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Sensitivity of shelf sea marine ecosystems to temporal resolution of meteorological 1 forcing 2

29 Phytoplankton phenology and the length of the growing season have implications that cascade through trophic levels 30 and ultimately impact the global carbon flux to the seafloor. Coupled hydrodynamic-ecosystem models must 31 accurately predict timing and duration of phytoplankton blooms in order to predict the impact of environmental 32 change on ecosystem dynamics. Meteorological conditions, such as solar irradiance, air temperature and wind-speed 33 are known to strongly impact the timing of phytoplankton blooms. Here, we investigate the impact of degrading the 34 temporal resolution of meteorological forcing (wind, surface pressure, air and dew point temperatures) from 1 - 24 35 hours using a 1D coupled hydrodynamic-ecosystem model at two contrasting shelf-sea sites: one coastal 36 intermediately stratified site (L4) and one offshore site with constant summer stratification (CCS). Higher temporal 37 resolutions of meteorological forcing resulted in greater wind stress acting on the sea surface increasing water 38 column turbulent kinetic energy. Consequently, the water column was stratified for a smaller proportion of the year 39 producing a delayed onset of the spring phytoplankton bloom by up to 6 days, often earlier cessation of the autumn 40 bloom, and shortened growing season of up to 23 days. Despite opposing trends in gross primary production 41 between sites, a weakened microbial loop occurred with higher meteorological resolution due to reduced dissolved 42 organic carbon production by phytoplankton caused by differences in resource limitation: light at CCS and nitrate at 43 L4. Caution should be taken when comparing model runs with differing meteorological forcing resolutions. 44 Recalibration of hydrodynamic-ecosystem models may be required if meteorological resolution is upgraded. 45 46 47


62
Phytoplankton phenology, that is, the timing of phytoplankton blooms, has consequences that cascade through 63 ecological trophic levels, with the potential to change ecosystem structure (Edwards & Richardson, 2004;Platt et al., 64 2003) and the flux of carbon to the sea floor. This is particularly important in shelf seas as they trap a 65 disproportionate amount of carbon from the atmosphere within their sediments compared to the deep global ocean 66 (Bauer et al., 2013;Sharples et al., 2019). The ability of marine ecosystem models to accurately represent and 67 capture changes in phytoplankton phenology, in addition to the magnitude and composition of phytoplankton 68 blooms, is imperative to predict the impacts of environmental change on ecosystem dynamics and the amount of 69 carbon trapped within global shelf seas. 70 Phytoplankton blooms occur when an optimal set of environmental conditions, in particular nutrient and light 71 availability, both of which are mediated by turbulent mixing, support growth rates that exceed losses (e.g. grazing). 72 There are several competing theories regarding the causes of the onset of the spring bloom. The critical depth 73 theory (Sverdrup, 1953) states that phytoplankton blooms will develop when the mixed layer is less than the critical 74 depth: the depth where vertically integrated phytoplankton growth exceeds phytoplankton losses. In more recent 75 years, at least two other hypotheses have been formulated. The critical turbulence theory postulates that a 76 phytoplankton bloom can occur in unstratified waters if turbulent mixing is weak enough that phytoplankton stay 77 within the photic zone long enough to photosynthesize (Huisman et al., 1999;Taylor & Ferrari, 2011) while the 78 disturbance-recovery hypothesis (Behrenfeld, 2010;Behrenfeld et al., 2013), states that the phytoplankton bloom is 79 dependent on the balance of phytoplankton loss and production due to grazing pressures and physical properties. On 80 shelves where light rather than nutrient availability limits phytoplankton growth, the spring bloom typically occurs 81 during a period of low grazing pressure when a reduction in turbulent mixing and shoaling of the actively mixing 82 surface layer eases light limitation. 83 In contrast to the spring bloom, the autumn phytoplankton bloom typically occurs when light is still non-limiting 84 and is fueled by entrainment of nutrients into the euphotic zone as convection and wind mixing deepen the surface 85 mixed layer. In addition, the phytoplankton composition within the autumn bloom is different to that of spring: more 86 motile species are present which have the ability to migrate across the mixed layer between nutrient rich and nutrient 87 poor regions of the water column (Smyth et al., 2014). Although not studied as intensively as the spring 88 phytoplankton bloom, the autumn phytoplankton bloom can also make a substantial contribution to annual gross 89 primary production (Wihsgott et al., 2019). 90 In all hypotheses for phytoplankton bloom initiation, the timing of the phytoplankton bloom is closely coupled to 91 meteorological indices such as light, temperature and wind speeds. Wind and temperature alter the timing of 92 stratification events in spring and autumn and the strength of stratification in summer, in addition to the amount of 93 turbulent kinetic energy present throughout the water column. Chiswell (2011)   This study uses the 1D L4 setup provided as a testcase in ERSEM 16.05 (Butenschön et al., 2016) as the baseline 176 model from which changes as outlined below and in Table S1 were made. The 1D model at both sites is relaxed on 177 a yearly timescale to observed temperature and salinity data (Fishwick, 2018;Wihsgott et al., 2016) to avoid drift in 178 these variables during the model run. Note this means the influence of changes in river flow on salinity at L4 and 179 CCS, and thus stratification, is not included in the model simulations. No relaxation was applied to any 180 biogeochemical variables at either site. Model calibration at CCS was performed with the aim of changing the 181 minimum number of parameters from the basic L4 setup as possible. 182 The model at CCS is initialized using average winter nutrient concentrations over 2014 and 2015 (BODC, 183 Woodward, 2016). The benthic model at CCS was spun up so that a quasi-steady state was achieved-this allowed 184 only a two year model spin-up period at the start of each model run. For L4 the published parameter set was 185 assumed to provide a quasi steady state. To prevent the increase of benthic particulate matter and benthic refractory 186 organic matter at both L4 and CCS, the affinity of benthic aerobic and anaerobic bacteria to benthic particulate 187 organic matter was increased to 4 x10 -5 m 2 (mg C) -1 d -1 , and affinity to benthic refractory organic matter to 4 x 10 -6 188 m 2 (mg C) -1 d -1 (Table S1). The k epsilon turbulent scheme was used within GOTM for both sites with the 189 minimum turbulent kinetic energy (kmin) at both sites increased to match temperature profiles with observations at 190 both sites. The absorption of silt was lowered at the CCS site to improve timing of the phytoplankton bloom and 191 depth of subsurface chlorophyll maximum in summer within the model relative to observations (Hopkins et al.,192 2019). The nitrification rate constant was also lowered at both sites to improve ammonium dynamics at depth. 193 Finally, the wind speed relative to current velocity, rather than the default setting of absolute wind speed, was used 194 to calculate air-sea fluxes. Both models were validated with observational data using robust statistics. Target

