Workshop on surface process coupling and its interactions with the atmosphere

The Earth System Modelling at the Weather scale (ESM-W) project, a collaboration between the German Weather Service (DWD) and GeoInfoDienst BW, aims to develop a coupled ocean-atmosphere forecasting system. This system integrates the ICON-O ocean model and the ICON-NWP atmospheric model for the NWP time scales, aligning with the ICON-Seamless development goal of a time scale independent, integrated modelling framework for both weather and climate applications.
We present a case study of Hurricane Fiona from September 2022 as an initial test for the coupled model, incorporating weakly coupled data assimilation. Hurricanes significantly im-pact both the atmosphere and the ocean, leading to noticeable changes in various physical quantities. Our primary focus is on comparing the modelled storm track with the real-world observations and analysing additional physical quantities to assess the model's performance. Additionally, we explore how the hurricane's behaviour is influenced by different technical configurations of the model. This poster presents our findings and outlines future development directions.

YAC (Yet Another Coupler) is a coupling library, which provides 2D interpolation for all grid types commonly used in coupled climate and weather simulations. Users can choose from a variety of different interpolation methods covering a diverse range of requirements. Its highly scalable online weight computation capabilities have proven to be able to handle simulations using globally defined km-scale grids with hundreds of millions of cells with ease. This presentation is an introduction to YAC and its abilities.

Machine learning models have emerged as powerful tools for simulating Earth system processes. Following their successful application in capturing atmospheric evolution for medium-range weather forecasts, attention has increasingly shifted towards other components of the Earth system, such as the marine and land environments. This interest is further driven by the potential to enhance forecasting capabilities beyond the medium-range. Machine learning frameworks offer remarkable flexibility in integrating these model components to achieve a coherent Earth system representation. At one end of the spectrum, model components can be trained jointly within a unified framework, optimized using a shared loss function. At the other end, components may be developed independently and coupled by exchanging physically relevant information at multiple interfaces, mirroring the traditional coupling strategies employed in numerical models. In this presentation, we will examine the advantages and challenges of these approaches, with a particular emphasis on coupling the atmospheric and marine components within the deterministic AIFS model, the machine learning-based forecast system developed at ECMWF. Furthermore, we will compare the coupling strategies of data-driven models with those of traditional numerical models, highlighting their respective strengths and limitations.

The present work discusses the results of applying a roughness sublayer parameterization to the land surface model SURFEX, which is extensively used both offline and coupled with different NWP atmospheric models and LES. By adding increased physical details in the classical similarity coupling scheme over a vegetated surface, the present work improves the surface-atmosphere coupling (the exchange of momentum and energy) in the NWP system above tall vegetation. However, it also reveals a few important questions (e.g. counter-gradient flow, effects of inaccurate physiography data, the coupling thickness) that deserve additional treatment.

The Met Office uses coupled atmosphere - ocean - sea-ice models across timescales from shortrange NWP to centennial climate predictions. Coupled NWP was the last system to go operational in May 2022.

In this presentation we will give an overview of the coupling methodology used in Met Office models. We will discuss the challenges of developing a model for coupled NWP and discuss the advantages of the seamless modelling approach. Finally, we will discuss some possible future developments for coupled NWP.

Several operational centres are either already using or planning to use a coupled modelling system with atmosphere, ocean, sea-ice and wave components for their numerical weather predictions (NWP). ECMWF implemented ocean/sea-ice coupling to all forecasting systems as part of the upgrade to CY45R1 on June 5 2018.
Experiences with ocean coupling for NWP time scales leading up to the operational implementation and lessons learned since then will be presented followed by an outlook of future developments.

The famous Craik-Leibovich eqautions are often used to incorporate sea state impacts in ocean circulation models. However, including the Stokes drift, in such phase-averaged equations for the Eulerian mean motion leads to an Eulerian energy budget which is physically difficult to interpret. Here, we show that a Lagrangian energy budget allows for a closed energy budget, in which all terms connecting the different energy compartments correspond to well known energy transfer terms. The Lagrangian energy budget is used to discuss an energetically consistent framework which can be used to couple a general circulation ocean model to a surface wave model. In this framework, the energy provided by the surface wave model is split-up into energy driving oceanic mean motions, inertial oscillations and turbulence in a consistent manner.

