Jian He

The Rapid Refresh model coupled to Chemistry (RAP-Chem) is an experimental forecast model built and operated by the NOAA Global Systems Laboratory (GSL). The RAP-Chem is a full chemistry extension to the operational smoke forecasting model, RAP-Smoke. RAP-Chem’s chemistry includes a reduced complexity, carbon-bond, gas-phase mechanism and an aerosol scheme that are coupled together to produce secondary aerosols and perform heterogeneous chemistry. The model includes chemical lateral boundary conditions, hourly anthropogenic emissions and online emissions from biomass burning, dust, sea salt, and biogenic sources. RAP-Chem also includes the direct aerosol effect on radiation and photolysis and a simplified indirect aerosol effect that couples prognostic aerosols to representative aerosols in the aerosol-aware microphysics scheme. The default ‘offline’ vertical mixing implemented in WRF-Chem is updated to instead mix chemical species alongside other scalars in the model’s planetary boundary layer (PBL) scheme, which tends to improve predictions across species – increasing surface ozone (O₃) and decreasing less reactive tracers (e.g., CO). Sub-grid boundary layer clouds in the PBL scheme are also coupled to a WRF-Chem photolysis scheme for the first time, overall reducing O₃ biases in the eastern US. Here we present the ability of the RAP-Chem model to simulate the extreme North American wildfires in 2020. We demonstrate that the relatively simple gas-phase chemical mechanism alongside our model physics developments and coupling with chemistry can predict maximum daily 8-h average O₃ with exceptional skill (Normalized Mean Bias (NMB) = 6.6 %) averaged over CONUS, improving on simulations that neglect the direct aerosol effect (NMB = 9.5 %). Ozone biases are large in regions with extreme aerosol loading, but the NMB improves more significantly. The model produces the observed extreme aerosol loading with less skill; tending to underpredict extreme wildfire induced concentrations while overpredicting urban particle pollution. As with O₃, daily average PM2.5 is predicted with better skill when the direct aerosol feedback is included (-15.8 % vs. -16.8 %). Overall we find that improving the coupling between chemistry and physics processes in the air quality model improves its predictions of both weather and atmospheric composition.

The Rapid Refresh model coupled to Chemistry (RAP-Chem) is an experimental forecast model built and operated by the NOAA Global Systems Laboratory (GSL). The RAP-Chem is a full chemistry extension to the operational smoke forecasting model, RAP-Smoke. RAP-Chem’s chemistry includes a reduced complexity, carbon-bond, gas-phase mechanism and an aerosol scheme that are coupled together to produce secondary aerosols and perform heterogeneous chemistry. The model includes chemical lateral boundary conditions, hourly anthropogenic emissions and online emissions from biomass burning, dust, sea salt, and biogenic sources. RAP-Chem also includes the direct aerosol effect on radiation and photolysis and a simplified indirect aerosol effect that couples prognostic aerosols to representative aerosols in the aerosol-aware microphysics scheme. The default ‘offline’ vertical mixing implemented in WRF-Chem is updated to instead mix chemical species alongside other scalars in the model’s planetary boundary layer (PBL) scheme, which tends to improve predictions across species – increasing surface ozone (O₃) and decreasing less reactive tracers (e.g., CO). Sub-grid boundary layer clouds in the PBL scheme are also coupled to a WRF-Chem photolysis scheme for the first time, overall reducing O₃ biases in the eastern US. Here we present the ability of the RAP-Chem model to simulate the extreme North American wildfires in 2020. We demonstrate that the relatively simple gas-phase chemical mechanism alongside our model physics developments and coupling with chemistry can predict maximum daily 8-h average O₃ with exceptional skill (Normalized Mean Bias (NMB) = 6.6 %) averaged over CONUS, improving on simulations that neglect the direct aerosol effect (NMB = 9.5 %). Ozone biases are large in regions with extreme aerosol loading, but the NMB improves more significantly. The model produces the observed extreme aerosol loading with less skill; tending to underpredict extreme wildfire induced concentrations while overpredicting urban particle pollution. As with O₃, daily average PM2.5 is predicted with better skill when the direct aerosol feedback is included (-15.8 % vs. -16.8 %). Overall we find that improving the coupling between chemistry and physics processes in the air quality model improves its predictions of both weather and atmospheric composition.

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect effects on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.

