layers on central Brøgger Peninsula, Svalbard, 2008-18. The doubleheaded arrow in (a) is the interquartile range of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 measured near Ny Ålesund in March 2008 by Aamaas et al.

Fig. 6 .Fig. 7 .

 67 Fig. 6. Scatterplot of (a) 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 EC and (b) 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 OC against hSWE based on measurements from Svalbard glacier snowpits from the April 2016 survey. The error bars are ± 1σ, and take into account uncertainties in hSWE, 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 .An exponential model, shown with 95% confidence bounds on predictions, was fitted by nonlinear least-squares regression, inversely weighted against data errors. In panel (a), data from earlier surveys by[START_REF] Forsström | Elemental carbon distribution in Svalbard snow[END_REF][START_REF] Forsström | Elemental carbon measurements in European Arctic snow packs[END_REF] are displayed for comparison.

Fig. 8 .

 8 Fig. 8. Simulated evolution of the snowpack from Sept 2015 to April 2016 at three glacier sites on Spitsbergen, compared with measured profiles of density, cumulative hSWE, δ 18 O, as well as cumulative 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 in the snowpack. The hSWE over the EC and OC sampling intervals was computed using the discrete snow layer density data. Where density measurements were missing, values from comparable layers in other snowpits were used. Snow layers with 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 <1 ng g -1 were assigned a value of 0.5 ng g -1 for 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 calculations. Icy snow and discrete

Fig. 9 .

 9 Fig. 9. Sub-seasonal increments of (a) 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and (b) 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 on Svalbard glaciers during the 2015-16 winter, as

Fig. 10 .Fig. 11 .

 1011 Fig. 10. Box-whisker plot of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 measured in surface layers on Austre Brøggerbreen in the spring months of 2011-18 (far left), compared with box-whisker plots of mean 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 calculated from 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 at Zeppelin Observatory, and using different values of the snow washout ratio W. Red plus signs (+) are outliers.

  , but outliers were removed for clarity. Published data sources for Svalbard glaciers: Forsström et al. (2009, 2013) and Ruppel et al. (2017); Ny-Ålesund and Longyearbyen: Aamaas et al. (2011); Greenland: Hagler et al. (2007); Alaska: Dou et al. (2007); northern Scandinavia: Forsström et al. (2013), Meinander et al. (2013), Svensson et al. (2013, 2018), and unpublished data (Table S5); Russia and Siberia: Evangeliou et al. (2018); Yukon and Sweden: unpublished data (TableS5). Data from Greenland and the Yukon span 3-6 years of accumulation in snow, while data from Holtedahlfonna span an estimated ~8 years. Five samples with 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 > 140 ng g -1 taken in Russian towns (Tomsk, Archangelsk) were excluded from the dataset by[START_REF] Evangeliou | Origin of elemental carbon in snow from western Siberia and northwestern European Russia during winter-spring 2014, 2015 and 2016[END_REF]. 36 https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

  sent to the Department of Environmental Science of Stockholm University. There, EC/OC analysis was performed using a

	Sunset Laboratory carbon aerosol analyzer (Sunset Laboratory Inc., Forest Grove, USA), following the European Supersites
	for Atmospheric Aerosol Research thermal evolution protocol (EUSAAR_2; Cavalli et al., 2010). A 1 x 1 cm 2 square section
	was used from each filter to determine separately the particulate EC and OC mass loading on each filter (𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝐸𝐸𝐸𝐸 , 𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑂𝑂𝐸𝐸 ), from
	which their mass concentrations in snow (𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 ) were calculated based on the volume of meltwater filtered. Blank
	filters (n = 6) had particulate carbon loadings below the limit of detection (LOD) of the carbon analyzer, so no blank correction
	was applied to the data. The coefficient of variation (CV) on 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	and 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸	was estimated to be ~40 % (see Supplement for
	details).
	The presence of mineral dust particles in snow can lead to understimations of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	relative to 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸	by the TOT
	method

EC and OC in the winter 2015-16 snowpack across Svalbard

	4 Discussion
	4.1. The April 2016 survey showed no discernible zonal or latitudinal gradient of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	or 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	across Svalbard. As noted earlier,
	only on Austfonna was 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	significantly lower than in some sectors of Spitsbergen. This contrasts with findings from
	surveys made in the springs of 2007-09, in which 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	on Austfonna snow was either comparable to, or larger than, that in
	central or northeastern Spitsbergen

The highest 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 occured in the spring of 2017, and the lowest in the spring of 2014. Depending on site, 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 in 2017 was 23 to 27 times higher than in 2014, and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 was 146 to 217 times higher, the largest differences being observed in the snow near Ny-Ålesund. The EC/OC in surface snow varied between 0.01 and 0.42, and variations in springtime EC/OC on Austre Brøggerbreen (2007-18 median: 0.08) generally tracked those at Ny-Ålesund (2010-18 median: 0.10) (Fig. 5d). However, variations in 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 or EC/OC between 2007 and 2018 did not correlate with

those in the median 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸 measured at Zeppelin Observatory, or with regional monthly snowfall anomalies (Fig.

5e,f

).

Table S1

 S1

		; 340 m a.s.l.) for the
	winter 2015-16 were ~0.3 and ~0.1 mg EC m -2 mo -1 , respectively, which are close to those reported by Jacobi et al. (2019) at
	these two sites for the winter 2011-12 (average: ~0.1-0.2 mg EC m -2 mo -1 ).
	We applied the exponential models for 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝑂𝑂𝐸𝐸

from Fig.

