scholarly journals Detecting anthropogenic effects in the observational evidence of climate change

2008 ◽  
Vol 87 (3) ◽  
pp. 217-217 ◽  
Author(s):  
H. von Storch
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Natural Variability ◽  
Ne Atlantic ◽  
Dynamical Models ◽  
Detection And Attribution ◽  
Attribution Analysis ◽  
Rational Debate ◽  
Recent Trends ◽  
Historical Range

AbstractThe issue of detecting changes beyond the range of natural variability (detection) and of attributing causes to such changes (attribution) are central to any rational debate about anthropogenic climate change. This concept, introduced by Klaus Hasselmann in the late 1970s, is usually not understood by either so-called sceptics or by activist scientists.Often rigorous detection and attribution analysis is replaced by mere declarations and by naïve applications of methods to determine if certain trends are ‘significant’ or not.In this talk, the concepts are introduced; the invoked assumptions and the roles of dynamical models and of time-scales are explained. The concept is illustrated with a few examples related to global and regional air temperature and to NE Atlantic storminess.For global and regional air temperature, the recent variations are found to be beyond the range of natural variability (detection) and the changes are best explained by a dominant contribution by elevated greenhouse gas concentrations. For NE Atlantic storminess recent trends are found to be intermittent and within the historical range of variations. The finding of significant GHG contributions to temperature change and the lack of such for changes in storminess are not contradictions.

scholarly journals Recent trends of snow avalanche regime in the Central Caucasus (Elbrus region as an example)

2019 ◽  
Vol 59 (2) ◽  
pp. 191-200
Author(s):  
A. D. Oleinikov ◽  
N. A. Volodicheva
Keyword(s):  
Climate Change ◽  
High Altitude ◽  
Air Temperature ◽  
Seasonal Variability ◽  
Frequency Of Occurrence ◽  
Greater Caucasus ◽  
Spring Period ◽  
Central Caucasus ◽  
Recent Trends ◽  
Destructive Potential

The climate change during cold seasons of 1995–2017 in the Central Caucasus is estimated, and its influence on the avalanche regime is shown. Data on the avalanche releases in the Central Caucasus for the period 1968– 2017 together with observations of high-altitude meteorological stations were used for the analysis. The paper presents estimates of snowiness of the winters and their frequency of occurrence in the area under investigation. The winter snowiness was noted to decrease since the beginning of the 2000s. The last decade of the period was not snowy, especially its series of six winters having very small amounts of snow. It is shown that in the second half of the XX century the heaviest snowfalls took place mostly in Januaries, and they were followed by releases of avalanches with the volumes exceeding 1 million cubic metres. In the early 2000‑ies, intensive January snowfalls were observed later, i.e. during the winter-spring period. In the warmer months March and April, the destructive potential of avalanches was noticeably smaller. In the present time, the warming and decrease of winter snowiness resulted in significant diminution of the avalanche hazard in the region. At the same time, on the background of general warming the certain increase in inter-seasonal variability of air temperature was noted. These changes may be compared to the warming of 1910–1945 when during its warmest phase the Europe suffered with one of the harshest winters in 1941/42. The swing of the «temperature pendulum» indicates that a harsh winter with heavy snowfalls and avalanches with catastrophic consequences may occur on the background of winters with mild and moderate avalanche danger. This is one of probable scenarios in the development of avalanche activity in the Greater Caucasus in the context of the current climate change.

scholarly journals Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus-aerosol and solar forced climate change

1997 ◽  
Vol 13 (9) ◽  
pp. 613-634 ◽  
Author(s):  
G. C. Hegerl ◽  
K. Hasselmann ◽  
U. Cubasch ◽  
J. F. B. Mitchell ◽  
E. Roeckner ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gas ◽  
Detection And Attribution ◽  
Attribution Analysis ◽  
Fingerprint Detection

Recent trends in regional air temperature and precipitation and links to global climate change in the Maharlo watershed, Southwestern Iran

