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Статті в журналах з теми "Feedback Rate"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Feldl, Nicole, Bruce T. Anderson, and Simona Bordoni. "Atmospheric Eddies Mediate Lapse Rate Feedback and Arctic Amplification." Journal of Climate 30, no. 22 (November 2017): 9213–24. http://dx.doi.org/10.1175/jcli-d-16-0706.1.

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Projections of amplified climate change in the Arctic are attributed to positive feedbacks associated with the retreat of sea ice and changes in the lapse rate of the polar atmosphere. Here, a set of idealized aquaplanet experiments are performed to understand the coupling between high-latitude feedbacks, polar amplification, and the large-scale atmospheric circulation. Results are compared to CMIP5. Simulated climate responses are characterized by a wide range of polar amplification (from none to nearly 15-K warming, relative to the low latitudes) under CO2 quadrupling. Notably, the high-latitude lapse rate feedback varies in sign among the experiments. The aquaplanet simulation with the greatest polar amplification, representing a transition from perennial to ice-free conditions, exhibits a marked decrease in dry static energy flux by transient eddies. Partly compensating for the reduced poleward energy flux is a contraction of the Ferrel cell and an increase in latent energy flux. Enhanced eddy energy flux is consistent with the upper-tropospheric warming that occurs in all experiments and provides a remote influence on the polar lapse rate feedback. The main conclusions are that (i) given a large, localized change in meridional surface temperature gradient, the midlatitude circulation exhibits strong compensation between changes in dry and latent energy fluxes, and (ii) atmospheric eddies mediate the nonlinear interaction between surface albedo and lapse rate feedbacks, rendering the high-latitude lapse rate feedback less positive than it would be otherwise. Consequently, the variability of the circulation response, and particularly the partitioning of energy fluxes, offers insights into understanding the magnitude of polar amplification.

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2

Graversen, Rune G., Peter L. Langen, and Thorsten Mauritsen. "Polar Amplification in CCSM4: Contributions from the Lapse Rate and Surface Albedo Feedbacks." Journal of Climate 27, no. 12 (June 5, 2014): 4433–50. http://dx.doi.org/10.1175/jcli-d-13-00551.1.

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Abstract A vertically nonuniform warming of the troposphere yields a lapse rate feedback by altering the infrared irradiance to space relative to that of a vertically uniform tropospheric warming. The lapse rate feedback is negative at low latitudes, as a result of moist convective processes, and positive at high latitudes, due to stable stratification conditions that effectively trap warming near the surface. It is shown that this feedback pattern leads to polar amplification of the temperature response induced by a radiative forcing. The results are obtained by suppressing the lapse rate feedback in the Community Climate System Model, version 4 (CCSM4). The lapse rate feedback accounts for 15% of the Arctic amplification and 20% of the amplification in the Antarctic region. The fraction of the amplification that can be attributed to the surface albedo feedback, associated with melting of snow and ice, is 40% in the Arctic and 65% in Antarctica. It is further found that the surface albedo and lapse rate feedbacks interact considerably at high latitudes to the extent that they cannot be considered independent feedback mechanisms at the global scale.

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3

Dabbagh, A. D., and D. J. Love. "Feedback rate-capacity loss tradeoff for limited feedback MIMO systems." IEEE Transactions on Information Theory 52, no. 5 (May 2006): 2190–202. http://dx.doi.org/10.1109/tit.2006.872864.

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4

Vahid, Alireza, Changho Suh, and A. Salman Avestimehr. "Interference Channels With Rate-Limited Feedback." IEEE Transactions on Information Theory 58, no. 5 (May 2012): 2788–812. http://dx.doi.org/10.1109/tit.2011.2181938.

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5

Bose, N. K., and C. Charoenlarpnopparut. "Minimax controller design using rate feedback." Circuits, Systems, and Signal Processing 18, no. 1 (January 1999): 17–25. http://dx.doi.org/10.1007/bf01206542.

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6

Ferraro, A. J., F. H. Lambert, M. Collins, and G. M. Miles. "Physical Mechanisms of Tropical Climate Feedbacks Investigated Using Temperature and Moisture Trends*." Journal of Climate 28, no. 22 (November 15, 2015): 8968–87. http://dx.doi.org/10.1175/jcli-d-15-0253.1.

