Reducing Model Uncertainty in Physical Parameterizations: Combinational Optimizations Using Genetic Algorithm
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Longwave
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Albedo (alchemy)
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The ARM goal is to help improve both longwave and shortwave models by providing improved radiometric shortwave data. These data can be used directly to test shortwave model predictions. As will be described below they can also provide inferred values for aerosol and cloud properties that are useful for longwave modeling efforts as well. The current ARM research program includes three tasks all related to the study of shortwave radiation transfer through clouds and aerosol. Two of the tasks involve the assembly of archived and new radiation and meteorological data sets; the third and dominant task has been the development and use of new shortwave radiometric sensors. Archived data from Golden, Colorado, and Albany, New York, were combined with National Weather Service ground and upper air data for testing radiation models for the era when the Earth Radiation Budget Experiment (ERBE) was operational. These data do not include optimum surface radiation measurements; consequently we are acquiring downwelling shortwave, including direct and diffuse irradiance, plus downwelling longwave, upwelling shortwave, and aerosol optical depth, at our own institution, as an additional dataset for ARM modelers.
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This research work is focused on the construction of an accurate longwave/shortwave radiation model on a tunnel greenhouse pseudo-bond graph model, widely used in Tunisia. This model includes sun position, useful incoming solar radiation model, sky longwave radiation model, inside longwave and shortwave radiation model. The key idea is to use bond graphs allowing a lumped modelling approach which is suitable for control applications. Furthermore, an evaluation of some longwave radiative model components was made, noting that these components are particularly sensitive regarding to the thermal behaviour of the model.Experimental tunnel greenhouse data are used as validation elements for the present model with globally good results. A comparative study was also performed between the present model and a previous bond graph model containing a simplistic radiative model. Practical simulation results show a clear improvement compared with the previous model.
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Dry Creek Experimental Watershed (DCEW) provides long-term environmental time series data to support hydrometerologic, ecosystem and climate change research. Numerous theses and peer-reviewed publications have utilized these data. This data set provides continuous hourly net radiation (incoming shortwave and longwave minus outgoing shortwave and longwave) data for five measurement sites in DCEW.
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Abstract. This study presents a new methodology, called temperature tagging. It keeps track of the contributions of individual processes to temperature within a climate model simulation. As a first step and as a test bed a simple box climate model is regarded. The model consists of an atmosphere, which absorbs and emits radiation and of a surface, which reflects, absorbs and emits radiation. The tagging methodology is used to investigate the impact of the atmosphere on surface temperature. Four processes are investigated in more detail and their contribution to the surface temperature quantified: (i) shortwave influx and shortwave atmospheric absorption ("sw"), (ii) longwave atmospheric absorption due to non-CO2 greenhouse gases ("nC"), (iii) due to a base case CO2 concentration ("bC"), and (iv) due to an enhanced CO2 concentration ("eC"). The differential equation for the temperature in the box climate model is decomposed into four equations for the tagged temperatures. This method is applied to investigate the contribution of longwave absorption to the surface temperature (greenhouse effect), which is calculated to be 68 K. This estimate contrasts an alternative calculation of the greenhouse effect of slightly more than 30 K based on the difference of the surface temperature with and without an atmosphere. The difference of the two estimates is due to a shortwave cooling effect and a reduced contribution of the shortwave to the total downward flux: The shortwave absorption of the atmosphere results in a reduced net shortwave flux at the surface of 192 W m−2, leading to a cooling of the surface by 14 K. Introducing an atmosphere results in a downward longwave flux at the surface due to atmospheric absorption of 189 W m−2, which roughly equals the net shortwave flux of 192 W m−2. This longwave flux is a result of both, the radiation due to atmospheric temperatures and its longwave absorption. Hence the longwave absorption roughly accounts for 91 W m−2 out of a total of 381 W m−2 (roughly 25%) and therefore accounts for a temperature of 68 K. In a second experiment, the CO2 concentration is doubled, which leads to an increase in surface temperature of 1.2 K, resulting from an temperature increase due to CO2 of 1.9 K, due to non-CO2 greenhouse gases of 0.6 K and a cooling of 1.3 K due to a reduced importance of the solar heating for the surface and atmospheric temperatures. These two experiments show the feasibility of temperature tagging and its potential as a diagnostic for climate simulations.
