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- Author or Editor: S. C. Pryor x
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Abstract
Many previous studies have indicated the importance of nitric acid (HNO3) reactions on sea salt particles for flux divergence of HNO3 in the marine surface layer. The potential importance of this reaction in determining the spatial and temporal patterns of nitrogen dry deposition to marine ecosystems is investigated using models of sea spray generation and particle- and gas-phase dry deposition. Under horizontally homogeneous conditions with near-neutral stability and for wind speeds between 3.5 and 10 m s−1, transfer of HNO3 to the particle phase to form sodium nitrate may decrease the deposition velocity of nitrogen by over 50%, leading to greater horizontal transport prior to deposition to the sea surface. Conversely, for wind speeds above 10 m s−1, transfer of nitrogen to the particle phase would increase the deposition rate and hence decrease horizontal transport prior to surface removal.
Abstract
Many previous studies have indicated the importance of nitric acid (HNO3) reactions on sea salt particles for flux divergence of HNO3 in the marine surface layer. The potential importance of this reaction in determining the spatial and temporal patterns of nitrogen dry deposition to marine ecosystems is investigated using models of sea spray generation and particle- and gas-phase dry deposition. Under horizontally homogeneous conditions with near-neutral stability and for wind speeds between 3.5 and 10 m s−1, transfer of HNO3 to the particle phase to form sodium nitrate may decrease the deposition velocity of nitrogen by over 50%, leading to greater horizontal transport prior to deposition to the sea surface. Conversely, for wind speeds above 10 m s−1, transfer of nitrogen to the particle phase would increase the deposition rate and hence decrease horizontal transport prior to surface removal.
Abstract
Wind speeds over the oceans are required for a range of applications but are difficult to obtain through in situ methods. Hence, remote sensing tools, which also offer the possibility of describing spatial variability, represent an attractive proposition. However, the uncertainties inherent in application of current remote sensing methodologies have yet to be fully quantified. Aside from known issues regarding absolute accuracy and precision, there are a number of biases inherent in remote retrieval of wind speeds using satellite-borne instrumentation that lead to overestimation of the wind resource and are demonstrated here to be of sufficient magnitude to merit further consideration. As an interim measure, error bounds are proposed for the wind speed probability distribution parameters, which may be applied to sparse datasets such as those likely to be obtained from satellite-borne instrumentation.
Abstract
Wind speeds over the oceans are required for a range of applications but are difficult to obtain through in situ methods. Hence, remote sensing tools, which also offer the possibility of describing spatial variability, represent an attractive proposition. However, the uncertainties inherent in application of current remote sensing methodologies have yet to be fully quantified. Aside from known issues regarding absolute accuracy and precision, there are a number of biases inherent in remote retrieval of wind speeds using satellite-borne instrumentation that lead to overestimation of the wind resource and are demonstrated here to be of sufficient magnitude to merit further consideration. As an interim measure, error bounds are proposed for the wind speed probability distribution parameters, which may be applied to sparse datasets such as those likely to be obtained from satellite-borne instrumentation.
Abstract
Markov chains are widely used tools for modeling daily precipitation occurrence. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a monthly basis for 831 stations in the contiguous United States using long-term data. The model order was first identified using the Bayesian information criteria (BIC). The maximum-likelihood estimates of the Markov transition probabilities were computed from 100 bootstrapped samples and were then used to generate 50-yr precipitation occurrence series. The distributions of dry- and wet-spell lengths in the resulting series were then compared with observations using a two-sample Kolmogorov–Smirnov (K-S) test. The results suggest that the most parsimonious model, as identified by the BIC, usually (in approximately 68% of the cases) reproduced the wet- and dry-spell length distributions. However, the K-S test often indicated a second-order model when the BIC indicated a first-order model. In a smaller number of cases, the BIC indicated a higher-order model than the K-S test. In both cases, the differences were found to be due to the distribution of wet spells rather than dry spells. It is concluded that models chosen on the basis of the BIC may not adequately reproduce the distributions of wet and dry spells for some locations and times of year.
