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Record W2333734087 · doi:10.5194/acpd-14-7187-2014

Modeling regional aerosol variability over California and its sensitivity to emissions and long-range transport during the 2010 CalNex and CARES campaigns

2014· article· en· W2333734087 on OpenAlexaff
Jerome D. Fast, J. D. Allan, R. Bahreini, J. S. Craven, L. K. Emmons, R. A. Ferrare, Patrick L. Hayes, Alma Hodžić, John W. Holloway, C. A. Hostetler, J. L. Jiménez, H. Jonsson, Shang Liu, Y. Liu, A. R. Metcalf, A. M. Middlebrook, J. B. Nowak, Mikhail Pekour, A. E. Perring, Lynn M. Russell, Arthur J. Sedlacek, John H. Seinfeld, A. Setyan, John E. Shilling, Manish Shrivastava, Stephen Springston, Chen Song, R. Subramanian, Jonathan Taylor, V. Vinoj, Qing Yang, R. A. Zaveri, Qi Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité de Montréal
FundersPacific Northwest National LaboratoryNational Oceanic and Atmospheric AdministrationCalifornia Air Resources BoardBattelleNational Aeronautics and Space AdministrationU.S. Department of Energy
KeywordsAerosolEnvironmental scienceWeather Research and Forecasting ModelTrace gasAir quality indexAtmospheric sciencesTransectRadiative transferRange (aeronautics)Air pollutionSpatial variabilityChemical transport modelMeteorologyClimatologyGeographyGeologyChemistry

Abstract

fetched live from OpenAlex

Abstract. The performance of the Weather Research and Forecasting regional model with chemistry (WRF-Chem) in simulating the spatial and temporal variations in aerosol mass, composition, and size over California is quantified using measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010. The extensive meteorological, trace gas, and aerosol measurements collected at surface sites and along aircraft and ship transects during CalNex and CARES were combined with operational monitoring network measurements to create a single dataset that was used to evaluate the one configuration of the model. Simulations were performed that examined the sensitivity of regional variations in aerosol concentrations to anthropogenic emissions and to long-range transport of aerosols into the domain obtained from a global model. The configuration of WRF-Chem used in this study is shown to reproduce the overall synoptic conditions, thermally-driven circulations, and boundary layer structure observed in region that controls the transport and mixing of trace gases and aerosols. However, sub-grid scale variability in the meteorology and emissions as well as uncertainties in the treatment of secondary organic aerosol chemistry likely contribute to errors at a primary surface sampling site located at the edge of the Los Angeles basin. Differences among the sensitivity simulations demonstrate that the aerosol layers over the central valley detected by lidar measurements likely resulted from lofting and recirculation of local anthropogenic emissions along the Sierra Nevada. Reducing the default emissions inventory by 50% led to an overall improvement in many simulated trace gases and black carbon aerosol at most sites and along most aircraft flight paths; however, simulated organic aerosol was closer to observed when there were no adjustments to the primary organic aerosol emissions. The model performance for some aerosol species was not uniform over the region, and we found that sulfate was better simulated over northern California whereas nitrate was better simulated over southern California. While the overall spatial and temporal variability of aerosols and their precursors were simulated reasonably well, we show cases where the local transport of some aerosol plumes were either too slow or too fast, which adversely affects the statistics regarding the differences between observed and simulated quantities. Comparisons with lidar and in-situ measurements indicate that long-range transport of aerosols from the global model was likely too high in the free troposphere even though their concentrations were relatively low. This bias led to an over-prediction in aerosol optical depth by as much as a factor of two that offset the under-predictions of boundary-layer extinction resulting primarily from local emissions. Lowering the boundary conditions of aerosol concentrations by 50% greatly reduced the bias in simulated aerosol optical depth for all regions of California. This study shows that quantifying regional-scale variations in aerosol radiative forcing and determining the relative role of emissions from local and distant sources is challenging during "clean" conditions and that a wide array of measurements are needed to ensure model predictions are correct for the right reasons. In this regard, the combined CalNex and CARES datasets are an ideal testbed that can be used to evaluate aerosol models in great detail and develop improved treatments for aerosol processes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.205
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2014
Admission routes1
Has abstractyes

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