Regional differences in modelled net production and shallow remineralization in the North Atlantic subtropical gyre

Biogeosciences, 9, 2831–2846, 2012 www.biogeosciences.net/9/2831/2012/ doi:10.5194/bg-9-2831-2012 © Author(s) 2012. CC Attribution 3.0 License. Biogeosciences Regional differences in modelled net production and shallow remineralization in the North Atlantic subtropical gyre B. Fern´ andez-Castro1, L. Anderson2, E. Mara˜ n´ on1, S. Neuer3, B. Aus´ ın1, M. Gonz´ alez-D´ avila4, J. M. Santana-Casiano4, A. Cianca4, R. Santana4, O. LLin´ as5, M. J. Rueda6, and B. Mouri˜ no-Carballido1 1Departamento de Ecolox´ ıa e Biolox´ ıa Animal, Universidade de Vigo, Vigo, Pontevedra, 36200, Spain 2Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, Woods Hole, MA 02543-1541, USA 3School of Life Sciences, Arizona State University, Tempe, AZ 85287-4501, USA 4Universidad de Las Palmas de Gran Canaria (ULPGC), Campus Universitario de Tafira s/n., 35017 Las Palmas de Gran Canaria, Spain 5Plataforma Oce´ anica de Canarias (PLOCAN), Crta. de Taliarte s/n, P.O. Box 413, 35200 Telde, Gran Canaria, Spain 6Instituto Canario de Ciencias Marinas, Gobierno de Canarias, Telde, Gran Canaria, Spain Correspondence to: B. Fern´ andez-Castro ([email protected]) Received: 5 December 2011 – Published in Biogeosciences Discuss.: 22 December 2011 Revised: 15 May 2012 – Accepted: 30 June 2012 – Published: 1 August 2012 Abstract. We used 5-yr concomitant data of tracer distribution from the BATS (Bermuda Time-series Study) and ESTOC (European Station for Time-Series in the Ocean, Canary Islands) sites to build a 1-D tracer model conservation including horizontal advection, and then compute net production and shallow remineralization rates for both sites. Our main goal was to verify if differences in these rates are consistent with the lower export rates of particulate organic carbon observed at ESTOC. Net production rates computed below the mixed layer to 110m from April to December for oxygen, dissolved inorganic carbon and nitrate at BATS (1.34±0.79molO2m−2,−1.73±0.52molCm−2 and −125±36mmolNm−2) were slightly higher for oxygen and carbon compared to ESTOC (1.03±0.62molO2m−2, −1.42±0.30molCm−2and −213±56mmolNm−2), although the differences were not statistically significant. Shallow remineralization rates between 110 and 250m computed at ESTOC (−3.9±1.0molO2m−2, 1.53±0.43molCm−2 and 38±155mmolNm−2) were statistically higher for oxygen compared to BATS (−1.81±0.37molO2m−2, 1.52± 0.30molCm−2and 147±43mmolNm−2). The lateral advective flux divergence of tracers, which was more significant at ESTOC, was responsible for the differences in estimated oxygen remineralization rates between both stations. According to these results, the differences in net production and shallow remineralization cannot fully explain the differences in the flux of sinking organic matter observed between both stations, suggesting an additional consumption of nonsinking organic matter at ESTOC. 1 Introduction The ocean is responsible for an annual photosynthetic fixation of ∼50Pg of carbon, which represents around half of theglobalprimaryproduction(Fieldetal., 1998). Carbon fixation by marine phytoplankton and its transport to the deep ocean, known as the biological carbon pump, plays a key role in the ocean–atmosphere CO2exchange, and hence affects climate on time-scales from decades to thousands of years. The carbon pump efficiency is not constant and depends on several factors, such as the input of nutrients into the euphotic layer, and the balance between synthesis and remineralization of organic matter as well as the planktonic community composition. Subtropical gyres represent the central part of the ocean and are characterized by a strong stratification of the water column. This translates into a weak input of nutrients into the euphotic layer and, as a consequence, low phytoplankton biomass and productivity. Because of their Published by Copernicus Publications on behalf of the European Geosciences Union. 2832 B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization oligotrophic characteristics, they have been traditionally considered oceanic deserts. However, from the point of view of carbon export from the euphotic zone, subtropicalgyres were found to be surprisingly productive (Emerson et al., 1997). This fact, combined with their vast extension (∼60% of the total ocean surface), results in their contribution of at least 30% of total marine new production (Najjar and Keeling, 2000). The North Atlantic subtropical gyre (NASG) is one of the best studied open ocean regions, and in the past decades, it has been a major contributor to the development of our understanding of biogeochemical cycles in subtropical regions. Traditionally, subtropical gyres have been considered relatively constant ecosystems in time and space. However, the comparative study of time-series data revealed that spatial heterogeneity is important in understanding the biogeochemistry of this biome (Neuer et al., 2002a; Mouri˜ no-Carballido and Neuer, 2008). Two time-series stations, BATS (Bermuda Atlantic Time-series Study, 31.7◦N–64.2◦W) and ESTOC (European