Nonstationary seasonality of upper ocean ... - Wiley Online Library

JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, C10015, doi:10.1029/2004JC002330, 2004

Nonstationary seasonality of upper ocean temperature in the California Current Roy Mendelssohn, Franklin B. Schwing, and Steven J. Bograd Pacific Fisheries Environmental Laboratory, National Marine Fisheries Service, NOAA, Pacific Grove, California, USA Received 17 February 2004; revised 15 July 2004; accepted 16 August 2004; published 30 October 2004.

[1] State-space models are used to examine long-term changes in the seasonal amplitude

and phase of upper ocean temperatures from a set of time series representing the meridional and offshore extent of the California Current System (CCS). We use global one-degree summaries from the World Ocean Database at 11 locations and 10 standard depths in the upper 200 m for the period 1950–1993. The seasonality of upper ocean temperature in the CCS is highly nonstationary, with significant interannual to decadal changes in seasonal amplitude and phase apparent over the period of study. The 1950s and early 1990s were characterized by high seasonal variability in upper ocean temperatures, while the intervening years were characterized by a reduced seasonal cycle. Long-term changes in phase were also observed, with seasonal extrema occurring 1–2 months earlier in the year by the 1990s. The leading common seasonal components, dominated by the longer (yearly, 6-month, 4-month) periodicities, explain most of the seasonal fluctuations in the temperature series and partition the variance into well-defined dynamic regions. In particular, long-term changes in seasonality at 30–75 m in the major coastal upwelling centers (34°N–38°N) differed from that observed north of Cape Mendocino, within the Southern California Bight, and farther offshore. The observed spatial patterns suggest that the changes in temperature seasonality off central California reflect a significant lowfrequency modulation of the intensity, timing, and duration of coastal upwelling in the CCS. This nonstationarity of the seasonal temperature cycle is superimposed on both a region-wide warming trend and spatially heterogeneous responses to low-frequency climate events, such as regime shifts and El Ninõ events. Results from this study demonstrate that the character and potential biological impacts of climate variability can only be fully realized by considering a nonstationary, nondeterministic seasonal INDEX TERMS: 4516 Oceanography: Physical: Eastern boundary currents; 4215 Oceanography: cycle. General: Climate and interannual variability (3309); 4227 Oceanography: General: Diurnal, seasonal, and annual cycles; 4279 Oceanography: General: Upwelling and convergences; KEYWORDS: CCS, state-space models, seasonal cycles, climate variability, upwelling, EBCs Citation: Mendelssohn, R., F. B. Schwing, and S. J. Bograd (2004), Nonstationary seasonality of upper ocean temperature in the California Current, J. Geophys. Res., 109, C10015, doi:10.1029/2004JC002330.

1. Introduction [2] A series of recent papers [Mendelssohn and Schwing, 1997, 2002; Schwing and Mendelssohn, 1997, 1998; Schwing et al., 1998] used state-space models to examine the seasonal and low-frequency behavior of surface environmental variables within the California Current System (CCS). The state-space models decompose the observed data into a nonparametric trend term, a nonstationary seasonal component, and an autoregressive component, which allows the variability in the time series to be separated into seasonal and long-term contributions. These studies have revealed important trends and change points in surface temperatures and winds, variations in their seasonal This paper is not subject to U.S. copyright. Published in 2004 by the American Geophysical Union.

amplitude and phase, and a spatially heterogeneous response within the CCS to large-scale climate forcing. [3] Mendelssohn et al. [2003] extended these earlier studies to look at nonparametric trends in subsurface ocean temperatures (upper 200 m) at a number of locations within the CCS over the period 1950 – 1993. All locations studied showed a significant warming trend over this period. However, different geographical regions and depth strata had clearly distinct temporal patterns of interannual to decadal variability. Coastal locations had greater variance and a stronger impact from most El Ninõ events and decadal-scale warming and cooling tendencies than locations farther offshore. Temperature fluctuations within the thermocline revealed higher variance south of 38°N, providing a measure of the northern extent of influence of El Ninõ events. (The main thermocline lies at 50– 75 m at these locations [see Mendelssohn et al., 2003, Figure 2].)

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representing the meridional and offshore extent of the CCS. A related study [Palacios et al., 2004] extends our results by describing seasonal and long-term variations in water column stratification in the CCS.

