Ed Blanchard Extreme Precipitation & Snowpack: Ruby Leung .

Extreme Precipitation & Snowpack:Model and ObservationsNaomi Goldenson *Ruby LeungCecilia BitzEd Blanchard* PhD CandidateUniversity of Washington, Department of Atmospheric Sciences

In western NorthAmerica,topography isimportant

mm/daybecause oforographicprecipitation andrain shadowswinter mean, NLDAS-2 forcing dataset

and where it is cold enough for snow.* winter mean SWEequivalent metersSNODAS(based on observations)

Global models don’t have the resolution.* winter mean SWECAM5 AMIP (1 )equivalent metersSNODAS(based on observations)(typical global model)

to understand where extreme precipitationcomes from.cm (total column precipitable water)We also need toknow about thelarge-scale.

Internal VariabilityVariations internal to the climate, notforced like warming that is forced byGreenhouse gases.

We can quantify internalvariability with models.ensembles of simulations that are identical but for random tweaks of initial conditions.

Any modelingsetup needs tobalance theseconsiderations.Model forPrediction AcrossScales CAM5**Community Atmosphere Model, version 5 physics

Here’s where we havehigher resolution:

So we can resolve mountain snowpack*.* winter mean SWEMPAS-CAM5CAM5 AMIP (1 )equivalent metersSNODAS(based on observations)(our model)(typical global model,same atmospheric physics)

precipitationMPAS-CAM5CAM5 AMIP (1 )mm/dayNLDAS-2(based on observations)(our model)(typical global model,same atmospheric physics)

cm (total column precipitable water)and atmospheric rivers (“Pineapple Express”) which cause the extreme precipitation.

I have a fastway of detectingevents.

Some years have more, some have fewer.

We make an AR-index from the counts to study interannual variability.

Count atmosphericrivers impactingthe Northwest for winter orspringNorthwestCalifornia in model orreanalysis

So what doatmospheric riversdo to snowpack, onthe whole?

Three 30-year ensemble membersfrom MPAS-CAM5 to study variability#2#3slope (m)#1snowpack (SWE) regressed on normalized winter AR index

Years with more atmospheric rivershave less snowpack in the Cascades#2#3slope (m)#1snowpack (SWE) regressed on normalized winter AR index

Compared with. SNODAS & SNOTEL (obs)regressed on reanalysis AR-indexslope (m)let usstudy variability.#1snowpack (SWE) regressed on normalized winter AR index

What about spring?from MPAS-CAM5#3slope (m)let usstudy variability.#1#2snowpack (SWE) regressed on normalized spring AR index

What about spring? .SNODAS & SNOTEL (obs)regressed on reanalysis AR-indexlet us study variability.snowpack (SWE) regressed on normalized spring AR indexslope (m)from#1 MPAS-CAM5

SummaryMulti-scale modeling is a good way tostudy relationships betweenlarge-scale circulation and regionalhydrology.More ARs in winter lead to lessCascades snowpack.&QuestionsIs MPAS-CAM5 less good at spring?Or is spring inherently more variable?What about other regions?

What about California?#2#3slope (m)#1snowpack (SWE) regressed on normalized winter AR index

A consistent picture in winter.slope (m)#1snowpack (SWE) regressed on normalized winter AR index

Like in the Northwest,spring is not robust.#2#3slope (m)#1snowpack (SWE) regressed on normalized spring AR index

And a different ensemble memberbest matches observations.slope (m)#3snowpack (SWE) regressed on normalized spring AR index

Questions?NaomiGoldensonPhD CandidateAtmospheric SciencesUniversity of Washingtonngoldens@uw.edu

large-scale. to understand where extreme precipitation comes from. cm (total column precipitable water) . and atmospheric rivers (“Pineapple Express”) . (total column precipitable water) I have a fast way of detecting events. Some years have more, some have fewer. We make an AR-index from the counts to study interannual variability.