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.

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.

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. 

Wang, W., P. Xie, S.-H. Yoo, Y. Xue, A. Kumar and X. Wu, 2011:  An assessment of the surface climate in the NCEP climate forecast system reanalysis. Climate Dynamics, 37, 1601-1620

http://link.springer.com/article/10.1007%2Fs00382-010-0935-7

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere–ocean–sea ice–land forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998–2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately.

Precipitation climatology (contour) and differences (shading) from the observation taken as the average of CMAP and GPCP. a Observation, b R1, c R2, d ERA40, and e CFSR. Contours are plotted at 2, 4, 8, and 12 mm/day, and shadings are at −4, −2, −1, −0.5, 0.5, 1, 2, and 4 mm/day. Global mean (GM) climatology is shown above each panel

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).

Kumar, A., L. Zhang and W. Wang, 2012: Sea Surface Temperature - Precipitation Relationship in Different Reanalyses. Monthly Weather Review, doi: http://dx.doi.org/10.1175/MWR-D-12-00214.1

The focus of this investigation is how the relationship at intraseasonal time scales between sea surface temperature (SST) 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 reanalysis – NCEP/NCAR reanalysis (R1) and NCEP/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 generation of atmosphere alone reanalyses, the Modern Era-Retrospective-analysis for Research and Applications (MERRA) and the ECMWF Re-Analysis Interim (ERAI). The results clearly demonstrate that the differences in 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. SST-P relationship in different reanalyses, when computed against a single SST for the benchmark, demonstrates a relationship that is common across all the reanalyses and observations.

Lead-lag SST-precipitation correlation for various reanalyses and for observations over the tropical western Pacific (averaged over 10°S–10°N, 130°–150°E) for respective SSTs were used (left panel), and for NCDC SST as the benchmark (right panel). Negative (positive) lag in days on the x axis indicates days by which the SST leads (lags) the precipitation.

Paek, H. and H.-P. Huang (2012), A comparison of the interannual variability in atmospheric angular momentum and length-of-day using multiple reanalysis data sets, J. Geophys. Res., 117, D20102, doi:10.1029/2012JD018105. 

This study performs an intercomparison of the interannual variability of atmospheric angular momentum (AAM) in eight reanalysis datasets for the post-1979 era.  The AAM data are further cross validated with the independent observation of length-of-day (LOD). The intercomparison reveals a close agreement among almost all reanalysis datasets, except that the AAM computed from the 20^th Century Reanalysis (20CR) has a noticeably lower correlation with LOD and with the AAM from other datasets.  This reduced correlation is related to the absence of coherent low-frequency variability, notably the Quasi-biennial Oscillation, in the stratospheric zonal wind in 20CR.  If the upper-level zonal wind in 20CR is replaced by its counterpart from a different reanalysis dataset, a higher value of the correlation is restored.  The correlation between the AAM and the Nino3.4 index of tropical Pacific SST is also computed for the reanalysis datasets. In this case, a close agreement is found among all, including 20CR, datasets. This indicates that the upward influence of SST on the tropospheric circulation is well captured by the data assimilation system of 20CR, which only explicitly incorporated the surface observations.  This study demonstrates the overall close agreement in the interannual variability of AAM among the reanalysis datasets.  This finding also reinforces the view expressed in a recent work by the authors that the most significant discrepancies among the reanalysis datasets are in the long-term mean and long-term trend. Paek and Huang 2012

   The time series of ∆LOD (red curve, converted to an equivalent ∆AAM using Eq. (3)) and ∆AAM (blue curve) from different reanalysis datasets: (a) NCEP R-1, (b) NCEP R-2, (c) CFSR, (d) 20CR, (e) ERA-40, (f) ERA-Interim, (g) JRA-25, and (h) MERRA. The time series of ∆Nino3.4 from HadISST is imposed as the green curve in panel (h). The units for ∆AAM and ∆Nino3.4 are 10²⁵ kg m² s^-1 and 1 °C, respectively. The time series for ∆AAM in panel (e) is slightly shorter due to the shorter record of the ERA-40 dataset.

   The time series of ∆M_R,STRAT for four selected reanalysis datasets including 20CR. (b) The original (dark blue) and the modified ∆AAM (light blue) for 20CR. The modified ∆AAM is calculated by replacing the zonal wind in the stratosphere by that from NCEP R-1. See text for detail.  The time series of ∆LOD (converted to an equivalent ∆AAM) is also shown as the red curve. The unit for ∆AAM is 10²⁵ kg m² s^-1.

