Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Tropical precipitation characteristics are investigated using the Tropical Rainfall Measuring Mission (TRMM) 3-hourly estimates, and the result is compared with five reanalyses including the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), Modern Era Retrospective Analysis for Research and Applications (MERRA), National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NCEP1), NCEP–U.S. Department of Energy (DOE) reanalysis (NCEP2), and NCEP–Climate Forecast System Reanalysis (CFSR). Precipitation characteristics are evaluated in terms of the mean, convectively coupled equatorial wave activity, frequency characteristics, diurnal cycle, and seasonality of regional precipitation variability associated with submonthly scale waves. Generally the latest reanalyses such as ERA-Interim, MERRA, and CFSR show better performances than NCEP1 and NCEP2. However, all the reanalyses are still different from observations. Besides the positive mean bias in the reanalyses, a spectral analysis revealed that the reanalyses have overreddened spectra with persistent rainfall. MERRA has the most persistent rainfall, and CFSR appears to have the most realistic variability. The diurnal cycle in NCEP1 is extremely exaggerated relative to TRMM. The low-frequency waves with the period longer than 3 days are relatively well represented in ERA-Interim, MERRA, and CFSR, but all the reanalyses have significant deficiencies in representing convectively coupled equatorial waves and variability in the high-frequency range.

Inter-comparison of extra-tropical cyclone activity in nine reanalysis datasets

In this study, surface and radiosonde data from staffed Antarctic observation stations are compared to output from five reanalyses [Climate Forecast System Reanalysis (CFSR), 40-yr ECMWF Re-Analysis (ERA-40), ECMWF Interim Re-Analysis (ERA-Interim), Japanese 25-year Reanalysis (JRA-25), and Modern Era Retrospective-Analysis for Research and Applications (MERRA)] over three decades spanning 1979–2008. Bias and year-to-year correlation between the reanalyses and observations are assessed for four variables: mean sea level pressure (MSLP), near-surface air temperature (Ts), 500-hPa geopotential height (H500), and 500-hPa temperature (T500). It was found that CFSR and MERRA are of a sufficiently high resolution for the height of the orography to be accurately reproduced at coastal observation stations. Progressively larger negative Ts biases at these coastal stations are apparent for reanalyses in order of decreasing resolution. However, orography height bias cannot explain large winter warm biases in CFSR, JRA-25, and MERRA (11.1°, 10.2°, and 7.9°C, respectively) at Amundsen–Scott and Vostok, which have been linked to problems with representing the surface energy balance. Linear trends in the annual-mean T500 and H500 averaged over Antarctica as a whole were found to be most reliable in CFSR, ERA-Interim, and MERRA, none of which show significant trends over the period 1979–2008. In contrast JRA-25 shows significant negative trends over 1979–2008 and ERA-40 gives significant positive trends during the 1980s (evident in both T500 and H500). Comparison to observations indicates that the positive trend in ERA-40 is spurious. At the smaller spatial scale of individual stations all five reanalyses have some spurious trends. However, ERA-Interim was found to be the most reliable for MSLP and H500 trends at station locations.

