Search Results
You are looking at 1 - 10 of 11 items for
- Author or Editor: Peter B. Gibson x
- Refine by Access: All Content x
Abstract
Even in locations endowed with excellent wind resources, the intermittent nature of wind is perceived as a barrier to reliable generation. However, recent studies have demonstrated that electrically interconnecting wind farms in a meteorologically oriented network can reduce supply variability and the observed frequency of zero-generation conditions. In this study a 5-yr synthetic dataset of 15 wind farms is utilized to investigate the benefits to supply reliability from wind farm interconnection in New Zealand. An examination is carried out primarily through a synoptic climatology framework, hypothesizing that benefits to reliability are primarily related to the degree to which wind farms are influenced differently by the synoptic-scale circulation. Using a weather-typing approach and composite analysis, regionality is observed in the linkages between synoptic-scale circulation and wind resources, particularly between wind farms located in the far northern and far southern regions of the country. Subsequently, and as compared with all other possible combinations, supply reliability is observed to be optimized in a network that includes wind farms connected between far northern and far southern regions, under which the frequency of hours with zero generation is almost eliminated. It is likely that the frequency of hours with zero generation could be further reduced on the basis of a more extensive meteorologically based selection of wind data from a greater number of locations. It is suggested that these findings should be taken into consideration in future planning and site selection of wind farm projects in New Zealand.
Abstract
Even in locations endowed with excellent wind resources, the intermittent nature of wind is perceived as a barrier to reliable generation. However, recent studies have demonstrated that electrically interconnecting wind farms in a meteorologically oriented network can reduce supply variability and the observed frequency of zero-generation conditions. In this study a 5-yr synthetic dataset of 15 wind farms is utilized to investigate the benefits to supply reliability from wind farm interconnection in New Zealand. An examination is carried out primarily through a synoptic climatology framework, hypothesizing that benefits to reliability are primarily related to the degree to which wind farms are influenced differently by the synoptic-scale circulation. Using a weather-typing approach and composite analysis, regionality is observed in the linkages between synoptic-scale circulation and wind resources, particularly between wind farms located in the far northern and far southern regions of the country. Subsequently, and as compared with all other possible combinations, supply reliability is observed to be optimized in a network that includes wind farms connected between far northern and far southern regions, under which the frequency of hours with zero generation is almost eliminated. It is likely that the frequency of hours with zero generation could be further reduced on the basis of a more extensive meteorologically based selection of wind data from a greater number of locations. It is suggested that these findings should be taken into consideration in future planning and site selection of wind farm projects in New Zealand.
Abstract
The occurrence of extreme precipitation events in New Zealand regularly results in devastating impacts to the local society and environment. An automated atmospheric river (AR) detection technique (ARDT) is applied to construct a climatology (1979–2019) of extreme midlatitude moisture fluxes conducive to extreme precipitation. A distinct seasonality exists in AR occurrence aligning with seasonal variations in the midlatitude jet streams. The formation of the Southern Hemisphere winter split jet enables AR occurrence to persist through all seasons in northern regions of New Zealand, while southern regions of the country exhibit a substantial (50%) reduction in AR occurrence as the polar jet shifts southward during the cold season. ARs making landfall on the western coast of New Zealand (90% of all events) are characterized by a dominant northwesterly moisture flux associated with a distinct dipole pressure anomaly, with low pressure to the southwest and high pressure to the northeast of New Zealand. Precipitation totals during AR events increase with AR rank (five-point scale) throughout the country, with the most substantial increase on the windward side of the Southern Alps (South Island). The largest events (rank 5 ARs) produce 3-day precipitation totals exceeding 1000 mm. ARs account for up to 78% of total precipitation and up to 94% of extreme precipitation on the west coast of the South Island. Assessment of the multiscale atmospheric processes associated with AR events governing extreme precipitation in the Southern Alps of New Zealand should remain a priority given their hydrological significance and impact on people and infrastructure.
