Atmospheric rivers (ARs) significantly impact the Arctic climate system by enhancing atmospheric heat and moisture transport and altering the local energy budget. Developing AR detection tools (ARDTs) is critical yet challenging. This study evaluates 12 ARDTs in the Arctic to assess their performance in representing atmospheric heat (represented by moist static energy) and moisture transport, as well as surface downward longwave radiation (LWD) and precipitation impacts, spanning 2000 to 2019 using ERA5 reanalysis. We find that AR occurrence frequency in the Arctic varies widely, from less than 1% to over 13%, depending on the ARDT. This variability leads to differences in contributions to poleward atmospheric heat (<1%-33%) and moisture (<1%-49%) transport. The highest AR frequency, and corresponding contributions to atmospheric heat and moisture transport, occurs over the Atlantic sector during non-summer seasons for most ARDTs. This region aligns with the primary poleward moisture pathway and the end of climatological mid-latitude storm tracks, highlighting strong connections between Arctic ARs and mid-latitude cyclones. ARs induce significant LWD anomalies, largest in winter, smallest in summer, and also substantially contribute to the seasonal precipitation. Global ARDTs detect fewer ARs with larger anomalies (>100 W m-2 in higher Arctic), but contribute <1% to seasonal climatological LWD and precipitation. In contrast, polar-specific ARDTs detect higher AR occurrences and account for up to 16% of seasonal LWD and 41% precipitation. This suggests that algorithms emphasizing extreme events with large anomalies do not necessarily indicate a large climate radiative and precipitation impact.

Weather and climate extremes are increasingly occurring in the Arctic. In this Review, we evaluate historical and projected changes in rare Arctic extremes across the atmosphere, cryosphere and ocean and elucidate their driving mechanisms. Clear shifts occur in mean and extreme distributions after ~2000. For instance, pre-2000 to post-2000 observational probabilities of 1.5 standard deviation events increase by 20% for atmospheric heat waves, 76.7% for Atlantic layer warm events, 83.5% for Arctic sea ice loss and 62.9% for Greenland Ice Sheet melt extent — in many cases, low probability, rare extreme events in the early period become the norm in the latter period. These observed changes can be explained using a ‘pushing and triggering’ concept, representing interplay between external forcing and internal variability: long-term warming destabilizes the climate system and ‘pushes’ it to a new state, allowing subsequent variability associated with large-scale atmosphere–ocean–ice interactions and synoptic systems to ‘trigger’ extreme events over different timescales. Ongoing anthropogenic warming is expected to further increase the frequency and magnitude of extremes, such that simulated probabilities of 1.5 standard deviation events increase by 72.6% for atmospheric heat waves, 68.7% for Atlantic layer warm events and 93.3% for Greenland Ice Sheet melt rate between historic (1984–2014) and future (2069–2099) periods under a very high emission scenario. Future research should prioritize the development of physically based metrics, enhance high-resolution observation and modelling capabilities and improve understanding of multiscale Arctic climate drivers. Rare and extreme climate events have increasingly occurred in the Arctic since ~2000. This Review outlines the observed and projected changes in atmospheric, oceanic and cryospheric extremes and explains their increasing occurrence through a ‘pushing and triggering’ framework.

We present a comprehensive analysis of Arctic surface energy budget (SEB) components during atmospheric river (AR) events identified by integrated water vapor transport exceeding the monthly 85th-percentile climatological threshold in 3-hourly ERA5 reanalysis data from January 1980 to December 2019. Analysis of average anomalies in SEB components, net SEB, and the overall AR contribution to both the seasonal SEB components and net SEB climatology reveals clear seasonality and distinct land-sea-sea ice contrast patterns. Over the sea-ice-covered central Arctic Ocean, ARs significantly impact net SEB, inducing substantial surface warming in fall, winter, and spring. This warming is primarily driven by large anomalies in surface downward longwave radiation (LWD), which average 29-45 W m(-2) during the cold seasons. In contrast, AR-related LWD anomalies are smaller in summer, averaging around 15 W m(-2), indicating a reduced impact during this season. Over sub-polar oceans, ARs have the most substantial positive impact on net SEB in cold seasons, mainly attributed to significant positive turbulent heat flux anomalies. AR-related turbulent heat anomalies reduce the upward turbulent flux, contributing up to -11 % relative to its seasonal climatology. In summer, ARs induce negative impacts on net SEB, primarily due to reduced shortwave radiation from increased cloud cover during AR events. Over continents, ARs generate smaller absolute impacts on net SEB because the large LWD anomalies are largely offset by corresponding increases in upward longwave radiation, particularly during cold seasons. Additionally, the seemingly large relative contributions of ARs to the net SEB over land primarily reflects the small magnitude of the climatological net SEB over continents. Greenland, especially western Greenland, exhibits significant downward longwave radiation anomalies associated with ARs, which drive large net SEB anomalies and contribute >54 % to mean SEB and induce amplified surface warming year-round. This holds significance for melt events, particularly during summer. Additionally, results of AR-related SEB impacts strongly depend on detection methods, as restrictive AR detection algorithms that emphasize extreme AR events, with large AR-related anomalies, do not necessarily indicate a large overall contribution to the SEB climatology due to the low occurrence frequency of these events. This study quantifies the role of ARs in the surface energy budget, contributing to our understanding of the Arctic warming and sea ice decline in ongoing Arctic amplification.

