Gabriel Jordá

Footprints of climate change on Mediterranean Sea biota

The Mediterranean Sea ranks among the ocean regions warming fastest. There is evidence for impacts of climate change on marine Mediterranean organisms but a quantitative assessment is lacking. We compiled the impacts of warming reported in the literature to provide a quantitative assessment for the Mediterranean Sea. During the last three decades the summer surface temperature has increased 1.15 oC. Strong heat wave events have occurred in years 1994, 2003 and 2009. Impacts of warming are evident on growth, survival, fertility, migration and phenology of pelagic and benthic organisms, from phytoplankton to marine vegetation, invertebrates and vertebrates. Overall, 50 % of biological impacts in the Mediterranean Sea occur at summer surface temperature anomaly ≤ 4.5 oC and at summer surface temperature of 27.5 oC. The activation energy (geometric mean 1.58 ± 0.48 eV), the slope of the Arrhenius equation describing the temperature-dependence of biological processes, for the response of Mediterranean marine biota to warming reveals that these responses in the Mediterranean are far steepest than possibly explained by the direct effect of warming alone. The observations are biased toward the northern and western sectors of the basin, likely underestimating the impacts of warming in areas where warming is particularly intense.

The Climate of the Mediterranean Region in Future Climate Projections

Future climate change over the Mediterranean area is investigated by means of climate model simulations covering the twenty-first century that take into account different anthropogenic greenhouse-gas-emission scenarios. This chapter first gives some new insights on these projections coming from the use of new methods, including the coupling at the regional scale of the atmospheric component to a Mediterranean Sea component. A synthesis of the expected changes of key aspects of the Mediterranean regional climate, obtained with a wide range of models and downscaling methods, is then presented. This includes an overview of not only expected changes in the mean climate and climate extremes but also possible changes in Mediterranean Sea temperature, salinity, circulation, water and heat budgets, and sea level. The chapter ends with some advanced results on the way to deal with uncertainties in climate projections and some discussion on the confidence that we can attribute to these projections.

Detection time for global and regional sea level trends and accelerations

Many studies analyze trends on sea level data with the underlying purpose of finding indications of a long-term change that could be interpreted as the signature of anthropogenic climate change. The identification of a long-term trend is a signal-to-noise problem where the natural variability (the “noise”) can mask the long-term trend (the “signal”). The signal-to-noise ratio depends on the magnitude of the long-term trend, on the magnitude of the natural variability, and on the length of the record, as the climate noise is larger when averaged over short time scales and becomes smaller over longer averaging periods. In this paper, we evaluate the time required to detect centennial sea level linear trends and accelerations at global and regional scales. Using model results and tide gauge observations, we find that the averaged detection time for a centennial linear trend is 87.9, 76.0, 59.3, 40.3, and 25.2 years for trends of 0.5, 1.0, 2.0, 5.0, and 10.0 mm/yr, respectively. However, in regions with large decadal variations like the Gulf Stream or the Circumpolar current, these values can increase up to a 50%. The spatial pattern of the detection time for sea level accelerations is almost identical. The main difference is that the length of the records has to be about 40–60 years longer to detect an acceleration than to detect a linear trend leading to an equivalent change after 100 years. Finally, we have used a new sea level reconstruction, which provides a more accurate representation of interannual variability for the last century in order to estimate the detection time for global mean sea level trends and accelerations. Our results suggest that the signature of natural variability in a 30 year global mean sea level record would be less than 1 mm/yr. Therefore, at least 2.2 mm/yr of the recent sea level trend estimated by altimetry cannot be attributed to natural multidecadal variability.