I will summarise recent progress on surface exchange over sea ice with a focus on using observations of turbulent fluxes of momentum, sensible and latent heat to constrain and develop parameterizations of surface exchange for numerical weather and climate prediction models. I will cover a recent parameterization development for scalar exchange. Plus the impacts of these new parameterizations on the atmosphere in operational weather forecasting models and state-of-the-art climate models.

Accurate tropical cyclone (TC) intensity forecasts remain a significant challenge due to the inadequate representation of upper-ocean dynamics and air-sea interaction processes. To address this issue, a regional Atmosphere-Ocean-Wave coupled model was developed, incorporating key ocean surface wave-related processes, including wave modulation of air-sea momentum flux, the sea spray effect on enthalpy flux, and non-breaking wave-induced mixing. Validated against airborne observations, our results demonstrate that ocean surface waves improve the representation of TC structure, size, and intensity. Furthermore, retrospective simulations of 21 TCs from 2013 were conducted to statistically assess the impacts of wave processes on TC simulations, showing a significant reduction in TC intensity simulation bias. This study highlights the critical role of ocean surface wave processes in modulating TC dynamics and improving intensity forecasts.

With the changing conditions in the Arctic, the need for improved forecasting of the meteorological and environmental conditions are increasing. Variability in surface conditions, especially over the ocean and sea ice, increases the uncertainties in heat, moisture and momentum fluxes between the atmosphere, ice and ocean, especially close to the marginal ice zone. These uncertainties increase the potential risks for operating in these harsh areas, both for aircraft and vessels.
Following the success of the Canadian Arctic Prediction System (CAPS) implemented at Environment and Climate Change Canada (ECCC) during the Year of Polar Prediction (YOPP), a second version of the Canadian Arctic Prediction System (CAPS) is currently being developed to address the growing need for accurate meteorological and environmental predictions in the pan-Arctic region. The GEM NWP model in CAPS covers the entire Arctic Ocean, from Bering Strait to south of Iceland (~60oN) at 3km resolution. The NEMO-CICE ice-ocean component follows the Regional Ice-Ocean Prediction System (RIOPS) covering Canada’s three oceans on a 1/12o ORCA grid. This presentation evaluates CAPS short-term 48h forecasts with a specific focus on the impact of coupling to the ice-ocean over and near the marginal ice zones.

Atlas is a library for Numerical Weather Prediction and Climate modelling developed at ECMWF which provides parallel distributed data structures for meshes and fields. Atlas has built-in support for various regional grids as well as global grids used in the community, such as the Gaussian grids used by the Integrated Forecasting System (IFS), ORCA grids used by ocean model NEMO, HEALPix grids, cubed sphere grids, and unstructured grids. Other grids can be supported via a plugin mechanism. Atlas provides domain decomposition strategies for meshes and fields and provides halo-exchange and gather-scatter parallel communication patterns. Atlas further contains several interpolation algorithms to enable remapping of fields defined in different meshes in a parallel distributed context. Currently following interpolation methods are supported: unstructured-linear, structured-linear, structured-cubic, structured-quasicubic, nearest-neighbour, k-nearest-neighbour, conservative-order-1 and conservative-order-2.
For parallel interpolation, the domain decomposition of the target grid must match that of the source grid. A new development started recently to enable different independent domain decompositions for each field, so that there is more flexibility in remapping fields defined in Earth System Model components with non-matching domain decompositions. This is e.g. the case for IFS ocean component using NEMO or FESOM2 and the IFS spectral atmosphere component. In this presentation we will present the current state of the art on remapping between the ocean surface and the atmosphere using Atlas, and the future plans on integration within the IFS model.

