• Belyshev, A. P., , Klevantsov Yu P. , , and Rozhkov V. A. , 1983: Probability Analysis of the Sea Currents. (in Russian). Gidrometeoizdat, 264 pp.

    • Search Google Scholar
    • Export Citation
  • Cherniawsky, J. Y., , Crawford W. R. , , Nikitin O. , , and Carmack E. C. , 2005: Bering Strait transports from satellite altimetry. J. Mar. Res., 63 , 887900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooley, W. W., , and Lohnes P. R. , 1971: Multivariate Data Analysis. John Wiley and Sons, 364 pp.

  • Emery, W. J., , and Thomson R. E. , 2003: Data Analysis Methods in Physical Oceanography. 2d ed. Elsevier, 638 pp.

  • Fissel, D. B., , and Tang C. L. , 1991: Response of sea ice drift to wind forcing on the northeastern Newfoundland shelf. J. Geophys. Res., 96 , C10. 1839718409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fowler, C. F., , Emery W. J. , , and Maslanik J. A. , 2004: Satellite-derived evolution of Arctic sea ice age: October 1978 to March 2003. IEEE Geophys. Remote Sens. Lett., 1 , 7174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1982: Atmosphere–Ocean Dynamics. Academic Press, 662 pp.

  • Greenan, B. J. W., , and Prinsenberg S. J. , 1998: Wind forcing of ice cover in the Labrador shelf marginal ice zone. Atmos.–Ocean, 36 , 2. 7193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leppäranta, M., 2005: The Drift of Sea Ice. Springer, 266 pp.

  • Maxwell, A. E., 1977: Multivariate Analysis in Behavioural Research. Chapman and Hall, 164 pp.

  • Overland, J. E., , and Pease C. H. , 1988: Modeling ice dynamics of coastal seas. J. Geophys. Res., 93 , C12. 1561915637.

  • Pokrashenko, S. A., , Truskov P. A. , , and Yakunin L. P. , 1987: Investigation of sea-ice drift on the shelf of Sakhalin Island using the radar methods. (in Russian). Proc. (Tr.) Far Eastern Res. Inst. (DVNII), 36 , 4952.

    • Search Google Scholar
    • Export Citation
  • Preller, R. H., , and Hogan P. J. , 1998: Oceanography of the Sea of Okhotsk and the Japan/East Sea. The Sea: The Global Coastal Ocean, A. R. Robinson and K. H. Brink, Eds., Regional Studies and Syntheses, Vol. 11, John Wiley and Sons, 429–481.

    • Search Google Scholar
    • Export Citation
  • Rigor, I., , and Wallace J. M. , 2004: Variations in the age of the Arctic sea-ice and summer sea-ice extent. Geophys. Res. Lett., 31 .L09401, doi:10.1029/2004GL019492.

    • Search Google Scholar
    • Export Citation
  • Shevchenko, G. V., , Rabinovich A. B. , , and Thomson R. E. , 2004: Sea-ice drift on the northeastern shelf of Sakhalin Island. J. Phys. Oceanogr., 34 , 24702491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tambovsky, V. S., , Tikhonchuk E. A. , , and Shevchenko G. V. , 2001: Characteristics of morphometry and dynamics of sea ice on the northeastern shelf of Sakhalin Island. 16th Int. Symp. on the Okhotsk Sea Ice, Mombetsu, Hokkaido, Japan, The Okhotsk Sea and Cold Ocean Research Association, 356–390.

  • Thorndike, A. S., 1986: Kinematics of the sea ice. The Geophysics of Sea Ice, N. Untersteiner, Ed., Plenum, 489–549.

  • Thorndike, A. S., , and Colony R. , 1982: Sea ice motion response to geostrophic winds. J. Geophys. Res., 87 , C8. 58455852.

  • Wadhams, P., 2000: Ice in the Ocean. Gordon and Breach, 351 pp.

  • Wang, D-P., , Oey L-Y. , , Ezer T. , , and Hamilton P. , 2003: Near-surface currents in DeSoto Canyon (1997–99): Comparison of current meters, satellite observation, and model simulation. J. Phys. Oceanogr., 33 , 313326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • View in gallery

    Test case C1. (a) Response ellipse describing rectilinear (one-dimensional) response of drift velocity (ice drift or ocean current “drift”) to the wind. Letters W, N, E, and S give the direction of the drift velocity toward the west, north, east, and south, respectively. Numbers 0°, 45°, . . . , 315° indicate the direction of the wind; Amax and ϕmax denote the magnitude of the maximum response and its direction, respectively. (b) Variations of the drift velocity response (wind factor) and turning angle as functions of the wind direction; λ1 and λ2 denote the eigenvalues, and ϕ1 and ϕ2 are the corresponding directions.

  • View in gallery

    Same as in Fig. 1, but for test case C2 describing a symmetric response of the drift velocity to the wind. Eigenvectors V1 and V2 are orthogonal with eigenvalues λ1 and λ2; Amax and Amin denote the magnitude of the maximum and minimum responses, and ϕmax and ϕmin indicate the corresponding directions of the wind. Shaded areas denote zones of positive turning angles.

  • View in gallery

    Same as in Fig. 1, but for test case C3 describing a nonsymmetric response of the drift velocity to the wind.

  • View in gallery

    Same as in Fig. 1, but for the test case C4 for which there is only one eigenvector, λ1 = λ2 = 1.50. In this case, the turning angle is always positive, except for two zero-value points of the eigenvector.

  • View in gallery

    Same as in Fig. 1, but for test case C5 for which there are no eigenvalues and the turning angle is always positive.

  • View in gallery

    Same as in Fig. 1, but for test case C6, describing an isotropic response of the drift velocity to the wind. Both the wind factor α0 = 2.24 and the turning angle θ = 26.6° are spatially uniform.

