Data assimilation for initialization of seasonal forecasts

This article reviews the requirements for a data assimilation system from the perspective of initializing seasonal forecasts. It provides a historical perspective of the developments in ocean data assimilation and ocean observing systems. It also discusses the differences between state estimation and initialization, and presents a brief assessment of different initialization strategies. The value of assimilating ocean data to estimate the ocean state and to initialize seasonal forecasts is demonstrated. However, it is also shown that the assumption of unbiased models in conventional data assimilation methods is not suitable for the production of long temporal records of ocean initial states. This is due to the combined effect of model-forcing error and the changing nature of the observing system. Bias correction algorithms are therefore important in the estimation of long records of ocean states. In the equatorial ocean, the delicate balance between the mass and the velocity fields should be preserved in order to maintain realistic circulations. The most common approach for initializing seasonal forecasts is the so-called full uncoupled initialization, which basically consists of producing an ocean reanalysis by assimilating ocean observations into an ocean model driven by atmospheric fluxes. Alternative approaches are the so-called anomaly initialization, which only attempts to initialize the anomalous state without any attempt of correcting mean; the latter is usually conducted in coupled mode, but coupled and anomaly initialization are not synonymous, and there are approaches where the initialization of the full state is done in coupled mode. The relative value of the approaches is system dependent, but as a long-term strategy the full initialization in coupled mode is more promising

Improving seasonal forecasting through tropical ocean bias corrections

Initialisation shock is often discussed in the context of coupled atmosphere-ocean forecasting, but its detection has remained elusive. In this paper, the presence of initialisation shock in seasonal forecasts is clearly identified in the variability of the tropical thermocline. The specific source of shock studied here is the use of a bias correction procedure to account for errors in equatorial wind stress forcing during ocean initialisation. It is shown that the abrupt removal of the bias correction at the beginning of the forecast leads to rapid adjustments in the upper ocean, creating a shock that remains in the system for at least three months. By contrast, gradual removal of the correction term, over 20 days, greatly reduces the initialisation shock. Evidence is presented of substantial increases in sea surface temperature (SST) seasonal forecast skill, at around 3–7 months’ lead time, when the gradual removal approach is used. Gains in skill of up to 0.05, as measured by the anomaly correlation coefficient for SST in the Nino4 region, are found, using a modest hindcast set covering four seasonal start dates. The results show that improvements in coupled initialisation aimed at reducing shocks may considerably benefit seasonal forecasting

Historical reconstruction of the Atlantic Meridional Overturning Circulation from the ECMWF operational ocean reanalysis

A reconstruction of the Atlantic Meridional Overturning Circulation (MOC) for the period 1959–2006 has been derived from the ECMWF operational ocean reanalysis. The reconstruction shows a wide range of time-variability, including a downward trend. At 26N, both the MOC intensity and changes in its vertical structure are in good agreement with previous estimates based on trans-Atlantic surveys. At 50N, the MOC and strength of the subpolar gyre are correlated at interannual time scales, but show opposite secular trends. Heat transport variability is highly correlated with the MOC but shows a smaller trend due to the warming of the upper ocean, which partially compensates for the weakening of the circulation. Results from sensitivity experiments show that although the time-varying upper boundary forcing provides useful MOC information, the sequential assimilation of ocean data further improves the MOC estimation by increasing both the mean and the time variability

The importance of wind and buoyancy forcing for the boundary density variations and the geostrophic component of the AMOC at 26°N

It is widely thought that changes in both the surface buoyancy fluxes and wind stress drive variability in the Atlantic meridional overturning circulation (AMOC), but that they drive variability on different time scales. For example, wind forcing dominates short-term variability through its effects on Ekman currents and coastal upwelling, whereas buoyancy forcing is important for longer time scales (multiannual and decadal). However, the role of the wind forcing on multiannual to decadal time scales is less clear. Here the authors present an analysis of simulations with the Nucleus for European Modelling of the Ocean (NEMO) ocean model with the aim of explaining the important drivers of the zonal density gradient at 26°N, which is directly related to the AMOC. In the experiments, only one of either the wind stress or the buoyancy forcing is allowed to vary in time, whereas the other remains at its seasonally varying climatology. On subannual time scales, variations in the density gradient, and in the AMOC minus Ekman, are driven largely by local wind-forced coastal upwelling at both the western and eastern boundaries. On decadal time scales, buoyancy forcing related to the North Atlantic Oscillation dominates variability in the AMOC. Interestingly, however, it is found that wind forcing also plays a role at longer time scales, primarily impacting the interannual variability through the excitation of Rossby waves in the central Atlantic, which propagate westward to interact with the western boundary, but also by modulating the decadal time-scale response to buoyancy forcing

Origin and impact of initialisation shocks in coupled atmosphere-ocean forecasts

Current methods for initialising coupled atmosphere-ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-range Weather Forecasts coupled system, the magnitude and extent of these so-called initialisation shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialised by separate ocean and atmospheric analyses do exhibit initialisation shocks in lower atmospheric temperature, when compared to forecasts initialised using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialisation on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialisation shocks are found to follow a change in either the atmospheric or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed

