Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Cloud feedback.
Статті в журналах з теми "Cloud feedback"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Cloud feedback".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Zhou, Chen, Mark D. Zelinka, Andrew E. Dessler, and Ping Yang. "An Analysis of the Short-Term Cloud Feedback Using MODIS Data." Journal of Climate 26, no. 13 (July 1, 2013): 4803–15. http://dx.doi.org/10.1175/jcli-d-12-00547.1.
Повний текст джерелаАнотація:
Abstract The cloud feedback in response to short-term climate variations is estimated from cloud measurements combined with offline radiative transfer calculations. The cloud measurements are made by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra satellite and cover the period 2000–10. Low clouds provide a strong negative cloud feedback, mainly because of their impact in the shortwave (SW) portion of the spectrum. Midlevel clouds provide a positive net cloud feedback that is a combination of a positive SW feedback partially canceled by a negative feedback in the longwave (LW). High clouds have only a small impact on the net cloud feedback because of a close cancellation between large LW and SW cloud feedbacks. Segregating the clouds by optical depth, it is found that the net cloud feedback is set by a positive cloud feedback due to reductions in the thickest clouds (mainly in the SW) and a cancelling negative feedback from increases in clouds with moderate optical depths (also mainly in the SW). The global average SW, LW, and net cloud feedbacks are +0.30 ±1.10, −0.46 ±0.74, and −0.16 ±0.83 W m−2 K−1, respectively. The SW feedback is consistent with previous work; the MODIS LW feedback is lower than previous calculations and there are reasons to suspect it may be biased low. Finally, it is shown that the apparently small control that global mean surface temperature exerts on clouds, which leads to the large uncertainty in the short-term cloud feedback, arises from statistically significant but offsetting relationships between individual cloud types and global mean surface temperature.
2
Zelinka, Mark D., Stephen A. Klein, Karl E. Taylor, Timothy Andrews, Mark J. Webb, Jonathan M. Gregory, and Piers M. Forster. "Contributions of Different Cloud Types to Feedbacks and Rapid Adjustments in CMIP5*." Journal of Climate 26, no. 14 (July 12, 2013): 5007–27. http://dx.doi.org/10.1175/jcli-d-12-00555.1.
Повний текст джерелаАнотація:
Abstract Using five climate model simulations of the response to an abrupt quadrupling of CO2, the authors perform the first simultaneous model intercomparison of cloud feedbacks and rapid radiative adjustments with cloud masking effects removed, partitioned among changes in cloud types and gross cloud properties. Upon CO2 quadrupling, clouds exhibit a rapid reduction in fractional coverage, cloud-top pressure, and optical depth, with each contributing equally to a 1.1 W m−2 net cloud radiative adjustment, primarily from shortwave radiation. Rapid reductions in midlevel clouds and optically thick clouds are important in reducing planetary albedo in every model. As the planet warms, clouds become fewer, higher, and thicker, and global mean net cloud feedback is positive in all but one model and results primarily from increased trapping of longwave radiation. As was true for earlier models, high cloud changes are the largest contributor to intermodel spread in longwave and shortwave cloud feedbacks, but low cloud changes are the largest contributor to the mean and spread in net cloud feedback. The importance of the negative optical depth feedback relative to the amount feedback at high latitudes is even more marked than in earlier models. The authors show that the negative longwave cloud adjustment inferred in previous studies is primarily caused by a 1.3 W m−2 cloud masking of CO2 forcing. Properly accounting for cloud masking increases net cloud feedback by 0.3 W m−2 K−1, whereas accounting for rapid adjustments reduces by 0.14 W m−2 K−1 the ensemble mean net cloud feedback through a combination of smaller positive cloud amount and altitude feedbacks and larger negative optical depth feedbacks.
3
Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part I: Cloud Radiative Kernels." Journal of Climate 25, no. 11 (June 2012): 3715–35. http://dx.doi.org/10.1175/jcli-d-11-00248.1.
Повний текст джерелаАнотація:
This study proposes a novel technique for computing cloud feedbacks using histograms of cloud fraction as a joint function of cloud-top pressure (CTP) and optical depth (τ). These histograms were generated by the International Satellite Cloud Climatology Project (ISCCP) simulator that was incorporated into doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. The authors use a radiative transfer model to compute top of atmosphere flux sensitivities to cloud fraction perturbations in each bin of the histogram for each month and latitude. Multiplying these cloud radiative kernels with histograms of modeled cloud fraction changes at each grid point per unit of global warming produces an estimate of cloud feedback. Spatial structures and globally integrated cloud feedbacks computed in this manner agree remarkably well with the adjusted change in cloud radiative forcing. The global and annual mean model-simulated cloud feedback is dominated by contributions from medium thickness (3.6 < τ ≤ 23) cloud changes, but thick (τ > 23) cloud changes cause the rapid transition of cloud feedback values from positive in midlatitudes to negative poleward of 50°S and 70°N. High (CTP ≤ 440 hPa) cloud changes are the dominant contributor to longwave (LW) cloud feedback, but because their LW and shortwave (SW) impacts are in opposition, they contribute less to the net cloud feedback than do the positive contributions from low (CTP > 680 hPa) cloud changes. Midlevel (440 < CTP ≤ 680 hPa) cloud changes cause positive SW cloud feedbacks that are 80% as large as those due to low clouds. Finally, high cloud changes induce wider ranges of LW and SW cloud feedbacks across models than do low clouds.
4
Dawson, Emma, and Kathleen A. Schiro. "Tropical High Cloud Feedback Relationships to Climate Sensitivity." Journal of Climate 38, no. 2 (January 15, 2025): 583–96. https://doi.org/10.1175/jcli-d-24-0218.1.
Повний текст джерелаАнотація:
Abstract Clouds constitute a large portion of uncertainty in predictions of equilibrium climate sensitivity (ECS). While low cloud feedbacks have been the focus of intermodel studies due to their high variability among global climate models, tropical high cloud feedbacks also exhibit considerable uncertainty. Here, we apply the cloud radiative kernel technique of Zelinka et al. to 22 models across the CMIP5 and CMIP6 ensembles to survey tropical high cloud feedbacks and analyze their relationship to ECS. We find that the net high cloud feedback and its altitude and optical depth feedback components are significantly positively correlated with ECS in the tropical mean. On the other hand, the tropical mean high cloud amount feedback is not correlated with ECS. These relationships are most pronounced outside of areas of strong climatological ascent, suggesting the importance of thin cirrus feedbacks. Finally, we explore connections between high cloud feedbacks, climate sensitivity, and mean state high cloud properties. In general, high ECS models are cloudier in the upper troposphere but have a thinner high cloud population. Moreover, we find that having more thin cirrus in the mean state relates to more positive high cloud altitude and optical depth feedbacks, and it either amplifies or dampens the high cloud amount feedback depending on the large-scale dynamical regime (amplifying in descent and dampening in ascent). In summary, our analysis highlights the importance of tropical high cloud feedbacks for driving intermodel spread in ECS and suggests that mean state high cloud characteristics might provide a unique opportunity for observationally constraining high cloud feedbacks. Significance Statement Clouds play an important role in modulating the effects of climate change through feedback processes involving changes to their amount, altitude, and opacity. In this study, we seek to understand how changes to tropical high clouds under warming are related to the magnitude of warming that global climate models simulate. We find that tropical high cloud feedbacks robustly relate to the amount of warming a model predicts and that warmer models tend to have a thinner tropical high cloud climatology. Our results highlight a potential opportunity to form a new constraint using these relationships in order to narrow the spread of warming estimates among global climate models.
5
Yoshimori, Masakazu, F. Hugo Lambert, Mark J. Webb, and Timothy Andrews. "Fixed Anvil Temperature Feedback: Positive, Zero, or Negative?" Journal of Climate 33, no. 7 (April 1, 2020): 2719–39. http://dx.doi.org/10.1175/jcli-d-19-0108.1.
Повний текст джерелаАнотація:
AbstractThe fixed anvil temperature (FAT) theory describes a mechanism for how tropical anvil clouds respond to global warming and has been used to argue for a robust positive longwave cloud feedback. A constant cloud anvil temperature, due to increased anvil altitude, has been argued to lead to a “zero cloud emission change” feedback, which can be considered positive relative to the negative feedback associated with cloud anvil warming when cloud altitude is unchanged. Here, partial radiative perturbation (PRP) analysis is used to quantify the radiative feedback caused by clouds that follow the FAT theory (FAT–cloud feedback) and to set this in the context of other feedback components in two atmospheric general circulation models. The FAT–cloud feedback is positive in the PRP framework due to increasing anvil altitude, but because the cloud emission does not change, this positive feedback is cancelled by an equal and opposite component of the temperature feedback due to increasing emission from the cloud. To incorporate this cancellation, the thermal radiative damping with fixed relative humidity and anvil temperature (T-FRAT) decomposition framework is proposed for longwave feedbacks, in which temperature, fixed relative humidity, and FAT–cloud feedbacks are combined. In T-FRAT, the cloud feedback under the FAT constraint is zero, while that under the proportionately higher anvil temperature (PHAT) constraint is negative. The change in the observable cloud radiative effect with FAT–cloud response is also evaluated and shown to be negative due to so-called cloud masking effects. It is shown that “cloud masking” is a misleading term in this context, and these effects are interpreted more generally as “cloud climatology effects.”
6
Zhu, Ping, James J. Hack, and Jeffrey T. Kiehl. "Diagnosing Cloud Feedbacks in General Circulation Models." Journal of Climate 20, no. 11 (June 1, 2007): 2602–22. http://dx.doi.org/10.1175/jcli4140.1.
