IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature

The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) an...

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Other Authors: SEEK
Format: Dataset
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Published: 2011
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Online Access:http://hdl.handle.net/10255/dryad.18370
http://metacat.lternet.edu/knb/metacat/dpennington.348.2/xml
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institution Open Polar
collection Dryad Digital Repository (Duke University)
op_collection_id ftdryad
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topic climate
global climate change
temperature
spellingShingle climate
global climate change
temperature
IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
topic_facet climate
global climate change
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description The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model.Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity.
author2 SEEK
format Dataset
title IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
title_short IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
title_full IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
title_fullStr IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
title_full_unstemmed IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature
title_sort ipcc climate change data: nies99 b2a model: 2020 minimum temperature
publishDate 2011
url http://hdl.handle.net/10255/dryad.18370
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op_coverage Worldwide
-180.0 W 180.0 E 90.0 N -90.0 S
2020-01-01 to 2020-12-31
genre Sea ice
genre_facet Sea ice
op_relation http://metacat.lternet.edu/knb/metacat/dpennington.348.2/xml
dpennington.348.2
http://hdl.handle.net/10255/dryad.18370
op_rights 1. The IPCC Data Distribution Centre permits the research results from seven climate modelling centres (Hadley Centre for Climate Prediction and Research, Deutsches Klimarechenzentrum, Canadian Centre for Climate Modelling and Analysis, Geophysical Fluids Dynamics Laboratory, the Commonwealth and Scientific Industrial Research Organisation, the National centre for Atmospheric Research and the Centre for Climate System Research) to be used freely for the purposes of bona fide research. (Bona fide research is deemed to be research which generates results that are freely and universally accessible to any interested party, i.e., if people use DDC data they must agree to publish results openly or respond willingly to requests from others for copies of the results.) 2. The climate modelling centres' research results should not be used for commercial exploitation, business use, resale or transfer to any third party. 3. No warranty is given as to the suitability of the climate modelling centres' research results for particular purposes. 4. No liability is accepted by the IPCC Data Distribution Centre and/or the climate modelling centres for any errors or omissions in the climate modelling centres' research results, associated information and/or documentation. 5. Please acknowledge the use of the corresponding climate modelling centres' research results in any publication. 6. The intellectual property rights on the climate modelling centres' research results remains the property of each of the climate modelling centres. 7. By registering with the DDC you agree to abide by this Data Statement.
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spelling ftdryad:oai:v1.datadryad.org:10255/dryad.18370 2023-05-15T18:19:04+02:00 IPCC Climate Change Data: NIES99 B2a Model: 2020 Minimum Temperature SEEK Worldwide -180.0 W 180.0 E 90.0 N -90.0 S 2020-01-01 to 2020-12-31 2011-04-26T18:01:41Z text/plain http://hdl.handle.net/10255/dryad.18370 http://metacat.lternet.edu/knb/metacat/dpennington.348.2/xml unknown http://metacat.lternet.edu/knb/metacat/dpennington.348.2/xml dpennington.348.2 http://hdl.handle.net/10255/dryad.18370 1. The IPCC Data Distribution Centre permits the research results from seven climate modelling centres (Hadley Centre for Climate Prediction and Research, Deutsches Klimarechenzentrum, Canadian Centre for Climate Modelling and Analysis, Geophysical Fluids Dynamics Laboratory, the Commonwealth and Scientific Industrial Research Organisation, the National centre for Atmospheric Research and the Centre for Climate System Research) to be used freely for the purposes of bona fide research. (Bona fide research is deemed to be research which generates results that are freely and universally accessible to any interested party, i.e., if people use DDC data they must agree to publish results openly or respond willingly to requests from others for copies of the results.) 2. The climate modelling centres' research results should not be used for commercial exploitation, business use, resale or transfer to any third party. 3. No warranty is given as to the suitability of the climate modelling centres' research results for particular purposes. 4. No liability is accepted by the IPCC Data Distribution Centre and/or the climate modelling centres for any errors or omissions in the climate modelling centres' research results, associated information and/or documentation. 5. Please acknowledge the use of the corresponding climate modelling centres' research results in any publication. 6. The intellectual property rights on the climate modelling centres' research results remains the property of each of the climate modelling centres. 