NASA Technical Reports Server (NTRS) 20130014363: Multi-model Mean Nitrogen and Sulfur Deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation Historical and Projected Changes

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10CanadianCentreforClimateModelingandAnalysis,EnvironmentCanada,Victoria,British pe Multi-model mean
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13NationalInstituteofWaterandAtmosphericResearch,Lauder,NewZealand isc J.-F.Lamarqueetal.
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Received:31January2013–Accepted:18February2013–Published:8March2013 sio
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Correspondenceto:J.-F.Lamarque([email protected]) P
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PublishedbyCopernicusPublicationsonbehalfoftheEuropeanGeosciencesUnion. e
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Abstract D
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We present multi-model global datasets of nitrogen and sulfate deposition covering s
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time periods from 1850 to 2100, calculated within the Atmospheric Chemistry and ion 13,6247–6294,2013
Climate Model Intercomparison Project (ACCMIP). The computed deposition fluxes P
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5 are compared to surface wet deposition and ice-core measurements. We use a new er Multi-model mean
dataset of wet deposition for 2000–2002 based on critical assessment of the qual- nitrogen and sulfur
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ity of existing regional network data. We show that for present-day (year 2000 AC- deposition
CMIP time-slice), the ACCMIP results perform similarly to previously published multi- D
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model assessments. For this time slice, we find a multi-model mean deposition of c J.-F.Lamarqueetal.
u
−1 −1 s
50Tg(N)yr from nitrogen oxide emissions, 60Tg(N)yr from ammonia emissions, s
10 and 83Tg(S)yr−1 from sulfur emissions. The analysis of changes between 1980 and ion
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2000 indicates significant differences between model and measurements over the a TitlePage
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United States but less so over Europe. This difference points towards misrepresenta- e
r Abstract Introduction
tion of 1980 NH emissions over North America. Based on ice-core records, the 1850
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deposition fluxes agree well with Greenland ice cores but the change between 1850 Conclusions References
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and 2000 seems to be overestimated in the Northern Hemisphere for both nitrogen is
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and sulfur species. Using the Representative Concentration Pathways to define the u
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projected climate and atmospheric chemistry related emissions and concentrations, sio
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we find large regional nitrogen deposition increases in 2100 in Latin America, Africa
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20 and parts of Asia under some of the scenarios considered. Increases in South Asia ap J I
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are especially large, and are seen in all scenarios, with 2100 values more than double r
2000 in some scenarios and reaching >1300mg(N)m−2yr−1 averaged over regional Back Close
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to continental scale regions in RCP 2.6 and 8.5, ∼30–50% larger than the values in
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anyregioncurrently(2000).ThenewACCMIPdepositiondatasetprovidesnovel,con- is
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25 sistentandevaluatedglobalgriddeddepositionfieldsforuseinawiderangeofclimate us
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and ecological studies. io
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1 Introduction D
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Theglobalnitrogencycleisofimportanceforanumberofkey-issues,suchasecology s
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and biodiversity (e.g. Phoenix et al., 2006; Bobbink et al., 2010; Butchart et al., 2010), ion 13,6247–6294,2013
eutrophicationandacidification(e.g.Bouwmanetal.,2002;Rodheetal.,2002;Fisher P
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5 etal.,2011),climatechange-carboncycleinteractions(e.g.Thorntonetal.,2007;Reay er Multi-model mean
et al., 2008; Zaehle et al. 2010), food and energy production (Sutton et al., 2011). nitrogen and sulfur
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Nitrogen emissions also impact human health through particulate matter and ozone deposition
formation. Clearly, nitrogen is central to many aspects of life on Earth (Galloway et al., D
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2008; Fowler et al., 2012). c J.-F.Lamarqueetal.
