A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model

Bassett, Richard; Young, Paul; Blair, Gordon S.; Samreen, Faiza; Simm, William

doi:10.1029/2019jd031286

Cited by 21 publications

(14 citation statements)

References 56 publications

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“…Additionally, MOSAIC treats the processes of aerosol nucleation, coagulation, condensation, water uptake, and aqueous chemistry. Aerosol dry deposition is handled via the method in Binkowski and Shankar (1995), which includes Brownian and turbulent diffusion as well as gravitational settling. Wet deposition of aerosols and gases Figure 1.…”

Section: Wrf-chemmentioning

confidence: 99%

Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem

et al. 2020

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Abstract. The Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) is run to quantify the in-snow and atmospheric radiative effects of black carbon (BC) and dust on a convective-allowing (4 km) grid for water year 2009 across a large area of the Rocky Mountains. The snow-darkening effect (SDE) due to the deposition of these light-absorbing particles (LAPs) on surface snow enhances snowmelt by 3 to 12 mm during late spring and early summer, effectuating surface runoff increases (decreases) prior to (after) June. Meanwhile, aerosol–radiation interactions (ARIs) associated with LAPs generally dim the surface from incoming solar energy, introducing an energy deficit at the surface and leading to snowpack preservation by 1 to 5 mm. Surface runoff alterations brought forth by LAP ARI are of opposite phase to those associated with LAP SDEs, and the BC SDE drives a majority of the surface energy and hydrological perturbations. More generally, changes in snow water equivalent (SWE) brought forth by LAP effects are more a result of perturbations to the surface energy budget rather than changes in precipitation amount or type. It is also found that perturbations to the surface energy budget by dust ARI can differ in sign from those of BC ARI, with the former being positive, enhancing snow melting, and changing runoff.

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Section: Wrf-chemmentioning

confidence: 99%

Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem

et al. 2020

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“…However, RACM17 shows higher correlation coefficients compared to MOZ17 at other stations. Although the difference in the correlation coefficients for the different chemistry mechanism is small, it is likely due to radiation feedbacks between the chemistry and meteorology in these mechanisms and internal model variability (Bassett et al, 2020). Furthermore, the temperature bias between the observed and simulated datasets is below 3 • C at all stations (Table 3), and all of the data points lie in close proximity to the one-to-one lines.…”

Section: Surface Meteorologymentioning

confidence: 84%

Simulations of winter ozone in the Upper Green River Basin, Wyoming, using WRF-Chem

Ghimire

Lebo

Murphy

et al. 2022

Preprint

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Abstract. In both the Upper Green River Basin (UGRB) of Wyoming and the Uintah Basin of Utah, strong wintertime ozone (O3) formation episodes leading to O3 concentrations exceeding the 8-hour O3 NAAQS (70 ppb) have been observed over the last two decades. Wintertime O3 events in the UGRB were first observed in 2005 and since then have continued to be observed intermittently when meteorological conditions are favorable, despite significant efforts to reduce emissions. While O3 formation has been successfully simulated using observed volatile organic compound (VOC) and nitrogen oxide (NOX) concentrations, successful simulation of these wintertime episodes using emission inventories in a 3-D photochemical model has remained elusive. An accurate 3-D photochemical model driven by an emission inventory is critical to understand which emission sources have the most impact on O3 formation. In the winter of 2016–2017 (December 2016–March 2017) several high O3 events were recorded with concentrations exceeding 70 ppb. This study uses the Weather Research Forecasting model with chemistry (WRF-Chem) to simulate one of the high O3 events observed in the UGRB during March of 2017. The WRF-Chem simulations were carried out using the 2014 edition of the Environmental Protection Agency National Emissions Inventory (EPA-NEI 2014v2), which includes estimates of emissions from non-point oil and gas production sources. Simulations were carried out with two different chemical mechanisms: the Model for Ozone and Related Chemical Tracers (MOZART) and the Regional Atmospheric Chemistry Mechanism (RACM), and the results were compared with the observed data from 7 weather and air quality monitoring stations in the UGRB operated by Wyoming Department of Environmental Quality (WYDEQ). The simulated meteorology compared favorably to observations in terms of predicting temperature inversions and surface temperature and wind speeds. Notably, because of snow cover present in the basin, the photolysis surface albedo was modified in all simulations. Without this modification, none of the simulations formed O3 exceeding 70 ppb, though the models were relatively insensitive to the exact photolysis albedo if it was over 0.65. The MOZART simulation produced more O3 in the basin than the RACM simulation and compares better with the observations. However, while O3 precursors NOX and NMHC are predicted similarly in simulations with both chemistry mechanisms, simulated NMHC mixing ratios are a factor of six lower than the observations, while NOX mixing ratios are also underpredicted but are much closer to the observations within the region of oil and gas production. The results show that both the RACM and MOZART chemical mechanisms were able to produce O3 even though the NMHC mixing ratios in the model were a factor of six too low, an intriguing result for future studies.

