Journal of Climate

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 MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams and converged to a single near-real-time stream in mid-2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

The authors propose a new climatic drought index: the standardized precipitation evapotranspiration index (SPEI). The SPEI is based on precipitation and temperature data, and it has the advantage of combining multiscalar character with the capacity to include the effects of temperature variability on drought assessment. The procedure to calculate the index is detailed and involves a climatic water balance, the accumulation of deficit/surplus at different time scales, and adjustment to a log-logistic probability distribution. Mathematically, the SPEI is similar to the standardized precipitation index (SPI), but it includes the role of temperature. Because the SPEI is based on a water balance, it can be compared to the self-calibrated Palmer drought severity index (sc-PDSI). Time series of the three indices were compared for a set of observatories with different climate characteristics, located in different parts of the world. Under global warming conditions, only the sc-PDSI and SPEI identified an increase in drought severity associated with higher water demand as a result of evapotranspiration. Relative to the sc-PDSI, the SPEI has the advantage of being multiscalar, which is crucial for drought analysis and monitoring.

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 in many aspects of climate variability, substantial differences remain in poorly constrained quantities such as precipitation and surface fluxes. These differences, due to variations both in the models and in the analysis techniques, are an important measure of the uncertainty in reanalysis products. It is also found that all reanalyses are still quite sensitive to observing system changes. Dealing with this sensitivity remains the most pressing challenge for the next generation of reanalyses.

Production has now caught up to the current period and MERRA is being continued as a near-real-time climate analysis. The output is available online through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).

Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.

The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

Observations of sea surface and land–near-surface merged temperature anomalies are used to monitor climate variations and to evaluate climate simulations; therefore, it is important to make analyses of these data as accurate as possible. Analysis uncertainty occurs because of data errors and incomplete sampling over the historical period. This manuscript documents recent improvements in NOAA’s merged global surface temperature anomaly analysis, monthly, in spatial 5° grid boxes. These improvements allow better analysis of temperatures throughout the record, with the greatest improvements in the late nineteenth century and since 1985. Improvements in the late nineteenth century are due to improved tuning of the analysis methods. Beginning in 1985, improvements are due to the inclusion of bias-adjusted satellite data. The old analysis (version 2) was documented in 2005, and this improved analysis is called version 3.

The second version of the NCEP Climate Forecast System (CFSv2) was made operational at NCEP in March 2011. This version has upgrades to nearly all aspects of the data assimilation and forecast model components of the system. A coupled reanalysis was made over a 32-yr period (1979–2010), which provided the initial conditions to carry out a comprehensive reforecast over 29 years (1982–2010). This was done to obtain consistent and stable calibrations, as well as skill estimates for the operational subseasonal and seasonal predictions at NCEP with CFSv2. The operational implementation of the full system ensures a continuity of the climate record and provides a valuable up-to-date dataset to study many aspects of predictability on the seasonal and subseasonal scales. Evaluation of the reforecasts show that the CFSv2 increases the length of skillful MJO forecasts from 6 to 17 days (dramatically improving subseasonal forecasts), nearly doubles the skill of seasonal forecasts of 2-m temperatures over the United States, and significantly improves global SST forecasts over its predecessor. The CFSv2 not only provides greatly improved guidance at these time scales but also creates many more products for subseasonal and seasonal forecasting with an extensive set of retrospective forecasts for users to calibrate their forecast products. These retrospective and real-time operational forecasts will be used by a wide community of users in their decision making processes in areas such as water management for rivers and agriculture, transportation, energy use by utilities, wind and other sustainable energy, and seasonal prediction of the hurricane season.

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

Significant climate anomalies persist through the summer (June–August) after El Niño dissipates in spring over the equatorial Pacific. They include the tropical Indian Ocean (TIO) sea surface temperature (SST) warming, increased tropical tropospheric temperature, an anomalous anticyclone over the subtropical northwest Pacific, and increased mei-yu–baiu rainfall over East Asia. The cause of these lingering El Niño effects during summer is investigated using observations and an atmospheric general circulation model (GCM). The results herein indicate that the TIO warming acts like a capacitor anchoring atmospheric anomalies over the Indo–western Pacific Oceans. It causes tropospheric temperature to increase by a moist-adiabatic adjustment in deep convection, emanating a baroclinic Kelvin wave into the Pacific. In the northwest Pacific, this equatorial Kelvin wave induces northeasterly surface wind anomalies, and the resultant divergence in the subtropics triggers suppressed convection and the anomalous anticyclone. The GCM results support this Kelvin wave–induced Ekman divergence mechanism. In response to a prescribed SST increase over the TIO, the model simulates the Kelvin wave with low pressure on the equator as well as suppressed convection and the anomalous anticyclone over the subtropical northwest Pacific. An additional experiment further indicates that the north Indian Ocean warming is most important for the Kelvin wave and northwest Pacific anticyclone, a result corroborated by observations.

These results have important implications for the predictability of Indo–western Pacific summer climate: the spatial distribution and magnitude of the TIO warming, rather than simply whether there is an El Niño in the preceding winter, affect summer climate anomalies over the Indo–western Pacific and East Asia.

Surface observations and subsurface ocean assimilation datasets are examined to contrast two distinct types of El Niño–Southern Oscillation (ENSO) in the tropical Pacific: an eastern-Pacific (EP) type and a central-Pacific (CP) type. An analysis method combining empirical orthogonal function (EOF) analysis and linear regression is used to separate these two types. Correlation and composite analyses based on the principal components of the EOF were performed to examine the structure, evolution, and teleconnection of these two ENSO types. The EP type of ENSO is found to have its SST anomaly center located in the eastern equatorial Pacific attached to the coast of South America. This type of ENSO is associated with basinwide thermocline and surface wind variations and shows a strong teleconnection with the tropical Indian Ocean. In contrast, the CP type of ENSO has most of its surface wind, SST, and subsurface anomalies confined in the central Pacific and tends to onset, develop, and decay in situ. This type of ENSO appears less related to the thermocline variations and may be influenced more by atmospheric forcing. It has a stronger teleconnection with the southern Indian Ocean. Phase-reversal signatures can be identified in the anomaly evolutions of the EP-ENSO but not for the CP-ENSO. This implies that the CP-ENSO may occur more as events or epochs than as a cycle. The EP-ENSO has experienced a stronger interdecadal change with the dominant period of its SST anomalies shifted from 2 to 4 yr near 1976/77, while the dominant period for the CP-ENSO stayed near the 2-yr band. The different onset times of these two types of ENSO imply that the difference between the EP and CP types of ENSO could be caused by the timing of the mechanisms that trigger the ENSO events.