scholarly journals Trends in Extreme Rainfall Frequency in the Contiguous United States: Attribution to Climate Change and Climate Variability Modes

2017 ◽  
Vol 31 (1) ◽  
pp. 369-385 ◽  
Author(s):  
Saman Armal ◽  
Naresh Devineni ◽  
Reza Khanbilvardi
Keyword(s):  
Climate Change ◽  
United States ◽  
Climate Variability ◽  
Surface Temperature ◽  
Multilevel Model ◽  
Extreme Rainfall ◽  
Temperature Anomalies ◽  
Global Surface Temperature ◽  
Global Surface ◽  
The U.S

AbstractThis study presents a systematic analysis for identifying and attributing trends in the annual frequency of extreme rainfall events across the contiguous United States to climate change and climate variability modes. A Bayesian multilevel model is developed for 1244 rainfall stations simultaneously to test the null hypothesis of no trend and verify two alternate hypotheses: trend can be attributed to changes in global surface temperature anomalies or to a combination of well-known cyclical climate modes with varying quasiperiodicities and global surface temperature anomalies. The Bayesian multilevel model provides the opportunity to pool information across stations and reduce the parameter estimation uncertainty, hence identifying the trends better. The choice of the best alternate hypothesis is made based on the Watanabe–Akaike information criterion, a Bayesian pointwise predictive accuracy measure. Statistically significant time trends are observed in 742 of the 1244 stations. Trends in 409 of these stations can be attributed to changes in global surface temperature anomalies. These stations are predominantly found in the U.S. Southeast and Northeast climate regions. The trends in 274 of these stations can be attributed to El Niño–Southern Oscillation, the North Atlantic Oscillation, the Pacific decadal oscillation, and the Atlantic multidecadal oscillation along with changes in global surface temperature anomalies. These stations are mainly found in the U.S. Northwest, West, and Southwest climate regions.

scholarly journals COSMIC-RAY-DRIVEN REACTION AND GREENHOUSE EFFECT OF HALOGENATED MOLECULES: CULPRITS FOR ATMOSPHERIC OZONE DEPLETION AND GLOBAL CLIMATE CHANGE

2013 ◽  
Vol 27 (17) ◽  
pp. 1350073 ◽  
Author(s):  
Q.-B. LU
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Global Climate Change ◽  
Greenhouse Effect ◽  
Ozone Depletion ◽  
Global Climate ◽  
Cosmic Ray ◽  
Atmospheric Ozone ◽  
Global Surface Temperature ◽  
Global Surface

This study is focused on the effects of cosmic rays (solar activity) and halogen-containing molecules (mainly chlorofluorocarbons — CFCs) on atmospheric ozone depletion and global climate change. Brief reviews are first given on the cosmic-ray-driven electron-induced-reaction (CRE) theory for O 3 depletion and the warming theory of halogenated molecules for climate change. Then natural and anthropogenic contributions to these phenomena are examined in detail and separated well through in-depth statistical analyses of comprehensive measured datasets of quantities, including cosmic rays (CRs), total solar irradiance, sunspot number, halogenated gases (CFCs, CCl 4 and HCFCs), CO 2, total O 3, lower stratospheric temperatures and global surface temperatures. For O 3 depletion, it is shown that an analytical equation derived from the CRE theory reproduces well 11-year cyclic variations of both polar O 3 loss and stratospheric cooling, and new statistical analyses of the CRE equation with observed data of total O 3 and stratospheric temperature give high linear correlation coefficients ≥ 0.92. After the removal of the CR effect, a pronounced recovery by 20 ~ 25 % of the Antarctic O 3 hole is found, while no recovery of O 3 loss in mid-latitudes has been observed. These results show both the correctness and dominance of the CRE mechanism and the success of the Montreal Protocol. For global climate change, in-depth analyses of the observed data clearly show that the solar effect and human-made halogenated gases played the dominant role in Earth's climate change prior to and after 1970, respectively. Remarkably, a statistical analysis gives a nearly zero correlation coefficient (R = -0.05) between corrected global surface temperature data by removing the solar effect and CO 2 concentration during 1850–1970. In striking contrast, a nearly perfect linear correlation with coefficients as high as 0.96–0.97 is found between corrected or uncorrected global surface temperature and total amount of stratospheric halogenated gases during 1970–2012. Furthermore, a new theoretical calculation on the greenhouse effect of halogenated gases shows that they (mainly CFCs) could alone result in the global surface temperature rise of ~0.6°C in 1970–2002. These results provide solid evidence that recent global warming was indeed caused by the greenhouse effect of anthropogenic halogenated gases. Thus, a slow reversal of global temperature to the 1950 value is predicted for coming 5 ~ 7 decades. It is also expected that the global sea level will continue to rise in coming 1 ~ 2 decades until the effect of the global temperature recovery dominates over that of the polar O 3 hole recovery; after that, both will drop concurrently. All the observed, analytical and theoretical results presented lead to a convincing conclusion that both the CRE mechanism and the CFC-warming mechanism not only provide new fundamental understandings of the O 3 hole and global climate change but have superior predictive capabilities, compared with the conventional models.