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Abstract Tropical climate feedback mechanisms are assessed using satellite-observed and model-simulated trends in tropical tropospheric temperature from the MSU/AMSU instruments and upper-tropospheric humidity from the HIRS instruments. Despite discrepancies in the rates of tropospheric warming between observations and models, both are consistent with constant relative humidity over the period 1979–2008. Because uncertainties in satellite-observed tropical-mean trends preclude a constraint on tropical-mean trends in models regional features of the feedbacks are also explored. The regional pattern of the lapse rate feedback is primarily determined by the regional pattern of surface temperature changes, as tropical atmospheric warming is relatively horizontally uniform. The regional pattern of the water vapor feedback is influenced by the regional pattern of precipitation changes, with variations of 1–2 W m−2 K−1 across the tropics (compared to a tropical-mean feedback magnitude of 3.3–4 W m−2 K−1). Thus the geographical patterns of water vapor and lapse rate feedbacks are not correlated, but when the feedbacks are calculated in precipitation percentiles rather than in geographical space they are anticorrelated, with strong positive water vapor feedback associated with strong negative lapse rate feedback. The regional structure of the feedbacks is not related to the strength of the tropical-mean feedback in a subset of the climate models from the CMIP5 archive. Nevertheless the approach constitutes a useful process-based test of climate models and has the potential to be extended to constrain regional climate projections.

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7

ZHU, P., L. TANG, Y. WANG, and X. YOU. "Impact of Feedback Error on Transmit Beamforming with Finite Rate Feedback." IEICE Transactions on Communications E90-B, no. 9 (September 1, 2007): 2600–2604. http://dx.doi.org/10.1093/ietcom/e90-b.9.2600.

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8

Taylor, Patrick C., Robert G. Ellingson, and Ming Cai. "Geographical Distribution of Climate Feedbacks in the NCAR CCSM3.0." Journal of Climate 24, no. 11 (June 1, 2011): 2737–53. http://dx.doi.org/10.1175/2010jcli3788.1.

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Abstract This study performs offline, partial radiative perturbation calculations to determine the geographical distributions of climate feedbacks contributing to the top-of-atmosphere (TOA) radiative energy budget. These radiative perturbations are diagnosed using monthly mean model output from the NCAR Community Climate System Model version 3 (CCSM3.0) forced with the Special Report Emissions Scenario (SRES) A1B emission scenario. The Monte Carlo Independent Column Approximation (MCICA) technique with a maximum–random overlap rule is used to sample monthly mean cloud frequency profiles to perform the radiative transfer calculations. It is shown that the MCICA technique provides a good estimate of all feedback sensitivity parameters. The radiative perturbation results are used to investigate the spatial variability of model feedbacks showing that the shortwave cloud and lapse rate feedbacks exhibit the most and second most spatial variability, respectively. It has been shown that the model surface temperature response is highly correlated with the change in the TOA net flux, and that the latter is largely determined by the total feedback spatial pattern rather than the external forcing. It is shown by representing the change in the TOA net flux as a linear combination of individual feedback radiative perturbations that the lapse rate explains the most spatial variance of the surface temperature response. Feedback spatial patterns are correlated with the model response and other feedback spatial patterns to investigate these relationships. The results indicate that the model convective response is strongly correlated with cloud and water vapor feedbacks, but the lapse rate feedback geographic distribution is strongly correlated with the climatological distribution of convection. The implication for the water vapor–lapse rate anticorrelation is discussed.

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9

Andrews, Timothy, and Mark J. Webb. "The Dependence of Global Cloud and Lapse Rate Feedbacks on the Spatial Structure of Tropical Pacific Warming." Journal of Climate 31, no. 2 (January 2018): 641–54. http://dx.doi.org/10.1175/jcli-d-17-0087.1.

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An atmospheric general circulation model (AGCM) is forced with patterns of observed sea surface temperature (SST) change and those output from atmosphere–ocean GCM (AOGCM) climate change simulations to demonstrate a strong dependence of climate feedback on the spatial structure of surface temperature change. Cloud and lapse rate feedbacks are found to vary the most, depending strongly on the pattern of tropical Pacific SST change. When warming is focused in the southeast tropical Pacific—a region of climatological subsidence and extensive marine low cloud cover—warming reduces the lower-tropospheric stability (LTS) and low cloud cover but is largely trapped under an inversion and hence has little remote effect. The net result is a relatively weak negative lapse rate feedback and a large positive cloud feedback. In contrast, when warming is weak in the southeast tropical Pacific and enhanced in the west tropical Pacific—a strong convective region—warming is efficiently transported throughout the free troposphere. The increased atmospheric stability results in a strong negative lapse rate feedback and increases the LTS in low cloud regions, resulting in a low cloud feedback of weak magnitude. These mechanisms help explain why climate feedback and sensitivity change on multidecadal time scales in AOGCM abrupt4xCO2 simulations and are different from those seen in AGCM experiments forced with observed historical SST changes. From the physical understanding developed here, one should expect unusually negative radiative feedbacks and low effective climate sensitivities to be diagnosed from real-world variations in radiative fluxes and temperature over decades in which the eastern Pacific has lacked warming.

