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

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

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

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP

Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.publishedVersio

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

Seasonal-to-decadal predictions with the ensemble Kalman filter and the Norwegian Earth System Model: a twin experiment

Here, we firstly demonstrate the potential of an advanced flow dependent data assimilation method for performing seasonal-to-decadal prediction and secondly, reassess the use of sea surface temperature (SST) for initialisation of these forecasts. We use the Norwegian Climate Prediction Model (NorCPM), which is based on the Norwegian Earth System Model (NorESM) and uses the deterministic ensemble Kalman filter to assimilate observations. NorESM is a fully coupled system based on the Community Earth System Model version 1, which includes an ocean, an atmosphere, a sea ice and a land model. A numerically efficient coarse resolution version of NorESM is used. We employ a twin experiment methodology to provide an upper estimate of predictability in our model framework (i.e. without considering model bias) of NorCPM that assimilates synthetic monthly SST data (EnKF-SST). The accuracy of EnKF-SST is compared to an unconstrained ensemble run (FREE) and ensemble predictions made with near perfect (i.e. microscopic SST perturbation) initial conditions (PERFECT). We perform 10 cycles, each consisting of a 10-yr assimilation phase, followed by a 10-yr prediction. The results indicate that EnKF-SST improves sea level, ice concentration, 2 m atmospheric temperature, precipitation and 3-D hydrography compared to FREE. Improvements for the hydrography are largest near the surface and are retained for longer periods at depth. Benefits in salinity are retained for longer periods compared to temperature. Near-surface improvements are largest in the tropics, while improvements at intermediate depths are found in regions of large-scale currents, regions of deep convection, and at the Mediterranean Sea outflow. However, the benefits are often small compared to PERFECT, in particular, at depth suggesting that more observations should be assimilated in addition to SST. The EnKF-SST system is also tested for standard ocean circulation indices and demonstrates decadal predictability for Atlantic overturning and sub-polar gyre circulations, and heat content in the Nordic Seas. The system beats persistence forecast and shows skill for heat content in the Nordic Seas that is close to PERFECT

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