ALBERT

Description: There is a clear need for the development of modelling frameworks for both climate change and air quality to help inform policies for addressing these issues. This paper presents an initial attempt to develop a single modelling framework, by introducing a greater degree of consistency in the modelling framework by using a two-step, one-way nested configuration of models, from a global composition-climate model (GCCM) (140 km resolution) to a regional composition-climate model covering Europe (RCCM) (50 km resolution) and finally to a high (12 km) resolution model over the UK (AQUM). The latter model is used to produce routine air quality forecasts for the UK. All three models are based on the Met Office's Unified Model (MetUM). In order to better understand the impact of resolution on the downscaling of projections of future climate and air quality, we have used this nest of models to simulate a five year period using present-day emissions and under present-day climate conditions. We also consider the impact of running the higher resolution model with higher spatial resolution emissions, rather than simply regridding emissions from the RCCM. We present an evaluation of the models compared to in situ air quality observations over the UK, plus a comparison against an independent 1 km resolution gridded dataset, derived from a combination of modelling and observations. We show that using a high resolution model over the UK has some benefits in improving air quality modelling, but that the use of higher spatial resolution emissions is important to capture local variations in concentrations, particularly for primary pollutants such as nitrogen dioxide and sulphur dioxide. For secondary pollutants such as ozone and the secondary component of PM10, the benefits of a higher resolution nested model are more limited and reasons for this are discussed. This study confirms that the resolution of models is not the only factor in determining model performance - consistency between nested models is also important.

Description: Major mid-winter stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Because SSWs are able to cause significant surface weather anomalies on intra-seasonal time scales, several previous studies have focused on their potential future change, as might be induced by anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to an actual decrease. Several factors might explain these contradictory results, notably the use of different metrics for the identification of SSWs, and the impact of large climatological biases in single-model studies. To bring some clarity, we here revisit the question of future SSWs changes, using an identical set of metrics applied consistently across 12 different models participating in the Chemistry Climate Model Initiative. Our analysis reveals that no statistically significant change in the frequency of SSWs will occur over the 21st century, irrespective of the metric used for the identification of the event. Changes in other SSWs characteristics, such as their duration and the tropospheric forcing, are also assessed: again, we find no evidence of future changes over the 21st century.

Description: We have derived values of the Ultraviolet Index (UVI) at solar noon from the Tropospheric Ultraviolet Model (TUV) driven by ozone, temperature and aerosol fields from the first phase of the Chemistry-Climate Model Initiative (CCMI-1). Since clouds remain one of the largest uncertainties in climate projections, we simulated only clear-sky UVI. We compared the UVI climatologies obtained from CCMI and TUV against present-day climatological values of UVI derived from satellite data (the OMI-Aura OMUVBd product) and ground-based measurements (from the NDACC network). Depending on the region, relative differences between the UVI obtained from CCMI and TUV and ground-based measurements ranged between −4% and 11%. We calculated the UVI evolution throughout the 21st century for the four Representative Concentration Pathways (RCPs 2.6, 4.5, 6.0 and 8.5). Compared to 1960s values, we found an average increase in UVI in 2100 (of 2–4%) in the tropical belt (30°N–30°S). For the mid-latitudes, we observed a 1.8 to 3.4% increase in the Southern Hemisphere for RCP 2.6, 4.5 and 6.0, and found a 2.3% decrease in RCP 8.5. Higher UV indices are projected in the Northern Hemisphere except for RCP 8.5. At high latitudes, ozone recovery is well identified and induces a complete return of mean UVI levels to 1960 values for RCP 8.5 in the Southern Hemisphere. In the Northern Hemisphere, UVI levels in 2100 are higher by 0.5 to 5.5% for RCP 2.6, 4.5 and 6.0 and they are lower by 7.9% for RCP 8.5. We analysed the impacts of greenhouse gases (GHGs) and ozone-depleting substances (ODSs) on UVI from 1960 by comparing CCMI sensitivity simulations (1960–2100) with fixed GHGs or ODSs at their respective 1960 levels. As expected with ODS fixed at their 1960 levels, there is no large decrease in ozone levels and consequently no sudden increase in UVI levels. With fixed GHG, we observed a delayed return of ozone to 1960 values, the same signal is observed on UVI, and looking at the UVI difference between 2090s values and 1960s values, we found an 8% increase in the tropical belt during the summer of each hemisphere. Finally, we show that, while in the Southern Hemisphere UVI is mainly driven by total ozone column, in the Northern Hemisphere both total ozone column and aerosol optical depth drive UVI levels, with aerosol optical depth having twice as much influence on UVI as total column does.

