Future Atmospheric Rivers and Impacts on Precipitation: Overview of the ARTMIP Tier 2 High‐Resolution Global Warming Experiment

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

The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Quantifying Uncertainties in Atmospheric River Climatology

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.Fil: Rutz, Jonathan J.. National Ocean And Atmospheric Administration; Estados UnidosFil: Shields, Christine A.. National Center for Atmospheric Research; Estados UnidosFil: Lora, Juan M.. University of Yale; Estados UnidosFil: Payne, Ashley E.. University of Michigan; Estados UnidosFil: Guan, Bin. California Institute of Technology; Estados UnidosFil: Ullrich, Paul. University of California at Davis; Estados UnidosFil: O'Brien, Travis. Lawrence Berkeley National Laboratory; Estados UnidosFil: Leung, Ruby. Pacific Northwest National Laboratory; Estados UnidosFil: Ralph, F. Martin. Center For Western Weather And Water Extremes; Estados UnidosFil: Wehner, Michael. Lawrence Berkeley National Laboratory; Estados UnidosFil: Brands, Swen. Meteogalicia; EspañaFil: Collow, Allison. Universities Space Research Association; Estados UnidosFil: Goldenson, Naomi. University of California at Los Angeles; Estados UnidosFil: Gorodetskaya, Irina. Universidade de Aveiro; PortugalFil: Griffith, Helen. University of Reading; Reino UnidoFil: Kashinath, Karthik. Lawrence Bekeley National Laboratory; Estados UnidosFil: Kawzenuk, Brian. Center For Western Weather And Water Extremes; Reino UnidoFil: Krishnan, Harinarayan. Lawrence Berkeley National Laboratory; Estados UnidosFil: Kurlin, Vitaliy. University of Liverpool; Reino UnidoFil: Lavers, David. European Centre For Medium-range Weather Forecasts; Estados UnidosFil: Magnusdottir, Gudrun. University of California at Irvine; Estados UnidosFil: Mahoney, Kelly. Universidad de Lisboa; PortugalFil: Mc Clenny, Elizabeth. University of California at Davis; Estados UnidosFil: Muszynski, Grzegorz. University of Liverpool; Reino Unido. Lawrence Bekeley National Laboratory; Estados UnidosFil: Nguyen, Phu Dinh. University of California at Irvine; Estados UnidosFil: Prabhat, Mr.. Lawrence Bekeley National Laboratory; Estados UnidosFil: Qian, Yun. Pacific Northwest National Laboratory; Estados UnidosFil: Ramos, Alexandre M.. Universidade Nova de Lisboa; PortugalFil: Sarangi, Chandan. Pacific Northwest National Laboratory; Estados UnidosFil: Viale, Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentin

Using Simpler Models to Understand Atmospheric River Responses to Sea-Surface Temperature Increases

