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.

Purpose: To perform basic extrapolations resulting in continuous fields of  NASA MERRA reanalyses for selected model levels where pressure values are greater than the surface pressure (i.e., filling in model grid points "below ground" with defined values). This could be useful where a continuous global field is needed, such as in modeling applications or spherical harmonic analyses. Code samples are included, below.  Geopotential heights are extrapolated by applying the hydrostatic equation and equation of state, as are temperature fields using adiabatic lapse rate approximations (moist or dry). 

Background reference from the MERRA FAQs (https://gmao.gsfc.nasa.gov/research/merra/faq.php)

Q. 6 "Why are there such large discrepancies at 1000MB and 850MB between MERRA and other Reanalyses?"

A. "The GEOS5 data assimilation system used to produce MERRA does not (or did not at the time of production) extrapolate data to pressure levels greater than the surface pressure. These grid points are marked by undefined values. The result is that area averages that include these points will not be representative compared to other data sets without additional screening. Time averages, such as monthly means, may also have substantial differences at the edges of topography. The lowest model level data and surface data are available so that users can produce their own extrapolation. A page discussing this issue is available. See http://gmao.gsfc.nasa.gov/research/merra/pressure_surface.php.   "

Below, note regions corresponding to high model topography where contours end due to undefined values in a sample MERRA Reanalysis plot (for example, portions of Rocky Mountains, Andes Mountains, Tibetan Plateau, Antarctica):

With extrapolation, the values are "filled in", as this figure shows (below):

Here is another example with Geopotential height:

And, after extrapolation:

Below are sample codes and scripts that perform extrapolation on 5 variables from a MERRA NetCDF file.

Software needed: GrADS, Fortran compiler, Unix/Linux shell environment.

Need a MERRA NetCDF or HDF file along with the following:

1.Shell executable extrap.csh:   Runs programs, creates output.  Calls grads scripts and fortran executable

*********************************** 

*********************************************

2. Two GrADS control files are needed:

check_filled_9999.ctl (views output w/-9999 set as undefined):

'reinit'

'sdfopen MERRA300.prod.assim.inst3_3d_asm_Cp.20100110.SUB_1.nc'

'set x 1 288'

'set y 1 144'

'set gxout fwrite'

'set fwrite tot_merra_filled_9999.prs'

t=1

while (t <= 8)

 'set t 't

say 'Analysis time is 't

*height

  z=1

  while (z<=17)

    'set  z 'z

    'd const(maskout(h,h),-9999.,-u)'

    z=z+1

  endwhile

*

*Temperature

  z=1

  while (z<=17)

    'set  z 'z

    'd const(maskout(t,t),-9999.,-u)'

    z=z+1

  endwhile

*

*u-wind

  z=1

  while (z<=17)

    'set  z 'z

    'd const(maskout(u,u+1000),-9999.,-u)'

    z=z+1

  endwhile

*

*v-wind

  z=1

  while (z<=17)

    'set  z 'z

    'd const(maskout(v,v+1000),-9999.,-u)'

    z=z+1

  endwhile

*

*RH

  z=1

  while (z<=17)

    'set  z 'z

    'd const(maskout(rh,rh),-9999.,-u)'

    z=z+1

  endwhile

*

 t = t + 1

endwhile

'disable fwrite'

'quit'

 4.  Fortran file extrap_merra.f:

 Code to perform extrapolation, called in extrap.csh.

