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- Author or Editor: Paul C. Loikith x
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Abstract
Non-Gaussian cold side temperature distribution tails occur in spatially coherent patterns in winter and summer across the globe. Under such conditions, future changes in extreme cold temperature exceedances may be manifested in more complex ways than if the underlying distribution were Gaussian. For example, under a uniform warm shift, locations with shorter- or longer-than-Gaussian cold side tails would experience a more or less rapid decrease in the number of extreme cold threshold exceedances, respectively, compared to if the tail were Gaussian. In many places in the mid- to high latitudes, shorter-than-Gaussian cold tails occur where there is a climatological limit on the magnitude of cold air to be transported by synoptic flow. For example, some high-latitude regions are already among the coldest in the hemisphere, thus limiting the availability of extremely cold air, in an anomalous sense, that can be transported to the region. In other short tail regions, anomalously cold air originates from or travels over large water bodies, which limits the magnitude of the cold anomaly. Long tails are often present when the cold source region is downstream of the climatological flow, requiring a highly anomalous circulation pattern to transport the cold air. The synoptic evolution of extreme cold days at several short- and long-tailed weather stations are presented to help diagnose the mechanisms behind extreme cold temperatures under conditions of non-Gaussianity. This provides a mechanistic view of how extreme cold occurs at each location, as well as an explanation for the notable deviations from Gaussianity.
Abstract
Non-Gaussian cold side temperature distribution tails occur in spatially coherent patterns in winter and summer across the globe. Under such conditions, future changes in extreme cold temperature exceedances may be manifested in more complex ways than if the underlying distribution were Gaussian. For example, under a uniform warm shift, locations with shorter- or longer-than-Gaussian cold side tails would experience a more or less rapid decrease in the number of extreme cold threshold exceedances, respectively, compared to if the tail were Gaussian. In many places in the mid- to high latitudes, shorter-than-Gaussian cold tails occur where there is a climatological limit on the magnitude of cold air to be transported by synoptic flow. For example, some high-latitude regions are already among the coldest in the hemisphere, thus limiting the availability of extremely cold air, in an anomalous sense, that can be transported to the region. In other short tail regions, anomalously cold air originates from or travels over large water bodies, which limits the magnitude of the cold anomaly. Long tails are often present when the cold source region is downstream of the climatological flow, requiring a highly anomalous circulation pattern to transport the cold air. The synoptic evolution of extreme cold days at several short- and long-tailed weather stations are presented to help diagnose the mechanisms behind extreme cold temperatures under conditions of non-Gaussianity. This provides a mechanistic view of how extreme cold occurs at each location, as well as an explanation for the notable deviations from Gaussianity.
Abstract
Motivated by a desire to understand the physical mechanisms involved in future anthropogenic changes in extreme temperature events, the key atmospheric circulation patterns associated with extreme daily temperatures over North America in the current climate are identified. The findings show that warm extremes at most locations are associated with positive 500-hPa geopotential height and sea level pressure anomalies just downstream with negative anomalies farther upstream. The orientation, physical characteristics, and spatial scale of these circulation patterns vary based on latitude, season, and proximity to important geographic features (i.e., mountains, coastlines). The anomaly patterns associated with extreme cold events tend to be similar to, but opposite in sign of, those associated with extreme warm events, especially within the westerlies, and tend to scale with temperature in the same locations. Circulation patterns aloft are more coherent across the continent than those at the surface where local surface features influence the occurrence of and patterns associated with extreme temperature days. Temperature extremes may be more sensitive to small shifts in circulation at locations where temperature is strongly influenced by mountains or large water bodies, or at the margins of important large-scale circulation patterns making such locations more susceptible to nonlinear responses to future climate change. The identification of these patterns and processes will allow for a thorough evaluation of the ability of climate models to realistically simulate extreme temperatures and their future trends.
