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Abstract
The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables.
Significance Statement
A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.
Abstract
The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables.
Significance Statement
A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.
Abstract
A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles and unstaffed aerial vehicles, is to ensure their proper functioning under adverse rain or snow conditions. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution were recorded at General Motors’s McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer and WXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of the University of Ontario Institute of Technology. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data.
Abstract
A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles and unstaffed aerial vehicles, is to ensure their proper functioning under adverse rain or snow conditions. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution were recorded at General Motors’s McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer and WXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of the University of Ontario Institute of Technology. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data.
Abstract
Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.25°) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions.
Abstract
Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.25°) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions.
Abstract
Much of the previous research on total and heavy precipitation trends across the northeastern United States (herein, the Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multiduration (1, 2, 3, 7, 14, and 30 days), multi-return-interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more-frequent, shorter-duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from rotated principal component analysis and k-means clustering analysis, which allowed for the main synoptic types present as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return intervals and included coastal low (Northeasters, tropical cyclones, and predecessor rain events), deep trough, East Coast trough, zonal, and high pressure patterns.
Abstract
Much of the previous research on total and heavy precipitation trends across the northeastern United States (herein, the Northeast) used daily precipitation totals over relatively short periods of record, which do not capture the full range of climate variability and change. Less well understood are the characteristics of long-term changes and synoptic patterns in longer-duration heavy precipitation events across the Northeast. A multiduration (1, 2, 3, 7, 14, and 30 days), multi-return-interval (2, 5, 10, and 50 years) precipitation dataset was used to diagnose changes in various types of precipitation events across the Northeast from 1895 to 2017. Increasing trends were found in all duration and return-interval event combinations with the rarest, longest duration events increasing at faster rates than more-frequent, shorter-duration ones. Daily 850-hPa geopotential height patterns associated with precipitation events were extracted from rotated principal component analysis and k-means clustering analysis, which allowed for the main synoptic types present as well as their structure and evolution to be analyzed. The daily synoptic patterns thus identified were found to be similar across all durations and return intervals and included coastal low (Northeasters, tropical cyclones, and predecessor rain events), deep trough, East Coast trough, zonal, and high pressure patterns.
Abstract
Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 10° ± 3°C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraurban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 6° ± 0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.
Significance Statement
Heatwaves are the leading cause of morbidity and mortality among all environmental hazards. Moreover, their frequency and intensity are on the rise due to climate change. Southern California is no stranger to extreme heat, but persistently humid heatwaves still test the adaptability limits of its residents. We find that the set of two contrasting heatwaves that afflicted Los Angeles in the summer of 2020 forms the perfect testbed for characterizing the impacts of humid versus dry heatwaves on urban environment. Because climate model forecasts and long-term observational trends point to more humid heatwaves in the future for Southern California, our results underscore the importance of including moist heat in extreme heat warning frameworks.
Abstract
Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 10° ± 3°C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraurban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 6° ± 0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.
Significance Statement
Heatwaves are the leading cause of morbidity and mortality among all environmental hazards. Moreover, their frequency and intensity are on the rise due to climate change. Southern California is no stranger to extreme heat, but persistently humid heatwaves still test the adaptability limits of its residents. We find that the set of two contrasting heatwaves that afflicted Los Angeles in the summer of 2020 forms the perfect testbed for characterizing the impacts of humid versus dry heatwaves on urban environment. Because climate model forecasts and long-term observational trends point to more humid heatwaves in the future for Southern California, our results underscore the importance of including moist heat in extreme heat warning frameworks.
Abstract
An accurate and reliable precipitation product on regular grids is essential for understanding trends and variability within climate studies, for weather forecasting, and in hydrology and agrometeorology applications. However, the construction of high-resolution spatiotemporal precipitation grid products is challenging for complex terrain with sparse rain gauge networks and when only coarse spatial resolutions of satellite data are available. The objective of this study was to consequently provide a practical method to create a grid precipitation product by merging accurate quantitative observations from weather stations with continuous spatial information and from a satellite-based estimate product. The new gridded precipitation product exhibits a monthly temporal resolution and a spatial resolution of 0.01° for the Tian Shan range, extending back to 1981. To overcome the limitation of low densities and sparse distributions of meteorological stations in the complex terrain of the Tian Shan, a suitable interpolation of Australian National University Spline (ANUSPLIN) was used to interpolate grid precipitation based on in situ data. The interpolation grid precipitation was then merged with the satellite precipitation product developed by the U.S. Geological Survey and the Climate Hazards Group. After evaluation and validation using withheld stations and comparison to reference datasets, the result indicated that the merged product exhibits considerable promise for application in complex terrain. The method can be widely applied and is expected to construct precipitation products with high spatial and temporal resolution by merging multiple precipitation data sources.
