El Niño events exhibit rich diversity in their spatial patterns, which can lead to distinct global impacts. Therefore, how El Niño pattern diversity will change in a warmer climate is one of the most critical issues for future climate projections. Based on the sixth Coupled Model Intercomparison Project simulations, we report an inter-model consensus on future El Niño diversity changes. Central Pacific (CP) El Niño events are projected to occur more frequently compared to eastern Pacific (EP) El Niño events. Concurrently, EP El Niño events are projected to increase in amplitude, leading to higher chances of extreme EP El Niño occurrences. We suggest that enhanced upper-ocean stability due to greenhouse warming can lead to a stronger surface-layer response for increasing positive feedbacks, more favorable excitation of CP El Niño. Whereas, enhanced nonlinear atmospheric responses to EP sea surface temperatures can lead to a higher probability of extreme EP El Niño.
Many deep learning technologies have been applied to the Earth sciences. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Niño–Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill (∼0.82) for a 9-month lead. For interpreting deep learning results beyond the prediction, we present a “contribution map” to estimate how much the grid box and variable contribute to the output and “contribution sensitivity” to estimate how much the output variable is changed to the small perturbation of the input variables. The contribution map and sensitivity are calculated by modifying the input variables to the pre-trained deep learning, which is quite similar to the occlusion sensitivity. Based on the two methods, we identified three precursors of ENSO and investigated their physical processes with El Niño and La Niña development. In particular, it is suggested here that the roles of each precursor are asymmetric between El Niño and La Niña. Our results suggest that the contribution map and sensitivity are simple approaches but can be a powerful tool in understanding ENSO dynamics and they might be also applied to other climate phenomena.
<p>Many deep learning technologies have been applied to the Earth sciences, including weather forecast, climate prediction, parameterization, resolution improvements, etc. Nonetheless, the difficulty in interpreting deep learning results still prevents their applications to studies on climate dynamics. Here, we applied a convolutional neural network to understand El Ni&#241;o&#8211;Southern Oscillation (ENSO) dynamics from long-term climate model simulations. The deep learning algorithm successfully predicted ENSO events with a high correlation skill of 0.82 for a 9-month lead. For interpreting deep learning results beyond the prediction skill, we first developed a &#8220;contribution map,&#8221; which estimates how much each grid point and variable contribute to a final output variable. Furthermore, we introduced a &#8220;sensitivity,&#8221; which estimates how much the output variable is sensitively changed to the small perturbation of the input variables by showing the differences in the output variables. The contribution map clearly shows the most important precursors for El Ni&#241;o and La Ni&#241;a developments. In addition, the sensitivity clearly reveals nonlinear relations between the precursors and the ENSO index, which helps us understand the respective role of each precursor. Our results suggest that the contribution map and sensitivity would be beneficial for understanding other climate phenomena.</p>
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