Повний текст джерелаАнотація:
Abstract In this study, it is shown that the NCAR and GFDL GCMs exhibit a marked difference in climate sensitivity of clouds and radiative fluxes in response to doubled CO2 and ±2-K SST perturbations. The GFDL model predicted a substantial decrease in cloud amount and an increase in cloud condensate in the warmer climate, but produced a much weaker change in net cloud radiative forcing (CRF) than the NCAR model. Using a multiple linear regression (MLR) method, the full-sky radiative flux change at the top of the atmosphere was successfully decomposed into individual components associated with the clear sky and different types of clouds. The authors specifically examined the cloud feedbacks due to the cloud amount and cloud condensate changes involving low, mid-, and high clouds between 60°S and 60°N. It was found that the NCAR and GFDL models predicted the same sign of individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate for all three types of clouds (low, mid, and high) despite the different cloud and radiation schemes used in the models. However, since the individual longwave and shortwave feedbacks resulting from the change in cloud amount and cloud condensate generally have the opposite signs, the net cloud feedback is a subtle residual of all. Strong cancellations between individual cloud feedbacks may result in a weak net cloud feedback. This result is consistent with the findings of the previous studies, which used different approaches to diagnose cloud feedbacks. This study indicates that the proposed MLR approach provides an easy way to efficiently expose the similarity and discrepancy of individual cloud feedback processes between GCMs, which are hidden in the total cloud feedback measured by CRF. Most importantly, this method has the potential to be applied to satellite measurements. Thus, it may serve as a reliable and efficient method to investigate cloud feedback mechanisms on short-term scales by comparing simulations with available observations, which may provide a useful way to identify the cause for the wide spread of cloud feedbacks in GCMs.
7
Zelinka, Mark D., Stephen A. Klein, and Dennis L. Hartmann. "Computing and Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II: Attribution to Changes in Cloud Amount, Altitude, and Optical Depth." Journal of Climate 25, no. 11 (June 2012): 3736–54. http://dx.doi.org/10.1175/jcli-d-11-00249.1.
Повний текст джерелаАнотація:
Cloud radiative kernels and histograms of cloud fraction, both as functions of cloud-top pressure and optical depth, are used to quantify cloud amount, altitude, and optical depth feedbacks. The analysis is applied to doubled-CO2 simulations from 11 global climate models in the Cloud Feedback Model Intercomparison Project. Global, annual, and ensemble mean longwave (LW) and shortwave (SW) cloud feedbacks are positive, with the latter nearly twice as large as the former. The robust increase in cloud-top altitude in both the tropics and extratropics is the dominant contributor to the positive LW cloud feedback. The negative impact of reductions in cloud amount offsets more than half of the positive impact of rising clouds on LW cloud feedback, but the magnitude of compensation varies considerably across the models. In contrast, robust reductions in cloud amount make a large and virtually unopposed positive contribution to SW cloud feedback, though the intermodel spread is greater than for any other individual feedback component. Overall reductions in cloud amount have twice as large an impact on SW fluxes as on LW fluxes, such that the net cloud amount feedback is moderately positive, with no models exhibiting a negative value. As a consequence of large but partially offsetting effects of cloud amount reductions on LW and SW feedbacks, both the mean and intermodel spread in net cloud amount feedback are smaller than those of the net cloud altitude feedback. Finally, the study finds that the large negative cloud feedback at high latitudes results from robust increases in cloud optical depth, not from increases in total cloud amount as is commonly assumed.
8
Sun, De-Zheng, Yongqiang Yu, and Tao Zhang. "Tropical Water Vapor and Cloud Feedbacks in Climate Models: A Further Assessment Using Coupled Simulations." Journal of Climate 22, no. 5 (March 1, 2009): 1287–304. http://dx.doi.org/10.1175/2008jcli2267.1.
Повний текст джерелаАнотація:
Abstract By comparing the response of clouds and water vapor to ENSO forcing in nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed the following two common biases in the models: 1) an underestimate of the strength of the negative cloud albedo feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other Intergovernmental Panel on Climate Change (IPCC) coupled models, it is found that these two biases persist. Relative to the earlier estimates from AMIP simulations, the overestimate of the positive feedback from water vapor is alleviated somewhat for most of the coupled simulations. Improvements in the simulation of the cloud albedo feedback are only found in the models whose AMIP runs suggest either a positive or nearly positive cloud albedo feedback. The strength of the negative cloud albedo feedback in all other models is found to be substantially weaker than that estimated from the corresponding AMIP simulations. Consequently, although additional models are found to have a cloud albedo feedback in their AMIP simulations that is as strong as in the observations, all coupled simulations analyzed in this study have a weaker negative feedback from the cloud albedo and therefore a weaker negative feedback from the net surface heating than that indicated in observations. The weakening in the cloud albedo feedback is apparently linked to a reduced response of deep convection over the equatorial Pacific, which is in turn linked to the excessive cold tongue in the mean climate of these models. The results highlight that the feedbacks of water vapor and clouds—the cloud albedo feedback in particular—may depend on the mean intensity of the hydrological cycle. Whether the intermodel variations in the feedback from cloud albedo (water vapor) in the ENSO variability are correlated with the intermodel variations of the feedback from cloud albedo (water vapor) in global warming has also been examined. While a weak positive correlation between the intermodel variations in the feedback of water vapor during ENSO and the intermodel variations in the water vapor feedback during global warming was found, there is no significant correlation found between the intermodel variations in the cloud albedo feedback during ENSO and the intermodel variations in the cloud albedo feedback during global warming. The results suggest that the two common biases revealed in the simulated ENSO variability may not necessarily be carried over to the simulated global warming. These biases, however, highlight the continuing difficulty that models have in simulating accurately the feedbacks of water vapor and clouds on a time scale of the observations available.
9
Zhang, Minghua, and Christopher Bretherton. "Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3)." Journal of Climate 21, no. 18 (September 15, 2008): 4859–78. http://dx.doi.org/10.1175/2008jcli2237.1.
Повний текст джерелаАнотація:
Abstract This study investigates the physical mechanism of low cloud feedback in the Community Atmospheric Model, version 3 (CAM3) through idealized single-column model (SCM) experiments over the subtropical eastern oceans. Negative cloud feedback is simulated from stratus and stratocumulus that is consistent with previous diagnostics of cloud feedbacks in CAM3 and its predecessor versions. The feedback occurs through the interaction of a suite of parameterized processes rather than from any single process. It is caused by the larger amount of in-cloud liquid water in stratus clouds from convective sources, and longer lifetimes of these clouds in a warmer climate through their interaction with boundary layer turbulence. Thermodynamic effects are found to dominate the negative cloud feedback in the model. The dynamic effect of weaker subsidence in a warmer climate also contributes to the negative cloud feedback, but with about one-quarter of the magnitude of the thermodynamic effect, owing to increased low-level convection in a warmer climate.
10
Lohmann, Ulrike, and David Neubauer. "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2." Atmospheric Chemistry and Physics 18, no. 12 (June 22, 2018): 8807–28. http://dx.doi.org/10.5194/acp-18-8807-2018.
Повний текст джерелаАнотація:
Abstract. How clouds change in a warmer climate remains one of the largest uncertainties for the equilibrium climate sensitivity (ECS). While a large spread in the cloud feedback arises from low-level clouds, it was recently shown that mixed-phase clouds are also important for ECS. If mixed-phase clouds in the current climate contain too few supercooled cloud droplets, too much ice will change to liquid water in a warmer climate. As shown by Tan et al. (2016), this overestimates the negative cloud-phase feedback and underestimates ECS in the CAM global climate model (GCM). Here we use the newest version of the ECHAM6-HAM2 GCM to investigate the importance of mixed-phase and ice clouds for ECS. Although we also considerably underestimate the fraction of supercooled liquid water globally in the reference version of the ECHAM6-HAM2 GCM, we do not obtain increases in ECS in simulations with more supercooled liquid water in the present-day climate, different from the findings by Tan et al. (2016). We hypothesize that it is not the global supercooled liquid water fraction that matters, but only how well low- and mid-level mixed-phase clouds with cloud-top temperatures in the mixed-phase temperature range between 0 and −35 ∘C that are not shielded by higher-lying ice clouds are simulated. These occur most frequently in midlatitudes, in particular over the Southern Ocean where they determine the amount of absorbed shortwave radiation. In ECHAM6-HAM2 the amount of absorbed shortwave radiation over the Southern Ocean is only significantly overestimated if all clouds below 0 ∘C consist exclusively of ice. Only in this simulation is ECS significantly smaller than in all other simulations and the cloud optical depth feedback is the dominant cloud feedback. In all other simulations, the cloud optical depth feedback is weak and changes in cloud feedbacks associated with cloud amount and cloud-top pressure dominate the overall cloud feedback. However, apart from the simulation with only ice below 0 ∘C, differences in the overall cloud feedback are not translated into differences in ECS in our model. This insensitivity to the cloud feedback in our model is explained with compensating effects in the clear sky.
11
Dagan, Guy. "Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency." Atmospheric Chemistry and Physics 22, no. 24 (December 16, 2022): 15767–75. http://dx.doi.org/10.5194/acp-22-15767-2022.
Повний текст джерелаАнотація:
Abstract. How Earth's climate reacts to anthropogenic forcing is one of the most burning questions faced by today's scientific community. A leading source of uncertainty in estimating this sensitivity is related to the response of clouds. Under the canonical climate-change perspective of forcings and feedbacks, the effect of anthropogenic aerosols on clouds is categorized under the forcing component, while the modifications of the radiative properties of clouds due to climate change are considered in the feedback component. Each of these components contributes the largest portion of uncertainty to its relevant category and is largely studied separately from the other. In this paper, using idealized cloud-resolving radiative–convective-equilibrium simulations, with a slab ocean model, we show that aerosol–cloud interactions could affect cloud feedback. Specifically, we show that equilibrium climate sensitivity increases under high aerosol concentration due to an increase in the short-wave cloud feedback. The short-wave cloud feedback is enhanced under high-aerosol conditions due to a stronger increase in the precipitation efficiency with warming, which can be explained by higher sensitivity of the droplet size and the cloud water content to the CO2 concentration rise. These results indicate a possible connection between cloud feedback and aerosol–cloud interactions.
12
Webb, Mark J., Adrian P. Lock, Christopher S. Bretherton, Sandrine Bony, Jason N. S. Cole, Abderrahmane Idelkadi, Sarah M. Kang, et al. "The impact of parametrized convection on cloud feedback." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2054 (November 13, 2015): 20140414. http://dx.doi.org/10.1098/rsta.2014.0414.
Повний текст джерелаАнотація:
We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that ‘ConvOff’ models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed.
