scholarly journals Potential Problems Measuring Climate Sensitivity from the Historical Record

2020 ◽  
Vol 33 (6) ◽  
pp. 2237-2248 ◽  
Author(s):  
Andrew E. Dessler
Keyword(s):  
Climate Sensitivity ◽  
Internal Variability ◽  
Historical Record ◽  
Cloud Feedback ◽  
Historical Period ◽  
Pattern Effect ◽  
Surface Warming ◽  
Two Factors ◽  
Low Cloud ◽  
The Impact

AbstractThis study investigates potential biases between equilibrium climate sensitivity inferred from warming over the historical period (ECShist) and the climate system’s true ECS (ECStrue). This paper focuses on two factors that could contribute to differences between these quantities. First is the impact of internal variability over the historical period: our historical climate record is just one of an infinity of possible trajectories, and these different trajectories can generate ECShist values 0.3 K below to 0.5 K above (5%–95% confidence interval) the average ECShist. Because this spread is due to unforced variability, I refer to this as the unforced pattern effect. This unforced pattern effect in the model analyzed here is traced to unforced variability in loss of sea ice, which affects the albedo feedback, and to unforced variability in warming of the troposphere, which affects the shortwave cloud feedback. There is also a forced pattern effect that causes ECShist to depart from ECStrue due to differences between today’s transient pattern of warming and the pattern of warming at 2×CO2 equilibrium. Changes in the pattern of warming lead to a strengthening low-cloud feedback as equilibrium is approached in regions where surface warming is delayed: the Southern Ocean, eastern Pacific, and North Atlantic near Greenland. This forced pattern effect causes ECShist to be on average 0.2 K lower than ECStrue (~8%). The net effect of these two pattern effects together can produce an estimate of ECShist as much as 0.5 K below ECStrue.

scholarly journals Observations of Local Positive Low Cloud Feedback Patterns and Their Role in Internal Variability and Climate Sensitivity

2018 ◽  
Vol 45 (9) ◽  
pp. 4438-4445 ◽  
Author(s):  
Tianle Yuan ◽  
Lazaros Oreopoulos ◽  
Steven E. Platnick ◽  
Kerry Meyer
Keyword(s):  
Climate Sensitivity ◽  
Internal Variability ◽  
Cloud Feedback ◽  
Low Cloud

scholarly journals The Strength of Low-Cloud Feedbacks and Tropical Climate: A CESM Sensitivity Study

2019 ◽  
Vol 32 (9) ◽  
pp. 2497-2516 ◽  
Author(s):  
Ehsan Erfani ◽  
Natalie J. Burls
Keyword(s):  
Climate Sensitivity ◽  
Cloud Cover ◽  
Hydrological Cycle ◽  
Tropical Climate ◽  
Cloud Feedback ◽  
Cloud Feedbacks ◽  
Feedback Strength ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
The Impact

Abstract Variability in the strength of low-cloud feedbacks across climate models is the primary contributor to the spread in their estimates of equilibrium climate sensitivity (ECS). This raises the question: What are the regional implications for key features of tropical climate of globally weak versus strong low-cloud feedbacks in response to greenhouse gas–induced warming? To address this question and formalize our understanding of cloud controls on tropical climate, we perform a suite of idealized fully coupled and slab-ocean climate simulations across which we systematically scale the strength of the low-cloud-cover feedback under abrupt 2 × CO2 forcing within a single model, thereby isolating the impact of low-cloud feedback strength. The feedback strength is varied by modifying the stratus cloud fraction so that it is a function of not only local conditions but also global temperature in a series of abrupt 2 × CO2 sensitivity experiments. The unperturbed decrease in low cloud cover (LCC) under 2 × CO2 is greatest in the mid- and high-latitude oceans, and the subtropical eastern Pacific and Atlantic, a pattern that is magnified as the feedback strength is scaled. Consequently, sea surface temperature (SST) increases more in these regions as well as the Pacific cold tongue. As the strength of the low-cloud feedback increases this results in not only increased ECS, but also an enhanced reduction of the large-scale zonal and meridional SST gradients (structural climate sensitivity), with implications for the atmospheric Hadley and Walker circulations, as well as the hydrological cycle. The relevance of our results to simulating past warm climate is also discussed.

scholarly journals Reply to “Comment on ‘The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity’”

2020 ◽  
Vol 33 (1) ◽  
pp. 397-404 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Time Windows ◽  
Internal Variability ◽  
Historical Record ◽  
Cmip5 Models ◽  
Temperature Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Total Uncertainty ◽  
The Impact ◽  
The Relationship

