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  • Author or Editor: A. J. Pitman x
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G. T. Narisma and A. J. Pitman

Abstract

The effect of land cover change on the Australian regional-scale climate is investigated using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). Four ensemble simulations are performed consisting of January and July experiments for eight different years with a 50-km grid spacing using natural (1788) and current (1988) vegetation cover. The statistical significance of changes that occurred following the replacement of natural vegetation with current vegetation on air temperature, rainfall, latent heat flux, and other related quantities is explored. Results show that the impact of land cover change on local air temperature is statistically significant at a 99% confidence level. Furthermore, there are indications that the observed increase in local maximum air temperatures in certain regions of Australia can be partially attributed to land cover change. The results are evidence of statistically significant changes in rainfall, and the sign of these changes over Western Australia in July, and the lack of any simulated changes in January, agree with observations. These results provide further evidence of large-scale reductions in rainfall following land cover change. Changes in wind speed are also simulated and are consistent with those expected following land cover change. The results indicate that attempts to identify greenhouse-related warming in Australian air temperature records should account for the effects of both land cover change and increasing CO2 concentrations since both types of anthropogenic forcing exist in long-term observational records. Since further land cover change will occur in the future, directly via human impact and indirectly via CO2 fertilization, the results support efforts to include land surface schemes that allow the vegetation to interact with changes in climate in climate models.

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A. L. Hirsch, A. J. Pitman, and V. Haverd

Abstract

This paper presents a methodology for examining land–atmosphere coupling in a regional climate model by examining how the resistances to moisture transfer from the land to the atmosphere control the surface turbulent energy fluxes. Perturbations were applied individually to the aerodynamic resistance from the soil surface to the displacement height, the aerodynamic resistance from the displacement height to the reference level, the stomatal resistance, and the leaf boundary layer resistance. Only perturbations to the aerodynamic resistance from the soil surface to the displacement height systematically affected 2-m air temperature for the shrub and evergreen boreal forest plant functional types (PFTs). This was associated with this resistance systematically increasing the terrestrial and atmospheric components of the land–atmosphere coupling strength through changes in the partitioning of the surface energy balance. Perturbing the other resistances did contribute to changing the partitioning of the surface energy balance but did not lead to systematic changes in the 2-m air temperature. The results suggest that land–atmosphere coupling in the modeling system presented here acts mostly through the aerodynamic resistance from the soil surface to the displacement height, which is a function of both the friction velocity and vegetation height and cover. The results show that a resistance pathway framework can be used to examine how changes in the resistances affect the partitioning of the surface energy balance and how this subsequently influences surface climate through land–atmosphere coupling. Limitations in the present analysis include grid-scale rather than PFT-scale analysis, the exclusion of resistance dependencies, and the linearity assumption of how temperature responds to a resistance perturbation.

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S. Fox, A. J. Pitman, A. Boone, and F. Habets

Abstract

Six modes of complexity of the Chameleon land surface model (CHASM) are used to explore the relationship between the complexity of the surface energy balance (SEB) formulation and the capacity of the model to explain intermodel variations in results from the Rhône-Aggregation Intercomparison Project (Rhône-AGG). At an annual time scale, differences between models identified in the Rhône-AGG experiments in the partitioning of available energy and water at the spatial scale of the Rhône Basin can be reproduced by CHASM via variations in the SEB complexity. Only two changes in the SEB complexity in the model generate statistically significant differences in the mean latent heat flux. These are the addition of a constant surface resistance to the simplest mode of CHASM and the addition of tiling and temporally and spatially variable surface resistance to produce the most complex model. Further, the only statistically significant differences in runoff occur following the addition of a constant surface resistance to the simplest mode of CHASM. As the time scale is reduced from annual to monthly, specific mechanisms begin to dominate the simulations produced by each Rhône-AGG model and introduce parameterization-specific behavior that depends on the time evolution of processes operating on longer time scales. CHASM cannot capture all this behavior by varying the SEB complexity, demonstrating the contribution to intermodel differences by hydrology and snow-related processes. Despite the increasing role of hydrology and snow in simulating processes at finer time scales, provided the constant surface resistance is included, CHASM's modes perform within the range of uncertainty illustrated by other Rhône-AGG models on seasonal and annual time scales.

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M. Decker, A. J. Pitman, and J. P. Evans

Abstract

A land surface scheme with and without groundwater–vegetation interactions is used to explore the impact of rainfall variability on transpiration over drought-vulnerable regions of southeastern Australia. The authors demonstrate that if groundwater is included in the simulations, there is a low correlation between rainfall variability and the response of transpiration to this variability over forested regions. Groundwater reduces near-surface water variability, enabling forests to maintain transpiration through several years of low rainfall, in agreement with independent observations of vegetation greenness. If groundwater is not included, the transpiration variability matches the rainfall variability independent of land cover type. The authors’ results suggest that omitting groundwater in regions where groundwater sustains forests will 1) probably overestimate the likelihood of forest dieback during drought, 2) overestimate a positive feedback linked with declining transpiration and a drying boundary layer, and 3) underestimate the impact of land cover change due to inadequately simulating the different responses to drought for different land cover types.

