Evaluation of ERA-20cm reanalysis dataset over South Korea

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Highlights

  • ERA-20cm ensemble has little temporal relationship with the observation.

  • For pdf-based comparisons, all ensemble members reproduce observation well.

  • The ensemble mean has different characteristics from the ten ensemble members.

  • The ensemble spread has no relationship with ENSO and it should be widened.

  • The applicability of ERA-20cm may vary by region.

Abstract

Long term climate data are key in assessing water related hazards in order to adapt and mitigate climate change. Reanalysis has been developed as a surrogate for local observations, but there is a lack of studies about the suitability at different parts of the world. In this study, our primary goal of this study was to identify the applicability of the ECMWF 20th century atmospheric model ensemble (ERA-20cm) in South Korea. Thus, we have evaluated the ensemble for precipitation and temperature by assessing the correlation coefficients, the long-term trend by the Mann-Kendall test, the skill score based on the probability density functions (PDFs) and the goodness of the ensemble spread. The relationship between the spread and the El Niño-Southern Oscillation (ENSO) has also been explored. ERA-20cm ensemble has difficulty in providing useful information on the long term trend as well as the temporal variability in South Korea, but, for the pdf-based comparison, all ensemble predictions represent significant agreements. It is found that the ensemble mean can misrepresent ten individual members, especially for statistical estimates, in regional-scale analyses. The ensemble does not spread well enough to cover the observation and there is no relationship between the spread and ENSO. This paper shows that the applicability of ERA-20cm may vary by region, hence these findings help to fill in the knowledge gaps about the applicability of the reanalysis in regional scale study including South Korea.

Introduction

To adapt and mitigate climate change, it is essential to analyse the reliable long-term climate dataset. It is generally considered that the gauged local data provide the best accuracy in the gauging points, but they are usually sparse and limited in the time range (Becker et al., 2013, Simmons et al., 2004). For this reason, the reliable gridded dataset, called “reanalysis”, derived using modern data assimilation techniques has been considered as a surrogate for local observations since 1990s. Representatively, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) have produced these kinds of products such as ERA-40 from 1957 to 2002, NCEP/NCAR Reanalysis from 1948 to present, ERA-interim from 1979 to present and NCEP-Department of Energy (DOE) reanalysis from 1979 to present (Dee et al., 2011, Kalnay et al., 1996, Kanamitsu et al., 2002, Uppala et al., 2005). These half-century reanalysis datasets were produced by assimilating a wide range of available observations for the reference time (Compo et al., 2011). However, the local gauged data prior to mid-twentieth century were mainly observed over land surface in specific regions and sparse, so there was vulnerability of generating reliable products for the early 20th century in these models (Compo et al., 2011, Donat et al., 2016). Nevertheless, a few century-long reanalyses such as the ECMWF 20th century atmospheric model ensemble (ERA-20cm) and NOAA-CIRES 20th century reanalysis v2c(20CR) have been developed by the advanced techniques which generate ensembles to account for uncertainties by the relatively sparse input data (Compo et al., 2011, Hersbach et al., 2015, Poli et al., 2016, Poli et al., 2013). These ensemble predictions are produced by applying different initial conditions and observations in their own atmospheric models. For instance, for 20CR, spanning 1850 to 2014 with the 1.875°×1.9° resolution, only surface pressure observations were assimilated with an Ensemble Kalman Filter, and monthly sea surface temperature (SST) and sea-ice cover (SIC) were used as boundary conditions (Compo et al., 2011, Donat et al., 2016). In contrast, ERA-20cm, covering 1900–2010, was produced with the Integrated Forecasting System (IFS) version Cy38r1, but it was simulated by observational SST and SIC with no data assimilation (Hersbach et al., 2015, Poli et al., 2016). Due to the difference of these simulation conditions, each ensemble model provides different representations of the observations (Gao et al., 2016). Hence, despite the availability of these reanalyses, their qualities are still an important issue in the climate change study.

