Mihaela Caian

An assessment of a multi-model ensemble of decadal climate predictions

Abstract A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.

Regional Arctic sea ice variations as predictor for winter climate conditions

Seasonal prediction skill of winter mid and high northern latitudes climate from sea ice variations in eight different Arctic regions is analyzed using detrended ERA-interim data and satellite sea ice data for the period 1980–2013. We find significant correlations between ice areas in both September and November and winter sea level pressure, air temperature and precipitation. The prediction skill is improved when using November sea ice conditions as predictor compared to September. This is particularly true for predicting winter NAO-like patterns and blocking situations in the Euro-Atlantic area. We find that sea ice variations in Barents Sea seem to be most important for the sign of the following winter NAO—negative after low ice—but amplitude and extension of the patterns are modulated by Greenland and Labrador Seas ice areas. November ice variability in the Greenland Sea provides the best prediction skill for central and western European temperature and ice variations in the Laptev/East Siberian Seas have the largest impact on the blocking number in the Euro-Atlantic region. Over North America, prediction skill is largest using September ice areas from the Pacific Arctic sector as predictor. Composite analyses of high and low regional autumn ice conditions reveal that the atmospheric response is not entirely linear suggesting changing predictive skill dependent on sign and amplitude of the anomaly. The results confirm the importance of realistic sea ice initial conditions for seasonal forecasts. However, correlations do seldom exceed 0.6 indicating that Arctic sea ice variations can only explain a part of winter climate variations in northern mid and high latitudes.

Vertical structure and physical processes of the Madden-Julian Oscillation: Biases and uncertainties at short range

Abstract An analysis of diabatic heating and moistening processes from 12 to 36 h lead time forecasts from 12 Global Circulation Models are presented as part of the “Vertical structure and physical processes of the Madden‐Julian Oscillation (MJO)” project. A lead time of 12–36 h is chosen to constrain the large‐scale dynamics and thermodynamics to be close to observations while avoiding being too close to the initial spin‐up of the models as they adjust to being driven from the Years of Tropical Convection (YOTC) analysis. A comparison of the vertical velocity and rainfall with the observations and YOTC analysis suggests that the phases of convection associated with the MJO are constrained in most models at this lead time although the rainfall in the suppressed phase is typically overestimated. Although the large‐scale dynamics is reasonably constrained, moistening and heating profiles have large intermodel spread. In particular, there are large spreads in convective heating and moistening at midlevels during the transition to active convection. Radiative heating and cloud parameters have the largest relative spread across models at upper levels during the active phase. A detailed analysis of time step behavior shows that some models show strong intermittency in rainfall and differences in the precipitation and dynamics relationship between models. The wealth of model outputs archived during this project is a very valuable resource for model developers beyond the study of the MJO. In addition, the findings of this study can inform the design of process model experiments, and inform the priorities for field experiments and future observing systems.

CECILIA Regional Climate Simulations for Future Climate: Analysis of Climate Change Signal

Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021–2050 and 2071–2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3°C in the first period and 2 and 5°C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of

Understanding the West African Monsoon from the analysis of diabatic heating distributions as simulated by climate models

Understanding the West African Monsoon from the analysis of diabatic heating distributions as simulated by climate models

Inter‐model comparison of subseasonal tropical variability in aquaplanet experiments: Effect of a warm pool

This study compares the simulation of sub-seasonal tropical variability by a set of six state-of-the-art AGCMs in two experiments in aquaplanet configuration: a zonally-symmetric experiment, and an experiment with a warm pool centered on the equator. In all six models, the presence of the warm pool generates zonal asymmetries in the simulated mean states in the form of a “Gill-type” response, made more complex by feedbacks between moisture, convective heating and circulation. Noticeable differences appear from one model to another. Only half the models simulate mean low-level equatorial westerlies over the warm pool area. The presence of the warm pool can also favor the development of large-scale variability consistent with observed Madden-Julian Oscillation (MJO) characteristics, but this happens only in half the models. Our results do not support the idea that the presence of the warm pool and/or of mean low-level equatorial westerlies are sufficient conditions for MJO-like variability to arise in the models. Comparing spectral characteristics of the simulated Convectively Coupled Equatorial Waves (CCEWs) in the aquaplanet experiments and the corresponding coupled atmosphere-ocean (i.e. CMIP) and atmosphere-only (i.e. AMIP) simulations, we also show that there is more consistency for a given model across its configurations, than for a given configuration across the six models. Overall, our results confirm that the simulation of sub-seasonal variability by given model is significantly influenced by the parameterization of sub-grid physical processes (most-likely cloud processes), both directly and through modulation of the mean state. This article is protected by copyright. All rights reserved.

Erratum to: An interannual link between Arctic sea-ice cover and the North Atlantic Oscillation

An interannual link between Arctic sea-ice cover and the North Atlantic Oscillation (vol 50, pg 423, 2017)