Surface ozone regulation policies rely heavily on air quality models, such as CAMx, as important guiding tools. Comparison with observations is crucial to validating a model’s ability to represent ozone production chemistry. Identifying factors influencing surface ozone formation is complicated because ozone photochemical production rates are non-linearly dependent on concentrations of precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs). We compare ozone production regimes (OPRs) identified from satellite observations and model simulations, as defined by the ratio of column formaldehyde to nitrogen dioxide (FNR, HCHO/NO2). We perform CAMx simulations for June-July-August 2016 over the Contiguous United States (CONUS) and compared these outputs against two OMI NO2and HCHO retrievals. Our analysis spans diurnal and altitudinal variations of OPRs, offering important insights for effective policy formulation. At the time of the OMI overpass (~1:30 PM LT), OPR is NOx-limited over most of the CONUS, as determined from OMI column ratios. Analysis of CAMx column ratios shows similar results. In contrast, more regions are VOC-limited when we constrain our ratio to within the Planetary Boundary Layer (PBL). In the morning (~9 AM LT), the CAMx PBL column ratios shift towards VOC-limited regime. We highlight areas of the CONUS for which satellite measurements of FNR may not be an accurate indicator of near-surface OPRs. Air quality regulations based on satellite observations should consider the diurnal variations of surface OPRs and assess how well their ratios represent near-surface OPR. Our results have implications for interpretation of TEMPO data for policy relevant applications.

During polar spring, periods of elevated tropospheric bromine known as “bromine explosion events” are associated with near complete removal of surface ozone. The satellite-based Ozone Monitoring Instrument (OMI) provides total column measurements of bromine monoxide (BrO) with daily global coverage. In this study, we estimate springtime bromine emissions over the Arctic using OMI retrievals of BrO in combination with the GEOS-Chem (version 12.0.1) chemical mechanism, run online within the GEOS Earth System Model. Tropospheric hotspots of BrO are identified over the Arctic where the difference between OMI and modeled columns of BrO exceeds the bias observed over regions not impacted by bromine explosion emissions. The resulting hotspot columns are a lower-limit estimate for the portion of the OMI BrO signal attributable to bromine explosion events and are well correlated with BrO measured in the lower troposphere by buoy-based instruments. Daily flux of molecular bromine is calculated from hotspot columns of BrO based on the modeled atmospheric lifetime of inorganic bromine in the lower troposphere and partitioning of bromine species into BrO at OMI overpass time. Following the application of Arctic emissions in GEOS-Chem, OMI-based tropospheric hotspots of BrO are successfully modeled for 2008 – 2012 and periods of isolated, large (> 50%) decreases in surface ozone are captured during April and May. While this technique does not fully capture the low ozone observed at coastal stations, if a lower threshold is used to identify tropospheric hotspots of BrO, the representation of surface ozone in late spring is improved.

We studied atmospheric methane observations from November 2016 to October 2017 from one rural and two urban towers in the Baltimore-Washington region (BWR). Methane observations at these three towers display distinct seasonal and diurnal cycles with maxima at night and in the early morning, reflecting local emissions and boundary layer dynamics. Peaks in winter concentrations and vertical gradients indicate strong local anthropogenic wintertime methane sources in urban regions. In contrast, our analysis shows larger local emissions in summer at the rural site, suggesting a dominant influence of wetland emissions. We compared observed enhancements (mole fractions above the 5th percentile) to simulated methane enhancements using the WRF-STILT model driven by two EDGAR inventories. When run with EDGAR 5.0, the low bias of modeled versus measured methane was greater (ratio of 1.9) than the bias found when using the EDGAR 4.2 emission inventory (ratio of 1.3). However, the correlation of modeled versus measured methane was stronger (~1.2 times higher) for EDGAR 5.0 compared to results found using EDGAR 4.2. In winter, the inclusion of wetland emissions using WETCHARTs had little impact on the mean bias, but during summer, the low bias for all hours using EDGAR 5.0 improved by from 63 to 23 nanomoles per mole of dry air or parts per billion (ppb) at the rural site. We conclude that both versions of EDGAR underestimate the regional anthropogenic emissions of methane, but version 5.0 has a more accurate spatial representation.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modelling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialised research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7{degree sign}C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8{degree sign}C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community’s goal to contain warming to below 1.5{degree sign}C above pre-industrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.

We use the Empirical Model of Global Climate (EM-GC) to show that human activity has been responsible for ~0.14 °C/decade (range: 0.08 to 0.20) of warming from 1979 to 2010. This EM-GC based quantification of Attributable Anthropogenic Warming Rate (AAWR) is constrained by the observed global mean surface temperature and ocean heat content records; the largest contribution to the uncertainty in our estimate of AAWR is imprecise knowledge of the radiative forcing due to tropospheric aerosols (AER RF). Our value of AAWR is noticeably lower than the mean value from the IPCC 2013 models, 0.22 °C/decade (range: 0.08 to 0.32) with no overlap of interquartile ranges. We also compute probabilistic forecasts of the rise in GMST where again the largest source of uncertainty is AER RF, and cast results in terms of the likelihood of achieving either 1.5 °C or 2.0 °C warmings relative to pre-industrial. We show that the likelihoods of limiting global warming to 2°C are 92%, 50%, and 20% if greenhouse gases follow the RCP 2.6, 4.5, and 6.0 scenarios; the likelihoods of limiting warming to 1.5°C drop to 67%, 10%, and 0.1% for these same three RCPs. Warming forecasts based upon our EM-GC are more optimistic than found by CMIP5 GCMs, following how many GCMs exhibit faster warming than inferred from the recent climate record. Our EM-GC forecasts show that aggressive controls on emissions of both CO2 and CH4 starting this decade are needed to limit global warming to 1.5°C with high probability.