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EXTREME STORMS AND CLIMATE

CHANGE

Rachel G. Mauk

Geography 8901

1 October 2012

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Monitoring and Understanding Trends in Extreme Storms: State of Knowledge

Kunkel et al. (in press), Bulletin of the American Meteorological Society

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Motivation

2011: 14 weather/climate disasters exceeding $1B in damage

Climate is changing Means AND extremes

How are extreme storms changing?How well can we detect changes?How well do we understand the changes?Next step: are the impacts changing?

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Extreme storms Short-duration events with levels/types of wind

and/or precipitation that are uncommon for a particular place and time of year

Severe convective storms Tornadoes, hail storms, severe thunderstorms

Extreme precipitation Tropical cyclones Winter weather

Snowstorms, ice storms

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Overriding Issues

Climate model resolution Knowledge of physical mechanisms Data heterogeneity

Changes in observing practices and technologies

Changes in station locationDefinitions of phenomenaAnalysis proceduresOften, changes are improvements….but still

impact validity of long-term trends

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Severe Thunderstorms: Reports and Environments

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Severe Thunderstorm Criteria A severe thunderstorm has at least one

of the following features:Hail ≥ 2.5cm (1”) in diameterWind gusts ≥ 95 km/h (58 mph)Tornado

Assess trends in severe thunderstorms in two waysReports (trend in observations)Environmental conditions (trend in

probabilities)

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Tornado Reports

Tornadoes: increased number of tornado reports since 1954No trend in F1+ tornadoes since 1954All of trend due to F0 tornadoes

?

Fig. 1

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WSR-88D: doppler radar enables detection of radial motion (toward/away from radar)

Identify mesocyclones (tornado precursor)

Send NWS survey teams to areas with radar-indicated tornadoes

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Tornado Reports

Upward trend in tornado frequency over last 50 years due to increase in (E)F0 reportsF0 are weak and short-lived tornadoesPrior to Doppler era, damage likely

attributed to straight-line winds or never observed

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Tornado damage reporting Fujita (F) scale replaced by Enhanced

Fujita (EF) scale in May 2007Adjusted damage ratings for construction

quality and maintenance (28 structures)Cannot directly compare F-scale ratings to

EF-scale ratingsNo plans for conversion

Changes in damage assessment over the decades (subjective/objective)

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From Changnon and Changnon (2000; J. Clim)

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From Changnon and Changnon (2000; J. Clim)

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From Changnon and Changnon (2000; J. Clim)

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Severe weather ingredients are well-knownLower-troposphere wind shear: self-supporting

thunderstorms; induce mesocyclones Instability: deep convection (tall cumulonimbus)

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Compare to wind event….

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How well are severe thunderstorm environments modeled?

Trapp et al. (2011), Clim. Dyn. Downscaled NCEP/NCAR Reanalysis

data (2.5°) using WRF to 4.25 km grid spacing

Identified severe thunderstorms as events exceeding minimum thresholds in updraft helicity (vertical motion + vorticity) and radar returns

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Too few events in May/June Too many in N. Gr. Plains in June, too few in E. US

[Clim. Dyn., 2011]

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How will GHGs affect severe weather frequency? Trapp et al. (2009; GRL, L01703)

5-member ensemble of CCSM3 (NCAR) Ran simulation from 1951-2099 Examined changes in parameters

known to be conducive to severe thunderstorm formation:CAPE (instability—vertically summed

difference between parcel & env. temp)Wind shear (0-6 km)—rotationWater vapor content (convection)

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Time series of regionally averaged: (a) CAPE2000, the number of days on which CAPE > 2000 J kg-1, (b) NDSEV, the number of days on which severe convective storms and associated significant surface winds, hail, and/or tornadoes could occur in the vicinity of a grid point, (c) NDSEV,P, the number of days with the joint occurrence of severe convective storm forcing and convective precipitation at a grid point (Trapp et al. 2009, GRL, L01703)

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Time series of regionally averaged: (d) S06, the magnitude of the vector differencebetween the horizontal wind at 6 km AGL and the wind at the lowest model level (m s -1), (e) CAPE (J kg-1), and (f) q, the surface specific humidity (x 10-3 kg kg-1) (Trapp et al. 2009, GRL, L01703)

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Summary: Severe Convective Storms

Data heterogeneity limits confidence in trends based on reports

Trends in favorability of environmental conditions over last century are not statistically significant now

Limited modeling of future conditions suggests increase in frequency of favorable environmentsMore studies needed

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Extreme Precipitation: Events and Favorable Conditions

