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Medeiros B.,U.S. National Center for Atmospheric Research | Sandu I.,European Center for Medium Range Weather ForecastsReading | Ahlgrimm M.,European Center for Medium Range Weather ForecastsReading
Journal of Advances in Modeling Earth Systems | Year: 2015

Guided by ground-based radar and lidar profiling at the Barbados Cloud Observatory (BCO), this study evaluates trade-wind cloudiness in ECMWF's Integrated Forecast System (IFS) and nine CMIP5 models using their single-timestep output at selected grid points. The observed profile of cloudiness is relatively evenly distributed between two important height levels: the lifting condensation level (LCL) and the tops of the deepest cumuli near the trade-wind inversion (2-3 km). Cloudiness at the LCL dominates the total cloud cover, but is relatively invariant. Variance in cloudiness instead peaks at the inversion. The IFS reproduces the depth of the cloud field and its variability, but underestimates cloudiness at the LCL and the inversion. A few CMIP5 models produce a single stratocumulus-like layer near the LCL, but more than half of the CMIP5 models reproduce the observed cloud layer depth in long-term mean profiles. At single-time steps, however, half of the models do not produce cloudiness near cloud tops along with the (almost ever-present) cloudiness near the LCL. In seven models, cloudiness is zero at both levels 10 to 65% of the time, compared to 3% in the observations. Models therefore tend to overestimate variance in cloudiness near the LCL. This variance is associated with longer time scales than in observations, which suggests that modeled cloudiness is too sensitive to large-scale processes. To conclude, many models do not appear to capture the processes that underlie changes in cloudiness, which is relevant for cloud feedbacks and climate prediction. © 2015. The Authors. Source


Hogan R.J.,European Center for Medium Range Weather ForecastsReading | Bozzo A.,European Center for Medium Range Weather ForecastsReading
Journal of Advances in Modeling Earth Systems | Year: 2015

Due to computational expense, the radiation schemes in many weather and climate models are called infrequently in time and/or on a reduced spatial grid. The former can lead to a lag in the diurnal cycle of surface temperature, while the latter can lead to large surface temperature errors at coastal land points due to surface fluxes computed over the ocean being used where the skin temperature and surface albedo are very different. This paper describes a computationally efficient solution to these problems, in which the surface longwave and shortwave fluxes are updated every time step and grid point according to the local skin temperature and albedo. In order that energy is conserved, it is necessary to compute the change to the net flux profile consistent with the changed surface fluxes. The longwave radiation scheme has been modified to compute also the rate of change of the profile of upwelling longwave flux with respect to the value at the surface. Then at each grid point and time step, the upwelling flux and heating-rate profiles are updated using the new value of skin temperature. The computational cost of performing approximate radiation updates in the ECMWF model is only 2% of the cost of the full radiation scheme, so increases the overall cost of the model by only of order 0.2%. Testing the new scheme by running daily 5 day forecasts over an 8 month period reveals significant improvement in 2 m temperature forecasts at coastal stations compared to observations. © 2015. The Authors. Source

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