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Dinitrogen(N )isthemostabundantcomponentintheatmosphere,butischemically s
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unreactive, only contributing to tropospheric chemistry through lightning and associ- n
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ated NO formation (Franzblau and Popp, 1989). More reactive atmospheric nitrogen a TitlePage
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components include oxidized NO (=NO+NO +other minor inorganic components e
y 2 r Abstract Introduction
andorganicnitrogens),thelong-livedgreenhousegasdinitrousoxide(N O;131±10yr,
Prather et al., 2012), and reduced nitrogen NH (=NH +NH+) as well2as organic ni- | Conclusions References
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trogencomponentssuchasamines(Kanadidouetal.,2012).NOy andNHx arecollec- is
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tively identified as reactive nitrogen (N ). Anthropogenic emissions of N components u
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areestimated(vanAardenneetal.,2001;Lamarqueetal.,2010)tohaveincreasedby sio
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afactorof5to10since1850.KnowledgeonthelinkbetweenN generationbyhuman
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20 activities and its subsequent impacts requires an accurate description of largescale ap J I
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emissions, atmospheric chemistry transport and removal, all of which occur on rela- r
tively fast timescales compared to nitrogen in the other compartments of relevance to Back Close
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the Earth system: terrestrial, coastal zones and open ocean.
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Globalatmospheric-chemistrytransportmodelsareroutinelyusedasatooltocalcu- is
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25 latetheglobaldispersionofNr.Sinceindividualmodelsarepronetospecificerrorsand uss Printer-friendlyVersion
multi-modelmeanstypicallyoutperformindividualmodels(e.g.ReichlerandKim,2008; io
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Shindell et al., 2012), it has become common to use model ensemble calculations to P InteractiveDiscussion
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improve the quality of the calculations. Previous deposition ensemble studies include p
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Lamarqueetal.(2005)using6modelsandfocusingonNO deposition,Denteneretal. D
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(2006) using 23 models and discussing NO , NH and SO deposition under current c ACPD
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and 2 future scenarios, and Sanderson et al. (2008) using 15 models and focusing on s
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export of NO and subsequent deposition. n
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The present study uses deposition fields generated by 10 models that participated a
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in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; e Multi-model mean
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Lamarque et al., 2012). ACCMIP was designed to inform aspects of the forthcom- nitrogen and sulfur
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ing Intergovernmental Panel on Climate Change Assessment Report #5 regarding deposition
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the role of long-term changes in atmospheric chemistry on climate and vice-versa. is
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In this ACCMIP study simulations were provided for time slices representative of the u
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periodaround1850,1980,2000,2050and2100(seeSect.2),wherethefutureatmo- sio
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spheric chemistry-climate conditions follow the 4 Representative Concentration Path-
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ways(Mossetal.,2010).Becauseoftheimportanceofsulfurdeposition(inassociation a TitlePage
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with nitrogen deposition) for the understanding of soil and water acidification (Fisher r Abstract Introduction
et al., 2011), we have included an analysis of sulfur deposition to the more traditional
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nitrogen deposition analysis. The models used in this analysis represent a combina- Conclusions References
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tion of the current generation of Chemistry-Transport Models and Chemistry-Climate is
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Models, with somewhat refined horizontal and vertical resolution and more detailed us
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descriptions of chemical processes compared to previous studies (namely the results io
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20 discussed in Dentener et al., 2006; Sanderson et al., 2008). Rather than represent- P
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ing specific meteorological years – as in previous studies – each model meteorology p J I
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is rather representative of the decade under consideration (e.g. 1850–1859; or 2000– r
2009) and hence include the effect of climate change. | Back Close
Thispaperisstructuredasfollows.Section2givesashortdescriptionofthemodels D FullScreen/Esc
25 and the ACCMIP experiments. We also define there the methodology for computing isc
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the multi-model mean (MMM). Section 3 describes the measurement datasets used s
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in this paper, from surface sites and from ice cores. In Sect. 4, we evaluate the multi- io
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model mean for the 2000 period – when extensive measured deposition datasets are P InteractiveDiscussion
