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Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica–San Pedro Basin off Southern California
Journal of Marine Systems 39 (2003) 185 – 202 www.elsevier.com/locate/jmarsys
Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica–San Pedro Basin off Southern California Nikolay P. Nezlin a,b,*, Bai-Lian Li b a
Institute of the Environment, UCLA, Los Angeles, CA, USA b Department of Botany and Plant Sciences, UCR, USA
Received 1 July 2002; received in revised form 16 October 2002; accepted 18 December 2002
Abstract The time-series of remote-sensed surface chlorophyll concentration measured by SeaWiFS radiometer from September 1997 to December 2001 and the relevant hydrological and meteorological factors (remote-sensed sea surface temperature, atmospheric precipitation, air temperature and wind stress) in Santa Monica Bay and adjacent waters off southern California were analyzed using wavelet and cross-correlation statistical methods. All parameters exhibited evident seasonal patterns of variation. Wavelet analysis revealed salient long-term variations most evident in air temperature during El Nin˜o 1997 – 1998 and in wind stress during La Nin˜a 1998 – 1999. Short-period (<100 days) variations of remote-sensed chlorophyll biomass were mostly typical to spring seasons. Chlorophyll biomass was significantly correlated with air temperature and wind stress: an increase of chlorophyll biomass followed with 5 – 6-day time lag an increase of wind stress accompanied by a simultaneous decrease of air temperature. The mechanism of these variations was an intensification of phytoplankton growth resulting from the mixing of water column by wind stress and entrainment of nutrients into the euphotic layer. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Remote sensing; Photosynthetic pigments; Environmental conditions; Time-series analysis; USA; California; Southern California Bight; 33 – 34.5jN; 119 – 118jW
1. Introduction One of the most important problems in studying phytoplankton dynamics in the ocean is the analysis of environmental factors controlling phytoplankton growth (Raymont, 1980). Various methods, including * Corresponding author. Southern California Coastal Water Research Project, 7171 Fenwick Lane, Westminister, CA 92683, USA. Tel.: +1-714-372-9227; fax: +1-714-894-9699. E-mail address: [email protected] (N.P. Nezlin).
laboratory experiments and mathematical simulation, are used to estimate the mechanism of this influence. However, a statistical analysis of extensive time-series data from field observations is also important. It can reveal correlations between the dynamics of phytoplankton biomass and environmental characteristics. Based on these correlations, we could hypothesize the mechanisms of environmental control of the biological processes. The most important limitation of this analysis is that the number of measurements is usually insufficient for statistical analysis because oceano-
0924-7963/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0924-7963(03)00030-7
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graphic observations are very expensive. This has changed during recent years because information collected by scientific satellites now provide extensive data from all regions of the World Ocean. About 60,000 images of ocean color collected by the Nimbus-7 Coastal Zone Color Scanner (CZCS) during 1978– 1986 have opened a ‘‘new epoch’’ in biological oceanography (Gordon et al., 1980; Hovis et al., 1980). The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) radiometer has collected information on ocean color since September 1997 (Acker et al., 2002). The availability of more than 4 years of remote-sensed data coupled with simultaneous observations of the physical environment now enable statistical analysis at a high confidence level. In this study, we analyzed the dynamics of remotesensed phytoplankton biomass in Santa Monica Bay
and the adjacent San Pedro Basin (hereafter SMB) off the southern California coast (Fig. 1). This basin is located in the vicinity of densely populated metropolitan Los Angeles. SMB is a semi-enclosed inshore part of the Southern California Bight. It is roughly 100 km long, 40 km wide and 900 m deep. The hydrographic processes in SMB are determined by a complex pattern of surface currents (Hickey, 1992). The relatively cold waters of the southward flowing California Current penetrate to SMB through the Santa Barbara Channel, this flow being especially intensive in spring (Bray et al., 1999; DiGiacomo and Holt, 2001). The relatively warm waters of the poleward Southern California Countercurrent (Sverdrup and Fleming, 1941) penetrate to the basin alongshore from the southeast. The temperature gradients near the coast are enhanced by the effects of local wind-driven
Fig. 1. Santa Monica Bay and adjacent waters. Rectangle indicates the region of averaging SeaWiFS chlorophyll and Pathfinder SST. Arrow with circle indicates the location of upwelling index (33jN, 119jW). Oblique cross indicates the center of 1j1j rectangle of GPCP atmospheric precipitation.
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upwelling, resulting in a decrease of sea surface temperature and local phytoplankton blooms. Thus, the extent of phytoplankton biomass in SMB is expected to result not only from local environmental factors but also from remote forcing, i.e., the hydrographic and meteorological conditions in other areas.
2. Data used for analysis The analysis of temporal variations of phytoplankton biomass in SMB was based on the data of SeaWiFS radiometer on the OrbView-2 satellite. The data were obtained from NASA Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC). We used daily averaged Level 3 Standard Mapped Images (SMI) data of SeaWiFS surface chlorophyll calculated during reprocessing #3 (Version 3). Level 3 SMI SeaWiFS chlorophyll data were interpolated to a regular grid of equidistant cylindrical projection of 360j/ 4096 pixels (about 9.28 km) resolution. Use of this data was in accord with the SeaWiFS Research Data Use Terms and Conditions Agreement. The algorithms used in GSFC for calculating surface chlorophyll concentration are described in O’Reilly et al. (1998). In this study, we use remote-sensed data (i.e., surface chlorophyll concentration derived from water color) to analyze the dynamics of phytoplankton biomass in the water column. The remotely sensed surface pigment concentration and total pigment concentration in water column are correlated (Chavez, 1995; Smith and Baker, 1978) but not identical. However, for this study, the absolute values are not as important as temporal gradients of phytoplankton biomass. We understand that the absolute values of surface chlorophyll concentration derived from satellite measurements are subject to significant inaccuracy due to technical difficulty of remote-sensed observations, and we do not compare the remote-sensed data directly to in situ absolute values of chlorophyll concentration and phytoplankton biomass. Instead, we consider the variations of satellite-measured chlorophyll concentration as an indicator of dynamics of phytoplankton biomass in the study region. The mean remote-sensed surface chlorophyll concentration was calculated as a median within the region located between the coast and the lines 119jW longitude, 33j30VN latitude and 118jW longitude (the
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rectangle at Fig. 1). We used a median instead of an arithmetic mean because the statistical distribution of remote-sensed chlorophyll in World Ocean has been shown to be asymmetric (Banse and English, 1994) and medians were much closer to modal values than means. This pattern was true for the study region as well. During all seasons (Fig. 2), log-transformation made the statistical distributions more symmetrical, but still far from normal (Kolmogorov –Smirnov normality test failed, P<0.001, for all four seasons). About 45% of observations contained no SeaWiFS observations within the chosen rectangle. To obtain the values for these days, the medians were log-transformed, then the missing data were linearly interpolated, and the resulting values were exp-transformed. To estimate the variations of sea surface temperature (SST) we used remote-sensed Advanced Very High Resolution Radiometer (AVHRR) data from Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (JPL PODAAC). These data were processed in JPL within the scope of NOAA/ NASA AVHRR Oceans Pathfinder Project. The SST data were derived from the AVHRR radiometers using an enhanced nonlinear algorithm (Walton, 1988). We used daily data for the descending pass (night-time) on global equal-angle grids of 4096 pixels/360j (f9.28 km) resolution, similar to SeaWiFS chlorophyll. Only the ‘‘best SST’’ data, (i.e., highest quality pixel values) was used. The versions V4.1 (January 1997– December 1999) and interim V4.1 (January 2000 –July 2001) were used to derive absolute SST values for time-series analysis. The arithmetic mean SST was calculated for each day within the rectangle between 118jW and 119jW and to the north of 33j30VN (similar to SeaWiFS chlorophyll; see Fig. 1). The daily values of ‘‘upwelling index’’ (i.e., the Ekman offshore drift calculated from the fields of atmospheric pressure (Bakun, 1973) for the location 33jN, 119jW (see Fig. 1) are obtained from the Pacific Fisheries Environmental Laboratory (PFEL) Internet site. Though expressed in the units of water transport (m3 s1 per 100 m of the coastline), in the Southern California Bight, the upwelling index is a measure of not only upwelling, but also of wind stress influence on the stratification of the water column. The data of remote-sensed atmospheric precipitation (Huffman et al., 2002) were obtained from NASA GSFC DAAC. These data were created as
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Fig. 2. Statistical distribution of remote-sensed surface chlorophyll concentrations in Santa Monica Bay and adjacent waters during four climatic seasons (Fall: September – November; Winter: December – February; Spring: March – May; Summer: June – August). x-scale is logarithmic; yscale is a percentage of observations.
