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The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

Most climate models in phase 6 of the Coupled Model Intercomparison Project (CMIP6) still suffer pronounced warm and dry summer biases in the central United States (CUS), even in high-resolution simulations. We found that the cloud base definition in the cumulus parameterization was the dominant factor determining the spread of the biases among models and those defining cloud base at the lifting condensation level (LCL) performed the best. To identify the underlying mechanisms, we developed a physically based analytical bias model (ABM) to capture the key feedback processes of land–atmosphere coupling. The ABM has significant explanatory power, capturing 80% variance of temperature and precipitation biases among all models. Our ABM analysis via counterfactual experiments indicated that the biases are attributed mostly by surface downwelling longwave radiation errors and second by surface net shortwave radiation errors, with the former 2–5 times larger. The effective radiative forcing from these two errors as weighted by their relative contributions induces runaway temperature and precipitation feedbacks, which collaborate to cause CUS summer warm and dry biases. The LCL cumulus reduces the biases through two key mechanisms: it produces more clouds and less precipitable water, which reduce radiative energy input for both surface heating and evapotranspiration to cause a cooler and wetter soil; it produces more rainfall and wetter soil conditions, which suppress the positive evapotranspiration–precipitation feedback to damp the warm and dry bias coupling. Most models using non-LCL schemes underestimate both precipitation and cloud amounts, which amplify the positive feedback to cause significant biases. [ABSTRACT FROM AUTHOR]

Watermelon [Citrullus lanatus (Thunb.) & Nakai] is widely grown on plastic mulches in the southeast United States, but responses of plants to plastic mulch color are not well understood. The objectives of this study were to determine the effects of colored plastic mulches and changes in root zone temperature (RZT) on fruit yield of watermelon fruit. The mulches (152 cm wide) evaluated were: black, blue, gray, red, silver (metallic), and white. During the first weeks after transplanting, canopy development was more rapid on black, blue, and red than silver and white mulches. As the season progressed and temperatures increased, differences in canopy development among mulch treatments tended to decrease. Watermelon plant growth benefited from soil warming using plastic film mulches, mainly blue, black, and red. There were no differences in fruit yield among different colored mulches, indicating that under the temperature conditions of the present study, the RZTs during fruiting were within optimal range for watermelon which is estimated to be 28.3°C. [ABSTRACT FROM AUTHOR]

Recent trends in wildfire area burned have been characterized by large patches with high densities of standing dead trees, well outside of historical range of variability in many areas and presenting forest managers with difficult decisions regarding post-fire management. Post-fire tree harvesting, commonly called salvage logging, is a controversial management tactic that is often undertaken to recoup economic loss and, more recently, also to reduce future fuel hazard, especially when coupled with surface fuel reduction. It is unclear, however, whether the reductions in future fuels translate to meaningful changes to reburn fire behavior, particularly in the context of potentially detrimental effects of harvest on other ecosystem services. We used observed post-fire snag structure in four high severity burn scars located in the Western United States that had variable post-fire snag basal area (13.3–63.9 mg ha−2) to initialize a simulation study of future coarse and fine woody fuel hazard and associated reburn fire behavior and effects. We compared untreated controls to intensive and intermediate intensity harvest treatments, both simulated and actual. All treatments showed some number of years of extreme fire behavior during which flame lengths exceeded thresholds associated with wildfire resistance to control, implying that future fuel reductions achieved by the treatments did not translate to conditions conducive for effective reburn fire management. Harvested stands had less severe soil fire effects (soil heating and smoldering duration) than untreated controls, explained by lower predicted peak coarse woody fuels (CWD) in the harvested stands. At higher pre-treatment snag basal area, harvested stands better maintained CWD within the range desired to maintain ecosystem functions such as nutrient cycling and wildlife habitat. These simulation results indicate that, even with reduced fuel hazard, salvage treatments may still be associated with severe fire behavior for some time after wildfire, but achieved reductions in coarse woody fuels may also reduce some soil fire effects. Tradeoffs in the effects of post-fire harvest must be considered carefully in the context of forest regeneration, local conditions that govern salvage methods, snag fall and decomposition, and associated potential reburn fire effects. • Post-fire harvest is a controversial practice following high severity wildfire. • Simulated harvest has higher initial fuel hazard with lower fuel hazard over time. • Lower fuel hazard in simulated harvest still exceed resistance to control thresholds. • Simulated harvest lowers soil fire effects over time. • Post-fire management is complicated by conflicting management objectives. [ABSTRACT FROM AUTHOR]

Wildfires cause direct and indirect CO2 emissions due to combustion and post-fire decomposition. Approximately half of temperate forest carbon (C) is stored in soil, so post-fire soil C cycling likely impacts forest C sink strength. Soil C sink strength is partly determined by soil microbial anabolism versus catabolism, which dictates the amount of C respired versus stored in microbial biomass. Fires affect soil C availability and composition, changes that could alter carbon use efficiency (CUE) and microbial biomass production, potentially influencing C sink recovery. Wildfire intensity is forecast to increase in forests of the western United States, and understanding the impacts of fire intensity on microbial anabolism is necessary for predicting fire-climate feedbacks. Our objective was to determine the influence of soil heating intensity and pyrogenic organic matter (PyOM) on microbial anabolism. We simulated the effects of fire intensity by heating soils to 100 or 200 °C for 30 min in a muffle furnace, and we amended the soils with charred or uncharred organic matter. Higher intensity soil heating (200 °C) led to lower microbial biomass carbon (MBC) accumulation, greater C respiration, and lower CUE proxies compared to unheated soils. Conversely, lower intensity heating (100 °C) yielded MBC accumulation and estimated CUE that was similar to unheated soils. Soils amended with PyOM exhibited similar MBC accumulation compared to uncharred organic matter, but lower CO2 emissions. These results indicate that high intensity soil heating decreases soil C-sink strength over the short-term by decreasing the amount of microbial anabolism relative to catabolism. [ABSTRACT FROM AUTHOR]