Abstract

Resolution in climate models is thought to be an important factor for advancing seasonal prediction capability. To test this hypothesis, seasonal ensemble reforecasts are conducted over 1993–2009 with the European community model EC-Earth in three configurations: standard resolution (~1° and ~60 km in the ocean and atmosphere models, respectively), intermediate resolution (~0.25° and ~60 km), and high resolution (~0.25° and ~39 km), the two latter configurations being used without any specific tuning. The model systematic biases of 2-m temperature, sea surface temperature (SST), and wind speed are generally reduced. Notably, the tropical Pacific cold tongue bias is significantly reduced, the Somali upwelling is better represented, and excessive precipitation over the Indian Ocean and over the Maritime Continent is decreased. In terms of skill, tropical SSTs and precipitation are better reforecasted in the Pacific and the Indian Oceans at higher resolutions. In particular, the Indian monsoon is better predicted. Improvements are more difficult to detect at middle and high latitudes. Still, a slight improvement is found in the prediction of the winter North Atlantic Oscillation (NAO) along with a more realistic representation of atmospheric blocking. The sea ice extent bias is unchanged, but the skill of the reforecasts increases in some cases, such as in summer for the pan-Arctic sea ice. All these results emphasize the idea that the resolution increase is an essential feature for forecast system development. At the same time, resolution alone cannot tackle all the forecast system deficiencies and will have to be implemented alongside new physical improvements to significantly push the boundaries of seasonal prediction.

Abstract

The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.

Abstract

This study presents a new simple approach for combining empirical with raw (i.e., not bias corrected) coupled model ensemble forecasts in order to make more skillful interval forecasts of ENSO. A Bayesian normal model has been used to combine empirical and raw coupled model December SST Niño-3.4 index forecasts started at the end of the preceding July (5-month lead time). The empirical forecasts were obtained by linear regression between December and the preceding July Niño-3.4 index values over the period 1950–2001. Coupled model ensemble forecasts for the period 1987–99 were provided by ECMWF, as part of the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) project. Empirical and raw coupled model ensemble forecasts alone have similar mean absolute error forecast skill score, compared to climatological forecasts, of around 50% over the period 1987–99. The combined forecast gives an increased skill score of 74% and provides a well-calibrated and reliable estimate of forecast uncertainty.

Abstract

The ranked probability skill score (RPSS) is a widely used measure to quantify the skill of ensemble forecasts. The underlying score is defined by the quadratic norm and is comparable to the mean squared error (mse) but it is applied in probability space. It is sensitive to the shape and the shift of the predicted probability distributions. However, the RPSS shows a negative bias for ensemble systems with small ensemble size, as recently shown. Here, two strategies are explored to tackle this flaw of the RPSS. First, the RPSS is examined for different norms L (RPSS _L ). It is shown that the RPSS _{L =1} based on the absolute rather than the squared difference between forecasted and observed cumulative probability distribution is unbiased; RPSS _L defined with higher-order norms show a negative bias. However, the RPSS _{L =1} is not strictly proper in a statistical sense. A second approach is then investigated, which is based on the quadratic norm but with sampling errors in climatological probabilities considered in the reference forecasts. This technique is based on strictly proper scores and results in an unbiased skill score, which is denoted as the debiased ranked probability skill score (RPSS _D ) hereafter. Both newly defined skill scores are independent of the ensemble size, whereas the associated confidence intervals are a function of the ensemble size and the number of forecasts.

The RPSS _{L =1} and the RPSS _D are then applied to the winter mean [December–January–February (DJF)] near-surface temperature predictions of the ECMWF Seasonal Forecast System 2. The overall structures of the RPSS _{L =1} and the RPSS _D are more consistent and largely independent of the ensemble size, unlike the RPSS _{L =2}. Furthermore, the minimum ensemble size required to predict a climate anomaly given a known signal-to-noise ratio is determined by employing the new skill scores. For a hypothetical setup comparable to the ECMWF hindcast system (40 members and 15 hindcast years), statistically significant skill scores were only found for a signal-to-noise ratio larger than ∼0.3.

Abstract

This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: (i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and (ii) a multimodel system composed of three European coupled ocean–atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated (i.e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Niño or La Niña years rather than in neutral years.