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  • View in gallery
    Fig. SB1.

    Time series of zonally and meridionally averaged, near-surface air temperature differences (anomalies). (a) The annually averaged differences of the near-surface air temperature relative to the corresponding long-term mean over the time period of 1951–80 for the Arctic (60°–90°N), midlatitudes (30°–60°N), tropics (20°S–20°N), and the globe. (b) The difference of the warming in the Arctic shown in (a), and the global average warming for winter (DJF) and summer (JJA). The thick lines in (a) and (b) without symbols indicate 5-yr running averages. The curves for spring (MAM) and fall (SON) are similar to those for DJF and, therefore, have been omitted in (b). The data are provided by the NASA GISTEMP Team, 2020: GISS Surface Temperature Analysis (GISTEMP), version 4. NASA Goddard Institute for Space Studies. Dataset accessed at https://data.giss.nasa.gov/gistemp/ on 8 Jun 2022.

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    Fig. 1.

    The (AC)3 simplified schematic of important local and remote processes and feedback mechanisms driving Arctic amplification. The figure illustrates the initial trigger by global warming (red), and shows examples of processes/feedback mechanisms such as (i) surface albedo feedback (black), (ii) upper-ocean effects (brown), (iii) local atmospheric processes (green), and (iv) Arctic–midlatitude linkages (yellow). Adopted from Wendisch et al. (2017) in modified form.

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    Fig. 2.

    Time series of selected atmospheric observations collected at the AWIPEV research base at Ny-Ålesund during (AC)3. (a) Monthly mean solar (red) and terrestrial (blue) net (downward minus upward) surface irradiance, (b) monthly mean 2 m temperature, (c) monthly mean vertically integrated water vapor from microwave radiometer (MWR; blue) and radiosondes (red), (d) monthly frequency of occurrence of any type of clouds (black), liquid clouds (blue), mixed-phase clouds (red), and ice clouds (orange) in the atmospheric column based on a cloud radar and ceilometer synergy, (e) monthly accumulated precipitation from Pluvio weighing gauge based on original (blue) and corrected values (gray). The error bars in (a) and (c) represent the standard deviation of the daily mean values. Hatched areas indicate times where no or insufficient data are available to calculate monthly mean values.

  • View in gallery
    Fig. 3.

    Horizontal and vertical coverage of the aircraft observations conducted during ACLOUD (82 + 82 flight hours with Polar 5 and Polar 6), PAMARCMiP (40 flight hours, Polar 5), AFLUX (66 flight hours, Polar 5), and MOSAiC-ACA (44 flight hours, Polar 5); altogether 314 h of measurements (about 10% within clouds) were collected. (a) Horizontal projections of flight pattern during the airborne measurements; LYR refers to Longyearbyen on Svalbard; (b) vertical distribution of number of flight hours spent in different altitudes over different sea ice conditions.

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    Fig. 4.

    Sea ice conditions from satellite observations during the (a)–(c) ACLOUD and (d)–(f) AFLUX campaigns. (a),(d) Sea ice concentration from the AMSR2 microwave radiometer. (b),(c) Sea ice albedo and fractional coverage of melt ponds on the sea ice from Sentinel-3 data (Pohl et al. 2020). (e) Thickness of thin sea ice from combined SMAP and SMOS L-band radiometer observations (Paţilea et al. 2019). (f) Snow depth on sea ice from AMSR2 observations (Rostosky et al. 2018, 2020). All data are available from https://seaice.uni-bremen.de.

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    Fig. 5.

    Radar reflectivity Ze (in dBZ) observations of airborne (curtain from lower-right to upper-left corners, measured by the flying downward-pointing 94 GHz MiRAC radar installed on Polar 5) and ground-based (curtain from lower-left to upper-right corners, measured by the locally fixed upward-pointing 94 GHz radar operated at the AWIPEV research base at Ny-Ålesund, whereby the vertically resolved column measurements were shifted with the simulated wind, indicated by the arrows) remote sensing measurements combined with high-resolution simulations (ICON-LEM, wind arrows, rendered clouds) around the area of Svalbard (topography).

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    Fig. 6.

    (a) Average March 2015 snow depth on sea ice from AMSR2 satellite microwave radiometer observations. The black line discriminates first-year from multiyear sea ice. (b) Trend of March snow depth for years 2003–20. Stippled areas mark statistically significant trends. Time series of yearly March snow depth on (c) first-year sea ice (FYI) and (d) multiyear sea ice (MYI). See Rostosky et al. (2018, 2020).

