Tropical intraseasonal rainfall variability in the CFSR

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.

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

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.

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.

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.

Eight current re-analyses—NCEP/NCAR Re-analysis (NCEPI), NCEP/DOE Re-analysis (NCEPII), NCEP Climate Forecast System Re-analysis (CFSR), ECMWF Interim Re-analysis (ERA-Interim), Japanese 55-year Re-analysis (JRA-55), NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA), NOAA Twentieth Century Re-analysis (20CR), and ECMWF’s first atmospheric re-analysis of the 20th century (ERA-20C)—are assessed to clarify their quality in capturing the East Asian summer monsoon (EASM) rainfall structure and its associated general circulation. They are found to present similar rainfall structures in East Asia, whereas they illustrate some differences in rainfall intensity, especially at lower latitudes. The third generation of re-analysis shows a better estimate of rainfall structure than that in the first and extended generation of re-analysis. Given the fact that the rainfall is ingested by the data assimilation system, the re-analysis cannot improve its production of rainfall quality. The mean sea level pressure is generated by re-analysis, showing a significant uncertainty over the Tibetan Plateau and its surrounding area. In that region, the JRA-55 and MERRA have a negative bias (BIAS), while the other six re-analyses present a positive BIAS to the observed mean sea level pressure. The 20CR and the ERA-20C are ancillary datasets to analyse the EASM due to the fact that they only apply limit observations into the data assimilation system. These two re-analyses demonstrate a prominent difference from the observed winds in the upper-air. Although the upper level winds exhibit difference, the EASM index is consistent in the eight re-analyses, which are based upon the zonal wind over 850 hPa.

Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice–ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981–2010: National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP–U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice–ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R = 0.82). The trend in the total ice volume in September is greatest with MERRA (−4.1 × 103 km3 decade−1) and least with CFSR (−2.7 × 103 km3 decade−1).

Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis

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.

The South American monsoon system (SAMS) is the most important climatic feature in South America and is characterized by pronounced seasonality in precipitation during the austral summer. This study compares several statistical properties of daily gridded precipitation from different data (1998–2008): 1) Physical Sciences Division (PSD), Earth System Research Laboratory [1.0° and 2.5° latitude (lat)/longitude (lon)]; 2) Global Precipitation Climatology Project (GPCP; 1° lat/lon); 3) Climate Prediction Center (CPC) unified gauge (CPC-uni) (0.5° lat/lon); 4) NCEP Climate Forecast System Reanalysis (CFSR) (0.5° lat/lon); 5) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis (0.5° lat/0.3° lon); and 6) Tropical Rainfall Measuring Mission (TRMM) 3B42 V6 data (0.25° lat/lon). The same statistical analyses are applied to data in 1) a common 2.5° lat/lon grid and 2) in the original resolutions of the datasets. All datasets consistently represent the large-scale patterns of the SAMS. The onset, demise, and duration of SAMS are consistent among PSD, GPCP, CPC-uni, and TRMM datasets, whereas CFSR and MERRA seem to have problems in capturing the correct timing of SAMS. Spectral analyses show that intraseasonal variance is somewhat similar in the six datasets. Moreover, differences in spatial patterns of mean precipitation are small among PSD, GPCP, CPC-uni, and TRMM data, while some discrepancies are found in CFSR and MERRA relative to the other datasets. Fitting of gamma frequency distributions to daily precipitation shows differences in the parameters that characterize the shape, scale, and tails of the frequency distributions. This suggests that significant uncertainties exist in the characterization of extreme precipitation, an issue that is highly important in the context of climate variability and change in South America.

The uncertainties related to atmospheric fields in the Arctic Ocean from commonly used and recently available reanalysis products are investigated. Fields from the 1) ECMWF Interim Re-Analysis (ERA-Interim), 2) Common Ocean–Ice Reference Experiment version 2 (CORE2), 3) Japanese 25-yr Reanalysis Project (JRA-25), 4) NCEP–NCAR reanalysis, 5) NCEP Climate Forecast System Reanalysis (CFSR), and 6) Modern-Era Retrospective Analysis for Research and Applications (MERRA) are evaluated against satellite-derived and in situ observations for zonal and meridional winds, precipitation, specific humidity, surface air temperature, and downwelling longwave and shortwave radiation fluxes. Comparison to reference observations shows that for variables such as air temperature and humidity, all reanalysis products have similar solutions. However, other variables such as winds, precipitation, and radiation show large spreads. The magnitude of uncertainties in all fields is large when compared to the signal. Biases in Arctic cloud parameterizations and predicted temperature and humidity profiles in reanalyses as discussed in other studies are likely common sources of error that affect surface downwelling radiation, air temperature, humidity, and precipitation.

