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.

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 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.   

Brönnimann, S., A.N. Grant, G.P. Compo,T. Ewen, T. Griesser, A.M. Fischer, M. Schraner, and A. Stickler, 2012: A multi-data set comparison of the vertical structure of temperature variability and change over the Arctic during the past 100 years. Cli. Dyn., 39, 1577-1598, doi:10.1007/s00382-012-1291-6.

We compare the daily, interannual, and decadal variability and trends in the thermal structure of the Arctic troposphere using eight observation-based, vertically resolved data sets, four of which have data prior to 1948. Comparisons on the daily scale between historical reanalysis data and historical upper-air observations were performed for Svalbard for the cold winters 1911/1912 and 1988/89, the warm winters 1944/1945 and 2005/2006, and the International Geophysical Year 1957/58. Excellent agreement is found at mid-tropospheric levels. Near the ground and at the tropopause level, however, systematic differences are identified. On the interannual time scale, the correlations between all data sets are high, but there are systematic biases in terms of absolute values as well as discrepancies in the magnitude of the variability. The causes of these differences are discussed. While none of the data sets individually may be suitable for trend analysis, consistent features can be identified from analyzing all data sets together. To illustrate this, we examine trends and 20-yr averages for those regions and seasons that exhibit large sea-ice changes and have enough data for comparison. In the summertime Pacific Arctic and the autumn eastern Canadian Arctic, the lower tropospheric temperature anomalies for the recent two decades are higher than in any previous 20-yr period. In contrast, mid-tropospheric temperatures of the European Arctic in the wintertime of the 1920s and 1930s may have reached values as high as those of the late 20^th and early 21^st centuries.

Time-height cross-section of seasonal mean temperature anomalies as a function of pressure and time for different data sets for the European Arctic (see Fig. 2) in winter. All anomalies are with respect to NNR (1961-1990) except CRUTEM3v (self-climatology, see Brohan et al. 2006). Note that for visualisation purposes, non-overlapping data sets have been combined in some cases, indicated by dashed lines). Between the end of the reconstruction period of REC2 (1957) and the start of ERA-Interim (1989) we show the calibration period of REC2. Yellow colours denote missing values.

Trend in seasonally-averaged temperature profiles over 20-yr periods as a function of pressure and time period for different data sets for the European Arctic (see Fig. 2) in winter. Note that for visualisation purposes, non-overlapping data sets have been combined in some cases, indicated by dashed lines). Between the end of the reconstruction period of REC2 (1957) and the start of ERA-Interim (1989) we show the calibration period of REC2. Yellow colours denote missing values.

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).

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.