2. School of Atmospheric Sciences, Sun Yat-Sen University, Guangdong 510275
Coupling of general circulation models (GCMs) to a well-designed, complicated, and multi-functional land surface model (LSM) is now a standard configuration. The land surface layer is widely regarded as a crucial component of the climate system (Stull, 1988; Rowntree, 1991). The impact of changes in land use and land cover on the earth’s climate is both large (Lee et al., 2011; Swann et al., 2012; Li et al., 2016) and uncertain (Pitman et al., 2009; Kumar et al., 2013). The Coupled Model Intercomparison Project Phase 6 features the Land Use Model Intercomparison Project (https://cmip.ucar.edu/lumip) as an important topic (Eyring et al., 2016).
Almost simultaneous with the development of GCMs, LSMs have evolved over several generations and include the Noah land surface model (Chen et al., 1996), Variable Infiltration Capacity model (Liang et al., 1994), Simple Biosphere model (Xue et al., 1991), and Community Land Model (CLM; Oleson et al., 2010). The majority of the earth’s land surface is covered by vegetation, which introduces complex structures, properties, and interactions in the surface layer. There is a general consensus that accurate simulations of land–atmosphere interactions require detailed representations of vegetation over the land surface (Dickinson et al., 1993; Bonan, 1998). Current LSMs, such as Noah (Chen et al., 1996) and Noah with multi-parameterization (Noah-MP; Niu et al., 2011), use a single-layer “big-leaf” parameterization to represent the complicated structure of the vegetation canopy. Compared with the “big-leaf” concept, the Common Land Model (CoLM; Dai et al., 2003) developed a two-big-leaf model, which represents the canopy temperature, photosynthesis, and stomatal conductance on sunlit and shaded leaves separately (Dai et al., 2004). Although some researchers have argued that the increase in model complexity reduces accuracy as a result of the increase in uncertainty (Jetten et al., 1999; Perrin et al., 2001), it is still important to properly account for all the biological and physical processes.
China is located in the East Asian monsoon region and has experienced a variety of extreme weather and climate events. Both the public and government have a vested interest in the prediction and mitigation of such disasters. Numerical weather and climate models make predictions and projections on daily to decadal timescales (IPCC, 2013) and are essential tools in investigations of the response of synoptic processes and the climate system to forcing. The Chinese Academy of Meteorological Sciences (CAMS) has been developing an integrated weather and climate model (CAMS-CSM) for several years with the aim of seamlessly predicting both weather and climate.
CAMS-CSM originated from coupling the fifth-generation atmospheric general circulation model of the Max Planck Institute (MPI) for Meteorology [ECmwf-HAMburg (ECHAM5)] to the Modular Ocean Model version 4 (Griffies, 2010). ECHAM5 initially had an embedded simple land surface scheme. However, since China’s terrain is so complex, with numerous different land types, this simple land surface scheme is unable to precisely simulate the interactions between the land and atmosphere over East Asia. CoLM is one of the newest generation of LSMs, which is developed and maintained by Yongjiu Dai et al. at Sun Yat-Sen University (Dai et al., 2013).
This study introduces the fifth-generation atmospheric general circulation model of the MPI for Meteorology (ECHAM5) embedded with CoLM, which is a complex LSM including a two-big-leaf model. CoLM has up to 5 snow layers and 10 soil layers. Section 2 describes the models and some key points of the coupling processes. The results, validation, and evaluation of the coupled models are reported in Section 3. And some concluding remarks are given in Section 4.2 Models, methodology, and data 2.1 ECHAM5
ECHAM5 (Roeckner et al., 2003, 2004) is a popular version of the ECHAM model developed by the MPI for Meteorology (www.mpimet.mpg.de). MPI-ECHAM5 was used in the IPCC’s Fourth Assessment Report, alongside many other GCMs from different countries. Major changes from its predecessor include an advection scheme for the positive definite variables, a longwave radiation code, a cloud cover parameterization scheme, separate treatment of cloud water and cloud ice, inclusion of cloud microphysics, and incorporation of sub-grid-scale orographic effects. In addition, ECHAM5 is more portable and flexible than previous versions because it is written in Fortran 95.
