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Abstract
Understanding links between the El Niño–Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, as well as for understanding natural variability and interpreting climate change predictions. Here, a 545-yr run of the third climate configuration of the Met Office Unified Model (HadCM3), with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution toward lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June–July–August (JJA) ENSO index onward, and are weakly detected in 50-yr subsections of the control run, but only a shifted North American response can be detected in the analysis of the 40-yr ECMWF Re-Analysis (ERA-40). The ENSO signal in SWE from the long run could still contribute to regional predictions, although it would only be a weak indicator.
Abstract
Understanding links between the El Niño–Southern Oscillation (ENSO) and snow would be useful for seasonal forecasting, as well as for understanding natural variability and interpreting climate change predictions. Here, a 545-yr run of the third climate configuration of the Met Office Unified Model (HadCM3), with prescribed external forcings and fixed greenhouse gas concentrations, is used to explore the impact of ENSO on snow water equivalent (SWE) anomalies. In North America, positive ENSO events reduce the mean SWE and skew the distribution toward lower values, and vice versa during negative ENSO events. This is associated with a dipole SWE anomaly structure, with anomalies of opposite sign centered in western Canada and the central United States. In Eurasia, warm episodes lead to a more positively skewed distribution and the mean SWE is raised. Again, the opposite effect is seen during cold episodes. In Eurasia the largest anomalies are concentrated in the Himalayas. These correlations with February SWE distribution are seen to exist from the previous June–July–August (JJA) ENSO index onward, and are weakly detected in 50-yr subsections of the control run, but only a shifted North American response can be detected in the analysis of the 40-yr ECMWF Re-Analysis (ERA-40). The ENSO signal in SWE from the long run could still contribute to regional predictions, although it would only be a weak indicator.
Abstract
The prediction of winter in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast to be better quantified than in past studies by greatly increasing the effective independent sample size. The procedures were repeated on three datasets: 1) all winters in the period 1950–79 (30 winters), 2) the 15 winters having the highest Variability Index (VI), and 3) the 15 winters having the lowest VI. The Variability Index was constructed to measure the intraseasonal variability of five-day period mean 700 mb heights for a portion of the Northern Hemisphere. Verification results showed that statistically significant skill was achieved in the complete sample (overall mean percent correct of 39 and 59 for three- and two-category forecasts respectively), but improved somewhat for the low VI sample. In that case, corresponding scores were (34 and 64 percent correct. In contrast, the high VI sample scores were lower (34 and 58 percent correct) than for the complete sample, indicating that skill is likely dependent on the degree of interaseasonal circulation variability.
Abstract
The prediction of winter in the United States from Pacific sea surface temperatures was examined using a jackknifed regression scheme and a measure of intraseasonal atmospheric circulation variability. Employing a jackknifed regression methodology when deriving objective prediction equations allowed forecast to be better quantified than in past studies by greatly increasing the effective independent sample size. The procedures were repeated on three datasets: 1) all winters in the period 1950–79 (30 winters), 2) the 15 winters having the highest Variability Index (VI), and 3) the 15 winters having the lowest VI. The Variability Index was constructed to measure the intraseasonal variability of five-day period mean 700 mb heights for a portion of the Northern Hemisphere. Verification results showed that statistically significant skill was achieved in the complete sample (overall mean percent correct of 39 and 59 for three- and two-category forecasts respectively), but improved somewhat for the low VI sample. In that case, corresponding scores were (34 and 64 percent correct. In contrast, the high VI sample scores were lower (34 and 58 percent correct) than for the complete sample, indicating that skill is likely dependent on the degree of interaseasonal circulation variability.
Abstract
Longwave emissivities and the vertical profile of cooling rates of tropical cirrus clouds are determined using broadband hemispheric irradiance data. Additionally, a broadband mass absorption coefficient is defined and used to relate emissivity to water content. The data used were collected by the National Center for Atmospheric Research (NCAR) Sabreliner during the GARP Atlantic Tropical Experiment (GATE) in the summer of 1974.
