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In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al. [ABSTRACT FROM AUTHOR]

The statistical method, Average Predictability Time (APT) decomposition, is used in the present paper to estimate the decadal predictability of upper ocean heat content over the global ocean, North Pacific and North Atlantic, respectively. The twentieth century simulations from CMIP5 outputs are the main data sources in this study. On global scale, the leading predictable component is characterized by a warming trend over the majority of oceans, which is related to the anthropogenic forced response. The second predictable component has significant loadings in the North Atlantic, especially in the subtropical region, which originates from the Atlantic Multidecadal Oscillation (AMO) predictability. To separate interactions among different ocean basins, we further maximize APT in individual North Pacific and North Atlantic oceans. It is found that the second and the third predictable component in North Pacific are significantly correlated with the well-known North Pacific Gyre Oscillation mode and the Pacific Decadal Oscillation respectively. Upper limit prediction skill of these two components are on the order of 6 years. In contrast, the most predictable component derived from the North Atlantic features an AMO-like spatial structure with its prediction skill up to 18 years, while the basin mode due to global warming only exists as the third component. This indicates the interdecadal variability in the North Atlantic is strong enough to mask the anthropogenic climate signals. Furthermore, predictability in the real world is also investigated and compared with model results by using observation-based data. [ABSTRACT FROM AUTHOR]

Previous studies have documented an abrupt decrease of tropical cyclone (TC) genesis frequency over the western North Pacific (WNP) since 1998. In this study, results from an objective clustering analysis demonstrated that this abrupt decrease is primarily related to the decrease in a cluster of TCs (C1) that mostly formed over the southeastern WNP, south of 15°N and east of the Philippines, and possessed long tracks. Further statistical analyses based on both best track TC data and global reanalysis data during 1980–2015 revealed that the genesis of C1 TCs was significantly modulated by the interdecadal Pacific oscillation (IPO), whose recent negative phase since 1998 corresponded to a La Niña–like sea surface temperature anomaly (SSTA) pattern, which strengthened the Walker circulation in the tropical Pacific and weakened the WNP monsoon trough, suppressing genesis of C1 TCs in the southeastern WNP and predominantly contributing to the decrease in TC genesis frequency over the entire WNP basin. These findings were further confirmed by results from similar analyses based on longer observational datasets and also the outputs from a 500-yr preindustrial general circulation model experiment using the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model, version 3. Additional analysis indicates that the decrease in C1 TC genesis frequency in the recent period was dominated during August–October, with the largest decrease in October. [ABSTRACT FROM AUTHOR]

Because of increased emissions of greenhouse gases oceans are warming, causing sea level to rise as the density of seawater falls. Predicting the rates of steric expansion is challenging because of the natural variability of the ocean and because observations are insufficient to adequately cover the ocean basins. Here, we investigate the ability of one ocean reanalysis, two objective analyses, and one combination of satellite geodetic measurements to accommodate data gaps and to reconstruct typical patterns of the steric sea level variability at interannual and multidecadal time scales. Six climate indices are used to identify robust features of the internal variability, using a Least Absolute Shrinkage and Selection Operator (LASSO) regression to select significant predictors of the steric variability. Spatially consistent fingerprints are revealed for all climate indices in the ocean reanalysis dataset, allowing the recovery of most of the steric variability observed in the tropical and North Pacific, as well as large fractions of the Atlantic and Indian Ocean signals. Robust climate mode fingerprints are also identified with high spatial resolution but limited temporal coverage in the geodetic observations. The objective analyses fail to detect many of the patterns expected from climate modes, especially before the Argo era. Climate indices constitute valuable yet underexploited tools to assess the performance of different techniques to reconstruct steric sea levels at interannual and multidecadal scales. Such progress will increase confidence in the historical reconstructions of steric sea levels, which is necessary to improve the closure of regional and global sea level budgets and to validate the predictions of climate models. [ABSTRACT FROM AUTHOR]

A new optimization strategy is proposed to identify the sensitivities of simulations of atmospheric and oceanic models to uncertain parameters. The strategy is based on a nonlinear optimization method that is able to estimate the maximum values of specific parameter sensitivity measures; meanwhile, it takes into account interactions among uncertain parameters. It is tested using the Lorenz'63 model and an intermediate complexity 2.5-layer shallow-water model of the North Pacific Ocean. For the Lorenz'63 model, it is shown that the parameter sensitivities of the model results depend on the initial conditions. For the 2.5-layer shallow-water model used to simulate the Kuroshio large meander (KLM) south of Japan, the optimization strategy reveals that the prediction of the KLM path is insensitive to the uncertainties in the bottom friction coefficient, the interfacial friction coefficient, and the lateral friction coefficient. Rather, the KLM prediction is relatively sensitive to the uncertainties of the reduced gravity representing ocean stratification and the wind stress coefficient. [ABSTRACT FROM AUTHOR]

Two types of El Niño evolution have been identified in terms of the lengths of their decaying phases: the first type is a short decaying El Niño that terminates in the following summer after the mature phase, and the second type is a long decaying one that persists until the subsequent winter. The responses of the western North Pacific anticyclone (WNPAC) anomaly to the two types of evolution are remarkably different. Using experiments from phase 5 of the Coupled Model Intercomparison Project (CMIP5), this study investigates how well climate models reproduce the two types of El Niño evolution and their impacts on the WNPAC in the historical period (1950-2005) and how they will change in the future under anthropogenic global warming. To reduce uncertainty in future projection, the nine best models are selected based on their performance in simulating El Niño evolution. In the historical run, the nine best models' multimodel ensemble (B9MME) well reproduces the enhanced (weakened) WNPAC that is associated with the short (long) decaying El Niño. The comparison between results of the historical run for 1950-2005 and the representative concentration pathway 4.5 run for 2050-99 reveals that individual models and the B9MME tend to project no significant changes in the two types of El Niño evolution for the latter half of the twenty-first century. However, the WNPAC response to the short decaying El Niño is considerably intensified, being associated with the enhanced negative precipitation anomaly response over the equatorial central Pacific. This enhancement is attributable to the robust increase in mean and interannual variability of precipitation over the equatorial central Pacific under global warming. [ABSTRACT FROM AUTHOR]

The annual cycle of sea surface temperature (SST) in the North Pacific Ocean is examined in terms of its response to global warming based on climate model simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). As the global ocean warms up, the SST in the North Pacific generally tends to increase and the warming is greater in summer than in winter, leading to a significant intensification of SST annual cycle. The mixed layer temperature equation is used to examine the mechanism of this intensification. Results show that the decrease of mixed layer depth (MLD) in summer is the main reason behind the intensification of SST annual cycle. Because the MLD in summer is much shallower than that in winter, the incoming net heat flux is trapped in a thinner surface layer in summer, causing a warmer summer SST and the amplification of SST annual cycle. The change of the SST annual cycle in the North Pacific may have profound ecological impacts. [ABSTRACT FROM AUTHOR]