Beyond long-term averages: making biological sense of a rapidly changing world

Biological responses to climate change are typically communicated in generalized terms such as poleward and altitudinal range shifts, but adaptation efforts relevant to management decisions often require forecasts that incorporate the interaction of multiple climatic and nonclimatic stressors at far smaller spatiotemporal scales. We argue that the desire for generalizations has, ironically, contributed to the frequent conflation of weather with climate, even within the scientific community. As a result, current predictions of ecological responses to climate change, and the design of experiments to understand underlying mechanisms, are too often based on broad-scale trends and averages that at a proximate level may have very little to do with the vulnerability of organisms and ecosystems. The creation of biologically relevant metrics of environmental change that incorporate the physical mechanisms by which climate trains patterns of weather, coupled with knowledge of how organisms and ecosystems respond to these changes, can offer insight into which aspects of climate change may be most important to monitor and predict. This approach also has the potential to enhance our ability to communicate impacts of climate change to nonscientists and especially to stakeholders attempting to enact climate change adaptation policies.

How should sparse marine in situ measurements be compared to a continuous model: an example

This work demonstrates an example of the importance of an adequate method to sub-sample model results when comparing with in situ measurements. A test of model skill was performed by employing a point-to-point method to compare a multi-decadal hindcast against a sparse, unevenly distributed historic in situ dataset. The point-to-point method masked out all hindcast cells that did not have a corresponding in situ measurement in order to match each in situ measurement against its most similar cell from the model. The application of the point-to-point method showed that the model was successful at reproducing the inter-annual variability of the in situ datasets. Furthermore, this success was not immediately apparent when the measurements were aggregated to regional averages. Time series, data density and target diagrams were employed to illustrate the impact of switching from the regional average method to the point-to-point method. The comparison based on regional averages gave significantly different and sometimes contradicting results that could lead to erroneous conclusions on the model performance. Furthermore, the point-to-point technique is a more correct method to exploit sparse uneven in situ data while compensating for the variability of its sampling. We therefore recommend that researchers take into account for the limitations of the in situ datasets and process the model to resemble the data as much as possible.

Monitoring changes in phytoplankton abundance and composition in the Northwest Atlantic: a comparison of results obtained by Continuous Plankton Recorder sampling and colour satellite imagery

Phytoplankton abundance in the NW Atlantic was measured by continuous plankton recorder (CPR) sampling along tracks between Iceland and the western Scotian Shelf from 1998 to 2006, when sea-surface chlorophyll (SSChl) measurements were also being made by ocean colour satellite imagery using the SeaWiFS sensor. Seasonal and inter-annual changes in phytoplankton abundance were examined using data collected by both techniques, averaged over each of four shelf regions and four deep ocean regions. CPR sampling had gaps (missing months) in all regions and in the four deep ocean regions satellite observations were too sparse between November and February to be of use. Average seasonal cycles of SSChl were similar to those of total diatom abundance in seven regions, to those of the phytoplankton colour index in six regions, but were not similar to those of total dinoflagellate abundance anywhere. Large inter-annual changes in spring bloom dynamics were captured by both samplers in shelf regions. Changes in annual (or 8 months) averages of SSChl did not generally follow those of the CPR indices within regions and multi-year averages of SSChl, and the three CPR indices were generally higher in shelf than in deep ocean regions. Remote sensing and CPR sampling provide complementary ways of monitoring phytoplankton in the ocean: the former has superior temporal and spatial coverage and temporal resolution, and the latter provides better taxonomic information.

The influence of phytoplankton productivity, temperature and environmental stability on the control of copepod diversity in the North East Atlantic

The patterns of copepod species richness (S) and their relationship with phytoplankton productivity, temperature and environmental stability were investigated at climatological, seasonal and year-to-year time scales as well as scales along latitudinal and oceanic–neritic gradients using monthly time series of the Continuous Plankton Recorder (CPR) Survey collected in the North East Atlantic between 1958 and 2006. Time series analyses confirmed previously described geographic patterns. Equatorward and towards neritic environments, the climatological average of S increases and the variance explained by the seasonal cycle decreases. The bi-modal character of seasonality increases equatorward and the timing of the seasonal cycle takes place progressive earlier equatorward and towards neritic environments. In the long-term, the climatological average of S decreased significantly (p < 0.001) between 1958 and 2006 in the Bay of Biscay and North Iberian shelf at a rate of ca. 0.04 year−1, and increased at the same rate between 1991 and 2006 in the northernmost oceanic location. The climatological averages of S correlate positively with those of the index of seasonality of phytoplankton productivity (ratio between the minimum and maximum monthly values of surface chlorophyll) and sea surface temperature, and negatively with those of the proxy for environmental stability (monthly frequency of occurrence of daily averaged wind speed exceeding 10 m s−1). The seasonal cycles of S and phytoplankton productivity (surface chlorophyll as proxy) exhibit similar features in terms of shape, timing and explained variance, but the relationship between the climatological averages of both variables is non-significant. From year-to-year, the annual averages of S correlate negatively with those of phytoplankton productivity and positively with those of sea surface temperature along the latitudinal gradient, and negatively with those of environmental stability along the oceanic–neritic gradient. The annual anomalies of S (i.e. factoring out geographic variation) show a unimodal relationship with those of sea surface temperature and environmental stability, with S peaking at intermediate values of the anomalies of these variables. The results evidence the role of seasonality of phytoplankton productivity on the control of copepod species richness at seasonal and climatological scales, giving support to the species richness–productivity hypothesis. Although sea surface temperature (SST) is indeed a good predictor of richness along the latitudinal gradient, it is unable to predict the increase of richness form oceanic to neritic environments, thus lessening the generality of the species richness–energy hypothesis. Meteo-hydrographic disturbances (i.e. SST and wind speed anomalies as proxies), presumably through its role on mixed layer depth dynamics and turbulence and hence productivity, maximise local diversity when occurring at intermediate frequency and or intensity, thus providing support to the intermediate disturbance hypothesis on the control of copepod diversity.