The heat budget of land surface processes is resolved implicitly in ECHAM5. However, this implicit scheme is unconditionally stable and allows the synchronous calculation of prognostic variables and surface fluxes, which means that there is no iteration to prognosticate the land surface temperature and heat flux. The interception of snow and rain by the plant canopy is considered in water budget calculations, but plant function types in ECHAM5 are simple and insufficient for accurate modeling.2.2 CoLM
CoLM is a land surface parameterization used in either the offline mode or with global and regional climate models. CoLM originated from the initial version of Common Land Model (Dai et al., 2003), which was adopted as the CLM (Oleson et al., 2010) for use with the Community Atmosphere Model in the Community Climate System Model. In addition to changes from a software engineering perspective, differences between CoLM and CLM in terms of their physics include: an improved two-stream approximation model of radiation transfer in the vegetation canopy, with attention to singularities in solution and with separate radiation absorptions by the sunlit and shaded canopy; separate photosynthesis-stomatal conductance models (Dai et al., 2004) for sunlit and shaded leaves, in which CO2 and water vapor are simultaneously transferred into and out of leaves; and a well-built quasi-Newton–Raphson method for the simultaneous solution of temperature on sunlit and shaded leaves. Unlike many other LSMs, such as Noah and Noah-MP, CoLM has surface sub-grid processes, which means that the model grid box is allowed to contain any number of tiles. Over the grid level of interface between GCMs and LSMs, tiles that contain the same land cover type are summed and area of every land cover type per grid square is calculated statistically. This is referred to as the mosaic method and can be used to flexibly adjust the resolution of CoLM to meet resolution of GCMs.2.3 Coupling and model setup
ECHAM5 was initially an atmospheric general circulation model with a simple land surface process scheme, whereas CoLM was an offline LSM driven by atmospheric forcing data. In an effort to improve the parameterization of land surface processes and their feedback to the atmosphere, CoLM was coupled to the ECHAM5 global circulation model as a new land surface scheme after consideration of the efficiency of parallel computing and removal of flux adjustments.
Figure 1 is a flow process diagram showing details of how CoLM was coupled into ECHAM5. ECHAM5 provides meteorological variables as the input to force the CoLM LSM at the lowest ECHAM5 sigma layer. These variables include the shortwave and longwave radiation, precipitation, humidity, wind speed, and barometric pressure. The simulated land surface albedo, land surface temperature, momentum flux, sensible heat flux, latent heat flux, and upward longwave radiation from CoLM are fed back into ECHAM5. Two AMIP-type numerical experiments are conducted: ECHAM5 and ECHAM5 coupled with CoLM by using historical data including the sea surface temperature, CO2 concentration, and volcanic eruptions, for external forcing. Simulations are run from 0000 UTC 1 January 1978 to 2359 UTC 31 December 2009 and output of the last 30 years is analyzed. The ECHAM5 run is referred to as ECHAM5 in this paper, whereas the run of ECHAM5 coupled with CoLM is referred to as ECHAM5-CoLM.2.4 Data and methodology
Datasets used in this study for assessment of ECHAM5-CoLM simulations include global reanalysis datasets and some in situ observations. The spatial distribution of land surface temperature is evaluated by using the NCEP-2 reanalysis dataset (Kanamitsu et al., 2002). The point-to-point comparison of soil temperature is based on in situ observations from the Valdai Observation Station (57.6°N, 33.1°E). This station has previously provided validation data for the Project for Intercomparison of Land Surface Parameterization Schemes (Schlosser et al., 2000; Slater et al., 2001). Monthly gridded soil moisture data from the NOAA’s Climate Prediction Center (CPC; Fan and van den Dool, 2004) are used to assess the simulation of soil moisture content. The monthly 0.5° × 0.5° gridded surface sensible heat flux and latent heat flux (Jung et al., 2011) based on the mo-del tree ensemble (MTE) upscaling of FLUXNET eddy covariance measurements from the MPI for Biogeochemistry (www.bgc-jena.mpg.de/geodb/projects/Home.php) are used to evaluate the sensible heat flux and latent heat flux (Li et al., 2017). The Global Precipitation Climatology Project (GPCP) version 2 monthly precipitation analysis (Adler et al., 2003) is used to evaluate the performance of models in simulating rainfall.
