Oceanic Rossby waves drive inter-annual predictability of net primary production in the central tropical Pacific

In the Pacific Ocean, off-equatorial Rossby waves, initiated by atmosphere-ocean interaction, modulate the inter-annual variability of the thermocline. In this study, we explore the resulting potential gain in predictability of central tropical Pacific primary production, which in this region strongly depends on the supply of macronutrients from below the thermocline. We use a decadal prediction system based on the Max Planck Institute Earth system model (MPI-ESM) to demonstrate that for the time period 1998-2014 properly initialized Rossby waves explain an increase in predictability of net primary productivity in the off-equatorial central tropical Pacific. We show that, for up to 5 years in advance, predictability of net primary productivity derived from the decadal prediction system is significantly larger than that derived from persistence alone, or an uninitialized historical simulation. The predicted signal can be explained by the following mechanism: off-equatorial Rossby waves are initiated in the eastern Pacific and travel towards the central tropical Pacific on a time scale of 2 to 6 years. On their arrival the Rossby waves modify the depths of both thermocline and nutricline, which is fundamental to the availability of nutrients in the euphotic layer. Local upwelling transports nutrients from below the nutricline into the euphotic zone, effectively transferring the Rossby wave signal to the near-surface ocean. While we show that skillful prediction of central off-equatorial tropical Pacific net primary productivity is possible, we open the door for establishing predictive systems for food web and ecosystem services in that region.

Influence of the NAO on Wintertime Surface Air Temperature over East Asia: Multidecadal Variability and Decadal Prediction

In this paper, we investigate the influence of the winter NAO on the multidecadal variability of winter East Asian surface air temperature (EASAT) and EASAT decadal prediction. The observational analysis shows that the winter EASAT and East Asian minimum SAT (EAmSAT) display strong in-phase fluctuations and a significant 60–80-year multidecadal variability, apart from a long-term warming trend. The winter EASAT experienced a decreasing trend in the last two decades, which is consistent with the occurrence of extremely cold events in East Asia winters in recent years. The winter NAO leads the detrended winter EASAT by 12–18 years with the greatest significant positive correlation at the lead time of 15 years. Further analysis shows that ENSO may affect winter EASAT interannual variability, but does not affect the robust lead relationship between the winter NAO and EASAT. We present the coupled oceanic-atmospheric bridge (COAB) mechanism of the NAO influences on winter EASAT multidecadal variability through its accumulated delayed effect of ∼15 years on the Atlantic Multidecadal Oscillation (AMO) and Africa-Asia multidecadal teleconnection (AAMT) pattern. An NAO-based linear model for predicting winter decadal EASAT is constructed on the principle of the COAB mechanism, with good hindcast performance. The winter EASAT for 2020–34 is predicted to keep on fluctuating downward until ∼2025, implying a high probability of occurrence of extremely cold events in coming winters in East Asia, followed by a sudden turn towards sharp warming. The predicted 2020/21 winter EASAT is almost the same as the 2019/20 winter.

Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.

A Preliminary Attempt On Decadal Prediction of The East Asian Summer Monsoon

The East Asian summer monsoon (EASM) is one of the major synoptic systems that affect the summer climate in China. The anomaly of the EASM is closely related to the occurrence of droughts and floods in China. Decadal prediction of the EASM is of great significance, yet few attempts have been made by far. This study represents a preliminary attempt that uses the decadal increment (DI) method to predict the decadal variability of the EASM. The 3-year increment of the decadal variability is used as the predictand, and predictors are selected from the previous circulation and external forcing. The predicted increment is combined with the observation three years ago to get the final prediction result. The results of cross validation and independent hindcast show that the decadal increment method can well predict decadal variability of the EASM during the recent century. In particular, the decadal regime shifts of the EASM are accurately captured. The decadal variability of the EASM in 2021 is further predicted with two previous predictors of the leading 4-year summer DI of the South Indian Ocean and the DI of the East Siberian Sea sea ice cover. The real-time prediction results show that the chance for the occurrence of strong decadal EASM would be rare in 2021 and 2022. The method developed in the present study provides a new approach for decadal prediction of the EASM.

Skillful decadal prediction of unforced southern European summer temperature variations

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970-2014. Using a multi-model ensemble average from 8 decadal prediction systems, we show that European summer temperatures are highly predictable for up to 10 years in CMIP6. Much of this predictive capability, or skill, is related to the externally forced response. Prediction skill for the unforced signal of European summer temperature is low. A link between unforced Southern European summer temperature and preceding spring Eastern North Atlantic - Mediterranean sea surface temperature (SST) observed during the period 1900-1969 motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature. Our model significantly increases decadal prediction skill of unforced European summer temperature variations: Unlike purely dynamical predictions, the dynamical-statistical model shows significant prediction skill for unforced Southern European summer temperature 2-9 years ahead. Our results highlight that dynamical-statistical models can serve to benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.

Predicting Climate Change over the multi-annual range: a perspective from CMCC Decadal Prediction System

<p>After the early pioneering studies during the 2000s, and the first coordinated multi-model effort within the framework of the 5th Coupled Model Inter-comparison Project (CMIP5) in early 2010s, decadal climate predictions are now entering a more mature phase of their historical development. Near-term climate prediction activities have been recently endorsed by the World Climate Research Programme (WCRP) as one of the Grand Challenges in climate science research, and the Lead Centre for Annual-to-Decadal Climate Prediction, collecting hindcasts and forecasts from several contributing centres worldwide has been established by the WMO.</p><p>Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 10-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2019, is performed using a full-field initialization strategy.</p><p>The predictive skill for key quantities is assessed and compared with a non-initialized historical simulation, so as to verify the added value of initialization. In particular, the CMCC DPS is capable to skilfully reproduce past-climate surface temperature over the North Atlantic ocean, the Indian ocean and the Western Pacific ocean, as well as over most part of the continents. Beyond the contribution of the climate change, predictive skill emerges, among other regions, for the subpolar North Atlantic sea-surface temperatures, resembling the imprint of the extra-tropical part of the Atlantic Multidecadal Variability.</p><p>In terms of precipitation, CMCC DPS is able to capture most of the decadal variability over the Northern part of the Eurasian continent. Indeed, a set of regional diagnostics is aimed to investigate the process at stake behind this high predictive skill.</p>