STUDIES ON MODEL PREDICTABILITY IN VARIOUS
CLIMATIC CONDITIONS: AN EFFORT USING CEOP EOP1
DATASET
KUN YANG, KATSUNORI TAMAGAWA, PETRA KOUDELOVA, TOSHIO KOIKE
Department of Civil Engineering, University of Tokyo,
Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
The Coordinated Enhanced Observing Period (CEOP) project provides an integrated,
globally covered dataset. The CEOP EOP1 dataset covers the period from July to
September, 2001. Using this dataset, this study investigates some aspects concerning
GCM predictability by model ~ observation comparison and model inter-comparison at
CEOP-reference sites. The model outputs are comparable to observations for air
temperature, humidity and net radiation while deviate far for surface temperature, surface
energy budget and precipitation. The model inter-comparison shows good agreements for
air temperature, humidity, net radiation, and soil heat flux, while quite different values
for other variables. These differences are not only caused by model errors, but also by the
footprint mismatch between the model grid and the spatial scale represented by in-situ
observations. We suggest that each variable may have a different spatial variability, and
models tend to well predict variables that have a low spatial variability.
INTRODUCTION
The Coordinated Enhanced Observing Period (CEOP) project provides an integrated
dataset consisting of in-situ data, satellite data, and model output at numerical weather
prediction centers. This globally covered dataset offers a unique opportunity to study
physical processes in various climatic conditions around the world, and makes possible to
investigate the predictability of GCMs in various conditions. Currently, CEOP EOP1
(July ~ September, 2001) dataset has been available. This study focuses on comparisons
between in-situ observations and Model Output Location Time Series (MOLTS) from
NASA-GEOS3 (Goddard Earth Observing system, version 3) and from NASA-GLDAS
(Global Land Data Assimilation System) for 16 GEWEX Continental-scale Experiments
(CSE) reference sites. As shown in Fig.1, these sites are marked with number 3
(Mongolia), 9 (Himalayas), 10 (South China sea), 15 (North Slope of Alaska or NSP), 17
(Berms-spruce), 18 (Fort Peck or Ftpeck), 19 (Bondville), 20 (Southern Great Plains or
SGP), 23 (Caxiuana), 25 (Manaus), 26 (Rondonia), 27 (Brasilia), 28 (Pantanal), 30
(Lindenberg), 31 (Cabauw), 34 (Tropical Western Pacific-Manus or TWP-Manus).
ANALYSIS METHOD
To evaluate the predictability of model outputs, two indexes are introduced: one is the
10-day mean value of each variable, and the other is the 10-day mean diurnal cycle of
1
2
each variable. The variables of interest are surface temperature, air temperature, air
humidity, surface radiations, surface heat fluxes, and precipitation.
Figure 1. CEOP reference sites
To account for the diurnal cycle in a period, the mean value for a variable
specific hour i can be calculated as follows
xi 
1 ni
 xi , j ,
ni j 1
(1)
The mean value for a variable
24 ni
x    xi , j
24
x at a
x in the period can be calculated by
 ni ,
(2)
1 24 1 ni
  xi , j ,
24 i 1 ni j 1
(3)
i 1 j 1
i 1
or
x
where i is the index of hour and
ni is the number of available data of variable x at hour
i in the period.
Eq.(2) and Eq.(3) give the same mean value if no data are missed. However, if some
data were missed, the result from Eq.(2) can be sensitive to the number and the value of
missed data. An example is the net radiation at BALTEX-Lindenberg, where the net
radiation was calculated from four radiation components. Because some observed data of
shortwave radiation at noon were missed, some high values of net radiation were thus
missed. As a result, the 10-day mean values of net radiation from Eq.(2) is unrealistically
low, as shown in the left panel of Fig.2, while the values from Eq.(3) reasonably
comparable to the output of two NASA models, as shown in the right panel of Fig.2.
3
Therefore, we adopt Eq.(3) to calculate 10-days mean values and compare it with model
outputs.
200
Net radiation - 10-days averages - "old"
]
-2
GLDAS
GEOS3
DAO
Observation
150
100
50
Rnsfc [wm
0
200
Net radiation - 10-days averages - "new"
GLDAS
DAO
GEOS3
Observation
150
100
50
180
200
220
240
(a) averaged from Eq.(2)
0
260 Julian day 180
200
220
240
260
(b) averaged from Eq.(3)
Figure 2. 10-day mean net radiation from observation, NASA/GEOS3 and
NASA/GLDAS at Lindenberg during CEOP EOP1
COMPARISONS BETWEEN MODEL OUTPUTS AND OBSERVATIONS
Fig. 3 shows the comparison of 10-day mean values of 10 variables between GLDAS
output and in-situ observations at the 16 CEOP reference sites. Similarly, we can
compare the 10-day mean diurnal cycle (not shown). In general, the comparisons show
good agreements for air temperature (Tair), humidity (qair), and surface net radiation
(Rnsfc), while very inconsistent for surface temperature (Tsfc), net shortwave radiation
(SWn), net longwave radiation (LWn), surface sensible heat (Hsfc), surface latent heat
(lEsfc), soil heat flux (Gsfc), and precipitation (prec). The following gives a preliminary
analysis on these results.
