Displays

Global coupled climate modeling requires the representation of multiple widely separated scales in each model component. In the ocean component, the separation of scales is especially dramatic as small scale turbulence exerts significant control on the global scale overturning circulation.  The importance of this control is demonstrated in the context of analyses of the Dansgaard-Oeschger oscillation of Marine Isotope Stage 3 (MIS 3; see Peltier and Vettoretti, 2014)). In the University of Toronto version of CCSM4 water column diapycnal diffusivity is represented using the KPP parameterization of Large et al (1994). This includes explicit contributions due to double diffusion processes which demonstrably play an important role in determining the period of the D-O oscillation itself.

We have developed a DNS-based methodology to test the accuracy of the doubly diffusive contributions to KPP. High-resolution turbulence data sets have been produced based upon two different models: the “unbounded gradient model” and the “interface model” with depth-dependent temperature and salinity gradients. By fitting the vertical fluxes in the unbounded gradient model (for equilibrium states) as a function of density ratio (the governing non-dimensional parameter) we derive a functional form on the basis of which KPP can be revised.  By applying the revised scheme to the interface model we demonstrate that the local fluxes predicted agree well with those from the numerical simulations. The difference between this new parametrization scheme and KPP demonstrates that KPP may significantly overestimate the diffusivities for both heat and salt at low-density ratio.

How to cite: Ma, Y. and Peltier, W.: A DNS-based Turbulence Parametrization for Global Climate Models: Doubly Diffusive Convection, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2035, https://doi.org/10.5194/egusphere-egu2020-2035, 2020.

When studying the life cycle of phytoplankton frequently one is interested in the survival or death conditions of a population (bloom/no bloom). These dynamics have been studied extensively in the literature through a range of modelling scenarios but in summary the main factors affecting the vertical dynamics are: Water column mixing intensity, solar energy distribution, nutrients availability and predatory activity. The later two can be represented by different biological models whereas the vertical mixing is usually parameterized by a diffusive process. Even though turbulence has been recognized as a paramount factor in the survival dynamics of sinking phytoplankton species, dealing with the multi scale nature of turbulence is a formidable challenge from the modelling point of view. In addition, convective motions are being recognized to play a role in the survival of phytoplankton throughout winter stocking. With this in mind, in this work we revisit a theoretically appealing  model for phytoplankton vertical dynamics with turbulent diffusivity and numerically study how large-scale fluid motions affect its survival and extinction conditions. To achieve this and to work with realistic parameter values, we adopt a kinematic flow field to account for the different spatial and temporal scales of turbulent motions. The dynamics of the population density are described by a reaction-advection-diffusion model with a growth term proportional to sun light availability. Light depletion is modelled accounting for water turbidity and plankton self-shading; advection is represented by a sinking speed and a two-dimensional, multiscale, chaotic flow. Preliminary results show that under appropriate conditions for the flow, our model reproduces past results based on turbulent diffusivity. Furthermore, the presence of large scale vortices (such as those one might expect during winter convection) seems to hinder survival, an effect that is partially mitigated by turbulent  diffusion.

How to cite: Beltram Tergolina, V., Berti, S., and Mompean, G.: Effects of large scale advection and small scale turbulence on vertical phytoplankton dynamics, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2398, https://doi.org/10.5194/egusphere-egu2020-2398, 2020.

The European Commission has invested in developing services such as the Copernicus Marine Environment Monitoring Services that offer opportunities to new downstream applications. This presentation describes the development of monitoring services in coastal areas at the submesoscale, by addressing synergies between different available marine technologies and products such as satellite images, autonomous surface and underwater vehicles, drone images, downscaled hydrodynamic models, etc, that get inspired in recent success cases [1, 2]. In particular ongoing efforts will be discussed that address the operational implementation of these tools for the management of marine pollution in harbors and coasts with a focus in the hydrodynamic modelling aspects.

Support is acknowledged  from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 821922 (IMPRESSIVE) and from Fundacion Biodiversidad and European Commission (BEWATS).

References

[1] A. G. Ramos, V. J. García-Garrido, A. M. Mancho, S. Wiggins, J. Coca, S. Glenn, O. Schofield, J. Kohut, D. Aragon, J. Kerfoot, T. Haskins, T. Miles, C. Haldeman, N. Strandskov, B. Allsup, C. Jones, J. Shapiro. Lagrangian coherent structure assisted path planning for transoceanic autonomous underwater vehicle missions.  Sci. Rep. 8, 4575 (2018).

[2] V. J. Garcia-Garrido, A. Ramos, A. M. Mancho, J. Coca, S. Wiggins. A dynamical systems perspective for a real-time response to a marine oil spill. Mar. Pollut. Bull. 112, 201-210, (2016).

How to cite: Mancho, A. M., Garcia-Sanchez, G., and Jimenez-Madrid, J. A.: Monitoring marine coastal areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4239, https://doi.org/10.5194/egusphere-egu2020-4239, 2020.

