What controls the variability of springtime fine dust in the...
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What controls the variability of springtime fine dust in the western United States? Implications for the recent dust increase in the Southwest Ploy Pattanun Achakulwisut Lu Shen Loretta J. Mickley Harvard University PM and Related Pollutants in a Changing World EPA RTP, 6-7 April 2017 Phoenix dust storm, 2015 Ploy (nearly all work) Lu (guidance)
Transcript of What controls the variability of springtime fine dust in the...
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
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