Discrepancy in radiative feedbacks between models and observations tied to models inability to reproduce historical surface temperature patterns over the tropical Indo-Pacific.

2020 ◽  
Author(s):  
Cristian Proistosescu ◽  
Yue Dong ◽  
Malte Stuecker ◽  
Kyle Armour ◽  
Robb Wills ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Atmospheric Model ◽  
Natural Variability ◽  
Forced Response ◽  
Large Ensemble ◽  
Radiative Feedback ◽  
Wide Range ◽  
Radiative Feedbacks

<p>How much Earth warms in response to radiative forcing is determined by the net radiative feedback, which quantifies how much more energy is radiated to space for a given increase in surface temperature.  Estimates from present day observations of temperature and earth's energetic imbalance yield a strongly negative radiative feedback, or, equivalently, a very low climate sensitivity, which lies outside the range of climate sensitivity in coupled climate models. This discrepancy in radiative feedbacks can be linked to discrepancies between models and observations in the pattern of historical sea-surface temperature (SST) anomalies driving tropical atmospheric circulation and radiative damping.  Indeed, we find that an atmospheric model (CAM5) forced with observed SSTs yields a net feedback that is consistent with observational estimates, but up to three times more negative than that from the same period (2000-2017) in historical simulations where the same atmospheric model is coupled to a dynamical ocean model (CESM1). </p><p>To understand the role natural variability can play in this discrepancy, we compare the radiative feedbacks generated by the observed pattern of SSTs to those within the CESM1 large ensemble over the same period. The large ensemble produces a wide range of feedbacks due to internal variability alone. Yet, global radiative feedbacks (cloud feedbacks in particular) generated by observed warming patterns are far outside the range of natural variability in the large ensemble. Using both a Green's function approach, as well as a simple metric based on the East-West tropical pacific gradient, we show that none of the control simulations of CMIP5 climate models can generate sufficiently large natural variability to explain the discrepancy between models and observations. We conclude that the discrepancy in SST patterns, and the resulting discrepancy in radiative feedbacks, is caused by an deficiency in models' ability to simulate either natural variabilty or the forced response over the recent historical period. We will also show preliminary analysis from CMIP6 simulations.</p>

scholarly journals Surface and top-of-atmosphere radiative feedback kernels for CESM-CAM5

2018 ◽  
Vol 10 (1) ◽  
pp. 317-324 ◽  
Author(s):  
Angeline G. Pendergrass ◽  
Andrew Conley ◽  
Francis M. Vitt
Keyword(s):  
Water Vapor ◽  
Greenhouse Gases ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Large Ensemble ◽  
Radiative Fluxes ◽  
Radiative Feedback ◽  
Ensemble Simulations ◽  
Temperature Water

Abstract. Radiative kernels at the top of the atmosphere are useful for decomposing changes in atmospheric radiative fluxes due to feedbacks from atmosphere and surface temperature, water vapor, and surface albedo. Here we describe and validate radiative kernels calculated with the large-ensemble version of CAM5, CESM1.1.2, at the top of the atmosphere and the surface. Estimates of the radiative forcing from greenhouse gases and aerosols in RCP8.5 in the CESM large-ensemble simulations are also diagnosed. As an application, feedbacks are calculated for the CESM large ensemble. The kernels are freely available at https://doi.org/10.5065/D6F47MT6, and accompanying software can be downloaded from https://github.com/apendergrass/cam5-kernels.

scholarly journals What Surface Observations Are Important for Separating the Influences of Anthropogenic Aerosols from Other Forcings?

