2.1 Satellite Data Analysis
 Seasonal cycles in North Atlantic phytoplankton were investigated using 8 day resolution remote sensing data from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) [McClain, 2009]. Our analysis deviates from that of Behrenfeld  in that (1) the satellite time series was extended to the period January 1998 to December 2007, (2) we added two North Atlantic bins (B-04 and B-11; Figure 1a) that were not included in the earlier study, and (3) the SeaWiFS data employed in the current study were from an additional 2011 reprocessing of the full SeaWiFS data set (see http://oceancolor.gsfc.nasa.gov). As in the earlier study, the 5° latitude by 10° longitude bins are defined to avoid continental margins (except bin B-11) and to minimize the influence of advection (relative to processes occurring within a bin) between 8 day periods while still capturing spatial variability in phytoplankton seasonal properties. The lowest latitude bins (B-01 to B-04) lie between 40°N and 45°N. In the northernmost bins (B-08 to B-14), satellite data are unavailable for a few weeks each year during midwinter due to high solar zenith angles. For orientation in Figure 1a, the site of the Joint Global Ocean Flux Study–North Atlantic Bloom Experiment (JGOFS-NABE) [Ducklow and Harris, 1993] is indicated by a white star, the North Atlantic Bloom 2008 study (NAB08) [Mahadevan et al., 2012] is marked by a pink star, and the profiling float study of Boss and Behrenfeld  is shown by a blue star.
 Satellite products used as indices of phytoplankton abundance were surface chlorophyll concentration, [Chl] (mg m−3), from the NASA standard OC4-V6 algorithm (the most familiar product to SeaWiFS users) and phytoplankton carbon concentration, [Cphyto] (mmol m−3), calculated using the particulate backscattering coefficient at 443 nm from the Garver-Siegel-Maritorena algorithm [Garver and Siegel, 1997; Maritorena et al., 2002] and following the [Cphyto] algorithm of Behrenfeld et al.  and Westberry et al. .
 Depth-integrated carbon and chlorophyll inventories (i.e., ΣCphyto and ΣChl), specific rates of biomass accumulation (r), and phytoplankton-specific division rates (μ) were assessed following Behrenfeld  and Boss and Behrenfeld . Briefly, ΣCphyto and ΣChl were calculated as the product of measured surface concentration and the greater of mixed layer depth (MLD) or euphotic depth (Zeu), where Zeu was calculated following Morel and Berthon . This assessment assumes uniform phytoplankton properties within an active mixing layer. This assumption is supported by in situ measurements during the late autumn-through-spring period when MLD exceeds Zeu [Townsend et al., 1992; Ward and Waniek, 2007; Boss et al., 2008]. In the recent study of Boss and Behrenfeld , 2 years of continuous profiling optical float data in the subarctic Atlantic showed that measured phytoplankton properties integrated through the mixed layer were closely approximated by the product of surface chlorophyll or carbon concentration and MLD over the autumn-through-spring period. This uniformity results from mixed layer transit times of a few days or less [Backhaus et al., 2003; D'Asaro, 2008]. In contrast to the autumn-through-spring period that is central to the current investigation of the subarctic Atlantic bloom, Zeu typically exceeds MLD between roughly June and October [e.g., Figure 4 in Behrenfeld, 2010; Figure 3 in Boss and Behrenfeld, 2010]. Under these conditions, phytoplankton properties below the mixed layer, but still within the euphotic zone, can be significantly different from those within the mixed layer. This situation can lead to errors when extrapolating surface properties to depth. However, this vertical structure in phytoplankton biomass has little impact on conclusions of the current study regarding the late autumn-through-spring blooming period.
 As described in Behrenfeld , understanding bloom dynamics requires an evaluation of the balance between phytoplankton division (μ) and loss (l) rates within the mixed layer. The difference between these two rates defines r (i.e., r = μ − l), where a positive value for r indicates an increasing phytoplankton population and a negative value indicates a decreasing population. When evaluating r from observed temporal changes in [Chl] or [Cphyto], it is important to consider whether the mixed layer is deepening or shoaling. Specifically, if light and nutrient conditions in the mixed layer allow μ to exceed l and the mixed layer depth is constant or shoaling, then phytoplankton concentration (mg m−3) will increase over time. Under such conditions, the specific rate of biomass accumulation (r) can be calculated from two measures of biomass concentration (C0, C1) separated by a period of time (Δt = t1 − t0) following
 This scenario is similar to calculating μ for a laboratory phytoplankton culture where the volume of the culture is either held constant or decreases (for example, if samples are removed) between t0 and t1. Equation ((1)), however, does not permit an accurate assessment of r if the mixed layer is deepening. Specifically, if we assume that light and nutrient conditions allow the same excess of μ over l (i.e., the same r) but that mixed layer deepening dilutes the phytoplankton population with plankton-free water from below, then the observed change in phytoplankton concentration from t0 to t1 will be smaller than the previous scenario (i.e., a fixed or shrinking volume) and may even decrease if the dilution effect is strong enough. Under these conditions, retrieval of r requires accounting for the change in volume (V) within which the phytoplankton population is suspended during t0 to t1 (i.e., V0 and V1, respectively). Thus, modifying equation ((1)) to account for the purely physical effects of dilution gives
 Application of equation ((2)) for the mixed layer deepening scenario is similar to calculating μ for a laboratory phytoplankton culture that is being diluted between t0 and t1, with the extreme case being a chemostat culture where a rapidly growing population is held at a constant concentration by equally rapid dilution. If equation ((2)) is also applied to a phytoplankton population in a fixed or shoaling mixed layer (the earlier scenario), then the resultant values of r would not only reflect the balance between μ and l within the mixed layer but would also include losses due to detrainment from the mixed layer (a purely physical process).
