scholarly journals A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

2020 ◽  
Vol 16 (5) ◽  
pp. 1715-1735 ◽  
Author(s):  
Martin Renoult ◽  
James Douglas Annan ◽  
Julia Catherine Hargreaves ◽  
Navjit Sagoo ◽  
Clare Flynn ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Bayesian Method ◽  
Regression Line ◽  
Ordinary Least Squares ◽  
Bayesian Framework ◽  
Future Climate Change ◽  
Filter Method ◽  
Structural Uncertainty ◽  
Uncertainty Estimates ◽  
Emergent Constraints

Abstract. In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (0.6–5.2, 5th–95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3 K (0.5–4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (0.7–5.2) using the LGM and 2.3 K (0.4–4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5 K (0.8–4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 K and only exceeding 6 K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity.

scholarly journals A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

2020 ◽  
Author(s):  
Martin Renoult ◽  
James Annan ◽  
Julia Hargreaves ◽  
Navjit Sagoo ◽  
Clare Flynn ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Bayesian Method ◽  
Ordinary Least Squares ◽  
Bayesian Framework ◽  
Future Climate Change ◽  
Filter Method ◽  
Data Sets ◽  
Uncertainty Estimates ◽  
Emergent Constraints ◽  
Model Ensembles

<p>In this study we introduce a Bayesian framework, which is flexible and explicit about the prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on Ordinary Least Squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (1.1 - 4.8, 5 - 95 percentiles) using the PMIP2, PMIP3 and PMIP4 data sets for the LGM, and 2.4 K (0.4 - 5.0) with the PlioMIP1 and PlioMIP2 data sets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (1.1 - 4.3) using the LGM and  2.4 K (0.4 - 5.1) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a slightly tighter constraint of 2.6 K (1.1 - 3.9). We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95% probability of climate sensitivity mostly below 5 and never exceeding 6 K. The approach is compared with other approaches based on OLS, a Kalman filter method and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, suggesting a higher bound by construction in case of weaker correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation of their potential use in future probabilistic estimation of climate sensitivity.</p>

scholarly journals A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP

2020 ◽  
Author(s):  
Martin Renoult ◽  
James Douglas Annan ◽  
Julia Catherine Hargreaves ◽  
Navjit Sagoo ◽  
Clare Flynn ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Bayesian Method ◽  
Ordinary Least Squares ◽  
Bayesian Framework ◽  
Future Climate Change ◽  
Filter Method ◽  
Data Sets ◽  
Uncertainty Estimates ◽  
Emergent Constraints ◽  
Model Ensembles

Abstract. In this paper we introduce a Bayesian framework, which is flexible and explicit about the prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on Ordinary Least Squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (1.1–4.8, 5–95 percentiles) using the PMIP2, PMIP3 and PMIP4 data sets for the LGM, and 2.4 K (0.4–5.0) with the PlioMIP1 and PlioMIP2 data sets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (1.1–4.3) using the LGM and 2.4 K (0.4–5.1) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a slightly tighter constraint of 2.6 K (1.1–3.9). We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 and never exceeding 6 K. The approach is compared with other approaches based on OLS, a Kalman filter method and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, suggesting a higher bound by construction in case of weaker correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation of their potential use in future probabilistic estimation of climate sensitivity.

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 Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

2020 ◽  
Vol 11 (4) ◽  
pp. 1233-1258
Author(s):  
Manuel Schlund ◽  
Axel Lauer ◽  
Pierre Gentine ◽  
Steven C. Sherwood ◽  
Veronika Eyring
Keyword(s):  
Climate Sensitivity ◽  
Statistical Significance ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Co2 Concentration ◽  
Intergovernmental Panel ◽  
Equilibrium Climate Sensitivity ◽  
Emergent Constraints ◽  
Statistical Relationships ◽  
Emergent Constraint

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

scholarly journals Structural uncertainty in air mass factor calculation for NO<sub>2</sub> and HCHO satellite retrievals

2017 ◽  
Vol 10 (3) ◽  
pp. 759-782 ◽  
Author(s):  
Alba Lorente ◽  
K. Folkert Boersma ◽  
Huan Yu ◽  
Steffen Dörner ◽  
Andreas Hilboll ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Structural Uncertainty ◽  
Ancillary Data ◽  
Air Mass ◽  
Cloud Parameters ◽  
Uncertainty Estimates ◽  
Satellite Retrievals ◽  
Mass Factor

