No abstract

<p>Daily atmospheric conditions before 1900 have been rarely investigated due to the limited availability of instrumental meteorological records. The documentary evidence is an alternative source that archives the atmospheric state. In Japan, the Historical Weather Database (HWDB, accessible by: http://tk2-202-10627.vs.sakura.ne.jp) provides descriptive daily weather information recorded in diaries at many stations since the 1660s. We utilize data assimilation to achieve high-temporal reconstructions by optimally combining observations with climate model forecasts. This study reconstructs daily weather conditions in the 1810s by assimilating diary weather information for the first time. We first categorize the descriptive records into “sunny”, “cloudy”, and “rainy”, and then assimilate these diary-based weather categories into the Global Spectral Model (GSM) through a local ensemble transform Kalman filter (LETKF) scheme. The reconstructed precipitation corresponds well with the daily synoptic pattern illustrated by documentary evidence in Japan. In a single-day case in August, 80% of non-assimilated diary categories are consistent with precipitation results. The atmospheric characteristics are also well reproduced in the Meiyu-Baiu season. Our results show better accuracy than the Twentieth Century Reanalysis (20CR) dataset due to their weak constraint in the Japan region. In addition, the Tambora eruption in April 1815 was among the largest in recent history, leading to the temperature decrease in Europe in the following year, commonly known as the “Year Without a Summer”. In our results, the surface air temperature anomaly indicates significant cooling also occurred in Japan in the summer of 1816, demonstrating the climate response to the Tambora eruption. This study shows the capability of diary data assimilation to reproduce daily atmospheric conditions, providing the basis to understand the cause of short-term variability in the past climate.</p>

No abstract

This a preprint and has not been peer reviewed. Data may be preliminary.

Old descriptive diaries are important sources of daily weather conditions before modern instrumental measurements were available. A previous study demonstrated the potential of reconstructing historical weather at a high temporal resolution by assimilating cloud cover converted from descriptive diaries. However, cloud cover often exhibits a non-Gaussian distribution, which violates the basic assumptions of most data assimilation schemes. In this study, we applied a Gaussian transformation (GT) approach to cloud cover data assimilation and conducted observing system simulation experiments (OSSEs) using 20 observation points over Japan. We performed experiments to assimilate cloud cover with large observational errors using the Global Spectral Model (GSM) and a local ensemble transform Kalman filter (LETKF). Without GT, meridional wind and temperature exhibited deteriorations in the lower troposphere compared with the experiment with no observations. In contrast, GT reduced the 2-month root-mean-square errors (RMSEs) by 5–15% throughout the troposphere for wind, temperature and specific humidity fields. Significant improvements include zonal wind at 500 hPa and temperature at 850 hPa with 6.4 and 7.3% improvements by GT, respectively, compared with the experiment without GT. We further demonstrate that the additional GT application to the precipitation background field improves precipitation estimation by 12.2%, with pronounced improvements over regions with monthly precipitation of less than 150 mm. We also explored the impact of cloud cover GT on a global scale and confirmed improvements extending from around the observation sites. Our results demonstrate the potential of GT in high-resolution historical weather reconstruction using old descriptive diaries.