2. Data and Methodology

Figure 1. Map of the eastern North Pacific, with shading denoting locations of World Ocean Database one-degree boxes that were examined (1950– 1993). The eleven solid and open colored boxes have sufficient data for analysis and are representative of the meridional and cross-shore extent of the California Current System, and are studied in detail here. Most locations also revealed different long-term trends within the mixed layer and thermocline, reflecting important changes in stratification. In general, there were significant regional and depth-dependent differences in the timing and amplitude of large-scale climate shifts and in the oceanic response to El Ninõ and La Ninã events. [4] It is well known that the CCS is characterized by strong seasonality in upper ocean circulation and thermal structure [Lynn and Simpson, 1987; Hickey, 1979, 1998], driven primarily by the seasonal cycle of large-scale atmospheric forcing [Strub and James, 1988; Bakun and Nelson, 1991; Winant and Dorman, 1997; Kelly et al., 1998; Murphree et al., 2003]. Thus climate variability might also be expected to affect the phase (timing and length) or amplitude of important seasonal processes, such as coastal upwelling and the ‘‘spring transition’’ from winter conditions [Lynn et al., 2003]. Schwing and Mendelssohn [1997, 1998], for example, found a shift in the seasonality of upwelling in the CCS during the period 1946– 1990, with a strong increasing trend to higher equatorward wind stress during the spring-summer upwelling period accompanied by increasing (decreasing) trends in sea surface salinity (sea surface temperature). Since many marine species have life cycles closely tuned to the seasonal cycle, a changing seasonality can lead to mismatches in characteristic trophic interactions [Cushing, 1969, 1975] and variations in ecosystem structure and productivity [Broekhuizen and McKenzie, 1995; Beare et al., 1998; Beare and McKenzie, 1999; Bograd et al., 2002]. [5] In this paper, the analysis of Mendelssohn et al. [2003] is extended to examine the seasonal dynamics of ocean thermal structure within the CCS. In particular, we describe low-frequency changes in seasonal amplitude and phase from a set of subsurface temperature time series

2.1. Data Sources [6] The data set used by Mendelssohn et al. [2003] is also examined here, so only a brief summary of the data used is given. Global one-degree summaries of temperature at 10 standard depths (0, 10, 20, 30, 50, 75, 100, 125, 150, and 200) were calculated from the World Ocean Database [Levitus and Boyer, 1994]. Eleven boxes having ample data over the study period (1950 –1993) and representing the meridional and offshore extent of the CCS were studied in detail (Figure 1). As a general caveat, there was lower data density in many of the boxes in the 1950s than in other decades; more detailed information is given by Mendelssohn et al. [2003]. State-space models were applied to the set of 110 monthly time series (11 boxes, 10 standard depths) for the years 1950 – 1993 as described by Mendelssohn et al. [2003] and summarized below. 2.2. State-Space Decomposition [7] For each of the 110 series, a state-space decomposition was estimated, yðt Þ ¼ T ðt Þ þ S ðt Þ þ I ðt Þ þ eðt Þ;

t ¼ 1; T ;

ð1Þ

where, at time t, y(t) is the observed series, T(t) is the unobserved time-dependent mean-level (nonlinear trend), S(t) is the seasonal component (zero-mean, nonstationary, and nondeterministic), I(t) is the irregular term (containing any stationary autocorrelated part of the data), and e(t) is the stationary uncorrelated component, which filters the observed data for ‘‘observation’’ error. The state-space methodology is one of the few that explicitly corrects estimates for measurement error, and that explicitly deals with missing data in terms of the model being estimated, rather than using a separate interpolator. A separate interpolation scheme can have properties that may bias any subsequent analysis being performed, particularly since the interpolated data usually are not differentiated from the real data in the subsequent analysis. The state-space algorithm produces minimum mean square error estimates of the states given all of the data, even when the observations are missing. The hyperparameters are estimated by maximum likelihood using a combination of a Kalman filter/smoother and the EM algorithm, and final estimates have been tested as significantly different than zero. (Detailed accounts of the state-space methodology are given by Harvey [1989], Shumway and Stoffer [2000], and Durbin and Koopman [2001]). This approach has some similarities to analysis of variance or regression models, in which there is a mean effect (the time-dependent nonlinear trend, or the intercept in a regression model) about which other zero-mean effects (in this case, the seasonal and irregular terms) vary. In a simple calculation of anomalies, by contrast, the fixed mean is usually subsumed in a deterministic seasonal component.

