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The parameterization of subgrid‐scale processes such as boundary layer (PBL) turbulence introduces uncertainty in Earth System Model (ESM) results. This uncertainty can contribute to or exacerbate existing biases in representing key physical processes. This study analyzes the influence of tunable parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBBX) scheme. CLUBB is the operational PBL parameterization in the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community ESM version 2 (CESM2). We perform the Morris one‐at‐a‐time (MOAT) parameter sensitivity analysis using short‐term (3‐day), initialized hindcasts of CAM6‐CLUBBX with 24 unique initial conditions. Several input parameters modulating vertical momentum flux appear most influential for various regionally‐averaged quantities, namely surface stress and shortwave cloud forcing (SWCF). These parameter sensitivities have a spatial dependence, with parameters governing momentum flux most influential in regions of high vertical wind shear (e.g., the mid‐latitude storm tracks). We next evaluate several experimental 20‐year simulations of CAM6‐CLUBBX with targeted parameter perturbations. We find that parameter perturbations produce similar physical mechanisms in both short‐term and long‐term simulations, but these physical responses can be muted due to nonlinear feedbacks manifesting over time scales longer than 3 days, thus causing differences in how output metrics respond in the long‐term simulations. Analysis of turbulent fluxes in CLUBBX indicates that the influential parameters affect vertical fluxes of heat, moisture, and momentum, providing physical pathways for the sensitivities identified in this study. Plain Language Summary: Models struggle with certain aspects of predicting the Earth's current and future climate. To achieve better predictions in the future, it is important to understand which parts of the model need to be improved. This study explores how changing certain model characteristics influences what the model outputs. We find that changing how the model estimates small‐scale motions in the atmosphere improves the model's accuracy. Furthermore, these changes affect both short‐term (several days) and long‐term (several decades) model simulations. The results of this study can help scientists understand the physical behavior of climate models and help inform future improvements to enhance model accuracy. Key Points: A computationally‐efficient sensitivity analysis identifies key parameters and physical mechanisms for global climate propertiesCertain parameter sensitivities in short‐term, initialized hindcasts are consistent with those seen in multidecadal climate simulationsParameters governing the degree of turbulent mixing in the presence of vertical wind shear are influential for surface stress representation [ABSTRACT FROM AUTHOR]

The challenging impacts of precipitation range from short-term public safety hazards associated with flooding to longer-term water storage concerns. In light of these impacts, there is a critical need for [...]