6

to a map of late winter (30 April) hSWE generated with the snowpack model in order to project the geographic pattern of EC and OC accumulation across the whole of Svalbard for the winter 2015-16 (Fig.

7

). The hSWE data used for this purpose were extracted from the output presented in

Van Pelt et al. (2019)

. Summing the predicted values for 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 and 𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸

Table 2 ).

 2

	The median EC/OC in snow at Ny-Ålesund and Austre Brøggerbreen was <0.10 prior to 2010, but rose to 0.43 and
	0.20, respectively, in 2015, and declined after 2016. The seasons with highest median EC/OC (2013-15) were also those with
	lowest 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

𝐸𝐸𝐸𝐸 and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 , which implies that meteorological and/or other conditions must have prevailed which limited atmospheric deposition of OC and EC in snow, OC being more affected than EC. Possible causal factors include sea ice cover or sea-surface winds, which partly modulate emissions of marine organic aerosol (e.g., Kirpes et al., 2019), or katabatic winds from Kongsfjorden, that can affect the thermal stratification of boundary layer air in winter months (Esau and Repina, 2012; Maturilli and Kayser, 2017). Between 2008 and 2018 (the years in which snow sampling was most thorough), 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 on Brøgger Peninsula varied by up to 35 ng g -1 , and 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 by up to 689 ng g -1 . However, there were no significant trends in either 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 or 𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸 over the whole period (Mann-Kendall test; p >> 0.05). Data from the April 2016 glacier survey (Fig.

Table 1 .

 1 Mass loading of EC in the seasonal snowpack, based on measurements of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Mass loading of OC in the seasonal snowpack, based on measurements of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 Main symbols and acronyms used in this paper. The various terms for black carbon (BC, EC, OC, eBC) are as defined in[START_REF] Petzold | Recommendations for reporting "black carbon" measurements[END_REF].

	Acronym
	or symbol	Units Definition
	BC		Black carbon: Light-absorbing, refractory particulate carbon aerosols emitted by the incomplete combustion of organic fuels (biomass or fossil fuels).
	TOT		Thermo-optical transmittance method used to analyze particulate carbon in snow
	EC		Elemental carbon: Refractory fraction of particulate carbon in snow determined by TOT
	OC		Organic carbon: Volatile fraction of particulate carbon in snow determined by TOT
	𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	ng g -1 Mass concentration of EC in snow determined by the TOT method
	𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸	ng g -1 Mass concentration of OC in snow determined by the TOT method
	𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	ng g -1 Median value of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸
	𝐶𝐶 ̃𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸	ng g -1 Median value of 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸
	𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝐸𝐸𝐸𝐸	µg cm -2 Mass loading of EC on filters determined by the TOT method
	𝐿𝐿 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑂𝑂𝐸𝐸	µg cm -2 Mass loading of OC on filters determined by the TOT method
	hSWE	cm	Snow depth expressed in water equivalent
	𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	mg m -2 𝐸𝐸𝐸𝐸
	𝐿𝐿 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑂𝑂𝐸𝐸	mg m -2 𝑂𝑂𝐸𝐸
	eBC		Equivalent black carbon: BC measured in air filters by light-absorption methods
	PSAP		Particle Soot Absorption Photometer used to measure eBC in air filters
	𝑒𝑒𝑒𝑒𝐸𝐸 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝐶𝐶 ̃𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸	ng m -3 Mass mixing ratio of eBC in air, determined by light-absorption methods ng m -3 Median value of 𝐶𝐶 𝑎𝑎𝑓𝑓𝑎𝑎 𝑒𝑒𝑒𝑒𝐸𝐸
	MACλ	m 2 g -1 Wavelength-dependent mass absorption coefficient of light by eBC

https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

Table 2 .

 2 Descriptive statistics for 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 , 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠

	𝑂𝑂𝐸𝐸	and EC/OC snow in samples analyzed
	in this study. n* is the number of values > 1 ng g -1 . Two outliers with 𝐶𝐶 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸	>1700 ng
	g -1 were excluded from calculations.

Acknowledgements.

The April 2016 survey on Svalbard was funded through a grant from the Svalbard Science Forum (RIS 10472) to J.-C. Gallet and others, and surface snow monitoring on Brøgger Peninsula was conducted by the Norwegian Polar Institute (NPI) with support from Svalbardmiljøfonds and from the Swedish Research Council Formas project "Black and White" (grant 2006-00210 to J. Ström and others). Logistical support for field work in 2016 was provided by NPI, the University of Oslo, the Swedish Research Council and the Swedish Polar Secretariat, the Ice and Climate and the Environment research group at Uppsala University, the Institute of Geophysics of the Polish Academy of Sciences (PAS), the Centre for Polar Studies at the University of Silesia (USi), the Polish Ministry of Science and Higher Education (statutory activities 3841/E-41/S/2020), and the Institut Paul-Emile Victor (France). A. Uszczyk (USi), D. Ignatiuk (Svalbard Integrated Arctic Earth Observing System), M. Grabiec and D. Kępski (PAS) participated in the snow sampling work near Horsund. Planning and collaborative work between the study co-authors was facilitated by funds from the Swedish Strategic Research Area initiative "Biodiversity and Ecosystem services in a Changing Climate" through Gothenburg University, the Gothenburg Air and Climate Network and the International Arctic Science Committee. https://doi.org/10.5194/acp-2020-491 Preprint. Discussion started: 16 June 2020 c Author(s) 2020. CC BY 4.0 License.