Abstract
Motivated by a desire to understand the physical mechanisms involved in future anthropogenic changes in extreme temperature events, the key atmospheric circulation patterns associated with extreme daily temperatures over North America in the current climate are identified. The findings show that warm extremes at most locations are associated with positive 500-hPa geopotential height and sea level pressure anomalies just downstream with negative anomalies farther upstream. The orientation, physical characteristics, and spatial scale of these circulation patterns vary based on latitude, season, and proximity to important geographic features (i.e., mountains, coastlines). The anomaly patterns associated with extreme cold events tend to be similar to, but opposite in sign of, those associated with extreme warm events, especially within the westerlies, and tend to scale with temperature in the same locations. Circulation patterns aloft are more coherent across the continent than those at the surface where local surface features influence the occurrence of and patterns associated with extreme temperature days. Temperature extremes may be more sensitive to small shifts in circulation at locations where temperature is strongly influenced by mountains or large water bodies, or at the margins of important large-scale circulation patterns making such locations more susceptible to nonlinear responses to future climate change. The identification of these patterns and processes will allow for a thorough evaluation of the ability of climate models to realistically simulate extreme temperatures and their future trends.
Abstract
Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled atmosphere–ocean general circulation models contributing to the fifth phase of the Coupled Model Intercomparison Project. Circulation patterns are defined as composites of anomalies in sea level pressure and 500-hPa geopotential height concurrent with days in the tails of temperature distribution. Several metrics used to systematically describe circulation patterns associated with extreme temperature days are applied to both the observed and model-simulated data. Additionally, self-organizing maps are employed as a means of comparing observed and model-simulated circulation patterns across the North American domain. In general, the multimodel ensemble resembles the observed patterns well, especially in areas removed from complex geographic features (e.g., mountains and coastlines). Individual model results vary; however, the majority of models capture the major features observed. The multimodel ensemble captures several key features, including regional variations in the strength and orientation of atmospheric circulation patterns associated with extreme temperatures, both near the surface and aloft, as well as variations with latitude and season. The results from this work suggest that these models can be used to comprehensively examine the role that changes in atmospheric circulation will play in projected changes in temperature extremes because of future anthropogenic climate warming.
Abstract
Circulation patterns associated with extreme temperature days over North America, as simulated by a suite of climate models, are compared with those obtained from observations. The authors analyze 17 coupled atmosphere–ocean general circulation models contributing to the fifth phase of the Coupled Model Intercomparison Project. Circulation patterns are defined as composites of anomalies in sea level pressure and 500-hPa geopotential height concurrent with days in the tails of temperature distribution. Several metrics used to systematically describe circulation patterns associated with extreme temperature days are applied to both the observed and model-simulated data. Additionally, self-organizing maps are employed as a means of comparing observed and model-simulated circulation patterns across the North American domain. In general, the multimodel ensemble resembles the observed patterns well, especially in areas removed from complex geographic features (e.g., mountains and coastlines). Individual model results vary; however, the majority of models capture the major features observed. The multimodel ensemble captures several key features, including regional variations in the strength and orientation of atmospheric circulation patterns associated with extreme temperatures, both near the surface and aloft, as well as variations with latitude and season. The results from this work suggest that these models can be used to comprehensively examine the role that changes in atmospheric circulation will play in projected changes in temperature extremes because of future anthropogenic climate warming.
Abstract
The influence of the Pacific–North American (PNA) pattern, the northern annular mode (NAM), and the El Niño–Southern Oscillation (ENSO) on extreme temperature days and months over North America is examined. Associations between extreme temperature days and months are strongest with the PNA and NAM and weaker for ENSO. In general, the association with extremes tends to be stronger on monthly than daily time scales and for winter as compared to summer. Extreme temperatures are associated with the PNA and NAM in the vicinity of the centers of action of these circulation patterns; however, many extremes also occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however, many of these smaller-scale events are concurrent with amplified PNA or NAM patterns. Associations are weaker in summer when other physical mechanisms affecting the surface energy balance, such as anomalous soil moisture content, also influence the occurrence of extreme temperatures.