13
Lee, Wan-Ho, and Richard C. J. Somerville. "Effects of alternative cloud radiation parameterizations in a general circulation model." Annales Geophysicae 14, no. 1 (January 31, 1996): 107–14. http://dx.doi.org/10.1007/s00585-996-0107-6.
Повний текст джерелаАнотація:
Abstract. Using the National Center for Atmospheric Research (NCAR) general circulation model (CCM2), a suite of alternative cloud radiation parameterizations has been tested. Our methodology relies on perpetual July integrations driven by ±2 K sea surface temperature forcing. The tested parameterizations include relative humidity based clouds and versions of schemes involving a prognostic cloud water budget. We are especially interested in testing the effect of cloud optical thickness feedbacks on global climate sensitivity. All schemes exhibit negative cloud radiation feedbacks, i.e., cloud moderates the global warming. However, these negative net cloud radiation feedbacks consist of quite different shortwave and longwave components between a scheme with interactive cloud radiative properties and several schemes with specified cloud water paths. An increase in cloud water content in the warmer climate leads to optically thicker middle- and low-level clouds and in turn negative shortwave feedbacks for the interactive radiative scheme, while a decrease in cloud amount leads to a positive shortwave feedback for the other schemes. For the longwave feedbacks, a decrease in high effective cloudiness for the schemes without interactive radiative properties leads to a negative feedback, while no distinct changes in effective high cloudiness and the resulting feedback are exhibited for the scheme with interactive radiative properties. The resulting magnitude of negative net cloud radiation feed-back is largest for the scheme with interactive radiative properties. Even though the simulated values of cloud radiative forcing for the present climate using this method differ most from the observational data, the approach shows great promise for the future.
14
Fu, Q., M. Baker, and D. L. Hartmann. "Tropical cirrus and water vapor: an effective Earth infrared iris feedback?" Atmospheric Chemistry and Physics 2, no. 1 (January 30, 2002): 31–37. http://dx.doi.org/10.5194/acp-2-31-2002.
Повний текст джерелаАнотація:
Abstract. We revisit a model of feedback processes proposed by Lindzen et al. (2001), in which an assumed 22% reduction in the area of tropical high clouds per degree increase in sea surface temperature produces negative feedbacks associated with upper tropospheric water vapor and cloud radiative effects. We argue that the water vapor feedback is overestimated in Lindzen et al. (2001) by at least 60%, and that the high cloud feedback is small. Although not mentioned by Lindzen et al. (2001), tropical low clouds make a significant contribution to their negative feedback, which is also overestimated. Using more realistic parameters in the model of Lindzen et al. (2001), we obtain a feedback factor in the range of -0.15 to -0.51, compared to their larger negative feedback factor of -0.45 to -1.03. It is noted that our feedback factor could still be overestimated due to the assumption of constant low cloud cover in the simple radiative-convective model.
15
Gettelman, A., J. E. Kay, and J. T. Fasullo. "Spatial Decomposition of Climate Feedbacks in the Community Earth System Model." Journal of Climate 26, no. 11 (May 31, 2013): 3544–61. http://dx.doi.org/10.1175/jcli-d-12-00497.1.
Повний текст джерелаАнотація:
Abstract An ensemble of simulations from different versions of the Community Atmosphere Model in the Community Earth System Model (CESM) is used to investigate the processes responsible for the intermodel spread in climate sensitivity. In the CESM simulations, the climate sensitivity spread is primarily explained by shortwave cloud feedbacks on the equatorward flank of the midlatitude storm tracks. Shortwave cloud feedbacks have been found to explain climate sensitivity spread in previous studies, but the location of feedback differences was in the subtropics rather than in the storm tracks as identified in CESM. The cloud-feedback relationships are slightly stronger in the winter hemisphere. The spread in climate sensitivity in this study is related both to the cloud-base state and to the cloud feedbacks. Simulated climate sensitivity is correlated with cloud-fraction changes on the equatorward side of the storm tracks, cloud condensate in the storm tracks, and cloud microphysical state on the poleward side of the storm tracks. Changes in the extent and water content of stratiform clouds (that make up cloud feedback) are regulated by the base-state vertical velocity, humidity, and deep convective mass fluxes. Within the storm tracks, the cloud-base state affects the cloud response to CO2-induced temperature changes and alters the cloud feedbacks, contributing to climate sensitivity spread within the CESM ensemble.
16
Lauer, Axel, Kevin Hamilton, Yuqing Wang, Vaughan T. J. Phillips, and Ralf Bennartz. "The Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern Pacific—A Regional Model Study." Journal of Climate 23, no. 21 (November 1, 2010): 5844–63. http://dx.doi.org/10.1175/2010jcli3666.1.
Повний текст джерелаАнотація:
Abstract Cloud simulations and cloud–climate feedbacks in the tropical and subtropical eastern Pacific region in 16 state-of-the-art coupled global climate models (GCMs) and in the International Pacific Research Center (IPRC) Regional Atmospheric Model (iRAM) are examined. The authors find that the simulation of the present-day mean cloud climatology for this region in the GCMs is very poor and that the cloud–climate feedbacks vary widely among the GCMs. By contrast, iRAM simulates mean clouds and interannual cloud variations that are quite similar to those observed in this region. The model also simulates well the observed relationship between lower-tropospheric stability (LTS) and low-level cloud amount. To investigate cloud–climate feedbacks in iRAM, several global warming scenarios were run with boundary conditions appropriate for late twenty-first-century conditions. All the global warming cases simulated with iRAM show a distinct reduction in low-level cloud amount, particularly in the stratocumulus regime, resulting in positive local feedback parameters in these regions in the range of 4–7 W m−2 K−1. Domain-averaged (30°S–30°N, 150°–60°W) feedback parameters from iRAM range between +1.8 and +1.9 W m−2 K−1. At most locations both the LTS and cloud amount are altered in the global warming cases, but the changes in these variables do not follow the empirical relationship found in the present-day experiments. The cloud–climate feedback averaged over the same east Pacific region was also calculated from the Special Report on Emissions Scenarios (SRES) A1B simulations for each of the 16 GCMs with results that varied from −1.0 to +1.3 W m−2 K−1, all less than the values obtained in the comparable iRAM simulations. The iRAM results by themselves cannot be connected definitively to global climate feedbacks; however, among the global GCMs the cloud feedback in the full tropical–subtropical zone is correlated strongly with the east Pacific cloud feedback, and the cloud feedback largely determines the global climate sensitivity. The present iRAM results for cloud feedbacks in the east Pacific provide some support for the high end of current estimates of global climate sensitivity.
17
Yue, Qing, Brian H. Kahn, Eric J. Fetzer, Sun Wong, Xianglei Huang, and Mathias Schreier. "Temporal and Spatial Characteristics of Short-Term Cloud Feedback on Global and Local Interannual Climate Fluctuations from A-Train Observations." Journal of Climate 32, no. 6 (March 11, 2019): 1875–93. http://dx.doi.org/10.1175/jcli-d-18-0335.1.
Повний текст джерелаАнотація:
AbstractObservations from multiple sensors on the NASA Aqua satellite are used to estimate the temporal and spatial variability of short-term cloud responses (CR) and cloud feedbacks λ for different cloud types, with respect to the interannual variability within the A-Train era (July 2002–June 2017). Short-term cloud feedbacks by cloud type are investigated both globally and locally by three different definitions in the literature: 1) the global-mean cloud feedback parameter λGG from regressing the global-mean cloud-induced TOA radiation anomaly ΔRG with the global-mean surface temperature change ΔTGS; 2) the local feedback parameter λLL from regressing the local ΔR with the local surface temperature change ΔTS; and 3) the local feedback parameter λGL from regressing global ΔRG with local ΔTS. Observations show significant temporal variability in the magnitudes and spatial patterns in λGG and λGL, whereas λLL remains essentially time invariant for different cloud types. The global-mean net λGG exhibits a gradual transition from negative to positive in the A-Train era due to a less negative λGG from low clouds and an increased positive λGG from high clouds over the warm pool region associated with the 2015/16 strong El Niño event. Strong temporal variability in λGL is intrinsically linked to its dependence on global ΔRG, and the scaling of λGL with surface temperature change patterns to obtain global feedback λGG does not hold. Despite the shortness of the A-Train record, statistically robust signals can be obtained for different cloud types and regions of interest.
18
Gettelman, A., L. Lin, B. Medeiros, and J. Olson. "Climate Feedback Variance and the Interaction of Aerosol Forcing and Feedbacks." Journal of Climate 29, no. 18 (August 29, 2016): 6659–75. http://dx.doi.org/10.1175/jcli-d-16-0151.1.
Повний текст джерелаАнотація:
Abstract Aerosols can influence cloud radiative effects and, thus, may alter interpretation of how Earth’s radiative budget responds to climate forcing. Three different ensemble experiments from the same climate model with different greenhouse gas and aerosol scenarios are used to analyze the role of aerosols in climate feedbacks and their spread across initial condition ensembles of transient climate simulations. The standard deviation of global feedback parameters across ensemble members is low, typically 0.02 W m−2 K−1. Feedbacks from high (8.5 W m−2) and moderate (4.5 W m−2) year 2100 forcing cases are nearly identical. An aerosol kernel is introduced to remove effects of aerosol cloud interactions that alias into cloud feedbacks. Adjusted cloud feedbacks indicate an “aerosol feedback” resulting from changes to climate that increase sea-salt emissions, mostly in the Southern Ocean. Ensemble simulations also indicate higher tropical cloud feedbacks with higher aerosol loading. These effects contribute to a difference in cloud feedbacks of nearly 50% between ensembles of the same model. These two effects are also seen in aquaplanet simulations with varying fixed drop number. Thus aerosols can be a significant modifier of cloud feedbacks, and different representations of aerosols and their interactions with clouds may contribute to multimodel spread in climate feedbacks and climate sensitivity in multimodel archives.
19
Fu, Q., M. Baker, and D. L. Hartmann. "Tropical cirrus and water vapor: an effective Earth infrared iris feedback?" Atmospheric Chemistry and Physics Discussions 1, no. 1 (September 3, 2001): 221–38. http://dx.doi.org/10.5194/acpd-1-221-2001.
