# Lidar-Derived Aerosol Properties from Ny-Ålesund, Svalbard during the MOSAiC Spring 2020

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## Abstract

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## 1. Introduction

## 2. Instruments, Methods and Data

#### 2.1. Microphysical Retrieval Methodology by Regularization

- Surface-area concentration (first (“fine”) mode, second (“coarse”) mode, total) ($\mathsf{\mu}{\mathrm{m}}^{2}{\mathrm{cm}}^{-3}$)${s}_{\mathrm{t}}=3\int \frac{v\left(r\right)}{r}\mathrm{d}r$;
- Volume concentration (first mode, second mode, total) ($\mathsf{\mu}{\mathrm{m}}^{3}{\mathrm{cm}}^{-3}$)${v}_{\mathrm{t}}=\int v\left(r\right)\mathrm{d}r$;
- Number concentration (first mode, second mode) (cm${}^{-3})$${n}_{\mathrm{t}}=3/4\pi \int \frac{v\left(r\right)}{{r}^{3}}\mathrm{d}r$;
- Effective radius ($\mathsf{\mu}\mathrm{m}$) ${r}_{\mathrm{eff}}=3\frac{{v}_{t}}{{s}_{t}}$.

## 3. Aerosol Properties in Spring 2020

## 4. Case Studies

#### 4.1. High Backscatter vs. High Lidar Ratio

#### 4.2. Aerosol Properties in the Mechanical Boundary Layer

## 5. Conclusions

- In 2020, aerosol backscatter below $1.5\phantom{\rule{0.166667em}{0ex}}$ km was found to be much higher than in 2019. Above that altitude, clear conditions with similar aerosol properties prevailed in both years. We found a dominance of small particles with radii below $100\phantom{\rule{0.166667em}{0ex}}$ nm. The almost constant aerosol properties above $2\phantom{\rule{0.166667em}{0ex}}$ km altitude suggest, if confirmed at other sites, that, in principle, regional climate models might be easily fed with realistic aerosol properties above this altitude for the Arctic;
- Even in the MOSAiC winter with additional meteorologic data, air backtrajectories alone may not be reliable (high and low aerosol for similar air masses from Siberia). Hence, a final proof of why 2020 was more turbid cannot be given;
- Backscatter histograms for 2020 and low altitudes show a bi-modal structure but the average LR and Ångström exponent for those high and low backscatter groups are very similar. Hence, high backscatter means usually “more of the same aerosol”;
- We generally found low aerosol depolarization. The dominance of nearly spherical particles means that Mie theory is justified to connect optical and microphysical aerosol properties;
- We found low to moderate RI (from four case studies only);
- The highest LR was found for a case with high humidity and low refractive index: likely a case of hygroscopic growth. This means that the LR alone, without knowledge of humidity, is not a good indicator of aerosol type in the Arctic;
- Similarly, other cases of high LR were already found in January for days with lower than average backscatter;
- The low depolarization, the low to moderate RI and the possibly hydrophilic behavior is in agreement with ground-based in situ observations showing nss-sulphate and marine aerosol to be the dominant aerosol species in this season:
- There is generally much higher backscatter and more variable aerosol properties below $2\phantom{\rule{0.166667em}{0ex}}$ km in altitude. Bi-modal volume distribution functions can occur. We found clear indications that (at least part) of this aerosol variability in the lowest $2\phantom{\rule{0.166667em}{0ex}}$ km is connected to elevated temperature inversions or gradients of humidity. This possible modification of aerosol properties over the undisturbed Arctic oceans compared to the local measurements over Svalbard needs more attention in the future.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A. Ångström Exponent

**Figure A1.**Frequencypolygon of the Ångström exponent for different height intervals and months. Left: calculated with $355\phantom{\rule{0.166667em}{0ex}}$ nm and $532\phantom{\rule{0.166667em}{0ex}}$ nm; right: calculated with $532\phantom{\rule{0.166667em}{0ex}}$ nm and $1064\phantom{\rule{0.166667em}{0ex}}$ nm. The trend differs for different wavelength pairs.

