Transcript Arctic Daily Temperature and Precipitation Extremes

ISU Atmospheric Component
Update – Part I
Justin Glisan
Iowa State University
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
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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
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
Alaska Analysis Region - Tukey HSD Rank Matrix
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*Coefficients that are significantly related are connected by a box.
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July Precipitation
Alaska Analysis Region - Tukey HSD Rank Matrix
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January 2m-Temperature
Alaska Analysis Region - Tukey HSD Rank Matrix
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EI
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July 2m-Temperature
Alaska Analysis Region - Tukey HSD Rank Matrix
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January 6th, 2012
Glisan Ph.D. Seminar – Iowa State University
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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
AN
AS
CW
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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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ERA-Interim
Pan-Arctic WRF
MSLP
[hPa]
850-hPa Winds
[ms-1]
500-hPa
Geopotential
Heights
[gpm]
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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
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extremes, despite lower precipitation intensity
Similar physical consistency between model and
observations appears for all analysis regions (not
shown)
Orographic processes producing a majority of
widespread extreme events in all analysis
regions except Western Canada
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
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