Повний текст джерелаАнотація:
Abstract. We revisit a model of feedback processes proposed by Lindzen et al. (2001), in which an assumed 22% reduction in the area of tropical high clouds per degree of sea surface temperature increase produces negative feedbacks associated with upper tropospheric water vapor and cloud radiative effects. We argue that the water vapor feedback is overestimated in Lindzen et al. (2001) by at least 60%, and that the high cloud feedback should be small. Although not mentioned by Lindzen et al, tropical low clouds make a significant contribution to their negative feedback, which is also overestimated. Using more realistic parameters in the model of Lindzen et al., we obtain a feedback factor in the range of −0.15 to −0.51, compared to their larger negative feedback factor of −0.45 to −1.03.
20
Bretherton, Christopher S. "Insights into low-latitude cloud feedbacks from high-resolution models." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2054 (November 13, 2015): 20140415. http://dx.doi.org/10.1098/rsta.2014.0415.
Повний текст джерелаАнотація:
Cloud feedbacks are a leading source of uncertainty in the climate sensitivity simulated by global climate models (GCMs). Low-latitude boundary-layer and cumulus cloud regimes are particularly problematic, because they are sustained by tight interactions between clouds and unresolved turbulent circulations. Turbulence-resolving models better simulate such cloud regimes and support the GCM consensus that they contribute to positive global cloud feedbacks. Large-eddy simulations using sub-100 m grid spacings over small computational domains elucidate marine boundary-layer cloud response to greenhouse warming. Four observationally supported mechanisms contribute: ‘thermodynamic’ cloudiness reduction from warming of the atmosphere–ocean column, ‘radiative’ cloudiness reduction from CO 2 - and H 2 O-induced increase in atmospheric emissivity aloft, ‘stability-induced’ cloud increase from increased lower tropospheric stratification, and ‘dynamical’ cloudiness increase from reduced subsidence. The cloudiness reduction mechanisms typically dominate, giving positive shortwave cloud feedback. Cloud-resolving models with horizontal grid spacings of a few kilometres illuminate how cumulonimbus cloud systems affect climate feedbacks. Limited-area simulations and superparameterized GCMs show upward shift and slight reduction of cloud cover in a warmer climate, implying positive cloud feedbacks. A global cloud-resolving model suggests tropical cirrus increases in a warmer climate, producing positive longwave cloud feedback, but results are sensitive to subgrid turbulence and ice microphysics schemes.
21
Dessler, A. E. "A Determination of the Cloud Feedback from Climate Variations over the Past Decade." Science 330, no. 6010 (December 9, 2010): 1523–27. http://dx.doi.org/10.1126/science.1192546.
Повний текст джерелаАнотація:
Estimates of Earth's climate sensitivity are uncertain, largely because of uncertainty in the long-term cloud feedback. I estimated the magnitude of the cloud feedback in response to short-term climate variations by analyzing the top-of-atmosphere radiation budget from March 2000 to February 2010. Over this period, the short-term cloud feedback had a magnitude of 0.54 ± 0.74 (2σ) watts per square meter per kelvin, meaning that it is likely positive. A small negative feedback is possible, but one large enough to cancel the climate’s positive feedbacks is not supported by these observations. Both long- and short-wave components of short-term cloud feedback are also likely positive. Calculations of short-term cloud feedback in climate models yield a similar feedback. I find no correlation in the models between the short- and long-term cloud feedbacks.
22
Zhang, Yuanchong, Zhonghai Jin, and Matteo Ottaviani. "Comparison of Clouds and Cloud Feedback between AMIP5 and AMIP6." Atmosphere 14, no. 6 (June 4, 2023): 978. http://dx.doi.org/10.3390/atmos14060978.
Повний текст джерелаАнотація:
We examine the changes in clouds and cloud feedback between Phase 5 (AMIP5) and Phase 6 (AMIP6) of the Atmospheric Model Intercomparison Project. Each model is perturbed by uniformly increasing the sea surface temperature by 4 K. The simulated cloud fraction, the perturbed states and cloud radiative kernels are used to derive cloud feedback in the shortwave (SW), longwave (LW) and their sum (Net). Compared to AMIP5, the cloud fraction in AMIP6 increases by 9.1%, while the perturbation leads to a 0.25% decrease. The Net cloud feedback at the top of the atmosphere (TOA) is almost double (174%). Statistical tests support that this change is mainly due to an increase in the surface SW cloud feedback caused by optically thick, middle and low clouds. The contribution of the atmospheric Net component (12%) stems from the increase in the atmospheric LW cloud feedback, likely to play a role in weakening (strengthening) the northward (southward) meridional atmospheric energy transport, while the opposite is true for the surface LW and Net cloud feedback in the meridional oceanic energy transport. The substantial increase in cloud feedback at the TOA primarily contributes to the higher climate sensitivity. The cloud feedback spread in AMIP6 is comparable to that in AMIP5.
23
Lin, Jia-Lin, Taotao Qian, and Toshiaki Shinoda. "Stratocumulus Clouds in Southeastern Pacific Simulated by Eight CMIP5–CFMIP Global Climate Models." Journal of Climate 27, no. 8 (April 10, 2014): 3000–3022. http://dx.doi.org/10.1175/jcli-d-13-00376.1.
Повний текст джерелаАнотація:
Abstract This study examines the stratocumulus clouds and associated cloud feedback in the southeast Pacific (SEP) simulated by eight global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and Cloud Feedback Model Intercomparison Project (CFMIP) using long-term observations of clouds, radiative fluxes, cloud radiative forcing (CRF), sea surface temperature (SST), and large-scale atmosphere environment. The results show that the state-of-the-art global climate models still have significant difficulty in simulating the SEP stratocumulus clouds and associated cloud feedback. Comparing with observations, the models tend to simulate significantly less cloud cover, higher cloud top, and a variety of unrealistic cloud albedo. The insufficient cloud cover leads to overly weak shortwave CRF and net CRF. Only two of the eight models capture the observed positive cloud feedback at subannual to decadal time scales. The cloud and radiation biases in the models are associated with 1) model biases in large-scale temperature structure including the lack of temperature inversion, insufficient lower troposphere stability (LTS), and insufficient reduction of LTS with local SST warming, and 2) improper model physics, especially insufficient increase of low cloud cover associated with larger LTS. The two models that arguably do best at simulating the stratocumulus clouds and associated cloud feedback are the only ones using cloud-top radiative cooling to drive boundary layer turbulence.
24
Sun, De-Zheng, John Fasullo, Tao Zhang, and Andres Roubicek. "On the Radiative and Dynamical Feedbacks over the Equatorial Pacific Cold Tongue." Journal of Climate 16, no. 14 (July 15, 2003): 2425–32. http://dx.doi.org/10.1175/2786.1.
Повний текст джерелаАнотація:
Abstract An analysis of the climatic feedbacks in the NCAR Community Climate Model, version 3 (CCM3) over the equatorial Pacific cold tongue is presented. Using interannual signals in the underlying SST, the radiative and dynamical feedbacks have been calculated using both observations and outputs from the NCAR CCM3. The results show that the positive feedback from the greenhouse effect of water vapor in the model largely agrees with that from observations. The dynamical feedback from the atmospheric transport in the model is also comparable to that from observations. However, the negative feedback from the solar forcing of clouds in the model is significantly weaker than the observed, while the positive feedback from the greenhouse effect of clouds is significantly larger. Consequently, the net atmospheric feedback in the CCM3 over the equatorial cold tongue region is strongly positive (5.1 W m−2 K−1), while the net atmospheric feedback in the real atmosphere is strongly negative (−6.4 W m−2 K−1). A further analysis with the aid of the International Satellite Cloud Climatology Project (ISCCP) data suggests that cloud cover response to changes in the SST may be a significant error source for the cloud feedbacks. It is also noted that the surface heating over the cold tongue in CCM3 is considerably weaker than in observations. In light of results from a linear feedback system, as well as those from a more sophisticated coupled model, it is suggested that the discrepancy in the net atmospheric feedback may have contributed significantly to the cold bias in the equatorial Pacific in the NCAR Climate System Model (CSM).
25
Stephens, Graeme L. "Cloud Feedbacks in the Climate System: A Critical Review." Journal of Climate 18, no. 2 (January 15, 2005): 237–73. http://dx.doi.org/10.1175/jcli-3243.1.
Повний текст джерелаАнотація:
Abstract This paper offers a critical review of the topic of cloud–climate feedbacks and exposes some of the underlying reasons for the inherent lack of understanding of these feedbacks and why progress might be expected on this important climate problem in the coming decade. Although many processes and related parameters come under the influence of clouds, it is argued that atmospheric processes fundamentally govern the cloud feedbacks via the relationship between the atmospheric circulations, cloudiness, and the radiative and latent heating of the atmosphere. It is also shown how perturbations to the atmospheric radiation budget that are induced by cloud changes in response to climate forcing dictate the eventual response of the global-mean hydrological cycle of the climate model to climate forcing. This suggests that cloud feedbacks are likely to control the bulk precipitation efficiency and associated responses of the planet’s hydrological cycle to climate radiative forcings. The paper provides a brief overview of the effects of clouds on the radiation budget of the earth–atmosphere system and a review of cloud feedbacks as they have been defined in simple systems, one being a system in radiative–convective equilibrium (RCE) and others relating to simple feedback ideas that regulate tropical SSTs. The systems perspective is reviewed as it has served as the basis for most feedback analyses. What emerges is the importance of being clear about the definition of the system. It is shown how different assumptions about the system produce very different conclusions about the magnitude and sign of feedbacks. Much more diligence is called for in terms of defining the system and justifying assumptions. In principle, there is also neither any theoretical basis to justify the system that defines feedbacks in terms of global–time-mean changes in surface temperature nor is there any compelling empirical evidence to do so. The lack of maturity of feedback analysis methods also suggests that progress in understanding climate feedback will require development of alternative methods of analysis. It has been argued that, in view of the complex nature of the climate system, and the cumbersome problems encountered in diagnosing feedbacks, understanding cloud feedback will be gleaned neither from observations nor proved from simple theoretical argument alone. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model’s credibility in representing these processes. While GCM climate and NWP models represent the most complete description of all the interactions between the processes that presumably establish the main cloud feedbacks, the weak link in the use of these models lies in the cloud parameterization imbedded in them. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress. Although progress in understanding the cloud feedback problem has been slow and confused by past analysis, there are legitimate reasons outlined in the paper that give hope for real progress in the future.