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Abstract. This study presents a new methodology, called temperature tagging. It keeps track of the contributions of individual processes to temperature within a climate model simulation. As a first step and as a test bed, a simple box climate model is regarded. The model consists of an atmosphere, which absorbs and emits radiation, and of a surface, which reflects, absorbs and emits radiation. The tagging methodology is used to investigate the impact of the atmosphere on surface temperature. Four processes are investigated in more detail and their contribution to the surface temperature quantified: (i) shortwave influx and shortwave atmospheric absorption ("sw"), (ii) longwave atmospheric absorption due to non-CO2 greenhouse gases ("nC"), (iii) due to a base case CO2 concentration ("bC"), and (iv) due to an enhanced CO2 concentration ("eC"). The differential equation for the temperature in the box climate model is decomposed into four equations for the tagged temperatures. This method is applied to investigate the contribution of longwave absorption to the surface temperature (greenhouse effect), which is calculated to be 68 K. This estimate contrasts an alternative calculation of the greenhouse effect of slightly more than 30 K based on the difference of the surface temperature with and without an atmosphere. The difference of the two estimates is due to a shortwave cooling effect and a reduced contribution of the shortwave to the total downward flux: the shortwave absorption of the atmosphere results in a reduced net shortwave flux at the surface of 192 W m−2, leading to a cooling of the surface by 14 K. Introducing an atmosphere results in a downward longwave flux at the surface due to atmospheric absorption of 189 W m−2, which roughly equals the net shortwave flux of 192 W m−2. This longwave flux is a result of both the radiation due to atmospheric temperatures and its longwave absorption. Hence the longwave absorption roughly accounts for 91 W m−2 out of a total of 381 W m−2 (roughly 25%) and therefore accounts for a temperature change of 68 K. In a second experiment, the CO2 concentration is doubled, which leads to an increase in surface temperature of 1.2 K, resulting from a temperature increase due to CO2 of 1.9 K, due to non-CO2 greenhouse gases of 0.6 K and a cooling of 1.3 K due to a reduced importance of the solar heating for the surface and atmospheric temperatures. These two experiments show the feasibility of temperature tagging and its potential as a diagnostic for climate simulations.
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There is direct evidence that excess net radiation calculated in general circulation models at continental surfaces [of about 11–17 W m−2 (20%–27%) on an annual basis is not only due to overestimates in annual incoming shortwave fluxes [of 9–18 W m−2 (6%–9%)], but also to underestimates in outgoing longwave fluxes. The bias in the outgoing longwave flux is deduced from a comparison of screen-air temperature observations, available as a global climatology of mean monthly values, and model-calculated surface and screen-air temperatures. An underestimate in the screen temperature computed in general circulation models over continents, of about 3 K on an annual basis, implies an underestimate in the outgoing longwave flux, averaged in six models under study, of 11–15 W m−2 (3%–4%). For a set of 22 inland stations studied previously, the residual bias on an annual basis (the residual is the net radiation minus incoming shortwave plus outgoing longwave) varies between 18 and −23 W m−2 for the models considered. Additional biases in one or both of the reflected shortwave and incoming longwave components cannot be ruled out.
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Shortwave radiation
Outgoing longwave radiation
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Shortwave
Shortwave radiation
Downwelling
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ORNL DAAC: This data set contains images of shortwave and longwave radiation at the surface and top of the atmosphere derived from collected GOES-7 data. The data cover the time period of 05-Feb-1994 to 20-Sep-1994. The images missing from the temporal series were zero-filled to create a consistent sequence of files.
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