  • View in gallery

    Response ellipses for ice drift motions recorded at coastal radar station (CRS) Odoptu on the northeastern shelf of Sakhalin Island (Sea of Okhotsk) for three time periods: (a) 31 Mar–17 Apr 1993; (b) 18 Apr–6 May 1993; and (c) 7–25 May 1993. Numbers 0°, 45°, . . . , 315° indicate the direction of the wind; Amax and ϕmax denote, respectively, the magnitude of the maximum response and its direction (semimajor ellipse axes), while Amin denotes the minimum response (semimajor ellipse axes). The arrows in (a) and (b) indicate the eigenvectors associated with eigenvalues λ1 and λ2. Shaded areas denote zones of positive turning angles.

  • View in gallery

    Variations of ice drift response (wind factor) and turning angle as functions of the wind direction at CRS Odoptu, northeastern shelf of Sakhalin Island, for three time periods: (a) 31 Mar–17 Apr 1993; (b) 18 Apr–6 May 1993; and (c) 7–25 May 1993. The corresponding response ellipses are shown in Fig. 7. Here λ1 and λ2 denote the eigenvalues; ϕ1 and ϕ2 are the corresponding directions; Amax and Amin give the magnitude of the maximum and minimum responses; and ϕmax and ϕmin indicate the corresponding wind directions. Shaded areas denote zones of positive turning angles.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 29 29 8
PDF Downloads 18 18 2

Sea Ice and Current Response to the Wind: A Vector Regressional Analysis Approach

View More View Less
  • 1 Department of Fisheries and Oceans, Institute of Ocean Sciences, Sidney, British Columbia, Canada, and P. P. Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia
  • | 2 Far Eastern Branch, Institute of Marine Geology and Geophysics, Russian Academy of Sciences, Yuzhno-Sakhalinsk, Russia
  • | 3 Department of Fisheries and Oceans, Institute of Ocean Sciences, Sidney, British Columbia, Canada
© Get Permissions
Full access

Abstract

The authors describe a two-dimensional (vector) regressional model for examining the anisotropic response of ice drift and ocean current velocity (“drift velocity”) to surface wind forcing. Illustration of the method is limited to sea ice response. The principal mathematical and physical properties of the model are outlined, together with estimates of the “response matrices” and the corresponding “response ellipses” (drift velocity response to a unity wind velocity forcing). For each direction, ϕ, of the wind vector the method describes a corresponding “wind factor” α(ϕ) (relative drift speed) and “turning angle” θ(ϕ) (the angle between the drift velocity and wind vector). The major ellipse axis corresponds to the direction of the “effective wind” (ϕ = ϕmax) and the minor axis to the direction of the “noneffective” wind. The eigenvectors of the response matrix define wind directions that are the same as the wind-induced drift velocity directions. Depending on the water depth and offshore distance, six analytical cases are possible, ranging from rectilinear response ellipses near the coast to purely circular response ellipses in the open ocean. The model is used to examine ice drift along the western shelf of Sakhalin Island (Sea of Okhotsk). Responses derived from the vector regression (four parameter) method are less constrained and therefore more representative of wind-induced surface motions than those derived using the traditional complex transfer function (two parameter) approach.

Corresponding author address: Richard E. Thomson, Department of Fisheries and Oceans, Institute of Ocean Sciences, 9860 West Saanich Road, Sidney, BC V8L 4B2, Canada. Email: thompsonr@pac.dfo-mgo.gc.ca

Abstract

The authors describe a two-dimensional (vector) regressional model for examining the anisotropic response of ice drift and ocean current velocity (“drift velocity”) to surface wind forcing. Illustration of the method is limited to sea ice response. The principal mathematical and physical properties of the model are outlined, together with estimates of the “response matrices” and the corresponding “response ellipses” (drift velocity response to a unity wind velocity forcing). For each direction, ϕ, of the wind vector the method describes a corresponding “wind factor” α(ϕ) (relative drift speed) and “turning angle” θ(ϕ) (the angle between the drift velocity and wind vector). The major ellipse axis corresponds to the direction of the “effective wind” (ϕ = ϕmax) and the minor axis to the direction of the “noneffective” wind. The eigenvectors of the response matrix define wind directions that are the same as the wind-induced drift velocity directions. Depending on the water depth and offshore distance, six analytical cases are possible, ranging from rectilinear response ellipses near the coast to purely circular response ellipses in the open ocean. The model is used to examine ice drift along the western shelf of Sakhalin Island (Sea of Okhotsk). Responses derived from the vector regression (four parameter) method are less constrained and therefore more representative of wind-induced surface motions than those derived using the traditional complex transfer function (two parameter) approach.

Corresponding author address: Richard E. Thomson, Department of Fisheries and Oceans, Institute of Ocean Sciences, 9860 West Saanich Road, Sidney, BC V8L 4B2, Canada. Email: thompsonr@pac.dfo-mgo.gc.ca

1. Introduction

Accurate predictions of ice drift and currents for shelf and coastal regions are important for navigation, commercial fisheries, oil and gas exploration, climatic studies, and other human endeavor (Fowler et al. 2004; Leppäranta 2005). Because the wind is the major factor affecting oceanic motions along continental margins (Gill 1982; Wadhams 2000; Rigor and Wallace 2004), it is important to know the wind response in these regions as precisely as possible. Complicated topography, resonant and wave-trapping effects, and the formation of land-fast ice (for freezing areas) are among the factors that make continental margins among the most challenging areas for coastal ocean prediction research (Wang et al. 2003). At the same time, the availability of near-real-time wind observations for these areas allow effective application of regression models both for diagnostic and forecasting purposes (cf. Thorndike and Colony 1982; Fissel and Tang 1991).