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Extreme precipitation: data US observing network well-suited to

assessing changes in heavy precipitation NWS Cooperative Observer network has used

same 8” nonrecording precipitation gaugeNo time-dependent instrumentation biasMinimal wind-driven bias and gauge undercatch for

large amounts of precipitation Network of observers relatively dense Does not distinguish convective from non-

convective events

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Extreme precipitation: causes Factors

Abundant atmospheric water vaporStrong upward motion

Associated with various systemsExtratropical cyclonesTropical cyclonesMesoscale convective systemsNorth American monsoon

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Extreme precipitation: metrics Analyze using statistics

Empirical (distribution, trends, threshold exceedance)

Theoretical (extreme value theory—maxima, return levels)

Choice of metrics involves tradeoff Low-probability events are most

societally relevant Less extreme but more frequent events

minimize sampling uncertainty Recurrence interval thresholds used

for runoff control structures

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Extreme precipitation: trends Many studies have found statistically

significant increases in extreme precipitation events of hours to daysNumber and intensity

Change in tails of distribution, not shift+0.6% / decade in mean precipitation+2% / decade in extreme precipitation

What about spatial trends?

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Figure 3. Time series of decadal values of an index (standardized to 1) of the number of 2-day precipitation totals exceeding a threshold for a 1 in 5-yr occurrence for 7 regions and the U.S. as a whole. This was based on an individual analysis of 930 long-term stations. Station time series of the annual number of events were gridded and then regional annual values were determined by averaging grid points within the region. Finally, the results were averaged over decadal periods.

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Extreme precipitation: trends U.S. as whole: more events in last 30

years Considerable spatial variation on

decadal scaleLess consistency in trends in west, more in

east Trend is significant for U.S. as whole,

Midwest and Southeast No significant trends in western U.S.

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Extreme precipitation: trends Estimated 1948-2010 change in 20-year

return values at individual stations calculated

76% of all stations experience increases in extreme precipitation

15% statistically significant (station-specific)

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Figure 4. Changes in observed twenty year return value of the daily accumulated precipitation from 1948 to 2010. Units: inches. Only locations for which data from at least 2/3 of the days in the 1948-2010 period were recorded are included in this analysis. The change in the return value at each station is shown by a circle whose relative size portrays its statistical significance: the large circles indicate the z-score (estimated change in the return value divided by its standard error) is greater than two in magnitude, medium circles indicate the z-score is between one and two in magnitude, and the small circles indicate the z-score is less than one in magnitude.

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Extreme precipitation: attribution Changes in extreme precipitation have

been linked to:Human-induced changes to atmospheric

compositionIncreased temperatures

○ For same annual or seasonal precipitation total, warmer climates generate more extreme events

Increased water vapor concentrations

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Water vapor and extreme precipitation Study documented significant increases in

water vapor associated with extreme precipitation eventsExtreme precip event assigned precipitable

water value: maximum value from any radiosonde station within 300 km and 24 h of event

Increase in water vapor in environment of precipitation-producing systems may be physical cause of increase in intense events

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Other causes: Physical changes to meteorological systems

Possible upward trend in number of extreme precipitation events associated with TCs (2 of 3 recent studies)

Upward trend in number of extreme precipitation events in the vicinity of fronts

Temporal redistribution of EN/LN events, land use changes

Possible changes to characteristics of meteorological systems?

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Summary: Extreme Precipitation

Evidence of increasing frequency of extreme precipitation eventsData quality is good

Water vapor content of air increasingLikely factor in extreme precipitation trend

More research needed to assess changes to meteorological systems producing extreme precipitation

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Winter Storms: Snow and Ice

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Winter storms Quantifying trends in frequency, duration,

and severity of winter storms requires ability to accurately and consistently measure:Amount of snow that fallsIce that accumulatesFor individual storms and throughout entire

season Data are complicated by changing observing

practices and technologies and reporting procedures

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Winter storm data heterogeneity The following cause “artifacts” in data

series:Transition from primarily afternoon to

morning observation timesMove to direct measurement from previous

estimation of precipitation by “10:1” snow to water ratio

Periodic changes in observer training practices

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What constitutes a winter storm? Characteristics

vary regionally 10” in NE is

fairly regular occurrence

10” in SE could cripple region for 1+ week

ILN just changed criteria for winter weather advisory

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What constitutes a winter storm? Regional Snowfall Index (RSI)

Combines population, snowfall amount, and spatial extent Thresholds differ for different regions

http://www.ncdc.noaa.gov/snow-and-ice/rsi/

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Winter storms: Influences Winter storm trajectories, frequency, and

intensity are affected by:

El Nino/Southern Oscillation (ENSO) North Atlantic/Arctic Oscillation

(NAO/AO)

