A model-data comparison for a multi-model ensemble of early Eocene atmosphere-ocean    simulations: EoMIP

Abstract. The early Eocene (~55 to 50 Ma) is a time period which has been explored in a large number of modelling and data studies. Here, using an ensemble of previously published model results, making up "EoMIP" – the Eocene Modelling Intercomparison Project, and syntheses of early Eocene terrestrial and SST temperature data, we present a self-consistent inter-model and model-data comparison. This shows that the previous modelling studies exhibit a very wide inter-model variability, but that at high CO2, there is good agreement between models and data for this period, particularly if possible seasonal biases in some of the proxies are considered. An energy balance analysis explores the reasons for the differences between the model results, and suggests that differences in surface albedo feedbacks, water vapour and lapse rate feedbacks, and prescribed aerosol loading are the dominant cause for the different results seen in the models, rather than inconsistencies in other prescribed boundary conditions or differences in cloud feedbacks. The CO2 level which would give optimal early Eocene model-data agreement, based on those models which have carried out simulations with more than one CO2 level, is in the range 2000 ppmv to 6500 ppmv. Given the spread of model results, tighter bounds on proxy estimates of atmospheric CO2 during this time period will allow a quantitative assessment of the skill of the models at simulating warm climates, which could be used as a metric for weighting future climate predictions.

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A model–data comparison for a multi-model ensemble of early Eocene atmosphere–ocean simulations: EoMIP

Climate of the Past ◽

10.5194/cp-8-1717-2012 ◽

2012 ◽

Vol 8 (5) ◽

pp. 1717-1736 ◽

Cited By ~ 142

Author(s):

D. J. Lunt ◽

T. Dunkley Jones ◽

M. Heinemann ◽

M. Huber ◽

A. LeGrande ◽

...

Keyword(s):

Lapse Rate ◽

Early Eocene ◽

Model Data ◽

Data Comparison ◽

Time Period ◽

Modelling Studies ◽

Balance Analysis ◽

Co2 Level ◽

Energy Balance Analysis ◽

Model Variability

Abstract. The early Eocene (~55 to 50 Ma) is a time period which has been explored in a large number of modelling and data studies. Here, using an ensemble of previously published model results, making up "EoMIP" – the Eocene Modelling Intercomparison Project – and syntheses of early Eocene terrestrial and sea surface temperature data, we present a self-consistent inter-model and model–data comparison. This shows that the previous modelling studies exhibit a very wide inter-model variability, but that at high CO2, there is good agreement between models and data for this period, particularly if possible seasonal biases in some of the proxies are considered. An energy balance analysis explores the reasons for the differences between the model results, and suggests that differences in surface albedo feedbacks, water vapour and lapse rate feedbacks, and prescribed aerosol loading are the dominant cause for the different results seen in the models, rather than inconsistencies in other prescribed boundary conditions or differences in cloud feedbacks. The CO2 level which would give optimal early Eocene model–data agreement, based on those models which have carried out simulations with more than one CO2 level, is in the range of 2500 ppmv to 6500 ppmv. Given the spread of model results, tighter bounds on proxy estimates of atmospheric CO2 and temperature during this time period will allow a quantitative assessment of the skill of the models at simulating warm climates. If it is the case that a model which gives a good simulation of the Eocene will also give a good simulation of the future, then such an assessment could be used to produce metrics for weighting future climate predictions.

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Investigating vegetation–climate feedbacks during the early Eocene

Climate of the Past ◽

10.5194/cp-10-419-2014 ◽

2014 ◽

Vol 10 (2) ◽

pp. 419-436 ◽

Cited By ~ 26

Author(s):

C. A. Loptson ◽

D. J. Lunt ◽

J. E. Francis

Keyword(s):

