scholarly journals Absence of internal multidecadal and interdecadal oscillations in climate model simulations

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Sonya K. Miller
Keyword(s):  
Climate Model ◽  
Southern Oscillation ◽  
Low Frequency ◽  
Climate Trends ◽  
Internal Oscillation ◽  
Model Simulations ◽  
Control Climate ◽  
Climate Model Simulations ◽  
Decadal Climate ◽  
And Control

AbstractFor several decades the existence of interdecadal and multidecadal internal climate oscillations has been asserted by numerous studies based on analyses of historical observations, paleoclimatic data and climate model simulations. Here we use a combination of observational data and state-of-the-art forced and control climate model simulations to demonstrate the absence of consistent evidence for decadal or longer-term internal oscillatory signals that are distinguishable from climatic noise. Only variability in the interannual range associated with the El Niño/Southern Oscillation is found to be distinguishable from the noise background. A distinct (40–50 year timescale) spectral peak that appears in global surface temperature observations appears to reflect the response of the climate system to both anthropogenic and natural forcing rather than any intrinsic internal oscillation. These findings have implications both for the validity of previous studies attributing certain long-term climate trends to internal low-frequency climate cycles and for the prospect of decadal climate predictability.

scholarly journals Teleconnections and relationship between the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM) in reconstructions and models over the past millennium

2020 ◽  
Vol 16 (2) ◽  
pp. 743-756 ◽  
Author(s):  
Christoph Dätwyler ◽  
Martin Grosjean ◽  
Nathan J. Steiger ◽  
Raphael Neukom
Keyword(s):  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
Southern Annular Mode ◽  
Last Millennium ◽  
Annular Mode ◽  
Model Simulations ◽  
The Past ◽  
Climate Model Simulations ◽  
Past Millennium

Abstract. The climate of the Southern Hemisphere (SH) is strongly influenced by variations in the El Niño–Southern Oscillation (ENSO) and the Southern Annular Mode (SAM). Because of the limited length of instrumental records in most parts of the SH, very little is known about the relationship between these two key modes of variability over time. Using proxy-based reconstructions and last-millennium climate model simulations, we find that ENSO and SAM indices are mostly negatively correlated over the past millennium. Pseudo-proxy experiments indicate that currently available proxy records are able to reliably capture ENSO–SAM relationships back to at least 1600 CE. Palaeoclimate reconstructions show mostly negative correlations back to about 1400 CE. An ensemble of last-millennium climate model simulations confirms this negative correlation, showing a stable correlation of approximately −0.3. Despite this generally negative relationship we do find intermittent periods of positive ENSO–SAM correlations in individual model simulations and in the palaeoclimate reconstructions. We do not find evidence that these relationship fluctuations are caused by exogenous forcing nor by a consistent climate pattern. However, we do find evidence that strong negative correlations are associated with strong positive (negative) anomalies in the Interdecadal Pacific Oscillation and the Amundsen Sea Low during periods when SAM and ENSO indices are of opposite (equal) sign.

Multi-decadal climate variability in the tropical stratosphere coupled to the Pacific

2021 ◽  
Author(s):  
Fernando Iglesias-Suarez ◽  
Oliver Wild ◽  
Douglas E. Kinnison ◽  
Rolando R. Garcia ◽  
Daniel R. Marsh ◽  
...  
Keyword(s):  
Climate Model ◽  
Decadal Variability ◽  
Stratospheric Ozone ◽  
Low Frequency ◽  
Decadal Climate Variability ◽  
The Pacific ◽  
Low Frequency Variability ◽  
The Pacific Ocean ◽  
Climate Model Simulations ◽  
Decadal Climate

<p><span>Recent studies have noted that tropical mid-stratospheric ozone decreased in the 1990s and has remained persistently low since. Current analyses suggest that these observations are linked to dynamical processes rather than being chemically-driven, although this has not been fully explored. Using measurements and chemistry-climate model simulations, we show that 50 ± 10% of these observed trends can be accounted for through multi-decadal variability in the Brewer-Dobson circulation (BDC) tied to the Pacific Ocean sea surface temperatures (the Interdecadal Pacific Oscillation, or IPO), via dynamical and chemical couplings. Moreover, accounting for this low frequency variability in the BDC can also help interpret previous observationally-derived changes in that circulation since year 1979. Overall, these findings demonstrate strong links between stratosphere-troposphere variability at decadal time scales and their potential importance for future ozone recovery detection.</span></p>

scholarly journals Can Bias Correction of Regional Climate Model Lateral Boundary Conditions Improve Low-Frequency Rainfall Variability?

2017 ◽  
Vol 30 (24) ◽  
pp. 9785-9806 ◽  
Author(s):  
Eytan Rocheta ◽  
Jason P. Evans ◽  
Ashish Sharma
Keyword(s):  
Boundary Conditions ◽  
Regional Climate Model ◽  
Bias Correction ◽  
Climate Model ◽  
Regional Climate ◽  
Low Frequency ◽  
Lateral Boundary ◽  
Model Simulations ◽  
Low Frequency Variability ◽  
Climate Model Simulations