Comparison of Mediterranean sea level variability as given by three baroclinic models

We compare the results of three baroclinic models with the aim of evaluating their skills in reproducing Mediterranean long-term sea level variability. The models are an ocean-ice coupled forced global model (ORCA), a regional forced ocean model (OM8) and a regional coupled atmosphere-ocean model (MITgcm). Model results are compared for the period 1961–2000 against hydrographic observations for water mass properties and steric sea level, and against satellite altimetry data and a reconstruction for sea level. All models represent the temperature variability of the upper layers reasonably well, but exhibit a considerable positive drift in the temperature of the deep layers due to an imbalance between the surface heat flux and the heat flux through Gibraltar. OM8 and MITgcm simulate the process of dense water formation better than ORCA thanks to their higher resolution in the model grid and in the atmospheric forcings. Concerning sea level variability, MITgcm is the only model that simulates well the inter-annual sea level variability associated with the Eastern Mediterranean Transient. However, none of the models is able to reproduce other features that have clear signatures on sea level. The inter-annual variability of Mediterranean mean sea level is better reproduced by the ORCA model because it is the only one considering the mass contribution from the Atlantic. The lack of that component in the regional models is a major shortcoming to reproduce Mediterranean sea level variability. Finally, mean sea level trends are overestimated by all models due to the spurious warming drift in the deep layers.

The SMOS L3 Mapping Algorithm for Sea Surface Salinity

The Soil Moisture and Ocean Salinity (SMOS) mission launched in November 2009 will provide, for the first time, satellite observations of sea surface salinity (SSS). At level 3 (L3) of the SMOS processing chain, the large amount of SSS data obtained by the satellite will be summarized in gridded products with the aim of synthesizing the information and reducing the error of individual SSS observations. In this paper, we present the algorithm adopted by the CP34 SMOS processing center to generate the SMOS L3 products and discuss the choices adopted. The algorithm is based on optimal statistical interpolation. This method needs the following: 1) the prescription of a background field; 2) a prefiltering procedure to reduce the data set size; 3) the definition of a suitable correlation model; and 4) the characterization of the observational error statistics. For the present initial stage, a monthly climatology is chosen as the best background field. The spatiotemporal correlations between the departures from the climatology are described using a bivariate Gaussian function. The correlation model parameters are obtained by fitting the function to the realistic ocean model data. The sensitivity experiments show that an accurate correlation model that permits local variations in the correlation parameters is the best option. The observational error statistics (bias, variance, and correlation) are addressed from the results of the SMOS level-2 processor simulator. Finally, several sensitivity experiments show that a bad prescription of observational errors in the L3 algorithm does result in a dramatic impact on the generation of L3 products.

The ability of a barotropic model to simulate sea level extremes of meteorological origin in the Mediterranean Sea, including those caused by explosive cyclones

Storm surges are responsible for great damage to coastal property and loss of life every year. Coastal management and adaptation practices are essential to reduce such damage. Numerical models provide a useful tool for informing these practices as they simulate sea level with high spatial resolution. Here we investigate the ability of a barotropic version of the HAMSOM model to simulate sea level extremes of meteorological origin in the Mediterranean Sea, including those caused by explosive cyclones. For this purpose, the output of the model is compared to hourly sea level observations from six tide gauge records (Valencia, Barcelona, Marseille, Civitavecchia, Trieste, and Antalya). It is found that the model underestimates the positive extremes significantly at all stations, in some cases by up to 65%. At Trieste, the model can also sometimes overestimate the extremes significantly. The differences between the model and the residuals are not constant for extremes of a given height, which limits the applicability of the numerical model for storm surge forecasting because calibration is difficult. The 50 and 10 year return levels are reasonably well captured by the model at all stations except Barcelona and Marseille, where they are underestimated by over 30%. The number of exceedances of the 99.9th and 99.95% percentiles over a period of 25 years is severely underestimated by the model at all stations. The skill of the model for predicting the timing and value of the storm surges seems to be higher for the events associated with explosive cyclones at all stations.

Spatial and temporal variations of the seasonal sea level cycle in the northwest Pacific

The seasonal sea level variations observed from tide gauges over 1900–2013 and gridded satellite altimeter product AVISO over 1993–2013 in the northwest Pacific have been explored. The seasonal cycle is able to explain 60–90% of monthly sea level variance in the marginal seas, while it explains less than 20% of variance in the eddy-rich regions. The maximum annual and semiannual sea level cycles (30 and 6 cm) are observed in the north of the East China Sea and the west of the South China Sea, respectively. AVISO was found to underestimate the annual amplitude by 25% compared to tide gauge estimates along the coasts of China and Russia. The forcing for the seasonal sea level cycle was identified. The atmospheric pressure and the steric height produce 8–12 cm of the annual cycle in the middle continental shelf and in the Kuroshio Current regions separately. The removal of the two attributors from total sea level permits to identify the sea level residuals that still show significant seasonality in the marginal seas. Both nearby wind stress and surface currents can explain well the long-term variability of the seasonal sea level cycle in the marginal seas and the tropics because of their influence on the sea level residuals. Interestingly, the surface currents are a better descriptor in the areas where the ocean currents are known to be strong. Here, they explain 50–90% of interannual variability due to the strong links between the steric height and the large-scale ocean currents.