26
Masters, T. "On the determination of the global cloud feedback from satellite measurements." Earth System Dynamics Discussions 3, no. 1 (February 3, 2012): 73–90. http://dx.doi.org/10.5194/esdd-3-73-2012.
Повний текст джерелаАнотація:
Abstract. A detailed analysis is presented in order to determine the sensitivity of the estimated short-term cloud feedback to choices of temperature datasets, sources of top-of-atmosphere (TOA) radiative flux data, and temporal averaging. It is shown that the results of a previous analysis, which suggested a likely positive value for the short-term cloud feedback, depended upon combining radiative fluxes from satellite and reanalysis data when determining the cloud radiative forcing (CRF). These results are contradicted when ΔCRF is derived from NASA's Clouds and Earth's Radiant Energy System (CERES) all-sky and clear-sky measurements over the same period, resulting in a likely negative feedback. The differences between the radiative flux data sources are thus explored, along with the potential problems with each method. Overall, there is little correlation between the changes in the CRF and surface temperatures on these timescales, suggesting that the net effect of clouds varies during this time period quite apart from global temperature changes. Attempts to diagnose long-term cloud feedbacks in this manner are unlikely to be robust.
27
Webb, Mark J., Timothy Andrews, Alejandro Bodas-Salcedo, Sandrine Bony, Christopher S. Bretherton, Robin Chadwick, Hélène Chepfer, et al. "The Cloud Feedback Model Intercomparison Project (CFMIP) contribution to CMIP6." Geoscientific Model Development 10, no. 1 (January 25, 2017): 359–84. http://dx.doi.org/10.5194/gmd-10-359-2017.
Повний текст джерелаАнотація:
Abstract. The primary objective of CFMIP is to inform future assessments of cloud feedbacks through improved understanding of cloud–climate feedback mechanisms and better evaluation of cloud processes and cloud feedbacks in climate models. However, the CFMIP approach is also increasingly being used to understand other aspects of climate change, and so a second objective has now been introduced, to improve understanding of circulation, regional-scale precipitation, and non-linear changes. CFMIP is supporting ongoing model inter-comparison activities by coordinating a hierarchy of targeted experiments for CMIP6, along with a set of cloud-related output diagnostics. CFMIP contributes primarily to addressing the CMIP6 questions How does the Earth system respond to forcing? and What are the origins and consequences of systematic model biases? and supports the activities of the WCRP Grand Challenge on Clouds, Circulation and Climate Sensitivity.A compact set of Tier 1 experiments is proposed for CMIP6 to address this question: (1) what are the physical mechanisms underlying the range of cloud feedbacks and cloud adjustments predicted by climate models, and which models have the most credible cloud feedbacks? Additional Tier 2 experiments are proposed to address the following questions. (2) Are cloud feedbacks consistent for climate cooling and warming, and if not, why? (3) How do cloud-radiative effects impact the structure, the strength and the variability of the general atmospheric circulation in present and future climates? (4) How do responses in the climate system due to changes in solar forcing differ from changes due to CO2, and is the response sensitive to the sign of the forcing? (5) To what extent is regional climate change per CO2 doubling state-dependent (non-linear), and why? (6) Are climate feedbacks during the 20th century different to those acting on long-term climate change and climate sensitivity? (7) How do regional climate responses (e.g. in precipitation) and their uncertainties in coupled models arise from the combination of different aspects of CO2 forcing and sea surface warming?CFMIP also proposes a number of additional model outputs in the CMIP DECK, CMIP6 Historical and CMIP6 CFMIP experiments, including COSP simulator outputs and process diagnostics to address the following questions. How well do clouds and other relevant variables simulated by models agree with observations?What physical processes and mechanisms are important for a credible simulation of clouds, cloud feedbacks and cloud adjustments in climate models?Which models have the most credible representations of processes relevant to the simulation of clouds?How do clouds and their changes interact with other elements of the climate system?
28
Chen, Lin, Yongqiang Yu, and De-Zheng Sun. "Cloud and Water Vapor Feedbacks to the El Niño Warming: Are They Still Biased in CMIP5 Models?" Journal of Climate 26, no. 14 (July 12, 2013): 4947–61. http://dx.doi.org/10.1175/jcli-d-12-00575.1.
Повний текст джерелаАнотація:
Abstract Previous evaluations of model simulations of the cloud and water vapor feedbacks in response to El Niño warming have singled out two common biases in models from phase 3 of the Coupled Model Intercomparison Project (CMIP3): an underestimate of the negative feedback from the shortwave cloud radiative forcing (SWCRF) and an overestimate of the positive feedback from the greenhouse effect of water vapor. Here, the authors check whether these two biases are alleviated in the CMIP5 models. While encouraging improvements are found, particularly in the simulation of the negative SWCRF feedback, the biases in the simulation of these two feedbacks remain prevalent and significant. It is shown that bias in the SWCRF feedback correlates well with biases in the corresponding feedbacks from precipitation, large-scale circulation, and longwave radiative forcing of clouds (LWCRF). By dividing CMIP5 models into two categories—high score models (HSM) and low score models (LSM)—based on their individual skills of simulating the SWCRF feedback, the authors further find that ocean–atmosphere coupling generally lowers the score of the simulated feedbacks of water vapor and clouds but that the LSM is more affected by the coupling than the HSM. They also find that the SWCRF feedback is simulated better in the models that have a more realistic zonal extent of the equatorial cold tongue, suggesting that the continuing existence of an excessive cold tongue is a key factor behind the persistence of the feedback biases in models.
29
Chen, Ying-Wen, Tatsuya Seiki, Chihiro Kodama, Masaki Satoh, Akira T. Noda, and Yohei Yamada. "High Cloud Responses to Global Warming Simulated by Two Different Cloud Microphysics Schemes Implemented in the Nonhydrostatic Icosahedral Atmospheric Model (NICAM)." Journal of Climate 29, no. 16 (August 4, 2016): 5949–64. http://dx.doi.org/10.1175/jcli-d-15-0668.1.
Повний текст джерелаАнотація:
Abstract This study examines cloud responses to global warming using a global nonhydrostatic model with two different cloud microphysics schemes. The cloud microphysics schemes tested here are the single- and double-moment schemes with six water categories: these schemes are referred to as NSW6 and NDW6, respectively. Simulations of one year for NSW6 and one boreal summer for NDW6 are performed using the nonhydrostatic icosahedral atmospheric model with a mesh size of approximately 14 km. NSW6 and NDW6 exhibit similar changes in the visible cloud fraction under conditions of global warming. The longwave (LW) cloud radiative feedbacks in NSW6 and NDW6 are within the upper half of the phase 5 of the Coupled Model Intercomparison Project (CMIP5)–Cloud Feedback Model Intercomparison Project 2 (CFMIP2) range. The LW cloud radiative feedbacks are mainly attributed to cirrus clouds, which prevail more in the tropics under global warming conditions. For NDW6, the LW cloud radiative feedbacks from cirrus clouds also extend to midlatitudes. The changes in cirrus clouds and their effects on LW cloud radiative forcing (LWCRF) are assessed based on changes in the effective radii of ice hydrometeors () and the cloud fraction. It was determined that an increase in has a nonnegligible impact on LWCRF compared with an increase in cloud fraction.
30
Crook, Julia A., Piers M. Forster, and Nicola Stuber. "Spatial Patterns of Modeled Climate Feedback and Contributions to Temperature Response and Polar Amplification." Journal of Climate 24, no. 14 (July 15, 2011): 3575–92. http://dx.doi.org/10.1175/2011jcli3863.1.
Повний текст джерелаАнотація:
Abstract Spatial patterns of local climate feedback and equilibrium partial temperature responses are produced from eight general circulation models with slab oceans forced by doubling carbon dioxide (CO2). The analysis is extended to other forcing mechanisms with the Met Office Hadley Centre slab ocean climate model version 3 (HadSM3). In agreement with previous studies, the greatest intermodel differences are in the tropical cloud feedbacks. However, the greatest intermodel spread in the equilibrium temperature response comes from the water vapor plus lapse rate feedback, not clouds, disagreeing with a previous study. Although the surface albedo feedback contributes most in the annual mean to the greater warming of high latitudes, compared to the tropics (polar amplification), its effect is significantly ameliorated by shortwave cloud feedback. In different seasons the relative importance of the contributions varies considerably, with longwave cloudy-sky feedback and horizontal heat transport plus ocean heat release playing a major role during winter and autumn when polar amplification is greatest. The greatest intermodel spread in annual mean polar amplification is due to variations in horizontal heat transport and shortwave cloud feedback. Spatial patterns of local climate feedback for HadSM3 forced with 2 × CO2, +2% solar, low-level scattering aerosol and high-level absorbing aerosol are more similar than those for different models forced with 2 × CO2. However, the equilibrium temperature response to high-level absorbing aerosol shows considerably enhanced polar amplification compared to the other forcing mechanisms, largely due to differences in horizontal heat transport and water vapor plus lapse rate feedback, with the forcing itself acting to reduce amplification. Such variations in high-latitude response between models and forcing mechanisms make it difficult to infer specific causes of recent Arctic temperature change.
31
Zhang, Bosong, Ryan J. Kramer, and Brian J. Soden. "Radiative Feedbacks Associated with the Madden–Julian Oscillation." Journal of Climate 32, no. 20 (September 23, 2019): 7055–65. http://dx.doi.org/10.1175/jcli-d-19-0144.1.
