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Abstract
A major goal of the Coupled Ocean-Atmosphere Response Experiment (COARE) was to achieve significantly more accurate and complete descriptions of the surface meteorology and air-sea fluxes in the western equatorial warm pool region. Time series of near-surface meteorology from a buoy moored near the center of the COARE Intensive Flux Array (IFA) are described here. The accuracies of the measurements and the derived fluxes are quantified; agreement between average net heat fluxes at the buoy and two nearby research ships is better than 10 W m−2 during three intercomparisons. Variability in the surface meteorology and fluxes associated with westerly wind bursts, periods of low winds, and short-lived, deep convective events characteristic of the region was large compared to the 4-month means. The ECMWF (European Centre for Medium-Range Weather Forecasts) analysis and prediction fields differed most from the buoy data during periods of short-lived, deep convective events, when several day averages of the net heat flux differed by more than 70 W m−2 and had the opposite sign. A one-dimensional ocean model run to examine the sensitivity of the upper-ocean response to differences between the observed and the ECMWF fluxes illustrates the importance of the short-lived events as well as of the wind bursts in maintaining the temperature of the warm pool.
Abstract
A major goal of the Coupled Ocean-Atmosphere Response Experiment (COARE) was to achieve significantly more accurate and complete descriptions of the surface meteorology and air-sea fluxes in the western equatorial warm pool region. Time series of near-surface meteorology from a buoy moored near the center of the COARE Intensive Flux Array (IFA) are described here. The accuracies of the measurements and the derived fluxes are quantified; agreement between average net heat fluxes at the buoy and two nearby research ships is better than 10 W m−2 during three intercomparisons. Variability in the surface meteorology and fluxes associated with westerly wind bursts, periods of low winds, and short-lived, deep convective events characteristic of the region was large compared to the 4-month means. The ECMWF (European Centre for Medium-Range Weather Forecasts) analysis and prediction fields differed most from the buoy data during periods of short-lived, deep convective events, when several day averages of the net heat flux differed by more than 70 W m−2 and had the opposite sign. A one-dimensional ocean model run to examine the sensitivity of the upper-ocean response to differences between the observed and the ECMWF fluxes illustrates the importance of the short-lived events as well as of the wind bursts in maintaining the temperature of the warm pool.
Abstract
A comparison is made of seven Antarctic sea ice data sets developed since 1980, on the basis of techniques of analysis and inferred temporal variations. Navy-NOAA Joint Ice Center sea ice charts are the basic data for all seven studies, but techniques used to derive ice areas vary significantly between studies. Sources of variation include the choice of a single week to represent a month, the characteristic measured (i.e., latitude of ice edge or actual ice area—with or without polynya), and the sea ice concentration used to determine the ice edge. The resulting data sets tend to indicate similar long term trends between 1973 and 1982. However, the estimates of mean annual and mean monthly ice areas vary distinctly between studies. This variability is often explainable in terms of the different techniques of analysis, but in some cases is not (e.g., Chiu's apparent overestimation of ice areas). The differences identified between these analyses suggest that caution should be taken in applying or extending these data sets.
Abstract
A comparison is made of seven Antarctic sea ice data sets developed since 1980, on the basis of techniques of analysis and inferred temporal variations. Navy-NOAA Joint Ice Center sea ice charts are the basic data for all seven studies, but techniques used to derive ice areas vary significantly between studies. Sources of variation include the choice of a single week to represent a month, the characteristic measured (i.e., latitude of ice edge or actual ice area—with or without polynya), and the sea ice concentration used to determine the ice edge. The resulting data sets tend to indicate similar long term trends between 1973 and 1982. However, the estimates of mean annual and mean monthly ice areas vary distinctly between studies. This variability is often explainable in terms of the different techniques of analysis, but in some cases is not (e.g., Chiu's apparent overestimation of ice areas). The differences identified between these analyses suggest that caution should be taken in applying or extending these data sets.
Abstract
The clustering of water vapor about ions is important because of its relevance to atmospheric electrical processes. For this reason we have placed our emphasis particularly on the description of the size distribution (concentrations) and mobilities of the small ion clusters at various humidities. From our present theoretical study, we find that most of the hydronium ions H3O+ tend to associate with a small number of water molecules to form a hydrated ion cluster even at extremely low humidities in the range of 5 × 10−3 to 1%. At atmospherically more realistic humidities and at the room temperature, our computed number of water molecules in the hydrated ion clusters is predicted to be relatively small. It is then conjectured that ion-induced nucleation process (if it occurs) starts rather from the small hydrated ion clusters which initially existed even at extremely low humidities in the atmosphere. In addition, we also find that, in general, the hydrated ion clusters of small sizes corresponding to the mass range of 2–5 water molecules are responsible for the ion mobility range of 2–2.5 cm−2 (V s)−1. For reduced mobility below 2.0 cm2 (V s)−1, the mass of the hydrated ion cluster is predicted to be greater than that of approximately five water molecules. The simultaneous estimation of size distribution and mobility aids us in better understanding observed mobility spectra and the nature of atmospherically important prenucleation clusters, including the information of their electric conductivities in the atmosphere.