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a part of the Global Precipitation Climatology Project (GPCP) from the measurements of Special Sensor Microwave/Imager (SSM/I) multichannel passive microwave radiometers on Defense Meteorological Satellite Program (DMSP) satellites, infrared (IR) sensors on Geosynchronous Operational Environmental Satellites (GOES, USA), Geosynchronous Meteorological Satellite (GMS, Japan), Meteorological Satellite (METEOSAT, European Community), the NOAA series low-earth-orbit satellite (LEO, USA) and the TIROS Operational Vertical Sounder (TOVS) data derived from the High-Resolution Infrared Sounder 2 (HIRS2), Microwave Sounding Unit (MSU) and Stratospheric Sounding Unit (SSU) instruments on the NOAA series of polar orbiting meteorological satellites. The daily data have 1j spatial resolution, and we used the 1j1j grid node located at 33j30VN, 118j30VW for analysis. The accuracy of remote-sensed atmospheric precipitation data is still questionable; therefore, we compared them with standard meteorological data measured by rain gauges. Air temperature and precipitation for the period January 1994 – December 2001 measured at the meteorological station of the Los Angeles International Airport (LAX) located at 33j55.8VN, 118j24VW were obtained from the Internet site of the National Climatic Data Center (NCDC).
3. Statistical methods used for analysis Air temperature (Tair), upwelling index (UI), SST and surface chlorophyll concentration (Chl) exhibit seasonal oscillations (Fig. 3). We estimated climatological values approximating the time-series of Tair, UI, SST and Chl using a sine function (Chl was logtransformed). The resulting climatological values were then subtracted from actual values of Tair, UI and SST to obtain seasonal anomalies. Seasonal anomalies of Chl were calculated in the following way: Chl data were log-transformed, the climatological cycle was subtracted from the actual values, and then the Chl data were exp-transformed. The time-series of seasonal anomalies of Tair, UI, SST and Chl, and total values of gauge-measured (GMP-LAX) and satellite-measured (GPCP) atmospheric precipitation were processed using wavelet
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analysis (Breaker et al., 2001; Gao and Li, 1993; Li, 1995; Li and Loehle, 1995; Lin, 1996; Torrence and Compo, 1998). The wavelet transform is a relatively new computational method for signal processing. Because of its characteristics of time/space-frequency localization and multi-resolution, the wavelet transform of a signal provides detailed information on underlying processes as a function of time and/or spatial scale. In wavelet representation, a geophysical signal is decomposed into a sum of elementary building blocks describing its local frequency content. The wavelet analysis provides both scale and time/space information and allows one to separate and sort different structures on different time/space scales at different times/locations. The integral wavelet transform is defined by 1 ðWw f Þða; bÞ ¼ pffiffiffiffiffiffiffiffiffi AaA
Z
tb f ðtÞw dt; a
pffiffiffiffiffiffiffiffiffi where the function wab(t)=w((tb)/a)/ AaA is called the ‘‘basis’’ or ‘‘mother wavelet’’. It must satisfy the following admissibility condition: Z l wðtÞdt ¼ 0 þl
The ‘‘Mexican hat’’ wavelet w(tV)=(1tV2 ) exp(tV2/2) was used as the wavelet basis function, which is the second-order derivative of the Gaussian function defined within 4VtVV4. The parameters a (a p 0) and b are used to adjust the shape pffiffiffiffiffiffiffiffiand ffi location of the wavelets, respectively. The 1/ AaA term keeps the energy of the scaled wavelet equal to the energy of the original basis wavelet. As a changes, the shape of the wavelet is compressed or stretched to cover different frequency ranges. Changing b allows one to move the time/space localization center and translate wavelets through all data points. In this way, the wavelet transform provides a time/space frequency description of a geophysical signal f(t). The wavelet transform, in essence, takes one-dimension function of time or space and expands it into a two-dimensional space consisting of time and scale. In our case, we analyzed temporal rather than spatial variations. The ability of wavelet analysis to determine the properties of spatial heterogeneity has not been used.
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Fig. 3. Time series of air temperature (jC) at LAX airport (A); upwelling index (m3 s1 per 100 m of coastline) at 33jN, 119jW (B); Pathfinder AVHRR sea surface temperature (jC) (C); and SeaWiFS surface chlorophyll (log mg m3) approximated by sine functions (dashed lines) (D).
We calculated wavelet power spectra defined as j(Wwf )(a,b)j2. The wavelet variance (or Global Wavelet Spectrum, GWS) was calculated by the integration of the squared transform coefficients at different scales for all data points within the ‘‘cone of influence’’, i.e., the spatial range within which transformation coefficients are computed for a given scale a (Li, 1995; Li and Loehle, 1995).
The standard time-dependent wavelet spectra were normalized by taking the original wavelet power spectra and dividing them by the average wavelet power (i.e., the global wavelet spectra) for the entire time series. Thus, the normalized wavelet spectra (Figs. 4B – 9B) show the ratio of the current wavelet power to the ‘‘normal’’ or expected wavelet power (Figs. 4C – 9C). The time scale corresponding to the
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first local maximum of the starting point of the plateau of the GWS function (i.e., BGWS(a)/Ba=0) is used to determine the dominating time scale of each parameter under analysis. Application of wavelet analysis to oceanographic data is relatively new. This innovative tool helps us to interpret multi-scale, nonstationary time-series data and reveals features we could not see otherwise. However, wavelet analysis should not be used alone, but in parallel with other statistical time-series methods. In practice, wavelet analysis emphasizes interpretation of a signal that is changing over time, with good time-frequency localization and the ‘‘zoom-in, zoom-out’’ property, an issue which classical Fourier analysis is incapable of addressing. The wavelet variance, which provides a scale-based analysis of variance, is also complementary to traditional frequency-based spectral analysis.
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4. Seasonal cycles of phytoplankton biomass and environmental factors Remote-sensed surface chlorophyll concentration, sea surface temperature, air temperature and upwelling index in SMB exhibit regular seasonal variations, each of which can be approximated by a sine function with a period of 1 year (Fig. 3). The seasonal minimum of UI (i.e., the alongshore wind stress in the Southern California Bight) occurs on December 19th (Julian day 353); the seasonal maximum occurs on June 20th (Julian day 171). These dates coincide with the dates of winter and summer solstice, respectively. Thus, the seasonal cycle of wind patterns in this region coincide with the seasonal solar cycle, with minimum wind stress during the minimum of incoming insolation and vise versa. The seasonal cycle of Tair is lagged by about 50
Fig. 4. Seasonal variations (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of gauge-measured atmospheric precipitation (GMP-LAX, mm/day). The x-axis is time in years; the y-axis is the period that corresponds to the wavelet scale. The contour levels at (B) are at 0.5 (gray) and 2.0 (black).
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days compared to the solar cycle, with a minimum in early February (Julian days 38 –39) and a maximum in early August (Julian days 220 – 221). The seasonal cycle of SST is lagged by 23 days compared to Tair; the total lag of SST as compared with solar cycle being about 2.5 months. The SST minimum occurs in early March (Julian days 61– 62) and the maximum in early September (Julian days 243– 244). The seasonal cycle of Chl in the study region is in antiphase to the seasonal cycle of SST (minimum in the end of August and maximum in the end of February, Julian days 241 and 58, respectively). Atmospheric precipitation (Figs. 4A and 5A) also exhibits seasonality, but it cannot be approximated by a sine function. Both gauge-measured (Fig. 4A) and satellite-measured (Fig. 5A) precipitation evidently increase during autumn– winter – spring periods. Interannual variability was also evident, and the increase in precipitation was especially pronounced in the end of
1997 and the beginning of 1998 (i.e., during the El Nin˜o event). The seasonal maximum of precipitation occurred during February; this period coincided with the SST seasonal minimum and the Chl seasonal maximum. The reciprocal relationship between the seasonal cycles of phytoplankton biomass (Chl) and sea temperature (SST) indicates typical subtropical seasonality of phytoplankton growth. It can be attributed to the Model 3 of the production/consumption seasonal cycles, called ‘‘winter – spring production with nutrient limitation’’ (Longhurst, 1995). In this cycle, productivity is not light limited and increases during winter as the progressive deepening of the mixed layer recharges nutrients in the euphotic zone. This model is applicable to a large part of the open ocean where winter winds are relatively mild. At the same time, Longhurst (1995), in his classification of biogeographical provinces in the World Ocean, attributed
Fig. 5. Seasonal variations (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of satellite-measured atmospheric precipitation (GPCP, mm/day). The notations and shading scale are similar to Fig. 3.