The authors examine five recent reanalysis products [NCEP Climate Forecast System Reanalysis (CFSR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Japanese 25-year Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Arctic System Reanalysis (ASR)] for 1) trends in near-surface radiation fluxes, air temperature, and humidity, which are important indicators of changes within the Arctic Ocean and also influence sea ice and ocean conditions, and 2) fidelity of these atmospheric fields and effects for an extreme event: namely, the 2007 ice retreat. An analysis of trends over the Arctic for the past decade (2000–09) shows that reanalysis solutions have large spreads, particularly for downwelling shortwave radiation. In many cases, the differences in significant trends between the five reanalysis products are comparable to the estimated trend within a particular product. These discrepancies make it difficult to establish a consensus on likely changes occurring in the Arctic solely based on results from reanalyses fields. Regarding the 2007 ice retreat event, comparisons with remotely sensed estimates of downwelling radiation observations against these reanalysis products present an ambiguity. Remotely sensed observations from a study cited herewith suggest a large increase in downwelling summertime shortwave radiation and decrease in downwelling summertime longwave radiation from 2006 and 2007. On the contrary, the reanalysis products show only small gains in summertime shortwave radiation, if any; however, all the products show increases in downwelling longwave radiation. Thus, agreement within reanalysis fields needs to be further checked against observations to assess possible biases common to all products.

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.

Four recent reanalyses—the 55-yr Japanese Reanalysis Project (JRA-55), Interim ECWMF Re-Analysis (ERA-I), NCEP Climate Forecast System Reanalysis (CFSR), and NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA)—are assessed to clarify their quality in representing the diurnal cycle over East Asia. They are found to present similar patterns/structure and summer progress of the mean wind diurnal cycle, whereas they exhibit some differences in diurnal amplitude, particularly for the low-level meridional wind. An evaluation with intense soundings suggests that the amplitude difference mainly results from the diurnal variation of mean bias that differs among reanalyses. The root-mean-square (RMS) error is found to have a diurnal variation more evident in CFSR and MERRA than that in JRA-55 and ERA-I, which strongly affects the representation of the varying diurnal amplitude at the peak hours of RMS error. Compared with satellite-derived rainfall, the four reanalyses are shown to reproduce well the rainfall diurnal cycle over East Asia in terms of large-scale terrain contrast, summer progress, and interannual variability. JRA-55 even presents a long-term increase of morning rainfall percentage over the east China plain over the past four decades, consistent with rain gauge observations. The four reanalyses exhibit some considerable discrepancies at regional scale; JRA-55 gives the best capture of the rainfall diurnal cycle over the Tibetan Plateau and the eastward propagation to the eastern lees. These results suggest that new reanalyses are potentially applicable for studying the large-scale diurnal variability over East Asia, whereas their different preferences, especially at regional scale, should be of concern in data application.

The China Meteorological Administration (CMA) recently produced a CMA Global Atmospheric Interim Reanalysis (CRAI) dataset for the years 2007–2016. A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation, in terms of both mean states and extreme indicators over China, is performed. Comparisons are made with other current reanalysis datasets, namely, the ECMWF interim reanalysis (ERAI), Japanese 55-yr reanalysis (JRA55), NCEP Climate Forecast System Reanalysis (CFSR), and NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), as well as NCEP Climate Prediction Center (CPC) observations. The results show that, for daily variations of rainfall during warm seasons in eastern China, CRAI and CFSR overestimate the precipitation of the main rain belt, while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2; whereas ERAI tends to underestimate the precipitation in most regions of eastern China. Two extreme metrics, the total amount of precipitation on days where daily precipitation exceeds the 95th percentile (R95pTOT) and the number of consecutive dry days (CDDs) in one month, are examined to assess the performance of reanalysis datasets. In terms of extreme events, CRAI, ERAI, and JRA55 tend to underestimate the R95pTOT in most of eastern China, whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2; and all of the reanalyses underestimate the CDDs. Among the reanalysis products, CRAI and JRA55 show better agreement with the observed R95pTOT than the other datasets, with fewer biases, higher correlation coefficients, and much more similar linear trend patterns, while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.