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Introduction

## Publications

Publications (130)

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...

Plain Language Summary
The Arctic has been warming at a larger rate than the rest of the world, resulting in a substantial loss of sea ice since the late 1970s. This has had and will continue to have an impact on our climate and societies. The exact causes of the ongoing sea‐ice loss are not entirely known, and understanding them is important in or...

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...

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...

The directional dependencies of different climate indices are explored using the Liang-Kleeman information flow in order to disentangle the influence of certain regions over the globe on the development of low-frequency variability of others. Seven key indices (the sea-surface temperature in El-Niño 3.4 region, the Atlantic Multidecadal Oscillation...

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 re...

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...

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...

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 ENSO forcing is used to mimick the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean‐atmosphere extratropical m...

The link to the document:
https://library.wmo.int/index.php?lvl=notice_display&id=21911#.YPEgbegzZPZ

The impact of the El Ni\~no-Southern Oscillation (ENSO) on the extratropics is investigated in an idealized, reduced-order model that has a tropical and an extratropical module. Unidirectional ENSO forcing is used to mimick the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean-atmosphere extratropical...

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 const...

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...

The new system for post-processing ECMWF ensemble forecasts at the stations of the Royal
Meteorological Institute (RMI) of Belgium was described previously in a short newsletter article (Vannitsem
& Demaeyer, 2020). This system has now been operational since the summer of 2020 and we provide a
description of its functionality and a preliminary anal...

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 ar...

The special issue on advances in post-processing and blending of deterministic and ensemble forecasts is the outcome of several successful successive sessions organized at the General Assembly of the European Geosciences Union. Statistical post-processing and blending of forecasts are currently topics of important attention and development in many...

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 re...

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 most suitable BLVs to initialize a (multiscale) coupled ocean–atmosphere forecasting system are the ones associated...

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 st...

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 pow...

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. Coup...

For most statistical postprocessing schemes used to correct weather forecasts, changes to the forecast model induce a considerable reforecasting effort. We present a new approach based on response theory to cope with slight model changes. In this framework, the model change is seen as a perturbation of the original forecast model. The response theo...

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 i...

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...

Coupled data assimilation (CDA) distinctively appears as a main concern in numerical weather and climate prediction with major efforts put forward worldwide. The core issue is the scale separation acting as a barrier that hampers the propagation of the information across model components. We provide a brief survey of CDA, and then focus on CDA usin...

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...

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 sytem (MAOOAM). It is found that the best set of BLvs to build a coupled ocean-atmosphere forecasting system are the ones associated with near-neutral or slightl...

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 theor...

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...

The predictability of the atmosphere at short and long time scales, associated with the coupling to the ocean, is explored in a new version of the Modular Arbitrary‐Order Ocean‐Atmosphere Model (MAOOAM). This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this paper co...

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 i...

The predictability of the atmosphere at short and long time scales, associated with the coupling to the ocean, is explored in a new version of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM). This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this paper co...

The ocean and atmosphere have very different characteristic timescales and display a rich range of interactions. Here, we investigate the sensitivity of the dynamical properties of the coupled atmosphere-ocean system when time-averaging of the trajectories of the original system is performed. We base our analysis on a conceptual model of the atmosp...

A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of the Modular Arbitrary-Order Ocean-Atmosphere Model (MAOOAM), a coupled ocean–atmosphere model of intermediate complexity. Two physically based parameterizations are investigated – the first one based on the singular perturbation of Markov...

The causal dependences (in a dynamical sense) between the dynamics of three different coupled ocean–atmosphere basins, the North Atlantic, the North Pacific and the tropical Pacific region (Nino3.4), have been explored using data from three reanalysis datasets, namely ORA-20C, ORAS4 and ERA-20C. The approach is based on convergent cross mapping (CC...

The CORDEX.be project created the foundations for Belgian climate services by producing high-resolution Belgian
climate information that (a) incorporates the expertise of the different Belgian climate modeling groups and
that (b) is consistent with the outcomes of the international CORDEX (“COordinated Regional Climate Downscaling
Experiment”) proj...

