گرمایش جهانی و فرارسیدن آن
Abstract
1- Introduction
2- Data and analysis
3- Discussion and results
4- Conclusions
References
Abstract
The enhancement of the atmospheric greenhouse effect due to the increase in the atmospheric greenhouse gases is often considered as responsible for global warming (known as greenhouse hypothesis of global warming). In this context, the temperature field of global troposphere and lower stratosphere over the period 12/1978–07/2018 is explored using the recent Version 6 of the UAH MSU/AMSU global satellite temperature dataset. Our analysis did not show a consistent warming with gradual increase from low to high latitudes in both hemispheres, as it should be from the global warming theory. In addition, in the lower stratosphere the temperature cooling over both poles is lower than that over tropics and extratropics. To study further the thermal field variability we investigated the long-range correlations throughout the global lower troposphere-lower stratosphere region. The results show that the temperature field displays power-law behaviour that becomes stronger by going from the lower troposphere to the tropopause.This power-law behaviour suggests that the fluctuations in global tropospheric temperature at short intervals are positively correlated with those at longer intervals in a power-law manner. The latter, however, does not apply to global temperature in the lower stratosphere. This suggests that the investigated intrinsic properties of the lower stratospheric temperature are not related to those of the troposphere, as is expected by the global warming theory.
Introduction
Over the last decades, the rise in surface air temperature in regions of our planet has led to a debate in the scientific community about the causes and impacts of this temperature rise, especially if it comes from anthropogenic activities or is of natural origin. We must bear in mind that by definition the climate system is part of the wider global system. In particular, it is composed of five subsystems the atmosphere, the cryosphere, the hydrosphere, the biosphere and the lithosphere, which interact with each other with mostly non-linear processes in space and time (e.g., IPCC, 2014; Lovejoy and Varotsos, 2016). Therefore, a change in a parameter of a climatic subsystem (e.g., atmospheric temperature) does not predict a climate change, as all other parameters of the atmosphere but also of other subsystems (known and measurable or not) are not necessarily known and stable. Also, by definition, the climate is a complicated (displaying many degrees of freedom) and a complex (non-linear, dynamical, sensitive) system (e.g. Lucarini, 2011). Therefore, it is a truism that climate has always been changing, and it will always be changing. Which subsystem dominates the climate change depends, for instance, on the time window, namely: For t < 10yrs the atmospheric degrees of freedom are active and the other sub-systems are frozen. For 100 < t < 1000yrs the ocean dominates, and for t > 5000yrs cryosphere dominates. Several analyses have been made on the key issues of scientific understanding of contemporary global climate change (e.g. Christy et al., 2007). The focus of most of these analyses is to discuss the uncertainties associated with existing observation data and the results of numerical modelling. These emphasize the need to analyze the ability of current models to simulate real climate change. As mentioned above, real climate change results from the non-linear interactions between numerous components of the climatic system. In these should also be taken into consideration and possible contributions by external forcings e.g., cosmic factors, such as solar activity. Despite the projected exponential growth in computer power, these processes will not be adequately resolved in numerical climate models in the near future (Franzke et al., 2015). Stochastic methods for numerical climate prediction may allow for an adequate representation of uncertainties, the reduction of systematic biases and improved representation of longterm climate variability (e.g., Droegemeier, 2009).