Atmospheric rivers (ARs) significantly impact the hydrological cycle and associated extremes in western continental regions. Recent studies suggest ARs also influence water resources and extremes in continental interiors. AR detection tools indicate that AR conditions are relatively frequent in areas east of the Rocky Mountains. The origin of these ARs, whether from synoptic-scale waves or mesoscale processes, is unclear. This study uses meteorological composite maps and transects of AR conditions during the four seasons. The analysis reveals that ARs east of the Rockies are associated with a long-wave baroclinic Rossby wave. This result demonstrates that eastern and midwestern ARs are dynamically similar to their western coastal counterparts, though mechanisms for vertical moisture flux differ between the two. These findings provide a foundation for understanding future climate change and ARs in this region and offer new methods for evaluating climate model simulations.

AbstractAtmospheric rivers (AR) are long, narrow jets of moisture transport responsible for over  90% of moisture transport from the tropics to higher latitudes, covering only between  2% and 10% of the earth’s surface. ARs have a significant impact on the hydrological cycle of midlatitudes and polar regions, which has resulted in a large effort to study ARs  and their impacts on these regions. It is not until recently that ARs in tropical latitudes are starting to generate interest within the scientific AR community. We use the European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis of the Twentieth Century (ERA-20C) dataset and the Bayesian AR detector Toolkit for Extreme Climate Analysis (TECA) Bayesian AR Detector (TECA–BARD) to show the relationship between extreme precipitation and ARs in central-western Mexico (CWM) during the dry seasons (November-March) in the 1900-2010 period. We find that more than 25% of extreme precipitation amount and frequency are associated with ARs, with a maximum of 60%-80% during December and January near the coast of Sinaloa (107.5°W,25°N).  Composites of the mean meteorological state show ”ideal” conditions for orographic precipitation due to landfalling ARs: high horizontal vapor transport perpendicular to the Sierra Madre. The horizontal vapor transport field and the tropospheric wave patterns in vertical velocity, surface pressure, and geopotential height indicate that these ARs are  related to tropical-extratropical interactions; however, this has yet to be quantified. Our results suggest that TECA–BARD reasonably estimates AR presence in CWM.

Many atmospheric river detectors (ARDTs) have been developed over the past few decades to capture atmospheric rivers (ARs). However, different ARDTs have been observed to capture different frequencies, shapes and sizes of ARs. Due to this, many questions including investigating the underlying phenomena for ARs in the ARDTs have been posed. In this paper, we assess four different ARDTs and investigate the underlying meteorological phenomena during landfalling ARs. We find that during landfalling ARs events, there exists a prevalent low-pressure and high-pressure confluence that enhances moisture influx toward the landfalling site. The strength of the pressure gradient in the confluence region enhances the influx of the integrated vapor transport. The four ARDTs predominantly capture similar atmospheric processes, nonetheless, they have statistically different magnitudes.

The Atmospheric River (AR) Tracking Method Intercomparison Project (ARTMIP) is a community effort to systematically assess how the uncertainties from AR detectors (ARDTs) impact our scientific understanding of ARs. This study describes the ARTMIP Tier 2 experimental design and initial results using the Coupled Model Intercomparison Project (CMIP) Phases 5 and 6 multi-model ensembles. We show that AR statistics from a given ARDT in CMIP5/6 historical simulations compare remarkably well with the MERRA-2 reanalysis. In CMIP5/6 future simulations, most ARDTs project a global increase in AR frequency, counts, and sizes, especially along the western coastlines of the Pacific and Atlantic oceans. We find that the choice of ARDT is the dominant contributor to the uncertainty in projected AR frequency when compared with model choice. These results imply that new projects investigating future changes in ARs should explicitly consider ARDT uncertainty as a core part of the experimental design.

Atmospheric rivers (AR) are large and narrow filaments of poleward horizontal water vapor transport. Because of its direct relationship with horizontal vapor transport, extreme precipitation, and overall AR impacts over land, the AR size is an important characteristic that needs to be better understood. Current AR detection and tracking algorithms have resulted in large uncertainty in estimating AR sizes, with areas varying over several orders of magnitude among different detection methods. We develop and implement five independent size estimation methods to characterize the size of ARs that make landfall over the west coast of North America in the 1980-2017 period and reduce the range of size estimation from ARTMIP. ARs that originate in the Northwest Pacific (WP) (100$^\circ$E-180$^\circ$E) have larger sizes and are more zonally oriented than those from the Northeast Pacific (EP) (180$^\circ$E-240$^\circ$E). ARs become smaller through their life cycle, mainly due to reductions in their width. They also become more meridionally oriented towards the end of their life cycle. Overall, the size estimation methods proposed in this work provide a range of AR areas (between 7x10$^{11}$m$^2$ and 10$^{13}$ m$^2$) that is several orders of magnitude narrower than current methods estimation. This methodology can provide statistical constraints in size and geometry for the AR detection and tracking algorithms; and an objective insight for future studies about AR size changes under different climate scenarios.

