Browsing by Author "Duncan, Bryan N."
Now showing 1 - 5 of 5
Results Per Page
Sort Options
Item Enhancing Long-Term Trend Simulation of Global Tropospheric OH and Its Drivers from 2005-2019: A Synergistic Integration of Model Simulations and Satellite Observations(EGU, 2024-02-29) Souri, Amir H.; Duncan, Bryan N.; Strode, Sarah A.; Anderson, Daniel; Manyin, Michael E.; Liu, Junhua; Oman, Luke D.; Zhang, Zhen; Weir, BradThe tropospheric hydroxyl radical (TOH) is a key player in regulating oxidation of various compounds in Earth’s atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO₂, tropospheric ozone (TO₃), and H₂O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005-2019) at 1x1 degrees. To achieve this, we use satellite observations of HCHO and NO₂ to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside an interpretable machine learning module named ECCOH, which is integrated into NASA’s GEOS global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers/proxies. Aura Ozone Monitoring Instrument (OMI) NO₂ observations suggest that the simulation has high biases over biomass burning activities in Africa and Eastern Europe, resulting in overestimation of up to 20 % in TOH, regionally. OMI HCHO primarily impacts oceans where TOH linearly correlates with this proxy. Five key parameters including TO₃, H₂O(v), NO₂, HCHO, and stratospheric ozone can collectively explain 65 % of variance in TOH trends. The overall trend of TOH influenced by NO₂ remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over oceans, TOH trends are primarily positive in the northern hemisphere, resulting from the upward trends in HCHO, TO₃, and H₂O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.Item A machine learning examination of hydroxyl radical differences among model simulations for CCMI-1(EGU Publications, 2020-02-05) Nicely, Julie M.; Duncan, Bryan N.; Hanisco, Thomas F.; Wolfe, Glenn M.; Salawitch, Ross J.; Deushi, Makoto; Haslerud, Amund S.; Jöckel, Patrick; Josse, Béatrice; Kinnison, Douglas E.; Klekociuk, Andrew; Manyin, Michael E.; Marécal, Virginie; Morgenstern, Olaf; Murray, Lee T.; Myhre, Gunnar; Oman, Luke D.; Pitari, Giovanni; Pozzer, Andrea; Quaglia, Ilaria; Revell, Laura E.; Rozanov, Eugene; Stenke, Andrea; Stone, Kane; Strahan, Susan; Tilmes, Simone; Tost, Holger; Westervelt, Daniel M.; Zeng, GuangThe hydroxyl radical (OH) plays critical roles within the troposphere, such as determining the lifetime of methane (CH₄), yet is challenging to model due to its fast cycling and dependence on a multitude of sources and sinks. As a result, the reasons for variations in OH and the resulting methane lifetime (τCH₄), both between models and in time, are difficult to diagnose. We apply a neural network (NN) approach to address this issue within a group of models that participated in the Chemistry-Climate Model Initiative (CCMI). Analysis of the historical specified dynamics simulations performed for CCMI indicates that the primary drivers of τCH₄ differences among 10 models are the flux of UV light to the troposphere (indicated by the photolysis frequency JO¹D), the mixing ratio of tropospheric ozone (O₃), the abundance of nitrogen oxides (NOₓ≡NO+NO₂), and details of the various chemical mechanisms that drive OH. Water vapour, carbon monoxide (CO), the ratio of NO:NOₓ, and formaldehyde (HCHO) explain moderate differences in τCH₄, while isoprene, methane, the photolysis frequency of NO₂ by visible light (JNO₂), overhead ozone column, and temperature account for little to no model variation in τCH₄. We also apply the NNs to analysis of temporal trends in OH from 1980 to 2015. All models that participated in the specified dynamics historical simulation for CCMI demonstrate a decline in τCH₄ during the analysed timeframe. The significant contributors to this trend, in order of importance, are tropospheric O₃, JO¹D, NOₓ, and H₂O, with CO also causing substantial interannual variability in OH burden. Finally, the identified trends in τCH₄ are compared to calculated trends in the tropospheric mean OH concentration from previous work, based on analysis of observations. The comparison reveals a robust result for the effect of rising water vapour on OH and τCH₄, imparting an increasing and decreasing trend of about 0.5 % decade⁻¹, respectively. The responses due to NOₓ, ozone column, and temperature are also in reasonably good agreement between the two studies.Item A Machine Learning Methodology for the Generation of a Parameterization of the Hydroxyl Radical: a Tool to Improve Computational-Efficiency in Chemistry Climate Models(EGU, 2022-03-09) Anderson, Daniel; Follette-Cook, Melanie B.; Strode, Sarah A.; Nicely, Julie M.; Liu, Junhua; Ivatt, Peter D.; Duncan, Bryan N.We present a methodology that uses gradient boosted regression trees (a machine learning technique) and a full-chemistry simulation (i.e., training dataset) from a chemistry climate model (CCM) to efficiently generate a parameterization of tropospheric hydroxyl radical (OH) that is a function of chemical, dynamical, and solar irradiance variables. This surrogate model of OH is designed to allow for computationally-efficient simulation of nonlinear feedbacks between OH and tropospheric constituents that have loss by reaction with OH as their primary sinks (e.g., carbon monoxide (CO), methane (CH₄), volatile organic compounds (VOCs)). Such a model framework is advantageous for studies that require multi-decadal simulations of CH₄ or multi-year sensitivity simulations to understand the causes of trends and variations of CO and CH₄. The methodology that we present provides for the relatively easy creation of a new parameterization in response to, for example, changes in the underlying CCM chemistry and/or dynamics schemes. We