Meteorological resolution forcing scenarios
198 Meteorological forcing scenarios were created using the instantaneous meteorological data, that is, 10m u and v 199 components of wind, sea surface pressure, 2m air temperature, 2m dew point temperature and total cloud cover. 200 Throughout this paper the term "meteorological forcing" refers to these variables. Six hourly, 12 hourly, and 24 201 hourly meteorological forcing data were subsampled from the hourly meteorological data to create the forcing 202 scenarios. This sampling method was chosen to reflect the potential changes from switching resolution of 203 meteorological products such as from 6 hourly ERA Interim data to hourly ERA 5. Precipitation and hourly net 204 short wave radiation were kept at an hourly resolution for all scenarios as these are time integrated variables that 205 already capture the change throughout the time-interval. Thus, while reduction in the time resolution in precipitation 206 and net shortwave radiation causes their variability to be underestimated, it does not affect total heat or freshwater 207 input. In addition, we chose not to adjust the resolution of shortwave radiation as resultant changes in meteorological 208 forcing may be highly dependent on individual model formulations for light and are also hard to disentangle from 209 other effects such as wind and temperature. Conversely, a reduction in resolution of instantaneous variables causes 210 biases of up to 3% (in u and cloud cover) and 0.8%(u) at L4 and CCS, respectively, in the mean of the timeseries of 211 meteorological inputs for the scenarios compared to the hourly simulation (Table S2); additionally, reduced 212 variability of some instantaneous variables (e.g., wind speed) will impact energy fluxes. In order to identify which 213 meteorological variables the model was sensitive to, the model was run a further 5 times, dropping one by one the 214 temporal resolution of each individual meteorological forcing variable to 12 hourly, leaving all other variables at 215 hourly resolution. 216 The exact method for defining the time periods is shown in Table S3. All calculations used either 10 day forward or 233 backward running means. The same time period for each regime is used across all years and all scenarios. To define 234 this, the minimum or maximum day of the year over all simulations and all scenarios which fulfilled the criteria in 235 Table S3 were used to delineate the exact start and end of each season in the final analysis. Note that the time 236 periods are slightly different for L4 and CCS. 237

