'Columbia University Libraries/Information Services'
Doi
Abstract
Climate prediction on decadal time scales is currently an active area of research. Although there are indications that predictions from dynamical models may have skill in some regions, assessment of this skill is still underway, and reliable model-based predictions of regional "near-term" climate change, particularly for terrestrial regions, have not yet been demonstrated. Given the absence of such forecasts, synthetic data sequences that capture the statistical properties of observed near-term climate variability have potential value. Incorporation of a climate change component in such sequences can aid in estimating likelihoods for a range of climatic stresses, perhaps lying outside the range of past experience. Such simulations can be used to drive agricultural, hydrological or other application models, enabling resilience testing of adaptation or decision systems. The use of statistically-based methods enables the efficient generation of a large ensemble of synthetic sequences as well as the creation of well-defined probabilistic risk estimates. In this report we discuss procedures for the generation of synthetic climate sequences that incorporate both the statistics of observed variability and expectations regarding future regional climate change. Model fitting and simulation are conditioned by requirements particular to the decadal climate problem. A method for downscaling annualized simulations to the daily time step while preserving both spatial and temporal subannual statistical properties is presented and other possible methods discussed. A "case-study" realization of the proposed framework is described
