This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3...This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3SMv2-MPAS)at 60 km to 10 km resolution.Multi-source observational data are utilized to validate sea surface temperature/salinity,sea ice,three-dimensional thermal-saline structures,mixed layer depth,ocean heat content,and sea surface height.Key results show the following:(1)E3SMv2-MPAS captures seasonal-to-decadal variability in surface fields and sea ice,but shows systematic biases in sea surface temperature of western boundary currents(inadequate eddy parameterization)and Arctic sea surface salinity(misrepresented freshwater fluxes and mixing processes).(2)The model robustly represents three-dimensional climate variability,yet underestimates mixed layer depth in key regions(Antarctic Circumpolar Current and North Atlantic),revealing deficiencies in extreme mixing.(3)Ocean heat content distributions are well-simulated.(4)Sea surface height spatial patterns and interannual variability are accurately reproduced.This work identifies critical refinements for unstructured-mesh models:mesoscale eddy parameterization,polar ocean-sea ice coupling,and multi-scale energy processes,advancing high-resolution climate model development and laying the groundwork for improved ocean forecasting systems.展开更多
Ozone has become one of the most important air pollution issues around the world. This article applied both O 3 /（NOy-NOx） and H 2 O 2 /HNO 3 indicators to analyze the ozone sensitivity in urban and rural areas of S...Ozone has become one of the most important air pollution issues around the world. This article applied both O 3 /（NOy-NOx） and H 2 O 2 /HNO 3 indicators to analyze the ozone sensitivity in urban and rural areas of Shanghai, with implementation of the MM5-CMAQ modeling system in July, 2007. The meteorological parameters were obtained by using the MM5 model. A regional emission inventory with spatial and temporal allocation based on the statistical data has been developed to provide input emission data to the MM5-CMAQ modeling system. Results showed that the ozone concentrations in Shanghai show clear regional differences. The ozone concentration in rural areas was much higher than that in the urban area. Two indicators showed that ozone was more sensitive to VOCs in urban areas, while it tended to be NOx sensitive in rural areas of Shanghai.展开更多
Surface ozone(O_(3))is influenced by regional background and local photochemical formation under favorable meteorological conditions.Understanding the contribution of these factors to changes in O_(3)is crucial to add...Surface ozone(O_(3))is influenced by regional background and local photochemical formation under favorable meteorological conditions.Understanding the contribution of these factors to changes in O_(3)is crucial to address the issue of O_(3)pollution.In this study,we propose a novel integrated method that combines random forest,principal component analysis,and Shapley additive explanations to distinguish observed O_(3)into meteorologically affected ozone(O_(3_MET)),chemically formed from local emissions(O_(3_LC)),and regional background ozone(O_(3_RBG)).Applied to three typical stations in Shanghai during the warm season from 2013 to 2021,the results indicate that O_(3_RBG)in Shanghai was 48.8±0.3 ppb,accounting for 79.6%–89.4%at different sites,with an overall declining trend of 0.018 ppb/yr.O_(3_LC)at urban and regional sites ranged from 5.9–9.0 ppb and 8.9–14.6 ppb,respectively,which were significantly higher than the contributions of 2.5–7.4 ppb at an upwind background site.O_(3_MET)can be categorized into those affecting O_(3)photochemical generation and those changing O_(3)dispersion conditions,with absolute contributions to O_(3)ranging from 13.4–19.0 ppb and 13.1–13.7 ppb,respectively.We found that the O_(3)rebound in 2017,compared to 2013,was primarily influenced by unfavorable O_(3)dispersion conditions and unbalanced emission reductions;while the O_(3)decline in 2021,compared to 2017,was primarily influenced by overall favorable meteorological conditions and further emissions reduction.These findings highlight the challenge of understanding O_(3)change due to meteorology and regional background,emphasizing the need for systematic interpretation of the different components of O_(3).展开更多
基金The National Key R&D Program of China under contract No.2021YFC3101503the Science and Technology Innovation Program of Hunan Province under contract No.2022RC3070+1 种基金the National Natural Science Foundation of China under contract Nos 42305176 and 42276205the Hunan Provincial Natural Science Foundation of China under contract No.2023JJ10053.