Physical/Phenological indices
The phytoplankton bloom duration is typically defined as the time period when chlorophyll exceeds 5% of the 238 annual median ( Here, we define the start of the spring phytoplankton bloom as the first day of the year when depth-integrated 241 chlorophyll is more than 10% of the annual median. The end of the phytoplankton bloom is defined when depth-242 integrated chlorophyll drops below 10% of the annual median for more than 6 consecutive days. The bloom duration 243 is the time between the start and end of the bloom. This metric however does not capture the autumn phytoplankton 244 blooms at our two sites. Therefore we also defined the growing season as the period of time when the 10 day 245 running average of mean water column gross primary production (GPP; gC m -3 d -1 ) is more than one tenth of the 246 annual maximum GPP at each site. The metrics for chlorophyll and GPP calculated for the hourly simulation are 247 used for all scenarios so that differences between each meteorological scenario can be fairly assessed. Finally, the 248 peak magnitude of the bloom represents the day of the year when depth integrated chlorophyll is greatest. 249 Changing surface pressure, air and dew point temperatures, and cloud cover have minimal impact on phenology and 254 ecosystem dynamics ( Figure S2). Thus throughout the rest this manuscript, we will focus on the impact of wind in 255 driving ecosystem dynamics. 256

257
The density structure of the water column in the hourly meteorological simulation at both sites in addition to the 258 MLD and assigned seasons for the years 2014 and 2015 are shown in Figure 2. The water column at L4 is well 259 mixed for a longer portion of the year than at CCS. There is also a weaker contrast between surface and bottom 260 water densities during summer at L4 indicating that seasonal stratification is weaker than at CCS. A more intense 261 spring bloom is predicted to occur at CCS and, during the summer, the subsurface chlorophyll maximum is deeper 262 (40 m at CCS compared to 15 m at L4; Figure 2). In addition, a later autumn bloom resulting in a longer growing 263 season is predicted in the model simulation at CCS than at L4 ( Figure S3). Comparing near-surface chlorophyll-a 264 concentrations observed at both sites with the baseline model ( Figure S3) provides confidence that the simulations 265 are satisfactorily predicting the observed phytoplankton phenology. 266

Impacts of meteorological resolution on physical dynamics
267 Lower meteorological resolution results in a 2-16% and 2-11% reduction in the annual mean magnitude of wind 268 stress acting on the surface water at L4 and CCS, respectively, between all scenarios and the hourly meteorological 269 simulation (Figure 3). This is a result of missing high intensity short lived wind events in the coarser, subsampled in a phytoplankton bloom that is up to 17 and 6 days longer at L4 and CCS, respectively, across all scenarios. 290 However, in some years an earlier and thus shorter bloom occurs at L4 in the scenarios compared to the hourly 291 resolution. The peak magnitude of depth-integrated chlorophyll as the meteorological resolution is reduced is up to 292 15 and 10% lower than the hourly resolution simulation at L4 and CCS respectively, although occasionally up to a 293 5% greater magnitude in chlorophyll concentration does occur ( Figure 5H). 294 The weaker trend in the change in the peak amplitude of the bloom to meteorological forcing than other 295 phenological indicators is likely due to the opposing impacts of wind stress on phytoplankton blooms. In some 296 years, differences in the MLDs due to changes in wind stress stimulates a higher magnitude bloom in the hourly 297 meteorological forcing compared to the lower resolution scenarios (i.e 2014, L4; Figure S5a) due to additional 298 nutrients being mixed into the photic zone. However, occasionally a large wind induced mixing event in the hourly 299 simulation relative to the lower resolution of meteorological forcing may cause the cessation of the bloom due to 300 phytoplankton being mixed down to low light environments and hence produce lower peak chlorophyll 301 concentrations (i.e. 2015 L4; Figure S5a). 302

