Understanding recent global hydroclimate change using multivariate detection and attribution techniques and GCM Experiments

that is, each forcing component, Greenhouse gas increase or changes in atmospheric concentration of industrial aerosols, tends to have a different T, P, and precipitation response patters. Thus the spatial patterns themselves yield insight into the physical processes that affect the response. In the proposed work, we will estimate the changes in the latitudinal and seasonal distribution of rainfall that are expected in response to each external forcing, and interpret those changes in terms of thermodynamically and dynamically induced precipitation changes. Thus, while this project is focused on recent climate change, the multivariate fingerprints developed for it will be more generally applicable.

In addition to applying D&A methods to climate model simulations, we will conduct a series of model simulations with the most advanced version of the coupled climate models of the National Center of Atmospheric Research (CESM- CAM5). These will be aimed at estimating the relative roles of radiative forcings and natural variability in the recent T and P trends. Our control mode integrations will be forced by the time history of observed sea surface temperatures (SST), sea ice, and trace gases, and based on similar past experiments are expected to reproduce the salient aspects of observed change in T and P. Additional simulations will be carried out to estimate the role of each forcing separately. For those we will use the changes in SST and sea ice induced by each radiative forcing separately as identified from 'single forcing' experiments that were done as part of the IPCC model runs. These experiments, in combination with those with actual historical SST forcing, will assess the extent to which changes in P and T over recent decades were driven by natural variability and by the various radiative forcings. These estimates will be compared to those derived from the D&A work to provide increased confidence in the results.

By addressing the question of whether model error in external forcing and response or internal variability is responsible for decadal differences between simulations and observations, the proposed project furthers the goals of DOE's Regional and Global Climate Modeling Program to assess and increase the reliability of climate change projections.