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Winter storms: ENSO

El Nino/Southern Oscillation (ENSO)La Nina: northerly storm track (more snow to

northern/central Rockies)El Nino: southerly storm track (rain/snow to

desert SW, rain to SE) Regime changes over last century

Low activity from 1930s-1940sEN favored early in 20th century, mid 1970s-

late 1990sLN favored in 1950s- mid 1970s

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Winter storms: (N)AO

(N)AO dominates eastern U.S. weather patternsPositive phase: mild winter weatherNegative phase: strong storms

Also a regime change:Positive phase in early/late 20th centuryNegative phase in mid 20th centuryLast 15-20 years more even split

○ Negative in 2009-2010, 2010-2011 winters○ Positive in 2011-2012 winter

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Figure 6. Number of extreme snowstorms (upper 10 percentile) occurring each decade within the six U.S. climate regions in the eastern two-thirds of the contiguous U.S. (Based on an analysis of the 50 strongest storms for each of the six climate regions from October 1900-April 2010). The inset map shows the boundaries of each climate region. These regions were selected for consistency with NOAA’s monthly to annual operational climate monitoring activities. The map includes standardized temperature anomalies and precipitation departures from the 20 th century mean calculated across all snow seasons in which each storm occurred. The snow season is defined as December-March for the South and Southeast regions and November-April for the other four regions.

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Extreme snowstorms: trends Based on RSI:

From 1961-2010, 21 extreme regional snowstormsFrom 1900-1960, 9 extreme snowstorms

Extreme storms occurred more frequently in colder and wetter winters (Fig. 6)35% were warmer than average30% drier than average

Even if temperatures continue to warm, for at least next few decades record storms are still possible

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Seasonal totals

Assessed variation of seasonal snowfall at 425 stations across contiguous U.S.No significant century-scale trends in either

high or low totalsAreal coverage of extremely low seasonal

snowfall has been steady or slightly increasing since mid 1970s

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Figure 7. (a). Area weighted annual percentage of U.S. homogenous snowfall stations exceeding their own 90th percentile seasonal totals, 1900-01 to 2010-11. Reference period is 1937-38 to 2006-07. Adapted from Kunkel et al. (2009c). Thick blue line: 11-year running mean of the percentages. Dashed line: Number of grid cells with active stations each year.

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Figure 7(b). Area weighted annual percentage of U.S. homogenous snowfall stations below their own 10th percentile seasonal totals, 1900-01 to 2010-11. Reference period is 1937-38 to 2006-07. Adapted from Kunkel et al. (2009c). Thick blue line: 11-year running mean of the percentages. Dashed line: Number of grid cells with active stations each year.

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Winter storm modeling Number of extreme snowstorms could

increase in latter half of 20th century Areal coverage of extremely low seasonal

snowfall totals constant or increasing Contradiction? No: comparing short-term extreme events

versus sum of all events over a seasonNorthern areas of U.S.: not uncommon for extreme

snowstorm to occur in year with below-average snowfall totals

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Ice storms

Ice storms disrupt transportation, can cause catastrophic damage to ecosystems and infrastructure

Most events occur east of Rockies Climatologies:

Begin in mid 20th centuryLimited to “days with”Coarsely distinguish magnitudeReporting highly inconsistent

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Ice trends

Difficult to predict Need shallow cold layer at surface, deeper >0°C layer above it Precipitation melts but won’t refreeze until it hits the surface

No systematic trends since 1960

Some regions experienced max in 1950s

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Summary: Snow and Ice

Many data issues with snow and ice reportsLow confidence in observed trends

Large-scale climate teleconnections affecting winter storms knownFrequency and location of storms

Frozen precipitation not likely to disappear in warmer worldHigher latitudes and elevations

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Trends in Impacts

Data quality and attribution issues also affect trends in impacts of extreme weather eventsSpatial/temporal changes in social

vulnerabilityEconomic loss reporting practices also changeMetrics vary in precision and adjustment

techniques (for population, wealth, mortality, type of loss)

Low confidence in loss trends

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Figure 8. Authors’ assessments of the adequacy of data and physical understanding to detect and attribute trends. Phenomena are put into one of three categories of knowledge from less to more. The dashed lines on the top and right sides denote that knowledge about phenomena in the top category is not complete.

Developed through extensive verbal discussion at a meeting of the author team to reach a group consensus.

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Recommendations Severe thunderstorms

Develop collecting procedures independent of weather warningsObjective remotely-sensed observations (radar)

Extreme precipitationMaintain observation networkExplore role of water vapor

Snow and ice stormsReduce uncertainties in historical record (new data sources,

techniques to account for changing observations technology/practices)

Improve observation network (density and technology)Consistent observation and reporting practices