2016 ◽  
Vol 29 (11) ◽  
pp. 4165-4184 ◽  
Author(s):  
Xiaoqin Yan ◽  
Timothy DelSole ◽  
Michael K. Tippett
Keyword(s):  
Spatial Structure ◽  
Surface Temperature ◽  
Sea Level ◽  
Climate Models ◽  
Accurate Estimate ◽  
Forced Response ◽  
Anthropogenic Aerosols ◽  
Anthropogenic Aerosol ◽  
Sea Level Pressure ◽  
Level Pressure

Abstract This paper shows that joint temperature–precipitation information over a global domain provides a more accurate estimate of aerosol forced responses in climate models than does any other combination of temperature, precipitation, or sea level pressure. This fact is demonstrated using a new quantity called potential detectability, which measures the extent to which a forced response can be detected in a model. In particular, this measure can be evaluated independently of observations and therefore permits efficient exploration of a large number of variable combinations before performing optimal fingerprinting on observations. This paper also shows that the response to anthropogenic aerosol forcing can be separated from that of other forcings using only spatial structure alone, leaving the time variation of the response to be inferred from data, thereby demonstrating that temporal information is not necessary for detection. The spatial structure of the forced response is derived by maximizing the signal-to-noise ratio. For single variables, the north–south hemispheric gradient and equator-to-pole latitudinal gradient are important spatial structures for detecting anthropogenic aerosols in some models but not all. Sea level pressure is not an independent detection variable because it is derived partly from surface temperature. In no case does sea level pressure significantly enhance potential detectability beyond that already possible using surface temperature. Including seasonal or land–sea contrast information does not significantly enhance detectability of anthropogenic aerosol responses relative to annual means over global domains.

scholarly journals What Caused the Observed Twentieth-Century Weakening of the Walker Circulation?

2011 ◽  
Vol 24 (24) ◽  
pp. 6501-6514 ◽  
Author(s):  
Scott B. Power ◽  
Greg Kociuba
Keyword(s):  
Twentieth Century ◽  
Climate Models ◽  
Southern Oscillation ◽  
Walker Circulation ◽  
Natural Variability ◽  
First Century ◽  
External Forcing ◽  
Wide Range ◽  
Twenty First Century ◽  
The Walker Circulation

Abstract The Walker circulation (WC) is one of the world’s most prominent and important atmospheric systems. The WC weakened during the twentieth century, reaching record low levels in recent decades. This weakening is thought to be partly due to global warming and partly due to internally generated natural variability. There is, however, no consensus in the literature on the relative contribution of external forcing and natural variability to the observed weakening of the WC. This paper examines changes in the strength of the WC using an index called BoxΔP, which is equal to the difference in mean sea level pressure across the equatorial Pacific. Change in both the observations and in World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) climate models are examined. The annual average BoxΔP declines in the observations and in 15 out of 23 models during the twentieth century (results that are significant at or above the 95% level), consistent with earlier work. However, the magnitude of the multimodel ensemble mean (MMEM) 1901–99 trend (−0.10 Pa yr−1) is much smaller than the magnitude of the observed trend (−0.52 Pa yr−1). While a wide range of trends is evident in the models with approximately 90% of the model trends in the range (−0.25 to +0.1 Pa yr−1), even this range is too narrow to encompass the magnitude of the observed trend. Twenty-first-century changes in BoxΔP under the Special Report on Emissions Scenarios (SRES) A1B and A2 are also examined. Negative trends (i.e., weaker WCs) are evident in all seasons. However, the MMEM trends for the A1B and A2 scenarios are smaller in magnitude than the magnitude of the observed trend. Given that external forcing linked to greenhouse gases is much larger in the twenty-first-century scenarios than twentieth-century forcing, this, together with the twentieth-century results mentioned above, would seem to suggest that external forcing has not been the primary driver of the observed weakening of the WC. However, 9 of the 23 models are unable to account for the observed change unless the internally generated component of the trend is very large. But indicators of observed variability linked to El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation have modest trends, suggesting that internally variability has been modest. Furthermore, many of the nine “inconsistent” models tend to have poorer simulations of climatic features linked to ENSO. In addition, the externally forced component of the trend tends to be larger in magnitude and more closely matches the observed trend in the models that are better able to reproduce ENSO-related variability. The “best” four models, for example, have a MMEM of −0.2 Pa yr−1 (i.e., approximately 40% of the observed change), suggesting a greater role for external forcing in driving the observed trend. These and other considerations outlined below lead the authors to conclude that (i) both external forcing and internally generated variability contributed to the observed weakening of the WC over the twentieth century and (ii) external forcing accounts for approximately 30%–70% of the observed weakening with internally generated climate variability making up the rest.

scholarly journals Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

2020 ◽  
Vol 33 (23) ◽  
pp. 10383-10402
Author(s):  
Giuliana Pallotta ◽  
Benjamin D. Santer
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Statistical Significance ◽  
Coupled Model ◽  
Natural Variability ◽  
Long Term Memory ◽  
Tropospheric Temperature ◽  
Wide Range ◽  
Multimodel Ensembles ◽  
Model Versus

AbstractStudies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

scholarly journals Does Antarctic glaciation cool the world?

