The version of the Norwegian Climate Prediction Model (NorCPM) that only assimilates sea surface temperature (SST) with the Ensemble Kalman Filter has been used to investigate the seasonal to decadal prediction skill of regional Arctic sea ice extent (SIE). Based on a suite of NorCPM retrospective forecasts, we show that seasonal prediction of pan-Arctic SIE is skillful at lead times up to 12 months, which outperforms the anomaly persistence forecast. The SIE skill varies seasonally and regionally. Among the five Arctic marginal seas, the Barents Sea has the highest SIE prediction skill, which is up to 10–11 lead months for winter target months. In the Barents Sea, the skill during summer is largely controlled by the variability of solar heat flux and the skill during winter is mostly constrained by the upper ocean heat content/SST and also related to the heat transport through the Barents Sea Opening. Compared with several state-of-the-art dynamical prediction systems, NorCPM has comparable regional SIE skill in winter due to the improved upper ocean heat content. The relatively low skill of summer SIE in NorCPM suggests that SST anomalies are not sufficient to constrain summer SIE variability and further assimilation of sea ice thickness or atmospheric data is expected to increase the skill.
Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback (α) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.
This study demonstrates that assimilating SST with an advanced data assimilation method yields prediction skill level with the best state-of-the-art systems. We employ the Norwegian Climate Prediction Model (NorCPM)-a fully-coupled forecasting system-to assimilate SST observations with the ensemble Kalman filter. Predictions of NorCPM are compared to predictions from the North American Multimodel Ensemble (NMME) project. The global prediction skill of NorCPM at 6-and 12-month lead times is higher than the averaged skill of the NMME. A new metric is introduced for ranking model skill. According to the metric, NorCPM is one of the most skilful systems among the NMME in predicting SST in most regions. Confronting the skill to a large historical ensemble without assimilation, shows that the skill is largely derived from the initialisation rather than from the external forcing. NorCPM achieves good skill in predicting El Niño-Southern Oscillation (ENSO) up to 12 months ahead and achieves skill over land via teleconnections. However, NorCPM has a more pronounced reduction in skill in May than the NMME systems. An analysis of ENSO dynamics indicates that the skill reduction is mainly caused by model deficiencies in representing the thermocline feedback in February and March. We also show that NorCPM has skill in predicting sea ice extent at the Arctic entrance adjacent to the north Atlantic; this skill is highly related to the initialisation of upper ocean heat content.
This paper presents a global picture of the dynamic processes and synoptic characteristics of extratropical extreme precipitation events (EPEs), defined as annual maximum daily precipitation averaged over 7.5° × 7.5° regional boxes. Based on the quasi-geostrophic omega equation, extreme precipitation can be decomposed into components forced by large-scale adiabatic disturbances and amplified by diabatic heating feedback. The spatial distribution of the diabatic feedback parameter is largely controlled by atmospheric precipitable water and captured by a simple model. Most spatial heterogeneities of EPEs in middle and high latitudes are due to the spatial variations of large-scale adiabatic forcing. The adiabatic component includes the processes of vorticity advection, in which the synoptic vorticity advection by background wind dominates; temperature advection, in which the total meridional temperature advection by synoptic wind dominates; and boundary forcing. The synoptic patterns of EPEs in all extratropical regions can be classified into six clusters using the self-organizing map method: two clusters in low latitudes and four clusters in middle and high latitudes. Synoptic disturbances are characterized by strong pressure anomalies throughout the troposphere over the coastal regions and oceans and feature upper-level short-wave disturbances and a large westward tilt with height over lands. Synoptic configurations favor moisture transport from oceans to lands over coastal regions.
Through a cluster analysis of daily NCEP–NCAR reanalysis data, this study demonstrates that the Arctic Oscillation (AO), defined as the leading empirical orthogonal function (EOF) of 250-hPa geopotential height anomalies, is not a unique pattern but a continuum that can be well approximated by five discrete, representative AO-like patterns. These AO-like patterns grow simultaneously from disturbances in the North Pacific, the North Atlantic, and the Arctic, and both the feedback from the high-frequency eddies in the North Pacific and North Atlantic and propagation of the low-frequency wave trains from the North Pacific across North America into the North Atlantic play important roles in the pattern formation. Furthermore, it is shown that the structures and frequencies of occurrence of the five AO-like patterns are significantly modulated by El Niño–Southern Oscillation (ENSO). Warm (cold) ENSO enhances the negative (positive) AO phase, compared with ENSO neutral winters. Finally, the surface weather effects of these AO-like patterns and their implications for the AO-related weather prediction and the AO-North Atlantic Oscillation (NAO) relationship are discussed.
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