Abstract
AbstractClimate predictions using coupled models in different time scales, from intraseasonal to decadal, are usually affected by initial shocks, drifts, and biases, which reduce the prediction skill. These arise from inconsistencies between different components of the coupled models and from the tendency of the model state to evolve from the prescribed initial conditions toward its own climatology over the course of the prediction. Aiming to provide tools and further insight into the mechanisms responsible for initial shocks, drifts, and biases, this paper presents a novel data set developed within the Long Range Forecast Transient Intercomparison Project, LRFTIP. This data set has been constructed by averaging hindcasts over available prediction years and ensemble members to form a hindcast climatology, that is a function of spatial variables and lead time, and thus results in a useful tool for characterizing and assessing the evolution of errors as well as the physical mechanisms responsible for them. A discussion on such errors at the different time scales is provided along with plausible ways forward in the field of climate predictions.
Highlights
The ever-growing field of climate predictions on subseasonal to multidecadal time scales can provide useful information for decision making on economic activities such as agriculture, water management and renewable energy (Robertson et al, 2015; Cassou et al, 2018; Merryfield et al, 2020)
The first term refers to the fast adjustment of the model immediately after initialization caused by inconsistencies between different components of the coupled model (Balmaseda & Anderson, 2009; Mulholland et al, 2015), whereas the model drift defines the tendency of the model state to evolve from the prescribed initial conditions toward the model’s own climatology over the course of the prediction (Mulholland et al, 2015), which differs from that of observations due to biases arising from systematic model errors (Zadra et al, 2018)
Bias reduction is typically accomplished by using retrospective forecasts or “hindcasts” (e.g. Arribas et al, 2011; Kharin et al, 2012; García-Serrano & Doblas-Reyes, 2012; Saha et al, 2014; Choudhury et al, 2017) to evaluate systematic errors as a function of the lead time of the prediction, which are robustly estimated for typical ensemble sizes (Manzanas, 2020)
Summary
The ever-growing field of climate predictions on subseasonal to multidecadal time scales can provide useful information for decision making on economic activities such as agriculture, water management and renewable energy (Robertson et al, 2015; Cassou et al, 2018; Merryfield et al, 2020). Meehl et al, 2014; Hawkins et al, 2014; Mulholland et al, 2015) which may severely reduce predictive skills (Doblas-Reyes et al, 2011; Smith et al, 2013; Kim et al, 2017; Yeager et al, 2018; Shukla et al, 2018) These errors are commonly described as initial shock and model drift. A further approach to identifying sources of model error leading to transient behavior and long term biases is to assess systematic tendencies in model equations integrated from observationally-constrained states This can be accomplished either by averaging initial tendencies near the start of a set of short-term forecasts (Rodwell & Palmer, 2007) or by estimating systematic tendencies from averages of analysis increments when data assimilation is applied (Batté & Doblas-Reyes, 2015; Bahrgava et al, 2018; Crawford et al, 2020).
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