Abstract
The influence of the Pacific–North American (PNA) pattern, the northern annular mode (NAM), and the El Niño–Southern Oscillation (ENSO) on extreme temperature days and months over North America is examined. Associations between extreme temperature days and months are strongest with the PNA and NAM and weaker for ENSO. In general, the association with extremes tends to be stronger on monthly than daily time scales and for winter as compared to summer. Extreme temperatures are associated with the PNA and NAM in the vicinity of the centers of action of these circulation patterns; however, many extremes also occur on days when the amplitude and polarity of these patterns do not favor their occurrence. In winter, synoptic-scale, transient weather disturbances are important drivers of extreme temperature days; however, many of these smaller-scale events are concurrent with amplified PNA or NAM patterns. Associations are weaker in summer when other physical mechanisms affecting the surface energy balance, such as anomalous soil moisture content, also influence the occurrence of extreme temperatures.
Abstract
During the last week of June 2021, the Pacific Northwest region of North America experienced a record-breaking heatwave of historic proportions. All-time high temperature records were shattered, often by several degrees, across many locations, with Canada setting a new national record, the state of Washington setting a new record, and the state of Oregon tying its previous record. Here we diagnose key meteorology that contributed to this heatwave. The event was associated with a highly anomalous midtropospheric ridge, with peak 500-hPa geopotential height anomalies centered over central British Columbia. This ridge developed over several days as part of a large-scale wave train. Back trajectory analysis indicates that synoptic-scale subsidence and associated adiabatic warming played a key role in enhancing the magnitude of the heat to the south of the ridge peak, while diabatic heating was dominant closer to the ridge center. Easterly/offshore flow inhibited marine cooling and contributed additional downslope warming along the western portions of the region. A notable surface thermally induced trough was evident throughout the event over western Oregon and Washington. An eastward shift of the thermal trough, following the eastward migration of the 500-hPa ridge, allowed an inland surge of cooler marine air and dramatic 24-h cooling, especially along the western periphery of the region. Large-scale horizontal warm-air advection played a minimal role. When compared with past highly amplified ridges over the region, this event was characterized by much higher 500-hPa geopotential heights, a stronger thermal trough, and stronger offshore flow.
Abstract
During the last week of June 2021, the Pacific Northwest region of North America experienced a record-breaking heatwave of historic proportions. All-time high temperature records were shattered, often by several degrees, across many locations, with Canada setting a new national record, the state of Washington setting a new record, and the state of Oregon tying its previous record. Here we diagnose key meteorology that contributed to this heatwave. The event was associated with a highly anomalous midtropospheric ridge, with peak 500-hPa geopotential height anomalies centered over central British Columbia. This ridge developed over several days as part of a large-scale wave train. Back trajectory analysis indicates that synoptic-scale subsidence and associated adiabatic warming played a key role in enhancing the magnitude of the heat to the south of the ridge peak, while diabatic heating was dominant closer to the ridge center. Easterly/offshore flow inhibited marine cooling and contributed additional downslope warming along the western portions of the region. A notable surface thermally induced trough was evident throughout the event over western Oregon and Washington. An eastward shift of the thermal trough, following the eastward migration of the 500-hPa ridge, allowed an inland surge of cooler marine air and dramatic 24-h cooling, especially along the western periphery of the region. Large-scale horizontal warm-air advection played a minimal role. When compared with past highly amplified ridges over the region, this event was characterized by much higher 500-hPa geopotential heights, a stronger thermal trough, and stronger offshore flow.
Abstract
Projected changes in atmospheric ridges and associated temperature and precipitation anomalies are assessed for the end of the twenty-first century in a suite of 27 models contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6) under a high-end emissions scenario over the Pacific–North American region. Ridges are defined as spatially coherent regions of positive zonal anomalies in 500-hPa geopotential height. The frequency of ridge days in the historical period varies by geography and season; however, ridge days are broadly more common over the region in winter and least common in summer. The CMIP6 models are credible in reproducing key features of reanalysis-derived ridge climatology. The CMIP6 models also reproduce historical temperature and precipitation anomalies associated with ridges. These associations include positive temperature anomalies over and to the west/northwest of the ridge peak and negative precipitation anomalies southeast of the ridge peak. Future projections show a general decrease in ridge days across most of the region in fall through spring, with considerable model agreement. Projections for summer are different, with robust projections of increases in the number of ridge days across parts of the interior western United States and Canada. The CMIP6 models project modest decreases in the probability of stronger ridges and modest increases in the probability of weaker ridges in fall and winter. Future ridges show similar temperature and precipitation anomaly associations as in the historical climate period, when future anomalies are computed relative to future climatology.