Three case studies are analyzed showing that these tropical cirrus clouds approached an emissivity of 1.0 within a vertical distance of 1.0 km. Broadband mass absorption coefficients ranging from 0.076 to 0.096 m2 g−1 are derived. A comparison of these results with other work suggests that tropical cirrus cloud emissivities may be significantly larger than heretofore believed.
Ice water content of the clouds was deduced from data collected by a one-dimensional particle spectrometer. Analyses of the ice water content and the observed particle size distributions are presented.
Abstract
Longwave emissivities and the vertical profile of cooling rates of tropical cirrus clouds are determined using broadband hemispheric irradiance data. Additionally, a broadband mass absorption coefficient is defined and used to relate emissivity to water content. The data used were collected by the National Center for Atmospheric Research (NCAR) Sabreliner during the GARP Atlantic Tropical Experiment (GATE) in the summer of 1974.
Three case studies are analyzed showing that these tropical cirrus clouds approached an emissivity of 1.0 within a vertical distance of 1.0 km. Broadband mass absorption coefficients ranging from 0.076 to 0.096 m2 g−1 are derived. A comparison of these results with other work suggests that tropical cirrus cloud emissivities may be significantly larger than heretofore believed.
Ice water content of the clouds was deduced from data collected by a one-dimensional particle spectrometer. Analyses of the ice water content and the observed particle size distributions are presented.
Abstract
Many applied climatological studies use teleconnection indices to characterize atmospheric flow. Although these indices are often associated with temperature and precipitation patterns, surprisingly little attention has been paid to changes in the flow patterns within the monthly period. This study analyzes the differences between the monthly and intramonthly indices (based on 5-day mean data) for the most important source of winter season low-frequency variability in the midtropospheric geopotential height field over North America, the Pacific/North American (PNA) pattern. Results suggest that the monthly index adequately represents flow patterns at shorter temporal scales. Months with PNA indices suggesting a typical configuration (moderate 700-mb ridging over western North America with moderate troughing in eastern North America) tend to be comprised of days with the same flow, rather than an offsetting of days with amplified ridge-trough and a reversed trough-ridge pattern across North America. It is found that consecutive days of amplified flow are common, and that the reverse pattern, although infrequent, can be quite persistent when it does occur. Even though there is a strong relationship between the monthly and intramonthly indices, intramonthly variability and persistence of flow must be considered if a truly accurate relationship between circulation and environmental phenomena is to be established.
Abstract
Many applied climatological studies use teleconnection indices to characterize atmospheric flow. Although these indices are often associated with temperature and precipitation patterns, surprisingly little attention has been paid to changes in the flow patterns within the monthly period. This study analyzes the differences between the monthly and intramonthly indices (based on 5-day mean data) for the most important source of winter season low-frequency variability in the midtropospheric geopotential height field over North America, the Pacific/North American (PNA) pattern. Results suggest that the monthly index adequately represents flow patterns at shorter temporal scales. Months with PNA indices suggesting a typical configuration (moderate 700-mb ridging over western North America with moderate troughing in eastern North America) tend to be comprised of days with the same flow, rather than an offsetting of days with amplified ridge-trough and a reversed trough-ridge pattern across North America. It is found that consecutive days of amplified flow are common, and that the reverse pattern, although infrequent, can be quite persistent when it does occur. Even though there is a strong relationship between the monthly and intramonthly indices, intramonthly variability and persistence of flow must be considered if a truly accurate relationship between circulation and environmental phenomena is to be established.
Abstract
This paper explores simulated changes to the cool-season (November–March) storm-surge and coastal-flooding events at the Battery in New York City, New York (NYC), during most of the twenty-first century using several climate models and a previously developed multilinear regression model. The surface wind and pressure forcing for the surge predictions are obtained from an ensemble of 6 coupled global climate models (GCM) and 30 members from the Community Earth System Model. Using the “RCP8.5” emission scenario, both the single-model and multimodel ensemble means yielded insignificant (significance level p > 0.05) simulated changes to the median surge event (>0.61 m above astronomical tide) between a historical period (1979–2004) and the mid-to-late twenty-first century (2054–79). There is also little change in the return interval for the moderate-to-high surge events. By the mid-to-late twenty-first century, there is a poleward shift of the mean surface cyclone track in many of the models and most GCMs demonstrate an intensification of the average cyclone. There is little effect on the future surge events at the Battery because most of these storm changes are not in a region that favors more or higher-amplitude surges at NYC. Rather, projected sea level rise dominates the future simulated change in the number of flooding events by the mid-to-late twenty-first century. For example, the projections show about 23 times as many coastal-flooding events (tide + surge ≥ 2.44 m above mean lower low water; 1983–2001) in 2079 when compared with 1979, and the return intervals for some major coastal floods (e.g., the December 1992 northeaster) decrease by a factor of 3–4.