## Appendix B. Backward Trajectories

**Figure A2.**(

**Left**): Comparison between the trajectories calculated with Reanalysis1 and with GDAS1. Both models used the same start parameters; (

**Right**): mean difference between the trajectories of GDAS1 and Reanalysis1. All trajectories were calculated for the whole month every 12 h with the same start parameters.

## Appendix C. Error Propagation from Optical to Microphysical Properties

**Table A1.**Microphysical parameters for the low layer on 21 February 2020, considering errors in the lidar input data.

Error Realization | Real (RI) | Imag (RI) | ${\mathit{r}}_{\mathbf{eff}}$ | ${\mathit{v}}_{\mathit{t}}$ | ${\mathbf{SSA}}_{355}$ | ${\mathbf{SSA}}_{532}$ |
---|---|---|---|---|---|---|

Exact solution | 1.526 | 0.020 | 0.0799 | 6.16 | 0.8540 | 0.8620 |

${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high | 1.520 | 0.021 | 0.0720 | 7.18 | 0.8553 | 0.8541 |

${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low | 1.538 | 0.020 | 0.1010 | 5.25 | 0.8514 | 0.8703 |

${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low | 1.533 | 0.020 | 0.3529 | 6.27 | 0.84128 | 0.87294 |

${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high | 1.561 | 0.022 | 0.0511 | 6.66 | 0.86155 | 0.8389 |

All ${\beta}^{\mathrm{aer}}$ low | 1.517 | 0.020 | 0.0865 | 6.30 | 0.8529 | 0.8609 |

All ${\beta}^{\mathrm{aer}}$ high | 1.529 | 0.020 | 0.0865 | 6.16 | 0.8508 | 0.8585 |

All ${\beta}^{\mathrm{aer}}$ low, ${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low | 1.520 | 0.017 | 0.3991 | 7.14 | 0.8515 | 0.8800 |

All ${\beta}^{\mathrm{aer}}$ low, ${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high | 1.547 | 0.021 | 0.0551 | 7.34 | 0.8585 | 0.8347 |

All ${\beta}^{\mathrm{aer}}$ high, ${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low | 1.537 | 0.021 | 0.2959 | 5.64 | 0.8337 | 0.8678 |

All ${\beta}^{\mathrm{aer}}$ high, ${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ low, ${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ high | 1.581 | 0.022 | 0.0505 | 6.35 | 0.8684 | 0.8501 |