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the Southern California Bight to Model 8 (‘‘the regions of intermittent production at coastal divergences’’), where the level of phytoplankton biomass is strictly related to upwelling-favorable wind stress. The seasonality of phytoplankton biomass and environmental factors (Fig. 3) illustrates that the seasonal cycle of phytoplankton in SMB is not directly regulated by upwelling-favorable wind, as is the case in a typical upwelling ecosystem (Cushing, 1971; Smith et al., 1983), e.g., along the central California and Oregon coast (Cushing, 1976). The Santa Monica – San Pedro Basin is sheltered by a mountain ridge from dominating in this latitudinal zone alongshore equatorward winds, and phytoplankton cycle is regulated by other factors influencing water column stratification, including ocean – atmosphere heat flux. Below, we analyze temporal variability and correlation between phytoplankton biomass and environmental factors on other than seasonal time scales.
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5. Variability at different frequency scales: wavelet analysis Hereafter, we analyze the time-series of total values of atmospheric precipitation (GMP-LAX and GPCP) and seasonal anomalies of the parameters with regular sine-type seasonal cycle: Tair, SST, UI and Chl. The seasonal cycles of both gauge-measured (GMP-LAX, Fig. 4A) and satellite-measured (GPCP, Fig. 5A) precipitation were almost identical, with maxima during winter seasons. The pattern of satellite-measured precipitation (Fig. 5A) exhibited more regular seasonal variations than gauge-measured data (Fig. 4A). The reason is that the GPCP data were derived from remote-sensed water vapor concentration in the atmosphere averaged over a large area (1j1j). In contrast, the gauge-measured precipitation characterizes a narrow location (LAX meteorological station at 33j55.8VN, 118j24VW), where precipitation is subject to small-scale spatio-temporal
Fig. 6. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of air temperature (Tair, jC). The notations and shading scale are similar to Fig. 3.
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variability. The location of gray and black areas at the wavelet power spectra (Figs. 4B –9B) in terms of time (x-axis) and frequency ( y-axis) coordinates indicates what frequency of variations dominated during that time period. The wavelet power spectra of precipitation (Figs. 4B and 5B) revealed oscillations in wide range of frequency during the period of rains in late winter and spring. During other periods, the precipitation was very small and its variability was almost absent. The most pronounced long-term variability was evident during the El Nin˜o period in the end of 1997 and in the first half of 1998. The first maximum of global wavelet spectrum (GWS) was 6– 8 days (Figs. 4C and 5C), which characterizes short rain events, observed in both satellite-measured and gauge-measured data. The seasonal anomalies of air temperature (Fig. 6A) were positive during the summer and autumn of 1997 (i.e., during the onset of the El Nin˜o event) and persistently negative during the transition from El
Nin˜o to La Nin˜a in the spring and early summer of 1998. Oscillations during the El Nin˜o period (1997) were characterized by long-term variations of 50– 400 days frequency (Fig. 6B). During the transition from El Nin˜o to La Nin˜a in 1998, the time-scale of oscillations decreased. From 1999 onwards, regular oscillations of frequency 20 –50 days were observed. The first GWS maximum was 98 days (Fig. 6C); this value seemingly results from the superposition of long-term variations dominating during 1997– 1998 and short-term variations dominating later in 1999– 2001. The latter was verified by analyzing the period 1999 – 2001 only, in which case, the first GWS maximum was as low as 50 days. The anomalies of SST (Fig. 7A) were evidently positive during the El Nin˜o period from early summer 1997 until spring 1998, and negative during the La Nin˜a period from autumn 1998 until winter 1999– 2000. Another period of negative SST was observed from autumn 2000 until summer 2001. In summer
Fig. 7. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of sea surface temperature (SST, jC). The notations and shading scale are similar to Fig. 3.
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1997 (during the onset of El Nin˜o), oscillations of frequency 40 –50 days dominated (Fig. 7B). Later, during the transition from El Nin˜o to La Nin˜a, the oscillations were longer, from 25 to 150 days. In summer 1999, pronounced oscillations occurred in all range of the time spectrum. A slightly changed pattern was observed in summer 2000. However, during this time period, the GWS function was monotonous without a local maximum (Fig. 7C), meaning that long-scale (interannual) SST variability dominated in SMB during 1997– 2001. The anomalies of UI were not evidently different from zero during the El Nin˜o period in 1997 and the beginning of 1998 (Fig. 8A). The offshore wind stress influence was persistently negative by the autumn of 1998 and no evident deviations occurred later, except short-time increase of the upwelling index in spring 1999. The first GWS maximum was 4 days (Fig. 8C), meaning that upwelling-favorable wind events were short compared to other environmental factors ana-
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lyzed. The wavelet power spectrum (Fig. 8B) revealed oscillations of 10 – 100 days typical to the spring seasons of 1998, 1999 and 2001, but less evident in spring 2000. Long-term oscillations (>100 days) occurred from summer 1998 to summer 1999 during the La Nin˜a period. It is interesting that no evident variations of wind stress were observed during the 1997– 1998 El Nin˜o period. This indicates that the 1997 – 1998 El Nin˜o event off southern California resulted from poleward propagation of coastally trapped waves rather than from the weakening of upwelling-favorable winds (see Discussion). The seasonal anomalies of remote-sensed chlorophyll biomass (Fig. 9A) exhibited numerous short spikes, explained as blooms of phytoplankton growth. From time-series data (Fig. 9A), it is hard to determine the seasons when these blooms dominate. The wavelet power spectrum (Fig. 9B) illustrates that the variations of less than 100 days period were mostly typical of spring seasons. In April 1998, during the
Fig. 8. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of upwelling index (UI, m3 s1 per 100 m of coastline) at 33jN, 119jW. The notations and shading scale are similar to Fig. 3.
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Fig. 9. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of SeaWiFS chlorophyll biomass (Chl, mg m3). The notations and shading scale are similar to Fig. 3.
late phase of El Nin˜o event, the spring period of variations was short. In spring 1999, this period was masked by pronounced variations of wide spectrum, which occurred during the 1998 –1999 autumn –winter period. We associate this variability with the 1998– 1999 La Nin˜a event. Later, during the spring seasons of 2000 and 2001, maximum variability was observed in the spring. Domination of variations of less than a period of 100 days was confirmed by the GWS pattern, where the GWS maximum was 66 days (Fig. 9C).
6. Correlations between phytoplankton biomass and environmental factors After removal of regular seasonal oscillations (see Fig. 3), the residuals, i.e., the variations of smaller time scale, appeared to be significantly correlated. Chl and SST anomalies are negatively correlated (Fig. 10A), which is a well known phenomenon in the
Southern California Bight (Eppley and Renger, 1988; Tont, 1976). The maximum correlation has zero time lag, but the cross-correlation diagram is asymmetric: correlations with negative time lag dominate over correlations with positive time lag. Therefore, high phytoplankton biomass is correlated with low SST observed simultaneously and a few days later. The negative correlation between SST and Chl can be explained by the upwelling of rich in nutrients subthermocline water into the upper euphotic layer, resulting in both a decrease of SST and an increase of phytoplankton growth rate. However, phytoplankton growth would be expected to occur not before, but a few days after the entrainment of nutrients into the euphotic layer. However, the observed negative time lag prima facie contradicts to this concept. The correlation between Chl and Tair is also negative but has a time lag of 5 days (Fig. 10B). In other words, the decrease of air temperature is followed by an increase of phytoplankton biomass 5 days later. The correlation between Chl and UI is significantly
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positive with 6-day time lag (Fig. 10C). Thus, a strengthening of upwelling-favorable wind stress results in phytoplankton bloom about 6 days later. However, it is important to distinguish between the influence of air temperature and wind stress on the stratification of water column and, in turn, on phyto-
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plankton growth. Both cooling of sea surface resulting from decrease of Tair and strengthening of wind stress favor mixing in the euphotic layer, and can recharge it with nutrients. SST is positively correlated with Tair; maximum correlation has zero time lag (Fig. 10D). The cross-
Fig. 10. Time-lagged correlation between (A) remote-sensed chlorophyll biomass (Chl) and sea surface temperature (SST); (B) Chl and air temperature (Tair), (C) Chl and upwelling index (UI), (D) SST and Tair, (E) SST and UI, (F) Tair and UI. Dashed lines indicate 95% confidence interval of the correlation coefficients.
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Fig. 10 (continued).
correlation diagram is asymmetric and this asymmetry emphasizes that the variations in air temperature precede the variations in SST. The correlation between SST and UI is negative with maximum time lag of 5 – 15 days (Fig. 10E). Therefore, strong upwelling-favorable wind events result in a decrease of SST during the next 1 – 2 weeks. Tair is also negatively correlated with UI with 1-day time lag (Fig. 10F). In other words, strong wind events coincide with next-day decrease of air temperature.
No significant correlation between Chl and atmospheric precipitation (GMP-LAX and GPCP) was revealed.
