Polar Boundary Layer Process & MOSAiC

Transcription

1 Polar Boundary Layer Process & MOSAiC Ian Brooks

2 Tjernstrom et al ARCMIP model temperature biases (black = autumn, grey = winter, black-dash = spring, grey-dash = summer)

3 Boundary Layer Issues Stable boundary layers Surface turbulent exchange Over ice/snow & mixed sea ice & water Stable stratification Strongly forced conditions (high wind / high T) Boundary-layer cloud Mixed phase Interactions with turbulence Interactions with aerosol Interactions with radiation

4 OBS Global models generally do a poor job of representing the Arctic BL and cloud Birch et al. 2012, ACP Operational UM

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6 ACCACIA: Aerosol-Cloud Coupling and Climate Interactions in the Arctic NERC Arctic Science Programme Consortium: RV Lance Leeds, Manchester, York, UEA BAS, Met Office (+ MPI, NOAA, Univ. Oslo) additional flying hours funded by NCAS and the MetOffice JCR Field campaigns: March/April & July/Aug 2013 Measurements: aerosol physics & chemistry (inc ice nuclei), cloud microphysics, BL thermodynamic & turbulent structure, surface aerosol & precursor gas (DMS) sources Modelling: detailed aerosol/microphysics process box modelling, LES, UM: operational model (case studies), SCM (parameterization tests), HADGEM (climate scale processes) WRF BL scheme test & development FAAM BAe 146 BAS Twin Otter (MASIN)

7 Large upward heat and moisture fluxes off the ice edge during cold air outbreaks convection and cloud formation. MetUM 4 km during B762: Ice concentration Winds (Model lev. 1) Sensible heat flux (Model lev. 1) Liquid water content (at ~700 m) Frozen water content (at ~700 m)

8 (MetUM - Obs) for potential temperature B759 B760 B761 B762-upwind B762 Upwind of ice edge B762-dnwind B764 B765 B767 B768 Consequently model is often too warm/cold following T sfc But B759 too warm; B760 cold over ice; B765 cold over MIZ

9 MetUM performance summary Discrepancies with sea-ice and T sfc prescription lead to some θ and SHFX differences e.g. SHFX too large due to sea-ice edge difference But other differences: too warm over MIZ (B759) too cold over sea-ice (B760) too cold in BL over MIZ (B765)

10 (MetUM - Obs) of sensible heat flux W/m 2 B759 B760 B761 B762-upwind B762 Upwind of ice edge B762-dnwind B764 B765 B767 B768 Model generally overestimates low level convective heat fluxes off the ice edge, whilst generally underestimating heat fluxes (too large negative values) higher up

11 MetUM performance summary Discrepancies with sea-ice and T sfc prescription lead to some θ and SHFX differences e.g. SHFX too large due to sea-ice edge difference But other differences: too warm over MIZ (B759) too cold over sea-ice (B760) too cold in BL over MIZ (B765) UM generally overestimates surface SHFX and underestimates BL SHFX (B759, 765) Is this due to BL height too high??

12 (MetUM - Obs) for liquid water content g/kg B759 B760 B761 B762-upwind B762 B762-dnwind B764 B765 B767 B768 Model generally simulates position of clouds accurately but substantially underestimates liquid water contents

13 MetUM performance summary Discrepancies with sea-ice and T sfc prescription lead to some θ and SHFX differences e.g. SHFX too large due to sea-ice edge difference But other differences: too warm over MIZ (B759) too cold over sea-ice (B760) too cold in BL over MIZ (B765) UM generally overestimates surface SHFX and underestimates BL SHFX (B759, 765) Is this due to BL height too high?? Underestimate of liquid water content (c.f. Field et al. 2013) Overestimate of frozen water content

14 Field et al. 2014: QJRMS

15 Default model Fails to represent Scu LWP too low Revised non-local BL scheme Produces SCu & transition of open cells BUT... LWP still too low Threshold T of -10C too high for onset of cloud ice (clean air mass default model aerosol too high?) Too efficient at removing liquid from cloud Microwave Obs Control Model 1.5km grid Modified Model Sh dom BL Thet=-18C New PSD

16 CMIP5 models: Mean bias of ~20 Wm -2 in surface SW flux SST bias of ~2K Major factor is poor representation of BL cloud, especially in cold sector of mid-latitude cyclones

17 ACSE: Summer 2014 measurements from icebreaker Oden of accelerated ice melt during warm advection & air mass modification Tjernström, M., et al., 2015: Warm-air advection, air mass transformation and fog causes rapid ice melt, Geophys. Res. Letts. 42. doi: /2015gl064373

18 Surface Exchange Surface turbulent flux bias (CMIP5 CERES/ERA)(W m -2 ) Loeb et al. 2015, Clim. Dyn.

19 Strongly Forced Conditions NCEP Aircraft EC % of time surface winds > 25 m s -1 Bourasa et al. 2013, BAMS ECMWF Obs from Petersen & Renfrew 2009