7. By registering with the DDC you agree to abide by this Data Statement. climate global climate change temperature dataset 2011 ftdryad 2020-01-01T14:38:38Z The model used here is a coupled ocean-atmosphere model that consists of the CCSR/NIES atmospheric GCM, the CCSR ocean GCM, a thermodynamic sea-ice model, and a river routing model (Abe-Ouchi et al., 1996). The spatial resolution is T21 spectral truncation (roughly 5.6 degrees latitude/longitude) and 20 vertical levels for the atmospheric part, and roughly 2.8 degrees horizontal grid and 17 vertical levels for the oceanic part. Flux adjustment for atmosphere-ocean heat and water exchange is applied to prevent a drift of the modelled climate. The atmospheric model adopts a radiation scheme based on the k-distribution, two-stream discrete ordinate method (DOM) (Nakajima and Tanaka, 1986). This scheme can deal with absorption, emission and scattering by gases, clouds and aerosol particles in a consistent manner. In the calculation of sulphate aerosol optical properties, the volumetric mode radius of the sulphate particle in dry environment is assumed to be 0.2 micron. The hygroscopic growth of the sulphate is considered by an empirical fit of d'Almeida et al. (1991). The vertical distribution of the sulphate aerosol is assumed to be constant in the lowest 2 km of the atmosphere. The concentrations of greenhouse gases are represented by equivalent-CO2. Three integrations are made for 200 model years (1890-2090). In the control experiment (CTL), the globally uniform concentration of greenhouse gases is kept constant at 345 ppmv CO2-equivalent and the concentration of sulphate is set to zero. In the experiment GG, the concentration of greenhouse gases is gradually increased, while that of sulphate is set to zero. In the experiments GS, the increase in anthropogenic sulphate as well as that in greenhouse gases is given and the aerosol scattering (the direct effect of aerosol) is explicitly represented in the way described above. The indirect effect of aerosol is not included in any experiment. The scenario of atmospheric concentrations of greenhouse gases and sulphate aerosols is given in accordance with Mitchell and Johns (1997). The increase in greenhouse gases is based on the historical record from 1890 to 1990 and is increased by 1 percent / yr (compound) after 1990. For sulphate aerosols, geographical distributions of sulphate loading for 1986 and 2050, which are estimated by a sulphur cycle model (Langer and Rodhe, 1991), are used as basic patterns. Based on global and annual mean sulphur emission rates, the 1986 pattern is scaled for years before 1990; the 2050 pattern is scaled for years after 2050; and the pattern is interpolated from the two basic ones for intermediate years to give the time series of the distribution. The sulphur emission rate in the future is based on the IPCC IS92a scenario. The sulphate concentration is offset in our run so that it starts from zero at 1890. The seasonal variation of sulphate concentration is ignored. Discussion on the results of the experiments will be found in Emori et al. (1999). Climate sensitivity of the CCSR/NIES model derived by equilibrium runs is estimated to be 3.5 degrees Celsius. Global-Mean Temperature, Precipitation and CO2 Changes (w.r.t. 1961-90) for the CCSR/NIES model.Like B1, the B2 world is one of increased concern for environmental and social sustainability, but the character of this world differs substantially. Education and welfare programs are widely pursued leading to reductions in mortality and, to a lesser extent, fertility. The population reaches about 10 billion people by 2100, consistent with both the United Nations and IIASA median projections. Income per capita grows at an intermediary rate to reach about US$12,000 by 2050. By 2100 the global economy might expand to reach some US$250 trillion. International income differences decrease, although not as rapidly as in scenarios of higher global convergence (A1, B1). Local inequity is reduced considerably through the development of stronger community support networks. Generally high educational levels promote both development and environmental protection. Indeed, environmental protection is one of the few remaining truly international priorities. However, strategies to address global environmental challenges are less successful than in B1, as governments have difficulty designing and implementing agreements that combine environmental protection with mutual economic benefits. The B2 storyline presents a particularly favorable climate for community initiative and social innovation, especially in view of high educational levels. Technological frontiers are pushed less than in A1 and B1 and innovations are also regionally more heterogeneous. Globally, investment in R and D continues its current declining trend, and mechanisms for international diffusion of technology and know-how remain weaker than in scenarios A1 and B1 (but higher than in scenario A2). Some regions with rapid economic development and limited natural resources place particular emphasis on technology development and bilateral co-operation. Technical change is therefore uneven. The energy intensity of GDP declines at about one percent per year, in line with the average historical experience of the last two centuries. Land-use management becomes better integrated at the local level in the B2 world. Urban and transport infrastructure is a particular focus of community innovation, contributing to a low level of car dependence and less urban sprawl. An emphasis on food self-reliance contributes to a shift in dietary patterns towards local products, with reduced meat consumption in countries with high population densities. Energy systems differ from region to region, depending on the availability of natural resources. The need to use energy and other resources more efficiently spurs the development of less carbon-intensive technology in some regions. Environment policy cooperation at the regional level leads to success in the management of some transboundary environmental problems, such as acidification due to SO2, especially to sustain regional self-reliance in agricultural production. Regional cooperation also results in lower emissions of NOx and VOCs, reducing the incidence of elevated tropospheric ozone levels. Although globally the energy system remains predominantly hydrocarbon-based to 2100, there is a gradual transition away from the current share of fossil resources in world energy supply, with a corresponding reduction in carbon intensity. Dataset Sea ice Dryad Digital Repository (Duke University)