Modeling and Observational Constraints of NH 3 Emissions and Sources of Nitrogen Deposition

Transcription

1 Modeling and Observational Constraints of NH 3 Emissions and Sources of Nitrogen Deposition Daven K. Henze University of Colorado, Boulder Hyungmin Lee, Juliet Zhu, Jana Milford (CU Boulder) Aika Davis, Ted Russell (GIT), Gill-Ran Jeong (KIAPS) Fabien Paulot, Daniel Jacob, Katie Travis (Harvard) Jesse Bash, Robert Pinder, Riche Scheffe, James Kelly (US EPA) Bret Schichtel, John Vimont (NPS), Linda Pardo (USFS)

2 mg(n)/m 2 /y Environmental impacts of NH 3 Estimated N deposition from NH x, Dentener et al. (2006) Areas where color approaches dark red --> deposited levels are hazardous to ecosystem. NH 3 emissions: - increased by factor of 2 5 since preindustrial era. - to double by 2050 (IPCC, Denman et al., 2007; Moss et al., 2010). - contribute to 46 Tg gap in global N budget (Schlesinger, 2009)?

3 Gg(NH 3 )/day Uncertainties in NH 3 emissions - Global inventories also uncertain (e.g., Beuson et al., 2008) - Substantial variability in estimates of total US NH 3 emissions. Gg(NH 3 ) day Optimized Alternate Gilliland (2006) Henze (2009) Zhang (2012) Pinder (2006) Park (2004) Pinder+Cooter - Large uncertainties at regional scales (e.g., Novak et al., 2012; Walker et al., 2012) 5 Paulot et al., 2014 J F M A M J J A S O N D Why so uncertain? - lack of direct source measurements (hard, expensive) - difficulty in relating associated species to NH 3 sources - constraints from observations of [NH 4+ ] or [NH x ] complicated by model/measurement error, precipitation - observations of [NH 3 ] less prevalent

4 GEOS-Chem [µg/m 3 ] GEOS-Chem [µg/m 3 ] GEOS-Chem [µg/m 3 ] Uncertainties in NH 3 emissions: Implications for air quality and environment contribute to errors in assessing PM 2.5 Ex: GEOS-Chem overestimates nitrate at IMPROVE / CASTNET (July) US CA measured [µg/m 3 ] measured [µg/m 3 ] measured [µg/m 3 ] Zhu et al., 2013 Heald et al., 2012 Walker et al., 2012 (also Liao et al., 2007; Henze et al., 2009; Zhang et al., 2012) undermine regulatory capabilities for secondary standards on SO x, NO x to control N r dep (e.g., Koo et al., 2012) uncertainties in projections of aerosol direct radiative forcing impacts (Henze et al., 2012)

5 Constraints on NH x deposition from inverse modeling Many US air quality models get NHx deposition correct via assimilation. Observations: wet NH x = aerosol NH gas NH 3 Method: adjust (w/kalman Filter) monthly nationwide scale factors Results: Gilliland et al., 2003; Gilliland et al., Assumptions: - uniform seasonality throughout broad regions of US EPA NEI NH 3 emission adjustment factors

6 Top-down constraints based on NH x Mendoza-Dominguez and Russell, 2001: constraints on NH 3 sources in the SE Zhang et al., 2012: Seasonality of NH 3 sources adjusted so that Modeled matched RPO and SEARCH NHx measurements - Resulting annual NHx and NO3 deposition unbiased. - Enforces a spatially uniform seasonality / correction factor across the US.

7 Spatial heterogeneity in source-receptor relationships for NH 3 Consider emissions perturbation, emiss: Spatial correlations of emiss with: [NH 3 ] wet dep [NH x ] April July Jeong et al., submitted Kg NH 3 /ha/month Spatially heterogeneous impacts of NH 3 emissions can be accounted for using 4D-Var / adjoint inversions

8 Source attribution techniques Forward Model (source-oriented) Sensitivity of all model concentrations to one model source or sector Forward Perturbation at source region t 0 Changes of concentration t n

9 Zhang et al., 2012 Source attribution techniques Forward Model (source-oriented) Sensitivity of all model concentrations to one model source or sector US Anthropogenic 5.0 Tg N / yr Forward Perturbation at source region Foreign Anthropogenic 0.42 Tg N / yr t 0 Changes of concentration Natural 1.0 Tg N / yr t n

10 Zhang et al., 2012 Source attribution techniques Forward Model (source-oriented) Sensitivity of all model concentrations to one model source or sector US Anthropogenic 5.0 Tg N / yr Adjoint Model (receptor-oriented) Sensitivity of model concentration in specific location to many model sources and sectors Forward Perturbation at source region Foreign Anthropogenic 0.42 Tg N / yr t 0 Concentration at the receptor t n Changes of concentration Natural 1.0 Tg N / yr t n adjoint area of possible origin t 0

