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Wintertime Cross-correlational Structures between Sea Surface Temperature Anomaly and Atmospheric-and-Oceanic Fields in the East/Japan Sea Under Arctic Oscillation

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Pith's one-line read During positive Arctic Oscillation winters, winter sea-surface temperature anomalies in the East/Japan Sea show near-ballistic persistence (Hurst exponent about 1.4–1.5) along the East Korean Bay–subpolar-front corridor, with long-lasting…

desk verdict Useful regional DFA/DCCA mapping, but the headline H values rest on an untested concatenation of winter segments that needs fixing. read the letter →

arxiv 2509.10233 v1 pith:SJRJ44FW submitted 2025-09-12 physics.ao-ph

Gyuchang Lim , JongJin Park This is my paper · ORCID
classification physics.ao-ph
keywords ArcticOscillationEast/JapanSeasurfacetemperatureanomalydetrendedfluctuationanalysiscross-correlationHurstexponentmarineheatwavescross-persistence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that winter sea-surface temperature anomalies in the East/Japan Sea are organized by two separate tiers of influence. Persistent, scale-invariant coupling comes from oceanic mesoscale structure—sea-surface height and meridional geostrophic flow—while synoptic winds and turbulent heat fluxes supply strong but essentially memoryless tendencies. Using 30 winters of daily fields, the authors estimate Hurst exponents and detrended cross-correlations on 5–50-day scales at every grid cell, separately for positive and negative Arctic Oscillation winters. The central result is that in positive-AO winters the East Korean Bay–subpolar-front corridor shows unusually high variance and near-ballistic persistence ($H \approx 1.4$–$1.5$), which the authors interpret as elevated susceptibility to winter marine heatwaves.

What carries the argument

The central machinery is grid-point detrended fluctuation analysis and detrended cross-correlation analysis (DFA/DCCA). At each cell, daily anomaly profiles are divided into 5–50-day segments, a local linear trend is removed, and the fluctuation function is fit as a power law; the slope gives the Hurst exponent $H$ for a single field and the cross-Hurst exponent $H_{XY}$ for a pair, while the normalized DCCA coefficient $\rho_{\mathrm{DCCA}}$ supplies the sign and scale-resolved strength of the coupling. Significance is assigned by surrogate-based Monte Carlo testing that preserves each series' amplitude distribution and spectrum, combined with false-discovery-rate control across grid cells and scales. The method's role is to separate persistent, scale-invariant co-evolution from strong but transient correlation.

What would settle it

Recompute all Hurst and cross-Hurst exponents separately for each individual winter season, or after explicitly removing year-boundary discontinuities from the stitched series; if the 1.4–1.5 exponents and the ocean-dominated cross-persistence collapse toward $0.5$ or lose their spatial pattern, the stated hierarchy would be an artifact of the concatenation.

Watch

Extended reading notes

Core claim

The paper claims to establish a two-tier mechanism for winter SST variability in the East/Japan Sea. On 5–50-day scales, SSTA cross-persistence is clear and spatially organized with oceanic fields—above all sea-surface height anomaly and meridional geostrophic velocity—along the East Korean Warm Current and subpolar-front pathways. In contrast, coupling with near-surface air temperature is positive but less persistent, coupling with turbulent heat fluxes is strongly negative with no cross-memory, and zonal wind and wind-stress curl show patchy, sign-changing correlations. The key phase contrast is that during positive Arctic Oscillation winters, SSTA variance and its Hurst exponent peak together ($H \approx 1.4$–$1.5$) in the East Korean Bay–subpolar-front corridor, which the authors read as a precondition for marine heatwaves. The overall picture is that the atmosphere and fluxes dictate the immediate tendency of SSTA, while the ocean's own height and advection fields decide which anomalies persist.

Load-bearing premise

The load-bearing premise is that stitching the January–February–March daily records of different years into one continuous series does not create artificial jumps that inflate the persistence estimates, since every reported Hurst and cross-Hurst exponent is computed from that stitched record.

