Accurate precipitation estimates are critical to simulating seasonal snowpack evolution. We conduct and evaluate high‐resolution (4‐km) snowpack simulations over the western United States (WUS) mountains in Water Year 2013 using the Noah with multi‐parameterization (Noah‐MP) land surface model driven by precipitation forcing from convection‐permitting (4‐km) Weather Research and Forecasting (WRF) modeling and four widely used high‐resolution datasets that are derived from statistical interpolation based on in situ measurements. Substantial differences in the precipitation amount among these five datasets, particularly over the western and northern portions of WUS mountains, significantly affect simulated snow water equivalent (SWE) and snow depth (SD) but have relatively limited effects on snow cover fraction (SCF) and surface albedo. WRF generally captures observed precipitation patterns and results in an overall best‐performed SWE and SD in the western and northern portions of WUS mountains, where the statistically interpolated datasets lead to underpredicted precipitation, SWE, and SD. Over the interior WUS mountains, all the datasets consistently underestimate precipitation, causing significant negative biases in SWE and SD, among which the results driven by the WRF precipitation show an average performance. Further analysis reveals systematic positive biases in SCF and surface albedo across the WUS mountains, with similar bias patterns and magnitudes for simulations driven by different precipitation datasets, suggesting an urgent need to improve the Noah‐MP snowpack physics. This study highlights that convection‐permitting modeling with proper configurations can have added values in providing decent precipitation for high‐resolution snowpack simulations over the WUS mountains in a typical ENSO‐neutral year, particularly over observation‐scarce regions.

W. Tang et al.: Evaluating high-resolution forecasts of atmospheric CO and CO 2 figurations. The correlation of CAMS CO bias with CO 2 bias is relatively high over these two regions (Seoul: 0.64-0.90, the West Sea: ∼ 0.80) suggesting that the contrast captured by CAMS may be dominated by anthropogenic emission ratios used in CAMS. However, CAMS shows poorer performance in terms of capturing local-to-urban CO and CO 2 variability. Along with measurements at ground sites over the Korean Peninsula, CAMS produces too high CO and CO 2 concentrations at the surface with steeper vertical gradients (∼ 0.4 ppmv hPa −1 for CO 2 and 3.5 ppbv hPa −1 for CO) in the morning samples than observed (∼ 0.25 ppmv hPa −1 for CO 2 and 1.7 ppbv hPa −1 for CO), suggesting weaker boundary layer mixing in the model. Lastly, we find that the combination of CO analyses (i.e., improved initial condition) and use of finer resolution (9 km vs. 16 km) generally produces better forecasts.

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Abstract. Global coupled chemistry–climate models underestimate carbon monoxide (CO) in the Northern Hemisphere, exhibiting a pervasive negative bias against measurements peaking in late winter and early spring. While this bias has been commonly attributed to underestimation of direct anthropogenic and biomass burning emissions, chemical production and loss via OH reaction from emissions of anthropogenic and biogenic volatile organic compounds (VOCs) play an important role. Here we investigate the reasons for this underestimation using aircraft measurements taken in May and June 2016 from the Korea–United States Air Quality (KORUS-AQ) experiment in South Korea and the Air Chemistry Research in Asia (ARIAs) in the North China Plain (NCP). For reference, multispectral CO retrievals (V8J) from the Measurements of Pollution in the Troposphere (MOPITT) are jointly assimilated with meteorological observations using an ensemble adjustment Kalman filter (EAKF) within the global Community Atmosphere Model with Chemistry (CAM-Chem) and the Data Assimilation Research Testbed (DART). With regard to KORUS-AQ data, CO is underestimated by 42 % in the control run and by 12 % with the MOPITT assimilation run. The inversion suggests an underestimation of anthropogenic CO sources in many regions, by up to 80 % for northern China, with large increments over the Liaoning Province and the North China Plain (NCP). Yet, an often-overlooked aspect of these inversions is that correcting the underestimation in anthropogenic CO emissions also improves the comparison with observational O3 datasets and observationally constrained box model simulations of OH and HO2. Running a CAM-Chem simulation with the updated emissions of anthropogenic CO reduces the bias by 29 % for CO, 18 % for ozone, 11 % for HO2, and 27 % for OH. Longer-lived anthropogenic VOCs whose model errors are correlated with CO are also improved, while short-lived VOCs, including formaldehyde, are difficult to constrain solely by assimilating satellite retrievals of CO. During an anticyclonic episode, better simulation of O3, with an average underestimation of 5.5 ppbv, and a reduction in the bias of surface formaldehyde and oxygenated VOCs can be achieved by separately increasing by a factor of 2 the modeled biogenic emissions for the plant functional types found in Korea. Results also suggest that controlling VOC and CO emissions, in addition to widespread NOx controls, can improve ozone pollution over East Asia.

Estimates of fire emissions remain uncertain due to limited constraints on the variations in fire characteristics. Here we demonstrate the utility of space‐based observations of smoke constituents in addressing this limitation. We introduce a satellite‐derived smoke index (SI) as an indicator of the dominant phase of large‐scale fires. This index is calculated as the ratio of the geometric mean of observed fractional enhancements (due to fire) in carbon monoxide and aerosol optical depth to that of nitrogen dioxide. We assess the usefulness of this index on fires in the Amazon. We analyze the seasonal, regional, and interannual joint distribution of SI and fire radiative power (FRP) in relation to fire hotspots, land cover, Drought Severity Index, and deforestation rate estimates. We also compare this index with an analogous quantity derived from field data or emission inventories. Our results show that SI changes from low (more flaming) to high (more smoldering) during the course of a fire season, which is consistent with the changes in observed maximum FRPs from high to low. We also find that flaming combustion is more dominant in areas where deforestation fires dominate, while smoldering combustion has a larger influence during drought years when understory fires are more likely enhanced. Lastly, we find that the spatiotemporal variation in SI is inconsistent with current emission inventories. Although we recognize some limitations of this approach, our results point to the utility of SI as a proxy for overall combustion efficiency in the parameterization of fire emission models.