Constraining Atmospheric Selenium Emissions Using Observations, Global Modeling, and Bayesian Inference.

Selenium (Se) is an essential dietary element for humans and animals, and the atmosphere is an important source of Se to soils. However, estimates of global atmospheric Se fluxes are highly uncertain. To constrain these uncertainties, we use a global model of atmospheric Se cycling and a database of more than 600 sites where Se in aerosol has been measured. Applying Bayesian inference techniques, we determine the probability distributions of global Se emissions from the four major sources: anthropogenic activities, volcanoes, marine biosphere, and terrestrial biosphere. Between 29 and 36 Gg of Se are emitted to the atmosphere every year, doubling previous estimates of emissions. Using emission parameters optimized by aerosol network measurements, our model shows good agreement with the aerosol Se observations (R2 = 0.66), as well as with independent aerosol (0.59) and wet deposition measurements (0.57). Both model and measurements show a decline in Se over North America in the last two decades because of changes in technology and energy policy. Our results highlight the role of the ocean as a net atmospheric Se sink, with around 7 Gg yr-1 of Se transferred from land through the atmosphere. The constrained Se emissions represent a substantial step forward in understanding the global Se cycle.

Emerging role of wetland methane emissions in driving 21st century climate change.

Wetland methane (CH4) emissions are the largest natural source in the global CH4 budget, contributing to roughly one third of total natural and anthropogenic emissions. As the second most important anthropogenic greenhouse gas in the atmosphere after CO2, CH4 is strongly associated with climate feedbacks. However, due to the paucity of data, wetland CH4 feedbacks were not fully assessed in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The degree to which future expansion of wetlands and CH4 emissions will evolve and consequently drive climate feedbacks is thus a question of major concern. Here we present an ensemble estimate of wetland CH4 emissions driven by 38 general circulation models for the 21st century. We find that climate change-induced increases in boreal wetland extent and temperature-driven increases in tropical CH4 emissions will dominate anthropogenic CH4 emissions by 38 to 56% toward the end of the 21st century under the Representative Concentration Pathway (RCP2.6). Depending on scenarios, wetland CH4 feedbacks translate to an increase in additional global mean radiative forcing of 0.04 W·m-2 to 0.19 W·m-2 by the end of the 21st century. Under the "worst-case" RCP8.5 scenario, with no climate mitigation, boreal CH4 emissions are enhanced by 18.05 Tg to 41.69 Tg, due to thawing of inundated areas during the cold season (December to May) and rising temperature, while tropical CH4 emissions accelerate with a total increment of 48.36 Tg to 87.37 Tg by 2099. Our results suggest that climate mitigation policies must consider mitigation of wetland CH4 feedbacks to maintain average global warming below 2 °C.

Revisiting the mystery of recent stratospheric temperature trends.

Simulated stratospheric temperatures over the period 1979-2016 in models from the Chemistry-Climate Model Initiative (CCMI) are compared with recently updated and extended satellite observations. The multi-model mean global temperature trends over 1979- 2005 are -0.88 ± 0.23, -0.70 ± 0.16, and -0.50 ± 0.12 K decade-1 for the Stratospheric Sounding Unit (SSU) channels 3 (~40-50 km), 2 (~35-45 km), and 1 (~25-35 km), respectively. These are within the uncertainty bounds of the observed temperature trends from two reprocessed satellite datasets. In the lower stratosphere, the multi-model mean trend in global temperature for the Microwave Sounding Unit channel 4 (~13-22 km) is -0.25 ± 0.12 K decade-1 over 1979-2005, consistent with estimates from three versions of this satellite record. The simulated stratospheric temperature trends in CCMI models over 1979-2005 agree with the previous generation of chemistry-climate models. The models and an extended satellite dataset of SSU with the Advanced Microwave Sounding Unit-A show weaker global stratospheric cooling over 1998-2016 compared to the period of intensive ozone depletion (1979-1997). This is due to the reduction in ozone-induced cooling from the slow-down of ozone trends and the onset of ozone recovery since the late 1990s. In summary, the results show much better consistency between simulated and satellite observed stratospheric temperature trends than was reported by Thompson et al. (2012) for the previous versions of the SSU record and chemistry-climate models. The improved agreement mainly comes from updates to the satellite records; the range of simulated trends is comparable to the previous generation of models.