Turbulence or turbulence-like phenomena are ubiquitous in nature, often showing a power-law behavior of the Fourier power spectrum in either spatial or temporal domains. This power-law behavior is due to interactions among different scales of motion, and to the absence of characteristic scale among several scale ranges. It can be further interpreted in the framework of turbulent cascade with movements on continuous range of scales. The power-law feature and the associate cascade picture are vitally important to our understanding of the ocean and atmosphere dynamics. In this work, we consider the China France Oceanography SATellite (CFOSAT) data in the general framework of ocean and atmosphere multi-scale dynamics. We apply both Fourier power spectrum analysis and second-order structure-function analysis, used in the fields of turbulence, to extract multiscale information from the wind speed (WS) and significant wave-height (Hs) data provided by CFOSAT project. The data analyzed here are along track data spatially collected from 29^th July to 31^th December 2019. The measured Fourier power spectrums for both WS and Hs illustrate a dual power-law behavior respectively from 5 to 25 km, and 30 to 500 km with measured scaling exponents β close to 2 and 5/3. The measured second-order structure-functions confirm the existence of the dual power-law behavior. The corresponding measured scaling exponents  ζ(2) close to 1 and 2/3 for the spatial scales mentioned above. Our preliminary results confirm the relevance of using multiscale statistical tools and turbulent theory to characterize the large-scale movements of both ocean and atmosphere.

How to cite: Gao, Y., Schmitt, F. G., Hu, J., and Huang, Y.: Scaling Analysis of the China France Oceanography Satellite Along Track Wave and Wind Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17781, https://doi.org/10.5194/egusphere-egu2020-17781, 2020.

The dynamics of the near-surface ocean currents result from the nonlinear interaction of simultaneous mechanisms at different scales. Of these, the submesoscale range  (a few hundred meters to 10 km, hours to a few days) remains particularly challenging to observe directly, due to the high variability in both time and space.  In this study, the scale-dependence of kinematic properties (divergence, vorticity and strain) in the submesoscale range, as well as their response to atmospheric forcing, is investigated in two distinct geographic regions: the Ligurian (NW-Mediterranean) Sea and the Northern Gulf of Mexico. The two applications are characterized by different dynamics, and the estimates of kinematic properties are derived from distinctly different observational approaches: in situ GPS drifters observations and remote HF radar data.

The Ligurian Sea application is based on HF radar measurements obtained for the JERICO-NEXT (Joint European Research Infrastructure network for Coastal Observatory – Novel European eXpertise for coastal observaTories) and IMPACT (Port Impact on Protected Marine Areas: Cooperative Cross-Border Actions) projects. Surface current measurements span 40 km off the coast with 1.5 km resolution, available every hour. The velocity fields are used to estimate the kinematic properties with an Eulerian approach, which allows the identification of structures such as eddies and jets of the order of a few km. We focus in particular on the response of the submesoscales to an extreme wind event that was captured by the observations. The deformation of the spatial structures suggests nonlinear interactions with the wind forcing, and the kinematic properties' magnitudes are almost doubled (exceeding the Coriolis parameter, f).

In the Gulf of Mexico, velocity observations are available from a series of massive, nearly simultaneous drifter releases conducted by CARTHE (Consortium for Advanced Research of Transport of Hydrocarbons in the Environment). Drifter triplets are analysed to estimate the kinematic properties of the flow at scales between 100 m and 5 km over a time scale of a day. Results show that the mean kinematic properties increase in magnitude with decreasing scales, with winter values generally higher than summer ones. For winter flows, vorticity and divergence distributions have more substantial tails of values multiple times the Coriolis paramater f at scales up to 2 km, while in the summer such large values are restricted to smaller scales (100-500 m).

The Lagrangian estimates of kinematic properties obtained in the Gulf of Mexico were also compared to Eulerian estimates from concurrent X-band radar measurements, showing good correlation and validating the comparison across observational methods. Moreover, the scale-dependence of the kinematic properties from drifter triplets was found to be consistent with turbulence scaling laws evaluated as two-particle statistics. We conclude that the kinematic properties metric provides a robust complementary methodology to characterize submesoscales and can be used both with Lagrangian and Eulerian observations.

How to cite: Berta, M., Griffa, A., Corgnati, L., Magaldi, M., Mantovani, C., Huntley, H., Poje, A., and Ozgokmen, T.: Submesoscales variability from surface drifter and HF radar measurements: scale and wind dependence of kinematic properties, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18620, https://doi.org/10.5194/egusphere-egu2020-18620, 2020.

Despite decades of development, global atmospheric models continue to have trouble in capturing the -5/3 slope of the atmospheric mesoscale kinetic energy (KE) spectrum suggested by conventional turbulence theory and upper tropospheric aircraft observations in the 1980s (e.g., Nastrom and Gage 1986). We have approached this issue in two ways: 1) How certain can we be that the “real” spectrum has a -5/3 slope? and 2) Are turbulent cascades the only determinants of the mesoscale spectrum? To address the first issue, especially in light of the vastly greater number of upper-air observations that have been analyzed since the 1980s, we have examined the 200-hPa KE spectra in several high-resolution global reanalysis datasets, including the NCEP GFS (resolution T1534 and T254), ERA-Interim (T255), ERA5 (T639), and JRA-55 (T319) datasets. We find that the mesoscale portions of the global spectra are highly mutually inconsistent. This is primarily because the global mesoscale KE has a large contribution from the KE in convective regions, which differs greatly among the various reanalyses. There is thus indeed some ambiguity concerning the slope of the “true” mesoscale spectrum.

