|Abstracts on Global Climate Change|
Periodic solutions for soil carbon dynamics equilibriums with time-varying forcing variables
Martin, MP Cordier, S Balesdent, J Arrouays, D
ECOLOGICAL MODELLING 204:3-4 523-530
Numerical models that simulate the dynamics of carbon in soil are increasingly used to improve our knowledge and help our management of the carbon cycle. Calculation of the long-term behavior of these models is necessary in many applications but encounters the difficulty of managing the periodic forcing variables, e.g. seasonal variations, such as carbon inputs and decomposition rates. This calculation is conventionally done by running the model over large time durations or by assuming constant forcing variables. Two methods, which make it possible to rapidly compute periodic solutions taking into account the time variations of these variables, are proposed. The first one works on discrete-time models and the second one on continuous-time models involving Fourier transforms. Both methods were tested on the Rothamsted carbon model (RothC), a discrete-time model which has also been given a continuous approximation, using realistic and unrealistic sets of time-varying forcing functions. Both methods provided an efficient way to compute the periodic solutions of the RothC model within the application domain of the model. Compared to running the discrete model to the equilibrium, reduction in the computational cost was of up to 95% at the expense of a maximum absolute error of 1% for the estimation of carbon stocks. For specific distributions of the forcing variables the use of Fourier transform of zero order, which was equivalent to assume constant forcing variables, led to a maximum absolute error of SS% in the estimation of the long-term behavior of the model. There, a Fourier transform of order higher than zero is required. (C) 2007 Elsevier B.V. All rights reserved.
Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs)
Tolika, K Maheras, P Vafiadis, M Flocasc, HA Arseni-Papadimitriou, A
INTERNATIONAL JOURNAL OF CLIMATOLOGY 27:7 861-881
A statistical downscaling technique based on artificial neural network (ANN) was employed for the estimation of local changes on seasonal (winter, spring) precipitation and raindays for selected stations over Greece. Empirical transfer functions were derived between large-scale predictors from the NCEP/NCAR reanalysis and local rainfall parameters. Two sets of predictors were used: (1) the circulation-based 500 hPa and (2) its combination along with surface specific humidity and raw precipitation data (nonconventional predictor). The simulated time series were evaluated against observational data and the downscaling model was found efficient in generating winter and spring precipitation and raindays. The temporal evolution of the estimated variables was well captured, for both seasons. Generally, the use of the nonconventional predictors are attributed to the improvement of the simulated results. Subsequently, the present day and future changes on precipitation conditions were examined using large-scale data from the atmospheric general circulation model HadAM3P to the statistical model. The downscaled climate change signal for both precipitation and raindays, partly for winter and especially for spring, is similar to the signal from the HadAM3P direct output: a decrease of the parameters is predicted over the study area. However, the amplitude of the changes was different. Copyright (c) 2006 Royal Meteorological Society
A maximum entropy method for combining AOGCMs for regional intra-year climate change assessment
Laurent, R Cai, XM
CLIMATIC CHANGE 82:3-4 411-435
This paper deals with different responses from various Atmosphere-Ocean Global Climate Models (AOGCMs) at the regional scale. What can be the best use of AOGCMs for assessing the climate change in a particular region? The question is complicated by the consideration of intra-year month-to-month variability of a particular climate variable such as precipitation or temperature in a specific region. A maximum entropy method (MEM), which combines limited information with empirical perspectives, is applied to assessing the probability-weighted multimodel ensemble average of a climate variable at the region scale. The method is compared to and coupled with other two methods: the root mean square error minimization method and the simple multimodel ensemble average method. A mechanism is developed to handle a comprehensive range of model uncertainties and to identify the best combination of AOGCMs based on a balance of two rules: depending equally on all models versus giving higher priority to models more strongly verified by the historical observation. As a case study, the method is applied to a central US region to compute the probability-based average changes in monthly precipitation and temperature projected for 2055, based on outputs from a set of AOGCMs. Using the AOGCM data prepared by international climate change study groups and local climate observation data, one can apply the MEM to precipitation or temperature for a particular region to generate an annual cycle, which includes the effects from both global climate change and local intra-year climate variability.
Seasonal-to-decadal predictability and prediction of South American climate
Nobre, P Marengo, JA Cavalcanti, IFA Obregon, G Barros, V Camilloni, I Campos, N Ferreira, AG
JOURNAL OF CLIMATE 19:23 5988-6004
The dynamical basis for seasonal to decadal climate predictions and predictability over South America is reviewed. It is shown that, while global tropical SSTs affect both predictability and predictions over South America, the current lack of SST predictability over the tropical Atlantic represents a limiting factor to seasonal climate predictions over some parts of the continent. The model’s skill varies with the continental region: the highest skill is found in the “Nordeste” region and the lowest skill over southeastern Brazil. It is also suggested that current two-tier approaches to predict seasonal climate variations might represent a major limitation to forecast coupled ocean-atmosphere phenomena like the South Atlantic convergence zone. Also discussed are the possible effects of global climate change on regional predictability of seasonal climate.
