Ocean Initialization for Seasonal Forecasts BALMASEDA, MAGDALENA A.; ALVES, OSCAR J.; ARRIBAS, ALBERTO ...
Oceanography (Washington, D.C.),
09/2009, Letnik:
22, Številka:
3
Journal Article
Recenzirano
Odprti dostop
Several operational centers routinely issue seasonal forecasts of Earth's climate using coupled ocean-atmosphere models, which require near-real-time knowledge of the state of the global ocean. This ...paper reviews existing ocean analysis efforts aimed at initializing seasonal forecasts. We show that ocean data assimilation improves the skill of seasonal forecasts in many cases, although its impact can be overshadowed by errors in the coupled models. The current practice, known as "uncoupled" initialization, has the advantage of better knowledge of atmospheric forcing fluxes, but it has the shortcoming of potential initialization shock. In recent years, the idea of obtaining truly "coupled" initialization, where the different components of the coupled system are well balanced, has stimulated several research activities that will be reviewed in light of their application to seasonal forecasts.
Coupled processes and associated subsurface dynamics near the eastern edge of the Indo/western Pacific (WP) Warm Pool are important for air‐sea interactions involved in tropical Pacific dynamics. We ...seek to shed light on the physical mechanisms governing air‐sea interactions in the region and the impacts of their biases in models. In this study, we use the Ocean ReAnalysis System 5 (ORAS5) to identify mean‐state biases in the National Center for Atmospheric Research Community Earth System Model version 2 (CESM2) with a particular focus on upper ocean properties and air‐sea interaction processes. We show that the CESM2 has warm and fresh surface biases in the tropical Pacific Ocean, a barrier layer that is too thin in the WP, and an isothermal layer depth (ILD) that is too deep in the eastern Pacific (EP). These biases impact air‐sea interaction processes involved in El Niño development. We compare the strong El Niño events in ORAS5 and CESM2 and show that biases in barrier layer thickness in the WP and in ILD in the EP are significant before the onset of the El Niño events. These biases then influence vertical mixing and entrainment processes, resulting in mixed layer cooling biases. Biases in the sea surface temperature seasonal cycle in the CESM2 also influence the development of the El Niño. We emphasize how the El Niño progression in models can be influenced by its sensitivity to the mean state biases in both subsurface ocean structure and seasonal cycle through local as well as the large‐scale physical processes.
Plain Language Summary
The western tropical Pacific has warm ocean surface temperatures that extend to over 100 m depth due to the prevailing trade winds. In contrast, the eastern tropical Pacific has cold ocean surface temperatures due to the upwelling of cold water from below. Changes to the trade winds can lead to changes in these temperatures and the whole ocean‐atmosphere coupled system. We shed light on the important physical processes that govern the ocean‐atmosphere interactions in the western tropical Pacific and how models have limitations in representing some of the processes, which potentially leads to errors in the simulation of important climate events such as El Niño. We show that the Community Earth System Model, a widely used global climate model, has warmer and fresher ocean surface waters compared to observations in this region which then influences the ocean‐atmosphere interactions adversely in this region. We also show that the so‐called “barrier” layer, which restricts the cold, deep water from reaching the surface when mixing occurs, is too thin in the climate model, which significantly hinders the model from reproducing the observed evolution of large El Niño events.
Key Points
Coupled processes and subsurface dynamics near the eastern edge of the western Pacific (WP) Warm Pool are important for air‐sea interactions
The CESM2 has warm and fresh surface biases in the tropical Pacific Ocean, a too thin barrier layer bias in WP
Barrier layer thickness biases in the west Pacific and isothermal layer depth biases in east influence the development of El Niño
Seasonal forecasts are subject to various types of errors: amplification of errors in oceanic initial conditions, errors due to the unpredictable nature of the synoptic atmospheric variability, and ...coupled model error. Ensemble forecasting is usually used in an attempt to sample some or all of these various sources of error. How to build an ensemble forecasting system in the seasonal range remains a largely unexplored area. In this paper, various ensemble generation methodologies for the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system are compared. A series of experiments using wind perturbations (applied when generating the oceanic initial conditions), sea surface temperature (SST) perturbations to those initial conditions, and random perturbation to the atmosphere during the forecast, individually and collectively, is presented and compared with the more usual lagged-average approach. SST perturbations are important during the first 2 months of the forecast to ensure a spread at least equal to the uncertainty level on the SST measure. From month 3 onward, all methods give a similar spread. This spread is significantly smaller than the rms error of the forecasts. There is also no clear link between the spread of the ensemble and the ensemble mean forecast error. These two facts suggest that factors not presently sampled in the ensemble, such as model error, act to limit the forecast skill. Methods that allow sampling of model error, such as multimodel ensembles, should be beneficial to seasonal forecasting.
