scholarly journals Comment on “Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations”

2017 ◽  
Vol 30 (23) ◽  
pp. 9763-9772 ◽  
Author(s):  
Sergey Kravtsov
Keyword(s):  
Climate Variability ◽  
Low Frequency ◽  
Accurate Estimate ◽  
Cmip5 Models ◽  
Pi Control ◽  
Individual Model ◽  
Internal Climate Variability ◽  
Alternative Methodology ◽  
The Difference ◽  
Model Ensembles

In a recent article, Cheung et al. applied a semiempirical methodology to isolate internal climate variability (ICV) in CMIP5 models and observations. The essence of their methodology is to subtract the scaled CMIP5 multimodel ensemble mean (MMEM) from individual model simulations and from the observed time series of several surface temperature indices. Cheung et al. detected large differences in both the magnitude and spatial patterns of the observed and simulated ICV, as well as large differences between the historical (simulated) ICV and preindustrial (PI) control CMIP5 simulations. Here it is shown that subtraction of the scaled MMEM from CMIP5 historical simulations produces a poor estimate of the modeled ICV due to the difference between the scaled MMEM and a given model’s true forced signal masquerading as ICV. The resulting phase and amplitude errors of the ICV so estimated are large, which compromises most of Cheung et al.’s conclusions pertaining to characterization of ICV in the historical CMIP5 simulations. By contrast, an alternative methodology based on forced signals computed from individual model ensembles produces a much more accurate estimate of the ICV in CMIP5 models, whose magnitude is consistent with the PI control simulations and is much smaller than any of the semiempirical estimates of the observed ICV on decadal and longer time scales.

scholarly journals Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations

2017 ◽  
Vol 30 (12) ◽  
pp. 4763-4776 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Climate Variability ◽  
Surface Temperature ◽  
Time Scales ◽  
Linear Regression Analysis ◽  
Low Frequency ◽  
Semiempirical Method ◽  
Cmip5 Models ◽  
Instrumental Data ◽  
Internal Climate Variability ◽  
Instrumental Record

Low-frequency internal climate variability (ICV) plays an important role in modulating global surface temperature, regional climate, and climate extremes. However, it has not been completely characterized in the instrumental record and in the Coupled Model Intercomparison Project phase 5 (CMIP5) model ensemble. In this study, the surface temperature ICV of the North Pacific (NP), North Atlantic (NA), and Northern Hemisphere (NH) in the instrumental record and historical CMIP5 all-forcing simulations is isolated using a semiempirical method wherein the CMIP5 ensemble mean is applied as the external forcing signal and removed from each time series. Comparison of ICV signals derived from this semiempirical method as well as from analysis of ICV in CMIP5 preindustrial control runs reveals disagreement in the spatial pattern and amplitude between models and instrumental data on multidecadal time scales (>20 yr). Analysis of the amplitude of total variability and the ICV in the models and instrumental data indicates that the models underestimate ICV amplitude on low-frequency time scales (>20 yr in the NA; >40 yr in the NP), while agreement is found in the NH variability. A multiple linear regression analysis of ICV in the instrumental record shows that variability in the NP drives decadal-to-interdecadal variability in the NH, whereas the NA drives multidecadal variability in the NH. Analysis of the CMIP5 historical simulations does not reveal such a relationship, indicating model limitations in simulating ICV. These findings demonstrate the need to better characterize low-frequency ICV, which may help improve attribution and decadal prediction.

Impacts of low-frequency internal climate variability and greenhouse warming on the El Niño-Southern Oscillation

2020 ◽  
pp. 1-38
Author(s):  
Benjamin Ng ◽  
Wenju Cai ◽  
Tim Cowan ◽  
Daohua Bi
Keyword(s):  
Standard Deviation ◽  
Climate Variability ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Cmip5 Models ◽  
Surface Warming ◽  
Internal Climate Variability ◽  
Cp El Niño ◽  
Eastern Equatorial Pacific