We present the Arctic atmospheric river (AR) climatology based on twelve sets of labels derived from ERA5 and MERRA-2 reanalyses for 1980–2019. The ARs were identified and tracked in the 3-hourly reanalysis data with a multifactorial approach based on either atmospheric column-integrated water vapor (IWV) or integrated water vapor transport (IVT) exceeding one of the three climate thresholds (75th, 85th, and 95th percentiles). Time series analysis of the AR event counts from the AR labels showed overall upward trends from the mid-1990s to 2019. The 75th IVT- and IWV-based labels, as well as the 85th IWV-based labels, are likely more sensitive to Arctic surface warming, therefore, detected some broadening of AR-affected areas over time, while the rest of the labels did not. Spatial exploratory analysis of these labels revealed that the AR frequency of occurrence maxima shifted poleward from over-land in 1980–1999 to over the Arctic Ocean and its outlying Seas in 2000–2019. Regions across the Atlantic, the Arctic, to the Pacific Oceans trended higher AR occurrence, surface temperature, and column-integrated moisture. Meanwhile, ARs were increasingly responsible for the rising moisture transport into the Arctic. Even though the increase of Arctic AR occurrence was primarily associated with long-term Arctic surface warming and moistening, the effects of changing atmospheric circulation could stand out locally, such as on the Pacific side over the Chukchi Sea. The changing teleconnection patterns strongly modulated AR activities in time and space, with prominent anomalies in the Arctic-Pacific sector during the latest decade. Besides, the extreme events identified by the 95th-percentile labels displayed the most significant changes and were most influenced by the teleconnection patterns. The twelve Arctic AR labels and the detailed graphics in the atlas can help navigate the uncertainty of detecting and quantifying Arctic ARs and their associated effects in current and future studies.

Atmospheric rivers (ARs) affect surface hydrometeorology in the US West Coast and Midwest. We systematically sought optimal AR indices for expressing surface precipitation impacts within the Atmospheric River Tracking Method Intercomparison Project (ARTMIP) framework. We adopted a multifactorial approach. Four factors, moisture fields, climatological thresholds, shape criteria, and temporal thresholds, collectively generated 81 West Coast AR indices and 81 Midwest indices from January 1980 to June 2017. Two moisture fields were extracted from the MERRA‐2 data for ARTMIP: integrated water vapor transport (IVT) and integrated water vapor (IWV). Metrics for precipitation effects included two‐way summary statistics, relating the concurrence of AR and that of precipitation, per event averaged precipitation rate, and per event precipitation accumulation. We found that an optimal AR index for precipitation depends on the types of impact to be addressed, associated physical mechanisms in the affected regions, timing, and duration. In West Coast and Midwest, IWV‐based AR indices identified the most abundant AR event time steps, most accurately associated AR to days with precipitation, and represented the presence of precipitation the best. With a lower climatological threshold, they detected the most accumulated precipitation with the longest event duration. Longer duration thresholds also led to higher accumulated precipitation, holding other factors constant. IWV‐based indices are the overall choice for Midwest ARs under varying seasonal precipitation drivers. IVT‐based indices suitably capture the accumulation of intense orographic precipitation on the West Coast. Indices combining IVT and IWV identify the fewest, shortest, but most intense AR precipitation episodes.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Journal of Geophysical Research - Atmospheres. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing an older version [v1]Go to new versionIn Search of The Optimal Atmospheric River Index for Precipitation: Big Data Analysis of Index Ensembles over the North American West Coast and US MidwestAuthorsChenZhangiDWen-WenTungiDWilliam SClevelandSee all authors Chen ZhangiDUnknowniDhttps://orcid.org/0000-0002-7018-1164view email addressThe email was not providedcopy email addressWen-Wen TungiDCorresponding Author• Submitting AuthorPurdue UniversityiDhttps://orcid.org/0000-0001-8627-1503view email addressThe email was not providedcopy email addressWilliam S ClevelandPurdue Universityview email addressThe email was not providedcopy email address

