1 What are reanalysis data?
Compare weather- or climate-model output to observations: Not only are the model outputs regularly gridded in time and space but are also complete. The availability of observations depends on the coverage, in space and time, of global observing systems, which includes weather stations, radiosondes, airplanes, and instruments onboard satellites. Despite this wealth of data, the coverage is not complete and data quality may vary with time. Some form of data selection and interpolation, or other processing, can bring observations into a uniform shape, which is appealing from a data analysis point of view. Purely mathematical in-terpolation can do this, but does not provide any extra information, as it is un-physical. Inreanalysis, physical models are used to provide the extra information, resulting in a physically consistent description.
Models used for reanalysis are meteorological numerical weather prediction (NWP) models that assimilate available observations and ensure that the numer-ical solution differs as little as possible from the observations where these are present, and offers physically consistent values for data sparse regions. The mod-els start at a state given by previous solutions, calculate one time step forward, compare the solution to observations that are relevant for the time, calculate the error, go back and modify the starting condition, and so on: by iteration, a so-lution (the ‘re-analysis’) is achieved in which the model and the observations are as consistent as possible to within their respective errors. The process is then repeated for the next time step. Reanalysisgrew out of the NWP field since NWP models base their forecasts on current observations and a physical model. As part of the data assimilation, a physics-based quality control is performed. Reanalysis models use similar model software but assimilate historical data – weather station
1DanishClimateCentre, DMI, Lyngbyvej 100, DK-2100 Copenhagen, Denmark
2Centre for Atmospheric Physics and Observations, DMI, Denmark c
EDP Sciences 2015 DOI: 10.1051/978-2-7598-1733-7.c119
data going back more than a century is now being assimilated. As more data describing the past are revealed in digitisation and archival work, new reanalyses can be calculated. At the same time, the descriptions of physical processes in the models are improved by researchers and new re-analyses of the updated body of data become available. This is computationally intensive and expensive and therefore only a limited number ofreanalysisprojects exist – the most important are described below.
In principle, paleo-climate data in the form ofclimatesensitive proxies could also be used forreanalysisif the inherent smoothing inproxydata, and the peculiar forms of climate sensitivity (e.g., non-linearities, and presence for some seasons only) in the proxies were represented by the model.
2 Global reanalysis data sets
There is a limited range of rather widely usedreanalysis datasets available, but the number is growing. While most reanalyses cover the globalatmosphere, there are also reanalyses for the oceans and for the land surfaces. More narrowly focused reanalyses, for polar regions or for details of the hydrological cycle, are also avail-able. The data differ in temporal and spatial resolution, in the time span covered, and in the set of observations that were assimilated by the model. This constitutes a fundamental limitation to the reanalyses – land- and sea-surface data records go back to the mid-19th century, while regular weather balloon data are available only from the 1950s. The 1970’s saw a rapid increase of the number and quality of satellite-based data. Hence, many reanalyses begin in 1979, which is used as the starting point of the “satellite era”.
There are three majorreanalysis projects: the European ERA project, the US NCEP/NCAR project, and the Japanese JRA project. In addition, several specialisedreanalysisprojects exist.
The European atmosphericreanalysisERA-40(Uppala et al.,2005), produced at the European Centre for Medium-range Weather Forecasts (ECMWF), covers the 45-year time period from September 1957 to August 2002. It has a 6-hour time resolution and covers 60 vertical levels up to an altitude of 64 km (0.1 hPa).
The follow-on reanalysis ERA-Interim starts in 1979, is being continued in real time, and has a similar vertical resolution and coverage as ERA-40 (Dee et al., 2011). In addition, a set of completely new reanalyses are currently in prepara-tion through theERA-CLIM3project led by ECMWF. The final globalreanalysis, covering the whole 20th century, 1900–2010, is not expected to be finalised before 2017. However, the results of a few specialised reanalyses, e.g., a high-resolution land-surfacereanalysis and a 20th century atmospheric reanalysisbased only on surface observations, are already available. TheERA-CLIMreanalyses constitute significant advancements, both in terms of resolution, time coverage (the whole
3http://www.era-clim.eu
P. Thejll and H. Gleisner: Reanalysis data 167
20th century, 1900–2010), and in the systematic handling and error character-isation of the observational data. For more in-depth information on ECMWF reanalyses, see lecture notes4prepared by the ECMWF.