A land surface model incorporated with soil freeze/thaw and its application in GAME/Tibet
Hu, HP Ye, BS Zhou, YH Tian, FQ
SCIENCE IN CHINA SERIES D-EARTH SCIENCES 49:12 1311-1322
Land surface process is of great importance in global climate change, moisture and heat exchange in the interface of the earth and atmosphere, human impacts on the environment and ecosystem, etc. Soil freeze/thaw plays an important role in cold land surface processes. In this work the diurnal freeze/thaw effects on energy partition in the context of GAME/Tibet are studied. A sophisticated land surface model is developed, the particular aspect of which is its physical consideration of soil freeze/thaw and vapor flux. The simultaneous water and heat transfer soil sub-model not only reflects the water flow from unfrozen zone to frozen fringe in freezing/thawing soil, but also demonstrates the change of moisture and temperature field induced by vapor flux from high temperature zone to low temperature zone, which makes the model applicable for various circumstances. The modified Picard numerical method is employed to help with the water balance and convergence of the numerical scheme. Finally, the model is applied to analyze the diurnal energy and water cycle characteristics over the Tibetan Plateau using the Game/Tibet datasets observed in May and July of 1998. Heat and energy transfer simulation shows that: (i) There exists a negative feedback mechanism between soil freeze/thaw and soil temperature/ground heat flux; (ii) during freezing period all three heat fluxes do not vary apparently, in spite of the fact that the negative soil temperature is higher than that not considering soil freeze; (iii) during thawing period, ground heat flux increases, and sensible heat flux decreases, but latent heat flux does not change much; and (iv) during freezing period, soil temperature decreases, though ground heat flux increases.
Prognosis of the impact of global climate change on zonal ecosystems of the Volga river basin
RUSSIAN JOURNAL OF ECOLOGY 37:6 391-401
On the basis of the GISS prognostic climatic model, landscape-ecological scenarios concerning the immediate future of the region are considered in the forms of cartographic and analytical models. These scenarios predict a growing thermoarid bioclimatic trend accompanied by a general northward displacement of zonal boundaries, with corresponding acceleration of the biological cycle and increase in the productivity of boreal forests.
Optimal endogenous carbon taxes for electric power supply chains with power plants
Nagurney, A Liu, ZG Woolley, T
MATHEMATICAL AND COMPUTER MODELLING 44:9-10 899-916
In this paper, we develop a modeling and computational framework that allows for the determination of optimal carbon taxes applied to electric power plants in the context of electric power supply chain (generation/distribution/consumption) networks. The adoption of carbon/pollution taxes both internationally and regionally has been fueled by global climate change and fuel security risks, with a significant portion of such policy interventions directed at the electric power industry. The general framework that we develop allows for three distinct types of carbon taxation environmental policies, beginning with a completely decentralized scheme in which taxes can be applied to each individual power generator/power plant in order to guarantee that each assigned emission bound is not exceeded, to two versions of a centralized scheme, one which assumes a fixed bound over the entire electric power supply chain in terms of total carbon emissions and the other which allows the bound to be a function of the tax. The behavior of the various decision-makers in the electric power supply chain network is described, along with the three taxation schemes, and the governing equilibrium conditions, which are formulated as finite-dimensional variational inequality problems. Twelve numerical examples are presented in which the optimal carbon taxes, as well as the equilibrium electric power flows and demands, are computed. The numerical results demonstrate, as the theory predicts, that the carbon taxes achieve the desired goal, in that the imposed bounds on the carbon emissions are not exceeded. Moreover, they illustrate the spectrum of scenarios that can be explored in terms of changes in the bounds on the carbon emissions; changes in emission factors; changes in the demand price functions, etc. (c) 2006 Elsevier Ltd. All rights reserved.
Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method
Wu, SH Liu, ZS Liu, BY
OPTICS COMMUNICATIONS 267:1 137-144
Lidar is being widely used to monitor meteorological parameters and atmospheric constituents. Applications include meteorology, environmental pollution, atmospheric dynamics and global climate change. Signal processing for lidar applications involve highly nonlinear models and consequently nonlinear filtering. In this paper, we applied a new method, empirical mode decomposition to the lidar signal processing. The denoising approach is done by removal of the proper intrinsic mode functions. The data from the simulation and measurements are analyzed to evaluate this method comparing with the traditional low-pass filter and the multi-pulse averaging. Results show that it is effective-and superior to the band-pass filter and the averaging method. The denoising method also allows less averaging laser shots which is important for the real-time monitoring and for the low cost laser transmitter. (c) 2006 Elsevier B.V. All rights reserved.
Assessing Goddard Institute for Space Studies ModelE aerosol climatology using satellite and ground-based measurements: A comparison study
Liu, L Lacis, AA Carlson, BE Mishchenko, MI Cairns, B
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 111:D20 -
A physically based aerosol climatology is important to address questions of global climate change. We evaluate the aerosol climatology used in the GISS ModelE (Schmidt et al., 2006), by characterizing and comparing the geographic distribution and seasonal variability of aerosol optical depth (AOD) and particle size via Angstrom exponent (A) against available satellite and ground-based measurements, i.e., MODIS, MISR, POLDER, AVHRR, and AERONET data. There are a number of model parameters, particularly those related to aerosol size specification, that can be better constrained by comparison to satellite data. Our comparison shows that there are large differences in the satellite and ground-based global distributions of AOD. The differences between the observations increase for the Angstrom exponent. Given the uncertainties associated with satellite retrieval results, the agreement in the distributions of global optical depth between GCM aerosols and satellite data is qualitatively reasonable. However, the Angstrom exponent of the GCM aerosol is clearly biased low compared to satellite data, implying that the GCM aerosol sizes are overestimated. There is qualitative agreement of the ModelE aerosol single scattering albedo pi with TOMS Aerosol Index (AI) and AERONET data. The comparisons show insufficient aerosol absorption at most locations, suggesting a possible underestimation of black carbon distributions in the GCM. However, a more quantitative comparison first requires a readjustment of the GCM aerosol size specification.
Marine research in the Latitudinal Gradient Project along Victoria Land, Antarctica
Berkman, PA Cattaneo-Vietti, R Chiantore, M Howard-Williams, C Cummings, V Kvitek, R
SCIENTIA MARINA 69: Suppl. 2 57-63
This paper describes the conceptual framework of the Latitudinal Gradient Project that is being implemented by the New Zealand, Italian and United States Antarctic programmes along Victoria Land, Antarctica, from 72 degrees S to 86 degrees S. The purpose of this interdisciplinary research project is to assess the dynamics and coupling of marine and terrestrial ecosystems in relation to global climate variability. Preliminary data about the research cruises from the R/V “Italica” and R/V “Tangaroa” along the Victoria Land Coast in 2004 are presented. As a global climate barometer, this research along Victoria Land provides a unique framework for assessing latitudinal shifts in ‘sentinel’ environmental transition zones, where climate changes have an amplified impact on the phases of water.
Fates of eroded soil organic carbon: Mississippi basin case study
Smith, SV Sleezer, RO Renwick, WH Buddemeier, R
ECOLOGICAL APPLICATIONS 15:6 1929-1940
We have developed a mass balance analysis of organic carbon (OC) across the five major river subsystems of the Mississippi (MS) Basin (an area of 3.2 X 10(6) km(2)) This largely agricultural landscape undergoes a bulk soil erosion rate of similar to 480 t center dot km(-2)center dot yr(-1) (similar to 1500 x 10(6) t/yr, across the MS Basin), and a,soil organic carbon (SOC) erosion rate of similar to 7 t center dot km(-2 center dot)yr(-1) (similar to 22 x 10(6) t/yr). Erosion translocates upland SOC to alluvial deposits, water impoundments, and the ocean. Soil erosion is generally considered to be a net source of CO2 release to the atmosphere in global budgets. However, our results indicate that SOC erosion and relocation of soil apparently can reduce the net SOC oxidation rate of the original upland SOC while promoting neu replacement of eroded SOC in upland soils that were eroded. Soil erosion at the MS Basin scale is, therefore, a net CO2 sink rather than a source.
