Decadal modulation of global surface temperature by internal climate variability

2015 ◽  
Vol 5 (6) ◽  
pp. 555-559 ◽  
Author(s):  
Aiguo Dai ◽  
John C. Fyfe ◽  
Shang-Ping Xie ◽  
Xingang Dai
Keyword(s):  
Climate Variability ◽  
Surface Temperature ◽  
Internal Climate Variability ◽  
Global Surface Temperature ◽  
Decadal Modulation ◽  
Global Surface

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.

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.

scholarly journals Estimating the influence of thermal inertia and feedbacks in the atmosphere–ocean system on the variability of global surface temperature

2019 ◽  
Vol 55 (6) ◽  
pp. 114-126
Author(s):  
S. A. Soldatenko ◽  
R. M. Yusupov
Keyword(s):  
Climate Variability ◽  
Surface Temperature ◽  
Decadal Variability ◽  
Global Climate ◽  
Thermal Inertia ◽  
Coupled Model ◽  
Radiation Balance ◽  
Global Surface Temperature ◽  
Wide Range ◽  
Global Surface

The present climate is characterized not only by the trend due to the increase in the concentrations of greenhouse gases in the atmosphere, but also by fluctuations covering a wide range of frequencies and scales. The global climate variability, calculated via the computational results from the Coupled Model Intercomparison Project Phase 5 of the World Climate Research Program, show significant inter-model differences. In particular, inter-model distinctions in decadal anomalies of the global and hemispheric temperatures reach four times the value. However, unlike the inter-model differences in climate sensitivity, the reasons for a wide range of estimates of climate variability are still unclear. Based on the two-component energy-balance stochastic model, the paper analyzes the inter-annual and inter-decadal variability of the mean global surface temperature (GST) to feedback and the thermal inertia of the atmosphere-ocean system, assuming that the external forcing is random fluctu-ations of the radiation balance at the top of the atmosphere. Using the obtained ab-solute and relative sensitivity functions, the influence of thermal inertia and feed-backs in the climate system on the inter-annual and inter-decimal variability (variance) of the GST and its spectrum is estimated.

scholarly journals Updated Temperature Data Give a Sharper View of Climate Trends

Eos ◽  
2019 ◽  
Vol 100 ◽  
Author(s):  
Huai-Min Zhang ◽  
Jay Lawrimore ◽  
Boyin Huang ◽  
Matthew Menne ◽  
Xungang Yin ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Temperature Data ◽  
Climate Trends ◽  
Historical Changes ◽  
Global Surface Temperature ◽  
Global Surface

The latest version of NOAA’s Global Surface Temperature Dataset improves coverage over land and sea and improves the treatment of historical changes in observational practices.

A Blended Satellite–In Situ Near–Global Surface Temperature Dataset

2000 ◽  
Vol 81 (9) ◽  
pp. 2157-2164 ◽  
Author(s):  
Thomas C. Peterson ◽  
Alan N. Basist ◽  
Claude N. Williams ◽  
Norman C. Grody
Keyword(s):  
Surface Temperature ◽  
Global Surface Temperature ◽  
Global Surface

scholarly journals Global Hydrological Cycle Response to Rapid and Slow Global Warming

2013 ◽  
Vol 26 (22) ◽  
pp. 8781-8786 ◽  
Author(s):  
Larissa Back ◽  
Karen Russ ◽  
Zhengyu Liu ◽  
Kuniaki Inoue ◽  
Jiaxu Zhang ◽  
...  
Keyword(s):  
Global Warming ◽  
Water Vapor ◽  
Surface Temperature ◽  
Global Surface Temperature ◽  
Rate Of Increase ◽  
The Tropics ◽  
Precipitation Changes ◽  
Global Surface ◽  
Global Mean ◽  
Global Water

Abstract This study analyzes the response of global water vapor to global warming in a series of fully coupled climate model simulations. The authors find that a roughly 7% K−1 rate of increase of water vapor with global surface temperature is robust only for rapid anthropogenic-like climate change. For slower warming that occurred naturally in the past, the Southern Ocean has time to equilibrate, producing a different pattern of surface warming, so that water vapor increases at only 4.2% K−1. This lower rate of increase of water vapor with warming is not due to relative humidity changes or differences in mean lower-tropospheric temperature. A temperature of over 80°C would be required in the Clausius–Clapeyron relationship to match the 4.2% K−1 rate of increase. Instead, the low rate of increase is due to spatially heterogeneous warming. During slower global warming, there is enhanced warming at southern high latitudes, and hence less warming in the tropics per kelvin of global surface temperature increase. This leads to a smaller global water vapor increase, because most of the atmospheric water vapor is in the tropics. A formula is proposed that applies to general warming scenarios. This study also examines the response of global-mean precipitation and the meridional profile of precipitation minus evaporation and compares the latter to thermodynamic scalings. It is found that global-mean precipitation changes are remarkably robust between rapid and slow warming. Thermodynamic scalings for the rapid- and slow-warming zonal-mean precipitation are similar, but the precipitation changes are significantly different, suggesting that circulation changes are important in driving these differences.

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