scholarly journals Detecting breakpoints in global temperature

2019 ◽  
Author(s):  
Junbo Duan ◽  
Lanling Zhao ◽  
Qing Wang ◽  
Pei Li
Keyword(s):  
Surface Temperature ◽  
20Th Century ◽  
Performance Studies ◽  
Temperature Data ◽  
Global Temperature ◽  
Piecewise Polynomials ◽  
Global Mean Surface Temperature ◽  
Temperature Records ◽  
Global Mean ◽  
Surface Temperature Data

Abstract. This paper aims to address the continuous debate of whether a hiatus occurred in the turn of this century. Several models have been employed to fit the global mean surface temperature data, and the results suggest that the allegation of an occurrence of hiatus lacks statistical evidence. However, these models had potential deficiencies in their capacity for detecting breakpoints, thereby weakening the arguments that deny the existence of a hiatus. To address this issue, we propose an improved sparse representation model, which can automatically segment and fit temperature records using piecewise polynomials. Simulations revealed improved detection performance; studies on five prominent global temperature records produced 2 to 6 breakpoints, none of which occurred after the year 1976, thus reinforcing arguments denying the existence of a 20th century hiatus.

scholarly journals Estimating the Impact of Artificially Injected Stratospheric Aerosols on the Global Mean Surface Temperature in the 21th Century

Climate ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 85 ◽  
Author(s):  
Sergei Soldatenko
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Global Temperature ◽  
Balance Model ◽  
Solar Radiation Management ◽  
Aerosol Layer ◽  
Aerosol Emission ◽  
Global Mean Surface Temperature ◽  
The Impact ◽  
Global Mean

In this paper, we apply the optimal control theory to obtain the analytic solutions of the two-component globally averaged energy balance model in order to estimate the influence of solar radiation management (SRM) operations on the global mean surface temperature in the 21st century. It is assumed that SRM is executed via injection of sulfur aerosols into the stratosphere to limit the global temperature increase in the year 2100 by 1.5 °C and keeping global temperature over the specified period (2020–2100) within 2 °C as required by the Paris climate agreement. The radiative forcing produced by the rise in the atmospheric concentrations of greenhouse gases is defined by the Representative Concentration Pathways and the 1pctCO2 (1% per year CO2 increase) scenario. The goal of SRM is formulated in terms of extremal problem, which entails finding a control function (the albedo of aerosol layer) that minimizes the amount of aerosols injected into the upper atmosphere to satisfy the Paris climate target. For each climate change scenario, the optimal albedo of the aerosol layer and the corresponding global mean surface temperature changes were obtained. In addition, the aerosol emission rates required to create an aerosol cloud with optimal optical properties were calculated.

High-output diesel engine heat transfer: Part 1 - comparison between piston heat flux and global energy balance

2021 ◽  
pp. 146808742110170
Author(s):  
Eric Gingrich ◽  
Michael Tess ◽  
Vamshi Korivi ◽  
Jaal Ghandhi
Keyword(s):  
Heat Transfer ◽  
Heat Flux ◽  
Diesel Engine ◽  
Surface Temperature ◽  
Fast Response ◽  
Temperature Data ◽  
Start Of Injection ◽  
Local Measurements ◽  
Surface Temperature Data ◽  
High Output

High-output diesel engine heat transfer measurements are presented in this paper, which is the first of a two-part series of papers. Local piston heat transfer, based on fast-response piston surface temperature data, is compared to global engine heat transfer based on thermodynamic data. A single-cylinder research engine was operated at multiple conditions, including very high-output cases – 30 bar IMEPg and 250 bar in-cylinder pressure. A wireless telemetry system was used to acquire fast-response piston surface temperature data, from which heat flux was calculated. An interpolation and averaging procedure was developed and a method to recover the steady-state portion of the heat flux based on the in-cylinder thermodynamic state was applied. The local measurements were spatially integrated to find total heat transfer, which was found to agree well with the global thermodynamic measurements. A delayed onset of the rise of spatially averaged heat flux was observed for later start of injection timings. The dataset is internally consistent, for example, the local measurements match the global values, which makes it well suited for heat transfer correlation development; this development is pursued in the second part of this paper.

scholarly journals Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature

2021 ◽  
Author(s):  
Philip G. Sansom ◽  
Donald Cummins ◽  
Stefan Siegert ◽  
David B Stephenson
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Surface Temperature ◽  
Climate Model ◽  
Future Projections ◽  
Perfect Model ◽  
Global Mean Surface Temperature ◽  
Time Series Approach ◽  
Energy Balance Models ◽  
Global Mean

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.

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