Impacts of stratospheric sulfate geoengineering on tropospheric ozone

A range of solar radiation management (SRM) techniques has been proposed to counter anthropogenic climate change. Here, we examine the potential effects of stratospheric sulfate aerosols and solar insolation reduction on tropospheric ozone and ozone at Earth's surface. Ozone is a key air pollutant, which can produce respiratory diseases and crop damage. Using a version of the Community Earth System Model from the National Center for Atmospheric Research that includes comprehensive tropospheric and stratospheric chemistry, we model both stratospheric sulfur injection and solar irradiance reduction schemes, with the aim of achieving equal levels of surface cooling relative to the Representative Concentration Pathway 6.0 scenario. This allows us to compare the impacts of sulfate aerosols and solar dimming on atmospheric ozone concentrations. Despite nearly identical global mean surface temperatures for the two SRM approaches, solar insolation reduction increases global average surface ozone concentrations, while sulfate injection decreases it. A fundamental difference between the two geoengineering schemes is the importance of heterogeneous reactions in the photochemical ozone balance with larger stratospheric sulfate abundance, resulting in increased ozone depletion in mid-A nd high latitudes. This reduces the net transport of stratospheric ozone into the troposphere and thus is a key driver of the overall decrease in surface ozone. At the same time, the change in stratospheric ozone alters the tropospheric photochemical environment due to enhanced ultraviolet radiation. A shared factor among both SRM scenarios is decreased chemical ozone loss due to reduced tropospheric humidity. Under insolation reduction, this is the dominant factor giving rise to the global surface ozone increase. Regionally, both surface ozone increases and decreases are found for both scenarios; that is, SRM would affect regions of the world differently in terms of air pollution. In conclusion, surface ozone and tropospheric chemistry would likely be affected by SRM, but the overall effect is strongly dependent on the SRM scheme. Due to the health and economic impacts of surface ozone, all these impacts should be taken into account in evaluations of possible consequences of SRM

T-loop phosphorylation of Arabidopsis CDKA;1 is required for its function and can be partially substituted by an aspartate residue

As in other eukaryotes, progression through the cell cycle in plants is governed by cyclin-dependent kinases. Phosphorylation of a canonical Thr residue in the T-loop of the kinases is required for high enzyme activity in animals and yeast. We show that the Arabidopsis thaliana Cdc21/Cdc28 homolog CDKA; 1 is also phosphorylated in the T-loop and that phosphorylation at the conserved Thr-161 residue is essential for its function. A phospho-mimicry T161D substitution restored the primary defect of cdka; 1 mutants, and although the T161D substitution displayed a dramatically reduced kinase activity with a compromised ability to bind substrates, homozygous mutant plants were recovered. The rescue by the T161D substitution, however, was not complete, and the resulting plants displayed various developmental abnormalities. For instance, even though flowers were formed, these plants were completely sterile as a result of a failure of the meiotic program, indicating that different requirements for CDKA; 1 function are needed during plant development

Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability

Low-cost air pollution sensors often fail to attain sufficient performance compared with state-of-the-art measurement stations, and they typically require expensive laboratory-based calibration procedures. A repeatedly proposed strategy to overcome these limitations is calibration through co-location with public measurement stations. Here we test the idea of using machine learning algorithms for such calibration tasks using hourly-averaged co-location data for nitrogen dioxide (NO2) and particulate matter of particle sizes smaller than 10 µm (PM10) at three different locations in the urban area of London, UK. We compare the performance of ridge regression, a linear statistical learning algorithm, to two non-linear algorithms in the form of random forest regression (RFR) and Gaussian process regression (GPR). We further benchmark the performance of all three machine learning methods relative to the more common multiple linear regression (MLR). We obtain very good out-of-sample R2 scores (coefficient of determination) >0.7, frequently exceeding 0.8, for the machine learning calibrated low-cost sensors. In contrast, the performance of MLR is more dependent on random variations in the sensor hardware and co-located signals, and it is also more sensitive to the length of the co-location period. We find that, subject to certain conditions, GPR is typically the best-performing method in our calibration setting, followed by ridge regression and RFR. We also highlight several key limitations of the machine learning methods, which will be crucial to consider in any co-location calibration. In particular, all methods are fundamentally limited in how well they can reproduce pollution levels that lie outside those encountered at training stage. We find, however, that the linear ridge regression outperforms the non-linear methods in extrapolation settings. GPR can allow for a small degree of extrapolation, whereas RFR can only predict values within the training range. This algorithm-dependent ability to extrapolate is one of the key limiting factors when the calibrated sensors are deployed away from the co-location site itself. Consequently, we find that ridge regression is often performing as good as or even better than GPR after sensor relocation. Our results highlight the potential of co-location approaches paired with machine learning calibration techniques to reduce costs of air pollution measurements, subject to careful consideration of the co-location training conditions, the choice of calibration variables and the features of the calibration algorithm

An unsupervised learning approach to identifying blocking events: the case of European summer

Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and in climate model data. However, BIs show significant regional and seasonal differences so that several indices are typically applied in combination to ensure scientific robustness. Here, we introduce a new BI using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European blocking based on expert judgement. We then demonstrate that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and reduces the dependence on arbitrary thresholds. Using ERA5 reanalysis data (1979–2019), we find that the SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI already performs well using only around 20 years of training data so that observational records are long enough to train our new method. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of SOM nodes can be an effective tool to diagnose such weather events, although the domain-based approach can still lead to errors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detection skill of SOM-BI depending on the meteorological variable used to study blocking, including geopotential height, sea level pressure and four variables related to potential vorticity, and the 500 hPa geopotential height anomaly field provides the best results with our new approach. We also demonstrate how SOM-BI can be used to identify different types of blocking events and their associated trends. Finally, we evaluate the SOM-BI performance on around 100 years of climate model data from a pre-industrial simulation with the new UK Earth System Model (UKESM1-0-LL). For the model data, all blocking detection methods have lower skill than for the ERA5 reanalysis, but SOM-BI performs noticeably better than the conventional indices. Overall, our results demonstrate the significant potential for unsupervised learning to complement the study of blocking events in both reanalysis and climate modelling contexts

Ozone changes under solar geoengineering:Implications for UV exposure and air quality

Various forms of geoengineering have been proposed to counter anthropogenic climate change. Methods which aim to modify the Earth's energy balance by reducing insolation are often subsumed under the term Solar Radiation Management (SRM). Here, we present results of a standard SRM modelling experiment in which the incoming solar irradiance is reduced to offset the global mean warming induced by a quadrupling of atmospheric carbon dioxide. For the first time in an atmosphere-ocean coupled climate model, we include atmospheric composition feedbacks such as ozone changes under this scenario. Including the composition changes, we find large reductions in surface UV-B irradiance, with implications for vitamin D production, and increases in surface ozone concentrations, both of which could be important for human health. We highlight that both tropospheric and stratospheric ozone changes should be considered in the assessment of any SRM scheme, due to their important roles in regulating UV exposure and air quality

Stratospheric ozone changes under solar geoengineering: Implications for UV exposure and air quality

Abstract. Various forms of geoengineering have been proposed to counter anthropogenic climate change. Methods which aim to modify the Earth's energy balance by reducing insolation are often subsumed under the term solar radiation management (SRM). Here, we present results of a standard SRM modelling experiment in which the incoming solar irradiance is reduced to offset the global mean warming induced by a quadrupling of atmospheric carbon dioxide. For the first time in an atmosphere–ocean coupled climate model, we include atmospheric composition feedbacks for this experiment. While the SRM scheme considered here could offset greenhouse gas induced global mean surface warming, it leads to important changes in atmospheric composition. We find large stratospheric ozone increases that induce significant reductions in surface UV-B irradiance, which would have implications for vitamin D production. In addition, the higher stratospheric ozone levels lead to decreased ozone photolysis in the troposphere. In combination with lower atmospheric specific humidity under SRM, this results in overall surface ozone concentration increases in the idealized G1 experiment. Both UV-B and surface ozone changes are important for human health. We therefore highlight that both stratospheric and tropospheric ozone changes must be considered in the assessment of any SRM scheme, due to their important roles in regulating UV exposure and air quality. We thank the European Research Council for funding through the ACCI project, project number 267760. In particular, we thank Jonathan M. Gregory (UK Met Office, University of Reading), Manoj M. Joshi (University of East Anglia) and Annette Osprey (University of Reading) for model development as part of the QUEST-ESM project supported by the UK Natural Environment Research Council (NERC) under contract numbers RH/H10/19 and R8/H12/124. We acknowledge use of the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the UK Met Office and NERC. For plotting, we used Matplotlib, a 2-D graphics environment for the Python programming language developed by Hunter (2007). We are grateful for advice of P. Telford during the model development stage of this project and thank the UKCA team at the UK Met Office for help and support.This is the final version of the article. It first appeared from Copernicus Publications via http://dx.doi.org/10.5194/acp-16-4191-2016

The Impact of Stratospheric Ozone Feedbacks on Climate Sensitivity Estimates

A number of climate modeling studies have shown that differences between typical choices for representing ozone can affect climate change projections. Here, we investigate potential climate impacts of a specific ozone representation used in simulations of the HadGEM model for the Coupled Model Intercomparison Project phase 5. The method considers ozone changes only in the troposphere and lower stratosphere and prescribes stratospheric ozone elsewhere. For a standard climate sensitivity simulation, we find that this method leads to significantly increased global warming and specific patterns of regional surface warming compared with a fully interactive atmospheric chemistry set-up. We explain this mainly by the suppressed part of the stratospheric ozone changes and the associated alteration of the stratospheric water vapor feedback. This combined effect is modulated by simultaneous cirrus cloud changes. We underline the need to understand better how representations of ozone can affect climate modeling results and, in particular, global and regional climate sensitivity estimates.European Research Council ACCI project (project number 267760

Universal phase shift and non-exponential decay of driven single-spin oscillations

We study, both theoretically and experimentally, driven Rabi oscillations of a single electron spin coupled to a nuclear spin bath. Due to the long correlation time of the bath, two unusual features are observed in the oscillations. The decay follows a power law, and the oscillations are shifted in phase by a universal value of ~pi/4. These properties are well understood from a theoretical expression that we derive here in the static limit for the nuclear bath. This improved understanding of the coupled electron-nuclear system is important for future experiments using the electron spin as a qubit.Comment: Main text: 4 pages, 3 figures, Supplementary material: 2 pages, 3 figure