WhatcontrolsthevariabilityofspringtimefinedustinthewesternUnitedStates?
ImplicationsfortherecentdustincreaseintheSouthwest
PloyPattanun AchakulwisutLuShen
LorettaJ.MickleyHarvardUniversity
PMandRelatedPollutantsinaChangingWorldEPARTP,6-7April2017
Phoenixduststorm,2015Ploy(nearlyallwork) Lu(guidance)
ClimatechangecouldhavealargeimpactondustandwildfirePMinthewesternUS.
DuststorminSanJoaquinValley,California RimFireinCalifornia,August2013.
Dry,hot,windyconditionscanleadtobothduststormsandwildfires.• Howwillchangingclimatechangethefrequencyofdusteventsandwildfires?• Whataretheimplicationsforairquality?
Yueetal.,(2013,2014,2015),Liu(2016….)
ClimatechangecouldhavealargeimpactondustandwildfirePMinthewesternUS.
DuststorminSanJoaquinValley,California
Dry,hot,windyconditionscanleadtobothduststormsandwildfires.• Howwillchangingclimatechangethefrequencyofdusteventsandwildfires?• Whataretheimplicationsforairquality?
AreaburnedinthewesternUS
Newinventory
GFED4GFED3
Wedevelopanewemissionsinventoryusingon-the-groundinteragencyfirereports.Yueinpreparation.
WestandSouthwestwilllikelyexperiencewarmeranddrierconditionsinthefutureclimate,withimplicationsfordust.
Anomaliesinprecipitationminusevaporation(P-E)
medianP-E
medianevap
medianprecip
ResultsovertheSouthwestfromanensembleof19climatemodels.
Seager etal.,2007
Reasonsfortrend:• ExpansionofHadleycell• Poleward shiftofpolarjet• Slowerwesterlies,lessmoisturedeliveredtomountains
drier
wetter
Drierconditions+landusechangecouldenhancedustconcentrations,withimplicationsforhumanhealth.
Hahnenberger etal.,2012ArrivalofaduststorminSaltLakeCity,Utah.
DusteventsinSaltLakeCityexceedtheNAAQSforPM10aboutonceperyear.
22August2010,3:30p.m.
20minuteslater
Dustcarriesmicroorganismsharmfultohumanhealth– e.g.,Cocciodiodes,thecauseofCocciodioidomycosis (a.k.a.valleyfever).
ValleyFevercanbefatalifsporesreachthebrain.ThediseaseismostcommoninArizonaandpartsofSouthernCalifornia.
NumberofreportedcasesinUS,1998-2015
CDC
OverseascontributiontoUS“worstdustdays”
%
PreviousstudiesonUSdust
Transpacificsourcesaccountfor~40%ofworstdustdaysintheWest.Fairlie etal.,2007
IMPROVEsitesshowdustincreasinginMarchintheWest,+5%yr-1.Handetal.,2016
UsingdatafromIMPROVEsites,ourstudyconfirmsthereportedtrendinMarchspringdust.
2002-2015TrendsindustatIMPROVEsitesintheWestMARCH
125W 115W 105W30N
40N
50NAPRIL
125W 115W 105W
MAY
125W 115W 105W
−0.4 −0.2 0.0 0.2 0.4(µg m−3 y−1)
March MayApril
Boxesindicatestatistically
significanttrends.
Weseektounderstand:• DriversofvariabilityofdustconcentrationsacrosstheWest.• CausesofMarchtrendindust.
Usingthisknowledge,wewillexaminetheimplicationsofclimatechangefordustinthewesternUS.
Approachoffirstdustproject.1.UseEOFanalysistodeterminethedominantpatternsofdustconcentrationsacrossthewest.
2.ProbetheEOFresultstoidentifymeteorologicaldriversthatdrivethevariabilityofdust.
3.Usinginfofrom#2,buildstatisticalmodelsthatrelatemeteorologyandteleconnectionpatternstodustconcentrations.
4.ApplythesemodelstoarchivedoutputfromIPCC.
