Modeled and observed atmospheric radiation balance during the West African dry season: role of mineral dust, biomass burning aerosol, and surface albedo

The global radiation balance of the atmosphere is still poorly observed, particularly at the surface. We investigate the observed radiation balance at (1) the surface using the ARM Mobile Facility in Niamey, Niger, and (2) the top of the atmosphere (TOA) over West Africa using data from the Geostationary Earth Radiation Budget (GERB) instrument on board Meteosat-8. Observed radiative fluxes are compared with predictions from the global numerical weather prediction (NWP) version of the Met Office Unified Model (MetUM). The evaluation points to major shortcomings in the NWP model's radiative fluxes during the dry season (December 2005 to April 2006) arising from (1) a lack of absorbing aerosol in the model (mineral dust and biomass burning aerosol) and (2) a poor specification of the surface albedo. A case study of the major Saharan dust outbreak of 6–12 March 2006 is used to evaluate a parameterization of mineral dust for use in the NWP models. The model shows good predictability of the large-scale flow out to 4–5 days with the dust parameterization providing reasonable dust uplift, spatial distribution, and temporal evolution for this strongly forced dust event. The direct radiative impact of the dust reduces net downward shortwave (SW) flux at the surface (TOA) by a maximum of 200 W m−2 (150 W m−2), with a SW heating of the atmospheric column. The impacts of dust on terrestrial radiation are smaller. Comparisons of TOA (surface) radiation balance with GERB (ARM) show the “dusty” forecasts reduce biases in the radiative fluxes and improve surface temperatures and vertical thermodynamic structure.

Data assimilation for morphodynamic prediction and predictability

This paper gives an overview of the project Changing Coastlines: data assimilation for morphodynamic prediction and predictability. This project is investigating whether data assimilation could be used to improve coastal morphodynamic modeling. The concept of data assimilation is described, and the benefits that data assimilation could bring to coastal morphodynamic modeling are discussed. Application of data assimilation in a simple 1D morphodynamic model is presented. This shows that data assimilation can be used to improve the current state of the model bathymetry, and to tune the model parameter. We now intend to implement these ideas in a 2D morphodynamic model, for two study sites. The logistics of this are considered, including model design and implementation, and data requirement issues. We envisage that this work could provide a means for maintaining up-to-date information on coastal bathymetry, without the need for costly survey campaigns. This would be useful for a range of coastal management issues, including coastal flood forecasting.

Consistent Dirichlet Boundary Conditions for Numerical Solution of Moving Boundary Problems

We consider the imposition of Dirichlet boundary conditions in the finite element modelling of moving boundary problems in one and two dimensions for which the total mass is prescribed. A modification of the standard linear finite element test space allows the boundary conditions to be imposed strongly whilst simultaneously conserving a discrete mass. The validity of the technique is assessed for a specific moving mesh finite element method, although the approach is more general. Numerical comparisons are carried out for mass-conserving solutions of the porous medium equation with Dirichlet boundary conditions and for a moving boundary problem with a source term and time-varying mass.

Aerosol optical depths over North Africa: 2. Modeling and model validation

An operational dust forecasting model is developed by including the Met Office Hadley Centre climate model dust parameterization scheme, within a Met Office regional numerical weather prediction (NWP) model. The model includes parameterizations for dust uplift, dust transport, and dust deposition in six discrete size bins and provides diagnostics such as the aerosol optical depth. The results are compared against surface and satellite remote sensing measurements and against in situ measurements from the Facility for Atmospheric Airborne Measurements for a case study when a strong dust event was forecast. Comparisons are also performed against satellite and surface instrumentation for the entire month of August. The case study shows that this Saharan dust NWP model can provide very good guidance of dust events, as much as 42 h ahead. The analysis of monthly data suggests that the mean and variability in the dust model is also well represented.

Correlated observation errors in data assimilation

Data assimilation provides techniques for combining observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting assigned to each observation in the analysis is determined by its associated error. Remote sensing data usually has correlated errors, but the correlations are typically ignored in NWP. Here, we describe three approaches to the treatment of observation error correlations. For an idealized data set, the information content under each simplified assumption is compared with that under correct correlation specification. Treating the errors as uncorrelated results in a significant loss of information. However, retention of an approximated correlation gives clear benefits.

Sensitivity of tropospheric forecasts to stratospheric initial conditions

A number of recent papers in the atmospheric science literature have suggested that a dynamical link exists between the stratosphere and troposphere. Numerical modelling studies have shown that the troposphere has a time-mean response to changes to the stratospheric climatological state. In this study the response of the troposphere to an imposed transient stratospheric change is examined. The study uses a high horizontal and vertical resolution numerical weather-prediction model. Experiments compare the tropospheric forecasts of two medium-range forecast ensembles which have identical tropospheric initial conditions and different stratospheric initial conditions. In three case studies described here, stratospheric initial conditions have a statistically significant impact on the tropospheric flow. The mechanism for this change involves, in its most basic step, a change to tropospheric synoptic-scale systems. A consistent change to the tropospheric synoptic-scale systems occurs in response to the stratospheric initial conditions. The aggregated impact of changes to individual synoptic systems maps strongly onto the structure of the Arctic Oscillation, particularly over the North Atlantic storm track. The relationship between the stratosphere and troposphere, while apparent in Arctic Oscillation diagnostics, does not occur on coherent, hemispheric scales.

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.

Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.