Station for Time series in the Ocean, Canary Islands, 29.16◦N–15.5◦W), are located at about the same latitude in the western (NASW) and eastern (NASE) portions of NASG. The monthly sampling program was initiated in October 1988 at BATS and in February 1994 at ESTOC, with the aim of tracking seasonal, inter-annual and inter-decadal changes in several biogeochemical variables (Steinberg et al., 2001; Neuer et al., 2007). Although both stations exhibit typical oligotrophic characteristics, they are characterized by different hydrographic dynamics. BATS is located in the recirculation of the Gulf Stream and it is strongly affected by mesoscale activity (Cianca et al., 2007). ESTOC is entrained by the Canary Current and is indirectly influenced by the coastal African upwelling, whichs exports nutrients and organic matter towards the center of the gyre by means of upwelling filaments and Ekman transport (Pelegr´ ı et al., 2005; ´ Alvarez-Salgado et al., 2007). It is important to note that upwelling filaments do not influence ESTOC directly (Davenport et al., 2002), and this station is considered oligotrophic based on nutrient scarcity, phytoplankton biomass and production rates (Neuer et al., 2007). Although both stations are characterized by similar phytoplankton biomass and primary production rates, annual mean carbon export measured by using sediment traps at 150, 200 and 300m is significantly lower at ESTOC, by a factor of 3– 5, than at BATS (Neuer et al., 2002a; Helmke et al., 2010). Assuming that the euphotic zone is in steady state, higher export production should be sustained by larger inputs of nutrients and/or organic matter into the euphotic zone. It has been proposed that differences in the nutrients input could be due to the more intense mesoscale activity observed at BATS (Siegel et al., 1999; Mouri˜ no et al., 2003). A comparative study using 10-yr BATS and ESTOC data combined with satellite altimetry data indicated that the higher physical forcing dominant at BATS – deeper mixed layers and more intense mesoscale dynamics – is partly compensated by the shallower nutricline observed at ESTOC (Cianca et al., 2007). According to these authors, NASE receives ∼75% of the nutrients avaliable for new production at NASW, although this difference is not statistically significant. Another source of new nitrogen that could explain the observed differences in carbon export between the two stations is biological nitrogen fixation. Recent studies indicate that this process supplies a significative contribution to total new nitrogen inputs in oligotrophic waters (Capone et al., 2005; Mouri˜ noCarballido et al., 2011; Bonnet et al., 2011). Biogeochemical estimates suggest a lower contribution of nitrogen fixation at ESTOC compared with BATS (Neuer et al., 2002a). The differences observed between the two stations in export rates of particulate organic carbon could also be a consequence of differences in the remineralization rates of the sinking organic matter. The comparative analysis of respiration rates determined from in vitro oxygen evolution experiments conducted in the NASE and NASW indicated higher oxygen consumption rates in the eastern part (Mouri˜ noCarballido and Neuer, 2008). It has been shown that atmospheric dust deposition can strongly stimulate bacterial respiration (Pulido-Villena et al., 2008). A recent study demonstrated that the most frequent and intense response of the microbial plankton to atmospheric dust deposition in the Tropical Atlantic is the stimulation of the bacterial activity rather than phytoplankton primary production (Mara˜ n´ on et al., 2010). This is consistent with the results found at ESTOC, which is strongly influenced by the natural atmospheric deposition of Saharan dust, and where phytoplanktonic production seems not to be affected by aerosol inputs (Neuer et al., 2004). For the first time, concomitant data of tracer distribution from the BATS and ESTOC time-series sites were analysed and used to build up a 1-D tracer conservation diagnostic model. The model was used to compute net production and shallow remineralization rates at both sites. The main goal of this study was to verify if differences in the synthesis and consumption of organic matter could explain the lower export rates of particulate organic carbon observed at ESTOC. 2 Comparison of the seasonal cycles at BATS and ESTOC Monthly climatologies were calculated for temperature, salinity, density anomaly (σT), oxygen, dissolved inorganic carbon (DIC), nitrate (actually nitrate+nitrite) and chlorophyll a, using the BATS and ESTOC data collected for the period 1996–2001 (Fig. 1). The BATS data were obtained from the BATS Web site (http://bats.bios.edu). BATS and ESTOC measurements were made monthly except during the spring bloom period at BATS (February–April) when biweekly samplings were conducted. Methodologies and results have been reported earlier for BATS (Michaels and Knap, 1996; Steinberg et al., 2001) and ESTOC (Neuer et al., Biogeosciences, 9, 2831–2846, 2012 www.biogeosciences.net/9/2831/2012/ B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization 2833 BATS 19 20 21 22 23 24 