オエットリ パスカル

Abstract Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical postprocessing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea surface temperature fields at 1–12-month lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month. Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent, in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics. The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent. Significance Statement The prediction of year-to-year climate variability patterns, such as El Niño, offers potential benefits to society by aiding mitigation and adaptation efforts. Current prediction systems, however, may still have substantial room for improvement due to systematic model errors and due to imperfect initialization of the oceanic state at the start of predictions. Here we develop a statistical correction scheme to improve prediction skill after forecasts have been completed. The scheme shows some moderate success in improving the skill for predicting El Niño and similar climate patterns in seven prediction systems. Our results not only indicate a potential for improving prediction skill after the fact but also point to the importance of improving the way prediction systems are initialized.

<jats:p>A large ensemble of 120 predictions of the Scale Interaction Experiment-Frontier Research Center for Global Change Version 2 (SINTEX-F2) coupled general circulation model were evaluated for their skill in predicting the surface air temperature (SAT) anomalies over Japan in the winter months December, January, and February. The predictions were initialized using November initial conditions. The members with skill scores based on anomaly correlation coefficient (ACC) were selected and an average of the selected predictions (SEM) was generated. Comparison of SAT anomaly predictions by the average of all the 120 members (ENS) to the SEM predictions shows SEM to outperform the ENS predictions in all the three winter months with higher ACC skill score, higher hit rate and low false alarm rate over the regions covering central Japan in December and January and over the northern region of Hokkaido in February. The improvement in the skill scores in the SEM is found to be due to improved representation of 200 hPa geopotential height anomalies in SEM compared to ENS predictions. The results indicate SEM to be useful for improving skill in predicting SAT anomalies over parts of Japan in the winter months.</jats:p>

<jats:p>A new type of hybrid prediction system (HPS) of the land surface air temperature (SAT) is described and its skill evaluated for one particular application. This approach utilizes sea-surface temperatures (SST) forecast by a dynamical prediction system, SINTEX-F2, to provide predictors of the SAT to a statistical modeling system consisting of a set of nine different machine learning algorithms. The statistical component is aimed to restore teleconnections between SST and SAT, particularly in the mid-latitudes, which are generally not captured well in the dynamical prediction system. The HPS is used to predict the SAT in the central region of Japan around Tokyo (Kantō) as a case study. Results show that at 2-month lead the hybrid model outperforms both persistence and the SINTEX-F2 prediction of SAT. This is also true when prediction skill is assessed for each calendar month separately. Despite the model's strong performance, there are also some limitations. The limited sample size makes it more difficult to calibrate the statistical model and to reliably evaluate its skill.</jats:p>

Utilizing the self-organizing map (SOM), a type of artificial neural network, a new classification of the climate of Japan is proposed. The SOM is applied on the monthly mean surface air temperature (SAT) anomalies, extracted from 762 stations. Considering the strong seasonal cycle in mid-latitudes, the classification is performed for two distinct seasons, boreal winter and boreal summer. Applied on monthly average temperature, to capture the seasonal signal, the SOM is an easily implementable interesting tool to (a) objectively capture the patterns present in the input data, and (b) identify the source of interannual variability, which is crucial for power demand forecasting. While modulated by local conditions, SAT in Japan is mainly influenced by large-scale circulation. It is found in this study that stronger relationships exist for tropics with southern regions and for extra-tropics with northern regions, in the seasonally oriented teleconnection patterns. In winter, the regions are organized along a north-south orientation, with a secondary west-east orientation in the central part of the country. This organization is a function of the strength of the link with the tropical Pacific and Indian Oceans sea-surface temperatures. Changes in the speed of the westerly jet, together with the modulation of the Western Pacific (WP)-like and Eurasian (EU)-like patterns, are also important for SAT in Japan in winter. In summer, the main organization follows a west-east orientation. The Pacific Japan (PJ)-like pattern plays an important role in the geographical distribution of the SAT, together with the existence of mid-latitude and subtropical wave trains, like the Silk Road (SR)-like pattern.

Abstract The selective ensemble mean (SEM) technique is applied to the late spring and summer months (May–August) surface air temperature anomaly predictions of the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), coupled general circulation model over Japan. Using the Köppen–Geiger climatic classification we chose four regions over Japan for applying the SEM technique. The SINTEX-F2 ensemble members for the SEM are chosen based on the anomaly correlation coefficients (ACC) of the SINTEX-F2 predicted and observed surface air temperature anomalies. The SEM technique is applied to generate the forecasts of the surface air temperature anomalies for the period 1983–2018 using the selected members. Analysis shows the ACC skill score of the SEM prediction to be higher compared to the ACC skill score of predictions obtained by averaging all the 24 members of the SINTEX-F2 (ENSMEAN). The SEM predicted surface air temperature anomalies also have higher hit rate and lower false alarm rate compared to the ENSMEAN predicted anomalies over a range of temperature anomalies. The results indicate the SEM technique to be a simple and easy to apply method to improve the SINTEX-F2 predictions of surface air temperature anomalies over Japan. The better performance of the SEM in generating the surface air temperature anomalies can be partly attributed to realistic prediction of 850-hPa geopotential height anomalies over Japan.