floods: what we know, what we don’t know, and a case study atmospheric-science seminar colin...
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Floods: What We Know, What We Don’t Know, and a Case Study
Atmospheric-Science SeminarColin Raymond October 2014
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Outline
• What We Know (IPCC Report)• What We Don’t Know [Yet] (Jain & Lall 2001)• Case Study (Martius et. al. 2013)
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What We Know
• CPT D. MIDAS• Ability to simulate floods qualitatively depends on
ability to predict extreme precip changes
• Extremes: circulation probably more important for rarer events
• C.C.: Insufficient evidence for attribution or even trends in magnitude – GCMs often disagree
--nonstationarity in river dynamics?--size of spring melt floods?
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What We Don’t Know [Yet]:Floods & Climate Change
• Strong correlations b/w ENSO/PDO indices & Similkameen River annual-max flows (AMF’s)
• Is this relationship robust over periods longer than obs. record? If so, what are the implications?
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What the Record Says
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What the Record Says
--Linear predictability of flood maxima a season in advance from ENSO-related indices
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ENSO Variability is Concentrated at Certain Frequencies
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ENSO Variability is Concentrated at Certain Frequencies
...but there’s longer timescales in there too
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ENSO Variability is Concentrated at Certain Frequencies
...but there’s longer timescales in there tooStructured Non-Stationarity in Flood Dist’ns?
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Non-Stationarity & ‘Snippet Biases’
• we’re likely overcounting extreme ENSO events & thus flood variability
• n-s: no short record can be fully representative selon ZC
• example (MATLAB) follows
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Same Principle Holds for Extremes
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Same Principle Holds for Extremes
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Conclusions from Jain & Lall
• Interannual stationarity in flood potential cannot be assumed even in a constant climate
• Flood extremes in WA closely correlated with ENSO over multiple timescales
• Good news: using extremes in the current obs. record as guideposts likely means overpreparation
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Case Study: 2010 Pakistan Floods
http://www.bbc.co.uk/news/world-south-asia-11068259
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http://www.bbc.co.uk/news/world-south-asia-10896849
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Magnitudes
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Related Findings
• In the Alps, long N-S upper-level troughs trigger heavy precip via:– creating favorable wind dirs for topographic lift– providing a persistent moisture source– reducing static stability & thus ‘activation energy’– forcing ascent quasi-geostrophically
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Other Known Extreme Factors
• ENSO phase – in Pakistan, climatologically higher precip during La Niña
• Soil-moisture feedbacks• Deeply saturated air• Warmer temps aloft
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Circulation and SSTs
Hupper-level wave-breaking zone;+PV anomaly
warm SSTswarm SSTs
monsoon low
Somali jet
Himalayan-foothills jetconvergence
& lifting
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Low-Level Temperature
H
cool air (enhanced evap.)
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Moisture
very moist air
very dry air
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Methodology
• back & forward trajectories to determine contributions of moisture-source regions, using potential-vorticity inversions
• simulation of sensitivity of precip to regional evapotranspiration
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Potential Vorticity Review
http://www.lpc2e.cnrs-orleans.fr/~enriched/images/News/Fullsize/SPIRALE_mimosa.png
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Potential Vorticity Review
• PV=-g(ζg+f)(∂θ/∂p)
http://www.eumetrain.org/data/2/28/Content/Images/pv2.jpg
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PV Inversion
• Given a distribution of PV in a domain (& some other basic
conditions), one can recover the balanced mass & momentum fields that produced it– piecewise technique just divides atmos in layers &
independently inverts each– this allows for analysis of the influence of discrete
portions of the total PV field on the total flow field
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Trajectory Calculations: 2 Approaches
• Lagrangian (Martius et. al.): Assumes Δq is cumulative sum of parcel’s E-P along route – ultimate sources of moisture appear less important if
intermediate precip & evap occur
• Eulerian: Inserts tagged tracers into model and follows them through the water cycle
Winschall, Pfahl, Sodemann, and Wernli, 2014. “Comparison of Eulerian and Lagrangian Moisture Source Diagnostics — the Flood Event in Eastern Europe in May 2010.” Atm. Chem. Phys. 14, 6605:6619.
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FindingsExtreme episode #1 #2
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FindingsHeavy precip assoc. with high PW, low T, low CAPE, deep saturation unusual set of anomalies
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Findings•Dynamics: heavy precip assoc. with high PW, low T, low CAPE, deep saturation (unusual set of anomalies)
•LL Circulation: heat low over northern Pakistan helped draw in moisture that would usually be near Bangladesh
•UL Circulation: as in similar Alpine events, forcing organized & intensified precip, and appeared to initiate it in the 2nd episode
•Moisture transport: 78% of moisture in 1st extreme episode originated in Pakistan or SW Asia, vs. 34% in 2nd episode; contribution of Indian subcontinent & bays incr. from 18% to 56% (but note Lagrangian def’n difficulties)
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Findings Cont.•Coupling of precip & ET critical (due to local sourcing of moisture), confirmed by ET sensitivity analysis• 80% lower precip in simulation when sfc ET over
Pakistan was eliminated, despite just a 15-18% decrease in PW
•High soil moisture meant higher availability for evap. than normal•ECMWF predictions & obs agreed remarkably well in both location & magnitude
similar dynamics as floods along Front Range of western US(Grumm and Du, 2013)
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Discussion Point: What Was the Relative Importance of Human Actions?
Syvitski, James, and Robert Brakenridge, 2013. “Causation and Avoidance of Catastrophic Flooding along the Indus River, Pakistan.” GSA Today. 23 (1), 4-10.
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What Can This Tell Us About Effects Under Climate Change?
• Depends partly on changes in frequency of blocking highs (c.f. heat-wave discussion)
• Displacement of moisture vs. overall moisture increase – we think we know extreme precip will increase
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Temperature: Lahore vs. Moscow