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A. J. Pitman

Abstract

The sensitivity of a land-surface scheme (the Biosphere Atmosphere Transfer Scheme, BATS) to its parameter values was investigated using a single column model. Identifying which parameters were important in controlling the turbulent energy fluxes, temperature, soil moisture, and runoff was dependent upon many factors. In the simulation of a nonmoisture-stressed tropical forest, results were dependent on a combination of reservoir terms (soil depth, root distribution), flux efficiency terms (roughness length, stomatal resistance), and available energy (albedo). If moisture became limited, the reservoir terms increased in importance because the total fluxes predicted depended on moisture availability and not on the rate of transfer between the surface and the atmosphere. The sensitivity shown by BATS depended on which vegetation type was being simulated, which variable was used to determine sensitivity, the magnitude and sign of the parameter change, the climate regime (precipitation amount and frequency), and soil moisture levels and proximity to wilting. The interactions between these factors made it difficult to identify the most important parameters in BATS. Therefore, this paper does not argue that a particular set of parameters is important in BATS, rather it shows that no general ranking of parameters is possible. It is also emphasized that using “stand-alone” forcing to examine the sensitivity of a land-surface scheme to perturbations, in either parameters or the atmosphere, is unreliable due to the lack of surface-atmospheric feedbacks.

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A. J. Pitman and S. E. Perkins

Abstract

A comparison of three global reanalyses is conducted based on probability density functions of daily maximum and minimum temperature at 2-m and 1000-hPa levels. The three reanalyses compare very favorably in both maximum and minimum temperatures at 1000 hPa, in both the mean and the 99.7th and 0.3rd percentiles of both quantities in most regions. At 2 m, there are large and widespread differences in the mean and 99.7th percentiles in maximum temperature between the three reanalyses over land commonly exceeding ±5°C and regionally exceeding ±10°C. The 2-m minimum temperatures compare unfavorably between the three reanalyses over virtually all continental surfaces with differences exceeding ±10°C over widespread areas. It is concluded that the three reanalyses are generally interchangeable in 1000-hPa temperatures. The three reanalyses of 2-m temperatures are very different owing to the methods used to diagnose these quantities. At this time, the probability distribution functions of the 2-m temperatures from the three reanalyses are sufficiently different that either the 2-m air temperatures should not be used or all three products should be used independently in any application and the differences highlighted.

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A. Henderson-Sellers, A. J. Pitman, B. Henderson-Sellers, D. Pollard, and J. M. Verner

Abstract

In addition to model validation techniques and intermodel comparison projects, the authors propose the use of software engineering metrics as an additional tool for the enhancement of “quality” in climate models. By discriminating between internal, directly measurable characteristics of structural complexity, and external characteristics, such as maintainability and comprehensibility, a way to benefit climate modeling by the use of easily derivable metrics is explored. As a small illustration, the results of a pilot project are presented. This is a subproject of the Project for Intercomparison of Landsurface Parameterization Schemes in which the authors use some typical structural complexity metrics, namely, for control flow, size, and coupling. Finally, and purely indicatively, the authors compare the results obtained from these metrics with scientists’ subjective views of the psychological complexity of the programs.

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S. E. Perkins, A. J. Pitman, N. J. Holbrook, and J. McAneney

Abstract

The coupled climate models used in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change are evaluated. The evaluation is focused on 12 regions of Australia for the daily simulation of precipitation, minimum temperature, and maximum temperature. The evaluation is based on probability density functions and a simple quantitative measure of how well each climate model can capture the observed probability density functions for each variable and each region is introduced. Across all three variables, the coupled climate models perform better than expected. Precipitation is simulated reasonably by most and very well by a small number of models, although the problem with excessive drizzle is apparent in most models. Averaged over Australia, 3 of the 14 climate models capture more than 80% of the observed probability density functions for precipitation. Minimum temperature is simulated well, with 10 of the 13 climate models capturing more than 80% of the observed probability density functions. Maximum temperature is also reasonably simulated with 6 of 10 climate models capturing more than 80% of the observed probability density functions. An overall ranking of the climate models, for each of precipitation, maximum, and minimum temperatures, and averaged over these three variables, is presented. Those climate models that are skillful over Australia are identified, providing guidance on those climate models that should be used in impacts assessments where those impacts are based on precipitation or temperature. These results have no bearing on how well these models work elsewhere, but the methodology is potentially useful in assessing which of the many climate models should be used by impacts groups.

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H. Zhang, A. Henderson-Sellers, A. J. Pitman, C. E. Desborough, J. L. McGregor, and J. J. Katzfey

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.