Abstract
The clustering of water vapor about ions is important because of its relevance to atmospheric electrical processes. For this reason we have placed our emphasis particularly on the description of the size distribution (concentrations) and mobilities of the small ion clusters at various humidities. From our present theoretical study, we find that most of the hydronium ions H3O+ tend to associate with a small number of water molecules to form a hydrated ion cluster even at extremely low humidities in the range of 5 × 10−3 to 1%. At atmospherically more realistic humidities and at the room temperature, our computed number of water molecules in the hydrated ion clusters is predicted to be relatively small. It is then conjectured that ion-induced nucleation process (if it occurs) starts rather from the small hydrated ion clusters which initially existed even at extremely low humidities in the atmosphere. In addition, we also find that, in general, the hydrated ion clusters of small sizes corresponding to the mass range of 2–5 water molecules are responsible for the ion mobility range of 2–2.5 cm−2 (V s)−1. For reduced mobility below 2.0 cm2 (V s)−1, the mass of the hydrated ion cluster is predicted to be greater than that of approximately five water molecules. The simultaneous estimation of size distribution and mobility aids us in better understanding observed mobility spectra and the nature of atmospherically important prenucleation clusters, including the information of their electric conductivities in the atmosphere.
Abstract
Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10%) W m−2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.
Abstract
Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10%) W m−2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.
Abstract
An untended instrument to measure ocean surface heat flux has been developed for use in support of field experiments and the investigation of heat flux parameterization techniques. The sensing component of the Skin-Layer Ocean Heat Flux Instrument (SOHFI) consists of two simple thermopile heat flux sensors suspended by a fiberglass mesh mounted inside a ring-shaped surface float. These sensors make direct measurements within the conduction layer, where they are held in place by a balance between surface tension and float buoyancy. The two sensors are designed with differing solar absorption properties so that surface heat flux can be distinguished from direct solar irradiance. Under laboratory conditions, the SOHFI measurements agree well with calorimetric measurements (generally to within 10%). Performance in freshwater and ocean environments is discussed in a companion paper.
Abstract
An untended instrument to measure ocean surface heat flux has been developed for use in support of field experiments and the investigation of heat flux parameterization techniques. The sensing component of the Skin-Layer Ocean Heat Flux Instrument (SOHFI) consists of two simple thermopile heat flux sensors suspended by a fiberglass mesh mounted inside a ring-shaped surface float. These sensors make direct measurements within the conduction layer, where they are held in place by a balance between surface tension and float buoyancy. The two sensors are designed with differing solar absorption properties so that surface heat flux can be distinguished from direct solar irradiance. Under laboratory conditions, the SOHFI measurements agree well with calorimetric measurements (generally to within 10%). Performance in freshwater and ocean environments is discussed in a companion paper.
Abstract
The Skin-Layer Ocean Heat Flux Instrument (SOHFI) described by Sromovsky et al. (Part I, this issue) was field-tested in a combination of freshwater and ocean deployments. Solar irradiance monitoring and field calibration techniques were demonstrated by comparison with independent measurements. Tracking of solar irradiance diurnal variations appears to be accurate to within about 5% of full scale. Preliminary field tests of the SOHFI have shown reasonably close agreement with bulk aerodynamic heat flux estimates in freshwater and ocean environments (generally within about 20%) under low to moderate wind conditions. Performance under heavy weather suggests a need to develop better methods of submergence filtering. Ocean deployments and recoveries of drifting SOHFI-equipped buoys were made during May and June 1995, during the Combined Sensor Program of 1996 in the western tropical Pacific region, and in the Greenland Sea in May 1997. The Gulf Stream and Greenland Sea deployments pointed out the need for design modifications to improve resistance to seabird attacks. Better estimates of performance and limitations of this device require extended intercomparison tests under field conditions.
