Representing uncertainty in ocean observations and the ocean model, for coupled ensemble data assimilation and ensemble extended-range prediction

目前正在大力发展以大气环流模式为基础的季节到年代际预测系统，以提供气候预报。这些技术相当复杂，在技术上具有挑战性，而且仍处于起步阶段。任何天气或气候预报都会受到三个不确定因素的影响，即观测不确定性、模式的初始不确定性，以及模式在预报期内的不确定性。这项建议的目的是提高天气和气候长期预报的可靠性，主要集中在耦合系统中的海洋部分。我们建议开发和采用基于随机物理学的各种工具，以提高预测的可靠性，重点是更准确地表示海洋观测和模型的不确定性。不同的发展对预测可靠性的个别影响将被量化，以提供预测中不同不确定因素来源的估计。发展可靠的扩展范围预报可以带来重大的经济和社会后果。

There is currently a large effort in the development of general circulation model (GCM)-based seasonal to decadal prediction systems to provide climate forecasts. Such techniques are rather complex, technically challenging and still in their infancy. Any weather or climate forecast will be subject to three sources of uncertainty, namely observation uncertainty, the model-component of initial uncertainty, and model uncertainty over the forecast period.The aim of this proposal is to improve the reliability of extended range forecast of weather and climate, mainly focusing on the ocean component of the coupled system. We propose to develop and incorporate various tools based on stochastic physics to improve the reliability of forecasts focusing on a more accurate representation of ocean observations and model uncertainties. The individual impacts of the different developments on the reliability of the forecasts will be quantified to provide estimates of the different sources of uncertainties in the forecasts. The development of reliable extended range forecasts can be extremely beneficial with major economical and societal consequences.