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Exchanges of mass, momentum and energy between the ocean and atmosphere are of large importance in regulating the climate system. Here, we apply for the first time a relatively novel approach, the rate of information transfer, to quantify interactions between the ocean surface and the lower atmosphere over the period 1988–2017 at a monthly timescale. More specifically, we investigate dynamical dependencies between sea surface temperature (SST), SST tendency and turbulent heat flux in satellite observations. We find a strong two-way influence between SST and/or SST tendency and turbulent heat flux in many regions of the world, with the largest values in the eastern tropical Pacific and Atlantic oceans, as well as in western boundary currents. The total number of regions with a significant influence by turbulent heat flux on SST and on SST tendency is reduced when considering the three variables (this case should be privileged, as it provides additional sources of information), while it remains large for the information transfer from SST and SST tendency to turbulent heat flux, suggesting an overall stronger ocean influence compared to the atmosphere. We also find a relatively strong influence by turbulent heat flux taken 1 month before on SST. Additionally, an increase in the magnitude of the rate of information transfer and in the number of regions with significant influence is observed when looking at interannual and decadal timescales compared to monthly timescales.

The impact of climate change on weather pattern dynamics over the North Atlantic is explored through the lens of the information theory of forced dissipative dynamical systems. The predictability problem is first tackled by investigating the evolution of block entropies on observational time series of weather patterns produced by the Met Office, which reveals that predictability is increasing as a function of time in the observations during the 19th century and beginning of the 20th century, while the trend is reversed at the end of the 20th century and beginning of the 21st century. This feature is also investigated in the 15-member ensemble of the UK Met Office CMIP5 model for the 20th and 21st centuries under two climate change scenarios, revealing a wide range of possible evolutions depending on the realization considered, with an overall decrease in predictability in the 21st century for both scenarios. Lower bounds of the information entropy production are also extracted, providing information on the degree of time asymmetry and irreversibility of the dynamics. The analysis of the UK Met Office model runs suggests that the information entropy production will increase by the end of the 21st century, by a factor of 10 % in the Representative Carbon Pathway RCP2.6 scenario and a factor of 30 %–40 % in the RCP8.5 one, as compared to the beginning of the 20th century. This allows one to make the conjecture that the degree of irreversibility is increasing, and hence heat production and dissipation will also increase under climate change, corroborating earlier findings based on the analysis of the thermodynamic entropy production.

In this paper, a new nonlinear forcing singular vector (NFSV) approach is proposed to provide mutually independent optimally combined modes of initial perturbations and model perturbations (C-NFSVs) in ensemble forecasts. The C-NFSVs are a group of optimally growing structures that take into account the impact of the interaction between the initial errors and the model errors effectively, generalizing the original NFSV for simulations of the impact of the model errors. The C-NFSVs method is tested in the context of the Lorenz-96 model to demonstrate its potential to improve ensemble forecast skills. This method is compared with the orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method for estimating only the initial uncertainties and the orthogonal NFSVs (O-NFSVs) for estimating only the model uncertainties. The results demonstrate that when both the initial perturbations and model perturbations are introduced in the forecasting system, the C-NFSVs are much more capable of achieving higher ensemble forecasting skills. The use of a deep learning approach as a remedy for the expensive computational costs of the C-NFSVs is evaluated. The results show that learning the impact of the C-NFSVs on the ensemble provides a useful and efficient alternative for the operational implementation of C-NFSVs in forecasting suites dealing with the combined effects of the initial errors and the model errors. Significance Statement A new ensemble forecasting method for dealing with combined effects of initial errors and model errors, i.e., the C-NFSVs, is proposed, which is an extension of the NFSV approach for simulating the model error effects in ensemble forecasts. The C-NFSVs provide mutually independent optimally combined modes of initial perturbations and model perturbations. This new method is tested for generating ensemble forecasts in the context of the Lorenz-96 model, and there are indications that the optimally growing structures may provide reliable ensemble forecasts. Furthermore, it is found that a hybrid dynamical–deep learning approach could be a potential avenue for real-time ensemble forecasting systems when perturbations combine the impact of the initial and the model errors.

Low-frequency variability (LFV) encompasses atmospheric and climate processes on time scales from a few weeks to decades.​ This includes atmospheric blockings, heat waves, cold spells, and at longer time scales long-term oscillations like the MJO, the NAO, ENSO….. Better understanding of LFV, could contribute to improved long term forecasts​. Identifying and evaluating LFVs in GCMs is computationally expensive, so in this study an idealised low order coupled model is used. They are climate models ‘stripped to the bone’, which links theoretical understanding to the complexity of more realistic models, made by key ingredients and approximations​; which hence helps us to study a particular phenomenon by tweaking the parameters affecting them with less computational cost​. The Quasi Geostrophic land atmosphere coupled model is a python implementation of mid-latitude atmospheric model​ with two layer quasi geostrophic channel atmosphere on beta -plane​ coupled to a simple land portion. The system exhibits blocking conditions at different time scales depending on the incoming solar radiation and also experiences transitions from blocking to zonal flow after applying different sets of parameters to the model. The predictability and persistence of these regimes is investigated by calculating the local lyapunov exponents at the specified transition points and around them. The findings are discussed in the perspective of the current literature on the predictability of blocking.