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Y. Xia, A. J. Pitman, H. V. Gupta, M. Leplastrier, A. Henderson-Sellers, and L. A. Bastidas

Abstract

The multicriteria methodology, which provides a means to estimate optimal ranges for land surface model parameter values via calibration, is evaluated. Following calibration, differences between schemes resulting from effective parameter values can be isolated from differences resulting from scheme structure or scheme parameterizations. The method is applied to the Project for the Intercomparison of Land Surface Parameterization Schemes (PILPS) phase-2a data from the Cabauw site in the Netherlands using the Chameleon Surface Model (CHASM) as the surrogate for a range of land surface schemes. Simulations are performed calibrating six modes of CHASM, representing a range of land surface complexity, against observed net radiation and latent and sensible heat fluxes. The six modes range from a simple bucket model to a complex mosaic-type structure with separate energy balances for each mosaic tile and explicit treatment of transpiration, canopy interception, and bare-ground evaporation. Results demonstrate that the performance of CHASM depends on the complexity of the representation of the surface energy balance. If the multicriteria method is used with two observed variables, the performance of the model improves little with incremental increases in complexity until the most complex version of the model is reached. If the multicriteria method is used with three observed variables, the most complex mode is shown to calibrate more accurately and more precisely than the simple modes. In all cases, every calibrated mode performs better than simulations using the default PILPS phase-2a parameters. The performance of the most complex mode of CHASM suggests that more complex representations of the surface energy balance generally improve the calibrated performance of land surface schemes. However, all modes, when calibrated, retain a residual error that most likely is due to parameterization errors included in the scheme. Most error is contained in the simulation of the latent heat flux, which suggests that, to improve CHASM further, the representation of the surface hydrological processes should be developed. Thus, the multicriteria method provides a means to assess the performance of a single model or group of land surface models and provides guidance as to the directions scheme development should take.

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A. M. Ukkola, A. J. Pitman, M. G. De Kauwe, G. Abramowitz, N. Herger, J. P. Evans, and M. Decker

Abstract

Global climate models play an important role in quantifying past and projecting future changes in drought. Previous studies have pointed to shortcomings in these models for simulating droughts, but systematic evaluation of their level of agreement has been limited. Here, historical simulations (1950–2004) for 20 models from the latest Coupled Model Intercomparison Project (CMIP5) were analyzed for a variety of drought metrics and thresholds using a standardized drought index. Model agreement was investigated for different types of drought (precipitation, runoff, and soil moisture) and how this varied with drought severity and duration. At the global scale, climate models were shown to agree well on most precipitation drought metrics, but systematically underestimated precipitation drought intensity compared to observations. Conversely, simulated runoff and soil moisture droughts varied significantly across models, particularly for intensity. Differences in precipitation simulations were found to explain model differences in runoff and soil moisture drought metrics over some regions, but predominantly with respect to drought intensity. This suggests it is insufficient to evaluate models for precipitation droughts to increase confidence in model performance for other types of drought. This study shows large but metric-dependent discrepancies in CMIP5 for modeling different types of droughts that relate strongly to the component models (i.e., atmospheric or land surface scheme) used in the coupled modeling systems. Our results point to a need to consider multiple models in drought impact studies to account for high model uncertainties.

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M. J. Best, G. Abramowitz, H. R. Johnson, A. J. Pitman, G. Balsamo, A. Boone, M. Cuntz, B. Decharme, P. A. Dirmeyer, J. Dong, M. Ek, Z. Guo, V. Haverd, B. J. J. van den Hurk, G. S. Nearing, B. Pak, C. Peters-Lidard, J. A. Santanello Jr., L. Stevens, and N. Vuichard

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

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Ned Haughton, Gab Abramowitz, Andy J. Pitman, Dani Or, Martin J. Best, Helen R. Johnson, Gianpaolo Balsamo, Aaron Boone, Matthias Cuntz, Bertrand Decharme, Paul A. Dirmeyer, Jairui Dong, Michael Ek, Zichang Guo, Vanessa Haverd, Bart J. J. van den Hurk, Grey S. Nearing, Bernard Pak, Joe A. Santanello Jr., Lauren E. Stevens, and Nicolas Vuichard

Abstract

The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) illustrated the value of prescribing a priori performance targets in model intercomparisons. It showed that the performance of turbulent energy flux predictions from different land surface models, at a broad range of flux tower sites using common evaluation metrics, was on average worse than relatively simple empirical models. For sensible heat fluxes, all land surface models were outperformed by a linear regression against downward shortwave radiation. For latent heat flux, all land surface models were outperformed by a regression against downward shortwave radiation, surface air temperature, and relative humidity. These results are explored here in greater detail and possible causes are investigated. It is examined whether particular metrics or sites unduly influence the collated results, whether results change according to time-scale aggregation, and whether a lack of energy conservation in flux tower data gives the empirical models an unfair advantage in the intercomparison. It is demonstrated that energy conservation in the observational data is not responsible for these results. It is also shown that the partitioning between sensible and latent heat fluxes in LSMs, rather than the calculation of available energy, is the cause of the original findings. Finally, evidence is presented that suggests that the nature of this partitioning problem is likely shared among all contributing LSMs. While a single candidate explanation for why land surface models perform poorly relative to empirical benchmarks in PLUMBER could not be found, multiple possible explanations are excluded and guidance is provided on where future research should focus.

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A. G. Slater, C. A. Schlosser, C. E. Desborough, A. J. Pitman, A. Henderson-Sellers, A. Robock, K. Ya Vinnikov, J. Entin, K. Mitchell, F. Chen, A. Boone, P. Etchevers, F. Habets, J. Noilhan, H. Braden, P. M. Cox, P. de Rosnay, R. E. Dickinson, Z-L. Yang, Y-J. Dai, Q. Zeng, Q. Duan, V. Koren, S. Schaake, N. Gedney, Ye M. Gusev, O. N. Nasonova, J. Kim, E. A. Kowalczyk, A. B. Shmakin, T. G. Smirnova, D. Verseghy, P. Wetzel, and Y. Xue

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.

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Zhichang Guo, Paul A. Dirmeyer, Randal D. Koster, Y. C. Sud, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, C. T. Gordon, J. L. McGregor, Shinjiro Kanae, Eva Kowalczyk, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.

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