The bias (model simulations minus observational data) and root-mean-square error (RMSE) are used to quantitatively evaluate the models.3 Results 3.1 Land surface temperature
The land surface temperature is a key feedback in the boundary layer of an atmosphere model. Figure 2 shows the performance of models in simulating the climatological annual mean land surface temperature. Spatial patterns of the land surface temperature obtained from the NCEP-2 dataset as well as ECHAM5 and ECHAM5-CoLM simulations are similar (Figs. 2a–c). Both ECHAM5-CoLM and ECHAM5 simulations show biases in many regions (Figs. 2d, e) and some of the bias patterns are similar, for example, a warm bias (about 2–6°C) appears in the mid- to low-latitude regions of both the Northern and Southern Hemispheres.
By contrast, the significant cold bias (about –2 to –6°C) found in ECHAM5 over the high latitudes of Eurasia does not appear in the ECHAM5-CoLM simulation, which implies that coupling to CoLM improves the simulation skill of ECHAM5 in the boreal region. Table 1 shows the land surface temperature RMSE between the model and NCEP-2 reanalysis data calculated over the whole globe and six regions. The RMSE of ECHAM5-CoLM is significantly smaller than that of ECHAM5, both globally and regionally, except over North America. ECHAM5-CoLM therefore significantly improves the simulation of land surface temperature compared with ECHAM5.
|Africa and Austria
To evaluate the simulation skill for seasonal cycles, Fig. 3 shows the seasonal variation of zonal mean soil temperature in the sub-surface layer (~1 cm). ECHAM5 and ECHAM5-CoLM simulations both show a decrease in soil temperature with latitude in both the Northern and Southern Hemispheres. Peaks in soil temperature are mostly simulated to occur during the summer months (June–July–August; JJA). The highest soil temperatures (> 30°C) are simulated in the subtropical regions in the two models.
The area over which the mean land surface temperature is > 30°C is clearly larger in the ECHAM5 simulation than that in the ECHAM5-CoLM simulation and NCEP-2 dataset. The soil temperature in the equatorial region (from about 10°S to 10°N) decreases by about 5°C at the beginning of June and then recovers from the end of July ( Fig. 3a). The ECHAM5-CoLM simulation almost captures this seasonal variation (Fig. 3c), but this feature is not completely simulated by ECHAM5, which shows no significant downward trend in soil temperature of the equatorial region over one year (Fig. 3b).
More robust comparisons with in situ observations are used to validate simulations. ECHAM5 and ECHAM5-CoLM are compared with observations of the monthly mean soil temperature at 20- (Fig. 4a) and 80-cm (Fig. 4b) depths at Valdai Station. ECHAM5 and ECHAM5-CoLM both capture the seasonal cycle of soil temperature. ECHAM5-CoLM is a better match to the observations, whereas the phase of ECHAM5 is ahead of the observations. RMSEs between the simulations and observations in Table 2 suggest that the ECHAM5-CoLM simulation is superior to ECHAM5 simulation.
CoLM defines 10 soil layers (0–1.8, 1.8–4.5, 4.5–9.1, 9.1–16.6, 16.6–28.9, 28.9–49.3, 49.3–82.9, 82.9–138.3, 138.3–229.6, and 229.6–343.3 cm), which means that there are 10 different soil moisture outputs from ECHAM5–CoLM. However, due to its simple physical parameterization of the mean transfer of heat and water between the layers of a soil, ECHAM5 can only produce a mean soil moisture content within one soil layer. As the CPC monthly soil moisture reanalysis dataset gives only one-layer soil moisture content at 1.6-m depth, we integrate the modeled soil moisture content from the surface to 1.6 m and then compare the results with the CPC reanalysis dataset. The total soil water content is integrated from the surface to the eighth (1.38 m) plus part of the ninth (0.22 m) layer in ECHAM5-CoLM, whereas in ECHAM5 the total soil water is calculated by multiplying the soil moisture content at 1.6-m depth.