Reduced order quasi-geostrophic ocean-atmosphere coupled models provide a platform that preserve key atmosphere behaviours, while still being simple enough to allow for analysis of the system dynamics. These models produce typical atmospheric dynamical features like atmospheric blocking and other low-frequency variability, while having a low number of degrees of freedom. For this reason, these models are well suited to investigating tipping points or bifurcations in the Earth's climate due to their simplified but insightful dynamics. In our present work we compare the dynamics of an ocean-atmosphere coupled model, previously implemented with linearised temperature equations (Vannitsem et al., 2015), but here we solve the equations including the non-linear Stefan-Boltzmann law in the radiative temperature term. When compared with the original version of the model with linearised temperature equations, the modified version of the model is found to produce multiple stable flows in the coupled ocean-atmosphere system. We find, for increasing atmospheric emissivity, there is an increase in the number of stable attractors, and these stable attractors present distinct flows in the ocean and atmosphere and distinct Lyapunov stability properties.Vannitsem, S., Demaeyer, J., De Cruz, L., & Ghil, M. (2015). Low-frequency variability and heat transport in a low-order nonlinear coupled ocean–atmosphere model. Physica D: Nonlinear Phenomena, 309, 71-85.

Statistical postprocessing of forecasts from numerical weather prediction systems is an important component of modern weather forecasting systems. A growing variety of postprocessing methods has been proposed, but a comprehensive, community-driven comparison of their relative performance is yet to be established. Important reasons for this lack include the absence of a fair intercomparison protocol, and, the difficulty of constructing a common comprehensive dataset that can be used to perform such intercomparison. Here we introduce the first version of the EUPPBench, a dataset of time-aligned medium-range forecasts and observations over Central Europe, with the aim to facilitate and standardize the intercomparison of postprocessing methods. This dataset is publicly available [1], includes station and gridded data, ensemble forecasts for training (20 years) and validation (2 years) based on the ECMWF system. The initial dataset is the basis of an ongoing activity to establish a benchmarking platform for postprocessing of medium-range weather forecasts. We showcase a first benchmark of several methods for the adjustment of near-surface temperature forecasts and outline the future plans for the benchmark activity. [1] https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark

The exceptional flood of July 2021 in central Europe impacted severely Belgium. As rainfall was the triggering factor of this event, this study aims at characterizing rainfall amounts in Belgium from July 13th to July 16th 2021 based on 2 types of observational data. First, observations recorded by high-quality rain gauges operated by weather and hydrological services in Belgium have been compiled and quality checked. Second, a radar-based rainfall product has been improved to provide a reliable estimation of quantitative precipitation at high spatial and temporal resolutions over Belgium. Several analyses of these data are here performed to describe the spatial and temporal distribution of rainfall during the event. These analyses indicate that the rainfall accumulations during the event have reached unprecedented levels over large areas. Accumulations over durations from 1 to 3 days significantly exceeded the 200-year return level at several places, with up to 90 % of exceedance over the 200-year return level for 2-day and 3-day values locally in the Vesdre basin. Such a record-breaking event needs to be documented as much as possible and available observational data must be shared with the scientific community for further studies in hydrology, in urban planning and, more generally, in all multi-disciplinary studies aiming at identifying and understanding factors leading to such disaster. The corresponding rainfall data are therefore provided freely as a supplement (Journée et al., 2023; Goudenhoofdt et al., 2023)..

Statistical postprocessing of medium-range weather forecasts is an important component of modern forecasting systems. Since the beginning of modern data science, numerous new postprocessing methods have been proposed, complementing an already very diverse field. However, one of the questions that frequently arises when considering different methods in the framework of implementing operational postprocessing is the relative performance of the methods for a given specific task. It is particularly challenging to find or construct a common comprehensive dataset that can be used to perform such comparisons. Here, we introduce the first version of EUPPBench (EUMETNET postprocessing benchmark), a dataset of time-aligned forecasts and observations, with the aim to facilitate and standardize this process. This dataset is publicly available at https://github.com/EUPP-benchmark/climetlab-eumetnet-postprocessing-benchmark (31 December 2022) and on Zenodo (https://doi.org/10.5281/zenodo.7429236, Demaeyer, 2022b and https://doi.org/10.5281/zenodo.7708362, Bhend et al., 2023). We provide examples showing how to download and use the data, we propose a set of evaluation methods, and we perform a first benchmark of several methods for the correction of 2 m temperature forecasts.

