Abstract
In this study, the characteristics of systematic errors in subseasonal prediction for East Asia are investigated using an ensemble hindcast (1991–2010) produced by the Global Seasonal Forecasting System version 5 (GloSea5). GloSea5 is a global prediction system for the subseasonal-to-seasonal time scale, based on a fully coupled atmosphere, land, ocean, and sea ice model. To examine the fidelity of the system with respect to reproducing and forecasting phenomena, this study assesses the systematic biases in the global prediction model focusing on the prediction skill for the East Asian winter monsoon (EAWM), which is a major driver of weather and climate variability in East Asia. To investigate the error characteristics of GloSea5, the hindcast period is analyzed by dividing it into two periods: 1991–2000 and 2001–2010. The main results show that the prediction skill for the EAWM with a lead time of 3 weeks is significantly decreased in the 2000s compared to the 1990s. To investigate the reason for the reduced EAWM prediction performance in the 2000s, the characteristics of the teleconnections relating to the polar and equatorial regions are examined. It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased EAWM prediction skill.
Highlights
Subseasonal to seasonal (S2S) prediction is becoming increasingly important in reducing the damage caused by extreme weather and climate events, since it bridges the gap between the weather and the seasonal climate forecast [1,2]
It is found that the simulated excessive weakening of the East Asian jet relating to the tropics and a failure in representing the Siberian high pressure relating to the Arctic are mainly responsible for the decreased East Asian winter monsoon (EAWM) prediction skill
To examine the the fifidelity the predictionsystem systemininreproducing reproducingand andforecasting forecastingphenomena, phenomena,this thisstudy study delity of of the prediction assesses the systematic biases in the global prediction model, focusing on the prediction assesses the systematic biases in the global prediction model, focusing on the prediction skill for EAWM, which is a major driver of weather and climate variability in East Asia
Summary
Subseasonal to seasonal (S2S) prediction is becoming increasingly important in reducing the damage caused by extreme weather and climate events, since it bridges the gap between the weather and the seasonal climate forecast [1,2]. The vast majority of global producing centers (GPCs) produce operational subseasonal forecasts These dynamical prediction models in operational centers are fully coupled climate system models that include the comprehensive dynamics and physics of the atmosphere, land surface, ocean, and sea ice interactions. The Korea Meteorological Administration (KMA) provides subseasonal and seasonal forecasts using a global prediction system based on a fully coupled atmosphere, land, ocean, and sea ice model. This study includes the identification and evaluation of systematic biases in the global prediction model and focuses on the prediction skill for the EAWM, with its interaction between the tropic and Arctic climates, which are major drivers of weather and climate variability in East Asia. Asian climate; Section 4 summarizes the results and provides the major conclusions
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