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth's hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. Plain Language Summary: Atmospheric rivers (ARs) are long and narrow weather features often referred to as "rivers in the sky." They often transport water from lower latitudes to higher latitudes typically across climate zones and produce precipitation necessary for local climates. Understanding ARs in a warming climate is challenging because of the variety of ways an AR can be defined on gridded data sets. Unlike weather features such as tropical cyclones where identification methodologies are similar, algorithms that determine the characteristics of ARs vary depending on the science question posed. Because there is no real consensus on AR identification methodology, we aim to quantify the algorithmic uncertainty in AR metrics and precipitation. We compare 16 different ways of defining an AR on gridded data sets and present the range of possibilities in which an AR could change under global warming. Generally, ARs are projected to increase but the amount of that increase is a function of the algorithm. Across all algorithms and focus regions, AR precipitation is projected to become more extreme. Key Points: High‐resolution historical and future simulations are used to evaluate atmospheric river detection tools (ARDT) uncertaintyARDTs mostly show increases in frequency and intensity of future atmospheric rivers (ARs) but the scale of response is dependent on algorithmic restrictivenessMost regions experience an increase in precipitation volume coming from extreme ARs [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated that high-resolution (~25 km) Earth System Models (ESMs) have the potential to skillfully predict tropical cyclone (TC) occurrence and intensity. However, biases in ESM TCs still exist, largely due to the need to parameterize processes such as boundary layer (PBL) turbulence. Building on past studies, we hypothesize that the depiction of the TC PBL in ESMs is sensitive to the configuration of the PBL parameterization scheme, and that the targeted perturbation of tunable parameters can reduce biases. The Morris one-at-a-time (MOAT) method is implemented to assess the sensitivity of the TC PBL to tunable parameters in the PBL scheme in an idealized configuration of the Community Atmosphere Model, version 6 (CAM6). The MOAT method objectively identifies several parameters in an experimental version of the Cloud Layers Unified by Binormals (CLUBB) scheme that appreciably influence the structure of the TC PBL. We then perturb the parameters identified by the MOAT method within a suite of CAM6 ensemble simulations and find a reduction in model biases compared to observations and a high-resolution, cloud-resolving model. We demonstrate that the high-sensitivity parameters are tied to PBL processes that reduce turbulent mixing and effective eddy diffusivity, and that in CAM6 these parameters alter the TC PBL in a manner consistent with past modeling studies. In this way, we provide an initial identification of process-based input parameters that, when altered, have the potential to improve TC predictions by ESMs. [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs) often generate extreme precipitation, with AR temperature strongly influencing hydrologic impacts by altering the timing and magnitude of runoff. Long‐term changes in AR temperatures therefore have important implications for regional hydroclimate—especially in locations where a shift to more rain‐dominated AR precipitation could affect flood risk and/or water storage in snowpack. In this study, we provide the first climatology of AR temperature across five U.S. West Coast subregions. We then assess trends in landfalling AR temperatures for each subregion from 1980 to 2016 using three reanalysis products. We find AR warming at seasonal and monthly scales. Cool‐season warming ranges from 0.69 to 1.65 °C over the study period. We detect monthly scale warming of >2 °C, with the most widespread warming occurring in November and March. To understand the causes of AR warming, we quantify the density of AR tracks from genesis to landfall and analyze along‐track AR temperature for each month and landfall region. We investigate three possible influences on AR temperature trends at landfall: along‐track temperatures prior to landfall, background temperatures over the landfall region, and AR temperature over the coastal ocean adjacent to the region of landfall. Generally, AR temperatures at landfall more closely match coastal and background temperature trends than along‐track AR temperature trends. The seasonal asymmetry of the AR warming and the heterogeneity of influences have important implications for regional water storage and flood risk—demonstrating that changes in AR characteristics are complex and may not be directly inferred from changes in the background climate. Plain Language Summary: Atmospheric river (AR) storms are well known for their ability to accumulate snowpack, provide drought relief, and generate extreme precipitation and flooding along the West Coast of the United States. AR temperature is an important variable for determining the water resource impacts of a given event, such as the ratio of rain to snow delivered by an individual storm. As a result, changes in AR temperature have implications for both water storage and flood risk. We find substantial warming in ARs at both the seasonal and monthly scales, as well as seasonal and regional variations in the amount of warming along the U.S. West Coast. To understand the warming of ARs at the landfall regions, we compare these trends with trends in temperature along the AR tracks, background temperature over the landfall region, and temperature over the coastal ocean adjacent to the landfall region. The most robust warming occurs in November and March, which has important implications for increased regional flood risk and decreased water storage, and motivates further investigation in other AR‐prone regions around the globe. Key Points: All U.S. West Coast subregions exhibit positive atmospheric river (AR) temperature trends, with warming ranging from 0.69 to 1.65 °C over the study periodAR warming magnitude exhibits seasonal and regional asymmetry; ARs have warmed by more than 2 °C in some months, particularly in MarchAR warming at landfall is influenced by temperature trends over the coastal ocean, over the landfall region, and along the AR tracks [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs)—narrow corridors of high atmospheric water vapor transport—occur globally and are associated with flooding and maintenance of the water supply. Therefore, it is important to improve forecasts of AR occurrence and characteristics. Although prior work has examined the skill of numerical weather prediction (NWP) models in forecasting atmospheric rivers, these studies only cover several years of reforecasts from a handful of models. Here, we expand this previous work and assess the performance of 10–30 years of wintertime (November–February) AR landfall reforecasts from the control runs of nine operational weather models, obtained from the International Subseasonal to Seasonal (S2S) Project database. Model errors along the west coast of North America at leads of 1–14 days are examined in terms of AR occurrence, intensity, and landfall location. Occurrence-based skill approaches that of climatology at 14 days, while models are, on average, more skillful at shorter leads in California, Oregon, and Washington compared to British Columbia and Alaska. We also find that the average magnitude of landfall integrated water vapor transport (IVT) error stays fairly constant across lead times, although overprediction of IVT is common at later lead times. Finally, we show that northward landfall location errors are favored in California, Oregon, and Washington, although southward errors occur more often than expected from climatology. These results highlight the need for model improvements, while helping to identify factors that cause model errors. [ABSTRACT FROM AUTHOR]

In the United States, severe weather poses a threat to society, producing tornadoes and hail that can result in hundreds of casualties and billions of dollars in damages. Fortunately, skillful predictions of severe weather for short lead times of 0–8 days and longer lead times exceeding 1 month have been realized. However, this leaves a forecast gap at subseasonal to seasonal lead times of 2–5 weeks, when early‐action decision making by stakeholders is typically made. Here we develop an empirical prediction model that fills this gap during March–June when severe weather is most prevalent across the United States. We demonstrate skillful weekly forecasts of opportunity with lead times of 2–5 weeks of environmental parameters favorable to severe weather, as well as actual tornado and hail activity. To attain this skill, we use as a predictor the current state of active phases of the Madden‐Julian Oscillation, known to have physical teleconnections with future weather over the United States. The model has significant skill in regions such as the Plains and the Southeast, providing stakeholders with valuable extended forewarning. Plain Language Summary: In the United States, severe thunderstorms produce tornadoes and large hail, responsible for hundreds of deaths and injuries and many billions of dollars in damages on average each year. Because of these devastating impacts, there is a keen interest to accurately forecast when and where severe thunderstorms are likely to occur. While meteorologists and computer models do reasonably well in forecasting severe thunderstorm activity up to a week in advance, their forecasts are less reliable in the 2‐ to 5‐week time frame. In our study, we develop a technique that can accurately forecast severe thunderstorm activity in this time frame by using knowledge of the current state of weather in the tropics. These accurate, extended forecasts offer valuable forewarning to both the general public and stakeholders of when and where potentially deadly severe thunderstorm activity is likely to occur. Key Points: An active Madden‐Julian Oscillation (MJO) modulates springtime tornado and hail activity over the next 2–5 weeks across the United StatesUsing the MJO as a predictor, skillful weekly forecasts of severe weather with lead times of 2–5 weeks can be realizedThese subseasonal to seasonal (S2S) forecasts offer important forewarning to stakeholders of when and where severe weather is likely [ABSTRACT FROM AUTHOR]