Description: We analyse simulations performed for the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion caused by anthropogenic stratospheric chlorine and bromine. We consider a total of 155 simulations from 20 models, including a range of sensitivity studies which examine the impact of climate change on ozone recovery. For the control simulations (unconstrained by nudging towards analysed meteorology) there is a large spread (±20 DU in the global average) in the predictions of the absolute ozone column. Therefore, the model results need to be adjusted for biases against historical data. Also, the interannual variability in the model results need to be smoothed in order to provide a reasonably narrow estimate of the range of ozone return dates. Consistent with previous studies, but here for a Representative Concentration Pathway (RCP) of 6.0, these new CCMI simulations project that global total column ozone will return to 1980 values in 2047 (with a 1-σ uncertainty of 2042–2052). At Southern Hemisphere mid-latitudes column ozone is projected to return to 1980 values in 2046 (2042–2050), and at Northern Hemisphere mid-latitudes in 2034 (2024–2044). In the polar regions, the return dates are 2062 (2055–2066) in the Antarctic in October and 2035 (2025–2040) in the Arctic in March. The earlier return dates in the NH reflect the larger sensitivity to dynamical changes. Our estimates of return dates are later than those presented in the 2014 Ozone Assessment by approximately 5–15 years, depending on the region. In the tropics only around half the models predict a return to 1980 values, at around 2040, while the other half do not reach this value. All models show a negative trend in tropical total column ozone towards the end of the 21st century. The CCMI models generally agree in their simulation of the time evolution of stratospheric chlorine, which is the main driver of ozone loss and recovery. However, there are a few outliers which show that the multi-model mean results for ozone recovery are not as tightly constrained as possible. Throughout the stratosphere the spread of ozone return dates to 1980 values between models tends to correlate with the spread of the return of inorganic chlorine to 1980 values. In the upper stratosphere, greenhouse gas-induced cooling speeds up the return by about 10–20 years. In the lower stratosphere, and for the column, there is a more direct link in the timing of the return dates, especially for the large Antarctic depletion. Comparisons of total column ozone between the models is affected by different predictions of the evolution of tropospheric ozone within the same scenario, presumably due to differing treatment of tropospheric chemistry. Therefore, for many scenarios, clear conclusions can only be drawn for stratospheric ozone columns rather than the total column. As noted by previous studies, the timing of ozone recovery is affected by the evolution of N2O and CH4. However, the effect in the simulations analysed here is small and at the limit of detectability from the few realisations available for these experiments compared to internal model variability. The large increase in N2O given in RCP 6.0 extends the ozone return globally by ~ 15 years relative to N2O fixed at 1960 abundances, mainly because it allows tropical column ozone to be depleted. The effect in extratropical latitudes is much smaller. The large increase in CH4 given in the RCP 8.5 scenario compared to RCP 6.0 also changes ozone return by ~ 15 years, again mainly through its impact in the tropics. For future assessments of single forcing or combined effects of CO2, CH4, and N2O on the stratospheric column ozone return dates, this work suggests that is more important to have multi-member (at least 3) ensembles for each scenario from each established participating model, rather than a large number of individual models.

Description: Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40–50% in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~30% in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2DU – approximately 33% larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, SOCOLv3.1, which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx, carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90% of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20% more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.

