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.

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

2020 ◽  
Vol 33 (1) ◽  
pp. 391-396 ◽  
Author(s):  
Kevin Cowtan ◽  
Peter Jacobs
Keyword(s):  
Climate Model ◽  
Time Windows ◽  
Temperature Response ◽  
Internal Variability ◽  
Baseline Period ◽  
Historical Record ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Window Method ◽  
The Impact

AbstractIn 2018, Lewis and Curry presented a method for estimating the transient climate response (TCR) of the climate system from the temperature change between two time windows: an early baseline period in the nineteenth century and a modern period primarily in the twenty-first century. The results suggest a lower value of TCR than estimates from climate model simulations. Previous studies have identified uncertainty in the historical forcings, the impact of the time evolution of the forcing on temperature response, and observational issues as contributory factors to this disagreement. We investigate a further factor: uncertainty in the bias corrections applied to historical sea surface temperature data. This uncertainty can particularly affect the estimation of variables on decadal time scales and therefore affect the estimation of TCR using the window method as well as estimates of internal variability. We demonstrate that use of the whole historical record can mitigate the impacts of working with short time windows to some extent, particularly with respect to the early part of the record.

scholarly journals Bayesian estimation of Earth's climate sensitivity and transient climate response from observational warming and heat content datasets

2021 ◽  
Vol 12 (2) ◽  
pp. 709-723
Author(s):  
Philip Goodwin ◽  
B. B. Cael
Keyword(s):  
Climate Sensitivity ◽  
Future Climate ◽  
Climate Feedback ◽  
Future Climate Change ◽  
Climate Projections ◽  
Climate Response ◽  
Ocean Heat Uptake ◽  
Transient Climate Response ◽  
Posterior Probability Distribution ◽  
The Impact

Abstract. Future climate change projections, impacts, and mitigation targets are directly affected by how sensitive Earth's global mean surface temperature is to anthropogenic forcing, expressed via the climate sensitivity (S) and transient climate response (TCR). However, the S and TCR are poorly constrained, in part because historic observations and future climate projections consider the climate system under different response timescales with potentially different climate feedback strengths. Here, we evaluate S and TCR by using historic observations of surface warming, available since the mid-19th century, and ocean heat uptake, available since the mid-20th century, to constrain a model with independent climate feedback components acting over multiple response timescales. Adopting a Bayesian approach, our prior uses a constrained distribution for the instantaneous Planck feedback combined with wide-ranging uniform distributions of the strengths of the fast feedbacks (acting over several days) and multi-decadal feedbacks. We extract posterior distributions by applying likelihood functions derived from different combinations of observational datasets. The resulting TCR distributions when using two preferred combinations of historic datasets both find a TCR of 1.5 (1.3 to 1.8 at 5–95 % range) ∘C. We find the posterior probability distribution for S for our preferred dataset combination evolves from S of 2.0 (1.6 to 2.5) ∘C on a 20-year response timescale to S of 2.3 (1.4 to 6.4) ∘C on a 140-year response timescale, due to the impact of multi-decadal feedbacks. Our results demonstrate how multi-decadal feedbacks allow a significantly higher upper bound on S than historic observations are otherwise consistent with.

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 Estimating trends in the global mean temperature record

2017 ◽  
Vol 3 (1) ◽  
pp. 33-53 ◽  
Author(s):  
Andrew Poppick ◽  
Elisabeth J. Moyer ◽  
Michael L. Stein
Keyword(s):  
Radiative Forcing ◽  
Temporal Correlation ◽  
Internal Variability ◽  
Historical Record ◽  
Temperature Record ◽  
Empirical Methods ◽  
Global Mean Temperature ◽  
Data Record ◽  
Historical Temperature ◽  
Global Mean

Abstract. Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the important characteristics of internal variability, can result in more accurate uncertainty statements about trends.

EXPRESS: Influence of Time to Endovascular Stroke Treatment on Outcomes in the Early versus Extended Window Paradigms

2021 ◽  
pp. 174749302110063
Author(s):  
Raul Nogueira ◽  
Tudor G Jovin ◽  
Diogo C. Haussen ◽  
Rishi Gupta ◽  
ashutosh Jadhav ◽  
...  
Keyword(s):  
Functional Disability ◽  
Time Window ◽  
Time Windows ◽  
Extended Time ◽  
Primary Analysis ◽  
Anterior Circulation ◽  
Time To Treatment ◽  
Stroke Treatment ◽  
The Impact ◽  
The Relationship

