Spatial Radiative Feedbacks from Internal Variability Using Multiple Regression

AbstractThe sensitivity of the climate to CO2 forcing depends on spatially varying radiative feedbacks that act both locally and nonlocally. We assess whether a method employing multiple regression can be used to estimate local and nonlocal radiative feedbacks from internal variability. We test this method on millennial-length simulations performed with six coupled atmosphere–ocean general circulation models (AOGCMs). Given the spatial pattern of warming, the method does quite well at recreating the top-of-atmosphere flux response for most regions of Earth, except over the Southern Ocean where it consistently overestimates the change, leading to an overestimate of the sensitivity. For five of the six models, the method finds that local feedbacks are positive due to cloud processes, balanced by negative nonlocal shortwave cloud feedbacks associated with regions of tropical convection. For four of these models, the magnitudes of both are comparable to the Planck feedback, so that changes in the ratio between them could lead to large changes in climate sensitivity. The positive local feedback explains why observational studies that estimate spatial feedbacks using only local regressions predict an unstable climate. The method implies that sensitivity in these AOGCMs increases over time due to a reduction in the share of warming occurring in tropical convecting regions and the resulting weakening of associated shortwave cloud and longwave clear-sky feedbacks. Our results provide a step toward an observational estimate of time-varying climate sensitivity by demonstrating that many aspects of spatial feedbacks appear to be the same between internal variability and the forced response.

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Forecast skill of autumn snow for European winter climate during the 20th century: A multi member seasonal prediction experiment

10.5194/egusphere-egu21-2586 ◽

2021 ◽

Author(s):

Martin Wegmann ◽

Yvan Orsolini ◽

Antje Weisheimer ◽

Bart van den Hurk ◽

Gerrit Lohmann

Keyword(s):

Snow Cover ◽

General Circulation ◽

Initial Conditions ◽

Internal Variability ◽

Seasonal Prediction ◽

General Circulation Models ◽

Reanalysis Data ◽

Winter Climate ◽

European Continent ◽

Near Surface

As the leading climate mode to explain wintertime climate variability over Europe, the North Atlantic Oscillation (NAO) has been extensively studied over the last decades. Recently, studies highlighted the state of the Northern Hemispheric cryosphere as possible predictor for the wintertime NAO (Cohen et al. 2014). Although several studies could find seasonal prediction skill in reanalysis data (Orsolini et al. 2016, Duville et al. 2017,Han & Sun 2018), experiments with ocean-atmosphere general circulation models (AOGCMs) still show conflicting results (Furtado et al. 2015, Handorf et al. 2015, Francis 2017, Gastineau et al. 2017).&#160;Here we use two kinds ECMWF seasonal prediction ensembles starting with November initial conditions taken from the long-term reanalysis ERA-20C and forecasting the following three winter months. Besides the 110-year ensemble of 50 members representing internal variability of the atmosphere, we investigate a second ensemble of 20 members where initial conditions are split between low and high snow cover years for the Northern Hemisphere. We compare two recently used Eurasian snow cover indices for their skill in predicting winter climate for the European continent. Analyzing the two forecast experiments, we found that prediction runs starting with high snow index values in November result in significantly more negative NAO states in the following winter (DJF), which in turn modulates near surface temperatures. We track the atmospheric anomalies triggered by the high snow index through the tropo- and stratosphere as well as for the individual winter months to provide a physical explanation for the formation of this particular climate state.&#160;

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Estimation of the Greenland ice sheet surface mass balance for the 20th and 21st centuries

The Cryosphere ◽

10.5194/tc-2-117-2008 ◽

2008 ◽

Vol 2 (2) ◽

pp. 117-129 ◽

Cited By ~ 68

Author(s):

X. Fettweis ◽

E. Hanna ◽

H. Gallée ◽

P. Huybrechts ◽

M. Erpicum

Keyword(s):