The commonly used regression analysis of two vector series is based on the complex functional relationship between input and output vector series (cf. Greenan and Prinsenberg 1998); specifically,
i1520-0426-24-6-1086-e1
where V = (U, V) is the input vector series (wind), u = (u, υ) is the output vector series (ice drift or current velocity; herein “drift velocity”) and α = a + ib is a complex coefficient determined using a least squares regressional fit for the entire suite of wind and drift velocity observations. In our Cartesian coordinate system, u and U are positive to the east, and υ and V are positive to the north. This approach is widely used for the analysis of ice motions (cf. Thorndike and Colony 1982; Fissel and Tang 1991; Greenan and Prinsenberg 1998) and ocean currents (Cherniawsky et al. 2005).
Equation (1) also can be written
i1520-0426-24-6-1086-e2
where α0 = |α| = a2 + b2 is the wind factor, and θ0 = −arctan(b/a) is the turning angle of the drift velocity direction relative to the wind direction. The turning angle is measured clockwise (counterclockwise) to the wind in the Northern (Southern) Hemisphere. The specific values α0 = 0.02 (a wind of 1 m s−1 forces a 2 cm s−1 ice drift) and θ0 = 28°, which describe the relationship between wind and free ice drift in the open ocean, denote the “Nansen–Ekman ice drift law” (cf. Thorndike 1986; Wadhams 2000). Similar values are obtained for surface currents (α0 = 0.01 − 0.04, θ0 = 10°–40°). The problem is that Eqs. (1) and (2) describe an isotropic response of ocean motions to wind for which the parameters α0 and θ0 are invariant to the wind direction, ϕ. This requirement is counter to Overland and Pease (1988) who observed a strongly anisotropic response of ice drift to the wind near coastal boundaries. Similarly, Fissel and Tang (1991) established that proximity to the coast and direction of the wind relative to the coastline are major factors influencing variability in the wind factor and turning angle. To account for these effects, and to better define the dependence of α(ϕ) and θ(ϕ) on wind direction, we consider a two-dimensional (matrix) regression model based on regional winds. Such models are widely used in oceanography and meteorology for analysis and prediction of vector processes (cf. Emery and Thomson 2003).

The paper is organized as follows. In section 2, we examine the principal mathematical properties of our regressional model and estimate the response matrices and corresponding response ellipses (the drift velocity response to a unity wind velocity forcing). In section 3, we consider six different wind response cases beginning with the extreme case of a flat response ellipse near the coast (describing fully rectilinear motion) and ending with the other extreme of a circular ellipse (describing isotropic motion) in the open ocean. The model has been used to examine radar-measured ice drift motions on the northeastern shelf of Sakhalin Island in the Sea of Okhotsk and moored current meter data on the west coast of Canada. Results for the sea ice data only are reported in this study (section 4). Comparison of our four-parameter vector regression model and the more traditional two-parameter complex transfer function model for ice-drift data enables us to characterize the advantages and limitations of each model. The main results are discussed in section 5.

2. Vector regressional model

The effect of coastlines on winds and wind-driven currents in the coastal zone can be quantified using a two-dimensional vector model, which relates the wind V(t) and the free ice drift or current u(t) through the two-dimensional regression equation (cf. Cooley and Lohnes 1971; Maxwell 1977):
i1520-0426-24-6-1086-e3a
Here
i1520-0426-24-6-1086-e3b
and aij are the regression (response) coefficients linking the cross-shore (u) and alongshore (υ) components of drift velocity with the corresponding components (U, V) of the wind velocity; coefficients (ɛu, ɛυ) denote random noise. [Without loss of generality, we assume that both the drift velocity and wind have zero mean speed; thus, Eq. (3a) does not require a term representing the mean drift velocity.]
The coefficients aij in (3) are found through a least squares method that minimizes the expressions
i1520-0426-24-6-1086-e4a
i1520-0426-24-6-1086-e4b
From (4) we obtain the following matrix relation (Cooley and Lohnes 1971):
i1520-0426-24-6-1086-e5a
where 𝗔 is defined by expression (3b), 𝗗 is the autocorrelation matrix of the input variable (wind), and 𝗥 is the cross-correlation matrix between input and output variables:
i1520-0426-24-6-1086-e5b
where the angle brackets 〈〉 denotes a time average. Terms involving random noise average to zero. From (5), it follows that
i1520-0426-24-6-1086-e6
where 𝗗−1 is the inverse of 𝗗. Equation (6) defines the four response coefficients: a11, a12, a21, and a22. We note that, while the matrix 𝗗 is symmetric, the matrix 𝗥 is generally nonsymmetric and, consequently, the matrix 𝗔 is also generally nonsymmetric. This nonsymmetry complicates the wind–ice and wind–current relationships.

a. Response ellipses

The regression coefficients in model (3) define a response ellipse. This ellipse is the curve traced by the tip of the output response vector α = (αu, αυ) of the drift velocity through one complete rotation of a unity amplitude input wind vector (U0, V0) = ( sinϕ, cosϕ), where ϕ is the angle of the unity wind vector measured clockwise from north:
i1520-0426-24-6-1086-e7a
i1520-0426-24-6-1086-e7b
According to (7), for each direction of the wind vector ϕ, there is a corresponding “wind factor” α, having relative drift speed α = |α(ϕ)| and direction ϕ = ϕ(ϕ). The angle between the drift velocity and wind vectors is the turning angle, θ = ϕϕ. For the commonly specified case of an isotropic response, αu = αυ = const, θ = const, so that the response ellipse becomes a circle.
In general, each response ellipse is described by four invariant parameters: the semimajor (α = Amax) and semiminor (α = Amin) axes corresponding to the maximum and minimum ice drift (current) responses, respectively; the orientation of the semimajor axis (ϕ = ϕmax); and the direction of the “effective wind” (ϕ = ϕmax), the wind direction producing the maximum ice drift or current response. These four parameters are as follows.
  • Direction of the “effective (noneffective) wind” ϕmax (ϕmin), the wind direction angle producing the maximum (minimum) drift response:
i1520-0426-24-6-1086-e8a
  • where ϕmax (ϕmin) is measured clockwise from north. Directions of ϕmax and ϕmin are related by ϕmin = ϕmax ± 90°.
  • Semimajor ellipse axes:
i1520-0426-24-6-1086-e8b
  • Semiminor ellipse axes:
i1520-0426-24-6-1086-e8c
  • Orientation of the semimajor axis:
i1520-0426-24-6-1086-e8d
The orientation of the semiminor axis is given by ϕmin = ϕmax ± 90°.

