Development of a gridded climate data tool for the Coordinated Regional Climate Downscaling Experiment data

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THESIS FOR DEGREE OF MASTER OF SCIENCE

Development of a gridded climate data tool for the

Coordinated Regional Climate Downscaling

Experiment data

CORDEX 자료 처리를 위한 격자형 기상 자료 처리 도구의 개발

BY

BYOUNG HYUN YOO

FEBURARY, 2017

MAJOR IN CROP SCIENCE AND BIOTECHNOLOGY

DEPARTMENT OF PLANT SCIENCE

i

Development of a gridded climate data tool for the

COordinated

Regional

Climate

Downscaling

EXperiment data

BYOUNG HYUN YOO

Crop Science and Biotechnology

Department of Plant Science

The Graduate School

Seoul National University

ABSTRACT

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,

ii

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.

Keywords: Grid analysis and display system, High-performance

computing, CORDEX, gridded data, CDSL

iv

CONTENTS

ABSTRACT

· · · ·

·

i

CONTENTS

· · · ·

·

iii

LIST OF TABLES

· · · ·

iv

LIST OF FIGURES

· · · ·

·

v

LIST OF ABBREVIATIONS

· · · viiI

INTRODUCTION

· · · ·

1

PROPERTIES OF CORDEX DATA

· · · ·

4

DESIGN OF CDSL

· · · ·

7

IMPLEMENTATION OF CDSL

· · · ·

11

CASE STUDY

· · · ·

14

DISCUSSION

· · · ·

32

REFERENCES

· · · ·

36

APPENDIX A

· · · ·

44

APPENDIX B

· · · ·

48

v

LIST OF TABLES

Table 1.

The description of functions implemented in the CORDEX

Data Support Library

Table 2.

Properties of CORDEX East Asia and AgMERRA data sets

Table 3.

Methods and variable names of CORDEX and AgMERRA

data used for the FAO 56 formula, respectively

vi

LIST OF FIGURES

Figure 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.

Figure 2.

The Nassi-Shneiderman diagram of R scripts to calculate

reference evapotranspiration using the CORDEX Data

Support Library (CDSL). The R scrips 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.

Figure 3.

Average of daily reference evapotranspiration in East Asia

during March-April-May in 1981–2005. The map of

ET

was obtained using AgMERRA data as inputs to

the FAO 56 formula.

Figure 4.

The bias of reference evapotranspiration estimates (ET0)

during March-April-May in 1981–2005. ET

represents

ET

determined using a given gridded data set M as inputs

to the FAO 56 formula. The bias was determined as

ET

- ET

where M includes the outputs of (a)

HadGEM3-vii

RA models, and (e) averages of these individual outputs. (f)

ET

includes MME

, which is the average of ET

for

given four RCMs.

Figure 5.

Probability density of bias of reference evapotranspiration

estimates (ET

) during March-April-May in 1981–2005.

ET

represents ET

determined using a given gridded data

set M as inputs to the FAO 56 formula. The bias was

determined as ET

- ET

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. ET

includes MME

, which is the

average of ET

for given four RCMs.

Figure 6.

Root mean square error (RMSE; A) and concordance

correlation

coefficient

(CCC;

B)

of

reference

evapotranspiration estimates (ET

). ET

represents ET

determined using a given gridded data set M as inputs to

the FAO 56 formula. ET

was used as reference data.

ET

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. ET

includes

MME

, which is the average of ET

for given four RCMs.

Figure 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

viii

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.

ix

LIST OF ABBREVIATIONS

CCC Concordance correlation coefficient

CDSL CORDEX Data Support Library

CORDEX COordinated Regional climate Downscaling EXperiment

ET

Reference evapotranspiration

GrADS Grid Analysis and Display System

HadGEMRA Hadley center Global Environmental Model version

3-Regional climate model

MAM March-April-May

netCDF Network Common Data Form

RegCM4 NCAR’s Regional Climate Model version 4

RMSE Root mean square error

SNU-WRF Seoul National University Weather Research Forecasting

Model

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

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 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.

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 pathway (RCP). Different sets of RCM 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

(~ 50km). 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 CORDEX 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 of projection would be

needed to analyze data sets that have different projections. It has been

recommended to include projection information of CORDEX data 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.

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.