2014 ◽  
Vol 126 (3-4) ◽  
pp. 177-192 ◽  
Author(s):  
Javad Abolverdi ◽  
Ghasem Ferdosifar ◽  
Davar Khalili ◽  
Ali Akbar Kamgar-Haghighi ◽  
Mohammad Abdolahipour Haghighi
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Global Climate Change ◽  
Global Climate ◽  
Temperature And Precipitation ◽  
Recent Trends ◽  
Southwestern Iran

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Projection of Hydro-Climatic Extreme Events under Climate Change in Yom and Nan River Basins, Thailand

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 665
Author(s):  
Chanchai Petpongpan ◽  
Chaiwat Ekkawatpanit ◽  
Supattra Visessri ◽  
Duangrudee Kositgittiwong
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Extreme Events ◽  
River Basins ◽  
Annual Rainfall ◽  
Extreme Floods ◽  
Extreme Flood ◽  
Rcp 8.5 ◽  
Near Future ◽  
Far Future

Due to a continuous increase in global temperature, the climate has been changing without sign of alleviation. An increase in the air temperature has caused changes in the hydrologic cycle, which have been followed by several emergencies of natural extreme events around the world. Thailand is one of the countries that has incurred a huge loss in assets and lives from the extreme flood and drought events, especially in the northern part. Therefore, the purpose of this study was to assess the hydrological regime in the Yom and Nan River basins, affected by climate change as well as the possibility of extreme floods and droughts. The hydrological processes of the study areas were generated via the physically-based hydrological model, namely the Soil and Water Assessment Tool (SWAT) model. The projected climate conditions were dependent on the outputs of the Global Climate Models (GCMs) as the Representative Concentration Pathways (RCPs) 2.6 and 8.5 between 2021 and 2095. Results show that the average air temperature, annual rainfall, and annual runoff will be significantly increased in the intermediate future (2046–2070) onwards, especially under RCP 8.5. According to the Flow Duration Curve and return period of peak discharge, there are fluctuating trends in the occurrence of extreme floods and drought events under RCP 2.6 from the future (2021–2045) to the far future (2071–2095). However, under RCP 8.5, the extreme flood and drought events seem to be more severe. The probability of extreme flood remains constant from the reference period to the near future, then rises dramatically in the intermediate and the far future. The intensity of extreme droughts will be increased in the near future and decreased in the intermediate future due to high annual rainfall, then tending to have an upward trend in the far future.

scholarly journals Effect of environmental history on the habitat-forming kelp Macrocystis pyrifera responses to ocean acidification and warming: a physiological and molecular approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pamela A. Fernández ◽  
Jorge M. Navarro ◽  
Carolina Camus ◽  
Rodrigo Torres ◽  
Alejandro H. Buschmann
Keyword(s):  
Climate Change ◽  
Ocean Acidification ◽  
Environmental History ◽  
Coastal Upwelling ◽  
Macrocystis Pyrifera ◽  
Natural Variability ◽  
Kelp Forests ◽  
Species Response ◽  
Enhanced Expression ◽  
Mesocosm Experiments

AbstractThe capacity of marine organisms to adapt and/or acclimate to climate change might differ among distinct populations, depending on their local environmental history and phenotypic plasticity. Kelp forests create some of the most productive habitats in the world, but globally, many populations have been negatively impacted by multiple anthropogenic stressors. Here, we compare the physiological and molecular responses to ocean acidification (OA) and warming (OW) of two populations of the giant kelp Macrocystis pyrifera from distinct upwelling conditions (weak vs strong). Using laboratory mesocosm experiments, we found that juvenile Macrocystis sporophyte responses to OW and OA did not differ among populations: elevated temperature reduced growth while OA had no effect on growth and photosynthesis. However, we observed higher growth rates and NO3− assimilation, and enhanced expression of metabolic-genes involved in the NO3− and CO2 assimilation in individuals from the strong upwelling site. Our results suggest that despite no inter-population differences in response to OA and OW, intrinsic differences among populations might be related to their natural variability in CO2, NO3− and seawater temperatures driven by coastal upwelling. Further work including additional populations and fluctuating climate change conditions rather than static values are needed to precisely determine how natural variability in environmental conditions might influence a species’ response to climate change.