Data availability.

The data presented in this article can be downloaded from Pangea [Dataset submitted on 19 May 2020, submission tracker # PDI-24118, doi pending].

Author contributions.

JCG, MB, CL, BL, TS, CZ and others initiated the April 2016 survey. JCG oversaw the snow sampling program on Brøgger Peninsula. UL and JC performed the EC and OC analyses, and TM the δ 18 O analyses. WvP carried out the snow model simulations. CZ wrote the manuscript, with contributions from all co-authors.

Competing interests.

The authors declare that they have no conflict of interest.


Sébastien Jenni
email: sjenni@unistra.fr

Angélique Sour
email: a.sour@unistra.fr

Molecular theranostic agents for PDT and MRI

Keywords: theranostic, MRI, PDT, gadolinium, porphyrin Porphyrin Dimer-GdDOTA Conjugate as a Theranostic Agent for One-and Two-Photon

MRI (magnetic resonance imaging) is a powerful non-invasive diagnostic tool that can bring important enlightenment for medical treatment monitoring and optimization. PDT (photodynamic therapy), a minimally invasive treatment for various types of tumors, is gaining increasing interest thanks to its temporal and spatial selectivity. The combination of MRI and PDT offers a real-time monitoring of the treatment and can bring significant informations for the drug-uptake and the light-delivery parameters optimization. In this review we will give an overview on molecular theranostic agents that have been designed for potential applications in MRI and PDT.

Introduction to the theranostic approach

Theranostics is an innovative medical treatment research field, which gathers the functions of therapy and diagnostic by imaging and paves a way for personalized medicine. [START_REF] Yoon | Inorganic Nanoparticles for Image-Guided Therapy[END_REF][START_REF] Crawley | Theranostics in the Growing Field of Personalized Medicine: An Analytical Chemistry Perspective[END_REF][START_REF] Terreno | Image guided therapy: The advent of theranostic agents[END_REF][START_REF] Kelkar | Combining Imaging and Therapy[END_REF] The emergence of this research field has been made possible by the tremendous progress in the development of instruments for imaging and treatment. Theranostic agents combine an imaging agent and a therapeutic agent within the same scaffold, both agents being thus delivered at the same time and with the same biodistribution. They bring important informations for pre-treatment planning, therapy monitoring, treatment outcome assessment and moreover, for the development of new therapeutic agents. Various imaging and therapeutic modalities can be gathered, with different assembly strategies, to generate theranostic agents. Here, we review on theranostic agents combining MRI (magnetic resonance imaging) and PDT (photodynamic therapy) applications, which have a molecular structure. Theranostic agents based on nanoparticles belong to another promising and active field and have recently been reviewed. [START_REF] Kunjachan | Noninvasive Imaging of Nanomedicines and Nanotheranostics: Principles, Progress, and Prospects[END_REF][START_REF] Chen | Nanochemistry and Nanomedicine for Nanoparticle-based Diagnostics and Therapy[END_REF][START_REF] Björnmalm | Bridging Bio-Nano Science and Cancer Nanomedicine[END_REF] 1.1. PDT treatment: strength and limitations PDT is a light activated treatment modality, clinically approved in the treatment of dermatological and ocular disorders and of various cancers. It is a localized treatment with minimal invasiveness and side effects. [START_REF] Fan | Overcoming the Achilles' heel of photodynamic therapy[END_REF][START_REF] Abrahamse | New photosensitizers for photodynamic therapy[END_REF][START_REF] Dąbrowski | Photodynamic therapy (PDT) of cancer: from local to systemic treatment[END_REF] PDT requires the administration of a drug called photosensitizer (PS) that is mainly a tetrapyrrole-based chromophore (Scheme 1). Light of an appropriate wavelength is then applied on the affected tissue and is absorbed by the photosensitizer. The latter is thus activated and reacts with surrounding oxygen and/or with surrounding molecules to generate cytotoxic species, which induce cellular damage, vascular occlusion and/or antitumor immune response. [START_REF] Mroz | Cell Death Pathways in Photodynamic Therapy of Cancer[END_REF] Most of the clinical PSs are activated with excitation wavelengths between 630 and 690 nm, which have limited tissue penetration depth mainly due to light scattering and absorption by endogenous molecules. The weak tissue penetration of light is a major concern for PDT development. Therefore PSs that can be activated in the optical transparency window of tissues (from 700 nm and up to 1000 nm with a two-photon absorption process) are very appealing. [START_REF] Fan | Overcoming the Achilles' heel of photodynamic therapy[END_REF] In addition to the wavelength range, strong absorption capacity (characterized by high absorption coefficient value  for one-photon absorption and by high two-photon absorption cross-section value 2 for two-photon absorption) increases the production of cytotoxic species and enhances the treatment efficacy. New PSs are designed and studied in order to respond to these criteria. Two porphyrin-based photosensitizers (Scheme 1c and 1d), with strong one-photon absorption in the near infrared, have recently been designed. Padeliporfin (Tookad ® WST11), with an excitation wavelength at 763 nm ( = 100 000 M -1 cm -1 ), has been approved for the treatment of prostate cancer. [START_REF] Azzouzi | Padeliporfin vascular-targeted photodynamic therapy versus active surveillance in men with low-risk prostate cancer (CLIN1001 PCM301): an open-label, phase 3, randomised controlled trial[END_REF] Redaporfin (LUZ11), that can be activated at 749 nm ( = 140 000 M -1 cm -1 ) has been approved for biliary tract cancer and is also moving to other clinical trials. [START_REF] Van Straten | Oncologic Photodynamic Therapy: Basic Principles, Current Clinical Status and Future Directions[END_REF][START_REF] Luz | Translating phototherapeutic indices from in vitro to in vivo photodynamic therapy with bacteriochlorins[END_REF] Scheme 1. Schematic representation of (a) Porfimer sodium (Photofrin ® ), (b) Verteporfin (Visudyne ® ), (c) Padeliporfin (Tookad ® WST11) and (d) Redaporfin (LUZ11) Some PSs are able to preferentially accumulate in tumor cells, however the mechanisms of this behavior are not fully understood and the tumor selectivity should be improved. There is also a need for precisely identifying the time period of maximum drug accumulation in the tumor. Light irradiation during this time period should generate many reactive oxygen species and lead to best PDT efficiency. [START_REF] Kunjachan | Noninvasive Imaging of Nanomedicines and Nanotheranostics: Principles, Progress, and Prospects[END_REF] Drug accumulation and light delivery is specific to each PS and tumor characteristics. Thus the optimization of the treatment planning is difficult to achieve. With these limitations, it has become evident that there is a need for treatment guidance by imaging that can pilot the pre-and post-treatment evaluation. [START_REF] Chen | Nanochemistry and Nanomedicine for Nanoparticle-based Diagnostics and Therapy[END_REF][START_REF] Celli | Imaging and Photodynamic Therapy: Mechanisms, Monitoring, and Optimization[END_REF][START_REF] Rai | Development and applications of photo-triggered theranostic agents[END_REF][START_REF] Bhaumik | Applications of phototheranostic nanoagents in photodynamic therapy[END_REF]