Editorial extensions

If this is right

  • During positive-AO winters, the East Korean Bay–subpolar-front corridor combines high SSTA variance with $H \approx 1.4$–$1.5$, i.e., near-ballistic persistence, which implies elevated marine-heatwave susceptibility there.
  • Among atmospheric fields, the 2-m air temperature anomaly shows basin-wide positive $\rho_{\mathrm{DCCA}}$ with SSTA and localized cross-persistence, while sea-level pressure and wind-stress curl produce patchy correlations that rarely persist.
  • Sensible and latent heat flux anomalies couple to SSTA with widespread negative $\rho_{\mathrm{DCCA}}$ and essentially no cross-persistence, consistent with fast damping feedbacks.
  • Sea-surface height anomaly shows the most extensive, AO-phase-stable positive coupling with SSTA, and meridional geostrophic velocity yields the clearest advective cross-persistence along the East Korean Warm Current and subpolar front.
  • The same grid-point DFA/DCCA workflow, with surrogate testing and false-discovery-rate control, can be transferred to other marginal seas.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the persistence hierarchy is fluxes < atmosphere < ocean, subseasonal predictability of winter SSTA in this basin is likely to come from monitoring sea-surface height and meridional geostrophic transport rather than from heat-flux or wind forecasts.
  • A transferable extension would apply the same FDR-controlled DCCA analysis to other marginal seas (for example the Yellow Sea or South China Sea) to test whether a two-tier 'ocean organizes, atmosphere forces' structure is generic.
  • The near-ballistic exponents over 5–50 days suggest that once a warm SSTA pattern is established in AO+ winters along the subpolar front, it may persist well beyond individual synoptic events; if so, winter marine-heatwave warnings could be conditioned on the pre-existing oceanic state rather than on the atmospheric forecast.
  • A causal test would compare these observational DCCA maps against ocean-model experiments with and without AO-forced wind and flux perturbations, isolating whether the oceanic cross-persistence is produced by advection or by mixed-layer re-emergence.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper analyzes 30 winters (1993–2022) of daily SSTA, atmospheric, flux, and oceanic fields in the East/Japan Sea, using grid-point DFA and DCCA to estimate Hurst exponents, cross-Hurst exponents, and DCCA coefficients over 5–50-day scales, separately for Arctic Oscillation positive (AO+) and negative (AO−) winters. Significance is assessed with iterative-AAFT surrogates and Benjamini–Hochberg false-discovery-rate control, and univariate/cross-scaling fits are screened by an R² threshold. The authors report that during AO+ winters the EKB–SPF corridor shows high SSTA variance and near-ballistic persistence (H ≈ 1.4–1.5); that persistent SSTA coupling is dominated by oceanic fields (SSHA, geostrophic meridional velocity) while synoptic winds and turbulent heat fluxes provide strong but non-persistent tendencies; and they frame the results as a two-tier process of mesoscale/advective organization versus synoptic/heat-flux forcing.

Significance. If the results hold, this would be a useful regional contribution with a transferable methodological template: the combination of pre-declared R² quality thresholds on the log–log fits, iAAFT surrogate nulls, and BH-FDR control across grid cells and scales is careful and reproducible, and the data sources are clearly documented. The paper’s proposed hierarchy (oceanic fields organize persistent coupling; atmospheric synoptic forcing and turbulent fluxes act as fast, non-persistent tendencies) is physically plausible and consistent with prior work on winter air–sea interaction in the East/Japan Sea. However, two load-bearing assumptions—the definition of the AO− phase and the concatenation of winter segments into a continuous daily series—are not adequately supported, so the central quantitative claims are not yet secure.

