Abstract/Summary

Atmospheric moist available potential energy (MAPE) has been traditionally defined as the potential energy of a moist atmosphere relative to that of the adiabatically sorted reference state defining a global potential energy minimum. Although the Munkres algorithm can in principle find such a reference state exactly, its computational cost has prompted much interest in developing heuristic methods for computing MAPE in practice. Comparisons of the accuracy of such approximate algorithms have so far been limited to a small number of test cases; this work provides an assessment of the algorithms’ performance across a wide range of atmospheric soundings, in two different locations. We determine that the divide-and-conquer algorithm is the best suited to practical application, but suffers from the previously unexplored shortcoming that it can produce a reference state with higher potential energy than the actual state, resulting in a negative value of MAPE. Additionally, we show that it is possible to construct an algorithm exploiting a previously derived theoretical expression linking MAPE to Convective Available Potential Energy (CAPE). This approach has a similar accuracy to existing approximate sorting algorithms, whilst providing greater insight into the physical source of MAPE. In light of these results, we discuss possible ways to improve on the construction of APE theory for a moist atmosphere.