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Ann Henderson-Sellers
and
Brian Henderson-Sellers

Abstract

No abstract available.

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Kendal McGuffie
and
Ann Henderson-Sellers

Abstract

This paper presents the case for improved interdisciplinarity in climate research in the context of assessing and discussing the caution required when utilizing some types of historical climate data. This is done by a case study examining the reliability of the instruments used for collecting weather data in Australia between 1788 and 1840, as well as the observers themselves, during the British settlement of New South Wales. This period is challenging because the instruments were not uniformly calibrated and were created, repaired, and used by a wide variety of people with skills that frequently remain undocumented. Continuing significant efforts to rescue such early instrumental records of climate are likely to be enhanced by more open, interdisciplinary research that encourages discussion of an apparent dichotomy of view about the quantitative value of early single-instrument data between historians of physics (including museum curators) and climate researchers.

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Diandong Ren
and
Ann Henderson-Sellers

Abstract

Besides the atmospheric forcing such as solar radiation input and precipitation, the heterogeneity of the surface cover also plays an important role, especially in the distribution characteristics of the latent heat flux (LE). In this study, scaling issues are discussed based on an analytical hydrological model that describes the transpiration and diffusion processes of soil water.

The solution of this analytical model is composed of a transient part that depends primarily on initial conditions and a steady part that depends on the boundary conditions. To know how sensitive the different averaging approaches are to the initial conditions, three initial profiles are chosen that cover the prevailing soil moisture regimes. After analyzing its solution, the study shows that 1) upon reaching the steady state, directly taking an average of soil properties will cause systematic overestimation in the calculation of area-averaged LE. For an initially very dry condition, averaging of a sandy soil and a clay soil can cause a percentage error as large as 40%. 2) For vegetation growing on sandy soils, a direct averaging of the transpiration rate results in persistent overestimation of LE. For vegetation growing on clay soil, however, even after reaching the steady state, averaging of two water extraction weights can be either an overestimation or an underestimation, depending on which two vegetation types are involved. 3) During the interim stage of drying down, averaging of the soil/vegetation properties can lead to either an overestimation or an underestimation, depending on the evolving stage of the soil moisture profile. 4) The initial soil moisture condition matters during the transient stage of drying down. Different initial soil moisture conditions yield different scenarios of underestimation and overestimation patterns and a differing severity of errors.

The simplicity of the analytical model and the heuristic initial soil profiles make the generalization easier than using sophisticated numerical models and make the causality mechanism clearer for physical interpretations.

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Diandong Ren
,
Ming Xue
, and
Ann Henderson-Sellers

Abstract

In comparison with the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) measurements, the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS), a multilayer soil hydrological model, simulates a much faster drying of the superficial soil layer (5 cm) for a densely vegetated area at the OASIS site in Norman, Oklahoma, under dry conditions. Further, the measured superficial soil moisture contents also show a counterintuitive daily cycle that moistens the soil during daytime and dries the soil at night. The original SHEELS model fails to simulate this behavior. This work proposes a treatment of hydraulic lift processes associated with stressed vegetation and shows via numerical experiments that both problems reported above can be much alleviated by including the hydraulic lift effect associated with stressed vegetation.

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Heather Tonkin
,
Greg J. Holland
,
Neil Holbrook
, and
Ann Henderson-Sellers

Abstract

This paper investigates the performance of two recently developed thermodynamic models of maximum tropical cyclone intensity (K. A. Emanuel’s referred to here as E1 and G. J. Holland’s referred to here as H1), which are designed to estimate the most intense storm possible given the ambient environmental conditions. The study involves estimating the maximum potential tropical cyclone intensity (MPI) from climatological information in three ocean regions, where relatively reliable atmospheric soundings and tropical cyclone intensity data exist. The monthly MPI was estimated for 28 locations across the northwest Pacific, southwest Pacific, and North Atlantic Ocean regions. Empirically derived relationships between observed maximum storm intensity and sea surface temperature were also utilized in the examination of regional MPI model performance.

Derived MPIs generally agreed well with observed maximum intensities during the tropical cyclone season. The H1 model tended to underestimate the maximum intensity of storms early and late in the tropical cyclone season and at stations between 10° and 20°N in the northwest Pacific, where the effect of continental air led to weak model estimates for the given surface energy conditions. Additionally, extremely intense H1 estimates were predicted at some stations in the Australian/southwest Pacific region where particularly unstable atmospheric conditions and low ambient surface pressure values are observed. These features of model performance are largely due to the sensitivity of H1 to warm environmental upper-level temperatures. The E1 model displayed a poor seasonality, frequently predicting the occurrence of storms during winter months. Emanuel MPI estimates were at times underestimated for stations in the North Atlantic and northwest Pacific. The E1 model estimates in the northwest Pacific were affected by particularly warm upper-level conditions, while relatively high ambient surface pressures in the North Atlantic at 25°N lead to MPI estimates, which are weaker than observed in this region.

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Sara A. Rauscher
,
Filippo Giorgi
,
Curt Covey
, and
Ann Henderson-Sellers

Abstract

No Abstract available.