This study examines the Indian summer monsoon hydroclimate in the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) Reanalysis (R2), the Climate Forecast System Reanalysis (CFSR), and the Modern Era Retrospective-Analysis for Research and Applications (MERRA). The three reanalyses show significant differences in the climatology of evaporation, low-level winds, and precipitable water fields over India. For example, the continental evaporation is significantly less in CFSR compared to R2 and MERRA. Likewise the mean boreal summer 925 hPa westerly winds in the northern Indian Ocean are stronger in R2. Similarly the continental precipitable water in R2 is much less while it is higher and comparable in MERRA and CFSR. Despite these climatological differences between the reanalyses, the climatological evaporative sources for rain events over central India show some qualitative similarities. Major differences however appear when interannual variations of the Indian summer monsoon are analyzed. The anomalous oceanic sources of moisture from the adjacent Bay of Bengal and Arabian Sea play a significant role in determining the wet or dry year of the Indian monsoon in CFSR. However in R2 the local evaporative sources from the continental region play a more significant role. We also find that the interannual variability of the evaporative sources in the break spells of the intraseasonal variations of the Indian monsoon is stronger than in the wet spells. We therefore claim that instead of rainfall, evaporative sources may be a more appropriate metric to observe the relationship between the seasonal monsoon strength and intraseasonal activity. These findings are consistent across the reanalyses and provide a basis to improve the predictability of intraseasonal variability of the Indian monsoon. This study also has a bearing on improving weather prediction for tropical cyclones in that we suggest targeting enhanced observations in the Bay of Bengal (where it is drawing the most moisture from) for improved analysis during active spells of the intraseasonal variability of the Indian monsoon. The analysis suggests that the land–atmosphere interactions contribute significant uncertainty to the Indian monsoon in the reanalyses, which is consistent with the fact that most of the global reanalyses do not assimilate any land-surface data because the data are not available. Therefore, the land–atmosphere interaction in the reanalyses is highly dependent on the land-surface model and it’s coupling with the atmospheric model.

While large-scale circulation fields from atmospheric reanalyses have been widely used to study the tropical intraseasonal variability, rainfall variations from the reanalyses are less focused. Because of the sparseness of in situ observations available in the tropics and strong coupling between convection and large-scale circulation, the accuracy of tropical rainfall from the reanalyses not only measures the quality of reanalysis rainfall but is also to some extent indicative of the accuracy of the circulations fields. This study analyzes tropical intraseasonal rainfall variability in the recently completed NCEP Climate Forecast System Reanalysis (CFSR) and its comparison with the widely used NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2). The R1 produces too weak rainfall variability while the R2 generates too strong westward propagation. Compared with the R1 and R2, the CFSR produces greatly improved tropical intraseasonal rainfall variability with the dominance of eastward propagation and more realistic amplitude. An analysis of the relationship between rainfall and large-scale fields using composites based on Madden-Julian Oscillation (MJO) events shows that, in all three NCEP reanalyses, the moisture convergence leading the rainfall maximum is near the surface in the western Pacific but is above 925 hPa in the eastern Indian Ocean. However, the CFSR produces the strongest large-scale convergence and the rainfall from CFSR lags the column integrated precipitable water by 1 or 2 days while R1 and R2 rainfall tends to lead the respective precipitable water. Diabatic heating related to the MJO variability in the CFSR is analyzed and compared with that derived from large-scale fields. It is found that the amplitude of CFSR-produced total heating anomalies is smaller than that of the derived. Rainfall variability from the other two recently produced reanalyses, the ECMWF Re-Analysis Interim (ERAI), and the Modern Era Retrospective-analysis for Research and Applications (MERRA), is also analyzed. It is shown that both the ERAI and MERRA generate stronger rainfall spectra than the R1 and more realistic dominance of eastward propagating variance than R2. The intraseasonal variability in the MERRA is stronger than that in the ERAI but weaker than that in the CFSR and CMORPH.

We compare and examine diurnal temperature tides including their migrating component (DW1) from the troposphere to the lower mesosphere, using data from Thermosphere‐Ionosphere‐Mesosphere‐Energetics and Dynamics/Sounding of the Atmosphere using Broadband Emission Radiometry (TIMED/SABER) and from six different reanalysis data sets: (1) the Modern Era Retrospective analysis for Research and Applications (MERRA), (2) the European Centre for Medium‐range Weather Forecasts (ECMWF) reanalysis (ERA‐Interim) (3) the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), (4) the Japanese 25‐year reanalysis by Japanese Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI) (JRA25), (5) the NCEP/National Center for Atmospheric Research reanalysis (NCEP1), and (6) the NCEP and Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP‐II) reanalysis data (NCEP2). The horizontal and vertical structures of the diurnal tides in SABER and reanalyses reasonably agree, although the amplitudes are up to 30–50% smaller in the reanalyses than in the SABER in the upper stratosphere to lower mesosphere. Of all tidal components, the DW1 is dominant while a clear eastward propagating zonal wave number 3 component (DE3) is observed at midlatitudes of the Southern Hemisphere in winter. Among the six reanalyses, MERRA, ERA‐Interim and CFSR are better at reproducing realistic diurnal tides. It is found that the diurnal tides extracted from SABER data in the winter‐hemisphere stratosphere suffer from sampling issues that are caused by short‐term variations of the background temperature. In addition, the GSWM underestimates the amplitude in the midlatitude upper stratosphere by about 50%.