Contact: Lee Byerle 

1. Executive Summary

Reanalyses have become an integral part of Earth system science research across many disciplines. While originating in the atmospheric sciences and Numerical Weather Prediction (NWP), the essential methodology has been adopted in the fields of oceanography and terrestrial ecosystems and hydrology, with emerging research in atmospheric composition, cryosphere and carbon cycle disciplines. Major challenges lie ahead as the disparate nature of each discipline become joined in Earth system analyses. Clearly, substantial progress has been made since the last reanalysis conference (Jan 2008, Tokyo Japan). Newer atmospheric reanalyses (MERRA, CFSR and ERA-Interim) have been evaluated in depth, and many strengths and weaknesses identified. Early results from JRA-55 are becoming available. There is tremendous potential in the NOAA/ESRL 20CR surface data only reanalysis, and in the uncertainty provided from the ensemble. Ensembles of multiple reanalysis systems can provide valuable information. Although there are several reanalyses efforts worldwide at present, the community consensus is that the diversity among them will enable deeper understanding of the reanalyses systems, their strengths and weaknesses and their representation of the underlying earth system processes/phenomena. This is then reflected in the producers’ plans (notably those of JMA and ECMWF) leaning toward “families” of reanalyses (each system producing various configurations of reanalysis). There is much to be learned about the observations, data assimilation, modelling, and coupling the Earth system but new data systems, efficient computing and processing of the multitude of reanalyses products are urgently needed. The integrating nature of reanalyses across components of the Earth system (land, ocean atmosphere) is a key benefit, but to date very few reanalyses systems include all relevant data and assimilate all Earth system components. Observations are the fundamental resource for reanalysis. The need for long-term records of continuous measurements cannot be overstated. Data recovery efforts for in situ and remotely sensed observations are essential to extend the records as far back in time as possible to support long historical reanalyses, while concerted efforts to maintain and develop the observing system forward in time to ensure continuity for the future. Documenting the observations and their uses in past reanalyses can be beneficial both to use in future reanalyses and to understanding of the observations. Expertise for all the observations exists around the world, and so, international coordination of observations for reanalysis should be an imperative for international observations and research coordination programs such as GCOS, GEOSS, CEOS and WCRP. Confronting models with observations continues to be important but also confronting models (e.g. ocean and atmospheric models) and observations (e.g. discrepancies between micro-wave satellite records and radiosonde measurements) among themselves are equally important research tasks for the future. Data assimilation methods continue to improve, but have more challenges ahead, such as the amelioration of shocks associated with changes to the observing system (also better characterizing and reducing model bias) and developing uncertainty estimates for reanalyses. Reanalyses have recently been getting attention from the climate monitoring community, and so, their strengths, weaknesses and uncertainty are increasingly exposed.

Reanalysis users often ask which reanalysis is best for a given topic. As newer reanalyses come along, the answer may not be widely known, if at all. In this regard, the community of users and developers must collaborate, given the diversity of applications. The web site, reanalysis.org, has been promoted as an open media for conveying the latest understanding of reanalyses data. Additionally, NCAR’s Climate Data Guide (https://climatedataguide.ucar.edu/) is a very useful go-to source for scientifically sound information and advice on the strengths, limitations and applications of climate data, including reanalyses. While fundamental information is available primarily on atmosphere and ocean reanalyses, discussions on the latest research and understanding are progressing more slowly. Ultimately, it is incumbent on the researchers to assess the multitude of reanalyses objectively. New data systems are required that allow for more efficient cross comparisons among the various reanalyses (such as those used for AMIP and CMIP studies and the Earth System Grid, ESG). The 4th WCRP International Conference on Reanalyses produced excellent discussions across all the important issues in reanalyses, but continuing the progress and improvements will require sustained efforts over the long term. While progress has been made across the major aspects of reanalyses, significant limitations persist. The conference has identified broad directions to continue the advancement of reanalysis:

1) Quantitative Uncertainty – Reanalyses are based on observations, and can include the errors of observations and the assimilating system. It is recommended to have reanalysis data available in a common framework so as to facilitate the analysis of their strengths and weaknesses. The notion of Families of reanalyses will likewise expose the impact of assimilating observations on the analyses. Ensemble methods can also provide quantitative uncertainty estimates. Lastly, passing observations and innovations into an easily accessible data format can promote deeper investigation of the use of observations in the reanalyses.