ALES: a multi-mission adaptive sub-waveform retracker for coastal and open ocean altimetry

Satellite altimetry has revolutionized our understanding of ocean dynamics thanks to frequent sampling and global coverage. Nevertheless, coastal data have been flagged as unreliable due to land and calm water interference in the altimeter and radiometer footprint and uncertainty in the modelling of high-frequency tidal and atmospheric forcing. Our study addresses the first issue, i.e. altimeter footprint contamination, via retracking, presenting ALES, the Adaptive Leading Edge Subwaveform retracker. ALES is potentially applicable to all the pulse-limited altimetry missions and its aim is to retrack both open ocean and coastal data with the same accuracy using just one algorithm. ALES selects part of each returned echo and models it with a classic ”open ocean” Brown functional form, by means of least square estimation whose convergence is found through the Nelder-Mead nonlinear optimization technique. By avoiding echoes from bright targets along the trailing edge, it is capable of retrieving more coastal waveforms than the standard processing. By adapting the width of the estimation window according to the significant wave height, it aims at maintaining the accuracy of the standard processing in both the open ocean and the coastal strip. This innovative retracker is validated against tide gauges in the Adriatic Sea and in the Greater Agulhas System for three different missions: Envisat, Jason-1 and Jason-2. Considerations of noise and biases provide a further verification of the strategy. The results show that ALES is able to provide more reliable 20-Hz data for all three missions in areas where even 1-Hz averages are flagged as unreliable in standard products. Application of the ALES retracker led to roughly a half of the analysed tracks showing a marked improvement in correlation with the tide gauge records, with the rms difference being reduced by a factor of 1.5 for Jason-1 and Jason-2 and over 4 for Envisat in the Adriatic Sea (at the closest point to the tide gauge).

Microbial acetone oxidation in coastal seawater

Acetone is an important oxygenated volatile organic compound (OVOC) in the troposphere where it influences the oxidizing capacity of the atmosphere. However, the air-sea flux is not well quantified, in part due to a lack of knowledge regarding which processes control oceanic concentrations, and, specifically whether microbial oxidation to CO2 represents a significant loss process. We demonstrate that 14C labeled acetone can be used to determine microbial oxidation to 14CO2. Linear microbial rates of acetone oxidation to CO2 were observed for between 0.75-3.5 h at a seasonally eutrophic coastal station located in the western English Channel (L4). A kinetic experiment in summer at station L4 gave a Vmax of 4.1 pmol L-1 h-1, with a Km constant of 54 pM. We then used this technique to obtain microbial acetone loss rates ranging between 1.2 and 42 pmol L-1 h-1.(monthly averages) over an annual cycle at L4, with maximum rates observed during winter months. The biological turnover time of acetone (in situ concentration divided by microbial oxidation rate) in surface waters varied from ~3 days in February 2011, when in situ concentrations were 3 ± 1 nM, to >240 days in June 2011, when concentrations were more than twofold higher at 7.5 ± 0.7 nM. These relatively low marine microbial acetone oxidation rates, when normalized to in situ concentrations, suggest that marine microbes preferentially utilize other OVOCs such as methanol and acetaldehyde.

Metocean Comparisons of Jason-2 and AltiKa—A Method to Develop a New Wind Speed Algorithm

As well as range, the AltiKa altimeter provides estimates of wave height, Hs and normalized backscatter, s0, that need to be assessed prior to statistics based on them being included in climate databases. An analysis of crossovers with the Jason-2 altimeter shows AltiKa Hs values to be biased high by only »0.05m, with a standard deviation (s.d.) of »0.1m for seven-point averages. AltiKa’s s 0 values are 2.5–3 dB less than those from Jason-2, with a s.d. of »0.3 dB, with these relatively large mismatches to be expected as AltiKa measures a different part of the spectrum of sea surface roughness. A new wind speed algorithm is developed through matchinghistogram of s0 values to that for Jason-2 wind speeds. The algorithm is robust to the use of short durations of data, with a consistency at roughly the 0.1 m/s level. Incorporation of Hs as a secondary input reduces the assessed error at crossovers from 0.82 m/s to 0.71 m/s. A comparison across all altimeter frequencies used to date demonstrates that the lowest wind speeds preferentially develop the shortest scales of roughness.