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Keith P. Shine
,
David A. Robinson
,
Ann Henderson-Sellers
, and
George Kukla

Abstract

Recent work has emphasized the potential importance of atmospheric aerosols in the Arctic. This paper presents results indicating the large-scale presence of arctic aerosols during late spring. Their screening effect may be sufficient to alter significantly the shortwave radiation budget. The ratios of brightness over sea and snow covered ice surfaces are shown to be considerably lower, using DMSP shortwave imagery, than those calculated for clear skies using a radiative transfer scheme. Our analysis shows that aerosols are the most likely cause of the discrepancy. With additional calibration the method offers the potential for remote sensing of the aerosol distribution and concentration over the Arctic.

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C. Adam Schlosser
,
Andrew G. Slater
,
Alan Robock
,
Andrew J. Pitman
,
Konstantin Ya. Vinnikov
,
Ann Henderson-Sellers
,
Nina A. Speranskaya
,
Ken Mitchell
, and
The PILPS 2(D) Contributors

Abstract

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966–83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

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Sarah J. Doherty
,
Stephan Bojinski
,
Ann Henderson-Sellers
,
Kevin Noone
,
David Goodrich
,
Nathaniel L. Bindoff
,
John A. Church
,
Kathy A. Hibbard
,
Thomas R. Karl
,
Lucka Kajfez-Bogataj
,
Amanda H. Lynch
,
David E. Parker
,
I. Colin Prentice
,
Venkatachalam Ramaswamy
,
Roger W. Saunders
,
Mark Stafford Smith
,
Konrad Steffen
,
Thomas F. Stocker
,
Peter W. Thorne
,
Kevin E. Trenberth
,
Michel M. Verstraete
, and
Francis W. Zwiers

The Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) concluded that global warming is “unequivocal” and that most of the observed increase since the mid-twentieth century is very likely due to the increase in anthropogenic greenhouse gas concentrations, with discernible human influences on ocean warming, continental-average temperatures, temperature extremes, wind patterns, and other physical and biological indicators, impacting both socioeconomic and ecological systems. It is now clear that we are committed to some level of global climate change, and it is imperative that this be considered when planning future climate research and observational strategies. The Global Climate Observing System program (GCOS), the World Climate Research Programme (WCRP), and the International Geosphere-Biosphere Programme (IGBP) therefore initiated a process to summarize the lessons learned through AR4 Working Groups I and II and to identify a set of high-priority modeling and observational needs. Two classes of recommendations emerged. First is the need to improve climate models, observational and climate monitoring systems, and our understanding of key processes. Second, the framework for climate research and observations must be extended to document impacts and to guide adaptation and mitigation efforts. Research and observational strategies specifically aimed at improving our ability to predict and understand impacts, adaptive capacity, and societal and ecosystem vulnerabilities will serve both purposes and are the subject of the specific recommendations made in this paper.

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Lifeng Luo
,
Alan Robock
,
Konstantin Y. Vinnikov
,
C. Adam Schlosser
,
Andrew G. Slater
,
Aaron Boone
,
Pierre Etchevers
,
Florence Habets
,
Joel Noilhan
,
Harald Braden
,
Peter Cox
,
Patricia de Rosnay
,
Robert E. Dickinson
,
Yongjiu Dai
,
Qing-Cun Zeng
,
Qingyun Duan
,
John Schaake
,
Ann Henderson-Sellers
,
Nicola Gedney
,
Yevgeniy M. Gusev
,
Olga N. Nasonova
,
Jinwon Kim
,
Eva Kowalczyk
,
Kenneth Mitchell
,
Andrew J. Pitman
,
Andrey B. Shmakin
,
Tatiana G. Smirnova
,
Peter Wetzel
,
Yongkang Xue
, and
Zong-Liang Yang

Abstract

The Project for Intercomparison of Land-Surface Parameterization Schemes phase 2(d) experiment at Valdai, Russia, offers a unique opportunity to evaluate land surface schemes, especially snow and frozen soil parameterizations. Here, the ability of the 21 schemes that participated in the experiment to correctly simulate the thermal and hydrological properties of the soil on several different timescales was examined. Using observed vertical profiles of soil temperature and soil moisture, the impact of frozen soil schemes in the land surface models on the soil temperature and soil moisture simulations was evaluated.

It was found that when soil-water freezing is explicitly included in a model, it improves the simulation of soil temperature and its variability at seasonal and interannual scales. Although change of thermal conductivity of the soil also affects soil temperature simulation, this effect is rather weak. The impact of frozen soil on soil moisture is inconclusive in this experiment due to the particular climate at Valdai, where the top 1 m of soil is very close to saturation during winter and the range for soil moisture changes at the time of snowmelt is very limited. The results also imply that inclusion of explicit snow processes in the models would contribute to substantially improved simulations. More sophisticated snow models based on snow physics tend to produce better snow simulations, especially of snow ablation. Hysteresis of snow-cover fraction as a function of snow depth is observed at the catchment but not in any of the models.

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