North Atlantic seasonal hurricane prediction: underlying science and an evaluation of statistical models

Statistically-based seasonal hurricane outlooks for the North Atlantic were initiated by Colorado State University (CSU) in 1984, and have been issued every year since that time by CSU. The National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center and the UK-based Tropical Storm Risk (TSR) have the next longest records (1998-present) of continuous outlooks. This chapter describes how these three forecasts have evolved with time, and documents the approaches, the environmental fields, and the lead times which underpin the models’ operation. Some of the environmental parameters used in early seasonal outlooks are no longer employed, but new predictive fields have been found which appear to be more important for seasonal hurricane prediction. An assessment is made of the deterministic skill of the seasonal hurricane outlooks issued in real-time by CSU, NOAA, and TSR between 2003 and 2014. All methods show moderate-to-good skill for early August outlooks (prior to the most active portion of the hurricane season), low-to-moderate skill for outlooks issued in early June, and lesser skill for outlooks issued in early April. Overall, the TSR model has the most skillful predictions of Accumulated Cyclone Energy (ACE), while NOAA has the best named storm predictions issued in early August

Tres gramàtiques per a tres moments de la història de la llengua del vuit-cents

This paper proposes to divide the sociolinguistic history of the Catalan language in the 19th century into three periods: a first period that would still be characterized by the linguistic ideologies of the Ancien Régime; a second period in which the ideology of the Renaixença, of regionalist Catalanism, would predominate; and a third period in which Catalanism, already consolidated, would promote a recovery of all sociolinguistic spheres for Catalan. To illustrate the ideologies of each of these three periods, a representative grammar is analysed ideologically: Josep Pau Ballot for the first, Antoni de Bofarull for the second and Pompeu Fabra for the third

Gradient flux measurements of sea–air DMS transfer during the Surface Ocean Aerosol Production (SOAP) experiment

Direct measurements of marine dimethylsulfide (DMS) fluxes are sparse, particularly in the Southern Ocean. The Surface Ocean Aerosol Production (SOAP) voyage in February–March 2012 examined the distribution and flux of DMS in a biologically active frontal system in the southwest Pacific Ocean. Three distinct phytoplankton blooms were studied with oceanic DMS concentrations as high as 25 nmol L−1. Measurements of DMS fluxes were made using two independent methods: the eddy covariance (EC) technique using atmospheric pressure chemical ionization–mass spectrometry (API-CIMS) and the gradient flux (GF) technique from an autonomous catamaran platform. Catamaran flux measurements are relatively unaffected by airflow distortion and are made close to the water surface, where gas gradients are largest. Flux measurements were complemented by near-surface hydrographic measurements to elucidate physical factors influencing DMS emission. Individual DMS fluxes derived by EC showed significant scatter and, at times, consistent departures from the Coupled Ocean–Atmosphere Response Experiment gas transfer algorithm (COAREG). A direct comparison between the two flux methods was carried out to separate instrumental effects from environmental effects and showed good agreement with a regression slope of 0.96 (r2 = 0.89). A period of abnormal downward atmospheric heat flux enhanced near-surface ocean stratification and reduced turbulent exchange, during which GF and EC transfer velocities showed good agreement but modelled COAREG values were significantly higher. The transfer velocity derived from near-surface ocean turbulence measurements on a spar buoy compared well with the COAREG model in general but showed less variation. This first direct comparison between EC and GF fluxes of DMS provides confidence in compilation of flux estimates from both techniques, as well as in the stable periods when the observations are not well predicted by the COAREG model

Using eddy covariance to measure the dependence of air–sea CO2 exchange rate on friction velocity