Daily maximum temperatures during the 2021 heatwave in the Pacific Northwest United States and Canada shattered century old records. Multiple causal factors, including anthropogenic climate change, contributed to these high temperatures, challenging traditional methods of attributing human influence. We demonstrate that the observed 2021 daily maximum temperatures are far above the bounds of Generalized Extreme Value distributions fitted from historical data. Hence, confidence in Granger causal inference statements about the human influence on this heatwave is low. Alternatively, we present a more conditional hindcast attribution study using two regional models. We performed ensembles of simulations of the heatwave to investigate how the event would have changed if it had occurred without anthropogenic climate change and with future warming. We found that human activities caused a 1C increase in heatwave temperatures. Future warming would lead to a 5C increase in heatwave temperature by the end of the 21st century.

Atmospheric rivers (ARs) are long and narrow filaments of vapor transport responsible for most poleward moisture transport outside of the tropics. Many AR detection algorithms have been developed to automatically identify ARs in climate data. The diversity of these algorithms has introduced appreciable uncertainties in quantitative measures of AR properties and thereby impedes the construction of a unified and internally consistent climatology of ARs. This paper compares eight global AR detection algorithms from the perspective of AR life cycles following the propagation of ARs from origin to termination in the MERRA2 reanalysis over the period 1980-2017. Uncertainties related to lifecycle characteristics, including number, lifetime, intensity, and frequency distribution are discussed. Notably, the number of AR events per year in the Northern Hemisphere can vary by a factor of 5 with different algorithms. Although all algorithms show that the maximum origin (termination) frequency locates over the northwestern (northeastern) Pacific, significant disagreements occur in regional distribution. Spreads are large in AR lifetime and intensity. The number of landfalling AR events produced by the algorithms can vary from 16 to 78 events per cool season, i.e. by almost a factor of five, although the agreement improves for stronger ARs. By examining the AR’s connection with the Madden-Julian Oscillation and El Niño Southern Oscillation, we find that the overall responses of ARs (such as changes in AR frequency, origin, and landfalling activity) to low-frequency climate variabilities are consistent among algorithms.

Cyclonic low-pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS in high-resolution datasets. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS dataset while minimizing disagreement between four atmospheric reanalyses in a training period. The streamfunction of the 850 hPa horizontal wind is found to be the best variable for tracking LPS; it is less noisy than vorticity and represents the complete non-divergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55-year Reanalysis (JRA-55) and the IMD dataset show a step-like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982 respectively; the 1958-2018 linear trend in JRA-55, however, is smaller than in the IMD dataset and its error bar includes zero. There are more LPS in seasons with above-average monsoon rainfall and also in La Nin ̵̃a years, but few other large-scale modes of interannual climate variability are found to modulate LPS counts, lifetimes, or track length consistently across all reanalyses.

Synoptic-scale cyclonic vortices produce abundant rainfall in South Asia, where these low pressure systems (LPS) are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department has tracked monsoon depressions for over a century, finding a large decline in the number of those storms in recent decades; their tracking methods, however, seem to have changed over time and do not include monsoon lows, which can produce intense rainfall despite their weak winds. This study presents a fast and objective tracking algorithm that can identify monsoon LPS in high-resolution datasets with a variety of grid structures. A sensitivity analysis has been performed to select a set of atmospheric variables and their corresponding thresholds for optimal tracking of LPS. Approximately 250 combinations of variables and thresholds are used to identify LPS over roughly a decade (the training period) in each of four atmospheric reanalyses, and these combinations are ranked using a skill score that compares the reanalyses with each other and with a preexisting track dataset that was compiled by subjective identification of LPS. This procedure finds the streamfunction of the 850 hPa horizontal wind to be the best variable for tracking LPS. The streamfunction is smoother than the vorticity field and represents the complete non-divergent component of the wind even when the flow is not geostrophic, unlike the geopotential height or sea level pressure. Using this tracking algorithm, LPS statistics are then computed in five reanalysis products that each span at least 40 years, with a primary goal being to determine whether the large decrease in monsoon depressions seen in the India Meteorological Department track dataset since the 1970s can be found in any reanalysis. This trend assessment is particularly relevant for the ERA5 reanalysis, which extends back to 1950 and which contains explicit climate forcings. In addition to secular trends, this study assesses the decadal variation of LPS, as well as interannual changes in LPS activity that are associated with the El Niño-Southern Oscillation and the Indian Ocean Dipole.