Background The effect of time from stroke onset to thrombectomy in the extended time window remains poorly characterized. Aim We aimed to analyze the relationship between time to treatment and clinical outcomes in the early versus extended time windows. Methods Proximal anterior circulation occlusion patients from a multicentric prospective registry were categorized into early (≤6-hours) or extended (>6-24-hours) treatment window. Patients with baseline NIHSS≥10 and intracranial ICA or MCA-M1-segment occlusion and pre-morbid mRS0-1 (“DAWN-like” cohort) served as the population for the primary analysis. The relationship between time to treatment and 90-day mRS, analyzed in ordinal (mRS shift) and dichotomized (good outcome, mRS0-2) fashion, was compared within and across the extended and early-windows. Results A total of 1603 out of 2008 patients qualified. Despite longer time to treatment (9[7-13.9]vs.3.4[2.5-4.3] hours,p<0.001), extended-window patients (n=257) had similar rates of symptomatic intracranial hemorrhage (0.8%vs.1.7%,p=0.293) and 90-day-mortality (10.5%vs.9.6%,p=0.714) with only slightly lower rates of 90-day good outcomes (50.4%vs.57.6%,p=0.047) versus early-window patients (n=709). Time to treatment was associated with 90-day disability in both ordinal (aOR,≥1-point mRS shift:0.75;95%CI[0.66-0.86],p<0.001) and dichotomized (aOR,mRS0-2:0.73;95%CI[0.62-0.86],p<0.001) analyses in the early- but not in the extended-window (aOR, mRS shift:0.96;95%CI[0.90-1.02],p=0.15; aOR,mRS0-2:0.97;95%CI[0.90-1.04],p=0.41). Early-window patients had significantly lower 90-day functional disability (aOR, mRS shift:1.533;95%CI[1.138-2.065],p=0.005) and a trend towards higher rates of good outcomes (aOR,mRS0-2:1.391;95%CI[0.972-1.990],p=0.071). Conclusions The impact of time to thrombectomy on outcomes appears to be time dependent with a steep influence in the early followed by a less significant plateau in the extended window. However, every effort should be made to shorten treatment times regardless of ischemia duration.

scholarly journals How does internal variability influence the ability of CMIP5 models to reproduce the recent trend in Southern Ocean sea ice extent?

2012 ◽  
Vol 6 (5) ◽  
pp. 3539-3573 ◽  
Author(s):  
V. Zunz ◽  
H. Goosse ◽  
F. Massonnet
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Climate Models ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Positive Trend ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The One ◽  
The Impact

Abstract. Observations over the last 30 yr have shown that the sea ice extent in the Southern Ocean has slightly increased since 1979. Mechanisms responsible for this positive trend have not been well established yet and climate models are generally unable to simulate correctly this expansion. In this study, we focus on two related hypotheses that could explain the misrepresentation of the positive trend in sea ice extent by climate models: an unrealistic internal variability and an inadequate initialization of the system. For that purpose, we analyze the evolution of sea ice around the Antarctic simulated by 24 different general circulation models involved in the 5th Coupled Model Intercomparison Project (CMIP5). On the one hand, historical simulations, driven by external forcing and initialized without observations, are examined. They provide information about the mean state, the variability and the trend in sea ice extent simulated by each model. On the other hand, decadal prediction experiments, driven by external forcing and initialized with some observed fields, allow us to assess the impact of the representation of the observed initial state on the quality of model predictions. Our analyses show that CMIP5 models respond to the forcing, including the one induced by stratospheric ozone depletion, by reducing the sea ice cover in the Southern Ocean. Some simulations display an increase in sea ice extent. However, models strongly overestimate the variability of sea ice extent and the initialization methods currently used in models do not improve systematically the simulated trends in sea ice extent. On the basis of those results, a critical role of the internal variability in the observed increase in the sea ice extent in the Southern Ocean could not be ruled out but current models results appear inadequate to test more precisely this hypothesis.

scholarly journals The Impact of Regional Multidecadal and Century-Scale Internal Climate Variability on Sea Level Trends in CMIP5 Models

2015 ◽  
Vol 28 (2) ◽  
pp. 853-861 ◽  
Author(s):  
Mark Carson ◽  
Armin Köhl ◽  
Detlef Stammer
Keyword(s):  
Sea Level ◽  
Time Scales ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Level Control ◽  
Ensemble Spread ◽  
Tropical Ocean ◽  
Regional Sea Level ◽  
The Impact

