What do historical temperature records tell us about natural variability in global temperature?

2016 ◽  
Author(s):  
Patrick T. Brown
Keyword(s):  
Global Warming ◽  
20Th Century ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Temperature Variability ◽  
Greenhouse Gasses ◽  
Natural Temperature ◽  
Temperature Records ◽  
Historical Temperature ◽  
Reduced Rate

Global average surface air temperature can change when it is either ‘forced’ to change by factors such as increasing greenhouse gasses, or it can change on its own through ‘unforced’ natural cycles like El-Niño/La-Niña. In this paper we estimated the magnitude of unforced temperature variability using historical datasets rather than the more commonly used computer climate models. We used data recorded by thermometers back to the year 1880 as well as data from “nature’s thermometers” – things like tree rings, corals, and lake sediments – that give us clues of how temperature varied naturally from the year 1000 to 1850. We found that unforced natural temperature variability is large enough to have been responsible for the decade-to-decade changes in the rate of global warming seen over the 20th century. However, the total warming over the 20th century cannot be explained by unforced variability alone and it would not have been possible without the human-caused increase in greenhouse gasses. We also found that unforced temperature variability may be the driver behind the reduced rate of global warming experienced at the beginning of the 21st century.

Application of linear dynamical mode decomposition to surface air temperature in 20th century

2020 ◽  
Author(s):  
Andrey Gavrilov ◽  
Sergey Kravtsov ◽  
Dmitry Mukhin ◽  
Evgeny Loskutov ◽  
Alexander Feigin
Keyword(s):  
20Th Century ◽  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
State Of The Art ◽  
Surface Air Temperature ◽  
Climate Dynamics ◽  
Bayesian Optimization ◽  
Climate Signal ◽  
Function Decomposition

<p>According to recent study [1], the current state-of-the-art climate models lack the substantial part of internal multidecadal climate signal which is observed in the 20th century surface air temperature reanalysis data as a global stadium wave (GSW). In the presented work we further investigate this phenomenon using the recently developed method [2] of empirical spatio-temporal data decomposition into linear dynamical modes (LDMs). The important property of LDMs is their ability to take into account the time scales of the system evolution (they are extracted from observed dataset by the Bayesian optimization technique) better than some other linear techniques, e.g. traditional empirical orthogonal function decomposition. Like any linear decomposition, it provides the time series of principal components and corresponding spatial patterns.<br>We modify the initially developed LDM decomposition to make it possible to take into account a prescribed external forcing (like CO2 emissions, sun activity etc.) and then find part of variability which may be considered as an internal climate dynamics decomposed into set of modes with different time scales, and hence may be helpful in GSW interpretation. The results of applying the method to the 20th century surface air temperature with different ways of forcing inclusion will be presented and discussed.</p><p>1. Kravtsov, S., Grimm, C., & Gu, S. (2018). Global-scale multidecadal variability missing in state-of-the-art climate models. Npj Climate and Atmospheric Science, 1(1), 34. https://doi.org/10.1038/s41612-018-0044-6<br>2. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2018). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics. https://doi.org/10.1007/s00382-018-4255-7</p>

scholarly journals Application of random walk to model temperature in 60 African cities for the 20th century

2021 ◽  
Vol 290 ◽  
pp. 02003
Author(s):  
Shaomin Yan ◽  
Guang Wu
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Random Walk ◽  
Temperature Change ◽  
20Th Century ◽  
Climate Models ◽  
Random Numbers ◽  
African Cities ◽  
Model Temperature ◽  
Recorded Temperature

The 20th century is marked with climate change led by global warming. So far, many models have been applied to analyze the temperature change. However, a simple but interesting model, a random walk is hardly used in his regard. In this study, we use the random walk to model the temperature in the format of random walk that is the conversion of recorded temperature and the real recorded temperature in 60 African cities including 53 capitals for the 20th century. The results show that the random walk can satisfyingly model either temperature in the format of random walk or real recorded temperature although the fitted results from other climate models are unavailable for comparison in these 60 cities. As nothing else besides random numbers is involved in this modelling, the results seem somewhat counter-intuitively.