Atmospheric rivers (ARs) are filamentary systems which perform nearly all of the poleward moisture transport through the midlatitudes. When these systems are lifted---for instance when impinging on local topography---they can produce substantial precipitation which ranges from beneficial to destructive, sometimes providing essential water resources and sometimes leading to devastating floods. With this in mind, it is critical for planning and preparedness purposes to project the response of ARs to climate change. Open questions remain however, highlighting the need for a better understanding of the physical processes behind the AR response to climate change. To address these gaps in understanding, ARs are simulated in an aquaplanet, an idealized global climate model without land, sea ice, topography, or seasonality. A time-invariant ``Baseline'' sea-surface temperature (SST) distribution is prescribed for a reference run, while each test run applies a uniform increase (plus two, four, and six Kelvin) over the Baseline distribution to isolate the response of ARs to warming SSTs and assess sensitivities. Under SST increases, zonal mean AR occurrence frequency increases everywhere, mostly due to enhancements in AR size. These AR occurrence frequency increases are greatest at higher latitudes as storm tracks shift poleward with SST warming. Zonal and areal means of AR integrated water vapor (IWV) show that AR moisture is enhanced at rates greater than predicted for surface moisture by the Clausius-Clapeyron relation with respect to SST warming. Vertical profiles of relative humidity (RH) and temperature show that this ``super-Clausius-Clapeyron'' IWV increase masks RH decreases at upper levels which are related to upper-tropospheric warming that far outpaces the prescribed SST increases. Zonal and areal means of AR IWV transport (IVT) show lower increases with SST warming than for IWV; a simple linear decomposition of AR IVT into moisture and wind components shows that a slowing of mean AR wind speeds attenuates the IVT response. Zonal mean AR precipitation rates exhibit a complicated response characterized by increases in some latitude bands and decreases in others. A linear decomposition of AR precipitation rates like that performed for AR IVT once again shows a compensatory relationship between AR moistening and weakening dynamics, in this case mid-tropospheric vertical velocities. Increases in AR size as SSTs warm are examined separately. Cross-sections of AR IVT are approximated with Gaussian functions with three fit parameters: AR background IVT, AR IVT exceedance above the background, and the breadth of the AR IVT profile, defined as the distance through which 95\% of the IVT exceedance occurs. From both the AR IVT cross-sections and their Gaussian approximations are derived four measures of AR width in total. All widths are enhanced with SST warming, mostly as a result of enhanced background IVT statistics and increased AR IVT profile breadth. IVT profile breadth changes are driven mostly by Clausius-Clapeyron moderated moisture increases, though changes to AR wind profiles also play a role

Sensitivity of Atmospheric River Vapor Transport and Precipitation to Uniform Sea Surface Temperature Increases

This is the companion data for the manuscript of the same title, submitted to JGR: Atmospheres on 02/08/2020. modelParameters: this folder contains the scripts I ran on NERSC Cori in 2019 to initialize the CESM2.0/CAM5 model runs. qobs_script_newcase.sh: creates all cases, for the Baseline as well as the +xK SST runs docn_comp_mod.F90: original CAM5 aquaplanet SST distributions; "QOBS" is used for my "Baseline" experiments plusxK_docn_comp_mod.F90: modified SST distributions, for x=(2,4,6); these are simply uniform additions to the QOBS SST distributions Macros.make & env_mach_specific.xml: configuration files to run CESM2.0 on Cori at the time user_nl_cam: namelist for CAM5; specifies some model run parameters as well as output variables. detectionParameters: this folder contains the scripts I ran on NERSC Cori in 2019 to detect tropical cyclones and atmospheric rivers. Use this code for reference purposes only (i.e., to see which parameters were used to detect ARs or TCs). It will not run as-is. All other subfolders provide working examples of code used to generate figures (all in Jupyter notebooks) for the manuscript; folder names are descriptive. Unfortunately, model output was large (~12 TB). Hence, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. For more details, refer to the manuscript, or contact me ([email protected])

Sensitivity of Atmospheric River Vapor Transport and Precipitation to Uniform Sea Surface Temperature Increases

Filaments of intense vapor transport called atmospheric rivers (ARs) are responsible for the majority of poleward vapor transport in the midlatitudes. Despite their importance to the hydrologic cycle, there remain many unanswered questions about changes to ARs in a warming climate. In this study we perform a series of escalating uniform SST increases (+2, +4, and +6K, respectively) in the Community Atmosphere Model version 5 in an aquaplanet configuration to evaluate the thermodynamic and dynamical response of AR vapor content, transport, and precipitation to warming SSTs. We find that AR column integrated water vapor (IWV) is especially sensitive to SST and increases by 6.3-9.7% per degree warming despite decreasing relative humidity through much of the column. Further analysis provides a more nuanced view of AR IWV changes: Since SST warming is modest compared to that in the midtroposphere, computing fractional changes in IWV with respect to SST results in finding spuriously large increases. Meanwhile, results here show that AR IWV transport increases relatively uniformly with temperature and at consistently lower rates than IWV, as modulated by systematically decreasing low-level wind speeds. Similarly, changes in AR precipitation are related to a compensatory relationship between enhanced near-surface moisture and damped vertical motions

Atmospheric River Tracking Method Intercomparison Project (ARTMIP): Project Goals and Experimental Design

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 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 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