Mean value | 1.537 | 0.02 | 0.1482 | 6.40 | 0.8528 | 0.8591 |

Standard deviation | 0.02 | 0.001 | 0.1321 | 0.64 | 0.0093 | 0.014 |

## References

- Serreze, M.C.; Barry, R.G. Processes and impacts of Arctic amplification: A research synthesis. Glob. Planet. Chang.
**2011**, 77, 85–96. [Google Scholar] [CrossRef] - Cohen, J.; Zhang, X.; Francis, J.; Jung, T.; Kwok, R.; Overl, J.; Ballinger, T.J.; Bhatt, U.S.; Chen, H.W.; Coumou, D.; et al. Divergent consensuses on Arctic amplification influence on midlatitude severe winter weather. Nat. Clim. Chang.
**2020**, 10, 20–29. [Google Scholar] [CrossRef] - Hall, R.J.; Hanna, E.; Chen, L. Winter Arctic Amplification at the synoptic timescale, 1979–2018, its regional variation and response to tropical and extratropical variability. Clim. Dyn.
**2021**, 56, 457–473. [Google Scholar] [CrossRef] - Schmale, J.; Zieger, P.; Ekman, A.M. Aerosols in current and future Arctic climate. Nat. Clim. Chang.
**2021**, 11, 95–105. [Google Scholar] [CrossRef] - Shupe, M.D.; Rex, M.; Dethloff, K.; Damm, E.; Fong, A.A.; Gradinger, R.; Heuze, C.; Loose, B.; Makarov, A.; Maslowski, W.; et al. Overview of the MOSAiC expedition: Atmosphere. Elem. Sci. Anth.
**2022**, 10, 00060. [Google Scholar] [CrossRef] - Engelmann, R.; Ansmann, A.; Ohneiser, K.; Griesche, H.; Radenz, M.; Hofer, J.; Althausen, D.; Dahlke, S.; Maturilli, M.; Veselovskii, I.; et al. UTLS wildfire smoke over the North Pole region, Arctic haze, and aerosol-cloud interaction during MOSAiC 2019/20: An introductory. Atmos. Chem. Phys. Discuss.
**2020**, 2020, 1–41. [Google Scholar] [CrossRef] - Dahlke, S.; Maturilli, M. Contribution of Atmospheric Advection to the Amplified Winter Warming in the Arctic North Atlantic Region. Adv. Meteorol.
**2017**, 2017, 4928620. [Google Scholar] [CrossRef] [Green Version] - Tunved, P.; Ström, J.; Krejci, R. Arctic aerosol life cycle: Linking aerosol size distributions observed between 2000 and 2010 with air mass transport and precipitation at Zeppelin station, Ny-Ålesund, Svalbard. Atmos. Chem. Phys.
**2013**, 13, 3643–3660. [Google Scholar] [CrossRef] [Green Version] - Udisti, R.; Bazzano, A.; Becagli, S.; Bolzacchini, E.; Caiazzo, L.; Cappelletti, D.; Ferrero, L.; Frosini, D.; Giardi, F.; Grotti, M.; et al. Sulfate source apportionment in the Ny-Ålesund (Svalbard Islands) Arctic aerosol. Rend. Fis. Acc. Lincei
**2016**, 27, 85–94. [Google Scholar] [CrossRef] - Graßl, S.; Ritter, C. Properties of Arctic Aerosol Based on Sun Photometer Long-Term Measurements in Ny-Ålesund, Svalbard. Remote Sens.
**2019**, 11, 1362. [Google Scholar] [CrossRef] [Green Version] - Shibata, T.; Shiraishi, K.; Shiobara, M.; Iwasaki, S.; Takano, T. Seasonal Variations in High Arctic Free Tropospheric Aerosols Over Ny-Ålesund, Svalbard, Observed by Ground-Based Lidar. J. Geophys. Res. Atmos.
**2018**, 123, 12353–12367. [Google Scholar] [CrossRef] - Hoffmann, A.; Ritter, C.; Stock, M.; Shiobara, M.; Lampert, A.; Maturilli, M.; Orgis, T.; Neuber, R.; Herber, A. Ground-based lidar measurements from Ny-Ålesund during ASTAR 2007. Atmos. Chem. Phys.
**2009**, 9, 9059–9081. [Google Scholar] [CrossRef] [Green Version] - Quinn, P.K.; Shaw, G.; Andrews, E.; Dutton, E.G.; Ruoho-Airola, T.; Gong, S.L. Arctic haze: Current trends and knowledge gaps. Tellus B Chem. Phys. Meteorol.
**2007**, 59, 99–114. [Google Scholar] [CrossRef] [Green Version] - Shaw, G.E. The Arctic haze phenomenon. Bull. Am. Meteorol. Soc.
**1995**, 76, 2403–2414. [Google Scholar] [CrossRef] - Stohl, A. Characteristics of atmospheric transport into the Arctic troposphere. J. Geophys. Res. Atmos.
**2006**, 111, 17. [Google Scholar] [CrossRef] - Warneke, C.; Bahreini, R.; Brioude, J.; Brock, C.; De Gouw, J.; Fahey, D.; Froyd, K.; Holloway, J.; Middlebrook, A.; Miller, L.; et al. Biomass burning in Siberia and Kazakhstan as an important source for haze over the Alaskan Arctic in April 2008. Geophys. Res. Lett.
**2009**, 36, 6. [Google Scholar] [CrossRef] [Green Version] - Schmeisser, L.; Backman, J.; Ogren, J.A.; Andrews, E.; Asmi, E.; Starkweather, S.; Uttal, T.; Fiebig, M.; Sharma, S.; Eleftheriadis, K.; et al. Seasonality of aerosol optical properties in the Arctic. Atmos. Chem. Phys.
**2018**, 18, 11599–11622. [Google Scholar] [CrossRef] [Green Version] - Hoffmann, A. Comparative Aerosol Studies Based on Multi-Wavelength Raman LIDAR at Ny-Ålesund, Spitsbergen. Ph.D. Thesis, Universität Potsdam, Potsdam, Germany, 2011. [Google Scholar]
- Ansmann, A.; Wandinger, U.; Riebesell, M.; Weitkamp, C.; Michaelis, W. Independent measurement of extinction and backscatter profiles in cirrus clouds by using a combined Raman elastic-backscatter lidar. Appl. Opt.
