Effective generation mechanisms of tropical instability waves as represented by high-resolution coupled atmosphere-ocean prediction experiments

Cusp-shaped fluctuations of the sea surface temperature (SST) front in the tropical Pacific, now known as tropical instability waves (TIWs), were discovered by remote sensing in the 1970s. Their discovery was followed by both theoretical and analytical studies, which, along with in situ observations, identified several possible generation mechanisms. Although modeling studies have shown that TIWs strongly influence the heat budget, their influence on local variations of realistically initialized predictions is not yet understood. We here evaluate a series of medium-range (up to ~ 10 days) coupled atmosphere-ocean predictions by a coupled model with different horizontal resolutions. Observational SST, surface wind stress, heat flux, and pressure data showed that representation of temporally and spatially local variations was improved by resolving fine-scale SST variations around the initialized coarse-scale SST front fluctuations of TIWs. Our study thus demonstrates the advantage of using high-resolution coupled models for medium-range predictions. In addition, analysis of TIW energetics showed two dominant sources of energy to anticyclonic eddies: barotropic instability between equatorial zonal currents and baroclinic instability due to intense density fronts. In turn, the eddy circulation strengthened both instabilities in the resolved simulations. This revealed feedback process refines our understanding of the generation mechanisms of TIWs.

Observational Needs for Improving Ocean and Coupled Reanalysis, S2S Prediction, and Decadal Prediction

Developments in observing system technologies and ocean data assimilation (DA) are symbiotic. New observation types lead to new DA methods and new DA methods, such as coupled DA, can change the value of existing observations or indicate where new observations can have greater utility for monitoring and prediction. Practitioners of DA are encouraged to make better use of observations that are already available, for example, taking advantage of strongly coupled DA so that ocean observations can be used to improve atmospheric analyses and vice versa. Ocean reanalyses are useful for the analysis of climate as well as the initialization of operational long-range prediction models. There are many remaining challenges for ocean reanalyses due to biases and abrupt changes in the ocean-observing system throughout its history, the presence of biases and drifts in models, and the simplifying assumptions made in DA solution methods. From a governance point of view, more support is needed to bring the ocean-observing and DA communities together. For prediction applications, there is wide agreement that protocols are needed for rapid communication of ocean-observing data on numerical weather prediction (NWP) timescales. There is potential for new observation types to enhance the observing system by supporting prediction on multiple timescales, ranging from the typical timescale of NWP, covering hours to weeks, out to multiple decades. Better communication between DA and observation communities is encouraged in order to allow operational prediction centers the ability to provide guidance for the design of a sustained and adaptive observing network.

Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System

Numerical weather prediction models are including an increasing number of components of the Earth system. In particular, every forecast now issued by the European Centre for Medium-Range Weather Forecasts (ECMWF) runs with a 3D ocean model and a sea ice model below the atmosphere. Initialisation of different components using different methods and on different timescales can lead to inconsistencies when they are combined in the full system. Historically, the methods for initialising the ocean and the atmosphere have been typically developed separately. This paper describes an approach for combining the existing ocean and atmospheric analyses into what we categorise as a weakly coupled assimilation scheme. Here, we show the performance improvements achieved for the atmosphere by having a weakly coupled ocean–atmosphere assimilation system compared with an uncoupled system. Using numerical weather prediction diagnostics, we show that forecast errors are decreased compared with forecasts initialised from an uncoupled analysis. Further, a detailed investigation into spatial coverage of sea ice concentration in the Baltic Sea shows that a much more realistic structure is obtained by the weakly coupled analysis. By introducing the weakly coupled ocean–atmosphere analysis, the ocean analysis becomes a critical part of the numerical weather prediction system and provides a platform from which to build ever stronger forms of analysis coupling.

Numerical simulations of debris drift from the Great Japan Tsunami of 2011 and their verification with observational reports

A suite of five ocean models is used to simulate the movement of floating debris generated by the Great Japan Tsunami of 2011. This debris was subject to differential wind and wave-induced motion relative to the ambient current (often termed "windage") which is a function of the shape, size, and buoyancy of the individual debris items. Model solutions suggest that during the eastward drift across the North Pacific the debris became "stratified" by the wind so that objects with different windages took different paths: high windage items reached North America in large numbers the first year, medium windage items recirculated southwest toward Hawaii and Asia, and low windage items collected in the Subtropical Gyre, primarily in the so-called "garbage patch" area located northeast of Hawaii and known for high concentrations of microplastics. Numerous boats lost during the tsunami were later observed at sea and/or found on the west coast of North America: these observations are used to determine optimal windage values for scaling the model solutions. The initial number of boats set adrift during the tsunami is estimated at about 1000, while about 100 boats are projected to still float in year 2018 with an e-folding decay of 2 to 8 years.

A new Approach to El Niño Prediction beyond the Spring Season

The enormous societal importance of accurate El Niño forecasts has long been recognized. Nonetheless, our predictive capabilities were once more shown to be inadequate in 2014 when an El Nino event was widely predicted by international climate centers but failed to materialize. This result highlighted the problem of the opaque spring persistence barrier, which severely restricts longer-term, accurate forecasting beyond boreal spring. Here we show that the role played by tropical seasonality in the evolution of the El Niño is changing on pentadal (five-year) to decadal timescales and thus that El Niño predictions beyond boreal spring will inevitably be uncertain if this change is neglected. To address this problem, our new coupled climate simulation incorporates these long-term influences directly and generates accurate hindcasts for the 7 major historical El Niños. The error value between predicted and observed sea surface temperature (SST) in a specific tropical region (5°N–5°S and 170°–120°W) can consequently be reduced by 0.6 Kelvin for one-year predictions. This correction is substantial since an “El Niño” is confirmed when the SST anomaly becomes greater than +0.5 Kelvin. Our 2014 forecast is in line with the observed development of the tropical climate.

Ocean Data Assimilation in Support of Climate Applications: Status and Perspectives

Ocean data assimilation brings together observations with known dynamics encapsulated in a circulation model to describe the time-varying ocean circulation. Its applications are manifold, ranging from marine and ecosystem forecasting to climate prediction and studies of the carbon cycle. Here, we address only climate applications, which range from improving our understanding of ocean circulation to estimating initial or boundary conditions and model parameters for ocean and climate forecasts. Because of differences in underlying methodologies, data assimilation products must be used judiciously and selected according to the specific purpose, as not all related inferences would be equally reliable. Further advances are expected from improved models and methods for estimating and representing error information in data assimilation systems. Ultimately, data assimilation into coupled climate system components is needed to support ocean and climate services. However, maintaining the infrastructure and expertise for sustained data assimilation remains challenging.

Decadal climate prediction (project GCEP)

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.