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se) concentrations were predicted to drop, particularly in Europe, due to increases in leaching attributed to increases in aridity. Given its international importance in agriculture, reductions of essential elements, including S and Se, in European soils could have important impacts on nutrition and human health. Our objectives were to model current soil S and Se levels in Europe and predict concentration changes for the 21st century. We interrogated four machine-learning (ML) techniques, but after critical evaluation, only outputs for linear support vector regression (Lin-SVR) models for S and Se and the multilayer perceptron model (MLP) for Se were consistent with known mechanisms reported in literature. Other models exhibited overfitting even when differences in training and testing performance were low or non-existent. Furthermore, our results highlight that similarly performing models based on RMSE or R2 can lead to drastically different predictions and conclusions, thus highlighting the need to interrogate machine learning models and to ensure they are consistent with known mechanisms reported in the literature. Both elements exhibited similar spatial patterns with predicted gains in Scandinavia versus losses in the central and Mediterranean regions of Europe, respectively, by the end of the 21st century for an extreme climate scenario. The median change was -5.5% for S (Lin-SVR) and -3.5% (MLP) and -4.0% (Lin-SVR) for Se. For both elements, modeled losses were driven by decreases in soil organic carbon, S and Se atmospheric deposition, and gains were driven by increases in evapotranspiration.

The influence of mixing on stratospheric age of air changes in the 21st century.

Climate models consistently predict an acceleration of the Brewer-Dobson circulation (BDC) due to climate change in the 21st century. However, the strength of this acceleration varies considerably among individual models, which constitutes a notable source of uncertainty for future climate projections. To shed more light upon the magnitude of this uncertainty and on its causes, we analyze the stratospheric mean age of air (AoA) of 10 climate projection simulations from the Chemistry Climate Model Initiative phase 1 (CCMI-I), covering the period between 1960 and 2100. In agreement with previous multi-model studies, we find a large model spread in the magnitude of the AoA trend over the simulation period. Differences between future and past AoA are found to be predominantly due to differences in mixing (reduced aging by mixing and recirculation) rather than differences in residual mean transport. We furthermore analyze the mixing efficiency, a measure of the relative strength of mixing for given residual mean transport, which was previously hypothesized to be a model constant. Here, the mixing efficiency is found to vary not only across models, but also over time in all models. Changes in mixing efficiency are shown to be closely related to changes in AoA and quantified to roughly contribute 10% to the long-term AoA decrease over the 21st century. Additionally, mixing efficiency variations are shown to considerably enhance model spread in AoA changes. To understand these mixing efficiency variations, we also present a consistent dynamical framework based on diffusive closure, which highlights the role of basic state potential vorticity gradients in controlling mixing efficiency and therefore aging by mixing.

Ultraviolet radiation modelling using output from the Chemistry Climate Model Initiative.

We have derived values of the Ultraviolet Index (UVI) at solar noon using the Tropospheric Ultraviolet Model (TUV) driven by ozone, temperature and aerosol fields from climate simulations of 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 the clear-sky UVI. We compared the modelled UVI climatologies against present-day climatological values of UVI derived from both 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/TUV calculations and the ground-based measurements ranged between -5.9% and 10.6%. We then 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 the 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 increases in UVI 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, with a corresponding pattern of change 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 the 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 the UVI as total ozone column does.

Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry-climate model.

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 40.2 DU - 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 8 DU, 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.

No Robust Evidence of Future Changes in Major Stratospheric Sudden Warmings: A Multi-model Assessment from CCMI.

Major stratospheric sudden warmings (SSWs) are the largest instance of wintertime variability in the Arctic stratosphere. Due to their relevance for the troposphere-stratosphere system, several previous studies have focused on their potential response to anthropogenic forcings. However, a wide range of results have been reported, from a future increase in the frequency of SSWs to a 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. Here we 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. From analyzing future integrations we find no statistically significant change in the frequency of SSWs over the 21st century, irrespective of the metric used for the identification of SSWs. 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.

Deriving Global OH Abundance and Atmospheric Lifetimes for Long-Lived Gases: A Search for CH3CCl3 Alternatives.