**1992**, 31, 7113–7131. [Google Scholar] [CrossRef] - Klett, J.D. Lidar inversion with variable backscatter/extinction ratios. Appl. Opt.
**1985**, 24, 1638–1643. [Google Scholar] [CrossRef] - Weitkamp, C. Lidar: Range-Resolved Optical Remote Sensing of the Atmosphere; Springer Series in Optical Sciences; Springer: New York, NY, USA, 2005; Volume 102. [Google Scholar]
- Behrendt, A.; Nakamura, T. Calculation of the calibration constant of polarization lidar and its dependency on atmospheric temperature. Opt. Express
**2002**, 10, 805–817. [Google Scholar] [CrossRef] - Böckmann, C. Hybrid regularization method for the ill-posed inversion of multiwavelength lidar data in the retrieval of aerosol size distributions. Appl. Opt.
**2001**, 40, 1329–1342. [Google Scholar] [CrossRef] [PubMed] - Böckmann, C.; Kirsche, A. Iterative regularization method for lidar remote sensing. Comput. Phys. Commun.
**2006**, 174, 607–615. [Google Scholar] [CrossRef] - Samaras, S.; Nicolae, D.; Böckmann, C.; Vasilescu, J.; Binietoglou, I.; Labzovskii, L.; Toanca, F.; Papayannis, A. Using Raman-lidar-based regularized microphysical retrievals and Aerosol Mass Spectrometer measurements for the characterization of biomass burning aerosols. J. Comput. Phys.
**2015**, 299, 156–174. [Google Scholar] [CrossRef] - Müller, D.; Böckmann, C.; Kolgotin, A.; Schneidenbach, L.; Chemyakin, E.; Rosemann, J.; Znak, P.; Romanov, A. Microphysical particle properties derived from inversion algorithms developed in the framework of EARLINET. Atmos. Meas. Tech.
**2016**, 9, 5007–5035. [Google Scholar] [CrossRef] [Green Version] - Kirsche, A.; Böckmann, C. Padé iteration method for regularization. Appl. Math. Comput.
**2006**, 180, 648–663. [Google Scholar] [CrossRef] - Sorrentino, A.; Sannino, A.; Spinelli, N.; Piana, M.; Boselli, A.; Tontodonato, V.; Castellano, P.; Wang, X. A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data. Atmos. Meas. Tech.
**2022**, 15, 149–164. [Google Scholar] [CrossRef] - Rader, F.; Traversi, R.; Severi, M.; Becagli, S.; Müller, K.J.; Nakoudi, K.; Ritter, C. Overview of Aerosol Properties in the European Arctic in Spring 2019 Based on In Situ Measurements and Lidar Data. Atmosphere
**2021**, 12, 271. [Google Scholar] [CrossRef] - Müller, D.; Mattis, I.; Ansmann, A.; Wehner, B.; Althausen, D.; Wandinger, U.; Dubovik, O. Closure study on optical and microphysical properties of a mixed urban and Arctic haze air mass observed with Raman lidar and Sun photometer. J. Geophys. Res. Atmos.
**2004**, 109, 10. [Google Scholar] [CrossRef] - Rolph, G.; Stein, A.; Stunder, B. Real-time Environmental Applications and Display sYstem: READY. Environ. Model. Softw.
**2017**, 95, 210–228. [Google Scholar] [CrossRef] - Stein, A.F.; Draxler, R.R.; Rolph, G.D.; Stunder, B.J.B.; Cohen, M.D.; Ngan, F. NOAA’s HYSPLIT Atmospheric Transport and Dispersion Modeling System. Bull. Am. Meteorol. Soc.
**2015**, 96, 2059–2077. [Google Scholar] [CrossRef] - Nicolaus, M.; Riemann-Campe, K.; Bliss, A.; Hutchings, J.K.; Granskog, M.A.; Haas, C.; Hoppmann, M.; Kanzow, T.; Krishfield, R.A.; Lei, R.; et al. Drift Trajectory of the Central Observatory 1 (CO1) of the Distributed Network of MOSAiC 2019/2020; PANGAEA: Bremerhven, Germany, 2021. [Google Scholar]
- Schulz, A. Die arktische Grenzschichthöhe auf der Basis von Sondierungen am Atmosphärenobservatorium in Ny-Alesund und im ECMWF-Modell. Ph.D. Thesis, Potsdam University, Potsdam, Germany, 2012. [Google Scholar]
- Richardson, H.; Basu, S.; Holtslag, A.A.M. Improving stable boundary-layer height estimation using a stability-dependent critical bulk Richardson number. Bound.-Layer Meteorol.
**2013**, 148, 93–109. [Google Scholar] [CrossRef] - Ferrero, L.; Cappelletti, D.; Busetto, M.; Mazzola, M.; Lupi, A.; Lanconelli, C.; Becagli, S.; Traversi, R.; Caiazzo, L.; Giardi, F.; et al. Vertical profiles of aerosol and black carbon in the Arctic: A seasonal phenomenology along 2 years (2011–2012) of field campaigns. Atmos. Chem. Phys.
**2016**, 16, 12601–12629. [Google Scholar] [CrossRef] [Green Version] - Zieger, P.; Fierz-Schmidhauser, R.; Gysel, M.; Ström, J.; Henne, S.; Yttri, K.E.; Baltensperger, U.; Weingartner, E. Effects of relative humidity on aerosol light scattering in the Arctic. Atmos. Chem. Phys.
**2010**, 10, 3875–3890. [Google Scholar] [CrossRef] [Green Version] - Inoue, J.; Yamazaki, A.; Ono, J.; Dethloff, K.; Maturilli, M.; Neuber, R.; Edwards, P.; Yamaguchi, H. Additional Arctic observations improve weather and sea-ice forecasts for the Northern Sea Route. Sci. Rep.
**2015**, 5, 16868. [Google Scholar] [CrossRef] [PubMed] [Green Version]