Повний текст джерелаАнотація:
Abstract Radiative kernels derived from CloudSat/CALIPSO measurements are used to diagnose radiative feedbacks induced by the Madden–Julian oscillation (MJO). Over the Indo-Pacific warm pool, positive cloud and water vapor feedbacks are coincident with the convective envelope of the MJO during its active phases, whereas the lapse rate feedback shows faster eastward propagation than the convective envelope. During phase 2/3, when the convective envelope is over the Indian Ocean, water vapor exhibits a vertically coherent response, with the largest anomalies and strongest feedback in the midtroposphere. Though spatial structures of the feedbacks vary, the most prominent difference lies in the magnitude. Cloud changes induce the largest radiative perturbations associated with the MJO. It is also found that the strength of the cloud feedback per unit of precipitation is greater for strong MJO events, suggesting that the strength of individual MJO events is largely dictated by the magnitude of cloud radiative heating of the atmosphere. In addition, stronger radiative heating due to water vapor and clouds helps the MJO survive the barrier effect of the Maritime Continent, leading to farther eastward propagation. These results offer process-oriented metrics that could help to improve model simulations and predictions of the MJO in the future.
32
Stephens, Graeme L., Susan van den Heever, and Lyle Pakula. "Radiative–Convective Feedbacks in Idealized States of Radiative–Convective Equilibrium." Journal of the Atmospheric Sciences 65, no. 12 (December 1, 2008): 3899–916. http://dx.doi.org/10.1175/2008jas2524.1.
Повний текст джерелаАнотація:
Abstract This paper examines feedbacks between the radiative heating of clouds and convection in the context of numerical radiative–convective equilibrium experiments conducted using both 2D and 3D versions of a cloud-resolving model. The experiments are conducted on a large domain, and equilibria develop as juxtaposed regions of dry and moist air that are connected and sustained by circulations between them. The scales of such variability are large and differ significantly between the 2D and 3D versions of the experiments. Three sensitivity experiments were conducted which, when compared to the control experiment, provide insight into the relative influences of cloud–radiation feedback mechanisms on the equilibrium state achieved. It emerges from the experiments conducted that radiation feedbacks operate via two main pathways, with the radiative heating by high clouds being the governing process of both. The predominant bimodal nature of the moist equilibrium is established by gradients in radiative heating that, in turn, are determined by high cloud differences between wet and dry regions that, in turn, are controlled by convection. Convection, on the other hand, is also influenced by the localized effects of cloud radiative heating by these extended layers of high clouds. The results of the experiments demonstrate how high cloud radiative heating, which is a by-product of the convection itself, provides a feedback that acts to regulate the high clouds produced in the wet convective areas of the equilibrium.
33
Kuma, Peter, Frida A. M. Bender, Alex Schuddeboom, Adrian J. McDonald, and Øyvind Seland. "Machine learning of cloud types in satellite observations and climate models." Atmospheric Chemistry and Physics 23, no. 1 (January 13, 2023): 523–49. http://dx.doi.org/10.5194/acp-23-523-2023.
Повний текст джерелаАнотація:
Abstract. Uncertainty in cloud feedbacks in climate models is a major limitation in projections of future climate. Therefore, evaluation and improvement of cloud simulation are essential to ensure the accuracy of climate models. We analyse cloud biases and cloud change with respect to global mean near-surface temperature (GMST) in climate models relative to satellite observations and relate them to equilibrium climate sensitivity, transient climate response and cloud feedback. For this purpose, we develop a supervised deep convolutional artificial neural network for determination of cloud types from low-resolution (2.5∘×2.5∘) daily mean top-of-atmosphere shortwave and longwave radiation fields, corresponding to the World Meteorological Organization (WMO) cloud genera recorded by human observers in the Global Telecommunication System (GTS). We train this network on top-of-atmosphere radiation retrieved by the Clouds and the Earth’s Radiant Energy System (CERES) and GTS and apply it to the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) model output and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) and the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) reanalyses. We compare the cloud types between models and satellite observations. We link biases to climate sensitivity and identify a negative linear relationship between the root mean square error of cloud type occurrence derived from the neural network and model equilibrium climate sensitivity (ECS), transient climate response (TCR) and cloud feedback. This statistical relationship in the model ensemble favours models with higher ECS, TCR and cloud feedback. However, this relationship could be due to the relatively small size of the ensemble used or decoupling between present-day biases and future projected cloud change. Using the abrupt-4×CO2 CMIP5 and CMIP6 experiments, we show that models simulating decreasing stratiform and increasing cumuliform clouds tend to have higher ECS than models simulating increasing stratiform and decreasing cumuliform clouds, and this could also partially explain the association between the model cloud type occurrence error and model ECS.
34
Wilson Kemsley, Sarah, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, and Peer Nowack. "A systematic evaluation of high-cloud controlling factors." Atmospheric Chemistry and Physics 24, no. 14 (July 24, 2024): 8295–316. http://dx.doi.org/10.5194/acp-24-8295-2024.
Повний текст джерелаАнотація:
Abstract. Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud controlling factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback.
35
Larson, Kristin, and Dennis L. Hartmann. "Interactions among Cloud, Water Vapor, Radiation, and Large-Scale Circulation in the Tropical Climate. Part I: Sensitivity to Uniform Sea Surface Temperature Changes." Journal of Climate 16, no. 10 (May 15, 2003): 1425–40. http://dx.doi.org/10.1175/1520-0442-16.10.1425.
Повний текст джерелаАнотація:
Abstract The responses of tropical clouds and water vapor to SST variations are investigated with simple numerical experiments. The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model is used with doubly periodic boundary conditions and a uniform constant sea surface temperature (SST). The SST is varied and the equilibrium statistics of cloud properties, water vapor, and circulation at different temperatures are compared. The top of the atmosphere (TOA) radiative fluxes have the same sensitivities to SST as in observations averaged from 20°N to 20°S over the Pacific, suggesting that the model sensitivities are realistic. As the SST increases, the temperature profile approximately follows a moist-adiabatic lapse rate. The rain rate and cloud ice amounts increase with SST. The average relative humidity profile stays approximately constant, but the upper-tropospheric relative humidity increases slightly with SST. The clear-sky mean temperature and water vapor feedbacks have similar magnitudes to each other and opposite signs. The net clear-sky feedback is thus about equal to the lapse rate feedback, which is about −2 W m−2 K−1. The clear-sky outgoing longwave radiation (OLR) thus increases with SST, but the high cloud-top temperature is almost constant with SST, and the high cloud amount increases with SST. The result of these three effects is an increase of cloud longwave forcing with SST and a mean OLR that is almost independent of SST. The high cloud albedo remains almost constant with increasing SST, but the increase in high cloud area causes a negative shortwave cloud radiative forcing feedback, which partly cancels the longwave cloud feedback. The net radiation decreases slightly with SST, giving a small net negative feedback, implying a stable, but very sensitive climate.
36
Lefèvre, Maxence, Xianyu Tan, Elspeth K. H. Lee, and R. T. Pierrehumbert. "Cloud-convection Feedback in Brown Dwarf Atmospheres." Astrophysical Journal 929, no. 2 (April 1, 2022): 153. http://dx.doi.org/10.3847/1538-4357/ac5e2d.
Повний текст джерелаАнотація:
Abstract Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and spectral properties of these atmospheres. The mechanism of such variability is still unclear, and neither 1D nor global circulation models can fully study this topic due to resolution. In this study, a convective-resolving model is coupled to gray-band radiative transfer in order to study the coupling between the convective atmosphere and the variability of clouds over a large temperature range with a domain of several hundred kilometers. Six types of clouds are considered, with microphysics including settling. The clouds are radiatively active through the Rosseland mean coefficient. Radiative cloud feedback can drive spontaneous atmospheric variability in both temperature and cloud structure, as modeled for the first time in three dimensions. Silicate clouds have the most effect on the thermal structure with the generation of a secondary convective layer in some cases, depending on the assumed particle size. Iron and aluminum clouds also have a substantial impact on the atmosphere. Thermal spectra were computed, and we find the strongest effect of the clouds is the smoothing of spectral features at optical wavelengths. Compared to observed L and T dwarfs on the color–magnitude diagram, the simulated atmospheres are redder for most of the cases. Simulations with the presence of cloud holes are closer to observations.
37
Lefèvre, Maxence, Xianyu Tan, Elspeth K. H. Lee, and R. T. Pierrehumbert. "Cloud-convection Feedback in Brown Dwarf Atmospheres." Astrophysical Journal 929, no. 2 (April 1, 2022): 153. http://dx.doi.org/10.3847/1538-4357/ac5e2d.
Повний текст джерелаАнотація:
Abstract Numerous observational evidence has suggested the presence of active meteorology in the atmospheres of brown dwarfs. A near-infrared brightness variability has been observed. Clouds have a major role in shaping the thermal structure and spectral properties of these atmospheres. The mechanism of such variability is still unclear, and neither 1D nor global circulation models can fully study this topic due to resolution. In this study, a convective-resolving model is coupled to gray-band radiative transfer in order to study the coupling between the convective atmosphere and the variability of clouds over a large temperature range with a domain of several hundred kilometers. Six types of clouds are considered, with microphysics including settling. The clouds are radiatively active through the Rosseland mean coefficient. Radiative cloud feedback can drive spontaneous atmospheric variability in both temperature and cloud structure, as modeled for the first time in three dimensions. Silicate clouds have the most effect on the thermal structure with the generation of a secondary convective layer in some cases, depending on the assumed particle size. Iron and aluminum clouds also have a substantial impact on the atmosphere. Thermal spectra were computed, and we find the strongest effect of the clouds is the smoothing of spectral features at optical wavelengths. Compared to observed L and T dwarfs on the color–magnitude diagram, the simulated atmospheres are redder for most of the cases. Simulations with the presence of cloud holes are closer to observations.
38
Ceppi, Paulo, and Dennis L. Hartmann. "Clouds and the Atmospheric Circulation Response to Warming." Journal of Climate 29, no. 2 (January 12, 2016): 783–99. http://dx.doi.org/10.1175/jcli-d-15-0394.1.