The Liang-Kleeman rate of information transfer is used to quantify interactions between the different terms of the ocean heat budget at monthly time scale over the period 1988–2017 in three coupled global climate models participating in the High Resolution Model Intercomparison Project (HighResMIP), as well as in the Ocean Reanalysis System 5 (ORAS5). In particular, we focus on the influences of ocean heat transport convergence (dynamical influence) and net surface heat flux (thermodynamical influence) on ocean heat content tendency. At least two different configurations are used for each model, allowing to investigate the impact of ocean resolution on these causal relationships. A very small number of regions with a dynamical influence is found at high ocean resolution (≤ 0.25°) and ORAS5 reanalysis when considering the upper 50 m, while a thermodynamical influence is present in a large number of regions. The number of regions with a dynamical influence increases when taking into account the upper 300 m and becomes comparable to the thermodynamical influence. Interestingly, low-resolution model configurations (1° in the ocean) show a much larger number of regions with a significant dynamical influence for both depth integrations (upper 50 m and 300 m) compared to high-resolution model configurations. The reason for the large difference in dynamical influence between low and high resolutions partly comes from the spatial distribution of ocean velocity field, which is highly variable at high resolution, leading to a smaller dynamical influence. High resolution is therefore key in representing realistically the causal interactions between the ocean and atmosphere.

Observations indicate that two types of El Niño events exist: one is the EP-El Niño with a warming center in the eastern tropical Pacific, and the other is the CP-El Niño with large positive SST anomalies in the central tropical Pacific. Most current numerical models are not able to accurately identify the different types of El Niño. The present study examines the dynamic properties of the ENSO forecast system NFSV-ICM which combines an intermediate-complexity ENSO model (ICM) with a nonlinear forcing singular vector (NFSV)-based tendency perturbation forecast model. This system is able to distinguish the different types of El Niño in predictions. Hindcasts show that the NFSV-ICM system is able to capture the horizontal distribution of the SST anomalies and their amplitudes in the mature phase of not only EP-El Niño events but also CP-El Niño events. The NFSV-ICM is also able to describe the evolution of SST anomalies associated with the two types of El Niño up to at least two-season lead times, while the corresponding forecasts with the ICM are limited to, at most, one-season lead times. These improvements are associated with the modifications of the atmospheric and ocean processes described by the ICM through the NFSV-based tendency perturbations. In particular, the thermocline and zonal advection feedback are strongly modified, and the conditions of the emergence of both EP- and CP-El Niño events are improved. The NFSV-ICM therefore provides a useful platform for studying ENSO dynamics and predictability associated with El Niño diversities.

Data assimilation for systems possessing many scales of motions is a substantial methodological and technological challenge. Systems with these features are found in many areas of computational physics and are becoming common thanks to increased computational power allowing to resolve finer scales and to couple together several sub-components. Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward by meteo services worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components (e.g. ocean and atmosphere). We provide a brief survey of CDA, and then focus on CDA using the ensemble Kalman filter (EnKF), a widely used Monte Carlo Gaussian method. Our goal is to elucidate the mechanisms behind information propagation across model components. We consider first a coupled system of equations with temporal scale difference, and deduce that: (i) cross components effects are strong from the slow to the fast scale, but, (ii) intra-component effects are much stronger in the fast scale. While observing the slow scale is desirable and benefits the fast, the latter must be observed with high frequency otherwise the error will grow up to affect the slow scale. Numerical experiments are performed using the atmosphere-ocean model, MAOOAM. Six configurations are considered, differing for the strength of the atmosphere-ocean coupling and/or the number of model modes. The performance of the EnKF depends on the model configuration, i.e. on its dynamical features. A comprehensive dynamical characterisation of the model configurations is provided by examining the Lyapunov spectrum, Kolmogorov entropy and Kaplan–Yorke attractor dimension. We also compute the covariant Lyapunov vectors and use them to explain how model instabilities act on different model’s modes according to the coupling strength. The experiments confirm the importance of observing the fast scale, but show also that, despite its slow temporal scale, frequent observations in the ocean are beneficial. The relation between the ensemble size, N, and the unstable subspace dimension, $$n_0$$ n 0 , has been studied. Results largely ratify what known for uncoupled system: the condition $$N\ge n_0$$ N ≥ n 0 is necessary for the EnKF to work satisfactorily. Nevertheless the quasi-degeneracy of the Lyapunov spectrum of MAOOAM, with many near-zero exponents, is potentially the cause of the smooth gradual reduction of the analysis error observed for some model configurations, even when $$N>n_0$$ N > n 0 . Future prospects for the EnKF in the context of coupled ocean-atmosphere systems are finally discussed.

