Exploiting large ensembles for a better yet simpler climate model evaluation

2021 ◽  
Author(s):  
Laura Suarez-Gutierrez ◽  
Sebastian Milinski ◽  
Nicola Maher
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Internal Variability ◽  
Forced Response ◽  
Evaluation Framework ◽  
Surface Areas ◽  
Global Mean Temperature ◽  
External Forcings ◽  
Large Ensembles

<p>We use a methodological framework exploiting the power of large ensembles to evaluate how well ten coupled climate models represent the internal variability and response to external forcings in observed historical surface temperatures. This evaluation framework allows us to directly attribute discrepancies between models and observations to biases in the simulated internal variability or forced response, without relying on assumptions to separate these signals in observations. The largest discrepancies result from the overestimated forced warming in some models during recent decades. In contrast, models do not systematically over- or underestimate internal variability in global mean temperature. On regional scales, all models misrepresent surface temperature variability over the Southern Ocean, while overestimating variability over land-surface areas, such as the Amazon and South Asia, and high-latitude oceans. Our evaluation shows that MPI-GE, followed by GFDL-ESM2M and CESM-LE offer the best global and regional representation of both the internal variability and forced response in observed historical temperatures.</p>

Which climate models capture the variability and forced response in observed temperatures: a large ensemble comparison

2020 ◽  
Author(s):  
Nicola Maher ◽  
Laura Suarez-Gutierrez ◽  
Sebastian Milinski
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Forced Response ◽  
Tropical Eastern Pacific ◽  
The North ◽  
Surface Temperatures ◽  
External Forcings ◽  
Coupled Climate Models ◽  
Large Ensembles ◽  
Adequate Representation

<p>We evaluate how large ensembles of ten coupled climate models represent the observed internal variability and response to external forcings in historical surface temperatures based on a novel methodological framework. This framework allows us to directly attribute whether discrepancies between models and observations arise due to biases in the simulated internal variability or rather in the forced response, without relying on assumptions to separate both signals in the observations. The largest discrepancies occur due to overestimated forced warming in some models during recent decades. The areas where most models, a maximum of nine, adequately simulate observed temperatures are the North Atlantic, Tropical Eastern Pacific, and the Northern Hemisphere land areas. In contrast, none of the models considered offers an adequate representation over the Southern Ocean. Our evaluation shows that CESM-LE, GFDL-ESM2M, and MPI-GE perform best at representing the internal variability and forced response in observed surface temperatures both globally and regionally. </p>

scholarly journals Impact of internal variability on climate change for the upcoming decades: analysis of the CanESM2-LE and CESM-LE large ensembles

2019 ◽  
Vol 156 (3) ◽  
pp. 299-314 ◽  
Author(s):  
Gabriel Rondeau-Genesse ◽  
Marco Braun
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Ghg Emissions ◽  
Internal Variability ◽  
Mean Annual Temperature ◽  
Large Ensemble ◽  
Climate Change Effects ◽  
Large Ensembles ◽  
The Mean

Abstract The pace of climate change can have a direct impact on the efforts required to adapt. For short timescales, however, this pace can be masked by internal variability (IV). Over a few decades, this can cause climate change effects to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climate models and thus combine both IV and inter-model differences. This study instead uses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) that comprise multiple realizations from a single climate model and a single GHG emission scenario, to quantify the relationship between IV and climate change over the next decades in Canada and the USA. The mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. However, members of the large ensembles in which a slowdown of warming is found during the 2021–2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of IV, remains fairly uncommon for the mean annual temperature, but occurs in 5 to 15% of the large ensemble members for the temperature extremes.

scholarly journals Indo-Pacific Climate Modes in Warming Climate: Consensus and Uncertainty Across Model Projections

2019 ◽  
Vol 5 (4) ◽  
pp. 308-321 ◽  
Author(s):  
Xiao-Tong Zheng
Keyword(s):  
Climate Models ◽  
Internal Variability ◽  
Forced Response ◽  
Future Climate Change ◽  
Climate Modes ◽  
Warming Climate ◽  
Warming Pattern ◽  
Large Ensembles ◽  
Projected Changes ◽  
Climate Mode

Abstract Purpose of Review Understanding the changes in climate variability in a warming climate is crucial for reliable projections of future climate change. This article reviews the recent progress in studies of how climate modes in the Indo-Pacific respond to greenhouse warming, including the consensus and uncertainty across climate models. Recent Findings Recent studies revealed a range of robust changes in the properties of climate modes, often associated with the mean state changes in the tropical Indo-Pacific. In particular, the intermodel diversity in the ocean warming pattern is a prominent source of uncertainty in mode changes. The internal variability also plays an important role in projected changes in climate modes. Summary Model biases and intermodel variability remain major challenges for reducing uncertainty in projecting climate mode changes in warming climate. Improved models and research linking simulated present-day climate and future changes are essential for reliable projections of climate mode changes. In addition, large ensembles should be used for each model to reduce the uncertainty from internal variability and isolate the forced response to global warming.