文摘This study evaluates the 1995-2020 global ocean-sea ice simulation using the unstructured-mesh model for prediction across scales(MPAS)-ocean/sea ice model within energy exascale earth system model(E3SM)version 2.1(E3SMv2-MPAS)at 60 km to 10 km resolution.Multi-source observational data are utilized to validate sea surface temperature/salinity,sea ice,three-dimensional thermal-saline structures,mixed layer depth,ocean heat content,and sea surface height.Key results show the following:(1)E3SMv2-MPAS captures seasonal-to-decadal variability in surface fields and sea ice,but shows systematic biases in sea surface temperature of western boundary currents(inadequate eddy parameterization)and Arctic sea surface salinity(misrepresented freshwater fluxes and mixing processes).(2)The model robustly represents three-dimensional climate variability,yet underestimates mixed layer depth in key regions(Antarctic Circumpolar Current and North Atlantic),revealing deficiencies in extreme mixing.(3)Ocean heat content distributions are well-simulated.(4)Sea surface height spatial patterns and interannual variability are accurately reproduced.This work identifies critical refinements for unstructured-mesh models:mesoscale eddy parameterization,polar ocean-sea ice coupling,and multi-scale energy processes,advancing high-resolution climate model development and laying the groundwork for improved ocean forecasting systems.
基金supported by the Chinese National Key Technology R&D Program (No. 2009BAK43B33)
文摘Ozone has become one of the most important air pollution issues around the world. This article applied both O 3 /（NOy-NOx） and H 2 O 2 /HNO 3 indicators to analyze the ozone sensitivity in urban and rural areas of Shanghai, with implementation of the MM5-CMAQ modeling system in July, 2007. The meteorological parameters were obtained by using the MM5 model. A regional emission inventory with spatial and temporal allocation based on the statistical data has been developed to provide input emission data to the MM5-CMAQ modeling system. Results showed that the ozone concentrations in Shanghai show clear regional differences. The ozone concentration in rural areas was much higher than that in the urban area. Two indicators showed that ozone was more sensitive to VOCs in urban areas, while it tended to be NOx sensitive in rural areas of Shanghai.
基金supported by the Shanghai Municipal Bureau of Ecology and Environment(China)([2022]37)National Natural Science Foundation of China(NOs.42075144,42005112)Key Research and Development Project of Shanghai Science and Technology Commission,China(No.20dz1204000).
文摘Surface ozone(O_(3))is influenced by regional background and local photochemical formation under favorable meteorological conditions.Understanding the contribution of these factors to changes in O_(3)is crucial to address the issue of O_(3)pollution.In this study,we propose a novel integrated method that combines random forest,principal component analysis,and Shapley additive explanations to distinguish observed O_(3)into meteorologically affected ozone(O_(3_MET)),chemically formed from local emissions(O_(3_LC)),and regional background ozone(O_(3_RBG)).Applied to three typical stations in Shanghai during the warm season from 2013 to 2021,the results indicate that O_(3_RBG)in Shanghai was 48.8±0.3 ppb,accounting for 79.6%–89.4%at different sites,with an overall declining trend of 0.018 ppb/yr.O_(3_LC)at urban and regional sites ranged from 5.9–9.0 ppb and 8.9–14.6 ppb,respectively,which were significantly higher than the contributions of 2.5–7.4 ppb at an upwind background site.O_(3_MET)can be categorized into those affecting O_(3)photochemical generation and those changing O_(3)dispersion conditions,with absolute contributions to O_(3)ranging from 13.4–19.0 ppb and 13.1–13.7 ppb,respectively.We found that the O_(3)rebound in 2017,compared to 2013,was primarily influenced by unfavorable O_(3)dispersion conditions and unbalanced emission reductions;while the O_(3)decline in 2021,compared to 2017,was primarily influenced by overall favorable meteorological conditions and further emissions reduction.These findings highlight the challenge of understanding O_(3)change due to meteorology and regional background,emphasizing the need for systematic interpretation of the different components of O_(3).
