extreme weather in a changing climate€¦ · pardeep pall, michael wehner, dáithí stone (2014)...

Extreme weather in a changing climate

CMIP Tutorial

NCAR

August 19, 2016

Michael F. Wehner

Lawrence Berkeley National Laboratory

[email protected]

Extreme Weather in a changing climate: Storms

Extreme Weather in a changing climate: Impacts

Folsom Lake, California

“snowmageddon”Texas

Boulder Creek

(behind the hotel)

• Simple metric of extremes

– ETCDDI indices

• Extreme Value Theory results

– Block Maxima

– Peaks over Threshold.

• Storm Tracking

• Are CMIP5 models fit for purpose?

– Projections

– Detection and Attribution

– Individual Extreme Event attribution

Outline of my talk

• 27 indices intended for detection and attribution of trends

• Tailored to available observational data

– often spatially sparse.

• Not descriptive of very rare events.

– Some are not really extreme at all.

• Gridded observations are available here:

– http://www.climdex.org/datasets.html

• Model results and code to calculate are available here:

– http://etccdi.pacificclimate.org/

Expert Team on Climate Change Detection Indices

(ETCCDI)

Txx=annual maximum hottest daytime temperature (hot days)

Tnn=annual minimum coldest nighttime temperature (cold nights)

Txn=annual maximum coldest nighttime temperature (warm nights)

Tnx=annual minimum hottest daytime temperature (cool days)

Useful ETCCDI temperature indices

End of century RCP8.5 changes. Hot days/cold nights

rx1day=annual maximum daily precipitation

rx5day=annual maximum pentad precipitation

cdd=consecutive dry days


rx1day=seasonal maximum daily precipitation

rx5day=seasonal maximum pentad precipitation

cdd=consecutive dry days

• Pentad precipitation is preferred over daily.

• Big storms often last longer than a day, but rarely more than 5

• 00:00 GMT may not be a convenient time

• Seasonal is preferable over annual

• Winter storms are different than summer storms!

• Consecutive dry days is not really extreme.

• A interesting measure of the dry season in some locales.


See Dan Cooley’s talk for more details.

Sampling for extremes

Block maxima Generalized Extreme Value (GEV) distribution

Many of the ETCCDI indices are block maxima.

Peaks over Threshold Generalized Pareto Distribution (GPD)

Sample from a parent high frequency data set.

.

• Interesting quantities

• Return value for a fixed period change in magnitude for fixed rarity.

• Return period for a fixed value change in frequency for a fixed threshold.

Extreme value statistics

• A “natural” block size is a season.

• May be too short in dry regions for precipitation.

• Winter is different than summer. Annualizing can lead to mixing the underlying seasonal distributions. Not a good thing.

• Thresholds.

• Arbitrary. 85%, 90%, 95% 99%?

• A balance between being in the tail vs. large enough datasets.

• Covariates are extremely useful.

• EV theory generally presumes stationarity but climate change is not

• Covariates can remove that restriction by adding more fitting parameters

• Useful covariates (are usually linear):

– CO2 (anthropogenic climate change)

– ENSO, NAO, PDO (natural modes of variability)

– Blocking

– This is new. Experience is limited.

Extreme value statistics: some guidance

Source: wikipedia

Seasonal high temperatures

Angelil et al. (2016) J. Clim

2013 was an extreme year for Australian temperatures.

The distribution of this model (CAM5.1) is comparable to two different observations.

Part of an attribution study. Yes, there is a big human influence.

Are models “fit for purpose” for extremes?

20 year return

value of the winter

daily precipitation

(mm/day)

CMIP5 class

models are not high

enough resolution.

Picture is much

worse for summer

due to defects in

convective

parameterizations.

Are models “fit for purpose” for extremes?

• AR5 ES:

– It is virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean temperatures increase

– Under RCP8.5 it is likely that, in most land regions, a current 20-year high temperature event will occur more frequently by the end of the 21st century (at least doubling its frequency, but in many regions becoming an annual or two-year event) and a current 20-year low temperature event will become exceedingly rare.

What does the IPCC have to say about temperature extremes?

• Changes in 20 year

return values of the

annual hottest and

coldest day.

• Used time dependent

GEV statistics.

• “Today’s rare hot events

become commonplace”

• Cold extremes increase

more than hot extremes.

IPCC AR5 Figure 12.14

• AR5 ES:

– Globally, for short-duration precipitation events, a shift to

more intense individual storms and fewer weak storms is

likely as temperatures increase.