b. Eigenvectors of matrix 𝗔

If 𝗔 is a square matrix, V is a column vector, and λ is a scalar quantity (eigenvalue) such that
i1520-0426-24-6-1086-e9
where
i1520-0426-24-6-1086-eq1
is the identity matrix, then V is said to be an eigenvector (latent vector) of the matrix 𝗔 (cf. Maxwell 1977). Each eigenvector is associated with a corresponding eigenvalue. The eigenvectors of the matrix 𝗔 determine the transformation from the vector V to the vector u. Because the directions of the eigenvectors are unchanged by this transformation (i.e., ϕλ = ϕλ), the eigenvectors give the direction along which the wind direction coincides with that of the wind-generated drift velocity. The eigenvalues, λ, are found from the characteristic equation (cf. Maxwell 1977):
i1520-0426-24-6-1086-e10
Equation (10) yields the quadratic equation
i1520-0426-24-6-1086-e11
which we use to define properties of the matrix 𝗔.
There are three invariants of the matrix 𝗔 determining the main properties of the transformation from wind to drift velocity (cf. Belyshev et al. 1983):
i1520-0426-24-6-1086-e12a
i1520-0426-24-6-1086-e12b
i1520-0426-24-6-1086-e12c
The characteristic Eq. (11) has two roots (eigenvalues):
i1520-0426-24-6-1086-e13a
whereby
i1520-0426-24-6-1086-e13b
The directions of the eigenvectors are found from (10a):
i1520-0426-24-6-1086-e14
whereby
i1520-0426-24-6-1086-e15
If the matrix 𝗔 is symmetric (J3 = 0), then for given eigenvalues λ1 and λ2, eigenvectors V1 and V2 are orthogonal and correspond to the principal ellipse axes associated with the maximum and minimum response (amplification) of the output series (drift velocity) relative to the input series (wind). However, if J3 ≠ 0, then the eigenvectors of the response ellipses are nonorthogonal and are rotated relative to the principal ellipse axes. Depending on the sign of J3, the turning angles θ have mainly positive or mainly negative values. The angle between two eigenvectors, which is equal to 90° if J3 = 0, becomes smaller with increasing |J3|. In the limit λ1 = λ2, there is only one eigenvector, and the turning angle always has the same sign except for the two zero-value points coincident with the eigenvector. The one-eigenvector condition (λ1 = λ2) may be presented as
i1520-0426-24-6-1086-e16
where D is the discriminant of Eq. (11). Case (16) is possible only if a12 and a21 have opposite signs. Moreover, if J21 < 4J2, that is
i1520-0426-24-6-1086-e17
then Eq. (13) does not have real roots and the turning angle has always the same sign.

c. Asymptotic cases

The two limiting cases are the flat ellipse (rectilinear) response and the circle (isotropic) response.

1) Flat ellipse (one-dimensional motion)

This case occurs when the matrix 𝗔 is singular, that is, when the determinant
i1520-0426-24-6-1086-e18
A two-dimensional matrix has a zero determinant if one row (column) is proportional to the other row (column),
i1520-0426-24-6-1086-e19
where k is a constant. This case describes wind-induced rectilinear motions, whereby regardless of the wind direction, the ice (or water) moves back and forth along one direction only. Motions of this type are possible in a narrow channel or near the coast, where the wind-induced motions are restricted by the boundary. The magnitude and sign of k determine the direction of these motions. For example, if k = 0, then the direction is west–east; if k = 1, then the direction is southwest–northeast.
Using (8a) and (18), and several simple transformations, the directions of the most and least effective wind (i.e., the wind direction producing the maximum and minimum drift response, respectively) are found from the equation
i1520-0426-24-6-1086-e20
It is obvious from (7a) and (18) that when the first term in the brackets of (20) is equal to zero there is no oceanic response to the wind. In this case,
i1520-0426-24-6-1086-e21
whereby winds in the direction ϕ = ϕ0 ± 180° produce no oceanic response; for any other direction, there is always a nonzero response. The direction of the maximum response may be found by setting the second term of (20) to zero:
i1520-0426-24-6-1086-e22
from which we obtain
i1520-0426-24-6-1086-e23
From (8b) and (23) it follows that the magnitude of the maximum response has the simple form,
i1520-0426-24-6-1086-e24
and that the corresponding direction of the maximum drift velocity depends only on coefficient k:
i1520-0426-24-6-1086-e25
More specifically, any drift motion in this case, including the maximum response, is in the direction ϕ = ±arctan(1/k). According to (13), the matrix (19) for this case has two eigenvectors:
i1520-0426-24-6-1086-e26a
i1520-0426-24-6-1086-e26b
Note that, in contrast to the earlier cases considered above, the case of rectilinear motion is described by three, not four, independent parameters: a11, a12, and k (or ϕmax, Amax, and ϕmax).