Statistical Postprocessing of Ensemble Forecasts brings together chapters contributed by international subject-matter experts describing the current state of the art in the statistical postprocessing of ensemble forecasts. The book illustrates the use of these methods in several important applications including weather, hydrological and climate for...

The stability properties of intermediate-order climate models are investigated by computing their Lyapunov exponents (LEs). The two models considered are PUMA (Portable University Model of the Atmosphere), a primitive-equation simple general circulation model, and MAOOAM (Modular Arbitrary-Order Ocean-Atmosphere Model), a quasi-geostrophic coupled...

The causal dependences between the dynamics of three different coupled ocean-atmosphere basins, The North Atlantic, the North Pacific and the Tropical Pacific region, NINO3.4, have been explored using data from three reanalyses datasets, namely the ORA-20C, the ORAS4 and the ERA-20C. The approach is based on the Convergent Cross Mapping (CCM) devel...

A new framework is proposed for the evaluation of stochastic subgrid-scale parameterizations in the context of MAOOAM, a coupled ocean-atmosphere model of intermediate complexity. Two physically-based parameterizations are investigated, the first one based on the singular perturbation of Markov operator, also known as homogenization. The second one...

We review some recent methods of subgrid-scale parameterization used in the context of climate modeling. These methods are developed to take into account (subgrid) processes playing an important role in the correct representation of the atmospheric and climate variability. We illustrate these methods on a simple stochastic triad system relevant for...

The stability properties of intermediate-order climate models are investigated by computing their Lyapunov exponents (LEs). The two models considered are PUMA (Portable University Model of the Atmosphere), a primitive-equation simple general circulation model, and MAOOAM (Modular Arbitrary-Order Ocean-Atmosphere Model), a quasi-geostrophic coupled...

Forecasts from numerical weather prediction models suffer from systematic and non-systematic errors, which originate from various sources such as model error and sub-grid variability. Statistical post-processing techniques can partly remove such errors.
Adaptive MOS techniques based on Kalman filters (here called AMOS), are used to sequentially po...

The deterministic equations describing the dynamics of the atmosphere (and of the climate system) are known to display the property of sensitivity to initial conditions. In the ergodic theory of chaos this property is usually quantified by computing the Lyapunov exponents. In this review, these quantifiers computed in a hierarchy of atmospheric mod...

Coupling between the ocean and the atmosphere is investigated in reanalysis datasets. Projecting the datasets onto a dynamically defined subspace allows one to isolate the dominant modes of variability of the coupled system. This coupled projection is then analyzed using multichannel singular spectrum analysis (M-SSA). The results suggest that a do...

We review some recent methods of subgrid-scale parameterization used in the context of climate modeling. These methods are developed to take into account (subgrid) processes playing an important role in the correct representation of the atmospheric and climate variability. We illustrate these methods on a simple stochastic triad system relevant for...

This chapter describes a novel approach for the treatment of model error in geophysical data assimilation. In this method, model error is treated as a deterministic process correlated in time. This allows for the derivation of the evolution equations for the relevant moments of the model error statistics required in data assimilation procedures, al...

This paper describes a reduced-order quasi-geostrophic coupled ocean–atmosphere model that allows for an arbitrary number of atmospheric and oceanic modes to be retained in the spectral decomposition. The modularity of this new model allows one to easily modify the model physics. Using this new model, coined the "Modular Arbitrary-Order Ocean-Atmos...

A stochastic subgrid-scale parameterization based on the Ruelle's response theory and proposed in Wouters 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...

The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation, in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events....

Climate model calibration relies on different working hypotheses. The simplest bias correction or delta change methods assume the invariance of bias under climate change. Recent works have questioned this hypothesis and proposed linear bias changes with respect to the forcing. However, when the system experiences larger forcings, these schemes coul...

The development of the Low-Frequency Variability (LFV) in the atmosphere at multi-decadal time scales is investigated in the context of a low-order coupled ocean-atmosphere model designed to emulate the interaction between the Ocean Mixed Layer (OML) and the atmosphere at midlatitudes, both subject to seasonal variations of the Sun's radiative inpu...

