Long-term evolution of Caspian Sea thermohaline properties reconstructed in an eddy-resolving ocean general circulation model

Abstract. Decadal variability in Caspian Sea thermohaline properties is investigated using a high-resolution ocean general circulation model including sea ice thermodynamics and air–sea interaction forced by prescribed realistic atmospheric conditions and riverine runoff. The model describes synoptic, seasonal and climatic variations of sea thermohaline structure, water balance, and sea level. A reconstruction experiment was conducted for the period of 1961–2001, covering a major regime shift in the global climate during 1976–1978, which allowed for an investigation of the Caspian Sea response to such significant episodes of climate variability. The model reproduced sea level evolution reasonably well despite the fact that many factors (such as possible seabed changes and insufficiently explored underground water infiltration) were not taken into account in the numerical reconstruction. This supports the hypothesis relating rapid Caspian Sea level rise in 1978–1995 with global climate change, which caused variation in local atmospheric conditions and riverine discharge reflected in the external forcing data used, as is shown in the paper. Other effects of the climatic shift are investigated, including a decrease in salinity in the active layer, strengthening of its stratification and corresponding diminishing of convection. It is also demonstrated that water exchange between the three Caspian basins (northern, middle and southern) plays a crucial role in the formation of their thermohaline regime. The reconstructed long-term trends in seawater salinity (general downtrend after 1978), temperature (overall increase) and density (general downtrend) are studied, including an assessment of the influence of main surface circulation patterns and model error accumulation. </jats:p

Compact Modeling Framework v3.0 for high-resolution global ocean-ice-atmosphere models

Abstract. We present new version of the Compact Modeling Framework (CMF3.0) developed for providing the software environment for stand-alone and coupled models of the Global geophysical fluids. The CMF3.0 designed for implementation high and ultra-high resolution models at massive-parallel supercomputers. The key features of the previous CMF version (2.0) are mentioned for reflecting progress in our researches. In the CMF3.0 pure MPI approach with high-level abstract driver, optimized coupler interpolation and I/O algorithms is replaced with PGAS paradigm communications scheme, while central hub architecture evolves to the set of simultaneously working services. Performance tests for both versions are carried out. As addition a parallel realisation of the EnOI (Ensemble Optimal Interpolation) data assimilation method as program service of CMF3.0 is presented. </jats:p

Assimilation of ice compactness data in a strong coupling regime in the ocean – sea ice coupled model

Abstract. The Arctic Ocean plays an important role in the global climate system, where sea ice regulates the exchange of heat, moisture and momentum between the atmosphere and the ocean. A comprehensive analysis and forecast of the Arctic ocean system requires a detailed numerical ocean and sea ice coupled model supplemented by assimilation of observational data at appropriate time scales. A new operative ocean – ice state forecast system was developed and implemented. It consists of the INMIO4.1 ocean general circulation model and the CICE5.1 sea ice dynamics and thermodynamics model with common spatial resolution of 0.25°. For the exchange of boundary conditions and service actions (data storage, time synchronization, etc.), the coupled model uses the Compact Modeling Framework (CMF3.0). Data assimilation is implemented in the form of the Data Assimilation Service (DAS) based on the Ensemble Optimal Interpolation (EnOI) method. This technique allows to simultaneously correct the ocean (temperature, salinity, surface level) and ice (concentration) model fields in the DAS service, so they are coordinated not only through the exchange of boundary conditions, but already at the stage of data assimilation (i.e. strong coupling data assimilation). Experiments with the INMIO – CICE model show that the developed algorithm provides a significant improvement in the accuracy of forecasting the state of the ice field in the Arctic Ocean. </jats:p

Concrete Autoencoder for the Reconstruction of Sea Temperature Field from Sparse Measurements

This paper presents a new method for finding the optimal positions for sensors used to reconstruct geophysical fields from sparse measurements. The method is composed of two stages. In the first stage, we estimate the spatial variability of the physical field by approximating its information entropy using the Conditional Pixel CNN network. In the second stage, the entropy is used to initialize the distribution of optimal sensor locations, which is then optimized using the Concrete Autoencoder architecture with the straight-through gradient estimator for the binary mask and with adversarial loss. This allows us to simultaneously minimize the number of sensors and maximize reconstruction accuracy. We apply our method to the global ocean under-surface temperature field and demonstrate its effectiveness on fields with up to a million grid cells. Additionally, we find that the information entropy field has a clear physical interpretation related to the mixing between cold and warm currents.</jats:p

Coupled atmosphere–ocean model SLAV–INMIO: implementation and first results

AbstractCoupled atmosphere–ocean models are widely used for climate change modelling. However, there is now more and more evidence on necessity to use such models in numerical weather prediction at different time scales. A coupled model is developed at the Institute of Numerical Mathematics, Shirshov Institute of Oceanology (Russian Academy of Sciences), and Hydrometeorological Research Centre of Russia. Particularities of program implementation for this model are discussed. The atmosphere model SLAV and the World Ocean model INMIO are coupled using the original program for models coupling. The results of numerical experiments with the coupled model demonstrate an agreement with observation data and show a possibility to use this model for probabilistic weather forecasts at time scales from weeks to year.</jats:p