Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data

Computers and Electronics in Agriculture 133 (2017) 128–140

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Original papers

Development of a gridded climate data tool for the COordinated Regional climate Downscaling EXperiment data Byoung Hyun Yoo a, Kwang Soo Kim a,b,⇑ a b

Seoul National University, Department of Plant Science, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Republic of Korea

a r t i c l e

i n f o

Article history: Received 28 April 2016 Received in revised form 24 November 2016 Accepted 1 December 2016

Keywords: Grid analysis and display system High-performance computing CORDEX Gridded data CDSL

a b s t r a c t The assessment of regional climate change impacts on agriculture would benefit from a climate data processing tool that aids preparation of input data to agricultural models. A gridded data tool was developed to process the outputs of regional climate models including the COordinated Regional climate Downscaling EXperiment (CORDEX) data. The CORDEX Data Support Library (CDSL) was designed to provide functionalities associated with high performance computing and the preparation of input data without additional storage requirement. A set of functions was implemented in the CDSL to facilitate the parallel processing of CORDEX data. The CDSL had functionalities to unify the spatial extent and resolution, projection and calendar system of gridded data for creating ensemble data sets that could be imported into a model of interest using a function call. As a case study, reference evapotranspiration (ET0) in East Asia was calculated using the CDSL to process the outputs of regional climate models (RCMs) available from the website of the CORDEX East Asia. Six sets of ET0 (ETCORDEX) were calculated using CORDEX data as inputs to the FAO 56 formula. Those sets were compared with ET0 calculated using AgMERRA data as inputs (ETAgMERRA). The processing time for climate data decreased with the increasing number of processor cores when the features of parallel processing were used for the CDSL. For example, the running time for data loading reduced by 88% using the CDSL with 16 processor cores. These results demonstrated that the CDSL would facilitate regional climate change impact assessment using a considerably large amount of climate data, e.g., >200 GB, as inputs to agricultural models. Ó 2016 Elsevier B.V. All rights reserved.

1. Introduction Models to simulate biophysical processes in agricultural ecosystems have been used at different spatial scales (Rosenzweig et al., 2014; Zhao et al., 2015). Arnell (1999) used a hydrological model with climate data from two global circulation models (GCM) and water use scenarios to project hydrological cycle in climate change condition. Fujihara et al. (2007) assessed climate change impact on Abbreviations: CCC, concordance correlation coefficient; CDSL, CORDEX Data Support Library; CORDEX, COordinated Regional climate Downscaling EXperiment; ET0, reference evapotranspiration; GrADS, Grid Analysis and Display System; HadGEM3-RA, Hadley center Global Environmental Model version 3-Regional climate model; MAM, March-April-May; netCDF, network common data form; RCM, regional climate model; RCP, representative concentration pathway; RegCM4, NCAR’s Regional Climate Model version 4; RMSE, root mean square error; SNUWRF, Seoul National University Weather Research Forecasting Model; YSU-RSM, Yonsei University Regional Spectral Model. ⇑ Corresponding author at: Seoul National University, Department of Plant Science, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea. E-mail address: [email protected] (K.S. Kim). http://dx.doi.org/10.1016/j.compag.2016.12.001 0168-1699/Ó 2016 Elsevier B.V. All rights reserved.

a river basin at regional scale using downscaled GCM data as inputs to hydrology and reservoir models. Andréasson et al. (2004) also examined changes in hydrological cycles at a national scale under future climate change conditions. Regional studies using agricultural models would provide information for the development of adaptation strategies in a region of interest (Lobell et al., 2006; Sultan et al., 2013). For example, Tramblay et al. (2013) used gridded data obtained from a regional climate model (RCM) as inputs to a hydrological model. In their study, it was found that the use of regional data at higher spatial resolution, e.g., 12 km, would allow more reliable assessment of water resources under current and future climate conditions. Regional impact assessment of climate change on agricultural ecosystems would benefit from the COordinated Regional Climate Downscaling EXperiment (CORDEX). The CORDEX program has been developed to increase our understanding of regional climate in the future (Giorgi et al., 2009). Through the CORDEX program, climate data at a high spatial resolution have been created for various regions, e.g., East Asia or Mediterranean regions. In each area

B.H. Yoo, K.S. Kim / Computers and Electronics in Agriculture 133 (2017) 128–140

of interest or domain, research groups have developed different approaches to obtain gridded climate scenario data. About 50 sets of gridded climate change scenario data from the CORDEX program are available at the Earth System Grid website (https://www.earthsystemgrid.org/). To our best knowledge, general purpose tools for gridded data have limited functionalities to prepare input data for agricultural models using the CORDEX data. Different data tools such as netCDF Operators (NCO), Climate Data Operator (CDO), Climate Data Analysis Tool (CDAT), climate data management system (CDMS), and NCAR Command Language (NCL) have been developed as an independent platform or application. Because an agricultural model would have its own data format, a conversion process would be needed for gridded data. When those tools would be used to prepare input data for a model, additional data storage would be required for gridded data because these input data would be loaded from a file system rather than those data tools. The CORDEX data could be processed using Application Programming Interfaces (API) to import gridded data into a model of interest. For example, Geospatial Data Abstraction Library (GDAL), netCDF API, and the raster package for R could be used to import gridded data into an agricultural model. However, the use of those APIs would require considerable efforts to process the CORDEX data of which spatial and temporal properties would differ by individual products. For example, a series of procedures would be needed for processing these data because the CORDEX data have been obtained from multiple RCMs. Some of the CORDEX data sets also have the projection that is rarely supported by common gridded data tools, e.g., Geospatial Data Abstraction Library (GDAL) and ArcGIS (ESRI, New York). The CORDEX data sets may have different calendar systems, e.g., 360 or 365 days in a year, which would require a customized procedure to prepare input data for agricultural models. A gridded data tool optimized for the CORDEX data would help preparation of input data with minimum knowledge of CORDEX data sets. Such a tool would facilitate simulation of agricultural ecosystems in a region using a wide range of regional climate data as inputs. The objectives of this study were to develop an application programming interface, CORDEX Data Support Library (CDSL), for the CORDEX data and to explore the application of the CDSL to an assessment of climate change impact on an agricultural ecosystem. It was also attempted to demonstrate that the CDSL would facilitate the use of high-performance computing and statistical analysis in simulation studies on agricultural ecosystems.