To address the second issue, especially given the sensitivity of the reanalysis spectra to representations of convection and damping in the reanalysis models, we assessed the sensitivity of the model spectra in two ways: (a) by stochastically perturbing the physical tendencies and (b) by decreasing the hyper-viscosity coefficient, in large ensembles of 10-day forecasts made with the NCEP GFS (T254) model. Both changes increased the mesoscale KE and decreased the steep spectral slope. The impact of the stochastic physics varied with the specified length scale of the stochastic perturbations. 

Our conclusions about issues 1) and 2) raised above are that (1) we do not really know the “true” mesoscale KE spectrum, and (2) model KE spectra are sensitive to and can be manipulated by stochastically perturbing the parameterized physical tendencies and tuning the horizontal diffusion in a model.  It may therefore be misleading for modelers to pursue the -5/3 slope of the Nastrom-Gage spectrum.

How to cite: Wang, J.-W. A. and Sardeshmukh, P.: Why can't models get the mesoscale atmospheric spectrum right?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3681, https://doi.org/10.5194/egusphere-egu2020-3681, 2020.

An abrupt climatic transition  could be triggered by a single extreme event, and an alpha-stable non-Gaussian Levy noise  is regarded as a   type of noise to generate such extreme events. In contrast  with the classic Gaussian noise, a comprehensive approach of the most probable transition path  for systems under alpha-stable Levy noise is still lacking. We develop here a  probabilistic framework, based on  the nonlocal Fokker-Planck equation, to investigate  the maximum likelihood climate change for  an energy balance system under the influence of  greenhouse effect and  Levy fluctuations.  We find that a period of the  cold climate state can be interrupted by a sharp shift to the warmer one due to  larger noise jumps with low frequency. Additionally,  the climate change for warming 1.5 degree under an enhanced greenhouse effect generates a step-like growth process. These results provide  important insights into  the underlying mechanisms of abrupt climate transitions triggered by a Levy process.

How to cite: Zheng, Y.: The maximum likelihood climate change for global warming under the influence of greenhouse effect and Levy noise, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6170, https://doi.org/10.5194/egusphere-egu2020-6170, 2020.

We employ the framework of the Koopman operator and dynamic mode decomposition to devise a computationally cheap and easily implementable method to detect transient dynamics and regime changes in time series. We argue that typically transient dynamics experiences the full state space dimension with subsequent fast relaxation towards the attractor. In equilibrium, on the other hand, the dynamics evolves on a slower time scale on a lower dimensional attractor. The reconstruction error of a dynamic mode decomposition is used to monitor the inability of the time series to resolve the fast relaxation towards the attractor as well as the effective dimension of the dynamics. We illustrate our method by detecting transient dynamics in the Kuramoto-Sivashinsky equation. We further apply our method to atmospheric reanalysis data; our diagnostics detects the transition from a predominantly negative North Atlantic Oscillation (NAO) to a predominantly positive NAO around 1970, as well as the recently found regime change in the Southern Hemisphere atmospheric circulation around 1970.

How to cite: Gottwald, G. and Gugole, F.: Detecting regime transitions in time series using dynamic mode decomposition, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1667, https://doi.org/10.5194/egusphere-egu2020-1667, 2020.

We analyze linear perturbations of a coupled quasi-geostrophic atmosphere-ocean model based on Covariant Lyapunov Vectors (CLVs). CLVs reveal the local geometrical structure of the attractor, and point into the direction of linear perturbations applied to the trajectory. Thus they represent a link between the geometry of the attractor and basic dynamical properties of the system, and they are physically meaningful. We compute the CLVs based on the so-called Ginelli method using the tangent linear version of the quasi-geostrophic atmosphere-ocean model MAOOAM (Modular Arbitrary-Order Ocean-Atmosphere Model). Based on the CLVs, we can quantify the contribution of each model variable on each scale to the development of linear instabilities. We also study the changes in the structure of the attractor - and, consequently, in the basic dynamical properties of our system - as an effect of the ocean-atmopshere coupling strength and the model resolution.

How to cite: Galfi, V. M., de Cruz, L., Lucarini, V., and Schubert, S.: Screening the coupled atmosphere-ocean system based on Covariant Lyapunov Vectors, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4849, https://doi.org/10.5194/egusphere-egu2020-4849, 2020.

Large scale motions in geophysical fluid models are often characterised by linear waves, which are obtained by linearising the equations. But there are also many explicit solutions of the fully nonlinear equations when posed the full space. The exact solutions we are investigating often characterise Rossby waves, since they are in geostrophic balance. They also can be compositions of waves, some are interacting with each other and some do not, showing wave interactions as explicit solutions in the fully nonlinear problem.

In this talk I will briefly introduce the idea behind these explicit nonlinear waves and show some of their properties, and their occurrence in different fluid models in extended domains.

As an application, we especially focus on a rotating shallow water model with simplified backscatter. In this case one finds not only geostrophic explicit solutions, but also ageostrophic ones. Moreover, here energy accumulates in selected scales due to the backscatter terms and causes exponentially and unboundedly growing ageostrophic nonlinear waves. This also relates to instability of coexisting stationary waves and is an instance of the role of nonlinear waves in energy transfer, and illustrates their role in preventing energy equidistribution for general data.

How to cite: Prugger, A. and Rademacher, J.: Explicit nonlinear waves of fluid models on extended domains and unbounded growth with backscatter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14052, https://doi.org/10.5194/egusphere-egu2020-14052, 2020.

