Rôle de la surface marine sur la variabilité intrasaisonnière estivale de l'atmosphère dans la région Nord Atlantique Europe

Le modèle océanique CNRMOM1D est développé pour l'étude du rôle de la surface marine sur la variabilité intrasaisonnière de la circulation atmosphérique de grande échelle. Les résultats suggèrent que les anomalies de températures de surface océanique induites par la circulation atmosphérique en été (juin-août) produisent de facon quasi-systématique une rétroaction négative sur cette même circulation à des échelles de temps de l'ordre de la semaine. D'autre part, cette thèse suggère qu'une représentation réaliste des variations diurnes océaniques peut améliorer la représentation de la variabilité intrasaisonnière des SST et les états moyens océaniques et atmosphériques estivaux.The ocean model CNRMOM1D was developed to study the role of the marine surface on the intraseasonal variability of the large-scale atmospheric circulation. The results suggest that the sea surface temperature anomalies forced by the summer (june-august) atmospheric circulation are able to produce a negative feedback onto this circulation on timescales of about one week. Besides, this thesis suggests that taking into account the ocean diurnal variations can improve the representation of intraseasonal SST variability and the mean summer states of the ocean and atmosphere

Multi-model skill assessment of seasonal temperature and precipitation forecasts over Europe

There is now a wide range of forecasts and observations of seasonal climatic conditions that can be used across a range of application sectors, including hydrological risk forecasting, planning and management. As we rely more on seasonal climate forecasts, it becomes essential to also assess its quality to ensure its intended use. In this study, we provide the most comprehensive assessment of seasonal temperature and precipitation ensemble forecasts of the EUROSIP multi-model forecasting system over Europe. The forecasts from the four individual climate models within the EUROSIP are assessed using both deterministic and probabilistic approaches. One equally and two unequally Weighted Multi-Models (WMMs) are also constructed from the individual models, for both climate variables, and their respective forecasts are also assessed. Consistent with existing literature, we find limited seasonal climate prediction skill over Europe. A simple equally WMM system performs better than both unequally WMM combination systems. However, the equally WMM system does not always outperform the single best model within the EUROSIP multi-model. Based on the results, it is recommended to assess seasonal temperature and precipitation forecast of individual climate models as well as their multi-model mean for a comprehensive overview of the forecast skill.The authors thank Prof. Francisco J. Doblas Reyes, Alicia Sanchez Lorente, Veronica Torralba and Louis-Philippe Caron for discussions and suggestions during the forming of this paper. Thanks to Nicolau Manubens, whose incredible technical support allowed steady implementation of the experiments in this study. Thanks are also due to the anonymous reviewers and their critical reviews. Their comments and suggestions improved the content of this paper. The research leading to these results has received funding from the EU H2020 Framework Programme under grant agreement #641811 (IMPREX).Peer ReviewedPostprint (author's final draft

Regional Arctic sea ice predictability and prediction on seasonal to interannual timescales

The fast depletion of the Arctic sea ice extent observed during the last three decades has awakened concerns about the consequences of such changes at hemispheric scales, and opened socio-economic opportunities such as maritime transport. This PhD project aims at investigating the sources of predictability and prediction skill of Arctic sea ice conditions at the regional scale. The first months have been dedicated to the investigation of the mechanisms behind the development of model systematic errors in seasonal regional predictions

Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state

Decadal prediction exploits sources of predictability from both the internal variability through the initialisation of the climate model from observational estimates, and the external radiative forcings. When a model is initialised with the observed state at the initial time step (Full Field Initialisation—FFI), the forecast run drifts towards the biased model climate. Distinguishing between the climate signal to be predicted and the model drift is a challenging task, because the application of a-posteriori bias correction has the risk of removing part of the variability signal. The anomaly initialisation (AI) technique aims at addressing the drift issue by answering the following question: if the model is allowed to start close to its own attractor (i.e. its biased world), but the phase of the simulated variability is constrained toward the contemporaneous observed one at the initialisation time, does the prediction skill improve? The relative merits of the FFI and AI techniques applied respectively to the ocean component and the ocean and sea ice components simultaneously in the EC-Earth global coupled model are assessed. For both strategies the initialised hindcasts show better skill than historical simulations for the ocean heat content and AMOC along the first two forecast years, for sea ice and PDO along the first forecast year, while for AMO the improvements are statistically significant for the first two forecast years. The AI in the ocean and sea ice components significantly improves the skill of the Arctic sea surface temperature over the FFI.The authors acknowledge funding support for this study from the SPECS (ENV-2012-308378) project funded by the Seventh Framework Programme (FP7) of the European Commission and the PICA-ICE (CGL2012-31987) project funded by the Ministry of Economy and Competitiveness of Spain. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center.Peer ReviewedPostprint (author's final draft

