scholarly journals The chemistry–climate model ECHAM6.3-HAM2.3-MOZ1.0

2018 ◽  
Vol 11 (5) ◽  
pp. 1695-1723 ◽  
Author(s):  
Martin G. Schultz ◽  
Scarlet Stadtler ◽  
Sabine Schröder ◽  
Domenico Taraborrelli ◽  
Bruno Franco ◽  
...  
Keyword(s):  
Climate Model ◽  
Model Simulation ◽  
Heterogeneous Reaction ◽  
Surface Ozone ◽  
Gas Phase Chemistry ◽  
Sea Salt Aerosol ◽  
The Stability ◽  
Phase Chemistry ◽  
The Impact ◽  
Lightning Nox

Abstract. The chemistry–climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parameterizations of aerosols using either a modal scheme (M7) or a bin scheme (SALSA). This article describes and evaluates the model version ECHAM6.3-HAM2.3-MOZ1.0 with a focus on the tropospheric gas-phase chemistry. A 10-year model simulation was performed to test the stability of the model and provide data for its evaluation. The comparison to observations concentrates on the year 2008 and includes total column observations of ozone and CO from IASI and OMI, Aura MLS observations of temperature, HNO3, ClO, and O3 for the evaluation of polar stratospheric processes, an ozonesonde climatology, surface ozone observations from the TOAR database, and surface CO data from the Global Atmosphere Watch network. Global budgets of ozone, OH, NOx, aerosols, clouds, and radiation are analyzed and compared to the literature. ECHAM-HAMMOZ performs well in many aspects. However, in the base simulation, lightning NOx emissions are very low, and the impact of the heterogeneous reaction of HNO3 on dust and sea salt aerosol is too strong. Sensitivity simulations with increased lightning NOx or modified heterogeneous chemistry deteriorate the comparison with observations and yield excessively large ozone budget terms and too much OH. We hypothesize that this is an impact of potential issues with tropical convection in the ECHAM model.

scholarly journals The Chemistry Climate Model ECHAM6.3-HAM2.3-MOZ1.0

2017 ◽  
pp. 1-43 ◽  
Author(s):  
Martin G. Schultz ◽  
Scarlet Stadtler ◽  
Sabine Schröder ◽  
Domenico Taraborrelli ◽  
Bruno Franco ◽  
...  
Keyword(s):  
Climate Model ◽  
Model Simulation ◽  
Heterogeneous Reaction ◽  
Surface Ozone ◽  
Ozone Monitoring Instrument ◽  
Gas Phase Chemistry ◽  
Chlorine Monoxide ◽  
The Stability ◽  
Phase Chemistry ◽  
The Impact

The chemistry climate model ECHAM-HAMMOZ contains a detailed representation of tropospheric and stratospheric reactive chemistry and state-of-the-art parametrisations of aerorols using either a modal scheme (M7) or a bin scheme (SALSA). This article describes and evaluates the model version ECHAM6.3-HAM2.3-MOZ1.0 with a focus on the tropospheric gas-phase chemistry. A ten-year model simulation was performed to test the stability of the model and provide data for its evaluation. The comparison to observations concentrates on the year 2008 and includes total column observations of ozone (O<sub>3</sub>) and carbon monoxide (CO) from Infrared Atmospheric Sounding Interferometer (IASI) and Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS) observations of temperature, nitric acid (HNO<sub>3</sub>), chlorine monoxide (ClO), and O<sub>3</sub> for the evaluation of polar stratospheric processes, an ozone sonde climatology, surface ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and surface CO data from the Global Atmosphere Watch network. Global budgets of ozone, hydroxide (OH), nitrogen oxides (NO<sub>x</sub>), aerosols, clouds, and radiation are analyzed and compared to the literature. ECHAM-HAMMOZ performs well in many aspects. However, in the base simulation, lightning NO<sub>x</sub> emissions are very low, and the impact of the heterogeneous reaction of HNO<sub>3</sub> on dust and seasalt aerosol is too strong. Sensitivity simulations with increased lightning NOx or modified heterogeneous chemistry deteriorate the comparison with observations and yield excessively large ozone budget terms and too much OH. We hypothesize that this is an impact of potential issues with tropical convection in the ECHAM model.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

scholarly journals Modelling the impact of megacities on local, regional and global tropospheric ozone and the deposition of nitrogen species

2013 ◽  
Vol 13 (24) ◽  
pp. 12215-12231 ◽  
Author(s):  
Z. S. Stock ◽  
M. R. Russo ◽  
T. M. Butler ◽  
A. T. Archibald ◽  
M. G. Lawrence ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Climate Model ◽  
Surface Ozone ◽  
Global Scale ◽  
Chemical Environment ◽  
Ozone Production ◽  
The Uk ◽  
The Impact ◽  
Local Emissions