Abstract
The occurrence of extreme precipitation events in New Zealand regularly results in devastating impacts to the local society and environment. An automated atmospheric river (AR) detection technique (ARDT) is applied to construct a climatology (1979–2019) of extreme midlatitude moisture fluxes conducive to extreme precipitation. A distinct seasonality exists in AR occurrence aligning with seasonal variations in the midlatitude jet streams. The formation of the Southern Hemisphere winter split jet enables AR occurrence to persist through all seasons in northern regions of New Zealand, while southern regions of the country exhibit a substantial (50%) reduction in AR occurrence as the polar jet shifts southward during the cold season. ARs making landfall on the western coast of New Zealand (90% of all events) are characterized by a dominant northwesterly moisture flux associated with a distinct dipole pressure anomaly, with low pressure to the southwest and high pressure to the northeast of New Zealand. Precipitation totals during AR events increase with AR rank (five-point scale) throughout the country, with the most substantial increase on the windward side of the Southern Alps (South Island). The largest events (rank 5 ARs) produce 3-day precipitation totals exceeding 1000 mm. ARs account for up to 78% of total precipitation and up to 94% of extreme precipitation on the west coast of the South Island. Assessment of the multiscale atmospheric processes associated with AR events governing extreme precipitation in the Southern Alps of New Zealand should remain a priority given their hydrological significance and impact on people and infrastructure.
Abstract
Understanding the physical drivers of heat waves is essential for improving short-term forecasts of individual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.
Abstract
Understanding the physical drivers of heat waves is essential for improving short-term forecasts of individual events and long-term projections of heat waves under climate change. This study provides the first analysis of the influence of the large-scale circulation on Australian heat waves, conditional on the land surface conditions. Circulation types, sourced from reanalysis, are used to characterize the different large-scale circulation patterns that drive heat wave events across Australia. The importance of horizontal temperature advection is illustrated in these circulation patterns, and the pattern occurrence frequency is shown to reorganize through different modes of climate variability. It is further shown that the relative likelihood of a particular synoptic situation being associated with a heat wave is strongly modulated by the localized partitioning of available energy between surface sensible and latent heat fluxes (as measured through evaporative fraction) in many regions in reanalysis data. In particular, a several-fold increase in the likelihood of heat wave day occurrence is found during days of reduced evaporative fraction under favorable circulation conditions. The atmospheric circulation and land surface conditions linked to heat waves in reanalysis were then examined in the context of CMIP5 climate model projections. Large uncertainty was found to exist for many regions, especially in terms of the direction of future land surface changes and in terms of the magnitude of atmospheric circulation changes. Efforts to constrain uncertainty in both atmospheric and land surface processes in climate models, while challenging, should translate to more robust regional projections of heat waves.
Abstract
Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.
Abstract
Climate model evaluation is complicated by the presence of observational uncertainty. In this study we analyze daily precipitation indices and compare multiple gridded observational and reanalysis products with regional climate models (RCMs) from the North American component of the Coordinated Regional Climate Downscaling Experiment (NA-CORDEX) multimodel ensemble. In the context of model evaluation, observational product differences across the contiguous United States (CONUS) are also deemed nontrivial for some indices, especially for annual counts of consecutive wet days and for heavy precipitation indices. Multidimensional scaling (MDS) is used to directly include this observational spread into the model evaluation procedure, enabling visualization and interpretation of model differences relative to a “cloud” of observational uncertainty. Applying MDS to the evaluation of NA-CORDEX RCMs reveals situations of added value from dynamical downscaling, situations of degraded performance from dynamical downscaling, and the sensitivity of model performance to model resolution. On precipitation days, higher-resolution RCMs typically simulate higher mean and extreme precipitation rates than their lower-resolution pairs, sometimes improving model fidelity with observations. These results document the model spread and biases in daily precipitation extremes across the full NA-CORDEX model ensemble. The often-large divergence between in situ observations, satellite data, and reanalysis, shown here for CONUS, is especially relevant for data-sparse regions of the globe where satellite and reanalysis products are extensively relied upon. This highlights the need to carefully consider multiple observational products when evaluating climate models.
Abstract
Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.