Global climate model simulations inherently contain multiple biases that, when used as boundary conditions for regional climate models, have the potential to produce poor downscaled simulations. Removing these biases before downscaling can potentially improve regional climate change impact assessment. In particular, reducing the low-frequency variability biases in atmospheric variables as well as modeled rainfall is important for hydrological impact assessment, predominantly for the improved simulation of floods and droughts. The impact of this bias in the lateral boundary conditions driving the dynamical downscaling has not been explored before. Here the use of three approaches for correcting the lateral boundary biases including mean, variance, and modification of sample moments through the use of a nested bias correction (NBC) method that corrects for low-frequency variability bias is investigated. These corrections are implemented at the 6-hourly time scale on the global climate model simulations to drive a regional climate model over the Australian Coordinated Regional Climate Downscaling Experiment (CORDEX) domain. The results show that the most substantial improvement in low-frequency variability after bias correction is obtained from modifying the mean field, with smaller changes attributed to the variance. Explicitly modifying monthly and annual lag-1 autocorrelations through NBC does not substantially improve low-frequency variability attributes of simulated precipitation in the regional model over a simpler mean bias correction. These results raise questions about the nature of bias correction techniques that are required to successfully gain improvement in regional climate model simulations and show that more complicated techniques do not necessarily lead to more skillful simulation.

scholarly journals Diagnosing ENSO and Global Warming Tropical Precipitation Shifts Using Surface Relative Humidity and Temperature

2018 ◽  
Vol 31 (4) ◽  
pp. 1413-1433 ◽  
Author(s):  
Alexander Todd ◽  
Matthew Collins ◽  
F. Hugo Lambert ◽  
Robin Chadwick
Keyword(s):  
Global Warming ◽  
Relative Humidity ◽  
Climate Model ◽  
Southern Oscillation ◽  
Precipitation Change ◽  
Method Performance ◽  
Model Simulations ◽  
Tropical Precipitation ◽  
Climate Model Simulations ◽  
Precipitation Changes

Large uncertainty remains in future projections of tropical precipitation change under global warming. A simplified method for diagnosing tropical precipitation change is tested here on present-day El Niño–Southern Oscillation (ENSO) precipitation shifts. This method, based on the weak temperature gradient approximation, assumes precipitation is associated with local surface relative humidity (RH) and surface air temperature (SAT), relative to the tropical mean. Observed and simulated changes in RH and SAT are subsequently used to diagnose changes in precipitation. Present-day ENSO precipitation shifts are successfully diagnosed using observations (correlation r = 0.69) and an ensemble of atmosphere-only (0.51 ≤ r ≤ 0.8) and coupled (0.5 ≤ r ≤ 0.87) climate model simulations. RH ( r = 0.56) is much more influential than SAT ( r = 0.27) in determining ENSO precipitation shifts for observations and climate model simulations over both land and ocean. Using intermodel differences, a significant relationship is demonstrated between method performance over ocean for present-day ENSO and projected global warming ( r = 0.68). As a caveat, the authors note that mechanisms leading to ENSO-related precipitation changes are not a direct analog for global warming–related precipitation changes. The diagnosis method presented here demonstrates plausible mechanisms that relate changes in precipitation, RH, and SAT under different climate perturbations. Therefore, uncertainty in future tropical precipitation changes may be linked with uncertainty in future RH and SAT changes.

The influence of the tropical troposphere on the QBO in model simulations

2020 ◽  
Author(s):  
Federico Serva ◽  
Chiara Cagnazzo ◽  
Bo Christiansen ◽  
Shuting Yang
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Horizontal Resolution ◽  
Quasi Biennial Oscillation ◽  
Model Simulations ◽  
Enso Events ◽  
Phase Alignment ◽  
Climate Model Simulations ◽  
The Tropics

<pre>The equatorial Quasi-Biennial Oscillation (QBO) and the El Ni<strong>ñ</strong>o-Southern Oscillation (ENSO) are major patterns of climate variability. Mutual interactions between these two modes have been highlighted in the observational record, but this has not been much tested using climate model simulations. In observations, the amplitude is larger during cold and smaller during warm events, while the descent rate is slower during cold and faster during warm events. Here we discuss how climate models represent this relationship, in terms of the changes of the QBO properties. The results for a multi-model ensemble, with atmosphere-only and ocean-atmosphere simulations, are discussed. The phase alignment of the QBO after the 1997/98 warm ENSO, already documented in previous works, is also present in a large ensemble of uncoupled experiments. It is found that a high horizontal resolution is needed to realistically reproduce the observed modulation of the QBO descent rate under strong ENSO events. The QBO amplitude response is instead weak at any horizontal resolution. Robust changes emerging in the projections and aspects of the modelled stratosphere-troposphere coupling in the tropics are presented. </pre>

scholarly journals Analysis of upper-tropospheric humidity in tropical descent regions using observed and modelled radiances

2013 ◽  
Vol 13 (4) ◽  
pp. 10547-10560
Author(s):  
V. O. John ◽  
D. E. Parker ◽  
S. A. Buehler ◽  
J. Price ◽  
R. W. Saunders
Keyword(s):  
El Niño ◽  
Climate Model ◽  
El Nino ◽  
Southern Oscillation ◽  
Model Simulations ◽  
Multiple Observations ◽  
Major Mode ◽  
Upper Tropospheric Humidity ◽  
Water Vapour Feedback ◽  
Climate Model Simulations

Abstract. We use multiple observations and climate model simulations to study upper tropospheric humidity (UTH) in tropical descent regions. A satellite simulator is used to generate UTH from model fields to ensure a like-to-like comparison. We have shown that HadGEM2 is generally able to reproduce the patterns and magnitude of UTH in these regions. In both models and observations, the major mode of UTH variability in these regions is associated with El Nino and Southern Oscillation (ENSO); a negative UTH anomaly is seen during El Nino years. There is no significant trend in UTH in these regions, where even a small negative trend would lead to an important reduction of the positive water vapour feedback on global warming.

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