We examine what can be learnt about climate sensitivity from variability in the surface air temperature record over the instrumental period, from around 1880 to the present. While many previous studies have used the trend in the time series to constrain equilibrium climate sensitivity, it has recently been argued that temporal variability may also be a powerful constraint. We explore this question in the context of a simple widely used energy balance model of the climate system. We consider two recently-proposed summary measures of variability and also show how the full information content can be optimally used in this idealised scenario. We find that the constraint provided by variability is inherently skewed and its power is inversely related to the sensitivity itself, discriminating most strongly between low sensitivity values and weakening substantially for higher values. As a result of this, is only when the sensitivity is very low that the variability can provide a tight constraint. Our results support the analysis of variability as a potentially useful tool in helping to constrain equilibrium climate sensitivity, but suggest caution in the interpretation of precise results.

How to cite: Annan, J., Hargreaves, J., Mauritsen, T., and Stevens, B.: What could we learn about climate sensitivity from variability in the surface temperature record?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5723, https://doi.org/10.5194/egusphere-egu2020-5723, 2020.

The equilibrium climate sensitivity (ECS) is widely used as a measure for possible future global warming. It has been determined from a wide range of climate models, observations and palaeoclimate records, however, it still remains relatively unconstrained. In particular, large values of warming as a consequence of atmospheric greenhouse gas increase cannot be excluded, with some of the most recent state-of-the-art climate models (CMIP6) supporting (much) more warming than previous generations of climate models. Moreover, a number of tipping elements have been identified within the climate system, some of which may affect the global mean temperature. Therefore, it is interesting to explore how the climate systems response (e.g. ECS) behaves when the system is close to a tipping point. 
A climate state close to a tipping point will have a degenerate linear response to perturbations, which can be associated with extreme values of the equilibrium climate sensitivity (ECS). In this talk we contrast linearized ('instantaneous') with fully nonlinear geometric ('two-point') notions of ECS, in both presence and absence of tipping points. For a stochastic energy balance model of the global mean surface temperature with two stable regimes, we confirm that tipping events cause the appearance of extremes in both notions of ECS. Moreover, multiple regimes with different mean sensitivities are visible in the two-point ECS. We confirm some of our findings in a physics-based multi-box model of the climate system.

Reference
P. Ashwin and A. S. von der Heydt (2019), Extreme Sensitivity and Climate Tipping Points, J. Stat. Phys.  370, 1166–24. http://doi.org/10.1007/s10955-019-02425-x.

How to cite: von der Heydt, A. and Ashwin, P.: Extreme sensitivity and climate tipping points, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4671, https://doi.org/10.5194/egusphere-egu2020-4671, 2020.

The atmospheric meridional energy transport in the Northern Hemisphere midlatitudes is mainly accomplished by planetary and synoptic waves. A decomposition into wave components highlights the strong seasonal dependence of the transport, with both the total transport and the contributions from planetary and synoptic waves peaking in winter. In both winter and summer months, poleward transport extremes primarily result from a constructive interference between planetary and synoptic motions. The contribution of the mean meridional circulation is close to climatology. Equatorward transport extremes feature a mean meridional equatorward transport in winter, while the planetary and synoptic modes mostly transport energy poleward. In summer, a systematic destructive interference occurs, with planetary modes mostly transporting energy equatorward and synoptic modes again poleward. This underscores that baroclinic conversion dominates regardless of season in the synoptic wave modes, whereas the planetary waves can be either free or forced, depending on the season.

How to cite: Lembo, V., Messori, G., Graversen, R., and Lucarini, V.: Wavenumber Decomposition and Extremes of Atmospheric Meridional Energy Transport in the Northern Hemisphere Midlatitudes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9262, https://doi.org/10.5194/egusphere-egu2020-9262, 2020.

From hourly to decadal time scales, atmospheric fields are characterized by two scaling regimes: at high frequencies the weather, with fluctuations increasing with the time scale, and at low frequencies, macroweather with fluctuations decreasing with scale, the transition between the two at τ_w. This transition time is the lifetime of planetary structures and is therefore close to the deterministic predictability limit of conventional numerical weather prediction models. While it is thus the outer scale of deterministic weather models, conversely, it is the inner scale of stochastic macroweather models.

Here we explore the spatial dependence of this transition time. Starting at the surface (2m temperature) we found that the monthly average temperature falls in the macroweather regime for almost any location in the globe, except for parts of the tropical ocean where τ_w ∼ 1 - 2 years. As we increase in altitude, the dependence of τ_w with the location becomes more homogeneous and above 850mb τ_w < 1 month almost everywhere. The longer tropical ocean transition scales are presumably the deterministic outer scales of the “ocean weather” regime.

Knowledge of τ_w is fundamental for stochastic macroweather forecasting.   Such forecasting is based on symmetries, primarily the power-law behavior of the fluctuations that implies a huge memory that can be exploited for forecasts up to several years. In addition, there is another approximate symmetry called “statistical space-time factorization” relating spatial and temporal statistics. Finally, while weather regime temperature fluctuations are highly intermittent, in macroweather the intermittency is much lower, fluctuations are quasi Gaussian.

The Stochastic Seasonal and Interannual Prediction System (StocSIPS^[1,2]) is a stochastic data-driven model that exploits these symmetries to perform macroweather (long-term) forecasts. Compared to traditional global circulation models (GCM), it has the advantage of forcing predictions to converge to the real-world climate (not the model climate). It extracts the internal variability (weather noise) directly from past data and does not suffer from model drift. Some other practical advantages include much lower computational cost, no need for downscaling and no ad hoc postprocessing.