Abstract
Markov chains are widely used tools for modeling daily precipitation occurrence. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a monthly basis for 831 stations in the contiguous United States using long-term data. The model order was first identified using the Bayesian information criteria (BIC). The maximum-likelihood estimates of the Markov transition probabilities were computed from 100 bootstrapped samples and were then used to generate 50-yr precipitation occurrence series. The distributions of dry- and wet-spell lengths in the resulting series were then compared with observations using a two-sample Kolmogorov–Smirnov (K-S) test. The results suggest that the most parsimonious model, as identified by the BIC, usually (in approximately 68% of the cases) reproduced the wet- and dry-spell length distributions. However, the K-S test often indicated a second-order model when the BIC indicated a first-order model. In a smaller number of cases, the BIC indicated a higher-order model than the K-S test. In both cases, the differences were found to be due to the distribution of wet spells rather than dry spells. It is concluded that models chosen on the basis of the BIC may not adequately reproduce the distributions of wet and dry spells for some locations and times of year.
Abstract
This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific–North American pattern (PNA), El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM–PNA first-order interaction and underestimate the NAM–AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term.
Abstract
This work quantitatively evaluates the fidelity with which the northern annular mode (NAM), southern annular mode (SAM), Pacific–North American pattern (PNA), El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Atlantic multidecadal oscillation (AMO), and the first-order mode interactions are represented in Earth system model (ESM) output from the CMIP6 archive. Several skill metrics are used as part of a differential credibility assessment (DCA) of both spatial and temporal characteristics of the modes across ESMs, ESM families, and specific ESM realizations relative to ERA5. The spatial patterns and probability distributions are generally well represented but skill scores that measure the degree to which the frequencies of maximum variance are captured are consistently lower for most ESMs and climate modes. Substantial variability in skill scores manifests across realizations from individual ESMs for the PNA and oceanic modes. Further, the ESMs consistently overestimate the strength of the NAM–PNA first-order interaction and underestimate the NAM–AMO connection. These results suggest that the choice of ESM and ESM realizations will continue to play a critical role in determining climate projections at the global and regional scale at least in the near term.
Abstract
Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. The approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact-relevant Climate Extreme (CLIMDEX) temperature indices that are assigned credibility based on 1) the reproduction of relevant large-scale predictors by the GCMs (i.e., fraction of regression beta weights derived from predictors that are well reproduced) and 2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations and between the two GCM and is generally higher for minimum temperature than for maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision-makers about projection confidence and/or can be extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.
Abstract
Climate science is increasingly using (i) ensembles of climate projections from multiple models derived using different assumptions and/or scenarios and (ii) process-oriented diagnostics of model fidelity. Efforts to assign differential credibility to projections and/or models are also rapidly advancing. A framework to quantify and depict the credibility of statistically downscaled model output is presented and demonstrated. The approach employs transfer functions in the form of robust and resilient generalized linear models applied to downscale daily minimum and maximum temperature anomalies at 10 locations using predictors drawn from ERA-Interim reanalysis and two global climate models (GCM; GFDL-ESM2M and MPI-ESM-LR). The downscaled time series are used to derive several impact-relevant Climate Extreme (CLIMDEX) temperature indices that are assigned credibility based on 1) the reproduction of relevant large-scale predictors by the GCMs (i.e., fraction of regression beta weights derived from predictors that are well reproduced) and 2) the degree of variance in the observations reproduced in the downscaled series following application of a new variance inflation technique. Credibility of the downscaled predictands varies across locations and between the two GCM and is generally higher for minimum temperature than for maximum temperature. The differential credibility assessment framework demonstrated here is easy to use and flexible. It can be applied as is to inform decision-makers about projection confidence and/or can be extended to include other components of the transfer functions, and/or used to weight members of a statistically downscaled ensemble.