7. Discussion The statistical analysis of remote-sensed phytoplankton biomass and environmental factors revealed three types of variability: seasonal variability directly
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related to solar cycles, interannual variations and short-period variability. The main feature of interannual variability in SMB during the study period was the El Nin˜o Southern Oscillation (ENSO) cycle, in particular the El Nin˜o –La Nin˜a events in 1997– 1999. The 1997– 1998 El Nin˜o event was the strongest in the 20th century (Chavez et al., 1999; Kahru and Mitchell, 2000; McPhaden, 1999). Typical ENSO periodicity is from 3 to 7 years (Rasmusson and Wallace, 1983). Each El Nin˜o event starts when strong westerly wind bursts over warmer than normal equatorial waters in the western tropical Pacific. These winds generate oceanic baroclinic downwelling Kelvin waves that propagate eastward along the equator to the South American coast (McPhaden, 1999; Wyrtki, 1975). These waves are then transformed into coastally trapped waves, which propagate north and south toward the poles (Chelton and Davis, 1982; Enfield and Allen, 1980; Huyer and Smith, 1985). The coastal waves deepen the thermocline and raise sea level along the American coast, and an accumulation of warm water in the upper mixed layer results in large positive sea surface temperature anomalies. The redistribution of sea surface temperature over the Pacific Ocean results in atmospheric circulation changes, reflected in an expansion of the Aleutian Low (Emery and Hamilton, 1985). Some authors (Breaker and Lewis, 1988; Breaker et al., 2001; Mysak, 1986; Simpson, 1983, 1984) consider the changes of local wind pattern to be the main source of hydrological and ecological changes off California (‘‘atmospheric teleconnection’’); other authors emphasize the direct influence of coastal waves (‘‘oceanic teleconnection’’) (Chavez, 1996; Rienecker and Mooers, 1986) or a combination of these two effects (Huyer and Smith, 1985; Lynn et al., 1995; Ramp et al., 1997). Our results reveal features of both atmospheric and oceanic teleconnections in SMB during the 1997– 1998 El Nin˜o event. The classical features of oceanic teleconnection were evident from SST and Chl patterns. During the second half of 1997 and the first half of 1998 (i.e., during the El Nin˜o event), persistent positive SST anomalies exceeded 2jC (Figs. 3C and 7A). Remote-sensed phytoplankton biomass was lower than normal, especially in autumn 1997 and in summer 1998, i.e., during the periods of nutrient
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limitation of phytoplankton growth (Figs. 3D and 9A). Both positive SST and negative Chl anomalies can be explained by a deepening of the thermocline caused by poleward propagation of coastally trapped waves. This deepening results in heat accumulation and depletion of nutrients in the upper mixed layer. The signature of El Nin˜o was particularly evident from the analysis of sea surface height derived from satellite altimetry (Nezlin and McWilliams, 2003). During the transition to La Nin˜a in late 1998 and in 1999, SST dramatically decreased (Figs. 3C and 7A) and the frequency of blooms of phytoplankton biomass changed. Longer oscillations typical to late spring period were observed between November 1998 and April 1999 (Fig. 9B). Changes in wind stress were observed from summer 1998 until mid-1999, i.e., during La Nin˜a period (Fig. 8A and B). In mid-1998, UI decreased, and then increased from autumn 1998 until summer 1999. No evident changes in wind stress were observed during the onset of El Nin˜o in summer 1997, meaning that in SMB, oceanic teleconnection dominated over atmospheric during the 1997 –1998 El Nin˜o event. However, salient features of El Nin˜o were evident in other meteorological parameters, such as air temperature and precipitation. Positive Tair anomalies were observed in autumn 1997 (Figs. 3A and 6A); then the frequency of Tair oscillations gradually decreased during 1998 (Fig. 6B). Atmospheric precipitation in winter 1997 –1998 exceeded normal levels, especially in terms of remotesensed values (GPCP, Fig. 5A). The accumulation of heat in the upper mixed layer resulted in increase of evaporation from sea surface, resulting in a rainy season in winter 1997 – 1998 that was longer lasting and more intense than during other years. ENSO is known as a main source of variability of precipitation in many regions and, in particular, in southern California (Diaz et al., 2001; New et al., 2001; Peel et al., 2002), where torrential rains during the 1997– 1998 El Nin˜o event resulted in dramatic increase of river discharge and pollution (Dwight et al., 2002). Nutrient transport with river runoff could stimulate phytoplankton growth; direct atmospheric precipitation could contain micronutrients (like iron) from dust transported by wind from continental regions (Piketh et al., 2000). That is why we expected an increase of chlorophyll biomass after rains resulting from nutrient
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transport with river discharge; however, no correlation was observed. The reason is that in the study region even dramatically increased during an El Nin˜o event river discharge to Southern California Bight is too small to provide a considerable amount of nutrients to phytoplankton. Thus, the two kinds of features of ENSO cycle were evident in SMB: oceanographic processes dominated by propagation of downwelling coastal waves, and meteorological processes related to heat and water flux between the ocean and the atmosphere. For short-period variations, the correlations between Chl, SST, Tair and UI indicate wind as the basic factor controlling short-period phytoplankton blooms in the Santa Monica– San Pedro Basin. This region is sheltered by the mountains from the alongshore equatorward wind typical of this area. Furthermore, the seasonal dynamic of phytoplankton biomass is not directly regulated by upwelling-favorable wind, as off northern and central California (Cushing, 1971, 1976). However, our data illustrate that short-period strengthening of upwelling-favorable northwesterly wind is related to algae blooms 5 –6 days later. The intensification of the northwesterly wind results in a decrease of air temperature on the next day. Both wind stress and sea surface cooling erode the thermocline and entrain cold subthermocline water that is rich in nutrients into the upper euphotic layer (Eppley and Renger, 1988). These mixing processes, in turn, result in a decrease of SST and an increase in phytoplankton growth rate. The maximum phytoplankton bloom usually occurs less than 1 week after the wind event because the increased phytoplankton biomass is rapidly consumed by herbivorous phytoplankton. In contrast, the decrease of SST lasts longer (about 2 weeks); therefore, hydrographic response (i.e., the process of formation of thermocline) is slower as compared with biological response, resulting from the balance between phytoplankton growth and its grazing.
8. Conclusions We analyzed the time-series of remote-sensed surface chlorophyll concentration and its relevant hydrological and meteorological factors (sea surface
temperature, atmospheric precipitation, air temperature and upwelling index) in the Santa Monica Bay and adjacent waters off southern California during the period from September 1997 to December 2001, using wavelet analysis and cross-correlation statistical methods. All parameters exhibited seasonal patterns of variations. The seasonal cycles of air temperature, sea surface temperature, upwelling index and chlorophyll concentration were approximated by sine function with annual periods. Upwelling index was closely related to solar cycles with the minimum and maximum coinciding with winter and summer solar solstices, respectively. The seasonal cycles of air temperature and sea surface temperature lagged behind the wind cycle about 2 and 2.5 months, respectively. Wavelet analysis revealed short-period (<100 days) variations of remote-sensed chlorophyll biomass during spring seasons; these variations were not evident from time-series data. The long-term variations were evident in air temperature during El Nin˜o 1997– 1998 and in wind stress during La Nin˜a 1998 –1999. Chlorophyll biomass was significantly correlated with sea surface temperature, air temperature and wind stress; no correlation with precipitation was observed. The variations of chlorophyll biomass and sea surface temperature lagged 5 – 6 and 5– 15 days behind the variations of wind stress, the latter being accompanied by a coherent variations of air temperature. The mechanism of these variations was an intensification of phytoplankton growth resulting from mixing of water column by wind stress and entrainment of cold rich in nutrients subthermocline water into the euphotic layer. The methodological aspect of this work consisted of a combined application of different types of timeseries statistical analysis to different parameters of the ocean system. It is worth mentioning that neither standard methods (spectral analysis and time-lagged correlation) nor newly introduced methodologies (wavelet analysis) can be recommended as the best tool for research. Indeed, each method reveals different features of the combined influence of meteorological and hydrological factors on the dynamics of phytoplankton in the coastal zone off California. We hope that the approach used in this study, in combination with other statistical methods, could be used in other regions of the World Ocean.
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Acknowledgements The authors would like to thank the SeaWiFS Project (Code 970.2) and the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center for the production and distribution of remote-sensed chlorophyll data, respectively. These activities are sponsored by NASA’s Mission to Planet Earth Program. We also thank the NASA Physical Oceanography Distributed Active Archive Center at the Jet Propulsion Laboratory, California Institute of Technology for sea surface temperature data and the Pacific Fisheries Environmental Laboratory for the data of upwelling index. We thank E. Stein for comments for the manuscript. The remarks of the anonymous reviewer were extremely helpful. The study was supported by California Seagrant (#R/CZ171), the UCLA Institute of the Environment and the University of California Agricultural Experiment Station.
