Decadal oscillation provides skillful multiyear predictions of Antarctic sea ice.
Nature communications 14:1 (2023) 8286
Abstract:
Over the satellite era, Antarctic sea ice exhibited an overall long-term increasing trend, contrary to the Arctic reduction under global warming. However, the drastic decline of Antarctic sea ice in 2014-2018 raises questions about its interannual and decadal-scale variabilities, which are poorly understood and predicted. Here, we identify an Antarctic sea ice decadal oscillation, exhibiting a quasi-period of 8-16 years, that is anticorrelated with the Pacific Quasi-Decadal Oscillation (r = -0.90). By combining observations, Coupled Model Intercomparison Project historical simulations, and pacemaker climate model experiments, we find evidence that the synchrony between the sea ice decadal oscillation and Pacific Quasi-Decadal Oscillation is linked to atmospheric poleward-propagating Rossby wave trains excited by heating in the central tropical Pacific. These waves weaken the Amundsen Sea Low, melting sea ice due to enhanced shortwave radiation and warm advection. A Pacific Quasi-Decadal Oscillation-based regression model shows that this tropical-polar teleconnection carries multi-year predictability.Moisture sources for precipitation variability over the Arabian Peninsula
Climate Dynamics Springer Nature 61:9-10 (2023) 4793-4807
Author Correction: The influence of natural variability on extreme monsoons in Pakistan
npj Climate and Atmospheric Science Springer Nature 6:1 (2023) 167
A Bayesian approach to atmospheric circulation regime assignment
Journal of Climate AMS (2023)
Abstract:
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes Theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes Theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time 푡 can be used to inform the prior probabilities at time 푡 +1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
The role of in situ ocean data assimilation in ECMWF subseasonal forecasts of sea‐surface temperature and mixed‐layer depth over the tropical Pacific ocean.
Quarterly Journal of the Royal Meteorological Society 149:757 (2023) 3513-3524
Abstract:
The tropical Pacific plays an important role in modulating the global climate through its prevailing sea-surface temperature spatial structure and dominant climate modes like El Niño–Southern Oscillation (ENSO), the Madden–Julian Oscillation (MJO), and their teleconnections. These modes of variability, including their oceanic anomalies, are considered to provide sources of prediction skill on subseasonal timescales in the Tropics. Therefore, this study aims to examine how assimilating in situ ocean observations influences the initial ocean sea-surface temperature (SST) and mixed-layer depth (MLD) and their subseasonal forecasts. We analyze two subseasonal forecast systems generated with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS), where the ocean states were initialized using two Observing-System Experiment (OSE) reanalyses. We find that the SST differences between forecasts with and without ocean data assimilation grow with time, resulting in a reduced cold-tongue bias when assimilating ocean observations. Two mechanisms related to air–sea coupling are considered to contribute to this growth of SST differences. One is a positive feedback between the zonal SST gradient, pressure gradient, and surface wind. The other is the difference in Ekman suction and mixing at the Equator due to surface wind-speed differences. While the initial mixed-layer depth (MLD) can be improved through ocean data assimilation, this improvement is not maintained in the forecasts. Instead, the MLD in both experiments shoals rapidly at the beginning of the forecast. These results emphasize how initialization and model biases influence air–sea interaction and the accuracy of subseasonal forecasts in the tropical Pacific.