Figure 5 shows the spatial distribution of climatological annual mean soil moisture content. The soil moisture from ECHAM5 simulation is much lower than that of CPC simulation, whereas ECHAM5-CoLM simulation is closer in magnitude to ECHAM5 simulation. Global mean values of the ECHAM5, ECHAM5-CoLM, and CPC simulations are about 30.9, 232.6, and 196.3 mm, respectively. The spatial pattern of ECHAM5-CoLM matches that of the CPC simulation better than that of ECHAM5 simulation. CPC (Fig. 5a) and ECHAM5-CoLM (Fig. 5c) simulations show a high soil moisture content in southern East Asia, Indonesia, central Africa, southeast North America, and the Amazon basin, consistent with previously reported results (Balsamo et al., 2011; Albergel et al., 2012). By contrast, ECHAM5 predicts a high soil moisture content in different regions: northern East Asia, southern Africa, northwest North America, and southern Amazon basin (Fig. 5b). ECHAM5 also simulates a wetter Australia and a drier Indonesia, opposite of the CPC simulation.
The seasonal variation in soil moisture content is mostly captured by ECHAM5-CoLM, but not ECHAM5 in the six regions of interest (Fig. 6). The observed soil moisture content from ERA-Interim dataset peaks during the summer months (JJA) on the Tibetan Plateau, in South America, and on the Indochina and Indian peninsulas. ECHAM5-CoLM simulates these peaks well and is close to ERA-Interim data. ECHAM5 systematically underestimates the soil moisture content in the six regions and is unable to reproduce the seasonal variation. ECHAM5-CoLM produces a large amplitude seasonal variation in Siberia and North America, which is probably due to that there is too much soil ice in soil layers of the model in these regions.3.4 Heat flux
Figure 7 shows the spatial distribution of the climatological annual mean sensible heat flux of the MTE product (Fig. 7a), ERA-Interim dataset (Fig. 7b), ECHAM5 simulation (Fig. 7c), and ECHAM5-CoLM simulation (Fig. 7d). Spatial patterns of the two models (ECHAM5 and ECHAM5-CoLM) are similar to those of the observational and reanalysis data (the MTE product and ERA-Interim dataset), especially in mid to low latitudes. Global mean (60°S–80°N) values for the MTE product, ERA-Interim dataset, and ECHAM5 and ECHAM5-CoLM simulations are 34.4, 24.0, 19.0, and 35.3 W m–2, respectively. Note that ECHAM5-CoLM simulates the closest sensible heat flux to observations (MTE product).
The spatial distribution of climatological annual mean latent heat flux is shown in Fig. 8. It is clear that spatial patterns of the modeled latent heat flux are close to those for the observations and reanalysis data. Values of the global mean (60°S–80°N) latent heat flux for the MTE product, ERA-Interim dataset, and ECHAM5 and ECHAM5-CoLM simulations are 35.7, 49.8, 27.7, and 42.7 W m–2, respectively. ECHAM5-CoLM simulates a latent heat flux that is closer to the MTE product and ERA-Interim dataset than that by ECHAM5.
