Upwelling and associated heat flux in the equatorial Atlantic inferred from helium isotope disequilibrium

Sea-to-air and diapycnal fluxes of nitrous oxide (N2O) into the mixed layer were determined during three cruises to the upwelling region off Mauritania. Sea-to-air fluxes as well as diapycnal fluxes were elevated close to the shelf break, but elevated sea-to-air fluxes reached further offshore as a result of the offshore transport of upwelled water masses. To calculate a mixed layer budget for N2O we compared the regionally averaged sea-to-air and diapycnal fluxes and estimated the potential contribution of other processes, such as vertical advection and biological N2O production in the mixed layer. Using common parameterizations for the gas transfer velocity, the comparison of the average sea-toair and diapycnal N2O fluxes indicated that the mean sea-toair flux is about three to four times larger than the diapycnal flux. Neither vertical and horizontal advection nor biological production were found sufficient to close the mixed layer budget. Instead, the sea-to-air flux, calculated using a parameterization that takes into account the attenuating effect of surfactants on gas exchange, is in the same range as the diapycnal flux. From our observations we conclude that common parameterizations for the gas transfer velocity likely overestimate the air-sea gas exchange within highly productive upwelling zones.

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.