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available, and which allows us also to make a retrospective analysis of the quality pe
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of data in this model dataset compared to the earlier and widely used Photocomp D
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(Dentener et al., 2006) and HTAP (Sanderson et al., 2008; Vet et al., 2013) deposi- c ACPD
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tion datasets. In Sect. 5, a more limited analysis is given for the changes in deposi- s
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tion (and its drivers) since the 1980s and, using ice-cores, for the change between n
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decadescenteredaround1850and2000.The1980–2000periodisofparticularinter- a
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est since, due to implementation of national and international emission control mea- e Multi-model mean
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sures, strong emission reductions have been reported, especially over North America nitrogen and sulfur
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andEurope,withcommensurateconsequencesfordeposition.Section6describesthe deposition
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overall global structure of total deposition (dry and wet) from 1850 to 2100. A discus- is
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sion and conclusions follow in Sect. 7. u
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2 ACCMIP simulations and multi-model mean a TitlePage
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AnoverviewoftheAtmosphericChemistryandClimateModelIntercomparisonProject r Abstract Introduction
(ACCMIP) simulations and participating models is given in Lamarque et al. (2012). |
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Consequently, we focus here on the aspects relevant to the analysis of nitrogen and D
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sulfate deposition. c Tables Figures
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ACCMIPprovidesanalysisontheroleofatmosphericchemistrychangesinthenear- ss
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term-climate forcing included in the CMIP5 (Climate Model Intercomparison Project n J I
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Phase 5; Taylor et al., 2012) simulations, including the chemical composition changes a
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associated with the CMIP5 prescribed forcings. The ACCMIP simulations used in the e
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present study (Table 1) consist of time slice experiments (for specific periods span-
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ning 1850 to 2100 with a minimum increment of 10yr) with chemistry diagnostics.
Each requested simulation is labeled as Primary (“P”) or Optional (“O”). Although sim- D FullScreen/Esc
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ulations using the Representative Concentration Pathway 6.0 (RCP6.0, Masui et al., cu
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2011)emissionswereperformed,notenoughgroupsprovidedthenecessaryfieldsfor s Printer-friendlyVersion
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a meaningful analysis of nitrogen and sulfur depositions; therefore, this work focuses n
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on the remaining three RCP projections RCP2.6, RCP4.5 and RCP8.5 (see van Vu- a
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uren et al., 2011 and references therein). A primary output of these simulations was er
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nitrogen and sulfate (dry and wet) deposition fields (see Table S1 in Lamarque et al., D
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2012). Although the ACCMIP models were required to specify the anthropogenic and c ACPD
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biomass burning emissions according to Lamarque et al. (2010) for 1850–2000 and s
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the RCP emissions (van Vuuren et al., 2011) beyond, there is a range of emissions n
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that were used in ACCMIP models, mostly from variations in the treatment of natural a
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emissions (Lamarque et al., 2012; Young et al., 2013). e Multi-model mean
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As a first step in the quality control of the model calculated depositions, we ana- nitrogen and sulfur
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lyze for each model the balance between emission and deposition as reported by the deposition
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modeling groups (Tables 2–4) for the year 2000 time slice experiment. This analysis is
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is done separately for nitrogen originating from nitrogen oxide emissions (deposition u
10 s
fields are referred as a group as NOy) or ammonia emissions (NHx) and sulfur emis- sio
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sions (SO ). We find that the NO deposition is larger than the emissions (surface and
x y P
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upper-air, including lightning) by approximately 1Tg(N)yr , representing the input of p
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nitrogen (mostly in the form of nitric acid) from the stratosphere, in general agreement r Abstract Introduction
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withobservationalestimatesof0.45Tg(N)yr fromMurphyandFahey(1998),except |
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−1 Conclusions References
for the GISS model where this influx is approximately 5Tg(N)yr . In the case of am- D
monium,thebalancebetweenemissionsanddepositionisclearlyattained.Inthecase isc Tables Figures
