assessing the ability of instantaneous aircraft and sonde ... v9.01.03; 4ºx5º; merra + maccity;...

Lee T. Murray1,2 ([email protected]) and Arlene M. Fiore2,31. ORAU / NASA Goddard Institute for Space Studies, New York, NY2. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY3. Department of Earth and Environmental Sciences, Columbia Univeristy, New York, NY

#A54E-05

Assessing the Ability of Instantaneous Aircraft and Sonde Measurements to Characterize

Climatological Means and Long-Term Trends in Tropospheric Composition

https://ntrs.nasa.gov/search.jsp?R=20160000367 2018-05-17T16:51:48+00:00Z

mailto:[email protected]

Over 40 yrs of 3-D in situ sampling of the troposphere

WOUDC (http://www.woudc.org)

NASA, NOAA, NSF/NCAR, NERC, DLR, et. al

IAGOS (http://iagos.org)MOZAIC/CARIBIC

Observations discretely sample a dynamic 4-D system

Sondes Commercial Field Campaigns

Can we use these observations to constrain CCMs?

Chemistry transport models (CTMs) may be evaluated by exact space-time matchingChemistry-climate models (CCMs) generate their own weather so cannot match observations exactly in space and timeCCMs are typically evaluated with observed climatologies

Are aggregated in situ observations indicative of background mean conditions?Where can these observations be used to constrain processes in CCMs?Can discrete sampling be used to constrain long-term trends?

Questions

Approach: Use CTM to compare in situ and “CCM” output

Compare all three to assess suitability of observations to characterize mean atmospheric composition

Decadal Monthly Mean Climatology

“CTM-like sampling”

Sample location at exact time of flight/sonde track

“CCM-like sampling”

Sample location’s clim. monthly mean for each observation

Simulated global “true” background

concentrations

GEOS-Chem CTMv9_02; 2ºx2.5º; 47L; MERRA

Jan 2003-Dec 2012

90ºS−30ºS 30ºS−EQ EQ−30ºN 30ºN−90ºN

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n = 1,272

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n = 921

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n = 2,507

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n = 1,388

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n = 1,595

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n = 7,599

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n = 2,057

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n = 2,101

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n = 12,274

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n = 12,018

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O3

(ppb

v)

Ozone most-sampled tropospheric trace gas distribution 2003-2012 Sondes + Passenger Programs + Field Campaigns

Ozone increases w/ latitude and altitude; large variability in FT; spring surface maxima

Observations


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n = 1,272

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n = 921

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n = 2,507

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n = 1,388

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n = 1,595

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n = 7,599

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n = 2,057

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n = 2,101

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n = 12,274

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n = 12,018

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O3

(ppb

v)

CTM sampled in space and time captures salient features

GEOS-Chem biased high ~7%; captures 87% of meridional, vertical, seas. variability (n=10 reg. x 12 mon.)

ObservationsCTM-Sampling

2003-2012 Sondes + Passenger Programs + Field Campaigns


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n = 1,272

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n = 921

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n = 2,507

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n = 1,388

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n = 1,595

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n = 7,599

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n = 2,057

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n = 2,101

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n = 12,274

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n = 12,018

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O3

(ppb

v)

Sampling ozone decadal monthly means reproduces mean of direct sampling


CCM decadal mean ozone patterns can be constrained with aggregated climatological observations


“CCM”-Sampling


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n = 1,272

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n = 921

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n = 2,507

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n = 1,388

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n = 1,595

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n = 7,599

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n = 2,057

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n = 2,101

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n = 12,274

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n = 12,018

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O3

(ppb

v)

O3 clim. fairly representative of “true” background mean & seasonality

CCM/CTM sampling biased 6% higher than “true” mean; captures 84% of spat./seas. monthly variability


Airports, Sonde StationsObs. biased toward South Atlantic

ozone maximum; add’l obs. needed to fully constrain zonal mean


“CCM”-SamplingTrue Model Mean


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n = 47

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n = 32

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n = 3,077

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n = 380

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n = 824

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n = 1,031

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n = 6,794

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CO (p

pbv)

CO reasonably represented by climatology, except in SH

Large IAV in CO skews statistics for “CCM” method

Model biased, but representative of

seasonality

2003-2012 Passenger Programs + Field Campaigns




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n = 1,031

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n = 6,794

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CO (p

pbv)


