Integrated Assessment Modeling: the Human Dimension of Earth System Research

2012 ◽  
Keyword(s):  
Integrated Assessment ◽  
Human Dimension ◽  
Earth System ◽  
System Research ◽  
Integrated Assessment Modeling

scholarly journals Estudo da Temperatura da Superfície do Mar para os Oceanos Atlântico e Pacífico Utilizando a Técnica de Análises de Componente Principal e de Agrupamento (Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of...)

2011 ◽  
Vol 4 (2) ◽  
pp. 264
Author(s):  
Madson Tavares Silva ◽  
Stephany C. F. Do Egito Costa ◽  
Manoel Francisco Gomes Filho ◽  
Daisy B. Lucena
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Research Laboratory ◽  
Principal Component ◽  
Component Analysis ◽  
Sea Surface ◽  
Earth System ◽  
System Research

Apresenta-se neste estudo a avaliação da metodologia de Análises Multivariadas: Análises em Componente Principal (ACP) e de Agrupamento (AA), aos dados de Temperatura da Superfície do Mar (TSM) para os Oceanos Atlântico (Norte (NATL), Tropical (TROP) e Sul (SATL)) e Pacifico (NIÑO1+2, NIÑO3.4, NIÑO3 e NIÑO4). Foram utilizados dados mensais de janeiro de 1950 a dezembro de 2010 de TSM obtidos na NOAA (National Oceanic and Atmospheric Administration/Earth System Research Laboratory). As regiões TROP e NIÑO4 apresentam as maiores TSM para os meses entre dezembro-julho. A região NATL apresenta no período de agosto-outubro seu maiores valores de TSM. A região NIÑo1+2 apresentou os menores valores de TSM. Os resultados da Análise em Componente Principal (ACP) identificaram maiores pesos na variação total explicada pelas duas primeiras componentes, que representam cerca de 100% da variância total dos dados de TSM. A Análise de Agrupamento (AA), pelo método Ward, permitiu o agrupamento das estações em três grupos homogêneos. Palavras - chave: Análises Multivariadas, Mudanças climáticas, Aquecimento Global.   Study of Sea Surface Temperature for the Atlantic and Pacific Oceans Using the Technique of Principal Component Analysis and Cluster   ABSTRACT Presented in this study was to evaluate the methodology of Multivariate Analysis: Principal Component Analysis (PCA) and cluster analysis (CA), the data of sea surface temperature (SST) for the Atlantic (North (NATL), Tropical (TROP) and South (Satler)) and Pacific (+2 NIÑO1, NIÑO3.4, and NIÑO3 NIÑO4). We used monthly data from January 1950 to December 2010 SST obtained from NOAA (National Oceanic and Atmospheric Administration / Earth System Research Laboratory). TROP and NIÑO4 regions have the highest SST for the months from December to July. NATL The region has in the period August-October SST your highest values +2 NIÑo1 The region had the lowest values of TSM. Results on Principal Component Analysis (PCA) identified higher weights in the total variation explained by the first two components, which represent about 100% of the total variance of SST. The Cluster Analysis (AA), the Ward method, allowed the grouping of stations into three homogeneous groups. Keywords: Multivariate Analysis, Climate Change, Global Warming.

The SYPR Integrative Assessment Model: Complexity in Development

2004 ◽  
Author(s):  
Steven Manson
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Environmental Change ◽  
Land Cover Change ◽  
Integrated Assessment ◽  
Global Environmental Change ◽  
Global Scale ◽  
Assessment Model ◽  
Integrated Assessment Modeling ◽  
Global Environmental