major comments (2)
  1. [Section 2.1.4, AO phase selection] The phase threshold is internally inconsistent: the text states 'Winters with JMF AO > +0.8σ ... were tagged AO+, and those with AO > −0.8σ were tagged AO−'. If taken literally, the AO− set includes all winters with AO greater than −0.8σ, which also contains the AO+ winters, making the phase composites overlapping and invalid. This must be corrected to AO < −0.8σ (or equivalent) and the resulting winter counts reported. Because every AO+ versus AO− comparison in Sections 3 and 4 depends on this partition, this is a central, load-bearing issue.
  2. [Section 2.1.4, cut-and-stitch concatenation] The DFA/DCCA analyses are performed on a series formed by concatenating JFM winter segments ('cut-and-stitch') without accounting for discontinuities at year boundaries. Each winter segment is about 90 days, and the concatenation joins 31 March to 1 January of the following winter; the SSTA fields, although defined relative to a day-of-year climatology, still contain year-specific offsets, producing an abrupt jump at every boundary. With linear (m = 1) detrending, DFA segments that straddle a boundary contain a step-like discontinuity that cannot be removed by a linear fit, inflating the fluctuation functions F(s) and F_XY(s) preferentially at scales where such segments are common. Since the headline H ≈ 1.4–1.5 and the cross-Hurst hierarchy are estimated from this stitched series, the authors must either demonstrate that boundary effects do not bias the scaling estimates (e.g., by a segment-aware analysis that drops or bridges boundary-straddling segments, or by comparing with per-winter H estimates) or the persistence hierarchy cannot be regarded as established. Section 2.2.4 reports only a scale-band robustness check and does not address this data-construction premise.
minor comments (6)
  1. [Section 2.1.4] The abbreviation 'JMF' appears consistently in the AO phase-selection paragraph, but the correct winter season everywhere else is 'JFM' (January–February–March). Please correct this typo.
  2. [Section 4.2] The sentence about oceanic drivers reads 'meridional geostrophic flow (V10)' maps the advective corridors; however, V10 in Section 2.1.2 denotes the 10-m meridional wind. The intended variable is geo-VA, the meridional geostrophic velocity anomaly. Please fix this notation clash.
  3. [Abstract and Section 2.2.1] The abstract refers to the cross-Hurst exponent as λ, while the main text consistently uses H_XY (also written h_XY in places). Please standardize the symbol throughout.
  4. [Section 2.2.2, Eqs. (10) and (11)] Two equations are both labeled (10): the initial Monte-Carlo p-values and the R²-conditioned p_H. The second equation should be numbered (11) to avoid confusion when readers refer to the quality-control step.
  5. [Figure 1 caption] The caption states 'Blanks in (C) and (F) mark insignificance at the 95% level', but the methods use Benjamini–Hochberg FDR control at q = 0.05, not a per-test 95% confidence threshold. Please align the caption with the actual inference procedure.
  6. [Section 2.2.4 and throughout] No confidence intervals are reported for H or H_XY. Given that the scaling estimates drive the interpretation, adding a surrogate-based or block-bootstrap interval would materially aid interpretation, even if only for the headline EKB–SPF corridor values.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: all exponents and coefficients are empirical statistics computed from the data, and the interpretive two-tier narrative does not enter any estimation equation.

full rationale

The paper's derivation chain is direct and self-contained: daily anomaly fields are formed by subtracting day-of-year climatologies, DFA/DCCA fluctuation functions are computed from these fields, and the reported H, H_XY, and rho_DCCA values are ordinary-least-squares slopes or normalized covariance ratios. No parameter is fitted to a subset of the data and then recycled as a prediction; the surrogate-based p-values and BH-FDR controls are independent statistical screens rather than fitted inputs. The 'two-tier process' interpretation in Sections 3.4 and 4.2 is a post-hoc summary of the spatial maps, not a constraint used to produce them, so it cannot make the results equivalent to the inputs by construction. The paper's self-citations (references 9, 13, 16) are methodological or contextual: DCCA itself is cited to the original literature (Podobnik and Stanley, Zebende), and the prior basin-scale study is mentioned as motivation rather than as the source of a load-bearing premise or uniqueness theorem. The skeptical concern about 'cut-and-stitch' concatenation of JFM segments in Section 2.1.4 is a legitimate internal-validity issue, because year-boundary discontinuities are not analyzed and could bias DFA/DCCA fluctuation functions; however, this is a data-construction risk, not circularity, since the outputs are not defined in terms of the conclusions and no fitted result is being relabeled as a prediction. Overall, the empirical quantities are measured from the data under stated assumptions, and the interpretive narrative does not feed back into the measurement equations.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