Abstract. We examine the effects of ozone precursor emissions from megacities on present-day air quality using the global chemistry–climate model UM-UKCA (UK Met Office Unified Model coupled to the UK Chemistry and Aerosols model). The sensitivity of megacity and regional ozone to local emissions, both from within the megacity and from surrounding regions, is important for determining air quality across many scales, which in turn is key for reducing human exposure to high levels of pollutants. We use two methods, perturbation and tagging, to quantify the impact of megacity emissions on global ozone. We also completely redistribute the anthropogenic emissions from megacities, to compare changes in local air quality going from centralised, densely populated megacities to decentralised, lower density urban areas. Focus is placed not only on how changes to megacity emissions affect regional and global NOx and O3, but also on changes to NOy deposition and to local chemical environments which are perturbed by the emission changes. The perturbation and tagging methods show broadly similar megacity impacts on total ozone, with the perturbation method underestimating the contribution partially because it perturbs the background chemical environment. The total redistribution of megacity emissions locally shifts the chemical environment towards more NOx-limited conditions in the megacities, which is more conducive to ozone production, and monthly mean surface ozone is found to increase up to 30% in megacities, depending on latitude and season. However, the displacement of emissions has little effect on the global annual ozone burden (0.12% change). Globally, megacity emissions are shown to contribute ~3% of total NOy deposition. The changes in O3, NOx and NOy deposition described here are useful for quantifying megacity impacts and for understanding the sensitivity of megacity regions to local emissions. The small global effects of the 100% redistribution carried out in this study suggest that the distribution of emissions on the local scale is unlikely to have large implications for chemistry–climate processes on the global scale.

Influence of Ocean and Atmosphere Components on Simulated Climate Sensitivities

2013 ◽  
Vol 26 (1) ◽  
pp. 231-245 ◽  
Author(s):  
Michael Winton ◽  
Alistair Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
Larry W. Horowitz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
Historical Simulation ◽  
Model Version ◽  
Salinity Response ◽  
The Impact

Abstract The influence of alternative ocean and atmosphere subcomponents on climate model simulation of transient sensitivities is examined by comparing three GFDL climate models used for phase 5 of the Coupled Model Intercomparison Project (CMIP5). The base model ESM2M is closely related to GFDL’s CMIP3 climate model version 2.1 (CM2.1), and makes use of a depth coordinate ocean component. The second model, ESM2G, is identical to ESM2M but makes use of an isopycnal coordinate ocean model. The authors compare the impact of this “ocean swap” with an “atmosphere swap” that produces the GFDL Climate Model version 3 (CM3) by replacing the AM2 atmospheric component with AM3 while retaining a depth coordinate ocean model. The atmosphere swap is found to have much larger influence on sensitivities of global surface temperature and Northern Hemisphere sea ice cover. The atmosphere swap also introduces a multidecadal response time scale through its indirect influence on heat uptake. Despite significant differences in their interior ocean mean states, the ESM2M and ESM2G simulations of these metrics of climate change are very similar, except for an enhanced high-latitude salinity response accompanied by temporarily advancing sea ice in ESM2G. In the ESM2G historical simulation this behavior results in the establishment of a strong halocline in the subpolar North Atlantic during the early twentieth century and an associated cooling, which are counter to observations in that region. The Atlantic meridional overturning declines comparably in all three models.

Different atmospheric O3 chemical environment in industrial regions of China, 2016: implications for emission control strategies and impacts on the globe in the future

2020 ◽  
Author(s):  
Zhenze Liu ◽  
Ruth M. Doherty ◽  
Oliver Wild ◽  
Fiona M. O’Connor
Keyword(s):  
Air Pollution ◽  
Gas Phase ◽  
Action Plan ◽  
Surface Ozone ◽  
Pollution Prevention ◽  
River Delta ◽  
Gas Phase Chemistry ◽  
Emission Controls ◽  
Industrial Regions ◽  
Phase Chemistry