**Figure 1.**Grid of the imaginary and real part of the CRI at 21 February 2020, lower layer from 1323 until 1586 m. The selected grid points in the diagonal pattern to determine the mean CRI are marked brightly.

**Figure 2.**Frequency polygon of the backscatter coefficient ${\beta}^{\mathrm{aer}}(532\phantom{\rule{0.166667em}{0ex}}$ nm) for different altitude ranges (labels on the right side) and months (indicated by colors). As a reference, the data from [29] for 2019 are plotted in grey.

**Figure 3.**Backscatter coefficient ${\beta}^{\mathrm{aer}}(532\phantom{\rule{0.166667em}{0ex}}$ nm) over time for different altitude ranges. The median value is plotted (different colors mark altitude ranges). The error bar is the mean value of all values larger and smaller than the median.

**Figure 5.**Profiles of the optical parameters for both case studies. The backscatter coefficient ${\beta}^{aer}$, aerosol extinction ${\alpha}^{aer}$, aerosol depolarization ${\delta}^{aer}$, lidar ratio LR and Ångström exponents Å are shown. The Ångström exponent is shown for all wavelength pairs: Å(355, 532 ) in blue, Å(355, 1064) in red, Å(532, 1064) in black. The investigated altitude intervals are marked with black lines. (

**a**) Parameters from 13 January (10:21–12:14) UT. (

**b**) Parameters from 21 February (13:40–15:10) UT.

**Figure 6.**Volume distribution function of the particle radius r for 13 January for two different altitudes, one within the MBL and one above. Blue: result of the inversion; green: log-normal fits. If the sum of the fits (in red) cannot be seen, it is congruent with the inversion. All values are given in Table 3. The mode with the smaller radius ${r}_{\mathrm{mod}}$ is referred to as the first mode.

**Figure 7.**Volume distribution function of the particle radius r for two different altitudes on 21 February, one within the MBL and one above. Blue: result of the inversion; green: log-normal fits. If the sum of the fits (in red) cannot be seen, it is congruent with the inversion. All values are given in Table 3. The mode with the smaller radius ${r}_{\mathrm{mod}}$ is referred to as the first mode.