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C. M. Holgate, J. P. Evans, A. I. J. M. van Dijk, A. J. Pitman, and G. Di Virgilio

Abstract

The relative importance of atmospheric advection and local land–atmosphere coupling to Australian precipitation is uncertain. Identifying the evaporative source regions and level of precipitation recycling can help quantify the importance of local and remote marine and terrestrial moisture to precipitation within the different hydroclimates across Australia. Using a three-dimensional Lagrangian back-trajectory approach, moisture from precipitation events across Australia during 1979–2013 was tracked to determine the source of moisture (the evaporative origin) and level of precipitation recycling. We show that source regions vary markedly for precipitation falling in different regions. Advected marine moisture was relatively more important than terrestrial contributions for precipitation in all regions and seasons. For Australia as a whole, contributions from precipitation recycling varied from ~11% in winter up to ~21% in summer. The strongest land–atmosphere coupling was in the northwest and southeast where recycled local land evapotranspiration accounted for an average of 9% of warm-season precipitation. Marine contributions to precipitation in the northwest of Australia increased in spring and, coupled with positive evaporation trends in the key source regions, suggest that the observed precipitation increase is the result of intensified evaporation in the Maritime Continent and Indian and Pacific Oceans. Less clear were the processes behind an observed shift in moisture contribution from winter to summer in southeastern Australia. Establishing the climatological source regions and the magnitude of moisture recycling enables future investigation of anomalous precipitation during extreme periods and provides further insight into the processes driving Australia’s variable precipitation.

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Jun Ge, Weidong Guo, Andrew J. Pitman, Martin G. De Kauwe, Xuelong Chen, and Congbin Fu

Abstract

China is several decades into large-scale afforestation programs to help address significant ecological and environmental degradation, with further afforestation planned for the future. However, the biophysical impact of afforestation on local surface temperature remains poorly understood, particularly in midlatitude regions where the importance of the radiative effect driven by albedo and the nonradiative effect driven by energy partitioning is uncertain. To examine this issue, we investigated the local impact of afforestation by comparing adjacent forest and open land pixels using satellite observations between 2001 and 2012. We attributed local surface temperature change between adjacent forest and open land to radiative and nonradiative effects over China based on the Intrinsic Biophysical Mechanism (IBM) method. Our results reveal that forest causes warming of 0.23°C (±0.21°C) through the radiative effect and cooling of −0.74°C (±0.50°C) through the nonradiative effect on local surface temperature compared with open land. The nonradiative effect explains about 79% (±16%) of local surface temperature change between adjacent forest and open land. The contribution of the nonradiative effect varies with forest and open land types. The largest cooling is achieved by replacing grasslands or rain-fed croplands with evergreen tree types. Conversely, converting irrigated croplands to deciduous broadleaf forest leads to warming. This provides new guidance on afforestation strategies, including how these should be informed by local conditions to avoid amplifying climate-related warming.

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T. H. Chen, A. Henderson-Sellers, P. C. D. Milly, A. J. Pitman, A. C. M. Beljaars, J. Polcher, F. Abramopoulos, A. Boone, S. Chang, F. Chen, Y. Dai, C. E. Desborough, R. E. Dickinson, L. Dümenil, M. Ek, J. R. Garratt, N. Gedney, Y. M. Gusev, J. Kim, R. Koster, E. A. Kowalczyk, K. Laval, J. Lean, D. Lettenmaier, X. Liang, J.-F. Mahfouf, H.-T. Mengelkamp, K. Mitchell, O. N. Nasonova, J. Noilhan, A. Robock, C. Rosenzweig, J. Schaake, C. A. Schlosser, J.-P. Schulz, Y. Shao, A. B. Shmakin, D. L. Verseghy, P. Wetzel, E. F. Wood, Y. Xue, Z.-L. Yang, and Q. Zeng

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.

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A. Boone, F. Habets, J. Noilhan, D. Clark, P. Dirmeyer, S. Fox, Y. Gusev, I. Haddeland, R. Koster, D. Lohmann, S. Mahanama, K. Mitchell, O. Nasonova, G.-Y. Niu, A. Pitman, J. Polcher, A. B. Shmakin, K. Tanaka, B. van den Hurk, S. Vérant, D. Verseghy, P. Viterbo, and Z.-L. Yang

Abstract

The Rhône-Aggregation (Rhône-AGG) Land Surface Scheme (LSS) intercomparison project is an initiative within the Global Energy and Water Cycle Experiment (GEWEX)/Global Land–Atmosphere System Study (GLASS) panel of the World Climate Research Programme (WCRP). It is a intermediate step leading up to the next phase of the Global Soil Wetness Project (GSWP) (Phase 2), for which there will be a broader investigation of the aggregation between global scales (GSWP-1) and the river scale. This project makes use of the Rhône modeling system, which was developed in recent years by the French research community in order to study the continental water cycle on a regional scale.

The main goals of this study are to investigate how 15 LSSs simulate the water balance for several annual cycles compared to data from a dense observation network consisting of daily discharge from over 145 gauges and daily snow depth from 24 sites, and to examine the impact of changing the spatial scale on the simulations. The overall evapotranspiration, runoff, and monthly change in water storage are similarly simulated by the LSSs, however, the differing partitioning among the fluxes results in very different river discharges and soil moisture equilibrium states. Subgrid runoff is especially important for discharge at the daily timescale and for smaller-scale basins. Also, models using an explicit treatment of the snowpack compared better with the observations than simpler composite schemes.

Results from a series of scaling experiments are examined for which the spatial resolution of the computational grid is decreased to be consistent with large-scale atmospheric models. The impact of upscaling on the domain-averaged hydrological components is similar among most LSSs, with increased evaporation of water intercepted by the canopy and a decrease in surface runoff representing the most consistent inter-LSS responses. A significant finding is that the snow water equivalent is greatly reduced by upscaling in all LSSs but one that explicitly accounts for subgrid-scale orography effects on the atmospheric forcing.

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