Thanks to recent advances in multisensory observation systems and high-resolution numerical weather prediction (NWP) models, a wealth of information is available to feed and improve operational weather forecasting systems. At the same time, end users such as the renewable energy sector and hydrological services require increasingly detailed and timely weather forecasts that take into account the latest observations.However, data assimilation in NWP models cannot yet leverage the full spatial or temporal resolution of today's observation systems. Moreover, the combined assimilation and model run takes significantly more time than an extrapolation-based nowcast, and cannot match its accuracy at short lead times. Therefore, many National Meteorological Services (NMSs) are moving towards seamless prediction systems. Seamless prediction aims to make optimal use of today’s rapidly available, high-resolution multisensory observations, nowcasting algorithms and state-of-the-art convection-permitting NWP models. This approach integrates multiple data and model sources to provide a single, frequently updating deterministic or probabilistic forecast for lead times from minutes to days.We present the seamless ensemble prediction system of the Royal Meteorological Institute of Belgium, called Project IMA (Japanese for "now" or "soon"). It provides rapidly updating seamless forecasts for the next 5 minutes to 24 hours. The nowcasting component is based on two systems: (1) the open-source probabilistic precipitation nowcasting scheme pySTEPS, which now features a scale-dependent blending with NWP ensemble forecasts (also presented in this session) and (2) an ensemble of INCA-BE nowcasts using two different NWP models, for other meteorological variables. The short-range NWP component consists of a multimodel lagged Mini-EPS of two convection-permitting configurations of the ACCORD system: AROME and ALARO, running at 1.3km resolution. It features a 3-hourly DA cycle and provides high-frequency precipitation output to facilitate the blending of precipitation nowcasts and forecasts. The system runs robustly using our NodeRunner tool based on EcFlow, ECMWF's operational work-flow package. We will give an overview of the development (past and future), some lessons learned, and use cases for Project IMA.

The prediction of the weather at subseasonal-to-seasonal (S2S) timescales is dependent on both initial and boundary conditions. An open question is how to best initialize a relatively small-sized ensemble of numerical model integrations to produce reliable forecasts at these timescales. Reliability in this case means that the statistical properties of the ensemble forecast are consistent with the actual uncertainties about the future state of the geophysical system under investigation. In the present work, a method is introduced to construct initial conditions that produce reliable ensemble forecasts by projecting onto the eigenfunctions of the Koopman or the Perron-Frobenius operators, which describe the time-evolution of observables and probability distributions of the system dynamics, respectively. These eigenfunctions can be approximated from data by using the Dynamic Mode Decomposition (DMD) algorithm. The effectiveness of this approach is illustrated in the framework of a low-order ocean-atmosphere model exhibiting multiple characteristic timescales, and is compared to other ensemble initialization methods based on the Empirical Orthogonal Functions (EOFs) of the model trajectory and on the backward and covariant Lyapunov vectors of the model dynamics. Projecting initial conditions onto a subset of the Koopman or Perron-Frobenius eigenfunctions that are characterized by time scales with fast-decaying oscillations is found to produce highly reliable forecasts at all lead times investigated, ranging from one week to two months. Reliable forecasts are also obtained with the adjoint covariant Lyapunov vectors, which are the eigenfunctions of the Koopman operator in the tangent space. The advantages of these different methods are discussed., Submitted to JAMES, 35 pages, 12 figures. 9 pages supplementary material at the end

Chaos is ubiquitous in physical systems. The associated sensitivity to initial conditions is a significant obstacle in forecasting the weather and other geophysical fluid flows. Data assimilation is the process whereby the uncertainty in initial conditions is reduced by the astute combination of model predictions and real-time data. This chapter reviews recent findings from investigations on the impact of chaos on data assimilation methods: for the Kalman filter and smoother in linear systems, analytic results are derived; for their ensemble-based versions and nonlinear dynamics, numerical results provide insights. The focus is on characterizing the asymptotic statistics of the Bayesian posterior in terms of the dynamical instabilities, differentiating between deterministic and stochastic dynamics. We also present two novel results. Firstly, we study the functioning of the ensemble Kalman filter in the context of a chaotic, coupled, atmosphere-ocean model with a quasi-degenerate spectrum of Lyapunov exponents, showing the importance of having sufficient ensemble members to track all of the near-null modes. Secondly, for the fully non-Gaussian method of the particle filter, numerical experiments are conducted to test whether the curse of dimensionality can be mitigated by discarding observations in the directions of little dynamical growth of uncertainty. The results refute this option, most likely because the particles already embody this information on the chaotic system. The results also suggest that it is the rank of the unstable-neutral subspace of the dynamics, and not that of the observation operator, that determines the required number of particles. We finally discuss how knowledge of the random attractor can play a role in the development of future data assimilation schemes for chaotic multiscale systems with large scale separation.

We explore a methodology to statistically downscale snowfall – the primary driver of surface mass balance in Antarctica – from an ensemble of historical (1850–present day) simulations performed with an earth system model over the coastal region of Dronning Maud Land (East Antarctica). This approach consists of associating daily snowfall simulations from a polar-oriented regional atmospheric climate model at 5.5 km spatial resolution with specific weather patterns observed over 1979–2010 CE with the atmospheric reanalyses ERA-Interim and ERA5. This association is then used to generate the spatial distribution of snowfall for the period from 1850 to present day for an ensemble of 10 members from the Community Earth System Model (CESM2). The new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset; https://doi.org/10.5281/zenodo.4287517; Ghilain et al., 2021). Based on a comparison with available ice cores and spatial reconstructions, our results show that the spatio-temporal distribution of snowfall is improved in the downscaled dataset compared with the CESM2 simulations. This dataset thus provides information that may be useful in identifying the large-scale patterns associated with the local precipitation conditions and their changes over the past century.