20 ACCACIA Elvidge, A. D., I. A. Renfrew, A. I. Weiss, I. M. Brooks, T. A. Lachlan-Cope, J. C. King, 2015: Observations of surface momentum exchange over the marginal-ice-zone and recommendations for its parameterization. Atmos. Chem. Phys. (submitted)

21 Coordinated sets of targeted observations are needed to refine flux parameterizations for high-latitude conditions and to provide calibration and validation data. Bourasa et al. 2013: High-Latitude Ocean and Sea Ice Surface Fluxes : Challenges for Climate Research, BAMS Challenges : Small spatial scales of flux variability (~10km) Rapid evolution of high latitude storm systems short time scales ~2 days to < 6 hours Surface heterogeneity Physics at scales down to metres matters Issues with satellite data at high latitudes (poleward of ~50 )...strong community consensus for an updated version of the SHEBA project and also for an Antarctic analog to SHEBA aimed at capturing differences between the sea ice zones of the Arctic (historically dominated by thick, multi-year ice) and the Antarctic (historically predominantly thinner first-year ice).

22 MOSAiC Multidisciplinary drifting Observatory for the Study of Arctic Climate Transpolar Drift track 2011 Objectives: Observe full sea-ice life cycle, starting in new ice. Trajectory that will last for at least (more than) 1 year Observe an understudied region Challenges: Central Arctic is isolated First year ice will be difficult

23 Sea Ice Energy Budget Decadal decline can be explained by ~1 W/m 2 excess. Kwok and Untersteiner (2011)

24 The MOSAiC Plan Central Arctic Basin, September 2019 > October ) Central Observatory: Coordinated atmos-ice-ocean-ecosystem observations 2) Distributed Network: Heterogeneity and variability on grid-box scales 3) Coordinated multi-scale modeling & analysis activities; link with YOPP

25 MOSAiC Key Milestones Planning Workshops: 2011, 2012, 2013 Polarstern proposal supported: 2015 US DOE ARM proposal supported: 2014 Draft Science Plan released: Agency/Funders Meeting: April 2015 Implementation Workshop: July 2015 Implementation Plan: MOSAiC Open Science Conference: 2016? Science-based Proposals:

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27 PACES Air Pollution in the Arctic: climate, environment and societies Co-sponsored by IGAC and IASC Research priorities needing focused, collaborative, and international effort: Sources of air pollution in the Arctic imported and locally emitted Processing, fate, and impacts on climate and ecosystems Interactions and feedbacks between anthropogenic pollution and natural sources Arctic climate response to forcing within and outside of Arctic Societal perspectives: health, ecosystems, sustainability, adaptation, economics, politics. PACESE aims to foster and coordinate: Trans-disciplinary collaboration, including social sciences Regular long-term and intensive field observations focused on processes Improved modeling over range of scales Improving collaborations using existing resources, including data sharing

28 Coordinated activities onwards (tie to YOPP) What to focus on? - Local emissions in the Arctic - Long-range transport, pollutant processing - Wet + dry deposition - Pollutant recycling (interactions with natural cycles) What would be needed to make real progress in understanding? - Local pollution aircraft, UAVs, ULAs - Processing, LRT Lagrangian approach. - Deposition - aircraft/ UAVs+surface+ ships - Recycling+natural - ground-based

29 PACES Working Group on Improving Predictive Capabilities Leads: Chuck Brock [NOAA], Knut von Salzen [Environment Canada], Steve Arnold [Leeds] - Exploring possibility of Lagrangian experiments in timeframe (coordination with YOPP) - focus on processing and scavenging in plumes transported into Arctic (interest from NOAA, UK?, Japan, Russia, Germany/ France, NASA??) - Initial exploration of regions of opportunity for Lagrangian experiment - transport patterns / aircraft ranges - 1D model comparisons to look at scavenging identify key process uncertainties.

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31 Observations of liquid water content (CDP) g/kg B759 B760 B761 B762-upwind B762 B762-dnwind B764 B765 B767 B768

32 MetUM for liquid water content [Note change in axis limits] g/kg B759 B760 B761 B762-upwind B762 B762-dnwind B764 B765 B767 B768 Model generally simulates position of clouds accurately but substantially underestimates liquid water contents

33 FAAM flights Mar-Apr 2013 Greyscale shading shows sea ice concentration (OSTIA): white = ice, grey = ocean B759 B760 B761 Group all legs roughly along a meridional or zonal vertical plane, giving 10 boundary cross sections (see next slides) Parallel with and upwind of ice edge Parallel with and downwind of ice edge All except two (highlighted red and blue) are across ice edge In all cases, flow is off ice-edge, i.e. cold air B762-upwind B dnwind outbreak off sea ice B764 B765 B767 B768