2) Circular ellipse (isotropic response)

The second limiting case is the isotropic response, for which the drift response ellipse becomes a circle. This situation occurs when the matrix 𝗔 is antisymmetric (a21 = −a12) and the main diagonal coefficients are equal (a22 = a11); namely,
i1520-0426-24-6-1086-e27
This case is equivalent to case (1) with 𝗔 = α, whereby matrix (27) takes the form
i1520-0426-24-6-1086-e28
where α0 = a211 + a212 and θ = arctan(a12/a11). Matrix (28) describes a combination of two elementary transformations, stretching and rotation, so that the isotropic response is described by only two independent parameters: a11 and a12 (or α0 and θ). In general, matrix (27) has no eigenvectors. The sole exception is the case of isotropic response without turning (θ = 0), which occurs when a12 = 0. In this case, wind vectors and response vectors always have the same directions and all vectors are the eigenvectors. The maximum turning angle θ = 90° (which is the same for all wind directions) corresponds to the case when the main diagonal elements of the matrix (27) are equal to zero (a11 = 0).

3. Test examples

We now consider matrices 𝗔 whose parameters (regression coefficients aij) are representative of the six different cases of ice or current response to the wind discussed in the previous section:
i1520-0426-24-6-1086-eq2
Here, the coefficients aij are expressed as the ratio of drift speed in CGS units to wind speed in MKS units [i.e., in cm s−1 (m s−1)−1], corresponding to a percentage. Table 1 presents the derived matrix invariants, ellipse parameters, and eigenvector parameters for the six cases.

a. Case C1

The determinant of the matrix 𝗔 is equal to zero (J2 = 0), so the matrix is for a rectilinear (one-dimensional) response (Fig. 1) of type (19) with k = 1.6. In near-shore regions, the orientation for this flat ellipse response (ϕmax = 32° for the present case) would typically coincide with the orientation of the coastline, indicating that regardless of the wind direction, only alongshore motions are possible. Note that, in general, a rectilinear response does not mean a symmetric response relative to the wind. Because of the earth’s rotation, the turning angle in the Northern (Southern) Hemisphere is expected to be mainly positive (negative), such that the response vector is directed clockwise (counterclockwise) relative to the wind vector.

b. Case C2

The matrix 𝗔 is symmetric (J3 = 0), so that the eigenvectors V1 and V2 for given eigenvalues λ1 and λ2 are orthogonal and correspond to the principal ellipse axes (λ1 = Amax = 2.21, ϕ1 = ϕmax = 22.5°; λ2 = Amin = 0.79, ϕ2 = ϕmin = 112.5°) (Fig. 2). The angle between the two eigenvectors is 90°. Because of the earth’s rotation, this symmetric response case is possible only near the equator, where the Coriolis parameter f ≈ 0.

c. Case C3

For this case, J3 ≠ 0 and the matrix 𝗔 is nonsymmetric, so the eigenvectors are nonorthogonal and do not correspond to the principal ellipse axes (Fig. 3). Because J3 is positive in our example (J3 = 0.5), the turning angles θ are mainly positive. The angle between the two eigenvectors is equal to 73.9°. For actual ocean conditions, we would expect the turning angle to become increasingly smaller with increasing offshore distance.

d. Case C4

For this case, the discriminant (16) is equal to zero (D = 0), so there is only one eigenvector (λ = λ1 = λ2 = 1.50). The turning angle is always positive except for two zero-value points corresponding to the eigenvector (Fig. 4). This situation is observed in confined regions near the coast.

e. Case C5

For this case, the discriminant is negative (D = −1.0), so that (13) does not have real roots and the turning angle is always positive (Fig. 5). We consider this representative of wind-driven drift motions for offshore regions in the Northern Hemisphere (for the Southern Hemisphere, the turning angle will be negative).

f. Case C6

For this case, the matrix 𝗔 is antisymmetric (a21 = −a12 = 1.0) and the main diagonal coefficients are equal (a22 = a11 = 2.0). In this isotropic response example, the wind factor α0 = 2.24 and turning angle θ = 26.6° are uniform (Fig. 6). This situation corresponds to open-ocean regions where the influence of coasts is negligible and the current or ice drift response to the wind is the same regardless of wind direction.

The five cases C1, C3, C4, C5, and C6 characterize how the response of surface drift currents to the wind changes with increasing offshore distance, from purely rectilinear (alongshore) wind-induced motions near the coast (C1) to almost circular responses in the open ocean (C6). (Case C2 applies only to equatorial regions.) In general, ice drift response changes in a similar way to the ocean currents. However, ice drift response is also dependent on ice concentration (Shevchenko et al. 2004). Higher ice concentration strengthens the internal ice stress, leading to marked attenuation in ice motions, especially in the cross-shore direction. In contrast, reduced ice concentration leads to intensification of cross-shore motions, analogous to the effect of increased offshore distance.

4. Ice drift on the Sakhalin shelf

To determine the ice drift response to the wind under observed oceanic conditions, we have used data collected in 1985–95 from coastal radar station “Odoptu” on the northeastern coast of Sakhalin Island in the Sea of Okhotsk (Pokrashenko et al. 1987; Shevchenko et al. 2004). The ice drift measurements were taken within 1-km circular areas at distances 4, 8, 12, and 16 km seaward of the coast. Present focus is on areas S1 and S4 (4 and 16 km, respectively). Ice drift vectors were determined three times per hour using major radar-reflecting ice flow targets located within radar coverage circles. Data were then interpolated and averaged into hourly time series of eastward (cross-shore, u) and northward (alongshore, υ) components of velocity. Local winds were measured simultaneously at hourly intervals at 10-m elevation. The year 1993 had the most extensive ice cover (Preller and Hogan 1998) and, therefore, the longest time series of ice drift. A continuous 73-day time series was collected during the period 12 March to 25 May 1993. The near-perfect agreement among estimated tidal motions from the various radar coverage circles signifies high-quality ice drift data. Tidal motions and mean drift were calculated and subtracted from the initial series.