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Figure 2. Plot of the AICi value versus the order of the state-space model. The minimum AICi occurs at a stateorder of 38. [8] In this paper we examine the dynamics of the seasonal components S(t). Partial residual series were calculated by removing the relevant terms, yðt Þ ðT ðt Þ þ I ðt ÞÞ;

ð2Þ

that is, the series were detrended. Mendelssohn et al. [2003] described the behavior of the long-term trend component T(t) from the same univariate models, by removing the component S(t), that is deseasonalizing the series. This differs from other analyses of seasonal interannual variability where the raw data for a selected month or group of months are analyzed through time. In these analyses, variability due to the trend is confounded with variability in the seasonal component, the latter being the focus of this paper. This point is discussed by Schwing and Mendelssohn [1997], where they show that the trend in SST is warming while at the same time the seasonal component, reflecting upwelling, is getting colder. They show that just examining the raw seasonal SST series through time would have produced a misleading result. 2.3. Canonical Variate Analysis [9] In previous papers, we have used the method of Aoki [1990] to identify common trends [Mendelssohn et al., 2003]. In this paper, we use a related subspace identification technique, Canonical Variate Analysis (CVA) [Larimore, 1983, 1990, 1996, 2000a], as implemented in the Matlab toolbox ADAPTx [Larimore, 1999, 2000b]. In this method, an ARX (AutoRegression with eXogenous variables) model is fit to the data up to a maximum lag, and an optimal lag is chosen using a modified AIC (Akaike Information Criterion) statistic. Then, as in the method of Aoki [1990], the Hankel matrix is calculated as the covariance between the past and future vectors up to the chosen lag. A canonical correlation analysis is performed based on the Hankel matrix in combination with a modified AIC test to determine the optimal state-order of the model. From this, estimates of the state vector over time and of the statespace system matrices are calculated. One iteration of the

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Kalman filter is then performed to get improved state estimates. A comparison of various subspace identification techniques showed CVA to have good statistical properties [Favoreel et al., 1998]. [10] For this analysis, the data being used in the model identification stage are the seasonal components plus noise, so the state estimates can be viewed as estimates of ‘‘common seasonals.’’ That is, they are reduced order dynamic factors that contain the seasonal information that is common to all of the series. (It should be re-emphasized that the raw series are not being modeled at this stage, but rather the seasonal components from the univariate statespace decompositions. The reduced order model provides a method for succinctly summarizing this seasonal variability and for identifying which regions and depths have similar seasonal variability.) The ARX determined a maximum lag of 2 to be used for state-order determination. This may seem surprising as the indicator variable method for modeling the seasonal component used in our univariate model (equation (1)) has a lag of 12, but, as was pointed out by Bograd et al. [2002], AR(2) models can give parsimonious and accurate representations of seasonal components. [11] The maximum state-order from the CVA analysis was 38 using a modified minimum AIC criteria [Akaike, 1973; Hurvich and Tsai, 1989] (Figure 2). This may seem like an unusually large state-order when compared to an empirical orthogonal function analysis of deseasonalized data, for example, where only a few components are typically examined. However, a univariate model requires a state-order of 12 to fully model a seasonal component. Modeled independently, the 110 series in this analysis would require a state-order of 1320. Thus the state-order of 38, which is approximately three full seasonal components, is a significant reduction in size from the independent model. The minimum AIC criterion estimates a tradeoff between improved predictability from increasing the stateorder and the bias thus introduced from increasing the number of parameters (and thus signifying when the next state added would add no more predictability than would noise) in calculating the optimal reduced model state-order. The final order is attempting to find a minimally sufficient state-order to describe the entire system dynamics; thus there can be components that are important for the overall predictive ability of the model but whose effects are only locally reflected in space and time. We are interested in the broader scale seasonal behavior within the CCS; thus our discussion will focus on the dominant components of the reduced order model.

3. Results 3.1. Reduction of the State-Order [12] The CVA analysis described in the previous section is based on a singular value decomposition on the Hankel matrix of the linear system. Because of this, the common seasonal terms will tend to be at given harmonics of the seasonal component, much as in the work of Bograd et al. [2002] or in the trigonometric seasonal model given by Harvey [1989]. This is in contrast to the ‘‘dummy’’ variable seasonal model that is used in the univariate state-space models, where there is a term for each month. If a singular value is complex, and therefore has a conjugate pair, then

