Tr a n s r e g i o n a l C o l l a b o r a t i v e Re s e a r c h C e n t r e TR 172

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1 Tr a n s r e g i o n a l C o l l a b o r a t i v e Re s e a r c h C e n t r e TR 172 ArctiC Amplification: Climate Relevant Atmospheric and SurfaCe Processes, and Feedback Mechanisms (AC) 3 Towards a better understanding of Arctic clouds using observations and highresolution modelling Kerstin Ebell 1, Vera Schemann 1, Rosa Gierens 1, Tatiana Nomkonova 1, Ulrich Löhnert 1, Marion Maturilli 2, Christoph Ritter 2, Roland Neuber 2 1) Institute for Geophysics and Meteorology, University of Cologne, Germany 2) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany

2 Why Arctic clouds? one of the main components driving the Arctic climate system key role in the radiation budget sparse knowledge of cloudradiation interactions and cloud properties at high latitudes Arctic clouds remain one of the greatest sources of uncertainties in the modelling of the Arctic response to climate change Longwave cooling microphysical processes? macro- and microphysical properties? variability on small and large scales? Longwave warming radiative impact? Shortwave cooling 2

3 spatial variability evaluation How can we improve our understanding? Observations in-situ (e.g., aircraft, Zeppelin Observatory) satellite remote sensing N (D) D High-resolution modelling large eddy simulations as virtual laboratory to test our understanding of cloud processes (parameterizations) bridges the link between small scales (observations) and large scales (climate models) Terra images NASA Worldview ground-based remote sensing exploitation of sensor synergy height time 3

4 New cloud remote sensing at AWIPEV since June GHz FMCW cloud radar (University of Cologne) profiles of radar reflectivity, Doppler velocity and spectral width every 3 s up to 5 m vertical resolution passive 89 GHz channel presence and amount of liquid in column vertical information on hydrometeors Brightness temperature at 89 GHz Radar reflectivity factor 4

5 Cloud remote sensing: sensor synergy since June GHz FMCW cloud radar (University of Cologne) profiles of radar reflectivity, Doppler velocity and spectral width every 3 s up to 5 m vertical resolution passive 89 GHz channel + Ceilometer CL51, Humidity and Temperature Profiler HATPRO (Alfred Wegener Institute) characterization of vertically resolved cloud properties Cloudnet (Illingworth et al., 2007), provided by Ewan O Connor retrieval of macrophysical and microphysical cloud properties process studies and long-term statistics (see poster by Nomokonova et al.) 5

6 High-resolution modelling with ICON-LEM developed by German Weather Service DWD and MPI Hamburg here: 4 nested grids centered around Ny-Ålesund initialization and forcing every hour at the boundary by ECMWF IFS data open boundaries land surface e.g. topography hor. resolution 600 m 300 m 150 m 75 m x NYA analysis of meteogram output for Ny- Ålesund; available every 9 s 110 km First step: Can the thermodynamic structure be captured by ICON-LEM? 6

7 Case studies 2 case studies from Arctic Research Collaboration for Radiosonde Observing SystEm (ARCROSE)-2013 (Inoue et al., 2015): several radiosondes/day! 13 September 2013 cold front passing Ny-Ålesund clouds, intermittent periods of rain 16 September 2013 stable atmospheric conditions high clouds Terra image NASA Worldview 7

8 Case study 13 Sep 2013: ICON vs. MWR temperature Microwave radiometer HATPRO (from elevation scans; typically 3-4 degrees of freedom for signal) ICON-LEM 8

9 ARCROSE case studies: ICON vs. radiosonde T (0-3 km) low-level T inversion on 16 Sep nicely be captured by ICON-LEM multiple T inversions challenging model perfomance depends on day analyzed 9 UTC 12 UTC 16 Sep Sep UTC 18 UTC 15 UTC 18 UTC ICON-LEM Radiosonde Temperature / K Temperature / K Temperature / K Temperature / K Temperature / K Temperature / K 9

10 ARCROSE case studies: ICON vs. radiosonde q vertical structure of atmospheric humidity reproduced by ICON- LEM 13 Sep UTC 18 UTC ICON-LEM Radiosonde 16 Sep Specific humidity / g/kg Specific humidity / g/kg 9 UTC 12 UTC 15 UTC 18 UTC Specific humidity / g/kg Specific humidity / g/kg Specific humidity / g/kg Specific humidity / g/kg 10

11 ARCROSE case studies: ICON vs. GPS/MWR Integrated Water Vapor 13 Sep 2013 Integrated water vapor MWR GPS ICON-LEM 16 Sep

12 Case study 16 Aug 2016: Target classification Cloudnet (synergy of cloud radar, MWR, ceilometer, NWP model) ICON-LEM 12

13 Case study 16 Aug 2016: ICON cloud water mixing ratio x 13

14 Case study 16 Aug 2016: Liquid water path OBS ICON-LEM 600 m OBS ICON-LEM 300 m OBS ICON-LEM 150 m OBS ICON-LEM 75 m high-resolution necessary to capture observed LWP variability 14

15 Challenges: mixed-phase cloud on 23 Nov 2017 Cloudnet (synergy of cloud radar, MWR, ceilometer, NWP model) ICON-LEM 15

16 Conclusion & Outlook ICON-LEM shows high potential to simulate atmospheric conditions at Ny-Ålesund reasonable representation of liquid boundary layer clouds but mixed-phase clouds more difficult high resolution (75 m) needed to capture cloud variability Next: 23 June 2017: Polar 5 and Polar 6 obs. as part of ACLOUD combined analysis of NYA column perspective, surroundings (aircraft) and full time-space description (ICON-LEM) Polar 5 flight track x NYA 16

17 For discussion How can we optimally exploit the various cloud observations in Ny-Ålesund (in-situ, e.g. Zeppelin observatory, and remote sensing) to better characterize Arctic clouds and the associated microphysical processes? Can we get a consistent picture from the different data sets? Can in-situ observation help us to constrain remote sensing retrieval methods? Role of aerosol? Discussion group on Thursday: Clouds and aerosol observations: what we can learn about aerosolclouds and clouds-aerosol interactions from combining remote sensing and in-situ observations? 17