This study utilizes six commonly used reanalysis products, including the NCEP–Department of Energy Reanalysis 2 (NCEP2), NCEP Climate Forecast System Reanalysis (CFSR), ECMWF interim reanalysis (ERA-Interim), Japanese 25-year Reanalysis Project (JRA-25), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and North American Regional Reanalysis (NARR), to evaluate features of the southern Great Plains low-level jet (LLJ) above the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) Southern Great Plains site. Two sets of radiosonde data are utilized: the six-week Midlatitude Continental Convective Clouds Experiment (MC3E) and a 10-yr period spanning 2001 through 2010. All six reanalyses are compared to MC3E data, while only the NARR, MERRA, and CFSR are compared to the 10-yr data. The reanalyses are able to represent most aspects of the composite LLJ profile, although there is a tendency for each reanalysis to overestimate the wind speed between the nose of the LLJ (at approximately 900 mb) and a pressure level of 700 mb. There are large discrepancies in the number of LLJs observed and derived from the reanalysis, particularly for strong LLJs, leading to an underestimate of the moisture transport associated with LLJs. When the 10-yr period is considered, the NARR and CFSR overestimate and MERRA underestimates the total moisture transport, but all three underestimate the transport associated with strong LLJs by factors of 1.4, 2.0, and 2.7 for CFSR, NARR, and MERRA, respectively. During MC3E there were differences in the patterns of moisture convergence and divergence, but the patterns are more consistent during the 10-yr period.

Reanalysis products produced at the various centers around the globe are utilized for many different scientific endeavors, including forcing land surface models and creating surface flux estimates. Here, flux tower observations of temperature, wind speed, precipitation, downward shortwave radiation, net surface radiation, and latent and sensible heat fluxes are used to evaluate the performance of various reanalysis products [NCEP–NCAR reanalysis and Climate Forecast System Reanalysis (CFSR) from NCEP; 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and ECMWF Interim Re-Analysis (ERA-Interim) from ECMWF; and Modern-Era Retrospective Analysis for Research and Applications (MERRA) and Global Land Data Assimilation System (GLDAS) from the Goddard Space Flight Center (GSFC)]. To combine the biases and standard deviation of errors from the separate stations, a ranking system is utilized. It is found that ERA-Interim has the lowest overall bias in 6-hourly air temperature, followed closely by MERRA and GLDAS. The variability in 6-hourly air temperature is again most accurate in ERA-Interim. ERA-40 is found to have the lowest overall bias in latent heat flux, followed closely by CFSR, while ERA-40 also has the lowest 6-hourly sensible heat bias. MERRA has the second lowest and is close to ERA-40. The variability in 6-hourly precipitation is best captured by GLDAS and ERA-Interim, and ERA-40 has the lowest precipitation bias. It is also found that at monthly time scales, the bias term in the reanalysis products are the dominant cause of the mean square errors, while at 6-hourly and daily time scales the dominant contributor to the mean square errors is the correlation term. Also, it is found that the hourly CFSR data have discontinuities present due to the assimilation cycle, while the hourly MERRA data do not contain these jumps.

