The impact of targeted observations made during the Greenland Flow Distortion Experiment

The impact of targeted sonde observations on the 1-3 day forecasts for northern Europe is evaluated using the Met Office four-dimensional variational data assimilation scheme and a 24 km gridlength limited-area version of the Unified Model (MetUM). The targeted observations were carried out during February and March 2007 as part of the Greenland Flow Distortion Experiment, using a research aircraft based in Iceland. Sensitive area predictions using either total energy singular vectors or an ensemble transform Kalman filter were used to predict where additional observations should be made to reduce errors in the initial conditions of forecasts for northern Europe. Targeted sonde data was assimilated operationally into the MetUM. Hindcasts show that the impact of the sondes was mixed. Only two out of the five cases showed clear forecast improvement; the maximum forecast improvement seen over the verifying region was approximately 5% of the forecast error 24 hours into the forecast. These two cases are presented in more detail: in the first the improvement propagates into the verification region with a developing polar low; and in the second the improvement is associated with an upper-level trough. The impact of cycling targeted data in the background of the forecast (including the memory of previous targeted observations) is investigated. This is shown to cause a greater forecast impact, but does not necessarily lead to a greater forecast improvement. Finally, the robustness of the results is assessed using a small ensemble of forecasts.

Predicted soil organic carbon stocks and changes in Jordan between 2000 and 2030 made using the GEFSOC modelling system

Estimates of soil organic carbon (SOC) stocks and changes under different land use systems can help determine vulnerability to land degradation. Such information is important for countries in and areas with high susceptibility to desertification. SOC stocks, and predicted changes between 2000 and 2030, were determined at the national scale for Jordan using The Global Environment Facility Soil Organic Carbon (GEFSOC) Modelling System. For the purpose of this study, Jordan was divided into three natural regions (The Jordan Valley, the Uplands and the Badia) and three developmental regions (North, Middle and South). Based on this division, Jordan was divided into five zones (based on the dominant land use): the Jordan Valley, the North Uplands, the Middle Uplands, the South Uplands and the Badia. This information was merged using GIS, along with a map of rainfall isohyets, to produce a map with 498 polygons. Each of these was given a unique ID, a land management unit identifier and was characterized in terms of its dominant soil type. Historical land use data, current land use and future land use change scenarios were also assembled, forming major inputs of the modelling system. The GEFSOC Modelling System was then run to produce C stocks in Jordan for the years 1990, 2000 and 2030. The results were compared with conventional methods of estimating carbon stocks, such as the mapping based SOTER method. The results of these comparisons showed that the model runs are acceptable, taking into consideration the limited availability of long-term experimental soil data that can be used to validate them. The main findings of this research show that between 2000 and 2030, SOC may increase in heavily used areas under irrigation and will likely decrease in grazed rangelands that cover most of Jordan giving an overall decrease in total SOC over time if the land is indeed used under the estimated forms of land use. (C) 2007 Elsevier B.V. All rights reserved.

Predictability and skill of boreal winter forecasts made with the ECMWF Seasonal Forecasting System II

The performance of boreal winter forecasts made with the European Centre for Medium-Range Weather Forecasts (ECMWF) System 11 Seasonal Forecasting System is investigated through analyses of ensemble hindcasts for the period 1987-2001. The predictability, or signal-to-noise ratio, associated with the forecasts, and the forecast skill are examined. On average, forecasts of 500 hPa geopotential height (GPH) have skill in most of the Tropics and in a few regions of the extratropics. There is broad, but not perfect, agreement between regions of high predictability and regions of high skill. However, model errors are also identified, in particular regions where the forecast ensemble spread appears too small. For individual winters the information provided by t-values, a simple measure of the forecast signal-to-noise ratio, is investigated. For 2 m surface air temperature (T2m), highest t-values are found in the Tropics but there is considerable interannual variability, and in the tropical Atlantic and Indian basins this variability is not directly tied to the El Nino Southern Oscillation. For GPH there is also large interannual variability in t-values, but these variations cannot easily be predicted from the strength of the tropical sea-surface-temperature anomalies. It is argued that the t-values for 500 hPa GPH can give valuable insight into the oceanic forcing of the atmosphere that generates predictable signals in the model. Consequently, t-values may be a useful tool for understanding, at a mechanistic level, forecast successes and failures. Lastly, the extent to which t-values are useful as a predictor of forecast skill is investigated. For T2m, t-values provide a useful predictor of forecast skill in both the Tropics and extratropics. Except in the equatorial east Pacific, most of the information in t-values is associated with interannual variability of the ensemble-mean forecast rather than interannual variability of the ensemble spread. For GPH, however, t-values provide a useful predictor of forecast skill only in the tropical Pacific region.