Dr Antje Weisheimer (she)

Abstract:

Seasonal climate forecasts are being used increasingly across a range of application sectors. A recent UK governmental report asked: how good are seasonal forecasts on a scale of 1-5 (where 5 is very good), and how good can we expect them to be in 30 years time? Seasonal forecasts are made from ensembles of integrations of numerical models of climate. We argue that 'goodness' should be assessed first and foremost in terms of the probabilistic reliability of these ensemble-based forecasts; reliable inputs are essential for any forecast-based decision-making. We propose that a '5' should be reserved for systems that are not only reliable overall, but where, in particular, small ensemble spread is a reliable indicator of low ensemble forecast error. We study the reliability of regional temperature and precipitation forecasts of the current operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, universally regarded as one of the world-leading operational institutes producing seasonal climate forecasts. A wide range of 'goodness' rankings, depending on region and variable (with summer forecasts of rainfall over Northern Europe performing exceptionally poorly) is found. Finally, we discuss the prospects of reaching '5' across all regions and variables in 30 years time.

Abstract:

The finite resolution of general circulation models of the coupled atmosphere-ocean system and the effects of sub-grid-scale variability present a major source of uncertainty in model simulations on all time scales. The European Centre for Medium-Range Weather Forecasts has been at the forefront of developing new approaches to account for these uncertainties. In particular, the stochastically perturbed physical tendency scheme and the stochastically perturbed backscatter algorithm for the atmosphere are now used routinely for global numerical weather prediction. The European Centre also performs long-range predictions of the coupled atmosphere-ocean climate system in operational forecast mode, and the latest seasonal forecasting system--System 4--has the stochastically perturbed tendency and backscatter schemes implemented in a similar way to that for the medium-range weather forecasts. Here, we present results of the impact of these schemes in System 4 by contrasting the operational performance on seasonal time scales during the retrospective forecast period 1981-2010 with comparable simulations that do not account for the representation of model uncertainty. We find that the stochastic tendency perturbation schemes helped to reduce excessively strong convective activity especially over the Maritime Continent and the tropical Western Pacific, leading to reduced biases of the outgoing longwave radiation (OLR), cloud cover, precipitation and near-surface winds. Positive impact was also found for the statistics of the Madden-Julian oscillation (MJO), showing an increase in the frequencies and amplitudes of MJO events. Further, the errors of El Niño southern oscillation forecasts become smaller, whereas increases in ensemble spread lead to a better calibrated system if the stochastic tendency is activated. The backscatter scheme has overall neutral impact. Finally, evidence for noise-activated regime transitions has been found in a cluster analysis of mid-latitude circulation regimes over the Pacific-North America region.

Abstract:

The influence of the snowpack on wintertime atmospheric teleconnections has received renewed attention in recent years, partially for its potential impact on seasonal predictability. Many observational and model studies have indicated that the autumn Eurasian snow cover in particular, influences circulation patterns over the North Pacific and North Atlantic. We have performed a suite of coupled atmosphere-ocean simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecast system to investigate the impact of accurate snow initialisation. Pairs of 2-month ensemble forecasts were started every 15 days from the 15th of October through the 1st of December in the years 2004-2009, with either realistic initialization of snow variables based on re-analyses, or else with "scrambled" snow initial conditions from an alternate autumn date and year. Initially, in the first 15 days, the presence of a thicker snowpack cools surface temperature over the continental land masses of Eurasia and North America. At a longer lead of 30-day, it causes a warming over the Arctic and the high latitudes of Eurasia due to an intensification and westward expansion of the Siberian High. It also causes a cooling over the mid-latitudes of Eurasia, and lowers sea level pressures over the Arctic. This "warm Arctic-cold continent" difference means that the forecasts of near-surface temperature with the more realistic snow initialization are in closer agreement with re-analyses, reducing a cold model bias over the Arctic and a warm model bias over mid-latitudes. The impact of realistic snow initialization upon the forecast skill in snow depth and near-surface temperature is estimated for various lead times. Following a modest skill improvement in the first 15 days over snow-covered land, we also find a forecast skill improvement up to the 30-day lead time over parts of the Arctic and the Northern Pacific, which can be attributed to the realistic snow initialization over the land masses. © 2013 Springer-Verlag Berlin Heidelberg.