show that a parameterization of OH generated from a CCM simulation is able to reproduce OH concentrations with a normalized root mean square error of approximately 5 %, as well as capturing the global mean methane lifetime within approximately 1 %. The accuracy of the parameterization is dependent on inputs being within the bounds of the training dataset. However, we show that the parameterization predicts large deviations in OH for an El Niño event that was not part of the training dataset, and that the spatial distribution and strength of these deviations are consistent with the event. This result gives confidence in the fidelity of the parameterization to simulate the spatial and temporal responses of OH to perturbations from large variations in the chemical, dynamical and solar irradiance drivers of OH. In addition, we discuss how two machine learning metrics, Gain feature importance and SHAP values, indicate that the behavior of the parameterization of OH generally comports with our understanding of OH chemistry, even though there are no physics- or chemistry-based constraints on the parameterization.Item Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers(Copernicus Publications, 2021-04-30) Anderson, Daniel C.; Duncan, Bryan N.; Fiore, Arlene M.; Baublitz, Colleen B.; Follette-Cook, Melanie B.; Nicely, Julie M.; Wolfe, GlennThe hydroxyl radical (OH) is the primary atmospheric oxidant responsible for removing many important trace gases, including methane, from the atmosphere. Although robust relationships between OH drivers and modes of climate variability have been shown, the underlying mechanisms between OH and these climate modes, such as the El Niño–Southern Oscillation (ENSO), have not been thoroughly investigated. Here, we use a chemical transport model to perform a 38 year simulation of atmospheric chemistry, in conjunction with satellite observations, to understand the relationship between tropospheric OH and ENSO, Northern Hemispheric modes of variability, the Indian Ocean Dipole, and monsoons. Empirical orthogonal function (EOF) and regression analyses show that ENSO is the dominant mode of global OH variability in the tropospheric column and upper troposphere, responsible for approximately 30 % of the total variance in boreal winter. Reductions in OH due to El Niño are centered over the tropical Pacific and Australia and can be as high as 10 %–15 % in the tropospheric column. The relationship between ENSO and OH is driven by changes in nitrogen oxides in the upper troposphere and changes in water vapor and O¹D in the lower troposphere. While the correlations between monsoons or other modes of variability and OH span smaller spatial scales than for ENSO, regional changes in OH can be significantly larger than those caused by ENSO. Similar relationships occur in multiple models that participated in the Chemistry–Climate Model Initiative (CCMI), suggesting that the dependence of OH interannual variability on these well-known modes of climate variability is robust. Finally, the spatial pattern and r² values of correlation between ENSO and modeled OH drivers – such as carbon monoxide, water vapor, lightning, and, to a lesser extent, NO₂ – closely agree with satellite observations. The ability of satellite products to capture the relationship between OH drivers and ENSO provides an avenue to an indirect OH observation strategy and new constraints on OH variability.Item Technical Note: Constraining the hydroxyl (OH) radical in the tropics with satellite observations of its drivers: First steps toward assessing the feasibility of a global observation strategy(2023-01-04) Anderson, Daniel; Duncan, Bryan N.; Nicely, Julie M.; Liu, Junhua; Strode, Sarah A.; Follette-Cook, Melanie B.Despite its importance in controlling the abundance of methane (CH ₄) and a myriad of other tropospheric species, the hydroxyl radical (OH) is poorly constrained due to its large spatial heterogeneity and the inability to measure tropospheric OH with satellites. Here, we present a methodology to infer tropospheric column OH (TCOH) in the tropics over the open oceans using a combination of a machine learning model, output from a simulation of the GEOS model, and satellite observations. Our overall goals are to assess the feasibility of our methodology, to identify potential limitations, and to suggest areas of improvement in the current observational network. The methodology reproduces the variability of TCOH from independent 3D model output and of observations from the Atmospheric Tomography mission (ATom). While the methodology also reproduces the magnitude of the 3D model validation set, the accuracy of the magnitude when applied to observations is uncertain because current observations are insufficient to fully evaluate the machine learning model. Despite large uncertainties in some of the satellite retrievals necessary to infer OH, particularly for NO₂ and HCHO, current satellite observations are of sufficient quality to apply the machine learning methodology, resulting in an error comparable to that of in situ OH observations. Finally, the methodology is not limited to a specific suite of satellite retrievals. Comparison of TCOH determined from two sets of retrievals does show, however, that systematic biases in NO₂, resulting both from retrieval algorithm and instrumental differences, lead to relative biases in the calculated TCOH. Further evaluation of NO₂ retrievals in the remote atmosphere is needed to determine their accuracy. With slight modifications, a similar methodology could likely be expanded to the extra-tropics and over land, with the benefits of increasing our understanding of the atmospheric oxidation capacity and, for instance, informing understanding of recent CH ₄ trends.