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- Author or Editor: Jonathan M. Winter x
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Abstract
The northeastern United States has experienced an increase in extreme precipitation over the last three decades, which has substantial implications for flooding. However, the relationship between extreme precipitation and societally relevant flooding in the Northeast is not well understood. Here, we investigate the association between precipitation, antecedent wetness, and damage-producing warm-season (June–November) flood reports in the Northeast, 1996–2020. We employ 4-km gridded daily precipitation data aggregated to the county scale to calculate 1- and 3-day precipitation and a standardized precipitation index at multiple time scales. From these, we determine extreme precipitation and extreme antecedent wetness thresholds to identify hydroclimatic extreme events and relate them to damaging flood occurrences from the NOAA Storm Events Database. We find that damaging floods frequently occur with relatively high precipitation and antecedent wetness, although there are exceptions in several counties. However, the majority (54%) of damaging floods are not associated with extreme precipitation or extreme antecedent wetness, and more than 90% of hydroclimatic extreme events are not associated with floods. We explore limitations to the definitions of extreme precipitation and extreme antecedent wetness by varying the threshold. Lower hydroclimatic extreme event thresholds not only have a greater likelihood of being associated with damaging floods but also have a greater number of hydroclimatic extreme events not associated with a damaging flood; the opposite holds for higher thresholds. We address these challenges by combining precipitation and antecedent wetness to identify coordinated thresholds that best capture damaging flooding in each county.
Abstract
The northeastern United States has experienced an increase in extreme precipitation over the last three decades, which has substantial implications for flooding. However, the relationship between extreme precipitation and societally relevant flooding in the Northeast is not well understood. Here, we investigate the association between precipitation, antecedent wetness, and damage-producing warm-season (June–November) flood reports in the Northeast, 1996–2020. We employ 4-km gridded daily precipitation data aggregated to the county scale to calculate 1- and 3-day precipitation and a standardized precipitation index at multiple time scales. From these, we determine extreme precipitation and extreme antecedent wetness thresholds to identify hydroclimatic extreme events and relate them to damaging flood occurrences from the NOAA Storm Events Database. We find that damaging floods frequently occur with relatively high precipitation and antecedent wetness, although there are exceptions in several counties. However, the majority (54%) of damaging floods are not associated with extreme precipitation or extreme antecedent wetness, and more than 90% of hydroclimatic extreme events are not associated with floods. We explore limitations to the definitions of extreme precipitation and extreme antecedent wetness by varying the threshold. Lower hydroclimatic extreme event thresholds not only have a greater likelihood of being associated with damaging floods but also have a greater number of hydroclimatic extreme events not associated with a damaging flood; the opposite holds for higher thresholds. We address these challenges by combining precipitation and antecedent wetness to identify coordinated thresholds that best capture damaging flooding in each county.
Abstract
A climate model must include an accurate surface physics scheme in order to examine the interactions between the land and atmosphere. Given an increase in the surface radiative forcing, the sensitivity of latent heat flux to available energy plays an important role in determining the energy budget and has a significant impact on the response of surface temperature.
The Penman–Monteith equation is used to construct a theoretical framework for evaluating the climatology of evapotranspiration and the sensitivity of latent heat flux to available energy. Regional Climate Model version 3 coupled to Integrated Biosphere Simulator (RegCM3–IBIS); RegCM3 with its native land surface model, Biosphere–Atmosphere Transfer Scheme 1e (RegCM3–BATS1e); and Flux Network (FLUXNET) micrometeorological tower observations are compared and contrasted using the developed methodology.
RegCM3–IBIS and RegCM3–BATS1e simulate the observed sensitivity of latent heat flux to available energy reasonably well during the summer on average; however, there are significant variations in the monthly values. Additional information provided by the physically based Penman–Monteith framework is employed for identifying deficiencies and guiding improvements in models, allowing calibration of both the climatology of evapotranspiration and the sensitivity of latent heat flux to available energy.
Abstract
A climate model must include an accurate surface physics scheme in order to examine the interactions between the land and atmosphere. Given an increase in the surface radiative forcing, the sensitivity of latent heat flux to available energy plays an important role in determining the energy budget and has a significant impact on the response of surface temperature.
