Some key photochemical uncertainties that cannot be readily eliminated by current observations translate into a range of stratospheric O₃ abundances in the tens of percent. The uncertainty in O₃ production due to that in the cross sections for O₂ in the Hertzberg continuum is studied here with the NCAR Community Atmosphere Model, which allows for interactive climate and ozone chemistry. A min-max range in the O₂ cross sections of 30%, consistent with current uncertainties, changes O₃ abundances in the lower tropical stratosphere by up to 30%, with a relatively smaller and opposite change above 30 hPa. Here we have systematically examined the changes in the time-mean state, the seasonal cycle, and the interannual variability of the temperature and circulation associated with the ±30% change in O₂ cross sections. This study points to the important role of O₃ in the lower tropical stratosphere in determining the physical characteristics of the tropical tropopause layer. Reducing O₂ cross sections by 30% increases ozone abundances which warms the lower stratosphere (60°S −60°N; 2 K maximum at equator) and lowers the tropopause height by 100–200 m (30°S –30°N). The large-scale warming leads to enhanced stratification near the tropopause which reduces upward wave propagation everywhere except for high latitudes. The lowermost tropical stratosphere is better ventilated during austral winter. The annual cycle of ozone is amplified. The interannual variability of the winter stratospheric polar vortices also increases, but the mechanism involves wave-mean flow interaction, and the exact role of ozone in it needs further investigation.

Abstract. Solar-J is a comprehensive model for radiative transfer over the solar spectrum that addresses the needs of both photochemistry and solar heating in Earth system models. Solar-J includes an 8-stream scattering, plane-parallel radiative transfer solver with corrections for sphericity. It uses the scattering phase function of aerosols and clouds expanded to 8th order and thus makes no isotropic-equivalent approximations that are prevalent in most solar heating codes. It calculates both chemical photolysis rates and the absorption of sunlight and thus the heating rates throughout the Earth's atmosphere. Solar-J is a spectral extension of Fast-J, a standard in many chemical models that calculates photolysis rates in the 0.18–0.85 μm region. For solar heating, Solar-J extends its calculation out to 12 μm using correlated-k gas absorption bins in the infrared from the shortwave Rapid Radiative Transfer Model for GCM applications (RRTMG-SW). Solar-J successfully matches RRTMG's atmospheric heating profile in a clear-sky, aerosol-free, tropical atmosphere. We compare both codes in cloudy atmospheres with a liquid-water stratus cloud and an ice-crystal cirrus cloud. For the stratus cloud both models use the same physical properties, and we find a systematic low bias in the RRTMG-SW of about 3 % in planetary albedo across all solar zenith angles, caused by RRTMG-SW's 2-stream scattering. Discrepancies with the cirrus cloud using any of RRTMG's three different parameterizations are larger, less systematic, and occur throughout the atmosphere. Effectively, Solar-J has combined the best components of RRTMG and Fast-J to build a high-fidelity module for the scattering and absorption of sunlight in the Earth's atmosphere, for which the three major components – wavelength integration, scattering, and averaging over cloud fields all have comparably small errors. More accurate solutions come with increased computational costs, about 5x that of RRTMG, but there are options for reduced costs or computational acceleration that would bring costs down while maintaining balanced errors across components and improved fidelity.