Significance Statement
Atmospheric ridges over the Pacific–North American region are a type of atmospheric circulation pattern associated with important weather and climate impacts. These impacts include heatwaves and drought. This study uses climate models to understand how ridges and their impacts may change under future climate warming. The results suggest that ridge days will be less common across parts of the domain in fall, winter, and spring. In summer, an increase in ridge days is projected in a region centered on Montana. Results suggest that temperature and precipitation patterns associated with ridges will change at a similar rate to the overall mean climate. This work provides evidence that continued climate warming will alter atmospheric circulation over the Pacific–North American region in complex ways.
Abstract
Projected changes in atmospheric ridges and associated temperature and precipitation anomalies are assessed for the end of the twenty-first century in a suite of 27 models contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6) under a high-end emissions scenario over the Pacific–North American region. Ridges are defined as spatially coherent regions of positive zonal anomalies in 500-hPa geopotential height. The frequency of ridge days in the historical period varies by geography and season; however, ridge days are broadly more common over the region in winter and least common in summer. The CMIP6 models are credible in reproducing key features of reanalysis-derived ridge climatology. The CMIP6 models also reproduce historical temperature and precipitation anomalies associated with ridges. These associations include positive temperature anomalies over and to the west/northwest of the ridge peak and negative precipitation anomalies southeast of the ridge peak. Future projections show a general decrease in ridge days across most of the region in fall through spring, with considerable model agreement. Projections for summer are different, with robust projections of increases in the number of ridge days across parts of the interior western United States and Canada. The CMIP6 models project modest decreases in the probability of stronger ridges and modest increases in the probability of weaker ridges in fall and winter. Future ridges show similar temperature and precipitation anomaly associations as in the historical climate period, when future anomalies are computed relative to future climatology.
Significance Statement
Atmospheric ridges over the Pacific–North American region are a type of atmospheric circulation pattern associated with important weather and climate impacts. These impacts include heatwaves and drought. This study uses climate models to understand how ridges and their impacts may change under future climate warming. The results suggest that ridge days will be less common across parts of the domain in fall, winter, and spring. In summer, an increase in ridge days is projected in a region centered on Montana. Results suggest that temperature and precipitation patterns associated with ridges will change at a similar rate to the overall mean climate. This work provides evidence that continued climate warming will alter atmospheric circulation over the Pacific–North American region in complex ways.
Abstract
The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales.
Abstract
The self-organizing maps (SOMs) approach is demonstrated as a way to identify a range of archetypal large-scale meteorological patterns (LSMPs) over the northwestern United States and connect these patterns with local-scale temperature and precipitation extremes. SOMs are used to construct a set of 12 characteristic LSMPs (nodes) based on daily reanalysis circulation fields spanning the range of observed synoptic-scale variability for the summer and winter seasons for the period 1979–2013. Composites of surface variables are constructed for subsets of days assigned to each node to explore relationships between temperature, precipitation, and the node patterns. The SOMs approach also captures interannual variability in daily weather regime frequency related to El Niño–Southern Oscillation. Temperature and precipitation extremes in high-resolution gridded observations and in situ station data show robust relationships with particular nodes in many cases, supporting the approach as a way to identify LSMPs associated with local extremes. Assigning days from the extreme warm summer of 2015 and wet winter of 2016 to nodes illustrates how SOMs may be used to assess future changes in extremes. These results point to the applicability of SOMs to climate model evaluation and assessment of future projections of local-scale extremes without requiring simulations to reliably resolve extremes at high spatial scales.