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Time-series analysis of remote-sensed chlorophyll and environmental factors in the Santa Monica–San Pedro Basin off Southern California Nikolay P. Nezlin a,b,*, Bai-Lian Li b a
Institute of the Environment, UCLA, Los Angeles, CA, USA b Department of Botany and Plant Sciences, UCR, USA
Received 1 July 2002; received in revised form 16 October 2002; accepted 18 December 2002
Abstract The time-series of remote-sensed surface chlorophyll concentration measured by SeaWiFS radiometer from September 1997 to December 2001 and the relevant hydrological and meteorological factors (remote-sensed sea surface temperature, atmospheric precipitation, air temperature and wind stress) in Santa Monica Bay and adjacent waters off southern California were analyzed using wavelet and cross-correlation statistical methods. All parameters exhibited evident seasonal patterns of variation. Wavelet analysis revealed salient long-term variations most evident in air temperature during El Nin˜o 1997 – 1998 and in wind stress during La Nin˜a 1998 – 1999. Short-period (<100 days) variations of remote-sensed chlorophyll biomass were mostly typical to spring seasons. Chlorophyll biomass was significantly correlated with air temperature and wind stress: an increase of chlorophyll biomass followed with 5 – 6-day time lag an increase of wind stress accompanied by a simultaneous decrease of air temperature. The mechanism of these variations was an intensification of phytoplankton growth resulting from the mixing of water column by wind stress and entrainment of nutrients into the euphotic layer. D 2003 Elsevier Science B.V. All rights reserved. Keywords: Remote sensing; Photosynthetic pigments; Environmental conditions; Time-series analysis; USA; California; Southern California Bight; 33 – 34.5jN; 119 – 118jW
1. Introduction One of the most important problems in studying phytoplankton dynamics in the ocean is the analysis of environmental factors controlling phytoplankton growth (Raymont, 1980). Various methods, including * Corresponding author. Southern California Coastal Water Research Project, 7171 Fenwick Lane, Westminister, CA 92683, USA. Tel.: +1-714-372-9227; fax: +1-714-894-9699. E-mail address: [email protected] (N.P. Nezlin).
laboratory experiments and mathematical simulation, are used to estimate the mechanism of this influence. However, a statistical analysis of extensive time-series data from field observations is also important. It can reveal correlations between the dynamics of phytoplankton biomass and environmental characteristics. Based on these correlations, we could hypothesize the mechanisms of environmental control of the biological processes. The most important limitation of this analysis is that the number of measurements is usually insufficient for statistical analysis because oceano-
0924-7963/03/$ - see front matter D 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0924-7963(03)00030-7
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graphic observations are very expensive. This has changed during recent years because information collected by scientific satellites now provide extensive data from all regions of the World Ocean. About 60,000 images of ocean color collected by the Nimbus-7 Coastal Zone Color Scanner (CZCS) during 1978– 1986 have opened a ‘‘new epoch’’ in biological oceanography (Gordon et al., 1980; Hovis et al., 1980). The Sea-viewing Wide Field-of-view Sensor (SeaWiFS) radiometer has collected information on ocean color since September 1997 (Acker et al., 2002). The availability of more than 4 years of remote-sensed data coupled with simultaneous observations of the physical environment now enable statistical analysis at a high confidence level. In this study, we analyzed the dynamics of remotesensed phytoplankton biomass in Santa Monica Bay
and the adjacent San Pedro Basin (hereafter SMB) off the southern California coast (Fig. 1). This basin is located in the vicinity of densely populated metropolitan Los Angeles. SMB is a semi-enclosed inshore part of the Southern California Bight. It is roughly 100 km long, 40 km wide and 900 m deep. The hydrographic processes in SMB are determined by a complex pattern of surface currents (Hickey, 1992). The relatively cold waters of the southward flowing California Current penetrate to SMB through the Santa Barbara Channel, this flow being especially intensive in spring (Bray et al., 1999; DiGiacomo and Holt, 2001). The relatively warm waters of the poleward Southern California Countercurrent (Sverdrup and Fleming, 1941) penetrate to the basin alongshore from the southeast. The temperature gradients near the coast are enhanced by the effects of local wind-driven
Fig. 1. Santa Monica Bay and adjacent waters. Rectangle indicates the region of averaging SeaWiFS chlorophyll and Pathfinder SST. Arrow with circle indicates the location of upwelling index (33jN, 119jW). Oblique cross indicates the center of 1j1j rectangle of GPCP atmospheric precipitation.
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upwelling, resulting in a decrease of sea surface temperature and local phytoplankton blooms. Thus, the extent of phytoplankton biomass in SMB is expected to result not only from local environmental factors but also from remote forcing, i.e., the hydrographic and meteorological conditions in other areas.
2. Data used for analysis The analysis of temporal variations of phytoplankton biomass in SMB was based on the data of SeaWiFS radiometer on the OrbView-2 satellite. The data were obtained from NASA Goddard Space Flight Center Distributed Active Archive Center (GSFC DAAC). We used daily averaged Level 3 Standard Mapped Images (SMI) data of SeaWiFS surface chlorophyll calculated during reprocessing #3 (Version 3). Level 3 SMI SeaWiFS chlorophyll data were interpolated to a regular grid of equidistant cylindrical projection of 360j/ 4096 pixels (about 9.28 km) resolution. Use of this data was in accord with the SeaWiFS Research Data Use Terms and Conditions Agreement. The algorithms used in GSFC for calculating surface chlorophyll concentration are described in O’Reilly et al. (1998). In this study, we use remote-sensed data (i.e., surface chlorophyll concentration derived from water color) to analyze the dynamics of phytoplankton biomass in the water column. The remotely sensed surface pigment concentration and total pigment concentration in water column are correlated (Chavez, 1995; Smith and Baker, 1978) but not identical. However, for this study, the absolute values are not as important as temporal gradients of phytoplankton biomass. We understand that the absolute values of surface chlorophyll concentration derived from satellite measurements are subject to significant inaccuracy due to technical difficulty of remote-sensed observations, and we do not compare the remote-sensed data directly to in situ absolute values of chlorophyll concentration and phytoplankton biomass. Instead, we consider the variations of satellite-measured chlorophyll concentration as an indicator of dynamics of phytoplankton biomass in the study region. The mean remote-sensed surface chlorophyll concentration was calculated as a median within the region located between the coast and the lines 119jW longitude, 33j30VN latitude and 118jW longitude (the
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rectangle at Fig. 1). We used a median instead of an arithmetic mean because the statistical distribution of remote-sensed chlorophyll in World Ocean has been shown to be asymmetric (Banse and English, 1994) and medians were much closer to modal values than means. This pattern was true for the study region as well. During all seasons (Fig. 2), log-transformation made the statistical distributions more symmetrical, but still far from normal (Kolmogorov –Smirnov normality test failed, P<0.001, for all four seasons). About 45% of observations contained no SeaWiFS observations within the chosen rectangle. To obtain the values for these days, the medians were log-transformed, then the missing data were linearly interpolated, and the resulting values were exp-transformed. To estimate the variations of sea surface temperature (SST) we used remote-sensed Advanced Very High Resolution Radiometer (AVHRR) data from Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (JPL PODAAC). These data were processed in JPL within the scope of NOAA/ NASA AVHRR Oceans Pathfinder Project. The SST data were derived from the AVHRR radiometers using an enhanced nonlinear algorithm (Walton, 1988). We used daily data for the descending pass (night-time) on global equal-angle grids of 4096 pixels/360j (f9.28 km) resolution, similar to SeaWiFS chlorophyll. Only the ‘‘best SST’’ data, (i.e., highest quality pixel values) was used. The versions V4.1 (January 1997– December 1999) and interim V4.1 (January 2000 –July 2001) were used to derive absolute SST values for time-series analysis. The arithmetic mean SST was calculated for each day within the rectangle between 118jW and 119jW and to the north of 33j30VN (similar to SeaWiFS chlorophyll; see Fig. 1). The daily values of ‘‘upwelling index’’ (i.e., the Ekman offshore drift calculated from the fields of atmospheric pressure (Bakun, 1973) for the location 33jN, 119jW (see Fig. 1) are obtained from the Pacific Fisheries Environmental Laboratory (PFEL) Internet site. Though expressed in the units of water transport (m3 s1 per 100 m of the coastline), in the Southern California Bight, the upwelling index is a measure of not only upwelling, but also of wind stress influence on the stratification of the water column. The data of remote-sensed atmospheric precipitation (Huffman et al., 2002) were obtained from NASA GSFC DAAC. These data were created as
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Fig. 2. Statistical distribution of remote-sensed surface chlorophyll concentrations in Santa Monica Bay and adjacent waters during four climatic seasons (Fall: September – November; Winter: December – February; Spring: March – May; Summer: June – August). x-scale is logarithmic; yscale is a percentage of observations.
