Papers by Stéphane Vannitsem
Earth System Science Data
We explore a methodology to statistically downscale snowfall-the primary driver of surface mass b... more 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 dependencies at monthly and interannual time scales in the Climate system: Study of the North Pacific and Atlantic regions
<p>The directional dependencies of different climate indices are explored u... more <p>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, the North Atlantic Oscillation, the North Pacific America pattern, the Arctic Oscillation, the Pacifid Decadal Oscillation, the Tropical North Atlantic index), together with three local time series located in Western Europe (Belgium), are selected. The analysis is performed on time scales from a month to 5 years by using a sliding window as filtering procedure.</p><p>A few key new results on the remote influence emerge: (i) The Arctic Oscillation plays a key role at short time (monthly) scales on the dynamics of the North Pacific and North Atlantic; (ii) the North Atlantic Oscillation is playing a global role at long time scales (several years); (iii) the Pacific Decadal Oscillation is indeed slaved to other influences; (iv) the local observables over Western Europe influence the variability on the ocean basins on long time scales. These results further illustrate the power of the Liang-Kleeman information flow in disentangling the dynamical dependencies.</p>
Zonally periodic ocean version of MAOOAM, parameter set S1: n= 1.7 and C = 0.01
66-year run of the channel-ocean version of the MAOOAM model with 40 variables, and parameters h ... more 66-year run of the channel-ocean version of the MAOOAM model with 40 variables, and parameters h = 1000 m, n= 1.7, and C = 0.01 kg / m^2 /s. Shown are the geopotential height difference between two locations in the spatial domain, a three-dimensional projection of the attractor of the system for the variables (ψa,1, ψo,1 To,1), and four panels representing the solution in the spatial domain. The spatial view is centered at the South pole.
A comparison of ensemble post-processing approaches that preserve correlation structures
Covariant Lyapunov Vectors in a Coupled Atmosphere-Ocean Model - Multiscale Effects and Geometric Degeneracy
Molecular Dynamics Simulations of Instabilities
info:eu-repo/semantics/publishe
Adaptive correction of ensemble forecasts
Journal of Advances in Modeling Earth Systems, 2021
• Two PBAs coexist in the model's midlatitudes for both periodic and chaotic ENSO forcing. • Thes... more • Two PBAs coexist in the model's midlatitudes for both periodic and chaotic ENSO forcing. • These local PBAs are nonlinearly unstable, with some trajectories that visit both of them. • The ENSO forcing synchronizes the midlatitude behavior in unexpected ways.
Bulletin of the American Meteorological Society, 2021
Statistical postprocessing techniques are nowadays key components of the forecasting suites in ma... more 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 in 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 toward running ensemble numerical weather prediction (NWP) systems at the kilometer scale that prod...
The surface mass balance (SMB) over the Antarctic Ice Sheet displays large temporal and spatial v... more 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, http://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. 1 Introduction In the context of the global climate warming, polar ice sheets have increasingly gained attention, due to the threat of a massive sea level rise at the global scale (Garbe et al. , 2020). While the Greenland Ice sheet is eroding at an increasing speed both from the base and the surface (Lenaerts et al.
Uncertain Forecasts From Deterministic Dynamics
Statistical Postprocessing of Ensemble Forecasts, 2018
Abstract Uncertainty in dynamical weather forecasts derives from sensitivity to initial-condition... more Abstract Uncertainty in dynamical weather forecasts derives from sensitivity to initial-condition uncertainty, and structural shortcomings of dynamical models. This chapter provides a brief and selective historical review of the development of the understanding of dynamical chaos in the atmosphere and models of it, early attempts to deal with the effects of initial-condition sensitivity leading to modern ensemble forecasting, and statistical postprocessing of both conventional single-integration dynamical forecasts and of ensemble forecasts.
Statistical post-processing of ensemble weather forecasts has become an essential step in the for... more 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.
Atmosphere and ocean dynamics display many complex features and are characterized by a wide varie... more 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 and multivariate generalized fractal dimensions. With these two complementary 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 timescales. 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 the statistics of the phase-space density of points for different regions, which is 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.