Mass Balance ◽

Multiple Regression ◽

21St Century ◽

General Circulation ◽

Ice Sheet ◽

Greenland Ice Sheet ◽

General Circulation Models ◽

Climate Simulation ◽

Surface Mass Balance ◽

Surface Mass

Abstract. Results from a regional climate simulation (1970–2006) over the Greenland ice sheet (GrIS) reveals that more than 97% of the interannual variability of the modelled Surface Mass Balance (SMB) can be explained by the GrIS summer temperature anomaly and the GrIS annual precipitation anomaly. This multiple regression is then used to empirically estimate the GrIS SMB since 1900 from climatological time series. The projected SMB changes in the 21st century are investigated with the set of simulations performed with atmosphere-ocean general circulation models (AOGCMs) of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). These estimates show that the high surface mass loss rates of recent years are not unprecedented in the GrIS history of the last hundred years. The minimum SMB rate seems to have occurred earlier in the 1930s and corresponds to a zero SMB rate. The AOGCMs project that the SMB rate of the 1930s would be common at the end of 2100. The temperature would be higher than in the 1930s but the increase of accumulation in the 21st century would partly offset the acceleration of surface melt due to the temperature increase. However, these assumptions are based on an empirical multiple regression only validated for recent/current climatic conditions, and the accuracy and time homogeneity of the data sets and AOGCM results used in these estimations constitute a large uncertainty.

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A Multimodel Study of Parametric Uncertainty in Predictions of Climate Response to Rising Greenhouse Gas Concentrations

Journal of Climate ◽

10.1175/2010jcli3498.1 ◽

2011 ◽

Vol 24 (5) ◽

pp. 1362-1377 ◽

Cited By ~ 50

Author(s):

Benjamin M. Sanderson

Keyword(s):

Climate Sensitivity ◽

General Circulation ◽

Parametric Uncertainty ◽

General Circulation Models ◽

Diverse Range ◽

Positive Feedbacks ◽

Clear Sky ◽

Model Version ◽

Atmosphere Model ◽

Hadley Centre

Abstract One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climateprediction.net ensemble are found to be proportional to significant biases in upper-tropospheric water vapor concentrations, which are not observed in the CAMcube ensemble. Both ensembles have a similar range of shortwave cloud feedback, making it unlikely that they are causing the larger climate sensitivities in climateprediction.net. In both cases, increased negative shortwave cloud feedbacks at high latitudes are generally compensated by increased positive feedbacks at lower latitudes.

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How Well Do We Understand and Evaluate Climate Change Feedback Processes?

Journal of Climate ◽

10.1175/jcli3819.1 ◽

2006 ◽

Vol 19 (15) ◽

pp. 3445-3482 ◽

Cited By ~ 673

Author(s):

Sandrine Bony ◽

Robert Colman ◽

Vladimir M. Kattsov ◽

Richard P. Allan ◽

Christopher S. Bretherton ◽

...

Keyword(s):

Climate Change ◽

Climate Sensitivity ◽

General Circulation ◽

External Perturbation ◽

General Circulation Models ◽

Lapse Rate ◽

Climate Feedback ◽

Climate Feedbacks ◽

Intergovernmental Panel ◽

Global Estimates

Abstract Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

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Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss ?

Journal of Climate ◽

10.1175/jcli-d-20-0613.1 ◽

2021 ◽

pp. 1-54

Author(s):

Y. Peings ◽

Z. Labe ◽

G. Magnusdottir

Keyword(s):