&lt;p&gt;Surface ozone (O&lt;sub&gt;3&lt;/sub&gt;) pollution became the main cause of atmospheric pollution over industrial regions in China since 2013, due to the effective mitigation of fine particulate matter (PM&lt;sub&gt;2.5&lt;/sub&gt;) by stringent emission controls by Air Pollution Prevention and Control Action Plan (APPCAP). O&lt;sub&gt;3&lt;/sub&gt;, as a secondary photochemical pollutant, poses a challenge to control due to its non-linear chemical relationship to precursors &amp;#8211; nitrogen oxides (NO&lt;sub&gt;x&lt;/sub&gt;), carbon monoxide (CO) and volatile organic compounds (VOCs).&lt;/p&gt;&lt;p&gt;We hence investigated the differences of atmospheric chemistry environment in the main industrial regions with high emissions &amp;#8211; North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Chongqing - in summer 2016, China by using a global climate-chemistry model, based on United Kingdom Chemistry and Aerosol (UKCA). Anthropogenic Multi-resolution Emission Inventory for China (MEIC) 2013 and Hemispheric Transport of Air Pollution (HTAP) emissions 2010 for the rest of globe were used but scaled to 2016 regionally and nationally separately. In addition, we improved the gas-phase chemistry scheme by adding more highly reactive VOC tracers to better simulate regional pollution. Diurnal cycles of O&lt;sub&gt;3&lt;/sub&gt; and NO&lt;sub&gt;x&lt;/sub&gt; have been evaluated and the results show very good model-observation comparisons after modifying the gas-phase chemistry scheme. Radical (OH, RO&lt;sub&gt;2&lt;/sub&gt; and HO&lt;sub&gt;2&lt;/sub&gt;), NO&lt;sub&gt;x&lt;/sub&gt; and VOC concentrations have also been examined. O&lt;sub&gt;3&lt;/sub&gt; production rates and budgets calculated based on these show the highest production rate in YRD and the lowest in PRD due to different NO&lt;sub&gt;x&lt;/sub&gt; and VOC concentration levels.&lt;/p&gt;&lt;p&gt;To investigate the O&lt;sub&gt;3 &lt;/sub&gt;sensitivity &amp;#8212; VOC limited or NO&lt;sub&gt;x&lt;/sub&gt; limited, we quantified the O&lt;sub&gt;3&lt;/sub&gt; response to VOCs and NO&lt;sub&gt;x&lt;/sub&gt; in total 64 scenarios by scaling NO&lt;sub&gt;x &lt;/sub&gt;and VOCs emissions. O&lt;sub&gt;3&lt;/sub&gt; isopleths suggest that most regions are VOC limited, but the sensitivities vary. O&lt;sub&gt;3&lt;/sub&gt; in YRD is more sensitive to NO&lt;sub&gt;x&lt;/sub&gt; emission change but PRD can be effectively controlled by decreasing VOC emissions. The ratio of H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; to HNO&lt;sub&gt;3&lt;/sub&gt; is applied to provide a quick examination method of O&lt;sub&gt;3&lt;/sub&gt; sensitivity. The contribution of O&lt;sub&gt;3&lt;/sub&gt; from China to the global O&lt;sub&gt;3&lt;/sub&gt; burden compared with other continents has also been quantified. The results show that the relatively higher O&lt;sub&gt;3&lt;/sub&gt; concentration in Asia is mainly contributed by China, and O&lt;sub&gt;3&lt;/sub&gt; becomes more sensitive to VOCs. The model allows us to provide a quantitative assessment of different emission controls on mitigating O&lt;sub&gt;3&lt;/sub&gt; over China and the impacts of Chinese emissions on the global O&lt;sub&gt;3&lt;/sub&gt; burden.&lt;/p&gt;

Forcings of mass-balance variability in Karakoram-Himalaya

2020 ◽  
Author(s):  
Pankaj Kumar ◽  
Vladimir A. Ryabchenko ◽  
Aaquib Javed ◽  
Dmitry V. Sein ◽  
Md. Farooq Azam
Keyword(s):  
Mass Balance ◽  
Large Scale ◽  
Climate Model ◽  
Model Simulation ◽  
Large Fraction ◽  
Water Security ◽  
Spatiotemporal Variability ◽  
Glacier Mass Balance ◽  
Russian Science ◽  
The Impact

&lt;p&gt;Glacier retreat is a key indicator of climate variability and change. Karakoram-Himalaya (KH) glaciers are the source of several perennial rivers protecting water security of a large fraction of the global population. The region is highly vulnerable to climate change impacts, hence the sensitivity of KH glaciers to regional microclimate, especially the impact of individual parameters forcing have been not quantified yet. The present study, using a coupled dynamical glacier-climate model simulation results, analyses the modelled interannual variability of mass-balance for the period 1989-2016. It is validated against available observations to quantify for the first time the sensitivity of the glaciers mass-balance to the individual forcing over KH. The snowfall variability emerges as the key factor, explaining ~60% of the variability of regional glacier mass balance. We provide insight into the recent divergent glacier response over the Karakoram Himalaya. The results underline the need for careful measurements and model representations of snowfall spatiotemporal variability, one of the HK's least-studied meteorological variables, to capture the large-scale, but region-specific, glacier changes at the third pole.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Acknowledgement:&lt;/p&gt;&lt;p&gt;The work was supported by Indian project no. DST/INT/RUS/RSF/P-33/G, and the Russian Science Foundation (Project 19-47-02015).&lt;/p&gt;

scholarly journals Improvements to the WRF-Chem 3.5.1 model for quasi-hemispheric simulations of aerosols and ozone in the Arctic

