Floods: What We Know, What We Don’t Know, and a Case Study

1. Floods: What We Know, What We Don’t Know, and a Case Study Atmospheric-Science Seminar Colin Raymond October 2014

2. Outline • What We Know (IPCC Report) • What We Don’t Know [Yet] (Jain & Lall 2001) • Case Study (Martius et. al. 2013)

4. 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?

5. 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?

6. What the Record Says

7. What the Record Says --Linear predictability of flood maxima a season in advance from ENSO-related indices

8. ENSO Variability is Concentrated at Certain Frequencies

9. ENSO Variability is Concentrated at Certain Frequencies ...but there’s longer timescales in there too

10. ENSO Variability is Concentrated at Certain Frequencies ...but there’s longer timescales in there too Structured Non-Stationarity in Flood Dist’ns?

11. 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

15. Same Principle Holds for Extremes

16. Same Principle Holds for Extremes

17. Conclusions from Jain & Lall • Interannualstationarity 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

18. Case Study: 2010 Pakistan Floods http://www.bbc.co.uk/news/world-south-asia-11068259

19. http://www.bbc.co.uk/news/world-south-asia-10896849

20. Magnitudes

21. 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

22. Other Known Extreme Factors • ENSO phase – in Pakistan, climatologically higher precip during La Niña • Soil-moisture feedbacks • Deeply saturated air • Warmer temps aloft

24. Circulation and SSTs H upper-level wave-breaking zone; +PV anomaly Himalayan-foothills jet convergence & lifting monsoon low warm SSTs warm SSTs Somali jet

25. Low-Level Temperature H cool air (enhanced evap.)

26. Moisture very dry air very moist air

27. Methodology • back & forward trajectories to determine contributions of moisture-source regions, using potential-vorticity inversions • simulation of sensitivity of precip to regional evapotranspiration

28. Potential Vorticity Review http://www.lpc2e.cnrs-orleans.fr/~enriched/images/News/Fullsize/SPIRALE_mimosa.png

29. Potential Vorticity Review • PV=-g(ζg+f)(∂θ/∂p) http://www.eumetrain.org/data/2/28/Content/Images/pv2.jpg

30. 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

31. 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.

32. Findings Extreme episode #1 #2

33. Findings Heavy precip assoc. with high PW, low T, low CAPE, deep saturation  unusual set of anomalies

34. 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 Lagrangiandef’n difficulties)

35. 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)

36. 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.

37. 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

38. Temperature: Lahore vs. Moscow