2. Properties of CORDEX data In the CORDEX program, the world is divided into 14 domains, which represent a region of interest for the future climate projection (Fig. 1). In each domain, dynamic or statistical downscaling approaches have been applied to the outputs of general circulation models (GCM) to produce the CORDEX data. Data products that represent historical climate were produced using outputs of GCM for the period from 1979 to 2005. The scenario data products were obtained from downscaling of GCM outputs under emission scenarios, e.g., representative concentration pathways (RCP). Different sets of RCMs were used to produce the regional climate data. For example, 29 RCMs were used for the Mediterranean domain whereas five RCMs were used for the East Asia domain. Spatial and temporal resolutions of CORDEX data differ by domain and data sets. A spatial resolution of CORDEX data is mostly 0.44° (
50 km). Data at a higher spatial resolution are available for a small number of domains. For example, MNA-22 and EUR-11 products have spatial resolutions of 0.22° (
25 km) and 0.11° (
12.5 km), respectively. Temporal resolutions of COR-

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DEX data include daily, monthly, and seasonal scale depending on products. Map projections used in CORDEX data differ by RCMs. For example, some of CORDEX data sets have the rotated latitude and longitude projection under which the rotated pole is defined specific to each domain (Christensen et al., 2014). Others have the Lambert conic conformal projection. About 34% of CORDEX data sets have a grid format other than Geographic latitude/longitude projection with the World Grid System (WGS) 84 datum. Thus, conversion would be needed to analyze data sets that have different projections. It has been recommended to include projection information of CORDEX data as metadata within data files. Still, such a protocol was not met for some of CORDEX data products, which makes it difficult to use existing gridded data tools for processing of those CORDEX data. For example, Yonsei University Regional Spectral Model (YSU-RSM) data set in the East Asia domain has no description of its projection in the data file. The network common data form (netCDF) format, which was developed to support a handling of multidimensional scientific data, has been recommended to create the CORDEX products (Christensen et al., 2014). A netCDF file consists of variable, dimension, and attribute. The variable is used to store multi-dimensional data. For example, CORDEX data files in netCDF format contain climate data by the variable, e.g., solar radiation, air temperature, specific humidity, or precipitation rate. The dimension and the attribute include descriptions of dimension and additional description of variables, respectively. For example, latitude, longitude, and time span of data are described in the dimension whereas a unit of variables, parameters of a given projection and other metadata of the file are stored in the attribute.

3. Design of CDSL The CDSL was developed to extend the functionalities of the Grid Analysis and Display System (GrADS), which is an independent platform to process gridded data. The GrADS has been used to extract, calculate and visualize multidimensional data in various file formats including binary and netCDF formats (Berman et al., 2001). For example, Reynolds et al. (2005) and Banzon et al. (2014) used the GrADS for displaying gridded climate data. Anandhi et al. (2014) and Pennelly et al. (2014) used the GrADS for interpolation of climate data. Additional functionalities of the CDSL to the GrADS were identified to facilitate the preparation of input data for agricultural models using the CORDEX data. An ensemble set of gridded climate data would be useful to minimize uncertainties in modeling studies (Tebaldi and Knutti, 2007). Preparation of ensemble data sets using the CORDEX data would require a procedure to combine multiple data sets that have different properties, e.g., spatial resolution and projection (Table 1). Because gridded climate files would require relatively large storage compared with data at a specific site, it would be preferable to develop an API instead of an independent application. Although the GrADS supports simple statistical analysis, e.g., calculation of averages (Barberà et al., 2015; Roy and Inamdar, 2014), it would also be desirable to allow a comprehensive set of statistical analyses for gridded climate data. One of the design goals for the CDSL was to obtain metadata with minimum effort. Metadata are often used to import gridded data into data tools including GrADS. For example, the GrADS configuration depends on a CTL file to indicate metadata including properties of the data file including filename, spatial and temporal resolution, projection, and variable names. Alternatively, metadata could be identified using the filename of the CORDEX data in accordance of naming convention (Christensen et al., 2014). For example, the NetCDF filename, ‘‘tas_EAS-44_HadGEM2-AO_

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B.H. Yoo, K.S. Kim / Computers and Electronics in Agriculture 133 (2017) 128–140

Fig. 1. The East Asia domain of the CORDEX project. Domains of other CORDEX project are included in the bottom left corner inset. The Polar Region domain was not included in this figure.