Benefitsofapproach:• Strongrelianceonobserved
relationships.• Useofanensembleofclimate
modelsandscenariostoobtainrobustresults.
29%
(a) 1st EOF loading
30% 45%
26%
(b) 2nd EOF loading
MARCH
20%
APRIL
16%
MAY
−0.2 −0.1 0.0 0.1 0.2
29%
(a) 1st EOF loading
30% 45%
26%
(b) 2nd EOF loading
MARCH
20%
APRIL
16%
MAY
−0.2 −0.1 0.0 0.1 0.2
EOFanalysisofMarchdustshowstwopatternsthattogetherexplain55%ofdustvariability:• Northwest-Southwestdipole• Uniformpattern
Whatdrivesthesepatterns?
HerewefocusonresultsforMarchdustinthewesternUS.
withJFMSSTs
2002 2004 2006 2008 2010 2012 2014−8
−4
0
4
8
12
−2
−1
0
1
2
3
JFM ENSO, r=0.74JFM PDO, r=0.69
(a) March PC1 time series (29%)
−1.0
−0.5
0.0
0.5
1.0
(b) Homogeneous corr map
−1.0
−0.5
0.0
0.5
1.0(c) Corr(PC, JFM SST)
−1.0
−0.5
0.0
0.5
1.0(d) Corr(PC, JFM Tmax)
−1.0
−0.5
0.0
0.5
1.0(e) Corr(PC, JFM precip)withJFMmaxTemps withJFMPrecip
CorrelationsofPC1withmeteorologicalvariables.
PC1ofMarchdustpatternscorrelateswellwithENSOindexandwiththePacificDecadalOscillation.
2002 2004 2006 2008 2010 2012 2014−8
−4
0
4
8
12
−2
−1
0
1
2
3
JFM ENSO, r=0.74JFM PDO, r=0.69
(a) March PC1 time series (29%)
−1.0
−0.5
0.0
0.5
1.0
(b) Homogeneous corr map
−1.0
−0.5
0.0
0.5
1.0(c) Corr(PC, JFM SST)
−1.0
−0.5
0.0
0.5
1.0(d) Corr(PC, JFM Tmax)
−1.0
−0.5
0.0
0.5
1.0(e) Corr(PC, JFM precip)
Timeseries ofPC1inMarch CorrelationofPC1withdust
ENSO
PDOPC1r =0.69, r =0.74
ElNinowintersareassociatedwithdecreaseddustinthedesertSouthwest.
OppositeoccursduringLaNinawinters,whensubtropicaljetisweaker.LaNinaleadstoincreasedsubsidenceoverSouthwestandgreaterdustconcentrations.
ElNiñoconditions:• Southwardshiftof
Pacificstormtrack.
• IncreasedwinterprecipitationacrossthesouthernUS.
• ReducednortherlyflowofcoldairfromCanadainNorthwest.
2002 2004 2006 2008 2010 2012 2014−8
−4
0
4
8
−100
0
100
200MGI, r=0.81AOD, r=0.62
(a) March PC2 time series (26%) (m)x 10−2
−1.0
−0.5
0.0
0.5
1.0
(b) Homogeneous corr map
−1.0
−0.5
0.0
0.5
1.0
(c) Corr(PC, Mar 500 mbar gph)
2002 2004 2006 2008 2010 2012 2014−8
−4
0
4
8
−100
0
100
200MGI, r=0.81AOD, r=0.62
(a) March PC2 time series (26%) (m)x 10−2
−1.0
−0.5
0.0
0.5
1.0
(b) Homogeneous corr map
−1.0
−0.5
0.0
0.5
1.0
(c) Corr(PC, Mar 500 mbar gph)
CorrelationofPC2withdust
CorrelationofPC2withMarch500mb heights
AOD
PC2ofMarchdustpatternsappearsrelatedwithtransportofAsiandustacrossthePacific.
