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Introduction

The Copernicus Arctic Regional Reanalysis (CARRA) is a high resolution (2.5 km) reanalysis system for two domains of the European part of the Arctic (Figure 1). The CARRA dataset covers the period from September, 1990 and onward (timely updates are planned to be provided until June 2025). CARRA adds value to the ERA5 global reanalysis with the use of more (local) observations, with the focus on the representation of important local processes like cold surfaces and with its higher spatial horizontal resolution which allows for more details. The CARRA system is described in its Full System Documentation, while some key points of the configuration of the system is compared to ERA5 in Table 1.

The main findings of a comprehensive evaluation of the CARRA dataset are presented in this page. However, the CARRA dataset is huge, and a fully exhaustive examination of all parts of the data is simply not possible. The data users are therefore also encouraged to perform their own evaluation, tailored towards their own use of the data and also to seek information in the literature. A selection of studies, which includes evaluation and comparisons of different parts of the CARRA data set can be found here.

Table 1. Some key features of CARRA compared to ERA5.

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Figure 1: The CARRA-West and CARRA-East (Copernicus Arctic Regional Reanalysis) domains.

Summary of added value with respect to ERA5

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CARRA provides more spatial and temporal details for near-surface parameters. In particular in regions with detailed local variations in surface forcing (e.g. topography, complex coastlines and other surface heterogeneity) the differences can be pronounced. Over more homogeneous surfaces (e.g. flat land surface, open ocean) the differences are smaller, but still present. Over sea ice, there also might be large differences between CARRA and ERA5 in periods due to the different representation of sea ice in the reanalysis. CARRA and ERA5 produce very similar large-scale structures, e.g. the location of synoptic scale low- and high- pressure systems. 

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Figure 5. Time series of Root Mean Square Error (RMSE) for the period of 1998-2018 averaged over both CARRA domains for CARRA (red) and ERA5 (blue) for Mean Sea Level Pressure, 2m air temperature, 10m wind speed and 2m  specific humidity. Figure is adapted from Schyberg et al. (in preparationspreparation). 

Regional differences 

As shown above, CARRA has a good general agreement with in-situ observations for a selection of near-surface parameters. In this section, it is shown in more detail how the agreement with observations for both CARRA and ERA5 varies for different parameters, regions, between systematic errors (measured by the mean error or bias) and the unsystematic errors (measured by the standard deviation of the error, SDE), and season. Although bias and SDE are presented for regions (with similar weather and climate conditions) it should be noted that the errors also vary between locations within each region.

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The error growth for 2m temperature and dew point temperature is rapid during the first hours and then slows down. MSLP grows slower, but continues to grow at a larger rate for longer forecast lengths. Opposite to this is the 10m wind speed, for which the RMSE grows very slowly with forecast length. An interpretation of these results is that the three first parameters are included in the assimilation process of the reanalysis and thereby more drawn towards the true state of the observed climate system, and then they tend to drift back to the climate of the underlying model system with increasing forecast lengths, while 10m wind speed is not assimilated and hence is already at the analysis time more similar to the characteristics of the underlying model system. This underlines that the construction of time series combining forecasts and analysis from CARRA should only be done with care.

It should  be noted that in this discussion errors (as represented by RMSE) are defined in terms of deviations from observations. But observations are point measurements which have their own errors, including representation errors relative to the mean value over a model grid box area which the reanalysis tries to represent. Taking observation errors (with possibly temporal error correlations) into account, the relative difference in the real errors between forecast and analysis may be smaller than indicated here. (See also discussion below on "factors influencing the agreement between CARRA and observations.)

Figure 10. RMSE of forecasts and analyses divided by the RMSE of the CARRA analysis as function of forecast length. Parameters shown are 2m air temperature (black), MSLP (red), 10m wind speed (blue) and 2m dew point temperature (cyan) for CARRA-East and CARRA-West domain. At +0 hour the RMSE of ERA5 divided by the RMSE of CARRA (circles) is also included.

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Copernicus Arctic Regional Reanalysis: List of references. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): list of references

Uncertainty information for the Copernicus Arctic Regional reanalysis. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): known issues and uncertainty information#Uncertaintyinformation

Known issues for the Copernicus Arctic Regional reanalysis. Copernicus Knowledge Base (CKB) article. Copernicus Arctic Regional Reanalysis (CARRA): known issues and uncertainty information#Knownissues

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