Following removal of the tides, we next low-pass-filtered simultaneous series of wind and residual (detided) ice velocities to remove fluctuations with frequencies ≥1 cpd. The resulting low-frequency local wind and ice velocities were highly correlated. To quantify the influence of ice concentration on the ice drift response to wind, we then divided the data into four sequential 18-day segments characterized by distinctly different ice type and concentration (Tambovsky et al. 2001): 1) 12–30 March had an ice concentration of approximately 80%–90% with the ice field consisting of large and small broken floes; 2) 31 March–17 April had the highest ice concentration (95%–100%), consisting of large ice fields; 3) 18 April–6 May had reduced concentration (60%–80%) and diminished floe sizes; and 4) 7–25 May was associated with intensive melting with ice concentration reduced by ∼40%–50%. For each of the four time segments and two observational circles (S1 and S4), we computed the matrix 𝗔 for eight different cases:
  • Location S1:
i1520-0426-24-6-1086-eq3
  • Location S4:
i1520-0426-24-6-1086-eq4
where the matrix coefficients aij are in units of cm s−1 (m s−1)−1, or percent.

Table 2 gives the matrix invariants, response ellipse parameters, and eigenvectors derived from expressions (7)(28). For the three specific examples (cases S4-2, S4-3, and S4-4) presented in Figs. 7 and 8, the response ellipses are oriented along the coastline (ϕmax = 9.5°–23.8° counterclockwise from the north). The pronounced flatness of the response ellipses indicates that the ice motions due to the wind are strongly anisotropic, with the ice response in the alongshore direction much greater than in the cross-shore direction (2.9%–6.1% versus 0.2%–1.9%, respectively). The alongshore values (2.6%–5.4%) of the response coefficients (the wind factor) are similar to those obtained by Fissel and Tang (1991) for the Newfoundland shelf. Response coefficients for the remote offshore observational area (S4) are greater by about 15%–20% than for the areas closest to shore (S1).

The above results reveal pronounced temporal changes in the ice drift response to the wind, apparently due to changes in ice properties. During the period of the highest ice concentration (period 2), the response ellipses are almost flat, indicating that the ice drift response was rectilinear (alongshore) (Fig. 7a). There are two eigenvalues, but the turning angles are mainly positive (Fig. 8a). In general, these ice-response ellipses resemble those for case C1 in section 3. For the early spring (period 1), and especially during the late spring (period 3), the response ellipses have larger magnitude and are more circular (Fig. 7b), indicating more intense cross-shore ice motions. Similarly, for the second period, the S1 and S4 matrices have two eigenvalues and a prevalence of positive turning angles (Fig. 8b). The response ellipses were of type C3. Finally, during the late spring (period 4), the response ellipses changed from flat to oval, similar to case C5 (Fig. 7c). For period 4, the matrices for both S1-4 and S4-4 had no eigenvalues and all turning angles were positive (Fig. 8c). According to our analysis, the last period was a time of free ice drift, while the three other periods were times of high internal ice stress and influence of the coast.

To compare the effectiveness of our vector regression model (3) versus the traditional complex transfer function model (1), we applied both models to the eight cases presented in Table 2 and estimated the residual (unexplained) variance. The results (Table 3) demonstrate that, for both offshore distances (S1, S4) and for the first three ice period segments (periods S1-1–S1-3 and S4-1–S4-3) (cases with substantial anisotropy of wind-induced ice motions), the vector regression model accounts for significantly greater variance than the traditional model. For example, for the strongest anisotropic case (period 2; Fig. 7a), the residual variances for the vector regression and traditional models have respective values: σ2res(S1-2) = 28.5% and 57.2%; σ2res(S4-2) = 17.9% and 54.2%. For our vector model, the residual variance becomes smaller for both velocity components (u, υ), while for the traditional model the residual variance of the cross-shore component (u) is increased relative to the initial value (Table 3). Thus, for markedly anisotropic ice motions, the complex transfer function model cannot accurately approximate the responses of both velocity components simultaneously, and performs well for highly energetic alongshore (υ) components of drift only. This contrasts with the last time segment (period 4), when the anisotropy was the smallest and the response ellipses were more circular (Fig. 7c), for which both models give almost identical results.

For the vector regression model, we define the mean wind factor α and the mean turning angle θ as
i1520-0426-24-6-1086-e29
Comparison of these parameters with those obtained for the traditional model (Table 3) indicates that model parameters are in reasonable agreement. However, the variance in α is significantly smaller than that of α0 for the traditional model (1.88–2.41 and 1.62–3.12, respectively). For all eight cases, the “mean turning angle” (θ) was positive and larger than θ0.

5. Summary and conclusions

Understanding the response of surface ice and currents to the wind is of critical importance in oceanography and marine engineering. The traditional approach for relating drift velocity to the wind is to assume a simple transfer function relationship involving two parameters: an amplitude variable that scales the drift speed to the wind speed and a directional variable that allows for an angular rotation of the drift velocity direction relative to the wind direction. Unfortunately, the two-parameter model, which was originally formulated to describe ice and current responses in the open ocean, is isotropic. We consider this unrealistic for coastal regions. In reality, how the ocean surface responds to the local winds depends on factors other than the wind, such as the orientation of the coastline and the regional bottom topography. For this reason, the assumption of isotropic response is likely invalid near the coast. To account for such effects, we have applied a two-dimensional (vector) regression model. In this model, the relationship between the wind and drift velocity (ice drift or current velocity) is described by four independent regression (response) coefficients, aij, linking the cross-shore (u) and alongshore (υ) components of the drift to the corresponding components (U, V) of the wind velocity. For each direction of the wind vector, ϕ, the method prescribes a “wind factor” α(ϕ) (relative drift speed) and “turning angle” θ(ϕ) (the angle between the drift velocity and wind vector).