Parameterisation of the air–sea gas transfer velocity of CO2 and other trace gases under open-ocean conditions has been a focus of air–sea interaction research and is required for accurately determining ocean carbon uptake. Ships are the most widely used platform for air–sea flux measurements but the quality of the data can be compromised by airflow distortion and sensor cross-sensitivity effects. Recent improvements in the understanding of these effects have led to enhanced corrections to the shipboard eddy covariance (EC) measurements. Here, we present a revised analysis of eddy covariance measurements of air–sea CO2 and momentum fluxes from the Southern Ocean Surface Ocean Aerosol Production (SOAP) study. We show that it is possible to significantly reduce the scatter in the EC data and achieve consistency between measurements taken on station and with the ship underway. The gas transfer velocities from the EC measurements correlate better with the EC friction velocity (u*) than with mean wind speeds derived from shipboard measurements corrected with an airflow distortion model. For the observed range of wind speeds (u10 N = 3–23 m s−1), the transfer velocities can be parameterised with a linear fit to u*. The SOAP data are compared to previous gas transfer parameterisations using u10 N computed from the EC friction velocity with the drag coefficient from the Coupled Ocean–Atmosphere Response Experiment (COARE) model version 3.5. The SOAP results are consistent with previous gas transfer studies, but at high wind speeds they do not support the sharp increase in gas transfer associated with bubble-mediated transfer predicted by physically based models

Air/sea DMS gas transfer in the North Atlantic: evidence for limited interfacial gas exchange at high wind speed

Shipboard measurements of eddy covariance dimethylsulfide (DMS) air–sea fluxes and seawater concentration were carried out in the North Atlantic bloom region in June/July 2011. Gas transfer coefficients (k660) show a linear dependence on mean horizontal wind speed at wind speeds up to 11 m s−1. At higher wind speeds the relationship between k660 and wind speed weakens. At high winds, measured DMS fluxes were lower than predicted based on the linear relationship between wind speed and interfacial stress extrapolated from low to intermediate wind speeds. In contrast, the transfer coefficient for sensible heat did not exhibit this effect. The apparent suppression of air–sea gas flux at higher wind speeds appears to be related to sea state, as determined from shipboard wave measurements. These observations are consistent with the idea that long waves suppress near-surface water-side turbulence, and decrease interfacial gas transfer. This effect may be more easily observed for DMS than for less soluble gases, such as CO2, because the air–sea exchange of DMS is controlled by interfacial rather than bubble-mediated gas transfer under high wind speed conditions

Methanethiol, dimethyl sulfide and acetone over biologically productive waters in the southwest Pacific Ocean

Atmospheric methanethiol (MeSHa), dimethyl sulfide (DMSa) and acetone (acetonea) were measured over biologically productive frontal waters in the remote southwest Pacific Ocean in summertime 2012 during the Surface Ocean Aerosol Production (SOAP) voyage. MeSHa mixing ratios varied from below the detection limit (<10ppt) up to 65ppt and were 3%–36% of parallel DMSa mixing ratios. MeSHa and DMSa were correlated over the voyage(R2=0.3,slope=0.07)with a stronger correlation over a coccolithophore-dominated phytoplankton bloom (R2= 0.5, slope 0.13). The diurnal cycle for MeSHa shows similar behaviour to DMSa with mixing ratios varying by a factor of ∼2 according to time of day with the minimum levels of both MeSHa and DMSa occurring at around 16:00LT (local time, all times in this paper are in local time). A positive flux of MeSH out of the ocean was calculated for three different nights and ranged from 3.5 to 5.8µmolm−2 d−1, corresponding to 14%–24% of the DMS flux (MeSH/(MeSH+DMS)). Spearman rank correlations with ocean biogeochemical parameters showed a moderate to-strong positive, highly significant relationship between both MeSHa and DMSa with seawater DMS (DMSsw) and a moderate correlation with total dimethylsulfoniopropionate (total DMSP). A positive correlation of acetonea with water temperature and negative correlation with nutrient concentrations are consistent with reports of acetone production in warmer subtropical waters. Positive correlations of acetonea with cryptophyte and eukaryotic phytoplankton numbers, and high-molecular-weight sugars and chromophoric dissolved organic matter (CDOM), suggest an organic source. This work points to a significant ocean source of MeSH, highlighting the need for further studies into the distribution and fate of MeSH, and it suggests links between atmospheric acetone levels and biogeochemistry over the midlatitude ocean. In addition, an intercalibration of DMSa at ambient levels using three independently calibrated instruments showed ∼15%–25% higher mixing ratios from an atmospheric pressure ionisation chemical ionisation mass spectrometer (mesoCIMS) compared to a gas chromatograph with a sulfurchemiluminescencedetector(GC-SCD)and proton transfer reaction mass spectrometer (PTR-MS). Some differences were attributed to the DMSa gradient above the sea surface and differing approaches of integrated versus discrete measurements. Remaining discrepancies were likely due to different calibrationscales,suggesting that further investigation of the stability and/or absolute calibration of DMSstandards used at sea is warranted

A genetic network that suppresses genome rearrangements in Saccharomyces cerevisiae and contains defects in cancers.