**Figure 8.**Atmospheric conditions on 13 January 2020. Meteorological data RH (in blue) and temperature (in red) from RS launch at 11:00 UTC, and lidar data ${\beta}^{\mathrm{aer}}$ from (10:21–12:14) UTC (in black). The resolution of the lidar data is 10 min and 7.5 m.

**Figure 9.**Atmospheric conditions on 21 February 2020. Meteorological data RH (in blue) and temperature (in red) from RS launch at 10:58 UTC, and lidar data ${\beta}^{\mathrm{aer}}$ from (13:40–15:10) UTC (in black). The resolution of the lidar data is 10 min and 7.5 m.

**Table 1.**Median of the lidar ratio $LR$ for different altitude ranges and months in 2020, with high and low backscatter events presented separately. The 25th and 75th percentiles are indicated in the brackets.

High | January | February | March | April |
---|---|---|---|---|

(0.7–1) km | 30.2 (20.1–49.8) | 27.1 (20.2–34.7) | 22.1 (12.6–33.7) | 30.9 (20.6–40.3) |

(1–1.5) km | 32.3 (16.3–52.6) | 24.9 (11.6–39.5) | 21.9 (7.0–42.2) | 26.5 (15.0–38.6) |

Low | January | February | March | April |

(0.7–1) km | 54.3 (35.7–79.4) | 33.7 (20.0–47.2) | 27.8 (11.4–53.0) | 28.6 (15.0–40.7) |

(1–1.5) km | 58.5 (32.6–82.0) | 29.1 (7.3–54.0) | 16.3 (0.9–38.9) | 29.1 (10.3–50.3) |

**Table 2.**Median of the retrieved optical parameters shown separately for the 3 days in February 2020 with the highest backscatter and for the rest of February. The 25th and 75th percentiles are indicated in the brackets.

High Backscatter Mode | Low Backscatter Mode | |
---|---|---|

Date | 21–23 February | Rest of February |

${\beta}_{532}^{\mathrm{aer}}$ | 1.12 (0.98–1.23) | 0.69 (0.56–1.01) |

$L{R}_{355}$ | 28.9 (23.6–36.7) | 29.5 (18.3–41.9) |

${\delta}_{532}^{\mathrm{aer}}$ [%] | 0.65 (0.57–0.74) | 0.85 (0.74–1.02) |

Å$(355,532\phantom{\rule{0.166667em}{0ex}}$ nm) | 1.04 (0.83–1.22) | 0.96 (0.56–1.18) |

**Table 3.**Optical and microphysical parameters for the different layers. The first mode is always the mode with the smaller radius ${r}_{\mathrm{mod}}$. For the optical parameters, the largest possible uncertainty according to the maximum error estimate is given.

13 January | 21 February | |||
---|---|---|---|---|

Time [UTC] | 10:21–12:14 | 10:21–12:14 | 13:40–15:10 | 13:40–15:10 |

Height [m] | 1050–1250 | 1600–1900 | 1323–1586 | 2150–2750 |

${\beta}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ [Mm${}^{-1}$sr${}^{-1}$] | 1.044 ± 0.08 | 0.465 ± 0.08 | 1.077 ±0.08 | 0.277 ± 0.07 |

${\beta}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ [Mm${}^{-1}$sr${}^{-1}$] | 0.545 ± 0.05 | 0.277 ± 0.05 | 0.7414 ± 0.04 | 0.207 ± 0.02 |

${\beta}_{1064\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ [Mm${}^{-1}$sr${}^{-1}$] | 0.244 ± 0.02 | 0.092 ± 0.02 | 0.219 ± 0.02 | 0.029 ± 0.02 |

${\alpha}_{355\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ [Mm${}^{-1}$] | 35.912 ± 8 | 20.777 ± 9 | 40.848 ± 8 | 12.650 ± 7 |

${\alpha}_{532\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}}^{\mathrm{aer}}$ [Mm${}^{-1}$] | 42.287 ± 19 | 5.343 ± 2.1 | 29.144 ±16 | 5.331 ± 2.3 |

$L{R}_{355}^{\mathrm{aer}}\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}$ [sr] | 34.40 (24.9–45.8) | 44.68 (23.3–81.2) | 37.93 (28.4–49.0) | 44.28 (16.3–94.9) |

