This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO₂ is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO₂ radiative forcing and stronger positive feedbacks.

Nitrogen oxides (NO_x ≡ NO + NO₂) produced by lightning make a major contribution to the global production of tropospheric ozone and OH. Lightning distributions inferred from standard convective parameterizations in global chemical transport models (CTMs) fail to reproduce observations from the Lightning Imaging Sensor (LIS) and the Optical Transient Detector (OTD) satellite instruments. We present an optimal regional scaling algorithm for CTMs to fit the lightning NO_xsource to the satellite lightning data in a way that preserves the coupling to deep convective transport. We show that applying monthly scaling factors over ∼37 regions globally significantly improves the tropical ozone simulation in the GEOS‐Chem CTM as compared to a simulation unconstrained by the satellite data and performs equally well to a simulation with local scaling. The coarse regional scaling preserves sufficient statistics in the satellite data to constrain the interannual variability (IAV) of lightning. After processing the LIS data to remove their diurnal sampling bias, we construct a monthly time series of lightning flash rates for 1998–2010 and 35°S–35°N. We find a correlation of IAV in total tropical lightning with El Niño but not with the solar cycle or the quasi‐biennial oscillation. The global lightning NO_xsource ± IAV standard deviation in GEOS‐Chem is 6.0 ± 0.5 Tg N yr⁻¹, compared to 5.5 ± 0.8 Tg N yr⁻¹ for the biomass burning source. Lightning NO_x could have a large influence on the IAV of tropospheric ozone because it is released in the upper troposphere where ozone production is most efficient.

The goal of the Tropospheric Ozone Assessment Report (TOAR) is to provide the research community with an up-to-date scientific assessment of tropospheric ozone, from the surface to the tropopause. While a suite of observations provides significant information on the spatial and temporal distribution of tropospheric ozone, observational gaps make it necessary to use global atmospheric chemistry models to synthesize our understanding of the processes and variables that control tropospheric ozone abundance and its variability. Models facilitate the interpretation of the observations and allow us to make projections of future tropospheric ozone and trace gas distributions for different anthropogenic or natural perturbations. This paper assesses the skill of current-generation global atmospheric chemistry models in simulating the observed present-day tropospheric ozone distribution, variability, and trends. Drawing upon the results of recent international multi-model intercomparisons and using a range of model evaluation techniques, we demonstrate that global chemistry models are broadly skillful in capturing the spatio-temporal variations of tropospheric ozone over the seasonal cycle, for extreme pollution episodes, and changes over interannual to decadal periods. However, models are consistently biased high in the northern hemisphere and biased low in the southern hemisphere, throughout the depth of the troposphere, and are unable to replicate particular metrics that define the longer term trends in tropospheric ozone as derived from some background sites. When the models compare unfavorably against observations, we discuss the potential causes of model biases and propose directions for future developments, including improved evaluations that may be able to better diagnose the root cause of the model-observation disparity. Overall, model results should be approached critically, including determining whether the model performance is acceptable for the problem being addressed, whether biases can be tolerated or corrected, whether the model is appropriately constituted, and whether there is a way to satisfactorily quantify the uncertainty.

Determining effective strategies for mitigating surface ozone (O₃) pollution requires knowledge of the relative ambient concentrations of its precursors, NO_x, and VOCs. The space‐based tropospheric column ratio of formaldehyde to NO₂ (FNR) has been used as an indicator to identify NO_x‐limited versus NO_x‐saturated O₃ formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NO_x‐limited and NO_x‐saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near‐surface environment, and (3) satellite products contain errors. We use the GEOS‐Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near‐surface O₃ production regime. Extending this surface‐based predictor to a column‐based FNR requires accounting for differences in the HCHO and NO₂ vertical profiles. We compare four combinations of two OMI HCHO and NO₂ retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite‐derived FNR vary with the choice of NO₂ product, while the mean offset depends on the choice of HCHO product. Space‐based FNR indicates that the spring transition to NO_x‐limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NO_x sensitivity implies that NO_x emission controls will improve O₃ air quality more now than it would have a decade ago.

The evolution of tropospheric ozone from 1850 to 2100 has been studied using data from Phase 6 of the Coupled Model Intercomparison Project (CMIP6). We evaluate long-term changes using coupled atmosphere–ocean chemistry–climate models, focusing on the CMIP Historical and ScenarioMIP ssp370 experiments, for which detailed tropospheric-ozone diagnostics were archived. The model ensemble has been evaluated against a suite of surface, sonde and satellite observations of the past several decades and found to reproduce well the salient spatial, seasonal and decadal variability and trends. The multi-model mean tropospheric-ozone burden increases from 247 ± 36 Tg in 1850 to a mean value of 356 ± 31 Tg for the period 2005–2014, an increase of 44 %. Modelled present-day values agree well with previous determinations (ACCENT: 336 ± 27 Tg; Atmospheric Chemistry and Climate Model Intercomparison Project, ACCMIP: 337 ± 23 Tg; Tropospheric Ozone Assessment Report, TOAR: 340 ± 34 Tg). In the ssp370 experiments, the ozone burden increases to 416 ± 35 Tg by 2100. The ozone budget has been examined over the same period using lumped ozone production ($P_{{\mathrm{O}}_{\mathrm{3}}}$ ) and loss ($L_{{\mathrm{O}}_{\mathrm{3}}}$ ) diagnostics. Both ozone production and chemical loss terms increase steadily over the period 1850 to 2100, with net chemical production ($P_{{\mathrm{O}}_{\mathrm{3}}}$ -$L_{{\mathrm{O}}_{\mathrm{3}}}$ ) reaching a maximum around the year 2000. The residual term, which contains contributions from stratosphere–troposphere transport reaches a minimum around the same time before recovering in the 21st century, while dry deposition increases steadily over the period 1850–2100. Differences between the model residual terms are explained in terms of variation in tropopause height and stratospheric ozone burden.