Global modelling of the total OH reactivity: investigations on the “missing” OH sink and its atmospheric implications

The hydroxyl radical (OH) plays a crucial role in the chemistry of the atmosphere as it initiates the removal of most trace gases. A number of field campaigns have observed the presence of a “missing” OH sink in a variety of regions across the planet. A comparison of direct measurements of the OH loss frequency, also known as total OH reactivity (kOH), with the sum of individual known OH sinks (obtained via the simultaneous detection of species such as volatile organic compounds and nitrogen oxides) indicates that, in some cases, up to 80 % of kOH is unaccounted for. In this work, the UM-UKCA chemistry-climate model was used to investigate the wider implications of the missing reactivity on the oxidising capacity of the atmosphere. Simulations of the present-day atmosphere were performed and the model was evaluated against an array of field measurements to verify that the known OH sinks were reproduced well, with a resulting good agreement found for most species. Following this, an additional sink was introduced to simulate the missing OH reactivity as an emission of a hypothetical molecule, X, which undergoes rapid reaction with OH. The magnitude and spatial distribution of this sink were underpinned by observations of the missing reactivity. Model runs showed that the missing reactivity accounted for on average 6 % of the total OH loss flux at the surface and up to 50 % in regions where emissions of the additional sink were high. The lifetime of the hydroxyl radical was reduced by 3 % in the boundary layer, whilst tropospheric methane lifetime increased by 2 % when the additional OH sink was included. As no OH recycling was introduced following the initial oxidation of X, these results can be interpreted as an upper limit of the effects of the missing reactivity on the oxidising capacity of the troposphere. The UM-UKCA simulations also allowed us to establish the atmospheric implications of the newly characterised reactions of peroxy radicals (RO2) with OH. Whilst the effects of this chemistry on kOH were minor, the reaction of the simplest peroxy radical, CH3O2, with OH was found to be a major sink for CH3O2 and source of HO2 over remote regions at the surface and in the free troposphere. Inclusion of this reaction in the model increased tropospheric methane lifetime by up to 3 %, depending on its product branching. Simulations based on the latest kinetic and product information showed that this reaction cannot reconcile models with observations of atmospheric methanol, in contrast to recent suggestions

Technical note: Unsupervised classification of ozone profiles in UKESM1

The vertical distribution of ozone in the atmosphere, which features complex spatial and temporal variability set by a balance of production, loss, and advection, is relevant for both surface air pollution and climate via its role in radiative forcing. At present, the way in which regions of coherent ozone structure are defined relies on somewhat arbitrarily drawn boundaries. Here we consider a more general, data-driven method for defining coherent regimes of ozone structure. We apply an unsupervised classification technique called Gaussian mixture modeling (GMM), which represents the underlying distribution of ozone profiles as a linear combination of multi-dimensional Gaussian functions. In doing so, GMM identifies coherent groups or subpopulations of the ozone profile distribution. As a proof-of-concept study, we apply GMM to ozone profiles from three subsets of the UKESM1 coupled climate model runs carried out for CMIP6: specifically, the seasonal mean of a historical subset (2009–2014) and two subsets from two different future climate projections (i.e., SSP1-2.6 and SSP5-8.5). Despite not being given any spatiotemporal information, GMM identifies several spatially coherent regions of ozone structure. Using a combination of statistical guidance and post hoc judgment, we select a six-class representation of global ozone, consisting of two tropical classes and four mid-to-high-latitude classes. The tropical classes feature a relatively high-altitude tropopause, while the higher-latitude classes feature a lower-altitude tropopause and low values of tropospheric ozone, as expected based on broad patterns observed in the atmosphere. Both of the future projections feature lower ozone concentrations at 850 hPa than the historical benchmark, with signatures of ozone hole recovery. We find that the area occupied by the tropical classes is expanded in both future projections, which are most prominent during austral summer. Our results suggest that GMM may be a useful method for identifying coherent ozone regimes, particularly in the context of model analysis

Reconciling the climate and ozone response to the 1257 CE Mount Samalas eruption.