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10

Soden, Brian J., and Isaac M. Held. "An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models." Journal of Climate 19, no. 14 (July 15, 2006): 3354–60. http://dx.doi.org/10.1175/jcli3799.1.

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Abstract The climate feedbacks in coupled ocean–atmosphere models are compared using a coordinated set of twenty-first-century climate change experiments. Water vapor is found to provide the largest positive feedback in all models and its strength is consistent with that expected from constant relative humidity changes in the water vapor mixing ratio. The feedbacks from clouds and surface albedo are also found to be positive in all models, while the only stabilizing (negative) feedback comes from the temperature response. Large intermodel differences in the lapse rate feedback are observed and shown to be associated with differing regional patterns of surface warming. Consistent with previous studies, it is found that the vertical changes in temperature and water vapor are tightly coupled in all models and, importantly, demonstrate that intermodel differences in the sum of lapse rate and water vapor feedbacks are small. In contrast, intermodel differences in cloud feedback are found to provide the largest source of uncertainty in current predictions of climate sensitivity.

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11

Dessler, A. E. "Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models*." Journal of Climate 26, no. 1 (January 1, 2013): 333–42. http://dx.doi.org/10.1175/jcli-d-11-00640.1.

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Abstract Feedbacks in response to climate variations during the period 2000–10 have been calculated using reanalysis meteorological fields and top-of-atmosphere flux measurements. Over this period, the climate was stabilized by a strongly negative temperature feedback (~−3 W m−2 K−1); climate variations were also amplified by a strong positive water vapor feedback (~+1.2 W m−2 K−1) and smaller positive albedo and cloud feedbacks (~+0.3 and +0.5 W m−2 K−1, respectively). These observations are compared to two climate model ensembles, one dominated by internal variability (the control ensemble) and the other dominated by long-term global warming (the A1B ensemble). The control ensemble produces global average feedbacks that agree within uncertainties with the observations, as well as producing similar spatial patterns. The most significant discrepancy was in the spatial pattern for the total (shortwave + longwave) cloud feedback. Feedbacks calculated from the A1B ensemble show a stronger negative temperature feedback (due to a stronger lapse-rate feedback), but that is cancelled by a stronger positive water vapor feedback. The feedbacks in the A1B ensemble tend to be more smoothly distributed in space, which is consistent with the differences between El Niño–Southern Oscillation (ENSO) climate variations and long-term global warming. The sum of all of the feedbacks, sometimes referred to as the thermal damping rate, is −1.15 ± 0.88 W m−2 K−1 in the observations and −0.60 ± 0.37 W m−2 K−1 in the control ensemble. Within the control ensemble, models that more accurately simulate ENSO tend to produce thermal damping rates closer to the observations. The A1B ensemble average thermal damping rate is −1.26 ± 0.45 W m−2 K−1.

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12

Lindeman, Stephan T., Wulfert P. Van Den Brink, and Johan Hoogstraten. "Effect of Feedback on Base-Rate Utilization." Perceptual and Motor Skills 67, no. 2 (October 1988): 343–50. http://dx.doi.org/10.2466/pms.1988.67.2.343.

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The effect of direct training with feedback on a base-rate problem of the engineers-lawyers type is assessed. Analysis indicates that feedback leads to adjusted probability estimates closer to the Bayesian norm than those in the no-feedback training-only condition. For a second base-rate problem (the so called “divorce problem”), however, no effect of training was shown, even though thinking-aloud protocols for this problem showed that subjects mentioned base-rates more often in the feedback than in the no-treatment condition.

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13

Dufresne, Jean-Louis, and Sandrine Bony. "An Assessment of the Primary Sources of Spread of Global Warming Estimates from Coupled Atmosphere–Ocean Models." Journal of Climate 21, no. 19 (October 1, 2008): 5135–44. http://dx.doi.org/10.1175/2008jcli2239.1.