25 26 27 Depth (m) T [oC] J A S O N D J F M A M J J A S O N D J F M A M J −250 −200 −150 −100 −50 0 36.5 36.6 36.6 36.65 36.7 36.7 S J A S O N D J F M A M J J A S O N D J F M A M J 24 24.5 25 25.5 26 26 26.25 Depth (m) Sigmat [kg m−3] J A S O N D J F M A M J J A S O N D J F M A M J −250 −200 −150 −100 −50 0 0.05 0.05 0.1 0.1 0.15 0.15 0.2 0.25 Chlorophyll [mg m−3] J A S O N D J F M A M J J A S O N D J F M A M J 205 210 210 215 215 220 225 230 225 Depth (m) O2[mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J −250 −200 −150 −100 −50 0 −25 −20 −15 −10 −5 0 5 10 15 O2Anomaly [mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J 2080 2090 2100 2110 2120 2130 2140 Depth (m) DIC [mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J −250 −200 −150 −100 −50 0 0.5 1 1.5 2 2.5 3 NO2+ NO3[mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J 16 17 18 19 20 21 22 23 T [oC] J A S O N D J F M A M J J A S O N D J F M A M J 36.2 36.3 36.4 36.5 36.6 36.7 36.8 S J A S O N D J F M A M J J A S O N D J F M A M J 25.4 25.6 26 26.2 26.4 26.4 26.6 Sigmat [Kg m−3] J A S O N D J F M A M J J A S O N D J F M A M J 0.05 0.1 0.1 0.15 0.15 0.2 0.25 0.3 Chlorophyll [mg m−3] J A S O N D J F M A M J J A S O N D J F M A M J 210 215 220 225 230 230 235 240 240 O2[mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J −40 −35 −30 −25 −20 − 15 −10 −5 0 5 10 O2Anomaly [mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J 2145 2150 2155 2160 2165 2180 2185 2190 DIC [mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J 1 2 3 4 5 6 7 8 NO3+NO2[mmol m−3] J A S O N D J F M A M J J A S O N D J F M A M J ESTOC 25.8 2170 2175 0.5 Fig. 1. Seasonal cycles of the vertical distribution of temperature (◦C), salinity (psu), density anomaly σT(kgm−3), chlorophyll a(mg m−3), nitrate + nitrite (mmolm−3), oxygen (mmolm−3), oxygen anomaly (mmolm−3) and dissolved inorganic carbon (DIC) (mmolm−3). Black discontinuous line represents the mixed layer depth. Black thick line represents the isoline of zero oxygen anomaly and the nitracline ([NO3]=0.5mmolm−3). 2007). Discrete data were first interpolated to a grid with the following depths: 5, 10, 20, 40, 60, 80, 100, 110, 120, 140, 160, 200 and 250m. The mean annual cycle was then constructed by averaging data from each depth onto a temporal grid of 15 days using a weighted moving average with a normal weighting factor, fn i, and a window of 50 days: hCni = Pifn iCi/Pifn i fn i=exp−tn−ti σ2,(1) where Cis the averaged variable, tis the time of the year in days (0–365), iis the index of the discrete data point, nis the index for the temporal grid and σ=35 days is a constant time scale. The mixed layer depth was calculated as the depth where temperature differs 0.1◦C from the 10m value. BATS and ESTOC exhibit a similar seasonality in the hydrographic conditions characterized by a period of winter mixing from November to March, followed by a period of summer stratification from May to September (Fig. 1). The maximum winter mixing occurs later (mid-March) at BATS compared to ESTOC (February). The transition between both periods is controlled by the seasonal cycle ofsolar irradiation and changes in the wind speed. Seasonality is more intense at BATS, where surface temperature increases from a minimum of ∼20◦C in mid-March to a maximum of ∼28◦C in August. At ESTOC, temperatures reach a minimum of 18– 19◦C in February and a maximum of ∼24◦C in September. This maximum appears later than at BATS because the intensification of the trade winds affects the ESTOC site during the summer period. Maxima mixed layers are deeper at BATS (193±26m) than at ESTOC (146±32m). Summer www.biogeosciences.net/9/2831/2012/ Biogeosciences, 9, 2831–2846, 2012 2834 B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization stratification is also more intense at BATS, where mixed layers during this period reach ∼20m. Mixed layers at ESTOC extend to a depth of ∼50m due to the trade winds intensification that enhances mixing during July–August. The main difference between both stations in salinity appears during the summer, when mixed layer values are fresher at BATS due to the rain intensification. Chlorophyll concentration is strongly influenced by the physical forcing at both stations. Phytoplankton blooms occur in late winter/early spring, after the mixed layer extends below the nutricline, with greatest monthly values of chlorophyll concentration similar for both stations (ca. 0.3mgm−3). As water column stratification increases, surface chlorophyll decreases and a deep chlorophyll maximum establishes at ca. 100m at BATS and ESTOC. This biological activity as well as the physical forcing determine the concentration of the chemical tracers considered in this study. The oxygen seasonal cycle in the mixed layer at both stations is characterized by maximum values during the winter period due to the increase in solubility. The temperature increase during the summer months causes a drop in solubility and hence in surface oxygen concentration. As stratification increases during summer, oxygen accumulates below the mixed layer (∼40m at BATS and ∼60m at ESTOC). This accumulation is clearly pictured by the oxygen