The Penman–Monteith equation is used to construct a theoretical framework for evaluating the climatology of evapotranspiration and the sensitivity of latent heat flux to available energy. Regional Climate Model version 3 coupled to Integrated Biosphere Simulator (RegCM3–IBIS); RegCM3 with its native land surface model, Biosphere–Atmosphere Transfer Scheme 1e (RegCM3–BATS1e); and Flux Network (FLUXNET) micrometeorological tower observations are compared and contrasted using the developed methodology.
RegCM3–IBIS and RegCM3–BATS1e simulate the observed sensitivity of latent heat flux to available energy reasonably well during the summer on average; however, there are significant variations in the monthly values. Additional information provided by the physically based Penman–Monteith framework is employed for identifying deficiencies and guiding improvements in models, allowing calibration of both the climatology of evapotranspiration and the sensitivity of latent heat flux to available energy.
Abstract
A description of the coupling of Integrated Biosphere Simulator (IBIS) to Regional Climate Model version 3 (RegCM3) is presented. IBIS introduces several key advantages to RegCM3, most notably vegetation dynamics, the coexistence of multiple plant functional types in the same grid cell, more sophisticated plant phenology, plant competition, explicit modeling of soil/plant biogeochemistry, and additional soil and snow layers.
A single subroutine was created that allows RegCM3 to use IBIS for surface physics calculations. A revised initialization scheme was implemented for RegCM3–IBIS, including an IBIS-specific prescription of vegetation and soil properties.
To illustrate the relative strengths and weaknesses of RegCM3–IBIS, one 4-yr numerical experiment was completed to assess ability of both RegCM3–IBIS (with static vegetation) and RegCM3 with its native land surface model, Biosphere–Atmosphere Transfer Scheme 1e (RegCM3–BATS1e), to simulate the energy and water budgets. Each model was evaluated using the NASA Surface Radiation Budget, FLUXNET micrometeorological tower observations, and Climate Research Unit Time Series 2.0. RegCM3–IBIS and RegCM3–BATS1e simulate excess shortwave radiation incident and absorbed at the surface, especially during the summer months. RegCM3–IBIS limits evapotranspiration, which allows for the correct estimation of latent heat flux, but increases surface temperature, sensible heat flux, and net longwave radiation. RegCM3–BATS1e better simulates temperature, net longwave radiation, and sensible heat flux, but systematically overestimates latent heat flux. This objective comparison of two different land surface models will help guide future adjustments to surface physics schemes within RegCM3.
Abstract
A description of the coupling of Integrated Biosphere Simulator (IBIS) to Regional Climate Model version 3 (RegCM3) is presented. IBIS introduces several key advantages to RegCM3, most notably vegetation dynamics, the coexistence of multiple plant functional types in the same grid cell, more sophisticated plant phenology, plant competition, explicit modeling of soil/plant biogeochemistry, and additional soil and snow layers.
A single subroutine was created that allows RegCM3 to use IBIS for surface physics calculations. A revised initialization scheme was implemented for RegCM3–IBIS, including an IBIS-specific prescription of vegetation and soil properties.
To illustrate the relative strengths and weaknesses of RegCM3–IBIS, one 4-yr numerical experiment was completed to assess ability of both RegCM3–IBIS (with static vegetation) and RegCM3 with its native land surface model, Biosphere–Atmosphere Transfer Scheme 1e (RegCM3–BATS1e), to simulate the energy and water budgets. Each model was evaluated using the NASA Surface Radiation Budget, FLUXNET micrometeorological tower observations, and Climate Research Unit Time Series 2.0. RegCM3–IBIS and RegCM3–BATS1e simulate excess shortwave radiation incident and absorbed at the surface, especially during the summer months. RegCM3–IBIS limits evapotranspiration, which allows for the correct estimation of latent heat flux, but increases surface temperature, sensible heat flux, and net longwave radiation. RegCM3–BATS1e better simulates temperature, net longwave radiation, and sensible heat flux, but systematically overestimates latent heat flux. This objective comparison of two different land surface models will help guide future adjustments to surface physics schemes within RegCM3.