Sea Ice ◽

General Circulation ◽

Internal Variability ◽

General Circulation Models ◽

Arctic Sea Ice ◽

Model Assessment ◽

Atmospheric Response ◽

Arctic Sea ◽

Ocean Atmosphere ◽

Arctic Sea Ice Loss

AbstractThis study presents results from the Polar Amplification Multimodel Intercomparison Project (PAMIP) single-year time-slice experiments that aim to isolate the atmospheric response to Arctic sea ice loss at global warming levels of +2°C. Using two General Circulation Models (GCMs), the ensemble size is increased up to 300 ensemble members, beyond the recommended 100 members. After partitioning the response in groups of 100-ensemble members, the reproducibility of the results is evaluated, with a focus on the response of the mid-latitude jet streams in the North Atlantic and North Pacific. Both atmosphere-only and coupled ocean-atmosphere PAMIP experiments are analyzed. Substantial differences in the mid-latitude response are found among the different experiment subsets, suggesting that 100-member ensembles are still significantly influenced by internal variability, which can mislead conclusions. Despite an overall stronger response, the coupled ocean-atmosphere runs exhibit greater spread due to additional ENSO-related internal variability when the ocean is interactive. The lack of consistency in the response is true for anomalies that are statistically significant according to Student’s-t and False Discovery Rate tests. This is problematic for the multi-model assessment of the response, as some of the spread may be attributed to different model sensitivities while it is due to internal variability. We propose a method to overcome this consistency issue, that allows for more robust conclusions when only 100 ensemble members are used.

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Hierarchy of climate and hydrological uncertainties in transient low flow projections

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-12-12649-2015 ◽

2015 ◽

Vol 12 (12) ◽

pp. 12649-12701 ◽

Cited By ~ 2

Author(s):

J.-P. Vidal ◽

B. Hingray ◽

C. Magand ◽

E. Sauquet ◽

A. Ducharne

Keyword(s):

General Circulation ◽

Large Scale ◽

Internal Variability ◽

General Circulation Models ◽

Low Flow ◽

Small Scale ◽

Total Uncertainty ◽

Low Flows ◽

The Difference ◽

Flow Variables

Abstract. This paper proposes a methodology for estimating the transient probability distribution of yearly hydrological variables conditional to an ensemble of projections built from multiple general circulation models (GCMs), multiple statistical downscaling methods (SDMs) and multiple hydrological models (HMs). The methodology is based on the quasi-ergodic analysis of variance (QE-ANOVA) framework that allows quantifying the contributions of the different sources of total uncertainty, by critically taking account of large-scale internal variability stemming from the transient evolution of multiple GCM runs, and of small-scale internal variability derived from multiple realizations of stochastic SDMs. The QE-ANOVA framework was initially developed for long-term climate averages and is here extended jointly to (1) yearly anomalies and (2) low flow variables. It is applied to better understand possible transient futures of both winter and summer low flows for two snow-influenced catchments in the southern French Alps. The analysis takes advantage of a very large dataset of transient hydrological projections that combines in a comprehensive way 11 runs from 4 different GCMs, 3 SDMs with 10 stochastic realizations each, as well as 6 diverse HMs. The change signal is a decrease in yearly low flows of around −20 % in 2065, except for the most elevated catchment in winter where low flows barely decrease. This signal is largely masked by both large- and small-scale internal variability, even in 2065. The time of emergence of the change signal on 30 year low-flow averages is however around 2035, i.e. for time slices starting in 2020. The most striking result is that a large part of the total uncertainty – and a higher one than that due to the GCMs – stems from the difference in HM responses. An analysis of the origin of this substantial divergence in HM responses for both catchments and in both seasons suggests that both evapotranspiration and snowpack components of HMs should be carefully checked for their robustness in a changed climate in order to provide reliable outputs for informing water resource adaptation strategies.

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An observation-based scaling model for climate sensitivity estimates and global projections to 2100

Climate Dynamics ◽

10.1007/s00382-020-05521-x ◽

2020 ◽

Author(s):

Raphaël Hébert ◽

Shaun Lovejoy ◽

Bruno Tremblay

Keyword(s):