Dynamical dependence between key observables and the surface mass balance (SMB) over Antarctica is analyzed in two historical runs performed with the MPI‐ESM‐P and the CESM1‐CAM5 climate models. The approach used is a novel method allowing for evaluating the rate of information transfer between observables that goes beyond the classical correlation analysis and allows for directional characterization of dependence. It reveals that a large proportion of significant correlations do not lead to dependence. In addition, three coherent results concerning the dependence of SMB emerge from the analysis of both models: (i) The SMB over the Antarctic Plateau is mostly influenced by the surface temperature and sea ice concentration and not by large‐scale circulation changes; (ii) the SMB of the Weddell Sea and the Dronning Maud Land coasts are not influenced significantly by the surface temperature; and (iii) the Weddell Sea coast is not significantly influenced by the sea ice concentration.

Atmosphere and ocean dynamics display many complex features and are characterized by a wide variety of processes and couplings across different timescales. Here we use Multivariate Empirical Mode Decomposition (MEMD; Rehman and Mandic, 2010) to investigate the multivariate and multiscale properties of a low-order model of the ocean-atmosphere coupled dynamics (Vannitsem, 2017). The MEMD allows us to decompose the original data into a series of oscillating patterns with time-dependent amplitude and phase by exploiting the local features of data and without any a priori assumptions on the decomposition basis. Moreover, each oscillating pattern, usually named Multivariate Intrinsic Mode Function (MIMF), can be used as a source of local (in terms of scale) fluctuations and information. This information allows us to derive multiscale measures when looking at the behavior of the generalized fractal dimensions at different scales (Hentschel and Procaccia, 1983) that can be seen as a sort of multivariate and multiscale generalized fractal dimensions (Alberti et al., 2020). With these two approaches, we demonstrate that the coupled ocean-atmosphere dynamics presents a rich variety of common features, although with a different nature of the fractal properties between the ocean and the atmosphere at different timescales. The MEMD results allow us to capture the main dynamics of the phase-space trajectory that can be used for reconstructing the skeleton of the phase-space dynamics, while the evaluation of the fractal dimensions at different timescales characterize the intrinsic complexity of oscillating patterns that can be related to the attractor properties. Our results support the interpretation of the coupled ocean-atmosphere dynamics as well as the investigation of general deterministic-chaotic dissipative dynamical systems in terms of invariant manifolds, bifurcations, as well as (strange) attractors in their phase-space, whose geometric and topological properties are a reflection of the dynamical regimes of the system at different scales. We compare the results obtained for the low-order dynamical model with those derived from the reanalysis data and demonstrate that a similar scale-dependent behavior is found, thus also confirming the suitability of the proposed system to model the ocean-atmosphere dynamics at different timescales and to describe topological and geometrical features of its phase-space. ReferencesAlberti, T., Consolini, G., Ditlevsen, P. D., Donner, R. V., Quattrociocchi, V. (2020). Multiscale measures of phase-space trajectories. Chaos 30, 123116.Alberti, T., Donner, R. V., and Vannitsem, S. (2021). Multiscale fractal dimension analysis of a reduced order model of coupled ocean-atmosphere dynamics. Earth Syst. Dynam. Discuss. [preprint], https://doi.org/10.5194/esd-2020-96, in review.Hentschel, H. G. E., Procaccia, I. (1983). The infinite number of generalized dimensions of fractals and strange attractors. Physica D 8, 435–444.Rehman, N., Mandic, D. P. (2010). Multivariate empirical mode decomposition. Proceedings of the Royal Society A, 466, 1291–1302.Vannitsem S., Predictability of large-scale atmospheric motions: Lyapunov exponents and error dynamics, Chaos, 27, 032101, 2017.

The impact of the El Niño-Southern Oscillation (ENSO) on the extratropics is investigated in an idealized, reduced-order model that has a tropical and an extratropical module. Unidirectional forcing is used to mimic the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean--atmosphere extratropical module is then investigated through the analysis of its pullback attractors (PBA). This analysis focuses on two ENSO-type forcings generated by the tropical module, one periodic and one aperiodic. For a substantial range of coupling parameters, multiple chaotic PBAs are found to coexist for the same set of parameter values. Different types of extratropical low-frequency variability are associated with each PBA over the parameter ranges explored. For periodic ENSO forcing, the coexisting PBAs are nonlinearly stable, while for the chaotic forcing, they are unstable and certain extratropical perturbations induce transitions between the PBAs. These distinct stability properties may have profound consequences for extratropical climate predictions, provided they are confirmed by studies using comprehensive climate models. Thus, for instance, ensemble averaging may no longer be a valid approach to isolate the low-frequency variability signal.

