# Uncertainty in the MAN Data Calibration & Trend Estimates

## TL;DR

This paper examines how calibration and data imputation affect trend detection in atmospheric ammonia data, revealing significant uncertainties and variability in trend estimates due to methodological choices.

## Contribution

It introduces a new method to account for calibration uncertainty and demonstrates its impact on trend detection in atmospheric ammonia datasets.

## Key findings

- Calibration uncertainty reduces detected trends by about 50%.
- Imputation alters the number and significance of identified trends.
- Trend sign and significance are highly sensitive to analysis choices.

## Abstract

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.