scholarly journals A new merge of global surface temperature datasets since the start of the 20th century

2019 ◽  
Vol 11 (4) ◽  
pp. 1629-1643 ◽  
Author(s):  
Xiang Yun ◽  
Boyin Huang ◽  
Jiayi Cheng ◽  
Wenhui Xu ◽  
Shaobo Qiao ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate Change ◽  
Global Climate ◽  
Sea Surface ◽  
Climate Change Research ◽  
Global Surface Temperature ◽  
Spatial Coverage ◽  
Global Surface

Abstract. Global surface temperature (ST) datasets are the foundation for global climate change research. Several global ST datasets have been developed by different groups in NOAA NCEI, NASA GISS, UK Met Office Hadley Centre & UEA CRU, and Berkeley Earth. In this study, a new global ST dataset named China Merged Surface Temperature (CMST) was presented. CMST is created by merging the China-Land Surface Air Temperature (C-LSAT1.3) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The merge of C-LSAT and ERSSTv5 shows a high spatial coverage extended to the high latitudes and is more consistent with a reference of multi-dataset averages in the polar regions. Comparisons indicated that CMST is consistent with other existing global ST datasets in interannual and decadal variations and long-term trends at global, hemispheric, and regional scales from 1900 to 2017. The CMST dataset can be used for global climate change assessment, monitoring, and detection. The CMST dataset presented here is publicly available at https://doi.org/10.1594/PANGAEA.901295 (Li, 2019a) and has been published on the Climate Explorer website of the Royal Netherlands Meteorological Institute (KNMI) at http://climexp.knmi.nl/select.cgi?id=someone@somewhere&field=cmst (last access: 11 August 2018; Li, 2019b, c).

scholarly journals A closer look at United States and global surface temperature change

2001 ◽  
Vol 106 (D20) ◽  
pp. 23947-23963 ◽  
Author(s):  
J. Hansen ◽  
R. Ruedy ◽  
M. Sato ◽  
M. Imhoff ◽  
W. Lawrence ◽  
...  
Keyword(s):  
United States ◽  
Surface Temperature ◽  
Temperature Change ◽  
Surface Temperature Change ◽  
Global Surface Temperature ◽  
Global Surface

REDUCTIONS OF NOISE AND UNCERTAINTY IN ANNUAL GLOBAL SURFACE TEMPERATURE ANOMALY DATA

2009 ◽  
Vol 01 (03) ◽  
pp. 447-460 ◽  
Author(s):  
NORDEN E. HUANG ◽  
ZHAOHUA WU ◽  
JORGE E. PINZÓN ◽  
CLAIRE L. PARKINSON ◽  
STEVEN R. LONG ◽  
...  
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Surface Temperature ◽  
Temperature Anomaly ◽  
Global Climate ◽  
Global Surface Temperature ◽  
Global Mean Temperature ◽  
Mode Decomposition ◽  
The Difference ◽  
Global Surface

Global climate variability is currently a topic of high scientific and public interest, with potential ramifications for the Earth's ecologic systems and policies governing world economy. Across the broad spectrum of global climate variability, the least well understood time scale is that of decade-to-century.1 The bases for investigating past changes across that period band are the records of annual mean Global Surface Temperature Anomaly (GSTA) time series, produced variously in many painstaking efforts.2–5 However, due to incipient instrument noise, the uneven distribution of sensors spatially and temporally, data gaps, land urbanization, and bias corrections to sea surface temperature, noise and uncertainty continue to exist in all data sets.1, 2, 6–8 Using the Empirical Mode Decomposition method as a filter, we can reduce this noise and uncertainty and produce a cleaner annual mean GSTA dataset. The noise in the climate dataset is thus reduced by one-third and the difference between the new and the commonly used, but unfiltered time series, ranges up to 0.1506°C, with a standard deviation up to 0.01974°C, and an overall mean difference of only 0.0001°C. Considering that the total increase of the global mean temperature over the last 150 years to be only around 0.6°C, we believe this difference of 0.1506°C is significant.

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