Multi-scale observation and cross-scale mechanistic modeling on terrestrial ecosystem carbon cycle
Cao, MK Yu, GR Liu, JY Li, KR
SCIENCE IN CHINA SERIES D-EARTH SCIENCES 48: Suppl. 1 17-32
To predict global climate change and to implement the Kyoto Protocol for stabilizing atmospheric greenhouse gases concentrations require quantifying spatio-temporal variations in the terrestrial carbon sink accurately. During the past decade multi-scale ecological experiment and observation networks have been established using various new technologies (e.g. controlled environmental facilities, eddy covariance techniques and quantitative remote sensing), and have obtained a large amount of data about terrestrial ecosystem carbon cycle. However, uncertainties in the magnitude and spatio-temporal variations of the terrestrial carbon sink and in understanding the underlying mechanisms have not been reduced significantly. One of the major reasons is that the observations and experiments were conducted at individual scales independently, but it is the interactions of factors and processes at different scales that determine the dynamics of the terrestrial carbon sink. Since experiments and observations are always conducted at specific scales, to understand cross-scale interactions requires mechanistic analysis that is best to be achieved by mechanistic modeling. However, mechanistic ecosystem models are mainly based on data from single-scale experiments and observations and hence have no capacity to simulate mechanistic cross-scale interconnection and interactions of ecosystem processes. New-generation mechanistic ecosystem models based on new ecological theoretical framework are needed to quantify the mechanisms from micro-level fast eco-physiological responses to macro-level slow acclimation in the pattern and structure in disturbed ecosystems. Multi-scale data-model fusion is a recently emerging approach to assimilate multi-scale observational data into mechanistic, dynamic modeling, in which the structure and parameters of mechanistic models for simulating cross-scale interactions are optimized using multi-scale observational data. The models are validated and evaluated at different spatial and temporal scales and real-time observational data are assimilated continuously into dynamic modeling for predicting and forecasting ecosystem changes realistically. In summary, a breakthrough in terrestrial carbon sink research requires using approaches of multi-scale observations and cross-scale modeling to understand and quantify interconnections and interactions among ecosystem processes at different scales and their controls over ecosystem carbon cycle.
The Earth Simulator: roles and impacts
PARALLEL COMPUTING 30:12 1279-1286
The Earth Simulator Research Project started in March 2002 with the primary objective of producing reliable prediction data for global climate change. Within a couple of months after the start of operation, the Earth Simulator achieved an amazing performance of 35.86 Teraflops (about 90% of the peak performance of 40.96 Teraflops) in the Linpack benchmark test and, more surprisingly, 26.58 Teraflops for a typical application program of global atmospheric circulation model (called AFES) with a horizontal resolution of 10km. These facts ensure us that the real contribution of the Earth Simulator be far greater than originally expected. Undoubtedly, the Earth Simulator would work to make a paradigm shift in science, industry, and human thinking, as well as finding the best human’s wisdom to keep a sustainable symbiotic relationship with nature. (C) 2004 Elsevier B.V. All rights reserved.
Interpretation of Arctic aerosol properties using cluster analysis applied to observations in the Svalbard area
Treffeisen, R Herber, A Strom, J Shiobara, M Yamagata, TY Holmen, K Kriews, M Schrems, O
TELLUS SERIES B-CHEMICAL AND PHYSICAL METEOROLOGY 56:5 457-476
Atmospheric aerosols play an important role in global climate change, directly through radiative forcing and indirectly through their effect on cloud properties. Numerous measurements have been performed in the last three decades in order to characterize polar aerosols. Information about aerosol characteristics is needed to calculate induced changes in the Earth’s heat balance. However, this forcing is highly variable in space and time. Accurate quantification of forcing by aerosols will require combined efforts, assimilating information from different sources such as satellite, aircraft and surface-based observations. Adding to the complexity of the problem is that the measurements themselves are often not directly comparable as they vary in spatial/temporal resolution and in the basic properties of the aerosol that they measure. Therefore it is desirable to close the gap between the differences in temporal and spatial resolution and coverage among the observational approaches. In order to keep the entire information content and to treat aerosol variability in a consistent and manageable way an approach has to be achieved which enables one to combine these data. This study presents one possibility for linking together a complex Arctic aerosol data set in terms of parameters, timescale and place of measurement as well as meteorological parameters. A cluster analysis was applied as a pattern recognition technique. The data set is classified in clusters and expressed in terms of mean statistical values, which represent the entire database and its variation. For this study, different time-series of microphysical, optical and chemical aerosol parameters as well as meteorological parameters were analysed. The database was obtained during an extensive aerosol measurement campaign, the ASTAR 2000 (Arctic Study of Tropospheric Aerosol and Radiation) field campaign, with coordinated simultaneous ground-based and airborne measurements in the vicinity of Spitsbergen (Svalbard). Furthermore, longterm measurements at two ground-based sites situated at different altitudes were incorporated into the analysis. The approach presented in this study allows the necessary linking of routine long-term measurements with short-term extensive observations. It also involves integration of intermittent vertical aerosol profile measurements. This is useful for many applications, especially in climate research where the required data coverage is large.