2012 ◽  
Vol 8 (4) ◽  
pp. 2645-2693 ◽  
Author(s):  
A. Goldner ◽  
M. Huber ◽  
R. Caballero
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Models ◽  
Ice Sheet ◽  
Cloud Feedback ◽  
Modern World ◽  
Antarctic Ice Sheet ◽  
Cloud Feedbacks ◽  
Wide Range ◽  
The Antarctic

Abstract. In this study we compare the simulated climatic impact of adding the Antarctic Ice Sheet to the "Greenhouse World" of the Eocene and removing the Antarctic Ice Sheet from the Modern world. The Modern surface temperature anomaly (ΔT) induced by Antarctic Glaciation ranges from −1.22 to −0.18 K when CO2 is dropped from 2240 to 560 ppm, whereas the Eocene ΔT is nearly constant at −0.3 K. We calculate the climate sensitivity parameter S[Antarctica] which is defined as the change in surface temperature (ΔT) divided by the change in radiative forcing (ΔQAntarctica) imposed by prescribing the glacial properties of Antarctica. While the ΔT associated with the imposed Antarctic properties is relatively consistent across the Eocene cases, the radiative forcing is not. This leads to a wide range of S[Antarctica], with Eocene values systematically smaller than Modern. This differing temperature response in Eocene and Modern is partially due to the smaller surface area of the imposed forcing over Antarctica in the Eocene and partially due to the presence of strong positive sea-ice feedbacks in the Modern. The system's response is further mediated by differing shortwave cloud feedbacks which are large and of opposite sign operating in Modern and Eocene configurations. A negative cloud feedback warms much of the Earth's surface as a large ice sheet is introduced in Antarctica in the Eocene, whereas in the Modern this cloud feedback is positive and acts to enhance cooling introduced by adding an ice sheet. Because of the importance of cloud feedbacks in determining the final temperature sensitivity of the Antarctic Ice Sheet our results are likely to be model dependent. Nevertheless, these model results show that the radiative forcing and feedbacks induced by the Antarctic Ice Sheet did not significantly decrease global mean surface temperature across the Eocene-Oligocene transition (EOT) and that other factors like declining atmospheric CO2 are more important for cooling across the EOT. The results indicate that climate transitions associated with glaciation depend on the climate background state. This means that using paleoclimate proxy data by itself, from the EOT to estimate Earth System Sensitivity, into the future, is made difficult without relying on climate models and consequently these modelling estimates will have large uncertainty, largely due to low clouds.

scholarly journals A method to encapsulate model structural uncertainty in ensemble projections of future climate: EPIC v1.0

2017 ◽  
Vol 10 (12) ◽  
pp. 4563-4575 ◽  
Author(s):  
Jared Lewis ◽  
Greg E. Bodeker ◽  
Stefanie Kremser ◽  
Andrew Tait
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Weather Generator ◽  
Future Climate ◽  
Daily Maximum ◽  
Training Phase ◽  
Large Ensemble ◽  
Stochastic Weather Generator ◽  
Wide Range ◽  
Ensemble Projections