 In summary, r is calculated from satellite data using equation ((2)) when the volume of the actively growing phytoplankton population is being expanded by mixed layer deepening and the newly entrained water is relatively phytoplankton free. This condition occurs in the subarctic Atlantic in the late autumn and winter when the MLD > Zeu. During springtime mixed layer shoaling, equation ((1)) is used to assess r, which allows the ecological processes regulating the dynamic balance between mixed layer μ and l to be isolated from the purely physical effects of detrainment. Finally, in the summer and early autumn period where the mixed layer is deepening but still shallower than Zeu, the newly entrained water from depth is not plankton free. If the phytoplankton concentration in the entrained deep water is equal to that in the mixed layer, then equation ((1)) can be used to assess r. As the actual phytoplankton concentration below the mixed layer is not known, this approach imparts an uncertainty in retrieved r, but this uncertainty has no impact on our conclusions regarding the winter-spring bloom in the subarctic Atlantic. In summary, r was calculated from the 8 day resolution SeaWiFS data for each North Atlantic bin following
where [Cphyto-0] and ΣCphyto-0 are phytoplankton carbon concentration and inventory, respectively, at t0; [Cphyto-1] and ΣCphyto-1 are phytoplankton carbon concentration and inventory, respectively, at t1, Δt = t1 − t0 = 8 days; and r is the average specific rate of biomass accumulation between time points t0 and t1. This approach is identical to that used in Behrenfeld  and Boss and Behrenfeld .
 Phytoplankton-specific division rates (μ) were calculated as water column-integrated daily net primary production (NPP) divided by ΣCphyto. NPP was calculated from the SeaWiFS data at 8 day resolution using the Vertically Generalized Production Model [Behrenfeld and Falkowski, 1997], where the maximum daily chlorophyll-specific light-saturated photosynthetic rate (Pbopt) was described as an exponential function of sea surface temperature (SST) [Morel, 1991; Campbell et al., 2002; Carr et al., 2006; Behrenfeld, 2010].
 Our analysis also employed data on incident surface photosynthetically active radiation (PAR), sea surface temperature (SST), and mixed layer depth (MLD). PAR data are SeaWiFS cloudiness-corrected values. SST data are from the Moderate-Resolution Imaging Spectrometer and the Advanced Very High Resolution Radiometer (http://web.science.oregonstate.edu/ocean.productivity). PAR and SST data are from the NASA Ocean Color website (oceancolor.gsfc.nasa.gov/). MLD values were from the Fleet Numerical Meteorology and Oceanography Center (FNMOC) model [Clancy and Sadler, 1992] and the Simple Ocean Data Assimilation (SODA) model, where MLD was defined as the first depth where density is 0.125 kg m−3 greater than the surface value. This MLD criterion does not account for short-term periods of low turbulence during the spring stratification period, but these transient events are not critical to bloom development [Mahadevan et al., 2012] nor during convective deepening of the mixed layer in the late autumn, early winter period of bloom initiation. The merged FNMOC and SODA data sets are described in detail and downloadable at www.science.oregonstate.edu/ocean.productivity. These MLD data are based on model estimates tuned to available in situ data (i.e., they are “data-assimilating models”). As described below (see section 2.2), the BEC-CCSM also generates MLD estimates, but in this case, the data are not tuned to in situ measurements. Nevertheless, spatial and temporal variations in MLD are similar to those from the data assimilating FNMOC and SODA estimates. All satellite-based properties requiring MLD estimates used FNMOC and SODA data, while all BEC-CCSM properties requiring MLD estimates used MLD data from the model. In section 3, MLD estimates from the data assimilating models are shown in Figures 1b, 3b, and 5a, while MLD data from the BEC-CCSM are shown in Figures 3b, 4a, and 5b.