We show that StocSIPS can predict monthly average surface temperature (nearly) to its stochastic predictability limits. Using monthly to annual lead time hindcasts, we compare StocSIPS predictions with those from the CanSIPS^[3] GCM. Beyond a month, and especially over land, StocSIPS generally has higher skill. For regular StocSIPS forecasts, see http://www.physics.mcgill.ca/StocSIPS/.

References

^[1] Del Rio Amador, L. and Lovejoy, S. (2019) Clim Dyn, 53: 4373. https://doi.org/10.1007/s00382-019-04791-4

^[2] Lovejoy, S., Del Rio Amador, L., Hébert, R. (2017) In Nonlinear Advances in Geosciences, A.A. Tsonis ed. Springer Nature, 305–355 DOI: 10.1007/978-3-319-58895-7

^[3] Merryfield WJ, Denis B, Fontecilla JS, Lee WS, Kharin S, Hodgson J, Archambault B (2011) Rep., 51pp, Environment Canada.

How to cite: Del Rio Amador, L. and Lovejoy, S.: Stochastic modelling and prediction of monthly surface temperatures: StocSIPS, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12286, https://doi.org/10.5194/egusphere-egu2020-12286, 2020.

How to cite: Schmitt, F., Yahia, H., Sudre, J., Garçon, V., and Charria, G.: Scaling and anisotropic heterogeneities of ocean SST images from satellite data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6031, https://doi.org/10.5194/egusphere-egu2020-6031, 2020.

Mesoscale eddies play an important role in lateral property fluxes. Observational studies often use sea level anomaly maps from satellite altimetry to estimate eddy statistics (incl. eddy kinetic energy). Recent findings suggest that altimetry derived eddy characteristics may suffer from the low spatial resolution of past and current satellite-tracks in high-latitude oceans associated with small Rossby radii. Here we present results of an eddy reconstruction based on a nonlinear damping Gauss-Newton optimisation algorithm using ship based current profiler observations from two research expeditions in the Labrador Sea in 2014 and 2016. Overall we detect 14 eddies with radii ranging from 7 to 35 km.

In order to verify the skill of the reconstruction we used the submesoscale permitting NATL60 model (1/60°) as a reference data set. Spectral analysis of the horizontal velocity implies that the mesoscale regime is well represented in NATL60 compared with the observations. The submesoscale regime in the model spectra shows deviations to the observations at scales smaller than 10km near the ocean surface. The representation of the submesoscale flow further decreases in the model with increasing depth.

By subsampling the NATL60 model velocities along artificial ship tracks, applying our eddy reconstruction algorithm, and comparing the results with the full model field, a skill assessment of the reconstruction is done. We show that the reconstruction of the eddy characteristics can be affected by the location of the ship track through the velocity field.

In comparison with the observed eddies the NATL60 eddies have smaller radii and higher azimuthal velocities and thus are more nonlinear. The inner core velocity structure for observations and NATL60 suggests solid body rotation for 2/3 of the radius. The maximum azimuthal velocity may deviate by up to 50% from solid body rotation.

The seasonality of the submesoscale regime can be seen in the data as the power spectrum is reduced from spring to summer in both the ship-based measurements and model.

How to cite: Bendinger, A., Karstensen, J., Le Sommer, J., Albert, A., and Dilmahamod, F.: Mesoscale eddy characteristics in the Labrador Sea from observations and a 1/60° numerical model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8195, https://doi.org/10.5194/egusphere-egu2020-8195, 2020.

The Bay of Biscay and the English Channel, in the North-eastern Atlantic, are considered as a natural laboratory to explore the coastal dynamics at different spatial and temporal scales. In those regions, the coastal circulation is constrained by a complex topography (e.g. varying width of the continental shelf, canyons), river runoffs, strong tides and a seasonally contrasted wind-driven circulation.

Based on different numerical model experiments (from 400m to 4km spatial resolution, from 40 to 100 sigma vertical layers using 3D primitive equation ocean models), different features of the Bay of Biscay and English Channel circulation are assessed and explored. Both spatial (submesoscale and mesoscale) and temporal (from hourly to monthly) scales are considered. Modelled spatial scales, with a specific focus on the variability of fine scale features (e.g. fronts, filaments, eddies), are compared with remotely sensed observations (i.e. Sea Surface Temperature). Different methodologies as singularity and Lyapunov exponents allow describing fine scales features and are applied on both modelled and observed datasets. For temporal scales, in situ high frequency surface temperature measurements from coastal moorings (from COAST-HF observing network) provide a reference for the temporal variability to be modelled. Exploring differences in the temporal scales (from an Empirical Mode Decomposition) advises on the efficiency of our coastal modelling approach.

This result overview in the Bay of Biscay and the English Channel aims illustrating the input of coastal modelling activities in understanding multi-scale interactions (spatial and temporal).

How to cite: Charria, G., Theetten, S., Ayouche, A., Poppeschi, C., Sudre, J., Yahia, H., Garçon, V., and Schmitt, F.: Multi-scale coastal surface temperature in the Bay of Biscay and the English Channel, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8222, https://doi.org/10.5194/egusphere-egu2020-8222, 2020.