2) Qualitative Uncertainty – Often researchers inquire to the applicability of a reanalysis for a given phenomena, or even, which reanalysis is best. Often, this is not satisfactorily known, varies with application and requires significant time and research. Therefore, sharing reanalysis knowledge and research in a timely manner, among researchers and developers is a critical need to allow subsequent exploitation by the climate community. The reanalysis.org effort has provided an initial effort along these lines, but more participation is encouraged. In addition, http://climatedataguide.ucar.edu provides informed commentary on analysis and other datasets. Likely, even more lines of communications are required. 

3) Earth System Coupling – The natural course of reanalysis development is toward, longer data sets with coupled Earth system components that will ultimately contribute to improved coupled predictions. The use of more varied observations (e.g. aerosols) will reinforce the physical representation of the Earth system processes in the reanalysis systems. There is a need to develop independent and innovative modeling, coupling and data assimilation methods to represent the Earth System throughout the time span of the observational record. More interdisciplinary collaborations in the system development and observational research will begin to address this need. 

4) Reanalyses, Observations and Stewardship – While the observational records have been greatly improved since the first reanalyses through research, reprocessing and homogenizations, research and improvements continue their development. Reprocessing and intercalibrations of observed records are critical to improve the quality and consistency of reanalyses. In situ and satellite data need to be found, rescued and archived into suitable formats to extend the reanalysis record back in time. Reanalysis systems for the atmosphere, ocean, cryosphere, land, and coupled earth system are needed that maximize use of the observations as far back as each instrumental record will allow. It is important for the observational data and reanalysis developers to maintain communication, so the latest data are used in reanalyses, and also that output of reanalyses may contribute to the understanding of observations. Such an endeavor should be coordinated at an international level.

 Link to Full Report

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.

 

Kumar, A., and Z.-Z. Hu, 2012: Uncertainty in the ocean-atmosphere feedbacks associated with ENSO in the reanalysis products. Clim. Dyn., 39 (3-4), 575-588. DOI: 10.1007/s00382-011-1104-3.



http://www.springerlink.com/content/w66j8vh1n826n711/



 

The evolution of El Niño-Southern Oscillation (ENSO) variability can be characterized by various ocean-atmosphere feedbacks, for example, the influence of ENSO related sea surface temperature (SST) variability on the low-level wind and surface heat fluxes in the equatorial tropical Pacific, which in turn affects the evolution of the SST.  An analysis of these feedbacks requires physically consistent observational data sets.  Availability of various reanalysis data sets produced during the last 15 years provides such an opportunity. A consolidated estimate of ocean surface fluxes based on multiple reanalyses also helps understand biases in ENSO predictions and simulations from climate models. 

In this paper, the intensity and the spatial structure of ocean-atmosphere feedback terms (precipitation, surface wind stress, and ocean surface heat flux) associated with ENSO are evaluated for six different reanalysis products.  The analysis provides an estimate for the feedback terms that could be used for model validation studies.  The analysis includes the robustness of the estimate across different reanalyses. Results show that one of the “coupled” reanalysis among the six investigated is closer to the ensemble mean of the results, suggesting that the coupled data assimilation may have the potential to better capture the overall atmosphere-ocean feedback processes associated with ENSO than the uncoupled ones.

From: Vose, R. S., S. Applequist, M. J. Menne, C. N. Williams Jr., and P. Thorne. 2012: An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses. Geophys. Res. Lett., 39, L10703, doi:10.1029/2012GL051387.

Least-squares trends (°C dec^-1) in mean annual temperature over the conterminous United States during the period 1979-2008.

Atmospheric Reanalyses – Recent Progress and Prospects for the Future.

A Report from a Technical Workshop, April 2010

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo, Kazutoshi Onogi, Ron Gelaro, Michael G. Bosilovich, Arlindo da Silva, Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, Hirotaka Kamahori, Robert Kistler, and Suranjana Saha

Abstract

In April 2010, developers representing each of the major reanalysis centers met at Goddard Space Flight Center to discuss technical issues – system advances and lessons learned – associated with recent and ongoing atmospheric reanalyses and plans for the future. The meeting included overviews of each center’s development efforts, a discussion of the issues in observations, models and data assimilation, and, finally, identification of priorities for future directions and potential areas of collaboration. This report summarizes the deliberations and recommendations from the meeting as well as some advances since the workshop.