AbstractCowtan and Jacobs assert that the method used by Lewis and Curry in 2018 (LC18) to estimate the climate system’s transient climate response (TCR) from changes between two time windows is less robust—in particular against sea surface temperature bias correction uncertainty—than a method that uses the entire historical record. We demonstrate that TCR estimated using all data from the temperature record is closely in line with that estimated using the LC18 windows, as is the median TCR estimate using all pairs of individual years. We also show that the median TCR estimate from all pairs of decade-plus-length windows is closely in line with that estimated using the LC18 windows and that incorporating window selection uncertainty would make little difference to total uncertainty in TCR estimation. We find that, when differences in the evolution of forcing are accounted for, the relationship over time between warming in CMIP5 models and observations is consistent with the relationship between CMIP5 TCR and LC18’s TCR estimate but fluctuates as a result of multidecadal internal variability and volcanism. We also show that various other matters raised by Cowtan and Jacobs have negligible implications for TCR estimation in LC18.

Observational constraints on low cloud feedback reduce uncertainty of climate sensitivity

2021 ◽  
Author(s):  
Timothy A. Myers ◽  
Ryan C. Scott ◽  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
Joel R. Norris ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Cloud Feedback ◽  
Observational Constraints ◽  
Low Cloud

scholarly journals Influence of Atmospheric Rivers on Mountain Snowpack in the Western United States

2018 ◽  
Vol 31 (24) ◽  
pp. 9921-9940 ◽  
Author(s):  
N. Goldenson ◽  
L. R. Leung ◽  
C. M. Bitz ◽  
E. Blanchard-Wrigglesworth
Keyword(s):  
United States ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Region Of Interest ◽  
Internal Variability ◽  
Western United States ◽  
Historical Period ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
The Impact

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

scholarly journals The Impact of Recent Forcing and Ocean Heat Uptake Data on Estimates of Climate Sensitivity

2018 ◽  
Vol 31 (15) ◽  
pp. 6051-6071 ◽  
Author(s):  
Nicholas Lewis ◽  
Judith Curry
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Temperature Data ◽  
Historical Period ◽  
Climate Feedbacks ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Surface Temperature Change ◽  
Aerosol Forcing ◽  
The Impact

Energy budget estimates of equilibrium climate sensitivity (ECS) and transient climate response (TCR) are derived based on the best estimates and uncertainty ranges for forcing provided in the IPCC Fifth Assessment Report (AR5). Recent revisions to greenhouse gas forcing and post-1990 ozone and aerosol forcing estimates are incorporated and the forcing data extended from 2011 to 2016. Reflecting recent evidence against strong aerosol forcing, its AR5 uncertainty lower bound is increased slightly. Using an 1869–82 base period and a 2007–16 final period, which are well matched for volcanic activity and influence from internal variability, medians are derived for ECS of 1.50 K (5%–95% range: 1.05–2.45 K) and for TCR of 1.20 K (5%–95% range: 0.9–1.7 K). These estimates both have much lower upper bounds than those from a predecessor study using AR5 data ending in 2011. Using infilled, globally complete temperature data give slightly higher estimates: a median of 1.66 K for ECS (5%–95% range: 1.15–2.7 K) and 1.33 K for TCR (5%–95% range: 1.0–1.9 K). These ECS estimates reflect climate feedbacks over the historical period, assumed to be time invariant. Allowing for possible time-varying climate feedbacks increases the median ECS estimate to 1.76 K (5%–95% range: 1.2–3.1 K), using infilled temperature data. Possible biases from non–unit forcing efficacy, temperature estimation issues, and variability in sea surface temperature change patterns are examined and found to be minor when using globally complete temperature data. These results imply that high ECS and TCR values derived from a majority of CMIP5 climate models are inconsistent with observed warming during the historical period.

scholarly journals On the Emergent Constraints of Climate Sensitivity

2018 ◽  
Vol 31 (2) ◽  
pp. 863-875 ◽  
Author(s):  
Xin Qu ◽  
Alex Hall ◽  
Anthony M. DeAngelis ◽  
Mark D. Zelinka ◽  
Stephen A. Klein ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Selection Process ◽  
Temperature Response ◽  
Cloud Feedback ◽  
Emergent Constraints ◽  
Low Cloud ◽  
Model Ensembles ◽  
Statistical Connection ◽  
Emergent Constraint

