Sea-ice effects on climate model sensitivity and low frequency variability

2000 ◽  
Vol 16 (4) ◽  
pp. 257-271 ◽  
Author(s):  
G. A. Meehl ◽  
J. M. Arblaster ◽  
W. G. Strand Jr
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Low Frequency ◽  
Model Sensitivity ◽  
Low Frequency Variability

scholarly journals Mechanisms for low-frequency variability of summer Arctic sea ice extent

2015 ◽  
Vol 112 (15) ◽  
pp. 4570-4575 ◽  
Author(s):  
Rong Zhang
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Low Frequency ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Low Frequency Variability ◽  
Sea Ice Decline ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.

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.

OSNAP and water mass transport in CMIP6 models

2021 ◽  
Author(s):  
Laura Jackson
Keyword(s):  
North Atlantic ◽  
Climate Models ◽  
Climate Model ◽  
Water Mass ◽  
Deep Convection ◽  
Low Frequency ◽  
Meridional Overturning Circulation ◽  
The West ◽  
Low Frequency Variability ◽  
Meridional Overturning

<p>The Atlantic Meridional Overturning Circulation (AMOC) influences our climate by transporting heat northwards in the Atlantic ocean. The subpolar North Atlantic plays an important role in this circulation, with transformation of water to higher densities, deep convection and formation of deep water. Recent OSNAP observations have shown that the overturning is stronger to the east of Greenland than the west.</p><p>Here we analyse a CMIP6 climate model at two resolutions (HadGEM3 GC3.1 LL and MM) and show both compare well with the OSNAP observations. We explore the source of low frequency variability of the AMOC and how it is related to the surface water mass transformation in different regions. We also investigate time-mean and low frequency water mass transformations in other CMIP6 climate models.</p>

scholarly journals Comparison of Mechanisms for Low-Frequency Variability of Summer Arctic Sea Ice in Three Coupled Models

2018 ◽  
Vol 31 (3) ◽  
pp. 1205-1226 ◽  
Author(s):  
Dawei Li ◽  
Rong Zhang ◽  
Thomas Knutson
Keyword(s):  
Sea Ice ◽  
Heat Transport ◽  
Low Frequency ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Coupled Models ◽  
Surface Warming ◽  
Low Frequency Variability ◽  
Sea Ice Decline ◽  
Arctic Sea

Abstract In this study the mechanisms for low-frequency variability of summer Arctic sea ice are analyzed using long control simulations from three coupled models (GFDL CM2.1, GFDL CM3, and NCAR CESM). Despite different Arctic sea ice mean states, there are many robust features in the response of low-frequency summer Arctic sea ice variability to the three key predictors (Atlantic and Pacific oceanic heat transport into the Arctic and the Arctic dipole) across all three models. In all three models, an enhanced Atlantic (Pacific) heat transport into the Arctic induces summer Arctic sea ice decline and surface warming, especially over the Atlantic (Pacific) sector of the Arctic. A positive phase of the Arctic dipole induces summer Arctic sea ice decline and surface warming on the Pacific side, and opposite changes on the Atlantic side. There is robust Bjerknes compensation at low frequency, so the northward atmospheric heat transport provides a negative feedback to summer Arctic sea ice variations. The influence of the Arctic dipole on summer Arctic sea ice extent is more (less) effective in simulations with less (excessive) climatological summer sea ice in the Atlantic sector. The response of Arctic sea ice thickness to the three key predictors is stronger in models that have thicker climatological Arctic sea ice.

On Robust Estimation of Low-Frequency Variability Trends in Discrete Markovian Sequences of Atmospheric Circulation Patterns

2009 ◽  
Vol 66 (7) ◽  
pp. 2059-2072 ◽  
Author(s):  
Illia Horenko
Keyword(s):  
Atmospheric Circulation ◽  
Kernel Smoothing ◽  
Low Frequency ◽  
Algorithm Analysis ◽  
Gaussian Kernel ◽  
Observation Data ◽  
Model Sensitivity ◽  
Atmospheric Circulation Patterns ◽  
Low Frequency Variability ◽  
Circulation Patterns

Abstract Identification and analysis of temporal trends and low-frequency variability in discrete time series is an important practical topic in the understanding and prediction of many atmospheric processes, for example, in analysis of climate change. Widely used numerical techniques of trend identification (like local Gaussian kernel smoothing) impose some strong mathematical assumptions on the analyzed data and are not robust to model sensitivity. The latter issue becomes crucial when analyzing historical observation data with a short record. Two global robust numerical methods for the trend estimation in discrete nonstationary Markovian data based on different sets of implicit mathematical assumptions are introduced and compared here. The methods are first compared on a simple model example; then the importance of mathematical assumptions on the data is explained and numerical problems of local Gaussian kernel smoothing are demonstrated. Presented methods are applied to analysis of the historical sequence of atmospheric circulation patterns over the United Kingdom between 1946 and 2007. It is demonstrated that the influence of the seasonal pattern variability on transition processes is dominated by the long-term effects revealed by the introduced methods. Despite the differences in the mathematical assumptions implied by both presented methods, almost identical symmetrical changes of the cyclonic and anticyclonic pattern probabilities are identified in the analyzed data, with the confidence intervals being smaller than in the case of the local Gaussian kernel smoothing algorithm. Analysis results are investigated with respect to model sensitivity and compared to a standard analysis technique based on a local Gaussian kernel smoothing. Finally, the implications of the discussed strategies on long-range predictability of the data-fitted Markovian models are discussed.

Low frequency variability in an coupled ocean-sea ice general circulation model of the Southern Ocean

2013 ◽  
Vol 54 ◽  
pp. 200 ◽  
Author(s):  
Terence John O'Kane ◽  
Richard Matear ◽  
Matthew Chamberlain ◽  
James Risbey ◽  
Illia Horenko ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
General Circulation Model ◽  
General Circulation ◽  
Low Frequency ◽  
Circulation Model ◽  
Low Frequency Variability

Atmosphere–sea-ice low-frequency variability with a simple model of the southern hemisphere

2003 ◽  
Vol 129 (592) ◽  
pp. 2347-2366 ◽  
Author(s):  
G. Garric ◽  
S. A. Venegas ◽  
C. E. Tansley ◽  
I. N. James
Keyword(s):  
Simple Model ◽  
Sea Ice ◽  
Southern Hemisphere ◽  
Low Frequency ◽  
Low Frequency Variability
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