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a part of the Global Precipitation Climatology Project (GPCP) from the measurements of Special Sensor Microwave/Imager (SSM/I) multichannel passive microwave radiometers on Defense Meteorological Satellite Program (DMSP) satellites, infrared (IR) sensors on Geosynchronous Operational Environmental Satellites (GOES, USA), Geosynchronous Meteorological Satellite (GMS, Japan), Meteorological Satellite (METEOSAT, European Community), the NOAA series low-earth-orbit satellite (LEO, USA) and the TIROS Operational Vertical Sounder (TOVS) data derived from the High-Resolution Infrared Sounder 2 (HIRS2), Microwave Sounding Unit (MSU) and Stratospheric Sounding Unit (SSU) instruments on the NOAA series of polar orbiting meteorological satellites. The daily data have 1j spatial resolution, and we used the 1j1j grid node located at 33j30VN, 118j30VW for analysis. The accuracy of remote-sensed atmospheric precipitation data is still questionable; therefore, we compared them with standard meteorological data measured by rain gauges. Air temperature and precipitation for the period January 1994 – December 2001 measured at the meteorological station of the Los Angeles International Airport (LAX) located at 33j55.8VN, 118j24VW were obtained from the Internet site of the National Climatic Data Center (NCDC).
3. Statistical methods used for analysis Air temperature (Tair), upwelling index (UI), SST and surface chlorophyll concentration (Chl) exhibit seasonal oscillations (Fig. 3). We estimated climatological values approximating the time-series of Tair, UI, SST and Chl using a sine function (Chl was logtransformed). The resulting climatological values were then subtracted from actual values of Tair, UI and SST to obtain seasonal anomalies. Seasonal anomalies of Chl were calculated in the following way: Chl data were log-transformed, the climatological cycle was subtracted from the actual values, and then the Chl data were exp-transformed. The time-series of seasonal anomalies of Tair, UI, SST and Chl, and total values of gauge-measured (GMP-LAX) and satellite-measured (GPCP) atmospheric precipitation were processed using wavelet
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analysis (Breaker et al., 2001; Gao and Li, 1993; Li, 1995; Li and Loehle, 1995; Lin, 1996; Torrence and Compo, 1998). The wavelet transform is a relatively new computational method for signal processing. Because of its characteristics of time/space-frequency localization and multi-resolution, the wavelet transform of a signal provides detailed information on underlying processes as a function of time and/or spatial scale. In wavelet representation, a geophysical signal is decomposed into a sum of elementary building blocks describing its local frequency content. The wavelet analysis provides both scale and time/space information and allows one to separate and sort different structures on different time/space scales at different times/locations. The integral wavelet transform is defined by 1 ðWw f Þða; bÞ ¼ pffiffiffiffiffiffiffiffiffi AaA
Z
tb f ðtÞw dt; a
pffiffiffiffiffiffiffiffiffi where the function wab(t)=w((tb)/a)/ AaA is called the ‘‘basis’’ or ‘‘mother wavelet’’. It must satisfy the following admissibility condition: Z l wðtÞdt ¼ 0 þl
The ‘‘Mexican hat’’ wavelet w(tV)=(1tV2 ) exp(tV2/2) was used as the wavelet basis function, which is the second-order derivative of the Gaussian function defined within 4VtVV4. The parameters a (a p 0) and b are used to adjust the shape pffiffiffiffiffiffiffiffiand ffi location of the wavelets, respectively. The 1/ AaA term keeps the energy of the scaled wavelet equal to the energy of the original basis wavelet. As a changes, the shape of the wavelet is compressed or stretched to cover different frequency ranges. Changing b allows one to move the time/space localization center and translate wavelets through all data points. In this way, the wavelet transform provides a time/space frequency description of a geophysical signal f(t). The wavelet transform, in essence, takes one-dimension function of time or space and expands it into a two-dimensional space consisting of time and scale. In our case, we analyzed temporal rather than spatial variations. The ability of wavelet analysis to determine the properties of spatial heterogeneity has not been used.
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Fig. 3. Time series of air temperature (jC) at LAX airport (A); upwelling index (m3 s1 per 100 m of coastline) at 33jN, 119jW (B); Pathfinder AVHRR sea surface temperature (jC) (C); and SeaWiFS surface chlorophyll (log mg m3) approximated by sine functions (dashed lines) (D).
We calculated wavelet power spectra defined as j(Wwf )(a,b)j2. The wavelet variance (or Global Wavelet Spectrum, GWS) was calculated by the integration of the squared transform coefficients at different scales for all data points within the ‘‘cone of influence’’, i.e., the spatial range within which transformation coefficients are computed for a given scale a (Li, 1995; Li and Loehle, 1995).
The standard time-dependent wavelet spectra were normalized by taking the original wavelet power spectra and dividing them by the average wavelet power (i.e., the global wavelet spectra) for the entire time series. Thus, the normalized wavelet spectra (Figs. 4B – 9B) show the ratio of the current wavelet power to the ‘‘normal’’ or expected wavelet power (Figs. 4C – 9C). The time scale corresponding to the
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first local maximum of the starting point of the plateau of the GWS function (i.e., BGWS(a)/Ba=0) is used to determine the dominating time scale of each parameter under analysis. Application of wavelet analysis to oceanographic data is relatively new. This innovative tool helps us to interpret multi-scale, nonstationary time-series data and reveals features we could not see otherwise. However, wavelet analysis should not be used alone, but in parallel with other statistical time-series methods. In practice, wavelet analysis emphasizes interpretation of a signal that is changing over time, with good time-frequency localization and the ‘‘zoom-in, zoom-out’’ property, an issue which classical Fourier analysis is incapable of addressing. The wavelet variance, which provides a scale-based analysis of variance, is also complementary to traditional frequency-based spectral analysis.
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4. Seasonal cycles of phytoplankton biomass and environmental factors Remote-sensed surface chlorophyll concentration, sea surface temperature, air temperature and upwelling index in SMB exhibit regular seasonal variations, each of which can be approximated by a sine function with a period of 1 year (Fig. 3). The seasonal minimum of UI (i.e., the alongshore wind stress in the Southern California Bight) occurs on December 19th (Julian day 353); the seasonal maximum occurs on June 20th (Julian day 171). These dates coincide with the dates of winter and summer solstice, respectively. Thus, the seasonal cycle of wind patterns in this region coincide with the seasonal solar cycle, with minimum wind stress during the minimum of incoming insolation and vise versa. The seasonal cycle of Tair is lagged by about 50
Fig. 4. Seasonal variations (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of gauge-measured atmospheric precipitation (GMP-LAX, mm/day). The x-axis is time in years; the y-axis is the period that corresponds to the wavelet scale. The contour levels at (B) are at 0.5 (gray) and 2.0 (black).
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days compared to the solar cycle, with a minimum in early February (Julian days 38 –39) and a maximum in early August (Julian days 220 – 221). The seasonal cycle of SST is lagged by 23 days compared to Tair; the total lag of SST as compared with solar cycle being about 2.5 months. The SST minimum occurs in early March (Julian days 61– 62) and the maximum in early September (Julian days 243– 244). The seasonal cycle of Chl in the study region is in antiphase to the seasonal cycle of SST (minimum in the end of August and maximum in the end of February, Julian days 241 and 58, respectively). Atmospheric precipitation (Figs. 4A and 5A) also exhibits seasonality, but it cannot be approximated by a sine function. Both gauge-measured (Fig. 4A) and satellite-measured (Fig. 5A) precipitation evidently increase during autumn– winter – spring periods. Interannual variability was also evident, and the increase in precipitation was especially pronounced in the end of
1997 and the beginning of 1998 (i.e., during the El Nin˜o event). The seasonal maximum of precipitation occurred during February; this period coincided with the SST seasonal minimum and the Chl seasonal maximum. The reciprocal relationship between the seasonal cycles of phytoplankton biomass (Chl) and sea temperature (SST) indicates typical subtropical seasonality of phytoplankton growth. It can be attributed to the Model 3 of the production/consumption seasonal cycles, called ‘‘winter – spring production with nutrient limitation’’ (Longhurst, 1995). In this cycle, productivity is not light limited and increases during winter as the progressive deepening of the mixed layer recharges nutrients in the euphotic zone. This model is applicable to a large part of the open ocean where winter winds are relatively mild. At the same time, Longhurst (1995), in his classification of biogeographical provinces in the World Ocean, attributed
Fig. 5. Seasonal variations (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of satellite-measured atmospheric precipitation (GPCP, mm/day). The notations and shading scale are similar to Fig. 3.