Summary of Recommendations

From Rienecker et al. (2012)

Target areas for improvements for the next generation of atmospheric reanalyses include:

• The hydrological cycle

• The quality of the reanalyses in the stratosphere

• The quality of the reanalyses over the polar regions

• Representation of surface fluxes

• Observational bias corrections and/or cross-calibration across platforms

• Estimates of uncertainty in the analyses, and

• Reductions of spurious trends and jumps associated with the changing observing system.

Several recommendations were made regarding areas for coordination between reanalysis centers in order to prepare for the next reanalyses:

• Preparation and sharing of lists of anomalous behavior or features to help identify how common anomalies are across the various reanalyses.

• Examination of data utilization, including QC decisions, innovation statistics, bias corrections, outcomes of data selection algorithms, cloud detection outcomes, etc.

• Identification of joint experiments to be conducted to elucidate issues found to be in common in different reanalyses.

• Sharing of results from jointly designed sensitivity experiments.

• Coordination of input observations and ancillary data and centralization of the serving of these observations where possible.

• Expansion of ACRE’s efforts for contributing surface observations to 20CR to contributing to all future reanalysis efforts, possibly acting as a data coordinator and provider of surface data for all future reanalyses in collaboration with working groups of GCOS and WCRP.

• Development of innovative diagnostics and metrics to help quantify observational issues, the quality and also agreement of the reanalyses.

The workshop recommended that a mechanism be established for the timely exchange of information about the quality of the reanalyses, results of experiments, and plans for future developments. This idea was quickly embraced with the establishment of Reanalysis.org. However, further progress is needed in the utilization of such a capability to enhance communications between reanalysis groups.

Finally, consistent with the Arkin et al. (2003) workshop report, the workshop participants recommended extending the reanalysis record for as long as possible, to include the 1970s for reanalyses focused on the satellite era, and to go back at least to 1850 with those reanalyses using sparse observations.

Arkin, P., E. Kalnay, J. Laver, S. Schubert, and K. Trenberth, 2003: Ongoing analysis of the climate system: A workshop report. NASA, NOAA, and NSF, 48 pp.  Link to Full Report.

Many white papers, published papers, and other reports have been issued relating to reanalyses, their current status, and recommendations for improvements.  Please include such reports below in reverse chronological order, with links to a Discussion reanalyses.org page which summarizes the report and a link to the Full Report itself. 

Fujiwara, M., J. S. Wright, G. L. Manney, L. J. Gray, J. Anstey, T. Birner, S. Davis, E. P. Gerber, V. L. Harvey, M. I. Hegglin, C. R. Homeyer, J. A. Knox, K. Krüger, A. Lambert, C. S. Long, P. Martineau, A. Molod, B. M. Monge-Sanz, M. L. Santee, S. Tegtmeier, S. Chabrillat, D. G. H. Tan, D. R. Jackson, S. Polavarapu, G. P. Compo, R. Dragani, W. Ebisuzaki, Y. Harada, C. Kobayashi, W. McCarty, K. Onogi, S. Pawson, A. Simmons, K. Wargan, J. S. Whitaker, and C.-Z. Zou: Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems, Atmos. Chem. Phys., 17, 1417-1452, doi:10.5194/acp-17-1417-2017, 2017. Link to Paper. 

Compo, G., J. Carton, X. Dong, A. Kumar, S. Saha, J. S. Woollen, L. Yu, and H. M. Archambault, 2016: Report from the NOAA Climate Reanalysis Task Force Technical Workshop. NOAA Technical Report OAR CPO-4, Silver Spring, MD. doi: 10.7289/V53J39ZZ. Full Report.

Bosilovich, M.G, J.-N.Thépaut, O. Kazutoshi, A. Kumar, D. Dee and Otis Brown, 2015: WCRP Task Team for Intercomparison of ReAnalyses (TIRA). White Paper. Full Report.

