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  • 10. Bellucci, A.
    et al.
    Haarsma, R.
    Gualdi, S.
    Athanasiadis, P. J.
    Caian, Mihaela
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Cassou, C.
    Fernandez, E.
    Germe, A.
    Jungclaus, J.
    Kroeger, J.
    Matei, D.
    Mueller, W.
    Pohlmann, H.
    Salas y Melia, D.
    Sanchez, E.
    Smith, D.
    Terray, L.
    Wyser, Klaus
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Yang, S.
    An assessment of a multi-model ensemble of decadal climate predictions2015Ingår i: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 44, nr 9-10, s. 2787-2806Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    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.

  • 11.
    Koenigk, Torben
    et al.
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Brodeau, Laurent
    Graversen, Rune Grand
    Karlsson, Johannes
    Svensson, Gunilla
    Tjernstrom, Michael
    Willén, Ulrika
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Wyser, Klaus
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Arctic climate change in 21st century CMIP5 simulations with EC-Earth2013Ingår i: Climate Dynamics, ISSN 0930-7575, E-ISSN 1432-0894, Vol. 40, nr 11-12, s. 2719-2743Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Arctic climate change is analyzed in an ensemble of future projection simulations performed with the global coupled climate model EC-Earth2.3. EC-Earth simulates the twentieth century Arctic climate relatively well but the Arctic is about 2 K too cold and the sea ice thickness and extent are overestimated. In the twenty-first century, the results show a continuation and strengthening of the Arctic trends observed over the recent decades, which leads to a dramatically changed Arctic climate, especially in the high emission scenario RCP8.5. The annually averaged Arctic mean near-surface temperature increases by 12 K in RCP8.5, with largest warming in the Barents Sea region. The warming is most pronounced in winter and autumn and in the lower atmosphere. The Arctic winter temperature inversion is reduced in all scenarios and disappears in RCP8.5. The Arctic becomes ice free in September in all RCP8.5 simulations after a rapid reduction event without recovery around year 2060. Taking into account the overestimation of ice in the twentieth century, our model results indicate a likely ice-free Arctic in September around 2040. Sea ice reductions are most pronounced in the Barents Sea in all RCPs, which lead to the most dramatic changes in this region. Here, surface heat fluxes are strongly enhanced and the cloudiness is substantially decreased. The meridional heat flux into the Arctic is reduced in the atmosphere but increases in the ocean. This oceanic increase is dominated by an enhanced heat flux into the Barents Sea, which strongly contributes to the large sea ice reduction and surface-air warming in this region. Increased precipitation and river runoff lead to more freshwater input into the Arctic Ocean. However, most of the additional freshwater is stored in the Arctic Ocean while the total Arctic freshwater export only slightly increases.

  • 12.
    Sheldon, Johnston, Marston
    et al.
    SMHI, Forskningsavdelningen, Atmosfärisk fjärranalys.
    Eliasson, S.
    Eriksson, P.
    Forbes, R. M.
    Wyser, Klaus
    SMHI, Forskningsavdelningen, Klimatforskning - Rossby Centre.
    Zelinka, M. D.
    Diagnosing the average spatio-temporal impact of convective systems - Part 1: A methodology for evaluating climate models2013Ingår i: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 13, nr 23, s. 12043-12058Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An earlier method to determine the mean response of upper-tropospheric water to localised deep convective systems (DC systems) is improved and applied to the EC-Earth climate model. Following Zelinka and Hartmann (2009), several fields related to moist processes and radiation from various satellites are composited with respect to the local maxima in rain rate to determine their spatio-temporal evolution with deep convection in the central Pacific Ocean. Major improvements to the earlier study are the isolation of DC systems in time so as to prevent multiple sampling of the same event, and a revised definition of the mean background state that allows for better characterisation of the DC-system-induced anomalies. The observed DC systems in this study propagate westward at similar to 4 ms(-1). Both the upper-tropospheric relative humidity and the outgoing longwave radiation are substantially perturbed over a broad horizontal extent and for periods > 30 h. The cloud fraction anomaly is fairly constant with height but small maximum can be seen around 200 hPa. The cloud ice water content anomaly is mostly confined to pressures greater than 150 hPa and reaches its maximum around 450 hPa, a few hours after the peak convection. Consistent with the large increase in upper-tropospheric cloud ice water content, albedo increases dramatically and persists about 30 h after peak convection. Applying the compositing technique to EC-Earth allows an assessment of the model representation of DC systems. The model captures the large-scale responses, most notably for outgoing longwave radiation, but there are a number of important differences. DC systems appear to propagate east-ward in the model, suggesting a strong link to Kelvin waves instead of equatorial Rossby waves. The diurnal cycle in the model is more pronounced and appears to trigger new convection further to the west each time. Finally, the modelled ice water content anomaly peaks at pressures greater than 500 hPa and in the upper troposphere between 250 hPa and 500 hPa, there is less ice than the observations and it does not persist as long after peak convection. The modelled upper-tropospheric cloud fraction anomaly, however, is of a comparable magnitude and exhibits a similar longevity as the observations.

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