The 1257 CE eruption of Mount Samalas (Indonesia) is the source of the largest stratospheric injection of volcanic gases in the Common Era. Sulfur dioxide emissions produced sulfate aerosols that cooled Earth's climate with a range of impacts on society. The coemission of halogenated species has also been speculated to have led to wide-scale ozone depletion. Here we present simulations from HadGEM3-ES, a fully coupled Earth system model, with interactive atmospheric chemistry and a microphysical treatment of sulfate aerosol, used to assess the chemical and climate impacts from the injection of sulfur and halogen species into the stratosphere as a result of the Mt. Samalas eruption. While our model simulations support a surface air temperature response to the eruption of the order of -1°C, performing well against multiple reconstructions of surface temperature from tree-ring records, we find little evidence to support significant injections of halogens into the stratosphere. Including modest fractions of the halogen emissions reported from Mt. Samalas leads to significant impacts on the composition of the atmosphere and on surface temperature. As little as 20% of the halogen inventory from Mt. Samalas reaching the stratosphere would result in catastrophic ozone depletion, extending the surface cooling caused by the eruption. However, based on available proxy records of surface temperature changes, our model results support only very minor fractions (1%) of the halogen inventory reaching the stratosphere and suggest that further constraints are needed to fully resolve the issue.DTP-1502139 NE/S000887/1 NE/N006038/1 ACSIS UKC

Simulating the Climate Response to Atmospheric Oxygen Variability in the Phanerozoic

Abstract. The amount of dioxygen (O2) in the atmosphere may have varied from as little as 10 % to as high as 35 % during the Phanerozoic eon (541 Ma–Present). These changes in the amount of O2 are large enough to have lead to changes in atmospheric mass, which may alter the radiative budget of the atmosphere, leading to this mechanism being invoked to explain discrepancies between climate model simulations and proxy reconstructions of past climates. Here we present the first fully 3D numerical model simulations to investigate the climate impacts of changes in O2 during different climate states using the HadGEM3-AO and HadCM3-BL models. We show that simulations with an increase in O2 content result in increased global mean surface air temperature under conditions of a pre-industrial Holocene climate state, in agreement with idealised 1D and 2D modelling studies. We demonstrate the mechanism behind the warming is complex and involves trade-off between a number of factors. Increasing atmospheric O2 leads to a reduction in incident shortwave radiation at Earth's surface due to Rayleigh scattering, a cooling effect. However, there is a competing warming effect due to an increase in the pressure broadening of greenhouse gas absorption lines and dynamical feedbacks, which alter the meridional heat transport of the ocean, warming polar regions and cooling tropical regions. Case studies from past climates are investigated using HadCM3-BL which show that in the warmest climate states, increasing oxygen may lead to a temperature decrease, as the equilibrium climate sensitivity is lower. For the Maastrichtian (72.1–66.0 Ma), increasing oxygen content leads to a better agreement with proxy reconstructions of surface temperature at that time irrespective of the carbon dioxide content. For the Asselian (298.9–295.0 Ma), increasing oxygen content leads to a warmer global mean surface temperature and reduced carbon storage on land, suggesting that high oxygen content may have been a contributing factor in preventing a Snowball Earth during this period of the early Permian. These climate model simulations reconcile the surface temperature response to oxygen content changes across the hierarchy of model complexity and highlight the broad range of Earth system feedbacks that need to be accounted for when considering the climate response to changes in atmospheric oxygen content. </jats:p

Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence

Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry-Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95 % confidence interval (2052-2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry-climate modelling communities

The NANOGrav 15 yr Data Set: Constraints on Supermassive Black Hole Binaries from the Gravitational-wave Background

El conjunto de datos de 15 años de NANOGrav muestra evidencias de la presencia de un fondo de ondas gravitacionales (GWB) de baja frecuencia. Aunque muchos procesos físicos pueden originar estas ondas gravitacionales de baja frecuencia, aquí analizamos la señal como procedente de una población de agujeros negros binarios supermasivos (SMBH) distribuidos por todo el Universo. Demostramos que los modelos astrofísicos de poblaciones binarias SMBH son capaces de reproducir tanto la amplitud como la forma del espectro de ondas gravitacionales de baja frecuencia observado. Aunque múltiples variaciones del modelo son capaces de reproducir el espectro GWB con nuestra precisión de medida actual, nuestros resultados subrayan la importancia de modelar con precisión la evolución binaria para producir espectros GWB realistas. Además, aunque unos parámetros razonables son capaces de reproducir las observaciones de 15 años, la amplitud implícita del GWB requiere que un gran número de parámetros se sitúen en los límites de los valores esperados o que un pequeño número de parámetros difieran notablemente de las expectativas estándar. Aunque todavía no somos capaces de establecer definitivamente el origen de la señal GWB inferida, la consistencia de la señal con las expectativas astrofísicas ofrece una perspectiva tentadora para confirmar que las binarias SMBH son capaces de formarse, alcanzar separaciones de sub-segundos y finalmente unirse. A medida que la importancia aumente con el tiempo, las características de orden superior del espectro del GWB determinarán definitivamente la naturaleza del GWB y permitirán nuevas restricciones sobre las poblaciones de SMBH. © 2023The NANOGrav 15 yr data set shows evidence for the presence of a low-frequency gravitational-wave background (GWB). While many physical processes can source such low-frequency gravitational waves, here we analyze the signal as coming from a population of supermassive black hole (SMBH) binaries distributed throughout the Universe. We show that astrophysically motivated models of SMBH binary populations are able to reproduce both the amplitude and shape of the observed low-frequency gravitational-wave spectrum. While multiple model variations are able to reproduce the GWB spectrum at our current measurement precision, our results highlight the importance of accurately modeling binary evolution for producing realistic GWB spectra. Additionally, while reasonable parameters are able to reproduce the 15 yr observations, the implied GWB amplitude necessitates either a large number of parameters to be at the edges of expected values or a small number of parameters to be notably different from standard expectations. While we are not yet able to definitively establish the origin of the inferred GWB signal, the consistency of the signal with astrophysical expectations offers a tantalizing prospect for confirming that SMBH binaries are able to form, reach subparsec separations, and eventually coalesce. As the significance grows over time, higher-order features of the GWB spectrum will definitively determine the nature of the GWB and allow for novel constraints on SMBH populations. © 2023. The Author(s). Published by the American Astronomical Society