Abstract
This paper explores simulated changes to the cool-season (November–March) storm-surge and coastal-flooding events at the Battery in New York City, New York (NYC), during most of the twenty-first century using several climate models and a previously developed multilinear regression model. The surface wind and pressure forcing for the surge predictions are obtained from an ensemble of 6 coupled global climate models (GCM) and 30 members from the Community Earth System Model. Using the “RCP8.5” emission scenario, both the single-model and multimodel ensemble means yielded insignificant (significance level p > 0.05) simulated changes to the median surge event (>0.61 m above astronomical tide) between a historical period (1979–2004) and the mid-to-late twenty-first century (2054–79). There is also little change in the return interval for the moderate-to-high surge events. By the mid-to-late twenty-first century, there is a poleward shift of the mean surface cyclone track in many of the models and most GCMs demonstrate an intensification of the average cyclone. There is little effect on the future surge events at the Battery because most of these storm changes are not in a region that favors more or higher-amplitude surges at NYC. Rather, projected sea level rise dominates the future simulated change in the number of flooding events by the mid-to-late twenty-first century. For example, the projections show about 23 times as many coastal-flooding events (tide + surge ≥ 2.44 m above mean lower low water; 1983–2001) in 2079 when compared with 1979, and the return intervals for some major coastal floods (e.g., the December 1992 northeaster) decrease by a factor of 3–4.
Abstract
In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.
Abstract
In arid and semiarid regions most of the solar radiation penetrates through the canopy and reaches the ground, and hence the turbulent exchange coefficient under canopy Cs becomes important. The use of a constant Cs that is only appropriate for thick canopies is found to be primarily responsible for the excessive warm bias of around 10 K in monthly mean ground temperature over these regions in version 2 of the Community Climate System Model (CCSM2). New Cs formulations are developed for the consistent treatment of undercanopy turbulence for both thick and thin canopies in land models, and provide a preliminary solution of this problem.
Abstract
The surface energy budget in Antarctic latitudes is evaluated for the medium-range numerical weather forecasts produced by the National Centers for Environmental Prediction (NCEP) and for the NCEP–National Center for Atmospheric Research reanalysis project during the winter, spring, and summer special observing periods (SOPs) of the Antarctic First Regional Observing Study of Troposphere project. A significant change in the energy balance resulted from an extensive model update beginning with the forecasts initialized on 11 January 1995 during the summer SOP. Both the forecasts and the reanalysis include significant errors in the surface energy balance over Antarctica. The errors often tend to cancel and thus produce reasonable surface temperature fields. General errors include downward longwave radiation about 30–50 W m−2 too small. Lower than observed cloudiness contributes to this error and to excessive downward shortwave radiation at the surface. The model albedo over Antarctica, about 75%, is lower than that derived from observations, about 81%. During the polar day, errors in net longwave and net shortwave radiation tend to cancel. The energy balance over Antarctica in the reanalysis is, in general, degraded from that of the forecasts.
Seasonal characteristics of the surface energy balance include cooling over East Antarctica and slight warming over West Antarctica during NCEP forecasts for the winter SOP. Wintertime surface warming by downward sensible heat flux is larger than observations by 21–36 W m−2 and tends to balance the excessive longwave cooling at the surface. During the spring SOP, forecast sensible heat flux produces an excessive heating contribution by about 20 W m−2. Latent heat flux during the Antarctic winter for the reanalysis is at least an order of magnitude larger than the very small observed values.
