Atmospheric rivers (ARs) are narrow, elongated, synoptic jets of water vapor that play important roles in the global water cycle and regional weather/hydrology. A technique is developed for objective detection of ARs on the global domain based on characteristics of the integrated water vapor transport (IVT). AR detection involves thresholding 6‐hourly fields of ERA‐Interim IVT based on the 85th percentile specific to each season and grid cell and a fixed lower limit of 100 kg m−1 s−1 and checking for the geometry requirements of length >2000 km, length/width ratio >2, and other considerations indicative of AR conditions. Output of the detection includes the AR shape, axis, landfall location, and basic statistics of each detected AR. The performance of the technique is evaluated by comparison to AR detection in the western North America, Britain, and East Antarctica with three independently conducted studies using different techniques, with over ~90% agreement in AR dates. Among the parameters tested, AR detection shows the largest sensitivity to the length criterion in terms of changes in the resulting statistical distribution of AR intensity and geometry. Global distributions of key AR characteristics are examined, and the results highlight the global footprints of ARs and their potential importance on global and regional scales. Also examined are seasonal dependence of AR frequency and precipitation and their modulation by four prominent modes of large‐scale climate variability. The results are in broad consistency with previous studies that focused on landfalling ARs in the west coasts of North America and Europe.

Aimed at reducing deficiencies in representing the Madden-Julian oscillation (MJO) in general circulation models (GCMs), a global model evaluation project on vertical structure and physical processes of the MJO was coordinated. In this paper, results from the climate simulation component of this project are reported. It is shown that the MJO remains a great challenge in these latest generation GCMs. The systematic eastward propagation of the MJO is only well simulated in about one fourth of the total participating models. The observed vertical westward tilt with altitude of the MJO is well simulated in good MJO models but not in the poor ones. Damped Kelvin wave responses to the east of convection in the lower troposphere could be responsible for the missing MJO preconditioning process in these poor MJO models. Several process-oriented diagnostics were conducted to discriminate key processes for realistic MJO simulations. While large-scale rainfall partition and low-level mean zonal winds over the Indo-Pacific in a model are not found to be closely associated with its MJO skill, two metrics, including the low-level relative humidity difference between high-and low-rain events and seasonal mean gross moist stability, exhibit statistically significant correlations with the MJO performance. It is further indicated that increased cloud-radiative feedback tends to be associated with reduced amplitude of intraseasonal variability, which is incompatible with the radiative instability theory previously proposed for the MJO. Results in this study confirm that inclusion of air-sea interaction can lead to significant improvement in simulating the MJO.

Abstract. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is an international collaborative effort to understand and quantify the uncertainties in atmospheric river (AR) science based on detection algorithm alone. Currently, there are many AR identification and tracking algorithms in the literature with a wide range of techniques and conclusions. ARTMIP strives to provide the community with information on different methodologies and provide guidance on the most appropriate algorithm for a given science question or region of interest. All ARTMIP participants will implement their detection algorithms on a specified common dataset for a defined period of time. The project is divided into two phases: Tier 1 will utilize the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) reanalysis from January 1980 to June 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is required. Tier 2 will be optional and include sensitivity studies designed around specific science questions, such as reanalysis uncertainty and climate change. High-resolution reanalysis and/or model output will be used wherever possible. Proposed metrics include AR frequency, duration, intensity, and precipitation attributable to ARs. Here, we present the ARTMIP experimental design, timeline, project requirements, and a brief description of the variety of methodologies in the current literature. We also present results from our 1-month “proof-of-concept” trial run designed to illustrate the utility and feasibility of the ARTMIP project.

[1] We perform an observationally based evaluation of the cloud ice water content (CIWC) and path (CIWP) of present-day GCMs, notably 20th century CMIP5 simulations, and compare these results to CMIP3 and two recent reanalyses. We use three different CloudSat + CALIPSO ice water products and two methods to remove the contribution from the convective core ice mass and/or precipitating cloud hydrometeors with variable sizes and falling speeds so that a robust observational estimate can be obtained for model evaluations. The results show that for annual mean CIWP, there are factors of 2-10 in the differences between observations and models for a majority of the GCMs and for a number of regions. However, there are a number of CMIP5 models, including CNRM-CM5, MRI, CCSM4 and CanESM2, as well as the UCLA CGCM, that perform well compared to our past evaluations. Systematic biases in CIWC vertical structure occur below the mid-troposphere where the models overestimate CIWC, with this bias arising mostly from the extratropics. The tropics are marked by model differences in the level of maximum CIWC ($250-550 hPa). Based on a number of metrics, the ensemble behavior of CMIP5 has improved considerably relative to CMIP3, although neither the CMIP5 ensemble mean nor any individual model performs particularly well, and there are still a number of models that exhibit very large biases despite the availability of relevant observations. The implications of these results on model representations of the Earth radiation balance are discussed, along with caveats and uncertainties associated with the observational estimates, model and observation representations of the precipitating and cloudy ice components, relevant physical processes and parameterizations. F., et al. (2012), An observationally based evaluation of cloud ice water in CMIP3 and CMIP5 GCMs and contemporary reanalyses using contemporary satellite data,

