Predictability of weather and climate

Abstract:

The potential to improve short-range forecasts of two extratropical storms by using supplementary observations in regions lacking accurate observations is investigated. In the idealized framework used here, a control and a truth experiment are selected from a set of forecasts initialized with analyses from different numerical weather- prediction centres. Synthetic soundings of wind and temperature are created from the truth experiment and are assimilated with four-dimensional variational analysis using the operational observation-error estimates for radiosondes and the initial condition of the control experiment as background. Through multiple analysis/forecast experiments we obtain a nonlinear estimate of the optimal zone for observing (OZO): that is the zone in which the use of a given number of supplementary observations leads to the largest reduction in forecast error. We evaluate targeting techniques based on either total-energy singular vectors (TESVs) or on Hessian singular vectors (HSVs) by comparison with the OZO and by comparison with experiments in which the same amount of supplementary observations are distributed in an untargeted manner, namely with a random distribution scheme (RDS). Overall, the HSV targeting is superior to the TESV targeting in the two cases. In one case there is a significant difference between the target regions determined with TESVs and HSVs. The HSV-based observing strategy resembles the OZO in terms of the observing region and the achieved forecast-error reduction. With the RDS, the forecast error is variable and likely to be larger than the forecast error obtained with singular-vector targeting. Experiments with target regions of different sizes show that supplementary observations in an area of about 3 × 106 km2 are required to achieve a significant forecast improvement. A two-dimensional sampling pattern with soundings spaced at a distance of about 1-2 times the horizontal correlation length-scale of the background- error estimate appears very efficient. In additional impact experiments for one case, observations were perturbed with noise to represent observational error. The perturbations are almost as likely to improve the forecast as to worsen it compared with the forecast using unperturbed observations.

Abstract:

Increasing concentrations of atmospheric carbon dioxide will almost certainly lead to changes in global mean climate. But because--by definition--extreme events are rare, it is significantly more difficult to quantify the risk of extremes. Ensemble-based probabilistic predictions, as used in short- and medium-term forecasts of weather and climate, are more useful than deterministic forecasts using a 'best guess' scenario to address this sort of problem. Here we present a probabilistic analysis of 19 global climate model simulations with a generic binary decision model. We estimate that the probability of total boreal winter precipitation exceeding two standard deviations above normal will increase by a factor of five over parts of the UK over the next 100 years. We find similar increases in probability for the Asian monsoon region in boreal summer, with implications for flooding in Bangladesh. Further practical applications of our techniques would be helped by the use of larger ensembles (for a more complete sampling of model uncertainty) and a wider range of scenarios at a resolution adequate to analyse average-size river basins.