Challenges and Observational Requirements for Advancement of Process-Oriented Regional Arctic Climate Modeling and Prediction

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1 Challenges and Observational Requirements for Advancement of Process-Oriented Regional Arctic Climate Modeling and Prediction Wieslaw Maslowski 2 and Annette Rinke 1 1 Alfred Wegener Institute (AWI), 2 Naval Postgraduate School (NPS)

2 Advancing Arctic Predictive Capabilities How can Regional Arctic System Models help advance understanding and prediction of Arctic climate change? 1. By resolving unresolved or under represented processes in individual system components 2. By addressing inadequacies along coupling channels between different system components 3. By exploring space-dependent sensitivities in the parameter space 4. Through a hierarchical modeling approach using regional and global models to help quantify and reduce uncertainty for seasonal to decadal prediction. Maslowski et al. 2012

3 Regional Modeling Based on & in Support of MOSAiC Many important Arctic physical processes and feedbacks are poorly represented or omitted in Earth System Models (ESMs) Process studies, 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) Ø Ø Ø More realistic representation of timedependent conditions of the Arctic sea ice cover and their effect on air-sea interactions is necessary in models and it requires coupling of the respective model components Range of complex and connected feedbacks occur between atmosphere, land, ocean and sea ice that cannot be fully understood, nor downscaled, without including their coupled interactions The realization of this coupled modeling requirement together with the need for observations of such coupling have been some of the primary drivers for the MOSAiC experiment (MOSAiCImplementation Plan, 2016) Synthesis of MOSAiC observations and regional Arctic climate model simulations will: (i) advance science, (ii) guide improvements of ESMs and (iii) reduce model bias in prediction of Arctic climate change

4 Background: Observed Sea Ice Concentration Trends (from NSIDC) Different processes and feedbacks involved in different regions and seasons!

5 Sensitivity of Sea Ice Thickness vice Extent C f = 8.5 C f = 17 C f = 34 March 2007 September 2007 C f = an empirical parameter that accounts for frictional energy dissipation Sea ice extent is NOT SUFFICIENT constraint of sea ice / climate model skill!

6 Sensitivity of Sea Ice Volume to Rheology: EAP & EVP R1002RBRxaaa01a PHC: mean EVP/EAP: >20% reduction in sea ice volume in Fully coupled RASM EVP/EAP: ~12% reduction in sea ice volume when atmosphere is decoupled from changes in sea ice - Change of rheology has a larger Impact in coupled climate simulations - Sea ice volume in fully coupled (top) and forced (bottom) RASM with EAP/EVP rheology

7 Arctic Air-Ice-Ocean Interactions Monthly mean winter (03/93) upward sfc heat fluxes Winter monthly-mean surface heat fluxes W/m 2 due to ice deformation Longwave downward radiation bias Surface winter monthly-mean heat fluxes in excess of 350 W/m 2 along the marginal ice zone Winter Tjernström et al., 2005

8 Winter atmospheric circulation: difference to reanalyses Winter MSLP ( ) difference CMIP5 - ERA-40 Winter cyclone frequency ( ) Arctic CORDEX RCMs vs. reanalyses (Era-I, MERRA2, CFSR, JRA55) (Akperov et al., 2017) (Maslowski et al., 2012)

9 Arctic CORDEX Arctic CORDEX Circum-Arctic domain horizontal resolutions cover 0.44, 0.22, and 0.15 Participating institutes (alphabetically) RCMs atmosphere/coupled AWI Potsdam, Germany HIRHAM5 HIRHAM-NAOSIM CCCma Victoria, Canada CanRCM4 Colorado Uni. Boulder, USA Polar-WRF / RASM DMI Copenhagen, Denmark HIRHAM5, HARMONIE HIRHAM-HYCOM-CICE EMUT Trier, Germany CCLM / * GERICS Hamburg, Germany REMO REMO-MPI/OM-HAMOC ISU Iowa, USA WRF3.6.1 Lund Uni. Lund, Sweden RCA4-GUESS MGO St. Petersburg, Russia RRCM SMHI Norrköping, Sweden RCA4 / RCAO UNI Bergen, Norway WRF3.3.1 / * Ulg Liège, Belgium MAR3.6 UQAM Montreal, Canada CRCM5 / *

10 RCM intercomparison using MOSAiC data Arctic MOSAiC CORDEXwill provide all of this but the expedition will not be completed until fall of 2020 and Q&A-secured data for the whole year will probably take another year à Now: ACSE (Arctic Clouds in Summer Experiment), 2014

11 Arctic Clouds in Summer Experiment (ACSE) (Swedish-Russian-US Arctic Ocean Investigation of Climate-Cryosphere-Carbon Arctic CORDEX Interactions: SWERUS-C 3 ) There... Tromsø Barrow 5 July to 19 August SWERUS-C ; R/V Oden and back Barrow Tromsø 21 August to 4 October, 2014 R/V Mirai 6-24 September Michael Tjernström, Stockholm University

12 RCM intercomparison using MOSAiC data Arctic MOSAiC CORDEXwill provide all of this but the expedition will not be completed until fall of 2020 and Q&A-secured data for the whole year will probably take another year à Now: ACSE (Arctic Clouds in Summer Experiment), 2014 Rich source of data for analysis and model evaluation, covering open water & sea ice for late summer melt & early autumn freeze Pre-MOSAiC Post-SHEBA/ARCMIP learning phase ACSE data will provide a smooth transition to the full MOSAiC model evaluations and improvement MOSAiC research phase : contrasting the central ice pack conditions with those closer to the ice edge

13 Simulation set up: Constrained and free runs Arctic Constrained CORDEX runs Test parameterizations under real synoptic conditions Force the model to stay close to observed weather situations: Circum-Arctic domain: nudging and/or forecast mode (reinitialization) Smaller sub-domains covering different MOSAiC legs Ensemble simulations physics, forcing, resolution, atmosphere-only/ice-oceanonly/coupled, Free runs Test parameterizations under climate conditions Climate mode simulations: Allow feedbacks to the large-scale & internal variability More realistic dynamic response to surface forcings (e.g., sea ice-cyclones) ensemble runs on seasonal time scale (perturbed initial state) evaluate statistics, not deterministic events Transfer to GCMs

14 Hierarchy of models: temporal-spatial scales and subsystem coupling Years Seasons Day s 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-Geo Chem. models Ecosystem models Integration?