The US NCEP/NCAR reanalyses come in two different versions:
NCEP/NCAR I, which covers the time span from 1948 to the present, and NCEP/NCAR II, which only covers the major satellite era after 1979 (Kalnay et al.,1996). Data from these reanalyses are available on 17 pressure levels up to 10 hPa. The latterreanalysisincorporates more observational data and addresses some deficiencies in the older data assimilation system. For the satellite era, 1979 and onward, there is also theNCEP CFSRreanalysis(Saha et al.,2010), which is unusual in the sense that thereanalysisis done with a fully coupled atmosphere-ocean-land surface-sea ice model to provide an optimal estimate of the state of these coupled domains.
The Japanese Meteorological Agency has also produced a set of global atmo-spheric reanalyses: the 55-yearreanalysisJRA-55 with 60 vertical levels, and the 25-yearreanalysisJRA-25 with 40 vertical levels (Onogi et al.,2007). The former starts in 1958 and the latter in 1979. Both are currently undergoing an update to the present.
While these threereanalysisprojects are the most widely used, there are also others available, some of them more specialised. MERRA5 is a NASA reanalysis covering the satellite era. It has partly a focus on the hydrological cycle and water vapour, and has been used extensively for water vapour budget studies. NOAA has produced a 20th century reanalysis6, covering the time 1871–2012. It has a 6-hourly time resolution, covers both thetroposphereand part of thestratosphere, and data are provided on a 2◦grid. A more specialised dataset is the Arctic System Reanalysis (ASR)7, covering the Arctic region at a high spatial resolution. The arctic contents of the majorreanalysis products are revieweded inLindsay et al.
(2014).
3 Limitations
Reanalysis data can be no better than the underlying observations, or than the models that process the data. On the modelling side, limitations exist because of the resolution chosen in the model and the parameterisations used for sub-grid processes. Since different reanalyses use somewhat overlapping, but not identical input data and methods, the quality of the reanalyses differ. Efforts are made to systematically assess and inter-compare reanalyses, e.g., as in the Ocean
4 http://www.ecmwf.int/en/training-course-ecmwf/eumetsat-nwp-saf-satellite-data-assimilation
5http://gmao.gsfc.nasa.gov/merra/
6http://www.esrl.noaa.gov/psd/data/20thC_Rean/
7http://polarmet.osu.edu/ASR/
Synthesis/Reanalysis Intercomparison Project8, and in the SPARC intercompar-ison project (S-RIP)9.
For atmospheric reanalyses, a typical model-caused limitation is the number of vertical levels included in the software – both number of levels and the height reached are important, as is the treatment of the upper boundary. At the upper limit, there is typically a ‘fade to nothing’ condition imposed: the ionosphere andthermosphere, etc. are simply not part of the model, so potential influences originating in those parts of the near-Earth environment cannot be propagated into thestratosphereandtroposphere. Solar UV irradiance does interact with the stratosphericozoneand might couple to thetropospherevia wave coupling (Haigh, 1994), but if the model is not given observations for the UV flux, this will not be part of thereanalysisdataset.
The inputs describing tropospheric and stratospheric conditions are limited to a subset of what is possible and includes temperature, pressure, and humidity.
Satellite and radio occultation (RO) data can be assimilated with methods based on forward modelling from the meteorological model to quantities the satellites measure, such as microwave fluxes and phase-changes in radio waves.
The observations come from surface observations, radiosondes (i.e., weather balloons), aircraft, satellites and also radio-occultations between GPS satellites.
