A Predictable AMO-Like Pattern in the GFDL Fully Coupled Ensemble Initialization and Decadal Forecasting System

Abstract

The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.

On the evolution of Atlantic Meridional Overturning Circulation Fingerprint and implications for decadal predictability in the North Atlantic

“…Our results provide a physical mechanism for the enhanced decadal prediction skills in the UOHC in the SPG shown in recent decadal prediction studies using different climate models [Robson et al, 2012;Yeager et al, 2012;Yang et al, 2013;Msadek et al, 2014]. The cooling in the GS region is less visible in these decadal prediction experiments, as the global warming induced by changes in anthropogenic radiative forcing is also included in these experiments.…”

Imprint of the Atlantic multi-decadal oscillation and Pacific decadal oscillation on southwestern US climate: past, present, and future

“…Ice core analysis suggests a shorter AMO quasi-periodicity (about 20 years) during the Little Ice Age and a longer periodicity in the Medieval Warm Period (Chylek et al 2012). Atmosphere-Ocean coupled climate models (Metha and Delworth 1995;Griffies and Bryan 1997;Delworth and Knutson 2000;Dong and Sutton 2001;Wei and Lohmann 2012;Mahajan et al 2011;Henriksson et al 2012;Yang et al 2013;Escudier et al 2013;Zanchettin et al 2013) as well as simplified conceptual ocean models (Frankcombe and Djikstra 2011), or statistical harmonic models (Humlum et al 2011;Mazzarella and Scafetta 2012;Scafetta 2012) suggest a future persistent AMO like multi-decadal oscillation. Based on this evidence of the past behavior we expect the AMO to retain its cyclic behavior during the twenty-first century with a cycle length of 60-70 years.…”

“…Knight et al 2005;Mahajan et al 2011;Yang et al 2013). Compo and Sardeshmukh (2009) found that the recent worldwide land warming has a significant component originating from warmer oceans rather than being a direct effect of increasing greenhouse gases.…”

“…The extreme complexity of the nonlinear climate system prevents AOGCMs from providing an unambiguous causal description of climate. Research establishing a meaningful decadal climate forecast by properly initiated dynamical models is only beginning (Yang et al 2013). In this situation, simplified semi-empirical and statistical models (e.g.…”

Impact of an observational time window on coupled data assimilation: simulation with a simple climate model

“…When the observed data included in one or more components of the coupled system framework are assimilated, the observational information will be able to be transferred among different media through the coupled dynamics so that all media gain consistent and coherent adjustments. Such an assimilation procedure is called coupled data assimilation (CDA), which can sustain the nature of multiple timescale interactions during climate estimation and prediction initialization (e.g., Zhang et al, 2007;Sugiura et al, 2008;Singleton, 2011), thus producing better climate analysis and prediction initialization and therefore improving the coupled models' predictability (Yang et al, 2013). Zhang et al (2007) developed the first CDA system in a fully coupled general circulation model, version 2 of the Geophysical Fluid Dynamics Laboratory Coupled Model (GFDL CM2).…”