Description: We examine the impact of model horizontal resolution on simulated surface ozone (O3) and particulate matter less than 2.5 μm (PM2.5) concentrations, and the associated health impacts over Europe, using the HadGEM3-UKCA chemistry-climate model to simulate pollutant concentrations over Europe at a global (~ 140 km) and a regional (~ 50 km) resolution. The attributable fraction (AF) of total mortality due to long-term exposure to warm season daily maximum 8-hour running mean (MDA8) O3 and annual-average PM2.5 concentrations is then calculated for each European country using pollutant concentrations simulated at each resolution. Our results highlight a strong seasonal variation in simulated O3 and PM2.5 differences between the two model resolutions in Europe. Compared to the regional resolution results, simulated European O3 concentrations at the global resolution are on average higher in winter and spring (10 % and 6 %, respectively). In contrast, simulated O3 concentrations at the global resolution are lower in summer and autumn (−1 % and −4 %, respectively). These differences may partly be explained by differences in nitrogen dioxide (NO2) concentrations simulated at the two resolutions. Compared to O3, we find the opposite seasonality in simulated PM2.5 differences between the two resolutions. In winter and spring, simulated PM2.5 concentrations are lower at the global compared to the regional resolution (−8 % and −27 %, respectively) but higher in summer and autumn (29 % and 8 %, respectively) and are mostly related to differences in convective rainfall between the two resolutions for all seasons. These differences between the two resolutions exhibit clear spatial patterns for both pollutants that vary by season, and exert a strong influence on country to country variations in estimated AF for the two resolutions. Warm season MDA8 O3 levels are higher in most of southern Europe, but lower in areas of northern and eastern Europe when simulated at the global resolution compared to the regional resolution. Annual-average PM2.5 concentrations are higher across most of northern and eastern Europe but lower over parts of southwest Europe at the global compared to the regional resolution. Across Europe, differences in the AF associated with long-term exposure to population-weighted MDA8 O3 range between −0.9 % and +2.6 % (largest positive differences in southern Europe) while differences in the AF associated with long-term exposure to population-weighted annual mean PM2.5 range from −4.7 % to +2.8 % (largest positive differences in eastern Europe) of the total mortality. Therefore this study, with its unique focus on Europe, demonstrates that health impact assessments calculated using modelled pollutant concentrations, are sensitive to a change in model resolution by up to ±5 % of the total mortality across Europe.

Description: The global secondary organic aerosol (SOA) budget is highly uncertain, with global annual SOA production rates, estimated from global models, ranging over an order of magnitude and simulated SOA concentrations underestimated compared to observations. In this study, we use a global composition-climate model (UKCA) with interactive chemistry and aerosol microphysics to provide an in-depth analysis of the impact of each SOA source on the global SOA budget and its seasonality. We further quantify the role of each source on SOA spatial distributions, and evaluate simulated seasonal SOA concentrations against a comprehensive set of observations. The annual global SOA production rates from monoterpene, isoprene, biomass burning and anthropogenic precursor sources is 19.9 19.6, 9.5 and 24.6 Tg (SOA) a−1 respectively. When all sources are included, the SOA production rate from all sources is 73.6 Tg (SOA) a−1, which lies within the range of estimates from previous modelling studies. SOA production rates and SOA burdens from biogenic and biomass burning SOA sources peak during northern hemisphere (NH) summer. In contrast, the an thropogenic SOA production rate is fairly constant all year round. However, the global anthropogenic SOA burden does have a seasonal cycle which is lowest during NH summer, which is probably due to enhanced wet removal. Inclusion of the new SOA sources also accelerates the ageing by condensation of primary organic aerosol (POA), making it more hydrophilic, leading to a reduction in the POA lifetime. With monoterpene as the only source of SOA, simulated SOA and total organic aerosol (OA) concentrations are underestimated by the model when compared to surface and aircraft measurements. Model agreement with observations improves with all new sources added, primarily due to the inclusion of the anthropogenic source of SOA, although a negative bias remains. A further sensitivity simulation was performed with an increased anthropogenic SOA reaction yield, corresponding to an annual global SOA production rate of 70.0 Tg (SOA) a−1. Whilst simulated SOA concentrations improved relative to observations, they were still underestimated in urban environments and overestimated further downwind and in remote environments respectively. On the other hand, the inclusion of SOA from isoprene and biomass burning did not improve model–observations biases substantially except at one out of two tropical locations. However, these findings may reflect the very limited availability of observations to evaluate the model, which are primarily located in the NH mid-latitudes where anthropogenic emissions are high. Our results highlight that, within the current uncertainty limits in SOA sources and reaction yields, over the NH mid-latitudes, a large anthropogenic SOA source results in good agreement with observations. However, more observations are needed to establish the importance of biomass burning and biogenic sources of SOA in model agreement with observations.

Description: The Northern Hemisphere and tropical circulation response to interannual variability in Arctic stratospheric ozone is analyzed in a set of the latest model simulations archived for the Chemistry-Climate Model Initiative (CCMI) project. All models simulate a connection between ozone variability and temperature/geopotential height in the lower stratosphere similar to that observed. A connection between Arctic ozone variability and polar cap sea-level pressure is also found, but additional analysis suggests that it is mediated by the dynamical variability that typically drives the anomalous ozone concentrations. The CCMI models also show a connection between Arctic stratospheric ozone and the El Nino Southern Oscillation (ENSO): the CCMI models show a tendency of Arctic stratospheric ozone variability to lead ENSO variability one to two years later. While this effect is much weaker than that observed, it is still statistically significant. Overall, Arctic stratospheric ozone is related to lower stratospheric variability and may also influence the surface in both polar and tropical latitudes, though these impacts can be masked by internal variability if data is only available for ~40 years.

