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- Author or Editor: C. E. Desborough x
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Abstract
By coupling a multimode land surface scheme with a regional climate model, three scientific issues are addressed in this paper: (i) the regional model's sensitivity to the different levels of complexity presented by the land surface parameterization, (ii) relative model sensitivity to the land surface parameterization as compared with that to other model physical representations, and, (iii) following offline calibration, whether different complexity in the land surface representation leads to different model performance in the coupled experiments. In this study, a version of a regional model [Division of Atmospheric Research Limited Area Model (DARLAM)] is coupled with the Chameleon Surface Model (CHASM). Three sets of experiments are analyzed in this paper, employing six different complexity modes of CHASM. Model results from these coupled experiments show that the regional model is sensitive overall to different complexities represented in the CHASM modes. Moreover, these model sensitivities are larger than the model's intrinsic sensitivity to the perturbation of its initial conditions. The sensitivity is retained in a series of model configurations employing different vertical resolutions and convection schemes. Different complexities in the land surface representation lead to 10–30 W m−2 changes in surface evaporation and 0.5–2.5-K changes in surface temperature. In comparing different sets of coupled experiments, it is noted that, because of the complex feedbacks involved in air–land interactions, land surface parameterizations can induce quantitatively similar model sensitivity to that from changing other model aspects such as vertical resolution and convection parameterization. Although different CHASM modes can be calibrated to show similar offline results, when coupled with DARLAM these similarities between different complexity modes are significantly reduced. The sensitivity revealed in the coupled model simulations underlines the importance of understanding the feedbacks between model land surface parameterization and other physical components. More important, these results show that complexity in land surface representation cannot be substituted by tuning of parameters such as the surface or stomatal resistance, because offline agreement is not maintained in coupled simulations.
Abstract
By coupling a multimode land surface scheme with a regional climate model, three scientific issues are addressed in this paper: (i) the regional model's sensitivity to the different levels of complexity presented by the land surface parameterization, (ii) relative model sensitivity to the land surface parameterization as compared with that to other model physical representations, and, (iii) following offline calibration, whether different complexity in the land surface representation leads to different model performance in the coupled experiments. In this study, a version of a regional model [Division of Atmospheric Research Limited Area Model (DARLAM)] is coupled with the Chameleon Surface Model (CHASM). Three sets of experiments are analyzed in this paper, employing six different complexity modes of CHASM. Model results from these coupled experiments show that the regional model is sensitive overall to different complexities represented in the CHASM modes. Moreover, these model sensitivities are larger than the model's intrinsic sensitivity to the perturbation of its initial conditions. The sensitivity is retained in a series of model configurations employing different vertical resolutions and convection schemes. Different complexities in the land surface representation lead to 10–30 W m−2 changes in surface evaporation and 0.5–2.5-K changes in surface temperature. In comparing different sets of coupled experiments, it is noted that, because of the complex feedbacks involved in air–land interactions, land surface parameterizations can induce quantitatively similar model sensitivity to that from changing other model aspects such as vertical resolution and convection parameterization. Although different CHASM modes can be calibrated to show similar offline results, when coupled with DARLAM these similarities between different complexity modes are significantly reduced. The sensitivity revealed in the coupled model simulations underlines the importance of understanding the feedbacks between model land surface parameterization and other physical components. More important, these results show that complexity in land surface representation cannot be substituted by tuning of parameters such as the surface or stomatal resistance, because offline agreement is not maintained in coupled simulations.
Abstract
In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m−2 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (±10 W m−2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models’ neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of30 W m−2 and 25 W m−2, respectively. Annual totals of evapotranspiration and runoff, into which the precipitation is partitioned, both have ranges of 315 mm. These ranges in annual heat and water fluxes were approximately halved upon exclusion of the three schemes that have no stomatal resistance under non-water-stressed conditions. Many schemes tend to underestimate latent heat flux and overestimate sensible heat flux in summer, with a reverse tendency in winter. For six schemes, root-mean-square deviations of predictions from monthly observations are less than the estimated upper bounds on observation errors (5 W m−2 for sensible heat flux and 10 W m−2 for latent heat flux). Actual runoff at the site is believed to be dominated by vertical drainage to groundwater, but several schemes produced significant amounts of runoff as overland flow or interflow. There is a range across schemes of 184 mm (40% of total pore volume) in the simulated annual mean root-zone soil moisture. Unfortunately, no measurements of soil moisture were available for model evaluation. A theoretical analysis suggested that differences in boundary conditions used in various schemes are not sufficient to explain the large variance in soil moisture. However, many of the extreme values of soil moisture could be explained in terms of the particulars of experimental setup or excessive evapotranspiration.
Abstract
In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m−2 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (±10 W m−2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models’ neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of30 W m−2 and 25 W m−2, respectively. Annual totals of evapotranspiration and runoff, into which the precipitation is partitioned, both have ranges of 315 mm. These ranges in annual heat and water fluxes were approximately halved upon exclusion of the three schemes that have no stomatal resistance under non-water-stressed conditions. Many schemes tend to underestimate latent heat flux and overestimate sensible heat flux in summer, with a reverse tendency in winter. For six schemes, root-mean-square deviations of predictions from monthly observations are less than the estimated upper bounds on observation errors (5 W m−2 for sensible heat flux and 10 W m−2 for latent heat flux). Actual runoff at the site is believed to be dominated by vertical drainage to groundwater, but several schemes produced significant amounts of runoff as overland flow or interflow. There is a range across schemes of 184 mm (40% of total pore volume) in the simulated annual mean root-zone soil moisture. Unfortunately, no measurements of soil moisture were available for model evaluation. A theoretical analysis suggested that differences in boundary conditions used in various schemes are not sufficient to explain the large variance in soil moisture. However, many of the extreme values of soil moisture could be explained in terms of the particulars of experimental setup or excessive evapotranspiration.
Abstract
Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra- and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models’ snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.
Abstract
Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra- and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models’ snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.