Abstract
Introduction of irrigated agriculture changes the partitioning of the surface energy flux between sensible and latent heat (H vs LE) and alters the albedo α and emissivity ε. In the absence of changes in the radiation components of the surface energy balance, the change in the Bowen ratio due to irrigation typically suppresses the local air temperature T but increases the total near-surface atmospheric heat content (as measured using equivalent potential temperature θ e ). While the effect of irrigation on surface energy partitioning due to enhanced surface and subsurface water availability has long been acknowledged, the roles of associated changes in ε and α have received less attention, and the scales and magnitudes of these effects remain uncertain. A new methodology designed for application to in situ and remote sensing data is presented and used to demonstrate that the net impact of irrigation on T and θ e is strongly dependent on the regional climate, land cover in surrounding areas, and the amount of irrigation in the upwind fetch. The results suggest that the impact of the radiative forcing terms on net available energy is not negligible and may amplify or offset the impact from changed energy partitioning on T and θ e depending on the specific regional climate and land cover.
Abstract
Introduction of irrigated agriculture changes the partitioning of the surface energy flux between sensible and latent heat (H vs LE) and alters the albedo α and emissivity ε. In the absence of changes in the radiation components of the surface energy balance, the change in the Bowen ratio due to irrigation typically suppresses the local air temperature T but increases the total near-surface atmospheric heat content (as measured using equivalent potential temperature θ e ). While the effect of irrigation on surface energy partitioning due to enhanced surface and subsurface water availability has long been acknowledged, the roles of associated changes in ε and α have received less attention, and the scales and magnitudes of these effects remain uncertain. A new methodology designed for application to in situ and remote sensing data is presented and used to demonstrate that the net impact of irrigation on T and θ e is strongly dependent on the regional climate, land cover in surrounding areas, and the amount of irrigation in the upwind fetch. The results suggest that the impact of the radiative forcing terms on net available energy is not negligible and may amplify or offset the impact from changed energy partitioning on T and θ e depending on the specific regional climate and land cover.
Abstract
ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.
Abstract
ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.
Abstract
The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios > 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF > 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (>25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.
Significance Statement
Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.
Abstract
The Southern Great Plains (SGP) region exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) Model applied at convection-permitting resolution with the Milbrandt–Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization radar, and gridded datasets and observations at the DOE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios > 1 indicate that WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mm h−1 over the domain. Detailed analyses of the 10 days with highest spatial coverage of cREF > 30 dBZ show spatially similar reflectivity fields and high RR in both radar data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (>25 mm h−1) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications, fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.
Significance Statement
Heavy rainfall and hail during convective events are challenging for numerical models to simulate in both space and time. For some applications, such as to estimate damage to wind turbine blades and solar panels, fidelity is also required with respect to hail size and joint probabilities of wind speed and hydrometeor type and rainfall rates (RR). This demands fidelity that is seldom evaluated. We show that, although this simulation exhibits fidelity for the marginal probabilities of wind speed, RR, and hail occurrence, the joint probabilities of these properties and the simulation of maximum size of hail are, as yet, not sufficient to characterize potential damage to these renewable energy industries.
Abstract
The Lower Fraser Valley of British Columbia is currently experiencing rapid population growth and episodically suffers elevated oxidant concentrations, the frequency of which is linked to meteorological conditions on the synoptic scale. This study is a first step toward developing and validating a methodology for “declimatizing” air quality data so that postulated effects of changing emissions patterns can be addressed. Principal component analysis of gridded fields at three atmospheric levels (sea level–reduced surface pressure, 850-mb height, and 500-mb height) yields four principal components (or modes of the atmospheric circulation) that account for over 83% of geophysical dataset variance. Daily component scores from these components are used as independent parameters in a region equation of the daily maximum ozone concentrations at a site (Rocky Point Park) in Vancouver over five summers (1984–88, inclusive). The coefficients in this equation are used to construct another algorithm that is used to predict maximum daily ozone concentrations at this site during the summers of 1989–92 on the basis of synoptic-scale meteorology. The algorithm correctly predicts the low frequency of ozone episodes in the July 1989–July 1992 period but cannot account for the reduction in daily maximum ozone concentrations on nonexceedance days at Rocky Point Park over this period. The implications of these findings are that during the summers of 1989–92 meteorological conditions on the synoptic scale were not conducive to the occurrence of ozone exceedances but that the reduction in average daily maximum ozone concentrations cannot be accounted for on the basis of synoptic-scale meteorological variability as parameterized by the component scores.