In recent years, the oceanographic community has devoted considerable interest to the study of SCVs (Submesoscale Coherent Vortices, i.e. vortices with radii between 2-30 km, below the first internal radius of deformation); indeed, both mesoscale and submesoscale eddies contribute to the transport and mixing of water masses and of tracers (active and passive), affecting the heat transport, the ventilation pathways and thus having an impact on the large scale circulation.

In different areas of the ocean, SCVs have been detected, via satellite or in-situ measurements, at the surface or at depth. From these data, SCVs were found to be of different shapes and sizes depending on their place of origin and on their location. Here, we will concentrate rather on the SCVs at depth.

In this study, we use a high resolution simulation of the North Atlantic ocean with the ROMS-CROCO model. In this simulation, we also identify the SCVs at different depths and densities; we analyse their site and mechanism of generation, their drift, the physical processes conducting to this drift and their interactions with the surrounding flows. We also quantify their physical characteristics (radius, thickness, intensity/vorticity, bias in polarity: cyclones versus anticyclones). We provide averages for these characteristics and standard deviations. 

We compare the model results with the observational data, in particular temperature and salinity profiles from Argo floats and velocity data from currentmeter recordings. 

This study is a first step in the understanding of the formation, occurrences and structure of SCVs in the North Atlantic Ocean, of help to improve their in-situ sampling.

How to cite: Chouksey, A., Carton, X., and Gula, J.: Detection and characterization of SCVs in the North Atlantic, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9821, https://doi.org/10.5194/egusphere-egu2020-9821, 2020.

Algal blooms, also known as ‘red tide’, are extremely harmful to the marine ecosystem since they infuse toxins into seawater and stifle oxygen in the water columns. Visually, they demonstrate rich patterns in spatial due to the interaction between the ocean current and the wind. Using the satelliate remote sensing data provided by the Chinese satellite Gaofeng 1, we first derive a normalized difference vegetation index (NDVI), which can be used to separate efficiently different types of cases, e.g., no algae bloom (NAB), macro algae bloom (MAB), and phytoplankton algae bloom (PAB), etc. The classical structure-function analysis is performed. Our preliminary results confirm the existence of the power-law behavior on the spatial scale range from 100 m to 400 m for the case of MAB. The corresponding scaling exponents are close to the ones of the classical passive scalar in three-dimension hydrodynamic turbulence. It suggests that the MAB could be treated as a passive scalar, which leads to not only a better understanding of the dynamics of algal blooms, but also a challenge of the modelling.

How to cite: Huang, Y., Gao, Y., Xing, Q., Schmitt, F., and Hu, J.: Scaling Analysis of the Algal Blooms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-17939, https://doi.org/10.5194/egusphere-egu2020-17939, 2020.

The parameterization of surface exchange coefficients (C_h) representing land–atmosphere coupling strength plays a key role in land surface modeling. Previous studies have found that land–atmosphere coupling in land surface models (LSMs) is overestimated, which affects the predictability of weather and climate evolution. To improve the representation of land–atmosphere interactions in LSMs, this study investigated the dynamic canopy-height-dependent coupling strength in the offline Noah LSM with multiparameterization options (Noah-MP) when applied to China. Comparison with the default Noah-MP LSM showed the dynamic scheme significantly improved the C_h calculations and realistically reduced the biases of simulated surface energy and water components against observations. It is noteworthy that the improvements brought by the dynamic scheme differed across land cover types. The scheme was found superior in reproducing the observed C_h as well as surface energy and water variables for short vegetation (grass, crop, and shrub), while the improvement for tall canopy (forest) was found not significant, although the estimations were reasonable. The improved version benefits from the treatment of the roughness length for heat. Overall, the dynamic coupling scheme markedly affects the simulation of land–atmosphere interactions, and altering the dynamics of surface coupling has potential for improving the representation of land–atmosphere interactions and thus furthering LSM development.

How to cite: Zhang, X., Chen, L., Ma, Z., and Gao, Y.: Assessment of Surface Exchange Coefficients in the Noah-MP Land Surface Model for Different Land Cover Types over China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3570, https://doi.org/10.5194/egusphere-egu2020-3570, 2020.

Precipitation is an important meteorological variable which is critical for weather risk assessment. For instance, intense but short precipitation events can lead to flash floods and landslides. Most statistical modelling studies assume that the occurrence of precipitation events is based on a Poisson process with exponentially distributed waiting times while precipitation intensities are typically described by a gamma distribution or a mixture of two exponential distributions. Here, we show by using hourly precipitation data over the United States that the waiting time between precipitation events is non-exponentially distributed and best described by a fractional Poisson process. A systematic model selection procedure reveals that the hourly precipitation intensities are best represented by a two-distribution model for about 90% of all stations. The two-distribution model consists of (a) a generalized Pareto distribution (GPD) model for bulk precipitation event sizes and (b) a power-law distribution for large and extreme events. Finally, we analyse regional climate model output to evaluate how the climate models represent the high-frequency temporal structure of U.S. precipitation. Our results reveal that these regional climate models fail to accurately reproduce the power-law behaviour of intensities and severely underestimate the long durations between events.

How to cite: Franzke, C., Yang, L., and Fu, Z.: Power-law behaviour of hourly precipitation intensity and dry spell duration over the United States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5632, https://doi.org/10.5194/egusphere-egu2020-5632, 2020.