Evaluation of Reanalysis Products with in situ GPS Sounding Observations in the Eastern Himalayas

We have produced a global ensemble mean of the four third-generation climate reanalysis models for the years 1981–2010. The reanalysis system models used in this study are National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Interim (ERA-I), Japan Meteorological Agency (JMA) 55-year Reanalysis (JRA-55), and National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA). Two gridded datasets are used as a baseline, for temperature the Global Historical Climatology Network (GHCN), and for precipitation the Global Precipitation Climatology Centre (GPCC). The reanalysis ensemble mean is used here as a comparison tool of the four reanalysis members. Meteorological fields investigated within the reanalysis models include 2-m air temperature, precipitation, and 500-hPa geopotential heights. Comparing the individual reanalysis models to the ensemble mean, we find that each perform similarly over large domains but exhibit significant differences over particular regions.

This study evaluates the temporal variability of the Antarctic surface mass balance, approximated as precipitation minus evaporation (P − E), and Southern Ocean precipitation in five global reanalyses during 1989–2009. The datasets consist of the NCEP/U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project 2 reanalysis (NCEP-2), the Japan Meteorological Agency (JMA) 25-year Reanalysis (JRA-25), ECMWF Interim Re-Analysis (ERA-Interim), NASA Modern Era Retrospective-Analysis for Research and Application (MERRA), and the Climate Forecast System Reanalysis (CFSR). Reanalyses are known to be prone to spurious trends and inhomogeneities caused by changes in the observing system, especially in the data-sparse high southern latitudes. The period of study has seen a dramatic increase in the amount of satellite observations used for data assimilation.The large positive and statistically significant trends in mean Antarctic P − E and mean Southern Ocean precipitation in NCEP-2, JRA-25, and MERRA are found to be largely spurious. The origin of these artifacts varies between reanalyses. Notably, a precipitation jump in MERRA in the late 1990s coincides with the start of the assimilation of radiances from the Advanced Microwave Sounding Unit (AMSU). ERA-Interim and CFSR do not exhibit any significant trends. However, the potential impact of the assimilation of rain-affected radiances in ERA-Interim and inhomogeneities in CFSR pressure fields over Antarctica cast some doubt on the reliability of these two datasets.The authors conclude that ERA-Interim likely offers the most realistic depiction of precipitation changes in high southern latitudes during 1989–2009. The range of the trends in Antarctic P − E among the reanalyses is equivalent to 1 mm of sea level over 21 years, which highlights the improvements still needed in reanalysis simulations to better assess the contribution of Antarctica to sea level rise. Finally, this work argues for continuing cautious use of reanalysis datasets for climate change assessment.

Drought monitoring in near–real time is essential for management of water resources, irrigation planning, and food security. However, lack of availability of quality real-time observations leads to slow decision making and relatively poor natural resources management, especially during and after severe and prolonged droughts. The global reanalysis products that are available in near–real time could be valuable for drought monitoring and assessment. Three high-resolution reanalysis products—the Modern-Era Retrospective Analysis for Research and Applications (MERRA), the Interim ECMWF Re-Analysis (ERA-Interim), and the NCEP Climate Forecast System Reanalysis (CFSR)—are examined for their effectiveness in reproducing retrospective droughts during the period 1980–2005. All the selected reanalysis products show biases in the monsoon season precipitation and temperature. MERRA, ERA-Interim, and CFSR showed median bias in the monsoon season precipitation (temperature) of 10% (−0.39°C), 34% (−0.21°C), and 11% (−0.44°C), respectively. The reanalysis products largely fail to reproduce the observed trends in the monsoon season precipitation and temperature over India. All-India median changes in the monsoon season precipitation (temperature) shown by the observations and by MERRA, ERA-Interim, and CFSR were −0.2% (0.13°C), 26% (−0.42°C), 7% (0.24°C), and −8% (0.54°C), respectively, during the period 1980–2005. Despite the differences in the observed areal extent and severity of drought from those obtained from the individual reanalysis products, ensemble mean drought indices of different reanalysis products showed better performance for drought assessment during the monsoon season in India.