AbstractThe El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate fluctuations with wide-ranging socio-economic and environmental impacts. Understanding the eastern Pacific (EP) and central Pacific (CP) El Niño response to a warmer climate is paramount, yet the role of internal climate variability in modulating their response is not clear. Using large ensembles, we find that internal variability generates a spread in the standard deviation and skewness of these two El Niño types that is similar to the spread of 17 Coupled Model Intercomparison Project phase 5 (CMIP5) models that realistically simulate ENSO diversity. Based on 40 Community Earth System Model Large Ensemble (CESM-LE) and 99 Max Planck Institute for Meteorology Grand Ensemble (MPI-GE) members, unforced variability can explain more than 90% of the historical EP and CP El Niño standard deviation and all of the ENSO skewness spread in the 17 CMIP5 models. Both CESM-LE and the selected CMIP5 models show increased EP and CP El Niño variability in a warmer climate, driven by a stronger mean vertical temperature gradient in the upper ocean and faster surface warming of the eastern equatorial Pacific. However, MPI-GE shows no agreement in EP or CP standard deviation change. This is due to weaker sensitivity to the warming signal, such that when the eastern equatorial Pacific surface warming is faster, the change in upper ocean vertical temperature gradient tends to be weaker. This highlights that individual models produce a different ENSO response in a warmer climate, and that considerable uncertainty within the CMIP5 ensemble may be caused by internal climate variability.

Separating Internal Variability from the Externally Forced Climate Response

2015 ◽  
Vol 28 (20) ◽  
pp. 8184-8202 ◽  
Author(s):  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
Michael E. Mann ◽  
Byron A. Steinman
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Low Frequency ◽  
Internal Variability ◽  
Atlantic Multidecadal Oscillation ◽  
Ensemble Member ◽  
Internal Climate Variability ◽  
Climate Simulations ◽  
Scaling Methods ◽  
Synthetic Time Series

Abstract Separating low-frequency internal variability of the climate system from the forced signal is essential to better understand anthropogenic climate change as well as internal climate variability. Here both synthetic time series and the historical simulations from phase 5 of CMIP (CMIP5) are used to examine several methods of performing this separation. Linear detrending, as is commonly used in studies of low-frequency climate variability, is found to introduce large biases in both amplitude and phase of the estimated internal variability. Using estimates of the forced signal obtained from ensembles of climate simulations can reduce these biases, particularly when the forced signal is scaled to match the historical time series of each ensemble member. These so-called scaling methods also provide estimates of model sensitivities to different types of external forcing. Applying the methods to observations of the Atlantic multidecadal oscillation leads to different estimates of the phase of this mode of variability in recent decades.

scholarly journals Reply to “Comment on ‘Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations’”

2017 ◽  
Vol 30 (23) ◽  
pp. 9773-9782 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Low Frequency ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Multimodel Ensemble ◽  
Internal Climate Variability ◽  
Instrumental Record ◽  
Ensemble Mean ◽  
Intercomparison Project ◽  
The Individual

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al.

El Niño variability mediates 21st century growth effects of climate change

2021 ◽  
Author(s):  
Christopher Callahan ◽  
Justin Mankin
Keyword(s):  
Climate Change ◽  
Economic Growth ◽  
Climate Variability ◽  
Climate Model ◽  
Internal Variability ◽  
Climate Impact ◽  
Growth Effects ◽  
Internal Climate Variability ◽  
Mitigation And Adaptation ◽  
Model Ensembles

<p>Understanding the effect of climate change on global economic growth is critical to informing optimal mitigation and adaptation policy. Many recent efforts have been made to empirically quantify the roles of weather and climate in economic growth, but these efforts have generally focused on changes in mean climate rather than changes in climate variability. Climate change is expected to alter modes of climate variability, so fully quantifying the costs of climate change requires both understanding the effects of climate variability on economic growth and constraining how this variability will evolve under forcing. Here we combine historical climate and economic data with multiple climate model ensembles to quantify the economic growth effects of El Niño and examine how these effects evolve in the 21<sup>st</sup> century. Preliminary results show substantial negative effects of El Niño on growth, with historical events reducing growth by >5 percentage points over 5 years in countries whose temperature variability is tightly correlated with ENSO. We then examine how climate change influences El Niño and its growth effects in both multi-model and single-model ensembles, allowing us to isolate the role of internal climate variability in shaping the evolution of ENSO statistics in the 21<sup>st</sup> century. Climate change is generally projected to increase El Niño frequency and thus the resulting growth penalties, but internal variability generates a wide spread of responses, all of which are consistent with the same forcing. These results highlight how internal variability can influence both interannual El Niño occurrence and long-term changes in its statistics, with consequences for future economic growth. Moreover, these results illustrate the range of climate impact trajectories that are consistent with the same emissions, providing critical information for adaptation decision-makers needing to construct robust socioeconomic systems in the face of 21<sup>st</sup> century climate change.</p>

scholarly journals Analysis of Low-Frequency Mutations Associated with Drug Resistance to Raltegravir before Antiretroviral Treatment