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 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 analysis for

gridded climate data.

effort. Metadata are often used to import gridded data into data tools including

GrADS. For example, the GrADS depends on a CTL file to indicate metadata

including properties of data file including file name, 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 a given file,

“tas_EAS-44_HadGEM2-AO_historical_r1i1p1_HadGEM3-RA_v1_day_19810101-19851230.nc”, contain

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 running time for data

preparation. The CDLS 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 requirement for input data preparation. Input data for a

model of interest, which would have a specific form, could be prepared from

gridded climate data using the GrADS. Still, additional data files would be created

for the preparation of input data because those gridded data could not be imported

into the model using the GrADS. When the CDSL would be used, no additional file

would be needed to prepare input data because 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 import the COREX

data sets into R for comprehensive sets of statistical analysis. Furthermore,

CORDEX data sets could also be imported into data tools that have a feature of

high-performance computing to process a large amount of data using the CDSL.

Table 1. The description of functions implemented in the CORDEX Data Support

Library.

Function

Functionalities / description

Dependencies

readCORDEX

Provide an Interface for gridded data.

readMetaCORDEX

CORDEXgrid

ensemCORDEX

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.

readMetaCORDEX

CORDEXgrid

readMetaCORDEX

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.

CORDEXgrid

Prepare the variable data from CORDEX file with projection

other than WGS84 for the conversion of projection.

MPprow

MPprow

Convert projection using bilinear interpolation, which is the

same functionality to gaprow implemented in Grid Analysis

and Display System.

IMPLEMENTATION OF CDSL

New functions were implemented within the CDSL to provide additional

functionalities to the GrADS (Table 1). For example, 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.

interpolation (Table 1). The MPprow has the same functionality as the gaprow

function in the GrADS. For example, the GrADS support 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 garow function in the GrADS. However, each cell of

the gridded data was processed without parallel processing functionality in the

gaprow function. In the MPprow function, conversion of projection and

interpolation of data were performed for multiple data lines concurrently using

parallel processing feature of openMP.

The readCORDEX function was implemented as an interface to other

application. The readCORDEX 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. In the ensemCORDEX function, a weighted average of individual

data sets is calculated to prepare an ensemble data set. By default, an equal

weighting scheme was used in the ensemCORDEX function. Still, weights for

individual data sets could be specified by a user. For example, weights determined

from different approaches, e.g. reliable ensemble average, Kolmogorov-Smirnov,

and Taylor index (Vidal and Wade, 2008) can be used as an additional 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. Still, the functionalities associated with

netCDF format were examined for the CDSL in this study.

CASE STUDY

The CDSL was used for the regional assessment of climate impact on

agricultural ecosystems using the CORDEX data as a case study. In the present

study, reference evapotranspiration (ET

) was calculated in the 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.

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 outputs of 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

(WRF), and the regional spectral model (YSU-RSM). HadGEM3RA and

SNU-WRF models are non-hydrostatic 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 AgMERRA 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°

Calculation and analysis of reference

evapotranspiration

Reference evapotranspiration ET

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 ET

for spatiotemporal analysis of

evapotranspiration (Gao et al., 2007; Chattopadhyay and Hulme (1997). The map

of ET

(mm d

) was obtained using CORDEX data and AgMERRA data as inputs

to the FAO-56 formula as follows (Allen et al., 1998):

=

Eq(1)

where R

and G represent the net radiation and the soil heat flux (MJ m

d

),

respectively. G was assumed to be zero for daily calculation of ET

(Allen et al.,

1998). T

and U

indicate daily mean air temperature (K) and wind speed (m s

) at

2 m height, respectively. e

and e

are saturated and actual vapor pressure (kPa),

respectively. Δ and γ are the slope of vapor pressure deficit (kPa) and

psychrometric constant (kPa °C

_{), respectively.}

AgMERRA data. Those variables were estimated using methods described in Allen

et al. (1998) (Table 3). 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 ET

with similar sets of

variables, outgoing longwave radiation was estimated for all the data sets as

follows (Allen et al., 1998):

= 4.903 × 10

(0.34 − 0.14

)(1.35

− 0.35) Eq (2)

where R

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.

R

and R

, which represents actual and clear sky radiation, respectively, were used

to calculate a fraction of cloud, i.e., R

/ R

. Clear sky radiation was calculated as

follows:

= ( + ) Eq (3)

where (a

+ b

) indicates a fraction of extraterrestrial radiation, R

, reaching an

earth surface. a

and b

were assumed to be 0.25 and 0.5, which were recommended

by Allen at al. (1998). R

was calculated as follows:

=

(

( )

( ) +

( )

( )

( )) Eq (4)

where G

, d

and w

represent solar constant, inverse relative distance from

the earth to the sun, and sunset hour angle. ϕ and δ indicate latitude of the each grid

cell and solar declination.