Solar-J is a comprehensive radiative transfer model for the solar spectrum that addresses the needs of both solar heating and photochemistry in Earth system models. Solar-J is a spectral extension of Cloud-J, a standard in many chemical models that calculates photolysis rates in the 0.18-0.8 μm region. The Cloud-J core consists of an eight-stream scattering, plane-parallel radiative transfer solver with corrections for sphericity. Cloud-J uses cloud quadrature to accurately average over correlated cloud layers. It uses the scattering phase function of aerosols and clouds expanded to eighth order and thus avoids isotropic-equivalent approximations prevalent in most solar heating codes. The spectral extension from 0.8 to 12 μm enables calculation of both scattered and absorbed sunlight and thus aerosol direct radiative effects and heating rates throughout the Earth's atmosphere. The Solar-J extension adopts the correlated-k gas absorption bins, primarily water vapor, from the shortwave Rapid Radiative Transfer Model for general circulation model (GCM) applications (RRTMG-SW). Solar-J successfully matches RRTMG-SW's tropospheric heating profile in a clear-sky, aerosol-free, tropical atmosphere. We compare both codes in cloudy atmospheres with a liquid-water stratus cloud and an ice-crystal cirrus cloud. For the stratus cloud, both models use the same physical properties, and we find a systematic low bias of about 3 % in planetary albedo across all solar zenith angles caused by RRTMG-SW's two-stream scattering. Discrepancies with the cirrus cloud using any of RRTMG-SW's three different parameterizations are as large as about 20-40 % depending on the solar zenith angles and occur throughout the atmosphere. Effectively, Solar-J has combined the best components of RRTMG-SW and Cloud-J to build a high-fidelity module for the scattering and absorption of sunlight in the Earth's atmosphere, for which the three major components - wavelength integration, scattering, and averaging over cloud fields - all have comparably small errors. More accurate solutions with Solar-J come with increased computational costs, about 5 times that of RRTMG-SW for a single atmosphere. There are options for reduced costs or computational acceleration that would bring costs down while maintaining improved fidelity and balanced errors.

Complete representation of rainfall–runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, −3.3, and 0.10, respectively, compared to 0.72, 0.66, −9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

Stratospheric ozone affects climate directly as the predominant heat source in the stratosphere and indirectly through chemical reactions controlling other greenhouse gases. The U.S. Department of Energy’s Energy Exascale Earth System Model version 1 (E3SMv1) implemented a new ozone chemistry module that improves the simulation of the sharp tropopause gradients, replacing a version based partly on long-term average climatologies that poorly represented heating rates in the lowermost stratosphere. The new O3v2 module extends seamlessly into the troposphere and preserves the naturally sharp cross-tropopause gradient, with 20 %–40 % less ozone in this region. Additionally, O3v2 enables the diagnosis of stratosphere–troposphere exchange flux of ozone, a key budget term lacking in E3SMv1. Here, we evaluate key features in ozone abundance and other closely related quantities in atmosphere-only E3SMv1 simulations driven by observed sea surface temperatures (SSTs, years 1990–2014), comparing them with satellite observations of ozone and also with the University of California, Irvine chemistry transport model (UCI CTM) using the same stratospheric chemistry scheme but driven by European Centre forecast fields for the same period. In terms of stratospheric column ozone, O3v2 shows reduced mean bias and improved northern midlatitude variability, but it is not quite as good as the UCI CTM. As expected, SST-forced E3SMv1 simulations cannot synchronize with observed quasi-biennial oscillations (QBOs), but they do show the typical QBO pattern seen in column ozone. This new O3v2 E3SMv1 model mostly retains the same climate state and climate sensitivity as the previous version, and we recommend its use for other climate models that still use ozone climatologies.

We examined the multiyear mean and variability of dimethyl sulfide (DMS) and its relationship to sulfate aerosols, as well as cloud microphysical and radiative properties. We conducted a 150 year simulation using preindustrial conditions produced by the Community Earth System Model embedded with a dynamic DMS module. The model simulated the mean spatial distribution of DMS emissions and burden, as well as sulfur budgets associated with DMS, SO2, H2SO4, and sulfate that were generally similar to available observations and inventories for a variety of regions. Changes in simulated sea-to-air DMS emissions and associated atmospheric abundance, along with associated aerosols and cloud and radiative properties, were consistently dominated by El Niño–Southern Oscillation (ENSO) cycle in the tropical Pacific region. Simulated DMS, aerosols, and clouds showed a weak positive feedback on sea surface temperature. This feedback suggests a link among DMS, aerosols, clouds, and climate on interannual timescales. The variability of DMS emissions associated with ENSO was primarily caused by a higher variation in wind speed during La Niña events. The simulation results also suggest that variations in DMS emissions increase the frequency of La Niña events but do not alter ENSO variability in terms of the standard deviation of the Niño 3 sea surface temperature anomalies.

