During the last four decades, global warming has intensified extreme precipitation events in the Midwestern United States (defined here as the region covering Illinois, Indiana, Ohio and Kentucky), leading to increased risks to human life, property, and infrastructure. To enable climate change adaptation and resilience across various economic and social sectors in this region, updated information about future climate changes, specifically at finer spatial scales, is essential. Leveraging a new 150-year dynamical downscaling dataset at convection-permitting resolution, this study introduces a framework to construct the projected future intensity-duration-frequency (IDF) curves of heavy precipitation, which are prominent tools for infrastructure design and water resources management. This framework generates IDF curves at both sub-daily and multi-day duration utilizing hourly in situ observations as well as quantile-based statistical techniques in bias-correction and return levels selection. The assumption of non-stationarity in the distribution parameter fitting process is also implemented in this workflow. Compared to historical IDF curves for 1980-2022, future projected IDF curves for 2058-2100 under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios indicate an average intensity increase of approximately 20% and 30%, respectively, across 74 stations and all four seasons of interest. The frequency of future extreme precipitation events in the Midwest region is also projected to double. Furthermore, current results reveal spatial heterogeneity of future trends across stations owing to the high-resolution input dataset.

Understanding the regional and temporal variability of atmospheric river (AR) seasonality is crucial for preparedness and mitigation of extreme events. Previously thought to peak mainly in winter, recent research reveals that ARs exhibit region-specific seasonality. However, AR analysis is heavily influenced by the chosen detection algorithm. Our study examines how AR seasonality varies based on both location, year and algorithm selection. We investigate the link between year-to-year consistency of peak AR activity and the presence of a dominant seasonal pattern. We categorize regions based on their year-to-year seasonality characteristics, including consistent patterns (e.g., India, Central Asia), patterns with occasional outliers (e.g., British Columbia coast, Gulf of Alaska), and regions lacking a clear dominant season of peak AR frequency (e.g., South Atlantic, parts of Australia). Hence, not all regions exhibit a consistent seasonal cycle of AR activity. Additionally, different algorithms may detect a consistent seasonal pattern for the same region but disagree on the exact dominant season. This is exemplified by the conflicting results obtained for China. Integrated Vapor Transport (IVT) often corroborates consistent or inconsistent patterns across regions. In conclusion, this study suggests that variations in the consistency of seasonal patterns are related not only to the detection technique but also to atmospheric circulation, synoptic and low-frequency anomalies. Understanding the variations in the consistency of seasonal pattern in areas like Britain remains challenging due to algorithmic and physical differences. These findings emphasize the need for a multi-faceted approach to AR research, considering not just detection methodologies but also regional characteristics and atmospheric processes. Understanding the specific reasons for inconsistent seasonal patterns is an important next step for future research to improve forecasts and preparedness.

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.

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.

Atmospheric rivers (ARs) are filamentary structures within the atmosphere that account for a substantial portion of poleward moisture transport and play an important role in Earth’s hydroclimate. However, there is no one quantitative definition for what constitutes an atmospheric river, leading to uncertainty in quantifying how these systems respond to global change. This study seeks to better understand how different AR detection tools (ARDTs) respond to changes in climate states utilizing single-forcing climate model experiments under the aegis of the Atmospheric River Tracking Method Intercomparison Project (ARTMIP). We compare a simulation with an early Holocene orbital configuration and another with CO2 levels of the Last Glacial Maximum to a pre-industrial control simulation to test how the ARDTs respond to changes in seasonality and mean climate state, respectively. We find good agreement among the algorithms in the AR response to the changing orbital configuration, with a poleward shift in AR frequency that tracks seasonal poleward shifts in atmospheric water vapor and zonal winds. In the low CO2 simulation, the algorithms generally agree on the sign of AR changes but there is substantial spread in their magnitude, indicating that mean-state changes lead to larger uncertainty. This disagreement likely arises primarily from differences between algorithms in their thresholds for water vapor and its transport used for identifying ARs. These findings warrant caution in ARDT selection for paleoclimate and climate change studies in which there is a change to the mean climate state, as ARDT selection contributes substantial uncertainty in such cases.

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.