2010 ◽  
Vol 55 (3) ◽  
pp. 1114-1119 ◽  
Author(s):  
Jia Liu ◽  
Michael D. Miller ◽  
Robert M. Danovich ◽  
Nathan Vandergrift ◽  
Fangping Cai ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Treatment Failure ◽  
Low Frequency ◽  
Hiv Treatment ◽  
Viral Population ◽  
Virologic Suppression ◽  
Resistance Mutations ◽  
The Difference ◽  
Allele Specific Sequencing ◽  
Hiv 1

ABSTRACTRaltegravir is highly efficacious in the treatment of HIV-1 infection. The prevalence and impact on virologic outcome of low-frequency resistant mutations among HIV-1-infected patients not previously treated with raltegravir have not been fully established. Samples from HIV treatment-experienced patients entering a clinical trial of raltegravir treatment were analyzed using a parallel allele-specific sequencing (PASS) assay that assessed six primary and six secondary integrase mutations. Patients who achieved and sustained virologic suppression (success patients,n= 36) and those who experienced virologic rebound (failure patients,n= 35) were compared. Patients who experienced treatment failure had twice as many raltegravir-associated resistance mutations prior to initiating treatment as those who achieved sustained virologic success, but the difference was not statistically significant. The frequency of nearly all detected resistance mutations was less than 1% of viral population, and the frequencies of mutations between the success and failure groups were similar. Expansion of pre-existing mutations (one primary and five secondary) was observed in 16 treatment failure patients in whom minority resistant mutations were detected at baseline, suggesting that they might play a role in the development of drug resistance. Two or more mutations were found in 13 patients (18.3%), but multiple mutations were not present in any single viral genome by linkage analysis. Our study demonstrates that low-frequency primary RAL-resistant mutations were uncommon, while minority secondary RAL-resistant mutations were more frequently detected in patients naïve to raltegravir. Additional studies in larger populations are warranted to fully understand the clinical implications of these mutations.

scholarly journals Climate Drift in the CMIP5 Models*

2013 ◽  
Vol 26 (21) ◽  
pp. 8597-8615 ◽  
Author(s):  
Alexander Sen Gupta ◽  
Nicolas C. Jourdain ◽  
Jaclyn N. Brown ◽  
Didier Monselesan
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Deep Ocean ◽  
Internal Variability ◽  
Correction Method ◽  
Physical Models ◽  
Cmip5 Models ◽  
Steric Sea Level ◽  
Biogeochemical Models ◽  
The Difference

Abstract Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

scholarly journals Meteorological conditions over Antarctic blue-ice areas and their influence on the local surface mass balance

2001 ◽  
Vol 47 (156) ◽  
pp. 37-50 ◽  
Author(s):  
Richard Bintanja ◽  
Carleen H. Reijmer
Keyword(s):  
Mass Balance ◽  
Low Frequency ◽  
Meteorological Conditions ◽  
Surface Mass Balance ◽  
Surface Mass ◽  
Wind Speeds ◽  
Dry Conditions ◽  
The Difference ◽  
Sublimation Rates ◽  
Different Characteristics

AbstractThis paper addresses the causes of the prevailing meteorological conditions observed over an Antarctic blue-ice area and their effect on the surface mass balance. Over blue-ice areas, net accumulation is zero and ablation occurs mainly through sublimation. Sublimation rates are much higher than over adjacent snowfields. The meteorological conditions favourable for high sublimation rates (warm, dry and gusty) are due to the specific orographic setting of this blue-ice area, with usually a steep upwind mountainous slope causing strong adiabatic heating. Diabatic warming due to radiation, and entrainment of warm air from aloft into the boundary layer augment the warming. The prevailing warm, dry conditions explain roughly 50% of the difference in sublimation, and the different characteristics of blue ice (mainly its lower albedo) the other 50%. Most of the annual sublimation (∼70%) takes place during the short summer (mainly in daytime), with winter ablation being restricted to occasional warm, dry föhn-like events. The additional moisture is effectively removed by entrainment and horizontal advection, which are maximum over the blue-ice area. Low-frequency turbulent motions induced by the upwind mountains enhance the vertical turbulent transports. Strong gusts and high peak wind speeds over blue-ice areas cause high potential snowdrift transports, which can easily remove the total precipitation, thereby maintaining zero accumulation.