MRI guidance of PDT

MRI is a non-invasive visualization tool of soft tissues with both high spatial and time resolution. [START_REF] Pierre | Contrast agents for MRI: 30+ years and where are we going? JBIC[END_REF] It has already proven to be powerful for therapy monitoring in oncology and for drug development. [START_REF] Evelhoch | In vivo MR in the drug pipeline[END_REF] The MR signal contrast arises from the differences of proton properties such as the relaxation times (T1 and T2) or the density of water molecules. [START_REF] De León-Rodríguez | Basic MR relaxation mechanisms and contrast agent design: MR Relaxation Mechanisms and Contrast Agents[END_REF] These properties are acquired using appropriate MR pulse sequences; they highlight different proton behaviors and allow tissue discrimination. In some cases, the contrast between healthy and diseased tissue is weak and the use of a contrast agent is necessary. Clinically used contrast agents are small gadolinium-based complexes containing octadentate chelators based on macrocyclic (GdDOTA with DOTA = 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetate) or linear (GdDTPA with DTPA = diethylenetriamine pentaacetate) structures (Scheme 2). The efficiency of a contrast agent is measured by its relaxivity that corresponds to the ability to decrease the relaxation time T1 of water protons in presence of a paramagnetic gadolinium complex at a concentration of 1 mM.

The PDT-induced changes in the tumoral area alter the proton behaviors and these changes can also be strongly emphasized by the presence of a contrast agent. The image modification can thus bring essential informations to estimate the PDT effect. It is then possible to establish relations between this effect and the drug and light parameters. The latters can then be finely tuned in order to increase the efficiency of the treatment. [START_REF] Josefsen | Unique Diagnostic and Therapeutic Roles of Porphyrins and Phthalocyanines in Photodynamic Therapy, Imaging and Theranostics[END_REF] Scheme 2. Chemical structures of two clinical contrast agents (a) GdDOTA (Dotarem ® or Clariscan ® ) and (b) GdDTPA (Magnevist ® ) MRI-guided PDT can be realized with repeated contrast agent injection at different time points after the PS injection. Depending on the choice of the MRI methodology, PDT response can be followed at different times after the treatment and give different informations. Contrast-enhanced MRI has shown to be very sensitive to PDT-induced vascular occlusion. [START_REF] Kennedy | Quantitative MRI of Gd-DTPA uptake in tumors: Response to photo dynamic therapy[END_REF][START_REF] Haider | Prostate Gland: MR Imaging Appearance after Vascular Targeted Photodynamic Therapy with Palladium-Bacteriopheophorbide[END_REF] It was used for the assessment of tumor response to PDT. [START_REF] Huang | Magnetic resonance imaging correlated with the histopathological effect of Pd-bacteriopheophorbide (Tookad) photodynamic therapy on the normal canine prostate gland[END_REF][START_REF] Schreurs | Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy[END_REF][START_REF] Zilberstein | Antivascular Treatment of Solid Melanoma Tumors with Bacteriochlorophyll-serine-based Photodynamic Therapy[END_REF] Informations obtained shortly after treatment could also be obtained. [START_REF] Schreurs | Quantitative Multi-Parametric Magnetic Resonance Imaging of Tumor Response to Photodynamic Therapy[END_REF] The optimization of the PDT treatment can be further improved using theranostic agents. With this approach, both the imaging and the therapeutic agents will have the same biodistribution and bioelimination behaviors. Gadolinium-based contrast agents linked to a porphyrin-based PS are expected to confer increased tumoral residence time, thus they provide a longer time window to monitor the treatment. They also have increased relaxivities compared to classical MRI contrast agents. Therefore, they can be administered at much lower dose than current clinical contrast agents and offer enhanced safety. The nanoparticle approaches for MRI and PDT applications have attracted increasing attention during the past decade. [START_REF] Kunjachan | Noninvasive Imaging of Nanomedicines and Nanotheranostics: Principles, Progress, and Prospects[END_REF][START_REF] Chen | Nanochemistry and Nanomedicine for Nanoparticle-based Diagnostics and Therapy[END_REF][START_REF] Björnmalm | Bridging Bio-Nano Science and Cancer Nanomedicine[END_REF][START_REF] Schleich | Iron oxide-loaded nanotheranostics: Major obstacles to in vivo studies and clinical translation[END_REF] In spite of the efficient targeting and high payload of some nanotheranostic agents, clinical translation has not yet been possible. [START_REF] Chen | Nanochemistry and Nanomedicine for Nanoparticle-based Diagnostics and Therapy[END_REF][START_REF] Björnmalm | Bridging Bio-Nano Science and Cancer Nanomedicine[END_REF][START_REF] Schleich | Iron oxide-loaded nanotheranostics: Major obstacles to in vivo studies and clinical translation[END_REF] Molecular theranostic agents for PDT and MRI have been developed to a lesser extent; however, interesting results have been obtained. These small or medium-size molecular agents have their own strengths and advantages (such as high reproducibility, stability, purity and good biocompatibility) and this approach continues to draw attention for cancer treatment. [START_REF] Kumar | Small conjugate-based theranostic agents: an encouraging approach for cancer therapy[END_REF]