The solar semidiurnal atmospheric tide (S2) was extracted from seven reanalysis data sets, including current data sets, such as CFSR (Climate Forecast System Reanalysis), MERRA (Modern‐Era Retrospective Analysis for Research and Applications), ERA‐Interim (ECMWF Reanalysis), and 20CR (Twentieth Century Reanalysis), and older frozen products, such as NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research), ERA‐40 (ECMWF Reanalysis), and JRA‐25 (Japanese 25 year Reanalysis). In this calculation, we emphasized the temporal variation of the tide. We also calculated the tidal error, which was sizable at high latitudes and over short averaging periods and large for 20CR at all latitudes. Because of the four standard daily samples, the interpolation scheme of van den Dool et al. (1997) was used when necessary. We found this method to be accurate for zonally averaged tides only. Comparing the climatology from the MERRA and CFSR S2 with a recent empirical tide model showed that MERRA better represented the geographical structure of the tide, especially its phase. We found a bias in the phase in all of the reanalysis data sets except for MERRA. The temporal evolution of the tide was inconsistent between the different data sets, although similar seasonal variations were observed. The seasonal cycle was also better depicted in MERRA. The S2 calculated from MERRA and satellite precipitation measurements from TRMM (Tropical Rainfall Measuring Mission) presented results that were inconsistent with the hypothesis in which rainfall latent heat release represents S2 forcing and functions as a source of S2 seasonal variability.

Arctic Fennoscandia has undergone significant climate change over recent decades. Reanalysis data sets allow us to understand the atmospheric processes driving such changes. Here we evaluate four reanalyses against observations of near‐surface air temperature (SAT) and precipitation (PPN) from 35 meteorological stations across the region for the 35‐year period from 1979 to 2013. The reanalyses compared are the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), the European Centre for Medium‐Range Weather Forecast (ECMWF) Interim reanalysis (ERA‐Interim), the Japanese Meteorological Agency (JMA) 55‐year reanalysis (JRA‐55) and National Aeronautics and Space Administration (NASA)’s Modern‐Era Retrospective Analysis for Research and Applications (MERRA).All four reanalyses have an overall small cool bias across Arctic Fennoscandia, with MERRA typically ~1 °C cooler than the others. They generally reproduce the broad spatial patterns of mean SAT across the region, although less well in areas of complex orography. Observations reveal a statistically significant warming across Arctic Fennoscandia, with the majority of trends significant at p < .01. Three reanalyses show similar regional warming but of smaller magnitude while CFSR is anomalous, even having a slight cooling in some areas. In general, the other reanalyses are sufficiently accurate to reproduce the varying significance of observed seasonal trends.There are much greater differences between the reanalyses when comparing PPN to observations. MERRA‐Land, which merges a gauge‐based data set, is clearly the best: CFSR is the least successful, with a significant wet bias. The smoothed reanalysis orography means that the high PPN associated with the western side of the Scandinavian Mountains extends too far inland. Spatial patterns of PPN trends across the region differ markedly between the reanalyses, which have varying success at matching observations and generally fail to replicate sites with significant observed trends. Therefore, using reanalyses to analyse PPN changes in Arctic Fennoscandia should be undertaken with caution.

The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature and precipitation (SST–P) varies among different reanalyses. The motivation for this work was spurred by a recent report that documented that the SST–P relationship in Climate Forecast System Reanalysis (CFSR) was much closer to that in the observation than it was for the older generation of reanalyses [i.e., NCEP–NCAR reanalysis (R1) and NCEP–Department of Energy (DOE) reanalysis (R2)]. Further, the reason was attributed either to the fact that the CFSR is a partially coupled reanalysis, while R1 and R2 are atmospheric-alone reanalyses, or that R1 and R2 use the observed weekly-averaged SST. The authors repeated the comparison of the SST–P relationship among R1, R2, and CFSR, as well as two recent generations of atmosphere-alone reanalyses, the Modern-Era Retrospective Analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERA-Interim). The results clearly demonstrate that the differences in the SST–P relationship at intraseasonal time scales across different reanalyses are not due to whether the reanalysis system is coupled or atmosphere alone, but are due to the specification of different SSTs. The SST–P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all of the reanalyses and observations.