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of sulfur, this balance cannot be fully evaluated due to the lack of diagnostics on the s
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formation of sulfate aerosols from the emitted dimethylsulfide (DMS). Boucher et al. ion J I
(2003) estimated the yield (DMS to sulfate aerosols) to be 87% when both gas-phase P
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and aqueous-phase reactions are taken into account, but this number will be some- pe J I
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what model dependent. Within that limitation, it is reasonable to assume that balance
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between sulfur deposition and emission is achieved for the listed models. |
The focus of this paper is on documenting the multi-model mean (MMM) generated D FullScreen/Esc
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for each time slice. The MMM is constructed by linearly interpolating the model gen- c
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erated monthly fields (for example wet deposition combined for all NO species) at s Printer-friendlyVersion
◦ ◦ y io
theirnativehorizontalresolution(TableS1)toacommon0.5 ×0.5 grid(finerthanany n
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model grid), identical to the emission grid used in Lamarque et al. (2010). Then, each a
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fieldisaveragedacrossmodelsattheoriginalmonthlytemporalresolutiontogenerate e
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its multi-model mean and standard deviation (Tables S2–S4). As indicated in these ta- D
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bles, the largest number of models (up to 9) generated the necessary fields is found c ACPD
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for the historical NO deposition, followed by the RCP8.5 simulations. The number of s
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models performing ammonium chemistry and deposition is however much smaller (2– n
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3 models, depending on the simulated time period) while between 2 and 6 models a
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have provided sulfate fields. In all cases, the MMM is constructed using all available e Multi-model mean
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model results, therefore leading to variations between the specific models used in the nitrogen and sulfur
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average. deposition
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3 Description of observational deposition data ss
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Under the auspices of the World Meteorological Organization (WMO) a precipitation P
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chemistry expert group has performed a critical analysis of available wet deposition p
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datafortheyears2000to2002.Whiledrydepositionmaybeavailableatspecificsites, r Abstract Introduction
thisisnotadirectlyobservedquantityandwethereforedonotusethisinformation.We |
Conclusions References
usetheWMO-processedwetdepositiondatasetsinouranalysisoftheperformanceof D
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the MMM in the 2000 time slice. c Tables Figures
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In the WMO dataset, a careful analysis is made of worldwide reported data of ss
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wet precipitation chemistry, and data quality qualifiers are provided to deposition n J I
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data obtained mainly from networks in Europe European Monitoring and Evaluation a
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Programme, EMEP; http://www.nilu.no/projects/ccc/emepdata.html; the United States e
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(National Atmospheric Deposition Program, NADP; http://nadp.sws.uiuc.edu/NTN/
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ntnData.aspx); Canada (Canadian Air and Precipitation Monitoring Network, CAP-
MoN; http://www.ec.gc.ca/rs-mn/default.asp?lang=En&n=752CE271-1); Asia (mainly D FullScreen/Esc
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fromtheAcidDepositionMonitoringNetworkinEastAsiaEANET;http://www.eanet.cc), cu
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and Africa (Deposition of Biogeochemically Important Trace Species (DEBITS), http: s Printer-friendlyVersion
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//debits.sedoo.fr). We will focus our analysis on the NADP, EMEP and EANET obser- n
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vations. For more detailed information on data networks, data selection criteria, and a
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an application to the HTAP Phase 1 deposition dataset, we refer to Vet et al. (2013), er
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as well as the data networks indicated above. The evaluated wet deposition datasets, D
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soon to be available through the World Data Centre for Precipitation Chemistry, only c ACPD
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containasubsetoftheavailablestationsforthatperiod,i.e.stationsthatcorrespondto s
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regionallyrepresentativesitesthatfulfilledspecificdatacompletenessandqualitycon- n
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5 trolmeasures.Inthispaper,weusethewetdepositionmeasurementsofnitrate(NO3), ap
ammonium (NH+) and non-sea salt sulfate (SO2−), which were qualified as “good”. er Multi-model mean
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Over Europe (EMEP) and the United States (NADP), deposition measurement sites nitrogen and sulfur
