Both of these are in review at “peer review” journals, because as we all know, peer review guarantees truth.
Lot of people use what’s known as the Ryden & McNeil Ammonia Flux Model, which is a semi-physical semi-statistical model of ammonia flux. The physics are simple, and a bit too simple. The statistics in it aren’t bad for wind, which behaves nicely in the boundary layer, but it’s not so good for the ammonia itself.
What we did was
We propose two simple fixes to the Ryden and McNeil ammonia flux model. These are necessary to prevent estimates from becoming unphysical, which very often happens and which has not yet been noted in the literature. The first fix is to constrain the limits of certain of the model’s parameters; without this limit, estimates from the model are seen to produce absurd values. The second is to estimate a point at which additional contributions of atmospheric ammonia are not part of a planned expert but are the result of natural background levels. These two fixes produce results that are everywhere physical. Some experiment types, such as surface broadcast, are not well cast in the Ryden and McNeil scheme, and lead to over-estimates of atmospheric ammonia.
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.
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