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    Fig. 7.

    Transfer coefficients for momentum Cd and heat Ch as a function of the bulk Richardson number Rib. Solid lines show 10 m values obtained with different parameterizations using surface roughnesses as given in the text. The curve notation is as follows: Black, Louis (1979); orange, Beljaars and Holtslag (1991); green, Grachev et al. (2007); red, new development of Gryanik et al. (2020); blue, Businger et al. (1971) and Dyer (1974). Black squares show SHEBA data obtained in the surface layer at different heights.

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    Fig. 8.

    Joint two-dimensional histogram of frequency distribution of terrestrial net irradiance (measured below clouds) as a function of all-sky surface albedo observed during (a) AFLUX and (b) ACLOUD. Two modes (cloud-free and opaquely cloudy) become obvious over both open ocean (low surface albedo) and sea ice (high surface albedo). The figure is taken from Stapf (2021).

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    Fig. 9.

    Two-dimensional histogram of frequency distribution of cloud radiative forcing (CRF) as a function of surface albedo derived from ACLOUD observations. Two cloud modes become obvious over open ocean (low surface albedo) and sea ice (high surface albedo). The figure is adapted from Stapf (2021).

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Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)3 Project

M. WendischLeipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;
Leipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;

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M. BrücknerLeipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;

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S. CrewellInstitut für Geophysik und Meteorologie, Universität zu Köln, Cologne, Germany;
Institut für Geophysik und Meteorologie, Universität zu Köln, Cologne, Germany;

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A. EhrlichLeipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;

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J. NotholtInstitut für Umweltphysik, Universität Bremen, Bremen, Germany;
Institut für Umweltphysik, Universität Bremen, Bremen, Germany;

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C. LüpkesAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;

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A. MackeLeibniz-Institut für Troposphärenforschung, Leipzig, Germany;
Leibniz-Institut für Troposphärenforschung, Leipzig, Germany;

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J. P. BurrowsInstitut für Umweltphysik, Universität Bremen, Bremen, Germany;

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A. RinkeAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;

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J. QuaasLeipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;

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M. MaturilliAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;

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V. SchemannInstitut für Geophysik und Meteorologie, Universität zu Köln, Cologne, Germany;

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M. D. ShupePhysical Sciences Laboratory, National Oceanic and Atmospheric Administration, and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado;
Physical Sciences Laboratory, National Oceanic and Atmospheric Administration, and Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado;

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E. F. AkansuLeibniz-Institut für Troposphärenforschung, Leipzig, Germany;

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C. Barrientos-VelascoLeibniz-Institut für Troposphärenforschung, Leipzig, Germany;

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K. BärfussInstitut für Flugführung, Technische Universität Braunschweig, Brunswick, Germany;
Institut für Flugführung, Technische Universität Braunschweig, Brunswick, Germany;

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A.-M. BlechschmidtInstitut für Umweltphysik, Universität Bremen, Bremen, Germany;

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K. BlockLeipziger Institut für Meteorologie, Universität Leipzig, Leipzig, Germany;

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I. BougoudisInstitut für Umweltphysik, Universität Bremen, Bremen, Germany;

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H. BozemInstitut für Physik der Atmosphäre, Johannes Gutenberg-Universität, Mainz, Germany;
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C. BöckmannAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, and Institut für Mathematik, Universität Potsdam, Potsdam, Germany;
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, and Institut für Mathematik, Universität Potsdam, Potsdam, Germany;

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A. BracherAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, and Institut für Umweltphysik, Universität Bremen, Bremen, Germany;
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, and Institut für Umweltphysik, Universität Bremen, Bremen, Germany;

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H. BressonAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;

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L. BretschneiderInstitut für Flugführung, Technische Universität Braunschweig, Brunswick, Germany;
Abteilung für Partikelchemie, Max-Planck-Institut für Chemie, Mainz, Germany;

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M. BuschmannInstitut für Umweltphysik, Universität Bremen, Bremen, Germany;

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D. G. ChechinAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar und Meeresforschung, Bremerhaven and Potsdam, Germany, and A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia;
Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar und Meeresforschung, Bremerhaven and Potsdam, Germany, and A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia;

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J. ChylikInstitut für Geophysik und Meteorologie, Universität zu Köln, Cologne, Germany;

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S. DahlkeAlfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven and Potsdam, Germany;

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