The Subseasonal Experiment (SubX) is a multimodel subseasonal prediction experiment designed around operational requirements with the goal of improving subseasonal forecasts. Seven global models have produced 17 years of retrospective (re)forecasts and more than a year of weekly real-time forecasts. The reforecasts and forecasts are archived at the Data Library of the International Research Institute for Climate and Society, Columbia University, providing a comprehensive database for research on subseasonal to seasonal predictability and predictions. The SubX models show skill for temperature and precipitation 3 weeks ahead of time in specific regions. The SubX multimodel ensemble mean is more skillful than any individual model overall. Skill in simulating the Madden–Julian oscillation (MJO) and the North Atlantic Oscillation (NAO), two sources of subseasonal predictability, is also evaluated, with skillful predictions of the MJO 4 weeks in advance and of the NAO 2 weeks in advance. SubX is also able to make useful contributions to operational forecast guidance at the Climate Prediction Center. Additionally, SubX provides information on the potential for extreme precipitation associated with tropical cyclones, which can help emergency management and aid organizations to plan for disasters.
[1] Understanding sources of uncertainty in aerosol direct radiative forcing (DRF), the difference in a given radiative flux component with and without aerosol, is essential to quantifying changes in Earth's radiation budget. We examine the uncertainty in DRF owing to measurement uncertainty in the quantities on which it depends: aerosol optical depth, single scattering albedo, asymmetry parameter, solar geometry, and surface albedo. Direct radiative forcing at the top of the atmosphere and at the surface is calculated at three locations representing distinct aerosol types and radiative environments. Sensitivities, the changes in DRF in response to unit changes in individual aerosol or surface properties, are also calculated for these conditions. The uncertainty in DRF associated with a given property is computed as the product of the sensitivity and typical measurement uncertainty in the respective property. Sensitivity and uncertainty values permit estimation of total uncertainty in calculated DRF and identification of properties that most limit accuracy in estimating forcing. Absolute total uncertainties in modeled local diurnally averaged forcing range from 0.2 to 3.1 W m À2 for the ranges of properties examined here. Relative total uncertainties range from $20 to 80% with larger values at higher latitudes, where fluxes are low. The largest contributor to total uncertainty in DRF is single scattering albedo; however, decreasing measurement uncertainties for any property would increase accuracy in DRF. Comparison of two radiative transfer models suggests the contribution of modeling error is small compared to the total uncertainty although comparable to uncertainty arising from some individual properties.
Approach and Model DescriptionThe general approach taken in this study was similar to that previously described by Kasibhatla et al.
[1] Airborne measurements of the ratio of spectral upward and downward irradiances (so-called spectral albedo) are used to derive the areal spectral surface albedo in the wavelength range from 330 to 1670 nm. The data were collected over different sea and land surfaces in cloudless atmospheric conditions during three field campaigns. Measurements from the Albedometer (developed at IfT) and the NASA Solar Spectral Flux Radiometer (SSFR) are employed. Spectral radiative transfer calculations show that atmospheric scattering and absorption within the layer beneath the flight level considerably contribute to the airborne albedo measurements reported here, even for low flight altitudes (0.2-0.5 km). To remove this atmospheric masking, a nonlinear extrapolation of the airborne albedo measurements to the ground is performed. The nonlinearity is due to the vertically inhomogeneous distribution of the particle microphysical properties. This fact underlines the importance of aerosol profile measurements for the proper correction of atmospheric masking. Examples of the extrapolated areal spectral surface albedos are discussed in terms of their solar zenith angle dependence, their small-scale, and general variability. Finally, typical areal spectral surface albedos for different sea and land surfaces, as derived from the three measurement campaigns, are supplied in parameterized form for use in radiative transfer applications.
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