Porphyrin-Gd-complexes conjugates with potential MRI and PDT applications

Several porphyrin analogues have been associated to Gd(III) complexes, their ability to accumulate in cancer cells and/or their relaxivity have been considered without exploring their potential as PDT PS. At an early stage, two compounds, gadophrin-2 and gadophrin-3, composed respectively of a free-base and a copper(II) porphyrin linked to two GdDTPA complexes have been investigated. Studies in mice have shown comparable pharmacological properties and these PSs were found to accumulate in necrotic areas. [START_REF] Hofmann | Mechanism of gadophrin-2 accumulation in tumor necrosis[END_REF][START_REF] Barkhausen | Imaging of myocardial infarction: comparison of magnevist and gadophrin-3 in rabbits[END_REF][START_REF] Daldrup-Link | Cell tracking with gadophrin-2: a bifunctional contrast agent for MR imaging, optical imaging, and fluorescence microscopy[END_REF][32][START_REF] Ni | Metalloporphyrins and Functional Analogues as MRI Contrast Agents[END_REF] A 5, 10, 15, 20-tetraphenylporphyrin (TPP) core has been linked to one and four GdDTPA complexes through amide bond formation. [START_REF] Haroon Ur | Synthesis and relaxivity measurement of porphyrin-based Magnetic Resonance Imaging (MRI) contrast agents[END_REF] Increased relaxivity values have been found. Free-base and copper(II) porphyrins linked to one, two and four GdDO3A-amide complexes have been developed for potential multimodal MRI/PET (positron emission tomography) applications. Preliminary relaxivity studies indicated promising contrast enhancement. [START_REF] Gros | New potential bimodal imaging contrast agents based on DOTA-like and porphyrin macrocycles[END_REF][START_REF] Eggenspiller | Design of Porphyrin-dota-Like Scaffolds as All-in-One Multimodal Heterometallic Complexes for Medical Imaging: Porphyrin-dota-Like Scaffolds for Medical Imaging[END_REF] Zinc(II) and copper(II) porphyrazine have been linked to two GdDO3A-amide complexes. The copper-containing compound showed the highest relaxivity, very good cellular internalization and tumor-bearing mice images indicated necrotic localization. [START_REF] Trivedi | Synthesis and characterization of a porphyrazine-Gd(III) MRI contrast agent and in vivo imaging of a breast cancer xenograft model: TUMOR IMAGING[END_REF]

Molecular theranostic agents with combined PDT and MRI studies

In 1993, a pioneering study has been realized with two bifunctional compounds containing porphyrin derivatives linked to one or two GdDTPA complexes. [START_REF] Hindré | Tetra-p-aminophenylporphyrin conjugated with Gd-DTPA: Tumor-specific contrast agent for MR imaging[END_REF] The compound TPP-Gd2(DTPA)4 consisting of a tetra-p-aminophenylporphyrin core coupled through amide bonds to four DTPA ligands with two of them metalated by Gd(III) ions, was the most promising. The relaxivity value per Gd(III) was found to be twice as high as that of GdDTPA at 20 MHz and the substantial image contrast enhancement of the tumor compared to adjacent normal tissue in tumor-bearing mice evidenced the affinity of the compound for tumor tissue. The photoinduced toxicity studies, realized with irradiation using a multiwavelength laser beam at 488 and 514 nm on two cell lines (HT29 and L1210), showed comparable phototoxicities than the one induced by a commercial HPD PS. This porphyrin-Gd complex conjugate was the first prototype built for MRI and PDT.