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n = 30

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n = 154

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n = 279

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n = 638

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n = 1,166

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CO (p

pbv)

Field campaign aggregation mitigates “plume chasing”



Field Campaigns Only


Campaigns; AMMA-SCOUT-F20; ARCTAS-DC8; AVE-04; AVE-05; COBRA-04; CR-AVE; DC3-GV; DISCOVER-AQ-DC-WP3B; FAAM; HIPPO; INTEX-B-C130;

INTEX-B-DUCHESS; INTEX-B-DC8; INTEX-NA; ITOP-UK; MAXMex-GV; NEAQS-ITCT; Polar-AVE; Pre-AVE; START-08; VOCALS-C130; VOCALS-G1

Short-lived, infrequently sampled species poorly characterized


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n = 75

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n = 411

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n = 33

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n = 31

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n = 311

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n = 481

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NOy

(ppb

v)




Additional observations required for characterizing reactive nitrogen budgets

Can observations constrain processes in CCMs?


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n = 1,128

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n = 853

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n = 4,255

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n = 1,373

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n = 1,774

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n = 9,892

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n = 1,998

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n = 2,118

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n = 12,996

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n = 12,656

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∆O

3 (p

pbv)

Lightning NOx contribution to ozone at in situ locations (2004-2012)∆O

3 fro

m L

NOx (

ppbv

)

CTM-Sampling“CCM”-SamplingTrue Model Mean

Model sampled at observations sensitive to zonal lightning-ozone relationship

Only local sens.

NH in situ clim. evenly sample zonal emission-ozone sensitivities (incl. Soil, FF, BB, BVOC); SH does not

Ongoing Work: Assessing Long-Term Trends

GEOS-Chem v9.01.03; 4ºx5º; MERRA + MACCity; Jan 1980-Dec 2010

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∆O3 = 4.24e−16 ppb/10−yr, p = 1∆O3 = 5.08e−16 ppb/10−yr, p = 1

∆O3 = 0.936 ppb/10−yr, p = 3.73e−11−20

−10

0

10

20

1980 1985 1990 1995 2000 2005 2010Time

Dese

ason

alize

d O

zone

(ppb

)

Deseasonalized Ozone @ 500 mb above Hohenpeißenberg, Germany

Currently assessing whether aggregated sonde + aircraft data may constrain multi-decadal trends in vertical structure

Statistically significant trend in simulated monthly mean ozone…

..but not in observations or model sampled at observations

Conclusions

Northern hemispheric sampling mostly indicative of background mean O3 and CO conditions; some biases toward polluted regionsSouthern hemisphere needs additional constraints on zonal asymmetries in O3 and CO and/or longer averaging intervalsReactive nitrogen species poorly characterizedSampling dense enough in northern hemisphere to constrain zonal emission-ozone/CO relationships; less so in the southern hemisphereOngoing work will assess the suitability of the aggregated in situ data to characterize long-term trends

AcknowledgementsThe many individuals and groups that collected and archived data through the yearsWMO/GAW/WOUDC, SHADOZ, MOZAIC, CARIBIC, IAGOS, *Pre-AVE, TROCCINOX-2004, COBRA04, INTEX-NA, NEAQS-ITCT, ITOP-DLR, ITOP-UK, FAAM, AVE-04, Polar-AVE, TROCCINOX-2005, AVE-05, AMMA-SCOUT-F20, CR-AVE, INTEX-B-J31, MAXMex-GV, INTEX-B-DC8, INTEX-B-C130, INTEX-B-DUCHESS, INTEX-B-CESSNA, TexAQS-P3B, TC4-DC8, TC4-WB57, ARCPAC, ARCTAS-WP3B, ARCTAS-DC8, START-08, VOCALS-G1, VOCALS-C130, VOCALS-TwinOtter, VOCALS-Dornier, HIPPO-1, HIPPO-2, HIPPO-3, HIPPO-4, DISCOVER-AQ-DC-WP3B, DISCOVER-AQ-DC-UMD, HIPPO-5, DC3-DC8, DC3-GV, DC3-F20, TACTS, ESMVal

assessing the ability of instantaneous aircraft and sonde ... v9.01.03; 4ºx5º; merra + maccity;...

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