historical_r1i1p1_HadGEM3-RA_v1_day_19810101-19851230.nc”, contains variable name, temporal resolution, and time span of data. Thus, it would be advantageous to obtain attributes of the CORDEX data file without additional files that contain metadata of the CORDEX data, e.g. CTL files. The CDSL was designed to prepare gridded climate data in a given spatial and temporal property. For example, the CDSL could be used to prepare data at a spatial resolution of 25 km from the CORDEX data of which a spatial resolution is 50 km. The CDSL could be used to convert the projection of gridded data into the projection specified by a user. Climate data with different calendar, e.g., 365-day and 360-day calendars could be combined together. For the preparation of an ensemble data set, however, it would be needed to create gridded data in a uniform property using the CORDEX data. The development of the CDSL was aimed to reduce processing time. Impact assessment of climate change could take a long time when gridded data are used as inputs to a process-based model (Zhao et al., 2013). A number of input files could be prepared to represent each grid cell in a region using gridded data. Multiple sets of gridded climate data from different RCMs could be used to prepare input data sets for ensemble simulations of agricultural ecosystems. A functionality of parallel processing within the CDSL

would be useful to minimize run time for data preparation. Further decrease in processing can be achieved because the CDSL was designed to extract climate data for the range of time and the extent of a region specified by a user rather than all of data. The CDSL was developed as an application programming interface (API) to minimize data storage requirements for input data preparation. Input data for a model of interest, having a specific data format, could be prepared from gridded climate data using the GrADS, but this would require additional data files because those gridded data could not be imported directly into the model using the GrADS. With CDSL, no additional files would be generated because the original gridded data could be imported into existing models and data tools. For example, the CDSL, implemented in C, would be used to build the R package that would allow the import of the CORDEX data sets into R for sophisticated statistical analysis. Furthermore, CORDEX data sets could be imported into data tools that have a feature of high-performance computing to process a large amount of data using the CDSL. 4. Implementation of CDSL New functions were implemented within the CDSL to provide additional functionalities to the GrADS (Table 1). For example,

B.H. Yoo, K.S. Kim / Computers and Electronics in Agriculture 133 (2017) 128–140 Table 1 The description of functions implemented in the CORDEX Data Support Library. Function

Functionalities/description

Dependencies

readCORDEX

Provide an Interface for gridded data Interface for read and ensemble gridded data of given data sets Each set of data is averaged from given weight Calendar system is unified upon user’s choice Parse the name of a given CORDEX file to get the name of variable and domain Parse attributes from CORDEX netCDF file, e.g. size of dimension and parameter of the projection of data For rotated latitude and longitude projection, each parameter of the projection was defined from CORDEX domain definition saved in the internal database Prepare the variable data from CORDEX file with projection other than WGS84 for the conversion of projection Convert projection using bilinear interpolation, which is the same functionality to gaprow implemented in Grid Analysis and Display System

readMetaCORDEX CORDEXgrid readMetaCORDEX CORDEXgrid

ensemCORDEX

readMetaCORDEX

CORDEXgrid

MPprow

MPprow

readMetaCORDEX, readCORDEX and ensemCORDEX functions were defined to parse the metadata of CORDEX data sets, to load the CORDEX data files, and to create an ensemble set, respectively. Those functions were implemented using C. Open Multi-Processing (openMP) was used to support parallel processing within the CDSL. The readMetaCORDEX function has functionalities to obtain metadata from individual CORDEX data files. A file for metadata of gridded data, e.g., a CTL file, is often prepared by users. Efforts for data handling and preparation could be minimized using the readMetaCORDEX function, which was implemented to retrieve those metadata automatically parsing the filename of CORDEX data. The domain of the given CORDEX data file is also identified by its filename using the readMetaCORDEX function. Spatial properties, e.g., rotated pole or extent of the domain, are retrieved from the internal database that stores spatial properties for all the domains (Christensen et al., 2014). The name of a variable is also identified by the filename of the CORDEX file in the readMetaCORDEX function. When CORDEX data requires conversion of projection, data were processed by row. At first, the CORDEXgrid function was used to read a chunk of raw data from the given file. Then, the MPprow function was called to process those rows of those data. The multidimensional data at a given time step was stored into an array of memory along with metadata prepared by the readMetaCORDEX function. The MPprow function was implemented to perform projection conversion and interpolation (Table 1). The MPprow has the same functionality as the gaprow function in the GrADS. For example, the GrADS supports multiple projections including rotated latitude and longitude, polar stereographic, oblique polar stereo and Lambert conic conformal projections. A projection of the gridded climate data can be converted to another through bilinear interpolation using gaprow function in the GrADS. However, parallel processing is not currently supported by the gaprow function. In the MPprow function, conversion of projection and interpolation of data were performed for multiple

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data lines concurrently using parallel processing feature of openMP. The readCORDEX function was implemented as an interface to other applications. This function requires the properties of input and output data, e.g., a filename of CORDEX data to be read and spatial resolution of output data. Once the CORDEX data file is accessed, a part of content specified by a user are transferred to a local array. In this process, openMP was used to reduce the processing time. The ensemCORDEX function was implemented to create an ensemble set of CORDEX data based on user-specified weights for individual data sets. By default, an equal weighting scheme was used in the ensemCORDEX function. However, weights determined from different approaches, e.g. reliable ensemble average, Kolmogorov-Smirnov, and Taylor index (Vidal and Wade, 2008) can be used as an option to the function. The CDSL can support different gridded data formats implemented in the GrADS. For example, data in Hierarchical Data Format (HDF), Gridded Binary (GRIB), and Binary Universal Form for the Representation of meteorological data (BUFR) formats can be used when the CDSL is compiled with options corresponding to those data formats. This study focuses on the functionalities associated with netCDF formats used in the CORDEX data files.