Abstract
The surface energy budget in Antarctic latitudes is evaluated for the medium-range numerical weather forecasts produced by the National Centers for Environmental Prediction (NCEP) and for the NCEP–National Center for Atmospheric Research reanalysis project during the winter, spring, and summer special observing periods (SOPs) of the Antarctic First Regional Observing Study of Troposphere project. A significant change in the energy balance resulted from an extensive model update beginning with the forecasts initialized on 11 January 1995 during the summer SOP. Both the forecasts and the reanalysis include significant errors in the surface energy balance over Antarctica. The errors often tend to cancel and thus produce reasonable surface temperature fields. General errors include downward longwave radiation about 30–50 W m−2 too small. Lower than observed cloudiness contributes to this error and to excessive downward shortwave radiation at the surface. The model albedo over Antarctica, about 75%, is lower than that derived from observations, about 81%. During the polar day, errors in net longwave and net shortwave radiation tend to cancel. The energy balance over Antarctica in the reanalysis is, in general, degraded from that of the forecasts.
Seasonal characteristics of the surface energy balance include cooling over East Antarctica and slight warming over West Antarctica during NCEP forecasts for the winter SOP. Wintertime surface warming by downward sensible heat flux is larger than observations by 21–36 W m−2 and tends to balance the excessive longwave cooling at the surface. During the spring SOP, forecast sensible heat flux produces an excessive heating contribution by about 20 W m−2. Latent heat flux during the Antarctic winter for the reanalysis is at least an order of magnitude larger than the very small observed values.
Abstract
An isolated thunderstorm formed in the southern United Kingdom on 15 June 2005 and moved through the area where a large number of observational instruments were deployed as part of the Convective Storm Initiation Project. Earlier, a convergence line had formed downstream of Devon in the southwest of the United Kingdom in a southwesterly airflow, along which a series of light showers had formed. The depth of these showers was limited by a capping inversion, or lid, at around 2.5 km. The deep thunderstorm convection developed from one of these showers when the convection broke through the lid and ascended up to the next inversion, associated with a tropopause fold at around 6 km. A series of clear-air reflectivity RHIs are used to map the height of the capping inversion and its lifting resulting from the ascent along the convergence line. The origins of the lid are tracked back to some descent from the midtroposphere along dry adiabats. The strength of the lid was weaker along a northwest-to-southeast-oriented region located behind an overrunning upper cold front. The transition from shallow to deep convection occurred where this region with a weaker lid intersected the region with a raised lid, oriented southwest to northeast, downstream of Devon. A very high resolution forecast model that is being developed by the Met Office predicted the isolated thunderstorm successfully. This success depended on the accurate representation of the following two scales: the synoptic-scale and the surface-forced mesoscale convergence line. The interaction between these scales localized the convection sufficiently in space and time for the initiation and subsequent development to be highly predictable despite the relatively poor representation in the model of processes at the cloud scale.
Abstract
An isolated thunderstorm formed in the southern United Kingdom on 15 June 2005 and moved through the area where a large number of observational instruments were deployed as part of the Convective Storm Initiation Project. Earlier, a convergence line had formed downstream of Devon in the southwest of the United Kingdom in a southwesterly airflow, along which a series of light showers had formed. The depth of these showers was limited by a capping inversion, or lid, at around 2.5 km. The deep thunderstorm convection developed from one of these showers when the convection broke through the lid and ascended up to the next inversion, associated with a tropopause fold at around 6 km. A series of clear-air reflectivity RHIs are used to map the height of the capping inversion and its lifting resulting from the ascent along the convergence line. The origins of the lid are tracked back to some descent from the midtroposphere along dry adiabats. The strength of the lid was weaker along a northwest-to-southeast-oriented region located behind an overrunning upper cold front. The transition from shallow to deep convection occurred where this region with a weaker lid intersected the region with a raised lid, oriented southwest to northeast, downstream of Devon. A very high resolution forecast model that is being developed by the Met Office predicted the isolated thunderstorm successfully. This success depended on the accurate representation of the following two scales: the synoptic-scale and the surface-forced mesoscale convergence line. The interaction between these scales localized the convection sufficiently in space and time for the initiation and subsequent development to be highly predictable despite the relatively poor representation in the model of processes at the cloud scale.