scholarly journals An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models

2013 ◽  
Vol 7 (1) ◽  
pp. 67-80 ◽  
Author(s):  
C. Brutel-Vuilmet ◽  
M. Ménégoz ◽  
G. Krinner
Keyword(s):  
Global Warming ◽  
Snow Cover ◽  
Northern Hemisphere ◽  
Air Temperature ◽  
Large Scale ◽  
Climate Models ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Climate Forcing ◽  
Snow Cover Extent

Abstract. The 20th century seasonal Northern Hemisphere (NH) land snow cover as simulated by available CMIP5 model output is compared to observations. On average, the models reproduce the observed snow cover extent very well, but the significant trend towards a reduced spring snow cover extent over the 1979–2005 period is underestimated (observed: (−3.4 ± 1.1)% per decade; simulated: (−1.0 ± 0.3)% per decade). We show that this is linked to the simulated Northern Hemisphere extratropical spring land warming trend over the same period, which is also underestimated, although the models, on average, correctly capture the observed global warming trend. There is a good linear correlation between the extent of hemispheric seasonal spring snow cover and boreal large-scale spring surface air temperature in the models, supported by available observations. This relationship also persists in the future and is independent of the particular anthropogenic climate forcing scenario. Similarly, the simulated linear relationship between the hemispheric seasonal spring snow cover extent and global mean annual mean surface air temperature is stable in time. However, the slope of this relationship is underestimated at present (observed: (−11.8 ± 2.7)% °C−1; simulated: (−5.1 ± 3.0)% °C−1) because the trend towards lower snow cover extent is underestimated, while the recent global warming trend is correctly represented.

scholarly journals Rapid attribution analysis of the extraordinary heatwave on the Pacific Coast of the US and Canada June 2021

2021 ◽  
Author(s):  
Sjoukje Y. Philip ◽  
Sarah F. Kew ◽  
Geert Jan van Oldenborgh ◽  
Faron S. Anslow ◽  
Sonia I. Seneviratne ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Pacific Northwest ◽  
Industrial Revolution ◽  
Climate Models ◽  
Well Being ◽  
Daily Maximum ◽  
Attribution Analysis ◽  
The Pacific ◽  
Temperature Records

Abstract. Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific northwest areas of the U.S. and Canada, leading to spikes in sudden deaths, and sharp increases in hospital visits for heat-related illnesses and emergency calls. Here we present a multi-model, multi-method attribution analysis to investigate to what extent human-induced climate change has influenced the probability and intensity of extreme heatwaves in this region. Based on observations and modeling, the occurrence of a heatwave with maximum daily temperatures (TXx) as observed in the area 45° N–52° N, 119° W–123° W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lie far outside the range of historically observed temperatures. This makes it hard to quantify with confidence how rare the event was. In the most realistic statistical analysis, which uses the assumption that the heatwave was a very low probability event that was not caused by new nonlinearities, the event is estimated to be about a 1 in 1000 year event in today’s climate. With this assumption and combining the results from the analysis of climate models and weather observations, an event, defined as daily maximum temperatures (TXx) in the heatwave region, as rare as 1 in a 1000 years would have been at least 150 times rarer without human-induced climate change. Also, this heatwave was about 2 °C hotter than a 1 in 1000-year heatwave that at the beginning of the industrial revolution would have been (when global mean temperatures were 1.2 °C cooler than today). Looking into the future, in a world with 2 °C of global warming (0.8 °C warmer than today), a 1000-year event would be another degree hotter. It would occur roughly every 5 to 10 years in such global warming conditions. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being, and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.

Global warming influences dynamics of plague’s epidemic manifestations in XX-XXI centuries

2017 ◽  
pp. 38-40
Author(s):  
E.S. Zenkevich ◽  
N.V. Popov
Keyword(s):  
Global Warming ◽  
20Th Century ◽  
Modern Trend ◽  
Climate Fluctuations ◽  
The World ◽  
Low Incidence ◽  
High Level

During the second half of 20th century, a high level of plague incidence in the world was in 1960–1979 and 1990–2009. The significant decrease of infection cases was in 1950–1959, 1980–1989, 2010–2015. It is noticed, that the observed cyclical nature of the alternation of high and low incidence plague’s periods, in many respects related to modern trend of climate fluctuations.

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