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Figure 3. Loadings per depth and location for the (a) first, (b) second, (c) third, and (d) fifth common seasonal components, based on analysis of all 110 time series. Coastal (offshore) locations are represented by thick (thin) lines and solid (open) symbols. Southern (northern) locations are represented by red (blue) lines. Compare with Figure 5 of Mendelssohn et al. [2003]. there will be two components that have similar harmonics but are in quadrature. [13] We will focus on the first, second, third, and fifth components of the 38 states identified by the model. These components are dominated by the longer (yearly, 6-month, 4-month) seasonal periodicities, and their factor loadings (these are the weights in the observation matrix of the multivariate state-space model, and, as in static factor analysis, show how each underlying dynamic factor loads on the observed series, in this case the detrended observations) separate the CCS into well-defined, large scale, area-depth regions (Figure 3) that are consistent with those found by Mendelssohn et al. [2003]. The higher order components, and the fourth component, load strongly on relatively local region-depth strata, and while important to the overall seasonal dynamics, are outside the large-scale focus of this paper. 3.2. First Seasonal Component [14] The first seasonal component is predominantly an annual harmonic, but with temporal changes in seasonal

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range and phase (Figures 4 and 5a). The amplitude of the seasonal peak and trough was high in the 1950s, lower from the 1960s through the 1980s, then back to the range of the earlier period by the early 1990s (Figure 4a). In particular, the sharp seasonal trough seen in the 1950s and again in the late 1980s to 1990s flattened through the intervening years (Figure 4b). While the amplitude of the spring trough and autumn peak returned to the earlier levels, the timing of the extrema had shifted by about a month. The spring trough was a month later and the autumn peak was a month earlier, leading to a compressed seasonal range and more rapid seasonal warming. [15] The factor loadings reveal that this component dominates in the upper 30 m of the water column, and has a stronger influence in the northern boxes (Figure 3a). There is a also a stronger influence in the offshore boxes relative to the coastal boxes, with the exception of 32°N 118°W, which is within the Southern California Bight (SCB). This component has minimal influence below the climatological mixed layer (50 m). This spatial structure, combined with the phase of the upper layer temperature changes (autumn peaks; Figure 4b), suggests that this component represents seasonal warming and cooling associated with the annual solar cycle. [16] The effect of this component can be seen more clearly by examining one location that has a high positive loading (49°N, 132°W at 20 m), one with a loading close to zero (38°N, 124°W at 50 m), and one with a negative loading (44°N, 126°W at 75 m; see Figure 3a). The relative impact of this component at each of these locations is particularly apparent when comparing their full seasonals (S(t); Figure 6, top row) with the projections of the first common seasonal onto these series (Figure 6, middle row). The seasonal behavior at 49°N, 132°W (20 m) is dominated by this component, although there is some high-frequency variation in the winter months that is not accounted for. In contrast, at 38°N, 124°W (50 m) the overall amplitude of the seasonal component is smaller than at 49°N, 132°W (20 m), and the first seasonal component has essentially no influence on the seasonal temperature cycle here. This component’s influence at 44°N, 126°W (75 m) is in between these extremes. Again, the seasonal range at this location is smaller than at 49°N, 132°W (20 m), and the relative impact of this component is also smaller. However, if the projection of the first common seasonal at this location is rescaled to the approximate range of the full seasonal, it is clear that this component captures a small but important portion of the seasonal variability during the winter months (Figure 6, bottom). As would be expected from the negative factor loading at this location, the pattern of the first seasonal component at 44°N, 126°W (75 m) is a half cycle out of phase with that at 49°N, 132°W (20 m). 3.3. Second Seasonal Component [17] The second seasonal component is also predominantly an annual harmonic, although it has additional energy at shorter periods and thus more variability over time (Figures 7 and 5b). This component has a complex conjugate root with the first component, so that the timing of their extrema are nearly in quadrature. [18] The factor loadings show a spatial separation of variance in both the alongshore and cross-shore directions

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Figure 4. First common seasonal component of subsurface temperature from all 110 time series. (a) Time series of the first common seasonal component; and (b) surface plot of the first common seasonal component.

(Figure 3b). A potentially more interesting spatial signal is the difference in loadings between the depth range around the bottom of the mixed layer and top of the thermocline (30 – 75 m) in the major upwelling centers (34°N –38°N nearshore) from the rest of the region. These series have similar negative loadings, while the southern offshore regions and the northernmost regions below 20 m are negligibly affected by this component. The surface layer in the northern regions loads strongly positive on this component. [19] The strong negative loadings near the mixed layer – thermocline boundary in the upwelling centers reflect cooler waters in these regions during the peak spring-summer upwelling period (Figures 3b and 7b). Thus, positive extrema in this component, which were highly variable in number per year, timing, and amplitude, probably reflect the relative strength of coastal upwelling. The multiple peaks seen in the early 1950s gave way to a single strong peak through much of the 1960s (Figure 7). By the late 1960s to early 1970s, a critical period of change in the common trend components in this region [Mendelssohn et al., 2003], the