The surface mass balance (SMB) over the Antarctic Ice Sheet displays large temporal and spatial variations. Due to the complex Antarctic topography, modelling the climate at high resolution is crucial to accurately represent the dynamics of SMB. While ice core records provide a means to infer the SMB over centuries, the view is very spatially constrained. General circulation models (GCMs) estimate its spatial distribution over centuries, but with a resolution that is too coarse to capture the large variations due to local orographic effects. We have therefore explored a methodology to statistically downscale snowfall accumulation, the primary driver of SMB, from climate model historical simulations (1850–present day) over the coastal region of Dronning Maud Land. An analog method is set up over a period of 30 years with the ERA-Interim and ERA5 reanalyses (1979–2010 AD) and associated with snowfall daily accumulation forecasts from the Regional Atmospheric Climate Model (RACMO2.3) at 5.5 km spatial resolution over Dronning Maud in East Antarctica. The same method is then applied to the period from 1850 to present day using an ensemble of ten members from the CESM2 model. This method enables to derive a spatial distribution of the accumulation of snowfall, the principal driver of the SMB variability over the region. A new dataset of daily and yearly snowfall accumulation based on this methodology is presented in this paper (MASS2ANT dataset, https://doi.org/10.5281/zenodo.4287517, Ghilain et al. (2021)), along with comparisons with ice core data and available spatial reconstructions. It offers a more detailed spatio-temporal view of the changes over the past 150 years compared to other available datasets, allowing a possible connection with the ice core records, and provides information that may be useful in identifying the large-scale patterns associated to the local precipitation conditions and their changes over the past century.

Atmosphere and ocean dynamics display many complex features and are characterized by a wide variety of processes and couplings across different timescales. Here we demonstrate the application of Multivariate Empirical Mode Decomposition (MEMD) to investigate the multivariate and multiscale properties of a reduced order model of the ocean-atmosphere coupled dynamics. MEMD provides a decomposition of the original multivariate time series into a series of oscillating patterns with time-dependent amplitude and phase by exploiting the local features of the data and without any a priori assumptions on the decomposition basis. Moreover, each oscillating pattern, usually named Multivariate Intrinsic Mode Function (MIMF), represents a local source of information that can be used to explore the behavior of fractal features at different scales by defining a sort of multiscale/multivariate generalized fractal dimensions. With these two approaches, we show that the ocean- atmosphere dynamics presents a rich variety of features, with different multifractal properties for the ocean and the atmosphere at different timescales. For weak ocean–atmosphere coupling, the resulting dimensions of the two model components are very different, while for strong coupling for which coupled modes develop, the scaling properties are more similar especially at longer time scales. The latter result reflects the presence of a coherent coupled dynamics. Finally, we also compare our model results with those obtained from reanalysis data demonstrating that the latter exhibit a similar qualitative behavior in terms of multiscale dimensions and the existence of a scale-dependency of topological and geometric features for different regions, being related to the different drivers and processes occurring at different timescales in the coupled atmosphere-ocean system. Our approach can therefore be used to diagnose the strength of coupling in real applications.

Statistical postprocessing techniques are nowadays key components of the forecasting suites in many National Meteorological Services (NMS), with for most of them, the objective of correcting the impact of different types of errors on the forecasts. The final aim is to provide optimal, automated, seamless forecasts for end users. Many techniques are now flourishing in the statistical, meteorological, climatological, hydrological, and engineering communities. The methods range in complexity from simple bias corrections to very sophisticated distribution-adjusting techniques that incorporate correlations among the prognostic variables. The paper is an attempt to summarize the main activities going on this area from theoretical developments to operational applications, with a focus on the current challenges and potential avenues in the field. Among these challenges is the shift in NMS towards running ensemble Numerical Weather Prediction (NWP) systems at the kilometer scale that produce very large datasets and require high-density high-quality observations; the necessity to preserve space time correlation of high-dimensional corrected fields; the need to reduce the impact of model changes affecting the parameters of the corrections; the necessity for techniques to merge different types of forecasts and ensembles with different behaviors; and finally the ability to transfer research on statistical postprocessing to operations. Potential new avenues will also be discussed., Comment: This work has been submitted to the Bulletin of the American Meteorological Society. Copyright in this work may be transferred without further notice