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the Southern California Bight to Model 8 (‘‘the regions of intermittent production at coastal divergences’’), where the level of phytoplankton biomass is strictly related to upwelling-favorable wind stress. The seasonality of phytoplankton biomass and environmental factors (Fig. 3) illustrates that the seasonal cycle of phytoplankton in SMB is not directly regulated by upwelling-favorable wind, as is the case in a typical upwelling ecosystem (Cushing, 1971; Smith et al., 1983), e.g., along the central California and Oregon coast (Cushing, 1976). The Santa Monica – San Pedro Basin is sheltered by a mountain ridge from dominating in this latitudinal zone alongshore equatorward winds, and phytoplankton cycle is regulated by other factors influencing water column stratification, including ocean – atmosphere heat flux. Below, we analyze temporal variability and correlation between phytoplankton biomass and environmental factors on other than seasonal time scales.
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5. Variability at different frequency scales: wavelet analysis Hereafter, we analyze the time-series of total values of atmospheric precipitation (GMP-LAX and GPCP) and seasonal anomalies of the parameters with regular sine-type seasonal cycle: Tair, SST, UI and Chl. The seasonal cycles of both gauge-measured (GMP-LAX, Fig. 4A) and satellite-measured (GPCP, Fig. 5A) precipitation were almost identical, with maxima during winter seasons. The pattern of satellite-measured precipitation (Fig. 5A) exhibited more regular seasonal variations than gauge-measured data (Fig. 4A). The reason is that the GPCP data were derived from remote-sensed water vapor concentration in the atmosphere averaged over a large area (1j1j). In contrast, the gauge-measured precipitation characterizes a narrow location (LAX meteorological station at 33j55.8VN, 118j24VW), where precipitation is subject to small-scale spatio-temporal
Fig. 6. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of air temperature (Tair, jC). The notations and shading scale are similar to Fig. 3.
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variability. The location of gray and black areas at the wavelet power spectra (Figs. 4B –9B) in terms of time (x-axis) and frequency ( y-axis) coordinates indicates what frequency of variations dominated during that time period. The wavelet power spectra of precipitation (Figs. 4B and 5B) revealed oscillations in wide range of frequency during the period of rains in late winter and spring. During other periods, the precipitation was very small and its variability was almost absent. The most pronounced long-term variability was evident during the El Nin˜o period in the end of 1997 and in the first half of 1998. The first maximum of global wavelet spectrum (GWS) was 6– 8 days (Figs. 4C and 5C), which characterizes short rain events, observed in both satellite-measured and gauge-measured data. The seasonal anomalies of air temperature (Fig. 6A) were positive during the summer and autumn of 1997 (i.e., during the onset of the El Nin˜o event) and persistently negative during the transition from El
Nin˜o to La Nin˜a in the spring and early summer of 1998. Oscillations during the El Nin˜o period (1997) were characterized by long-term variations of 50– 400 days frequency (Fig. 6B). During the transition from El Nin˜o to La Nin˜a in 1998, the time-scale of oscillations decreased. From 1999 onwards, regular oscillations of frequency 20 –50 days were observed. The first GWS maximum was 98 days (Fig. 6C); this value seemingly results from the superposition of long-term variations dominating during 1997– 1998 and short-term variations dominating later in 1999– 2001. The latter was verified by analyzing the period 1999 – 2001 only, in which case, the first GWS maximum was as low as 50 days. The anomalies of SST (Fig. 7A) were evidently positive during the El Nin˜o period from early summer 1997 until spring 1998, and negative during the La Nin˜a period from autumn 1998 until winter 1999– 2000. Another period of negative SST was observed from autumn 2000 until summer 2001. In summer
Fig. 7. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of sea surface temperature (SST, jC). The notations and shading scale are similar to Fig. 3.
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1997 (during the onset of El Nin˜o), oscillations of frequency 40 –50 days dominated (Fig. 7B). Later, during the transition from El Nin˜o to La Nin˜a, the oscillations were longer, from 25 to 150 days. In summer 1999, pronounced oscillations occurred in all range of the time spectrum. A slightly changed pattern was observed in summer 2000. However, during this time period, the GWS function was monotonous without a local maximum (Fig. 7C), meaning that long-scale (interannual) SST variability dominated in SMB during 1997– 2001. The anomalies of UI were not evidently different from zero during the El Nin˜o period in 1997 and the beginning of 1998 (Fig. 8A). The offshore wind stress influence was persistently negative by the autumn of 1998 and no evident deviations occurred later, except short-time increase of the upwelling index in spring 1999. The first GWS maximum was 4 days (Fig. 8C), meaning that upwelling-favorable wind events were short compared to other environmental factors ana-
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lyzed. The wavelet power spectrum (Fig. 8B) revealed oscillations of 10 – 100 days typical to the spring seasons of 1998, 1999 and 2001, but less evident in spring 2000. Long-term oscillations (>100 days) occurred from summer 1998 to summer 1999 during the La Nin˜a period. It is interesting that no evident variations of wind stress were observed during the 1997– 1998 El Nin˜o period. This indicates that the 1997 – 1998 El Nin˜o event off southern California resulted from poleward propagation of coastally trapped waves rather than from the weakening of upwelling-favorable winds (see Discussion). The seasonal anomalies of remote-sensed chlorophyll biomass (Fig. 9A) exhibited numerous short spikes, explained as blooms of phytoplankton growth. From time-series data (Fig. 9A), it is hard to determine the seasons when these blooms dominate. The wavelet power spectrum (Fig. 9B) illustrates that the variations of less than 100 days period were mostly typical of spring seasons. In April 1998, during the
Fig. 8. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of upwelling index (UI, m3 s1 per 100 m of coastline) at 33jN, 119jW. The notations and shading scale are similar to Fig. 3.
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Fig. 9. Seasonal anomalies (A) and wavelet power spectrum (B) normalized by global wavelet spectrum (C) of SeaWiFS chlorophyll biomass (Chl, mg m3). The notations and shading scale are similar to Fig. 3.
late phase of El Nin˜o event, the spring period of variations was short. In spring 1999, this period was masked by pronounced variations of wide spectrum, which occurred during the 1998 –1999 autumn –winter period. We associate this variability with the 1998– 1999 La Nin˜a event. Later, during the spring seasons of 2000 and 2001, maximum variability was observed in the spring. Domination of variations of less than a period of 100 days was confirmed by the GWS pattern, where the GWS maximum was 66 days (Fig. 9C).
6. Correlations between phytoplankton biomass and environmental factors After removal of regular seasonal oscillations (see Fig. 3), the residuals, i.e., the variations of smaller time scale, appeared to be significantly correlated. Chl and SST anomalies are negatively correlated (Fig. 10A), which is a well known phenomenon in the
Southern California Bight (Eppley and Renger, 1988; Tont, 1976). The maximum correlation has zero time lag, but the cross-correlation diagram is asymmetric: correlations with negative time lag dominate over correlations with positive time lag. Therefore, high phytoplankton biomass is correlated with low SST observed simultaneously and a few days later. The negative correlation between SST and Chl can be explained by the upwelling of rich in nutrients subthermocline water into the upper euphotic layer, resulting in both a decrease of SST and an increase of phytoplankton growth rate. However, phytoplankton growth would be expected to occur not before, but a few days after the entrainment of nutrients into the euphotic layer. However, the observed negative time lag prima facie contradicts to this concept. The correlation between Chl and Tair is also negative but has a time lag of 5 days (Fig. 10B). In other words, the decrease of air temperature is followed by an increase of phytoplankton biomass 5 days later. The correlation between Chl and UI is significantly
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positive with 6-day time lag (Fig. 10C). Thus, a strengthening of upwelling-favorable wind stress results in phytoplankton bloom about 6 days later. However, it is important to distinguish between the influence of air temperature and wind stress on the stratification of water column and, in turn, on phyto-
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plankton growth. Both cooling of sea surface resulting from decrease of Tair and strengthening of wind stress favor mixing in the euphotic layer, and can recharge it with nutrients. SST is positively correlated with Tair; maximum correlation has zero time lag (Fig. 10D). The cross-
Fig. 10. Time-lagged correlation between (A) remote-sensed chlorophyll biomass (Chl) and sea surface temperature (SST); (B) Chl and air temperature (Tair), (C) Chl and upwelling index (UI), (D) SST and Tair, (E) SST and UI, (F) Tair and UI. Dashed lines indicate 95% confidence interval of the correlation coefficients.
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Fig. 10 (continued).
correlation diagram is asymmetric and this asymmetry emphasizes that the variations in air temperature precede the variations in SST. The correlation between SST and UI is negative with maximum time lag of 5 – 15 days (Fig. 10E). Therefore, strong upwelling-favorable wind events result in a decrease of SST during the next 1 – 2 weeks. Tair is also negatively correlated with UI with 1-day time lag (Fig. 10F). In other words, strong wind events coincide with next-day decrease of air temperature.
No significant correlation between Chl and atmospheric precipitation (GMP-LAX and GPCP) was revealed.
