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During polar spring, periods of elevated tropospheric bromine drive near complete removal of surface ozone. These events impact the tropospheric oxidative capacity and are an area of active research with multiple approaches for representing the underlying processes in global models. We present a method for parameterizing emissions of molecular bromine (Br2) over the Arctic using satellite retrievals of bromine monoxide (BrO) from the Ozone Monitoring Instrument (OMI). OMI retrieves column BrO with daily near global coverage, and we use the GEOS‐Chem chemical mechanism, run online within the Goddard Earth Observing System Earth System Model to identify hotspots of BrO likely associated with polar processes. To account for uncertainties in modeling background BrO, hotspots are only identified where the difference between OMI and modeled columns exceeds a statistical threshold. The resulting hotspot columns are a lower‐limit for the portion of OMI BrO attributable to bromine explosion events. While these hotspots are correlated with BrO measured in the lower troposphere over the Arctic Ocean, a case study of missing detections of near‐surface BrO is identified. Daily flux of Br2 is estimated from hotspot columns of BrO using internal model parameters. When the emissions are applied, BrO hotspots are modeled with a 5% low bias. The sensitivity of the resulting ozone simulations to the treatment of background uncertainties in the BrO column is demonstrated. While periods of isolated, large (>50%) decreases in surface ozone are modeled, this technique does not simulate the low ozone observed at coastal stations and consistently underestimates ozone loss during March. Plain Language Summary: During polar spring, high levels of bromine‐containing molecules drive near complete removal of surface ozone (O3), impacting the chemistry of the troposphere and the biological uptake of mercury. Global models currently have multiple mechanisms for representing the underlying processes that produce brominated molecules in polar regions. We estimate molecular bromine (Br2) emissions from measurements of bromine monoxide (BrO) collected over the Arctic by a satellite instrument. An atmospheric model, run without polar emissions of Br2, is used to estimate how much of the satellite BrO signal is due to background processes in the stratosphere and troposphere and isolate the portion of the signal likely associated with Arctic emissions. We account for uncertainties in the model representation of background BrO using a statistical threshold. Because of the catalytic nature of bromine‐mediated ozone depletion, we focus our initial efforts on developing a lower‐limit estimate of Arctic emissions. The amount of BrO attributed to polar processes and the resulting impact on O3 are sensitive to the magnitude of the statistical threshold, with a better representation of surface O3 achieved with a lower threshold. While the satellite‐based emissions result in periodic decreases in surface O3 in late spring, modeled O3 is consistently high with respect to observations, particularly during early spring. Key Points: BrO hotspots are isolated from satellite signals using modeled columns of BrO and a bias threshold to account for model uncertaintiesWe estimate Arctic Br2 emissions from BrO signals and demonstrate the sensitivity of modeled O3 to the BrO hotspot detection thresholdSimulations with satellite‐based Br2 emissions overestimate springtime Arctic surface O3 with few ozone depleting events modeled in March [ABSTRACT FROM AUTHOR]

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 modeling 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 specialized 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°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°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°C above preindustrial 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. Plain Language Summary: Our best tools to capture state‐of‐the‐art knowledge are complex, fully coupled Earth System Models (ESMs). However, ESMs are expensive to run and no single ESM can easily produce responses which represent the full range of uncertainties. Instead, for some applications, computationally efficient reduced complexity climate models (RCMs) are used in a probabilistic setup. An example of these applications is estimating the likelihood that an emissions scenario will stay below a certain global‐mean temperature change. Here we present a study (referred to as the Reduced Complexity Model Intercomparison Project (RCMIP) Phase 2) which investigates the extent to which different RCMs can be probabilistically calibrated to reproduce knowledge from specialized research communities. We find that the agreement between each RCM and the benchmarks varies, although the best performing models show good agreement across the majority of benchmarks. Under a very‐low‐emissions scenario median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, assuming historical warming of 0.8°C between 1850–1900 and 1995–2014). Investigating new ways to reduce these projection uncertainties seems paramount given the international community's goal to limit warming to below 1.5°C above preindustrial in the long‐term. Key Points: Probabilistic global‐mean temperature projections often use reduced complexity climate models (RCMs) because of their low computational costWe evaluate how well RCMs' probabilistic setups can synthesize knowledge from multiple research domains for policy relevant projectionsNo RCM is able to capture all forcing, warming, heat uptake and carbon cycle metrics we evaluate, however some come close across a range [ABSTRACT FROM AUTHOR]