– Regional to global-scale projected decreases in soil

moisture and increased risk of agricultural drought are likely

in presently dry regions and are projected with medium

confidence by the end of this century under the RCP8.5

scenario.

Models were judged to not be of high enough quality to justify

presenting changes in long period return values.

But there was pressure to present something…

What does the IPCC have to say about precipitation extremes?

• CMIP5 models are limited by their coarse resolution to reproduce severe storms

• Return value and period expressed as the amount per oC local temperature

increase rather than give the scenario projections

– Results consistent with Clausius-Clapeyron scaling.

• A deliberate choice to downplay the attention paid to this figure.

IPCC AR5 Figure 12.27: Rare precipitation extremes

• “Frequency change multiplier” = “Risk Ratio”

= RP2000 / RP2100

3rd US National Climate Assessment

• High resolution is required to accurately simulate intense storms.

– CMIP6 subproject HIRESMIP will provide 5-10 models in the 25km range.

– Extremely computationally expensive.

– Database is much larger. Sub-daily becomes much more interesting!

• ~25km global Community Atmospheric Model (CAM5.1)

– Able to simulate hurricanes up to Category 5.

– Far superior extreme precipitation statistics.

– Thanks to the large US DOE investment in high performance computing.

• Was 2 wall-clock days per simulated year.

• Now is 1 day/year.

High resolution global climate modeling

High resolution global climate modeling

Tropical

Storm

Cat1

Cat2

Cat3

Cat4

Cat5

Figures by Prabhat

Cat1

Cat2

Cat3

Cat4

Cat5

Figures by Prabhat

Cat4

Cat5

Figures by Prabhat

• With the advent of tropical cyclone permitting models, extreme storms are

much more interesting.

• In order say anything about storms, you have to find them first.

• Large datasets. Computationally expensive

The Toolkit for Extreme Climate Analysis (TECA2.0) is a general purpose tool

built upon the MapReduce algorithm to efficiently track storms contained in

very high resolution simulation in a highly parallel manner.

• Currently tropical cyclone tracking is ready for release

• Scaled to 750000 processors. 10000 is more typical.

• Reduces time to solution from weeks to hours

• Extra-tropical cyclone (ETC) tracking is beta

• Atmospheric River (AR) identification is pre-beta

Supervised machine learning has been demonstrated

as a non-parametric storm tracker (frontal systems).

Storm Tracking

Do CMIP5-class models have any value in projections of future tropical cyclone frequency?

1o fvCAM5 makes more tropical cyclones in the warmer world

0.25o fvCAM5 make fewer tropical cyclones in the warmer world but more intense TCs.

NO! Not by direct tracking anyways.

Limited by tracking of weak storms.

Tropical Cyclones in a warmer world

1o 0.25o

All storms

Do the bulk characteristics of CMIP5-class models have value in projections of future tropical cyclone statistics? Part 1. Intense Hurricanes

Emanuel’s Maximum Potential Intensity (MPI)

Direct tracking of high resolution models in a warmer world indicates the strongest storms get stronger consistent with DMPI.

DMPI is quite similar between coarse and high resolution

Yes, CMIP5-class models can predict the behavior of TC relevant fields.


Do the bulk characteristics of CMIP5-class models have value in projections of future tropical cyclone statistics? Part 2. Cyclogenesis

•Hi-res model produces fewer TC in a

warmer world.

•GPI correctly reflects the change in

the bulk cyclogenesis-relevant fields.

•But GPI is missing any information

about changes in short term instabilities.

•The warmer worlds are more stable.

•Fewer opportunities for TC to be

realized even though cyclogenesis

potential is more

favorable.

No, GPI fails.


Emanuel’s Genesis Potential Index (GPI)

Actual tracks

Data transfer of sub-daily data is a serious problem.

Case study: Track ETC in all historical, rcp8.5 and piControl CMIP5 experiments

160TB of model output. It took a network professional 3 months to download it.

This will improve when

Globus is fully enabled.

Avoid wget if at all possible.

HIRESMIP data for TC tracking ~10TB/decade/model

Storm tracking in CMIP5/6

Data transfer of sub-daily is a serious problem.

Case study: Track ETC in all historical, rcp8.5 and piControl experiments

160TB of model output. It took a network professional 3 months to download it.

Netflix to my house

.

Storm tracking in CMIP5/6

• When extreme weather happens, the public wants to know

– “Is this climate change?”

• Not quite the correct question, better to ask:

– “How has the risk of this event changed because of climate change?”

Or

– “How did climate change affect the magnitude of this event?”

• We approach these questions in two different ways.

1. Use extreme value statistics and the existing CMIP5 “ensemble of

opportunity”.