Change in length of growing seasons 303
The increasingly longer period of stratification with lower meteorological resolution supports an increasingly longer 304 growing season of phytoplankton ( Figure 5). The start of the growing season is up to 10 days earlier in the 24h 305 scenario at L4 and 11 days earlier at CCS compared to the hourly simulation. The end of the growing season is up to 306 13 and 10 days later at L4 and CCS respectively, although there is little change in the end of the growing season 307 between the hourly and six hourly scenario at L4. The overall effect of reducing meteorological resolution at both 308 sites is to increase the growing season by up to 23 days at L4 and 11 days at CCS. 309 peninsula. Our results also suggest a longer bloom with decreased wind stress due to both an earlier start and later 395 finish to the bloom ( Figure 5). In addition, although there is a tendency at both sites towards a diminished bloom 396 magnitude when the bloom starts earlier this does not always happen. In cases where the wind disrupts the 397 formation of stratification, Waniek (2003) predicts that zooplankton biomass will increase relative to years with 398 uninterrupted formation of stratification, due to having more time to respond to changes in phytoplankton biomass. 399

Annual changes in carbon reservoirs
Hence lower phytoplankton biomass and greater primary production, would occur, in addition to greater 400 zooplankton biomass. This mechanism appears to arise during 2014 at CCS and 2015 at L4 when there is a lower 401 peak magnitude of phytoplankton biomass and higher peak zooplankton in the hourly simulation compared to the 24 402 hourly scenario (Figure S5b), although the peak magnitude of GPP is also lower during these years. In all other 403 years where a decrease in phytoplankton biomass occurs in the hourly simulation relative to the scenarios (i.e. 2012 404 L4 and CCS; Figure S5a and b), a lower peak in zooplankton also occurs. 405 In addition to changing phytoplankton bloom length, interannual changes in meteorological variables have also been 406 linked to an increase in the length of the growing season. Increasing delays between spring and autumn blooms 407 have been observed in the temperate North Atlantic, and are attributed to enhanced stratification due to the warming 408 of the ocean (González Taboada

What drives the enhanced microbial loop?
425 An enhanced microbial loop occurs at both sites with lowering meteorological resolution (Figure 8). However, the 426 impact on the ecosystem structure and nutrient dynamics due to the change in stratification is different between the 427 two sites, despite similarities in the phenology. A key driver of the microbial loop is the change in DOC production. 428 DOC production provides food for bacteria which enhances remineralization of carbon back to DIC. Extracellular 429 release of DOC by phytoplankton is the main source of DOC to marine systems (Borchard & Engel, 2015). 430 Extracellular release may occur through passive diffusion of low molecular weight compounds across cell 431 membranes (Bjornsen, 1988) or through active release of DOC by exudation (Fogg, 1983)  We propose that different mechanisms are driving the enhanced microbial loop at each site reflecting the site-437 specific response of GPP, phytoplankton and zooplankton to the meteorological forcing. The small increase in GPP 438 at CCS with lowering meteorological forcing resolution likely reflects the longer growing season due to the 439 increased amount of time that the water column is stratified (Figure 4). The higher GPP could further reflect less 440 turbulent conditions and thus a greater time that phytoplankton remain in the photic zone. The greater GPP in spring 441 as meteorological resolution decreases (Figure 7) supports both these hypotheses. In contrast, at L4 there is a weak 442 trend towards a decreasing GPP on an annual scale with coarsening meteorological resolution which reflects the 443 lower phytoplankton biomass in the scenarios (Figure 7). The decreasing trend is greatest during summer and likely 444 reflects the weaker flux of nutrients across the nutricline during this time period ( Figure 10A) as there is reduced 445 kinetic energy within the water column. This mechanism is much weaker at CCS ( Figure 10B) as there the nutricline 446 is positioned at greater depth, out of reach of the turbulence produced by surface wind stress. Thus greater wind 447 stress is required at CCS to break down stratification up to the depth of the nutricline. Lower GPP at L4 in the 448 scenarios may also be driven by the thinner mixed layer resulting in a reduction in the total mass of nutrients within 449 the mixed layer available for phytoplankton growth (Figure 4, S5c). This last process is hypothesized to be 450 important for bloom timing in nutrient limited subtropical seas (Henson et al., 2009). 451 The contrasting trends in GPP at L4 and CCS highlight the role that resource limitation plays in the response of We have investigated the impact that wind in a 1D model has on physical and biogeochemical dynamics. The 492 impacts in 3D may be greater than presented here as the spatial resolution of the horizontal grid from ERA-Interim 493 to ERA 5 improves from 79km to 31km adding further fluctuations in wind stress to the surface water. In addition, 494 hourly light and cloud cover data will also result in changes between ERA5 and ERA-Interim. Here, we 495 purposefully kept net shortwave radiation constant in all scenarios as the effect of changes in incoming shortwave 496 radiation as a result of switching from ERA5 to ERA-Interim are likely to be model specific, depending on the 497 model formulations for light. Changes in bias in the ERA-Interim and ERA5 datasets, for example, the higher 498 precipitation rates over Europe in ERA5 than ERA-Interim (ECMWF/C3S/CAMS, 2018.), should also be 499 considered when changing meteorological forcing, but are beyond the scope of this study. 500 There may also be projects when time averaged meteorological variables (i.e. Blackford, 2002;Ridderinkhof, 1992)