In order to examine the quality of reanalysis datasets, there have been a lot of global-, continental-, or local-scale studies, and the evaluation of the ensemble predictions also has been explored (Donat et al., 2016, Donat et al., 2014, Ferguson and Villarini, 2012, Gao et al., 2016, Simmons et al., 2004). For example, Simmons et al. (2004) evaluated ERA-40 and NCEP/NCAR reanalysis by comparing them with CRU, the interpolated global gridded observations, for air temperature at 5°×5° resolution on global and continental scales and concluded that there were very similar interannual patterns between ERA-40 and CRU, especially in Northern Hemisphere from 1979 onward. In another global study, Donat et al. (2016) compared 20CR, ERA-20c and ERA-20cm with the interpolated gridded observations (HadEX2), and suggested that these reanalyses agreed well after about 1950 although they often had discrepancies during the early twentieth century. In the case of national scale evaluation, Ferguson and Villarini (2012) Compared 20CR over the central United States with CRU and suggested that there were inhomogeneities for 20CR from 1940 to 1950 during the warm season so it was recommended to use the second half century of it, not all period, over the central United States. A recent study over China by Gao et al. (2016) statistically evaluated ERA-20cm. After comparing the ensemble ten members at 0.5°×0.5° grids for precipitation and temperature, it was concluded that generally all ensemble simulations were able to represent the real condition on a comparable level.

It is important that comparative studies should cover a wide range of locations around the world and gaps should be filled in for the sites lacking such studies so that a clear pattern could be understood. In Korea, the long-term climate trend analysis on precipitation and temperature has generally been based on the observed values and the time range of these studies were limited in the late 20th century (Bae et al., 2008, Chang and Kwon, 2007, Chung et al., 2004, Chung and Yoon, 2000, Jung et al., 2011). There were a few trials to apply the reanalysis products on the climate trend research over Korea, but these datasets were applied to estimate the features of the comparable region like East-Asia as a whole, not Korea itself at the country level (Choi et al., 2016, Ho et al., 2003, Jeong et al., 2015). In other words, the climate datasets were used in Asian area in order to compare with the climate trend of Korea examined by the observation data. However, if researchers would like to extend the analysis period up to the early of the 20th century, it is essential to attempt to find out the reliable long-term dataset with high resolution, which should be explored.

Of the 20th century ensemble reanalyses, ERA-20cm is one of the representative datasets. While 20CR provides the ensemble mean and the spread with the relatively coarse resolution, 1.875°×1.9°, in the public web server, ERA-20cm is able to support all predictions of ten ensemble members at a higher resolution such as 0.5°×0.5°. Thus, in this study, we focus on whether ERA-20cm can provide reliable data in regional-scale analyses over South Korea. It is known that ERA-20cm cannot reproduce actual synoptic situation, but it is able to detect the long term trend of the climate variables such as temperature as well as providing statistically meaningful values (Hersbach et al., 2015). However, this perception is mainly based on the ensemble mean in global- or continental-scale analysis (Hersbach et al., 2015, Poli et al., 2016), and the evaluation based on the regional-scale is relatively rare. Gao et al. (2016) assessed ERA-20cm ensemble members over China, but it was limited in statistical analyses. Kim and Han (2018) evaluated the ERA-20cm mean compared with other century-long datasets in South Korea, but did not analyse individual ensemble members. For this reason, we have evaluated ERA-20cm ensemble members for precipitation and temperature, which are commonly used in hydro-meteorological analysis, over South Korea in this study. By estimating the correlation coefficient r, the significance of trend by the Mann-Kendall test, the skill score based on the probability density functions (PDFs) and the percent of the observation within the ensemble spread, this paper assesses the temporal variability and statistical agreement of each ensemble member, and the goodness of the ensemble spread in South Korea. The relationship between the spread and the El Niño-Southern Oscillation (ENSO) has also been explored. To show the specific process, the data and methodology applied in this study are introduced in Section 2 and Section 3. Section 4 presents the main results for precipitation and temperature and finally the discussion and conclusions are described in Section 5 and Section 6, respectively.