2. Design our own ensembles of climate model simulations tailored to event

attribution.

– Climate of the 20th Century C20C (~50-400 ensemble members)

– climateprediction.net (~1000+ ensemble members)

Extreme Event Attribution

• Consider three different summer heat wave events

– Europe 2003 (~70,000 excess deaths)

– Russia 2010 (~50,000 excess deaths, massive fires)

– Texas 2011 (lots of dead cows, massive drought, $8BN to ag industry)

• These are very rare events. We are interested in how the rarity of these

events has changed.

• We calculated the change in risk by comparing the extreme value

statistics in these regions from realistic historical simulations to those in

the pre-industrial simulations and the observations.Pardeep Pall, Michael Wehner, Dáithí Stone (2014) Probabilistic Extreme Event Attribution in Dynamics and

Predictability of Large-Scale, High-Impact Weather and Climate Events, Richard Grojahn, Jianping Li, Richard

Swinbank, Hans Volkert, editors. Cambridge University Press. 37-46, ISBN 978-1-107-07142-1.

Extreme event attribution: CMIP statistical analysis

• Real World: with industrialized humans

• Not Real World: without industrialized humans

• Fractional attributable risk is often used to determine liability.

Fractional attributable risk (FAR)

Risk _ ratio =PrealPNot _ real

FAR=1-PNot _ real

Preal

Event Risk Change at

time of event

Change in risk

2023

Change in risk

2040

Europe 2003 ~2X 35X 154X

Russia 2010 2-3 X 2.5-4 X 5-8 X

Texas 2011 1.5-4 X 2-5 X 4-10 X

Extreme event attribution: CMIP statistical analysis

The risk of each of these events has least doubled since the preindustrial era

• CMIP provides prescribed emissions or prescribed concentration scenarios.

• Fully coupled atmosphere, ocean, land, ice models

• A more constrained scenario is “prescribed temperature”

• AMIP is the traditional case where sea surface temperature and ice

concentration are prescribed by historical estimates.

• C20C+ is a factual/counterfactual experiment designed for extreme event

attribution.

– Counterfactual world preserves observed interannual variations but

anthropogenic changes are removed.

– 5-10 models. Ensemble sizes from 50 to 400 over the period 1959-2015

– http://portal.nersc.gov/c20c/

• HAPPI1.5/HAPPI2.0

• Half a degree Additional warming, Projections, Prognosis and Impacts

• Stabilized 1.5 & 2K above preindustrial prescribed SSTs

• In response to the Paris COP21 agreement

• Currently underway.

Extreme event attribution: other datasets

• Real world systems are multivariate.

• Impacts can depend on the combinations

– Hot, dry, windy wildfires

– Hot, moist, stagnant human health

• Past and future statistics also depends on the combinations.

– Mechanisms of changes vary.

• Our project brings climate analysts, impacts scientists and statisticians

together

– Impacts scientists help us define what is “extreme”.

– Statisticians are developing non-asymptotic methods.

– Climate analysts design targeted numerical experiments

Multivariate extremes

Read this book!

Coles, S. (2001), An Introduction to Statistical Modeling of Extreme Values, Springer, London.

ExtRemes: R based package with many useful EV functions so you don’t have to code up Coles(2001).

http://www.ral.ucar.edu/~ericg/extRemes/

climextRemes: A python based subset of ExtRemes useful for non-stationary problems of interest to me. (projections & attributions)

https://cran.r-project.org/web/packages/climextRemes/index.html

TECA2.0. Parallel storm tracking tool (soon to be released to the public). With python and c++ interfaces

First release. Tropical Storms

Later: Extratropical Cyclones, Atmospheric Rivers, Jet Stream

Useful tools

• A wide variety of research into extreme weather is enabled by CMIP5.

• CMIP6 will be considerably better because of the HIRESMIP.

• Biases in extreme temperature and precipitation are generally much worse than

for seasonal means.

• Some extreme weather phenomena should not be analyzed with CMIP5-class

models.

• Model evaluation is critical. Are the models “fit for purpose”?

• Analysis of seasonal extremes is limited by the smaller ensembles of CMIP5

• Large CESM ensemble is of use.

• Resist the temptation to annualize data.

• Daily and subdaily dataset sizes are challenging.

• Most analyses are embarrassingly parallel in some dimension.

• Parallel processing can substantially increase throughput.

• I/O can be a bottleneck in parallel codes.

Conclusions

Thank you!

[email protected]

extreme weather in a changing climate€¦ · pardeep pall, michael wehner, dáithí stone (2014)...

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