Central claims rest on the validity of DFA/DCCA scaling, iAAFT null distributions, FDR control under spatial dependence, the fidelity of reanalysis and altimetry products, and the unstated assumption that stitching JFM segments does not create artificial long-range memory. The free parameters are methodological thresholds chosen by hand; no physical constants are fitted. No invented entities are introduced.

free parameters (5)
  • Scale band S = 5-50 days
    Central H and rho_DCCA estimates depend on this pre-declared band; changing to 7-45 days alters magnitudes modestly but not patterns (Section 2.2.4).
  • DFA/DCCA detrending order m = 1
    Local polynomial detrending order chosen by hand; it affects fluctuation functions and fitted exponents.
  • Quality threshold R0 = 0.90
    Cells with log-log fit R2 below 0.90 are masked; this spatial selection affects the extent of reported H_XY.
  • AO phase threshold = +/-0.8 sigma
    Winters are classified AO+/AO- using JFM AO thresholds; the text contains an inconsistent inequality for AO- (Section 2.1.4).
  • FDR level q = 0.05
    Benjamini-Hochberg target false discovery rate chosen by hand; it controls which cells are declared significant.
assumptions (5)
  • domain assumption DFA/DCCA power-law scaling holds over the 5-50 day scale band for daily anomaly fields.
    The entire H and H_XY interpretation assumes log F versus log s is linear over S; R2 screening is used, but the truth of scaling is an assumption.
  • domain assumption iAAFT surrogates provide a valid null distribution for cross-correlation under preserved marginals and linear autocorrelation.
    Monte-Carlo p-values in Section 2.2.2 assume surrogates match the null; iAAFT is standard but not guaranteed for non-Gaussian, nonlinear fields.
  • ad hoc to paper Concatenating JFM winter segments does not introduce artifacts that bias fluctuation functions.
    Section 2.1.4 stitches winters together with no treatment of year-boundary discontinuities; DFA segments can cross these boundaries.
  • domain assumption BH-FDR control remains valid for spatially correlated geophysical fields.
    Section 2.2.3 assumes positive spatial dependence does not break FDR; authors note it is appropriate but do not test it.
  • domain assumption ERA5 and DUACS fields faithfully represent the relevant atmospheric fluxes and geostrophic currents at 0.25 degree resolution.
    All predictor fields come from reanalysis and mapped altimetry, which have their own errors; the paper acknowledges along-track noise in DUACS (Section 4.5).

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Cite this review

Pith. "Pith review of Wintertime Cross-correlational Structures between Sea Surface Temperature Anomaly and Atmospheric-and-Oceanic Fields in the East/Japan Sea Under Arctic Oscillation." pith.science (2026). https://pith.science/paper/SJRJ44FW

@misc{pith2026250910233,
  author       = {Pith},
  title        = {Pith review of: Wintertime Cross-correlational Structures between Sea Surface Temperature Anomaly and Atmospheric-and-Oceanic Fields in the East/Japan Sea Under Arctic Oscillation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SJRJ44FW}},
  note         = {Machine review of arXiv:2509.10233}
}
abstract

