Suggested Colors for Intercomparing Reanalysis Timeseries from S-RIP:

The Sparc Reanalysis Intercomparison Project (S-RIP --> https://s-rip.github.io/) has a suggested list of colors to use for comparing reanalysis time-series. The list can be viewed here https://s-rip.ees.hokudai.ac.jp/mediawiki/index.php/Notes_for_Authors --> https://s-rip.github.io/report/colourdefinition.html The colors are available in CVS format below, in NCL, and in XLS. The link to the S-RIP page has examples for Python and IDL --> https://s-rip.github.io/report/colourtables.html.

Note that the URLs have changed. 

The colors are available in CVS format below, in NCL, and in XLS. 

CSV:

r,g,b,c,m,y,k,RGB Hexadecimal,reanalysis

226, 31, 38,  0,86.28,83.19,11.37,  E21F26,  MERRA-2

246, 153, 153,  0,37.8,37.8,3.53,  F69999,  MERRA

41, 95, 138,  70.29,31.16,0,45.88,  295F8A,  ERA-Interim

95, 152, 198,  52.02,23.23,0,22.35,  5F98C6,  ERA5

175, 203, 227,  22.91,10.57,0,10.98,  AFCBE3,  ERA-40

114, 59, 122,  6.56,51.64,0,52.16,  723B7A,  JRA-55

173, 113, 181,  4.42,37.57,0,29.02,  AD71B5,  JRA-55C or JRA-55 AMIP

214, 184, 218,  1.83,15.6,0,14.51,  D6B8DA,  JRA-25

245, 126, 32,  0,48.57,86.94,3.92,  F57E20,  NCEP-R1

253, 191, 110,  0,24.51,56.52,0.78,  FDBF6E,  NCEP-R2

236, 0, 140,  0,100,40.68,7.45,  EC008C,  20CRv2c

247, 153, 209,  0,38.06,15.38,3.14,  F799D1,  20CRv2

0, 174, 239,  100,27.2,0,6.27,  00AEEF,  CERA-20C

96, 200, 232,  58.62,13.79,0,9.02,  60C8E8,  ERA-20C

52, 160, 72,  67.5,0,55,37.25,  34A048,  CFSR

179, 91, 40,  0,49.16,77.65,29.8,  B35B28,  REM

255,215,0,  0,15.69,100,0,  FFD700,  Other

0, 0, 0,  0,0,0,100,  000000,  Obs

119, 119, 119,  0,0,0,53.33,  777777,  Other Obs

This image shows how the colors can be used.

Suggested Practices: Climatologies:

Use 1981-2010.

GOAT (Geophysical Observation Analysis Tool)

Analysis:

GOAT archives all geophysical data in a consistent format, thereby reducing the need to bother with the esoteric details of the particular datasets.

GOAT icludes a GUI based interface for intuitive browsing, subsetting and display of data in all spatial and temporal projections.

The GUI also provides some advanced analysis features such as:

• Calculation of climatology and anomalies
• Spatial and temporal filters
• Display differences between two fields
• Display superposition of two fields
• Include topography in display or analysis   
• Code generation (similar to the 'Generate Code' option in MATLAB figures)

Data retrieval:

GOAT intgrates with OPeNDAP sources and NetCDF files via a process referred to as 'linking' (GOAT LINK Format). GOAT also integrates with *.mat file archived in a GOAT standard format (GOAT MAT Format). The GOAT GUI also enables converting linked OPeNDAP and NetCDF archives to MAT Format. Information on GOAT archiving formats can be found in the GOAT documentation. 

OPeNDAP sources (GOAT LINK Format):

LINK archives do not require much disk space (typically a few MB per archive). A full list of available OPeNDAP LINK archive can be found here. Examples of available OPeNDAP LINK archives are

• MERRA (entire archive)
• 20th Century Reanalysis (entire archive)
• NCEP I and II (entire archives)
• ERA-40 (partial archive)
• ERA-Interim (entire archive. Uses a specially designed perl based protocol for communicating with the MARS server)
• ORA-S4 ocean reanalysis. 

NetCDF files (GOAT LINK Format):

GOAT indludes functions for automatically linking NetCDF files following the CMIP5 standard. In particular, the GOAT_CMIP5_LINK function (which can also be launched from the GOAT GUI) automatically finds all CMIP5 files in a specified folder and creates LINK archives for these files.

This functionality can be further extended to other datasets as a growing number of observational datasets are now available in CMIP5 archiving standard via the obs4MIPs and ana4MIPs projects.

Conversion of datasets to *.mat files (GOAT MAT Format)

GOAT includes a GUI based data download tool for automatically downloading and converting datasets to GOAT MAT Format (*.mat files).