Zhang, Q., H. Körnich and K. Holmgren, 2012: How well do reanalyses represent the southern African precipitation? Clim. Dyn., DOI: 10.1007/s00382-012-1423-z.

http://dx.doi.org/10.1007/s00382-012-1423-z

Monthly-mean precipitation observations over southern Africa are used to evaluate the performance of eight global reanalyses: ERA-40, ERA-interim, JRA-25, MERRA, CFSR, NCEP-R1, NCEP-R2 and 20CRv2. All eight reanalyses reproduce the regionally averaged seasonal cycle fairly well; a few spatial mismatches with the observations are found in the climate mean for the rainy season. Principal component analyses show a dipole in the leading modes of all reanalyses, however with crucial differences in its spatial position.

Possible reasons for the differences between the reanalyses are discussed on the basis of the ERA-interim and 20CRv2 results. A comparison between the moisture transports shows that ERA-interim manifests a very strong moisture convergence over the eastern equatorial Atlantic, resulting in the strong precipitation here. This excessive convergence may be due to the water–vapor assimilation and convection parameterization. Over the Indian Ocean, the ITCZ is shifted northward in ERA-interim compared to its position in 20CRv2. This discrepancy is most likely attributable to the meridional SST gradients in the Indian Ocean which are significantly larger in the ERA-interim than those in the 20CRv2, and the resulting atmospheric response prevents a southward shift of the ITCZ.

Overall, the consistent description of the dynamical circulation of the atmosphere and the hydrological cycle appears as a crucial benchmark for reanalysis data. Based on our evaluation, the preferential reanalysis for investigating the climate variability over southern Africa is 20CRv2 that furthermore spans the longest time period, hence permitting the most precise investigations of interannual to decadal variability.

 

An intercomparison of the global relative angular momentum M_R in five reanalysis datasets, including the Twentieth Century Reanalysis (20CR), is performed for the second half of the twentieth century. The intercomparison forms a stringent test for 20CR because the variability of M_R is known to be strongly influenced by the variability of upper-tropospheric zonal wind whereas 20CR assimilated only surface observations. The analysis reveals good agreement for decadal-to-multidecadal variability among all of the datasets, including 20CR, for the second half of the twentieth century. The discrepancies among different datasets are mainly in the slowest component, the long-term trend, of M_R. Once the data are detrended, the resulting decadal-to-multidecadal variability shows even better agreement among all of the datasets. This result indicates that 20CR can be reliably used for the analysis of decadal-to-interdecadal variability in the pre-1950 era, provided that the data are properly detrended. As a quick application, it is found that the increase in M_Rduring the 1976/77 climate-shift event remains the sharpest over the entire period from 1871 to 2008 covered by 20CR. The nontrivial difference in the long-term trend between 20CR and the other reanalysis datasets found in this study provides a caution against using 20CR to determine the trend on the centennial time scale that is relevant to climate change. These conclusions are restricted to the quantities that depend strongly on the upper-tropospheric zonal wind, but the approach adopted in this work will be useful for future intercomparisons of the low-frequency behavior of other climate indices in the reanalysis datasets. Paek and Huang 2012.

   (a) The 5-year running averaged monthly anomalies of global relative angular momentum, ΔM_R, for the 5 reanalysis datasets. (b) Same as (a) but for ΔM₂₀₀, the angular momentum (per unit pressure thickness) calculated at only 200 hPa level.

   (a) The least-square quadratic fit (dashed curve) to ΔM_R for NCEP Reanalysis I, ERA-40, and 20CR for 1949-1978. (b) the “detrended” time series for the same period, with the quadratic curve removed from the time series. (c) Same as (a) but for 1979-2008 and with the addition of NCEP Reanalysis II and ERA-Interim.  (d) Same as (b) but for 1979-2008 and with the addition of NCEP Reanalysis II and ERA-Interim.

  (a) The global relative angular momentum, M_R, with the long-term mean retained, for the five reanalysis datasets. (b) Same as (a) but for M₂₀₀.  (c) Same as (a) but with the integration carried out only from 150-10hPa and 10°S-10°N to show the contribution from tropical upper atmosphere.

  (a) The summer (JJA)  climatology of zonal mean zonal wind from 1979-2008 for 20CR. (b) Same as (a) but for ERA-Interim. (c) The difference between 20CR and ERA-Interim, i.e., (b) minus (a). (d) is similar to (c) but for the difference between NCEP Reanalysis II and ERA-Interim.  (e)-(h) are similar to (a)-(d) but for winter (DJF). (i)-(l) are similar to (a)-(d) but for the annual mean.  (m) and (n) are the 1979-2008 linear trends for 20CR and ERA-Interim.  Contour intervals are 4 m s^-1 for (a), (b), (e), (f), (i), and (j);  1 m s^-1 for (c), (d), (g), (h), (k), (l), (m) and (n).

The extended intercomparison to CFSR and MERRA can be found at http://icr4.org/posters/Paek_AT-43.pdf.