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“…8d). We believe this to be the result of internal model variability rather than a physical manifestation (Bassett et al 2020). Examination of several grid points where the March-through-June mean SWE anomalies were positive revealed that fine-scale storm location and intensity differences between, for instance, CNT and noBCSDE were leading to positive SWE anomalies (not shown).…”

mentioning

confidence: 94%

RC2 response

Rahimi¹

2020

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Based on model simulations, the authors examine the skill of high resolution WR-FChem on the impact of snow albedo darkening and radiative forcing over western USA. They evaluate the model simulation with various observations. The authors also C1 ACPD Interactive comment Printer-friendly version Discussion paper discussed the radiative differences between BC and dust and intercompared two different pathways (snow direct radiative effect and atmosphere direct radiative effect) of aerosol effect on surface water budget. The spatial and temporal variation of radiative effects are also discussed. The experiments and results are interesting and suitable for publication in ACP after major revision to address following concerns. Reply: We thank the reviewer for the positive comments. The manuscript has undergone significant revisions. 4 Figures have been moved to the supplement, Of note, the acronym "BCD" has been changed to "light-absorbing particles" (LAPs) for better consistency with the literature. 5 appendices are now used to house more technical descriptions that weigh the paper down, and other text has been moved to the supplement. Sec. 5.4 is now included along with Table 5 which highlights changes in meltout date. Major comments (MaCs) MaC1: More discussion on role of dust in the manuscript is needed along with clear explanation and analysis. A) why is dust induced SWE positive over Northern Rockies in Figure 8f,which is inconsistent with the fact that dust-ISRE is positive (Figure 7d) and dust-induced change in albedo is negative (Figure 8i). B) More aerosols always have a negative radiative effect at surface as it either scatters or absorbs incoming radiation at surface. Why is dust surface RE in Figure 12i positive? Although, the authors have tried to explain this by stating the differences in aerosol microphysics, I feel it is not clear. C) Also, this is found over the entire domain not only over the brighter snow surfaces as discussed latter. Therefore, explain in detail line 545 to 550. D)Detailed analysis and discussion should be done to explain why on doubling the dust concentration changes the sign/magnitude of dust induced perturbations on various variables nonlinearly (compared to that with initial dust). For example, in figure 14e, peak dust SDE in may end is ∼1.5 Wm2 and the corresponding dust-induced reduction in SWE SWE is ∼7% for Northern Rockies. This is very different from the situation in Figure 10 and 11. Dust SDE in Figure 11 is ∼1W/m2 and the corresponding dust-induced reduction in SWE is ∼ 2%. Please explain and discuss this nonlinearity.

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A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model

Cited by 21 publications

References 56 publications

Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem

Examining the atmospheric radiative and snow-darkening effects of black carbon and dust across the Rocky Mountains of the United States using WRF-Chem

Simulations of winter ozone in the Upper Green River Basin, Wyoming, using WRF-Chem

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