Information on available datasets for download can be found here. 

The following datasets are available via the GUI based tool:

Atmosphere

• NCEP I and NCEP II
• 20th Century Reanalysis
• MERRA Daily

Clouds

• CloudsatCalipso (a combination of Cloudsat and Calipso by Jennifer. E. Kay)

Surface

• GISTEMP
• TRMM Daily
• ERSST (V3b)

Ocean:

• GODAS
• ECMWF ORA-S4
• World Ocean Atlas 2013

From: Bosilovich, Michael G., 2013: Regional Climate and Variability of NASA MERRA and Recent Reanalyses: U.S. Summertime Precipitation and Temperature. J. Appl. Meteor. Climatol., 52, 1939–1951. doi: http://dx.doi.org/10.1175/JAMC-D-12-0291.1

The ability of reanalyses to reproduce the seasonal variations of precipitation and temperature over the United States during summer, when model forecasts have characteristically weak forecast skill, is assessed. Precipitation variations are reproduced well over much of the United States, especially in the Northwest, where ENSO contributes to the large-scale circulation. Some significant biases in the seasonal mean do exist. The weakest regions are the Midwest and Southeast, where land–atmosphere interactions strongly affect the physical parameterizations in the forecast model. In particular, the variance of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) is lower than observed (extreme seasonal averages are weak), and the variability of the Interim ECMWF Re-Analysis (ERA-Interim) is affected by spurious low-frequency trends. Surface temperature is generally robust among the reanalyses examined, though; reanalyses that assimilate near-surface observations have distinct advantages. Observations and forecast error from MERRA are used to assess the reanalysis uncertainty across U.S. regions. These data help to show where the reanalysis is realistically replicating physical processes, and they provide guidance on the quality of the data and needs for further development.

Figure: MERRA Summer Seasonal mean precipitaiton correlated to CPC Gauge observations. The white contour indicates statistically significant positive correlation at 99%.

Land surface air temperature (SAT) is one of the most important variables in weather and climate studies, and its diurnal cycle and day-to-day variation are also needed for a variety of applications. Global long-term hourly SAT observational data, however, do not exist. While such hourly products could be obtained from global reanalyses, they are strongly affected by model parameterizations and hence are found to be unrealistic in representing the SAT diurnal cycle (even after the monthly mean bias correction) (see Figure below; left panels: SAT anomalies; right panels: SAT differences between reanalyses and CRU).

Global hourly 0.5-degree SAT datasets are developed here based on four reanalysis products [MERRA (1979-2009), ERA-40 (1958-2001), ERA-Interim (1979-2009), and NCEP/NCAR (1948-2009)] and the CRU TS3.10 data for 1948-2009. Our three-step adjustments include the spatial downscaling to 0.5-degree grid cells, the temporal interpolation from 6-hourly (in ERA-40 and NCEP/NCAR) to hourly using the MERRA hourly SAT climatology for each day (and the linear interpolation from 3-hourly in ERA-Interim to hourly), and the mean bias correction in both monthly mean maximum and minimum SAT using the CRU data.

The final products have exactly the same monthly maximum and minimum SAT as the CRU data, and perform well in comparison with in situ hourly measurements over six sites and with a regional daily SAT dataset over Europe. They agree with each other much better than the original reanalyses, and the spurious SAT jumps of reanalyses over some regions are also substantially eliminated. One of the uncertainties in our final products can be quantified by their differences in the true monthly mean (using 24 hourly values) and the monthly averaged diurnal cycle. The datasets will be available to the community in late 2013.

The paper is available at: http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00682.1

Data Access DS193.0: NCAR

From Daehyun Kim, M.-I. Lee, D. Kim, S.D. Schubert, D. E. Waliser, and B. Tian, Clim. Dyn., doi 10. 1007/s00382-013-1890-x. Online First.

     The study examined the representation of tropical subseasonal variability of precipitation in five global reanalyses (RAs) – the three generations of global RAs from NCEP (NCEP/NCAR, NCEP-DOE, and CFSR), and two other RAs from ECMWF (ERA-I) and NASA/GSFC (MERRA). The modern RAs show significant improvement in their representation of the mean state and subseasonal variability of precipitation when compared to the two older NCEP RAs. The modern RAs also show higher coherence of CCEWs with observed variability and more realistic eastward propagation of the MJO precipitation. However, the probability density of rain intensity in the modern RAs shows discrepancies from observations that are similar to what the old RAs have. The study also indicates that the modern RAs exhibit common systematic deficiencies in the variability and the phase relationship of high-frequency CCEWs other than the MJO. The study leaves a detailed analysis of impacts driven by assimilating moisture-related satellite radiances in the modern RAs for further study, which are speculated as at least one of the potential sources for the improvement from the old RAs in the representation of MJO and CCEWs.