Abstract. A method, based on climate pattern scaling, has been developed to expand a small number of projections of fields of a selected climate variable (X) into an ensemble that encapsulates a wide range of indicative model structural uncertainties. The method described in this paper is referred to as the Ensemble Projections Incorporating Climate model uncertainty (EPIC) method. Each ensemble member is constructed by adding contributions from (1) a climatology derived from observations that represents the time-invariant part of the signal; (2) a contribution from forced changes in X, where those changes can be statistically related to changes in global mean surface temperature (Tglobal); and (3) a contribution from unforced variability that is generated by a stochastic weather generator. The patterns of unforced variability are also allowed to respond to changes in Tglobal. The statistical relationships between changes in X (and its patterns of variability) and Tglobal are obtained in a training phase. Then, in an implementation phase, 190 simulations of Tglobal are generated using a simple climate model tuned to emulate 19 different global climate models (GCMs) and 10 different carbon cycle models. Using the generated Tglobal time series and the correlation between the forced changes in X and Tglobal, obtained in the training phase, the forced change in the X field can be generated many times using Monte Carlo analysis. A stochastic weather generator is used to generate realistic representations of weather which include spatial coherence. Because GCMs and regional climate models (RCMs) are less likely to correctly represent unforced variability compared to observations, the stochastic weather generator takes as input measures of variability derived from observations, but also responds to forced changes in climate in a way that is consistent with the RCM projections. This approach to generating a large ensemble of projections is many orders of magnitude more computationally efficient than running multiple GCM or RCM simulations. Such a large ensemble of projections permits a description of a probability density function (PDF) of future climate states rather than a small number of individual story lines within that PDF, which may not be representative of the PDF as a whole; the EPIC method largely corrects for such potential sampling biases. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts and implications of climate change in a probabilistic way. A web-based tool, using the EPIC method to provide probabilistic projections of changes in daily maximum and minimum temperatures for New Zealand, has been developed and is described in this paper.

scholarly journals Contributions of Climate Feedbacks to Changes in Atmospheric Circulation

2017 ◽  
Vol 30 (22) ◽  
pp. 9097-9118 ◽  
Author(s):  
Paulo Ceppi ◽  
Theodore G. Shepherd
Keyword(s):  
Water Vapor ◽  
Atmospheric Circulation ◽  
Surface Albedo ◽  
Climate Models ◽  
Cmip5 Models ◽  
Climate Feedbacks ◽  
Wide Range ◽  
Radiative Feedbacks ◽  
Opposing Effects ◽  
The Impact

The projected response of the atmospheric circulation to the radiative changes induced by CO2 forcing and climate feedbacks is currently uncertain. In this modeling study, the impact of CO2-induced climate feedbacks on changes in jet latitude and speed is assessed by imposing surface albedo, cloud, and water vapor feedbacks as if they were forcings in two climate models, CAM4 and ECHAM6. The jet response to radiative feedbacks can be broadly interpreted through changes in midlatitude baroclinicity. Clouds enhance baroclinicity, favoring a strengthened, poleward-shifted jet; this is mitigated by surface albedo changes, which have the opposite effect on baroclinicity and the jet, while water vapor has opposing effects on upper- and lower-level baroclinicity with little net impact on the jet. Large differences between the CAM4 and ECHAM6 responses illustrate how model uncertainty in radiative feedbacks causes a large spread in the baroclinicity response to CO2 forcing. Across the CMIP5 models, differences in shortwave feedbacks by clouds and albedo are a dominant contribution to this spread. Forcing CAM4 with shortwave cloud and albedo feedbacks from a representative set of CMIP5 models yields a wide range of jet responses that strongly correlate with the meridional gradient of the anomalous shortwave heating and the associated baroclinicity response. Differences in shortwave feedbacks statistically explain about 50% of the intermodel spread in CMIP5 jet shifts for the set of models used, demonstrating the importance of constraining radiative feedbacks for accurate projections of circulation changes.

scholarly journals Relationship of tropospheric stability to climate sensitivity and Earth’s observed radiation budget

2017 ◽  
Vol 114 (50) ◽  
pp. 13126-13131 ◽  
Author(s):  
Paulo Ceppi ◽  
Jonathan M. Gregory
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Sea Surface ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Climate Feedback ◽  
Temperature Pattern ◽  
Climate Feedbacks