Since 1997, the European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new ...version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification.
The main cause of seasonal predictability is El Niño and La Niña perturbing the average weather in many regions and seasons throughout the world. As a baseline to compare the dynamical models with, a set of simple statistical models (STAT) is constructed. These are based on persistence and a lagged regression with the first few EOFs of SST from 1901 to 1986 wherever the correlations are significant. The first EOF corresponds to ENSO, and the second corresponds to decadal ENSO. The temperature model uses one EOF, the sea level pressure (SLP) model uses five EOFs, and the precipitation model uses two EOFs but excludes persistence.
As the number of verification data points is very low (15), the simplest measure of skill is used: the correlation coefficient of the ensemble mean. To further reduce the sampling uncertainties, we restrict ourselves to areas and seasons of known ENSO teleconnections.
The dynamical ECMWF models show better skill in 2-m temperature forecasts over sea and the tropical land areas than STAT, but the modeled ENSO teleconnection pattern to North America is shifted relative to observations, leading to little pointwise skill. Precipitation forecasts of the ECMWF models are very good, better than those of the statistical model, in southeast Asia, the equatorial Pacific, and the Americas in December–February. In March–May the skill is lower. Overall, S1 (S2) shows better skill than STAT at lead time of 2 months in 29 (32) out of 40 regions and seasons of known ENSO teleconnections.
Global ocean forecast systems, developed under the Global Ocean Data Assimilation Experiment (GODAE), are a powerful means of assessing the impact of different components of the Global Ocean ...Observing System (GOOS). Using a range of analysis tools and approaches, GODAE systems are useful for quantifying the impact of different observation types on the quality of analyses and forecasts. This assessment includes both existing and future observation platforms. Many important conclusions can be drawn from these studies. It is clear that altimeter data are extremely important for constraining mesoscale variability in ocean forecast systems. The number of altimeters is also important. For example, near-real-time applications need data from four altimeters to achieve skill that is similar to systems using data from two altimeters in delayed mode. Another important result is that sea surface temperature is the only observation parameter that adequately monitors ocean properties in coastal regions and shallow seas. Assimilation of Argo data provides a significant, measurable improvement to GODAE systems, and is the only observation platform that provides global-scale information for constraining salinity. The complementary nature of different components of GOOS is now clear and the emergence of new assimilation techniques for observing system evaluation provides the GODAE community with a practical path toward routine GOOS monitoring.
This paper demonstrates the value of Observing System Evaluation (OS-Eval) efforts which have been made or are ongoing to contribute to observing system review and design with the support of Ocean ...Data Assimilation and Prediction (ODAP) communities such as GODAE OceanView and CLIVAR-GSOP, by highlighting examples that illustrate the potential of the related OS-Eval methodologies and recent achievements. For instance, Observing System Experiment (OSE) studies illustrate the impacts of the severe decrease in the number of TAO buoys during 2012-2014 and TRITON buoys since 2013 on ODAP system performance. Multi-system evaluation of the impacts of assimilating satellite sea surface salinity data based on OSEs has been performed to demonstrate the need to continue and enhance satellite salinity missions. Impacts of underwater gliders have been assessed using Observing System Simulation Experiments (OSSEs) to provide guidance on effective coordination of the western North Atlantic observing system elements. OSSEs are also being performed under H2020 AtlantOS project with the goal to enhance and optimize the Atlantic in-situ networks. Potential of future satellite missions of wide-swash altimetry and surface ocean currents monitoring is explored through OSSEs and evaluation of Degrees of Freedom for Signal (DFS). Forecast Sensitivity Observation Impacts (FSOI) are routinely evaluated for monitoring the ocean observation impacts in the US Navy’s ODAP system. Perspective on the extension of OS-Eval to the deep ocean, polar regions, coupled data assimilation, and biogeochemical applications are also presented. Based on the examples above, we identify the limitations of OS-Eval, indicating that the most significant limitation is reduction of robustness and reliability of the results due to their system-dependency. Inability of performing evaluation in near real time is also critical. A strategy to mitigate the limitation and to strengthen the impact of evaluations is discussed. In particular, we emphasize the importance of collaboration within the ODAP community for multi-system evaluation and communication with ocean observational communities on the design of OS-Eval, required resources, and effective distribution of the results. Finally, we recommend to further develop OS-Eval activities at international level with the support of the international ODAP (e.g., OceanPredict and CLIVAR-GSOP) and observational communities.