Climate Sensitivity ◽

Coupled Model ◽

Internal Variability ◽

Forced Response ◽

Scaling Exponent ◽

Climate Response ◽

Transient Climate Response ◽

Rcp 8.5 ◽

Historical Temperature ◽

Inner Scale

AbstractWe directly exploit the stochasticity of the internal variability, and the linearity of the forced response to make global temperature projections based on historical data and a Green’s function, or Climate Response Function (CRF). To make the problem tractable, we take advantage of the temporal scaling symmetry to define a scaling CRF characterized by the scaling exponent H, which controls the long-range memory of the climate, i.e. how fast the system tends toward a steady-state, and an inner scale $$\tau \approx 2$$ τ ≈ 2 years below which the higher-frequency response is smoothed out. An aerosol scaling factor and a non-linear volcanic damping exponent were introduced to account for the large uncertainty in these forcings. We estimate the model and forcing parameters by Bayesian inference which allows us to analytically calculate the transient climate response and the equilibrium climate sensitivity as: $$1.7^{+0.3} _{-0.2}$$ 1 . 7 - 0.2 + 0.3 K and $$2.4^{+1.3} _{-0.6}$$ 2 . 4 - 0.6 + 1.3 K respectively (likely range). Projections to 2100 according to the RCP 2.6, 4.5 and 8.5 scenarios yield warmings with respect to 1880–1910 of: $$1.5^{+0.4}_{-0.2}K$$ 1 . 5 - 0.2 + 0.4 K , $$2.3^{+0.7}_{-0.5}$$ 2 . 3 - 0.5 + 0.7 K and $$4.2^{+1.3}_{-0.9}$$ 4 . 2 - 0.9 + 1.3 K. These projection estimates are lower than the ones based on a Coupled Model Intercomparison Project phase 5 multi-model ensemble; more importantly, their uncertainties are smaller and only depend on historical temperature and forcing series. The key uncertainty is due to aerosol forcings; we find a modern (2005) forcing value of $$[-1.0, -0.3]\, \,\,\mathrm{Wm} ^{-2}$$ [ - 1.0 , - 0.3 ] Wm - 2 (90 % confidence interval) with median at $$-0.7 \,\,\mathrm{Wm} ^{-2}$$ - 0.7 Wm - 2 . Projecting to 2100, we find that to keep the warming below 1.5 K, future emissions must undergo cuts similar to RCP 2.6 for which the probability to remain under 1.5 K is 48 %. RCP 4.5 and RCP 8.5-like futures overshoot with very high probability.

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Evaluating parametric sensitivity of climate feedbacks in CNRM-CM6

10.5194/egusphere-egu21-15730 ◽

2021 ◽

Author(s):

Saloua Peatier ◽

Benjamin Sanderson ◽

Laurent Terray

Keyword(s):

Climate Sensitivity ◽

General Circulation ◽

Temperature Response ◽

Coupled Model ◽

General Circulation Models ◽

Future Climate Change ◽

Climate Feedbacks ◽

Geophysical Research ◽

Physics Ensemble ◽

Earth’S Climate

The global surface temperature response to CO2 doubling (Equilibrium Climate Sensitivity or ECS) is a key uncertain parameter determining the extent of future climate change. Sherwood et al. (2020) estimated the ECS to be within [2.6K - 4.5K], but in the Coupled Model Intercomparison Project phase 6 (CMIP6), 1/3 of the General Circulation Models (GCMs) show ECS exceeding 4.5K (Zelinka et al., 2020). CNRM-CM6-1 is one of these models, with an ECS of 4.9K. In this paper, we sampled 30 atmospheric parameters of CNRM-CM6-1 and produced a Perturbed Physics Ensemble (PPE) of atmospheric-only simulations to explore the feedback parameters diversity and the climatological plausibility of the members. This PPE showed a comparable&#160; range of feedback parameters to the multi-model archive, from 0.8 W.m-2/K to 1.8 W.m-2/K. Emulators of climatological performance and feedback parameters were used together with&#160; observational datasets to search for optimal model configurations conditional on different net climate feedbacks. The climatological constraints considered here did not themselves rule out the higher end ECS values of 5K and above. An optimal subset of parameter configurations were chosen to sample the range of ECS allowing the assessment of feedback constraints in future fully coupled experiments.&#160;References :Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M., Hargreaves, J. C., ... & Zelinka, M. D. (2020). An assessment of Earth's climate sensitivity using multiple lines of evidence. Reviews of Geophysics, 58(4), e2019RG000678.Zelinka, M. D., Myers, T. A., McCoy, D. T., Po&#8208;Chedley, S., Caldwell, P. M., Ceppi, P., ... & Taylor, K. E. (2020). Causes of higher climate sensitivity in CMIP6 models. Geophysical Research Letters, 47(1), e2019GL085782.