seasonal range in this component was considerably reduced, similar to that seen in the first seasonal component (Figure 4). Also similar to the first seasonal component, the seasonal range increased after the late 1980s, especially with stronger winter troughs (Figure 7). Multiple peaks and troughs were again seen in the early 1990s, but the timing of the extrema had shifted 1 – 2 months earlier compared to the 1950s. [20] These changes in the annual temperature cycle at the major upwelling centers reflect changes in the timing, length, and amplitude of the upwelling season. In particular, the early and late portions of the record are characterized by relatively strong upwelling but a shorter upwelling season (and a later peak in more recent years). The intervening years are characterized by generally weaker upwelling but a longer upwelling season. These results from the subsurface temperature seasonals are similar to the long-term seasonal variability seen in nearshore sea surface temperature (SST) in the CCS [Schwing and Mendelssohn, 1997], consistent with the idea that changes in the timing and intensity of local upwelling are reflected in the seasonality of coastal

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Figure 5. Spectral properties of the (a) first, (b) second, (c) third, and (d) fifth seasonal components.

thermal structure. The later onset of upwelling, as compared to the 1950s, and the longer upwelling season in the 1970 – 1980s agree with the changing seasonal pattern of the North Pacific High [Bograd et al., 2002], which is the principal large-scale driver of upwelling winds along the west coast. The strong positive loadings in the northern surface regions suggest that periods of stronger upwelling off central and northern California are associated with warmer SSTs farther north along the coast. This may reflect a southerly shift in the position of maximum equatorward wind stress, with downwelling favorable conditions farther north. [21] These long-term fluctuations in upwelling seasonality can be seen more clearly by looking at areas that load at the extremes of the second seasonal component (Figure 8). At the offshore locations with loadings close to zero throughout the water column (e.g., 36°N, 127°W at 20 m), this component has virtually no effect on the seasonal temperature cycle. However, at the nearshore location at this latitude (36°N, 123°W at 50 m), which is strongly impacted by seasonal upwelling, this component captures a significant amount of the variation in the full seasonal. At 47°N, 126°W, with a strong positive loading at the surface, the projection of the second component and the full seasonal appear to have little relationship to each other. However, if the projection of the first seasonal component at this location is removed from the full seasonal (Figure 8,

bottom), it is clear that much of the remaining variation is captured by the second component. 3.4. Third Seasonal Component [22] The third seasonal component contains both annual and semi-annual harmonics, but is affected by higher order harmonics as well (Figures 9 and 5c). Significant changes in the amplitude and phase of the extrema are evident throughout the record. Both the seasonal minima and maxima decrease in amplitude from the mid-1950s through the 1960s, such that the seasonal range by the early 1970s is less than half that 20 years earlier. Starting around 1990, the deeper seasonal minima reappear as do the late autumn maxima, but, as with the lower order components, the extrema had shifted to earlier in the year. [23] The corresponding factor loadings reveal a partition of variance with latitude and depth in this component, implying meridional differences in the seasonality of thermal stratification (Figure 3c). Most locations load negatively on this component in the upper 30 m, particularly the northern and offshore boxes. Only at 38°N, 124°W and 40°N, 126°W, within the region Schwing and Mendelssohn [1997] refer to as a ‘‘transition zone’’ in upwelling intensity, does the upper water column load weakly. Regional differences are more pronounced below 75 m, where the northern coastal locations (40°N – 47°N) and the most

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Figure 6. (top row) Full seasonal components and (middle row) projection of the first seasonal component onto the series at a location with a large positive loading (49°N, 132°W at 20 m), a location with a negative loading (44°N, 126°W at 75 m), and a location with a loading near zero (38°N, 124°W at 50 m). (The projection is the estimate of the original series, in this case the partial residual series at a given location and depth, from the stated common seasonal(s).) (bottom) Projection of the first seasonal component for 44°N, 126°W at 75 m is replotted with a different scaling.