CORE CLIMAX, 2014: Procedure for comparing reanalyses, and comparing reanalyses to assimilated observations and CDRs. COordinating Earth observation data validation for RE-analysis for CLIMAte ServiceS, Grant agreement No: 313085, Deliverable D5.53, pp 45. Full report

Bosilovich, M. G., J. Kennedy, D. Dee, R. Allan and A. O’Neill, 2013: On the Reprocessing and Reanalysis of Observations for Climate, Climate Science for Serving Society: Research, Modelling and Prediction Priorities. G. R. Asrar and J. W. Hurrell, Eds. Springer, in press. Full Report. Discussion Page. 

Bosilovich, M.G., M. Rixen, P. van Oevelen, G. Asrar, G. Compo, K. Onogi, A. Simmons, K. Trenberth, D. Behringer, T. H. Bhuiyan, S. Capps, A. Chaudhuri, J. Chen, L. Chen, N. Colasacco-Thumm, M. G. Escobar, C.R. Ferguson, T. Ishibashi, M.L.R. Liberato, J. Meng, A. Molod, P. Poli, J. Roundy, K. Willett, J. Woollen,R. Yang, 2012: Report of the 4th World Climate Research Programme International Conference on Reanalyses, Silver Spring, Maryland, USA, 7-11 May 2012. WCRP Report 12/2012. Full Report. Discussion Page.

Rienecker, M.M., D. Dee, J. Woollen, G.P. Compo, K. Onogi, R. Gelaro, M.G. Bosilovich, A. da Silva, S. Pawson, S. Schubert, M. Suarez, D. Barker, H. Kamahori, R. Kistler, and S. Saha, 2012: Atmospheric Reanalyses—Recent Progress and Prospects for the Future. A Report from a Technical Workshop, April 2010. NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM–2012-104606, Vol. 29, 56 pp. Full Report. Discussion Page.

Climate Change Science Program, 2008: Weather and Climate Extremes in a Changing Climate. Regions of Focus: North America, Hawaii, Caribbean, and U.S. Pacific Islands. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research. [Thomas R. Karl, Gerald A. Meehl, Christopher D. Miller, Susan J. Hassol, Anne M. Waple, and William L. Murray (eds.)]. Department of Commerce, NOAA's National Climatic Data Center, Washington, D.C., USA, 164 pp. Full Report.

Climate Change Science Program, 2008: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Randall Dole, Martin Hoerling, and Siegfried Schubert (eds.)]. National Oceanic and Atmospheric Administration, National Climatic Data Center, Asheville, NC, 156 pp. Full Report.

Lermusiaux, P.F.J.,  P. Malanotte-Rizzoli, D. Stammer, J. Carton, J. Cummings, and A.M. Moore, 2006: Progress and Prospects in U.S. Data Assimilation in Ocean Research. Oceanography, 19, 172-183. Full Report.

Arkin, P., E. Kalnay, J. Laver, S. Schubert, and K. Trenberth, 2003: Ongoing analysis of the climate system: A workshop report. NASA, NOAA, and NSF, 48 pp.  Full Report.

A recent publication on different Arctic cloud climatologies intercomparison shows a wide spread among observations and reanalyses. Reanalyses generally are not in a close agreement with satellite and surface observations of cloudiness in the Arctic. Several reanalyses show the highest values of total cloud fraction over the central part of the Arctic Ocean but not over the Norwegian Sea and the Barents Sea as observations do. The maximum and minimum of total cloud fraction in the annual cycle are shifted by 1-2 months compared to observations.

December-January-February and June-July-August mean of TCF over the Arctic (north of 60◦N) from different data.

The annual cycle of TCF.

Normalized pattern statistics showing differences among different observational and reanalyses TCF spatial distribution (Taylor diagrams).

Alexander Chernokulsky and Igor I. Mokhov, “Climatology of Total Cloudiness in the Arctic: An Intercomparison of Observations and Reanalyses,” Advances in Meteorology, vol. 2012, Article ID 542093, 15 pages, 2012. doi:10.1155/2012/542093