Statistical post-processing of ensemble weather forecasts has become an essential step in the forecasting chain as it enables the correction of biases and reliable uncertainty estimates of ensembles (Gneiting, 2014). One algorithm recently proposed to perform the correction of ensemble weather forecasts is a linear member-by-member (MBM) Model Output Statistics (MOS) system, post-processing each member of the ECMWF ensemble (Van Schaeybroeck & Vannitsem, 2015). This method consists in correcting the mean and variability of the ensemble members in line with the observed climatology. At the same time, it calibrates the ensemble spread such as to match, on average, the mean square error of the ensemble mean. The MBM method calibrates the ensemble forecasts based on the station observations by minimizing the continuous ranked probability score (CRPS).Using this method, the Royal Meteorological Institute of Belgium has started in 2020 its new postprocessing program by developing an operational application to perform the calibration of the ECMWF ensemble forecasts at the stations points for the minimum and maximum temperature, and for wind gusts. In this report, we will first describe briefly the postprocessing methods being used and the architecture of the application. We will then present the results over the first few months of operation. Finally, we will discuss the future developments of this application and of the program. Gneiting, T., 2014: Calibration of medium-range weather forecasts. ECMWF Technical Memorandum No. 719 Van Schaeybroeck, B. & Vannitsem, S., 2015: Ensemble post-processing using member-by-member approaches: theoretical aspects. Quarterly Journal of the Royal Meteorological Society, 141, 807–818.

Over recent decades, the Antarctic Ice Sheet has witnessed large spatial variations at its surface through the surface mass balance (SMB). Since the complex Antarctic topography, working at high resolution is crucial to represent accurately the dynamics of SMB. While ice cores provide a mean to infer the SMB over centuries, the view is very spatially constrained. Global Climate models estimate the spatial distribution of SMB over centuries, but with a too coarse resolution with regards to the large variations due to local orographic effects. We have therefore explored a methodology to statistically downscale the SMB components from the climate model historical simulations (1850-present day). An analogue method is set up over a period of 30 years with the ERA-Interim reanalysis (1979-2010 AD) and associated with SMB components from the Regional Atmospheric Climate Model (RACMO) at 5 km spatial resolution over Dronning Maud in East Antarctica. The same method is then applied to the period from 1850 to present days using an ensemble of 10 simulations from the CESM2 model. This method enables to derive a spatial distribution of SMB. In addition, the changes in precipitation delivery mechanisms can be unveiled.

In an era where communicating your science goes hand in hand with doing your science, many scientists devote time to develop tools and learn new skills and strategies for Science Communication. The European Geosciences Union (EGU) has put in place one of those tools: the Divisions’ Blog. Most of the current EGU Divisions has an active blog run mainly by one or more volunteer early-career scientists. Regularly, both editors, and regular and guest authors write about research in their field, talk about relevant topics discussed within the scientific community, and highlight interesting facts for scientists and the general public. The goal is to provide a platform for enhancing communication among geoscientists in ways that go beyond the means of peer-reviewed publication or scientific conferences. At the same time, we aim at engaging with the general public, by writing in a technically sound, but more accessible form. Each Division’s blog has its character, like the teams behind it, making the blogs a diversified and exciting digital environment.Here we show the main numbers, statistics, and feedback from each Division Blog, thus providing a measure of the efforts put in and the impact made so far by the broad Geoscience community. We discuss best practices, blog styles and topics which do work well or not, based on readership statistics. We also show the channels chosen for advertising the blogs, such as social media, and the impact of the choices made. Finally, we show that even though EGU has its base in Europe, we reach an audience beyond Europe thanks to active members based outside Europe and to topics addressing particular geographical areas.We conclude that, within the increasingly essential role played by Science Communication in every research field, the EGU Divisions’ Blogs are successful at sharing research related to their fields with the broad geoscientific and non-scientific community. This success mainly relies on the time, effort, motivation, and creativity of editors and guest authors.

Forecasts from numerical weather prediction models suffer from systematic and nonsystematic errors, which originate from various sources such as subgrid-scale variability affecting large scales. Statistical postprocessing techniques can partly remove such errors. Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially postprocess the forecasts, by continuously updating the correction parameters as new ground observations become available. These techniques, originally proposed for deterministic forecasts, are valuable when long training datasets do not exist. Here, a new adaptive postprocessing technique for ensemble predictions (called AEMOS) is introduced. The proposed method implements a Kalman filtering approach that fully exploits the information content of the ensemble for updating the parameters of the postprocessing equation. A verification study for the region of Campania in southern Italy is performed. Two years (2014–15) of daily meteorological observations of 10-m wind speed and 2-m temperature from 18 ground-based automatic weather stations are used, comparing them with the corresponding COSMO-LEPS ensemble forecasts. It is shown that the proposed adaptive method outperforms the AMOS method, while it shows comparable results to the member-by-member batch postprocessing approach.

One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges, it can be described by scaling relationships in the form of power laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate prediction.