Predicting sudden stratospheric warming 2018 and its climate impacts with a multi‐model ensemble

Sudden stratospheric warmings (SSWs) are significant source of enhanced subseasonal predictability, but whether this source is untapped in operational models remains an open question. Here we report on the prediction of the SSW on 12 February 2018, its dynamical precursors, and surface climate impacts by an ensemble of dynamical forecast models. The ensemble forecast from 1 February predicted 3 times increased odds of an SSW compared to climatology, although the lead time for SSW prediction varied among individual models. Errors in the forecast location of a Ural high and underestimated magnitude of upward wave activity flux reduced SSW forecast skill. Although the SSW's downward influence was not well forecasted, the observed northern Eurasia cold anomaly following SSW was predicted, albeit with a weaker magnitude, due to persistent tropospheric anomalies. The ensemble forecast from 8 February predicted the SSW, its subsequent downward influence, and a long‐lasting cold anomaly at the surface

The ECMWF Ocean Analysis System: ORA-S3

International audienceA new operational ocean analysis/reanalysis system (ORA-S3) has been im- plemented at ECMWF. The reanalysis started from 01/01/1959: it is continuosly maintained up to 11 days behind real time and is used to initialize seasonal fore- casts as well as to provide a historical representation of the ocean for climate studies. It has several innovative features, including an on-line bias-correction algorithm, the assimilation of salinity data on temperature surfaces and assimi- lation of altimeter-derived sea level anomalies and global sea level trends. It is designed to reduce spurious climate variability in the resulting ocean reanaly- sis due to the non-stationary nature of the observing system, while still taking advantage of the observation information. In addition to the basic analysis, a real-time analysis is produced (RT-S3). This is needed for monthly forecasts and in future may be needed for shorter- range forecasts. It is initialised from the near-real-time ORA-S3 and run each day from it. The new analysis system is compared with the previous operational version; the equatorial temperature biases are reduced and equatorial currents are im- proved. The impact of assimilation in the ocean state is discussed by diagnosis of the assimilation increment and bias correction terms. The resulting analysis not only improves the fit to the data, but also improves the respresentation of the interannual variability

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

A Multivariate Treatment of Bias for Sequential Data Assimilation: Application to the Tropical Oceans

International audienceThis paper discusses the problems arising from the presence of system bias in ocean data assimilation taking examples from the ECMWF ocean reanalysis used for seasonal forecasting. The examples illustrate how in a biased system, the non-stationary nature of the observing system is a handicap for the reliable representation of climate variability. It is also shown how the bias can be aggravated by the assimilation process, as is the case for the temperature bias in the eastern equatorial Pacific, linked to a spurious vertical circulation generated by the data assimilation. A generalized algorithm for treatment of bias in sequential data assimilation has been implemented. The scheme allows the control variables of the bias to be different from those for the state vector. Experiments were conducted to evaluate the sensitivity of the results to the choice of bias variables. Results highlight the importance of the correct choice of variables for the bias: while correcting the bias in the pressure field reduces the bias in temperature and in the velocity field, the direct correction of the bias in the temperature field reduces the temperature bias, but significantly increases the error in the velocity field. Analysis of the error statistics reveals that the bias term is not constant in time, but exhibits large interannual fluctuations. The bias algorithm has been generalized further to include temporal variations of the bias term. A memory factor is included to allow for the slow variations of the bias, and a prescribed bias term is added to represent errors known a-priori. Several experiments have been conducted to illustrate the sensitivity of the results to the time evolution of the bias

A Drift-Free Decadal Climate Prediction System for the Community Earth System Model

Performance of a newly developed decadal climate prediction system is examined using the low-resolution Community Earth System Model (CESM). To identify key sources of predictability and determine the role of upper and deeper ocean data assimilation, we first conduct a series of perfect model experiments. These experiments reveal the importance of upper ocean temperature and salinity assimilation in reducing sea surface temperature biases. However, to reduce biases in the sea surface height, data assimilation below 300 m in the ocean is necessary, in particular for high-latitude regions. The perfect model experiments clearly emphasize the key role of combined three-dimensional ocean temperature and salinity assimilation in reproducing mean state and model trajectories. Applying this knowledge to the realistic decadal climate prediction system, we conducted an ensemble of ocean assimilation simulations with the fully coupled CESM covering the period 1960–2014. In this system, we assimilate three-dimensional ocean temperature and salinity data into the ocean component of CESM. Instead of assimilating direct observations, we assimilate temperature and salinity anomalies obtained from the ECMWF Ocean Reanalysis version 4 (ORA-S4). Anomalies are calculated relative to the sum of the ORA-S4 climatology and an estimate of the externally forced signal. As a result of applying the balanced ocean conditions to the model, our hindcasts show only very little drift and initialization shocks. This new prediction system exhibits multiyear predictive skills for decadal climate variations of the Atlantic meridional overturning circulation (AMOC) and North Pacific decadal variability