  Environmental model performances need to be assessed using some statistical
parameters, such as mean absolute error (MAE) and root mean square error
(RMSE). The advantages and disadvantages of these parameters are still in
controversial. The purpose of this study is to introduce a statistical
parameter, type A uncertainty (UA), into model performance evaluations. We
particularly focus on the relations between sample sizes and three evaluation
parameters, and tested a few ocean color remote sensing algorithms and
datasets. The results indicate that RMSE, MAE and UA all vary with the sample
size n but present different trends. Based on our tested results and
theoretical analysis, we therefore conclude that UA is better than RMSE and MAE
to express model uncertainty, because its downward trends indicate that the
more samples we take, the less uncertainty we get. RMSE and MAE are good
parameters for assessing model accuracy rather than uncertainty.