anomaly distribution that represents the excess of oxygen concentration above the solubility equilibrium. The oxygen anomaly distribution shows that whereas the photic layer (ca. 100m at both sites, Cianca et al., 2007) at ESTOC is oversaturated during the whole year, at BATS it is undersaturated during the winter period. This difference is probably due to the different intensity in the mixing and ventilation of deep waters that characterizes both stations. The seasonal variability in the oxygen cycle is also weaker at ESTOC, where the maximum amplitude of the accumulation in oxygen anomaly below the mixed layer was ∼9mmolm−3(from February to August), versus ∼17mmolm−3(from February to September) at BATS. The oxygen anomaly distribution in the euphotic zone has a mirror-like image below this layer. Oxygen concentration increases during winter–spring as a result of the mixing with oxygen-rich surface waters and it is maximum in April–May. Then, during summer stratification oxygen concentration decreases, with the minimum values observed in January (BATS) and November (ESTOC) at 200m. Despite the differences observed in the euphotic zone, the maximum amplitude of the oxygen signal at 200m (∼7mmolm−3) is similar in both stations. DIC concentrations in the mixed layer are greatest in April (BATS) and March (ESTOC), due to mixing with deeper waters and, to a lesser extent, to air-sea exchange (Marchal et al., 1996; Gruber et al., 1998; Gonz´ alez-D´ avila et al., 2003). Surface DIC concentrations decline to minimum values in October, this decline being more pronounced at BATS (∼44mmolm−3) compared to ESTOC (∼15mmolm−3). Below the euphotic zone, DIC accumulation occurs in concert with the observed oxygen decline. The DIC accumulation at 200m from April to January (BATS) and from April to October (ESTOC) is 6mmolm−3and 8.7mmolm−3, respectively. Nitrate concentration is very low in the euphotic zone at both stations. The nitracline (>0.5mmolm−3) is generally shallower at ESTOC (90.3±5.0m) compared to BATS (104±14m), but with a greater vertical variability at the western station. As a consequence of the influence of the nutrient poor 18◦C Subtropical Mode Water at BATS, nitrate concentration below the nitracline is higher at ESTOC. Below the euphotic zone, nitrate accumulates from June to January at BATS and from May to December at ESTOC; the amplitude of the accumulation at 200m is very similar in both stations (0.7mmolm−3). The seasonal variability of the tracers considered in this study, in agreement with previous reports for BATS (Menzel and Ryther, 1959; Jenkins and Goldman, 1985; Marchal et al., 1996) and ESTOC (Gonz´ alez-D´ avila et al., 2003; Neuer et al., 2007; Santana-Casiano et al., 2007), is consistent with the fact that synthesis of organic matter occurs in the euphotic layer and that this matter is remineralized, at least partially, in the shallow aphotic zone between 100– 250m. In order to quantify the contribution of the biological processes and the physical forcing to the observed seasonal variability of the chemical tracers, a 1-D diagnostic model was implemented. 3 Model implementation 3.1 Model general description We adapted the tracer conservation model based on Ono et al. (2001), who estimated the shallow remineralization at BATS for the period 1992–1998. This approach includes the main physical processes that occur below the mixed layer when convective winter mixing is not the dominant process. These processes are vertical diffusion, vertical advection (Ekman transport) and lateral advection. We used the following tracer conservation equation ∂C ∂t = −u∂C ∂x −v∂C ∂y −w∂C ∂z +K∂2C ∂z2+JC(2) where C=C(t,z) represents temperature (T) or the tracer (oxygen, DIC, nitrate) concentration for each depth, z, and time t;u(t,z) and v(t,z) are the longitudinal and latitudinal geostrophical velocities, respectively; ∂C/∂x and ∂C/∂y are the longitudinal and latitudinal gradients of temperature and tracer concentration; K(t) is the vertical diffusivity, and JC(t,z) represents the sources minus sinks term. For temperature, JCrepresents the effect of the solar shortwave radiation that penetrates below the mixed layer depth; whereas for oxygen, DIC and nitrate it represents the net effect of Biogeosciences, 9, 2831–2846, 2012 www.biogeosciences.net/9/2831/2012/ B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization 2835 BATS ESTOC −0.03 −0.025 −0.02 −0.025 −0.015 −0.02 U [m s−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 −0.03 −0.02 −0.03 −0.02 −0.02 −0.015 −0.015 −0.015 −0.015 −0.01 V [m s−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 −0.02 −0.02 −0.01 0 0.01 0.01 0.02 −0.01 −0.01 −0.01 0.03 U [m s−1] J F M A M J J A S O N D −0.06 −0.04 −0.02 0 −0.04 −0.02 V [m s−1] J F M A M J J A S O N D Fig. 2. Computed geostrophic horizontal velocities (u,v) for BATS and ESTOC. The thick black line represents the null velocity component. photosynthesis and respiration, and therefore net production and shallow remineralization. The model was executed 23 times for each variable in periods of 15 days during the seasonal cycle, with a time step dt=0.005daysandanuniformverticalgrid (dz=1 m) from the mixed layer depth to 250m. In each of these runs the model was initialized with the profile of the tracer for the initial time t,Cobs(t,z), and produces the modelled profile for the final time t+1t,Cmodel(t +1t,z), where 1t =15 days. For the biological tracers JCwas computed diagnostically, as described by Mouri˜ no-Carballido and Anderson (2009), so that the output of the model for each run fits the profile for the corresponding final time. An initial guess for the JC(t,z) term was made as (Cobs(t +1t,z) −Cobs(t,z))/1t. The model was run forward from time tto time t+1t and a mean squared misfit was computed as: Cost =  250 m Z MLD (Cobs(t +1t,z) −Cmod(t +1t,z))2dz  1/2 .