STUDIES ON MODEL PREDICTABILITY IN VARIOUS CLIMATIC CONDITIONS: AN EFFORT USING CEOP EOP1 DATASET

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1 STUDIES ON MODEL PREDICTABILITY IN VARIOUS CLIMATIC CONDITIONS: AN EFFORT USING CEOP EOP DATASET KUN YANG, KATSUNORI TAMAGAWA, PETRA KOUDELOVA, TOSHIO KOIKE Department of Civil Engineering, University of Tokyo, Hongo 7-3-, Bunkyo-ku, Tokyo , Japan The Coordinated Enhanced Observing Period (CEOP) project provides an integrated, globally covered dataset. The CEOP EOP dataset covers the period from July to September, 2. 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 EOP (July ~ September, 2) 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 6 GEWEX Continental-scale Experiments (CSE) reference sites. As shown in Fig., these sites are marked with number 3 (), 9 (), (South China sea), 5 (North Slope of Alaska or NSP), 7 (Berms-spruce), 8 (Fort Peck or ), 9 (), 2 (Southern Great Plains or ), 23 (), 25 (), 26 (), 27 (), 28 (), 3 (), 3 (), 34 (Tropical Western Pacific-Manus or TWP-Manus). ANALYSIS METHOD To evaluate the predictability of model outputs, two indexes are introduced: one is the -day mean value of each variable, and the other is the -day mean diurnal cycle of

2 each variable. The variables of interest are surface temperature, air temperature, air humidity, surface radiations, surface heat fluxes, and precipitation. 2 Figure. CEOP reference sites To account for the diurnal cycle in a period, the mean value for a variable x at a specific hour i can be calculated as follows n i xi = xi, j, () n j= i The mean value for a variable 24 ni 24 = =, j i i j i= x in the period can be calculated by x = x i n, (2) or 24 ni x = xi, j, (3) 24 i= n j= i where i is the index of hour and is the number of available data of variable at hour i n 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-, 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 -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. x

3 ] Therefore, we adopt Eq.(3) to calculate -days mean values and compare it with model outputs. 3 2 Net radiation - -days averages - "old" 2 GLDAS 5 DAO GEOS3 Observation 2 5 Net radiation - -days averages - "new" GLDAS DAO GEOS3 Observation Rnsfc [w (a) averaged from Eq.(2) 5 Julian day (b) averaged from Eq.(3) Figure 2. -day mean net radiation from observation, NASA/GEOS3 and NASA/GLDAS at during CEOP EOP COMPARISONS BETWEEN MODEL OUTPUTS AND OBSERVATIONS Fig. 3 shows the comparison of -day mean values of variables between GLDAS output and in-situ observations at the 6 CEOP reference sites. Similarly, we can compare the -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 ~km 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. () 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 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 -2 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 3 (a) Tsfc 3 (b) Tair GLDAS-output surface temperature (K GLDAS-output air temperature (K Observed surface temperature (K) Observed air temperature (K) 25 (c) qair 35 (d) SWN GLDAS-output air humidity (g kg - ) Observed air humidity (g kg - ) GLDAS-output net shortwave radiation (W m -2 ) Observed net shortwave radiation (W m -2 ) Figure 3. Comparison of -day mean values between GLDAS output and in-situ observations at 6 CEOP reference sites

5 5 GLDAS-output net longwave radiation (W -2 m ) -5 - (e) LWN GLDAS-output net radiation (W m -2 ) (f) Rnsfc Observed net longwave radiation (W m -2 ) Observed net radiation (W m -2 ) 5 (g) Hsfc 2 (h) lesfc GLDAS-output surface sensible heat (W m -2 ) Observed surface sensible heat (W m-2) GLDAS-output surface latent heat flux (W -2 m ) Observed surface latent heat flux (W m -2 ) (I) Gsfc 25 (j) Rainfall GLDAS-output surface soil heat flux (W -2 m ) Observed surface soil heat flux (W m -2 ) GLDAS-output precipitation (mm day - ) Observed precipitation (mm day - ) Figure 3. Comparison of -day mean values between GLDAS output and in-situ observations at 6 CEOP reference sites (Continued)

6 6 Point m Moisture, Tsfc Spatial variability km km Rainfall Rsw, Rlw, Cloud Hsfc, Esfc Rnsfc, Gsfc, H PBL km Tair, qair 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 GEOS3 air temperature (K) (a) Tair GLDAS air temperature (K) GEOS3 air humidity (g kg - ) (b) qair GLDAS air humidity (g kg - ) Figure 5. Comparison of -day mean values of variables having low spatial variability between GLDAS and GEOS3 at 3 CEOP reference sites

7 7 25 (c) Rnsfc (d) Gsfc GEOS3 net radiation (W m -2 ) GLDAS net radiation (W m -2 ) GEOS3 surface soil heat flux (W m -2 ) GLDAS surface soil heat flux (W m -2 ) Figure 5. Comparison of -day mean values of variables having low spatial variability between GLDAS and GEOS3 at 3 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, 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. 35 (a) SWn (b) LWn GEOS3 net shortwave radiation (W m -2 ) GLDAS net shortwave radiation (W m -2 ) GEOS3 net longwave radiation (W m -2 ) GLDAS net longwave radiation (W m -2 ) Figure 6. Comparison of -day mean of variables having high spatial variability between GLDAS and GEOS3 at 3 CEOP reference sites

8 8 5 (c) Hsfc 2 (d) lesfc GEOS3 surface sensible heat flux (W m -2 ) GLDAS surface sensible heat flux (W m -2 ) 3 35 GEOS3 surface latent heat flux (W m -2 ) (e) Tsfc GLDAS surface latent heat flux (W m -2 ) GEOS3 surface temperature (K) GLDAS surface temperature (K) Figure 6. Comparison of -day mean of variables having high spatial variability between GLDAS and GEOS3 at 3 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 -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 EOP 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.