Climate feedbacks generally become smaller in magnitude over time under CO2 forcing in coupled climate models, leading to an increase in the effective climate sensitivity, the estimated global-mean surface warming in steady state for doubled CO2. Here, we show that the evolution of climate feedbacks in models is consistent with the effect of a change in tropospheric stability, as has recently been hypothesized, and the latter is itself driven by the evolution of the pattern of sea-surface temperature response. The change in climate feedback is mainly associated with a decrease in marine tropical low cloud (a more positive shortwave cloud feedback) and with a less negative lapse-rate feedback, as expected from a decrease in stability. Smaller changes in surface albedo and humidity feedbacks also contribute to the overall change in feedback, but are unexplained by stability. The spatial pattern of feedback changes closely matches the pattern of stability changes, with the largest increase in feedback occurring in the tropical East Pacific. Relationships qualitatively similar to those in the models among sea-surface temperature pattern, stability, and radiative budget are also found in observations on interannual time scales. Our results suggest that constraining the future evolution of sea-surface temperature patterns and tropospheric stability will be necessary for constraining climate sensitivity.

scholarly journals New Perspectives on Observed and Simulated Antarctic Sea Ice Extent Trends Using Optimal Fingerprinting Techniques*

2015 ◽  
Vol 28 (4) ◽  
pp. 1543-1560 ◽  
Author(s):  
William Richard Hobbs ◽  
Nathaniel L. Bindoff ◽  
Marilyn N. Raphael
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Ross Sea ◽  
Internal Variability ◽  
Ice Cover ◽  
Natural Variability ◽  
Forced Response ◽  
Cmip5 Models ◽  
Sea Ice Extent ◽  
Optimal Fingerprinting

Abstract Using optimal fingerprinting techniques, a detection analysis is performed to determine whether observed trends in Southern Ocean sea ice extent since 1979 are outside the expected range of natural variability. Consistent with previous studies, it is found that for the seasons of maximum sea ice cover (i.e., winter and early spring), the observed trends are not outside the range of natural variability and in some West Antarctic sectors they may be partially due to tropical variability. However, when information about the spatial pattern of trends is included in the analysis, the summer and autumn trends fall outside the range of internal variability. The detectable signal is dominated by strong and opposing trends in the Ross Sea and the Amundsen–Bellingshausen Sea regions. In contrast to the observed pattern, an ensemble of 20 CMIP5 coupled climate models shows that a decrease in Ross Sea ice cover would be expected in response to external forcings. The simulated decreases in the Ross, Bellingshausen, and Amundsen Seas for the autumn season are significantly different from unforced internal variability at the 95% confidence level. Unlike earlier work, the authors formally show that the simulated sea ice response to external forcing is different from both the observed trends and simulated internal variability and conclude that in general the CMIP5 models do not adequately represent the forced response of the Antarctic climate system.

scholarly journals The Greenhouse Effect Definition

2019 ◽  
pp. 1-5
Author(s):  
Antero Ollila
Keyword(s):  
Global Warming ◽  
Elevated Temperature ◽  
Energy Balance ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Greenhouse Effect ◽  
Climate Models ◽  
Climate Model ◽  
Heating Effects ◽  
Downward Radiation

The greenhouse effect concept explains the Earth’s elevated temperature. The IPCC endorses the anthropogenic global warming theory, and it assumes that the greenhouse (GH) effect is due to the longwave (LW) absorption by GH gases and clouds. The IPCC’s GH definition lets to understand that the LW absorption is responsible for the downward radiation to the surface. According to the energy laws, it is not possible that the LW absorption of 155.6 Wm-2 by the GH gases could re-emit downward LW radiation of 345.6 Wm-2 on the Earth’s surface. When the shortwave (SW) absorption is decreased from this total LW radiation, the rest of the radiation is 270.6 Wm-2. This LW radiation downward is the imminent cause for the GH effect increasing the surface temperature by the 33°C. It includes LW absorption by the GH gases and clouds in the atmosphere and the latent and sensible heating effects. Without the latent and sensible heating impacts in the atmosphere, the downward LW radiation could not close the energy balance of the surface. The contribution of CO2 in the GH effect is 7.4% corresponding to 2.5°C in temperature. This result does not only mutilate the image of CO2 as a strong GH gas, but it has further consequences in climate models. It turned out that the IPCC’s climate model showing a climate sensitivity (CS) of 1.2°C (caused by CO2 effects only) could not be fitted into the total GH effect of CO2. A climate model showing a CS of 0.6°C matches the CO2 contribution in the GH effect.

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