Section snippets

Observed local data

To analyse the precipitation and temperature change over the mainland of South Korea, daily total precipitations and daily mean 2-m air temperatures of 13 ground gauge stations are taken from the data archive of Korea Meteorological Administration (KMA) (https://data.kma.go.kr/cmmn/main.do) and merged to the monthly values. The stations are evenly selected excluding islands of Korea from 1961 to 2010 with no empty values, although three of stations are available from 1966, 1968 and 1973,

Evaluation of interannual variability

To explore the temporal variability of each ensemble member compared with the observed values, the Pearson’s linear correlation coefficients (r) between the ensemble and the observations of 13 stations from 1961 to 2010 are calculated. For this analysis, the seasonal/yearly total precipitation and mean temperature variables are derived from all the dataset. Every seasonal dataset is collected for spring from March to May, summer from June to August, autumn from September to November, and winter

Interannual variability

Table 2 quantitatively explains the seasonal or annual correlation between the observation and the simulated precipitation from 1961 to 2010. In the seasonal comparison, the r values for all ten ensemble members are located between −0.171 and 0.228, and Mean has the values between −0.035 and 0.278. This indicates that there are little temporal correlations between ERA-20cm ensemble members as well as Mean and the observations for precipitation. The annual comparison also suggests the similar

Discussion

As aforementioned in Introduction, it has been known that despite the inconsistency in synoptic events, ERA-20cm can represent the long term as well as statistical estimates of climate variables in global- or continental-scale analyses (Hersbach et al., 2015, Poli et al., 2016). However, it is also found that there still exists knowledge gap about the applicability of ERA-20cm in regional-scale studies. In this context, we have evaluated the long-term change and statistical estimate of ERA-20cm

Concluding remarks

In this study, we have evaluated the century-long ERA-20cm ensemble for precipitation and temperature in South Korea. From the evaluations, it could be concluded that ERA-20cm has difficulty in providing useful information on the long term trend as well as the temporal variability for temperature and precipitation in South Korea, although the temperature ensemble has a partial relationship with the observation. For the pdf-based comparison, all ensemble predictions represent significant

Acknowledgements

The ERA-20cm ensemble data were collected via the ECMWF’s public server (http://apps.ecmwf.int/datasets/). Support for the ONI was provided by the NOAA Climate Prediction Centre (http://www.cpc.noaa.gov/). The first author is grateful for the financial support from the Government of South Korea for carrying out his PhD studies at the University of Bristol.

Dongik Kim received his B.Eng. degree in KAIST (Korea Advanced Institute of Science and Technology) in South Korea, and since 2004, he has served as a deputy director in the Ministry of Land, Infrastructure and Transport affairs (MOLIT) of South Korea. He has mainly worked on river planning and water resource policy in the government, and also supervised several research projects on natural hazards and water resources management. He recently received his Ph.D. degree in the University of

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  • Dongik Kim received his B.Eng. degree in KAIST (Korea Advanced Institute of Science and Technology) in South Korea, and since 2004, he has served as a deputy director in the Ministry of Land, Infrastructure and Transport affairs (MOLIT) of South Korea. He has mainly worked on river planning and water resource policy in the government, and also supervised several research projects on natural hazards and water resources management. He recently received his Ph.D. degree in the University of Bristol.

    Dawei Han received his B.Eng. and M.Sc. degrees in water conservancy from North China Institute of Water Conservancy and Electric Power, China, and the Ph.D. degree in radar hydrology from the University of Salford, U.K. He is currently a Professor of Hydroinformatics with the Department of Civil Engineering, University of Bristol, U.K. He has carried out various projects on weather radar rainfall and numerical weather prediction to aid flood risk assessment, downscaling of global circulation model for climate change, resilience to multi-natural hazards, etc. He has published over two hundred peer-reviewed journal and conference papers.

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