Significance Statement
The purpose of this study is to construct a gridded precipitation dataset by merging the interpolation precipitation based on in situ observation and a satellite precipitation product in arid mountain regions. This is important because gridded precipitations are essential to the evaluation of climate model output and detection of trends in mean climate and climate extremes. Our results present a guide on merging frameworks to construct precipitation datasets used multisource data sources for regions with complex topography, precipitation scarcity, and ungauged and sparse collection.
Abstract
An accurate and reliable precipitation product on regular grids is essential for understanding trends and variability within climate studies, for weather forecasting, and in hydrology and agrometeorology applications. However, the construction of high-resolution spatiotemporal precipitation grid products is challenging for complex terrain with sparse rain gauge networks and when only coarse spatial resolutions of satellite data are available. The objective of this study was to consequently provide a practical method to create a grid precipitation product by merging accurate quantitative observations from weather stations with continuous spatial information and from a satellite-based estimate product. The new gridded precipitation product exhibits a monthly temporal resolution and a spatial resolution of 0.01° for the Tian Shan range, extending back to 1981. To overcome the limitation of low densities and sparse distributions of meteorological stations in the complex terrain of the Tian Shan, a suitable interpolation of Australian National University Spline (ANUSPLIN) was used to interpolate grid precipitation based on in situ data. The interpolation grid precipitation was then merged with the satellite precipitation product developed by the U.S. Geological Survey and the Climate Hazards Group. After evaluation and validation using withheld stations and comparison to reference datasets, the result indicated that the merged product exhibits considerable promise for application in complex terrain. The method can be widely applied and is expected to construct precipitation products with high spatial and temporal resolution by merging multiple precipitation data sources.
Significance Statement
The purpose of this study is to construct a gridded precipitation dataset by merging the interpolation precipitation based on in situ observation and a satellite precipitation product in arid mountain regions. This is important because gridded precipitations are essential to the evaluation of climate model output and detection of trends in mean climate and climate extremes. Our results present a guide on merging frameworks to construct precipitation datasets used multisource data sources for regions with complex topography, precipitation scarcity, and ungauged and sparse collection.
Abstract
We propose a statistical downscaling model based on multiway functional principal component analysis (FPCA) for rainfall prediction. The model mainly explains the relationship between the winter mean sea level pressure (MSLP) and rainfall in southern Australia from the perspective of functional data. In comparison with the traditional approach of feature extraction based on principal component analysis, the multiway FPCA needs fewer principal components not only to capture the most variance in MSLP but also to greatly avoid the loss of spatial information. A functional principal component (FPC) regression is further developed to simulate both current and future rainfall. The main results show that the first five leading FPCs are sufficient to capture the spatial characteristics of winter MSLP, achieving the purpose of efficient dimensionality reduction. Specifically, no more than three FPCs are required to develop the functional downscaling models for the winter rainfall over four studied regions. The functional downscaling model provides a good skill in terms of the correlation higher than 0.7 between the predictions and observations and the ratio of root-mean-square error to the climatology of winter rainfall below 20% over four regions. The developed downscaling models are further used to interpret the MSLP patterns from four CMIP5 climate models [ACCESS1.3, BCC_CSM1.1(m), CESM1(CAM5), and MPI-ESM-MR], which have been used to simulate both present-day and future climate. The resulting downscaled values based on ensemble MSLP provide 1) a closer representation of observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes in rainfall that arise from changes in MSLP.