Abstract. Air mass factor (AMF) calculation is the largest source of uncertainty in NO2 and HCHO satellite retrievals in situations with enhanced trace gas concentrations in the lower troposphere. Structural uncertainty arises when different retrieval methodologies are applied within the scientific community to the same satellite observations. Here, we address the issue of AMF structural uncertainty via a detailed comparison of AMF calculation methods that are structurally different between seven retrieval groups for measurements from the Ozone Monitoring Instrument (OMI). We estimate the escalation of structural uncertainty in every sub-step of the AMF calculation process. This goes beyond the algorithm uncertainty estimates provided in state-of-the-art retrievals, which address the theoretical propagation of uncertainties for one particular retrieval algorithm only. We find that top-of-atmosphere reflectances simulated by four radiative transfer models (RTMs) (DAK, McArtim, SCIATRAN and VLIDORT) agree within 1.5 %. We find that different retrieval groups agree well in the calculations of altitude resolved AMFs from different RTMs (to within 3 %), and in the tropospheric AMFs (to within 6 %) as long as identical ancillary data (surface albedo, terrain height, cloud parameters and trace gas profile) and cloud and aerosol correction procedures are being used. Structural uncertainty increases sharply when retrieval groups use their preference for ancillary data, cloud and aerosol correction. On average, we estimate the AMF structural uncertainty to be 42 % over polluted regions and 31 % over unpolluted regions, mostly driven by substantial differences in the a priori trace gas profiles, surface albedo and cloud parameters. Sensitivity studies for one particular algorithm indicate that different cloud correction approaches result in substantial AMF differences in polluted conditions (5 to 40 % depending on cloud fraction and cloud pressure, and 11 % on average) even for low cloud fractions (<  0.2) and the choice of aerosol correction introduces an average uncertainty of 50 % for situations with high pollution and high aerosol loading. Our work shows that structural uncertainty in AMF calculations is significant and that it is mainly caused by the assumptions and choices made to represent the state of the atmosphere. In order to decide which approach and which ancillary data are best for AMF calculations, we call for well-designed validation exercises focusing on polluted conditions in which AMF structural uncertainty has the highest impact on NO2 and HCHO retrievals.

scholarly journals Comparison of VOC measurements made by PTR-MS, adsorbent tubes–GC-FID-MS and DNPH derivatization–HPLC during the Sydney Particle Study, 2012: a contribution to the assessment of uncertainty in routine atmospheric VOC measurements

2018 ◽  
Vol 11 (1) ◽  
pp. 141-159 ◽  
Author(s):  
Erin Dunne ◽  
Ian E. Galbally ◽  
Min Cheng ◽  
Paul Selleck ◽  
Suzie B. Molloy ◽  
...  
Keyword(s):  
High Performance ◽  
Climate Models ◽  
Regression Line ◽  
Measurement Techniques ◽  
Major Axis ◽  
Proton Transfer Reaction ◽  
Bottom Up ◽  
Routine Monitoring ◽  
Uncertainty Estimates ◽  
Benzene Toluene

Abstract. Understanding uncertainty is essential for utilizing atmospheric volatile organic compound (VOC) measurements in robust ways to develop atmospheric science. This study describes an inter-comparison of the VOC data, and the derived uncertainty estimates, measured with three independent techniques (PTR-MS, proton-transfer-reaction mass spectrometry; GC-FID-MS, gas chromatography with flame-ionization and mass spectrometric detection; and DNPH–HPLC, 2,4-dinitrophenylhydrazine derivatization followed by analysis by high-performance liquid chromatography) during routine monitoring as part of the Sydney Particle Study (SPS) campaign in 2012. Benzene, toluene, C8 aromatics, isoprene, formaldehyde and acetaldehyde were selected for the comparison, based on objective selection criteria from the available data. Bottom-up uncertainty analyses were undertaken for each compound and each measurement system. Top-down uncertainties were quantified via the inter-comparisons. In all seven comparisons, the correlations between independent measurement techniques were high with R2 values with a median of 0.92 (range 0.75–0.98) and small root mean square of the deviations (RMSD) of the observations from the regression line with a median of 0.11 (range 0.04–0.23 ppbv). These results give a high degree of confidence that for each comparison the response of the two independent techniques is dominated by the same constituents. The slope and intercept as determined by reduced major axis (RMA) regression gives a different story. The slopes varied considerably with a median of 1.25 and a range of 1.16–2.01. The intercepts varied with a median of 0.04 and a range of −0.03 to 0.31 ppbv. An ideal comparison would give a slope of 1.00 and an intercept of 0. Some sources of uncertainty that are poorly quantified by the bottom-up uncertainty analysis method were identified, including: contributions of non-target compounds to the measurement of the target compound for benzene, toluene and isoprene by PTR-MS as well as the under-reporting of formaldehyde, acetaldehyde and acetone by the DNPH technique. As well as these, this study has identified a specific interference of liquid water with acetone measurements by the DNPH technique. These relationships reported for Sydney 2012 were incorporated into a larger analysis with 61 similar published inter-comparison studies for the same compounds. Overall, for the light aromatics, isoprene and the C1–C3 carbonyls, the uncertainty in a set of measurements varies by a factor of between 1.5 and 2. These uncertainties (∼50 %) are significantly higher than uncertainties estimated using standard propagation of error methods, which in this case were ∼22 % or less, and are the result of the presence of poorly understood or neglected processes that affect the measurement and its uncertainty. The uncertainties in VOC measurements identified here should be considered when assessing the reliability of VOC measurements from routine monitoring with individual, stand-alone instruments; when utilizing VOC data to constrain and inform air quality and climate models; when using VOC observations for human exposure studies; and for comparison with satellite retrievals.

