Reconstructing the recent past ocean variability: Status and perspective

In the last two decades, climate research has benefited from the continuous development of analysis systems dedicated to operational monitoring and forecasting, which opened up the possibility of exploiting the state of the art modeling and data assimilation tools to reconstruct and study the ocean during the past decades. This activity became feasible also thanks to the increasing availability of long time series of high-quality in situ and remotely-sensed observations. Retrospective analyses (or simply reanalyses or ocean syntheses), indeed combine quality controlled reprocessed ocean observations with a state-of-the-art ocean general circulation model (OGCM) using data assimilation methods to estimate the time-varying, three-dimensional state of the ocean. Ocean reanalyses benefit from data assimilation algorithms that are usually inherited from operational oceanography, although they require specific treatment in order to avoid spurious drifts stemming from instrumental or model biases. Unlike observation-only products, ocean reanalyses take advantage of time-varying atmospheric forcing, usually coming from an atmospheric reanalysis, and dynamical and physical balances implied by the OGCM. Here we give an excursus on the availability of global and regional ocean reanalyses, their applications, their strengths and weaknesses, and their future developments foreseen at the present time

The Rapid Warming of the North Atlantic Ocean in the Mid-1990s in an Eddy-Permitting Ocean Reanalysis (1982–2013)

Abstract The rapid warming in the mid-1990s in the North Atlantic Ocean is investigated by means of an eddy-permitting ocean reanalysis. Both the mean state and variability, including the mid-1990s warming event, are well captured by the reanalysis. An ocean heat budget applied to the subpolar gyre (SPG) region (50°–66°N, 60°–10°W) shows that the 1995–99 rapid warming is primarily dictated by changes in the heat transport convergence term while the surface heat fluxes appear to play a minor role. The mean negative temperature increment suggests a warm bias in the model and data assimilation corrects the mean state of the model, but it is not crucial to reconstruct the time variability of the upper-ocean temperature. The decomposition of the heat transport across the southern edge of the SPG into time-mean and time-varying components shows that the SPG warming is mainly associated with both the anomalous advection of mean temperature and the mean advection of temperature anomalies across the 50°N zonal section. The relative contributions of the Atlantic meridional overturning circulation (AMOC) and gyre circulation to the heat transport are also analyzed. It is shown that both the overturning and gyre components are relevant to the mid-1990s warming. In particular, the fast adjustment of the barotropic circulation response to the NAO drives the anomalous transport of mean temperature at the subtropical/subpolar boundary, while the slowly evolving AMOC feeds the large-scale advection of thermal anomalies across 50°N. The persistently positive phase of the NAO during the years prior to the rapid warming likely favored the cross-gyre heat transfer and the following SPG warming

The role of upper-ocean heat content in the regional variability of Arctic sea ice at sub-seasonal timescales

In recent decades, the Arctic Ocean has undergone changes associated with enhanced poleward inflow of Atlantic and Pacific waters and increased heat flux exchange with the atmosphere in seasonally ice-free regions. The associated changes in upper-ocean heat content can alter the exchange of energy at the ocean–ice interface. Yet, the role of ocean heat content in modulating Arctic sea ice variability at sub-seasonal timescales is still poorly documented. We analyze ocean heat transports and surface heat fluxes between 1980–2021 using two eddy-permitting global ocean reanalyses, C-GLORSv5 and ORAS5, to assess the surface energy budget of the Arctic Ocean and its regional seas. We then assess the role of upper-ocean heat content, computed in the surface mixed layer (Qml) and in the 0–300 m layer (Q300), as a sub-seasonal precursor of sea ice variability by means of lag correlations. Our results reveal that in the Pacific Arctic regions, sea ice variability in autumn is linked with Qml anomalies leading by 1 to 3 months, and this relationship has strengthened in the Laptev and East Siberian seas during 2001–2021 relative to 1980–2000, primarily due to reduced surface heat loss since the mid-2000s. Q300 anomalies act as a precursor for wintertime sea ice variability in the Barents and Kara seas, with considerable strengthening and expansion of this link from 1980–2000 and 2001–2021 in both reanalyses. Our results highlight the role played by upper-ocean heat content in modulating the interannual variability of Arctic sea ice at sub-seasonal timescales. Heat stored in the ocean has important implications for the predictability of sea ice, calling for improvements in forecast initialization and a focus upon regional predictions in the Arctic region.</p

