Institute of Environmental Geosciences

Abstract A persistent low‐level cloud cover (LCC) is a major climatic feature of western equatorial Africa during the long dry season (June–September). We investigate the ability of the mesoscale atmospheric model Meso‐NH at 2‐km horizontal grid spacing, forced by ERA5 reanalysis data, to simulate the LCC features. We chose a six‐day period in June 2008, presenting temporal changes in cloud cover, to better understand the atmospheric mechanism associated with the LCC formation. The main meteorological variables of the model are firstly validated extensively against tri‐hourly station data and ERA5. The LCC diurnal cycle shows an early‐morning maximum consistent with station data, then a decrease in the afternoon which matches the evolution of the stratiform cloud cover, but some of the transition to cumulus clouds is missed. Discrepancies are found with respect to satellite data, but the latter have issues of reliability. Over the ocean, Meso‐NH overestimates the LCC compared to satellite data, while ERA5 underestimates it. Over land, Meso‐NH enabled the depiction of subregionally coherent LCC dynamics in relatively good agreement with observations, though discrepancies often occurred on individual days. This result does not depend on methodological issues such as the cloud overlap assumption or the low‐level cloud fraction threshold retained to define cloudy observations. Dynamical analysis suggested that local and regional‐scale wind direction changes, related to the synoptic weather pattern, are key to the maintenance or clear‐up of the LCC in the afternoon. The LCC persistence is associated with surface westerlies advecting cooler maritime air increasing the lower‐tropospheric stability. Moreover, the moistening of the top of the Planetary Boundary Layer promotes the formation of the LCC. These local atmospheric changes are driven by a mid‐tropospheric easterly wave further north, a weakening of the Kalahari High over southern Africa and the weakening of the transequatorial flow.

Dynamic recrystallization can have a strong impact on texture development during the deformation of polycrystalline materials at high temperatures, particularly for materials with strong viscoplastic anisotropy such as ice. Owing to this anisotropy, recrystallization is essential for ensuring strain compatibility, and the development of textures leads to anisotropic softening. Accurate prediction of the effect of recrystallization on the texture evolution of ice is therefore crucial to adequately account for texture-induced mechanical anisotropy in large-scale models of glacial ice flow. However, this prediction remains a challenge. We propose a new formulation for modeling texture evolution due to dynamic recrystallization on the basis of observations of the evolution of the microstructure and texture of ice deforming by dislocation creep and dynamic recrystallization. This formulation relies on an orientation attractor that maximizes the resolved shear stress on the easiest slip system in the crystal. It is implemented in the equation describing the evolution of the crystal orientation with deformation and is coupled with an anisotropic viscoplastic law that provides the mechanical response of the ice crystal. This set of equations, which is the core of the R 3 iCe model is solved by a finite-element method with a semi-implicit scheme coded using the Rheolef library. The resulting open-source software R 3 iCe is validated by comparison with laboratory creep data for ice polycrystals under uniaxial compression, simple shear, and uniaxial tension. It correctly reproduces the texture evolution and mechanical softening observed in the experiment during tertiary creep. Although the present formulation is too time-consuming for direct implementation in large-scale ice flow models, R 3 iCe can be used to adjust the parameterization used to implement texture-induced anisotropy in these models. The validation was performed for ice, but the R 3 iCe implementation is generic and applies to any material whose behavior may be adequately described using an anisotropic flow law.

The Antarctic Peninsula (AP) experienced a new extreme warm event and record-high surface melt in February 2022, rivaling the recent temperature records from 2015 and 2020, and contributing to the alarming series of extreme warm events over this region showing stronger warming compared to the rest of Antarctica. Here, the drivers and impacts of the event are analyzed in detail using a range of observational and modeling data. The northern/northwestern AP was directly impacted by an intense atmospheric river (AR) attaining category 3 on the AR scale, which brought anomalous heat and rainfall, while the AR-enhanced foehn effect further warmed its northeastern side. The event was triggered by multiple large-scale atmospheric circulation patterns linking the AR formation to tropical convection anomalies and stationary Rossby waves, with an anomalous Amundsen Sea Low and a record-breaking high-pressure system east of the AP. This multivariate and spatial compound event culminated in widespread and intense surface melt across the AP. Circulation analog analysis shows that global warming played a role in the amplification and increased probability of the event. Increasing frequency of such events can undermine the stability of the AP ice shelves, with multiple local to global impacts, including acceleration of the AP ice mass loss and changes in sensitive ecosystems.