$L{R}_{532}^{\mathrm{aer}}\phantom{\rule{0.166667em}{0ex}}\mathrm{nm}$ [sr] | 77.59 (38.4–122.4) | 19.29 (9.6–38.8) | 39.33 (16.8–64.4) | 25.79 (13.3–40.8) |

$\mathrm{Mean}\phantom{\rule{4.pt}{0ex}}{\mathrm{RI}}_{\mathrm{real}}$ | 1.311 ± 0.010 | 1.458 ± 0.009 | 1.526 ± 0.015 | 1.447 ± 0.011 |

$\mathrm{Mean}\phantom{\rule{4.pt}{0ex}}{\mathrm{RI}}_{\mathrm{imag}}$ | 0.0006 ± 0.0005 | 0.0010 ± 0.0004 | 0.020 ± 0.004 | 0.0039 ± 0.0017 |

Total: ${r}_{\mathrm{eff}}$ [$\mathsf{\mu}$m] | 0.73 ± 0.06 | 0.053 ± 0.003 | 0.0799 ± 0.0033 | 0.0538 ± 0.0014 |

First mode: ${r}_{\mathrm{mod}}$ [$\mathsf{\mu}$m] | 0.54 | 0.016 | 0.004 | 0.015 |

Second mode: ${r}_{\mathrm{mod}}$ [$\mathsf{\mu}$m] | 1.44 | 2.26 | 0.53 | 0.69 |

First mode: $\sigma $ | 1.38 | 2.03 | 3.19 | 2.16 |

Second mode: $\sigma $ | 1.16 | 1.09 | 1.14 | 1.09 |

First mode: ${v}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{3}$cm${}^{-3}$] | 11.06 | 4.80 | 5.04 | 1.98 |

Second mode: ${v}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{3}$cm${}^{-3}$] | 2.68 | 2.17 | 1.17 | 0.65 |

Total: ${v}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{3}$cm${}^{-3}$] | 14.01 ± 1.22 | 7.27 ± 0.28 | 6.16 ± 0.19 | 2.90 ± 0.13 |

First mode: ${s}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{2}$cm${}^{-3}$] | 47.36 | 261.14 | 143.52 | 90.15 |

Second mode: ${s}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{2}$cm${}^{-3}$] | 5.32 | 2.83 | 6.37 | 2.79 |

Total: ${s}_{\mathrm{t}}$ [$\mathsf{\mu}$m${}^{2}$cm${}^{-3}$] | 57.24 ± 0.86 | 409.2 ± 14.53 | 231.66 ± 9.56 | 161.55 ± 0.13 |

First mode: ${n}_{\mathrm{t}}$ [cm${}^{-3}$] | 10.46 | 30 899 | 58 420 | 9 848 |

Second mode: ${n}_{\mathrm{t}}$ [cm${}^{-3}$] | 0.20 | 0.04 | 1.76 | 0.47 |

${\mathrm{SSA}}_{355}$ | 0.9863 ± 0.0013 | 0.98637 ± 0.00025 | 0.854 ± 0.012 | 0.960 ± 0.004 |

${\mathrm{SSA}}_{532}$ | 0.9917 ± 0.0006 | 0.9777 ± 0.0007 | 0.862 ± 0.012 | 0.946 ± 0.006 |

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**MDPI and ACS Style**

Dube, J.; Böckmann, C.; Ritter, C.
Lidar-Derived Aerosol Properties from Ny-Ålesund, Svalbard during the MOSAiC Spring 2020. *Remote Sens.* **2022**, *14*, 2578.
https://doi.org/10.3390/rs14112578

**AMA Style**

Dube J, Böckmann C, Ritter C.
Lidar-Derived Aerosol Properties from Ny-Ålesund, Svalbard during the MOSAiC Spring 2020. *Remote Sensing*. 2022; 14(11):2578.
https://doi.org/10.3390/rs14112578

**Chicago/Turabian Style**

Dube, Jonas, Christine Böckmann, and Christoph Ritter.
2022. "Lidar-Derived Aerosol Properties from Ny-Ålesund, Svalbard during the MOSAiC Spring 2020" *Remote Sensing* 14, no. 11: 2578.
https://doi.org/10.3390/rs14112578