ENSO and its effects on the atmospheric heating processes

El Nino-Southern Oscillation (ENSO) is an important air-sea coupled phenomenon that plays a dominant role in the variability of the tropical regions. Observations, atmospheric and oceanic reanalysis datasets are used to classify ENSO and non-ENSO years to investigate the typical features of its periodicity and atmospheric circulation patterns. Among non-ENSO years, we have analyzed a group, called type-II years, with very small SST anomalies in summer that tend to weaken the correlation between ENSO and precipitation in the equatorial regions. A unique character of ENSO is studied in terms of the quasi-biennial periodicity of SST and heat content (HC) fields over the Pacific-Indian Oceans. While the SST tends to have higher biennial frequency along the Equator, the HC maximizes it into two centers in the western Pacific sector. The north-western center, located east of Mindanao, is strongly correlated with SST in the NINO3 region. The classification of El Nino and La Nina years, based on NINO3 SST and north-western Pacific HC respectively, has been used to identify and describe temperature and wind patterns over an extended-ENSO region that includes the tropical Pacific and Indian Oceans. The description of the spatial patterns within the atmospheric ENSO circulation has been extended to tropospheric moisture fields and low-level moisture divergence during November-December-January, differentiating the role of El Nino, when lame amounts of condensational heat are concentrated in the central Pacific, from La Nina that tends to mainly redistribute heat to Maritime Continents and higher latitudes. The influence of the described mechanisms on equatorial convection in the context of the variability of ENSO on longer timescales for the end of the 20th century is questioned. However, the inaccuracy of the atmospheric reanalysis products in terms of precipitation and the shorter time length of more reliable datasets hamper a final conclusion on this issue

Evaluation of Amip-Type Atmospheric Fields as Forcing For Mediterranean Sea and Global Ocean Reanalyses

Oceanic reanalyses are powerful products to reconstruct the historical 3D-state of the ocean and related circulation. At present a challenge is to have oceanic reanalyses covering the whole 20th century. This study describes the exercise of comparing available datasets to force Mediterranean Sea and global oceanic reanalyses from 1901 to present. In particular, we compared available atmospheric reanalyses with a set of experiments performed with an atmospheric general circulation model where sea surface temperature (SST) and sea-ice concen- tration are prescribed. These types of experiments have the advantage of covering long time records, at least for the period for which global SST is available, and they can be performed at relatively high horizontal resolutions, a very important requisite for regional oceanic re- analyses. However, they are limited by the intrinsic model biases in representing the mean atmospheric state and its variability. In this study, we show that, within some limits, the atmospheric model performance in representing the basic variables needed for the bulk-formulae to force oceanic data assimilation systems can be comparable to the differences among available atmospheric reanalyses. In the case of the Mediterranean Sea the high horizontal resolution of the set of SST-prescribed experiments combined with their good performance in rep- resenting the surface winds in the area made them the most appropriate atmospheric forcing. On the other hand, in the case of the global ocean, atmospheric reanalyses have been proven to be still preferable due to the better representation of spatial and temporal variability of surface winds and radiative fluxes. Because of their intrinsic limitations AMIP experiments cannot provide atmospheric fields alterna- tive to atmospheric reanalyses. Nevertheless, here we show how in the specific case of the Mediterranean Sea, they can be of use, if not preferable, to available atmospheric reanalyses

Seasonal forecast skill of upper-ocean heat content in coupled high-resolution systems

AbstractOcean heat content (OHC) anomalies typically persist for several months, making this variable a vital component of seasonal predictability in both the ocean and the atmosphere. However, the ability of seasonal forecasting systems to predict OHC remains largely untested. Here, we present a global assessment of OHC predictability in two state-of-the-art and fully-coupled seasonal forecasting systems. Overall, we find that dynamical systems make skilful seasonal predictions of OHC in the upper 300 m across a range of forecast start times, seasons and dynamical environments. Predictions of OHC are typically as skilful as predictions of sea surface temperature (SST), providing further proof that accurate representation of subsurface heat contributes to accurate surface predictions. We also compare dynamical systems to a simple anomaly persistence model to identify where dynamical systems provide added value over cheaper forecasts; this largely occurs in the equatorial regions and the tropics, and to a greater extent in the latter part of the forecast period. Regions where system performance is inadequate include the sub-polar regions and areas dominated by sharp fronts, which should be the focus of future improvements of climate forecasting systems

Performance and results of the high-resolution biogeochemical model PELAGOS025 v1.0 within NEMO v3.4