Scatter plot between East/West power ratios of symmetric and antisymmetric MJO. The ratio is defined as the sum of power over the MJO band (wavenumber 1-5, period 30-60 days) divided by that of the westward propagating counterpart.

Coherence squared (colors) and phase lag (vectors) between GPCP precipitation and precipitation from a) NCEP/NCAR, b) NCEP-DOE, c) CFSR, d) ERA-I, e) MERRA, and f) TRMM. The symmetric spectrum is shown. Spectra were computed at individual latitude, and then averaged over 15^oS–15^oN. Vectors represent the phase by which reanalysis precipitation lags GPCP, increasing in the clockwise direction. A phase of 0^o is represented by a vector directed upward.

From Chung, E.-S., B. J. Soden, B. J. Sohn, and J. Schmetz (2013), An assessment of the diurnal variation of upper tropospheric humidity in reanalysis data sets, J. Geophys. Res. Atmos., 118, doi:10.1002/jgrd.50345.

Diurnal variations of upper tropospheric humidity in five different reanalysis datasets are compared over convective land and ocean regions, and evaluated using multiple satellite observations as a reference. All reanalysis datasets reproduce the day/night contrast of upper tropospheric humidity and the land/ocean contrast in the diurnal amplitude. The infrared satellite measurements indicate a slightly later diurnal minimum over land relative to most reanalyses and the microwave satellite measurements, suggesting that cloud masking of the infrared radiances may introduce a small (~ 3 hr) bias in the phase. One reanalysis exhibits a substantially different diurnal cycle over land which is inconsistent with both infrared and microwave satellite measurements and other reanalysis products. This product also exhibits a different covariance between vertical velocity, cloud water and humidity than other reanalyses, suggesting that the phase bias is related to deficiencies in the parameterization of moist convective processes.

Diurnal anomaly of Meteosat-5 water vapor channel brightness temperature simulated from NCEP/DOE, 20th Century Reanalysis, ERA-40, ERA-Interim, and MERRA over the convectively active regions of Africa and the Atlantic Ocean for the period 1984-2004. The red lines denote the diurnal anomaly of observed brightness temperature for the same period.

Citation: L'Heureux, M.L., S. Lee, and B. Lyon, 2013: Recent multidecadal strengthening of the Walker circulation across the tropical Pacific. Nature Clim Change, doi: 10.1038/nclimate1840.

Ten different datasets from reanalyses, reconstructions, and in situ measurements are examined for sea level pressure (SLP) trends over the tropical Pacific.  Instead of fitting a single least squares linear trend through the entire 1900-2011 record, running linear trends are calculated every six months over varying length windows (10-, 20-, 30-, and 40-year).  For the 20-40 year windows over Indonesia (110-160E, 10S-10N), a gradual increase from negative to positive SLP trends is evident beginning in 1910-1920 (see top figure below).  Then, starting in 1955-1965, the the positive trends cease growing and eventually trends become significantly negative over the last several decades. In contrast, over the Eastern Pacific (130-80W, 10S-10N), trends are less significant but there is evidence for a tendency towards positive SLP trends starting in the late 1950s or 1960s (see bottom figure below).  The tendency towards lower SLP over Indonesia and higher SLP over the eastern Pacific suggests a strengthening Walker circulation over the last half of the 20^th century.  This increase in the Walker circulation becomes even more apparent when ENSO variability (using the Nino-3.4 index) is linearly removed from the datasets.  Moreover, the tendency for a strengthening Walker circulation appears to be nearly concurrent with the shift toward positive trends in global average temperatures.

While not shown below (see supplementary info), we note that a significant lack of in situ SLP data (ICOADS.v2.5) over the tropical Pacific is linked to larger disagreement in the datasets during the first half of the 20th century. Therefore, caution is recommended when interpreting the observed linear SLP trends prior to the 1950s.   

Lavers, D.A., G. Villarini, R.P. Allan, E.F. Wood, and A.J. Wade, The detection of atmospheric rivers in atmospheric reanalyses and their links to British winter floods and the large-scale climatic circulation, Journal of Geophysical Research, 117, D20106, doi:10.1029/2012JD018027, 2012.

