The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1
The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set version 1
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DOI：
10.5194/gmd-9-2255-2016
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发表时间：
2016-01-01
影响因子：
5.1
通讯作者：
Hawkins, Ed
中科院分区：
文献类型：
--
作者：
Day, Jonathan J.;Tietsche, Steffen;Hawkins, Ed
近几十年来，在从季节到年际的时间尺度上，气候预测能力取得了重大发展。然而，直到最近，这种系统预测北极气候的潜力还很少得到评估。本文描述了作为北极季节到年际时间尺度的可预报性和预测(APPER)项目的一部分进行的多模式可预报性试验。APPATE的主要目标是量化北极气候可预测的时间尺度。为了实现这一点，进行了一组理想化的初值可预报性实验，其中包括7个大气环流模式。这是第一个模式相互比较项目，旨在量化北极气候在季节和年际时间尺度上的可预测性。在这里，我们描述了存档的数据集(可在英国大气数据中心获得)，对这些模式中的北极海冰范围和海量可预测性估计进行了评估，并调查了可预测性在多大程度上依赖于初始状态。附加模式的加入扩大了海冰量和范围可预测性估计的范围，表明在进行季节性到年际时间尺度预测的可能性方面存在模型多样性。我们还调查了从极高和极低海冰初始状态开始的海冰预报是否显示出比从接近模式平均状态的预报开始的潜在可预测性更高的水平，并发现结果取决于度量值。尽管旨在解决北极地区的可预测性问题，但我们在这里描述了存档数据，以便其他人可以使用该数据集来评估其他地区和气候变化模式在这些时间尺度上的可预测性，如厄尔尼诺-南方涛动。
Recent decades have seen significant developments in climate prediction capabilities at seasonal-to-interannual timescales. However, until recently the potential of such systems to predict Arctic climate had rarely been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to interannual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre), an assessment of Arctic sea ice extent and volume predictability estimates in these models, and an investigation into to what extent predictability is dependent on the initial state.The inclusion of additional models expands the range of sea ice volume and extent predictability estimates, demonstrating that there is model diversity in the potential to make seasonal-to-interannual timescale predictions. We also investigate whether sea ice forecasts started from extreme high and low sea ice initial states exhibit higher levels of potential predictability than forecasts started from close to the models' mean state, and find that the result depends on the metric.Although designed to address Arctic predictability, we describe the archived data here so that others can use this data set to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Nino-Southern Oscillation.