scholarly journals Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user

2010 ◽  
Vol 48 (3) ◽  
Author(s):  
D. Maraun ◽  
F. Wetterhall ◽  
A. M. Ireson ◽  
R. E. Chandler ◽  
E. J. Kendon ◽  
...  
Keyword(s):  
Climate Change ◽  
End User ◽  
Dynamical Models ◽  
Recent Developments

scholarly journals Heatwaves and Summer Urban Heat Islands: A Daily Cycle Approach to Unveil the Urban Thermal Signal Changes in Lisbon, Portugal

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 292 ◽  
Author(s):  
Ana Oliveira ◽  
António Lopes ◽  
Ezequiel Correia ◽  
Samuel Niza ◽  
Amílcar Soares
Keyword(s):  
Climate Change ◽  
Air Temperature ◽  
Wind Direction ◽  
Urban Heat Islands ◽  
Daily Cycle ◽  
Local Climate ◽  
Thermal Signal ◽  
Air Temperatures ◽  
Local Climate Change ◽  
Urban Heat

Lisbon is a European Mediterranean city, greatly exposed to heatwaves (HW), according to recent trends and climate change prospects. Considering the Atlantic influence, air temperature observations from Lisbon’s mesoscale network are used to investigate the interactions between background weather and the urban thermal signal (UTS) in summer. Days are classified according to the prevailing regional wind direction, and hourly UTS is compared between HW and non-HW conditions. Northern-wind days predominate, revealing greater maximum air temperatures (up to 40 °C) and greater thermal amplitudes (approximately 10 °C), and account for 37 out of 49 HW days; southern-wind days have milder temperatures, and no HWs occur. Results show that the wind direction groups are significantly different. While southern-wind days have minor UTS variations, northern-wind days have a consistent UTS daily cycle: a diurnal urban cooling island (UCI) (often lower than –1.0 °C), a late afternoon peak urban heat island (UHI) (occasionally surpassing 4.0 °C), and a stable nocturnal UHI (1.5 °C median intensity). UHI/UCI intensities are not significantly different between HW and non-HW conditions, although the synoptic influence is noted. Results indicate that, in Lisbon, the UHI intensity does not increase during HW events, although it is significantly affected by wind. As such, local climate change adaptation strategies must be based on scenarios that account for the synergies between potential changes in regional air temperature and wind.

scholarly journals A Bayesian Climate Change Detection and Attribution Assessment

2005 ◽  
Vol 18 (13) ◽  
pp. 2429-2440 ◽  
Author(s):  
Terry C. K. Lee ◽  
Francis W. Zwiers ◽  
Gabriele C. Hegerl ◽  
Xuebin Zhang ◽  
Min Tsao
Keyword(s):  
Climate Change ◽  
Strong Evidence ◽  
Climate Model ◽  
Consistency Test ◽  
Change Question ◽  
Detection And Attribution ◽  
Complex Process ◽  
Detection Approach ◽  
Limited Length ◽  
Observational Record

Abstract A Bayesian analysis of the evidence for human-induced climate change in global surface temperature observations is described. The analysis uses the standard optimal detection approach and explicitly incorporates prior knowledge about uncertainty and the influence of humans on the climate. This knowledge is expressed through prior distributions that are noncommittal on the climate change question. Evidence for detection and attribution is assessed probabilistically using clearly defined criteria. Detection requires that there is high likelihood that a given climate-model-simulated response to historical changes in greenhouse gas concentration and sulphate aerosol loading has been identified in observations. Attribution entails a more complex process that involves both the elimination of other plausible explanations of change and an assessment of the likelihood that the climate-model-simulated response to historical forcing changes is correct. The Bayesian formalism used in this study deals with this latter aspect of attribution in a more satisfactory way than the standard attribution consistency test. Very strong evidence is found to support the detection of an anthropogenic influence on the climate of the twentieth century. However, the evidence from the Bayesian attribution assessment is not as strong, possibly due to the limited length of the available observational record or sources of external forcing on the climate system that have not been accounted for in this study. It is estimated that strong evidence from a Bayesian attribution assessment using a relatively stringent attribution criterion may be available by 2020.

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