(3) When the difference of Cost with the previous estimate was less than 0.002mmolm−2, the simulation was ended and the simulation of the next 15-day period was initiated. Otherwise, JC(t,z) was corrected as: Jnew C(t,z)=JC(t,z)+0.75(Cobs(t+1t,z)−Cmod(t +1t,z))/1t, (4) and a new run was conducted. In this way, JC(t,z) was optimized so that Cmod(t +1t,z) fits Cobs(t +1t,z). In order to compute the biological source term (JC), tracer data, horizontal and vertical velocities were calculated as described below and linearly interpolated to the model grid. 3.2 Model inputs 3.2.1 Horizontal advection Horizontal gradients of temperature, oxygen and nitrate were calculated using the four grid points surrounding BATS and ESTOC at the World Ocean Atlas 2009 monthly climatology (WOA09) (Locarnini et al., 2010; Garcia et al., 2010b,a). Longitudinal and latitudinal gradients were computed using averaged values for each xand y-components. DIC data for the same locations were obtained from the Global Distribution of Total Inorganic Carbon and Total Alkalinity Below the Deepest Winter Mixed Layer Depths climatology (Goyet et al., 2000), which includes lower temporal resolution trimonthly averaged data. Horizontal velocities, uand v, were assumed to be geostrophic and computed from the monthly temperature and salinity WOA09 data (Locarnini et al., 2010; Antonov et al., 2010) and the thermal wind equations. Water density was calculated using the Millero and Poisson (1981) parametrization. The thermal wind equations were integrated down to 3000m, which was assumed as the level of no motion following Siegel and Deuser (1997). The calculated geostrophic flow for BATS (0.02–0.04ms−1) was directed southwest during the whole year (see Fig. 2), consistent with the fact that BATS is influenced by the Gulf Stream recirculation (Siegel and Deuser, 1997; Ono et al., 2001). The flow intensity was more variable near ESTOC (0.02–0.07ms−1), where the current was directed southwest during most of the year except between March and April, when uwas eastward, and during the transition from spring to summer (May–July) when vwas northward. This pattern is consistent with the results described by Neuer et al. (2007) and Pelegr´ ı et al. (2005). Due to the implementation imposed to ensure volume conservation (see next section), lateral transport included a nongeostrophic component. Further on we will use geostrophic horizontal advection to refer to the geostrophic component of the lateral transport and horizontal advection to refer to the total corrected lateral transport. Additionally, model runs where the geostrophic transport term was set to zero (u=v=0) were used to determine the www.biogeosciences.net/9/2831/2012/ Biogeosciences, 9, 2831–2846, 2012 2836 B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization influence of the geostrophic transport in the computed biological rates. 3.2.2 Vertical advection Ekman downwelling/upwelling velocity, w, was computed for both stations from the wind stress monthly climatological data included in the International Comprehensive OceanAtmosphere Data Set, with a spatial resolution of 2◦×2◦ (Leetmaa and Bunker, 1978). The computed Ekman velocity at BATS was negative during most of the year and characterized by a clear seasonal cycle (see Fig. 3a). Downwelling was maximum in February (−96myr−1) and minimum in September, when a weak upwelling was computed (10myr−1). These results are in agreement with the harmonic function used by Musgrave et al. (1988) and Ono et al. (2001) at BATS. The Ekman velocity was lower at ESTOC, where the seasonal cycle was characterized by relatively weak upwelling during the summer (ca. 20myr−1) and relatively weak downwelling during the rest of the year. The Ekman velocity was set to zero at the surface and increased linearly to the Ekman depth, which was considered as the minimum value of 30m and the mixed layer depth, and decreased linearly to zero down to 250m (Ono et al., 2001). As the depth-dependent wrequires horizontal convergence or divergence for volume conservation, horizontal advection included a correction term. This was accomplished numerically by implicitly evaluating w∂C/∂z at the grid box interfaces. 