ORCID Profile
0000-0002-5462-6880
Current Organisation
The Chinese University of Hong Kong
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Publisher: Wiley
Date: 20-06-2017
DOI: 10.1002/JOC.5159
Publisher: Copernicus GmbH
Date: 15-11-2019
DOI: 10.5194/ACP-2019-824
Abstract: Abstract. Surface ozone (O3) is an important air pollutant and greenhouse gas. Land use and land cover (LULC) is one of the critical factors influencing ozone, in addition to anthropogenic emissions and climate. LULC change can on the one hand affect ozone biogeochemically, i.e., via dry deposition and biogenic emissions of volatile organic compounds (VOCs). LULC change can on the other hand alter regional- to large-scale climate through modifying albedo and evapotranspiration, which can lead to changes in surface temperature, hydrometeorology and atmospheric circulation that can ultimately impact ozone biogeophysically over local and remote areas. Such biogeophysical effects of LULC on ozone are largely understudied. This study investigates the in idual and combined biogeophysical and biogeochemical effects of LULC on ozone, and explicitly examines the critical pathway for how LULC change impacts ozone pollution. A global coupled atmosphere–chemistry–land model is driven by projected LULC changes from the present day (2000) to future (2050) under RCP4.5 and RCP8.5 scenarios, focusing on the boreal summer. Results reveal that when considering biogeochemical effects only, surface ozone is predicted to have slight changes by up to 2 ppbv maximum in some areas due to LULC changes. It is primarily driven by changes in isoprene emission and dry deposition counteracting each other in shaping ozone. In contrast, when considering the integrated effect of LULC, ozone is more substantially altered by up to 6 ppbv over several regions, reflecting the importance of biogeophysical effects on ozone changes. Furthermore, large areas of these ozone changes are found over the regions without LULC changes where the biogeophysical effect is the only pathway for such changes. The mechanism is likely that LULC change induces a regional circulation response, in particular the formation of anomalous stationary high-pressure systems, shifting of moisture transport, and near-surface warming over the middle-to-high northern latitudes in boreal summer, owing to associated changes in albedo and surface energy budget. Such temperature changes then alter ozone substantially. We conclude that the biogeophysical effect of LULC is an important pathway for the influence of LULC change on ozone air quality over both local and remote regions, even in locations without significant LULC changes. Overlooking the impact of biogeophysical effect may cause evident underestimation of the impacts of LULC change on ozone pollution.
Publisher: American Geophysical Union (AGU)
Date: 31-10-2023
DOI: 10.1029/2023JD038813
Publisher: Research Square Platform LLC
Date: 15-04-2021
DOI: 10.21203/RS.3.RS-424811/V1
Abstract: Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. Here, we construct a set of bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a nonlinear trend from the mean of 18 CMIP6 models. The dataset spans the historical period of 1979–2014 and future scenarios (SSP245 and SSP585) of 2015–2100 with a horizontal resolution of 1.25° × 1.25° and 6-hourly intervals. Our evaluation suggests that the bias-corrected data shows clearly better quality than in idual CMIP6 models evaluated in terms of climatological mean, interannual variance and extreme events. The presented dataset will be useful for the dynamical downscaling projections of future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
Publisher: Springer Science and Business Media LLC
Date: 04-11-2021
DOI: 10.1038/S41597-021-01079-3
Abstract: Dynamical downscaling is an important approach to obtaining fine-scale weather and climate information. However, dynamical downscaling simulations are often degraded by biases in the large-scale forcing itself. We constructed a bias-corrected global dataset based on 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) dataset. The bias-corrected data have an ERA5-based mean climate and interannual variance, but with a non-linear trend from the ensemble mean of the 18 CMIP6 models. The dataset spans the historical time period 1979–2014 and future scenarios (SSP245 and SSP585) for 2015–2100 with a horizontal grid spacing of (1.25° × 1.25°) at six-hourly intervals. Our evaluation suggests that the bias-corrected data are of better quality than the in idual CMIP6 models in terms of the climatological mean, interannual variance and extreme events. This dataset will be useful for dynamical downscaling projections of the Earth’s future climate, atmospheric environment, hydrology, agriculture, wind power, etc.
Publisher: Springer Science and Business Media LLC
Date: 07-07-2015
No related grants have been discovered for Francis Tam.