Based on 1999-2000 observations made by the first Arctic and sixteenth Antactic scientific voyages,a study is undertaken about the meridional surface UV-B (B band ultraviolet rays) variations in 75°N-70°S.It is mitigated as a function of latitudes and marked by lower radiation averaged over the Northern Hemisphere (NH) than over the Southern Hemisphere (SH),with its daily course basically similar to that of total radiation.Around polar summer noon hours (local time) and where ice albedo is maximum,the strongest UV-B irradiance on the surface perpendicular to sun's beams as found at equatorial latitudes is measured sometimes.In the areas near Zhongshan Station the increase of surface UV-B radiation shows a close relation to the decrease of ozone in the higher atmosphere but it has a less intimate relation with its concentration at ground.
Abstract In this study, a method based on singular vector analysis is proposed to improve El Niño–Southern Oscillation (ENSO) predictions. Its essential idea is that the initial errors are projected onto their optimal growth patterns, which are propagated by the tangent linear model (TLM) of the original prediction model. The forecast errors at a given lead time of predictions are obtained, and then removed from the raw predictions. This method is applied to a realistic ENSO prediction model for improving prediction skill for the period from 1980 to 1999. This correction method considerably improves the ENSO prediction skill, compared with the original predictions without the correction.
[1] Ensemble predictions are performed using the LDEO5 model for the period from 1856 to 2003 based on a well developed El Niño–Southern Oscillation (ENSO) ensemble system. Information-based and ensemble-based potential predictability measures of ENSO are explored using ensemble predictions and the recently developed framework of predictability. Relationships of these potential predictability measures and actual predictability measures are investigated on multiple time scales from interannual to decadal. Results show that among three information-based potential predictability measures, relative entropy (RE) is better than predictive information (PI) and predictive power (PP) in quantifying correlation-based prediction skill, whereas PI and PP are better indicators in estimating mean square error (MSE)-based prediction skill. The primary reason for these relationships is analyzed and the control factors of the potential predictability measures are identified. It is found that RE is dominated by the signal component, but the dispersion component has a comparable contribution during weak ENSO periods.
By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model's capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño–Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill's close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21–22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere–western Indian Ocean area and across the eastern Indian Ocean–Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier.
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