Abstract
Despite an urgent demand for reliable seasonal prediction of precipitation in California (CA) due to the recent recurrent and severe drought conditions, our predictive skill for CA winter precipitation remains limited. October hindcasts by the coupled dynamical models typically show a correlation skill of about 0.3 for CA winter (November–March) precipitation. In this study, an attempt is made to understand the underlying processes that limit seasonal prediction skill for CA winter precipitation. It is found that only about 25% of interannual variability of CA winter precipitation can be attributed to influences by El Niño–Southern Oscillation (ENSO). Instead, the year-to-year CA winter precipitation variability is primarily due to circulation anomalies independent from ENSO, featuring a circulation center over the west coast United States as a portion of a short Rossby wave train pattern over the North Pacific. Analyses suggest that dynamical models show nearly no skill in predicting these ENSO-independent circulation anomalies, thus leading to limited predictive skill for CA winter precipitation. Low predictability of these ENSO-independent circulation anomalies is further demonstrated by a large ensemble of atmospheric-only climate model simulations. While low predictability of the ENSO-independent circulation anomalies could be due to chaotic internal atmospheric processes over the mid- to high latitudes, possible underexploited predictability sources for CA precipitation in models are also discussed. This study pinpoints an urgent need for improved understanding of the formation mechanisms of ENSO-independent circulation anomalies over the U.S. West Coast for a breakthrough in seasonal prediction of CA winter precipitation.
Abstract
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections.
Significance Statement
This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.
Abstract
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections.
Significance Statement
This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.
Abstract
Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Using k-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Niño–Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.
Abstract
Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Using k-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Niño–Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.
Abstract
Persistent winter ridging events are a consistent feature of meteorological drought across the western and southwestern United States. In this study, a ridge detection algorithm is developed and applied on daily geopotential height anomalies to track and quantify the diversity of individual ridge characteristics (e.g., position, frequency, magnitude, extent, and persistence). Three dominant ridge types are shown to play important, but differing, roles for influencing the location of landfalling atmospheric rivers (ARs), precipitation, and subsequently meteorological drought. For California, a combination of these ridge types is important for influencing precipitation deficits on daily through seasonal time scales, indicating the various pathways by which ridging can induce drought. Furthermore, both the frequency of ridge types and reduced AR activity are necessary features for explaining drought variability on seasonal time scales across the western and southwestern regions. The three ridge types are found to be associated in different ways with various remote drivers and modes of variability, highlighting possible sources of subseasonal-to-seasonal (S2S) predictability. A comparison between ridge types shows that anomalously large and persistent ridging events relate to different Rossby wave trains across the Pacific with different preferential upstream locations of tropical heating. For the “South-ridge” type, centered over the Southwest, a positive trend is found in both the frequency and persistence of these events across recent decades, likely contributing to observed regional drying. These results illustrate the utility of feature tracking for characterizing a wider range of ridging features that collectively influence precipitation deficits and drought.
Abstract
Persistent winter ridging events are a consistent feature of meteorological drought across the western and southwestern United States. In this study, a ridge detection algorithm is developed and applied on daily geopotential height anomalies to track and quantify the diversity of individual ridge characteristics (e.g., position, frequency, magnitude, extent, and persistence). Three dominant ridge types are shown to play important, but differing, roles for influencing the location of landfalling atmospheric rivers (ARs), precipitation, and subsequently meteorological drought. For California, a combination of these ridge types is important for influencing precipitation deficits on daily through seasonal time scales, indicating the various pathways by which ridging can induce drought. Furthermore, both the frequency of ridge types and reduced AR activity are necessary features for explaining drought variability on seasonal time scales across the western and southwestern regions. The three ridge types are found to be associated in different ways with various remote drivers and modes of variability, highlighting possible sources of subseasonal-to-seasonal (S2S) predictability. A comparison between ridge types shows that anomalously large and persistent ridging events relate to different Rossby wave trains across the Pacific with different preferential upstream locations of tropical heating. For the “South-ridge” type, centered over the Southwest, a positive trend is found in both the frequency and persistence of these events across recent decades, likely contributing to observed regional drying. These results illustrate the utility of feature tracking for characterizing a wider range of ridging features that collectively influence precipitation deficits and drought.
Abstract
California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.
Abstract
California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.