More than a decade later, Pandey and collaborators extensively investigated several theranostic agents that combine diagnostic imaging (MR and fluorescence imaging) and PDT treatment properties. They are based on different photosensitizers (pyropheophorbide analogues with different lipophilic/hydrophilic chains) linked to one, two, three or six GdDTPA complexes. [START_REF] Li | Chlorophyll-a Analogues Conjugated with Aminobenzyl-DTPA as Potential Bifunctional Agents for Magnetic Resonance Imaging and Photodynamic Therapy †[END_REF][START_REF] Pandey | Nature: A rich source for developing multifunctional agents. tumor-imaging and photodynamic therapy[END_REF][START_REF] Spernyak | Hexylether Derivative of Pyropheophorbide-a (HPPH) on Conjugating with 3 Gadolinium(III) Aminobenzyldiethylenetriaminepentaacetic Acid Shows Potential for in Vivo Tumor Imaging (MR, Fluorescence) and Photodynamic Therapy[END_REF][START_REF] Goswami | Synthesis of Tumor-Avid Photosensitizer-Gd(III)DTPA Conjugates: Impact of the Number of Gadolinium Units in T1/T2 Relaxivity, Intracellular localization, and Photosensitizing Efficacy[END_REF] In these compounds, the linkage is realized through the C-functionalization of the diethylenetriamine backbone and the stability of the GdDTPA core is preserved with the five anionic carboxylate groups. The theranostic compounds containing one and two Gd complexes required liposomal formulation to resolve the poor water-solubility problem. With the presence of 3 and 6