Recent improvements in the description of the surface-atmosphere interactions in the ICON atmospheric model of the German Meteorological Service are presented. The continental surfaces, including vegetation cover, represent an important component of the earth’s climate system. On the one hand, they are the habitat of humanity, which makes it important to try to understand the governing processes and living conditions at the land surface and how they may evolve in the future. On the other hand, from the point of view of atmospheric sciences, the land surface and biosphere interact with the lower atmosphere, and they have a significant impact on near-surface meteorological and climatological phenomena. During the recent years, the formulations of several processes in the ICON multi-layer land surface scheme TERRA were either deeply revised, or replaced by more realistic versions, for instance the formulation of the bare soil evaporation. A very persistent problem in atmospheric models is the computation of the surface temperature, in particular over vegetation. In TERRA, this was addressed by a simplified canopy approach, inspired by ECMWF’s IFS skin temperature formulation, embedded in the tile approach of ICON. Furthermore, with the trend of an increasing resolution of atmospheric models for numerical weather prediction or climate simulations, more fine-scale processes at the land surface can be resolved. Consequently, an urban canopy parameterization was developed and implemented in ICON, heading for km-scale or even hectometric scale applications. Beside further developing the physical parameterizations and using them with well-tuned, but static, parameter values, another possibility to improve the model skill is to apply an adaptive parameter tuning, based on the information from the data assimilation system. Meanwhile, this method is applied to several ICON land surface parameters. It is demonstrated that all these measures, and others, improve the interactions and feedbacks between land surface and atmosphere in ICON.

Windstorms associated with extratropical cyclones are destructive natural hazards. At the Laboratoire d’Aérologie (LAERO in Toulouse, France) we are interested in elucidating the processes involved in the formation of near-surface extreme winds and my focus is on wave and wave breaking related processes. Though crucial for their societal impact, these processes are not well understood and too small scale to be explicitly represented in numerical weather prediction models. Waves modulate air-sea exchanges, mix the upper ocean, and inject sea spray into the atmosphere when breaking. Air-sea fluxes of enthalpy and momentum greatly influence the dynamics of the marine atmospheric boundary layer (MABL). Waves increase the surface roughness but sea spray loading may act as a buffer layer reducing drag and stabilizing the MABL. Larger droplets increase air-sea enthalpy and decrease momentum transfers thus promoting the intensification of tropical cyclones, but what of extra tropical cyclones?

The role of waves and the ocean in extra tropical cyclones surface wind extremes will be illustrated by two case studies : 1) the North Atlantic storm Alex where wave coupling influences mesoscale jets and the downward momentum transport, and 2) the cold wake producing medicane (Mediterranean Hurricane) Ianos where the ocean induces a negative feedback similar to that seen in certain tropical cyclones. These were simulated using the coupled framework which consists of the atmospheric model Meso-NH, the 3rd generation wave model WAVEWATCH III®, and the oceanic model CROCO. As computing advances, such as GPU porting of Meso-NH, allow realistic storm scale LES-type simulations, we need to question 1) What do we gain from kilometer to sub kilometer scale coupling ? and 2) How should coupling strategies and air-sea parameterizations be adapted depending on resolution ?

Anomalous tidal waves triggered by atmospheric disturbances, occurring at frequencies similar to tsunamis, are called meteorological tsunamis or meteo-tsunamis. These waves typically form when atmospheric disturbances, often caused by mesoscale phenomena such as thunderstorms, atmospheric gravity waves, squalls, and cyclones, induce rapid changes in mean sea level pressure. Even small fluctuations can generate sea level oscillations of several centimetres. Typically, 1 mbar of pressure disturbance can cause 1cm sea surface change. When the speed of the atmospheric disturbance closely aligns with the wave speed (approximately equal to the phase speed, √gh), continuous amplification of the water level occurs through a mechanism known as Proudman resonance (Proudman, 1929; Renzi et al., 2023).
Although research on meteotsunamis in the US and Mediterranean is advanced, studies in the UK remain limited. This is largely because meteotsunamis are considered rare and of minimal risk in the UK (Lewis, 2023). However, recent events suggest that these phenomena are often missed by observations, as the frequency of tide gauge recordings is typically insufficient to detect them. Furthermore, feedback from the UK Environmental Agency (EA) during such events indicates that meteotsunamis can cause significant damage if actions are not taken.
This study explores the understanding and prediction of meteotsunami events using the atmosphere-ocean-wave (AOW) coupled model, which integrates the Unified Model (UM), NEMO, and WaveWatchIII, developed at the Met Office. The model was configured with a 10-minute coupling frequency to evaluate its capability for capturing meteotsunamis. We analyse two events: one associated with a slow-moving frontal system, and the other with a mesoscale convective system. Analysis of the first event on 31 October 2021, recorded by the Portsmouth tide gauge, demonstrates that the 10-minute coupling successfully captures the event, whereas the previously used 1-hour coupling does not. The model can also capture the seiche in the Channel.

