Evaluating Precipitation Errors Using the Environmentally Conditioned Intensity‐Frequency Decomposition Method - Di Luca, A.; Argüeso, D.; Sherwood, S.; Evans, J. P. (Journal of Advances in Modeling Earth Systems, 2021)
Abstract A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors. , Plain Language Summary The simulations of complex physical processes always entail various sources of errors. These errors can be of different sign and can consequently cancel each other out when using traditional performance metrics such as the bias error metric. We present a formal framework that allows us to approximate precipitation according to three terms that describe different aspects of the rainfall field including large‐scale environmental conditions and the frequency and intensity of rainfall. We apply the methodology to a large ensemble of high‐resolution simulations representing the precipitation associated with strong cyclones in eastern Australia. We show that simulations produce precipitation too often, with an intensity that is too weak leading to strong compensation. We further define new error metrics that explicitly quantify the degree of error compensation when simulating precipitation. We show that convection‐permitting simulations consistently improve the performance compared to coarser resolution simulations using parameterized convection and that these improvements are only revealed when using the new error metrics but are not apparent in traditional metrics (e.g., bias). , Key Points Multiple high‐resolution simulations produce precipitation too often with underestimated intensity leading to strong error compensation Errors in precipitation are quantified using novel metrics that prevent error compensation showing value compared with traditional metrics Convection permitting simulations outperform the representation of precipitation compared to simulations using parameterized convection

Future Changes in the Occurrence of Hybrid Cyclones: The Added Value of Cyclone Classification for the East Australian Low‐Pressure Systems - Cavicchia, L.; Pepler, A.; Dowdy, A.; Evans, J.; Di Luca, A.; Walsh, K. (Geophysical Research Letters, 2020)
Abstract Several regions of the world, including the east coast of Australia, are characterized by the occurrence of low‐pressure systems with a range of different dynamical structures, including tropical, extratropical, and hybrid cyclones. Future changes in the occurrence of cyclones are better understood if storms are classified according to their dynamical structure. Therefore, we apply a classification of cyclones according to their cold‐core or warm‐core structure to an ensemble of regional climate model simulations. First, we show that historical simulations reproduce well the reanalysis results in terms of cyclone classification. We then show that once cyclone classification is applied, projections of future cyclone activity become more robust, including a decrease in the occurrence of both cold‐core and warm‐core cyclones. Finally, we show that in a warmer climate warm‐core hybrid cyclone activity could increase close to the coast, while the associated rainfall and wind are projected to increase. , Plain Language Summary Cyclones in the tropics derive their energy from the temperature difference between warm ocean waters and the atmosphere and their interior is warmer than the environment (warm core), while cyclones in the midlatitudes derive their energy from differences in the atmospheric temperature and density at different locations and their interior is colder than the environment (cold core). In subtropical regions both types of cyclone can form. Also in those regions cyclones known as hybrid cyclones form, with mixed tropical‐extratropical features, such as a partial lower tropospheric warm core and a partial upper tropospheric cold core. This study is focused on cyclones along the eastern coast of Australia. Here we show that dividing cyclones in different classes according to their thermal structure provides a better framework to interpret changes in cyclone activity at subtropical latitudes. This study has two main results. First, classifying cyclones adds value to climate projection robustness. A large number of models agree on the decrease in the occurrence of both cold‐core and warm‐core cyclones. The study also indicates increased occurrence of hybrid cyclones close to the Australian coast. Second, the study shows evidence of future changes in cyclone‐related impacts, such as an increase in the associated rainfall. , Key Points A physically based classification of hybrid cyclones is applied to an ensemble of regional climate model simulations The cyclone classification method adds value to the projections of future cyclone activity, making them more robust Results indicate future changes (2060–2079) toward more intense impacts associated with hybrid cyclones