The 3rd ARTMIP Workshop What: Over 30 participants from multiple universities and research insititutions met to discuss new results from the Atmospheric River Tracking Method Intercomparison Project. Where: Lawrence Berkeley National Lab, Berkeley, CA, USA When: 16-18 October 2019

The focus of data science is data analysis. This article begins with a categorization of the data science technical areas that play a direct role in data analysis. Next, big data are addressed, which create computational challenges due to the data size, as does the computational complexity of many analytic methods. Divide and recombine (D&R) is a statistical approach whose goal is to meet the challenges. In D&R, the data are divided into subsets, an analytic method is applied independently to each subset, and the outputs are recombined. This enables a large component of embarrassingly-parallel computation, the fastest parallel computation. DeltaRho open-source software implements D&R. At the front end, the analyst programs in R. The back end is the Hadoop distributed file system and parallel compute engine. The goals of D&R are the following: access to thousands of methods of machine learning, statistics, and data visualization; deep analysis of the data, which means analysis of the detailed data at their finest granularity; easy programming of analyses; and high computational performance. To succeed, D&R requires research in all of the technical areas of data science. Network cybersecurity and climate science are two subject-matter areas with big, complex data benefiting from D&R. We illustrate this by discussing two datasets, one from each area. The first is the measurements of 13 variables for each of 10,615,054,608 queries to the Spamhaus IP address blacklisting service. The second has 50,632 3-hourly satellite rainfall estimates at 576,000 locations.

This paper presents an adaptive procedure for estimating the variability and determining error bars as confidence intervals for climate mean states by accounting for both short- and long-range dependence. While the prevailing methods for quantifying the variability of climate means account for short-range dependence, they ignore long memory, which is demonstrated to lead to underestimated variability and hence artificially narrow confidence intervals. To capture both short- and long-range correlation structures, climate data are modeled as fractionally integrated autoregressive moving-average processes. The preferred model can be selected adaptively via an information criterion and a diagnostic visualization, and the estimated variability of the climate mean state can be computed directly from the chosen model. The procedure was demonstrated by determining error bars for four 30-yr means of surface temperatures observed at Potsdam, Germany, from 1896 to 2015. These error bars are roughly twice the width as those obtained using prevailing methods, which disregard long memory, leading to a substantive reinterpretation of differences among mean states of this particular dataset. Despite their increased width, the new error bars still suggest that a significant increase occurred in the mean temperature state of Potsdam from the 1896–1925 period to the most recent period, 1986–2015. The new wider error bars, therefore, communicate greater uncertainty in the mean state yet present even stronger evidence of a significant temperature increase. These results corroborate a need for more meticulous consideration of the correlation structures of climate data—especially of their long-memory properties—in assessing the variability and determining confidence intervals for their mean states.

Previous observational studies have found a persistent maximum in stratospheric water vapor (SWV) in the upper troposphere lower stratosphere (UTLS) confined by the upper-level anticyclone over the Asian summer monsoon region. This study investigates the simulation of SWV in the Community Earth System Model, version 1 with the Whole Atmosphere Community Climate Model as its atmospheric component [CESM1(WACCM)]. CESM1(WACCM) generally tends to simulate a SWV maximum over the central Pacific Ocean, but this bias is largely improved in the high vertical resolution version. The high vertical resolution model with increased vertical layers in the UTLS is found to have a less stratified UTLS over the central Pacific Ocean compared with the low vertical resolution model. It therefore simulates a steepened potential vorticity gradient over the central Pacific Ocean that better closes the upper-level anticyclone and confines the SWV within the enhanced transport barrier.

