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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- and 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.

Charge transport in conjugated polymer semiconductors has traditionally been thought to be limited to a low-mobility regime by pronounced energetic disorder. Much progress has recently been made in advancing carrier mobilities in field-effect transistors through developing low-disorder conjugated polymers. However, in diodes these polymers have to date not shown much improved mobilities, presumably reflecting the fact that in diodes lower carrier concentrations are available to fill up residual tail states in the density of states. Here, we show that the bulk charge transport in low-disorder polymers is limited by water-induced trap states and that their concentration can be dramatically reduced through incorporating small molecular additives into the polymer film. Upon incorporation of the additives we achieve space-charge limited current characteristics that resemble molecular single crystals such as rubrene with high, trap-free SCLC mobilities up to 0.2 cm 2 /Vs and a width of the residual tail state distribution comparable to k B T . Charge transport in organic diodes based on conjugated polymers is severely limited by the high water-related trap concentration and energetic disorder. Here, the authors report high-mobility trap-free charge transport in low-disorder conjugated polymers by incorporating small molecular additives.

Many modelling studies suggest that the El Niño–Southern Oscillation (ENSO), in interaction with the tropical Pacific background climate, will change with rising atmospheric greenhouse gas concentrations. Solar geoengineering (reducing the solar flux from outer space) has been proposed as a means to counteract anthropogenic climate change. However, the effectiveness of solar geoengineering concerning a variety of aspects of Earth's climate is uncertain. Robust results are particularly challenging to obtain for ENSO because existing geoengineering simulations are too short (typically ∼ 50 years) to detect statistically significant changes in the highly variable tropical Pacific background climate. We here present results from a 1000-year-long solar-geoengineering simulation, G1, carried out with the coupled atmosphere–ocean general circulation model HadCM3L. In agreement with previous studies, reducing the solar irradiance (4 %) to offset global mean surface warming in the model more than compensates the warming in the tropical Pacific that develops in the 4 × CO2 scenario. We see an overcooling of 0.3 ∘C and a 0.23 mm d−1 (5 %) reduction in mean rainfall over the tropical Pacific relative to preindustrial conditions in the G1 simulation, owing to the different latitudinal distributions of the shortwave (solar) and longwave (CO2) forcings. The location of the Intertropical Convergence Zone (ITCZ) in the tropical Pacific, which moved 7.5∘ southwards under 4 × CO2, is restored to its preindustrial position. However, other aspects of the tropical Pacific mean climate are not reset as effectively. Relative to preindustrial conditions, in G1 the time-averaged zonal wind stress, zonal sea surface temperature (SST) gradient, and meridional SST gradient are each statistically significantly reduced by around 10 %, and the Pacific Walker Circulation (PWC) is consistently weakened, resulting in conditions conducive to increased frequency of El Niño events. The overall amplitude of ENSO strengthens by 9 %–10 % in G1, but there is a 65 % reduction in the asymmetry between cold and warm events: cold events intensify more than warm events. Notably, the frequency of extreme El Niño and La Niña events increases by ca. 60 % and 30 %, respectively, while the total number of El Niño events increases by around 10 %. All of these changes are statistically significant at either 95 or 99 % confidence level. Somewhat paradoxically, while the number of total and extreme events increases, the extreme El Niño events become weaker relative to the preindustrial state, while the extreme La Niña events become even stronger. That is, such extreme El Niño events in G1 become less intense than under preindustrial conditions but also more frequent. In contrast, extreme La Niña events become stronger in G1, which is in agreement with the general overcooling of the tropical Pacific in G1 relative to preindustrial conditions.

