Justin Glisan Iowa 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

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

Update Since Boulder…

CORDEX Arctic Domain

3. RESEARCH METHODOLOGY AND DEVELOPMENT Key research questions

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?

4. NORTH AMERICAN OBSERVATIONAL STUDY NCDC North American stations Precipitation and Temperature

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

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

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

Pan-Arctic SIMULATIONS Analysis 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)

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

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

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

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

SN Namelist Settings

PAW VALIDATION AND ANALYSIS Differencing and Statistical Analysis Temporally Persistent Extreme Analysis

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)

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

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

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