Abstract. The present work aims at evaluating the scalability performance of a high-resolution global ocean biogeochemistry model (PELAGOS025) on massive parallel architectures and the benefits in terms of the time-to-solution reduction. PELAGOS025 is an on-line coupling between the Nucleus for the European Modelling of the Ocean (NEMO) physical ocean model and the Biogeochemical Flux Model (BFM) biogeochemical model. Both the models use a parallel domain decomposition along the horizontal dimension. The parallelisation is based on the message passing paradigm. The performance analysis has been done on two parallel architectures, an IBM BlueGene/Q at ALCF (Argonne Leadership Computing Facilities) and an IBM iDataPlex with Sandy Bridge processors at the CMCC (Euro Mediterranean Center on Climate Change). The outcome of the analysis demonstrated that the lack of scalability is due to several factors such as the I/O operations, the memory contention, the load unbalancing due to the memory structure of the BFM component and, for the BlueGene/Q, the absence of a hybrid parallelisation approach

A relocatable ocean modelling platform for downscaling to shelf-coastal areas to support disaster risk reduction

High-impact ocean weather events and climate extremes can have devastating effects on coastal zones and small islands. Marine Disaster Risk Reduction (DRR) is a systematic approach to such events, through which the risk of disaster can be identified, assessed and reduced. This can be done by improving ocean and atmosphere prediction models, data assimilation for better initial conditions and developing an efficient and sustainable impact forecasting methodology for Early Warnings Systems. A common user request during disaster remediation actions is for high-resolution information, which can be derived from easily deployable numerical models nested into operational larger-scale ocean models. The Structured and Unstructured Relocatable Ocean Model for Forecasting (SURF) enables users to rapidly deploy a nested high-resolution numerical model into larger-scale ocean forecasts. Rapidly downscaling the currents, sea level, temperature, and salinity fields is critical in supporting emergency responses to extreme events and natural hazards in the world’s oceans. The most important requirement in a relocatable model is to ensure that the interpolation of low-resolution ocean model fields (analyses and reanalyses) and atmospheric forcing is tested for different model domains. The provision of continuous ocean circulation forecasts through the Copernicus Marine Environment Monitoring Service (CMEMS) enables this testing. High-resolution SURF ocean circulation forecasts can be provided to specific application models such as oil spill fate and transport models, search and rescue trajectory models, and ship routing models requiring knowledge of meteooceanographic conditions. SURF was used to downscale CMEMS circulation analyses in four world ocean regions, and the high-resolution currents it can simulate for specific applications are examined. The SURF downscaled circulation fields show that the marine current resolutions affect the quality of the application models to be used for assessing disaster risks, particularly near coastal areas where the coastline geometry must be resolved through a numerical grid, and high-frequency coastal currents must be accurately simulated

Drivers and impact of the seasonal variability of the organic carbon offshore transport in the Canary upwelling system

The Canary upwelling system (CanUS) is a productive coastal region characterized by strong seasonality and an intense offshore transport of organic carbon (Corg) to the adjacent oligotrophic offshore waters. There, the respiration of this Corg substantially modifies net community production (NCP). While this transport and the resulting coupling of the biogeochemistry between the coastal and open ocean has been well studied in the annual mean, the temporal variability, and especially its seasonality, has not yet been investigated. Here, we determine the seasonal variability of the offshore transport of Corg, its mesoscale component, latitudinal differences, and the underlying physical and biological drivers. To this end, we employ the Regional Ocean Modeling System (ROMS) coupled to a nutrient–phytoplankton–zooplankton–detritus (NPZD) ecosystem model. Our results reveal the importance of the mesoscale fluxes and of the upwelling processes (coastal upwelling and Ekman pumping) in modulating the seasonal variation of the offshore Corg transport. We find that the region surrounding Cape Blanc (21∘ N) hosts the most intense Corg offshore flux in every season, linked to the persistent, and far reaching Cape Blanc filament and its interaction with the Cape Verde Front. Coastal upwelling filaments dominate the seasonality of the total offshore flux up to 100 km from the coast, contributing in every season at least 80 % to the total flux. The seasonality of the upwelling modulates the offshore Corg seasonality hundreds of kilometers from the CanUS coast via lateral redistribution of nearshore production. North of 24.5∘ N, the sharp summer–fall peak of coastal upwelling results in an export of more than 30 % of the coastal Corg at 100 km offshore due to a combination of intensified nearshore production and offshore fluxes. To the south, the less pronounced upwelling seasonality regulates an overall larger but farther-reaching and less seasonally varying lateral flux, which exports between 60 % and 90 % of the coastal production more than 100 km offshore. Overall, we show that the temporal variability of nearshore processes modulates the variability of Corg and NCP hundreds of kilometers offshore from the CanUS coast via the offshore transport of the nearshore production

Skill assessment of ECV/EOV from seasonal forecast

Assess the seasonal forecast skill of selected ocean variables - SST, OHC300m, and SSH - from the ensemble of ECMWF and CMCC seasonal forecasts systems contributing to C3