GdDTPA units, the water solubility was improved. The compound HPPH-3GdDTPA bearing 3 GdDTPA complexes (Figure 1a) was found to be the best candidate regarding its imaging and treatment results. It showed remarkable MR contrast enhancement of tumor in mice 24 hours after injection, with a 10-fold lower dose than Magnevist and preferential uptake in tumor compared to muscle (Figure 1b). Fluorescence imaging, resulting from the light emission of the HPPH derivative, also showed maximum intensity 24 hours after injection. Finally this compound also showed efficient PDT effect after one irradiation at 665 nm (70 J/cm²) 24 hours after injection. Phthalocyanine and porphyrazine are tetraazaporphyrins known for their intense electronic absorption in the NIR region, they require addition of peripheric substitution groups to avoid aggregation and favor water solubilization. They have been linked to GdDO3A-amide complexes that are GdDOTA derivatives where one carboxylate arm is replaced by an acetamide group. The amide-bond formation allows rapid access to Gd(III) complex functionalization but it induces strongly reduced thermodynamic stability while keeping the same kinetic inertness as the GdDOTA complex. [START_REF] Lattuada | The synthesis and application of polyamino polycarboxylic bifunctional chelating agents[END_REF] The phthalocyanine-based PS has been linked to one GdDO3A-amide complex (ZnPht-1Gd, Figure 2a). [START_REF] Tekdaş | Design of a Gd-DOTA-Phthalocyanine Conjugate Combining MRI Contrast Imaging and Photosensitization Properties as a Potential Molecular Theranostic[END_REF] It exhibited low relaxivity (1.43 mM -1 s -1 at 128 MHz) and the authors proposed that this weak value could be due to the presence of the amide function on the arm that could block the water access to the metallic center. The compound showed a good ability to produce cytotoxic singlet oxygen under irradiation with a quantum yield of 0.67 (in DMSO). The porphyrazine based PS has been linked to one, four and eight GdDO3A-amide complexes to give the bifunctional compounds ZnPz-nGd (with n = 1, 4, 8, Figure 2b). [START_REF] Song | Synthesis and Characterization of New Porphyrazine-Gd(III) Conjugates as Multimodal MR Contrast Agents[END_REF] They showed strong relaxivity increase (up to 12.8 mM -1 s -1 for ZnPz-8Gd at 60 MHz and 37°C) with the number of Gd-complexes attached to the PS. Cellular uptake was observed only with the compounds bearing one Gd complex (ZnPz-1Gd) and notable phototoxic effect (with 50% cell killing) was assessed after 10 min irradiation with white light (Figure 2c). Porphyrin derivatives incorporating a Gd(III) ion are at first sight appealing agents due to their simplified structure and synthesis and to their promising in vitro properties as phosphorescence-based oxygen sensors, PDT photosensitizers and MRI contrast agents. However, their in vivo study have resulted in disparate conclusions. Koenig [START_REF] Lyon | Tissue distribution and stability of metalloporphyrin MRI contrast agents[END_REF] and Furmanski [START_REF] Furmanski | Metalloporphyrin Enhancement of Magnetic Resonance Imaging of Human Tumor Xenografts in Nude Mice[END_REF] studied gadolinium-incorporated porphyrins as MRI contrast agents, they observed stability problems with ion dissociation from the porphyrin during their studies in plasma and in mice. Recently, two porphyrazine-based compounds incorporating a Gd(III) ion, GdPz1 and GdPz2, which differ in the nature of the peripheral groups, have been obtained (Figure 3a). [START_REF] Yuzhakova | In vivo multimodal tumor imaging and photodynamic therapy with novel theranostic agents based on the porphyrazine framework-chelated gadolinium (III) cation[END_REF] A good relaxivity value was obtained at very high magnetic field (4.67 mM -1 s -1 at 9.4 T) for GdPz1 once solubilized in polymer polyimide brushes. Cellular uptake, dual in vivo fluorescence and MR imaging and in vivo PDT activity were studied. Significant in vivo tumor accumulation was demonstrated by fluorescence and MR images for both compounds. PDT activity was assessed in cancerous CT26 cells with light irradiation performed at 615-635 nm (10-20 J/cm 2 , 10 -7 to 10 -4 M incubation concentration). PDT treatment of CT26 tumor-bearing Balb/c mice (Figure 3b) was realized 3 hours post-injection by irradiation at 593 nm (30 min, 120 J/cm 2 ). Moderate tumor death was observed and this study indicated the need to optimize different parameters such as the drug dose and the light application. In order to obtain contrast agents with high relaxivity at high magnetic fields, the strategy of increasing the number of coordinated water molecules at the Gd(III) center is particularly appealing as it allows to nearly double the r1 relaxivity value, independently of the magnetic field. In this case, careful design of hepta-or hexadentate ligands is necessary in order to obtain Gd(III) complexes with good thermodynamic stability and kinetic inertness and to avoid ternary complex formation with endogenous molecules. Chen et al. developed a potential theranostic agent (Scheme 3a) consisting of a tetraphenylporphyrin core linked to four GdDTTA complexes. [START_REF] Luo | Tetranuclear Gadolinium(III) Porphyrin Complex as a Theranostic Agent for Multimodal Imaging and Photodynamic Therapy[END_REF] High relaxivity has been measured (14.1 mM -1 s -1 at 0.55 T in Hepes (4-(2-hydroxyethyl)-1-piperazine ethanesulfonic acid) buffer) and this value was doubled in presence of human serum albumin, indicating strong binding of the conjugate to this blood pool protein. The fluorescence of the PS has been evidenced in H1299 lung cancer cells and showed harmless cellular uptake. Singlet oxygen was efficiently produced upon irradiation at 650 nm in deuterated water. These studies showed the potential of this compound to behave as a contrast agent for multimodal (MR and luminescence) imaging and as PS for PDT. Scheme 3. Chemical structure of the theranostic compounds developped by (a) Chen et al. [START_REF] Luo | Tetranuclear Gadolinium(III) Porphyrin Complex as a Theranostic Agent for Multimodal Imaging and Photodynamic Therapy[END_REF] and (b) Sour et al. [START_REF] Sour | Four Gadolinium(III) Complexes Appended to a Porphyrin: A Water-Soluble Molecular Theranostic Agent with Remarkable Relaxivity Suited for MRI Tracking of the Photosensitizer[END_REF] The development of a theranostic agent gathering four GdDTTA complexes and a TPP core has also been realized with a design differing in the nature of the linkers separating the two agents (Scheme 3b). Short and relatively rigid benzyl linkers have been used, they allow to minimize rotational flexibility and thus to optimize the relaxivity gain brought by the increase of the molecular weight. [START_REF] Sour | Four Gadolinium(III) Complexes Appended to a Porphyrin: A Water-Soluble Molecular Theranostic Agent with Remarkable Relaxivity Suited for MRI Tracking of the Photosensitizer[END_REF] This water-soluble bifunctional system has the highest relaxivity reported for a medium-size system with a maximum of 43.7 mM -1 s -1 (per Gd(III) ion at 20 MHz). A 27 % relaxivity increase has also been observed in presence of BSA (bovine serum albumin). Phantom images of cell pellets (Figure 5a) obtained at high magnetic field (7 T) evidenced the cellular uptake. ICP-MS measurements showed that the cellular uptake of Gd ions was 60 times more effective with the theranostic agent than with the commercial GdDTPA contrast agent at 10 M Gd incubation concentration. This result can be explained by the amphiphillic character of the theranostic compound that favors cell internalization. A good PDT effect was observed on HeLa cells (Figure 5b) upon irradiation at 636 nm (1 hour) and was found to increase with light intensity and with the incubation concentration of the theranostic agent. The theranostic agents reported so far are activated by one-photon absorption, with excitation wavelengths below 700 nm. The two-photon excitation process allows to use excitation wavelengths in the near infrared. [START_REF] Bolze | Molecular photosensitisers for two-photon photodynamic therapy[END_REF][START_REF] Sun | Photosensitizers for Two-Photon Excited Photodynamic Therapy[END_REF] This process allows deep treatment and minimal photodamages to healthy tissues. The two-photon irradiation is possible only in a very small area, high spatial treatment precision can be obtained but the application to bulky tumors is currently limited. The design of new PSs with high two-photon absorption capability requires large pi-electronic delocalization.

A one-and two-photon activatable PS based on a diketopyrrolopyrrole-zinc-porphyrin component (DPP-ZnP) and linked to a GdDOTA complex as an imaging probe has been studied (Scheme 4). [START_REF] Schmitt | A Theranostic Agent Combining a Two-Photon-Absorbing Photosensitizer for Photodynamic Therapy and a Gadolinium(III) Complex for MRI Detection[END_REF] The GdDOTA attachment to the PS has been A theranostic agent containing a PS with high one-and two-photon absorption capacity in the near infrared and two stable contrast agents for MR imaging has been studied. [55] The structure of this agent is composed of a Zn-porphyrin dimer (ZnP-ZnP) linked to two GdDOTA complexes (Figure 6a). High relaxivity values have been obtained with a maximum of r1 = 14.4 mM -1 s -1 (at 40