Our description of the vector regressional model begins with the principal mathematical and physical properties of the model together with estimates of the response matrices and the corresponding response ellipses (ice or current velocity response to a unity wind velocity forcing). The major ellipse axes coincide with the direction of the “effective wind” (ϕ = ϕmax) (the wind direction generating the strongest ice or current motions) while the minor axes of the response ellipse coincide with the direction of the “noneffective” wind (ϕ = ϕmin) (the wind producing the weakest motions). In most cases, ellipses have corresponding eigenvectors. These eigenvectors denote the directions for which the wind and the induced drift motions are aligned. As a consequence, the eigenvectors separate zones, which have different signs for the turning angles, θ(ϕ). The angles are positive when the drift vector is directed to the right of the surface wind and negative when it is directed to the left of the wind. This anisotropy arises directly from the proximity to coastal boundaries. As we illustrate through the test examples in section 3, there are six different solutions (cases) for the matrix discriminant defined in section 2, ranging from a flat (rectilinear) response ellipse near the coast to an entirely circular response ellipse in the open ocean.

The vector regression model was applied to observed ice drift motions on the shelf of Sakhalin Island (section 4). Because the wind is the main factor determining low-frequency ice motions, the ice drift series is ideal for testing various models of wind-induced motions. These data not only have high signal/noise ratio, but they also indicate that the influence of other factors is relatively small (Wadhams 2000; Leppäranta 2005). The high-quality long-term ice drift data obtained in 1993 on the Sakhalin Island shelf (Shevchenko et al. 2004) enabled us to effectively estimate the efficiency of the four-parameter vector regression technique compared with the more traditional two-parameter complex transfer function approach. Estimates for different time periods and different offshore distances find that the vector regression model explains from 54% to 86% of the ice drift variance, while the traditional model only explains from 39% to 70% of the observed ice drift variance. For sea surface ice drift, ice concentration is a major factor influencing the response to the local wind field.

Because of its greater number of free coefficients, the four-parameter vector regression model should yield a smaller residual variance than the traditional two-parameter model. However, because the number of degrees of freedom in the dataset being analyzed decreases with an increase in the number of coefficients, calculation of the vector regression coefficients to the same level of confidence as the traditional model coefficients requires a slightly longer duration time series. The stability of the response ellipse parameters (relative to small changes in the parameters of the input functions) is the main criterion for determining the reliability of the results. Good agreement between the response ellipses for the Sakhalin shelf for two independent observation sites (S1 at 4 km offshore and S4 at 16 km offshore) and four different time periods (12 March–30 March, 31 March–17 April, 18 April–6 May, and 7 May–25 May 1993) indicates that the results in Tables 2 and 3 and Figs. 7 and 8 are highly reliable. It is also important to note that the structure of these ellipses have physical meaning in the sense that they account for the significant difference in ice drift response to alongshore and cross-shore winds. Our results confirm that an anisotropic, vector regression model is better for examining wind–ice and wind–current processes in coastal zone regions than an isotropic, complex transform model. Moreover, the vector regression model is more likely to capture surface dynamical features of the wind response than the traditional model.

Acknowledgments

We thank Josef Cherniawsky and Isaac Fine for useful discussions and Victor Tambovsky for helping us with ice drift data. We further thank Patricia Kimber for helping draft the figures and the two anonymous reviewers for their helpful suggestions and comments.

REFERENCES

  • Belyshev, A. P., , Klevantsov Yu P. , , and Rozhkov V. A. , 1983: Probability Analysis of the Sea Currents. (in Russian). Gidrometeoizdat, 264 pp.

    • Search Google Scholar
    • Export Citation
  • Cherniawsky, J. Y., , Crawford W. R. , , Nikitin O. , , and Carmack E. C. , 2005: Bering Strait transports from satellite altimetry. J. Mar. Res., 63 , 887900.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooley, W. W., , and Lohnes P. R. , 1971: Multivariate Data Analysis. John Wiley and Sons, 364 pp.

  • Emery, W. J., , and Thomson R. E. , 2003: Data Analysis Methods in Physical Oceanography. 2d ed. Elsevier, 638 pp.

  • Fissel, D. B., , and Tang C. L. , 1991: Response of sea ice drift to wind forcing on the northeastern Newfoundland shelf. J. Geophys. Res., 96 , C10. 1839718409.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fowler, C. F., , Emery W. J. , , and Maslanik J. A. , 2004: Satellite-derived evolution of Arctic sea ice age: October 1978 to March 2003. IEEE Geophys. Remote Sens. Lett., 1 , 7174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gill, A. E., 1982: Atmosphere–Ocean Dynamics. Academic Press, 662 pp.

  • Greenan, B. J. W., , and Prinsenberg S. J. , 1998: Wind forcing of ice cover in the Labrador shelf marginal ice zone. Atmos.–Ocean, 36 , 2. 7193.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leppäranta, M., 2005: The Drift of Sea Ice. Springer, 266 pp.

  • Maxwell, A. E., 1977: Multivariate Analysis in Behavioural Research. Chapman and Hall, 164 pp.

  • Overland, J. E., , and Pease C. H. , 1988: Modeling ice dynamics of coastal seas. J. Geophys. Res., 93 , C12. 1561915637.

  • Pokrashenko, S. A., , Truskov P. A. , , and Yakunin L. P. , 1987: Investigation of sea-ice drift on the shelf of Sakhalin Island using the radar methods. (in Russian). Proc. (Tr.) Far Eastern Res. Inst. (DVNII), 36 , 4952.

    • Search Google Scholar
    • Export Citation
  • Preller, R. H., , and Hogan P. J. , 1998: Oceanography of the Sea of Okhotsk and the Japan/East Sea. The Sea: The Global Coastal Ocean, A. R. Robinson and K. H. Brink, Eds., Regional Studies and Syntheses, Vol. 11, John Wiley and Sons, 429–481.