Dynamics of Ferroelectric and Nematic Liquid Crystals Confined in Porous Matrices

1994 ◽  
Vol 366 ◽  
Author(s):  
Fouad M. Aliev
Keyword(s):  
Liquid Crystals ◽  
Heterogeneous Media ◽  
Volume Fraction ◽  
Low Frequency ◽  
Pore Wall ◽  
Polar Molecules ◽  
Average Pore ◽  
Surface Polarization ◽  
The Difference ◽  
Frequency Relaxation

ABSTRACTWe performed dielectric spectroscopy measurements to study dynamics of collective modes of ferroelectric (FLC) and molecular motion of nematic (NLC) liquid crystals with polar molecules confined in silica macroporous and microporous glasses with average pore sizes of 1000 Å (volume fraction of pores 40%) and 100 Å (27%) respectively. For FLC the Goldstone and the soft modes are found in macropores. The rotational viscosity associated with the soft mode is about 10 times higher in pores than in the bulk. These modes are not detected in micropores although low frequency relaxation is present. The last one probably is not connected with the nature of liquid crystal but is associated with surface polarization effects typical for two component heterogeneous media. The difference between the dynamics of orientational motion of the polar molecules of NLC in confined geometries and in the bulk is qualitatively determined by the total energy Fs of the interaction between molecules and the surface of the pore wall, which is found Fs ≈ 102erg/cm2.

The North Atlantic Ocean as a Modulator of Vegetation Greening/Browning in the Northern High Latitudes?

2021 ◽  
Author(s):  
Leonard F. Borchert ◽  
Alexander J. Winkler
Keyword(s):  
Climate Variability ◽  
North Atlantic ◽  
North Atlantic Ocean ◽  
Internal Variability ◽  
Dominant Mode ◽  
High Latitudes ◽  
Model Simulations ◽  
Internal Climate Variability ◽  
The North ◽  
The North Atlantic

<p>Vegetation in the northern high latitudes shows a characteristic pattern of persistent changes as documented by multi-decadal satellite observations. The prevailing explanation that these mainly increasing trends (greening) are a consequence of external CO<sub>2</sub> forcing, i.e., due to the ubiquitous effect of CO2-induced fertilization and/or warming of temperature-limited ecosystems, however does not explain why some areas also show decreasing trends of vegetation cover (browning). We propose here to consider the dominant mode of multi-decadal internal climate variability in the north Atlantic region, the Atlantic Multidecadal Variability (AMV), as the missing link in the explanation of greening and browning trend patterns in the northern high latitudes. Such a link would also imply potential for decadal predictions of ecosystem changes in the northern high latitudes.</p><p>An analysis of observational and reanalysis data sets for the period 1979-2019 shows that locations characterized by greening trends largely coincide with warming summer temperature and increasing precipitation. Wherever either cooling or decreasing precipitation occurs, browning trends are observed over this period. These precipitation and temperature patterns are significantly correlated with a North Atlantic sea surface temperature index that represents the AMV signal, indicating its role in modulating greening/browning trend patterns in the northern high latitudes.</p><p>Using two large ensembles of coupled Earth system model simulations (100 members of MPI-ESM-LR Grand Ensemble and 32 members of the IPSL-CM6A-LR Large Ensemble), we separate the greening/browning pattern caused by external CO<sub>2</sub> forcing from that caused by internal climate variability associated with the AMV. These sets of model simulations enable a clean separation of the externally forced signal from internal variability. While the greening and browning patterns in the simulations do not agree with observations in terms of magnitude and location, we find consistent internally generated greening/browning patterns in both models caused by changes in temperature and precipitation linked to the AMV signal. These greening/browning trend patterns are of the same magnitude as those caused by the external forcing alone. Our work therefore shows that internally-generated changes of vegetation in the northern lands, driven by AMV, are potentially as large as those caused by external CO<sub>2</sub> forcing. We thus argue that the observed pattern of greening/browning in the northern high latitudes could originate from the combined effect of rising CO<sub>2</sub> as well as the AMV.</p>

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