Abstract. This study is anchored in the H2020 SEAMLESS project (www.seamlessproject.org), which aims to develop ensemble assimilation methods to be implemented in Copernicus Marine Service monitoring and forecasting systems, in order to operationally estimate a set of targeted ecosystem indicators in various regions, including uncertainty estimates. In this paper, a simplified approach is introduced to perform a 4D (space-time) ensemble analysis describing the evolution of the ocean ecosystem. An example application is provided, which covers a limited time period in a limited subregion of the North Atlantic (between 31° W and 21° W, between 44° N and 50.5° N, between March 15 and June 15, 2019, at a 1/4° and a 1 day resolution). The ensemble analysis is based on prior ensemble statistics from a stochastic NEMO/PISCES simulator. Ocean colour observations are used as constraints to condition the 4D prior probability distribution. As compared to classic data assimilation, the simplification comes from the decoupling between the forward simulation using the complex modelling system and the update of the 4D ensemble to account for the observation constraint. The shortcomings and possible advantages of this approach for biogeochemical applications are discussed in the paper. The results show that it is possible to produce a multivariate ensemble analysis continuous in time and consistent with the observations. Furthermore, we study how the method can be used to extrapolate analyses calculated from past observations into the future. The resulting 4D ensemble statistical forecast is shown to contain valuable information about the evolution of the ecosystem for a few days after the last observation. However, as a result of the short decorrelation time scale in the prior ensemble, the spread of the ensemble forecast is increasing quickly with time. Throughout the paper, a special emphasis is given to discussing the statistical reliability of the solution. Two different methods have been applied to perform this 4D statistical analysis and forecast: the analysis step of the Ensemble Transform Kalman Filter (with domain localization) and a Monte Carlo Markov Chain (MCMC) sampler (with covariance localization), both enhanced by the application of anamorphosis to the original variables. Despite being very different, the two algorithms produce very similar results, thus somehow validating each other. As shown in the paper, the decoupling of the statistical analysis from the dynamical model allows us to restrict the analysis to a few selected variables and, at the same time, to produce estimates of additional ecological indicators (in our example: phenology, trophic efficiency, downward flux of particulate organic matter). This approach can easily be appended to existing operational systems to focus on dedicated users' requirements, at small additional cost, as long as a reliable prior ensemble simulation is available. It can also serve as a baseline to compare with the dynamical ensemble forecast, and as a possible substitute whenever useful.

The Beaufort Sea has experienced a significant decline in sea ice, with thinner first-year ice replacing thicker multi-year ice. This transition makes the ice cover weaker and more mobile, making it more vulnerable to breakup during winter. Using a coupled ocean-sea-ice model, we investigated the impact of these changes on sea-ice breakup events and lead formation from 2000 to 2018. The simulation shows an increasing trend in the Beaufort Sea lead area fraction during winter, with a pronounced transition around 2007. A high lead area fraction in winter promotes a significant growth of new, thin ice within the Beaufort region while also leading to enhanced sea ice transport out of the area. The export offsets ice growth, resulting in negative volume anomalies and preconditioning a thinner and weaker ice pack at the end of the cool season. Our results indicate that large breakup events may become more frequent as the sea-ice cover thins and that such events only became common after 2007. This result highlights the need to represent these processes in global-scale climate models to improve projections of the Arctic.

Earth's global climate system is vast, complex, and intricately interrelated. Many areas are influenced by global-scale phenomena, including the "triple dip" La Niña conditions that

Future sea-level change is characterized by both quantifiable and unquantifiable uncertainties. Effective communication of both types of uncertainties is a key challenge for translating sea-level science to inform long-term coastal planning. Scientific assessments play a key role in the translation process and have taken diverse approaches to communicating sea-level projection uncertainty. Here, we review how past Intergovernmental Panel on Climate Change (IPCC) and regional assessments have presented sea-level projection uncertainty, how IPCC presentations have been

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.

The mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum and nutrients from the surface to the deep ocean. We propose here a generic framework to evaluate the mixed layer depth (MLD) dynamics across a wide range of forcings and preconditioning conditions. To do so, we propose to use a physically-based parameter space formed by two dimensionless numbers: $\lambda_s$ the relative contribution of the buoyancy flux and the wind in the surface layer, and the Richardson number $R_h$ which characterizes the stability of the water column at the mixed layer base. Four MLD dynamics regimes (“restratification”, “stable”, “deepening” and “strong deepening”) are defined based on the values of the normalized temporal evolution of the MLD. We show the robustness and the predictive skill of the parameter space in a context of 1D simulations by showing that to a given ($\lambda_s$, $R_h$) corresponds a predictable MLD dynamics regime. Finally, we present two applications showing how the parameter space can be used with 3D ocean realistic models. We discuss the impact of the horizontal resolution (1°, 1/12° or 1/60°) and the Gent McWilliams parameterization on the MLD dynamics regimes.

Study regionThe western Amazon basin at Tamshiyacu gauging station (near the Iquitos City) hosts floodplain agriculture that can be affected by the sudden reversal in direction of water levels known as “repiquetes” that produce intermittent flooding. Study focusThis study assesses repiquete flooding risk in riparian crop areas based on statistical analyses of repiquete events registered from 1996 to 2018, hydraulic modeling to estimate flooded extension, and assessment of climatological characteristics during the formation of repiquetes. New hydrological insightsFloods (≥ 20 cm) produced by repiquetes in riparian crop areas between 83.00 and 88.00 m above sea level (masl) occur 1.8 times per year. However, not all elevation ranges have the same flooding risk to crops. Terrain elevations between 85.31 and 87.00 masl have a reduced flooding risk of 0.35 per year. Likewise, areas with elevations between 87.00 and 88.00 masl (43% of the total area) were not affected by repiquetes. Extreme repiquetes (study cases of 2002 and 2008) have been influenced by the increase of atmospheric moisture flux convergence and precipitation over both the northern Ucayali and Marañón basins through the six previous days. Flood impacts from the extreme event of 2002 (2008) could have reached 40% (25%) of the available area for agriculture at the initiation of the repiquete.