Повний текст джерелаАнотація:
Abstract The authors study the effect of clouds on the atmospheric circulation response to CO2 quadrupling in an aquaplanet model with a slab ocean lower boundary. The cloud effect is isolated by locking the clouds to either the control or 4xCO2 state in the shortwave (SW) or longwave (LW) radiation schemes. In the model, cloud radiative changes explain more than half of the total poleward expansion of the Hadley cells, midlatitude jets, and storm tracks under CO2 quadrupling, even though they cause only one-fourth of the total global-mean surface warming. The effect of clouds on circulation results mainly from the SW cloud radiative changes, which strongly enhance the equator-to-pole temperature gradient at all levels in the troposphere, favoring stronger and poleward-shifted midlatitude eddies. By contrast, quadrupling CO2 while holding the clouds fixed causes strong polar amplification and weakened midlatitude baroclinicity at lower levels, yielding only a small poleward expansion of the circulation. The results show that 1) the atmospheric circulation responds sensitively to cloud-driven changes in meridional and vertical temperature distribution and 2) the spatial structure of cloud feedbacks likely plays a dominant role in the circulation response to greenhouse gas forcing. While the magnitude and spatial structure of the cloud feedback are expected to be highly model dependent, an analysis of 4xCO2 simulations of CMIP5 models shows that the SW cloud feedback likely forces a poleward expansion of the tropospheric circulation in most climate models.
39
Eitzen, Zachary A., Kuan-Man Xu, and Takmeng Wong. "An Estimate of Low-Cloud Feedbacks from Variations of Cloud Radiative and Physical Properties with Sea Surface Temperature on Interannual Time Scales." Journal of Climate 24, no. 4 (February 15, 2011): 1106–21. http://dx.doi.org/10.1175/2010jcli3670.1.
Повний текст джерелаАнотація:
Abstract Simulations of climate change have yet to reach a consensus on the sign and magnitude of the changes in physical properties of marine boundary layer clouds. In this study, the authors analyze how cloud and radiative properties vary with SST anomaly in low-cloud regions, based on five years (March 2000–February 2005) of Clouds and the Earth’s Radiant Energy System (CERES)–Terra monthly gridded data and matched European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological reanalaysis data. In particular, this study focuses on the changes in cloud radiative effect, cloud fraction, and cloud optical depth with SST anomaly. The major findings are as follows. First, the low-cloud amount (−1.9% to −3.4% K−1) and the logarithm of low-cloud optical depth (−0.085 to −0.100 K−1) tend to decrease while the net cloud radiative effect (3.86 W m−2 K−1) becomes less negative as SST anomalies increase. These results are broadly consistent with previous observational studies. Second, after the changes in cloud and radiative properties with SST anomaly are separated into dynamic, thermodynamic, and residual components, changes in the dynamic component (taken as the vertical velocity at 700 hPa) have relatively little effect on cloud and radiative properties. However, the estimated inversion strength decreases with increasing SST, accounting for a large portion of the measured decreases in cloud fraction and cloud optical depth. The residual positive change in net cloud radiative effect (1.48 W m−2 K−1) and small changes in low-cloud amount (−0.81% to 0.22% K−1) and decrease in the logarithm of optical depth (–0.035 to –0.046 K−1) with SST are interpreted as a positive cloud feedback, with cloud optical depth feedback being the dominant contributor. Last, the magnitudes of the residual changes differ greatly among the six low-cloud regions examined in this study, with the largest positive feedbacks (∼4 W m−2 K−1) in the southeast and northeast Atlantic regions and a slightly negative feedback (−0.2 W m−2 K−1) in the south-central Pacific region. Because the retrievals of cloud optical depth and/or cloud fraction are difficult in the presence of aerosols, the transport of heavy African continental aerosols may contribute to the large magnitudes of estimated cloud feedback in the two Atlantic regions.
40
Sejas, Sergio A., Ming Cai, Aixue Hu, Gerald A. Meehl, Warren Washington, and Patrick C. Taylor. "Individual Feedback Contributions to the Seasonality of Surface Warming." Journal of Climate 27, no. 14 (July 10, 2014): 5653–69. http://dx.doi.org/10.1175/jcli-d-13-00658.1.
Повний текст джерелаАнотація:
Abstract Using the climate feedback response analysis method, the authors examine the individual contributions of the CO2 radiative forcing and climate feedbacks to the magnitude, spatial pattern, and seasonality of the transient surface warming response in a 1% yr−1 CO2 increase simulation of the NCAR Community Climate System Model, version 4 (CCSM4). The CO2 forcing and water vapor feedback warm the surface everywhere throughout the year. The tropical warming is predominantly caused by the CO2 forcing and water vapor feedback, while the evaporation feedback reduces the warming. Most feedbacks exhibit noticeable seasonal variations; however, their net effect has little seasonal variation due to compensating effects, which keeps the tropical warming relatively invariant all year long. The polar warming has a pronounced seasonal cycle, with maximum warming in fall/winter and minimum warming in summer. In summer, the large cancelations between the shortwave and longwave cloud feedbacks and between the surface albedo feedback warming and the cooling from the ocean heat storage/dynamics feedback lead to a warming minimum. In polar winter, surface albedo and shortwave cloud feedbacks are nearly absent due to a lack of insolation. However, the ocean heat storage feedback relays the polar warming due to the surface albedo feedback from summer to winter, and the longwave cloud feedback warms the polar surface. Therefore, the seasonal variations in the cloud feedback, surface albedo feedback, and ocean heat storage/dynamics feedback, directly caused by the strong annual cycle of insolation, contribute primarily to the large seasonal variation of polar warming. Furthermore, the CO2 forcing and water vapor and atmospheric dynamics feedbacks add to the maximum polar warming in fall/winter.
41
Virgin, John G., Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski. "Cloud Feedbacks from CanESM2 to CanESM5.0 and their influence on climate sensitivity." Geoscientific Model Development 14, no. 9 (August 31, 2021): 5355–72. http://dx.doi.org/10.5194/gmd-14-5355-2021.
Повний текст джерелаАнотація:
Abstract. The newest iteration of the Canadian Earth System Model (CanESM5.0.3) has an effective climate sensitivity (EffCS) of 5.65 K, which is a 54 % increase relative to the model's previous version (CanESM2 – 3.67 K), and the highest sensitivity of all current models participating in the sixth phase of the coupled model inter-comparison project (CMIP6). Here, we explore the underlying causes behind CanESM5's increased EffCS via comparison of forcing and feedbacks between CanESM2 and CanESM5. We find only modest differences in radiative forcing as a response to CO2 between model versions. We find small increases in the surface albedo and longwave cloud feedback, as well as a substantial increase in the SW cloud feedback in CanESM5. Through the use of cloud area fraction output and cloud radiative kernels, we find that more positive low and non-low shortwave cloud feedbacks – particularly with regards to low clouds across the equatorial Pacific, as well as subtropical and extratropical free troposphere cloud optical depth – are the dominant contributors to CanESM5's increased climate sensitivity. Additional simulations with prescribed sea surface temperatures reveal that the spatial pattern of surface temperature change exerts controls on the magnitude and spatial distribution of low-cloud fraction response but does not fully explain the increased EffCS in CanESM5. The results from CanESM5 are consistent with increased EffCS in several other CMIP6 models, which has been primarily attributed to changes in shortwave cloud feedbacks.
42
Vaillant de Guélis, Thibault, Hélène Chepfer, Vincent Noel, Rodrigo Guzman, Philippe Dubuisson, David M. Winker, and Seiji Kato. "The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated." Atmospheric Measurement Techniques 10, no. 12 (December 4, 2017): 4659–85. http://dx.doi.org/10.5194/amt-10-4659-2017.
Повний текст джерелаАнотація:
Abstract. According to climate model simulations, the changing altitude of middle and high clouds is the dominant contributor to the positive global mean longwave cloud feedback. Nevertheless, the mechanisms of this longwave cloud altitude feedback and its magnitude have not yet been verified by observations. Accurate, stable, and long-term observations of a metric-characterizing cloud vertical distribution that are related to the longwave cloud radiative effect are needed to achieve a better understanding of the mechanism of longwave cloud altitude feedback. This study shows that the direct measurement of the altitude of atmospheric lidar opacity is a good candidate for the necessary observational metric. The opacity altitude is the level at which a spaceborne lidar beam is fully attenuated when probing an opaque cloud. By combining this altitude with the direct lidar measurement of the cloud-top altitude, we derive the effective radiative temperature of opaque clouds which linearly drives (as we will show) the outgoing longwave radiation. We find that, for an opaque cloud, a cloud temperature change of 1 K modifies its cloud radiative effect by 2 W m−2. Similarly, the longwave cloud radiative effect of optically thin clouds can be derived from their top and base altitudes and an estimate of their emissivity. We show with radiative transfer simulations that these relationships hold true at single atmospheric column scale, on the scale of the Clouds and the Earth's Radiant Energy System (CERES) instantaneous footprint, and at monthly mean 2° × 2° scale. Opaque clouds cover 35 % of the ice-free ocean and contribute to 73 % of the global mean cloud radiative effect. Thin-cloud coverage is 36 % and contributes 27 % of the global mean cloud radiative effect. The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated provides a simple formulation of the cloud radiative effect in the longwave domain and so helps us to understand the longwave cloud altitude feedback mechanism.
43
Ceppi, Paulo, and Peer Nowack. "Observational evidence that cloud feedback amplifies global warming." Proceedings of the National Academy of Sciences 118, no. 30 (July 19, 2021): e2026290118. http://dx.doi.org/10.1073/pnas.2026290118.
Повний текст джерелаАнотація:
Global warming drives changes in Earth’s cloud cover, which, in turn, may amplify or dampen climate change. This “cloud feedback” is the single most important cause of uncertainty in Equilibrium Climate Sensitivity (ECS)—the equilibrium global warming following a doubling of atmospheric carbon dioxide. Using data from Earth observations and climate model simulations, we here develop a statistical learning analysis of how clouds respond to changes in the environment. We show that global cloud feedback is dominated by the sensitivity of clouds to surface temperature and tropospheric stability. Considering changes in just these two factors, we are able to constrain global cloud feedback to 0.43 ± 0.35 W⋅m−2⋅K−1 (90% confidence), implying a robustly amplifying effect of clouds on global warming and only a 0.5% chance of ECS below 2 K. We thus anticipate that our approach will enable tighter constraints on climate change projections, including its manifold socioeconomic and ecological impacts.
44
Li, R. L., T. Storelvmo, A. V. Fedorov, and Y. S. Choi. "A Positive Iris Feedback: Insights from Climate Simulations with Temperature-Sensitive Cloud–Rain Conversion." Journal of Climate 32, no. 16 (July 24, 2019): 5305–24. http://dx.doi.org/10.1175/jcli-d-18-0845.1.