5. Case study The CDSL was used for a regional assessment of evapotranspiration during an early season under climate change conditions as a case study. In the present study, reference evapotranspiration (ET0) was calculated in East Asia using multiple sets of the CORDEX data. The East Asia domain includes China, Indonesia, Japan, and Korea. It has been reported that the East Asia region would have a relatively high vulnerability to climate change conditions (IPCC, 2012). In particular, irrigation requirements for crop production would be relatively high in the region where rice is usually cultivated in a paddy field. For computation of ET0 using parallel processing, a custom-built workstation was used. The computer was equipped with two central processing units of dodeca core AMD Opteron (6176SE, Advanced Micro Devices, Inc., Sunnyvale, California).

5.1. CORDEX and AgMERRA data The CORDEX data were downloaded from the website of the CORDEX East Asia (http://cordex-ea.climate.go.kr). In total, four sets of CORDEX data at a spatial resolution of 0.44° were used to calculate reference evapotranspiration (Table 2). Those data sets include the outputs of the Hadley center Global Environmental Model version 3-Regional climate model (HadGEM3-RA), the regional climate model version 4 (RegCM4), the weather research and forecasting model (SNU-WRF), and the regional spectral model (YSU-RSM). HadGEM3RA and SNU-WRF models are nonhydrostatic models whereas RegCM4, and YSU-RSM models are hydrostatic models (Suh et al., 2012). Estimates of evapotranspiration using CORDEX data were compared with those using AgMERRA data (http://data.giss.nasa.gov/ impacts/agmipcf/agmerra/). The gridded climate data, which are available at the Goddard Space Flight Center (GSFC), have been used as a climate forcing data for Agricultural Model Intercomparison and Improvement Project (Ruane et al., 2015). The reanalysis data provide daily weather variables for calculation of evapotranspiration with the geographic latitude/longitude projection at a spatial resolution of 0.25°. Ruane et al. (2015) reported that AgMERRA data would have an accurate representation of the

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Table 2 Properties of CORDEX East Asia and AgMERRA data sets.

Resolution Grid size Projectiona Calendarb a b

HadGEM3-RA

RegCM4

SNU-WRF

YSU-RSM

AgMERRA

0.44° 167  204 rotll 360-day

0.44° 195  241 lcc 360-day

0.44° 196  232 lcc Regular

0.44° 198  241 lcc 360-day

0.25° 720  1440 ll Regular

rotll, lcc, and ll indicate rotated latitude and longitude, Lambert conic conformal, and Geographic (lat/lon) projections, respectively. Regular calendar represents the 365-day calendar with a leap year.

diurnal cycle of humidity, which would be useful for reliable predictions of evapotranspiration.

Rl ¼ 4:903  103

  pffiffiffiffiffi Tx4 þ Tn4 RS  0:35 ð0:34  0:14 ea Þ 1:35 2 Rso ð2Þ

5.2. Calculation and analysis of reference evapotranspiration 2

Estimations of reference evapotranspiration, ET0, are used for simulations of crop growth and hydrological processes (Grismer et al., 2002; Utset et al., 2004). Gridded climate data have been used to calculate ET0 for spatiotemporal analysis of evapotranspiration (Gao et al., 2007; Chattopadhyay and Hulme, 1997). CORDEX and AgMERRA data were used as inputs to the FAO-56 formula (Allen et al., 1998) to generate maps of ET0 (mm d1) as follows:

ET 0 ¼

0:408DðRn  GÞ þ c 900 U 2 ðes  ea Þ T2

ð1Þ

D þ cð1 þ 0:34U 2 Þ

where Rn and G represent the net radiation and the soil heat flux (MJ m2 d1), respectively. G was assumed to be zero for daily calculation of ET0 (Allen et al., 1998). T2 and U2 indicate daily mean air temperature (K) and wind speed (m s2) at 2 m height, respectively. es and ea are saturated and actual vapor pressure (kPa), respectively. D and c are the slope of vapor pressure deficit (kPa) and psychrometric constant (kPa °C1), respectively. Some variables required for Eq. (1) were not available from CORDEX or AgMERRA data (Table 3). Those variables were estimated using methods described by Allen et al. (1998). For example, outgoing longwave radiation was estimated because the data are available in the data sets from HadGEM3-RA and YSU-RSM products but not from the other products. To calculate ET0 with similar sets of variables, outgoing longwave radiation was estimated for all the data sets as follows (Allen et al., 1998):

Table 3 Methods and variable names of CORDEX and AgMERRA data used for the FAO 56 formula, respectively.

Rn Rl Rns Rs

a Rso U2 T es ea D

c Tx Tn SH PS (kPa)

CORDEX

AgMERRA

Rns - Rnl Eq. (2) (1 – a) Rs rsds 0.23 Eq. (3) sfcWinda tas Eq. (4) Eq. (3) D = 4098es/(T  35.85)b c = 0.665  103 PS tasmaxb tasminb huss ps

Rns - Rnl Eq. (2) (1 – a) Rs srad 0.23 Eq. (3) wndspd (tmax + tmin)/2 Eq. (4) es  rhstmax D = 4098es/(T  35.85)b c = 0.665  103 PS tmax tmin – 101.3

a For SNU-WRF and YSU-RSM datasets, wind speed was calculated using uas and vas variables that represent velocity of easterly and northerly winds, respectively. b The values of tasmax and tasmin for the SNU-WRF model were replaced by that of tas, respectively.