Because the CORDEX data had specific humidity rather than relative

humidity, e

and e

were calculated as follows:

=

, and Eq (5)

= 0.6108 ×

(17.27 × /( + 237.3)) Eq (6)

where SH and PS indicate specific humidity (g kg

_{) and air pressure (kPa),}

respectively. T represents average daily temperature (°C) available from the outputs

of RCMs (Table 3).

Calculation of ET

was performed only in a part of growing season to

minimize computing resources as our focus was on exploring the functionalities of

the CDSL. Daily ET

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. ET

was calculated for the periods from 1981 to

2005 during which both CORDEX and AgMERRA data were available.

In total, seven data sets of ET

were obtained in the East Asia region. Four sets

of ET

were obtained using CORDEX data from individual RCMs as inputs to eq.

1. Those sets of ET

were denoted by the name of RCM corresponding to the data

set. For example, ET

calculated using HadGEM3-RA model data was denoted by

ET

. To compare ensemble approaches, two sets of ensemble data, ET

and MME

, were created. An ensemble of CORDEX data, which was averages of

create another set of ET

, ET

. Average of ET

obtained from individual data sets,

MME

, was calculated as follows:

MME

= ∑ET

/ 4 Eq (7)

where M represent individual RCMs including HadGEM3RA, YSU-RSM,

SNU-WRF, and RegCM4. Collectively, ET

was used to represent ET

obtained from the CORDEX data. AgMERRA data were used as inputs to the

FAO-56 formula to create ET

, which was used as a reference set.

Probability density of differences between ET

and ET

was

determined to compare the reliability of ET

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 ET

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 ET

and

ET

as follows:

=

∑

(

[ ] −

[ ]) , Eq (7)

where n is the number of the valid grid cell [i] represents each grid cell of

ET

and ET

. Concordance correlation coefficient (CCC), which has

been used to represent both precision and accuracy, was determined as follows (Lin,

1989):

=

, Eq (8)

indicate standard deviation and average of ET

and ET

respectively.

Implementation of R scripts

A customized script for R was prepared to calculate ET

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 ET

, and to create output files of ET

. 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 with other than R language. Those packages consist of

functions written in R or other languages, e.g., C, a configuration file for compiling,

the namespace of the R functions and description about the package.

A package for R was built to facilitate use of CDSL in R (Appendix). 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 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 ET

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 ET

using parallel

processing. Climate data for each grid cell were used as inputs to functions that

implemented eqs. 1 - 6.

Geospatial map of ET

was created after ET

was calculated. Once ET

was

calculated for each grid cell, the average values of ET

for a given period, e.g.,

from 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 ET

were

created using “raster” package, which has specialized functionalities to manage

gridded data (Hijmans and Etten, 2015).

The degree of agreement statistics was determined between ET

and

ET

. The values of RMSE were determined using eq. 5. The value of CCC

was obtained for each grid cell between two sets of ET

using the epiR package

(Stevenson et al., 2015). Probability density of the bias was determined using R.

Three sets of R script were prepared to examine computing time under

high-performance computing (Fig. 2). 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. Two sets of R scripts were written to compare two versions of R

package for the CDSL with and without functionalities of parallel processing based

on openMP, respectively. Another set of the script was written to examine

computing time under high-performance computing environment provided in R.

Thus, the CDSL without openMP functionalities were used in the script. The

package doSNOW, which provides functionalities of high-performance computing

(Revolution Analytics, 2014), was used for ET

calculation. The functions included

in the doSNOW package was used to read gridded climate data in parallel. A range

of processor cores, e.g., from 1-24, was used for the preparation of data and

calculation of ET

to examine the performance of parallel computing with

increasing number of processors.

Analysis of Reference evapotranspiration estimates

from gridded climate data

The difference between ET

and ET

was relatively large in regions

with complex terrains (Figs. 3-4). For example, parts of Myanmar where lowland

plains and mountains locate within a small area had considerably large differences

between ET

and ET

, e.g., 64% higher for ET

. In contrast, the

difference was relatively small in large plains and coastal areas, e.g., in southern

China and Indonesia.

For example, MME

was relatively similar to ET

in southern regions where

rice would be cultivated, e.g., plains of China, Philippines, Indonesia, and Japan

whereas ET

had relatively large biases in those areas. In the northern area, e.g.,

central regions of China, MME

tended to have higher ET

than ET

whereas

ET

had a similar magnitude of ET

compared with ET

.

Probability density functions of the difference between ET

and

ET

differed by RCMs (Fig. 5). Tong et al. (2007) reported that the RMSE of

ET

was about 0.4 mm d

when ET

was estimated using spatial interpolation.