Abstract
A multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%–38.0%. A bias-correction technique reduces the average mean absolute error by 10%–15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010–14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model’s [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.
Abstract
A multilinear regression (MLR) approach is developed to predict 3-hourly storm surge during the cool-season months (1 October–31 March 31) between 1979 and 2012 using two different atmospheric reanalysis datasets and water-level observations at three stations along the New York–New Jersey coast (Atlantic City, New Jersey; the Battery in New York City; and Montauk Point, New York). The predictors of the MLR are specified to represent prolonged surface wind stress and a surface sea level pressure minimum for a boxed region near each station. The regression underpredicts relatively large (≥95th percentile) storm maximum surge heights by 6.0%–38.0%. A bias-correction technique reduces the average mean absolute error by 10%–15% at the various stations for storm maximum surge predictions. Using the same forecast surface winds and pressures from the North American Mesoscale (NAM) model between October and March 2010–14, raw and bias-corrected surge predictions at the Battery are compared with raw output from a numerical hydrodynamic model’s [the Stevens Institute of Technology New York Harbor Observing and Prediction System (SIT-NYHOPS)] predictions. The accuracy of surge predictions between the SIT-NYHOPS output and bias-corrected MLR model at the Battery are similar for predictions that meet or exceed the 95th percentile of storm maximum surge heights.
Abstract
The Realistic Urban Spread and Transport of Intrusive Contaminants (RUSTIC) model has been developed as a simplified computational fluid dynamics model with a k–ω turbulence model to be used to provide moderately fast simulations of turbulent airflow in an urban environment. RUSTIC simulations were compared with wind tunnel measurements to refine and “calibrate” the parameters for the k–ω model. RUSTIC simulations were then run and compared with data from five different periods during the Joint Urban 2003 experiment. Predictions from RUSTIC were compared with data from 33 near-surface sonic anemometers as well as 8 sonic anemometers on a 90-m tower and a sodar wind profiler located in the Oklahoma City, Oklahoma, central business district. The data were subdivided into daytime and nighttime datasets and then the daytime data were further subdivided into exposed and sheltered sonic anemometers. While there was little difference between day and night for wind speed and direction comparisons, there was considerable difference for the turbulence kinetic energy (TKE) comparisons. In the nighttime cases, RUSTIC overpredicted the TKE but without any correlation between model and observations. On the other hand, for the daytime cases, RUSTIC underpredicted the TKE values and correlated well with the observations. RUSTIC predicted both winds and TKE much better for the exposed sonic anemometers than for the sheltered ones. For the 90-m tower location downwind of the central business district, RUSTIC predicted the vertical profile of wind speed and direction very closely but underestimated the TKE.
Abstract
The Realistic Urban Spread and Transport of Intrusive Contaminants (RUSTIC) model has been developed as a simplified computational fluid dynamics model with a k–ω turbulence model to be used to provide moderately fast simulations of turbulent airflow in an urban environment. RUSTIC simulations were compared with wind tunnel measurements to refine and “calibrate” the parameters for the k–ω model. RUSTIC simulations were then run and compared with data from five different periods during the Joint Urban 2003 experiment. Predictions from RUSTIC were compared with data from 33 near-surface sonic anemometers as well as 8 sonic anemometers on a 90-m tower and a sodar wind profiler located in the Oklahoma City, Oklahoma, central business district. The data were subdivided into daytime and nighttime datasets and then the daytime data were further subdivided into exposed and sheltered sonic anemometers. While there was little difference between day and night for wind speed and direction comparisons, there was considerable difference for the turbulence kinetic energy (TKE) comparisons. In the nighttime cases, RUSTIC overpredicted the TKE but without any correlation between model and observations. On the other hand, for the daytime cases, RUSTIC underpredicted the TKE values and correlated well with the observations. RUSTIC predicted both winds and TKE much better for the exposed sonic anemometers than for the sheltered ones. For the 90-m tower location downwind of the central business district, RUSTIC predicted the vertical profile of wind speed and direction very closely but underestimated the TKE.