1

where Rl is outgoing longwave radiation (MJ m d ). Tx and Tn indicate maximum and minimum temperature (°C). It was found that maximum and minimum temperature data of SNU-WRF model were incomplete. Thus, Tx and Tn in Eq. (2) were replaced by the average temperature data for the SNU-WRF model. Rs and Rso, which represents actual and clear sky radiation, respectively, were used to calculate a fraction of cloud, i.e., Rs/Rso. Clear sky radiation was calculated as follows:

Rso ¼ ðas þ bs ÞRa

ð3Þ

where (as + bs) indicates a fraction of extraterrestrial radiation, Ra, reaching an earth surface. as and bs were assumed to be 0.25 and 0.5, which were recommended by Allen et al. (1998). Ra was calculated as follows:

Ra ¼

12ð60Þ

p

Gsc dr ðws sinðuÞ sinðdÞ þ cosðdÞ cosðdÞsinðws ÞÞ

ð4Þ

where Gsc, dr and ws represent solar constant, inverse relative distance from the earth to the sun, and sunset hour angle. / and d indicate latitude of each grid cell and solar declination. Because the CORDEX data include specific humidity rather than relative humidity, ea and es were calculated as follows:

ea ¼

SH  PS ; ð622 þ 0:378  SHÞ

and

es ¼ 0:6108  expð17:27  T=ðT þ 237:3ÞÞ

ð5Þ ð6Þ

where SH and PS indicate specific humidity (g kg1) and air pressure (kPa), respectively. T represents average daily temperature (°C) available from the outputs of RCMs. Calculation of ET0 was performed only in an early part of the growing season to minimize computing resources as our focus was on exploring the functionalities of the CDSL. Daily ET0 was calculated from March to May (MAM) because it is likely that a large amount of irrigation water would be needed for rice paddy fields in these periods. Once gridded sets of daily evapotranspiration were obtained using each set of CORDEX data, seasonal averages of evapotranspiration were calculated for the period of MAM. ET0 was calculated for the periods from 1981 to 2005 during which both CORDEX and AgMERRA data were available. The spatial resolution of each CORDEX dataset was increased to 0.25° using the bilinear interpolation function implemented in the CDSL, to allow comparison of ET0 between CORDEX and AgMERRA data. The outputs of the SNU-WRF model had the 365-day calendar whereas those of the other RCMs had the 360-day calendar. Thus, the CDSL was configured to convert the calendar system of the SNU-WRF model outputs into the 360-day calendar by removing data on the 29th of February and the 31st of each month after March from these data sets.

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In total, seven data sets of ET0 were obtained in the East Asia region. Four sets of ET0 were obtained using CORDEX data from individual RCMs as inputs to Eq. (1). Those sets of ET0 were denoted by the name of RCM corresponding to the data set. For example, ET0 calculated using HadGEM3-RA model data was denoted by ETHadGEM3-RA. To compare ensemble approaches, two sets of ensemble data, ETMME and MMEET, were created. An ensemble of CORDEX data, which was averages of each variable for data sets from RCMs, was used as input to the FAO-56 formula to create another set of ET0, ETMME. Average of ET0 obtained from individual data sets, MMEET, was calculated as follows:

MMEET ¼

X

ETM =4

ð7Þ

where M represent individual RCMs including HadGEM3-RA, YSURSM, SNU-WRF, and RegCM4. Collectively, ETCORDEX was used to represent ET0 obtained from the CORDEX data. AgMERRA data were used as inputs to the FAO-56 formula to create ETAgMERRA, which was used as a reference set. Probability density of differences between ETCORDEX and ETAgMERRA was determined to compare the reliability of ET0 estimates using CORDEX data. The Gaussian method was used as the smoothing kernel. The bandwidth of each data set was calculated from Silverman’s rule of thumb (Silverman, 1986). The degree of agreement statistics was determined to compare ET0 estimates using different products of climate data as inputs to the FAO-56 formula. The root mean square error (RMSE) was determined between the values of ETAgMERRA and ETCORDEX as follows:

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi u n u1 X 2 RMSE ¼ t ðET CORDEX ½i  ET AgMERRA ½iÞ ; n i¼1

ð8Þ

where n is the number of the valid grid cell [i] represents each grid cell of ETCORDEX and ETAgMERRA. Concordance correlation coefficient (CCC), which has been used to represent both precision and accuracy, was determined as follows (Lin, 1989):

CCC ¼

2qrx ry ; r2x þ r2y þ ðlx  ly Þ

ð9Þ

where q is the correlation coefficient between ETAgMERRA and ETCORDEX. r and l indicate standard deviation and average of ETAgMERRA and ETCORDEX, respectively.

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a gridded data file. The R list object created using user inputs is converted to an object in a composite data type in C, which is used as an input to the readCORDEX function. When the R list object has multiple filenames with an ensemble option, the loadCDSL function calls the ensemCORDEX function to create ensemble data. Gridded data returned from the readCORDEX or ensemCORDEX functions are stored as an array object of C in the loadCDSL function. Then, the array data object of R is returned to readGrid function from the loadCDSL function. Multiple steps were taken to calculate ET0 in the East Asia region once climate data were imported using the R package for the CDSL. Climate surfaces were divided into groups by latitude to facilitate calculation of ET0 using parallel processing. Climate data for each grid cell were used as inputs to functions that implemented Eqs. (1)–(6). Once ET0 was calculated for each grid cell, the average values of ET0 for March to May were obtained. Those average values were stored in a two-dimensional array, which was used to create GIS compatible data file, e.g., the Geospatial Tagged Image File Format (GeoTIFF) files. The maps of ET0 were created using ‘‘raster” package, which has specialized functionalities to manage gridded data (Hijmans and Van Etten, 2015). The degree of agreement statistics was determined between ETCORDEX and ETAgMERRA. The values of RMSE were determined using Eq. (5). The value of CCC was obtained for each grid cell between two sets of ET0 using the epiR package (Stevenson et al., 2015). Probability density of the bias was determined using R. Because the total size of gridded climate data was about 220 GB, a high-performance computing approach was used in the R script as well as the CDSL. Three sets of R script were prepared to examine computing time under high-performance computing (Fig. 2): (1) using no parallel processing capability as a reference, (2) using the CDSL with openMP parallel functionality, (3) using the package doSNOW (Revolution Analytics, 2014) to provide a highperformance computing environment within in R. The functions included in the doSNOW package were used to read gridded climate data and to calculate ET0 in parallel. A range of processor cores, e.g., from 1 to 24, was used for the preparation of data and calculation of ET0 to examine the performance of parallel computing with increasing number of processors. 5.4. Analysis of reference evapotranspiration estimates from gridded climate data