11 Source attribution techniques Forward Model (source-oriented) Sensitivity of all model concentrations to one model source or sector US Anthropogenic 5.0 Tg N / yr Adjoint Model (receptor-oriented) Sensitivity of model concentration in specific location to many model sources and sectors Forward Perturbation at source region Foreign Anthropogenic 0.42 Tg N / yr t 0 Concentration at the receptor t n Zhang et al., 2012 Changes of concentration Natural 1.0 Tg N / yr t n [unitless] Using receptor = sum of squared model error, these relationships can be used for high resolution inverse modeling

12 Constraints from NH x deposition, and an alternate bottom up inventory Paulot et al., GEOS-Chem 4D-Var (Henze et al., 2007) - Global 2x2.5 - Assimilate NTN, EMEP,

13 Constraints from NH x deposition, and an alternate bottom up inventory Paulot et al., 2014 New bottom-up inventory No support for homogeneous seasonality in the US. New bottom-up inventory (MASSAGE) can reproduce optimized emissions in some areas.

14 Constraints from NH x deposition, and an alternate bottom up inventory Comparison to surface NH3 measurements (Puchalski et al., 2011) before and after assimilation: Paulot et al., 2014

15 Constraints from NH x deposition, and an alternate bottom up inventory Comparison to surface NH 3 measurements (Puchalski et al., 2011) before and after assimilation: Paulot et al., 2014 Closure for NH x deposition does not necessarily imply better model NH 3

16 Potential for making new inroads on this problem: ambient measurements of NH 3 Remote sensing with TES and IASI: TES NH 3 sensitivity TES: - 5 km x 8 km footprint - sensitive to BL - detection limit of ~ 1 ppb - bias of +0.5 ppb more precise & sparse than IASI (Beer et al., 2008; Clarisse et al., 2009; Clarisse et al., 2010; Mark Shephard et al., 2011) Passive surface measurements: EPA s AMoN sites (>2007) (Puchalski et al., 2011) Also LADCO, SEARCH, CSU, ANARChE

17 TES reflects real-world spatial gradients and seasonal trends Pinder et al., 2011 Validating TES NH 3 with surface observations Overlap surface obs with TES Transects for 2009: NH3 Emission Density [kg NH3 / km 2 ] < >10000 TES Transect CAMNet Monitoring Site

18 October GEOS-Chem NH 3 (ppb) July April he true model were ascribed error of the same size as the meas Constraining emissions of NH 3 in GEOS-Chem using 4D-Var technique (Zhu et al., 2013) NH 3 emissions in GEOS-Chem Initial Optimized ln(optimized/initial) (c) April +80% July ribes the second subfigure; +57% October +33% Figure [10 6 kg] [ unitless] Agrees Constraints with constraints from TES improve using NH estimates x deposition of NH& 3 new at AMoN bottom sites up inventory April and from October. Paulot in April (+/- Contradicting 20%) but in not July. in July NH 3 emissions from GEOS-Chem before and after AMoN surface obs (ppb)

19 NH 3 [µg/m 3 ] Diurnal variability of NH 3 : case study in Warsaw, NC, with CMAQ regional model CMAQ* CMAQ* modified diurnal NH 3 emissions Observations downwind of livestock facility (Walker et al., 2006) * Using NEI05 emissions, simulated year not same as observations Time of day Improved diurnal variability (Bash) can help resolve discrepancies between in situ and satellite obs (Jeong et al., submitted)

20 GC [g/(yr m2)] Optimized (Zhu et al 2013) Impacts of bidirectional exchange in GEOS-Chem Bidi applied to optimized emissions Base M=1.13 Bidi M=1.23 AMoN (ppb) NTN NHx [g/(yr m2)] Improved (mechanistic) representation of NH3 fluxes may help resolve inconsistencies between NH 3 and [NH x ] dep constraints. Other considerations in remote-sensing constraints: - temporal sampling bias - spatial sampling bias Zhu

21 NASA AQAST Tiger Team Overview: multi-model assessment of current and future sources of reactive nitrogen deposition in Class I and at-risk ecosystems in the US Members: Daven Henze, Jana Milford (CUB) Fabien Paulot, Daniel Jacob (Harvard) Aika Yano,Ted Russell (Georgia Tech) Bret Schichtel, John Vimont (NPS) Rich Scheffe, James Kelly (US EPA) Linda Pardo (USFS) Ellis et al., 2013) What are the sources contributing to exceedences in Federal Class I Areas? Tools / Observations: NH 3 remote sensing, in situ observations (RMNP, ) GEOS-Chem and CMAQ models Source attribution techniques: sector perturbations, DDM, adjoint

22 model configurations and domains CMAQ v5: - 36km CONUS - 4km over NPs NEI 2005 scaled to Bidirectional NH 3 exchange - CB05 with Pleim-Xiu LSM - WRF v3 GEOS-Chem: x NEI GFEDv3

23 Footprints of reactive Nitrogen deposition NH 3 Area (livestock) Fert NO x Area (mobile) Point Nonpt Lightng Soil Rocky Mountain NP: - CMAQ: 0.85 kgn/ha/a from livestock NH3, 1.55 kgn/ha / from mobile NO x - Gebhart et al. (2011): 50% of NH3 inputs from out-of-state - Benedict et al. (2013): Lee, Paulot, Davis

