  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.
