气候变化领域集成服务门户

It is essential for the preservation of cultural heritage that the effects of climate change are investigated. With this in mind, the daily temperature and relative humidity (RH) cycles within the Brown Gallery at Knole House, Kent, have been reconstructed for the period 1605-2015 enabling the study of low-cycle environmental fatigue on a set of seventeenth century panel paintings. By establishing a relationship between the temperature in the Brown Gallery and the Hadley Centre Central England Temperature (HadCET) dataset over a sixteen year period (2000-2015), it is possible to use the full HadCET dataset to obtain the daily minimum and maximum temperatures in the Brown Gallery for the period 1878-2015. Using a Fourier series to fit the periodic data it is then possible to extrapolate back to 1605. Furthermore, correction factors derived using the HadCET average daily temperature in the period 1772-1877 and average monthly temperature in the period 1659-1771 are applied to the temperature data to increase the model accuracy. The daily minimum and maximum RH for the period 1605-2015 are obtained using the Brown Gallery maximum and minimum temperatures respectively, and assuming that the daily dew point temperature at Knole is calculated by subtracting a monthly-dependent constant from the daily minimum temperature at Knole, thus enabling the calculation of the daily actual water vapour pressure of air. Changes in RH are a result of the daily temperature cycle changing the saturation vapour pressure of air in the gallery. This data is valuable as it enables a study of the effects of low-cycle fatigue on the seventeenth century panel paintings housed in the Brown Gallery at Knole House, Kent due to these temperature and relative humidity cycles. Furthermore, the method presented offers a technique that can be utilised to replicate the internal environment for any unheated monument building so that the effects of past and future temperature and humidity cycles on cultural heritage can be examined. (C) 2019 Elsevier Masson SAS. All rights reserved.