Examining reliability of seasonal to decadal sea surface temperature forecasts: The role of ensemble dispersion

Useful probabilistic climate forecasts on decadal timescales should be reliable (i.e., forecast probabilities match the observed relative frequencies) but this is seldom examined. This paper assesses a necessary condition for reliability, which the ratio of ensemble spread to forecast error being close to one, for seasonal to decadal sea surface temperature retrospective forecasts from the Met Office Decadal Prediction System. Factors which may affect reliability are diagnosed by comparing this spread-error ratio for an initial condition ensemble and two perturbed physics ensembles for initialized and uninitialized predictions. At lead times less than 2 years, the initialized ensembles tend to be underdispersed and produce overconfident and hence unreliable forecasts. For longer lead times, all three ensembles are predominantly overdispersed. Such overdispersion is primarily related to excessive interannual variability in the climate model. These findings highlight the need to carefully evaluate simulated variability in seasonal and decadal prediction systems.

在十年时间尺度上有用的概率性气候预测应该是可靠的（即预测概率与观测到的相对频率相符），但这一点很少被检验。本文评估了可靠性的一个必要条件，即集合离散度与预测误差的比率接近1，针对英国气象局十年预测系统的季节到十年海表温度回顾性预测。通过比较初始条件集合以及两个扰动物理集合（针对初始化和未初始化预测）的这种离散度 - 误差比率，对可能影响可靠性的因素进行诊断。在小于2年的预报时效内，初始化集合往往离散不足，会产生过度自信因而不可靠的预测。对于更长的预报时效，所有三个集合主要是离散过度。这种离散过度主要与气候模型中过度的年际变率有关。这些研究结果强调了需要仔细评估季节和十年预测系统中的模拟变率。