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Abstract Climate feedback analysis constitutes a useful framework for comparing the global mean surface temperature responses to an external forcing predicted by general circulation models (GCMs). Nevertheless, the contributions of the different radiative feedbacks to global warming (in equilibrium or transient conditions) and their comparison with the contribution of other processes (e.g., the ocean heat uptake) have not been quantified explicitly. Here these contributions from the classical feedback analysis framework are defined and quantified for an ensemble of 12 third phase of the Coupled Model Intercomparison Project (CMIP3)/Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) coupled atmosphere–ocean GCMs. In transient simulations, the multimodel mean contributions to global warming associated with the combined water vapor–lapse-rate feedback, cloud feedback, and ocean heat uptake are comparable. However, intermodel differences in cloud feedbacks constitute by far the most primary source of spread of both equilibrium and transient climate responses simulated by GCMs. The spread associated with intermodel differences in cloud feedbacks appears to be roughly 3 times larger than that associated either with the combined water vapor–lapse-rate feedback, the ocean heat uptake, or the radiative forcing.

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14

Pithan, Felix, and Thorsten Mauritsen. "Comments on “Current GCMs' Unrealistic Negative Feedback in the Arctic”." Journal of Climate 26, no. 19 (September 24, 2013): 7783–88. http://dx.doi.org/10.1175/jcli-d-12-00331.1.

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Abstract In contrast to prior studies showing a positive lapse-rate feedback associated with the Arctic inversion, Boé et al. reported that strong present-day Arctic temperature inversions are associated with stronger negative longwave feedbacks and thus reduced Arctic amplification in the model ensemble from phase 3 of the Coupled Model Intercomparison Project (CMIP3). A permutation test reveals that the relation between longwave feedbacks and inversion strength is an artifact of statistical self-correlation and that shortwave feedbacks have a stronger correlation with intermodel spread. The present comment concludes that the conventional understanding of a positive lapse-rate feedback associated with the Arctic inversion is consistent with the CMIP3 model ensemble.

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15

Po-Chedley, Stephen, Kyle C. Armour, Cecilia M. Bitz, Mark D. Zelinka, Benjamin D. Santer, and Qiang Fu. "Sources of Intermodel Spread in the Lapse Rate and Water Vapor Feedbacks." Journal of Climate 31, no. 8 (March 23, 2018): 3187–206. http://dx.doi.org/10.1175/jcli-d-17-0674.1.

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Abstract Sources of intermodel differences in the global lapse rate (LR) and water vapor (WV) feedbacks are assessed using CO2 forcing simulations from 28 general circulation models. Tropical surface warming leads to significant warming and moistening in the tropical and extratropical upper troposphere, signifying a nonlocal, tropical influence on extratropical radiation and feedbacks. Model spread in the locally defined LR and WV feedbacks is pronounced in the Southern Ocean because of large-scale ocean upwelling, which reduces surface warming and decouples the surface from the tropospheric response. The magnitude of local extratropical feedbacks across models and over time is well characterized using the ratio of tropical to extratropical surface warming. It is shown that model differences in locally defined LR and WV feedbacks, particularly over the southern extratropics, drive model variability in the global feedbacks. The cross-model correlation between the global LR and WV feedbacks therefore does not arise from their covariation in the tropics, but rather from the pattern of warming exerting a common control on extratropical feedback responses. Because local feedbacks over the Southern Hemisphere are an important contributor to the global feedback, the partitioning of surface warming between the tropics and the southern extratropics is a key determinant of the spread in the global LR and WV feedbacks. It is also shown that model Antarctic sea ice climatology influences sea ice area changes and southern extratropical surface warming. As a result, model discrepancies in climatological Antarctic sea ice area have a significant impact on the intermodel spread of the global LR and WV feedbacks.

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16

E. Schlesinger, Michael, C. Bruce Entwistle, and Bin Li. "Temperature-Profile/Lapse-Rate Feedback: A Misunderstood Feedback of the Climate System." Atmospheric and Climate Sciences 02, no. 04 (2012): 474–78. http://dx.doi.org/10.4236/acs.2012.24041.

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17

De Pascalis, Vilfredo, Antonella Anello, and Riccardo Venturing. "Changes in Heart Rate during Feedback Control of Respiration." Perceptual and Motor Skills 63, no. 1 (August 1986): 87–96. http://dx.doi.org/10.2466/pms.1986.63.1.87.

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Subjects received one of eight treatments: (1) practice at increasing heart rate with heart-rate feedback, (2) practice at decreasing heart rate with heart-rate feedback, (3) practice at increasing heart rate without heart-rate feedback, (4) practice at decreasing heart rate without heart-rate feedback, (5) practice at increasing respiration rate with respiratory feedback, (6) practice at decreasing respiration rate with respiratory feedback, (7) practice at increasing respiration rate with respiratory instructions only, (8) practice at decreasing respiration rate with respiratory instructions only. Heart rate, Respiration rate, and Respiration depth were measured. Analysis indicated that (a) subjects who controlled respiration with respiratory feedback reliably increased and decreased heart rate; (b) subjects who controlled respiration with respiratory instructions only reliably increased but not decreased heart rate; (c) subjects in the respiratory-feedback conditions showed higher heart-rate increase and decrease than heart-rate increase and decrease of subjects in the other six conditions.

