Dynamical Adjustment of Surface Air Temperature Fields from Large Initial-Condition Ensemble Simulations: Ensemble Spread and Multi-Decadal Trend Attribution

Thursday, 18 December 2014
Brian V Smoliak1, Clara Deser2 and Adam Phillips2, (1)Climate Corporation Seattle, Seattle, WA, United States, (2)NCAR, Boulder, CO, United States
Boreal cold season surface air temperature (SAT) variability and trends attributable to fluctuations in large-scale patterns of sea-level pressure (SLP) anomalies are investigated in a large initial-condition (IC) ensemble of general circulation model climate change simulations using partial least squares (PLS) regression, a statistical method that specifies a predictand time series or field using multiple mutually orthogonal factors generated from a field of predictors. This specification represents a dynamical adjustment to the raw SAT field, which reduces ensemble spread by accounting for the influence of spontaneously-occurring atmospheric circulation changes unique to each ensemble member (EM). When applied independently to the simulated Northern Hemisphere (NH) SAT field from individual EMs, the methodology yields a dynamical adjustment that accounts for nearly 50% of the cold season SAT variance in each EM, validating a previous application of PLS regression to observations. Dynamical adjustment is also applied to SAT trends across the large IC ensemble, enabling the attribution of multi-decadal trends to external forcing and internal variability as well as dynamical and radiative/thermodynamic processes. When applied to the ensemble of SAT and SLP trends over a multi-decadal mid-21st Century reference interval, the methodology yields a dynamical adjustment that explains over 75% of the intra-ensemble variance of the NHSAT warming trend and accounts for about 5% of the ensemble-mean NHSAT warming trend. This methodological framework may be applied to other large IC ensembles, other climatological variables, and across a wider variety of time scales in order to investigate the role of internal variability in modulating the magnitude and patterns of historical and projected climate change.