Climate Noise Has Shades of Pink

Physics ◽  
2018 ◽  
Vol 11 ◽  
Author(s):  
Anonymous
Keyword(s):  
Climate Noise

Multidecadal climate oscillations during the past millennium driven by volcanic forcing

Science ◽  
2021 ◽  
Vol 371 (6533) ◽  
pp. 1014-1019
Author(s):  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Daniel J. Brouillette ◽  
Sonya K. Miller
Keyword(s):  
Past Research ◽  
Atlantic Multidecadal Oscillation ◽  
Oscillatory Behavior ◽  
Time Varying ◽  
Sulfate Aerosols ◽  
Climate Oscillations ◽  
The Past ◽  
Volcanic Forcing ◽  
Climate Noise ◽  
Past Millennium

Past research argues for an internal multidecadal (40- to 60-year) oscillation distinct from climate noise. Recent studies have claimed that this so-termed Atlantic Multidecadal Oscillation is instead a manifestation of competing time-varying effects of anthropogenic greenhouse gases and sulfate aerosols. That conclusion is bolstered by the absence of robust multidecadal climate oscillations in control simulations of current-generation models. Paleoclimate data, however, do demonstrate multidecadal oscillatory behavior during the preindustrial era. By comparing control and forced “Last Millennium” simulations, we show that these apparent multidecadal oscillations are an artifact of pulses of volcanic activity during the preindustrial era that project markedly onto the multidecadal (50- to 70-year) frequency band. We conclude that there is no compelling evidence for internal multidecadal oscillations in the climate system.

scholarly journals Late quaternary temperature variability described as abrupt transitions on a 1/<i>f</i> noise background

2015 ◽  
Vol 6 (2) ◽  
pp. 2323-2337
Author(s):  
M. Rypdal ◽  
K. Rypdal
Keyword(s):  
Time Scales ◽  
Late Quaternary ◽  
Scaling Law ◽  
Temperature Variability ◽  
Climate System ◽  
Scaling Analysis ◽  
Noise Background ◽  
Quaternary Climate ◽  
Last Glaciation ◽  
Climate Noise

Abstract. We show that in order to have a scaling description of the climate system that is not inherently non-stationary, the rapid shifts between stadial and interstadial conditions during the last glaciation cannot be included in the scaling law. The same is true for the shifts between the glacial and interglacial states in the quaternary climate. When these events are omitted from a scaling analysis we find that the climate noise is consistent with a 1/f law on time scales from months to 105 years.

scholarly journals Multi-scale analysis of teleconnection indices: climate noise and nonlinear trend analysis

2009 ◽  
Vol 16 (1) ◽  
pp. 65-76 ◽  
Author(s):  
C. Franzke
Keyword(s):  
Time Series ◽  
Decadal Variability ◽  
Statistical Significance ◽  
Stationary Time Series ◽  
Intrinsic Mode Functions ◽  
Multi Scale ◽  
The North ◽  
Teleconnection Indices ◽  
Nonlinear Trends ◽  
Climate Noise

Abstract. The multi-scale nature and climate noise properties of teleconnection indices are examined by using the Empirical Mode Decomposition (EMD) procedure. The EMD procedure allows for the analysis of non-stationary time series to extract physically meaningful intrinsic mode functions (IMF) and nonlinear trends. The climatologically relevant monthly mean teleconnection indices of the North Atlantic Oscillation (NAO), the North Pacific index (NP) and the Southern Annular Mode (SAM) are analyzed. The significance of IMFs and trends are tested against the null hypothesis of climate noise. The analysis of surrogate monthly mean time series from a red noise process shows that the EMD procedure is effectively a dyadic filter bank and the IMFs (except the first IMF) are nearly Gaussian distributed. The distribution of the variance contained in IMFs of an ensemble of AR(1) simulations is nearly χ2 distributed. To test the statistical significance of the IMFs of the teleconnection indices and their nonlinear trends we utilize an ensemble of corresponding monthly averaged AR(1) processes, which we refer to as climate noise. Our results indicate that most of the interannual and decadal variability of the analysed teleconnection indices cannot be distinguished from climate noise. The NP and SAM indices have significant nonlinear trends, while the NAO has no significant trend when tested against a climate noise hypothesis.

scholarly journals Potential for long-range regional precipitation prediction over India

MAUSAM ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 47-56
Author(s):  
NEELIMA A. SONTAKKE ◽  
DENNIS J. SHEA ◽  
ROLAND A. MADDEN ◽  
RICHARD W. KATZ
Keyword(s):  
Long Range ◽  
Spatial Scales ◽  
Monsoon Season ◽  
Regional Scale ◽  
Climate Signal ◽  
Precipitation Prediction ◽  
Northwest India ◽  
Sw Monsoon ◽  
Climate Noise ◽  
Long Range Prediction

The potential for long-range precipitation prediction over the Indian monsoon region is generally good where climate noise (i.e., variability due to daily weather fluctuations) is small as compared to the climate signal (i.e., variability due to year to year fluctuations in monthly/seasonal means) being in the tropical belt. In order to understand the potential on smaller spatial scales, the ratios of inter-annual variability to that associated with climate noise have been computed for precipitation of four seasons as well as SW monsoon sub-seasons/months over 1656 stations in the Indian subcontinent.   Precipitation in SW monsoon has been found potentially predictable on seasonal as well as intra-seasonal scale. The west coast and contiguous northwest India, part of the 'northeast India are more predictable. Potential for long-range prediction over northwest India is highest during the active monsoon period from July to September. Over eastern peninsula potential for prediction is generally found low whereas over north-central India it is always moderate. Over northern latitudes precipitation due to western disturbances during January to May is potentially predictable. Precipitation over southeast India and Sri Lanka during October to February due to northeast (NE) monsoon shows good potential for long-range prediction. It is manifested that long-range precipitation forecasting schemes for SW monsoon season, sub-seasons and months and for the other seasons over India on point to regional scale have good scope by taking into account the potential predictability at the individual stations as well as at contiguous resemblance areas over the country.

