Impact of assimilating a merged sea-ice thickness from CryoSat-2 and SMOS in the Arctic reanalysis

Accurately forecasting the sea-ice thickness (SIT) in the Arctic is a major challenge. The new SIT product (referred to as CS2SMOS) merges measurements from the CryoSat-2 and SMOS satellites on a weekly basis during the winter. The impact of assimilating CS2SMOS data is tested for the TOPAZ4 system – the Arctic component of the Copernicus Marine Environment Monitoring Services (CMEMS). TOPAZ4 currently assimilates a large set of ocean and sea-ice observations with the Deterministic Ensemble Kalman Filter (DEnKF). Two parallel reanalyses are conducted without (Official run) and with (Test run) assimilation of CS2SMOS data from 19 March 2014 to 31 March 2015. Since only mapping errors were provided in the CS2SMOS observation, an arbitrary term was added to compensate for the missing errors, but was found a posteriori too large. The SIT bias (too thin) is reduced from 16 to 5&thinsp;cm and the standard errors decrease from 53 to 38&thinsp;cm (by 28&thinsp;%) when compared to the assimilated SIT. When compared to independent SIT observations, the error reduction is 24&thinsp;% against the ice mass balance (IMB) buoy 2013F and by 12.5&thinsp;% against SIT data from the IceBridge campaigns. The improvement of sea-ice volume persists through the summer months in the absence of CS2SMOS data. Comparisons to sea-ice drift from the satellites show that dynamical adjustments reduce the drift errors around the North Pole by about 8&thinsp;%–9&thinsp;% in December 2014 and February 2015. Finally, using the degrees of freedom for signal (DFS), we find that CS2SMOS makes the prime source of information in the central Arctic and in the Kara Sea. We therefore recommend the assimilation of C2SMOS for Arctic reanalyses in order to improve the ice thickness and the ice drift.</p

An eddy resolving tidal-driven model of the South China Sea assimilating along-track SLA data using the EnOI

The upper ocean circulation in the South China Sea (SCS) is driven by the Asian monsoon, the Kuroshio intrusion through the Luzon Strait, strong tidal currents, and a complex topography. Here, we demonstrate the benefit of assimilating along-track altimeter data into a nested configuration of the HYbrid Coordinate Ocean Model that includes tides. Including tides in models is important because they interact with the main circulation. However, assimilation of altimetry data into a model including tides is challenging because tides and mesoscale features contribute to the elevation of ocean surface at different time scales and require different corrections. To address this issue, tides are filtered out of the model output and only the mesoscale variability is corrected with a computationally cheap data assimilation method: the Ensemble Optimal Interpolation (EnOI). This method uses a running selection of members to handle the seasonal variability and assimilates the track data asynchronously. The data assimilative system is tested for the period 1994–1995, during which time a large number of validation data are available. Data assimilation reduces the Root Mean Square Error of Sea Level Anomalies from 9.3 to 6.9 cm and improves the representation of the mesoscale features. With respect to the vertical temperature profiles, the data assimilation scheme reduces the errors quantitatively with an improvement at intermediate depth and deterioration at deeper depth. The comparison to surface drifters shows an improvement of surface current by approximately −9% in the Northern SCS and east of Vietnam. Results are improved compared to an assimilative system that does not include tides and a system that does not consider asynchronous assimilation

On dynamical downscaling of ENSO-induced oceanic anomalies off Baja California Peninsula, Mexico: role of the air-sea heat flux

The El Niño Southern Oscillation (ENSO) phenomenon is responsible for important physical and biogeochemical anomalies in the Northeastern Pacific Ocean. The event of 1997-98 has been one of the most intense in the last decades and it had large implications for the waters off Baja California (BC) Peninsula with a pronounced warm sea surface temperature (SST) anomaly adjacent to the coast. Downscaling of reanalysis products was carried out using a mesoscale-resolving numerical ocean model to reproduce the regional SST anomalies. The nested model has a 9 km horizontal resolution that extend from Cabo Corrientes to Point Conception. A downscaling experiment that computes surface fluxes online with bulk formulae achieves a better representation of the event than a version with prescribed surface fluxes. The nested system improves the representation of the large scale warming and the localized SST anomaly adjacent to BC Peninsula compared to the reanalysis product. A sensitivity analysis shows that air temperature and to a lesser extent wind stress anomalies are the primary drivers of the formation of BC temperature anomaly. The warm air-temperature anomalies advect from the near-equatorial regions and the central north Pacific and is associated with sea-level pressure anomalies in the synoptic-scale atmospheric circulation. This regional warm pool has a pronounced signature on sea level anomaly in agreement with observations, which may have implications for biogeochemistry.publishedVersio