Abstract
We propose a statistical downscaling model based on multiway functional principal component analysis (FPCA) for rainfall prediction. The model mainly explains the relationship between the winter mean sea level pressure (MSLP) and rainfall in southern Australia from the perspective of functional data. In comparison with the traditional approach of feature extraction based on principal component analysis, the multiway FPCA needs fewer principal components not only to capture the most variance in MSLP but also to greatly avoid the loss of spatial information. A functional principal component (FPC) regression is further developed to simulate both current and future rainfall. The main results show that the first five leading FPCs are sufficient to capture the spatial characteristics of winter MSLP, achieving the purpose of efficient dimensionality reduction. Specifically, no more than three FPCs are required to develop the functional downscaling models for the winter rainfall over four studied regions. The functional downscaling model provides a good skill in terms of the correlation higher than 0.7 between the predictions and observations and the ratio of root-mean-square error to the climatology of winter rainfall below 20% over four regions. The developed downscaling models are further used to interpret the MSLP patterns from four CMIP5 climate models [ACCESS1.3, BCC_CSM1.1(m), CESM1(CAM5), and MPI-ESM-MR], which have been used to simulate both present-day and future climate. The resulting downscaled values based on ensemble MSLP provide 1) a closer representation of observed present-day rainfall than the raw climate model values and 2) alternative estimates of future changes in rainfall that arise from changes in MSLP.
Abstract
The continually changing atmospheric conditions over densely populated coastal urban regions make it challenging to produce models that accurately capture the complex interactions of anthropogenic and environmental emissions, chemical reactions, and unique meteorological processes, such as sea- and land-breeze circulations. The purpose of this study is to determine and identify the influence of synoptic-scale wind patterns on the development of local-scale sea-breeze circulations and air quality over the New York City (NYC), New York, metropolitan area. This study utilizes column-integrated nitrogen dioxide observations made during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, ground-level ozone observations, the HRRR numerical weather prediction model, and trajectory model simulations using the NOAA HYSPLIT model. A cluster analysis within the HYSPLIT modeling system was performed to determine that there were six unique synoptic-scale transport pathways for NYC. Stagnant conditions or weak transport out of the northwest resulted in the worst air quality for NYC. Weak synoptic-scale forcings associated with these conditions allowed for local-scale sea-breeze circulations to develop, resulting in air pollution being able to recirculate and mix with freshly emitted pollutants.
Significance Statement
The purpose of this work is to understand how synoptic-scale wind patterns influence air quality and sea-breeze circulations in the New York City, New York, metropolitan area. This work shows that clean air can be imported into the region from rural New England and over the Atlantic Ocean, whereas polluted air can be transported into the region from the northwest and southwest. This work also shows the importance of the strength in synoptic-scale forcings in the development of sea-breeze circulations. Weak synoptic-scale winds allow for strong sea-breeze circulations to develop over all coastlines in the New York City region, resulting in air pollutants recirculating and mixing with freshly emitted air pollution and contributing to poor air quality.
Abstract
The continually changing atmospheric conditions over densely populated coastal urban regions make it challenging to produce models that accurately capture the complex interactions of anthropogenic and environmental emissions, chemical reactions, and unique meteorological processes, such as sea- and land-breeze circulations. The purpose of this study is to determine and identify the influence of synoptic-scale wind patterns on the development of local-scale sea-breeze circulations and air quality over the New York City (NYC), New York, metropolitan area. This study utilizes column-integrated nitrogen dioxide observations made during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, ground-level ozone observations, the HRRR numerical weather prediction model, and trajectory model simulations using the NOAA HYSPLIT model. A cluster analysis within the HYSPLIT modeling system was performed to determine that there were six unique synoptic-scale transport pathways for NYC. Stagnant conditions or weak transport out of the northwest resulted in the worst air quality for NYC. Weak synoptic-scale forcings associated with these conditions allowed for local-scale sea-breeze circulations to develop, resulting in air pollution being able to recirculate and mix with freshly emitted pollutants.
Significance Statement
The purpose of this work is to understand how synoptic-scale wind patterns influence air quality and sea-breeze circulations in the New York City, New York, metropolitan area. This work shows that clean air can be imported into the region from rural New England and over the Atlantic Ocean, whereas polluted air can be transported into the region from the northwest and southwest. This work also shows the importance of the strength in synoptic-scale forcings in the development of sea-breeze circulations. Weak synoptic-scale winds allow for strong sea-breeze circulations to develop over all coastlines in the New York City region, resulting in air pollutants recirculating and mixing with freshly emitted air pollution and contributing to poor air quality.
