The Climate Prediction group aims at developing regional and global climate prediction capability for time scales ranging from a few weeks to a few decades into the future (sub-seasonal to decadal climate prediction). This objective relies on expanding our understanding of the climate processes through a deep analysis of the strengths and weaknesses of state-of-the-art climate forecast systems in comparison with the most up-to-date observational datasets, and on exploiting these detailed analyses to refine the representation of climate processes in our climate forecast systems and as well as their initialization. Although our primary tool is the EC-Earth European climate model (http://www.ec-earth.org/), we also make frequent use of large multi-model databases made available in the context of cooperative international projects (CMIP, SPECS, NMME …) for process analysis. To achieve our objectives, we rely on a wide variety of expertise, both in terms of on climate processes and regions within our group: from the stratosphere down to the deep ocean and from tropical to polar latitudes, as well as on expertise on climate modelling and data assimilation. We have contributed in the past and plan to continue contributing to near-operational climate prediction exercises: on decadal (http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/decadal-fc) and on seasonal (http://www.arcus.org/search-program/seaiceoutlook) time scales.
* Inclusion and/or testing in EC-Earth of new model components (biogeochemistry, vegetation, aerosols, new alternative sea ice model) or sub-grid cell parameterizations (ocean mixing, sea ice and snow cover surface scheme for albedo, soil hydrological cycling …) developed by other research centers to account for additional potential predictability sources.
* Tuning of sub-grid cell parameterisations (sea ice albedo, accretion and strength, ocean mixing and diffusion …) in EC-Earth to reduce the climate prediction drift and improve the prediction skill.
* Generation of ground-breaking high resolution climate predictions and assessment of the added-value from such a resolution increase (from 0.25 down to 0.08 degrees for the ocean, 40 to 25 km for the atmosphere) in terms of prediction drift and prediction skill.
* Use of a large set of ocean and atmospheric reanalyses (physical combination of observational data and model outputs) produced by other research centers and generation of in-house ensemble sea ice reconstructions to obtain a large variety of initial conditions (IC) for climate predictions to be compared in their merits and limitations.
* Comparison of the performance of various initialisation techniques (full-field and different variants of anomaly initialisation).
* Benefit assessment of weakly coupled observational data assimilation (through nudging and Ensemble Kalman Filter) in obtaining initial climate conditions.
* Assessment of the state-of-the-art climate forecast performance from a large set of multi-model databases using multifaceted forecast quality assessment in the presence of climate drift for a large range of variables (continental temperatures and precipitation, tropical cyclones, weather regimes, climate variability modes, heat waves, ocean circulation, sea ice conditions …) to inform the Earth Sciences service group.
* Production of sensitivity experiments with EC-Earth (to new model components, different sources of initial conditions, radiative forcings, resolution, HPC plaform) to highlight sources of skill (land surface, soil moisture and sea ice initialization, volcanic, natural and anthropogenic aerosols, snow cover, ocean heat contents and transports …) and estimation of the contribution of these sources to the prediction skill.
* Detailed analysis of successful climate predictions from multi-model databases for attribution purposes (extreme events including droughts and heat waves, recent global warming hiatus, Arctic summer sea ice lows and Antarctic winter sea ice highs) and sensitivity experiments with EC-Earth to understand the physical mechanisms explaining these events.
* Investigation of the mechanisms leading to climate prediction drift (through heat, salt, momentum, energy budgets …) and the relations between the drift and the prediction skill (through a statistical analysis).
* Investigation of the causes which can explain the failure of Earth systems to reproduce particular climate events in forecasts.
* Feedbacks toward climate forecast system development: information about the sources of model errors and suggestions of ways to improve the model quality.
Interests: Seasonal prediction and attribution of extreme climate events
Interests: Verification of prediction systems, model and observational uncertainty
Interests: Predictability of tropical cyclones at seasonal and multi-annual timescale
Interests: tropical cyclones, hurricanes, Atlantic variability and predictability
Interests: climate extremes, climate variability
Interests: Understanding driving mechanisms and predictability of climate extremes
Interests: Sea ice, seasonal and interannual forecast.
Master Thesis: http://digibug.ugr.es/handle/10481/39110#.VnKkvzIgcWM
Juan de la Cierva-incorporación postdoctoral fellow
Interests: climate dynamics and prediction, sea ice, ocean role in climate, intraseasonal-to-decadal predictions, statistical methods and bias correction
Head of the climate prediction group
Ramon y Cajal Fellow (Highly competitive national grant : 2% success rate)
Interests: sea ice predictability, initialization, prediction, impact on the northern hemisphere climate, ocean predictability, global warming slowdown
Interests: Seasonal forecasting, Tropical Variability, soil moisture, extreme events, heatwave, high resolution
Interests: Ocean modeller, seasonal forecast.
Interests: Initialization of decadal predictions.
PhD thesis: https://www.dropbox.com/s/ivh0qpdasang1rf/Thesis.pdf?dl=0
Interests: initialization and data assimilation, sea ice, ocean tides and mixing
Interests: Aerosols and snow cover in climate forecasts
Interests: Sea ice, data assimilation, polar prediction
Thesis (provisional) title: ENSO influence on the North Atlantic-European winter: mechanisms and implications for predictability
Interests: food, sci-fi, koalas
Marie Curie Post-Doc
Focuses: Climate variability and predictability (mainly at interannual to multi-decadal timescales), large scale ocean and atmosphere dynamic processes, ocean-atmopshere interactions.
Interests: Ocean biogeochemistry
“La Caixa Junior Leader” Post-Doc
Interests: ocean biogeochemistry, carbon cycle, plankton, sulfur cycle, biogenic aerosols, light-driven biogeochemistry, vertical mixing
Interests: decadal climate prediction, forecast quality assessment, cryosphere-climate interactions (glaciers, snow cover), subantarctic and Antarctica
Juan de la Cierva Post Doc
Interests: cryosphere-climate interactions, atmospheric dynamics, extreme events, sub-seasonal to seasonal prediction, climate model initialization, aerosol effects on climate.
Marie-Curie Post Doc
Interests: atmospheric dynamics, Rossby waves (sources and propagation), extreme events, sub-seasonal to seasonal prediction, tropospheric jet variability, troposphere-stratosphere interactions
Interests: marine biogeochemistry, ocean meso- and submeso-scale dynamics, plankton, biogeochemical modelling, iron, carbon cycle, Southern Ocean
Interests: carbon cycle, vegetation & wildfire modelling, climate change, climate prediction, generation of initial conditions
Interests: Inter-annual variability in the tropics, particularly ENSO and its diversity, Low-frequency variability of climatic modes, Air-sea interactions, Impact of climate change on large-scale dynamics
Interests: Climate prediction, Climate Services, Weather regimes, Software development, Artificial Intelligence, Numerical weather prediction.
CV in Portuguese
Interests: climate predictability, forecast verification, multidisciplinary research