This monograph on convection-coupled systems in the tropics was inspired by the life and career of Professor Michio Yanai, whose major contributions to the subject spanned more than five decades. From a distant perspective, Professor Yanai's career can be understood in the context of Japanese scientists who immigrated to the United States in the decades of the 1950s and 1960s, enriching the meteorological research community in the United States as well as abroad (Lewis 1993). A closer look reminds us that the tapestry of scientific progress is created by the contributions of individual scientists with their unique backgrounds, motivations, and talents, and the serendipity of events that shape their lives.

The authors examined the maintenance mechanisms of perturbation kinetic energy (PKE) in the tropical regions for multiple time scales by computing and analyzing its budget equation. The emphasis has been placed on the mean features of synoptic and subseasonal variabilities using a 33-yr dataset. From analysis of the contributions from u-wind and y-wind components, the PKE maximum in the Indian Ocean is attributed less to synoptic variability and more to intraseasonal variability in which the Madden-Julian oscillation (MJO) dominates; however, there is strong evidence of seasonal variability affiliated with the Asian monsoon systems. The ones in the eastern Pacific and Atlantic Oceans are closely related to both intraseasonal and synoptic variability that result from the strong MJO and the relatively large amplitude of equatorial waves. The maintenance of the PKE budget mainly depends on the structure of time mean horizontal flows, the location of convection, and the transport of PKE from the extratropics. In the regions with strong convective activities, such as the eastern Indian Ocean to the western Pacific, the production of PKE occurs between 700 and 200 hPa at the expense of perturbation available potential energy (PAPE), which is generated by convective heating. This gain in PKE is largely offset by divergence of the geopotential component of vertical energy flux; that is, it is redistributed to the upper-and lower-atmospheric layers by the pressure field. Strong PKE generation through the horizontal convergence of the extratropical energy flux takes place in the upper troposphere over the eastern Pacific and Atlantic Ocean, and is largely balanced by a PKE loss due to barotropic conversion, which is determined solely by the sign of longitudinal stretching deformation. However, over the Indian Ocean, there is a net PKE loss due to divergence of energy flux, which is compensated by PKE gain through the shear generation.

The authors survey a series of modeling studies that have examined the influences that cloud microphysical processes can have on tropical cyclone (TC) motion, the strength and breadth of the wind field, inner-core diabatic heating asymmetries, outer-core convective activity, and the characteristics of the TC anvil cloud. These characteristics are sensitive to the microphysical parameterization (MP) in large part owing to the cloud-radiative forcing (CRF), the interaction of hydrometeors with radiation. The most influential component of CRF is that due to absorption and emission of longwave radiation in the anvil, which via gentle lifting directly encourages the more extensive convective activity that then leads to a radial expansion of the TC wind field. On a curved Earth, the magnitude of the outer winds helps determine the speed and direction of TC motion via the beta drift. CRF also influences TC motion by determining how convective asymmetries develop in the TC inner core. Further improvements in TC forecasting may require improved understanding and representation of cloud-radiative processes in operational models, and more comprehensive comparisons with observations are clearly needed.

This work studies moisture and heat budgets within two atmospheric rivers (ARs) that made landfall on the west coast of North America during January 2009. Three-dimensional kinematic and thermodynamic fields were constructed using ECMWF Year of Tropical Convection data and global gridded precipitation datasets. Differences between the two ARs are observed, even though both had embedded precipitating convective organizations of the same spatial scale. AR1 extended from 20 degrees to 50 degrees N in an almost west-east orientation. It had excessive warm and moist near-surface conditions. Its precipitating systems were mainly distributed on the southwest and northeast sides of the AR, and tended to exhibit stratiform-type vertical heat and moisture transports. In contrast, AR2 spanned latitudes between 20 degrees and 60 degrees N in a north-south orientation. It was narrower and shorter than AR1, and was mostly covered by pronounced precipitating systems, dominated by a deep convection type of heating throughout the troposphere. In association with these distinctions, the atmosphere over the northeastern Pacific on average experienced episodic cooling and drying despite the occurrence of AR1, yet underwent heating and drying during AR2, when latent heating was strong. Downward sensible heat flux and weak upward surface latent heat flux were observed particularly in AR1. In addition, cloud radiative forcing (CRF) was very weak in AR1, whereas it was strongly negative in AR2. In short, it is found that the oceanic convection in ARs both impacts the moisture transport of ARs, as well as modifies the heat balance in the midlatitudes through latent heat release, convective heat transport, surface heat fluxes, and CRF.