Accuracy of SMOS Level 3 SSS Products Related to Observational Errors

The Soil Moisture and Ocean Salinity (SMOS) mission will provide, for the first time, satellite observations of sea surface salinity (SSS). The satellite will acquire a large amount of data but with a high observational noise. At level 3 (L3) of the SMOS processing chain, the information will be summarized in gridded products with the aim of synthesizing the information and reducing the error of individual SSS observations. The technique chosen to generate the maps is optimal statistical interpolation (OI). The goal of this paper is to quantify the impact of the observational error on the accuracy of L3 gridded products. The accuracy of the products is estimated using the OI error formulation, which has been extended to include the convolution with a normal error filter. For a given observational error, we estimate the minimum scales that can be resolved with a prescribed accuracy (at the expense of losing the variance associated with shorter scales). Conversely, for a prescribed accuracy and spatio-temporal resolution of L3 products, we estimate the maximum observational error that will allow the fulfillment of the requirements. Results indicate that a maximum SSS error of about 0.8 (1.1) psu would be enough to obtain an accuracy of 0.1 psu for L3 products with a resolution of 100 km/30 day (200 km/10 day). The statistical errors produced by the OI formulation are compared with the errors obtained for several case studies in order to assess their robustness.

Mediterranean circulation perturbations over the last five centuries: Relevance to past Eastern Mediterranean Transient-type events

The Eastern Mediterranean Transient (EMT) occurred in the Aegean Sea from 1988 to 1995 and is the most significant intermediate-to-deep Mediterranean overturning perturbation reported by instrumental records. The EMT was likely caused by accumulation of high salinity waters in the Levantine and enhanced heat loss in the Aegean Sea, coupled with surface water freshening in the Sicily Channel. It is still unknown whether similar transients occurred in the past and, if so, what their forcing processes were. In this study, sediments from the Sicily Channel document surface water freshening (SCFR) at 1910 ± 12, 1812 ± 18, 1725 ± 25 and 1580 ± 30 CE. A regional ocean hindcast links SCFR to enhanced deep-water production and in turn to strengthened Mediterranean thermohaline circulation. Independent evidence collected in the Aegean Sea supports this reconstruction, showing that enhanced bottom water ventilation in the Eastern Mediterranean was associated with each SCFR event. Comparison between the records and multi-decadal atmospheric circulation patterns and climatic external forcings indicates that Mediterranean circulation destabilisation occurs during positive North Atlantic Oscillation (NAO) and negative Atlantic Multidecadal Oscillation (AMO) phases, reduced solar activity and strong tropical volcanic eruptions. They may have recurrently produced favourable deep-water formation conditions, both increasing salinity and reducing temperature on multi-decadal time scales.

Comment on "Storm surge frequency reduction in Venice under climate change" by Troccoli et al.

AbstractTroccoli et al. (Climatic Change, published online 14th May, DOI: 10.1007/s10584-011-0093-x), analysed different projections from global climate models in order to assess the frequency of storm surges in Venice during the 21st century under a climate change context. They concluded that the frequency of storm surges would decrease by about 30%, and that this reduction would compensate the expected mean sea level rise. Their final statement was that “the frequency of extreme tides in Venice might largely remain unaltered”. Although we agree in the expected reduction of storm surges, we strongly disagree in their final conclusion. First, because the impact of storm surges not only depends on the number of extreme surge events, but also on their intensity, that was not explicitely addressed. Second, because their estimates of mean sea level change for the 21st century are largely underestimated, as they miss some of the components driving sea level variability. Using state-of-the-art estimates for the thermosteric, mass and tidal contributions we show that the flooding events in Venice are expected to dramatically increase in a climate change scenario.