Semi Empirical Model of Global Warming Including Cosmic Forces, Greenhouse Gases, and Volcanic Eruptions

<p>The Empirical Model of Global Climate (EM-GC) (Canty et al., ACP, 2013, McBride et al., ESDD, 2020) is a multiple linear regression, energy balance model that accounts for the natural influences on global mean surface temperature due to ENSO, the 11-year solar cycle, major volcanic eruptions, as well as the anthropogenic influence of greenhouse gases and aerosols and the efficiency of ocean heat uptake. First, we will analyze the human contribution of global warming from 1975-2014 based on the climate record, also known as the attributable anthropogenic warming rate (AAWR). We will compare the values of AAWR found using the EM-GC with values of AAWR from the CMIP6 multi-model ensemble. Preliminary analysis indicates that over the past three decades, the human component of global warming inferred from the CMIP6 GCMs is larger than the human component of warming from the climate record. Second, we will compare values of equilibrium climate sensitivity inferred from the historical climate record to those determined from CMIP6 GCMs using the Gregory et al., GRL, 2004 method. Third, we will use the future abundances of greenhouse gases and aerosols provided by the Shared Socioeconomic Pathways (SSPs) to project future global mean surface temperature change. We will compare the projections of future temperature anomalies from CMIP6 GCMs to those determined by the EM-GC. We will conclude by assessing the probability of the CMIP6 and EM-GC projections of achieving the Paris Agreement target (1.5&#176;C) and upper limit (2.0&#176;C) for several of the SSP scenarios.</p>

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How Much Human-Caused Global Warming Should We Expect with Business-As-Usual (BAU) Climate Policies? A Semi-Empirical Assessment

Energies ◽

10.3390/en13061365 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1365 ◽

Cited By ~ 4

Author(s):

Ronan Connolly ◽

Michael Connolly ◽

Robert M. Carter ◽

Willie Soon

Keyword(s):

Climate Change ◽

Global Warming ◽

Greenhouse Gases ◽

Climate Sensitivity ◽

Climate Model ◽

Global Climate Model ◽

Future Climate Change ◽

Transient Climate Response ◽

Semi Empirical ◽

Business As Usual

In order to assess the merits of national climate change mitigation policies, it is important to have a reasonable benchmark for how much human-caused global warming would occur over the coming century with “Business-As-Usual” (BAU) conditions. However, currently, policymakers are limited to making assessments by comparing the Global Climate Model (GCM) projections of future climate change under various different “scenarios”, none of which are explicitly defined as BAU. Moreover, all of these estimates are ab initio computer model projections, and policymakers do not currently have equivalent empirically derived estimates for comparison. Therefore, estimates of the total future human-caused global warming from the three main greenhouse gases of concern (CO2, CH4, and N2O) up to 2100 are here derived for BAU conditions. A semi-empirical approach is used that allows direct comparisons between GCM-based estimates and empirically derived estimates. If the climate sensitivity to greenhouse gases implies a Transient Climate Response (TCR) of ≥ 2.5 °C or an Equilibrium Climate Sensitivity (ECS) of ≥ 5.0 °C then the 2015 Paris Agreement’s target of keeping human-caused global warming below 2.0 °C will have been broken by the middle of the century under BAU. However, for a TCR < 1.5 °C or ECS < 2.0 °C, the target would not be broken under BAU until the 22nd century or later. Therefore, the current Intergovernmental Panel on Climate Change (IPCC) “likely” range estimates for TCR of 1.0 to 2.5 °C and ECS of 1.5 to 4.5 °C have not yet established if human-caused global warming is a 21st century problem.

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Global Warming and Its Multiple Causes

Journal of Atmospheric Science Research ◽

10.30564/jasr.v3i2.2013 ◽

2020 ◽

Vol 3 (2) ◽

Author(s):

Romdhane Ben Slama

Keyword(s):

Global Warming ◽

Greenhouse Gases ◽

Greenhouse Effect ◽

Infrared Radiation ◽

Fossil Fuels ◽

Ambient Air ◽

The Earth ◽

The One ◽

Heat Released ◽

Emission Of Greenhouse Gases

The global warming which preoccupies humanity, is still considered to be linked to a single cause which is the emission of greenhouse gases, CO2 in particular. In this article, we try to show that, on the one hand, the greenhouse effect (the radiative imprisonment to use the scientific term) took place in conjunction with the infrared radiation emitted by the earth. The surplus of CO2 due to the combustion of fossil fuels, but also the surplus of infrared emissions from artificialized soils contribute together or each separately, to the imbalance of the natural greenhouse effect and the trend of global warming. In addition, another actor acting directly and instantaneously on the warming of the ambient air is the heat released by fossil fuels estimated at 17415.1010 kWh / year inducing a rise in temperature of 0.122 ° C, or 12.2 ° C / century.

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A Semi-Empirical Model for Predicting Frost Properties

Processes ◽

10.3390/pr9030412 ◽

2021 ◽

Vol 9 (3) ◽

pp. 412

Author(s):

Shao-Ming Li ◽

Kai-Shing Yang ◽

Chi-Chuan Wang

Keyword(s):

Thermal Conductivity ◽

Linear Programming ◽

Numerical Model ◽

Empirical Model ◽

Quantitative Method ◽

Predictive Ability ◽

Dimensionless Temperature ◽

Crystal Shape ◽

Proposed Model ◽

Semi Empirical

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal conductivities, but also gives good predictions for density and is especially accurate when the frost density is lower than 125 kg/m3. Yet, the predictive ability for frost density is improved by 24% when compared to the most accurate correlation available.

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