Mikhail Dobrynin

Turbulent Mixing due to Surface Waves Indicated by Remote Sensing of Suspended Particulate Matter and Its Implementation into Coupled Modeling of Waves, Turbulence, and Circulation

This paper studies the impact of the surface waves on the turbulent mixing. The satellite observations of suspended particulate matter (SPM) at the ocean surface as an indicator of turbulent quantities of the flow are used. In a water column, SPM builds a vertical profile depending on settling velocities of the particles and on vertical mixing processes; thus, SPM is a perfect marker to study the turbulent quantities of the flow. Satellite observations in the North Sea show that surface SPM concentrations, in locations of its deposition, grow rapidly and build plume-shaped, long (many kilometers) uninterrupted and consistent structures during a storm. Also, satellites reveal that SPM rapidly sinks to the seabed after the storm peak has passed and wave height decreases (i.e., in the absence of strong turbulence). The nonbreaking wave-induced turbulence has been discussed, parameterized, and implemented into an equation of evolution of turbulent kinetic energy (TKE) in the frame of mean-flow concept, which can be used in existing circulation models. The ratio between dissipated and total wave energy is used to describe the influence of wave damping on the mean flow. The numerical tests reproduce experiments in a wave tank very well and are supported by observations of SPM in the North Sea. Their results show that the motion of an individual nonbreaking wave includes turbulent fluctuations if the critical Reynolds number for wave motion is exceeded, independent of the presence of currents due to wind or tides. These fluctuations can produce high diffusivity and strongly influence mixing in the upper water layer of the ocean.

Detection and attribution of climate change signal in ocean wind waves

AbstractSurface waves in the ocean respond to variability and changes of climate. Observations and modeling studies indicate trends in wave height over the past decades. Nevertheless, it is currently impossible to discern whether these trends are the result of climate variability or change. The output of an Earth system model (EC-EARTH) produced within phase 5 of the Coupled Model Intercomparison Project (CMIP5) is used here to force a global Wave Model (WAM) in order to study the response of waves to different climate regimes. A control simulation was run to determine the natural (unforced) model variability. A simplified fingerprint approach was used to calculate positive and negative limits of natural variability for wind speed and significant wave height, which were then compared to different (forced) climate regimes over the historical period (1850–2010) and in the future climate change scenario RCP8.5 (2010–2100). Detectable climate change signals were found in the current decade (2010–20) in the No...

Improved Seasonal Prediction of European Summer Temperatures With New Five‐Layer Soil‐Hydrology Scheme

We evaluate the impact of a new five-layer soil-hydrology scheme on seasonal hindcast skill of 2 m temperatures over Europe obtained with the Max Planck Institute Earth System Model (MPI-ESM). Assimilation experiments from 1981 to 2010 and 10-member seasonal hindcasts initialized on 1 May each year are performed with MPI-ESM in two soil configurations, one using a bucket scheme and one a new five-layer soil-hydrology scheme. We find the seasonal hindcast skill for European summer temperatures to improve with the five-layer scheme compared to the bucket scheme and investigate possible causes for these improvements. First, improved indirect soil moisture assimilation allows for enhanced soil moisture-temperature feedbacks in the hindcasts. Additionally, this leads to improved prediction of anomalies in the 500 hPa geopotential height surface, reflecting more realistic atmospheric circulation patterns over Europe.

Improved teleconnection-based dynamical seasonal predictions of boreal winter

Climate and weather variability in the North Atlantic region is determined largely by the North Atlantic Oscillation (NAO). The potential for skillful seasonal forecasts of the winter NAO using an ensemble-based dynamical prediction system has only recently been demonstrated. Here we show that the winter predictability can be significantly improved by refining a dynamical ensemble through subsampling. We enhance prediction skill of surface temperature, precipitation, and sea level pressure over essential parts of the Northern Hemisphere by retaining only the ensemble members whose NAO state is close to a “first guess” NAO prediction based on a statistical analysis of the initial autumn state of the ocean, sea ice, land, and stratosphere. The correlation coefficient between the reforecasted and observation-based winter NAO is significantly increased from 0.49 to 0.83 over a reforecast period from 1982 to 2016, and from 0.42 to 0.86 for a forecast period from 2001 to 2017. Our novel approach represents a successful and robust alternative to further increasing the ensemble size, and potentially can be used in operational seasonal prediction systems. Plain Language Summary Predicting Northern Hemisphere winter conditions, which are controlled largely by fluctuations in the pressure filed over the North Atlantic (North Atlantic Oscillation, NAO), for the next season is a major challenge. Most state-of-the-art seasonal prediction systems show a correlation between observed and predicted NAOs of less than 0.30. Our novel approach uses dynamical links (teleconnections) between the autumn state of sea surface temperature in the North Atlantic, Arctic sea ice, snow in Eurasia, and stratosphere temperature over the Northern Hemisphere as predictors of the NAO in the subsequent winter to subsample a dynamical reforecast ensemble. We select only the ensemble members that consistently reproduce winter NAO states that evolve in accordance with the autumn state of these predictors. As a result the winter NAO prediction skill increases to a correlation value of 0.83. Considering these well established NAO teleconnections in our Earth system model leads to an improved prediction skill of European winter conditions, that is, surface temperature, precipitation, and sea level pressure. Our results advance seasonal prediction of European weather to a level that is usually limited to tropical regions and are relevant for a variety of societal sectors, such as global and national economies and energy and water resources.

Assimilation of satellite data in a Suspended Particulate Matter transport model

The GKSS-BSH three-dimensional suspended particulate matter (SPM) transport module was combined with the hydrodynamic circulation model HAMSOM to simulate 2 years of SPM distributions in the North Sea with fine spatial resolution (3 km horizontally and 21 vertical layers). In addition, the model was forced by wave fields computed with a WAM model set-up on the same grid. The SPM model calculates distributions of three SPM fractions with different settling velocities in the water column and the corresponding fine sediment fractions in the upper 20 cm of the bottom. The local shear stress velocities, derived from currents and waves control the processes of sedimentation, re-suspension and erosion. Waves, currents and the different sinking velocities of the three sediment fractions govern the vertical exchange. In a first step, the results were compared to ENVISAT MERIS satellite data and to in-situ measurements, and a quality control system for the satellite data was developed. In a second step, the satellite data were assimilated into the model using a sequential optimum interpolation scheme. Our focus is to develop a tool for SPM calculations based on modelling and data assimilation, which can be used for operational purpose. Results of the SPM simulations with and without assimilation will be presented and compared with independent observations.