About 64% of grid cells for ET

had biases similar to the previous study, e.g.,

from -0.4 to 0.4 mm d

. In contrast, ET

had biases between -0.4 and 0.4 mm

d

_{for 49% of grid cells.}

The degree of agreement between ET

and ET

was greater than

other models (Fig. 6). Average CCC of ET

during 25 years was about 0.80

whereas CCC of other data sets ranged from 0.73 to 0.77. ET

had the lowest

value of CCC. MME

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. ET

and MME

had relatively low RMSE, which was 0.73 and 0.68

respectively, whereas ET

had the largest RMSE (0.93).

The degree of agreement between ET

and ET

tended to decrease

over time (Fig. 6). The CCC values of ET

and MME

had a smaller

negative trend than other sets. CCC of ET

decreased more than that of other

models. In the most of the study period, MME

had the greatest value of CCC and

other sets for 25 years.

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 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 minutes

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%.

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

HadGEM3-RA

RegCM4

SNU-WRF

YSU-RSM

AgMERRA

Resolution

0.44°

0.44°

0.44°

0.44°

0.25°

Grid size

167 x 204

195 x 241

196 x 232

198 x 241

720 x 1440

Projection

_rotll

_lcc

_lcc

_lcc

_ll

Calendar

360-day

360-day

Regular

360-day

Regular

1.

rotll, lcc,

and ll indicate rotated latitude and longitude, Lambert conic

conformal, and Geographic(lat/lon) projections, respectively.

Table 3. Variable names of CORDEX and AgMERRA products for calculation of

reference evapotranspiration

CORDEX

AgMERRA

R

R

- R

R

- R

R

Eq. 2

Eq. 2

R

(1 – α) R

R

- R

R

rsds

srad

α

0.23

0.23

R

Eq. 3

Eq. 3.

U

sfcWind

wndspd

K

tas

(tmax + tmin) / 2

e

Eq. 4

Eq. 4

e

Eq. 3

_e

ⅹ rhstmax

Δ

Δ = 4098e

/ (K -35.85)

Δ = 4098e

/ (K -35.85)

γ

γ = 0.665 x 10

_PS

_{γ = 0.665 x 10}

_PS

Tx

tasmax

_tmax

Tn

tasmin

_tmin

SH

huss

-PS (kPa)

ps

101.3

1. For SNU-WRF and YSU-RSM datasets, wind speed was calculated from “uas” and “vas”

data which represent wind velocity of east and north direction, respectively.

2. the value of tasmax and tasmin for the SNU-WRF model was replaced by that of tas,

respectively.

Fig. 2. The Nassi-Shneiderman diagram of R scripts to calculate reference

evapotranspiration using the CORDEX Data Support Library (CDSL). Two sets of

the script were implemented to compare computing time (A) with and (B) without

parallel processing functionality using the doSNOW package, respectively.

Fig. 3. Average daily reference evapotranspiration in East Asia during

March-April-May in 1981-2005. The map of ET

was obtained using AgMERRA

Fig. 4. The bias of reference evapotranspiration estimates (ET

) during

March-April-May in 1981–2005. ET

represents ET

determined using a given gridded

data set M as inputs to the FAO 56 formula. The bias was determined as ET

- ET

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) ET

Fig. 5. Probability density of bias of reference evapotranspiration estimates (ET

)

during March-April-May in 1981–2005. ET

represents ET

determined using a

given gridded data set M as inputs to the FAO 56 formula. The bias was determined

as ET

- ET

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. ET

includes

MME

, which is the average of ET

for given four RCMs.

Fig. 6. Root mean square error (RMSE; A) and concordance correlation coefficient

(CCC;

B)

of

reference

evapotranspiration

estimates

(ET

). ET

represents ET

determined using a given gridded data set M as inputs to

the FAO 56 formula. ET

was used as reference data. ET

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. ET

includes MME

, which is the average of ET

for

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.

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 sets of CORDEX data were prepared as inputs to the FAO-56

formula using the CDSL in R, which allowed reliable estimation of ET

in the

region of interest

It was found that reasonable ET

could be estimated from the ensemble of ET

obtained from individual data sets. ET

in areas with complex terrains tended to

were caused by an algorithm used in the regional climate models, e.g., hydrostatic

and non-hydrostatic models. Nevertheless, the ensemble of ET

estimates, e.g.,

MME

, had less bias than other estimates using individual or ensemble data sets as

inputs, compared with ET

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 Kajfez-Bogataj, 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 ET

than other regions in the MAM.

Estimation of ET

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.

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 ET

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, 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 simulation 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 considerably 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,

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 high-resolution 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.

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