The winter Arctic Oscillation (AO) modulates East Asian climate and the East/Japan Sea (EJS), yet local, scale-dependent air-sea couplings linking atmosphere, ocean and sea-surface temperature anomalies (SSTA) remain unclear. Using 30 years of daily fields (1993--2022), we compute detrended fluctuation/cross-correlation metrics over 5--50-day scales at every grid: the Hurst exponent ($H$), the cross-Hurst exponent ($\lambda$), and the DCCA coefficient ($\rho_{DCCA}$). Significance is assessed with iterative-AAFT surrogates and Benjamini--Hochberg false-discovery-rate control. Three robust features emerge. (1) During AO+ winters, the EKB--SPF corridor exhibits high SSTA variance and near-ballistic persistence ($H \approx 1.4$--$1.5$), indicating increased susceptibility to marine heatwaves. (2) SSTA co-fluctuates positively with near-surface air-temperature anomalies, whereas turbulent heat-flux anomalies are largely anti-phased and show negligible cross-persistence, consistent with fast damping. (3) Oceanic fields impart persistent coupling: sea-surface height anomalies display basin-wide positive links with SSTA; meridional geostrophic velocity imprints advective cross-coupling along EKWC/SPF pathways, while zonal flow and vorticity yield patchy signatures. Winter SST variability in the EJS thus reflects a two-tier process in which mesoscale structure and along-front advection organize persistence, while synoptic forcing and turbulent heat exchange supply strong but non-persistent tendencies. The FDR-controlled, grid-point DFA/DCCA framework is transferable to other marginal seas.

Figures

Figures reproduced from arXiv: 2509.10233 by the authors.

Figure 2
Figure 2. SSTA↔ATMPA. (A, B) DCCA cross-persistence 𝐻𝑋𝑌 during AO+ and AO−. Cells are shown only where the log–log fit of 𝐹𝑋𝑌(𝑠) vs. 𝑠 over 5–50 days meets the pre-declared quality criterion (e.g., 𝑅 2 ≥ 𝑅0) and the iAAFT Monte￾Carlo p-value is BH-significant at FDR 𝑞 = 0.05 (family: all grid cells). (C, D) Scale-averaged 𝜌̅𝑑𝑐𝑐𝑎 (mean over 5–50 days). A cell is colored only if, for at least [PITH_FULL_IMAGE:figures/full_fig_… view at source ↗
Figure 3
Figure 3. SSTA↔CurlTauA. Same layout and significance rules as [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. SSTA↔SLPA. Same layout and significance rules as [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: SSTA↔UA10. Same layout and significance rules as [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: SSTA↔VA10. Same layout and significance rules as [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]

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Works this paper leans on

30 extracted references · 30 canonical work pages

  1. [1]

    Y.; Wang, S

    Gong, D. Y.; Wang, S. W. ; Zhu, J. H. East Asian winter monsoon and Arctic oscillation. Geophys. Res. Lett. 2001, 28, 2073–6

    work page 2001

  2. [2]

    H.; Ho, C

    Jeong, J. H.; Ho, C. H. Changes in occurrence of cold surges over East Asia in association with Arctic oscillation. Geophys. Res. Lett. 2005, 32, 85–93. doi: 10.1029/2005GL023024

    work page doi:10.1029/2005gl023024 2005

  3. [3]

    Impact of Arctic Oscillation on the East Asian climate: A review

    He, S.; Gao, Y.; Li, F.; Wang, H.; He, Y. Impact of Arctic Oscillation on the East Asian climate: A review. Earth-Science Reviews 2017, 164, 48–62

    work page 2017

  4. [4]

    -Y.; Kim, Y

    Song, S. -Y.; Kim, Y. -J.; Lee, E. -J.; Yeh, S. -W.; Park, J. -H.; Park, Y. -G. Wintertime sea sur face temperature variability modulated by Arctic Oscillation in the northwestern part of the East/Japan Sea and its relationship with marine heatwaves. Front. Mar. Sci. 2023, 10: 1198418

    work page 2023

  5. [5]

