On the soil moisture memory and influence on coupled seasonal forecasts over Australia
On the soil moisture memory and influence on coupled seasonal forecasts over Australia
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DOI：
10.1007/s00382-018-4566-8
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发表时间：
2019-01
期刊：
影响因子：
4.6
通讯作者：
Mei Zhao;Huqiang Zhang;I. Dharssi
中科院分区：
文献类型：
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作者：
Mei Zhao;Huqiang Zhang;I. Dharssi
在本研究中，我们通过名为 ACCESS-S1（澳大利亚共同体气候和地球系统模拟器的季节性预测版本 1）的耦合模型评估了地表初始化对次季节和季节性预测技能的影响。进行了一系列敏感性实验，以探索不同的地表初始化对模型技能和平均偏差的影响程度。通过将分析重点放在澳大利亚大陆，我们的研究试图解决三个问题：（1）模型中的土壤湿度记忆有多强，与一些观测证据相比，其真实性如何？ （2）土壤湿度记忆如何影响地表通量； (3) 这些对地表通量的影响如何转化为对降雨、温度和大气环流预报的影响。首先，我们利用 ACCESS-S1 地表模型 JULES，利用 ERA 中期强迫数据对 1990-2012 年期间进行了离线实验，并通过月度观测进一步调整降水量。这产生了与“观测到的”气象强迫相对应的 23 年土壤湿度时间序列。不同水平和不同月份土壤湿度之间的滞后相关性与马兰比吉流域的一些现场观测结果进行了比较。我们显示，非洲大陆东南部的顶层和地下根区之间的模拟和观测的土壤湿度耦合之间具有良好的一致性，并且这些区域具有显着的土壤湿度记忆。然后，我们使用 JULES 离线土壤湿度数据来初始化 ACCESS-S1 的 3 个月后报，开始日期为 5 月 1 日，为期 23 年。与默认的 ACCESS-S1 设置（在地表初始化中使用气候土壤湿度）相比，我们的结果表明，通过使用 JULES 离线数据初始化模型，ACCESS-S1 的地表最高温度 (Tmax) 和蒸散量预测能力显着提高。它还对地表最低温度 (Tmin) 和降水预报进行了适度改进。在非洲大陆东部地区，技能的提高尤其明显，那里的模拟和观测的土壤湿度记忆很强。我们的研究表明，在未来的预报系统开发中，我们不仅需要用更新的土壤湿度异常条件来初始化模型，而且还需要确保这些异常与正确且一致的土壤湿度气候相结合，以进行后报和实时预报。
In this study we assess impacts of land-surface initialization on sub-seasonal and seasonal forecast skills from a coupled model named ACCESS-S1 (a seasonal prediction version 1 of the Australian Community Climate and Earth-System Simulator). A series of sensitivity experiments is conducted to explore to what extent the model skill and mean bias can be affected by different land-surface initialisations. By focusing the analysis on the Australian continent, our study tries to address three questions: (1) how strong is soil moisture memory in the model and how realistic is that compared with some observational evidence; (2) how does the soil moisture memory affect surface fluxes; and (3) how these impacts on surface fluxes are translated into the impacts on forecasting rainfall, temperature and atmospheric circulation. Firstly, we run an offline experiment with the ACCESS-S1 land surface model JULES for the period of 1990–2012 using ERA-interim forcing data, with precipitation further adjusted by monthly observations. This produces 23-year soil moisture time series corresponding to “observed” meteorological forcing. Lagged correlations between soil moisture at different levels and at different months are compared with some in-situ observations over Murrumbidgee catchment. We show good agreement between modelled and observed soil moisture coupling between its top-layer and sub-surface root-zone in the southeast part of the continent, and notable soil moisture memory over these regions. We then use the JULES offline soil moisture data to initialize ACCESS-S1 in its 3-month hindcasts with start date of 1st May for the 23-year period. In contrast to the default ACCESS-S1 setup which uses climatological soil moisture in its land-surface initialisation, our results show significant improvements to ACCESS-S1 forecast skill of surface maximum temperature (Tmax) and evapotranspiration by initialising the model with JULES offline data. It also has moderate improvements of surface minimum temperature (Tmin) and precipitation forecasts. The skill gain is particularly evident over the eastern part of the continent where the modelled and observed soil moisture memory is strong. Our study demonstrates that in future forecast system development, we not only need to initialise the model with updated soil moisture anomalous conditions, but also need to make sure these anomalies are combined with correct and consistent soil moisture climatology for both hindcast and real-time forecast.