Dynamical prediction of Arctic sea ice modes of variability

This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.The authors acknowledge funding support for this study from the PICA-ICE (CGL2012-31987) Project funded by the Ministry of Economy and Competitiveness of Spain, the SPECS (GA 308378) Project funded by the Seventh Framework Programme (FP7) and the PRIMAVERA (GA 641727) project funded by the Horizon 2020 framework of the European Commission. NSF was a recipient of the Juan de la Cierva-incorporación postdoctoral fellowship from the Ministry of Economy and Competitiveness of Spain. NCJ was supported by NOAA’s Climate Program Office. The authors acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center in Barcelona, Spain, and by the European Centre for Medium–Range Weather Forecasts in Reading, UK. The authors thank Stefan Siegert and an anonymous reviewer for their constructive inputs, and Francois Massonnet, Javier Garcia-Serrano, Omar Bellprat, Louis-Philippe Caron, Matthieu Chevallier, Torben Koening, Mitch Bushuk and Jonathan Day for valuable discussions. Analyzed global sea ice historical reconstruction with ORCA1 NEMO-LIM2 is available upon request.Peer ReviewedPostprint (published version

Origin of the warm eastern tropical Atlantic SST bias in a climate model

The substantial warm sea surface temperature bias in the eastern Tropical Atlantic reported in most CMIP5 climate simulations with various models, in particular along the coast of Namibia and Angola, remains an issue in more recent and CMIP6-ready versions of climate models such as EC-Earth. A complete and original set of experiments with EC-Earth3.1 is performed to investigate the causes and mechanisms responsible for the emergence and persistence of this bias. The fully-developed bias is studied in a historical experiment that has reached quasi-equilibrium, while retrospective prediction experiments are used to highlight the development/growth from an observed initial state. Prediction experiments are performed at both low and high resolution to assess the possible dependence of the bias on horizontal resolution. Standalone experiments with the ocean and the atmosphere components of EC-Earth are also analyzed to separate the respective contributions of the ocean and atmosphere to the development of the bias. EC-Earth3.1 exhibits a bias similar to that reported in most climate models that took part in CMIP5. The magnitude of this bias, however, is weaker than most CMIP5 models by few degrees. Increased horizontal resolution only leads to a minor reduction of the bias in EC-Earth. The warm SST bias is found to be the result of an excessive solar absorption in the ocean mixed layer, which can be linked to the excessive solar insolation due to unrealistically low cloud cover, and the absence of spatial and temporal variability of the biological productivity in the ocean component. The warm SST bias is further linked to deficient turbulent vertical mixing of cold water to the mixed layer. Our study points at a need for better representation of clouds in the vicinity of eastern boundaries in atmosphere models, and better representation of solar penetration and turbulent mixing in the ocean models in order to eliminate the Tropical Atlantic biases.We would like to acknowledge the anonymous reviewer who provided constructive comments that led to a considerable improvement of the manuscript. We would also like to thank Aurore Voldoire and Anna-Lena Deppenmeier for the useful discussion, and Yann Planton for providing the code for implementing the tendency diagnostics in NEMO. This research has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 603521 (PREFACE) and the Horizon 2020 EU program under grand agreements 641727 (PRIMAVERA). We acknowledge RES and ECMWF for awarding access to supercomput- ing facilities in the Barcelona Supercomputing Center in Spain and the ECMWF Supercomputing Center in the UK, through the HiResClim and SPESICCF projects, recpectively. We acknowledge the work of the developers of the s2dverification R-based package (http://cran.r-project. org/web/packages/s2dverification/index.html). The visualization of some of the figures was done with the NCAR Command Language (NCL, Version 6.3.0, 2016, Boulder, Colorado: UCAR/NCAR/CISL/TDD, http://dx.doi.org/10.5065/D6WD3XH5).Peer ReviewedPostprint (author's final draft

