Alpha values could be quite big and thus introduce results far from reality

[hongyuanjia]

For instance, for tas_day_AWI-CM-1-1-MR_ssp585_r1i1p1f1_gn_20500101-20501231.nc, the alpha value for November could be -19.95. Should introduce a logic to issue warnings about this case and fallback to Shift method.

data_mean[, .(lon, lat, dist, epw_mean, gcm_mean = value, delta, alpha)]
#>          lon      lat     dist        epw_mean       gcm_mean            delta           alpha
#>        <num>    <num>    <num>         <units>        <units>          <units>         <units>
#>  1: 106.4062 35.99986 89.25595 -7.5504032 [°C] -4.804854 [°C]  2.74554916 [°C]   0.6363705 [1]
#>  2: 106.4062 35.99986 89.25595 -3.9571429 [°C] -1.769299 [°C]  2.18784401 [°C]   0.4471152 [1]
#>  3: 106.4062 35.99986 89.25595  1.3489247 [°C]  3.581754 [°C]  2.23282969 [°C]   2.6552663 [1]
#>  4: 106.4062 35.99986 89.25595  8.2494444 [°C]  8.227525 [°C] -0.02191962 [°C]   0.9973429 [1]
#>  5: 106.4062 35.99986 89.25595 13.4138441 [°C] 19.642524 [°C]  6.22867986 [°C]   1.4643471 [1]
#>  6: 106.4062 35.99986 89.25595 16.8897222 [°C] 22.220674 [°C]  5.33095225 [°C]   1.3156329 [1]
#>  7: 106.4062 35.99986 89.25595 19.6094086 [°C] 26.158875 [°C]  6.54946686 [°C]   1.3339961 [1]
#>  8: 106.4062 35.99986 89.25595 18.2104839 [°C] 24.773162 [°C]  6.56267795 [°C]   1.3603791 [1]
#>  9: 106.4062 35.99986 89.25595 13.3270833 [°C] 21.648293 [°C]  8.32121006 [°C]   1.6243834 [1]
#> 10: 106.4062 35.99986 89.25595  7.0000000 [°C] 13.851030 [°C]  6.85103005 [°C]   1.9787186 [1]
#> 11: 106.4062 35.99986 89.25595 -0.2101389 [°C]  4.192878 [°C]  4.40301659 [°C] -19.9528880 [1]
#> 12: 106.4062 35.99986 89.25595 -6.1481183 [°C] -2.254458 [°C]  3.89366038 [°C]   0.3666907 [1]