Seasonal cycles of the zonal mean sensible heat flux and latent heat flux are shown in Figs. 9, 10, respectively. The two centers of sensible heat flux > 40 W m –2 in subtropical regions are reproduced by both EHCAM5 and ECHAM5-CoLM simulations. ECHAM5-CoLM simulates two stronger centers than ECHAM as well as observational and reanalysis data (Fig. 9). The pattern of latent heat flux simulated by ECHAM5-CoLM matches the observations better than that by EHCAM5 (Fig. 10). The latent heat flux in tropical regions simulated by ECHAM5 is too weak, perhaps because physical processes of evapotranspiration by vegetation are too simple in ECHAM5.3.5 Rainfall
Figure 11 compares the spatial distribution of the climatological annual mean precipitation from the models with reanalysis data. The general pattern simulated by the models resembles that of GPCP reanalysis data. However, the simulated precipitation is slightly heavier than the observed precipitation in many of the major rainfall centers, for example, the intertropical convergence zone, south Pacific convergence zone, and tropi-cal Indian Ocean. The simulated south Pacific convergence zone extends further east than GPCP rainfall. However, the precipitation averaged over the low-latitude region (30°S to 30°N) (or the global land surface) by the GPCP, ECHAM5, and ECHAM5-CoLM simulations is 2.97 (1.82), 3.39 (1.87), and 3.26 (1.82) mm day–1, respectively. Precipitation over the land surface simulated by ECHAM5-CoLM is closer to the observed data than that of ECHAM5. The positive bias of summer (JJA) precipitation over China simulated by ECHAM5 is efficiently reduced by ECHAM5-CoLM, especially over the southern Tibetan Plateau as well as middle and lower reaches of the Yangtze River (Fig. 12). The overestimated rainfall around the southern slope of Himalaya decreases by > 6 mm day –1. Note that the two-step shape-preserving advection scheme (Yu, 1994) is not used in ECHAM5 in this study. Precipitation over the Yangtze River region also decreases by about 2–4 mm day–1. ECHAM5-CoLM therefore improves the simulation skill of summer monsoon precipitation in China.3.6 Taylor diagram
A Taylor diagram is used to compare the performance of ECHAM5-CoLM and ECHAM5 simulations (Fig. 13). ECHAM5-CoLM shows a better pattern correlation in the simulation of the land surface temperature, sensible heat flux, latent heat flux, and precipitation from 60°S to 90°N. RMSEs of the land surface temperature and sensible heat flux in ECHAM5-CoLM are better than those in ECHAM5, whereas RMSEs of the latent heat flux and precipitation are close to those in ECHAM5. Precipitation in ECHAM5-CoLM clearly has a smaller bias, whereas there is no improvement in sensible and latent heat fluxes. The Taylor diagram therefore indicates the better performance of ECHAM5-CoLM in modeling the spatial distribution and variability of meteorological variables.4 Conclusions
To improve the simulation skill of interaction between the land surface and atmosphere in the integrated weat-her and climate model of the Chinese Academy of Meteorological Sciences (CAMS-CSM), the CoLM was encapsulated and transplanted into the fifth-generation atmospheric general circulation model from ECHAM5 as a new land surface scheme. Two AMIP-type numerical experiments from ECHAM5 and ECHAM5-CoLM were compared with reanalysis datasets and observational data. The results show that ECHAM5-CoLM simulates land surface processes well and produces reasonable feedback to the atmospheric model. The land surface temperature simulated by ECHAM5-CoLM is significantly better than that from ECHAM5. The cool bias over Eurasia is erased and the RMSE of ECHAM5-CoLM is reduced in most regions. The soil temperature and moisture are both simulated better by ECHAM5-CoLM than by ECHAM5. The sub-surface (~1 cm) soil temperature and soil moisture content from ECHAM5-CoLM match the observed spatial and temporal patterns better than those from ECHAM5. ECHAM5-CoLM gives a better reproduction of the spatial and seasonal patterns of sensible and latent heat fluxes. Although a strong positive bias in precipitation still exists in ECHAM5-CoLM, replacement of the land surface scheme in ECHAM5 by CoLM seems to improve the prediction of rainfall, especially on the southern Tibetan Plateau as well as in the middle and lower reaches of the Yangtze River.
The module-encapsulated CoLM was embedded into ECHAM5 and the two-way coupling of ECHAM5-CoLM runs smoothly with no flux adjustment over many years. Our results show that increase in complexity in the ECHAM5-CoLM model not only maintains the model accuracy, but also accounts for dominant biological and physical processes. There are many uncertainties (Wei et al., 2010, 2017; Li et al., 2013) in this coupled version, which should be evaluated. It is planned to transplant ECHAM5-CoLM into the coupled earth simulation systems, which will necessitate further tuning.
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