Modelling approaches for MOSAiC Klaus Dethloff, A. Rinke, A. Sommerfeld, D. Klaus T. Vihma, M. Müller, J. Inoue, W. Maslowski & modelling team
Why do we need MOSAiC? High quality co-observations of A-O-I-BGC-E system in coldest region of NH Data assimilation in data sparse region during drift Improved Arctic sub-grid scale parameterizations 1. Weather prediction models 2. Climate models (seasons, annual, decades) 3. Climate projections PBL turbulence, low- & mid-level clouds, aerosols Sea ice, Snow, Ocean meso-scale dyn., Biogeoch. Linkages Arctic and mid-latitudes, Tropo-Stratosphere
Process studies, parameterizations, model hierarchy 3D-GCM Global Models NWPs (ECHAM6-FESOM) 3D-RCM Regional Models NWPs (HIRHAM5) 1D-SCM Single Column Models (HIRHAM5-SCM) Measurements at stations, in-situ, field campaign MOSAiC, airborne, satellites Non-hydrostatic mesoscale and large-scale eddy simulations Projections Predictions Regional process understanding Validation Evaluation Measurements Sub-grid scale parameterisations
Weather & climate model hierarchy NWPs, RCMs, GCMs NWP Models: Operational Weather and sea ice forecasts Guide the MOSAiC drift track Integrate process information via near-real time data assimilation (Met.No, JAMSTEC, ECMWF) Provide spatial distributions Process studies, LES & Process Models Regional Models A, O-I, ARCMIP; CORDEX; FAMOS Coupled models A-O-I-L CARCMIP Sub-grid scale paramet. development Initial conditions Ensemble simul. Global Models: Linkages with mid-latitudes Year of polar prediction YOPP 2017/19 Consolidation phase 2020/2022 measurement campaigns Satellite data International Driftstation MOSAiC Data assimilation Arctic climate system models with improved sub-grid scale parameterizations (e. g. sea ice, soil, fluxes, albedo, clouds, aerosols)
Measurements in a model grid, MOSAiC observatory Central Observatory Ship based Interdisciplinary Atmosphere, Ocean, Sea ice, Ecosystem Process scale observations < 5 km Distributed Network in a model grid box Network of spatial heterogeneity Variability measurements ( Planes, UAVs) Satellite data Parameterizations < 50 km Regional models Large-scale linkages Synoptical linkages > 1000 km Coordinated measurements Data assimilation studies ARCROSE Teleconnection patterns Predictability studies AGCMs YOPP
Data assimilation studies MOSAiC Great cyclone case in August 2012 (RV Polarstern) Case study September 2013 (RV Mirai, Ny-Ålesund, Alert & Eureka) September 2014 (RVs Polarstern, Mirai, Oden; Ny-Ålesund, Alert & Eureka) Global observations Extra Observations (radiosondes) JAMSTEC ALERA2 Observing system experiments Control Reanalysis Atmospheric forecast Sea-ice forecast Reanalysis w/o extra obs Atmospheric forecast Sea-ice forecast Data assimilation Predictability of extreme events Predictability of sea ice over NSR
Great Arctic cyclone on 6 Aug 2012 Polarstern Radiosondes Assimilation in Japanese Earth simulator Thick lines: Mean of 63 ensemble runs, resolution T119, L48 13-29 July 2012 Radiosonde observations at RV Polarstern (2 RS per day) Central sea level pressure (hpa) of the cyclone August 4 6 8 10 LOW ERA-I Data With RS assimilation Without RS assimilation Improved prediction of low pressure with assimilated radiosondes from Polarstern Yamazaki et al. J.Geophpys. Res. 2015
Regional atmospheric climate models: Arctic Cordex COordinated Regional climate Downscaling EXperiment Circum-Arctic domain horiz. resol. of 0.44 (ca. 50 km x 50 km) (also higher resolution) ERA-Interim-driven simulations ) 13 participating institutes AWI CCCma Colorado Uni. DMI EMUT GERICS ISU Lund Uni. MGO SMHI UNI Ulg UQAM Potsdam, Germany Victoria, Canada Boulder, USA Copenhagen, Denmark Trier, Germany Hamburg, Germany Iowa, USA Lund, Sweden St. Petersburg, Russia Norrkoping, Sweden Bergen, Norway Liège, Belgium Montreal, Canada
Arctic Ocean Oscillation index; State of the Ocean Courtesy of A. Proshutinsky H L Anti-Cyclonic Circulation Regime High pressure over Arctic Ocean Basin Atmospheric cyclone trajectories shifted toward Siberia. L L Cyclonic Circulation Regime Low pressure over Arctic Ocean Atmospheric cyclones penetrate into central Arctic Ocean Basin
Models of the coupled Arctic Climate System A-O-I-L US: RASM NPS, CIRES S: RCAO SMHI G: HIRHAM-NAOSIM AWI DK: HIRHAM-HYCOM-CICE- PISM DMI G: REMO-MPI-HAMOCC GERICS CA: CRCM UQAM NO: WRF-COAWST UNI/BCCR
Global Ice-Ocean-Prediction System GIOPS G. Smith, Environment Canada will contribute to MOSAiC
Bio-Geochemical & Ecosystem models Seasonality in bloom development and downward carbon export Wassmann & Reigstad 2011 Understanding effects of melting sea ice on carbon and nutrient cycles and marine ecosystem responses Under water light & seasonal cycle Stratification, salinity and temperature Upwelling and mixing in all seasons Oceanic currents and acidification Exchange of carbon dioxide & methane Nutrients and primary production Arctic BGC-E Model SINMOD 3-D chemical & biological model coupled to Arctic Ocean-sea ice model Reigstad et al. Forced by atmospheric driving data and freshwater input
Hierarchy of models (temporal-spatial scales and subsystem coupling) Years Seasons Days Temporal Scale LES Clouds Turbulence Meso-scale Process models Regional models Global models Spatial Scale Local Regional Global Atmosphere- Land models Ocean- Sea ice Models Coupled A-O-I-L models Bio- Geochem. models Ecosystem models Integration?