2008-06-08 HTAP Aerosol Science Review

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Title: 2008-06-08: HTAP Aerosol Science Review Date: 2008/6/8 Location: Washington DC Report Formats: PPT | [No Screencast] [No Word] | [No PDF]

Contents 1 Discussion 1.1 Oinstein Hov 1.2 HTAP 2010 Policy Relevant Issues - Aerosols 2 Introduction 3 Interdependence of Emissions, Models and Observations 3.1 Aerosol Characterization 3.2 Links

[edit] Discussion

[edit] Oinstein Hov

• WMO - WIS. Goal is to add air quality data into the met data stream. Users would be assimilation, forecasting
  • ToDo: Study WIS
  • Link to Unidata
  • Contact Len, Oinstein, Christiane,
  • Architecture the GEOSS way
  • Outcome: GEOSS

[edit] HTAP 2010 Policy Relevant Issues - Aerosols

• Spatial-temporal Pattern, transport, apportionment of sources
• Trends..)
• Source contributions, ship, dust, biomass
• Uncertainty

Back to Integrated Global Dataset

This wiki page is intended to be the collaborative workspace for Task Force members interested in the HTAP Information System development, implementation and use. The contents can be freely edited by anyone who is logged in. Comments and feedback regarding the wiki page or its content can also be sent by email to rhusar@me.wustl.edu.

[edit] Introduction

The purpose of Chapter 6 is to discuss the need to integrate information from observations, models, and emissions inventories to better understand intercontinental transport. A draft outline of Chapter 6 is also on this wiki. This section focuses on the information system in support of the integration of observations, emissions and models for HTAP.

The previous chapters have assessed the current knowledge of HTAP through the review of existing literature and through a set of model intercomparison studies. These assessments highlight the complexities of atmospheric transport and our limited ability to consistently estimate the magnitude of hemispheric transport. A reconciliation of the observations with the current set of models is an even larger challenge.

Recent developments in air quality monitoring, modeling and information technologies offer outstanding opportunities to fulfill the information needs for the HTAP integration effort. The data from surface-based air pollution monitoring networks now routinely provide spatio-temporal and chemical patterns of ozone and PM. Satellite sensors with global coverage and kilometer-scale spatial resolution now provide real-time snapshots which depict the pattern of industrial haze, smoke, dust, as well as some gaseous species in stunning detail. Detailed phisico-chemical models are now capable of simulating the spatial-temporal pollutant pattern on regional and global scales. The ‘terabytes’ of data from observations and models can now be stored, processed and delivered in near-real time. The instantaneous ‘horizontal’ diffusion of information via the Internet now permits, in principle, the delivery of the right information to the right people at the right place and time. Standardized computer-computer communication languages and Service-Oriented Architectures (SOA) now facilitate the flexible access, quality assurance and processing of raw data into high-grade ‘actionable’ knowledge suitable for HTAP policy decisions. Last but not least, the World Wide Web has opened the way to generous sharing of data, models and tools leading to collaborative analysis in virtual workgroups. Nevertheless, air quality data analyses and data-model integration face significant hurdles. The section below presents an architectural framework, an implementation strategy, and a set of action-oriented recommendations for the proposed HTAP information system.

[edit] Interdependence of Emissions, Models and Observations

The assessment of the current observational evidence (Chapter 3), emission inventories (Chapter 4) and global chemical transport modeling (Chapter 5) indicates that these three aspects of HTAP are interdependent. A particularly burning issue is the large uncertainty in emissions for some species. The linkages and dependencies for observations, emission inventories and modeling are shown schematically in Figure 1. The boxes represent information resources from observations, emission inventories and modeling. The purple arrows indicate connections and operations within each of the three boxes, such as inter-comparisons, reconciliation and homogenization of data.

The blue arrows represent the major connections and operations that link observations, emission inventories and modeling. Observations are providing the data for model validation as well as for real-time or retrospective assimilation into the models (reanalysis). Through inverse modeling, observations also allow emission estimation for sources that do not have reliable emission inventories (e.g. sulfur-nitrogen in developing countries, biomass burning; dust emissions; biogenics). In many areas of the world such poorly characterized emission dominate the concentration of the species. Lacking reliable emission inventories, chemical models cannot provide simulations or forecasts that are verifiable by observations.

The second problem associated with model verification is the lack of suitable global-scale air quality data that are suitable for model validation. At this time, only satellite data for aerosols NO2, CO, and formaldehyde are available with the necessary spatial and temporal coverage. However, such satellite data represent columnar integrals throughout the atmosphere and therefore do not help in verifying which vertical elevation a pollutant resides. In the case of aerosols, a further limitation is that the aerosol optical thickness retrieved from the satellites includes all chemical species regardless of their source and composition. Hence, even if there is a matching AOT between model and observations, it is no guarantee that the chemical apportionment is correct.

Complete aerosol chemical composition data are available only for limited sampling locations in few regions of the world. Fortunately, in the US territories, rich datasets are available from the IMPROVE aerosol chemistry network (over 200 sites, since the 1980s) and from the EPA Speciation Trends Network (about 150 sites, since 2002). Hourly surface ozone data from 600+ stations along with limited speciated gas chemistry data are available for model validation. The emission inventories for anthropogenic pollutants over North America are also reasonably well established. Given the dual difficulty of poor emission inventories and poor observational coverage, it is recommended that the systematic chemical model validation be initiated using the 350 station aerosol chemistry data and the 600 station surface ozone monitoring data over the US. Within Europe, extensive chemical monitoring is being conducted, ..... model evaluation with European data (expand Europend model-data description?). Subsequently, the verification could be expanded to other global locations with sparser data coverage.

It is evident, that a full reconciliation will require an integrated approach where the best available knowledge from of observations, emissions and models is combined. Furthermore, reconciliation will probably require considerable iteration until the deviations are minimized. Thus, the supporting information system will need to connect and facilitate the data flow of observations, emissions and models.

Within HTAP, a number of integration and reconciliation projects are already under way....need more on this??...(a chart like below, would convey the diverse projects that emerged through or in response of HTAP)

[edit] Aerosol Characterization

[edit] Links

Obs Emiss Model Assimilation