Abstract
The Lower Fraser Valley of British Columbia is currently experiencing rapid population growth and episodically suffers elevated oxidant concentrations, the frequency of which is linked to meteorological conditions on the synoptic scale. This study is a first step toward developing and validating a methodology for “declimatizing” air quality data so that postulated effects of changing emissions patterns can be addressed. Principal component analysis of gridded fields at three atmospheric levels (sea level–reduced surface pressure, 850-mb height, and 500-mb height) yields four principal components (or modes of the atmospheric circulation) that account for over 83% of geophysical dataset variance. Daily component scores from these components are used as independent parameters in a region equation of the daily maximum ozone concentrations at a site (Rocky Point Park) in Vancouver over five summers (1984–88, inclusive). The coefficients in this equation are used to construct another algorithm that is used to predict maximum daily ozone concentrations at this site during the summers of 1989–92 on the basis of synoptic-scale meteorology. The algorithm correctly predicts the low frequency of ozone episodes in the July 1989–July 1992 period but cannot account for the reduction in daily maximum ozone concentrations on nonexceedance days at Rocky Point Park over this period. The implications of these findings are that during the summers of 1989–92 meteorological conditions on the synoptic scale were not conducive to the occurrence of ozone exceedances but that the reduction in average daily maximum ozone concentrations cannot be accounted for on the basis of synoptic-scale meteorological variability as parameterized by the component scores.
Abstract
Humidity is a key determinant of heat wave impacts, but studies investigating changes in extreme heat events have not differentiated between events characterized by high temperatures and those characterized by simultaneously elevated temperature and humidity. The authors present a framework, using air temperature (T) and equivalent temperature (T E ; a measure combining temperature and specific humidity), to examine changes in local percentile-based extreme heat events characterized by high temperature (T only) and those with high temperature and humidity (T-and-T E events). Application to one observational dataset (PRISM), four reanalysis products (1981–2015), and seven U.S. regions reveals widespread changes in heat wave characteristics over the 35-yr period. Agreement among the datasets employed on several heat wave metrics suggests that many of the findings are robust. With the exception of the northern plains region, all regions experienced increases in both T-only and T-and-T E heat wave day (HWD) frequency in each of the reanalyses. In the northern plains, all datasets have negative trends in T-only HWD frequency and positive trends in T-and-T E HWD frequency. Trends in HWD frequency were generally accompanied by changes in the spatial footprint in heat wave conditions. Temperature has increased significantly during T-only HWDs in the western regions, while increases in T E during T-and-T E HWDs have occurred in the central United States and Northeast region. These findings suggest that equivalent temperature provides an alternative perspective on the evolution of regional heat wave climatology. Studies considering changes in regional heat wave impacts should carefully consider the role of atmospheric moisture.
Abstract
Humidity is a key determinant of heat wave impacts, but studies investigating changes in extreme heat events have not differentiated between events characterized by high temperatures and those characterized by simultaneously elevated temperature and humidity. The authors present a framework, using air temperature (T) and equivalent temperature (T E ; a measure combining temperature and specific humidity), to examine changes in local percentile-based extreme heat events characterized by high temperature (T only) and those with high temperature and humidity (T-and-T E events). Application to one observational dataset (PRISM), four reanalysis products (1981–2015), and seven U.S. regions reveals widespread changes in heat wave characteristics over the 35-yr period. Agreement among the datasets employed on several heat wave metrics suggests that many of the findings are robust. With the exception of the northern plains region, all regions experienced increases in both T-only and T-and-T E heat wave day (HWD) frequency in each of the reanalyses. In the northern plains, all datasets have negative trends in T-only HWD frequency and positive trends in T-and-T E HWD frequency. Trends in HWD frequency were generally accompanied by changes in the spatial footprint in heat wave conditions. Temperature has increased significantly during T-only HWDs in the western regions, while increases in T E during T-and-T E HWDs have occurred in the central United States and Northeast region. These findings suggest that equivalent temperature provides an alternative perspective on the evolution of regional heat wave climatology. Studies considering changes in regional heat wave impacts should carefully consider the role of atmospheric moisture.