We study a simplified coupled atmosphere-ocean model using the formalism of
covariant Lyapunov vectors (CLVs), which link physically-based directions of
perturbations to growth/decay rates. The model is obtained via a severe
truncation of quasi-geostrophic equations for the two fluids, and includes a
simple yet physically meaningful representation...

The ensemble spread is often used as a measure of the forecast quality or uncertainty. However, it is not clear whether the spread is a good measure of uncertainty and how the spread-error relationship can be properly assessed. Even for perfectly reliable forecasts the error for a given spread varies considerably in amplitude and the spread-error r...

The impact of errors in the forcing, errors in the model structure and parameters, and errors in the initial conditions are investigated in a simple hydrological ensemble prediction system. The hydrological model is based on an input non-linearity connected with a linear transfer function and forced by precipitation forecasts from the ECMWF Ensembl...

This chapter describes a novel approach for the treatment of model error in
geophysical data assimilation. In this method, model error is treated as a
deterministic process fully correlated in time. This allows for the derivation
of the evolution equations for the relevant moments of the model error
statistics required in data assimilation procedur...

We formulate and study a low-order nonlinear coupled ocean-atmosphere model
with an emphasis on the impact of radiative and heat fluxes and of the
frictional coupling between the two components. This model version extends a
previous 24-variable version by adding a dynamical equation for the passive
advection of temperature in the ocean, together wi...

There is a growing interest in developing stochastic schemes for the description of processes that are poorly represented in atmospheric and climate models, in order to increase their variability and reduce the impact of model errors. The use of such noise could however have adverse effects by modifying in undesired ways a certain number of moments...

Linear post-processing approaches are proposed and fundamental mechanisms are analyzed by which the probabilistic skill of an ensemble forecast can be improved. The ensemble mean of the corrected forecast is a linear function of the ensemble mean(s) of the predictor(s). Likewise, the ensemble spread of the corrected forecast depends linearly on tha...

A new low-order coupled ocean-atmosphere model for mid-latitudes is
derived. It is based on quasi-geostrophic equations for both the ocean
and the atmosphere, coupled through momentum transfer at the interface.
The systematic reduction of the number of modes describing the dynamics
leads to an atmospheric low-order component of 20 ordinary differen...

The dynamics of a low-order coupled wind-driven ocean–atmosphere system is investigated with emphasis on its predictability properties. The low-order coupled deterministic system is composed of a baroclinic atmosphere for which 12 dominant dynamical modes are only retained (Charney and Straus in J Atmos Sci 37:1157–1176, 1980) and a wind-driven, qu...

A deep understanding of the error dynamics in turbulent systems is
crucial to estimate the horizon of predictability, and to quantify the
impact of initial-condition (IC) and model errors on the statistical
characteristics of ensemble prediction systems. We present a study of
the dynamics of combined IC and model errors in a turbulent system. We
us...

We develop post-processing or calibration approaches based on linear
regression that make ensemble forecasts more reliable. We enforce
climatological reliability in the sense that the total variability of
the prediction is equal to the variability of the observations. Second,
we impose ensemble reliability such that the spread around the ensemble
m...

The nomenclature “data assimilation” arises from applications in the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation is closely related to filtering and smoothing on the one hand and inverse problems and statistical inference on the other. Ke...

This paper presents a soil analysis scheme based on an extended Kalman filter (EKF), the short time augmented extended Kalman filter (STAEKF), where the model parameters are estimated along with the system state. We use an off-line version of the interaction soil-biosphere-atmosphere model for the assimilation of screen-level temperature and relati...

With an operational implementation of post-processing at RMI in mind, we study
possible approaches of correcting the ECMWF ensemble forecast for stations in Belgium
using the ensemble hindcast data set. This data set is enlarged each week by eighteen
independent five-member ensemble forecasts using the current IFS system. Therefore, the
hindcasts c...