Atmospheric rivers (ARs) are narrow bands of enhanced water vapor transport in the lower troposphere, and are the cause of extreme precipitation and floods over mid-latitude regions.  This study introduces an algorithm (based on the vertically-integrated horizontal Water Vapor Transport, IVT) for the detection of persistent ARs (lasting 18 hours or longer) in five atmospheric reanalysis products.  The reanalyses considered were: (1) NCEP Climate Forecast System (CFSR), (2) ECMWF ERA-Interim (ERAIN), (3) Twentieth Century Reanalysis (20CR), (4) NASA Modern Era Retrospective-Analysis for Research and Applications (MERRA), and (5) NCEP–NCAR.

Figure 1: Time series of the number of persistent ARs in each winter half-year (October to March) over 1980–2010 in the five reanalyses (left y-axis).  The black dashed line represents the winter half-year Scandinavian Pattern index (anomaly values shown on the right y-axis).  The total number of ARs for each reanalysis product is given in the legend.

Time series of the number of detected ARs in each winter half-year over 1980–2010 in the five reanalyses are shown in Figure 1 (taken from JGR paper).  The number of ARs varies between about 2 and 14 events per winter.  Each product identifies a different number of ARs ranging from 190 in CFSR to 264 in 20CR, which may be partly caused by the different IVT threshold values used for each reanalysis, as well as the different assimilating models and data used. As shown in Figure 1, a negative dependence was found between AR frequency and the winter half-year Scandinavian Pattern. In conclusion, the generally good agreement of AR occurrence between the reanalyses suggests that realistic sea surface temperatures and atmospheric circulation, used in the five products, are sufficient for simulating the AR structures.

Figure 2: The IVT (in kg m^-1 s^-1) for (a) 20CR, (c) CFSR, (d) ERAIN, (e) MERRA, (f) NCEP–NCAR and (b) 20CR MSLP field (in hPa) at 1200 UTC 10^th December 1994 before the largest flood event on 11^th December 1994 in the Ayr at Mainholm basin in Scotland.  The “L” and “H” in panel (b) refer to the Low and High pressure centres respectively; the black dots in the panels mark the location of the Ayr at Mainholm basin.

An example of an AR captured in the five reanalyses is shown in Figure 2 (taken from JGR paper); this AR was behind the largest flood in one of the study river basins. The effect of the different reanalysis grid resolutions is shown, with the peak IVT and hence AR region (as shown by the red and orange colors) in the finer resolution CFSR, ERAIN and MERRA products occupying a smaller region than in the 20CR or NCEP-NCAR. 

A strong link exists between the detected ARs and the biggest winter floods in the nine study basins. In one western British basin about 80% of the 31 largest floods followed a persistent AR. As the largest floods in these basins occur in the winter, these results provide evidence that ARs control a large part of the upper tail of the flood peak distribution.   

Wang, J., W. Wang, X. Fu, K.-H. Seo, 2012: Tropical intraseasonal rainfall variability in the CFSR. Climate Dynamics, 38, 2191-2207, http://link.springer.com/article/10.1007/s00382-011-1087-0

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.

Wavenumber-frequency spectra of 10ºS–10ºN average of raw daily–mean anomalies of precipitation. a CMORPH; b R1; c R2; and d CFSR The unit is 0.001 mm² days⁻². Contours are shaded starting at 6 with an interval of 3.

Wavenumber-frequency spectra of 10ºS–10ºN average of raw daily–mean anomalies of precipitation. a ERAI; and b MERRA. 

Ebisuzaki, W. and L. Zhang, 2011: Assessing the performance of the CFSR by an ensemble of analyses. Climate Dynamics, 37, 2541-2550

http://link.springer.com/article/10.1007/s00382-011-1074-5#

The Climate Forecast System Reanalysis (CFSR, Saha et al. in Bull Am Meteor Soc 91:1015–1057, 2010) is the latest global reanalysis from the National Centers of Environmental Prediction (NCEP). In this study, we compare the CFSR tropospheric analyses to two ensembles of analyses. The first ensemble consists of 12 h analyses from various operational analyses for the year 2007. This ensemble shows how well the CFSR analyses can capture the daily variability. The second ensemble consists of monthly means from the available reanalyses from the years 1979 to 2009 which is used to examine the trends. With the 2007 ensemble, we find that the CFSR captures the daily variability in 2007 better than the older reanalyses and is comparable to the operational analyses. With the ensemble of monthly means, the CFSR is often the outlier. The CFSR shows a strong warming trend in the tropics which is not seen in the observations or other reanalyses.

The 200 hPa height (m) for Singapore (grid cell average). The time series were low passed filtered by a 12 months running mean. Shown are CFSR (red), ERA-40 (orange), JRA-25 (light blue), MERRA (green), R1 (blue), R2 (black) and the observation (thick black line).