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Figure 7. Second common seasonal component of subsurface temperature from all 110 time series. (a) Time series of the second common seasonal component. (b) Surface plot of the second common seasonal component. southerly offshore location (31°N, 123°W) have strong positive loadings while the southern coastal areas are relatively unaffected. This is a similar spatial partition of variance to that seen in the third common trend component [Mendelssohn et al., 2003], which picked up much of the interannual variance (e.g., El Ninõ events) in the time series. An interpretation is that during periods of maxima in this component (generally broad spring and brief autumn peaks), upper layer temperatures tend to decrease relative to a static thermocline south of Cape Mendocino (38°N) but relative to a warming thermocline north of Cape Mendocino. Thus this component reflects seasonal changes in thermal stratification primarily in the northern upwelling zone of the CCS, and to a lesser degree at all locations. It should be noted that in the northern CCS, stratification is driven more by salinity than by temperature. [24] The projections of the third seasonal component onto series having positive (40°N, 126°W at 125 m), negative (44°N, 126°W at 20 m), and near zero (38°N, 124°W at

75 m) loadings reveal a smaller amplitude than the first two seasonal components, but important seasonal variation in the northern regions nonetheless (Figure 10). The impact of this component is amplified in the northern upwelling zone, where the upper layer and thermocline temperatures are varying a half cycle out of phase. Thus this component acts to decrease thermal stratification during the middle part of the record (1970s), when the seasonal range is low, relative to the 1950s and 1990s (Figure 9a). Shifts in phase to earlier extrema are also apparent (Figure 10). 3.5. Fifth Seasonal Component [25] Like the third component, the fifth seasonal component also contains energy at the annual, semi-annual, and higher order harmonics, but with the strongest peak at the annual harmonic (Figures 11 and 5d). This component also reveals a compression of the seasonal range in the middle years of the record, so that the overall seasonal variability expressed in this component is substantially reduced by the

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Figure 8. (top row) Full seasonal components and (middle row) projection of the second seasonal component onto the series at a location with a large positive loading (47°N, 126°W at 0 m), a location with a negative loading (36°N, 123°W at 50 m), and a location with a loading near zero (36°N, 127°W at 20 m). (bottom) Projection of the first seasonal component for 47°N, 126°W at 0 m subtracted from the full seasonal component.

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Figure 9. Third common seasonal component of subsurface temperature from all 110 time series. (a) Time series of the third common seasonal component. (b) Surface plot of the third common seasonal component.

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Figure 10. (top row) Full seasonal components and (middle row) projection of the third seasonal component onto the series at a location with a large positive loading (40°N, 126°W at 125 m), a location with a negative loading (44°N, 126°W at 20 m), and a location with a loading near zero (38°N, 124°W at 75 m).

late 1960s. The phase of the relative maxima in summer and fall also shifts to earlier in the year during this period. The amplitude of the winter maxima begins to increase in the 1980s, and the deep relative minimum in April begins to reappear near the end of the record. As seen in the other components, seasonal variability in the 1990s appeared to be returning to conditions more like those of the 1950s, after an extended period of reduced amplitude and changes in phase. [26] The corresponding factor loadings (Figure 3d) generally separate the southern (strong positive loadings peaking near 50 m) from the northern (small negative peaks near the surface) locations. In addition, the transitional location at 40°N, 126°W has strong negative loadings within the thermocline. Most offshore locations are only minimally affected by this component, with the exception of 36°N, 123°W, which has moderate positive loadings everywhere in the upper 50 m. The spatial patterns delineated by this component partly reinforce the variability of the second common

seasonal, which also had strongest loadings near the base of the mixed layer in the major upwelling centers. Thus the deep spring trough seen through the 1950s (Figure 11a), which is particularly apparent at 32°N, 118°W at 50 m (Figure 12, top row), implies enhanced upwelling and cooler temperatures at that time. This effect is not evident at 40°N, 126°W at 100 m (Figure 12, bottom row).

4. Discussion [27] The strong seasonality seen in CCS subsurface temperatures is driven largely by the annual cycle of winds, particularly the evolution of the alongshore wind stress which drives coastal upwelling and wind stress curl [Bakun and Nelson, 1991; Hickey, 1998; Kelly et al., 1998; Murphree et al., 2003]. Local wind-forcing of the CCS is, in turn, linked to basin-scale atmospheric processes [Strub and James, 1988]. It has been shown that these atmospheric