Be it global environmental change or environment and development, landuse and land-cover change is central to the dynamics and consequences in question in the southern Yucatán peninsular region. Designing policies to address these impacts is hampered by the difficulty of projecting land use and land cover, not only because the dynamics are complex but also because consequences are strongly place-based. This chapter describes an integrated assessment modeling framework that builds on the research detailed in earlier chapters in order to project land-use and land-cover change in a geographically explicit way. Integrated assessment is a term that describes holistic treatments of complex problems to assess both science and policy endeavors in global environmental change (Rotmans and Dowlatabadi 1998). The most common form of integrated assessment is computer modeling that combines socioeconomic and biogeophysical factors to predict global climate. Advanced in part by the successes of these global-scale models, integrated assessment has expanded to structure knowledge and set research priorities for a large range of coupled human–environment problems. Increasing recognition is given to the need for integrated assessment models to address regionalscale problems that are masked by global-scale assessments (Walker 1994). Such models must address two issues to project successfully land-use and land-cover change at the regional scale. First, change occurs incrementally in spatially distinct patterns that have different implications for global change (Lambin 1994). Second, a model must account for the complexity of, and relationships among, socio-economic and environmental factors (B. L. Turner et al. 1995). The SYPR integrated assessment model, therefore, has a fine temporal and spatial grain and it places land-use and landcover change at the intersection of land-manager decision-making, the environment, and socio-economic institutions. What follows is a description of an ongoing integrated assessment modeling endeavor of the SYPR project (henceforth, SYPR IA model). The depth and breadth of the SYPR project poses a challenge to the integrated assessment modeling effort since some unifying framework must reconcile a broad array of issues, theories, and data. The global change research community offers a general conception of how environmental change results from infrastructure development, population pressure, market opportunities, resource institutions, and environmental or resource policies (Stern, Young, and Drukman 1992).

scholarly journals From land use to land cover: restoring the afforestation signal in a coupled integrated assessment–earth system model and the implications for CMIP5 RCP simulations

2014 ◽  
Vol 11 (22) ◽  
pp. 6435-6450 ◽  
Author(s):  
A. V. Di Vittorio ◽  
L. P. Chini ◽  
B. Bond-Lamberty ◽  
J. Mao ◽  
X. Shi ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Integrated Assessment ◽  
Coupled Model ◽  
Assessment Model ◽  
Carbon Gain ◽  
Climate Projections ◽  
Full Potential ◽  
Earth System ◽  
Intergovernmental Panel

Abstract. Climate projections depend on scenarios of fossil fuel emissions and land use change, and the Intergovernmental Panel on Climate Change (IPCC) AR5 parallel process assumes consistent climate scenarios across integrated assessment and earth system models (IAMs and ESMs). The CMIP5 (Coupled Model Intercomparison Project Phase 5) project used a novel "land use harmonization" based on the Global Land use Model (GLM) to provide ESMs with consistent 1500–2100 land use trajectories generated by historical data and four IAMs. A direct coupling of the Global Change Assessment Model (GCAM), GLM, and the Community ESM (CESM) has allowed us to characterize and partially address a major gap in the CMIP5 land coupling design: the lack of a corresponding land cover harmonization. For RCP4.5, CESM global afforestation is only 22% of GCAM's 2005 to 2100 afforestation. Likewise, only 17% of GCAM's 2040 afforestation, and zero pasture loss, were transmitted to CESM within the directly coupled model. This is a problem because GCAM relied on afforestation to achieve RCP4.5 climate stabilization. GLM modifications and sharing forest area between GCAM and GLM within the directly coupled model did not increase CESM afforestation. Modifying the land use translator in addition to GLM, however, enabled CESM to include 66% of GCAM's afforestation in 2040, and 94% of GCAM's pasture loss as grassland and shrubland losses. This additional afforestation increases CESM vegetation carbon gain by 19 PgC and decreases atmospheric CO2 gain by 8 ppmv from 2005 to 2040, which demonstrates that CESM without additional afforestation simulates a different RCP4.5 scenario than prescribed by GCAM. Similar land cover inconsistencies exist in other CMIP5 model results, primarily because land cover information is not shared between models. Further work to harmonize land cover among models will be required to increase fidelity between IAM scenarios and ESM simulations and realize the full potential of scenario-based earth system simulations.

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