Climate models include many processes that may be simplified to save computational time. This work shows that model representation of upper atmosphere ozone can impact on the projected climate sensitivity. State-of-the-art climate models now include more climate processes simulated at higher spatial resolution than ever 1 . Nevertheless, some processes, such as atmospheric chemical feedbacks, are still computationally expensive and are often ignored in climate simulations 1 , 2 . Here we present evidence that the representation of stratospheric ozone in climate models can have a first-order impact on estimates of effective climate sensitivity. Using a comprehensive atmosphere–ocean chemistry–climate model, we find an increase in global mean surface warming of around 1 °C (~20%) after 75 years when ozone is prescribed at pre-industrial levels compared with when it is allowed to evolve self-consistently in response to an abrupt 4×CO 2 forcing. The difference is primarily attributed to changes in long-wave radiative feedbacks associated with circulation-driven decreases in tropical lower stratospheric ozone and related stratospheric water vapour and cirrus cloud changes. This has important implications for global model intercomparison studies 1 , 2 in which participating models often use simplified treatments of atmospheric composition changes that are consistent with neither the specified greenhouse gas forcing scenario nor the associated atmospheric circulation feedbacks 3 , 4 , 5 .

There are discrepancies between global climate models regarding the evolution of the tropical tropopause layer (TTL) and also whether changes in ozone impact the surface under climate change. We use a 1D clearsky radiative–convective equilibriummodel to determine how a variety of factors can affect the TTL and how they influence surface climate. We develop a new method of convective adjustment, which relaxes the temperature profile toward the moist adiabat and allows for cooling above the level of neutral buoyancy. The TTL temperatures in our model are sensitive to CO₂ concentration, ozone profile, the method of convective adjustment, and the upwelling velocity, which is used to calculate a dynamical cooling rate in the stratosphere. Moreover, the temperature response of the TTL to changes in each of the above factors sometimes depends on the others. The surface temperature response to changes in ozone and upwelling at and above the TTL is also strongly amplified by both stratospheric and tropospheric water vapor changes. With all these influencing factors, it is not surprising that global models disagree with regard to TTL structure and evolution and the influence of ozone changes on surface temperatures. On the other hand, the effect of doubling CO₂ on the surface, including just radiative, water vapor, and lapse-rate feedbacks, is relatively robust to changes in convection, upwelling, or the applied ozone profile.

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.

Stratospheric ozone and water vapour are key components of the Earth system, and past and future changes to both have important impacts on global and regional climate. Here, we evaluate long-term changes in these species from the pre-industrial period (1850) to the end of the 21st century in Coupled Model Intercomparison Project phase 6 (CMIP6) models under a range of future emissions scenarios. There is good agreement between the CMIP multi-model mean and observations for total column ozone (TCO), although there is substantial variation between the individual CMIP6 models. For the CMIP6 multi-model mean, global mean TCO has increased from ∼ 300 DU in 1850 to ∼ 305 DU in 1960, before rapidly declining in the 1970s and 1980s following the use and emission of halogenated ozone-depleting substances (ODSs). TCO is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 scenarios, and under the SSP3-7.0 and SSP5-8.5 scenarios TCO values are projected to be ∼ 10 DU higher than the 1960s values by 2100. However, under the SSP1-1.9 and SSP1-1.6 scenarios, TCO is not projected to return to the 1960s values despite reductions in halogenated ODSs due to decreases in tropospheric ozone mixing ratios. This global pattern is similar to regional patterns, except in the tropics where TCO under most scenarios is not projected to return to 1960s values, either through reductions in tropospheric ozone under SSP1-1.9 and SSP1-2.6, or through reductions in lower stratospheric ozone resulting from an acceleration of the Brewer–Dobson circulation under other Shared Socioeconomic Pathways (SSPs). In contrast to TCO, there is poorer agreement between the CMIP6 multi-model mean and observed lower stratospheric water vapour mixing ratios, with the CMIP6 multi-model mean underestimating observed water vapour mixing ratios by ∼ 0.5 ppmv at 70 hPa. CMIP6 multi-model mean stratospheric water vapour mixing ratios in the tropical lower stratosphere have increased by ∼ 0.5 ppmv from the pre-industrial to the present-day period and are projected to increase further by the end of the 21st century. The largest increases (∼ 2 ppmv) are simulated under the future scenarios with the highest assumed forcing pathway (e.g. SSP5-8.5). Tropical lower stratospheric water vapour, and to a lesser extent TCO, shows large variations following explosive volcanic eruptions.

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.

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60∘ N–60∘ S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models.