Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information, from proxy records and documentary data to instrumental measurements, were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region, and generally do not follow a Gaussian distribution. In this paper, experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea-level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating the precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. The wet day records can become an especially important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.

中文翻译：

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在古气候数据同化框架中吸收月降水量数据

诸如集合卡尔曼滤波方法之类的数据同化方法已经成为古气候重建和再分析的重要技术。在以前的研究中，从代理记录和文献数据到仪器测量，已经吸收了不同的信息来源，以重建过去的气候场。但是，降水重建通常基于间接来源（例如代理记录）。进行降水量测量是一项艰巨的任务，因为它们具有很高的不确定性，通常仅代表一个很小的区域，并且通常不遵循高斯分布。在本文中，进行了实验，以通过单独吸收每月的仪器降水量或每月的湿天数，或仅与其他气候变量（例如温度和海平面压力）同化，以月度分辨率来检验将有关降水的信息用于气候重建的可能性，集成到气候模型模拟中 当仅每月降水量被同化时，所有变量（温度，降水，海平面压力）的技巧都比纯模型模拟提高。与同化降水量相比，同化潮湿天数的技能相似或更好。对不同类型仪器观测值进行的实验表明，降水数据可能是有用的，特别是在给定区域没有其他变量可用的情况下。总体而言，实验显示出令人鼓舞的结果，因为随着降水信息的同化，可以利用新的数据源进行气候重建。湿日记录可以成为未来气候重建中特别重要的数据源，因为许多现有记录的历史可以追溯到几个世纪前，并且不受气象仪器的限制。