Using Machine Learning to Make Computationally Inexpensive Projections of 21st Century Stratospheric Column Ozone Changes in the Tropics

Stratospheric ozone projections in the tropics, modeled using the UKESM1 Earth system model, are explored under different Shared Socioeconomic Pathways (SSPs). Consistent with other studies, it is found that tropical stratospheric column ozone does not return to 1980s values by the end of the 21st century under any SSP scenario as increased ozone mixing ratios in the tropical upper stratosphere are offset by continued ozone decreases in the tropical lower stratosphere. Stratospheric column ozone is projected to be largest under SSP scenarios with the smallest change in radiative forcing, and smallest for SSP scenarios with larger radiative forcing, consistent with a faster Brewer-Dobson circulation at high greenhouse gas loadings. This study explores the use of machine learning (ML) techniques to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Four ML techniques are investigated: Ridge regression, Lasso regression, Random Forests and Extra Trees. All four techniques investigated here are able to make projections of future tropical stratospheric column ozone which agree well with those made by the UKESM1 Earth system model, often falling within the ensemble spread of UKESM1 simulations for a broad range of SSPs. However, all techniques struggle to make accurate projects for the final decades of the SSP5-8.5 scenario. Accurate projections can only be achieved when the ML methods are trained on sufficient data, including both historical and future simulations. When trained only on historical data, the projections made using models based on ML techniques fail to accurately predict tropical stratospheric ozone changes. Results presented here indicate that, when sufficiently trained, ML models have the potential to make accurate, computationally inexpensive projections of tropical stratospheric column ozone. Further development of these models may reduce the computational burden placed on fully coupled chemistry-climate and Earth system models and enable the exploration of tropical stratospheric column ozone recovery under a much broader range of future emissions scenarios

Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

Funder: Tsinghua University Initiative Scientific Research ProgramFunder: National Centre for Atmospheric Science; doi: http://dx.doi.org/10.13039/501100000662Funder: Met Office; doi: http://dx.doi.org/10.13039/501100000847Abstract: The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector

An excess of star-forming galaxies in the fields of high-redshift QSOs

We present submillimetre (submm) and mid-infrared (MIR) imaging observations of five fields centred on quasi-stellar objects (QSOs) at 1.7< z<2.8. All five QSOs were detected previously at submm wavelengths. At 850 (450) m, we detect 17 (11) submillimetre galaxies (SMGs) in addition to the QSOs. The total area mapped at 850 m is arcmin2 down to rms noise levels of 1–2 mJy beam−1, depending on the field. Integral number counts are computed from the 850-m data using the same analytical techniques adopted by ‘blank-field’ submm surveys. We find that the ‘QSO-field’ counts show a clear excess over the blank-field counts at deboosted flux densities of mJy; at higher flux densities, the counts are consistent with the blank-field counts. Robust MIR counterparts are identified for all four submm detected QSOs and per cent of the SMGs. The MIR colours of the QSOs are similar to those of the local ultraluminous infrared galaxy (ULIRG)/active galactic nuclei (AGN) Mrk 231 if placed at 1< z<3 whilst most of the SMGs have colours very similar to those of the local ULIRG Arp 220 at 1< z<3. MIR diagnostics therefore find no strong evidence that the SMGs host buried AGN although we cannot rule out such a possibility. Taken together our results suggest that the QSOs sit in regions of the early universe which are undergoing an enhanced level of major star formation activity, and should evolve to become similarly dense regions containing massive galaxies at the present epoch. Finally, we find evidence that the level of star formation activity in individual galaxies appears to be lower around the QSOs than it is around more powerful radio-loud AGN at higher redshifts