Differences among climate models in equilibrium climate sensitivity (ECS; the equilibrium surface temperature response to a doubling of atmospheric CO2) remain a significant barrier to the accurate assessment of societally important impacts of climate change. Relationships between ECS and observable metrics of the current climate in model ensembles, so-called emergent constraints, have been used to constrain ECS. Here a statistical method (including a backward selection process) is employed to achieve a better statistical understanding of the connections between four recently proposed emergent constraint metrics and individual feedbacks influencing ECS. The relationship between each metric and ECS is largely attributable to a statistical connection with shortwave low cloud feedback, the leading cause of intermodel ECS spread. This result bolsters confidence in some of the metrics, which had assumed such a connection in the first place. Additional analysis is conducted with a few thousand artificial metrics that are randomly generated but are well correlated with ECS. The relationships between the contrived metrics and ECS can also be linked statistically to shortwave cloud feedback. Thus, any proposed or forthcoming ECS constraint based on the current generation of climate models should be viewed as a potential constraint on shortwave cloud feedback, and physical links with that feedback should be investigated to verify that the constraint is real. In addition, any proposed ECS constraint should not be taken at face value since other factors influencing ECS besides shortwave cloud feedback could be systematically biased in the models.

scholarly journals Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

2020 ◽  
Vol 33 (18) ◽  
pp. 7755-7775 ◽  
Author(s):  
Yue Dong ◽  
Kyle C. Armour ◽  
Mark D. Zelinka ◽  
Cristian Proistosescu ◽  
David S. Battisti ◽  
...  
Keyword(s):  
Time Scales ◽  
Climate Sensitivity ◽  
Atmospheric Model ◽  
Warm Pool ◽  
Cmip5 Models ◽  
Fast Time ◽  
Pattern Effect ◽  
Surface Warming ◽  
Early Portion ◽  
Radiative Feedbacks

AbstractRadiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. This study investigates intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the west Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

Estimate of equilibrium climate sensitivity and uncertainties by using observations and CMIP6 global coupled climate models

2021 ◽  
Author(s):  
Jonathan Chenal ◽  
Benoit Meyssignac
Keyword(s):  
Temperature Dependence ◽  
Climate Sensitivity ◽  
Energy Budget ◽  
Climate Model ◽  
Regression Method ◽  
Historical Period ◽  
Ocean Heat Uptake ◽  
Equilibrium Climate Sensitivity ◽  
Pattern Effect ◽  
Uncertainty Range

<p>Energy budget estimates of the effective climate sensitivity (effCS) are derived based on estimates of the historical forcing and of observations of the sea surface temperature variations and the ocean heat uptake. Recent revisions to Greenhouse gas forcing and aerosol forcing estimates are included and the data is extended to 2018. We consider two different approaches to derive the effCS from the energy budget: 1) a difference of the energy budget between the recent period 2005-2018 and a base period 1861-1880 (following Sherwood 2020) and 2)  a regression of the differential form of energy budget over the period 1955-2017 (following Gregory et al. 2020). These estimates of the effCS over the historical period are representative of the climate feedback experienced by the climate during the historical period. When accounting for the uncertainty in the forcing, the surface temperature and the ocean heat uptake estimates plus the uncertainty due to the internal variability we find a range of effCS of [1.0;9.7] (at the 95%CL) with a median of 2.0 K with approach (1) and [1.2;2.7] with a median of 1.7 K with approch (2). We find that the lower and the upper tail of the distribution in effCS arise dominantly from the uncertainty in the historical forcing, particularly for the regression method, and at a lower extent for the difference method. This is consistent with previous studies (e.g. Lewis and Curry 2018 and Sherwood et al. 2020).</p><p>Using the same approach based on historical observations but accounting for the pattern effect and the temperature dependence of the feedback estimated with climate model simulations, we derive new estimates of the effECS that should encompass the equilibrium climate sensitivity (assuming that climate model simulate properly the pattern effect and the temperature dependence of feedback). We find that adding the pattern effect and the temperature dependence of the feedbacks shifts upwards the median of the effECS and increases significantly the uncertainty range. For the difference method, the median is now 2.5 K and the uncertainty range [1.1;17.2]. For the regression method the median is now 2.0 K and the uncertainty range is [1.2;4.7] K ((5-95%). On the overall, we find that the regression method performs better to constraint the equilibrium climate sensitivity and that the major source of uncertainties comes from the differences in the simulation of the pattern effect among climate models rather than the uncertainties on the historical forcing.</p>

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