Climate data generotor for SEIB-DGVM

This online system generates meteorological data for inputting the SEIB-DGVM v3.00 and later. This generator is based on CRU-NCEP-SRB. Another generator based on CRU-JRA2.4 is available here.

Citation of the climate data, generated by this web system: Tei S., Sugimoto A., Liang M. et al., (2017) Journal of Geophysical Research: Biogeosciences, doi:10.1002/2016jg003745.
Following description about the recipe of generated data was taken from the above literature.

The integrated model requires daily climatic variables for the following items: mean air temperature, daily range of air temperature, precipitation, downward shortwave and longwave radiations, wind velocity, and relative humidity. We employed the CRU-TS4.00 observation-based climatic data set (0.5°, 1901-2015) (Harris et al., 2014). Because this is a monthly based data set, we supplemented daily climatic variability within each month using the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily climate data set (Kalnay et al., 1996) for 1950.

The NCEP/NCAR data set had a spatial resolution of 192 × 94 global grids and was linearly interpolated to a 0.5° grid mesh, which corresponds to the CRU data set. Using the CRU and this interpolated NCEP/NCAR data sets, we obtained the following driving data. For air temperature, NCEP/NCAR reanalysis was linearly scaled by adding a constant (month and location specific) so that monthly mean values became identical to the CRU data set values for the corresponding month and location. For precipitation and relative humidity, NCEP/NCAR reanalysis was linearly scaled by multiplying a constant (month and location specific) so that monthly mean values became identical to the CRU data set values for the corresponding month and location. For daily air temperature ranges, CRU data were directly employed, assuming that it is constant during a month. For daily wind, NCEP/NCAR reanalysis was directly employed.

Neither CRU nor NCEP/NCAR data sets contain radiation values, and hence, these were estimated from cloudiness. First, daily cloudiness values in the NCEP/NCAR data set were linearly scaled by multiplying a constant (month and location specific) so that monthly means became identical to CRU cloudiness values. We substituted these scaled daily cloudiness in the empirical functions presented in Appendices A2 and A3 of Sato et al. (2007) and obtained daily means of downward shortwave and longwave radiations. Finally, each downward shortwave and longwave radiation value was linearly scaled by multiplying a constant (location specific) so that its average from 1991 to 2000 became identical to the averages of the NASA/Global Energy and Water Cycle Experiment Surface Radiation Budget Release-3.0 monthly data set (https://eosweb.larc.nasa.gov/project/srb/srb_table), for the same period.

Citations
Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister (2014), Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset, Int. J. Climatol., 34(3), 623-642, doi:10.1002/joc.3711.
Kalnay, E., et al. (1996), The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77(3), 437-471.
Sato, H., A. Itoh, and T. Kohyama (2007), SEIB-DGVM: A new dynamic global vegetation model using a spatially explicit individual-based approach, Ecol. Model., 200(3-4), 279-307.

Data generator

Modification History

12 Dec 2017
Climatic data generator for SEIB ver3.0 and later is now opend to public.

Conditions of use

1. This web-system can be used by any person and by any organs for fair usages.
2. The data is provided with no guarantees as to the accuracy, correctness or utility of the output produced.
3. Publications should give adequately citation to the original climate dataset (see section "About original climate dataset").

About original meteorological data sets

CRU TS4.00 Climate Time-Series (0.5 degree global, 1901-2015, Monthly)

Surface climate data extending over global land areas, excluding Antarctica. These gridded data are based on an archive of monthly mean temperatures provided by more than 4000 weather stations distributed around the world.

The original data was downloaded from following website:
British Atmospheric sData Centre (BADC)
Detailed description of the data set is also available on the website.

The citation paper of the original data:
Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014)
Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset
International Journal of Climatology, 34(3), 623-642, doi: 10.1002/joc.3711

NCEP/NCAR reanalysis datasets (192×94 global, 1948-Near present, Daily)

An outcome of data assimilation technique using a climate forecasting model. Observed climatic data from 1948 to the present are analyzed, interpolated onto a system of grids, then employed for initialization and forcing of the model.

The citation paper of the original data:
Kalnay et al.,The NCEP/NCAR 40-year reanalysis project, Bull. Amer. Meteor. Soc., 77, 437-470, 1996.

SRB Release-3.0 (1 degree global; July01/1983 - Dec31/2007; 3-hourly, daily, monthly/3-hourly, and monthly averages)

This data set was generated by a model, of which require data includes visible and infrared radiances, cloud, surface temperature, surface moisture, and column ozone amounts.

The original data was downloaded from following website:
SRB Data and Information
Detailed description of the data set is also available. Note that we used monthly averaged data.

How this system generate data?

Procedure

(0) 1 year daily climatic data (air temperature, soil temperature, precipitation, total cloud cover, specific humidity, wind velocity, and downward short wave radiation) during 1950 was obtained from the NCEP/NCAR reanalysis daily climatic dataset. Spatial resolution of the original data was 192×94 global grids, and was linearly interpolated to 0.5 degree grid mesh, which corresponds to the spatial resolution in CRU TS3.22 data. Using this interpolated daily data, all items (except for daily range of air temperature) of CRU TS3.22 monthly climatic data will be scaled to daily as follows.

For air temperature, NCEP/NCAR reanalysis will be linearly scaled by adding a constant (month and location specific) so that its monthly mean becomes as same as the values of corresponding month and location of CRU-TS3.22.
For cloudness, NCEP/NCAR reanalysis will be linearly scaled by multiplying a constant (month and location specific) so that its monthly mean becomes as same as the values of corresponding month and location of CRU-TS3.22.
For precipitation and specific humidity, daily values of NCEP/NCAR reanalysis will be linearly scaled by multiplying a constant (month and location specific) so that its monthly total becomes as same as the values of corresponding month of CRU-TS3.22.
For soil temperatures and wind velocity, NCEP/NCAR reanalysis was simply employed.

(1) From specified latitude and longitude, select 4 grids that will be referred to generate data (see "Data interpolation method").

(2) Generate data through linear interpolation, which is described below. This interpolation procedure is omitted for ocean grids, which do not contain any values.

Data interpolation method

From coarse original data, this web system generate climate data of your specified location through simple liner interpolation as below.

The simple example of liner interpolation. In this case, the value at yellow point is 6.8 y/(x+y) + 2.4 x/(x+y)

To obtain the interpolated value at your selected location, values at most proximate 4 grids will be referred. First, values at green dots will be obtained by above method. Then, applying the same method to the green dots, value at yellow dot will be calculated.

For SEIB users in Japan

(Sorry in Japanese)
このシステムで使用している全球気象データ（0.5度、Daily、115年）を提供します。無料です。上のConditions of useをお守り頂き、そして論文のacknowledgmentにでも私の名前を書いて下さるのが提供条件です。データセットの大体の大きさは、tarzip圧縮したもので約56GBです。データの諸元をまとめたテキストファイルと、データ変換に使用したFortran90のコードも添付します。
ご希望の方は、事前にコンタクトを取った後、ポータブルHDD等を郵送して下さい。データをコピーしてから、着払い便（応相談）にて返送いたします。

	(degree)	(minutes)
Latitude
Longitude

Meteorological data generator for SEIB-DGVM

based on the CRU-NCEP-SRB