Temporal–spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans

Li, Minglu; Ding, Ruiqiang

doi:10.1002/joc.3562

Cited by 62 publications

(37 citation statements)

References 76 publications

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“…Slab models provide as much predictability and forecast skill (∼1 y) as coupled models, which is surprising because slab models do not contain the oceanic Rossby and Kelvin waves associated with ENSO mechanisms. Also, the prediction skill of ENSO in current dynamical and statistical models is less than a year (39)(40)(41). Subsequent predictable components (i.e., components 4, 5, and 6) in slab and coupled models show similar spatial structure, predictability, and skill, but these are not shown, for brevity.…”

Section: Resultsmentioning

confidence: 97%

Decadal predictability without ocean dynamics

Srivastava

DelSole

2017

Proc. Natl. Acad. Sci. U.S.A.

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This paper shows that the most predictable components of internal variability in coupled atmosphere-ocean models are remarkably similar to the most predictable components of climate models without interactive ocean dynamics (i.e., models whose ocean is represented by a 50-m-deep slab ocean mixed layer with no interactive currents). Furthermore, a linear regression model derived solely from dynamical model output can skillfully predict observed anomalies in these components at least a year or two in advance, indicating that these model-derived components and associated linear dynamics are realistic. These results suggest that interactive ocean circulation is not essential for the existence of multiyear predictability previously identified in coupled models and observations. decadal prediction | decadal predictability | average predictability time | CMIP I n recent years, climate prediction on decadal time scales has gained increased attention because of its growing scientific, geopolitical, and societal importance (1-5). Sources of decadal predictability often are divided into two kinds: predictability caused by external forcing, such as changes in solar insolation, volcanic aerosols, and anthropogenic greenhouse gases, and predictability due to internal variability arising naturally from the coupled atmosphere-ocean-land-ice climate system (6). Climate models suggest that certain structures of internal variability in sea surface temperatures (SST), such as the Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO), are predictable on decadal/multidecadal time scales (5, 7-9). The precise mechanisms for this decadal predictability are not clear, although the dynamics of ocean circulation are widely believed to play a major role (10-17).Recently, a few studies have challenged the notion that interactive ocean dynamics play a dominant role in decadal predictability (18,19). These studies are based on integrations of atmospheric global circulation models coupled to a slab ocean mixed layer, in which ocean circulation is a prescribed function of time and the ocean interacts with the atmosphere only thermodynamically, through radiative, sensible, and latent heat fluxes. Despite the absence of interactive ocean dynamics, these models can produce realistic variability that is predictable on interannual and longer time scales. For example, El Niño-Southern Oscillation (ENSO)-like variability can arise on interannual and decadal time scales from such models (20)(21)(22). Also, the main features of the observed AMO (e.g., spatial pattern, power spectra, and associated atmospheric circulation) have been reproduced in models without interactive ocean dynamics (23). However, not all details of these simulations are perfect: In some locations, the lag correlation between AMO and surface heat flux has the opposite sign relative to coupled models and observations (14,15). On the other hand, these heat fluxes tend to be nearly canceled by the ocean heat transport convergence on long time scales (24), raisin...

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Section: Resultsmentioning

confidence: 97%

Decadal predictability without ocean dynamics

Srivastava

DelSole

2017

Proc. Natl. Acad. Sci. U.S.A.

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“…Generally, exponential distribution (Equation (2)) is used to model drought duration, while gamma distribution (Equation (3)) fits drought severity. However, in this study the entropy-based distribution was used to model the marginal distributions of both variables (see Equations (11) and (12)), with the empirical distribution being used for comparison. For the evenly selected stations in Figure 1, the theoretical cumulative distribution, entropy-based cumulative distribution and empirical cumulative distribution are shown in Figures 5 and 6.…”

Section: Drought Characteristic and Distribution Testmentioning

confidence: 99%

“…To date, many drought indices have been proposed for quantifying, monitoring and analyzing droughts [4,5], but the drought problem is a complex one involving a great many factors [6][7][8][9][10][11][12]. There is no unified index because of the complexity of the physical processes involved; however, many studies have shown that the intensity, duration and spatial extent are the main characteristics of droughts [13,14].…”

Section: Introductionmentioning

confidence: 99%

An Entropy-Based Investigation into Bivariate Drought Analysis in China

Zuo

Hou

Jia

2017

Water

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Abstract:Because of the high correlation between random variables of drought duration and severity, their joint distribution is difficult to obtain by traditional mathematical methods. However, the copula method has proved to be a useful tool for analyzing the frequency of drought duration and severity. Most studies have used different marginal distribution functions to fit the drought duration and severity distributions. This requires a great deal of contrast analysis, and sometimes two or more distributions fit the data well. Based on entropy theory, however, a unified probability distribution function is derived which reduces complex contrast analysis and improves the filtering distribution function. Based on monthly precipitation data at 162 stations in China for 1961-2015, the monthly standardized precipitation index was calculated and used to extract drought duration and severity. Then the entropy distribution was used to fit the distributions of drought duration and severity, and to establish the correspondence between them. The probabilities of the interval and return periods were then determined using the copula method. An analysis of the discrepancy between the conventional and entropy-based methods indicated that the entropy distribution showed a better fit than conventional methods for drought duration distribution, although no obvious difference was found in drought severity distribution. The entropy-based results were more consistent with the empirical data, whereas conventional methods showed apparent deviation in some drought types. Hence, the entropy-based method is proposed as an alternative method of deriving the marginal distributions of drought duration and severity, and for analyzing the interval probability and return period in China.

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“…The experimental results of Ding and Li (2007) show that, compared with a linear local or finitetime Lyapunov exponent, the NLLE is more appropriate for the quantitative determination of the predictability limit of a chaotic system. Based on observational or reanalysis data, the NLLE method has been used to investigate the atmospheric predictability at various timescales (Ding et al, , 2010(Ding et al, , 2016Li and Ding, 2008, 2013.…”

Section: Introductionmentioning

confidence: 99%

Determining the spectrum of the nonlinear local Lyapunov exponents in a multidimensional chaotic system

Ding

2017

Adv. Atmos. Sci.

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For an n-dimensional chaotic system, we extend the definition of the nonlinear local Lyapunov exponent (NLLE) from one-to n-dimensional spectra, and present a method for computing the NLLE spectrum. The method is tested on three chaotic systems with different complexity. The results indicate that the NLLE spectrum realistically characterizes the growth rates of initial error vectors along different directions from the linear to nonlinear phases of error growth. This represents an improvement over the traditional Lyapunov exponent spectrum, which only characterizes the error growth rates during the linear phase of error growth. In addition, because the NLLE spectrum can effectively separate the slowly and rapidly growing perturbations, it is shown to be more suitable for estimating the predictability of chaotic systems, as compared to the traditional Lyapunov exponent spectrum.Key words: Lyapunov exponent, nonlinear local Lyapunov exponent, predictability Citation: Ding, R. Q., J. P. Li, and B. S. Li, 2017: Determining the spectrum of the nonlinear local Lyapunov exponents in a multidimensional chaotic system.

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Temporal–spatial distribution of the predictability limit of monthly sea surface temperature in the global oceans

Cited by 62 publications

References 76 publications

Decadal predictability without ocean dynamics

Decadal predictability without ocean dynamics

An Entropy-Based Investigation into Bivariate Drought Analysis in China

Determining the spectrum of the nonlinear local Lyapunov exponents in a multidimensional chaotic system

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