Abstract
The northeastern United States has experienced a large increase in precipitation over recent decades. Annual and seasonal changes of total and extreme precipitation from station observations in the Northeast were assessed over multiple time periods spanning 1901–2014. Spatially averaged, both annual total and extreme precipitation across the Northeast increased significantly since 1901, with changepoints occurring in 2002 and 1996, respectively. Annual extreme precipitation experienced a larger increase than total precipitation; extreme precipitation from 1996 to 2014 is 53% higher than from 1901 to 1995. Spatially, coastal areas receive more total and extreme precipitation on average, but increases across the changepoints are distributed fairly uniformly across the domain. Increases in annual total precipitation across the 2002 changepoint are driven by significant total precipitation increases in fall and summer, while increases in annual extreme precipitation across the 1996 changepoint are driven by significant extreme precipitation increases in fall and spring. The ability of gridded observed and reanalysis precipitation data to reproduce station observations was also evaluated. Gridded observations perform well in reproducing averages and trends of annual and seasonal total precipitation, but extreme precipitation trends show significantly different spatial and domain-averaged trends than station data. The North American Regional Reanalysis generally underestimates annual and seasonal total and extreme precipitation means and trends relative to station observations, and also shows substantial differences in the spatial pattern of total and extreme precipitation trends within the Northeast.
Abstract
The northeastern United States has experienced a large increase in precipitation over recent decades. Annual and seasonal changes of total and extreme precipitation from station observations in the Northeast were assessed over multiple time periods spanning 1901–2014. Spatially averaged, both annual total and extreme precipitation across the Northeast increased significantly since 1901, with changepoints occurring in 2002 and 1996, respectively. Annual extreme precipitation experienced a larger increase than total precipitation; extreme precipitation from 1996 to 2014 is 53% higher than from 1901 to 1995. Spatially, coastal areas receive more total and extreme precipitation on average, but increases across the changepoints are distributed fairly uniformly across the domain. Increases in annual total precipitation across the 2002 changepoint are driven by significant total precipitation increases in fall and summer, while increases in annual extreme precipitation across the 1996 changepoint are driven by significant extreme precipitation increases in fall and spring. The ability of gridded observed and reanalysis precipitation data to reproduce station observations was also evaluated. Gridded observations perform well in reproducing averages and trends of annual and seasonal total precipitation, but extreme precipitation trends show significantly different spatial and domain-averaged trends than station data. The North American Regional Reanalysis generally underestimates annual and seasonal total and extreme precipitation means and trends relative to station observations, and also shows substantial differences in the spatial pattern of total and extreme precipitation trends within the Northeast.
Abstract
The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~⅛°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30″), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (⅛°) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.
Abstract
The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~⅛°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30″), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (⅛°) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.
Abstract
The Lake Champlain basin is a critical ecological and socioeconomic resource of the northeastern United States and southern Quebec, Canada. While general circulation models (GCMs) provide an overview of climate change in the region, they lack the spatial and temporal resolution necessary to fully anticipate the effects of rising global temperatures associated with increasing greenhouse gas concentrations. Observed trends in precipitation and temperature were assessed across the Lake Champlain basin to bridge the gap between global climate change and local impacts. Future shifts in precipitation and temperature were evaluated as well as derived indices, including maple syrup production, days above 32.2°C (90°F), and snowfall, relevant to managing the natural and human environments in the region. Four statistically downscaled, bias-corrected GCM simulations were evaluated from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by two representative concentration pathways (RCPs) to sample the uncertainty in future climate simulations. Precipitation is projected to increase by between 9.1 and 12.8 mm yr−1 decade−1 during the twenty-first century while daily temperatures are projected to increase between 0.43° and 0.49°C decade−1. Annual snowfall at six major ski resorts in the region is projected to decrease between 46.9% and 52.4% by the late twenty-first century. In the month of July, the number of days above 32.2°C in Burlington, Vermont, is projected to increase by over 10 days during the twenty-first century.
Abstract
The Lake Champlain basin is a critical ecological and socioeconomic resource of the northeastern United States and southern Quebec, Canada. While general circulation models (GCMs) provide an overview of climate change in the region, they lack the spatial and temporal resolution necessary to fully anticipate the effects of rising global temperatures associated with increasing greenhouse gas concentrations. Observed trends in precipitation and temperature were assessed across the Lake Champlain basin to bridge the gap between global climate change and local impacts. Future shifts in precipitation and temperature were evaluated as well as derived indices, including maple syrup production, days above 32.2°C (90°F), and snowfall, relevant to managing the natural and human environments in the region. Four statistically downscaled, bias-corrected GCM simulations were evaluated from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by two representative concentration pathways (RCPs) to sample the uncertainty in future climate simulations. Precipitation is projected to increase by between 9.1 and 12.8 mm yr−1 decade−1 during the twenty-first century while daily temperatures are projected to increase between 0.43° and 0.49°C decade−1. Annual snowfall at six major ski resorts in the region is projected to decrease between 46.9% and 52.4% by the late twenty-first century. In the month of July, the number of days above 32.2°C in Burlington, Vermont, is projected to increase by over 10 days during the twenty-first century.