Abstract
October–September runoff increased 6% and 17% in the upper (UMRB) and lower (LMRB) Missouri River basins, respectively, in a recent (1990–2019) climate in comparison with a past (1960–89) climate. The runoff increases were unanticipated, given various projections for semipermanent drought and/or aridification in the North American Great Plains. Here, five transient coupled climate model ensembles are used to diagnose the effects of natural internal variability and anthropogenic climate change on the observed runoff increases and to project UMRB and LMRB runoff to the mid-twenty-first century. The runoff increases observed in the recent climate in comparison with the past climate were not due to anthropogenic climate change but rather resulted mostly from an extreme occurrence of internal multidecadal variability. High runoff resulted from large, mostly internally generated, precipitation increases (6% in the UMRB and 5% in the LMRB) that exceeded simulated increases attributable to climate change forcing alone (0%–2% intermodel range). The precipitation elasticity of runoff, which relates runoff sensitivity to precipitation differences in the recent climate in comparison with the past climate, led to one–threefold and two–fourfold amplifications of runoff versus precipitation in the UMRB and LMRB, respectively. Without the observed precipitation increases in the recent climate in comparison with the past climate, effects of human-induced warming of about 1°C would alone have most likely induced runoff declines of 7% and 13% in the UMRB and LMRB, respectively. Ensemble model simulations overwhelmingly project lower UMRB and LRMB runoff by 2050 when compared with 1990–2019, a change found to be insensitive to whether individual realizations experienced high flows in the recent climate.
Significance Statement
Declines in Missouri River basin runoff under climate change pose serious threats to communities that depend on riverine transport, irrigated agriculture, and aquatic recreation. Concerns arising from reports and projections of semipermanent drought in the basin have yet to be realized; observed runoff was greater in a recent climate (1990–2019) than in a past climate (1960–89). We found that the observed runoff increase from past to recent climates was due not to anthropogenic influences but rather to internal multidecadal variability that led to unlikely precipitation increases (<10% probability) that overwhelmed the drying effect of warming temperatures. Model simulations indicate that a modest reduction in runoff of ∼7%–15% was most likely from the past climate to the recent climate.
Abstract
October–September runoff increased 6% and 17% in the upper (UMRB) and lower (LMRB) Missouri River basins, respectively, in a recent (1990–2019) climate in comparison with a past (1960–89) climate. The runoff increases were unanticipated, given various projections for semipermanent drought and/or aridification in the North American Great Plains. Here, five transient coupled climate model ensembles are used to diagnose the effects of natural internal variability and anthropogenic climate change on the observed runoff increases and to project UMRB and LMRB runoff to the mid-twenty-first century. The runoff increases observed in the recent climate in comparison with the past climate were not due to anthropogenic climate change but rather resulted mostly from an extreme occurrence of internal multidecadal variability. High runoff resulted from large, mostly internally generated, precipitation increases (6% in the UMRB and 5% in the LMRB) that exceeded simulated increases attributable to climate change forcing alone (0%–2% intermodel range). The precipitation elasticity of runoff, which relates runoff sensitivity to precipitation differences in the recent climate in comparison with the past climate, led to one–threefold and two–fourfold amplifications of runoff versus precipitation in the UMRB and LMRB, respectively. Without the observed precipitation increases in the recent climate in comparison with the past climate, effects of human-induced warming of about 1°C would alone have most likely induced runoff declines of 7% and 13% in the UMRB and LMRB, respectively. Ensemble model simulations overwhelmingly project lower UMRB and LRMB runoff by 2050 when compared with 1990–2019, a change found to be insensitive to whether individual realizations experienced high flows in the recent climate.
Significance Statement
Declines in Missouri River basin runoff under climate change pose serious threats to communities that depend on riverine transport, irrigated agriculture, and aquatic recreation. Concerns arising from reports and projections of semipermanent drought in the basin have yet to be realized; observed runoff was greater in a recent climate (1990–2019) than in a past climate (1960–89). We found that the observed runoff increase from past to recent climates was due not to anthropogenic influences but rather to internal multidecadal variability that led to unlikely precipitation increases (<10% probability) that overwhelmed the drying effect of warming temperatures. Model simulations indicate that a modest reduction in runoff of ∼7%–15% was most likely from the past climate to the recent climate.
