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Abstract
Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.
Abstract
Statistical processing of numerical model output has been a part of both weather forecasting and climate applications for decades. Statistical techniques are used to correct systematic biases in atmospheric model outputs and to represent local effects that are unresolved by the model, referred to as downscaling. Many downscaling techniques have been developed, and it has been difficult to systematically explore the implications of the individual decisions made in the development of downscaling methods. Here we describe a unified framework that enables the user to evaluate multiple decisions made in the methods used to statistically postprocess output from weather and climate models. The Ensemble Generalized Analog Regression Downscaling (En-GARD) method enables the user to select any number of input variables, predictors, mathematical transformations, and combinations for use in parametric or nonparametric downscaling approaches. En-GARD enables explicitly predicting both the probability of event occurrence and the event magnitude. Outputs from En-GARD include errors in model fit, enabling the production of an ensemble of projections through sampling of the probability distributions of each climate variable. We apply En-GARD to regional climate model simulations to evaluate the relative importance of different downscaling method choices on simulations of the current and future climate. We show that choice of predictor variables is the most important decision affecting downscaled future climate outputs, while having little impact on the fidelity of downscaled outcomes for current climate. We also show that weak statistical relationships prevent such approaches from predicting large changes in extreme events on a daily time scale.
Abstract
Large changes in the hydrology of the western United States have been observed since the mid-twentieth century. These include a reduction in the amount of precipitation arriving as snow, a decline in snowpack at low and midelevations, and a shift toward earlier arrival of both snowmelt and the centroid (center of mass) of streamflows. To project future water supply reliability, it is crucial to obtain a better understanding of the underlying cause or causes for these changes. A regional warming is often posited as the cause of these changes without formal testing of different competitive explanations for the warming. In this study, a rigorous detection and attribution analysis is performed to determine the causes of the late winter/early spring changes in hydrologically relevant temperature variables over mountain ranges of the western United States. Natural internal climate variability, as estimated from two long control climate model simulations, is insufficient to explain the rapid increase in daily minimum and maximum temperatures, the sharp decline in frost days, and the rise in degree-days above 0°C (a simple proxy for temperature-driven snowmelt). These observed changes are also inconsistent with the model-predicted responses to variability in solar irradiance and volcanic activity. The observations are consistent with climate simulations that include the combined effects of anthropogenic greenhouse gases and aerosols. It is found that, for each temperature variable considered, an anthropogenic signal is identifiable in observational fields. The results are robust to uncertainties in model-estimated fingerprints and natural variability noise, to the choice of statistical downscaling method, and to various processing options in the detection and attribution method.
Abstract
Large changes in the hydrology of the western United States have been observed since the mid-twentieth century. These include a reduction in the amount of precipitation arriving as snow, a decline in snowpack at low and midelevations, and a shift toward earlier arrival of both snowmelt and the centroid (center of mass) of streamflows. To project future water supply reliability, it is crucial to obtain a better understanding of the underlying cause or causes for these changes. A regional warming is often posited as the cause of these changes without formal testing of different competitive explanations for the warming. In this study, a rigorous detection and attribution analysis is performed to determine the causes of the late winter/early spring changes in hydrologically relevant temperature variables over mountain ranges of the western United States. Natural internal climate variability, as estimated from two long control climate model simulations, is insufficient to explain the rapid increase in daily minimum and maximum temperatures, the sharp decline in frost days, and the rise in degree-days above 0°C (a simple proxy for temperature-driven snowmelt). These observed changes are also inconsistent with the model-predicted responses to variability in solar irradiance and volcanic activity. The observations are consistent with climate simulations that include the combined effects of anthropogenic greenhouse gases and aerosols. It is found that, for each temperature variable considered, an anthropogenic signal is identifiable in observational fields. The results are robust to uncertainties in model-estimated fingerprints and natural variability noise, to the choice of statistical downscaling method, and to various processing options in the detection and attribution method.
Abstract
Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.
Abstract
Observations show snowpack has declined across much of the western United States over the period 1950–99. This reduction has important social and economic implications, as water retained in the snowpack from winter storms forms an important part of the hydrological cycle and water supply in the region. A formal model-based detection and attribution (D–A) study of these reductions is performed. The detection variable is the ratio of 1 April snow water equivalent (SWE) to water-year-to-date precipitation (P), chosen to reduce the effect of P variability on the results. Estimates of natural internal climate variability are obtained from 1600 years of two control simulations performed with fully coupled ocean–atmosphere climate models. Estimates of the SWE/P response to anthropogenic greenhouse gases, ozone, and some aerosols are taken from multiple-member ensembles of perturbation experiments run with two models. The D–A shows the observations and anthropogenically forced models have greater SWE/P reductions than can be explained by natural internal climate variability alone. Model-estimated effects of changes in solar and volcanic forcing likewise do not explain the SWE/P reductions. The mean model estimate is that about half of the SWE/P reductions observed in the west from 1950 to 1999 are the result of climate changes forced by anthropogenic greenhouse gases, ozone, and aerosols.