3.2.3 Shortwave solar radiation The effect of the solar shortwave radiation that penetrates below the mixed layer (JCterm for the temperature model) was computed as: JT C(t,z) =1 ρ(t,z)Cp(t,z) ∂I (t,z) ∂z ,(5) where ρis the water density computed from temperature and salinity seasonal cycles using the Millero and Poisson (1981) formulation, Cpis the specific heat (Fofonoff and Millard, 1983), and I (t,z) is the shortwave radiation flux computed by using the attenuation model of Paulson and Simpson (1977) for Type I water and the surface shortwave radiation values (Fig. 3b). These values were obtained by fitting to an harmonic function monthly data for the period 1996–2001 obtained from the CORE.2 Global Air-Sea flux dataset close to both sites. Annual means and amplitudes of the harmonic function were 182±5Wm−2and 87±6Wm−2 at BATS, and 191±5Wm−2and 68±8Wm−2at ESTOC, respectively. Surface Iwas minimum in January for both stations. BATS ESTOC −120 −100 −80 −60 −40 −20 0 20 E[m yr−1] J F M A M J J A S O N D 50 100 150 200 250 300 Shortwave radiation flux, [W m ] −2 J F M A M J J A S O N D (a) (b) Fig. 3. (a) Vertical Ekman velocity (w) at the Ekman depth (wE) and (b) surface shortwave solar radiation computed for the period 1996–2001 near BATS and ESTOC. The continuous lines represent the fit to a single harmonic function, whereas the dotted line correspond to the 95% confidence intervals. Fitting of wEto the harmonic function was not used in the model. 3.3 Temperature model and Koptimization Similar to the approach followed by Musgrave et al. (1988), Ono et al. (2001) and Mouri˜ no-Carballido and Anderson (2009), we computed the vertical diffusivity (K) from the optimization of the simulated temperature (T) seasonal cycle (Fig. 4). An optimized value of Kwas obtained every 15 days by minimizing the following function for each run: Cost(t) =   250 m Z MLD(t) (Tobs(t,z) −Tmod(t,z))2dz   1/2 .(6) The annual mean Kwas 2.0±1.5cm2s−1for BATS and 1.5±1.6cm2s−1for ESTOC. The maximum Kat BATS was set to 5cm2s−1on account of numerical limitations in the biological source term calculations due to the high values computed during the winter mixing. At ESTOC, higher K-values were computed in July and August, possibly related to trade wind intensification during the summer months. The comparison of the temperature rate of change computed from the observed and simulated seasonal cycles showed a good agreement for both stations (Fig. 5). The errors in the simulation of the temperature seasonal cycle were computed as: Err(t,z) =absTmod(t,z) −Tobs(t,z) Tobs(t,z) .(7) Model errors were related to a deficient parametrization of the constant vertical diffusivity, especially during strong Biogeosciences, 9, 2831–2846, 2012 www.biogeosciences.net/9/2831/2012/ B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization 2837 J F M A M J J A S O N D 0 1 2 3 4 5 6x 10−4 Optimized K [m2/s] ESTOC BATS Fig. 4. Optimized diffusivity (K) computed for BATS and ESTOC. Values higher than 5 cm2s−1where set to 5 cm2s−1on account of numerical limitations in the biological source term calculations. mixing events. Higher errors were computed during the periods of strong winter mixing for both stations, just below the mixed layer in June–July (BATS) and August–September (ESTOC), and between 100 and 250m in September at BATS and June–September at ESTOC. Ono et al. (2001) used a single Koptimized for the whole year; however, we observed that optimizing Kfor each 15-day period reduced the total integrated error in the simulation of the temperature seasonal cycle by 13% at BATS and 20% at ESTOC (data not shown). Figure 5d shows the contribution of the geostrophic horizontal advection to the temperature rate of change. This contribution was more important at ESTOC, where an input of warmer water was computed in June–July and colder water in September. 3.4 Integrated budgets and errors estimation Net production rates below the mixed layer were computed by integrating thebiological source term (JC) from this depth to the estimated compensation depth. This depth was set to 110m based on the JCdistribution and the examination of rates sensitivity to this limit (see below). To compute shallow remineralization, JCwas integrated from the compensation depth down to 250m. Both rates were integrated between April and December in order to avoid the period of intense and intermittent winter mixing, which is not accurately simulated by the model. The physical model terms were also integrated for the same period and depth intervals to evaluate their contribution to the change in the tracer inventories. Uncertainties associated with the integrated budgets were estimated by using Monte Carlo simulations. A high number (N > 100) of model runs for all the tracers (including temperature for Koptimization) were performed with each BATS ESTOC (a) (b) (c) (d) −0.06 −0.04 −0.02 0 0 0.02 0.04 0 0.06 0 T rate of change [oC d−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 −0.04 −0.03 −0.02 −0.01 0 0 0.01 0.02 0 0.03 0.01 T rate of change [oC d−1] J F M A M J J A S O N D 0 0 0.02 0.04 0.06 T modelled rate of change [oC d−1] Depth [m] J F M A M J J A S O N D −200 −150 −100 −50 −0.02 −0.01 0 0 0.01 0.02 0 0 0.03 −0.01 T modelled rate of change [oC d−1] J F M A M J J A S O N D 0.4 0.4 0.8 0.4 0.8 0.4 1.2 1.2 1.2 1.6 0.8 1.6 % Error in T simulation Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 