An accurate estimate of global hydroxyl radical (OH) abundance is important for projections of air quality, climate, and stratospheric ozone recovery. As the atmospheric mixing ratios of methyl chloroform (CH3CCl3) (MCF), the commonly used OH reference gas, approaches zero, it is important to find alternative approaches to infer atmospheric OH abundance and variability. The lack of global bottom-up emission inventories is the primary obstacle in choosing a MCF alternative. We illustrate that global emissions of long-lived trace gases can be inferred from their observed mixing ratio differences between the Northern Hemisphere (NH) and Southern Hemisphere (SH), given realistic estimates of their NH-SH exchange time, the emission partitioning between the two hemispheres, and the NH versus SH OH abundance ratio. Using the observed long-term trend and emissions derived from the measured hemispheric gradient, the combination of HFC-32 (CH2F2), HFC-134a (CH2FCF3, HFC-152a (CH3CHF2), and HCFC-22 (CHClF2), instead of a single gas, will be useful as a MCF alternative to infer global and hemispheric OH abundance and trace gas lifetimes. The primary assumption on which this multispecies approach relies is that the OH lifetimes can be estimated by scaling the thermal reaction rates of a reference gas at 272 K on global and hemispheric scales. Thus, the derived hemispheric and global OH estimates are forced to reconcile the observed trends and gradient for all four compounds simultaneously. However, currently, observations of these gases from the surface networks do not provide more accurate OH abundance estimate than that from MCF.

Formaldehyde in the Tropical Western Pacific: Chemical sources and sinks, convective transport, and representation in CAM-Chem and the CCMI models.

Formaldehyde (HCHO) directly affects the atmospheric oxidative capacity through its effects on HOx. In remote marine environments, such as the Tropical Western Pacific (TWP), it is particularly important to understand the processes controlling the abundance of HCHO because model output from these regions is used to correct satellite retrievals of HCHO. Here, we have used observations from the CONTRAST field campaign, conducted during January and February 2014, to evaluate our understanding of the processes controlling the distribution of HCHO in the TWP as well as its representation in chemical transport/climate models. Observed HCHO mixing ratios varied from ~500 pptv near the surface to ~75 pptv in the upper troposphere. Recent convective transport of near surface HCHO and its precursors, acetaldehyde and possibly methyl hydroperoxide, increased upper tropospheric HCHO mixing ratios by ~33% (22 pptv); this air contained roughly 60% less NO than more aged air. Output from the CAM-Chem chemistry transport model (2014 meteorology) as well as nine chemistry climate models from the Chemistry-Climate Model Initiative (free-running meteorology) are found to uniformly underestimate HCHO columns derived from in situ observations by between 4 and 50%. This underestimate of HCHO likely results from a near factor of two underestimate of NO in most models, which strongly suggests errors in NOx emissions inventories and/or in the model chemical mechanisms. Likewise, the lack of oceanic acetaldehyde emissions and potential errors in the model acetaldehyde chemistry lead to additional underestimates in modeled HCHO of up to 75 pptv (~15%) in the lower troposphere.

Skin cancer risks avoided by the Montreal Protocol--worldwide modeling integrating coupled climate-chemistry models with a risk model for UV.

The assessment model for ultraviolet radiation and risk "AMOUR" is applied to output from two chemistry-climate models (CCMs). Results from the UK Chemistry and Aerosols CCM are used to quantify the worldwide skin cancer risk avoided by the Montreal Protocol and its amendments: by the year 2030, two million cases of skin cancer have been prevented yearly, which is 14% fewer skin cancer cases per year. In the "World Avoided," excess skin cancer incidence will continue to grow dramatically after 2030. Results from the CCM E39C-A are used to estimate skin cancer risk that had already been inevitably committed once ozone depletion was recognized: excess incidence will peak mid 21st century and then recover or even super-recover at the end of the century. When compared with a "No Depletion" scenario, with ozone undepleted and cloud characteristics as in the 1960s throughout, excess incidence (extra yearly cases skin cancer per million people) of the "Full Compliance with Montreal Protocol" scenario is in the ranges: New Zealand: 100-150, Congo: -10-0, Patagonia: 20-50, Western Europe: 30-40, China: 90-120, South-West USA: 80-110, Mediterranean: 90-100 and North-East Australia: 170-200. This is up to 4% of total local incidence in the Full Compliance scenario in the peak year.