The North Atlantic Oscillation (NAO) is the dominant mode of climate variability over the North Atlantic basin and has a significant impact on seasonal climate and surface weather conditions. This is the result of complex and nonlinear interactions between many spatio-temporal scales. Here, the authors study a number of linear and nonlinear models for a station-based time series of the daily winter NAO index. It is found that nonlinear autoregressive models including both short and long lags perform excellently in reproducing the characteristic statistical properties of the NAO, such as skewness and fat tails of the distribution and the different time scales of the two phases. As a spinoff of the modelling procedure, we are able to deduce that the interannual dependence of the NAO mostly affects the positive phase and that timescales of one to three weeks are more dominant for the negative phase. The statistical properties of the model makes it useful for the generation of realistic climate noise.

How to cite: Hannachi, A., Önskog, T., and Franzke, C.: Nonlinear time series models for the North Atlantic Oscillation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13481, https://doi.org/10.5194/egusphere-egu2020-13481, 2020.

Long-term persistence (LTP) and multifractality in river runoff fluctuations have been well recognized over the recent decades, but the origins of these characteristics are still under debate. In this study, runoff and precipitation data from China are analyzed using detrended fluctuation analysis (DFA) and its generalized version, multifractal detrended fluctuation analysis (MF-DFA). By comparing the results between runoff and the nearby precipitation data, we find the multifractal behaviors in river runoff may be propagated from the nearby precipitation data, but the LTP is not inherited from precipitation. The LTP in river runoff may arise from the spatial aggregation effect, as it is closely related with the catchment area, especially for stations with large catchment areas. These findings are based on data from China, which was not analyzed systematically due to the poor data availability. Since the existence of LTP and multifractality makes the runoff change not completely random, one should further introduce these characteristics into hydrological models, for improved water managements and better estimations of hazard risks.

How to cite: Yuan, N., Wu, W., Xie, F., and Qi, Y.: Understanding long-term persistence and multifractal behaviors in river runoff: A detailed study over China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8544, https://doi.org/10.5194/egusphere-egu2020-8544, 2020.

The El-Niño index behaves as a nonlinear and non-Gaussian stochastic process. A well-known characteristic is its positive skewness coming from the occurrence of stronger episodes of El-Niño than of La Niña. Here, we use the period 1870-2018 of the standardized El-Niño index x(t), sampled in trimesters to analyze the spectral origin of the bicorrelation: sk(t1,t2)=E[x(t)x(t+t1)x(t+t2)] and skewness sk(0,0). For that, we estimate the two-dimensional Fourier transform of sk(t1,t2) or bispectrum B(f1,f2). Its sum over bi-frequencies (f1,f2) equals the skewness (0.45 in our case). Positive and negative bispectrum peaks are due to phase locking of frequency triplets: (f1,f2,f1+f2), contributing to extreme El-Niños and La Niñas respectively. Moreover, the most significant positive and/or negative bispectrum regions are rather well localized in the bispectrum domain. Here, we propose a partition of the El Niño signal into a set of band-pass spectrally separated components whose self and cross interactions can explain the broad structure of bispectrum. In the simplest case where the signal is decomposed into a fast and a slow component (with a cutoff frequency of (1/2.56) cycles/yr.), we verifty that slow-slow interactions (or phase locking) explain most of La-Niñas, particularly at the frequency triplet (1/4.9, 1/15 and 1/3.7 cycles/yr) whereas the fast-slow interactions explain most of El Niños, particularly at the frequency triplet (1/4.9, 1/4.9 and 1/2.5 cycles/yr). In order to simulate this stochastic behavior, we calibrate a set of nonlinearly coupled oscillators (Auto-regressive processes, forced by self and cross quadratic component terms), one for each component. In the case of weak cross-component interactions, and thus weak nonlinearity, the coupling coefficients between spectral-band components are proportional to the corresponding cross-skewnesses, which represent good first guesses in the calibration of the model parameters. The predictability of the model is then assessed, in particular for the anticipation of big El Niños and la Niñas. The authors would like to acknowledge the financial support FCT through project UIDB/50019/2020 – IDL.

How to cite: Pires, C. and Hannachi, A.: Stochastic modeling of extreme El-Niño and La Niña events by nonlinearly coupled oscillators, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10741, https://doi.org/10.5194/egusphere-egu2020-10741, 2020.

Using observations and model simulations, the impact of the November Eurasian (EU) teleconnection on the following January Arctic Oscillation (Arctic Oscillation) and the possible mechanisms are investigated in this study.        We found that the positive (negative) phase of the November EU pattern favors the negative (positive) phase of AO during the subsequent January, and both the stratosphere-troposphere interactionsand the tropospheric Blocking High (BH) activity anomalies over the Euro-Atlantic sector play an important role in their connections. When the EU pattern is positive (negative) phase in November, the increased (decreased) vertical wave activity over Eurasia and North Atlantic gradually weakens (enhances) the Stratospheric polar vortex (SPV)from November to the following early January, which is then conducive to a downward propagation of positive height anomalies from the stratosphere to troposphere. On the other hand, due to the persistent stronger (weaker) and southward (northward) shifted storm tracks over the Euro-Atlantic sector from November to the following early January, the BH activities over this region are significantly decreased (increased) during the same period, whichthen contributesto positive (negative) height anomalies over the Arctic via the propagation of a zonal wave number 1-3. As both the SPVand BHanomalies over the Euro-Atlantic sector reach the maximum around the late December-early January, the resultant equivalent barotropic AO dipole patterndevelops and finally establishes during the following January.These results are useful for the predictability of the middle winter climate

How to cite: Qiao, S.: Impact of the November Eurasian teleconnection on the following January Arctic Oscillation, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12542, https://doi.org/10.5194/egusphere-egu2020-12542, 2020.