Abstract
Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.
Abstract
Atmospheric rivers (ARs) are long, narrow filamentary regions of enhanced vertically integrated water vapor transport (IVT) that play an important role in regional water supply and hydrometeorological extremes. Here, an AR detection algorithm is applied to global reanalysis from Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), to objectively and consistently characterize ARs regionally across the continental United States (CONUS). The characteristics of AR and associated precipitation are computed at the gridpoint scale and summarized over the seven U.S. National Climate Assessment regions. ARs are most frequent in the autumn and winter in the West, spring in the Great Plains, and autumn in the Midwest and Northeast. ARs show regional and seasonal variability in basic geometry and IVT. AR IVT composites reveal annually consistent northeastward-directed moisture transport from the Pacific Ocean in the West, whereas moisture transport patterns vary seasonally across the Southern Great Plains and Midwest. Linked AR precipitation characteristics suggest that a substantial proportion of extreme events, defined as the top 5% of 3-day precipitation totals, are associated with ARs over many parts of CONUS, including the East. Regional patterns of AR-associated precipitation highlight that seasonally varying moisture transport and lifting mechanisms differ between the East and the West where orographic lifting is key. Our study aims to contribute a comprehensive and consistent CONUS-wide, regional-scale analysis of ARs in support of ongoing NCA efforts. Given the CONUS-wide role ARs play in extreme precipitation, findings motivate continued study of associated climate change impacts.
Abstract
Regions of shorter-than-Gaussian warm-side temperature anomaly distribution tails are shown to occur in spatially coherent patterns in global reanalysis. Under such conditions, future warming may be manifested in more complex ways than if the underlying distribution were close to Gaussian. For example, under a uniform warm shift, the simplest prototype for future warming, a location with a short tail would experience a greater increase in extreme warm exceedances relative to a fixed threshold compared to if the distribution were Gaussian. The associated societal and environmental impacts make realistic representation of these short tails an important target for climate models. Global evaluation of the ability for a suite of global climate models (GCMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that most models approximately capture the principal observed coherent regions of short tails. This suggests the underlying dynamics and physics occur on scales resolved by the models, and helps build confidence in model simulations of extremes. Furthermore, most GCMs show more rapid future increases in exceedances of the historical 95th percentile in regions exhibiting short tails in the historical climate. These regions, where the ratio of exceedances projected by the GCM compared to that expected from a Gaussian sometimes exceeds 1.5, are termed hot spots. Prominent hot spots include western North America, Central America, a broad swath of northwestern Eurasia, and the Indochina Peninsula during boreal winter. During boreal summer, central and western Australia, parts of southern Africa, and portions of central South America are major hot spots.
Abstract
Regions of shorter-than-Gaussian warm-side temperature anomaly distribution tails are shown to occur in spatially coherent patterns in global reanalysis. Under such conditions, future warming may be manifested in more complex ways than if the underlying distribution were close to Gaussian. For example, under a uniform warm shift, the simplest prototype for future warming, a location with a short tail would experience a greater increase in extreme warm exceedances relative to a fixed threshold compared to if the distribution were Gaussian. The associated societal and environmental impacts make realistic representation of these short tails an important target for climate models. Global evaluation of the ability for a suite of global climate models (GCMs) contributing to phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that most models approximately capture the principal observed coherent regions of short tails. This suggests the underlying dynamics and physics occur on scales resolved by the models, and helps build confidence in model simulations of extremes. Furthermore, most GCMs show more rapid future increases in exceedances of the historical 95th percentile in regions exhibiting short tails in the historical climate. These regions, where the ratio of exceedances projected by the GCM compared to that expected from a Gaussian sometimes exceeds 1.5, are termed hot spots. Prominent hot spots include western North America, Central America, a broad swath of northwestern Eurasia, and the Indochina Peninsula during boreal winter. During boreal summer, central and western Australia, parts of southern Africa, and portions of central South America are major hot spots.