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typically started around the 1980s. We use these early measurements to evaluate the deposition
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change in deposition over these two decades. To this purpose, we computed from the is
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raw data (available at the aforementioned web sites), and using the filtering protocols u
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defined by the specific networks, two sets of 6-yr averages (1980–1985 and 1997– io
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2002) for sites that had sufficient observations for both time slices. P
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Although a quality control similar to the WMO evaluation was not available for the p
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1980s data, comparison of the WMO processed 2000–2002 data with our 1997–2002 r Abstract Introduction
15 indicate very good agreement over the respective regions (not shown), validating the |
Conclusions References
suitability of our processed dataset to study trends in deposition from the 1980s to the D
2000s. is
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Historical records of nitrate, ammonium, and sulfur were also developed from high- s
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depth-resolution measurements in ice cores (see Table S5 for their geographical infor- ion J I
mation) using an established continuous ice core analytical system (e.g., McConnell P
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and Edwards, 2008). Nitrate and ammonium were measured using spectrophotome- p J I
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try and fluorimetry, respectively, with standard flow through methods (Roethlisberger
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et al., 2000). Total sulfur concentrations were measured using magnetic sector Induc- |
tively Coupled Plasma Mass Spectrometry (McConnell and Edwards, 2008). At core D FullScreen/Esc
siteswithsufficientannualsnowfall,theicecorerecordsweredatedusingannuallayer isc
25 u
countingofarangeofseasonallyvaryingchemicalspecies(Sigletal.,2012).Synchro- ss Printer-friendlyVersion
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nization to well-known volcanic layers was used for dating at core sites with very low n
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snowfall (e.g., East Antarctica) where annual layers are not preserved (Anschu¨tz et al. a
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2011) or at lower elevation sites (e.g., Akademmi Nauk, McCall Glacier, Flade Isblink) e
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where surface melting and percolation make annual layer identification difficult (Opel D
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et al., 2009). Uncertainty (1σ) in the dating is estimated at ±1yr for the annually dated c ACPD
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sites and ±3yr for all other sites. Decadal averages centered on 1855 and 2000 were s
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computed from the high-resolution measurements. n
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4 Evaluation of ACCMIP year 2000 time slice deposition
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deposition
While observation and model data are available at the monthly time resolution, we fo- D
cusouranalysisontheannualmean.Thischoiceismadetolimitthediscussiontothe is
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long-termtrendsinnitrogenandsulfatedeposition.Inadditiontothepresentintercom- s
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parison project, MMM results from two previous studies are available for comparison io
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andanalysisofpotentialimprovements:PhotoComp(Denteneretal.,2006)andHTAP P
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(Sanderson et al., 2012; Vet et al., 2013). These previous studies are partially inde- p
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pendent of the present one, with different emissions and a different sets of models or r Abstract Introduction
different versions of the same models. Using the WMO dataset and the model results |
Conclusions References
interpolated to the location of the observing stations, we can statistically analyze the D
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ability of the models to reproduce the observations. c Tables Figures
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NHW+e(Fdiigs.p1laby) ianndFiSg.O12−th(Feigg.lo1bca)lwaentdderepgoisointioanl dinisttrhibeuMtioMnMs ocfomNOpa3reNdOto−3 t(hFeigW. 1MaO), ssion J I
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dataset (over Europe and the United States). Deposition is clearly strongly correlated a
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withemissions(i.e.largestintheNorthernHemisphere,butalsowithlargeramountsin e
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20 areas of biomass burning and large soil emissions such as Central Africa), albeit with | Back Close
significant downwind propagation for each compound. Unlike the nitrogen sources,
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sulfur emissions from degassing volcanoes (Andres and Kasgnoc, 1998) lead to the is
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formation of deposition hotspots in areas with low anthropogenic emission levels such u
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as Central America. io
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In the case of NO deposition, we find that all 3 experiments (ACCMIP, HTAP and
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PhotoComp) perform rather similarly (Fig. 2 and Table 5). However, none of the model p
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simulations are able to capture the observed high deposition rates over East Asia or r
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