Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux adjustment.” A suite of 12-month duration retrospective forecasts is performed over the 1981–2012 period, after initializing the climate model to observationally constrained conditions at the start of each forecast period, using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basinwide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally aggregated regional TC activity months in advance are feasible.

A fully coupled data assimilation (CDA) system, consisting of an ensemble filter applied to the Geophysical Fluid Dynamics Laboratory’s global fully coupled climate model (CM2), has been developed to facilitate the detection and prediction of seasonal-to-multidecadal climate variability and climate trends. The assimilation provides a self-consistent, temporally continuous estimate of the coupled model state and its uncertainty, in the form of discrete ensemble members, which can be used directly to initialize probabilistic climate forecasts. Here, the CDA is evaluated using a series of perfect model experiments, in which a particular twentieth-century simulation—with temporally varying greenhouse gas and natural aerosol radiative forcings—serves as a “truth” from which observations are drawn, according to the actual ocean observing network for the twentieth century. These observations are then assimilated into a coupled model ensemble that is subjected only to preindustrial forcings. By examining how well this analysis ensemble reproduces the “truth,” the skill of the analysis system in recovering anthropogenically forced trends and natural climate variability is assessed, given the historical observing network. The assimilation successfully reconstructs the twentieth-century ocean heat content variability and trends in most locations. The experiments highlight the importance of maintaining key physical relationships among model fields, which are associated with water masses in the ocean and geostrophy in the atmosphere. For example, when only oceanic temperatures are assimilated, the ocean analysis is greatly improved by incorporating the temperature–salinity covariance provided by the analysis ensemble. Interestingly, wind observations are more helpful than atmospheric temperature observations for constructing the structure of the tropical atmosphere; the opposite holds for the extratropical atmosphere. The experiments indicate that the Atlantic meridional overturning circulation is difficult to constrain using the twentieth-century observational network, but there is hope that additional observations—including those from the newly deployed Argo profiles—may lessen this problem in the twenty-first century. The challenges for data assimilation of model systematic biases and evolving observing systems are discussed.

Decadal prediction experiments were conducted as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) using the GFDL Climate Model, version 2.1 (CM2.1) forecast system. The abrupt warming of the North Atlantic Subpolar Gyre (SPG) that was observed in the mid-1990s is considered as a case study to evaluate forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-1990s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 1990s show a warming of the SPG; however, only the ensemble-mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predicting the mid-1990s warming. The successful initialized forecasts show an increased Atlantic meridional overturning circulation and North Atlantic Current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and, subsequently, a warming and spindown of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea ice concentration over the Arctic, an enhanced warming over the central United States during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic multidecadal oscillation, which is encouraging for future predictions of North Atlantic climate.

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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.

Reliable estimates of historical and current biogeochemistry are essential for understanding past ecosystem variability and predicting future changes. Efforts to translate improved physical ocean state estimates into improved biogeochemical estimates, however, are hindered by high biogeochemical sensitivity to transient momentum imbalances that arise during physical data assimilation. Most notably, the breakdown of geostrophic constraints on data assimilation in equatorial regions can lead to spurious upwelling, resulting in excessive equatorial productivity and biogeochemical fluxes. This hampers efforts to understand and predict the biogeochemical consequences of El Niño and La Niña. We develop a strategy to robustly integrate an ocean biogeochemical model with an ensemble coupled‐climate data assimilation system used for seasonal to decadal global climate prediction. Addressing spurious vertical velocities requires two steps. First, we find that tightening constraints on atmospheric data assimilation maintains a better equatorial wind stress and pressure gradient balance. This reduces spurious vertical velocities, but those remaining still produce substantial biogeochemical biases. The remainder is addressed by imposing stricter fidelity to model dynamics over data constraints near the equator. We determine an optimal choice of model‐data weights that removed spurious biogeochemical signals while benefitting from off‐equatorial constraints that still substantially improve equatorial physical ocean simulations. Compared to the unconstrained control run, the optimally constrained model reduces equatorial biogeochemical biases and markedly improves the equatorial subsurface nitrate concentrations and hypoxic area. The pragmatic approach described herein offers a means of advancing earth system prediction in parallel with continued data assimilation advances aimed at fully considering equatorial data constraints.

Labrador Sea forcing can dominate multidecadal AMOC variability even while contributing minimally to mean circulation strength.

As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing. A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.