Gross chromosomal rearrangements (GCRs) play an important role in human diseases, including cancer. The identity of all Genome Instability Suppressing (GIS) genes is not currently known. Here multiple Saccharomyces cerevisiae GCR assays and query mutations were crossed into arrays of mutants to identify progeny with increased GCR rates. One hundred eighty two GIS genes were identified that suppressed GCR formation. Another 438 cooperatively acting GIS genes were identified that were not GIS genes, but suppressed the increased genome instability caused by individual query mutations. Analysis of TCGA data using the human genes predicted to act in GIS pathways revealed that a minimum of 93% of ovarian and 66% of colorectal cancer cases had defects affecting one or more predicted GIS gene. These defects included loss-of-function mutations, copy-number changes associated with reduced expression, and silencing. In contrast, acute myeloid leukaemia cases did not appear to have defects affecting the predicted GIS genes

Recommendations for dealing with waste contaminated with Ebola virus: a Hazard Analysis of Critical Control Points approach

Objective To assess, within communities experiencing Ebola virus outbreaks, the risks associated with the disposal of human waste and to generate recommendations for mitigating such risks. Methods A team with expertise in the Hazard Analysis of Critical Control Points framework identified waste products from the care of individuals with Ebola virus disease and constructed, tested and confirmed flow diagrams showing the creation of such products. After listing potential hazards associated with each step in each flow diagram, the team conducted a hazard analysis, determined critical control points and made recommendations to mitigate the transmission risks at each control point. Findings The collection, transportation, cleaning and shared use of blood-soiled fomites and the shared use of latrines contaminated with blood or bloodied faeces appeared to be associated with particularly high levels of risk of Ebola virus transmission. More moderate levels of risk were associated with the collection and transportation of material contaminated with bodily fluids other than blood, shared use of latrines soiled with such fluids, the cleaning and shared use of fomites soiled with such fluids, and the contamination of the environment during the collection and transportation of blood-contaminated waste. Conclusion The risk of the waste-related transmission of Ebola virus could be reduced by the use of full personal protective equipment, appropriate hand hygiene and an appropriate disinfectant after careful cleaning. Use of the Hazard Analysis of Critical Control Points framework could facilitate rapid responses to outbreaks of emerging infectious disease

Predictability of Seawater DMS During the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES)

This work presents an overview of a unique set of surface ocean dimethylsulfide (DMS) measurements from four shipboard field campaigns conducted during the North Atlantic Aerosol and Marine Ecosystem Study (NAAMES) project. Variations in surface seawater DMS are discussed in relation to biological and physical observations. Results are considered at a range of timescales (seasons to days) and spatial scales (regional to sub-mesoscale). Elevated DMS concentrations are generally associated with greater biological productivity, although chlorophyll a (Chl) only explains a small fraction of the DMS variability (15%). Physical factors that determine the location of oceanic temperature fronts and depth of vertical mixing have an important influence on seawater DMS concentrations during all seasons. The interplay of biomass and physics influences DMS concentrations at regional/seasonal scales and at smaller spatial and shorter temporal scales. Seawater DMS measurements are compared with the global seawater DMS climatology and predictions made using a recently published algorithm and by a neural network model. The climatology is successful at capturing the seasonal progression in average seawater DMS, but does not reproduce the shorter spatial/temporal scale variability. The input terms common to the algorithm and neural network approaches are biological (Chl) and physical (mixed layer depth, photosynthetically active radiation, seawater temperature). Both models predict the seasonal North Atlantic average seawater DMS trends better than the climatology. However, DMS concentrations tend to be under-predicted and the episodic occurrence of higher DMS concentrations is poorly predicted. The choice of climatological seawater DMS product makes a substantial impact on the estimated DMS flux into the North Atlantic atmosphere. These results suggest that additional input terms are needed to improve the predictive capability of current state-of-the-art approaches to estimating seawater DMS