Abstract: Estimating interannual variability in carbon export is a key goal of many marine biogeochemical studies. However, due to variations in export mechanisms between regions, generalized models used to estimate global patterns in export often fail when used for intra-regional analysis. We present here a region-specific model of export production for the California Current Ecosystem (CCE) parameterized using intensive Lagrangian process studies conducted during El Niño-Southern Oscillation (ENSO) warm and neutral phases by the CCE Long-Term Ecological Research (LTER) program. We find that, contrary to expectations from prominent global algorithms, export efficiency (e-ratio = export / primary productivity) is positively correlated with temperature and negatively correlated with net primary productivity (NPP). We attribute these results to the substantial horizontal advection found within the region, and verify this assumption by using a Lagrangian particle tracking model to estimate water mass age. We further suggest that sinking particles in the CCE are comprised of a recently-produced, rapidly-sinking component (likely mesozooplankton fecal pellets) and a longer-lived, slowly-sinking component that is likely advected long distances prior to export. We determine a new algorithm for estimating particle export in the CCE from NPP (Export = 0.08 · NPP + 72 mg C m-2 d-1). We apply this algorithm to a two-decade long time series of NPP in the CCE to estimate spatial and interannual variability across multiple ENSO phases. Reduced export during the warm anomaly of 2014-2015 and El Niño 2015-2016 resulted primarily from decreased export in the coastal upwelling region of the CCE; the oligotrophic offshore region exhibited comparatively low seasonal and interannual variability in flux. The model resolves intra-regional patterns of in situ export measurements, and provides a valuable contrast to global export models.

Abstract: Eastern China (27-41 degrees N, 110-123 degrees E) is heavily polluted by nitrogen dioxide (NO2), particulate matter with aerodynamic diameter below 2.5 mu m (PM2.5), and other air pollutants. These pollutants vary on a variety of temporal and spatial scales, with many temporal scales that are nonperiodic and nonstationary, challenging proper quantitative characterization and visualization. This study uses a newly compiled EOF-EEMD analysis visualization package to evaluate the spatiotemporal variability of ground-level NO2, PM2.5, and their associations with meteorological processes over Eastern China in fall-winter 2013. Applying the package to observed hourly pollutant data reveals a primary spatial pattern representing Eastern China synchronous variation in time, which is dominated by diurnal variability with a much weaker day-to-day signal. A secondary spatial mode, representing north-south opposing changes in time with no constant period, is characterized by wind-related dilution or a buildup of pollutants from one day to another.
We further evaluate simulations of nested GEOS-Chem v9-02 and WRF/CMAQ v5.0.1 in capturing the spatiotemporal variability of pollutants. GEOS-Chem underestimates NO2 by about 17 mu g m(-3) and PM2.5 by 35 mu g m(-3 )on average over fall-winter 2013. It reproduces the diurnal variability for both pollutants. For the day-to-day variation, GEOS-Chem reproduces the observed north-south contrasting mode for both pollutants but not the Eastern China synchronous mode (especially for NO2). The model errors are due to a first model layer too thick (about 130 m) to capture the near-surface vertical gradient, deficiencies in the nighttime nitrogen chemistry in the first layer, and missing secondary organic aerosols and anthropogenic dust. CMAQ overestimates the diurnal cycle of pollutants due to too-weak boundary layer mixing, especially in the nighttime, and overestimates NO2 by about 30 mu g m(-3) and PM2.5 by 60 mu g m(-3). For the day-to-day variability, CMAQ reproduces the observed Eastern China synchronous mode but not the north-south opposing mode of NO2. Both models capture the day-to-day variability of PM2.5 better than that of NO2. These results shed light on model improvement. The EOF-EEMD package is freely available for noncommercial uses.

Abstract: Advances in in situ imaging allow enumeration of abundant populations of large Rhizarians that compose a substantial proportion of total mesozooplankton biovolume. Using a quasi-Lagrangian sampling scheme, we quantified the abundance, vertical distributions, and sinking&#8208;related mortality of Aulosphaeridae, an abundant family of Phaeodaria in the California Current Ecosystem. Inter&#8208;cruise variability was high, with average concentrations at the depth of maximum abundance ranging from < 10 to > 300 cells m&#8722;3, with seasonal and interannual variability associated with temperature&#8208;preferences and regional shoaling of the 10°C isotherm. Vertical profiles showed that these organisms were consistently most abundant at 100&#65533;150&#8201;m depth. Average turnover times with respect to sinking were 4.7&#65533;10.9 d, equating to minimum in situ population growth rates of ~ 0.1&#65533;0.2 d&#8722;1. Using simultaneous measurements of sinking organic carbon, we find that these organisms could only meet their carbon demand if their carbon : volume ratio were ~ 1 &#956;g C mm&#8722;3. This value is substantially lower than previously used in global estimates of rhizarian biomass, but is reasonable for organisms that use large siliceous tests to inflate their cross&#8208;sectional area without a concomitant increase in biomass. We found that Aulosphaeridae alone can intercept > 20% of sinking particles produced in the euphotic zone before these particles reach a depth of 300&#8201;m. Our results suggest that the local (and likely global) carbon biomass of Aulosphaeridae, and probably the large Rhizaria overall, needs to be revised downwards, but that these organisms nevertheless play a major role in carbon flux attenuation in the twilight zone.