ISU Atmospheric Component Update – Part I

Justin GlisanIowa State University

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Update

• PhD work completed last semester!• Dissertation title: Arctic Daily Temperature

and Precipitation Extremes: Observed and Simulated Behavior– Composed of three papers– Will be submitted to J. Clim. and JGR

• Postdoctoral work on NSF extremes project

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PhD Research Questions

• Are there certain atmospheric circulation regimes favorable for extreme events?

• Does seasonality and geography affect extremes?

• Can WRF simulate well Arctic extreme and spatially wide-spread events?

• What is the effect of “spectral nudging” on extremes?

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Case Study 1: Effects of spectral nudging on temperature and precipitation simulations

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RACM Domain and Analysis Regions

Case Study 1 Background

• Long and short PAW simulations were run on the RACM domain

• A systematic, atmosphere-deep circulation bias formed within the northern Pacific storm track

• Various remedies tested, but with little success• Spectral or interior nudging was introduced

Hypothesis

• A set of short simulations was run using the WRF default nudging strength with promising results

• This case study examines the effects of a range of nudging strengths on temperature and precipitation means and extremes

• We hypothesize that too much interior nudging can smooth out extreme events while leaving mean behavior observationally consistent

Case Study Setup

• PAW six-member ensemble on RACM• Two study months:

– January and July 2007– Simulations begun in December and June, with first three

weeks discarded for spin-up• Four analysis regions selected to study geographical

effects of nudging on means and extremes– 2-m T: 1st, 5th, 50th, 95th, and 99th percentiles– Daily precipitation: 50th, 95th, and 99th percentiles

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Nudging Coefficient Table

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Tukey HSD Rank Matrix

• Compares the means of all possible pairs in the nudging coefficient pool– Including applicable observation sets– Also includes ANOVA

• Calculates how large the mean difference among group members must be for any two members to be significantly related

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January Precipitation

1st 2nd 3rd 4th 5th 6th 7th 8th 9th

Double

Full

Half

Quarter

Eighth

Sixteenth

128th

Zero

NCDC

*Coefficients that are significantly related are connected by a box.

Alaska Analysis Region - Tukey HSD Rank Matrix

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July PrecipitationAlaska Analysis Region - Tukey HSD Rank Matrix

1st 2nd 3rd 4th 5th 6th 7th 8th 9th

Double

Full

Half

Quarter

Eighth

Sixteenth

128th

Zero

NCDC

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January 2m-Temperature  1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Double

Full

   

Half

Quarter

Eighth

Sixteenth

128th

Zero

EI

NCDC

  1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Double

Full

   

Half

Quarter

Eighth

Sixteenth

128th

Zero

EI

NCDC

Alaska Analysis Region - Tukey HSD Rank Matrix

Glisan Ph.D. Seminar – Iowa State University 14

July 2m-Temperature

January 6th, 2012

  1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Double      

Full      

Half      

Quarter      

Eighth      

Sixteenth      

128th

Zero

EI      

NCDC

  1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th

Double      

Full      

Half      

Quarter      

Eighth      

Sixteenth      

128th

Zero

EI      

NCDC

Alaska Analysis Region - Tukey HSD Rank Matrix

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Conclusions• Winter behavior more sensitive to nudging • Improve Cold Season Mean and Extreme Behavior

– Stronger SN for precipitation– Weaker SN for surface temperatures

• Improve Warm Season Mean and Extreme Behavior– Weaker SN for precipitation– Stronger SN for surface temperatures

Optimal range for pan-Arctic simulations: 1/8th – 1/16th the WRF default

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Case Study 2: WRF Summer extreme daily precipitation over the CORDEX Arctic

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CORDEX Arctic Domain

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Case Study 2 Setup

• 19-year, six-member ensemble simulation• Summer season (JAS), defined by

climatological sea ice minimum• Four analysis regions over North America• Daily precipitation analysis

– Mean behavior – Individual extreme events– Spatially wide-spread extreme events

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Analysis Regions

CE

CWAS

AN

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Frequency vs. Intensity

• Grid point daily events (> 2.5 mm) pooled separately for PAW and NCDC observations

• Extremes defined at the 95th and 99th percentiles

• Histograms normalized to account for differences in spatial sampling

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Frequency vs. Intensity for WC

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Simultaneity of Extremes

• We define simultaneous extremes as 25 or more concurrent grid point events

• NCDC scaled to match model resolution• Plots give an indication of the spatial scale of

the extremes

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Simultaneity of Events

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Extreme Composites

• From the simultaneity plot, we extract days matching our wide-spread criterion

• Using the EI and PAW output, we construct composites of pertinent surface and atmospheric fields– Diagnose relevant physical conditions conducive for wide-

spread extremes– Anomaly plots also used to show how extremes depart

from climatology– Are PAW and obs. consistent in their treatment of

circulation behavior?

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MSLP [hPa]

850-hPa Winds[ms-1]

500-hPa Geopotential

Heights [gpm]

ERA-Interim Pan-Arctic WRF

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Figure 1: (top left) Composited summer extreme precipitation [mm-d-1] and (top right) location occurrence [%] of spatially widespread extreme events.

(bottom) Convective contribution anomaly [%] of total daily precipitation during extreme event days for Western Canada.

Figure 2: (left) Composited Convective Available Potential Energy anomaly [J-kg-1] and (right) Level of Free Convection anomaly [m] for Western Canada.

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Summer Conclusions

o The model produces well the physical causes of extremes, despite lower precipitation intensity

o Similar physical consistency between model and observations appears for all analysis regions (not shown)

o Orographic processes producing a majority of widespread extreme events in all analysis regions except Western Canada

o Convective processes contribute significantly to widespread extreme precipitation in Western Canada

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Future Work

• The use of SOMs to better understand seasonally dominant circulation features

• Produce future climate simulations with PAW– Determine if contemporary causes of extreme

behaviors are present and if not, how and why they evolve in a warming climate

– Force PAW with GCM BCs to determine how extreme events may be altered