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

10.5194/egusphere-egu2020-12353 ◽

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

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. &#160;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. &#160;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).&#160;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.

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Using the fractional energy balance equation for accurate temperature projections through 2100

10.5194/egusphere-egu2020-11825 ◽

2020 ◽

Author(s):

Roman Procyk ◽

Shaun Lovejoy ◽

Lenin Del Rio Amador

Keyword(s):

Energy Storage ◽

Energy Balance ◽

Power Law ◽

Balance Equation ◽

General Circulation ◽

Numerical Models ◽

General Circulation Models ◽

Energy Balance Equation ◽

Forced Response ◽

Linear Differential

The conventional energy balance equation (EBE) is a first order linear differential equation driven by solar, volcanic and anthropogenic forcings. &#160;The differential term accounts for energy storage usually modelled as one or two &#8220;boxes&#8221;. &#160;Each box obeys Newton&#8217;s law of cooling, so that when perturbed, the Earth&#8217;s temperature relaxes exponentially to a thermodynamic equilibrium.However, the spatial scaling obeyed by the atmosphere and its numerical models implies that the energy storage process is a scaling, power law process, a consequence largely of turbulent, hierarchically organized oceans currents but also hierarchies of land ice, soil moisture and other processes whose rates depend on size.Scaling storage leads to power law relaxation and can be modelled via a seemingly trivial change - from integer to fractional order derivatives - the Fractional EBE (FEBE): with temperature derivatives order 0 < H &#160;< 1 rather than the EBE value H = 1. &#160;Mathematically the FEBE is a past value problem, not an initial value problem. &#160;&#160;&#160;Recent support for the FEBE comes from [Lovejoy, 2019a] who shows that the special H = 1/2 case (close to observations), the &#8220;Half-order EBE&#8221; (HEBE), can be analytically obtained from classical Budyko-Sellers energy balance models by improving the boundary conditions.The FEBE simultaneously models the deterministic forced response to external (e.g. anthropogenic) forcing as well as the stochastic response to internal forcing (variability) [Lovejoy, 2019b]. &#160;We directly exploit both aspects to make projections based on historical data estimating the parameters using Bayesian inference.&#160; Using global instrumental temperature series, alongside CMIP5 and CMIP6 standard forcings, the basic FEBE parameters are H &#8776; 0.4 with a relaxation time &#8776; 4 years. &#160;This observation-based model also produces projections for the coming century with forcings prescribed by the CMIP5 Representative Concentration Pathways scenarios and the CMIP6 Shared Socioeconomic Pathways.We compare both generations of General Circulation Models (GCMs) outputs from CMIP5/6 alongside with the projections produced by the FEBE model which are entirely independent from GCMs, contributing to our understanding of forced climate variability in the past, present and future.&#160; When comparing to CMIP5 projections, we find that the mean projections are about 10- 15% lower while the uncertainties are roughly half as large. &#160;Our global temperature projections are therefore within the&#160; CMIP5 90% confidence limits and thus give them strong, independent support.&#160;ReferencesLovejoy, S., The half-order energy balance equation, J. Geophys. Res. (Atmos.), (submitted, Nov. 2019), 2019a.Lovejoy, S., Fractional Relaxation noises, motions and the stochastic fractional relxation equation Nonlinear Proc. in Geophys. Disc., https://doi.org/10.5194/npg-2019-39, 2019b.

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