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.

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.

10The 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 15 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 MERRA-2 reanalysis from January 1980 to June of 2017 and will be used as a baseline for all subsequent comparisons. Participation in Tier 1 is 20 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 25 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.Geosci. Model Dev. Discuss., https://doi

We present the results of a search for substellar companions to members of the star-forming cluster IC 348. Using the Wide Field Planetary Camera 2 on board the Hubble Space Telescope, we have obtained deep, high-resolution images of the cluster through the F791W and F850LP filters. These data encompass 150 known members of IC 348, including 14 primaries that are likely to be substellar (M 1 ¼ 0:015 0:08 M ). The detection limits for companions to low-mass stars and brown dwarfs in the PC images are Ám 791 ¼ 0, 2.5, and 5.5 at separations of 0B05, 0B1, and 0B3, respectively, which correspond to M 2 / M 1 ¼ 1, 0.3, and 0.1 at 15, 30, and 90 AU. Meanwhile, for heavily saturated solar-mass primaries in the WFC images, the limits are Ám 791 ¼ 0 and 6 (M 2 / M 1 ¼ 1 and 0.04) at 0B2 and 0B4. The sky limiting magnitude of m 791 $ 26 at large separations from a primary corresponds to a mass of $0.006 M according to the evolutionary models of Chabrier and Baraffe. Point sources appearing near known and candidate cluster members are classified as either field stars or likely cluster members through their positions on the color-magnitude diagram constructed from the WFPC2 photometry. For the two faintest candidate companions appearing in these data, we have obtained 0.8-2.5 m spectra with SpeX at the IRTF. Through a comparison to spectra of optically classified dwarfs, giants, and pre-main-sequence objects, we classify these two sources as cluster members with spectral types near M6, corresponding to masses of $0.1 M with the models of Chabrier and Baraffe. Thus, no probable substellar companions are detected in this survey. After considering all potential binaries within our WFPC2 images, we find that the frequencies of stellar and substellar companions within 0B4-5 00 (120-1600 AU ) from low-mass stars (M 1 ¼ 0:08 0:5) in IC 348 agree within the uncertainties with measurements in the field. The factor of $3-10 deficiency in brown dwarfs relative to stars among companions at wide separations in IC 348 and across the much larger range of separations probed for field stars in previous work is equal within the uncertainties to the deficiency in brown dwarfs in measurements of mass functions of isolated objects. In other words, when defined relative to stars, the brown dwarf ''desert'' among companions is also present among isolated objects, which is expected if stellar and substellar companions form in the same manner as their free-floating counterparts. Meanwhile, among the 14 substellar primaries in our survey of IC 348, no companions are detected. This absence of wide binary brown dwarfs is statistically consistent with the frequency of wide binary stars in IC 348.

Abstract. The presence of light-absorbing aerosol particles deposited on arctic snow and sea ice influences the surface albedo, causing greater shortwave absorption, warming, and loss of snow and sea ice, lowering the albedo further. The Community Earth System Model version 1 (CESM1) now includes the radiative effects of light-absorbing particles in snow on land and sea ice and in sea ice itself. We investigate the model response to the deposition of black carbon and dust to both snow and sea ice. For these purposes we employ a slab ocean version of CESM1, using the Community Atmosphere Model version 4 (CAM4), run to equilibrium for year 2000 levels of CO 2 and fixed aerosol deposition. We construct experiments with and without aerosol deposition, with dust or black carbon deposition alone, and with varying quantities of black carbon and dust to approximate year 1850 and 2000 deposition fluxes. The year 2000 deposition fluxes of both dust and black carbon cause 1-2 • C of surface warming over large areas of the Arctic Ocean and subArctic seas in autumn and winter and in patches of Northern land in every season. Atmospheric circulation changes are a key component of the surface-warming pattern. Arctic sea ice thins by on average about 30 cm. Simulations with year 1850 aerosol deposition are not substantially different from those with year 2000 deposition, given constant levels of CO 2 . The climatic impact of particulate impurities deposited over land exceeds that of particles deposited over sea ice. Even the surface warming over the sea ice and sea ice thinning depends more upon light-absorbing particles deposited over land. For CO 2 doubled relative to year 2000 levels, the climate impact of particulate impurities in snow and sea ice is substantially lower than for the year 2000 equilibrium simulation.