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18

Jindal, N. "MIMO Broadcast Channels With Finite-Rate Feedback." IEEE Transactions on Information Theory 52, no. 11 (November 2006): 5045–60. http://dx.doi.org/10.1109/tit.2006.883550.

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19

Galloway, J. Andrew. "Auditory Feedback for Golfers' Face Closure Rate." Journal of the Acoustical Society of America 130, no. 2 (2011): 1088. http://dx.doi.org/10.1121/1.3625679.

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20

Bross, Shraga I., and Amos Lapidoth. "The Rate-and-State Capacity with Feedback." IEEE Transactions on Information Theory 64, no. 3 (March 2018): 1893–918. http://dx.doi.org/10.1109/tit.2017.2777389.

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21

Ardestanizadeh, Ehsan, Massimo Franceschetti, Tara Javidi, and Young-Han Kim. "Wiretap Channel With Secure Rate-Limited Feedback." IEEE Transactions on Information Theory 55, no. 12 (December 2009): 5353–61. http://dx.doi.org/10.1109/tit.2009.2032814.

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22

Li, K., and J. Baillieul. "Data-rate requirements for nonlinear Feedback control." IFAC Proceedings Volumes 37, no. 13 (September 2004): 997–1002. http://dx.doi.org/10.1016/s1474-6670(17)31356-3.

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23

Bose, N. K., and C. Charoenlarpnopparut. "Minimax controller using rate feedback: Latest results." IFAC Proceedings Volumes 32, no. 2 (July 1999): 3714–19. http://dx.doi.org/10.1016/s1474-6670(17)56635-5.

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24

Amaral, Luı́s A. Nunes, Ary L. Goldberger, Plamen Ch Ivanov, and H. Eugene Stanley. "Modeling heart rate variability by stochastic feedback." Computer Physics Communications 121-122 (September 1999): 126–28. http://dx.doi.org/10.1016/s0010-4655(99)00295-7.

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25

Huang, B., L. Xu, and D. Chen. "Slew rate enhancement via excessive transient feedback." Electronics Letters 49, no. 15 (July 2013): 930–32. http://dx.doi.org/10.1049/el.2013.0496.

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26

Steptoe, Andrew, and Frank Eves. "Ambulatory heart rate feedback: A preliminary study." Biological Psychology 23, no. 1 (August 1986): 88. http://dx.doi.org/10.1016/0301-0511(86)90118-3.

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27

Li, Liang, Marius Pesavento, and Alex B. Gershman. "Downlink Opportunistic Scheduling with Low-Rate Channel State Feedback: Error Rate Analysis and Optimization of the Feedback Parameters." IEEE Transactions on Communications 58, no. 10 (October 2010): 2871–80. http://dx.doi.org/10.1109/tcomm.2010.083110.090183.

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28

Shell, Karen M., and Richard C. J. Somerville. "A Generalized Energy Balance Climate Model with Parameterized Dynamics and Diabatic Heating." Journal of Climate 18, no. 11 (June 1, 2005): 1753–72. http://dx.doi.org/10.1175/jcli3373.1.

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Abstract Energy balance models have proven useful in understanding mechanisms and feedbacks in the climate system. An original global energy balance model is presented here. The model is solved numerically for equilibrium climate states defined by zonal average temperature as a function of latitude for both a surface and an atmospheric layer. The effects of radiative, latent, and sensible heating are parameterized. The model includes a variable lapse rate and parameterizations of the major dynamical mechanisms responsible for meridional heat transport: the Hadley cell, midlatitude baroclinic eddies, and ocean circulation. The model reproduces both the mean variation of temperature with latitude and the global average heat budget within the uncertainty of observations. The utility of the model is demonstrated through examination of various climate feedbacks. One important feedback is the effect of the lapse rate on climate. When the planet warms as a result of an increase in the solar constant, the lapse rate acts as a negative feedback, effectively enhancing the longwave emission efficiency of the atmosphere. The lapse rate is also responsible for an increase in global average temperature when the meridional heat transport effectiveness is increased. The water vapor feedback enhances temperature changes, while the latent and sensible heating feedback reduces surface temperature changes.