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Figure 11. Fifth common seasonal component of subsurface temperature from all 110 time series. (a) Time series of the fifth common seasonal component. (b) Surface plot of the fifth common seasonal component. processes also display low-frequency changes in seasonality. The North Pacific High, the dominant sea level pressure center over the eastern subtropical Pacific Ocean, has experienced significant changes in both seasonal amplitude and phase over the past 50 years [Bograd et al., 2002]. Since the North Pacific High is the principal driver of wind variability over the CCS, it might be expected that the upper ocean will share its nonstationary seasonality. [28] However, in the CCS, latitudinal differences in surface (wind and heat fluxes) and buoyancy (freshwater inflow) forcing interact with topography to create regional differences in the seasonality of upper ocean circulation and water column structure [Hickey, 1998]. Climate forcing will likely impact each of these processes differently, leading to unique regional responses to variable large-scale forcing. Understanding the complex interactions between large-scale

forcing and regional ocean processes is beyond the scope of the present study. Future analyses will further investigate the mechanisms driving the observed changes in CCS seasonality, and relate these changes to global climate variability. 4.1. Combining Nonseasonal and Seasonal Changes [29] Examination of individual nonseasonal (trend) and seasonal components from a state-space modeling approach is valuable for understanding the important spatial and temporal partitions of variance in these CCS temperature time series. However, the overall behavior of these time series is clear only when combining the effects of the leading trend and seasonal components. For example, temperature variability at the locations and depths most directly affected by coastal upwelling (near the top of the

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Figure 12. (top row) Full seasonal components and (middle row) projection of the fifth seasonal component onto the series at a location with a large positive loading (32°N, 118°W at 50 m), a location with a negative loading (40°N, 126°W at 100 m), and a location with a loading near zero (47°N, 126°W at 50 m). thermocline in the nearshore boxes between 34°N and 38°N) is best described by the second common trend [see Mendelssohn et al., 2003, Figure 5] and the second common seasonal components (Figure 3). While the first common trend expresses an overall warming tendency over the 44-year study period, the second common trend accentuates this warming at the subsurface upwelling centers [see Mendelssohn et al., 2003, Figure 4]. Also apparent in these series are accelerated warming trends beginning around 1970 and 1990. [30] The second common seasonal expresses a significant modulation of the seasonal temperature cycle at the locations most impacted by coastal upwelling, with a reduction in seasonal range through the middle portion of the record and an increase in the winter minima near the end of the record (Figure 7). Thus the first period of accelerated warming (late 1960s to early 1970s [Mendelssohn et al., 2003]) is associated with a decrease in seasonal upwelling intensity, even though the duration of the upwelling season

increased. This change would likely have accentuated the regional warming trend. The second period of accelerated warming (late 1980s to early 1990s), on the other hand, occurred at a time of increasing upwelling intensity, though with a shorter, and earlier, upwelling season. This change in upwelling seasonality may have mitigated the impact of the broader low-frequency warming trend at this time. This is consistent with the results of Schwing and Mendelssohn [1997], who performed a comparable analysis on coastal SST and alongshore wind stress. [31] Figure 13 shows the time series of the trend and seasonal terms for one of the upwelling locations (36°N, 123°W at 50 m). A highly variable seasonal cycle is superimposed on long-term warming and cooling trends. Throughout the CCS, significant modulations of seasonal amplitude and phase, as revealed by the first two common seasonals (Figures 5 and 7), were superimposed on a significant region-wide warming trend, as seen in the first two common trends [Mendelssohn et al., 2003]. Examina-

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Figure 13. Time series of the trend (T(t); solid black line) and trend + seasonal (T(t) + S(t); solid red line) components from the decomposition of 50 m temperature at 36°N, 123°W. The inset shows the annual temperature cycle for two representative years. tion of the trends alone would not have revealed the full complexity of climate variability in the CCS. 4.2. Changing Seasonality of California Current Upwelling [32] Seasonal temperature variability associated with long-term changes in the intensity, timing, and duration of coastal upwelling can be seen more clearly by looking at representative annual series from the first, second, third, and fifth common seasonal components (Figure 14). In the first common seasonal component, the sharp temperature peak in October (e.g., 1952) is replaced with a broader and weaker autumn temperature peak by 1970 (Figure 14a). A sharp peak is again evident by 1991, but it occurs a month sooner than in the earlier portion of the record. A similar phase shift is seen in the winter minimum. The second common seasonal component again reveals a phase shift between the early and late portions of the record, with multiple peaks and troughs offset by 1 – 2 months (Figure 14b). Other years (e.g., 1960 and 1985) show a single summer peak, but with differences in seasonal range. [33] Even more significant seasonal variability is evident in the higher harmonics. In the third seasonal component, sharp peaks are evident in both March and November of 1952 but only in September of 1991 (Figure 14c). Thus, not only were there changes in phase and amplitude, but also in the relative importance of the annual and semi-annual harmonics. As seen in the first common seasonal, the period around 1970 is characterized by a relatively flat seasonal cycle. The fifth common seasonal component contains energy at a number of frequencies, but consistently shows

a winter peak and an early spring trough (Figure 14d). The compression of seasonal variability is again seen in the middle portion of the record (e.g., 1970 and 1982).