    B.; Ramachandran, R.; Rani, S

    Subrahamanyam, D. B.; Ramachandran, R.; Rani, S. I.; Kumar, B. P. Air -sea interaction processes over the east-asian marginal seas surrounding the Korean peninsula. Ann. Ge ophys. 2007, 25, 1477 –1486

    work page 2007

  6. [6]

    Chung, J.Y

    Park, K. ; Chung, J.Y. Spatial and temporal scale variations of sea surface temperature in the East Sea using NOAA/AVHRR data. J. Oceanogr. 1999, 55, 271 –288

    work page 1999

  7. [7]

    Sako, A

    Minobe, S. ; Sako, A. ; Nakamura, M. Interannual to interdecadal variability in the Japan Sea based on a new gridded upper water temperature dataset. J. Phys. Oceanogr. 2004, 34, 2382–2397

    work page 2004

  8. [8]

    -P.; Phillips, A.S

    Deser, C.; Alexander, M.A.; Xie, S. -P.; Phillips, A.S. Sea Surface Tempera ture Variability: Patterns and Mechanisms. Annu. Rev. Mar. Sci. 2010, 2, 115 –143. https://doi.org/10.1146 /annurev-marine-120408-151453

    work page 2010

Show all 30 references
  1. [9]

    Lim, G. ; Park. J. -J. Exploring Long -Term Persistence in Sea Surface Temperature and Oc ean Parameters via Detrended Cross -Correlation Approach. Remote Sens. 2024, 16(13), 2 501

    2024

  2. [10]

    -J.; Hong, S

    Kim, E. -J.; Hong, S. -Y. Impact of air -sea interaction on East Asian su mmer monsoon cli mate in WRF. J. Geophys. Res. 2010, 115, D19118

    2010

  3. [11]

    -K.; Buldyrev, S.V.; Havlin, S.; Simons, M.; Stanley, H.E.; Goldberger, A.L

    Peng, C. -K.; Buldyrev, S.V.; Havlin, S.; Simons, M.; Stanley, H.E.; Goldberger, A.L. Mosa ic organization of DNA nucleotides. Phys. Rev. E 1994, 49, 1685 –1689

    1994

  4. [12]

    Detrended Cross -Correlation Analysis: A New Method for A nalyzing Two Nonstationary Time Series

    Podobnik, B.; Stanley, H.E. Detrended Cross -Correlation Analysis: A New Method for A nalyzing Two Nonstationary Time Series. Phys. Rev. Lett. 2008, 100, 084102

    2008

  5. [13]

    Lim, G.; Park, J. -J. Auto - and Cross -Correlation Multifractal Analysis of Sea Surface Te mperature Varia bility. Fractal Fract. 2024,8, 239. https://doi.org/10.3390/fractalfract8040239

    2024 doi

  6. [14]

    DCCA cross -correlation coefficient: quantifying level of cross -correlation

    Zebende, G. DCCA cross -correlation coefficient: quantifying level of cross -correlation. P h ysica A, 2011, 390 (4), 614–618

    2011

  7. [15]

    Stochastic climate models Part I

    Hasselmann, K. Stochastic climate models Part I. Theory. Tellus. 1976, 28(6), 473 –485

    1976

  8. [16]

    Park, J

    Lim, G. ; Park, J. -J. Arctic Oscillation Modulation of Winter Air –Sea Coupling in the East /Japan Sea: Persistence, Timescales, and Extre mes. arXiv preprint. 2025, arXiv:2509.09628. https://doi.org/10.48550/arXiv.2509.09628

    2025 doi arXiv

  9. [17]

    Improved Surrogate Data for Nonlinearity Tests

    Schreiber, T .; Schmitz, A. Improved Surrogate Data for Nonlinearity Tests. Phys. Rev. L ett. 1996, 77, 635

    1996

  10. [18]