We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.

This study provides an overview of the coupled high-resolution Version 1 of the Energy Exascale Earth System Model (E3SMv1) and documents the characteristics of a 50-year-long high-resolution control simulation with time-invariant 1950 forcings following the HighResMIP protocol. In terms of global root-mean-squared error metrics, this high-resolution simulation is generally superior to results from the low-resolution configuration of E3SMv1 (due to resolution, tuning changes, and possibly initialization procedure) and compares favorably to models in the CMIP5 ensemble. Ocean and sea ice simulation is particularly improved, due to better resolution of bathymetry, the ability to capture more variability and extremes in winds and currents, and the ability to resolve mesoscale ocean eddies. The largest improvement in this regard is an ice-free Labrador Sea, which is a major problem at low resolution. Interestingly, several features found to improve with resolution in previous studies are insensitive to resolution or even degrade in E3SMv1. Most notable in this regard are warm bias and associated stratocumulus deficiency in eastern subtropical oceans and lack of improvement in El Niño. Another major finding of this study is that resolution increase had negligible impact on climate sensitivity (measured by net feedback determined through uniform +4K prescribed sea surface temperature increase) and aerosol sensitivity. Cloud response to resolution increase consisted of very minor decrease at all levels. Large-scale patterns of precipitation bias were also relatively unaffected by grid spacing.

Emissions of air pollutants and their precursors determine regional air quality and can alter climate. Climate change can perturb the long-range transport, chemical processing, and local meteorology that influence air pollution. We review the implications of projected changes in methane (CH₄), ozone precursors (O₃), and aerosols for climate (expressed in terms of the radiative forcing metric or changes in global surface temperature) and hemispheric-to-continental scale air quality. Reducing the O₃ precursor CH₄ would slow near-term warming by decreasing both CH₄ and tropospheric O₃. Uncertainty remains as to the net climate forcing from anthropogenic nitrogen oxide (NO _x ) emissions, which increase tropospheric O₃ (warming) but also increase aerosols and decrease CH₄ (both cooling). Anthropogenic emissions of carbon monoxide (CO) and non-CH₄ volatile organic compounds (NMVOC) warm by increasing both O₃ and CH₄. Radiative impacts from secondary organic aerosols (SOA) are poorly understood. Black carbon emission controls, by reducing the absorption of sunlight in the atmosphere and on snow and ice, have the potential to slow near-term warming, but uncertainties in coincident emissions of reflective (cooling) aerosols and poorly constrained cloud indirect effects confound robust estimates of net climate impacts. Reducing sulfate and nitrate aerosols would improve air quality and lessen interference with the hydrologic cycle, but lead to warming. A holistic and balanced view is thus needed to assess how air pollution controls influence climate; a first step towards this goal involves estimating net climate impacts from individual emission sectors. Modeling and observational analyses suggest a warming climate degrades air quality (increasing surface O₃ and particulate matter) in many populated regions, including during pollution episodes. Prior Intergovernmental Panel on Climate Change (IPCC) scenarios (SRES) allowed unconstrained growth, whereas the Representative Concentration Pathway (RCP) scenarios assume uniformly an aggressive reduction, of air pollutant emissions. New estimates from the current generation of chemistry–climate models with RCP emissions thus project improved air quality over the next century relative to those using the IPCC SRES scenarios. These two sets of projections likely bracket possible futures. We find that uncertainty in emission-driven changes in air quality is generally greater than uncertainty in climate-driven changes. Confidence in air quality projections is limited by the reliability of anthropogenic emission trajectories and the uncertainties in regional climate responses, feedbacks with the terrestrial biosphere, and oxidation pathways affecting O₃ and SOA.