A Stochastic Climate Generator for Agriculture in Southeast Asian Domains

Abstract:

We extend a previously-described method for generating future climate scenarios, suitable for driving agricultural models, to selected domains in Lao PDR, Bangladesh and Indonesia. There are notable differences in climatology among the study regions, most importantly the inverse seasonal relationship of southeast Asian and Australian monsoons. These differences necessitate a partially-differentiated modeling approach, utilizing common features for better estimation while allowing independent modeling of divergent attributes. The method attempts to constrain uncertainty due to both anthropogenic and natural influences, providing a measure of how these effects may combine during specified future decades. Seasonal climate fields are downscaled to the daily time step by resampling the AgMERRA dataset, providing a full suite of agriculturally relevant variables and enabling the propagation of climate uncertainty to agricultural outputs. The role of this research in a broader project, conducted under the auspices of the International Fund for Agricultural Development (IFAD), is discussed.