7. Discussion The statistical analysis of remote-sensed phytoplankton biomass and environmental factors revealed three types of variability: seasonal variability directly
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related to solar cycles, interannual variations and short-period variability. The main feature of interannual variability in SMB during the study period was the El Nin˜o Southern Oscillation (ENSO) cycle, in particular the El Nin˜o –La Nin˜a events in 1997– 1999. The 1997– 1998 El Nin˜o event was the strongest in the 20th century (Chavez et al., 1999; Kahru and Mitchell, 2000; McPhaden, 1999). Typical ENSO periodicity is from 3 to 7 years (Rasmusson and Wallace, 1983). Each El Nin˜o event starts when strong westerly wind bursts over warmer than normal equatorial waters in the western tropical Pacific. These winds generate oceanic baroclinic downwelling Kelvin waves that propagate eastward along the equator to the South American coast (McPhaden, 1999; Wyrtki, 1975). These waves are then transformed into coastally trapped waves, which propagate north and south toward the poles (Chelton and Davis, 1982; Enfield and Allen, 1980; Huyer and Smith, 1985). The coastal waves deepen the thermocline and raise sea level along the American coast, and an accumulation of warm water in the upper mixed layer results in large positive sea surface temperature anomalies. The redistribution of sea surface temperature over the Pacific Ocean results in atmospheric circulation changes, reflected in an expansion of the Aleutian Low (Emery and Hamilton, 1985). Some authors (Breaker and Lewis, 1988; Breaker et al., 2001; Mysak, 1986; Simpson, 1983, 1984) consider the changes of local wind pattern to be the main source of hydrological and ecological changes off California (‘‘atmospheric teleconnection’’); other authors emphasize the direct influence of coastal waves (‘‘oceanic teleconnection’’) (Chavez, 1996; Rienecker and Mooers, 1986) or a combination of these two effects (Huyer and Smith, 1985; Lynn et al., 1995; Ramp et al., 1997). Our results reveal features of both atmospheric and oceanic teleconnections in SMB during the 1997– 1998 El Nin˜o event. The classical features of oceanic teleconnection were evident from SST and Chl patterns. During the second half of 1997 and the first half of 1998 (i.e., during the El Nin˜o event), persistent positive SST anomalies exceeded 2jC (Figs. 3C and 7A). Remote-sensed phytoplankton biomass was lower than normal, especially in autumn 1997 and in summer 1998, i.e., during the periods of nutrient
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limitation of phytoplankton growth (Figs. 3D and 9A). Both positive SST and negative Chl anomalies can be explained by a deepening of the thermocline caused by poleward propagation of coastally trapped waves. This deepening results in heat accumulation and depletion of nutrients in the upper mixed layer. The signature of El Nin˜o was particularly evident from the analysis of sea surface height derived from satellite altimetry (Nezlin and McWilliams, 2003). During the transition to La Nin˜a in late 1998 and in 1999, SST dramatically decreased (Figs. 3C and 7A) and the frequency of blooms of phytoplankton biomass changed. Longer oscillations typical to late spring period were observed between November 1998 and April 1999 (Fig. 9B). Changes in wind stress were observed from summer 1998 until mid-1999, i.e., during La Nin˜a period (Fig. 8A and B). In mid-1998, UI decreased, and then increased from autumn 1998 until summer 1999. No evident changes in wind stress were observed during the onset of El Nin˜o in summer 1997, meaning that in SMB, oceanic teleconnection dominated over atmospheric during the 1997 –1998 El Nin˜o event. However, salient features of El Nin˜o were evident in other meteorological parameters, such as air temperature and precipitation. Positive Tair anomalies were observed in autumn 1997 (Figs. 3A and 6A); then the frequency of Tair oscillations gradually decreased during 1998 (Fig. 6B). Atmospheric precipitation in winter 1997 –1998 exceeded normal levels, especially in terms of remotesensed values (GPCP, Fig. 5A). The accumulation of heat in the upper mixed layer resulted in increase of evaporation from sea surface, resulting in a rainy season in winter 1997 – 1998 that was longer lasting and more intense than during other years. ENSO is known as a main source of variability of precipitation in many regions and, in particular, in southern California (Diaz et al., 2001; New et al., 2001; Peel et al., 2002), where torrential rains during the 1997– 1998 El Nin˜o event resulted in dramatic increase of river discharge and pollution (Dwight et al., 2002). Nutrient transport with river runoff could stimulate phytoplankton growth; direct atmospheric precipitation could contain micronutrients (like iron) from dust transported by wind from continental regions (Piketh et al., 2000). That is why we expected an increase of chlorophyll biomass after rains resulting from nutrient
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transport with river discharge; however, no correlation was observed. The reason is that in the study region even dramatically increased during an El Nin˜o event river discharge to Southern California Bight is too small to provide a considerable amount of nutrients to phytoplankton. Thus, the two kinds of features of ENSO cycle were evident in SMB: oceanographic processes dominated by propagation of downwelling coastal waves, and meteorological processes related to heat and water flux between the ocean and the atmosphere. For short-period variations, the correlations between Chl, SST, Tair and UI indicate wind as the basic factor controlling short-period phytoplankton blooms in the Santa Monica– San Pedro Basin. This region is sheltered by the mountains from the alongshore equatorward wind typical of this area. Furthermore, the seasonal dynamic of phytoplankton biomass is not directly regulated by upwelling-favorable wind, as off northern and central California (Cushing, 1971, 1976). However, our data illustrate that short-period strengthening of upwelling-favorable northwesterly wind is related to algae blooms 5 –6 days later. The intensification of the northwesterly wind results in a decrease of air temperature on the next day. Both wind stress and sea surface cooling erode the thermocline and entrain cold subthermocline water that is rich in nutrients into the upper euphotic layer (Eppley and Renger, 1988). These mixing processes, in turn, result in a decrease of SST and an increase in phytoplankton growth rate. The maximum phytoplankton bloom usually occurs less than 1 week after the wind event because the increased phytoplankton biomass is rapidly consumed by herbivorous phytoplankton. In contrast, the decrease of SST lasts longer (about 2 weeks); therefore, hydrographic response (i.e., the process of formation of thermocline) is slower as compared with biological response, resulting from the balance between phytoplankton growth and its grazing.
8. Conclusions We analyzed the time-series of remote-sensed surface chlorophyll concentration and its relevant hydrological and meteorological factors (sea surface
temperature, atmospheric precipitation, air temperature and upwelling index) in the Santa Monica Bay and adjacent waters off southern California during the period from September 1997 to December 2001, using wavelet analysis and cross-correlation statistical methods. All parameters exhibited seasonal patterns of variations. The seasonal cycles of air temperature, sea surface temperature, upwelling index and chlorophyll concentration were approximated by sine function with annual periods. Upwelling index was closely related to solar cycles with the minimum and maximum coinciding with winter and summer solar solstices, respectively. The seasonal cycles of air temperature and sea surface temperature lagged behind the wind cycle about 2 and 2.5 months, respectively. Wavelet analysis revealed short-period (<100 days) variations of remote-sensed chlorophyll biomass during spring seasons; these variations were not evident from time-series data. The long-term variations were evident in air temperature during El Nin˜o 1997– 1998 and in wind stress during La Nin˜a 1998 –1999. Chlorophyll biomass was significantly correlated with sea surface temperature, air temperature and wind stress; no correlation with precipitation was observed. The variations of chlorophyll biomass and sea surface temperature lagged 5 – 6 and 5– 15 days behind the variations of wind stress, the latter being accompanied by a coherent variations of air temperature. The mechanism of these variations was an intensification of phytoplankton growth resulting from mixing of water column by wind stress and entrainment of cold rich in nutrients subthermocline water into the euphotic layer. The methodological aspect of this work consisted of a combined application of different types of timeseries statistical analysis to different parameters of the ocean system. It is worth mentioning that neither standard methods (spectral analysis and time-lagged correlation) nor newly introduced methodologies (wavelet analysis) can be recommended as the best tool for research. Indeed, each method reveals different features of the combined influence of meteorological and hydrological factors on the dynamics of phytoplankton in the coastal zone off California. We hope that the approach used in this study, in combination with other statistical methods, could be used in other regions of the World Ocean.
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Acknowledgements The authors would like to thank the SeaWiFS Project (Code 970.2) and the Distributed Active Archive Center (Code 902) at the Goddard Space Flight Center for the production and distribution of remote-sensed chlorophyll data, respectively. These activities are sponsored by NASA’s Mission to Planet Earth Program. We also thank the NASA Physical Oceanography Distributed Active Archive Center at the Jet Propulsion Laboratory, California Institute of Technology for sea surface temperature data and the Pacific Fisheries Environmental Laboratory for the data of upwelling index. We thank E. Stein for comments for the manuscript. The remarks of the anonymous reviewer were extremely helpful. The study was supported by California Seagrant (#R/CZ171), the UCLA Institute of the Environment and the University of California Agricultural Experiment Station.
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