507
This study investigates the response of shelf-sea ecosystems to the resolution of meteorological forcing in 508 hydrodynamic-ecosystem models. This is especially important given the increased availability of hourly datasets 509 such as the ERA5 and NCEP Climate Forecast System products. In general, a higher temporal resolution of 510 meteorological forcing results in greater mixing within the water column with a later development of the surface 511 mixed layer in spring and earlier breakdown in autumn. This produces a shorter growing season and later start to the 512 phytoplankton bloom which directly impacts higher trophic levels within the ecosystem, and at CCS, weakens 513 deposition of POC to the sea floor during spring. The strength of the microbial loop at both sites is reduced: at the 514 coastal L4 station this is a consequence of the relief of nutrient stress resulting in less DOC expelled by 515 phytoplankton, at the offshore CCS station, this is a consequence of the decrease in GPP due to the reduced growing 516 season. 517 Our results show that it is important to consider the impact that changes in meteorological forcing of coupled 518 hydrodynamic-ecosystem models will have on interpreting physical and ecosystem dynamics. Although this work 519 only includes two sites on shelf seas, we believe that our work can be extrapolated to other sites globally and other 520 model setups. We envision that the sites which will show the biggest response to meteorological forcing are those 521 that are seasonally or intermittently stratified, similar to the ones studied here. These sites represent approximately 522 44% of the surface area of the North Sea (van Leeuwen et al., 2015). Permanently mixed sites are unlikely to show 523 any strong impact to changes in meteorological forcing resolution, while permanently stratified sites might show a 524 small response to meteorological forcing. The conceptual model that we present can be used to guide researchers on 525 expected outcomes using their knowledge of stratification of an individual site, resource limitation status and model 526 design (i.e. whether there is a stress release mechanism for DOC). The main effect of changing the meteorological 527 forcing in this study was to increase the variability of winds, consequently adding more energy into the water 528 column. A main limitation of our study is that changes in the frequency of prescribed shortwave radiation, or cloud 529 cover, were not investigated. The ecosystem response to such changes are likely to be dependent on individual 530 model formulations for light which should also be considered when switching meteorological forcing. Thus, 531 recalibration of models may be required when switching meteorological forcing which may give new insights to 532 ecosystem dynamics. 533

ACKNOWLEDGEMENTS 534
This work was supported by NERC funded project, Combining Autonomous observations and Models for Predicting 535 and Understanding Shelf seas (CAMPUS; NE/R006849/1 and NE/R006822/2) and the NERC single centre national 536 capability programme -Climate Linked Atlantic Sector Science (NE/R015953/1). All observational data used in 537 this work can be downloaded from www.bodc.ac.uk and is referenced in the main text, except for the following dois 538 corresponding to the CCS CTD data collected during the cruises mentioned in section 2.1 of the main text: 539 doi:10/c4mj, doi:10/c4pk, doi:10/c4mk, doi:10/c4ph, doi:10/c4pk, doi:10/c4pj, doi:10/c4pf,. doi:10/c4pg. The  Table S3. Positive values indicate 1 hourly meteorological simulation 799 was lower than the defined scenario. Line across box represents median, black, filled triangle represents the mean, 800 whiskers in the boxplots represent the maximum and minimum range of the data. Note difference in scales between 801 stations. Numbers on bottom of graph indicate the result of the 1h meteorological forcing simulation (Fluxes: mg C 802 m -2 d -1 , biomass: mg C m -2 ). GPP = gross primary production, P-Z=Phytoplankton to zooplankton flux, P-DOC = 803 phytoplankton to dissolved organic carbon flux ,P = phytoplankton, Z= zooplankton.