MHz in water containing 2 % of pyridine to ensure complete water solubility). Due to the presence of the two Gd(III) complexes, the corresponding molecular relaxivities are doubled, and this trait is important to realize the imaging and therapeutic studies at the same low concentration required for PDT treatment. It is also interesting to note that the relaxivity value was doubled in presence of BSA at 20 MHz. This compound showed strong one-photon absorption capacity in the near infrared (with a maximum at 746 nm with  = 10 5 M -1 cm -1 in DMSO). It also showed very strong two-photon absorption ability with a maximum between 880 and 930 nm (2 ≈ 8000 GM in DMSO). Efficient PDT effect was observed in HeLa cells after one-photon irradiation at 740 nm (30 min, 1 M incubation concentration). Two-photon PDT effect was observed at 910 nm (300 scans, 2 M incubation concentration) as a function of the light power and 100% cell death was observed with an average power of 108 mW at the back pupil of the objective (Figure 6b). ). Adapted with permission from [55]. Copyright 2018 American Chemical Society.

Conclusions

The design and study of molecular theranostic agents for potential applications in MR imaging and PDT treatment has been highlighted and discussed.

Compared to small commercial contrast agents, the MR imaging properties of these theranostic agents are improved by the presence of the lipophilic PS. Increased cellular uptake and/or tumor accumulation have been observed and together with the increased relaxivity brought by the large size of these compounds, the use of such theranostic agent requires much lower doses than the one used with clinical contrast agents. To further improve the imaging efficiency of the theranostic agent, the same criteria as those for classical contrast agents need to be considered. In particular, the local rigidity and the stability are two important parameters to take into account. In addition to the MR imaging, the fluorescence properties of the tetrapyrrolic core have also been explored in some cases. They bring a second imaging modality with high sensitivity.

Compared to an individual tetrapyrrolic PS, the presence of hydrophilic Gd(III) complexes brings increased water solubility and modulates the in vivo distribution and elimination. A good ratio between the number of Gd(III) complexes and the PS has to be found in order to keep the cellular uptake ability. The design of PDT sensitizers with strong absorption in the biological transparency window is necessary for increased tissue penetration depth and high cytotoxic species production.

Cellular and animal studies ask for strong and long experimentation efforts. Studies showing the influence of the drug concentration and the light application on the PDT efficiency should be developed. Finally, a chemical design that allows better tumoral selectivity will undoubtedly explode the capacity of these theranostic agents.

Figure 1 .

 1 Figure 1. (a) Chemical structure of the theranostic compound HPPH-3GdDTPA, (b) MR images of a rat (Fischer) bearing Ward colon tumors before (left) and 24 h after (right) injection of HPPH-3-Gd(III) conjugate (dose: 10.0 mol/kg). Adapted with permission from [41] . Copyright 2010 American Chemical Society.

Figure 2 .

 2 Figure 2. Chemical structure of (a) ZnPht-1Gd[44] and (b) ZnPz-1Gd[45]; (c) Phototoxic effect in WI-38 VA13 cells incubated with Zn-Pz-nGd (n = 1, 4, 8; 50 M, 24 h) and irradiated by white light for 0 or 10 min. The same protocol was realized with Photofrin as a positive control. Adapted with permission from [45]. Copyright 2010 American Chemical Society.

Figure 3 .

 3 Figure 3. (a) Molecular structure of GdPz1 and GdPz2; (b) Tumor volume variation as a function of time.Intravenous injection of the theranostic agent and PDT treatment 3 hours after this injection were realized on day 10 after tumor inoculation. Adapted with permission from[START_REF] Yuzhakova | In vivo multimodal tumor imaging and photodynamic therapy with novel theranostic agents based on the porphyrazine framework-chelated gadolinium (III) cation[END_REF]. Copyright 2017 Elsevier.

Figure 4 .

 4 Figure 4. (a) Molecular representation of Gd/Pt-P1; (b) C6 cell viability as a function of three porphyrin (P1, Pt-P1 and Gd/Pt-P1) concentration without irradiation (6 h incubation) and 44 h after irradiation at 630 nm (0.2 W/cm, 10 min). Adapted from Ref. [49] with permission from The Royal Society of Chemistry.

Figure 5 .

 5 Figure 5. (a) T1 maps of HeLa cell pellets at 7 T incubated for 24 h with different concentrations of the theranostic compound; (b) Phototoxicity of the theranostic compound following 1 h irradiation at 636 nm after 24 h incubation at 2.5 µM (dark gray) and 6 µM (light gray). Adapted with permission from [51]. Copyright 2016 American Chemical Society.

Scheme 4 .

 4 Scheme 4. Chemical structure of the theranostic compound DPP-ZnP-GdDOTA developed by Heitz et al.[START_REF] Schmitt | A Theranostic Agent Combining a Two-Photon-Absorbing Photosensitizer for Photodynamic Therapy and a Gadolinium(III) Complex for MRI Detection[END_REF]

Figure 6 .

 6 Figure 6. (a) Structure of the zinc porphyrin dimer linked to two GdDOTA complexes; (b) Phototoxicity induced by a two-photon excitation at 910 nm after 24 h incubation in absence (left) or presence (right) of the compound. Scale bar is 100 µm. ). Adapted with permission from [55]. Copyright 2018 American Chemical

Acknowledgments: : The authors thank the group members and collaborators involved in the work done in the frame of the LSAMM team.

Conflicts of Interest: the authors declare no conflict of interest.