Increasing the complexity of both weather and climate forecasting modelling systems allows better prediction skills in coastal areas, where equilibrium assumptions between Earth system components break down. It also allows better consistence between earth system components, enabling multi-hazard forecasting. We present recent advances in the regional coupled environmental prediction system developed in the UK through the release of the Regional Coupled Suite – UKC4 configuration. The coupled system runs at km-scale over the northwest European shelf region, coupling a convection-permitting atmosphere with land, surface waves and shelf-enabled ocean models. The new UKC4 configuration is an upgrade to all model components, it includes a new Regional Atmosphere and Land configuration (RAL3.3), an online diagnostic of rivers, an option for higher frequency coupling, additional coupling to a biogeochemistry model, the possibility of running near-real time ensemble forecasts and to run climate hindcasts.

The new atmospheric configuration leads to a beneficial increase in shortwave radiation reaching the ocean in summer months and a beneficial reduction in wind speed over sea, which is further reduced with wave coupling. RCS-UKC4 has good skills in terms of ensemble wave forecasts during storms compared to the current Met Office operational wave ensemble because it captures tidal current/wave/wind interactions. We show coupling either inflates or deflates the ensemble spread in screen temperature depending on whether latent heat flux or radiative heat flux dominates the spread in near-surface fluxes. Finally, we show an example of use of the coupled system for increasing our understanding of regional marine heatwaves and their impacts on a multi-hazard storm.

Ocean surface waves can significantly influence the wind within the wave boundary layer, especially under swell conditions. However, current models fail to capture the wind profile in this layer, such as the swell-induced low-level wind jet. In this study, we propose a new turbulence closure model to estimate wind stress in the wave boundary layer by separately considering viscous stress, shear-induced turbulent stress, wind-sea induced stress, and swell-induced upward stress. The model also accounts for the misalignment between wind stress and wind direction. Single-column simulations indicate that (a) swell-induced upward momentum flux increases surface wind speed and alters wind direction, (b) the misalignment between upward momentum flux and wind has a more significant impact on the wind profile than downward momentum flux, and (c) the impact of swell-induced upward momentum flux decreases with atmospheric convection. The proposed closure scheme was implemented into an atmosphere-wave coupled model.

The fractional or percentage whitecap coverage of the ocean surface (W) is often parameterised in terms of wind speed. Datasets of W typically show order of magnitude scatter at a given wind speed value. Here we compare modelled values of W to measured values from the North Atlantic Ocean. The modelled W is forced by the spectrally-integrated whitecap dissipation source function in the European Centre for Medium-Range Weather Forecasts spectral wave model, ecWAM. Without tuning, best agreement is found for mature sea states, with an average modelled to measured
ratio of 0.87. This ratio approaches unity with the introduction of a dissipation rate threshold value and an explicit wave-age dependence. The study suggests that accurate estimates of W can be routinely produced by ecWAM and opens new opportunities to model bubble-mediated fluxes of CO2 and sea spray aerosol with ecWAM.

Sofar Ocean has developed a coupled atmosphere-wave-ocean forecast system to improve forecast skill of surface winds and waves over the ocean. Using data from ECMWF and modeling components from NOAA, along with the U.S. Earth System Modeling Framework (ESMF) National Unified Operational Prediction Capability (NUOPC) coupling software, Sofar runs this forecast system containerized and on the cloud using virtual HPC.

Initial results indicate that improvements to the atmospheric model planetary boundary layer (PBL) scheme, coupled interactions with the ocean mixed layer, improved representation of interactions between the atmosphere and wave components, and influence of surface currents on waves all help to improve the skill of wind and waves forecasts over the ocean surface. We also apply a Bayesian Optimization Machine Learning approach to calibrate key coupled model parameters to improve the fit of forecasts with Sofar's global network of Spotter buoys, which collect measurements of directional wave spectra that are processed to provide wind and wave observations.