Data for: A fiery past: a comparison of glacial and contemporary fire regimes on the Palaeo-Agulhas Plain, Cape Floristic Region

We calculated daily FDI from daily measurements recorded at 14h00 (local time) of temperature, relative humidity, wind speed, and rainfall (of the past 24 hours) (cf. Kraaij et al., 2013b). Conditions at this time of day approximate daily maximum temperature, minimum relative humidity, wind at its strongest, and thus maximum daily FDI. This index also incorporates a drought factor and the Keetch Byram Drought Index (Keetch and Byram, 1968; Noble et al., 1980). For each of the eight locations we calculated daily FDI spanning 30 years for three types of series:
(i) ‘Contemporary actual’, i.e. weather data actually observed during the past four decades (the exact dates of the series depended on the availability and completeness of weather records for the different locations);
(ii) ‘Contemporary model’, i.e. data modelled for contemporary conditions (1981-2010) by the AMIPw simulation (Engelbrecht et al., 2019) but bias-corrected as described by Cowling et al. (this issue); and
(iii) ‘LGM models’, i.e. data modelled for conditions during the LGM, obtained from the eight dynamically downscaled climate simulations (CCSIM4a, CNRM, FGOALS, GISS, IPSL, MIROC, MPI, and MRI) of Engelbrecht et al. (2019) that were bias-corrected for rainfall and temperature as described by Cowling et al. (this issue).

For each location and series type we then calculated mean monthly FDI and the mean number of days per month with at least moderate (FDI > 5) and at least high (FDI > 12) fire weather (respectively) to assess the seasonality and severity of fire danger weather and how this differed among locations and periods. We also computed mean monthly values for each of the input variables, i.e. maximum temperature, minimum relative humidity, wind speed at mid-day, and daily rainfall, to aid our interpretation of the FDI results.

本研究基于对过去24小时内14时（当地时间）所记录的气温、相对湿度、风速和降雨量（每日数据）的每日外商直接投资（FDI）进行计算（参照Kraaij等人，2013b）。该时点的气象条件与日最高气温、最低相对湿度、风力最强时段相吻合，因此能够反映每日最大FDI。此指数还融合了干旱因素及Keetch-Byram干旱指数（Keetch和Byram，1968；Noble等人，1980）。对于八个地点，我们计算了涵盖30年的每日FDI，涉及以下三种数据序列类型：
(i) '当代实际'，即过去四十年实际观测到的气象数据（数据序列的确切日期取决于不同地点气象记录的可用性和完整性）；
(ii) '当代模型'，即通过AMIPw模拟（Engelbrecht等人，2019）对当代条件（1981-2010）进行建模的数据，但如Cowling等人（本期）所述进行了偏差校正；
(iii) 'LGM模型'，即模拟冰期最大（LGM）条件下获得的数据，由Engelbrecht等人（2019）进行的八个动态降尺度气候模拟（CCSIM4a、CNRM、FGOALS、GISS、IPSL、MIROC、MPI和MRI）获得，如Cowling等人（本期）所述，对降雨量和温度进行了偏差校正。
对于每个地点和序列类型，我们进一步计算了每月平均FDI以及每月至少中等（FDI>5）和至少高度（FDI>12）火灾气象日的平均数，以评估火灾危险天气的季节性和严重性，并分析不同地点和时期之间的差异。此外，我们还计算了每个输入变量的平均月值，包括最大气温、最低相对湿度、正午风速和每日降雨量，以帮助解释FDI的结果。