Повний текст джерелаАнотація:
AbstractEstimates for equilibrium climate sensitivity from current climate models continue to exhibit a large spread, from 2.1 to 4.7 K per carbon dioxide doubling. Recent studies have found that the treatment of precipitation efficiency in deep convective clouds—specifically the conversion rate from cloud condensate to rain Cp—may contribute to the large intermodel spread. It is common for convective parameterization in climate models to carry a constant Cp, although its values are model and resolution dependent. In this study, we investigate how introducing a potential iris feedback, the cloud–climate feedback introduced by parameterizing Cp to increase with surface temperature, affects future climate simulations within a slab ocean configuration of the Community Earth System Model. Progressively stronger dependencies of Cp on temperature unexpectedly increase the equilibrium climate sensitivity monotonically from 3.8 to up to 4.6 K. This positive iris feedback puzzle, in which a reduction in cirrus clouds increases surface temperature, is attributed to changes in the opacity of convectively detrained cirrus. Cirrus clouds reduced largely in ice content and marginally in horizontal coverage, and thus the positive shortwave cloud radiative feedback dominates. The sign of the iris feedback is robust across different cloud macrophysics schemes, which control horizontal cloud cover associated with detrained ice. These results suggest a potentially strong but highly uncertain connection among convective precipitation, detrained anvil cirrus, and the high cloud feedback in a climate forced by increased atmospheric carbon dioxide concentrations.
45
Middlemas, Eleanor A., Amy C. Clement, Brian Medeiros, and Ben Kirtman. "Cloud Radiative Feedbacks and El Niño–Southern Oscillation." Journal of Climate 32, no. 15 (July 2, 2019): 4661–80. http://dx.doi.org/10.1175/jcli-d-18-0842.1.
Повний текст джерелаАнотація:
Abstract Cloud radiative feedbacks are disabled via “cloud-locking” in the Community Earth System Model, version 1.2 (CESM1.2), to result in a shift in El Niño–Southern Oscillation (ENSO) periodicity from 2–7 years to decadal time scales. We hypothesize that cloud radiative feedbacks may impact the periodicity in three ways: by 1) modulating heat flux locally into the equatorial Pacific subsurface through negative shortwave cloud feedback on sea surface temperature anomalies (SSTA), 2) damping the persistence of subtropical southeast Pacific SSTA such that the South Pacific meridional mode impacts the duration of ENSO events, or 3) controlling the meridional width of off-equatorial westerly winds, which impacts the periodicity of ENSO by initiating longer Rossby waves. The result of cloud-locking in CESM1.2 contrasts that of another study, which found that cloud-locking in a different global climate model led to decreased ENSO magnitude across all time scales due to a lack of positive longwave feedback on the anomalous Walker circulation. CESM1.2 contains this positive longwave feedback on the anomalous Walker circulation, but either its influence on the surface is decoupled from ocean dynamics or the feedback is only active on interannual time scales. The roles of cloud radiative feedbacks in ENSO in other global climate models are additionally considered. In particular, it is shown that one cannot predict the role of cloud radiative feedbacks in ENSO through a multimodel diagnostic analysis. Instead, they must be directly altered.
46
Caldwell, Peter M., Mark D. Zelinka, Karl E. Taylor, and Kate Marvel. "Quantifying the Sources of Intermodel Spread in Equilibrium Climate Sensitivity." Journal of Climate 29, no. 2 (January 7, 2016): 513–24. http://dx.doi.org/10.1175/jcli-d-15-0352.1.
Повний текст джерелаАнотація:
Abstract This study clarifies the causes of intermodel differences in the global-average temperature response to doubled CO2, commonly known as equilibrium climate sensitivity (ECS). The authors begin by noting several issues with the standard approach for decomposing ECS into a sum of forcing and feedback terms. This leads to a derivation of an alternative method based on linearizing the effect of the net feedback. Consistent with previous studies, the new method identifies shortwave cloud feedback as the dominant source of intermodel spread in ECS. This new approach also reveals that covariances between cloud feedback and forcing, between lapse rate and longwave cloud feedbacks, and between albedo and shortwave cloud feedbacks play an important and previously underappreciated role in determining model differences in ECS. Defining feedbacks based on fixed relative rather than specific humidity (as suggested by Held and Shell) reduces the covariances between processes and leads to more straightforward interpretations of results.
47
Yoshimori, Masakazu, Tokuta Yokohata, and Ayako Abe-Ouchi. "A Comparison of Climate Feedback Strength between CO2 Doubling and LGM Experiments." Journal of Climate 22, no. 12 (June 15, 2009): 3374–95. http://dx.doi.org/10.1175/2009jcli2801.1.
Повний текст джерелаАнотація:
Abstract Studies of the climate in the past potentially provide a constraint on the uncertainty of climate sensitivity, but previous studies warn against a simple scaling to the future. Climate sensitivity is determined by a number of feedback processes, and they may vary according to climate states and forcings. In this study, the similarities and differences in feedbacks for CO2 doubling, a Last Glacial Maximum (LGM), and LGM greenhouse gas (GHG) forcing experiments are investigated using an atmospheric general circulation model coupled to a slab ocean model. After computing the radiative forcing, the individual feedback strengths of water vapor, lapse-rate, albedo, and cloud feedbacks are evaluated explicitly. For this particular model, the difference in the climate sensitivity between the experiments is attributed to the shortwave cloud feedback, in which there is a tendency for it to become weaker or even negative in cooling experiments. No significant difference is found in the water vapor feedback between warming and cooling experiments by GHGs. The weaker positive water vapor feedback in the LGM experiment resulting from a relatively weaker tropical forcing is compensated for by the stronger positive lapse-rate feedback resulting from a relatively stronger extratropical forcing. A hypothesis is proposed that explains the asymmetric cloud response between the warming and cooling experiments associated with a displacement of the region of mixed-phase clouds. The difference in the total feedback strength between the experiments is, however, relatively small compared to the current intermodel spread, and does not necessarily preclude the use of LGM climate as a future constraint.
48
Abbot, Dorian S., Chris C. Walker, and Eli Tziperman. "Can a Convective Cloud Feedback Help to Eliminate Winter Sea Ice at High CO2 Concentrations?" Journal of Climate 22, no. 21 (November 1, 2009): 5719–31. http://dx.doi.org/10.1175/2009jcli2854.1.
Повний текст джерелаАнотація:
Abstract Winter sea ice dramatically cools the Arctic climate during the coldest months of the year and may have remote effects on global climate as well. Accurate forecasting of winter sea ice has significant social and economic benefits. Such forecasting requires the identification and understanding of all of the feedbacks that can affect sea ice. A convective cloud feedback has recently been proposed in the context of explaining equable climates, for example, the climate of the Eocene, which might be important for determining future winter sea ice. In this feedback, CO2-initiated warming leads to sea ice reduction, which allows increased heat and moisture fluxes from the ocean surface, which in turn destabilizes the atmosphere and leads to atmospheric convection. This atmospheric convection produces optically thick convective clouds and increases high-altitude moisture levels, both of which trap outgoing longwave radiation and therefore result in further warming and sea ice loss. Here it is shown that this convective cloud feedback is active at high CO2 during polar night in the coupled ocean–sea ice–land–atmosphere global climate models used for the 1% yr−1 CO2 increase to the quadrupling (1120 ppm) scenario of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. At quadrupled CO2, model forecasts of maximum seasonal (March) sea ice volume are found to be correlated with polar winter cloud radiative forcing, which the convective cloud feedback increases. In contrast, sea ice volume is entirely uncorrelated with model global climate sensitivity. It is then shown that the convective cloud feedback plays an essential role in the elimination of March sea ice at quadrupled CO2 in NCAR’s Community Climate System Model (CCSM), one of the IPCC models that loses sea ice year-round at this CO2 concentration. A new method is developed to disable the convective cloud feedback in the Community Atmosphere Model (CAM), the atmospheric component of CCSM, and to show that March sea ice cannot be eliminated in CCSM at CO2 = 1120 ppm without the aide of the convective cloud feedback.
49
Cesana, Grégory, Anthony D. Del Genio, and Hélène Chepfer. "The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)." Earth System Science Data 11, no. 4 (November 25, 2019): 1745–64. http://dx.doi.org/10.5194/essd-11-1745-2019.
Повний текст джерелаАнотація:
Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).
50
Yokohata, Tokuta, Mark J. Webb, Matthew Collins, Keith D. Williams, Masakazu Yoshimori, Julia C. Hargreaves, and James D. Annan. "Structural Similarities and Differences in Climate Responses to CO2 Increase between Two Perturbed Physics Ensembles." Journal of Climate 23, no. 6 (March 15, 2010): 1392–410. http://dx.doi.org/10.1175/2009jcli2917.1.
Повний текст джерелаАнотація:
Abstract The equilibrium climate sensitivity (ECS) of the two perturbed physics ensembles (PPE) generated using structurally different GCMs, Model for Interdisciplinary Research on Climate (MIROC3.2) and the Third Hadley Centre Atmospheric Model with slab ocean (HadSM3), is investigated. A method to quantify the shortwave (SW) cloud feedback by clouds with different cloud-top pressure is developed. It is found that the difference in the ensemble means of the ECS between the two ensembles is mainly caused by differences in the SW low-level cloud feedback. The ensemble mean SW cloud feedback and ECS of the MIROC3.2 ensemble is larger than that of the HadSM3 ensemble. This is likely related to the 1XCO2 low-level cloud albedo of the former being larger than that of the latter. It is also found that the largest contribution to the within-ensemble variation of ECS comes from the SW low-level cloud feedback in both ensembles. The mechanism that causes the within-ensemble variation is different between the two ensembles. In the HadSM3 ensemble, members with large 1XCO2 low-level cloud albedo have large SW cloud feedback and large ECS; ensemble members with large 1XCO2 cloud cover have large negative SW cloud feedback and relatively low ECS. In the MIROC3.2 ensemble, the 1XCO2 low-level cloud albedo is much more tightly constrained, and no relationship is found between it and the cloud feedback. These results indicate that both the parametric uncertainties sampled in PPEs and the structural uncertainties of GCMs are important and worth further investigation.