We propose a hybrid Artificial Intelligence (AI) climate modeling approach that enables climate modelers in scientific discovery using a climate-targeted simulation methodology based on a novel combination of deep neural networks and mathematical methods for modeling dynamical systems. The simulations are grounded by a neuro-symbolic language that both enables question answering of what is learned by the AI methods and provides a means of explainability. We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC). We show how this methodology is able to predict AMOC collapse with a high degree of accuracy using a surrogate climate model for ocean interaction. We also show preliminary results of neuro-symbolic method performance when translating between natural language questions and symbolically learned representations. Our AI methodology shows promising early results, potentially enabling faster climate tipping point related research that would otherwise be computationally infeasible.

翻译：我们建议一种混合人工智能(AI)气候建模方法,使气候建模者能够利用基于深神经网络和模拟动态系统数学方法新颖组合的气候定向模拟方法进行科学发现,模拟以神经-共振语言为基础,既能回答人工智能方法所学知识的答案,又能提供解释手段。我们描述了这一方法如何应用到气候倾斜点的发现,特别是大西洋环流环流的崩溃。我们展示了这一方法如何能够利用海洋互动的替代气候模型以高度准确的方式预测大气中枢的崩溃。我们还展示了在翻译自然语言问题和象征性学习表现时神经-共振学方法的性能初步结果。我们的人工智能方法显示了有希望的早期结果,有可能使气候倾斜点相关研究更快,否则是无法进行计算。