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29

Pfister, Patrik L., and Thomas F. Stocker. "Changes in Local and Global Climate Feedbacks in the Absence of Interactive Clouds: Southern Ocean–Climate Interactions in Two Intermediate-Complexity Models." Journal of Climate 34, no. 2 (January 2021): 755–72. http://dx.doi.org/10.1175/jcli-d-20-0113.1.

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AbstractThe global-mean climate feedback quantifies how much the climate system will warm in response to a forcing such as increased CO2 concentration. Under a constant forcing, this feedback becomes less negative (increasing) over time in comprehensive climate models, which has been attributed to increases in cloud and lapse-rate feedbacks. However, out of eight Earth system models of intermediate complexity (EMICs) not featuring interactive clouds, two also simulate such a feedback increase: Bern3D-LPX and LOVECLIM. Using these two models, we investigate the causes of the global-mean feedback increase in the absence of cloud feedbacks. In both models, the increase is predominantly driven by processes in the Southern Ocean region. In LOVECLIM, the global-mean increase is mainly due to a local longwave feedback increase in that region, which can be attributed to lapse-rate changes. It is enhanced by the slow atmospheric warming above the Southern Ocean, which is delayed due to regional ocean heat uptake. In Bern3D-LPX, this delayed regional warming is the main driver of the global-mean feedback increase. It acts on a near-constant local feedback pattern mainly determined by the sea ice–albedo feedback. The global-mean feedback increase is limited by the availability of sea ice: faster Southern Ocean sea ice melting due to either stronger forcing or higher equilibrium climate sensitivity (ECS) reduces the increase of the global mean feedback in Bern3D-LPX. In the highest-ECS simulation with 4 × CO2 forcing, the feedback even becomes more negative (decreasing) over time. This reduced ice–albedo feedback due to sea ice depletion is a plausible mechanism for a decreasing feedback also in high-forcing simulations of other models.

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30

Eiselt, Kai-Uwe, and Rune Grand Graversen. "Change in Climate Sensitivity and Its Dependence on the Lapse-Rate Feedback in 4 × CO2 Climate Model Experiments." Journal of Climate 35, no. 9 (May 1, 2022): 2919–32. http://dx.doi.org/10.1175/jcli-d-21-0623.1.

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Abstract Robust estimates of climate sensitivity are important for decision-making on mitigation of climate change. However, climate sensitivity and its governing processes are still subject to large uncertainty. Recently it has been established that climate sensitivity changes over time in numerical climate model experiments with abrupt quadrupling of the CO2 concentration. Here we conduct an analysis of such experiments from a range of climate models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP). Climate feedbacks associated with clouds, lapse rate, Planck radiation, surface albedo, and water vapor and their changes over time are diagnosed based on a radiative kernel method. We find two clearly distinct model groups, one with weak and one with strong lapse-rate feedback change. The Arctic is the region showing the largest differences between these two model groups, with respect to both warming change and individual feedback changes. We retrace this change to the development over time of the Arctic sea ice, which impacts both the surface-albedo and lapse-rate feedbacks. Generally, models that warm quickly, both globally and in the Arctic, also quickly lose their Arctic sea ice and change their total global-mean climate feedback only little, and vice versa. However, it remains unclear if the Arctic changes are a cause or rather a by-product of the total global-mean feedback change. Finally, we find support for the results of previous studies finding that the relative warming in the tropical Indo-Pacific region may control the change of total climate feedback over time.

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31

Liu, Fei, and Bin Wang. "Roles of the Moisture and Wave Feedbacks in Shaping the Madden–Julian Oscillation." Journal of Climate 30, no. 24 (December 2017): 10275–91. http://dx.doi.org/10.1175/jcli-d-17-0003.1.

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This study investigates the moisture and wave feedbacks in the Madden–Julian oscillation (MJO) dynamics by applying the general three-way interaction theoretical model. The three-way interaction model can reproduce observed large-scale characteristics of the MJO in terms of horizontal quadrupole-vortex structure, vertically tilted structure led by planetary boundary layer (PBL) convergence, slow eastward propagation with a period of 30–90 days, and planetary-scale circulation. The moisture feedback effects can be identified in this model by using diagnostic thermodynamic and momentum equations, and the wave feedback effects are investigated by using a diagnostic moisture equation. The moisture feedback is found to be responsible for producing the MJO dispersive modes when the convective adjustment process is slow. The moisture feedback mainly acts to reduce the frequency and growth rate of the short waves, while leaving the planetary waves less affected, so neglecting the moisture feedback is a good approximation for the wavenumber-1 MJO. The wave feedback is shown to slow down the eastward propagation and increase the growth rate of the planetary waves. The wave feedback becomes weak when the convective adjustment time increases, so neglecting the wave feedback is a good approximation for the MJO dynamics during a slow adjustment process. Sensitivities of these two feedbacks to other parameters are also discussed. These theoretical findings suggest that the two feedback processes, and thus the behaviors of the simulated MJO mode, should be sensitive to the parameters used in cumulus parameterizations.