5.3. Implementation of R scripts A customized script for R was prepared to calculate ET0 using gridded climate data (Fig. 2; supplementary information). The script includes procedures to prepare the climate data using the CDSL, to determine variables required for Eq. (1), to calculate ET0, and to create output files of ET0. For functions associated with geographic information system (GIS), high-performance computing, and statistical analysis, the R packages were used in the script. The R packages allow use of external functions written in both R and other languages, e.g., C. A package for R was built to facilitate use of CDSL in R (Appendix A). In the R package, functions including readGrid and loadCDSL were implemented to import gridded data into R using the CDSL. The readGrid written in R is used to call the loadCDSL function. The readGrid function requires filename, spatial extent, temporal range, and file format for a gridded data file. When an ensemble data are to be prepared using the loadCDSL function, additional options including a list of filenames and weight values for each file are required to create ensemble data. The loadCDSL function was implemented to provide an interface between the CDSL and R. In the loadCDSL function, the readCORDEX function of the CDSL is called using an option to specify

The difference between ETCORDEX and ETAgMERRA was relatively large in regions with complex terrains (Figs. 3 and 4). For example, parts of Myanmar where lowland plains and mountains locate within a small area had considerably large differences between ETAgMERRA and ETCORDEX, e.g., 64% higher for ETRegCM4. In contrast, the difference was relatively small in large plains and coastal areas, e.g., in southern China and Indonesia. Biases of the ensemble sets, MMEET and ETMME, had different spatial patterns. For example, MMEET was relatively similar to ETAgMERRA in southern regions where rice would be cultivated, e.g., plains of China, Philippines, Indonesia, and Japan whereas ETMME had relatively large biases in those areas. In the northern area, e.g., central regions of China, MMEET tended to have higher ET0 than ETAgMERRA whereas ETMME had a similar magnitude of ET0 compared with ETAgMERRA. Probability density functions of the difference between ETCORDEX and ETAgMERRA differed by RCMs (Fig. 5). Tong et al. (2007) reported that the RMSE of ET0 was about 0.4 mm d1 when ET0 was estimated using spatial interpolation. About 64% of grid cells for ETSNU-WRF had biases similar to the previous study, e.g., from 0.4 to 0.4 mm d1. In contrast, ETRegCM had biases between 0.4 and 0.4 mm d1 for 49% of grid cells.

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Fig. 2. The Nassi-Shneiderman diagram of R scripts to calculate reference evapotranspiration using the CORDEX Data Support Library (CDSL). The R scripts were classified into two groups depending on use of the doSNOW package, which provides functionalities of parallel processing. For the script without no doSNOW package (a), the CDSL was compiled with and without openMP to compare data loading time using two versions of CDSL, respectively. Another set of script using doSNOW (b) was dependent on the CDSL built without openMP.

The degree of agreement between ETHadGEM3-RA and ETAgMERRA was greater than other models (Fig. 6). Average CCC of ETHadGEM3RA during 25 years was about 0.80 whereas CCC of other data sets ranged from 0.73 to 0.77. ETSNU-WRF had the lowest value of CCC. MMEET had greater CCC, which was 0.83 for the average CCC during 25 years, than other CORDEX data set. The RMSE had a similar pattern to CCC. ETHadGEM3-RA and MMEET had relatively low RMSE, which was 0.73 and 0.68 respectively, whereas ETSNU-WRF had the largest RMSE (0.93). The degree of agreement between ETAgMERRA and ETCORDEX tended to decrease over time (Fig. 6). The CCC values of ETHadGEM3RA and MMEET had a smaller negative trend than other sets. CCC of ETSNU-WRF decreased more than that of other models. In the most of the study period, MMEET had the greatest value of CCC and the smallest fluctuation of CCC. However, CCC of ETMME stayed in the middle of other sets for 25 years.

5.5. Performance of gridded data processing New functions implemented in the CDSL, e.g., MPprow, was effective to reduce the processing time in data loading (Fig. 7A). Even when a single processor core was used, the running time for data loading decreased by 82% using the functions implemented in the CDSL compared with using the original functions of the GrADS. Use of multiple processor cores decreased the running time further. For example, the running time for data loading decreased by 30% when 16 processor cores were used. However, the effectiveness of using multiple processor cores to load a gridded data file became relatively lower as the number of processor cores increased up to 24 cores.

Application of a package for parallel processing to R was useful to reduce the processing time for loading CORDEX data (Fig. 7B). It took about 67.6 min with one processor core to load 150 CORDEX data files for preparation of an ensemble data set for the study periods. When 24 processor cores were used for loading data concurrently using the doSNOW package, the running time decreased by 86%.

