ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

The ERA5 global reanalysis

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA’s Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA’s Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF Re-Analysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses i...

MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), is the latest atmospheric reanalysis of the modern satellite era produced by NASA’s Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRA’s terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of M...

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The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

For the tropical Pacific and Atlantic oceans, internal modes of variability that lead to climatic oscillations have been recognized1,2, but in the Indian Ocean region a similar ocean–atmosphere interaction causing interannual climate variability has not yet been found3. Here we report an analysis of observational data over the past 40 years, showing a dipole mode in the Indian Ocean: a pattern of internal variability with anomalously low sea surface temperatures off Sumatra and high sea surface temperatures in the western Indian Ocean, with accompanying wind and precipitation anomalies. The spatio-temporal links between sea surface temperatures and winds reveal a strong coupling through the precipitation field and ocean dynamics. This air–sea interaction process is unique and inherent in the Indian Ocean, and is shown to be independent of the El Nino/Southern Oscillation. The discovery of this dipole mode that accounts for about 12% of the sea surface temperature variability in the Indian Ocean—and, in its active years, also causes severe rainfall in eastern Africa and droughts in Indonesia—brightens the prospects for a long-term forecast of rainfall anomalies in the affected countries.

A Dipole Mode in the Tropical Indian Ocean

The Indian Ocean underwent substantial changes in 1997–1998. The observations show not only the appearance of a dipole mode in the tropical region but also a persistent basin-scale warming. We present in this study an analysis of the basin-scale sea surface temperature (SST) variations during 1997–1998 using satellite observations, in situ temperature measurements, and National Centers for Environmental Prediction reanalyses. We find that the Indian SST anomaly peaks occurred at two periods, i.e., November–December–January coinciding with the Nino3 peak and the following April–May–June, and were phase locked to the Indian Ocean seasonal cycle. The changes of SST in the equatorial ocean were related to a coupled interaction between the atmosphere and the ocean. Oceanic upwelling in the east and downwelling in the west played a major role in giving rise to the SST anomalies associated with the dipole mode structure. The upwelling off the coast of Sumatra elevated the regional thermocline by more than 80 m in December 1997. On the other hand, the changes of SST in the southern Indian Ocean were largely induced by the changes of local latent flux. During boreal fall-winter of 1997 the southeasterly trades were displaced and abnormally prolonged in their northernmost equatorial position. This shifted the center of the trades toward the equator, weakened the winds in the central Southern Ocean, reduced the latent heat flux in the region, and subsequently induced a surface warming. The total change of the SST anomalies integrated over the two periods, July–December 1997 and January–May 1998, were explained well by the same period latent flux integral in both intensity and pattern. The cross-basin upper ocean temperature sections show that the extratropical warming was rather uniformly distributed in the upper 60 m, further supporting the role of mixed-layer processes in the warming.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2000JC900068

Indian Ocean warming of 1997-1998

To compensate for a poorly known geoid, satel- lite altimeter data is usually analyzed in terms of anomalies from the time mean record. When such anomalies are as- similated into an ocean model, the bias between the clima- tologies of the model and data is problematic. An ensemble Kalman filter (EnKF) is modified to account for the presence of a forecast-model bias and applied to the assimilation of TOPEX/Poseidon (T/P) altimeter data. The online bias cor- rection (OBC) algorithm uses the same ensemble of model state vectors to estimate biased-error and unbiased-error co- variance matrices. Covariance localization is used but the bias covariances have different localization scales from the unbiased-error covariances, thereby accounting for the fact that the bias in a global ocean model could have much larger spatial scales than the random error. The method is applied to a 27-layer version of the Posei- don global ocean general circulation model with about 30- million state variables. Experiments in which T/P altime- ter anomalies are assimilated show that the OBC reduces the RMS observation minus forecast difference for sea-surface height (SSH) over a similar EnKF run in which OBC is not used. Independent in situ temperature observations show that the temperature field is also improved. When the T/P data and in situ temperature data are assimilated in the same run and the configuration of the ensemble at the end of the run is used to initialize the ocean component of the GMAO cou- pled forecast model, seasonal SSH hindcasts made with the coupled model are generally better than those initialized with optimal interpolation of temperature observations without al- timeter data. The analysis of the corresponding sea-surface temperature hindcasts is not as conclusive.

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Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction

The horizontal and vertical mesoscale physical structure of the broad, cold filament observed off Point Arena in advanced very high resolution radiometer (AVHRR) images in June and July 1986 are investigated. Small-scale intrusive features are shown in vertical sections of temperature and salinity from tow-yo conductivity-temperature-depth data. A regional perspective of the filament structure and its evolution is given using the AVHRR satellite imagery.

Mesoscale eddies, jets, and fronts off Point Arena, California, July 1986

The potential for climate predictability at seasonal time scales resides in information provided by the ocean initial conditions, in particular the upper thermal structure. Currently, several operational centres issue routine seasonal forecasts produced with coupled ocean-atmosphere models, requiring real-time knowledge of the state of the global ocean. Seasonal forecasting needs the calibration of the numerical output of the coupled model, which in turn requires an historical ocean reanalysis, as will be discussed in this paper. Assimilation of observations into an ocean model forced by prescribed atmospheric fluxes is the most common practice for initialization of the ocean component of a coupled model. It is shown that the assimilation of ocean data reduces the uncertainty in the ocean estimation arising from the uncertainty in the forcing fluxes. Although data assimilation also improves the skill of seasonal forecasts in many cases, its impact is often overshadowed by errors in the coupled models. This paper offers a review of the existing ocean analysis efforts aiming at the initialization of seasonal forecasts. The current practice, known as "uncoupled" initialization, has often been criticized as having several shortcomings, the initialization shock being one of them. On the other hand, the uncoupled initialization usually benefits from better knowledge of the atmospheric forcing fluxes, an advantage that should not be overlooked. 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.

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Ocean Initialization for Seasonal Forecasts

The OPTOMA (Ocean Prediction Through Observation, Modeling and Analysis) program is developing an ocean descriptive-predictive system for four-dimensional data assimilation. It is presently concerned with the mesoscale variability in the California Current System (CCS). The present paper has the objective to assess the relationship of surface temperature structure to subsurface temperature structure and flow fields. Surface temperature anomalies are related to the mesoscale horizontal advective and subsurface mass fields. The cool anomaly off northern California in summer 1982 is discussed, taking into account the temperature structure along transects, T-S variations and inferred water masses, variation of horizontal temperature patterns with depth, and synoptic information from IR data.

Michele M. Rienecker

Papers

Indian Ocean warming of 1997-1998

Ensemble Kalman filter assimilation of temperature and altimeter data with bias correction and application to seasonal prediction

Mesoscale eddies, jets, and fronts off Point Arena, California, July 1986

Ocean Initialization for Seasonal Forecasts

A cool anomaly off northern California: An investigation using IR imagery and in situ data