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Justin GlisanIowa State University
Department of Geological and Atmospheric Sciences
RACM Project Update: ISU Atmospheric Modeling Component: Part 1
7th DOE/RACM Meeting: Ames, IA 1Justin Glisan, Iowa State University
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Presentation Outline• Update since Boulder• Research Methodology and Development• North American Observational Study• Proposed PAW Simulations– PAW CORDEX Ensemble Simulation– PAW RACM Spectral Nudging
• Model Validation and Analysis• Some results
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Update Since Boulder…
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CORDEX Arctic Domain
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3. RESEARCH METHODOLOGY AND DEVELOPMENT
Key research questions
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Key Research Questions
• The underlying premise of this research is the study/analysis of extreme atmospheric behavior– Temperature and precipitation– Large-scale, quasi-stationary flow regimes
• Do extremes produced in PAW represent real-world occurrences?
• Does spectral nudging act to filter out extreme events?
• Do quasi-stationary persistent flows affect downstream extremes?
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4. NORTH AMERICAN OBSERVATIONAL STUDY
NCDC North American stationsPrecipitation and Temperature
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Domain of Interest
• Arctic CORDEX Domain• NCDS Global Summary of the Day– Around 150 stations– Daily Precipitation and Temperature
• Four analysis boxes– Based on the climatological record, weather
patterns– Geographical and topographical characteristics
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Analysis Boxes Selection
• Is station located within forcing frame?• Does station data exhibit a significant degree
of temporal continuity (20% threshold)?• Four boxes:– Canada A: The Canadian Archipelago– Canada B: Sub-Arctic Canadian Plains– Alaska A: North of the Brooks Range, Arctic Sea– Alaska B: South of Brooks Range, Gulf of Alaska
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Observation Analysis
• Each station is considered an individual realization within each box; each realization has a large number of samples =>DoF
• Observations are ordered and ranked by precipitation amount and temperature
• Using the 95th percentile, extreme values are extracted from the data
• Further analysis will be performed to determine extreme temporal and spatial regimes
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Pan-Arctic SIMULATIONSAnalysis of extreme and persistent model behavior as manifested in:
• Short-term spectrally-nudged PAW simulations on the RACM domain • Long-term non-nudged PAW simulations on the CA domain• Large-scale quasi-stationary atmospheric flow regimes
Development of the Baseline Arctic System Climatology (BASC)
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PAW CORDEX Ensembles
• Long-term simulations spanning E-I period• Six-members created via 1-day stagger• Simulations run over CORDEX Arctic domain• Used to study large, quasi-persistent flows and
associated temperature and precip. extremes
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PAW CORDEX Ensembles (con’t)
• Study how PAW produces large-scale atmospheric flows in the Arctic– Associated T and precip. events– Are extremes evolving with sea ice changes?
• Determine if PAW replicates historic events• Baseline Arctic System Climatology– Diagnostic for extreme events– Used in fully-coupled RACM
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PAW RACM Spectral Nudging
• Spectral nudging constrains the model to be more consistent with observed behavior– Usually activated at a specific level– Adds nudging terms to largest waves
• What strength of nudging is ideal/efficient without smoothing extreme behavior?– Strong nudging may push PAW to a smooth, large-
scale state while keeping mean behavior intact– Weak nudging may not correct RACM anomalies
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PAW RACM Spectral Nudging
• WRFV3.1.1 w/ CU physics• Full spectral nudging options• Six-member ensemble (one day stagger)• Two cases:– Winter case: January 2007 (initialized in Dec.)– Summer case: July 2007 (initialized in June)
• Eight nudging coefficients – Full (WRF default)– Triple, Double, 1/2, 1/4, 1/8, 1/16, 1/128– Baseline cases
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SN Namelist Settings
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PAW VALIDATION AND ANALYSIS
Differencing and Statistical AnalysisTemporally Persistent Extreme Analysis
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Bias and Statistical Analysis
• Data sets used in model validation:– ECMWF Era-Interim Reanalysis– NCDC Global Summary of the Day– Washington gridded 50-km Arctic Station data– HARA*
• Analysis tools: – NCL (plotting, climatology)– JMP (statistics)– Excel (statistics, binning)
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Temporally Persistent Extreme Analysis
• Large-scale quasi-stationary flows located by:– Blocking Index (strength)– Sum of Lyapunov Exponents (episode duration)
• These features have been shown to influence weather and extremes:– Downstream of system– For multiple seasons after episode
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Blocking Index
• The BI has a scale from 1 to 10• Proportional to the height gradients in the
blocking region• Can be use to diagnose the strength of large-
scale circulations
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Lyapunov Exponents
• Analog to flow stability• Best used as a diagnostic for locating quasi-
persistent anticyclones• Decreasing positive values indicate flow
stabilization – Significant shifts in planetary-scale flow– Found prior to block initiation
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