    • Search Google Scholar
    • Export Citation
  • Rigor, I., , and Wallace J. M. , 2004: Variations in the age of the Arctic sea-ice and summer sea-ice extent. Geophys. Res. Lett., 31 .L09401, doi:10.1029/2004GL019492.

    • Search Google Scholar
    • Export Citation
  • Shevchenko, G. V., , Rabinovich A. B. , , and Thomson R. E. , 2004: Sea-ice drift on the northeastern shelf of Sakhalin Island. J. Phys. Oceanogr., 34 , 24702491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tambovsky, V. S., , Tikhonchuk E. A. , , and Shevchenko G. V. , 2001: Characteristics of morphometry and dynamics of sea ice on the northeastern shelf of Sakhalin Island. 16th Int. Symp. on the Okhotsk Sea Ice, Mombetsu, Hokkaido, Japan, The Okhotsk Sea and Cold Ocean Research Association, 356–390.

  • Thorndike, A. S., 1986: Kinematics of the sea ice. The Geophysics of Sea Ice, N. Untersteiner, Ed., Plenum, 489–549.

  • Thorndike, A. S., , and Colony R. , 1982: Sea ice motion response to geostrophic winds. J. Geophys. Res., 87 , C8. 58455852.

  • Wadhams, P., 2000: Ice in the Ocean. Gordon and Breach, 351 pp.

  • Wang, D-P., , Oey L-Y. , , Ezer T. , , and Hamilton P. , 2003: Near-surface currents in DeSoto Canyon (1997–99): Comparison of current meters, satellite observation, and model simulation. J. Phys. Oceanogr., 33 , 313326.

    • Crossref
    • Search Google Scholar
    • Export Citation

Fig. 1.
Fig. 1.

Test case C1. (a) Response ellipse describing rectilinear (one-dimensional) response of drift velocity (ice drift or ocean current “drift”) to the wind. Letters W, N, E, and S give the direction of the drift velocity toward the west, north, east, and south, respectively. Numbers 0°, 45°, . . . , 315° indicate the direction of the wind; Amax and ϕmax denote the magnitude of the maximum response and its direction, respectively. (b) Variations of the drift velocity response (wind factor) and turning angle as functions of the wind direction; λ1 and λ2 denote the eigenvalues, and ϕ1 and ϕ2 are the corresponding directions.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 2.
Fig. 2.

Same as in Fig. 1, but for test case C2 describing a symmetric response of the drift velocity to the wind. Eigenvectors V1 and V2 are orthogonal with eigenvalues λ1 and λ2; Amax and Amin denote the magnitude of the maximum and minimum responses, and ϕmax and ϕmin indicate the corresponding directions of the wind. Shaded areas denote zones of positive turning angles.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 3.
Fig. 3.

Same as in Fig. 1, but for test case C3 describing a nonsymmetric response of the drift velocity to the wind.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 4.
Fig. 4.

Same as in Fig. 1, but for the test case C4 for which there is only one eigenvector, λ1 = λ2 = 1.50. In this case, the turning angle is always positive, except for two zero-value points of the eigenvector.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 5.
Fig. 5.

Same as in Fig. 1, but for test case C5 for which there are no eigenvalues and the turning angle is always positive.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 6.
Fig. 6.

Same as in Fig. 1, but for test case C6, describing an isotropic response of the drift velocity to the wind. Both the wind factor α0 = 2.24 and the turning angle θ = 26.6° are spatially uniform.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 7.
Fig. 7.

Response ellipses for ice drift motions recorded at coastal radar station (CRS) Odoptu on the northeastern shelf of Sakhalin Island (Sea of Okhotsk) for three time periods: (a) 31 Mar–17 Apr 1993; (b) 18 Apr–6 May 1993; and (c) 7–25 May 1993. Numbers 0°, 45°, . . . , 315° indicate the direction of the wind; Amax and ϕmax denote, respectively, the magnitude of the maximum response and its direction (semimajor ellipse axes), while Amin denotes the minimum response (semimajor ellipse axes). The arrows in (a) and (b) indicate the eigenvectors associated with eigenvalues λ1 and λ2. Shaded areas denote zones of positive turning angles.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Fig. 8.
Fig. 8.

Variations of ice drift response (wind factor) and turning angle as functions of the wind direction at CRS Odoptu, northeastern shelf of Sakhalin Island, for three time periods: (a) 31 Mar–17 Apr 1993; (b) 18 Apr–6 May 1993; and (c) 7–25 May 1993. The corresponding response ellipses are shown in Fig. 7. Here λ1 and λ2 denote the eigenvalues; ϕ1 and ϕ2 are the corresponding directions; Amax and Amin give the magnitude of the maximum and minimum responses; and ϕmax and ϕmin indicate the corresponding wind directions. Shaded areas denote zones of positive turning angles.

Citation: Journal of Atmospheric and Oceanic Technology 24, 6; 10.1175/JTECH2015.1

Table 1.

Response ellipse parameters and eigenvectors for the six test examples.

Table 1.
Table 2.

Response ellipse parameters and eigenvectors for ice drift recorded by the CRS Odoptu (northeastern shelf of Sakhalin Island) at two offshore observation circles: S1 (4 km offshore) and S4 (16 km offshore) for four different time periods: 1) 12–30 Mar 1993; 2) 31 Mar–17 Apr 1993; 3) 18 Apr–6 May 1993; 4) 7–25 May 1993.

Table 2.
Table 3.

Initial variance, σ2init = σ2u init + σ2υ init, of ice drift motions recorded in 1993 by the CRS Odoptu (northeastern shelf of Sakhalin Island), residual (unexplained) variance, σ2res = σ2u res + σ2υ res, for vector regression and complex transform models and the model parameters.

Table 3.
Save