5. Summary and Conclusions [34] State-space models were used to examine long-term changes in the seasonal amplitude and phase of upper ocean temperatures from a set of time series representing the meridional and offshore extent of the California Current System. This study complements and extends the work presented by Mendelssohn et al. [2003], which described the spatial structure of long-term nonparametric trends from the same data series. The results from the present analysis reveal significant modulation of the intensity, timing, and duration of seasonal upwelling in the CCS. This nonstationarity of the seasonal temperature cycle is superimposed on a region-wide warming trend and spatially heterogeneous responses to low-frequency climate events, thus modifying the character and potential biological impacts of the observed climate variability. A summary of the changing seasonality of CCS thermal structure revealed in this analysis is presented below: [35] 1. The seasonality of upper ocean temperature in the CCS is highly nonstationary. Significant interannual to decadal changes in seasonal amplitude and phase were apparent over the period 1950 – 1993. [36] 2. The 1950s and early 1990s were characterized by high seasonal variability in upper ocean temperatures in the CCS, while the intervening years were characterized by a reduced seasonal cycle. Long-term changes in phase were

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also observed, with seasonal extrema generally occurring 1– 2 months earlier in the year by the 1990s. [37] 3. The leading common seasonal components partition the seasonal variance into specific dynamic regimes within the CCS. Long-term changes in seasonality at 30– 75 m in the major coastal upwelling centers (34°N –38°N) differed from that observed north of Cape Mendocino, within the Southern California Bight, and farther offshore. The first common seasonal reflects warming and cooling associated with the annual solar cycle, while the second common seasonal captures the impact of seasonal coastal upwelling. The third common seasonal expresses different seasonality above and within the thermocline. Each of the leading components reveals a robust partitioning of seasonal variance north and south of Cape Mendocino. [38] 4. These spatial patterns of seasonal variance suggest that the observed changes in temperature seasonality reflect a significant low-frequency modulation of the intensity, timing, and duration of coastal upwelling in the CCS. In particular, the upwelling season changed from strong but short in the 1950s to weaker but longer in the 1970s, and then back to strong and short in the 1990s, but shifted to earlier in the year. These results are consistent with observed changes in the seasonality of coastal SST in the CCS [Schwing and Mendelssohn, 1997] and in sea level pressure offshore of the CCS [Bograd et al., 2002]. [39] 5. Significant changes in CCS temperature seasonality were concomitant with periods of accelerated change in the long-term temperature trends. This changing seasonality is likely to modify the local impacts of large-scale climate variability. More generally, marine populations may have regionally distinct responses to global climate change [Parrish et al., 1983]. [40] Different behavior in the trend and seasonal temperature decompositions above and within the thermocline, as demonstrated by Mendelssohn et al. [2003] and in the present study, imply long-term changes in water column stratification. This could alter the efficacy of wind-driven coastal upwelling to bring nutrients from below into the euphotic zone, thus altering ecosystem structure. Long-term and seasonal trends in the CCS in thermocline strength, depth, and temperature, as well as in integrated heat content, derived from the vertical temperature gradients for a subset of the data presented here, are described in detail by Palacios et al. [2004]. Together, these studies speak to the importance of considering a nondeterministic and nonstationary seasonal cycle when exploring the impacts of climate variability on environmental processes, habitat modification, biological production, and ecosystem structure [Peterson and Schwing, 2003].

Figure 14. Time series of the amplitude of the seasonal components for selected years. (a) First seasonal component for the years 1953, 1970, and 1991; (b) second seasonal component for the years 1953, 1960, 1985, and 1993; (c) third seasonal component for the years 1952, 1970, and 1991; and (d) fifth seasonal component for the years 1953, 1970, 1982, and 1990. 15 of 16

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[41] Acknowledgments. This work was supported by a grant from the U.S. GLOBEC Northeast Pacific Program (contribution 485). We thank Daniel Palacios and Richard Parrish for comments on an earlier draft. The manuscript has benefited from the comments of the anonymous reviewers.

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S. Bograd, R. Mendelssohn, and F. Schwing, Pacific Fisheries Environmental Laboratory, National Marine Fisheries Service, NOAA, 1352 Lighthouse Avenue, Pacific Grove, CA 93950-2097, USA. (rmendels@ pfeg.noaa.gov)

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