The Rapid Refresh model coupled to Chemistry (RAP-Chem) is an experimental forecast model built and operated by the NOAA Global Systems Laboratory (GSL). The RAP-Chem is a full chemistry extension to the operational smoke forecasting model, RAP-Smoke. RAP-Chem’s chemistry includes a reduced complexity, carbon-bond, gas-phase mechanism and an aerosol scheme that are coupled together to produce secondary aerosols and perform heterogeneous chemistry. The model includes chemical lateral boundary conditions, hourly anthropogenic emissions and online emissions from biomass burning, dust, sea salt, and biogenic sources. RAP-Chem also includes the direct aerosol effect on radiation and photolysis and a simplified indirect aerosol effect that couples prognostic aerosols to representative aerosols in the aerosol-aware microphysics scheme. The default ‘offline’ vertical mixing implemented in WRF-Chem is updated to instead mix chemical species alongside other scalars in the model’s planetary boundary layer (PBL) scheme, which tends to improve predictions across species – increasing surface ozone (O3) and decreasing less reactive tracers (e.g., CO). Sub-grid boundary layer clouds in the PBL scheme are also coupled to a WRF-Chem photolysis scheme for the first time, overall reducing O3 biases in the eastern US. Here we present the ability of the RAP-Chem model to simulate the extreme North American wildfires in 2020. We demonstrate that the relatively simple gas-phase chemical mechanism alongside our model physics developments and coupling with chemistry can predict maximum daily 8-h average O3 with exceptional skill (Normalized Mean Bias (NMB) = 6.6 %) averaged over CONUS, improving on simulations that neglect the direct aerosol effect (NMB = 9.5 %). Ozone biases are large in regions with extreme aerosol loading, but the NMB improves more significantly. The model produces the observed extreme aerosol loading with less skill; tending to underpredict extreme wildfire induced concentrations while overpredicting urban particle pollution. As with O3, daily average PM2.5 is predicted with better skill when the direct aerosol feedback is included (-15.8 % vs. -16.8 %). Overall we find that improving the coupling between chemistry and physics processes in the air quality model improves its predictions of both weather and atmospheric composition.

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect feedback on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.

We thank Millet et al. for their comment in discussing the methodology and conclusions reached in Francoeur et al. (2025, https://doi.org/10.1029/2025gl115286 ). In general, we agree with the key points of the comment, although with significant caveats. Mainly, we agree that the thermal infrared is capable of constraining ethane emissions from space. While we also agree that spectral signals do not change in a 1:1 manner with ethane emissions, our chemical transport and radiative transfer modeling suggest that the relationship is 0.72:1. Lastly, while spectral signals are affected by vertical sensitivity and regional background, these factors are not large enough to significantly alter how trends in spectral signals reflect trends in ethane emissions. We hope that the differences observed between Francoeur et al. (2025, https://doi.org/10.1029/2025gl115286 ) and Brewer et al. (2024, https://doi.org/10.1038/s41467‐024‐52247‐z ) lead to further study that advances the ability to detect atmospheric composition in the thermal infrared.

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect feedback on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect feedback on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.

The Global Ensemble Forecast System (GEFS) version 12 has been operational at the National Centers for Environmental Prediction since September 2020, with GEFS-Aerosols serving as its global aerosol forecasting member. In 2023, GEFS-Aerosols was upgraded to version 12.3 in operations, incorporating improvements to wet deposition, anthropogenic and biomass burning emissions, aerosol optical depth calculations, and the FENGSHA dust emission scheme. While GEFS-Aerosols provides valuable operational aerosol forecasts on medium-range timescales, its one-way coupling strategy restricts aerosol–atmosphere interactions to prescribed climatological fields, thereby precluding fully interactive aerosol feedbacks for extended-range prediction. To overcome this limitation, we develop the Unified Forecast System coupled with Chemistry (UFS-Chem), which incorporates the Configurable Atmospheric Chemistry (CATChem) library and modeling component to include both aerosol and gas-phase chemistry schemes. In the current UFS-Chem configuration, the aerosol component is based on the operational GEFS-Aerosols v12.3 and implemented using the Common Community Physics Package (CCPP) framework. Relative to GEFS-Aerosols v12.3, UFS-Chem incorporates several enhancements, including inline aerosol radiative feedback, inline large-scale wet deposition, and updated FENGSHA dust scheme. In particular, the integration of inline large-scale wet deposition and aerosol indirect effects through the Thompson aerosol-aware microphysics scheme improves the representation of aerosol–cloud interactions and supports weather and subseasonal-to-seasonal forecasting when fully coupled with ocean, sea ice, land, and wave components. The performance of UFS-Chem in weather and subseasonal prediction is evaluated against reanalysis data, ground-based measurements, and satellite observations. The relative impacts of aerosol radiative and indirect feedback on weather and subseasonal predictions are investigated and quantified, advancing our understanding of how aerosol–radiation and aerosol–cloud interactions influence forecast skill across timescales.