PC2correlateswith:1. MeridionalGradientIndex
(differenceinheightsbetweenthetwoboxes)
2. AODincentralPacific.
2002 2004 2006 2008 2010 2012 2014−8
−4
0
4
8
−100
0
100
200MGI, r=0.81AOD, r=0.82
(a) March PC2 time series (26%) (m)x 10−2
−1.0
−0.5
0.0
0.5
1.0
(b) Homogeneous corr map
−1.0
−0.5
0.0
0.5
1.0
(c) Corr(PC, Mar 500 mbar gph)
Timeseries ofPC2inMarch
AOD
Meridionalgradientindex
PC2 r =0.62r =0.81
120W 115W 110W 105W32N
36N
40N
Trends and concentrations in March fine dust, 2002−2015 average
California Southwest
−0.30
−0.15
0.00
0.15
0.30(µg m−3 y−1) (µg m−3)
1
1
1
1
1
< 1.5
< 2.5
< 3.5
< 4.5
< 5.5
WelookmorecloselyatrecenttrendsinMarchdustinCaliforniaandSouthwest.
Weuseastepwiseapproachtobuildalinearregressionmodeltounderstandobservedincreases.
UsinginformationfromEOFanalysis,weconsiderthesevariables:ENSO,PDO,localmeteorologicalvariables,droughtindices….
Size=meanconcentrationBox=significanttrend
2002-2015Trendsandmeanconcentrationsoffinedust
Phoenix
2002 2004 2006 2008 2010 2012 20140.5
1.5
2.5
3.5 Observed (Trend = 0.11 µg m−3 y−1)Modeled (R2 = 0.76)
(a) Southwest regional−mean March fine dust conc. (µg m−3) time series
−3−2−1
012
−1.5−1−0.500.51
2002 2004 2006 2008 2010 2012 2014
PDOSPEI48
(b) JFM PDO and SPEI48 time series
MarchvariabilityindustintheSouthwestcanbeexplainedbyvariationsinPDOanddrought.
Timeseries ofregionalmeandustintheSouthwestinMarch
Timeseries ofpredictors
PDO
SPEI48
Droughtindex
observations
model
CoolPacific+increasingdrought,increasingdust
WarmPacific+littledrought,lowdust?
Dustco
ncentrationµg
m-3
R2 =0.76
2002 2004 2006 2008 2010 2012 20140.5
1.5
2.5Observed (Trend = 0.05 µg m−3 y−1)Modeled (R2 = 0.81)
(a) California regional−mean March fine dust conc. (µg m−3) time series
−2
−1
0
1
2
2002 2004 2006 2008 2010 2012 2014
SPEI48SMGIRH
(b) JFM SPEI48, March SMGI and RH (%) time series
45
55
65
75
MarchvariabilityinCaliforniadustcanbeexplainedbyvariationsinrelativehumidity,transportfromAsia,anddrought.
Timeseries ofCaliforniaregionalmeandust
Dustco
ncentrationµg
m-3
Timeseries ofpredictors
RH
SPEI48Meridionalgradientindex
Droughtindex
model
observationsR2 =0.81
NextstepwillbetoapplyourstatisticalmodelofmonthlymeandusttotheensembleofCMIP5models.
2000 2002 2004 2006 2008 2010 2012 2014
0
20
40
60
80
100 None D0 D1 D2 D3 D4
Area (%) of different drought types averaged over AZ, NM, TX, and OK for Jan−MarPercentofSouthweststatesindifferentstatesofdrought
nodrought
Extremedrought
Severedrought
Southwesthasseenshifttowardincreaseddroughtsince2000.Willthistrendcontinue?IsitrelatedtotoexpansionofHadleyCell?
Policyrelevantmessages.
• ObserveddustincreaseinMarchinSouthwestandCaliforniaappearsrelatedtonaturalvariability– i.e.,thesignalofclimatechangehasnotemergedfromthenoise.
• Projectionsoflikelydroughtintheregioncouldleadtogreaterfrequencyofduststorms,withimplicationsforhumanhealth.
WearecurrentlyinvestigatingtrendsinfuturedustandsmokeconcentrationsacrosstheWest,usingbothstatisticalanddynamicmodels.
Ourresearchgroup,spring2016