24 Footprints of reactive Nitrogen deposition NH 3 Area (livestock) Fert NO x Area (mobile) Point Nonpt Lightng Soil Lee, Paulot

25 NH NH 3 (an 20%) NO x (an 20%) 3 (an N (an 20%) 20%) NO CL x (Ellis) (an 20%) CL (EPA) N (an 20%) CL (Ellis) C Great Voyageurs Smoky Mountains NP NP Great Voyageurs Smoky NP Mountains NP Effectiveness of NH3 vs NOx emission controls for approaching deposition Critical Loads kgn ha 1 a 1 NH 3 (an 20%) NO x (an 20%) Voyageurs NP 5 4 kgn ha 1 a What is the impact of reducing anthropogenic emissions by % as a function of distance (area) away from the park? kgn ha 1 a Voyager Great Smokey Shenandoa NH 3 NH (an 3 (an 20%) 20%) NO x NO (an x (an 20%) 20%) Shenandoah Voyageurs NP NP N (an N (an 20%) 20%) CL (Ellis) CL (Ellis) CL (EPA) CL (EPA) Great Great Shenandoah Smoky Smoky Adirondack Mountains NP NP NP Shenandoah Adirondack NP NH 3 NO x Both EPA CL Ellis CL kgn ha 1 a 1 kgn ha 1 a 1 kgn ha 1 1 a Area (km 2 ) x 10 5 Shenandoah NP NP 12 kgn ha 1 a Area Area (km 2 (km ) 2 ) x 10x Area Area (km 2 )(km ) x 10 5 x 1 Adirondack All regions are far from attaining 6 CL values with small reductions to emissions over a wide 10 6 km 2 area (size of France!) kgn ha 1 a 1 -local NOx -distant NH kgn ha 1 a 1 - NOx - same Paulot

26 What is the nitrogen deposition efficiency? NH 3 NO x SO 2 Grand Teton Joshua Tree (kg N dep / ha / yr) / (mol emission / yr) Implications for impacts of new sources Lee

27 Projections of Nr deposition Projections of the evolving roles of NH 3 and NO x on Nr deposition following emission projections from IPCC AR5 (Moss et al., 2010) Paulot et al., 2012; also Ellis et al While Nr may be decreasing, role of NH 3 increasing

28 Final comments Constraints from multiple sources (remote sensing, deposition, in situ measurements) helping reduce uncertainty in NH 3 emissions. 4D-Var techniques allow inversion process to consider spatially heterogeneous biases in emissions inventories. It s an iterative procedure, and we re learning more about process-level emissions (diurnal variability, bi-directional fluxes). NH 3 and NO x sources can contribute significantly to reactive nitrogen deposition several states away. Substantial controls required to approach critical loads, particularly given projected increases in NH 3 emissions.

29 End

30 Remote sensing of NH 3 : IASI 16 Van Damme et al., ACPD, 2013

31 kg N / ha / season Model evaluation: GEOS-Chem vs observed (NTN) N deposition 3 2 MAM (meas) JJA (meas) SON (meas) DJF (meas) MAM (model) JJA (model) SON (model) DFJ (model) Grand Teton Joshua Tree Rocky Mountain Sequoia

32 TES NH 3 visualization

33 TES NH3 constraints in GEOS-Chem: spatial sampling / retrieval bias Consider all 12 x 12 km2 CMAQ grid cells Of these, in which did we have a successful TES retrieval? => TES constraints may be ~30% high

34 Constraints from NH x deposition, and an alternate bottom up inventory Annual NH 3 emissions in GEOS-Chem a priori Seasonality in SE China from TES NH 3 observations (Shephard et al., 2011) optimized Monthly SE Asia NH 3 emissions Optimized Alternative bottom-up Top-down constraints agree with recent bottom up inventories: Huang (2012) and Alternate. Paulot in prep

35 Constraints from CASTNet NH 4 +? n(nh 4+ ) : 2n(SO 4 2- ) + n(no 3- ) CASTNet, all sites, (R. Pinder) Field campaigns (Sorooshian et al.) January SJ Valley April Houston Issues with evaporation

36 Conundrum of nitrate (too high) and ammonia (too high at surface, too low higher up) in July in GEOS-Chem NO 3 - (surface) NH 3 (2 km) NH 3 (surface) Base Diurnal - Base NO 3- * Base (Heald 2012) Mechanistic NH 3 emissions an important future direction for global models. Other factors: - BL heights (Dalhousie, following Lin and McElroy, 2010) - excessive N 2 O 5 (Zhang et al., 2012; Paulot et al., submitted)

37 Jeong et al., submitted Impacts of bidirectional exchange in GEOS-Chem NH3: CMAQ bidi - CMAQ base April July Decreased deposition in July leads to enhanced NH3 lifetime throughout the US. October