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32

Lee, J. W. "Achievable rate of statistical beamforming with finite-rate transmit correlation feedback." Electronics Letters 46, no. 10 (2010): 726. http://dx.doi.org/10.1049/el.2010.0847.

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33

Kaur, Harpreet, Govindasamy Bala, and Ashwin K. Seshadri. "Why Is Climate Sensitivity for Solar Forcing Smaller than for an Equivalent CO2 Forcing?" Journal of Climate 36, no. 3 (February 1, 2023): 775–89. http://dx.doi.org/10.1175/jcli-d-21-0980.1.

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Abstract Previous studies have shown that climate sensitivity, defined as the global mean surface temperature change per unit radiative forcing, is smaller for solar radiative forcing compared to an equivalent CO2 radiative forcing. We investigate the causes for this difference using the NCAR CAM4 model. The contributions to the climate feedback parameter, which is inversely related to climate sensitivity, are estimated for water vapor, lapse rate, Planck, albedo, and cloud feedbacks using the radiative kernel technique. The total feedback estimated for CO2 and solar radiative forcing from our model simulations is −1.23 and −1.45 W m−2 K−1, respectively. We find that the difference in feedback between the two cases is primarily due to differences in lapse rate, water vapor, and cloud feedbacks, which together explain 65% of the difference in total feedback. The rest comes from Planck and albedo feedbacks. The differences in feedbacks arise mainly from differences in the horizontal (meridional) structure of forcing and the consequent warming. Our study provides important insights into the effects of the meridional structure of forcing on climate feedback, which is important for evaluating global climate change from different forcing agents. Significance Statement An increase in atmospheric CO2 concentration or an increase in incoming solar radiation leads to a rise in the radiative budget and consequent climate warming, which is amplified by the presence of multiple climate feedbacks. These feedbacks, from changes in surface albedo, combined effect of water vapor and the vertical lapse rate of temperature, and changes in clouds, differ between solar and CO2 forcing. Using radiative kernels, this study quantifies these individual feedbacks for an equivalent radiative change caused by an increase in CO2 or incoming solar radiation, showing how the differences arise from differences in the meridional patterns of warming. In agreement with prior studies, these differences can explain the smaller efficacy of solar forcing compared to CO2 forcing.

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34

Zhou, Shengli, Baosheng Li, and Peter Willett. "Recursive and Trellis-Based Feedback Reduction for MIMO-OFDM with Rate-Limited Feedback." IEEE Transactions on Wireless Communications 5, no. 12 (December 2006): 3400–3405. http://dx.doi.org/10.1109/twc.2006.256963.

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35

Port, U., M. Claussen, and V. Brovkin. "Radiative forcing by forest and subsequent feedbacks in the early Eocene climate." Climate of the Past Discussions 11, no. 2 (March 30, 2015): 997–1029. http://dx.doi.org/10.5194/cpd-11-997-2015.

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Abstract. Using the Max Planck Institute for Meteorology Earth System Model, we investigate the forcing of forests and the feedback triggered by forests in the pre-industrial climate and in the early Eocene climate (about 54 to 52 million years ago). Other than the interglacial, pre-industrial climate, the early Eocene climate was characterised by high temperatures which led to almost ice-free poles. We compare simulations in which all continents are covered either by dense forest or by bare soil. To isolate the effect of soil albedo, we choose either bright soils or dark soils, respectively. Considering bright soil, forests warm in both, the early Eocene climate and the current climate, but the warming differs due to differences in climate feedbacks. The lapse-rate and water-vapour feedback is stronger in early Eocene climate than in current climate, but strong and negative cloud feedbacks and cloud masking in the early Eocene climate outweigh the stronger positive lapse-rate and water-vapour feedback. In the sum, global mean warming is weaker in the early Eocene climate. Sea-ice related feedbacks are weak in the almost ice-free climate of the early Eocene leading to a weak polar amplification. Considering dark soil, our results change. Forests cools stronger in the early Eocene climate than in the current climate because the lapse-rate and water-vapour feedback is stronger in the early Eocene climate while cloud feedbacks and cloud masking are equally strong in both climates. The different temperature change by forest in both climates highlights the state-dependency of vegetation's impact on climate.