Description: The modeling study presented here aims to estimate how uncertainties in global hydroxyl radical (OH) distributions, variability, and trends may contribute to resolve discrepancies between simulated and observed methane (CH4) changes since 2000. A multi-model ensemble of 14 OH fields were analysed and were aggregated into 64 scenarios to force the offline atmospheric chemistry transport model LMDz with a standard CH4 emission scenario over the period 2000–2016. The multi-model simulated global volume-weighted tropospheric mean OH concentration ([OH]) averaged over 2000–2010 ranges between 8.7 × 105 and 12.8 × 105 molec cm−3. The inter-model differences in tropospheric OH burden and vertical distributions are mainly determined by the differences in the nitrogen oxide (NO) distributions, while the spatial discrepancies between OH fields are mostly due to differences in natural emissions and VOC chemistry. From 2000 to 2010, most simulated OH fields show an increase of 0.1–0.3 × 105 molec cm−3 in the tropospheric mean [OH], with year-to-year variations much smaller than during the historical period 1960–2000. Once ingested into the LMDz model, these OH changes translated into a 5 to 15 ppbv reduction in CH4 mixing ratio in 2010, which represent 7 %–20 % of the model simulated CH4 increase due to surface emissions. Between 2010 and 2016, the ensemble of simulations showed that OH changes could lead to a CH4 mixing ratio uncertainty of 〉 ±30 ppbv. Over the full 2000–2016 time period, using a common state-of-the-art but non-optimized emission scenario, the impact of [OH] changes tested here can explain up to 54 % of the gap between model simulations and observations. This result emphasizes the importance of better representing OH abundance and variations in CH4 forward simulations and emission optimizations performed by atmospheric inversions.

Description: The representation of volatile organic compound (VOC) deposition and oxidation mechanisms in the context of secondary organic aerosol (SOA) formation are developed in the United Kingdom Chemistry and Aerosol (UKCA) chemistry-climate model. Impacts of these developments on both the global SOA budget and model agreement with observations is quantified. Firstly, global model simulations were performed with varying VOC dry deposition and wet deposition. Including VOC dry deposition reduces the global annual-total SOA production rate by 2–32 %, with the range reflecting uncertainties in surface resistances. Including VOC wet deposition reduces the global annual-total SOA production rate by 15 % and is relatively insensitive to changes in effective Henry's Law coefficients. With precursor deposition, simulated SOA concentrations are lower than observed, with a normalised mean bias (NMB) of −51 %. Hence, including SOA precursor deposition worsens model agreement with observations even further (NMB = −66 %). Secondly, for the anthropogenic and biomass burning VOC precursors of SOA (VOCANT/BB), model simulations were performed varying: a) the parent hydrocarbon reactivity, b) the number of reaction intermediates, and c) accounting for differences in volatility between oxidation products from various pathways. These changes were compared to a scheme where VOCANT/BB adopts the reactivity of monoterpene (α-pinene), and is oxidised in a single-step mechanism with a fixed SOA yield. By using the chemical reactivity of either benzene, toluene or naphthalene for VOCANT/BB, the global annual-total VOCANT/BB oxidation rate changes by −3, −31 or −66 %, respectively, compared to when using monoterpene. Increasing the number of reaction intermediates, by introducing a peroxy radical (RO2), slightly slows the rate of SOA formation, but has no impact on the global annual-total SOA production rate. However, RO2 undergoes competitive oxidation reactions, forming products with substantially different volatilities. Accounting for the differences in product volatility between RO2 oxidation pathways increases the global SOA production rate by 153 % compared to using a single SOA yield. Overall, for relatively reactive compounds, such as toluene and naphthalene, the reduction in reactivity for VOCANT/BB oxidation is outweighed by accounting for the difference in volatility of RO2 products, leading to a net increase in the global annual-total SOA production rate of 85 and 145 %, respectively, and improvemtns in model agreement (NMB of −46 and 56 %, respectively). However, for benzene, the reduction in VOCANT/BB oxidation is not outweighed by accounting for the difference in SOA yield pathways, leading to a small change in the global annual-total SOA production rate of −3 %, and a slight worsening of model agreement with observatiobs (NMB = −77 %). These results highlight that variations in both VOC deposition and oxidation mechanisms contribute to substantial uncertainties in the global SOA budget and model agreement with observations.