2017 ◽  
Vol 10 (10) ◽  
pp. 3661-3677 ◽  
Author(s):  
Louis Marelle ◽  
Jean-Christophe Raut ◽  
Kathy S. Law ◽  
Larry K. Berg ◽  
Jerome D. Fast ◽  
...  
Keyword(s):  
Gas Phase ◽  
Trace Gases ◽  
Surface Ozone ◽  
The Arctic ◽  
Surface Model ◽  
Gas Phase Chemistry ◽  
Surface Temperatures ◽  
Phase Chemistry ◽  
Mean Square Errors ◽  
Snow And Ice

Abstract. In this study, the WRF-Chem regional model is updated to improve simulated short-lived pollutants (e.g., aerosols, ozone) in the Arctic. Specifically, we include in WRF-Chem 3.5.1 (with SAPRC-99 gas-phase chemistry and MOSAIC aerosols) (1) a correction to the sedimentation of aerosols, (2) dimethyl sulfide (DMS) oceanic emissions and gas-phase chemistry, (3) an improved representation of the dry deposition of trace gases over seasonal snow, and (4) an UV-albedo dependence on snow and ice cover for photolysis calculations. We also (5) correct the representation of surface temperatures over melting ice in the Noah Land Surface Model and (6) couple and further test the recent KF-CuP (Kain–Fritsch + Cumulus Potential) cumulus parameterization that includes the effect of cumulus clouds on aerosols and trace gases. The updated model is used to perform quasi-hemispheric simulations of aerosols and ozone, which are evaluated against surface measurements of black carbon (BC), sulfate, and ozone as well as airborne measurements of BC in the Arctic. The updated model shows significant improvements in terms of seasonal aerosol cycles at the surface and root mean square errors (RMSEs) for surface ozone, aerosols, and BC aloft, compared to the base version of the model and to previous large-scale evaluations of WRF-Chem in the Arctic. These improvements are mostly due to the inclusion of cumulus effects on aerosols and trace gases in KF-CuP (improved RMSE for surface BC and BC profiles, surface sulfate, and surface ozone), the improved surface temperatures over sea ice (surface ozone, BC, and sulfate), and the updated trace gas deposition and UV albedo over snow and ice (improved RMSE and correlation for surface ozone). DMS emissions and chemistry improve surface sulfate at all Arctic sites except Zeppelin, and correcting aerosol sedimentation has little influence on aerosols except in the upper troposphere.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2018 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry models. Our training data consists of one month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model (v10). From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine learning driven GEOS-Chem model compares well to the standard simulation. For O3, error from using the random forests grow slowly and after 5 days the normalised mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 %, 0.9 respectively; after 30 days the errors increase to 13 %, 67 %, 0.75. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short lived nitrogen species errors become large, with NMB, RMSE and R2 reaching >2100 % >400 %, <0.1 respectively. This proof-of-concept implementation is 85 % slower than the direct integration of the differential equations but optimisation and software engineering would allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations and its applicability to operational air quality activities.

The Impact of Poleward Moisture and Sensible Heat Flux on Arctic Winter Sea Ice Variability*

2015 ◽  
Vol 28 (13) ◽  
pp. 5030-5040 ◽  
Author(s):  
Hyo-Seok Park ◽  
Sukyoung Lee ◽  
Seok-Woo Son ◽  
Steven B. Feldstein ◽  
Yu Kosaka
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Model Simulation ◽  
Early Stage ◽  
Sensible Heat Flux ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Surface Warming ◽  
The Impact

Abstract The surface warming in recent decades has been most rapid in the Arctic, especially during the winter. Here, by utilizing global reanalysis and satellite datasets, it is shown that the northward flux of moisture into the Arctic during the winter strengthens the downward infrared radiation (IR) by 30–40 W m−2 over 1–2 weeks. This is followed by a decline of up to 10% in sea ice concentration over the Greenland, Barents, and Kara Seas. A climate model simulation indicates that the wind-induced sea ice drift leads the decline of sea ice thickness during the early stage of the strong downward IR events, but that within one week the cumulative downward IR effect appears to be dominant. Further analysis indicates that strong downward IR events are preceded several days earlier by enhanced convection over the tropical Indian and western Pacific Oceans. This finding suggests that sea ice predictions can benefit from an improved understanding of tropical convection and ensuing planetary wave dynamics.

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