Footprint mismatch
GCM outputs represent an average over ~100km grids, while observations usually carried
out at a point-scale or patch-scale. If the point-scale observations cannot represent a mean
over a GCM grid, then it is not surprising that the model out is not consistent with
observations. Viewing the fact that some variables agree with observations while others
do not, we speculate that each variable may have a different spatial variability. Several
factors may determine their spatial variability. (1) Surface heterogeneity like land cover,
soil type and terrain variability. They are the major factors determining the spatial scale
of the surface variables like surface temperature and soil moisture, surface wind, and
energy budget. (2) Spatial heterogeneity such as convective cloud and rainfall. Their
scales are associated with the shortwave and longwave radiations. (3) Horizontal
advection. If the wind is very weak, the surface air temperature and humidity would
strongly determined by surface conditions and thus have a spatial variability similar as
surface temperature; however, strong horizontal advection plays a role in upscaling these
variables, and makes them represent an average over a large area. (4) Physical internal
relationships. Shortwave radiation can be reduced by cloud while longwave radiation can
4
be enhanced by cloud. As a result, the net radiation can represent a spatial scale larger
than that for individual radiation components. Soil heat flux is affected by many factors
such as soil thermal properties and soil moisture, but the dominant factor is the surface
net radiation and thus has a scale close to that of the net radiation. (5) Observing
approach. Although surface energy budget has a spatial heterogeneity similar to surface
temperature and soil moisture, heat fluxes measured by the eddy-correlation technique
usually represent values averaged over the distance 100-200 times the sensor’s reference
height in the upwind direction, so the measured turbulent fluxes have a scale much larger
than that for the surface temperature and the soil moisture. Therefore, we propose the
schematic of the spatial variability of each variable in Fig. 4
310
310
(b) Tair
305
305
GLDAS-output air temperature (K)
GLDAS-output surface temperature (K)
(a) Tsfc
300
295
290
285
280
275
300
295
290
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Pantanal
Caxiuana
Pantanal
BERMS_Spruc
Brasilia
BERMS_Spruc
NSA
TWP_Manus
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Brasilia
NSA
TWP_Manus
285
280
275
270
270
270
275
280
285
290
295
300
Observed surface temperature (K)
305
270
310
275
305
310
(d) SWN
-2
GLDAS-output net shortwave radiation (W m)
(c) qair
-1
285
290
295
300
Observed air temperature (K)
350
25
20
GLDAS-output air humidity (g kg)
280
300
250
15
200
10
5
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
150
100
50
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
0
0
0
5
10
15
Observed air humidity (g kg-1 )
20
25
0
50
100
150
200
250
300
Observed net shortwave radiation (W m-2 )
350
400
Figure 3. Comparison of 10-day mean values between GLDAS output and in-situ
observations at 16 CEOP reference sites
5
250
0
(f) Rnsfc
-50
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
200
-2
Cabauw
GLDAS-output net radiation (W m)
-2
GLDAS-output net longwave radiation (W m)
(e) LWN
Lindenberg
150
100
-100
50
0
-150
150
-100
-50
Observed net longwave radiation (W m-2 )
-50
0
0
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
50
100
150
Observed net radiation (W m-2 )
200
200
250
(h) lEsfc
-2
GLDAS-output surface latent heat flux (W m)
(g) Hsfc
-2
GLDAS-output surface sensible heat (W m)
Cabauw
Mongolia
-50
-150
150
100
100
50
0
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
-50
50
0
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
-50
-50
100
0
50
100
Observed surface sensible heat (W m-2)
150
-50
0
25
(I) Gsfc
50
100
150
Observed surface latent heat flux (W m-2 )
200
(j) Rainfall
-1
GLDAS-output precipitation (mm day)
-2
GLDAS-output surface soil heat flux (W m)
Lindenberg
50
20
15
0
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
10
-50
5
Lindenberg
Cabauw
Mongolia
China_sea
Himalayas
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
NSA
TWP_Manus
0
-50
0
50
Observed surface soil heat flux (W m-2 )
100
0
5
10
15
Observed precipitation (mm day-1 )
20
Figure 3. Comparison of 10-day mean values between GLDAS output and in-situ
observations at 16 CEOP reference sites (Continued)
25
6
Point
1m
Spatial variability
Moisture, Tsfc
1km
Hsfc, Esfc
Rainfall
10km
Rsw, Rlw, Cloud
Rnsfc, Gsfc, HPBL
Tair, qair
100km
Grid
Figure 4. Spatial variability of each variable represented by the point-scale observations
Because the air temperature (Tair), humidity (qair), surface net radiation (Rnsfc) and
soil heat flux (Gsfc) can represent mean values over an area much larger than that for
other variables, the footprint mismatch problem can be alleviated to some extent for these
variables; therefore, they should be closer to GCM outputs than other variables. We note
that the modeled Gsfc in Fig. 3 deviates far from the observations. However, this does not
mean our analysis on its spatial variability is definitely wrong. The Gsfc cannot be
directly measured and thus it is derived from the energy budget equation, so any error in
other energy fluxes would be added to the term Gsfc, which may make its value
unrealistic. On the other hand, the inconsistency for highly variable quantities such as the
surface temperature cannot be simply attributed to the problem of GCM predictability.