In atmospheric and climate sciences, research and development is often first conducted with a simple idealized system like the Lorenz-N models (N ∈ {63, 84, 96}) which are toy models of atmospheric variability. On the other hand, reduced-order spectral quasi-geostrophic models of the atmosphere with a sufficient number of modes offer a good representation of the dry atmospheric dynamics. They allow one to identify typical features of the atmospheric circulation, such as blocked and zonal circulation regimes, and low-frequency variability. However, these models are less often considered in literature, despite their demonstration of more realistic behavior.qgs (Demaeyer et al., 2020) aims to popularize these systems by providing a fast and easy-to-use Python framework for researchers and teachers to integrate this kind of model. The documentation makes it clear and efficient to handle the model, by explaining the equations and parameters and linking these to the code. The choice to use Python was specifically made to facilitate its use in Jupyter Notebooks and with the multiple recent machine learning libraries that are available in this language.In this talk, we will present the modeling capabilities of qgs and show its usage in a varieties of didactical and research use cases.ReferenceDemaeyer, J., De Cruz, L., & Vannitsem, S. (2020). qgs: A flexible Python framework of reduced-order multiscale climate models. Journal of Open Source Software, 5(56), 2597, https://doi.org/10.21105/joss.02597 .

For most statistical post-processing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforcasting effort. We present a new approach based on response theory to cope with slight model change. In this framework, the model change is seen as a perturbation of the original forecast model. The response theory allows then to evaluate the variation induced on the averages involved in the statistical post-processing, provided that the magnitude of this perturbation is not too large. This approach is studied in the context of simple Ornstein-Uhlenbeck models, and then on a more realistic, yet simple, quasi-geostrophic model. The analytical results for the former case allow for posing the problem, while the application to the latter provide a proof-of-concept of the potential performances of response theory in a chaotic system. In both cases, the parameters of the statistical post-processing used – an Error-in-Variables Model Output Statistics (EVMOS) – are appropriately corrected when facing a model change. The potential application in a more operational environment is also discussed.

The use of coupled Backward Lyapunov Vectors (BLV) for ensemble forecast is demonstrated in a coupled ocean-atmosphere system of reduced order, the Modular Arbitrary Order Ocean-Atmosphere Model (MAOOAM). It is found that overall the best set of BLVs to initialize a (multiscale) coupled ocean-atmosphere forecasting system are the ones associated with near-neutral or slightly negative Lyapunov exponents. This unexpected result is related to the fact that these sets display larger projections on the ocean variables than the others, leading to an appropriate spread for the ocean, and at the same time a rapid transfer of these errors toward the most unstable BLVs affecting predominantly the atmosphere is experienced. The latter dynamics is a natural property of any generic perturbation in nonlinear chaotic dynamical systems, allowing for a reliable spread with the atmosphere too. Furthermore, this specific choice becomes even more crucial when the goal is the forecasting of low-frequency variability at annual and decadal time scales. The implications of these results for operational ensemble forecasts in coupled ocean-atmosphere systems are briefly discussed., 34 pages, 9 figures

Seasonal predictions from climate models are increasingly invoked in various sectors like water management, energy and transport to cite a few. This study investigates the post-processing of the seasonal predictions of the EUROSIP multi-model system. The hindcasts comprise samples of 23 to 36 years and ensembles of 10 to 28 members depending on the 5 models included. Skill scores both deterministic and probabilistic are calculated in order to compare the impact of the post-processing and help selecting – if any – the multi- or single-model and the post-processing method best suited for a specific location, target season and lead-time. The presence of trends and the cross-validation setting add some complexity to the already heterogeneous database. This study focuses on six cases in Western Europe and the Mediterranean Region. The forecasts of three monthly averages of surface temperature and of sea mean sea level pressure are compared with the corresponding ERA Interim reanalysis data whereas the forecasts of precipitation are evaluated with the rain-gauge data from the Global Precipitation Climatology Centre. The skills of seasonal predictions in the extra-tropics are limited and our results are no exception. There is a significant skill for the spring temperature forecast with model initiation in March for all but one case studies and the skill is extending to the initiation begin of February for Belgium. There is is also a significant skill for the summer temperature for the case studies in the Mediterranean region. For these area, the skill comes in large part from the global warming so that after having de-trended the data, a null improvement cannot be excluded. Autumn temperature in UK and in the Turkey is forecast with some skill as well as winter temperature in UK and Greece. Precipitation is even more difficult to forecast: the two spots where skill scores are significantly positive are Sweden and Greece during winter with initialisation on the first December. It has been shown that multi-model ensemble improve the skills in many cases and that taking into account the longest common period of hindcasts results in better and less uncertain skill scores. For all these cases, the post-processing method and the model or model combination resulting in the best skill score have been selected.

Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep convection parameterization and the target model without deep convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that that the total mass, heat and momentum remain conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations, not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

A stochastic subgrid-scale parameterization based on the Ruelle's response theory and proposed inWouters and Lucarini (2012) is tested in the context of a low-order coupled ocean-atmosphere model for which a part of the atmospheric modes are considered as unresolved. A natural separation of the phase-space into an invariant set and its complement allows for an analytical derivation of the different terms involved in the parameterization, namely the average, the fluctuation and the long memory terms. In this case, the fluctuation term is an additive stochastic noise. Its application to the loworder system reveals that a considerable correction of the low-frequency variability along the invariant subset can be obtained, provided that the coupling is sufficiently weak. This new approach of scale separation opens new avenues of subgrid-scale parameterizations in multiscale systems used for climate forecasts.