To study emissions of CO2 in the Baltimore, MD‐Washington, D.C. (Balt‐Wash) area, an aircraft campaign was conducted in February 2015, as part of the Fluxes of Atmospheric Greenhouse‐Gases in Maryland (FLAGG‐MD) project. During the campaign, elevated mole fractions of CO2 were observed downwind of the urban center and local power plants. Upwind flight data and Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model analyses help account for the impact of emissions outside the Balt‐Wash area. The accuracy, precision, and sensitivity of CO2 emissions estimates based on the mass balance approach were assessed for both power plants and cities. Our estimates of CO2 emissions from two local power plants agree well with their Continuous Emissions Monitoring Systems (CEMS) records. For the 16 power plant plumes captured by the aircraft, the mean percentage difference of CO2 emissions was −0.3%. For the Balt‐Wash area as a whole, the 1𝜎 CO2 emission rate uncertainty for any individual aircraft‐based mass balance approach experiment was ±38%. Treating the mass balance experiments, which were repeated seven times within 9 days, as individual quantifications of the Balt‐Wash CO2 emissions, the estimation uncertainty was ±16% (standard error of the mean at 95% CL). Our aircraft‐based estimate was compared to various bottom‐up fossil fuel CO2 (FFCO2) emission inventories. Based on the FLAGG‐MD aircraft observations, we estimate 1.9 ± 0.3 MtC of FFCO2 from the Balt‐Wash area during the month of February 2015. The mean estimate of FFCO2 from the four bottom‐up models was 2.2 ± 0.3 MtC. Key Points: 1.9 ± 0.3 MtC of fossil fuel CO2 was emitted in Baltimore‐Washington during February 2015 based on data collected during seven aircraft flightsFour bottom‐up inventories indicate 2.2 ± 0.3 MtC of fossil fuel CO2 was emitted, in good agreement with our top‐down estimateThe uncertainty from a single flight segment was ±38% (1𝜎); data from seven flights yielded a precision of 16% at the 95% confidence level [ABSTRACT FROM AUTHOR]

Abstract: Bromine radicals (Br + BrO) are important atmospheric species owing to their ability to catalytically destroy ozone as well as their potential impacts on the oxidative pathways of many trace gases, including dimethylsulfide and mercury. Using space‐based observations of BrO, recent studies have reported rapid enhancements of tropospheric BrO over large areas (so called “BrO explosions”) connected to near‐surface ozone depletion occurring in polar spring. However, the source(s) of reactive bromine and mechanism(s) that initiate these BrO explosions are uncertain. In this study, we investigate the relationships between Arctic BrO explosions and two of the proposed sources of reactive bromine: sea‐salt aerosol (SSA) generated from blowing snow and first‐year (seasonal) sea ice. We use tropospheric column BrO derived from the Ozone Monitoring Instrument (OMI) in conjunction with the Goddard Earth Observing System Version 5 (GEOS‐5) data assimilation system provided by National Aeronautics and Space Administration Global Modeling and Assimilation Office. Case studies demonstrate a strong association between the temporal and spatial extent of OMI‐observed BrO explosions and the GEOS‐5 simulated blowing snow‐generated SSA during Arctic spring. Furthermore, the frequency of BrO explosion events observed over the 11‐year record of OMI exhibits significant correlation with a time series of the simulated SSA emission flux in the Arctic and little to no correlation with a time series of satellite‐based first‐year sea ice area. Therefore, we conclude that SSA generated by blowing snow is an important factor in the formation of the BrO explosion observed from space during Arctic spring. [ABSTRACT FROM AUTHOR]