A set of ensemble seasonal reforecasts for 1958–2014 is conducted using the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2. In comparison with other current ...reforecasts, this dataset extends the seasonal reforecasts to the 1960s–70s. Direct comparison of the predictability of the ENSO events occurring during the 1960s–70s with the more widely studied ENSO events since then demonstrates the seasonal forecast system’s capability in different phases of multidecadal variability and degrees of global climate change. A major concern for a long reforecast is whether the seasonal reforecasts before 1979 provide useful skill when observations, particularly of the ocean, were sparser. This study demonstrates that, although the reforecasts have lower skill in predicting SST anomalies in the North Pacific and North Atlantic before 1979, the prediction skill of the onset and development of ENSO events in 1958–78 is comparable to that for 1979–2014. In particular, the ENSO predictions initialized in April during 1958–78 show higher skill in the summer. However, the skill of the earlier predictions declines faster in the ENSO decaying phase, because the reforecasts initialized after boreal summer persistently predict lingering wind and SST anomalies over the eastern equatorial Pacific during such events. Reforecasts initialized in boreal fall overestimate the peak SST anomalies of strong El Niño events since the 1980s. Both phenomena imply that the model’s air–sea feedback is overly active in the eastern Pacific before ENSO event termination. Whether these differences are due to changes in the observing system or are associated with flow-dependent predictability remains an open question.
Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool ...for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
Coastal high water level events are increasing in frequency and severity as global sea‐levels rise, and are exposing coastlines to risks of flooding. Yet, operational seasonal forecasts of sea‐level ...anomalies are not made for most coastal regions. Advancements in forecasting climate variability using coupled ocean‐atmosphere global models provide the opportunity to predict the likelihood of future high water events several months in advance. However, the skill of these models to forecast seasonal sea‐level anomalies has not been fully assessed, especially in a multi‐model framework. Here, we construct a 10‐model ensemble of retrospective forecasts with future lead times of up to 11 months. We compare predicted sea levels from bias‐corrected forecasts with 20 years of observations from satellite‐based altimetry and shore‐based tide gauges. Forecast skill, as measured by anomaly correlation, tends to be highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along some continental coasts. For most locations, multi‐model averaging produces forecast skill that is comparable to or better than the best performing individual model. We find that the most skillful predictions typically come from forecast systems with more accurate initializations of sea level, which is generally achieved by assimilating altimetry data. Having relatively higher horizontal resolution in the ocean is also beneficial, as such models seem to better capture dynamical processes necessary for successful forecasts. The multi‐model assessment suggests that skillful seasonal sea‐level forecasts are possible in many, though not all, parts of the global ocean.
Plain Language Summary
We assess 10 global climate forecasting systems to predict monthly and seasonal anomalies of local sea levels up to a year into the future. We find that skillful seasonal sea‐level forecasts are possible in many parts of the global ocean. Forecast skill is generally highest in the tropical and subtropical open oceans, whereas the skill is lower in the higher latitudes and along continental coasts. For most locations, multi‐model averaging improves the forecast skill, compared to considering the models individually. Overall, the most skillful predictions are from forecasting systems with more accurate initializations of sea level and higher horizontal resolutions of the ocean.
Key Points
Prediction skill of seasonal sea‐level anomalies up to a year in the future is assessed in 10 global climate forecasting systems
Skillful seasonal sea‐level forecasts are found in the tropics, whereas the skill is lower in higher latitudes and along continental coasts
The most skillful predictions are from models with more accurate initializations of sea level and higher resolutions of the ocean
When forecasting sea surface temperature (SST) in the Equatorial Pacific on a timescale of several seasons, most prediction schemes have a spring barrier; that is, they have skill scores that are ...substantially lower when predicting northern spring and summer conditions compared to autumn and winter. This feature is investigated by examining predictions during the 1970s and the 1980s, using a dynamic ocean model of intermediate complexity coupled to a statistical atmosphere. Results show that predictions initialized during the 1970s exhibit the typical prominent skill decay in spring, whereas the seasonal dependence in those predictions initialized during the 1980s is rather small. Similar changes in seasonal dependence are also found in predictions based on simple persistence of observed SST anomalies. This decadal change in the spring barrier is related to decadal variations found in the seasonal phase locking of the SST anomalies, which is largely determined by the timing of El Niño events. The spring barrier was strong in the 1970s, when El Niño was strongly phaselocked to the annual cycle. An analysis of observed SST anomalies from 1900 to 1990 shows several changes in behavior on a decadal scale, with the largest change being from the 1970s to the 1980s. The seasonal dependence of model heat content predictions is investigated and found to be similar to that for SST, except that it shows a winter barrier rather than the spring barrier evident in SST.
PDF