0.4 0.4 0.4 0.4 0.8 0.8 0.4 0.8 1.2 1.2 1.2 0.8 1.2 % Error in T simulation J F M A M J J A S O N D −0.006 −0.004 −0.003 −0.002 −0.001 0 0.001 0.002 T geostrophic hor. adv. flux divergence [ oC d−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 −0.01 −0.005 0 −0.005 −0.005 0.005 0.01 −0.01 0 −0.01 0.015 T geostrophic hor. adv. flux divergence [ oC d−1] J F M A M J J A S O N D Fig. 5. (a) Observed and (b) simulated temperature rate of change, (c) errors in the simulated temperature seasonal cycle, and (d) geostrophic horizontal advection flux divergence at BATS and ESTOC. The black discontinuous line represents the mixed layer depth and the black thick line represents the zero rate of change isoline. element of the model inputs being randomly generated from a Gaussian distribution. The standard deviation of the Gaussian distribution was taken from the estimated error of the model inputs. The weighted standard error was used for the seasonal cycles of temperature and the chemical tracers, the standard error provided by the WOA09 data base was used for the variables obtained from this climatology, and a 75% error was assigned to the DIC lateral gradients – consistent with the magnitude of computed errors for the lateral gradients of oxygen and nitrate. For the Ekman velocity (w), the error considered was 25% according to Ono et al. (2001). The uncertainties associated with the integrated budgets were www.biogeosciences.net/9/2831/2012/ Biogeosciences, 9, 2831–2846, 2012 2838 B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization BATS ESTOC O2 biological source [mmol m−3 d−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 DIC biological source [mmol m−3 d−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 DIC geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D DIC biological source [mmol m−3 d−1] J F M A M J J A S O N D DIC geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D −1 −0.5 0 0.5 1 NO3 biological source [mmol m−3 d−1] Depth [m] J F M A M J J A S O N D −250 −200 −150 −100 −50 NO3 geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D NO3 biological source [mmol m−3 d−1] J F M A M J J A S O N D NO3 geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D −0.1 −0.05 0 0.05 0.1 O2 biological source [mmol m−3 d−1] J F M A M J J A S O N D O2 geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D −1.5 −1 −0.5 0 0.5 1 1.5 O2 geostrophic hor. adv. flux divergence [mmol m −3 d−1] J F M A M J J A S O N D Fig. 6. Biological source term (JC) and geostrophic horizontal advection flux divergence computed for oxygen, DIC and nitrate at BATS and ESTOC. The discontinuous line represents the mixed-layer depth and the black thick line the zero isoline. Isoline separations are 0.1 mmol O2m−2d−1, 0.1mmolCm−2d−1and 0.01mmolNm−2d−1, for oxygen, DIC and nitrate, respectively. the standard deviation of the values of the integrated budgets produced by each model realization. 4 Results and discussion 4.1 Distribution of biological sources and sinks Figure 6 shows the biological source term (JC) computed for oxygen, DIC and nitrate by using the diagnostic model at BATS and ESTOC and the geostrophic horizontal flux divergence (i.e. the contribution of the geostrophic horizontal advection to the total rate of tracer change). Despite the signal being noisier, probably due to using a shorter data set and a time-dependent Koptimization, the distribution of JCcomputed for all the tracers at BATS was in general agreement with the results reported by Ono et al. (2001). The biological oxygen (DIC and nitrate) production (consumption) computed below the mixed layer was consistent with the synthesis of organic matter, whereas oxygen (DIC and nitrate) consumption (production) computed in the shallow aphotic zone indicated remineralization of organic matter. Oxygen production occurred below the mixed layer during summer stratification with the maximum at ca. 40m. The compensation depth (JC=0) was between 80–100m. Below the compensation depth, maximum oxygen consumption was computed at ca. 120m during late spring and early summer. The high oxygen consumption computed just below the mixed layer coincided with large errors in the temperature simulation (see Fig. 5c), which are probably related to limitations in the diffusivity optimization. DIC consumption ocurred just below the mixed layer during summer stratification. For this tracer the compensation depth was slightly shallower (ca. 60–80m), and maximum production was computed at 120 m during the summer. The distribution of JCfor nitrate was very similar to DIC but the compensation depth was located deeper (ca. 110–115 m). Maximum nitrate consumption was computed at 80m during May–June. Maximum production occurred at 120m, coinciding with the maximum rates of DIC (oxygen) production (consumption), and slightly shallower than the maximum described by Ono et al. (2001) at 140m. A second maximum in nitrate remineralization was observed at 250m close to the model border. The distribution of JCat ESTOC was in general very similar to the patterns described for BATS, although a few differences were observed. The oxygen compensation depth was