Search Results

You are looking at 1 - 10 of 28 items for :

  • Regression analysis x
  • Climate Implications of Frontal Scale Air–Sea Interaction x
  • Refine by Access: All Content x
Clear All
Morio Nakayama, Hisashi Nakamura, and Fumiaki Ogawa

than in the CTL experiment. Table 1. Fraction (%) of the total variance of [TKE] explained by EOF1 (EOF2). Figure 4 shows anomalies of [TKE] and poleward [ υ ′ T ′] both regressed against the BAM indices, which are thus typical for its positive phase. In the CTL experiment, broad meridional monopole structures are evident in both the [TKE] and [ υ ′ T ′] anomalies in each of the hemispheres ( Figs. 4c,d ), which is overall consistent with the observed BAM ( Figs. 4a,b ) in our analysis and TW14

Open access
Hyodae Seo, Hajoon Song, Larry W. O’Neill, Matthew R. Mazloff, and Bruce D. Cornuelle

. Tellus , 59A , 127 – 140 , https://doi.org/10.1111/j.1600-0870.2006.00213.x . 10.1111/j.1600-0870.2006.00213.x Carton , J.A. , and B. S. Giese , 2008 . A reanalysis of ocean climate using Simple Ocean Data Assimilation (SODA) . Mon. Wea. Rev. , 136 , 2999 – 3017 , https://doi.org/10.1175/2007MWR1978.1 . 10.1175/2007MWR1978.1 Chang , E. K. M. , 1993 : Downstream development of baroclinic waves as inferred from regression analysis . J. Atmos. Sci. , 50 , 2038 – 2053 , https

Restricted access
Adèle Révelard, Claude Frankignoul, Nathalie Sennéchael, Young-Oh Kwon, and Bo Qiu

atmospheric response seems to be primarily driven by the decadal variability of the KE. Indeed, repeating the analysis, but regressing onto a high-pass- and low-pass-filtered KE index with a cutoff at 6 years gave very similar results when using the low-pass filtered KE index, but different and more noisy ones when using the high-pass filtered one (not shown). b. SST anomalies and heat flux feedback The KE variability influences the atmosphere through SST changes that generate air–sea heat flux anomalies

Full access
Yi-Hui Wang and W. Timothy Liu

associated with the Niño-3.4 index are removed. After separating the large-scale and frontal-scale signals and removing the tropical variability, we perform regression analysis to the KE index and the individual climate variables point by point. The KE index is normalized by its standard deviation. The regression coefficient at each point represents the changes in one specific variable as the normalized KE index increases by one unit. The light green contours in the figures enclose areas where the

Full access
Xiaohui Ma, Ping Chang, R. Saravanan, Dexing Wu, Xiaopei Lin, Lixin Wu, and Xiuquan Wan

related variables is computed when carrying out SVD analysis. Additionally, we carried out lag-regression analyses to examine relationships between SST and extreme flux events. These analyses are based on seasonal mean anomalies and the results will be discussed in section 4 . 3. Event-day and non-event-day fluxes and the associated storms a. Characteristics of extreme flux events Figure 2a shows the NDJFM daily climatology of THF and the associated standard deviation for the KER (top graph) and GSR

Full access
Bunmei Taguchi, Niklas Schneider, Masami Nonaka, and Hideharu Sasaki

latter. The westward intensification and concentration of the signals with initially broad meridional scale is consistent with jet-trapped Rossby waves proposed by Sasaki et al. (2013) . Fig . 6. (a) Lagged correlation (color shading) and regression (contours, every 0.1 K) coefficients of annual mean based on the Ishii analysis associated with the standardized reference time series of averaged over the KE region (33°–38°N, 145°–170°E; cyan boxes in the panels for lag 0 yr). Positive (negative

Full access
Hyodae Seo, Young-Oh Kwon, Terrence M. Joyce, and Caroline C. Ummenhofer

Joyce 2013 ). In (b) and (c), the mean position of the GS is shown as thick black lines; and in (b) the 6°, 8°, and 10°C isotherms by thin black contours. Tropical influence is removed based on the linear regression on the leading principal components of the tropical Indo-Pacific SST and tropical Atlantic SST. Focusing on interannual to longer time scales, Kwon and Joyce (2013) used lead–lag regression analysis to find a significant relationship between the GSI and the North Atlantic SST when the

Full access
R. Justin Small, Frank O. Bryan, Stuart P. Bishop, and Robert A. Tomas

://doi.org/10.1175/JCLI-D-12-00062.1 . 10.1175/JCLI-D-12-00062.1 Clement , A. , K. Bellomo , L. N. Murphy , M. A. Cane , T. Mauritsen , G. Rädel , and B. Stevens , 2015 : The Atlantic multidecadal oscillation without a role for ocean circulation . Science , 350 , 320 – 324 , https://doi.org/10.1126/science.aab3980 . 10.1126/science.aab3980 Cohen , J. , and P. Cohen , 1983 : Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences . Lawrence Erlbam

Full access
Masayo Ogi, Bunmei Taguchi, Meiji Honda, David G. Barber, and Søren Rysgaard

these marginal seas. Figure 2 shows September sea-ice concentration regressed on the maximum Okhotsk sea-ice coverage in the following winter. The regression and correlation patterns yield a large loading and high correlation over the East Siberian Sea. Figure 3 show the time series of the September sea-ice concentration averaged over the East Siberian Sea (the box area in Fig. 2 ) and the maximum Okhotsk sea ice in the following winter. These time series are hereafter referred to as “the

Full access
Young-Oh Kwon and Terrence M. Joyce

variability of the winter meridional heat fluxes by transient eddies is correlated with the north–south fluctuations of the three ocean fronts, with more detailed analysis in the case of the synoptic transient eddy heat flux and GS. The forcing and response causality is implied from the lag regressions as already explained in section 2d . The regression pattern with the atmosphere leading the ocean index is interpreted as the atmospheric pattern responsible for (i.e., forcing) the changes in the ocean

Full access