Abstract:

Practitioners rely heavily on historical climate data and future climate projections to assess risks from climate hazards and develop infrastructural standards. In much the same way, researchers rely on this data for driving, for example, socioeconomic or environmental impacts models to understand how systems at local to regional scales will be impacted by climate change. As a result, both practitioners and researchers have been increasingly demanding climate datasets that are both detailed and accurate down to local scales, and are credible sources for probabilistic estimates of the future climate. These datasets are sometimes referred to as decision-relevant climate data products.

In recent years, the number of decision-relevant climate data products has grown rapidly, particularly new statistical and dynamical downscaling products aimed at addressing user needs. However, a lack of coordination among product developers and a lack of standards for evaluating these products has driven confusion among end-users. It has become increasingly difficult to determine which product should be used for what purpose, or what insights could be gained from considering multiple products in the same framework. This talk lays out the current state of the science, and makes the case for better organization and coordinated evaluation of these climate data products to minimize future uncertainties and ensure the wider user community is acting on our best possible projections.