located deeper at ca. 115m. Highest rates of oxygen production were also located deeper (ca. 100m), coinciding with the deep chlorophyll maximum. The vertical structure of the spring–summer remineralization was also different compared to BATS, as highest rates – instead of being located at Biogeosciences, 9, 2831–2846, 2012 www.biogeosciences.net/9/2831/2012/ B. Fern´ andez-Castro et al.: Regional differences in NP and shallow remineralization 2839 specific depths – occurred through out the whole water column. Another important difference with BATS was observed in September–November when high oxygen (DIC, nitrate) consumption (production) was computed through the whole water column. This feature coincided with significant inputs of oxygen and nitrate, and also cold water (Fig. 5d), through horizontal advection. The role of the horizontal transport in the seasonality of the synthesis and remineralization of organic matter has been previously reported in the Canary region, which is under the influence of the coastal African upwelling (Neuer et al., 2002b; Ar´ ıstegui et al., 2003; Pelegr´ ı et al., 2005; Alonso-Gonz´ alez et al., 2009). Upwelling filaments and Ekman transport export particulate and dissolved organic matter from the coastal upwelling to the open ocean. During this transport organic matter is remineralized, at least partially, providing an external source for nutrients (Pelegr´ ı et al., 2005). According to these authors, the relevance of this process is higher during the fall. These facts indicate that at ESTOC, even though removed from direct influence of the upwelling zone, allochthonous inputs of organic and inorganic matter could represent sources and sinks for oxygen, DIC and nutrients that must be considered when interpreting our model results. 4.2 Net production rates We set 110m as the mean compensation depth because it is the approximate depth of maxima in the integrated net production and remineralization rates for nitrate and for oxygen at ESTOC (see Fig. 7). DIC and oxygen at BATS actually have maxima at 80–90m, but they all have inflection points near 110m, and the 110m integrals are not greatly different (20% lower for DIC at ESTOC and O2 at BATS; 2% lower for DIC at BATS). Net production rates and all the integrated model terms, including the associated errors computed below the mixed layer to 110m from April to December for both stations, are shown in Fig. 8. Net production rates computed for oxygen (1.34± 0.79molO2m−2), dissolved inorganic carbon (−1.73± 0.52molCm−2) and nitrate (−125±36mmolNm−2) at BATS were slightly higher for oxygen and carbon compared to ESTOC (1.03±0.62molO2m−2,−1.42±0.30 mol C m−2 and −213±56mmolNm−2), although the differences were not statistically significant. In agreement with previous geochemical estimates (Riser and Johnson, 2008), our results indicate that in these regions, at least for the depths (mixed layer base to 110m) and time period (April–December) we considered, photosynthesis exceeds remineralization of organic matter. The comparison of the observed changes in the tracer inventories and the modelled net production evidences the relevance of the physical fluxes, as the change in the tracers, in general, underestimated biological net production. The change in the tracer inventories underestimated oxygen net production rates by 163% at BATS and 136% at ESTOC. 0 0.5 1 1.5 2 O2 −DIC −NO3 BATS O2, −DIC integrated rate [mol m−2] 0 0.05 0.1 0.15 0.2 −140 −130 −120 −110 −100 −90 −80 −70 −NO3 integrated rate [mol m−2] Lower integration limit [m] 0 0.5 1 1.5 2−140 −130 −120 −110 −100 −90 −80 −70 O2 −DIC −NO3 ESTOC 0 0.1 0.2 0.3 0.4 Fig. 7. Time and depth-integrated net production rates as a function of the lower limit used for the integration at BATS and ESTOC for oxygen (solid), DIC (dashed) and nitrate (dot-dashed). For DIC the underestimation was 63% at ESTOC, whereas it was overestimated by 17% at BATS. For nitrate the underestimation was 97% at BATS and 102% at ESTOC. The contribution of diffusion was larger than total advection (horizontal+vertical) for all the tracers at both stations except DIC at BATS. Computed net production rates compared with a summary of previous values reported for BATS and ESTOC are presented in Table 1, where different integration periods must be interpreted carefully. Gruber et al. (1998) and Brix et al. (2006) estimated that net production at BATS during the spring–summer period represents 60–80% of the total annual net production. Similar results were obtained at ESTOC by Gonz´ alez-D´ avila et al. (2003). As the integration interval (April–December) used for our estimates misses part of the winter–spring bloom (Brix et al., 2006; Gonz´ alez-D´ avila et al., 2007, 2010), our rates are probably lower than annual estimates. According to Marchal et al. (1996), net production in the mixed layer in the Sargasso Sea represented 60% of the photic layer net production. Assuming this is also true for ESTOC, our rates would represent about 40% of the total net production, i.e. 3.4molO2m−2and −4.3molCm−2 at BATS, and 2.6molO2m−2and −3.6molCm−2at ESTOC. 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