scholarly journals Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble

2018 ◽  
Vol 31 (16) ◽  
pp. 6505-6525 ◽  
Author(s):  
Margot Bador ◽  
Markus G. Donat ◽  
Olivier Geoffroy ◽  
Lisa V. Alexander
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Internal Variability ◽  
Global Scale ◽  
Grid Cells ◽  
Wet Season ◽  
Tropical Regions ◽  
Projected Changes ◽  
Background Climate ◽  
Climate Noise

Abstract A warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

scholarly journals GENETIC MARKERS AS AN INDICATOR OF HUMAN RESILIENCE TO VARIOUS ECOLOGICAL AND PROFESSIONAL FACTORS

2017 ◽  
Vol 19 (4) ◽  
pp. 6-13
Author(s):  
M K Rzhepetskaya
Keyword(s):  
Physical Activity ◽  
Genetic Markers ◽  
Cold Climate ◽  
Working Capacity ◽  
Occupational Selection ◽  
Noise Vibration ◽  
Efficiency Of Selection ◽  
The Military ◽  
Intense Physical Activity ◽  
Climate Noise

Various ecological and professional factors (hypoxia, a hot or cold climate, noise, vibration, radiation, intense physical activity, etc.) are peculiarities of the military activities. The introduction of molecular and genetic methods in medical practice and occupational selection can greatly improve the efficiency of selection and rational appointment of people into separate specialties, optimize working capacity, maintain human health, reduce the costs for rehabilitation and (or) specialists’ treatment

scholarly journals Climate noise effect on uncertainty of hydrological extremes: numerical experiments with hydrological and climate models

2015 ◽  
Vol 369 ◽  
pp. 49-53 ◽  
Author(s):  
A. N. Gelfan ◽  
V. A. Semenov ◽  
Yu. G. Motovilov
Keyword(s):  
Numerical Experiments ◽  
Climate Models ◽  
Hydrological Model ◽  
Climate Model ◽  
Initial Conditions ◽  
Hydrological Regime ◽  
Internal Variability ◽  
Climate Impact ◽  
Noise Effect ◽  
Climate Noise

Abstract. An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere.

The Multi-Model Large Ensemble Archive as a climate noise generator: opportunities and outlooks for Observational Large Ensemble construction

2021 ◽  
Author(s):  
Anna L. Merrifield ◽  
Flavio Lehner ◽  
Ruth Lorenz ◽  
Reto Knutti
Keyword(s):  
Sea Level ◽  
Regional Climate ◽  
Internal Variability ◽  
Forced Response ◽  
Large Ensemble ◽  
Sea Level Pressure ◽  
Future Projections ◽  
Dynamic Noise ◽  
Residual Signal ◽  
Climate Noise

&lt;p&gt;The Multi-Model Large Ensemble Archive (MMLEA) is a collection of CMIP5-generation single model initial condition large ensembles (SMILEs) and thus provides estimates of internal variability from several independently developed coupled climate models. Work is underway to determine whether these simulations provide a range of historical regional climate variability suitable for statistically increasing the observed temperature sample.&amp;#160; Alternative sequences of historical temperature can be constructed by combining a forced signal with estimates of internal climate noise; prior studies have used the forced response from one SMILE in concert with observational noise resampling to form an &amp;#8220;observational large ensemble&amp;#8221; (McKinnon et al. 2018). Analogous to a SMILE, an observational large ensemble can be used to statistically contextualize monthly to half-yearly extreme events, such as the persistently mild Siberian winter of 2020, and to develop additional extended hot or cold spell storylines to explore in future projections of regional climate.&lt;/p&gt;&lt;p&gt;In this study, an alternative approach to constructing an observational large ensemble of European surface air temperature over the historical period (1950-2014), made possible by the MMLEA, is explored. Rather than relying on forced response and internal variability, components not well-defined in the single realization of observed climate, the constructed circulation analogue method of dynamical adjustment is employed to separate temperature anomalies related to atmospheric circulation (&amp;#8220;dynamic noise&quot;) from a more thermodynamically driven residual signal. The approach is advantageous because it can be applied in a similar manner to single realizations from both models and observations. Here, dynamic noise is computed by dividing each of the seven CMIP5-generation SMILEs in half and empirically estimating the component of temperature associated with interannual sea level pressure variability in one half of the SMILE using circulation analogues from members in the other half. Because ensemble means can be computed in SMILEs, it is possible to use the relationship between unforced temperature and unforced sea level pressure anomalies to construct dynamic noise. In observations, weekly-averaged analogues are assessed as a means to increase the size of the analogue pool such that the separation between dynamic noise and thermodynamic residual signal occurs in a manner more similar to that computed in the SMILEs.&lt;/p&gt;&lt;p&gt;The extent to which dynamic noise fields from different SMILEs are distinguishable from each other and from observational estimates is determined via spectral and spatial pattern analyses. To avoid introducing regional model bias into dynamic noise estimates, a mosaic approach will be taken; noise estimates from different models are mosaiced such that observed statistical properties are maintained at each grid point of the European domain. Upon validation, SMILE-derived dynamic noise and observational thermodynamic residual signal estimates are combined into a 50-member European observational large ensemble and evaluated via a multi-month extreme temperature frequency metric against the observational large ensemble developed by McKinnon et al. (2018). Anomalously persistent hot and cold spells found in the European observational large ensemble are further compared to events in out-of-sample future projections of climate from the CMIP6 archive.&lt;/p&gt;

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