  We investigate trend identification in the LML and MAN atmospheric ammonia
data. The signals are mixed in the LML data, with just as many positive,
negative, and no trends found. The start date for trend identification is
crucial, with the trends claimed changing sign and significance depending on
the start date. The MAN data is calibrated to the LML data. This calibration
introduces uncertainty never heretofore accounted for in any downstream
analysis, such as identifying trends. We introduce a method to do this, and
find that the number of trends identified in the MAN data drop by about 50%.
The missing data at MAN stations is also imputed; we show that this imputation
again changes the number of trends identified, with more positive and fewer
significant trends claimed. The sign and significance of the trends identified
in the MAN data change with the introduction of the calibration and then again
with the imputation. The conclusion is that great over-certainty exists in
current methods of trend identification.
