scholarly journals he Impact of Primary Marine Aerosol on Atmospheric Chemistry, Radiation and Climate: A CCSM Model Development Study

2013 ◽  
Author(s):  
William C. Keene ◽  
◽  
Michael S. Long ◽  
Keyword(s):  
Atmospheric Chemistry ◽  
Model Development ◽  
Marine Aerosol ◽  
Development Study

scholarly journals Using GOME NO<sub>2</sub> satellite data to examine regional differences in TOMCAT model performance

2004 ◽  
Vol 4 (3) ◽  
pp. 2569-2613
Author(s):  
N. H. Savage ◽  
K. S. Law ◽  
J. A. Pyle ◽  
A. Richter ◽  
H. Nüß ◽  
...  
Keyword(s):  
North America ◽  
Case Studies ◽  
Biomass Burning ◽  
Satellite Data ◽  
Atmospheric Chemistry ◽  
Regional Differences ◽  
Model Development ◽  
Vertical Mixing ◽  
Model Performance ◽  
Model Treatment

Abstract. This paper compares column measurements of NO2 made by the GOME instrument on ERS-2 to model results from the TOMCAT global CTM. The overall correlation between the model and observations is good (0.79 for the whole world, and 0.89 for north America) but the modelled columns are too large over polluted areas (gradient of 1.4 for North America and 1.9 for Europe). NO2 columns in the region of outflow from North America into the Atlantic seem too high in winter in the model compared to the GOME results, whereas the modelled columns are too small off the coast of Africa where there appear to be biomass burning plumes in the satellite data. Several hypotheses are presented to explain these discrepancies. Weaknesses in the model treatment of vertical mixing and chemistry appear to be the most likely explanations. It is shown that GOME and other satellite data will be of great value in furthering our understanding of atmospheric chemistry and in targeting and testing future model development and case studies.

A user-orientated approach to provenance capture and representation for in silico experiments, explored within the atmospheric chemistry community

2009 ◽  
Vol 367 (1898) ◽  
pp. 2753-2770 ◽  
Author(s):  
Chris J. Martin ◽  
Mohammed H. Haji ◽  
Peter K. Jimack ◽  
Michael J. Pilling ◽  
Peter M. Dew
Keyword(s):  
Atmospheric Chemistry ◽  
In Silico ◽  
Model Development ◽  
Design Development ◽  
Laboratory Notebook ◽  
Iterative Model ◽  
Science Domain ◽  
And Storage ◽  
Chemistry Community

We present a novel user-orientated approach to provenance capture and representation for in silico experiments, contrasted against the more systems-orientated approaches that have been typical within the e-Science domain. In our approach, we seek to capture the scientist's reasoning in the form of annotations as an experiment evolves, while using the scientist's terminology in the representation of process provenance. Our user-orientated approach is applied in a case study within the atmospheric chemistry domain: we consider the design, development and evaluation of an electronic laboratory notebook, a provenance capture and storage tool, for iterative model development.

scholarly journals Evaluation and Error Apportionment of an Ensemble of Atmospheric Chemistry Transport Modelling Systems: Multi-variable Temporal and Spatial Breakdown

2016 ◽  
Author(s):  
Efisio Solazzo ◽  
Roberto Bianconi ◽  
Christian Hogrefe ◽  
Gabriele Curci ◽  
Ummugulsum Alyuz ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Model Comparison ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Strong Impact ◽  
Air Quality Model ◽  
Quality Model ◽  
Transport Modelling

Abstract. Through the comparison of several regional-scale chemistry transport modelling systems that simulate meteorology and air quality over the European and American continents, this study aims at i) apportioning the error to the responsible processes using time-scale analysis, ii) helping to detect causes of models error, and iii) identifying the processes and scales most urgently requiring dedicated investigations. The analysis is conducted within the framework of the third phase of the Air Quality Model Evaluation International Initiative (AQMEII) and tackles model performance gauging through measurement-to-model comparison, error decomposition and time series analysis of the models biases for several fields (ozone, CO, SO2, NO, NO2, PM10, PM2.5, wind speed, and temperature). The operational metrics (magnitude of the error, sign of the bias, associativity) provide an overall sense of model strengths and deficiencies, while apportioning the error to its constituent parts (bias, variance and covariance) can help to assess the nature and quality of the error. Each of the error components is analysed independently and apportioned to specific processes based on the corresponding timescale (long scale, synoptic, diurnal, and intra-day) using the error apportionment technique devised in the former phases of AQMEII. The application of the error apportionment method to the AQMEII Phase 3 simulations provides several key insights. In addition to reaffirming the strong impact of model inputs (emissions and boundary conditions) and poor representation of the stable boundary layer on model bias, results also highlighted the high inter-dependencies among meteorological and chemical variables, as well as among their errors. This indicates that the evaluation of air quality model performance for individual pollutants needs to be supported by complementary analysis of meteorological fields and chemical precursors to provide results that are more insightful from a model development perspective. The error embedded in the emissions is dominant for primary species (CO, PM, NO) and largely outweighs the error from any other source. The uncertainty in meteorological fields is most relevant to ozone. Some further aspects emerged whose interpretation requires additional consideration, such as, among others, the uniformity of the synoptic error being region and model-independent, observed for several pollutants; the source of unexplained variance for the diurnal component; and the type of error caused by deposition and at which scale.

scholarly journals Semantically enhanced provenance capture for chamber model development with a master chemical mechanism

2008 ◽  
Vol 367 (1890) ◽  
pp. 987-990 ◽  
Author(s):  
Chris J Martin ◽  
Mohammed H Haji ◽  
Peter M Dew ◽  
Michael J Pilling ◽  
Peter K Jimack
Keyword(s):  
Atmospheric Chemistry ◽  
Model Development ◽  
Computational Modelling ◽  
Chemical Mechanism ◽  
Scientific Communities ◽  
Chamber Model ◽  
Master Chemical Mechanism ◽  
Semantically Enhanced ◽  
Chemistry Community

The development and maintenance of benchmark databases within scientific communities is reliant on interactions with database users. We explore the role of semantically enhanced provenance for computational modelling processes that make use of one such database: the master chemical mechanism, a key resource within the atmospheric chemistry community.

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