ENSO response to changes in the tropical Indian ocean temperature

<p>Since the start of the 21st century, El Niño-Southern Oscillation (ENSO) variability has changed, supporting generally weaker Central Pacific El Niño events. Recent studies suggest that stronger trade winds in the equatorial Pacific could be a key driving force contributing to this shift. One possible mechanism to drive such changes in the mean tropical Pacific climate state is the enhanced warming trends in the tropical Indian Ocean (TIO) relative to the rest of the tropics. TIO warming can affect the Walker circulation in both the Pacific and Atlantic basins by inducing quasi-stationary Kelvin and Rossby wave patterns. Using the latest coupled-model from Insitut Pierre Simon Laplace (IPSL-CM6), ensemble experiments are conducted to investigate the effect of TIO sea surface temperature (SST) on ENSO variability. Applying a weak SST nudging over the TIO region, in four ensemble experiments we change mean Indian ocean SST by -1.4°C, -0.7°C, +0.7°C, and +1.4°C and find that TIO warming changes the magnitude of the mean equatorial Pacific zonal wind stress proportionally to the imposed forcing, with stronger trades winds corresponding to a warmer TIO. Surprisingly, ENSO variability increases in both TIO cooling and warming experiments, relative to the control. While a stronger ENSO for weaker trade winds, associated with TIO cooling, is expected from previous studies, we argue that the ENSO strengthening for stronger trade winds, associated with TIO cooling, is related to the induced changes in ocean stratification. We illustrate this effect by computing different contributions to the Bjerknes stability index. Thus, our results suggest that the tropical Indian ocean temperatures are an important regulator of TIO mean state and ENSO dynamics.</p>

Stability Studies of a Coupled Model to Perturbation of Initial Data

The stability problem is considered in terms of the classical Lyapunov definition. For this, a set of initial conditions is set, consisting of their preliminary calculations, and the spread of the trajectories obtained as a result of numerical simulation is analyzed. This procedure is implemented as a series of ensemble experiments with a joint MPI-ESM model of the Institute of Meteorology M. Planck (Germany). For numerical modeling, a series of different initial values of the characteristic fields was specified and the model was integrated, starting from each of these fields for different time periods. Изучались экстремальные характеристики уровня океана за период 30 лет. The statistical distribution was built, the parameters of this distribution were estimated, and the statistical forecast for 5 years in advance was studied. It is shown that the statistical forecast of the level corresponds to the calculated forecast obtained by the model. The localization of extreme level values was studied and an analysis of these results was carried out. Numerical calculations were performed on the Lomonosov-2 supercomputer of Lomonosov Moscow State University.

Ensemble quantification of short-term predictability of the ocean fine-scale dynamics: a western mediterranean test case at kilometric-scale resolution.

<p>“Predictability” in operational forecasting systems can be viewed as the ability to meet the forecast accuracy that is required for a given application. In the literature, the most usual approach is to assume that predictability is mainly limited by model instability (i.e. the chaotic behaviour of the system), which means assuming that initial and model errors are small. But, in operational systems, initial and model errors cannot usually be assumed small, because of the complexity of the system and because observations and model resources are limited. In this study, we propose  a practical approach to take into account such model and initial condition errors, in the aim to evaluate the predictability of the fine-scale dynamics in a CMEMS-like operational system, based on ensemble experiments with the ocean numerical model NEMO.</p><p>    To do so, we set up a regional model configuration MEDWEST60 with NEMO v3.6,  212 vertical levels and a kilometric-scale horizontal resolution (1/60º). Such a resolution allows to simulate the fine-scale dynamics up to an effective resolution of  ~10 km. The domain covers the Western Mediterranean sea from Gibraltar to Corsica-Sardinia. The configuration includes tides and is forced at the western and eastern boundaries with hourly outputs from a reference simulation on a larger domain, also including tides, and based on the exact same horizontal and vertical grid.</p><p>    The practical approach we follow consists first in performing a set of several  short (~1month) ensemble forecast experiments to study the growth of forecast errors for different levels of  model error and initial condition error. In practice, we need to implement a tunable source of model error in MEDWEST60, that might represent e.g. numerical errors, forcing errors, missing or uncertain physics via stochastic parameterization (in this presentation, we will focus on a first set of ensemble experiments where stochastic perturbations are added on the model vertical grid). It is then used to generate different levels of error on the initial conditions. </p><p>    In a second step, by inverting the dependence between forecast error on the one hand and initial and model error on the other hand, we aim to diagnose the level of initial and model accuracy needed for a given targeted accuracy of the forecasting system. </p><p>Practical questions addressed by such experiments relate to the relative importance of model accuracy vs initial condition accuracy for the  forecast of the finest scales in a CMEMS system. From this we can infer information about (a) predictability - for instance, the time along which a forecast remains meaningful for the fine scales. And information about (b) controllability by the observations, for instance, the minimal time to consider between two passes of a future satellite to be able to follow a given observed fine-scale structure - front, eddy, etc</p>

Navigating Cascades of Uncertainty — As Easy as ABC? Not Quite…

The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.