Well-known inhomogeneities are present in reanalyses due to the gradual and time-dependent introduction of satellite, aircraft and RO data, and the disappearance of surface and radiosonde observations. Reanalysisoutput contains many quanti-ties not all of which are primary (such as pressure and temperature). Secondary variables are those (for instance, snow depth, and other surface fluxes) that depend on the state of the system but are not provided as observational input or are only indirectly connected to input observations – they may thus be ‘consistent’ but are not used to define the solution (Kalnay et al.,1996).
4 Reanalysisdata in Sun-Climate research
Reanalysis data have been used extensively to detect and characterise the rela-tions betweensolar activityand atmospheric structure. Most of these studies have focused on variability related to the solar cycle, but also day-to-day variability and periodicities related to the solar rotation rate have been studied. The stan-dard approach has been to detect statistically significant correlations between an indicator ofsolar activity or solar irradiance, and various atmospheric quantities, through multiple regression, also allowing for an impact from greenhouse gasses, volcanic emissions, or internal variability in theclimate system.
Examples include studies ofsolar cycle-related variability in zonally averaged temperatures and Hadley cell structure (Gleisner and Thejll,2003;van Loon et al.,
8 http://www.clivar.org/organization/gsop/resources/ocean-synthesisreanalysis-intercomparison-project
9http://s-rip.ees.hokudai.ac.jp/
P. Thejll and H. Gleisner: Reanalysis data 169
2007), in the strength and location of mid-latitude jet winds (Haigh et al., 2005), in troposphere-stratospherewind changes apparently coupled to ozonedynamics (Crooks and Gray, 2005), and solar cycle variability in the height of the pres-sure surface at 300 hPa (Br¨onnimann et al., 2007). These studies used either NCEP/NCAR reanalyses or ERA-40, and applied various types of multivariate regression. There are also examples of spectral methods being used to study so-lar effects in reanalysis data, (e.g., Mayr et al. (2007) and Mayr et al. (2009)).
Not only the freeatmosphere – troposphereand stratosphere – but also surface temperature variations have been investigated, e.g., bySoon et al.(2011).
The last decade has seen a wealth of studies on Sun-climate relations us-ing reanalysis data sets (see, e.g., Gray et al. (2012) and Mitchell et al. (2014) for overviews of studies and datasets). As the reanalysis methods improve, and the satellite data records become longer, there is considerable potential, not only for an improved characterisation of Sun-climate relations, but also for a better understanding of the underlying physical mechanisms.
5 Accessingreanalysis data
A good starting point for selecting and accessing reanalysis data is {reanalysis.org}10, which provides overview summaries of the most widely used datasets. Gettingreanalysisdata is relatively simple – they are provided over the Internet and can be downloaded simply. The NCEP is particularly simple to pull down, while the ERA and JRA require a password (easily enough obtained in a day or so). ERA-interim11, and NCEP12 are easily reached over the Internet. A summary of most access options is given atreanalysis.org13. Once downloaded you have to have the right sort of software for reading the files. Formats include NetCDF and GRIB, and there exists software to read these formats, or to trans-form one into the other. CDO14 is a tool for converting GRIB format to NetCDF (amongst many other things). There are libraries in many programming languages for reading NetCDF and GRIB: IDL provides simple ways to read NetCDF files;
FORTRAN can15 also do it, with patience; Python has a NUMPY interface16; and Java also has a library17. ‘NCL’ and ‘GrADS’ are programmes that allow visualisation of data, mathematical manipulation and the ability to read NetCDF files. The ESRL at NOAA18provides access to several such tools. Online services
10http://reanalysis.org
allow access and inspection of reanalysis datasets, such as the KNMI climate explorer19.
6 Take-Home message
Reanalyses are a convenient source of realistic, high-resolution data about the climate system. Global coverage extends to one century, although the best data are available since about the start of the satellite era – for just the Northern Hemisphere somewhat earlier.
19http://climexp.knmi.nl/start.cgi?id=someone@somewhere