Abstract
High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.
Abstract
High-resolution, bias-corrected climate data are necessary for climate impact studies at local scales. Gridded historical data are convenient for bias correction but may contain biases resulting from interpolation. Long-term, quality-controlled station data are generally superior climatological measurements, but because the distribution of climate stations is irregular, station data are challenging to incorporate into downscaling and bias-correction approaches. Here, we compared six novel methods for constructing full-coverage, high-resolution, bias-corrected climate products using daily maximum temperature simulations from a regional climate model (RCM). Only station data were used for bias correction. We quantified performance of the six methods with the root-mean-square-error (RMSE) and Perkins skill score (PSS) and used two ANOVA models to analyze how performance varied among methods. We validated the six methods using two calibration periods of observed data (1980–89 and 1980–2014) and two testing sets of RCM data (1990–2014 and 1980–2014). RMSE for all methods varied throughout the year and was larger in cold months, whereas PSS was more consistent. Quantile-mapping bias-correction techniques substantially improved PSS, while simple linear transfer functions performed best in improving RMSE. For the 1980–89 calibration period, simple quantile-mapping techniques outperformed empirical quantile mapping (EQM) in improving PSS. When calibration and testing time periods were equivalent, EQM resulted in the largest improvements in PSS. No one method performed best in both RMSE and PSS. Our results indicate that simple quantile-mapping techniques are less prone to overfitting than EQM and are suitable for processing future climate model output, whereas EQM is ideal for bias correcting historical climate model output.
Abstract
Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.
Abstract
Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.
Abstract
General circulation models (GCMs) are essential for projecting future climate; however, despite the rapid advances in their ability to simulate the climate system at increasing spatial resolution, GCMs cannot capture the local and regional weather dynamics necessary for climate impacts assessments. Temperature and precipitation, for which dense observational records are available, can be bias corrected and downscaled, but many climate impacts models require a larger set of variables such as relative humidity, cloud cover, wind speed and direction, and solar radiation. To address this need, we develop and demonstrate an analog-based approach, which we call a “weather estimator.” The weather estimator employs a highly generalizable structure, utilizing temperature and precipitation from previously downscaled GCMs to select analogs from a reanalysis product, resulting in a complete daily gridded dataset. The resulting dataset, constructed from the selected analogs, contains weather variables needed for impacts modeling that are physically, spatially, and temporally consistent. This approach relies on the weather variables’ correlation with temperature and precipitation, and our correlation analysis indicates that the weather estimator should best estimate evaporation, relative humidity, and cloud cover and do less well in estimating pressure and wind speed and direction. In addition, while the weather estimator has several user-defined parameters, a sensitivity analysis shows that the method is robust to small variations in important model parameters. The weather estimator recreates the historical distributions of relative humidity, pressure, evaporation, shortwave radiation, cloud cover, and wind speed well and outperforms a multiple linear regression estimator across all predictands.
Abstract
General circulation models (GCMs) are essential for projecting future climate; however, despite the rapid advances in their ability to simulate the climate system at increasing spatial resolution, GCMs cannot capture the local and regional weather dynamics necessary for climate impacts assessments. Temperature and precipitation, for which dense observational records are available, can be bias corrected and downscaled, but many climate impacts models require a larger set of variables such as relative humidity, cloud cover, wind speed and direction, and solar radiation. To address this need, we develop and demonstrate an analog-based approach, which we call a “weather estimator.” The weather estimator employs a highly generalizable structure, utilizing temperature and precipitation from previously downscaled GCMs to select analogs from a reanalysis product, resulting in a complete daily gridded dataset. The resulting dataset, constructed from the selected analogs, contains weather variables needed for impacts modeling that are physically, spatially, and temporally consistent. This approach relies on the weather variables’ correlation with temperature and precipitation, and our correlation analysis indicates that the weather estimator should best estimate evaporation, relative humidity, and cloud cover and do less well in estimating pressure and wind speed and direction. In addition, while the weather estimator has several user-defined parameters, a sensitivity analysis shows that the method is robust to small variations in important model parameters. The weather estimator recreates the historical distributions of relative humidity, pressure, evaporation, shortwave radiation, cloud cover, and wind speed well and outperforms a multiple linear regression estimator across all predictands.