Decadal climate prediction with a refined anomaly initialisation approach

In decadal prediction, the objective is to exploit both the sources of predictability from the external radiative forcings and from the internal variability to provide the best possible climate information for the next decade. Predicting the climate system internal variability relies on initialising the climate model from observational estimates. We present a refined method of anomaly initialisation (AI) applied to the ocean and sea ice components of the global climate forecast model EC-Earth, with the following key innovations: (1) the use of a weight applied to the observed anomalies, in order to avoid the risk of introducing anomalies recorded in the observed climate, whose amplitude does not fit in the range of the internal variability generated by the model; (2) the AI of the ocean density, instead of calculating it from the anomaly initialised state of temperature and salinity. An experiment initialised with this refined AI method has been compared with a full field and standard AI experiment. Results show that the use of such refinements enhances the surface temperature skill over part of the North and South Atlantic, part of the South Pacific and the Mediterranean Sea for the first forecast year. However, part of such improvement is lost in the following forecast years. For the tropical Pacific surface temperature, the full field initialised experiment performs the best. The prediction of the Arctic sea-ice volume is improved by the refined AI method for the first three forecast years and the skill of the Atlantic multidecadal oscillation is significantly increased compared to a non-initialised forecast, along the whole forecast time.The authors acknowledge funding support for this study from the SPECS (ENV-2012-308378) Project funded by the Seventh Framework Programme (FP7) of the European Commission and the PICA-ICE (CGL2012-31987) Project funded by the Ministry of Economy and Competitiveness of Spain. E.H. was also funded by the UK Natural Environment Research Council and N.K.N. was funded in part by the UK Natural Environment Research Council. D.V. gratefully acknowledges financial support from the University of Reading. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Red Española de Supercomputación through the Barcelona Supercomputing Center.Peer ReviewedPostprint (author's final draft

Uncertainties of drag coefficient estimates above sea ice from field data

Surface turbulent exchanges play a key role on sea ice dynamics, on ocean and sea ice heat budgets and on the polar atmosphere. Uncertainties in parameterizations of surface turbulent fluxes are mostly held by the transfer coefficients and estimates of those transfer coefficients from field data are required for parameterization development. Measurement errors propagate through the computation of transfer coefficients and contribute to its total error together with the uncertainties in the empirical stability functions used to correct for stability effects. Here we propose a methodology to assess their contributions individually to each coefficient estimate as well as the total drag coefficient uncertainty and we apply this methodology on the example of the SHEBA campaign. We conclude that for most common drag coefficient values (between 1.0×10 -3 and 2.5×10 -3), the relative total uncertainty ranges from 25 and 50%. For stable or unstable conditions with a stability parameter |ζ|>1 on average, the total uncertainty in the neutral drag coefficient exceeds the neutral drag coefficient value itself, while for |ζ|<1 the total uncertainty is around 25% of the drag coefficient. For closer-to-neutral conditions, this uncertainty is dominated by measurement uncertainties in surface turbulent momentum fluxes which should therefore be the target of efforts in uncertainty reduction. We also propose an objective data-screening procedure for field data, which consists of retaining data for which the relative error on neutral drag coefficient does not exceed a given threshold. This method, in addition to the commonly used flux quality control procedure, allows for a reduction of the drag coefficient dispersion compared to other data-screening methods, which we take as an indication of better dataset quality

Projeccions climàtiques i escenaris de futur

Aquest capítol tracta de la projecció dels impactes de climes futurs per a trams vulnerables de la costa catalana. Al començament, s’hi revisa la geodiversitat de la costa en termes meteorològics i geològics. El ventall d’impactes que en resulta (sota climes presents i futurs) presenta uns nivells d’incertesa que s’han de considerar per a poder prendre decisions. L’anàlisi es basa en les projeccions del nivell mitjà del mar i en les característiques de l’onatge per a les famílies d’escenaris RCP (trajectòries de concentracions representatives). La projecció dels impactes d’erosió i inundació per a platges i d’agitació i ultrapassament per a ports permet determinar quin és el domini costaner sotmès a aquests impactes, i també quins seran els nivells de risc que es poden esperar en platges i ports. Les conclusions del capítol s’estructuren com un seguit d’actuacions seqüencials per a afavorir la sostenibilitat de la costa. Aquest «camí d’adaptació» permetrà d’assolir uns nivells de riscs presents i futurs explícits, que han de ser considerats per a les activitats socioeconòmiques de la zona litoral.Peer ReviewedPostprint (published version