Using hindcast and forecastdata from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2)for the period 1982-2017, we comprehensively assess the predictability of the climatology, interannual variability, and dominant modes of the wintertime 500 hPa geopotential height over Ural-Siberia (40-80°Nand 30-100°E). Although the climatic mean 500 hPa heightover Ural-Siberia simulated by NCEP CFSv2has a negative bias, especially over the eastern part of the region, NCEP CFSv2 well predicts the spatial distribution of the two major modes(EOF1 and EOF2) over this region 2 months in advance.The forecasting skill of the principal component (PC) of the two major modes,PC1 (PC2), is highest1 (0) month in advance, where the linear correlation coefficient between the predicted and observed time series reaches +0.36 (+0.67), exceeding the 95% confidence level. Conversely, the forecasting skill of PC1 (PC2) is very low 0 (1) month in advance. The main reason for the poorer(better) prediction of PC1 0 (1) month in advance is associated with a less (more) accurate response of the Eurasian teleconnection to SST anomalies over the southwestern Atlantic. For PC2, the better (poorer) prediction of PC2 0 (1) month in advance may be due to more (less) accurate responses of the stratospheric polar vortex and the Scandinavian teleconnection to the dipole SST anomalies over the North Pacific. These results are useful for evaluating the predictability of the East Asian winter climate.

How to cite: Zou, M.: Predictability of the Wintertime 500 hPa Geopotential Height over Ural-Siberia in the NCEP Climate Forecast System, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13084, https://doi.org/10.5194/egusphere-egu2020-13084, 2020.

The estimations of various factors influence on weather regimes formation in Russian regions in transitional (spring, fall) seasons are presented. Changes in those regimes comparing to the middle of 20th century are analyzed, considering atmospheric circulation features under the changes in meridional heat transfer and Rossby waves stationary modes. Using long-term observations of surface air temperature from several locations across Russia, the multimodal features of the probability density functions (PDF) in several decades of 20th and 21st centuries are identified. Focusing on surface temperature anomalies in transitional seasons, we examine the connection between the multimodal features of their PDFs and the nonlinear dependence of surface albedo on temperature during the formation and melting of snow cover. We investigate the impacts of other mechanisms that can facilitate these features, including blocking of zonal atmospheric transport in middle latitudes and formation of blocking anticyclones (blockings) and stationary Rossby waves.

How to cite: Parfenova, M. and Mokhov, I. I.: Intraseasonal temperature variability features in Northern Eurasia regions under changing climate, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20698, https://doi.org/10.5194/egusphere-egu2020-20698, 2020.

Sea surface temperature (SST) is the only oceanic parameter on which depend heat fluxes between ocean and atmosphere and, therefore, SST is one of the key factors that influence climate and its variability. Over the twentieth century, SSTs have significantly increased around the global ocean, warming that has been attributed to anthropogenic climate change, although it is not yet clear how much of it is related to natural causes and how much is due to human activities. A considerable part of available literature regarding climate change has been built based on the global or hemispheric analysis of surface temperature trends. There are, however, some key open questions that need to be answered and for this task estimates of long-term SST trend patterns represent a source of valuable information. Unfortunately, long-term SST trend patterns have large uncertainties and although SST constitutes one of the most-measured ocean variables of our historic records, their poor spatial and temporal sampling, as well as inhomogeneous measurements technics, hinder an accurate determination of long-term SST trends, which increases their uncertainty and, therefore, limit their physical interpretation as well as their use in the verification of climate simulations.
Most of the long-term SST trend patterns have been built using linear techniques, which are very usefull when they are used to extract information of measurements satisfying two key assumptions: linearity and stationarity. The global warming resulting of our economic activities, however, affect the state of the World Ocean and the atmosphere inducing changes in the climate that may result in oscillatory modes of variability of different frequencies, which may undergo non-stationary and non-linear evolutions. In this work, we construct long-term SST trend patterns by using non-linear techniques to extract non-linear, long-term trends in each grid-point of two available global SST datasets: the National Oceanic and Atmospheric Administration Extended Reconstructed SST (ERSST) and from the Hadley Centre sea ice and SST (HadISST). The used non-linear technique makes a good job even if the SST data are non-linear and non-stationary. Additionally, the nonlinearity of the extracted trends allows the use of the first and second derivative to get more information about the global, long-term evolution of the SST fields, favoring thus a deeper understanding and interpretation of the observed changes in SST. Particularly, our results clearly show, in both ERSST and HadISST datasets, the non-uniform warming observed in the tropical Pacific, which seems to be related to the enhanced vertical heat flux in the eastern equatorial Pacific and the strengthening of the warm pool in the western Pacific. By using the second derivative of the nonlinear SST trends, emerges an interesting pattern delimiting several zones in the Pacific Ocean which have been responded in a different way to the impose warming of the last century.

How to cite: Martinez-Lopez, B.: Non-linear, long-term evolution of sea surface temperature across the World Ocean, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22546, https://doi.org/10.5194/egusphere-egu2020-22546, 2020.