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Chris D. Jones, Matthew Collins, Peter M. Cox, and Steven A. Spall

Abstract

There is significant interannual variability in the atmospheric concentration of carbon dioxide (CO2) even when the effect of anthropogenic sources has been accounted for. This variability is well correlated with the El Niño–Southern Oscillation (ENSO) cycle. This behavior of the natural carbon cycle provides a valuable mechanism for validating carbon cycle models. The model in turn is a valuable tool for examining the processes involved in the relationship between ENSO and the carbon cycle.

A GCM coupled climate–carbon cycle model is used to study the mechanisms involved. The model simulates the observed temperature, precipitation, and CO2 response of the climate to the ENSO cycle. Climatic changes over land during El Niño events lead to decreased gross primary productivity and increased plant and soil respiration, and hence the terrestrial biosphere becomes a source of CO2 to the atmosphere. Conversely, during El Niño events, the ocean becomes a sink of CO2 because of reduction of equatorial Pacific outgassing as a result of decreased upwelling of carbon-rich deep water. During La Niña events the opposite occurs; the land becomes a sink and the ocean a source of CO2.

The magnitude of the model's response is such that the terrestrial biosphere releases about 1.8 GtC yr−1 for an El Niño with a Niño-3 index of magnitude 1 °C, and the oceans take up about 0.5 GtC yr−1. (1 GtC = 1015 g of carbon). The net global response is thus an increase in atmospheric CO2 of about 0.6 ppmv yr−1. This is in close agreement with the sensitivity of the observed CO2 record to ENSO events.

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Randal D. Koster, Paul A. Dirmeyer, Andrea N. Hahmann, Ruben Ijpelaar, Lori Tyahla, Peter Cox, and Max J. Suarez

Abstract

The strength of the coupling between the land and the atmosphere, which controls, for example, the degree to which precipitation-induced soil moisture anomalies affect the overlying atmosphere and thereby the subsequent generation of precipitation, has been examined and quantified with many atmospheric general circulation models (AGCMs). Generally missing from such studies, however, is an indication of the extent to which the simulated coupling strength is model dependent. Four modeling groups have recently performed a highly controlled numerical experiment that allows an objective intermodel comparison of land–atmosphere coupling strength, focusing on short (weekly down to subhourly) timescales. The experiment essentially consists of an ensemble of 1-month simulations in which each member simulation artificially maintains the same (model specific) time series of surface prognostic variables. Differences in atmospheric behavior between the ensemble members then indicate the degree to which the state of the land surface controls atmospheric processes in that model. A comparison of the four sets of experimental results shows that coupling strength does indeed vary significantly among the AGCMs.

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Jose A. Marengo, Luiz E.O.C. Aragão, Peter M. Cox, Richard Betts, Duarte Costa, Neil Kaye, Lauren T. Smith, Lincoln M. Alves, and Vera Reis
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Cynthia Rosenzweig, William D. Solecki, Lily Parshall, Barry Lynn, Jennifer Cox, Richard Goldberg, Sara Hodges, Stuart Gaffin, Ronald B. Slosberg, Peter Savio, Frank Dunstan, and Mark Watson

This study of New York City, New York's, heat island and its potential mitigation was structured around research questions developed by project stakeholders working with a multidisciplinary team of researchers. Meteorological, remotely-sensed, and spatial data on the urban environment were brought together to understand multiple dimensions of New York City's heat island and the feasibility of mitigation strategies, including urban forestry, green roofs, and high-albedo surfaces. Heat island mitigation was simulated with the fifth-generation Pennsylvania State University-NCAR Mesoscale Model (MM5). Results compare the possible effectiveness of mitigation strategies at reducing urban air temperature in six New York City neighborhoods and for New York City as a whole. Throughout the city, the most effective temperature-reduction strategy is to maximize the amount of vegetation, with a combination of tree planting and green roofs. This lowered simulated citywide surface urban air temperature by 0.4°C on average, and 0.7°C at 1500 Eastern Standard Time (EST), when the greatest temperature reductions tend to occur. Decreases of up to 1.1°C at 1500 EST occurred in some neighborhoods in Manhattan and Brooklyn, where there is more available area for implementing vegetation planting. New York City agencies are using project results to guide ongoing urban greening initiatives, particularly tree-planting programs.

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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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Randal D. Koster, Y. C. Sud, Zhichang Guo, Paul A. Dirmeyer, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, Harvey Davies, Eva Kowalczyk, C. T. Gordon, Shinjiro Kanae, 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 Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

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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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