scholarly journals The Early Eocene equable climate problem: can perturbations of climate model parameters identify possible solutions?

2013 ◽  
Vol 371 (2001) ◽  
pp. 20130123 ◽  
Author(s):  
Navjit Sagoo ◽  
Paul Valdes ◽  
Rachel Flecker ◽  
Lauren J. Gregoire
Keyword(s):  
Ocean Circulation ◽  
Climate Sensitivity ◽  
Climate Model ◽  
Mean Value ◽  
Future Climate Change ◽  
Model Parameters ◽  
Early Eocene ◽  
Equable Climate ◽  
The Mean ◽  
Climate Problem

Geological data for the Early Eocene (56–47.8 Ma) indicate extensive global warming, with very warm temperatures at both poles. However, despite numerous attempts to simulate this warmth, there are remarkable data–model differences in the prediction of these polar surface temperatures, resulting in the so-called ‘equable climate problem’. In this paper, for the first time an ensemble with a perturbed climate-sensitive model parameters approach has been applied to modelling the Early Eocene climate. We performed more than 100 simulations with perturbed physics parameters, and identified two simulations that have an optimal fit with the proxy data. We have simulated the warmth of the Early Eocene at 560 ppmv CO 2 , which is a much lower CO 2 level than many other models. We investigate the changes in atmospheric circulation, cloud properties and ocean circulation that are common to these simulations and how they differ from the remaining simulations in order to understand what mechanisms contribute to the polar warming. The parameter set from one of the optimal Early Eocene simulations also produces a favourable fit for the last glacial maximum boundary climate and outperforms the control parameter set for the present day. Although this does not ‘prove’ that this model is correct, it is very encouraging that there is a parameter set that creates a climate model able to simulate well very different palaeoclimates and the present-day climate. Interestingly, to achieve the great warmth of the Early Eocene this version of the model does not have a strong future climate change Charney climate sensitivity. It produces a Charney climate sensitivity of 2.7 ° C, whereas the mean value of the 18 models in the IPCC Fourth Assessment Report (AR4) is 3.26 ° C±0.69 ° C. Thus, this value is within the range and below the mean of the models included in the AR4.

scholarly journals Impact of GCM structure uncertainty on hydrological processes in an arid area of China

2017 ◽  
Vol 49 (3) ◽  
pp. 893-907 ◽  
Author(s):  
Gonghuan Fang ◽  
Jing Yang ◽  
Yaning Chen ◽  
Zhi Li ◽  
Philippe De Maeyer
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Assessment Tool ◽  
Circulation Model ◽  
Hydrological Processes ◽  
Future Climate Change ◽  
Management Decision ◽  
Structural Uncertainty ◽  
Rcp Scenarios ◽  
The Impact

Abstract Quantifying the uncertainty sources in assessment of climate change impacts on hydrological processes is helpful for local water management decision-making. This paper investigated the impact of the general circulation model (GCM) structural uncertainty on hydrological processes in the Kaidu River Basin. Outputs of 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two representative concentration pathway (RCP) scenarios (i.e., RCP4.5 and RCP8.5), representing future climate change under uncertainty, were first bias-corrected using four precipitation and three temperature methods and then used to force a well-calibrated hydrological model (the Soil and Water Assessment Tool, SWAT) in the study area. Results show that the precipitation will increase by 3.1%–18% and 7.0%–22.5%, the temperature will increase by 2.0 °C–3.3 °C and 4.2 °C–5.5 °C and the streamflow will change by −26% to 3.4% and −38% to −7% under RCP4.5 and RCP8.5, respectively. Timing of snowmelt will shift forward by approximately 1–2 months for both scenarios. Compared to RCPs and bias correction methods, GCM structural uncertainty contributes most to streamflow uncertainty based on the standard deviation method (55.3%) while it is dominant based on the analysis of variance approach (94.1%).

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