Spatial Projection of Multiple Climate Variables using Hierarchical Multitask Learning
This is an incremental improvement for climate scientists, offering a more structured approach to multitask learning for spatial climate projections.
The paper tackles the problem of projecting multiple climate variables by introducing a hierarchical multitask learning (HMTL) framework that groups tasks into super-tasks with group lasso regularization, showing it outperforms decoupled methods and baselines in experiments on synthetic and real climate data.
Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask learning (MTL) framework for projections of individual climate variables. In this paper we introduce a framework for hierarchical multitask learning (HMTL) with two levels of tasks such that each super-task, i.e., task at the top level, is itself a multitask learning problem over sub-tasks. For climate projections, each super-task focuses on projections of specific climate variables spatially using an MTL formulation. For the proposed HMTL approach, a group lasso regularization is added to couple parameters across the super-tasks, which in the climate context helps exploit relationships among the behavior of different climate variables at a given spatial location. We show that some recent works on MTL based on learning task dependency structures can be viewed as special cases of HMTL. Experiments on synthetic and real climate data show that HMTL produces better results than decoupled MTL methods applied separately on the super-tasks and HMTL significantly outperforms baselines for climate projection.