Modeling uncertainties
310
25
(a) Tair
(b) qair
305
-1
GEOS3 air humidity (g kg)
GEOS3 air temperature (K)
20
300
295
15
290
285
280
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
275
10
5
Brasilia
BERMS_Spruc
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
270
0
270
275
280
285
290
295
GLDAS air temperature (K)
300
305
310
0
5
10
15
-1
GLDAS air humidity (g kg )
20
25
Figure 5. Comparison of 10-day mean values of variables having low spatial variability
between GLDAS and GEOS3 at 13 CEOP reference sites
7
250
100
(d) Gsfc
GEOS3 surface soil heat flux (W m)
(c) Rnsfc
-2
GEOS3 net radiation (W m )
-2
200
150
100
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
50
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
50
0
BERMS_Spruc
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
0
-50
0
50
100
150
-2
GLDAS net radiation (W m )
200
250
-50
0
50
-2
GLDAS surface soil heat flux (W m )
100
Figure 5. Comparison of 10-day mean values of variables having low spatial variability
between GLDAS and GEOS3 at 13 CEOP reference sites (continued)
Fig. 5 and Fig. 6 show the comparison between the outputs of two NASA models,
respectively, for variables having a low and a high spatial variability. At Himalayas, NSA
and TWP-manus sites, different land properties are set in the two models, so the
comparisons at the three sites are removed. Fig. 5 clearly indicates that the model outputs
give close values for the variables with a low spatial variability (Tair, qair, Rnsfc, Gsfc),
and thus have small uncertainties.
350
0
(a) SWn
(b) LWn
GEOS3 net longwave radiation (W m)
-2
-2
GEOS3 net shortwave radiation (W m)
300
250
200
150
100
50
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
-50
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
-100
BERMS_Spruc
0
0
50
100
150
200
250
300
-2
GLDAS net shortwave radiation (W m )
350
400
-150
-150
-100
-50
-2
GLDAS net longwave radiation (W m )
Figure 6. Comparison of 10-day mean of variables having high spatial variability
between GLDAS and GEOS3 at 13 CEOP reference sites
0
8
150
200
(d) lEsfc
-2
GEOS3 surface latent heat flux (W m)
-2
GEOS3 surface sensible heat flux (W m)
(c) Hsfc
100
150
100
50
Lindenberg
Cabauw
Mongolia
China_sea
SGP
0
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
50
0
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
BERMS_Spruc
-50
-50
-50
0
50
100
-2
GLDAS surface sensible heat flux (W m )
150
-50
0
50
100
150
-2
GLDAS surface latent heat flux (W m )
200
310
(e) Tsfc
GEOS3 surface temperature (K)
305
300
295
290
285
280
275
Lindenberg
Cabauw
Mongolia
China_sea
SGP
Bondville
Ftpeck
Rondonia
Manaus
Caxiuana
Pantanal
Brasilia
BERMS_Spruc
270
270
275
280
285
290
295
300
GLDAS surface temperature (K)
305
310
Figure 6. Comparison of 10-day mean of variables having high spatial variability
between GLDAS and GEOS3 at 13 CEOP reference sites (continued)
On the other hand, Fig. 6 shows the model outputs give quite different values for the
variables with a high spatial variability (SWn, LWn, rainfall, Hsfc, lEsfc) except Tsfc,
and thus have large uncertainties. Even though the modeled 10-day mean values of
surface temperature are closed to each other, but its diurnal cycle is not consistent in the
two models (not shown). Therefore, the model predictability is associated with the spatial
variability of each variable.
SUMMARY
Based on the analysis of CEOP EOP1 dataset, we speculate that each variable has its own
spatial variability and thus the GCM performance cannot be simply evaluated by
comparing model output with point-scale observations. The uncertainties in modeling
results may be related to the spatial variability of variables.