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36

Soto, Paul L., Jack J. McDowell, and Jesse Dallery. "FEEDBACK FUNCTIONS, OPTIMIZATION, AND THE RELATION OF RESPONSE RATE TO REINFORCER RATE." Journal of the Experimental Analysis of Behavior 85, no. 1 (January 2006): 57–71. http://dx.doi.org/10.1901/jeab.2006.13-05.

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37

O’Rourke, Shawn, and Tim Sawicki. "Online Tools for Improving Student Feedback Reading Rate." International Journal for Digital Society 9, no. 2 (June 30, 2018): 1387–92. http://dx.doi.org/10.20533/ijds.2040.2570.2018.0171.

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38

ISHII, Hideaki, and Koji TSUMURA. "Data Rate Limitations in Feedback Control over Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95-A, no. 4 (2012): 680–90. http://dx.doi.org/10.1587/transfun.e95.a.680.

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39

Nair, Girish N., Fabio Fagnani, Sandro Zampieri, and Robin J. Evans. "Feedback Control Under Data Rate Constraints: An Overview." Proceedings of the IEEE 95, no. 1 (January 2007): 108–37. http://dx.doi.org/10.1109/jproc.2006.887294.

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40

Belyea, Lisa R., and R. S. Clymo. "Feedback control of the rate of peat formation." Proceedings of the Royal Society of London. Series B: Biological Sciences 268, no. 1473 (June 22, 2001): 1315–21. http://dx.doi.org/10.1098/rspb.2001.1665.

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41

Sadka, A. H., F. Eryurtlu, and A. M. Kondoz. "Rate control feedback mechanism for packet video networks." Electronics Letters 32, no. 8 (1996): 716. http://dx.doi.org/10.1049/el:19960480.

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42

Eswaran, Krishnan, Anand D. Sarwate, Anant Sahai, and Michael C. Gastpar. "Zero-Rate Feedback Can Achieve the Empirical Capacity." IEEE Transactions on Information Theory 56, no. 1 (January 2010): 25–39. http://dx.doi.org/10.1109/tit.2009.2034779.

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43

Cole, Daniel G., and William R. Saunders. "Acoustic absorptivity of direct acoustic rate feedback control." Journal of the Acoustical Society of America 97, no. 5 (May 1995): 3339. http://dx.doi.org/10.1121/1.412799.

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44

Jammeh, E. A., M. Fleury, and M. Ghanbari. "Rate-adaptive video streaming through packet dispersion feedback." IET Communications 3, no. 1 (2009): 25. http://dx.doi.org/10.1049/iet-com:20080047.

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45

Horner, S. M., C. F. Murphy, B. Coen, D. J. Dick, F. G. Harrison, Z. Vespalcova, and M. J. Lab. "Contribution to Heart Rate Variability by Mechanoelectric Feedback." Circulation 94, no. 7 (October 1996): 1762–67. http://dx.doi.org/10.1161/01.cir.94.7.1762.

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46

González-Otero, Digna M., Sofía Ruiz de Gauna, Jesus Ruiz, Mohamud R. Daya, Lars Wik, James K. Russell, Jo Kramer-Johansen, Trygve Eftestøl, Erik Alonso, and Unai Ayala. "Chest compression rate feedback based on transthoracic impedance." Resuscitation 93 (August 2015): 82–88. http://dx.doi.org/10.1016/j.resuscitation.2015.05.027.

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47

Kirkpatrick, Michael A., and Andrew S. Groves. "Verbal Feedback Facilitates Heart Rate Discrimination and Differentiation." European Journal of Behavior Analysis 12, no. 2 (December 2011): 431–39. http://dx.doi.org/10.1080/15021149.2011.11434393.

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48

Chen, Zhuo, Iain B. Collings, Zhendong Zhou, and Branka Vucetic. "Transmit antenna selection schemes with reduced feedback rate." IEEE Transactions on Wireless Communications 8, no. 2 (February 2009): 1006–16. http://dx.doi.org/10.1109/twc.2009.080296.

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49

Marques, A. G., G. B. Giannakis, F. F. Digham, and F. J. Ramos. "Power-efficient wireless OFDMA using limited-rate feedback." IEEE Transactions on Wireless Communications 7, no. 2 (February 2008): 685–96. http://dx.doi.org/10.1109/twc.2008.060629.

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50

Ferrante, Francesco, and Luca Zaccarian. "Dynamic reset output feedback with guaranteed convergence rate." IFAC-PapersOnLine 52, no. 16 (2019): 102–7. http://dx.doi.org/10.1016/j.ifacol.2019.11.763.

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