Nonlinear Processes in Geophysics Discussions, 2019
Seasonal predictions from climate models are increasingly invoked in various sectors like water m... more 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 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. 1 Introduction Information about the climate during the next seasons is useful in sectors like health, energy, transport, agriculture and water management. In the context of the latter, a dynamical approach consists in initializing a hydrological model with the current hydrological conditions and forcing this model with calibrated and downscaled seasonal forecasts of atmospheric variables 1
Quarterly Journal of the Royal Meteorological Society, 2019
The predictability of the atmosphere at short and long time scales, associated with the coupling ... more 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), based on a 2-layer quasi-geostrophic atmosphere and a 1-layer reduced-gravity quasi-geostrophic ocean. This version features a new ocean basin geometry with periodic boundary conditions in the zonal direction. The analysis presented in this paper considers a low-order version of the model with 40 dynamical variables. First the increase of surface friction (and the associated heat flux) with the ocean can either induce chaos when the aspect ratio between the meridional and zonal directions of the domain of integration is small, or suppress chaos when it is large. This reflects the potentially counter-intuitive role that the ocean can play in the coupled dynamics. Second, and perhaps more importantly, the emergence of long-term predictability within the atmosphere for specific values of the friction coefficient occurs through intermittent excursions in the vicinity of a (long-period) unstable periodic solution. Once close to this solution the system is predictable for long times, i.e. a few years. The intermittent transition close to this orbit is, however, erratic and probably hard to predict. This new route to long-term predictability contrasts with the one found in the closed oceanbasin low-order version of MAOOAM, in which the chaotic solution is permanently wandering in the vicinity of an unstable periodic orbit for specific values of the friction coefficient. The model solution is thus at any time influenced by the unstable periodic orbit and inherits from its long-term predictability.
Climate Services, 2018
The CORDEX.be project created the foundations for Belgian climate services by producing high-reso... more 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") project. The key practical tasks for the project were the coordination of activities among different Belgian climate groups, fostering the links to specific international initiatives and the creation of a stakeholder dialogue. Scientifically, the CORDEX.be project contributed to the EURO-CORDEX project, created a small ensemble of High-Resolution (H-Res) future projections over Belgium at convection-permitting resolutions and coupled these to seven Local Impact Models. Several impact studies have been carried out. The project also addressed some aspects of climate change uncertainties. The interactions and feedback from the stakeholder dialogue led to different practical applications at the Belgian national level. Practical Implications The signing of the Paris Agreement requires the engagement of nations worldwide to limit the global temperature rise well below 2°C. On different levels, initiatives are undertaken to assess the impact of climate change and to meet the associated societal challenges. These range from the continental level (e.g. European Environmental Agency (EEA, 2017), EU strategy on climate change, Climate-ADAPT) down to the urban level (Covenant of Mayors). 1 In order to achieve the national adaptation goals, the Belgian Adapation Plan 2017-2020 (www.climat.be) specifies as a first step the production of high-resolution climate scenarios for Belgium. In March 2015 the Belgian project CORDEX.be started, funded by the Belgian Science Policy Office (BELSPO). This initiative aims to gather existing and ongoing Belgian research activities in the domain of climate modeling. In essence, CORDEX.be is a platform
Nonlinear Processes in Geophysics Discussions, 2019
Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties... more 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.
Earth System Dynamics, 2018
The causal dependences (in a dynamical sense) between the dynamics of three different coupled oce... more 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 (CCM) developed by Sugihara et al. (2012) that allows for evaluating the dependences between variables beyond the classical teleconnection patterns based on correlations. The use of CCM on these data mostly reveals that (i) the tropical Pacific (Nino3.4 region) only influences the dynamics of the North Atlantic region through its annual climatological cycle; (ii) the atmosphere over the North Pacific is dynamically forcing the North Atlantic on a monthly basis; (iii) on longer timescales (interannual), the dynamics of the North Pacific and the North Atlantic are influencing each other through the ocean dynamics, suggesting a connection through the thermohaline circulation. These findings shed a new light on the coupling between these three different regions of the globe. In particular, they call for a deep reassessment of the way teleconnections are interpreted and for a more rigorous way to evaluate dynamical dependences between the different components of the climate system.
Geoscientific Model Development Discussions, 2016
This paper describes a reduced-order quasi-geostrophic coupled ocean-atmosphere model that allows... more 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 "Modular Arbitrary-Order Ocean-Atmosphere Model" (MAOOAM), we analyse the dependence of the model dynamics on the truncation level of the spectral expansion, and unveil spurious behaviour that may exist at low resolution by a comparison with the higher resolution versions. In particular, we assess the robustness of the coupled low-frequency variability when the number of modes is increased. An "optimal" version is proposed for which the ocean resolution is sufficiently high while the total number of modes is small enough to allow for a tractable and extensive analysis of the dynamics.
Monthly Weather Review, 2016
The ensemble spread is often used as a measure of forecast quality or uncertainty. However, it is... more The ensemble spread is often used as a measure of 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 relationship is therefore strongly heteroscedastic. This implies that the forecast of the uncertainty based only on the knowledge of spread should itself be probabilistic. Simple probabilistic models for the prediction of the error as a function of the spread are introduced and evaluated for different spread–error metrics. These forecasts can be verified using probabilistic scores and a methodology is proposed to determine what the impact is of estimating uncertainty based on the spread only. A new method is also proposed to verify whether the flow-dependent spread is a realistic indicator of uncertainty. This method cancels the heteroscedastic...
Uploads
Papers by Stéphane Vannitsem