6. Discussion Our results demonstrated that the CDSL would be useful to prepare input data for agricultural models using gridded climate data including CORDEX data. Using the CDSL, for example, data sets that have uniform properties, e.g., projection, spatial resolution, and calendar system, were prepared with minimum effort and short running time for the outputs of different RCMs. Regional impact assessment of climate change would be useful for identifying adaptation measures, which would increase sustainability in the region (Yin, 2003; Gosain et al., 2006). Such studies would benefit from the CDSL, which would help preparation of gridded climate data in a region, e.g., CORDEX data, as inputs to agricultural models. In an assessment of climate change impact on agricultural production, it is crucial to obtain reliable outcomes from a simulation of agricultural ecosystems using models (Aggarwal and Mall, 2002). Ensemble approaches have been considered one of the methods to minimize uncertainty (Asseng et al., 2013; Martre et al., 2015). Because the CDSL have functionalities to aid preparation of input data from different regional gridded data and to create ensemble sets from multiple data sets, it would facilitate regional studies with ensemble approaches. For example, different

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Fig. 3. Average of daily reference evapotranspiration in East Asia during March-April-May in 1981–2005. The map of ETAgMERRA was obtained using AgMERRA data as inputs to the FAO 56 formula.

sets of CORDEX data were prepared as inputs to the FAO-56 formula using the CDSL in R, which allowed reliable estimation of ET0 in the region of interest. It was found that reasonable ET0 could be estimated from the ensemble of ET0 obtained from individual data sets. ET0 in areas with complex terrains tended to have relatively large differences between RCMs. It appeared that such differences were caused by an algorithm used in the regional climate models, e.g., hydrostatic and non-hydrostatic models. Nevertheless, the ensemble of ET0 estimates, e.g., MMEET, had less bias than other estimates using individual or ensemble data sets as inputs, compared with ET0 using AgMERRA data. The impact assessment of climate change on agricultural ecosystems could be improved using ensemble sets obtained from weighting scheme or bias correction approach (Ceglar and KajfezBogataj, 2012). The CDSL has functionalities to import gridded climate data into a data analysis tool for different weight schemes and bias correction methods. For example, the plotrix package of R (Lemon, 2006) can be used to calculate the Taylor index for each weather variables or evapotranspiration estimates. In the further studies, estimation of evapotranspiration would be needed using CORDEX data or ensemble set to which bias correction or weighting scheme were applied.

Although our analysis was focused on spring periods to demonstrate functionalities of the CDSL, it was possible to identify areas where water demands would be considerably high during the early growing season. For example, Philippines and Myanmar have higher ET0 than other regions in the MAM. Estimation of ET0 during a whole season would help reliable assessment of climate change impact on water demands from rice fields along with an analysis of precipitation. Further assessment of actual evapotranspiration would be helpful for prepare of climate change, which merits further studies on assessment of evapotranspiration and drought. It was shown that use of the CDSL would reduce the running time for impact assessment of climate on agricultural ecosystems. Because the CDSL was implemented as an interface to a data analysis tool that supports high-performance computing, various approaches for high-performance computing could be used. For example, the total running time for calculation of ET0 decreased by 82% using the doSNOW package within R. The CDSL that allows parallel processing for loading climate data also resulted in reduction of the running time further for preparation of input data, e.g., by 30% with 16 processor cores. Still, more processor core did not necessarily improve computing time when parallel processing functionalities was used. In general,

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Fig. 4. The bias of reference evapotranspiration estimates (ET0) during March-April-May in 1981–2005. ETM represents ET0 determined using a given gridded data set M as inputs to the FAO 56 formula. The bias was determined as ETAgMERRA - ETM where M includes the outputs of (a) RegCM4, (b) YSU-RSM, (c) SNU-WRF, (d) HadGEM3-RA models, and (e) averages of these individual outputs. (f) ETM includes MMEET, which is the average of ETM for given four RCMs.

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Fig. 5. Probability density of bias of reference evapotranspiration estimates (ET0) during March-April-May in 1981–2005. ETM represents ET0 determined using a given gridded data set M as inputs to the FAO 56 formula. The bias was determined as ETAgMERRA - ETM where M includes the outputs of (a) RegCM4 and YSU-RSM, which are non-hydrostatic models, (b) SNU-WRF and HadGEM3-RA, which are hydrostatic models, and (c) averages of these individual outputs. ETM includes MMEET, which is the average of ETM for given four RCMs.

gain of computing time from high performance computing would decrease with the increasing number of processors due to time for data distribution and collection between multiple cores. Thus, it would be recommended to identify an optimum number of processors for parallel processing. For example, the CDSL with openMP functionality had the least loading time at 16 processor cores. The CDSL would minimize data storage requirements for preparation of inputs to agricultural models. To perform ensemble simulations of agricultural ecosystems, multiple sets of climate data could be used as inputs. As regional climate data often have high spatial resolution, total file size for regional impact assessment studies could become large, e.g. >200 GB. Using the CDSL, no additional data files are created because it is used as an interface to other data analysis tools. Because different types of gridded data have been available for regional studies, support for multiple data format would facilitate regional impact assessment studies. By default, the CDSL has functionalities to support the netCDF format and the gridded binary format. In further studies, it would be merited to examine functionalities to prepare inputs from data files in different file formats,

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Fig. 6. Root mean square error (RMSE; A) and concordance correlation coefficient (CCC; B) of reference evapotranspiration estimates (ET0). ETM represents ET0 determined using a given gridded data set M as inputs to the FAO 56 formula. ETAgMERRA was used as reference data. ETM where M includes the outputs of (a) RegCM4 and YSU-RSM, which are non-hydrostatic models, (b) SNU-WRF and HadGEM3-RA, which are hydrostatic models, and (c) averages of these individual outputs. ETM includes MMEET, which is the average of ETM for given four RCMs.

e.g., GRIB and HDF formats. For example, input data from NASA data sets and MODIS datasets can be prepared for crop growth simulation models using the CDSL. Implementation of additional interpolation schemes would be helpful for the preparation of reliable input data to agricultural models. For example, Zhao et al. (2015) showed that highresolution climate data would be preferable to assess climate change impact in regions with a complex terrain. Rezaei et al. (2015) suggested that the spatial resolution of climate input data would affect the reliability of output from the model. Because the CDSL depends on bilinear interpolation, it is limited to take into account the effects of a complex terrain for spatial interpolation of climate data. Agricultural ecosystems are usually influenced by local terrains. Thus, implementation of reliable spatial interpolation approaches into the CDSL would help the preparation of reliable input data at a higher spatial resolution for agricultural models.

