Atmospheric composition and climate/Data uncertainties limitations: Difference between revisions
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|Reference=Forster et al., 2007; Randall et al., 2007; Müller et al., a (unpublished); | |Reference=Forster et al., 2007; Randall et al., 2007; Müller et al., a (unpublished); | ||
|Description=<h2>Data, uncertainties and limitations</h2> | |Description=<h2>Data, uncertainties and limitations</h2> | ||
The external data used in the climate model are the radiative forcing factors from IPCC’s AR4 ([[Forster et al., 2007]]), [[MAGICC model|MAGICC]] parameterisations, and the patterns of climate change obtained from [[AOGCM model|AOGCMs]]. In general terms, the main uncertainties in the climate system relate to greenhouse gas concentration levels in an emission scenario, to the radiative forcing per greenhouse gas concentration, to global mean temperature change as a result of a change in radiative forcing, and to the spatial distribution of temperature and precipitation changes. For CO2, there is a substantial uncertainty on how much of the carbon enters the ocean and the terrestrial biosphere, now and in the future, and how much remains in the atmosphere. For all other greenhouse gases, atmospheric concentrations following the release of a certain amount of emissions is less uncertain and also less relevant, as these gases contribute less to global climate change ([[Forster et al., 2007]]). In terms of radiative forcing, there is little uncertainty for the long-lived greenhouse gases (CO2, CH4, N2O, and halocarbons), but AR4 identified the largest uncertainties for direct and indirect aerosol effects, albedo, and tropospheric ozone ([[Forster et al., 2007]]). For all forcing agents, IMAGE uses the mean values from AR4 as best estimates ([[Forster et al., 2007]]). The uncertainty in global mean temperature change, as derived from results of AOGCM calculations, is still quite large, with climate sensitivity likely to be in the range of 2 to 4.5 °C (with lower values being very unlikely, and values above 4.5 not possible to be excluded). Instead of using the best estimate of 3 °C ([[Randall et al., 2007]]), this uncertainty can be accounted for by emulating different AOGCMs with the MAGICC model. For the spatial distribution of climate change, most models agree that the changes will be largest at higher latitudes. For changes in precipitation, however, there is a large degree of uncertainty, with models even disagreeing on what would be a sign of change, in many regions (AR4, Chapter 10[[*****?**]]). This uncertainty in the spatial patterns of climate change can be taken into account by applying pattern scaling for temperature and precipitation based on a range of AOGCMs in IMAGE. In addition to CO2 concentrations and climate change, also atmospheric concentrations are relevant for some IMAGE modules. Air pollutants are used to calculate the effect on human health ([[Human development]]). The effect of ozone on crop yields is explored in a first study, but is not yet part of the standard model set-up. | The external data used in the climate model are the radiative forcing factors from IPCC’s AR4 ([[Forster et al., 2007]]), [[MAGICC model|MAGICC]] parameterisations, and the patterns of climate change obtained from [[AOGCM model|AOGCMs]]. In general terms, the main uncertainties in the climate system relate to greenhouse gas concentration levels in an emission scenario, to the radiative forcing per greenhouse gas concentration, to global mean temperature change as a result of a change in radiative forcing, and to the spatial distribution of temperature and precipitation changes. For CO2, there is a substantial uncertainty on how much of the carbon enters the ocean and the terrestrial biosphere, now and in the future, and how much remains in the atmosphere. For all other greenhouse gases, atmospheric concentrations following the release of a certain amount of emissions is less uncertain and also less relevant, as these gases contribute less to global climate change ([[Forster et al., 2007]]). In terms of radiative forcing, there is little uncertainty for the long-lived greenhouse gases (CO2, CH4, N2O, and halocarbons), but AR4 identified the largest uncertainties for direct and indirect aerosol effects, albedo, and tropospheric ozone ([[Forster et al., 2007]]). For all forcing agents, IMAGE uses the mean values from AR4 as best estimates ([[Forster et al., 2007]]). The uncertainty in global mean temperature change, as derived from results of AOGCM calculations, is still quite large, with climate sensitivity likely to be in the range of 2 to 4.5 °C (with lower values being very unlikely, and values above 4.5 not possible to be excluded). Instead of using the best estimate of 3 °C ([[Randall et al., 2007]]), this uncertainty can be accounted for by emulating different AOGCMs with the MAGICC model. For the spatial distribution of climate change, most models agree that the changes will be largest at higher latitudes. For changes in precipitation, however, there is a large degree of uncertainty, with models even disagreeing on what would be a sign of change, in many regions ([[AR|AR4]], Chapter 10[[*****?**]]). This uncertainty in the spatial patterns of climate change can be taken into account by applying pattern scaling for temperature and precipitation based on a range of AOGCMs in IMAGE. In addition to CO2 concentrations and climate change, also atmospheric concentrations are relevant for some IMAGE modules. Air pollutants are used to calculate the effect on human health ([[Human development]]). The effect of ozone on crop yields is explored in a first study, but is not yet part of the standard model set-up. | ||
Although the use of a fully fledged AOGCM is unworkable for integrated assessment, some IAMs use an earth system model of intermediate complexity for climate modelling. This allows for much more detail and consistency in climate change impacts and feedbacks, but is fixed to one representation of the system, not accounting for the large uncertainties. | Although the use of a fully fledged AOGCM is unworkable for integrated assessment, some IAMs use an earth system model of intermediate complexity for climate modelling. This allows for much more detail and consistency in climate change impacts and feedbacks, but is fixed to one representation of the system, not accounting for the large uncertainties. | ||
Using the simple climate model MAGICC 6.0 also allows research on the consequences of parameter values outside the existing, emulated AOGCMs. For example, the impact of high climate sensitivity values that go beyond the typical range found in AOGCMs (2 to 4.5 °C) can be studied ([[Müller et al., a (unpublished)]]). This extends the range of uncertainty that can be covered by the IMAGE model in its projections of future climate change and the related impacts. | Using the simple climate model MAGICC 6.0 also allows research on the consequences of parameter values outside the existing, emulated AOGCMs. For example, the impact of high climate sensitivity values that go beyond the typical range found in AOGCMs (2 to 4.5 °C) can be studied ([[Müller et al., a (unpublished)]]). This extends the range of uncertainty that can be covered by the IMAGE model in its projections of future climate change and the related impacts. | ||
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Revision as of 17:37, 16 December 2013
Parts of Atmospheric composition and climate/Data uncertainties limitations
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