Propagation of thermohaline anomalies and their predictive potential along the Atlantic water pathway

Abstract We assess to what extent seven state-of-the-art dynamical prediction systems can retrospectively predict winter sea surface temperature (SST) in the subpolar North Atlantic and the Nordic seas in the period 1970–2005. We focus on the region where warm water flows poleward (i.e., the Atlantic water pathway to the Arctic) and on interannual-to-decadal time scales. Observational studies demonstrate predictability several years in advance in this region, but we find that SST skill is low with significant skill only at a lead time of 1–2 years. To better understand why the prediction systems have predictive skill or lack thereof, we assess the skill of the systems to reproduce a spatiotemporal SST pattern based on observations. The physical mechanism underlying this pattern is a propagation of oceanic anomalies from low to high latitudes along the major currents, the North Atlantic Current and the Norwegian Atlantic Current. We find that the prediction systems have difficulties in reproducing this pattern. To identify whether the misrepresentation is due to incorrect model physics, we assess the respective uninitialized historical simulations. These simulations also tend to misrepresent the spatiotemporal SST pattern, indicating that the physical mechanism is not properly simulated. However, the representation of the pattern is slightly degraded in the predictions compared to historical runs, which could be a result of initialization shocks and forecast drift effects. Ways to enhance predictions could include improved initialization and better simulation of poleward circulation of anomalies. This might require model resolutions in which flow over complex bathymetry and the physics of mesoscale ocean eddies and their interactions with the atmosphere are resolved. Significance Statement In this study, we find that dynamical prediction systems and their respective climate models struggle to realistically represent ocean surface temperature variability in the eastern subpolar North Atlantic and Nordic seas on interannual-to-decadal time scales. In previous studies, ocean advection is proposed as a key mechanism in propagating temperature anomalies along the Atlantic water pathway toward the Arctic Ocean. Our analysis suggests that the predicted temperature anomalies are not properly circulated to the north; this is a result of model errors that seems to be exacerbated by the effect of initialization shocks and forecast drift. Better climate predictions in the study region will thus require improving the initialization step, as well as enhancing process representation in the climate models.Acknowledgments. The research leading to these results has received funding from the Blue-Action Project (European Union’s Horizon 2020 research and innovation program, Grant 727852), the Trond Mohn Foundation with the project Bjerknes Climate Prediction Unit (BCPU, Grant BFS2018TMT01), the NordForsk under the Nordic Centre of Excellence (ARCPATH, 76654), and from the Bjerknes Centre with the project SKD MEDEVAC. The research leading to these results has also received funding from the German Federal Ministry of Education and Research (BMBF) through the JPI Climate/JPI Oceans NextG-Climate Science-ROADMAP (FKZ: 01LP2002A; DM and Norwegian Grant 316618/JPIC/JPIO-04; HRL and NK and ANR-19-JPOC-003; JM). PO was funded by the Spanish Ministry for the Economy, Industry and Competitiveness through the grant RYC-2017-22772. SY also receives financial support from the Danish National Center for Climate Research (NCKF). The National Center for Atmospheric Research is a major facility sponsored by the U.S. National Science Foundation (NSF) under Cooperative Agreement 1852977. EM is supported by the U.S. NSF Office of Polar Programs Grant 1737377. The prediction simulations using EC-EARTH anomaly initialization system were performed by SMHI on resources provided by the Swedish National Infrastructure for Computing (SNIC).Peer Reviewed"Article signat per 16 autors/es: H. R. Langehaug, P. Ortega, F. Counillon, D. Matei, E. Maroon, N. Keenlyside, J. Mignot, Y. Wang, D. Swingedouw, I. Bethke, S. Yang, G. Danabasoglu, A. Bellucci, P. Ruggieri, D. Nicolì, and M. Årthun"Postprint (published version

TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic

We present a detailed description of TOPAZ4, the latest version of TOPAZ – a coupled ocean-sea ice data assimilation system for the North Atlantic Ocean and Arctic. It is the only operational, large-scale ocean data assimilation system that uses the ensemble Kalman filter. This means that TOPAZ features a time-evolving, state-dependent estimate of the state error covariance. Based on results from the pilot MyOcean reanalysis for 2003–2008, we demonstrate that TOPAZ4 produces a realistic estimate of the ocean circulation in the North Atlantic and the sea-ice variability in the Arctic. We find that the ensemble spread for temperature and sea-level remains fairly constant throughout the reanalysis demonstrating that the data assimilation system is robust to ensemble collapse. Moreover, the ensemble spread for ice concentration is well correlated with the actual errors. This indicates that the ensemble statistics provide reliable state-dependent error estimates – a feature that is unique to ensemble-based data assimilation systems. We demonstrate that the quality of the reanalysis changes when different sea surface temperature products are assimilated, or when in-situ profiles below the ice in the Arctic Ocean are assimilated. We find that data assimilation improves the match to independent observations compared to a free model. Improvements are particularly noticeable for ice thickness, salinity in the Arctic, and temperature in the Fram Strait, but not for transport estimates or underwater temperature. At the same time, the pilot reanalysis has revealed several flaws in the system that have degraded its performance. Finally, we show that a simple bias estimation scheme can effectively detect the seasonal or constant bias in temperature and sea-level

Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway

Dinophysis acuminata produces Diarrhetic Shellfish Toxins (DST) that contaminate natural and farmed shellfish, leading to public health risks and economically impacting mussel farms. For this reason, there is a high interest in understanding and predicting D. acuminata blooms. This study assesses the environmental conditions and develops a sub-seasonal (7 - 28 days) forecast model to predict D. acuminata cells abundance in the Lyngen fjord located in northern Norway. A Support Vector Machine (SVM) model is trained to predict future D. acuminata cells abundance by using the past cell concentration, sea surface temperature (SST), Photosynthetic Active Radiation (PAR), and wind speed. Cells concentration of Dinophysis spp. are measured in-situ from 2006 to 2019, and SST, PAR, and surface wind speed are obtained by satellite remote sensing. D. acuminata only explains 40% of DST variability from 2006 to 2011, but it changes to 65% after 2011 when D. acuta prevalence reduced. The D. acuminata blooms can reach concentration up to 3954 cells l−1 and are restricted to the summer during warmer waters, varying from 7.8 to 12.7 °C. The forecast model predicts with fair accuracy the seasonal development of the blooms and the blooms amplitude, showing a coefficient of determination varying from 0.46 to 0.55. SST has been found to be a useful predictor for the seasonal development of the blooms, while the past cells abundance is needed for updating the current status and adjusting the blooms timing and amplitude. The calibrated model should be tested operationally in the future to provide an early warning of D. acuminata blooms in the Lyngen fjord. The approach can be generalized to other regions by recalibrating the model with local observations of D. acuminata blooms and remote sensing data.publishedVersio

Teaching new dogs old tricks: membrane biophysical properties in drug delivery and resistance

"How do drugs cross the plasma membrane?" this may seem like a trivial question. This question is often over-looked to focus primarily on the different complex macro-molecular aspects involved in drug delivery or drug resistance. However, recent studies have highlighted the theme that to be fully understood, more knowledge of the underlying biology of the most complex biological processes involved in the delivery and resistance to drugs is needed. After all, why would a drug interact with a transporter then subsequently be excluded from P-glycoprotein (P-gp) expressing drug resistant cells? What are the determinants of this transition in behavior? Full consideration of the physical biology of drug delivery has allowed a better understanding of the reasons why specific membrane proteins are upregulated or overexpressed in drug resistant cells. This, in turn, allows us to identify new targets for drug chemicals. Better yet, it increases the significance of recents patents and underlines their importance in multi drug resistance

Seasonal predictability of Kiremt rainfall in coupled general circulation models

The Ethiopian economy and population is strongly dependent on rainfall. Operational seasonal predictions for the main rainy season (Kiremt, June–September) are based on statistical approaches with Pacific sea surface temperatures (SST) as the main predictor. Here we analyse dynamical predictions from 11 coupled general circulation models for the Kiremt seasons from 1985–2005 with the forecasts starting from the beginning of May. We find skillful predictions from three of the 11 models, but no model beats a simple linear prediction model based on the predicted Niño3.4 indices. The skill of the individual models for dynamically predicting Kiremt rainfall depends on the strength of the teleconnection between Kiremt rainfall and concurrent Pacific SST in the models. Models that do not simulate this teleconnection fail to capture the observed relationship between Kiremt rainfall and the large-scale Walker circulation.publishedVersio