Decadal predictability in the North Atlantic as seen by EC-Earth3.

<p>We study the decadal predictability in the North Atlantic region using  ensembles of historical and decadal prediction experiments with EC-Earth3  and other CMIP models. In particular, the focus is on the NAO and the sub-polar gyre region. In general the impact of initialization is weak  for lead-times larger than one to two years and we investigate different ways to isolate and estimate the statistical significance of this impact. For the sub-polar gyre region the prediction skill is found to be mainly due to an abrupt change in the late 90ies and models disagree on whether this skill is due to forcing or initial conditions. Also the predictability of the NAO is weak and varies with lead-time and length of the predicted period. We only see weak evidence of the 'signal-to-noise paradox'. The importance of the ensemble size is also studied.                                                              </p>

Decadal Prediction of Marine Heatwaves in MPI-ESM

<p>Marine Heatwaves (MHWs) are Sea Surface Temperature (SST) extremes that can have devastating impacts on marine ecosystems but can also impact circulation patterns in the ocean and the atmosphere. The variability of MHWs has been studied in historical observations and longterm climate projections, but predictability has only been analyzed on seasonal timescales. Here, we we present the first attempt to study the decadal predictability of MHW days per year in an ensemble of decadal hindcasts based on the Max Planck Institute Earth System Model (MPI-ESM-LR).</p><p>Our results show that there are strong regional differences in prediction skill. While many regions show little to no skill, we find in the Subpolar North Atlantic correlation coefficients up to 0.7 for MHW days up to lead year 8. We demonstrate that these correlations mainly arise from correctly predicting the absence of MHWs in individual years. MHW days per year might be successfully predicted by only using yearly mean SST as a proxy, which also demonstrates that in the Subpolar North Atlantic, any increase in SST is accompanied by more MHWs and vice versa.</p>

A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability

Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.

Assessing the role of internal ocean dynamics and external forcings on the decadal-scale predictability in the North Atlantic: a large ensemble analysis

<p>Understanding processes and mechanisms which contribute to decadal climate variability is a crucial step in the development of a reliable prediction system, and as such it constitutes an important segment of the activities carried forward by the EU-funded Horizon 2020 EUCP project.</p><p>Sea surface temperature (SST) variability in the North Atlantic is known to be a key source of decadal predictability for the Euro-Atlantic sector. However, the nature of the observed variability is at the core of a long-standing debate.</p><p>In this work, we investigate the origins of North Atlantic SST variability, focusing on a specific event: the mid-20<sup>th</sup> century (1940-1975) “warm-to-cold” transition. This event is particularly interesting as it represents a well documented decadal-scale fluctuation of the observed climate record and can be used as a suitable test-bed to evaluate the relative skill of initialized versus non-initialized (historical) climate simulations.</p><p>Several mechanisms and processes have been taken into account to explain the cooling in the middle of 20th century, ranging from a slowdown of the Atlantic Meridional Overturning Circulation (AMOC) to an increase in anthropogenic aerosol. Here the 1940-1975 transition is examined firstly in the NCAR Large Ensemble (NCAR-LENS), aiming to further explore the role of the possible drivers. Despite the lack of a realistic model state initialization, the NCAR-LENS shows some skill in capturing the North Atlantic SST transition, suggesting a non-negligible influence of the external forcing. Some lag between observations and model results is found, with the ensemble mean SST leading the onset of the observed transition by about ten years. This is consistent with previous studies, where some evidence was found of the driving role of anthropogenic aerosol and greenhouse gas forcing. In contrast, the simultaneous ocean dynamic response (AMOC) exhibits a large intra-member spread. This finding corroborates the hypothesis of a non-oceanic driver for the decadal-scale SST fluctuation. The same episode is then analysed in the NCAR Decadal Prediction Large Ensemble (NCAR-DPLE), which shares the same model code, configuration details, component resolutions, and external forcing datasets as for the non-initialized LENS ensemble. This allows a rigorous attribution of the relative roles of initialization, (mainly constraining the ocean-driven internal variability) and external forcing conditions on the overall skill in reproducing the Atlantic decadal variability, with clear implications for decadal predictability and predictions.</p><p> </p>