Abstract
The Skin-Layer Ocean Heat Flux Instrument (SOHFI) described by Sromovsky et al. (Part I, this issue) was field-tested in a combination of freshwater and ocean deployments. Solar irradiance monitoring and field calibration techniques were demonstrated by comparison with independent measurements. Tracking of solar irradiance diurnal variations appears to be accurate to within about 5% of full scale. Preliminary field tests of the SOHFI have shown reasonably close agreement with bulk aerodynamic heat flux estimates in freshwater and ocean environments (generally within about 20%) under low to moderate wind conditions. Performance under heavy weather suggests a need to develop better methods of submergence filtering. Ocean deployments and recoveries of drifting SOHFI-equipped buoys were made during May and June 1995, during the Combined Sensor Program of 1996 in the western tropical Pacific region, and in the Greenland Sea in May 1997. The Gulf Stream and Greenland Sea deployments pointed out the need for design modifications to improve resistance to seabird attacks. Better estimates of performance and limitations of this device require extended intercomparison tests under field conditions.
Abstract
The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.
Abstract
The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.
Abstract
Global-scale variations in the climate system over the last half of the twentieth century, including long-term increases in global-mean near-surface temperatures, are consistent with concurrent human-induced emissions of radiatively active gases and aerosols. However, such consistency does not preclude the possible influence of other forcing agents, including internal modes of climate variability or unaccounted for aerosol effects. To test whether other unknown forcing agents may have contributed to multidecadal increases in global-mean near-surface temperatures from 1950 to 2000, data pertaining to observed changes in global-scale sea surface temperatures and observed changes in radiatively active atmospheric constituents are incorporated into numerical global climate models. Results indicate that the radiative forcing needed to produce the observed long-term trends in sea surface temperatures—and global-mean near-surface temperatures—is provided predominantly by known changes in greenhouse gases and aerosols. Further, results indicate that less than 10% of the long-term historical increase in global-mean near-surface temperatures over the last half of the twentieth century could have been the result of internal climate variability. In addition, they indicate that less than 25% of the total radiative forcing needed to produce the observed long-term trend in global-mean near-surface temperatures could have been provided by changes in net radiative forcing from unknown sources (either positive or negative). These results, which are derived from simple energy balance requirements, emphasize the important role humans have played in modifying the global climate over the last half of the twentieth century.
Abstract
Global-scale variations in the climate system over the last half of the twentieth century, including long-term increases in global-mean near-surface temperatures, are consistent with concurrent human-induced emissions of radiatively active gases and aerosols. However, such consistency does not preclude the possible influence of other forcing agents, including internal modes of climate variability or unaccounted for aerosol effects. To test whether other unknown forcing agents may have contributed to multidecadal increases in global-mean near-surface temperatures from 1950 to 2000, data pertaining to observed changes in global-scale sea surface temperatures and observed changes in radiatively active atmospheric constituents are incorporated into numerical global climate models. Results indicate that the radiative forcing needed to produce the observed long-term trends in sea surface temperatures—and global-mean near-surface temperatures—is provided predominantly by known changes in greenhouse gases and aerosols. Further, results indicate that less than 10% of the long-term historical increase in global-mean near-surface temperatures over the last half of the twentieth century could have been the result of internal climate variability. In addition, they indicate that less than 25% of the total radiative forcing needed to produce the observed long-term trend in global-mean near-surface temperatures could have been provided by changes in net radiative forcing from unknown sources (either positive or negative). These results, which are derived from simple energy balance requirements, emphasize the important role humans have played in modifying the global climate over the last half of the twentieth century.
Abstract
The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.
Abstract
The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.
Abstract
Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.
Abstract
Probabilistic forecasts of U.S. Drought Monitor (USDM) intensification over 2-, 4-, and 8-week time periods are developed based on recent anomalies in precipitation, evapotranspiration, and soil moisture. These statistical forecasts are computed using logistic regression with cross validation. While recent precipitation, evapotranspiration, and soil moisture do provide skillful forecasts, it is found that additional information on the current state of the USDM adds significant skill to the forecasts. The USDM state information takes the form of a metric that quantifies the “distance” from the next-higher drought category using a nondiscrete estimate of the current USDM state. This adds skill because USDM states that are close to the next-higher drought category are more likely to intensify than states that are farther from this threshold. The method shows skill over most of the United States but is most skillful over the north-central United States, where the cross-validated Brier skill score averages 0.20 for both 2- and 4-week forecasts. The 8-week forecasts are less skillful in most locations. The 2- and 4-week probabilities have very good reliability. The 8-week probabilities, on the other hand, are noticeably overconfident. For individual drought events, the method shows the most skill when forecasting high-amplitude flash droughts and when large regions of the United States are experiencing intensifying drought.
