Atmospheric rivers in 20 year weather and climate simulations: A multimodel, global evaluation

Abstract

Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and meteorological/hydrological extremes. Increasing evidence shows that ARs have signatures and impacts in many regions across different continents. However, global‐scale characterizations of AR representations in weather and climate models have been very limited. Using a recently developed AR detection algorithm oriented for global applications, the representation of AR activities in multidecade weather/climate simulations is evaluated. The algorithm is applied to 6‐hourly (daily) integrated water vapor transport (IVT) from 22 (2) global weather/climate models that participated in the Global Energy and Water Cycle Experiment Atmospheric System Study–Year of Tropical Convection Multimodel Experiment, including four models with ocean‐atmosphere coupling and two models with superparameterization. Multiple reanalysis products are used as references to help quantify model errors in the context of reanalysis uncertainty. Model performance is examined for key aspects of ARs (frequency, intensity, geometry, and seasonality), with the focus on identifying and understanding systematic errors in simulated ARs. The results highlight the range of model performances relative to reanalysis uncertainty in representing the most basic features of ARs. Among the 17 metrics considered, AR frequency, zonal IVT, fractional zonal circumference, fractional total meridional IVT, and three seasonality metrics have consistently large errors across all models. Possible connections between AR simulation qualities and aspects of model configurations are discussed. Despite the lack of a monotonic relationship, the importance of model horizontal resolution to the overall quality of AR simulation is suggested by the evaluation results.

Atmospheric Blocking and Other Large‐Scale Precursor Patterns of Landfalling Atmospheric Rivers in the North Pacific: A CESM2 Study

“…For each grid cell, AR frequency is defined as the number of days in which any identified AR exists within that grid cell divided by the total number of days in the season. In both model and reanalysis, ARs are most common along and just south of the North Pacific storm track (Figures d and e), consistent with the findings of previous studies (e.g., Guan & Waliser, ; Mundhenk et al., ). AR frequencies within the MERRA data set exceed 16% of days in the central part of the basin.…”

“…CESM2 underestimates AR frequencies by roughly 5–20% relative to the MERRA data set where ARs are climatologically most active across the North Pacific storm track region (Figure ), a pattern reminiscent of that found by Guan and Waliser () for two CAM5 simulations. However, only in the far western Pacific are these CESM2‐MERRA AR frequency differences statistically significant.…”

Sensitivity of Seasonal Snowfall Attribution to Atmospheric Rivers and Their Reanalysis‐Based Detection

“…We characterize the sensitivity of AR‐attributed snowfall patterns to the atmospheric reanalyses used for identifying AR conditions, to illustrate how different atmospheric data can influence the interpretation of AR impacts for hydrologic studies. AR algorithms have previously been applied to a variety of atmospheric reanalyses (Guan & Waliser, ), but information about the hydrological implications of such applications over snow‐covered mountains has been missing. Utilizing a single detection method, namely, the GW2015 IVT‐based algorithm, we find reasonable agreement among the AR‐attributed CS values using external snow data and ARs identified by four atmospheric reanalyses.…”

“…We use the following steps to attribute snowfall to ARs: Compute IVT from an atmospheric reanalysis between 1,000 and 300 hPa, inclusive (GW2015; Guan & Waliser, ). Apply the GW2015 algorithm to diagnose ARs from IVT at 6‐hourly time steps (section ). Identify the days ARs persist over the study domain (Figure S1 in the supporting information) to form the AR catalogs used here (section ). Use AR dates to extract the amount of snowfall that occurred on those days from an independent snow data set (sections –). We repeat these steps for each of the (four) atmospheric reanalyses. Steps ii–iv are further described below.…”

Life Cycle of Atmospheric Rivers: Identification and Climatological Characteristics

“…Approximately 30–50% of precipitation and snow water equivalent over the west coast of United States are attributed to landfalling ARs (Dettinger et al, ; Guan et al, , ). On the other hand, coastal landmass during active AR season is at the risk of experiencing disastrous events like floodings, extreme winds, and precipitations (Bao et al, ; Guan & Waliser, ; Neiman et al, ; Neiman et al, ; Ralph et al, ; Waliser & Guan, ).…”