Water Vapour ◽

Global Scale ◽

The Arctic ◽

Early Eocene ◽

High Latitudes ◽

Climate Feedbacks ◽

Modelling Framework ◽

Modelling Studies ◽

Balance Analysis ◽

Energy Balance Analysis

Abstract. Evidence suggests that the early Eocene was a time of extreme global warmth. However, there are discrepancies between the results of many previous modelling studies and the proxy data at high latitudes, with models struggling to simulate the shallow temperature gradients of this time period to the same extent as the proxies indicate. Vegetation–climate feedbacks play an important role in the present day, but are often neglected in these palaeoclimate modelling studies, and this may be a contributing factor to resolving the model–data discrepancy. Here we investigate these vegetation–climate feedbacks by carrying out simulations of the early Eocene climate at 2 × and 4 × pre-industrial atmospheric CO2 with fixed vegetation (homogeneous shrubs everywhere) and dynamic vegetation. The results show that the simulations with dynamic vegetation are warmer in the global annual mean than the simulations with fixed shrubs by 0.9 °C at 2 × and 1.8 °C at 4 ×. Consequently, the warming when CO2 is doubled from 2 × to 4 × is 1 °C higher (in the global annual mean) with dynamic vegetation than with fixed shrubs. This corresponds to an increase in climate sensitivity of 26%. This difference in warming is enhanced at high latitudes, with temperatures increasing by over 50% in some regions of Antarctica. In the Arctic, ice–albedo feedbacks are responsible for the majority of this warming. On a global scale, energy balance analysis shows that the enhanced warming with dynamic vegetation is mainly associated with an increase in atmospheric water vapour but changes in clouds also contribute to the temperature increase. It is likely that changes in surface albedo due to changes in vegetation cover resulted in an initial warming which triggered these water vapour feedbacks. In conclusion, dynamic vegetation goes some way to resolving the discrepancy, but our modelled temperatures cannot reach the same warmth as the data suggest in the Arctic. This suggests that there are additional mechanisms, not included in this modelling framework, behind the polar warmth or that the proxies have been misinterpreted.

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Investigating vegetation-climate feedbacks during the early Eocene

Climate of the Past Discussions ◽

10.5194/cpd-9-4705-2013 ◽

2013 ◽

Vol 9 (4) ◽

pp. 4705-4744

Author(s):

C. A. Loptson ◽

D. J. Lunt ◽

J. E. Francis

Keyword(s):

Water Vapour ◽

Global Scale ◽

The Arctic ◽

Early Eocene ◽

High Latitudes ◽

Climate Feedbacks ◽

Modelling Framework ◽

Modelling Studies ◽

Balance Analysis ◽

Energy Balance Analysis

Abstract. Evidence suggests that the early Eocene was a time of extreme global warmth, extending to the high latitudes. However, there are discrepancies between the results of many previous modelling studies and the proxy data at high latitudes, with models struggling to simulate the shallow temperature gradients of this time period to the same extent as the proxies indicate. Vegetation-climate feedbacks play an important role in the present day, but are often neglected in paleoclimate modelling studies and this may be a contributing factor to resolving the model-data discrepancy. Here we investigate these vegetation-climate feedbacks by carrying out simulations of the early Eocene climate at 2 × and 4 × pre-industrial atmospheric CO2 with fixed vegetation (homogeneous shrubs everywhere) and dynamic vegetation. The results show that the simulations with dynamic vegetation are warmer in the global annual mean than the simulations with fixed shrubs by 0.9 °C at 2 × and 1.8 °C at 4 ×. In addition, the warming when CO2 is doubled from 2 × to 4 × is 1 °C higher (in the global annual mean) with dynamic vegetation than with fixed shrubs. This corresponds to an increase in climate sensitivity of 26%. This difference in warming is enhanced at high latitudes, with temperatures increasing by over 50% in some regions of Antarctica. In the Arctic, ice-albedo feedbacks are responsible for the majority of this warming. On a global scale, energy balance analysis shows that the enhanced warming with dynamic vegetation is mainly associated with an increase in atmospheric water vapour but changes in clouds also contribute to the temperature increase. It is likely that changes in surface albedo due to changes in vegetation cover resulted in an initial warming which triggered these water vapour feedbacks. In conclusion, dynamic vegetation goes some way to resolving the discrepancy, but our modelled temperatures cannot reach the same warmth as the data suggests in the Arctic. This suggests that there are additional mechanisms, not included in this modelling framework, behind the polar warmth.