Atmospheric rivers (ARs) are now widely known for their association with high‐impact weather events and long‐term water supply in many regions. Researchers within the scientific community have developed numerous methods to identify and track of ARs—a necessary step for analyses on gridded data sets, and objective attribution of impacts to ARs. These different methods have been developed to answer specific research questions and hence use different criteria (e.g., geometry, threshold values of key variables, and time dependence). Furthermore, these methods are often employed using different reanalysis data sets, time periods, and regions of interest. The goal of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) is to understand and quantify uncertainties in AR science that arise due to differences in these methods. This paper presents results for key AR‐related metrics based on 20+ different AR identification and tracking methods applied to Modern‐Era Retrospective Analysis for Research and Applications Version 2 reanalysis data from January 1980 through June 2017. We show that AR frequency, duration, and seasonality exhibit a wide range of results, while the meridional distribution of these metrics along selected coastal (but not interior) transects are quite similar across methods. Furthermore, methods are grouped into criteria‐based clusters, within which the range of results is reduced. AR case studies and an evaluation of individual method deviation from an all‐method mean highlight advantages/disadvantages of certain approaches. For example, methods with less (more) restrictive criteria identify more (less) ARs and AR‐related impacts. Finally, this paper concludes with a discussion and recommendations for those conducting AR‐related research to consider.

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A uniform, global approach is used to quantify how atmospheric rivers (ARs) change between Coupled Model Intercomparison Project Phase 5 historical simulations and future projections under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 warming scenarios. The projections indicate that while there will be~10% fewer ARs in the future, the ARs will be~25% longer,~25% wider, and exhibit stronger integrated water vapor transports (IVTs) under RCP8.5. These changes result in pronounced increases in the frequency (IVT strength) of AR conditions under RCP8.5:~50% (25%) globally,~50% (20%) in the northern midlatitudes, and~60% (20%) in the southern midlatitudes. The models exhibit systematic low biases across the midlatitudes in replicating historical AR frequency (~10%), zonal IVT (~15%), and meridional IVT (~25%), with sizable intermodel differences. A more detailed examination of six regions strongly impacted by ARs suggests that the western United States, northwestern Europe, and southwestern South America exhibit considerable intermodel differences in projected changes in ARs.Plain Language Summary Atmospheric rivers (ARs) are elongated strands of horizontal water vapor transport, accounting for over 90% of the poleward water vapor transport across midlatitudes. These "rivers in the sky" have important implications for extreme precipitation when they make landfall, particularly along the west coasts of many midlatitude continents (e.g., North America, South America, and West Europe) due to orographic lifting. ARs are important contributors to extreme weather and precipitation events, and while their presence can contribute to beneficial rainfall and snowfall, which can mitigate droughts, they can also lead to flooding and extreme winds. This study takes a uniform, global approach that is used to quantify how ARs change between Coupled Model Intercomparison Project Phase 5 historical simulations and future projections under the Representative Concentration Pathway (RCP) 4.5 and RCP8.5 warming scenarios globally. The projections indicate that while there will be~10% fewer ARs in the future, the ARs will be~25% longer,~25% wider, and exhibit stronger integrated water vapor transports under RCP8.5. These changes result in pronounced increases in the frequency (integrated water vapor transport strength) of AR conditions under RCP8.5:~50% (25%) globally,~50% (20%) in the northern midlatitudes, and~60% (20%) in the southern midlatitudes.

[1] Narrow bands of strong atmospheric water vapor transport, referred to as "atmospheric rivers" (ARs), are responsible for the majority of wintertime extreme precipitation events with important contributions to the seasonal water balance. We investigate relationships between snow water equivalent (SWE), precipitation, and surface air temperature (SAT) across the Sierra Nevada for 45 wintertime AR events. Analysis of assimilated and in situ data for water years [2004][2005][2006][2007][2008][2009][2010] indicates that ARs on average generate ∼4 times daily SWE accumulation of non-AR storms. In addition, AR events contributed ∼30-40% of total seasonal SWE accumulation in most years, with the contribution dominated by just 1-2 extreme events in some cases. In situ and remotely sensed observations show that SWE changes associated with ARs are closely related to SAT. These results reveal the previously unexplored significance of ARs with regard to the snowpack and associated sensitivities of AR precipitation to SAT. Citation: Guan, B., N. P. Molotch, D. E. Waliser, E. J. Fetzer, and P. J. Neiman (2010), Extreme snowfall events linked to atmospheric rivers and surface air temperature via satellite measurements, Geophys.