Abstract
Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
Abstract
Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human–natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using human-generated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.
On the Susceptibility of Cold Tropical Cirrus to Ice Nuclei AbundanceAbstract
Numerical simulations of cirrus formation in the tropical tropopause layer (TTL) during boreal wintertime are used to evaluate the impact of heterogeneous ice nuclei (IN) abundance on cold cloud microphysical properties and occurrence frequencies. The cirrus model includes homogeneous and heterogeneous ice nucleation, deposition growth/sublimation, and sedimentation. Reanalysis temperature and wind fields with high-frequency waves superimposed are used to force the simulations. The model results are constrained by comparison with in situ and satellite observations of TTL cirrus and relative humidity. Temperature variability driven by high-frequency waves has a dominant influence on TTL cirrus microphysical properties and occurrence frequencies, and inclusion of these waves is required to produce agreement between the simulated and observed abundance of TTL cirrus. With homogeneous freezing only and small-scale gravity waves included in the temperature curtains, the model produces excessive ice concentrations compared with in situ observations. Inclusion of relatively numerous heterogeneous ice nuclei (N IN ≥ 100 L−1) in the simulations improves the agreement with observed ice concentrations. However, when IN contribute significantly to TTL cirrus ice nucleation, the occurrence frequency of large supersaturations with respect to ice is less than indicated by in situ measurements. The model results suggest that the sensitivity of TTL cirrus extinction and ice water content statistics to heterogeneous ice nuclei abundance is relatively weak. The simulated occurrence frequencies of TTL cirrus are quite insensitive to ice nuclei abundance, both in terms of cloud frequency height distribution and regional distribution throughout the tropics.
Abstract
Numerical simulations of cirrus formation in the tropical tropopause layer (TTL) during boreal wintertime are used to evaluate the impact of heterogeneous ice nuclei (IN) abundance on cold cloud microphysical properties and occurrence frequencies. The cirrus model includes homogeneous and heterogeneous ice nucleation, deposition growth/sublimation, and sedimentation. Reanalysis temperature and wind fields with high-frequency waves superimposed are used to force the simulations. The model results are constrained by comparison with in situ and satellite observations of TTL cirrus and relative humidity. Temperature variability driven by high-frequency waves has a dominant influence on TTL cirrus microphysical properties and occurrence frequencies, and inclusion of these waves is required to produce agreement between the simulated and observed abundance of TTL cirrus. With homogeneous freezing only and small-scale gravity waves included in the temperature curtains, the model produces excessive ice concentrations compared with in situ observations. Inclusion of relatively numerous heterogeneous ice nuclei (N IN ≥ 100 L−1) in the simulations improves the agreement with observed ice concentrations. However, when IN contribute significantly to TTL cirrus ice nucleation, the occurrence frequency of large supersaturations with respect to ice is less than indicated by in situ measurements. The model results suggest that the sensitivity of TTL cirrus extinction and ice water content statistics to heterogeneous ice nuclei abundance is relatively weak. The simulated occurrence frequencies of TTL cirrus are quite insensitive to ice nuclei abundance, both in terms of cloud frequency height distribution and regional distribution throughout the tropics.
Abstract
The February–March 2014 deployment of the National Aeronautics and Space Administration (NASA) Airborne Tropical Tropopause Experiment (ATTREX) provided unique in situ measurements in the western Pacific tropical tropopause layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the Convective Transport of Active Species in the Tropics (CONTRAST) and the Coordinated Airborne Studies in the Tropics (CAST) airborne campaigns based in Guam using lower-altitude aircraft (see companion articles in this issue). The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes, as well as for evaluation and improvement of global-model representations of TTL processes. The ATTREX data are publicly available online (at https://espoarchive.nasa.gov/).
Abstract
The February–March 2014 deployment of the National Aeronautics and Space Administration (NASA) Airborne Tropical Tropopause Experiment (ATTREX) provided unique in situ measurements in the western Pacific tropical tropopause layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the Convective Transport of Active Species in the Tropics (CONTRAST) and the Coordinated Airborne Studies in the Tropics (CAST) airborne campaigns based in Guam using lower-altitude aircraft (see companion articles in this issue). The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes, as well as for evaluation and improvement of global-model representations of TTL processes. The ATTREX data are publicly available online (at https://espoarchive.nasa.gov/).