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Comprehensive energy balance analysis of photon-enhanced thermionic power generation considering concentrated solar absorption distribution

Solar Energy Materials and Solar Cells ◽

10.1016/j.solmat.2021.111067 ◽

2021 ◽

Vol 226 ◽

pp. 111067

Author(s):

A.N.M. Taufiq Elahi ◽

Devon Jensen ◽

Mohammad Ghashami ◽

Keunhan Park

Keyword(s):

Power Generation ◽

Energy Balance ◽

Solar Absorption ◽

Balance Analysis ◽

Energy Balance Analysis

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Mass and Energy Balance Analysis of Methanol Production Using Atmospheric CO2 Capture with Energy Source from PCMSR

E3S Web of Conferences ◽

10.1051/e3sconf/20184201004 ◽

2018 ◽

Vol 42 ◽

pp. 01004

Author(s):

Andang W. Harto ◽

Mella Soelanda

Keyword(s):

Energy Source ◽

Global Warming ◽

Co2 Capture ◽

Co2 Sequestration ◽

Co2 Concentration ◽

Atmospheric Co2 ◽

Methanol Production ◽

Sufficient Energy ◽

Balance Analysis ◽

Energy Balance Analysis

The rising of atmospheric CO2 concentration is the major source to global warming system. Many methods have been proposed to mitigate global warming, such as carbon penalty, carbon trading, CO2 sequestration, etc. However these proposed methods are usually uneconomical, i.e., these methods do not produce economic valuable substances. This paper will propose a method to absorb atmospheric CO2 to produce economic valuable substances such as methanol, dimethyl ether, ethylene, several hydrocarbon substances and derivatives and several graphite substances. This paper is focused on methanol production using atmospheric CO2 capture. The overall process is endothermic. Thus a sufficient energy source is needed. To avoid more CO2 emission, the energy source must not use conventional fuels. To assure the continuity of energy deliberation, nuclear energy will be used as the energy source of the process. In this paper, the Passive Compact Molten Salt Reactor (PCMSR) will be used as the energy source. The 460 MWth PCMSR is coupled with atmospheric CO2 capture, desalination, hydrogen production and methanol production facilities. The capturing CO2 capacity is 7.2 ton/h of atmospheric CO2. The valuable outputs of this system are 3.34 ton/h of H2, 34.56 ton/h of O2, 5.24 ton/h of methanol and 86.74 MWe of excess electricity.

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The Last Glacial Maximum and Heinrich Event 1 in terms of climate and vegetation around the Alboran Sea: a preliminary model-data comparison

Comptes Rendus Geoscience ◽

10.1016/j.crte.2005.04.012 ◽

2005 ◽

Vol 337 (10-11) ◽

pp. 983-992 ◽

Cited By ~ 46

Author(s):

Masa Kageyama ◽

Nathalie Combourieu Nebout ◽

Pierre Sepulchre ◽

Odile Peyron ◽

Gerhard Krinner ◽

...

Keyword(s):

Last Glacial Maximum ◽

Glacial Maximum ◽

Model Data ◽

Last Glacial ◽

Data Comparison ◽

Heinrich Event ◽

Preliminary Model ◽

The Last Glacial Maximum ◽

Heinrich Event 1 ◽

The Last Glacial

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Energy balance analysis of combined photovoltaic–diesel powered telecommunication stations

International Journal of Electrical Power & Energy Systems ◽

10.1016/j.ijepes.2011.08.016 ◽

2011 ◽

Cited By ~ 3

Author(s):

J.K. Kaldellis ◽

Ioanna Ninou

Keyword(s):

Energy Balance ◽

Balance Analysis ◽

Energy Balance Analysis

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An alternative way to evaluate chemistry-transport model variability

Geoscientific Model Development ◽

10.5194/gmd-10-1199-2017 ◽

2017 ◽

Vol 10 (3) ◽

pp. 1199-1208 ◽

Cited By ~ 1

Author(s):

Laurent Menut ◽

Sylvain Mailler ◽

Bertrand Bessagnet ◽

Guillaume Siour ◽

Augustin Colette ◽

...

Keyword(s):

Transport Model ◽

Model Performance ◽

Error Statistics ◽

Chemistry Transport Model ◽

Temporal Correlations ◽

Time Period ◽

The Difference ◽

Model Variability ◽

Spatial Locations ◽

Statistical Indicators

Abstract. A simple and complementary model evaluation technique for regional chemistry transport is discussed. The methodology is based on the concept that we can learn about model performance by comparing the simulation results with observational data available for time periods other than the period originally targeted. First, the statistical indicators selected in this study (spatial and temporal correlations) are computed for a given time period, using colocated observation and simulation data in time and space. Second, the same indicators are used to calculate scores for several other years while conserving the spatial locations and Julian days of the year. The difference between the results provides useful insights on the model capability to reproduce the observed day-to-day and spatial variability. In order to synthesize the large amount of results, a new indicator is proposed, designed to compare several error statistics between all the years of validation and to quantify whether the period and area being studied were well captured by the model for the correct reasons.

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