    -Q.; Zhou, W

    Podobnik, B.; Jiang, Z. -Q.; Zhou, W. -X.; Stanley, H.E. Statistical tests for power -law cros s-correlated processes. Phys. Rev. E, 2011, 84, 066118

    2011

  11. [19]

    Hochberg, Y

    Benjamini, Y. ; Hochberg, Y. Controlling the false discovery rate: a practical and powerf ul approach to multiple testing. J. R. Stat. Soc. B , 1995, 57, 289 –300

    1995

  12. [20]

    Liu, C

    Huang, B. ; Liu, C. ; Freeman, E. ; Graham, G. ; Smith, T. ; Zhang, H. -M. Assessment and i ntercomparison of NOAA daily optimum interpolation sea surface temperature (DOISST) version 2 .1. J. Clim. 2021, 34, 7421–7441

    2021

  13. [21]

    Smith, T.M

    Reynolds, R.W. ; Smith, T.M. ; Liu, C. ; Chelton, D.B. ; Casey, K.S. ; Schlax, M.G. Daily high -resolution-blended analyses for sea surface temperature. J. Clim. 2007, 20, 5473–5496

    2007

  14. [22]

    Bell, B

    Hersbach, H. ; Bell, B. ; Berrisford, P. ; Hirahara, S. ; Horányi, A. ; Muñoz‐Sabater, J. ; et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 2020, 146(730), 1999–2049

    2020

  15. [23]

    -I.; Faugère, Y

    Pujol, M. -I.; Faugère, Y. ; Taburet, G. ; Dupuy, S. ; Pelloquin, C. ; Ablain, M. ; Picot, N. DU ACS DT2014: the new multi -mission altimeter data set reprocessed over 20 years, Ocean Sci. 2016, 12, 1067 –1090

    2016

  16. [24]

    Sanchez-Roman, A

    Taburet, G. ; Sanchez-Roman, A. ; Ballarotta, M. ; Pujol, M. -I.; Legeais, J. -F.; Fournier, F. ; F augere, Y. ; Dibarboure, G. DUACS DT2018: 25 years of reprocessed sea level altimetry p roducts, Ocean Sci. 2019, 15, 1207 –1224

    2019

  17. [25]

    Arctic Oscillation (AO) Index

    NOAA CPC (2023). Arctic Oscillation (AO) Index . National Oceanic and Atmospheric A dministration Climate Prediction Center. [Online] Available at: https://www.cpc.ncep.noa a.gov/products/precip/CWlink/daily_ao_index/ao.shtml (Accessed: 10 April 2024)

    2023

  18. [26]

    Thompson, D. W. ; Wallace, J. M. Annular modes in the extratropical circulation. part I: month-to-month variability. J. Climate, 2000, 13, 1000 –1016

    2000

  19. [27]

    Mu ltifractal detrended fluctuation analysis of nonstationary time series

    Kantelhardt, J.W.; Zschiegner, S.A.; Bunde, E.K.; Havlin, S.; Bunde, A.; Stanley, H.E. Mu ltifractal detrended fluctuation analysis of nonstationary time series. Physica A , 2002, 31 6, 87 –114

    2002

  20. [28]

    Hasselmann, K

    Frankignoul, C. ; Hasselmann, K. Stochastic climate models, part ii application to sea -surf ace temperature anomalies and thermocline variability . Tellus, 1997, 29(4), 289–305

    1997

  21. [29]

    Detrended cross -correlation analysis for non-stationary time series with periodic trends

    Podobnik, B.; Horvatic, D.; Ilic, S.; Stanley, H.E. Detrended cross -correlation analysis for non-stationary time series with periodic trends . Eur. Phys. J. B, 2011, 84, 445 –449

    2011

  22. [30]

    Min, S

    Lim, G. ; Min, S. Effect of Outliers and Non -consecutive Data Points on the Detrended Cross-Correlation Analysis. J. Korean Phys. Soc. 2018, 72, 545–550. Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the indiv...

    2018

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