Abstract Regional sea surface height variability due to internal climate fluctuations is estimated using preindustrial control runs of 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Projected sea level trends of the representative concentration pathway 4.5 (RCP4.5) scenario for 20-, 50-, and 100-yr intervals grow from being largely dominated by internal variability on shorter time scales to being the dominant sea level signal on long time scales. The internal variability is estimated by calculating overlapping trends for the various time scales on the regional sea level control run output from each model. When compared to the ensemble spread of the RCP4.5 scenario trends, the internal variability remains a substantial portion of the spread even after 50 years. The regional ensemble mean trends are mostly larger than the ensemble spread for the 50-yr interval and are larger everywhere, except for part of the central Arctic and the Southern Ocean for the 100-yr projection. Although it is unclear whether the model internal variability estimate will be comparable to long-term variability in the real ocean, the authors compare the strength of the estimate to satellite altimetry and find that altimetry-based trends may be larger in tropical ocean regions, with only limited extratropical regions rising above the internal variability. The authors also analyze a single model’s internal variability against its future RCP4.5-projected sea level and show that, by 50 years, many regional sea level trends are larger than the underlying internal variability, though this variability still accounts for more than a third of the trend magnitude for almost half of the extratropical ocean.

scholarly journals How effectively do drought indices capture health outcomes? An investigation from rural Australia

2021 ◽  
Author(s):  
Emma Austin ◽  
Anthony S. Kiem ◽  
Jane Rich ◽  
David Perkins ◽  
Brian Kelly
Keyword(s):  
Time Window ◽  
Empirical Studies ◽  
Time Windows ◽  
Drought Indices ◽  
Drought Duration ◽  
South Wales ◽  
The Individual ◽  
Global Threat ◽  
The Impact ◽  
The Relationship

AbstractDrought is a global threat to public health. Increasingly, the impact of drought on mental health and wellbeing is being recognised. This paper investigates the relationship between drought and wellbeing to determine which drought indices most effectively capture wellbeing outcomes. A thorough understanding of the relationship between drought and wellbeing must consider the: (i) three aspects of drought (duration, frequency and magnitude); (ii) different types of drought (e.g. meteorological, agricultural, etc.); and (iii) the individual context of specific locations, communities and sectors. For this reason, we used a variety of drought types, drought indices, and time windows to identify the thresholds for wet and dry epochs that enhance and suppress impacts to wellbeing. Four postcodes in New South Wales (NSW), Australia are used as case studies in the analysis to highlight the spatial variability in the relationship between drought and wellbeing. The results demonstrate that the relationship between drought indices and wellbeing outcomes differs temporally, spatially and according to drought type. This paper objectively tests the relationship between commonly used drought indices and wellbeing outcomes to establish if current methods of quantifying drought effectively capture wellbeing outcomes. For funding, community programs and interventions to result in successful adaptation, it is essential to critically choose which drought index, time window and wellbeing outcome to use in empirical studies. The uncertainties associated with these relationships must be accounted for and it must also be realized that results will differ based on these decisions.

What warms the warmest waters during El Niño?

2021 ◽  
Author(s):  
Allison Hogikyan ◽  
Stephan Fueglistaler ◽  
Laure Resplandy
Keyword(s):  
Surface Temperature ◽  
Climate Model ◽  
Deep Convection ◽  
Internal Variability ◽  
Historical Record ◽  
Forced Response ◽  
Ocean Dynamics ◽  
Ocean Heat Uptake ◽  
Eastern Equatorial Pacific ◽  
Heat Uptake

&lt;p&gt;During El Ni&amp;#241;o, the upwelling in the eastern equatorial Pacific (EEP) slows, leading to a warm sea surface temperature (SST) anomaly, and the tropical troposphere warms. Only SSTs in regions with atmospheric deep convection, typically the warmest SSTs, affect the temperature of the tropical free troposphere. The warming of the EEP, which is home to the coldest tropical SSTs and does not experience atmospheric convection, therefore appears insufficient to explain the observed warming of the troposphere. Here, we examine the physical processes that lead to the warming of the warmest SSTs using both a global atmosphere-ocean coupled climate model and the ECMWF reanalysis. We show that SSTs in convecting regions do not warm as a result of ocean dynamics (upwelling), but as a result of a net heat flux from the atmosphere to the ocean following a weakening of surface winds and decrease in evaporation. This increased ocean heat uptake in convecting regions opposes the decrease in ocean heat uptake in the rest of the tropics during El Nino. This process may be important for linking surface temperature to ocean heat uptake changes, and the contribution of internal variability in the form of ENSO and IPO to the forced response observed over the historical record.&lt;/p&gt;

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.

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