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.
Abstract. The Last Glacial Maximum (LGM, ∼ 21 000 years ago) has been a major focus for evaluating how well state-of-the-art climate models simulate climate changes as large as those expected in the future using paleoclimate reconstructions. A new generation of climate models has been used to generate LGM simulations as part of the Paleoclimate Modelling Intercomparison Project (PMIP) contribution to the Coupled Model Intercomparison Project (CMIP). Here, we provide a preliminary analysis and evaluation of the results of these LGM experiments (PMIP4, most of which are PMIP4-CMIP6) and compare them with the previous generation of simulations (PMIP3, most of which are PMIP3-CMIP5). We show that the global averages of the PMIP4 simulations span a larger range in terms of mean annual surface air temperature and mean annual precipitation compared to the PMIP3-CMIP5 simulations, with some PMIP4 simulations reaching a globally colder and drier state. However, the multi-model global cooling average is similar for the PMIP4 and PMIP3 ensembles, while the multi-model PMIP4 mean annual precipitation average is drier than the PMIP3 one. There are important differences in both atmospheric and oceanic circulations between the two sets of experiments, with the northern and southern jet streams being more poleward and the changes in the Atlantic Meridional Overturning Circulation being less pronounced in the PMIP4-CMIP6 simulations than in the PMIP3-CMIP5 simulations. Changes in simulated precipitation patterns are influenced by both temperature and circulation changes. Differences in simulated climate between individual models remain large. Therefore, although there are differences in the average behaviour across the two ensembles, the new simulation results are not fundamentally different from the PMIP3-CMIP5 results. Evaluation of large-scale climate features, such as land–sea contrast and polar amplification, confirms that the models capture these well and within the uncertainty of the paleoclimate reconstructions. Nevertheless, regional climate changes are less well simulated: the models underestimate extratropical cooling, particularly in winter, and precipitation changes. These results point to the utility of using paleoclimate simulations to understand the mechanisms of climate change and evaluate model performance.
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
Southwestern North America was wetter than present during the Last Glacial Maximum. The causes of increased water availability have been recently debated, and quantitative precipitation reconstructions have been underutilized in model‐data comparisons. We investigate the climatological response of North Pacific atmospheric rivers to the glacial climate using model simulations and paleoclimate reconstructions. Atmospheric moisture transport due to these features shifted toward the southeast relative to modern. Enhanced southwesterly moisture delivery between Hawaii and California increased precipitation in the southwest while decreasing it in the Pacific Northwest, in agreement with reconstructions. Coupled climate models that are best able to reproduce reconstructed precipitation changes simulate decreases in sea level pressure across the eastern North Pacific and show the strongest southeastward shifts of moisture transport relative to a modern climate. Precipitation increases of ∼1 mm d−1, due largely to atmospheric rivers, are of the right magnitude to account for reconstructed pluvial conditions in parts of southwestern North America during the Last Glacial Maximum.
Simulation results are presented from a new general circulation model (GCM) of Titan, the Titan Atmospheric Model (TAM), which couples the Flexible Modeling System (FMS) spectral dynamical core to a suite of external/sub-grid-scale physics. These include a new nongray radiative transfer module that takes advantage of recent data from Cassini-Huygens, large-scale condensation and quasi-equilibrium moist convection schemes, a surface model with "bucket" hydrology, and boundary layer turbulent diffusion. The model produces a realistic temperature structure from the surface to the lower mesosphere, including a stratopause, as well as satisfactory superrotation. The latter is shown to depend on the dynamical core's ability to build up angular momentum from surface torques. Simulated latitudinal temperature contrasts are adequate, compared to observations, and polar temperature anomalies agree with observations. In the lower atmosphere, the insolation distribution is shown to strongly impact turbulent fluxes, and surface heating is maximum at mid-latitudes. Surface liquids are unstable at mid-and low-latitudes, and quickly migrate poleward. The simulated humidity profile and distribution of surface temperatures, compared to observations, corroborate the prevalence of dry conditions at low latitudes. Polar cloud activity is well represented, though the observed mid-latitude clouds remain somewhat puzzling, and some formation alternatives are suggested.
Dramatic hydroclimate shifts occurred in western North America during the last deglaciation, but the timing and mechanisms driving these changes are uncertain and debated, and previous modeling has largely relied on linear interpolations between equilibrium snapshot simulations. Using a published transient climate simulation and a range of proxy records, we analyze the region's climate evolution in order to identify the mechanisms governing hydroclimate shifts. A rapid loss of ice around 14,000 years ago causes an abrupt reorganization of the circulation, which precipitates drying and moistening of southwestern and northwestern North America, respectively. The atmospheric circulation transitions between two states on a timescale of decades to centuries, during which time the westerly jet shifts north by about 7°. In contrast to previous studies, we find that changes in the water budget of western North America prior to this event are not attributable to variations in the position of the jet, but rather to the intensity of moisture transport into the continent.
Atmospheric rivers (ARs) constitute an important mechanism for water vapor transport, but research on their characteristics and impacts has relied on a diverse assortment of detection methodologies, complicating comparisons. The AR Tracking Method Intercomparison Project (ARTMIP) provides a platform for comparing such methodologies, but analysis of ARTMIP catalogues has heretofore focused primarily on specific regions. Here we investigate ARs as detected by an ensemble of algorithms with global coverage. We find that the frequency of occurrence of the majority-consensus ARs produces a robust distribution, featuring five hot spots over the extratropical oceans, against which we compare individual algorithm results. We further explore the underlying similarities and differences via two case studies of AR evolution. The dominant source of disagreement between detection methodologies globally consists of detections (or lack thereof) of weak features, and the algorithms otherwise tend to agree remarkably well on the footprints of ARs. Plain Language Summary Atmospheric rivers-filamentary structures in the atmosphere in which water vapor is concentrated and transported-have received increasing attention over the past decade, but studies of their occurrence have relied on many disparate methods for objectively detecting them in various data sets. Here we explore the results of fourteen different such methods applied to the same data set, to better understand the resulting consensus or disagreement on atmospheric river occurrence, using both statistics from 37 years and two specific case studies. We find that most methods tend to agree quantitatively when detecting moderate and stronger features (in which the water vapor transport is high), and what appears to be significant disagreement is simply due to regions of only slightly enhanced vapor transport that are obviously disputable as true atmospheric rivers.
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