the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Global O3–CO correlations in a chemistry and transport model during July–August: evaluation with TES satellite observations and sensitivity to input meteorological data and emissions
Hyun-Deok Choi
James H. Crawford
David B. Considine
Dale J. Allen
Bryan N. Duncan
Larry W. Horowitz
Jose M. Rodriguez
Susan E. Strahan
Lin Zhang
Xiong Liu
Megan R. Damon
Stephen D. Steenrod
Related authors
No articles found.
hiddensource of inter-model variability and may be leading to bias in some climate model results.
Several machine learning models are applied to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains ARM site during the summer months of 2012–2020. We find that the random forest model is the best predictor among common classifiers. We rank variables in terms of their effectiveness in nowcasting ENTLN lightning and identify geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors.
detergent, removing air pollutants and greenhouse gases like methane from the atmosphere. Thus, understanding how it is changing and responding to its various drivers is important for air quality and climate. We found that OH has increased by about 5 % globally from 1980 to 2014 in our model, mostly driven by increasing nitrogen oxide (NOx) emissions. This suggests potential climate tradeoffs from air quality policies solely targeting NOx emissions.
hottest20 % of parcels.
Related subject area
Accurate national methane (CH4) emission estimates are essential for tracking progress towards climate goals. This study compares estimates from Finland, which use different methods and scales, and shows how well a global model estimates emissions within a country. The bottom-up estimates vary a lot but constraining them with atmospheric CH4 measurements brought the estimates closer together. We also highlight the importance of quantifying natural emissions alongside anthropogenic emissions.
coal-to-gasenergy transition in China. However, this small loss rate can be misleading given China's high gas imports.