Acknowledgement This work carried out with the support of ‘‘Cooperative Research for Agriculture Science & Technology Development (PJ010115012016)” Rural Development Administration, Republic of Korea. We thank C.H. Porter and the anonymous reviewers for their constructive comments to improve this work.

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struct datacode {char ctlname[512]; int Ystart; int Yend; int stime; int etime; int isiz; int jsiz; float xllcorner; float yllcorner; float cellsize; float nodata; char ncfile;};

(3)

The loadCDSL function assigns the values of list variable to each member for the code variable. The name of attributes included in the list variable is identified as follows: names = getAttrib(list, R_NamesSymbol);

(4)

Using the name of attribute of the list variable, its value is assigned to corresponding member of the code variable. For example, the value of ‘‘ctlname” is assigned to ctlname member of the code variable as follows: for(i = 0; i < length(list); i++) { if(strcmp(CHAR(STRING_ELT(names, i)), ‘‘ctlname”) == 0) { for(j = 0; j < length(STRING_ELT(VECTOR_ELT(list,i),0)); j++) code->ctlname[j] = CHAR(STRING_ELT(VECTOR_ELT(list,i), 0))[j]; code->ctlname[j] = ‘n00 ; }} (5)

The variable in R numeric data types, e.g., integer and real, is converted to the corresponding variable type of C as follows:

Fig. 7. The rate of computing time reduction using multiple processor cores. Computing time was compared between (A) two versions of the CDSL with and without parallel processing based on openMP, respectively. Computing time was also compared for (B) the use of doSNOW package for parallel processing functionality within R. The CDSL. The task of importing CORDEX data into R environment was indicated by ‘‘Data loading”. ‘‘Data distribution” indicates the task of dividing and transferring gridded climate data to each processor core for parallel processing. ‘‘ET0 calculation” represents the tasks of calculating reference evapotranspiration using the FAO-56 formula.

Appendix A

(1)

The loadCDSL prepares inputs to those functions of the CDSL using the list variable obtained from R environment. For example, the readCORDEX function was declared as follows: double ⁄ readCORDEX(struct datacode ⁄ code);

The data type of datacode variable is defined as follows:

(2)

(6) (7)

The loadCDSL function returns a SEXP object to provide an array of gridded data in an R data type. The output of the readCORDEX function is assigned to the result variable of one-dimensional array of double data type in C as follows: result = readCORDEX(ctl);

The R package for the CDSL was built to provide a simple interface between the CDSL and R. Those packages consist of functions written in R or other languages, a configuration file for compiling, the namespace of the R functions and description about the package. The loadCDSL function was implemented in C to call functions of the CDSL including readCORDEX and ensemCORDEX functions. The loadCDSL function was declared using data types specific to R as follows: SEXP loadCDSL(SEXP list);

if(strcmp(CHAR(STRING_ELT(names,i)),‘‘etime”)==0) code->etime = INTEGER(VECOTR_ELT(list,i))[0]; if(strcmp(CHAR(STRING_ELT(names,i)),‘‘xllcorner”)==0) code->xllcorner = REAL(VECOTR_ELT(list,i))[0];

(8)

Because the data type of the result variable is not compatible to R, the values of result variable are assigned to another variable, result_R, in a SEXP data type in the loadGrid function. The PROTECT function is used to allocate the same amount of memory of the result variable to the result_R variable as follows: PROTECT(result_R = allocVector(REALSXP, (ctl->isiz) ⁄ (ctl>jsiz) ⁄ ((ctl->etime) – (ctl->stime))));

(9)

Finally, the result_R variable is returned to caller at the end of the loadGrid function as follows: return (result_R);

(10)

The readGrid function was implemented in R to provide an interface between loadCDSL and R. At first, the loadCDSL function is called using a given variable x to indicate properties of gridded data file as follows:

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output <- .Call(‘‘loadCDSL”,x)

(11)

Once the output of the loadCDSL is assigned to the output variable, a multidimensional array is created to obtain the value of X variable using the output variable as follows: X <- array(output, dim = c(x$jsiz,x$isiz, (x$etime[1] – x $stime[1])))

(12)

The X variable is returned to R environment when the readGrid function is called from a command prompt of R as follows: return (X)

(13)

The NAMESPACE file was prepared to indicate library and functions provided in the package as follows: useDynLib(‘‘loadCDSL”); export(‘‘readGrid”)

(14)

Makevars file was prepared to provide compiler options as follows: PKG_CPPFLAGS= -DHAVE_CONFIG_H -DCDSL_OPENMP DN_THREAD=24 – I/usr/local/include –I/usr/lib64/R/include –I./include –I/usr/ lib64/R/include/R_exts PKG_LIBS=-lnetcdf –L/usr/lib64 –ludunits2 -lgomp –L/usr/ local/lib64 –L/usr/bin/lib64

(15)

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