scholarly journals Optimising the FAMOUS climate model: inclusion of global carbon cycling

2013 ◽  
Vol 6 (1) ◽  
pp. 141-160 ◽  
Author(s):  
J. H. T. Williams ◽  
R. S. Smith ◽  
P. J. Valdes ◽  
B. B. B. Booth ◽  
A. Osprey
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
System Modelling ◽  
Computationally Efficient ◽  
Ensemble Technique ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Upper Level ◽  
Hadley Centre

Abstract. FAMOUS fills an important role in the hierarchy of climate models, both explicitly resolving atmospheric and oceanic dynamics yet being sufficiently computationally efficient that either very long simulations or large ensembles are possible. An improved set of carbon cycle parameters for this model has been found using a perturbed physics ensemble technique. This is an important step towards building the "Earth System" modelling capability of FAMOUS, which is a reduced resolution, and hence faster running, version of the Hadley Centre Climate model, HadCM3. Two separate 100 member perturbed parameter ensembles were performed; one for the land surface and one for the ocean. The land surface scheme was tested against present-day and past representations of vegetation and the ocean ensemble was tested against observations of nitrate. An advantage of using a relatively fast climate model is that a large number of simulations can be run and hence the model parameter space (a large source of climate model uncertainty) can be more thoroughly sampled. This has the associated benefit of being able to assess the sensitivity of model results to changes in each parameter. The climatologies of surface and tropospheric air temperature and precipitation are improved relative to previous versions of FAMOUS. The improved representation of upper atmosphere temperatures is driven by improved ozone concentrations near the tropopause and better upper level winds.

scholarly journals Pattern Recognition Methods to Separate Forced Responses from Internal Variability in Climate Model Ensembles and Observations

2020 ◽  
Vol 33 (20) ◽  
pp. 8693-8719 ◽  
Author(s):  
Robert C. J. Wills ◽  
David S. Battisti ◽  
Kyle C. Armour ◽  
Tapio Schneider ◽  
Clara Deser
Keyword(s):  
Climate Change ◽  
Pattern Recognition ◽  
North Atlantic Oscillation ◽  
North Atlantic ◽  
El Niño ◽  
Climate Model ◽  
El Nino ◽  
Internal Variability ◽  
Forced Response ◽  
Large Ensembles

AbstractEnsembles of climate model simulations are commonly used to separate externally forced climate change from internal variability. However, much of the information gained from running large ensembles is lost in traditional methods of data reduction such as linear trend analysis or large-scale spatial averaging. This paper demonstrates how a pattern recognition method (signal-to-noise-maximizing pattern filtering) extracts patterns of externally forced climate change from large ensembles and identifies the forced climate response with up to 10 times fewer ensemble members than simple ensemble averaging. It is particularly effective at filtering out spatially coherent modes of internal variability (e.g., El Niño, North Atlantic Oscillation), which would otherwise alias into estimates of regional responses to forcing. This method is used to identify forced climate responses within the 40-member Community Earth System Model (CESM) large ensemble, including an El Niño–like response to volcanic eruptions and forced trends in the North Atlantic Oscillation. The ensemble-based estimate of the forced response is used to test statistical methods for isolating the forced response from a single realization (i.e., individual ensemble members). Low-frequency pattern filtering is found to skillfully identify the forced response within individual ensemble members and is applied to the HadCRUT4 reconstruction of observed temperatures, whereby it identifies slow components of observed temperature changes that are consistent with the expected effects of anthropogenic greenhouse gas and aerosol forcing.

scholarly journals Marginal ice zone fraction benchmarks sea ice and climate model skill

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Christopher Horvat
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Internal Variability ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Global Mean Temperature ◽  
Marginal Ice Zone ◽  
The Impact

AbstractGlobal climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979–2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January–September (but not October–December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.

scholarly journals Optimising the FAMOUS climate model: inclusion of global carbon cycling

2012 ◽  
Vol 5 (4) ◽  
pp. 3089-3129
Author(s):  
J. H. T. Williams ◽  
R. S. Smith ◽  
P. J. Valdes ◽  
B. B. B. Booth ◽  
A. Osprey
Keyword(s):  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
System Modelling ◽  
Computationally Efficient ◽  
Ensemble Technique ◽  
Temperature And Precipitation ◽  
Large Ensembles ◽  
Upper Level ◽  
Hadley Centre

Abstract. FAMOUS fills an important role in the hierarchy of climate models, both explicitly resolving atmospheric and oceanic dynamics yet being sufficiently computationally efficient that either very long simulations or large ensembles are possible. An improved set of carbon cycle parameters for this model has been found using a perturbed physics ensemble technique. This is an important step towards building the "Earth System" modelling capability of FAMOUS, which is a reduced resolution, and hence faster running, version of the Hadley Centre Climate model, HadCM3. Two separate 100 member perturbed parameter ensembles were performed; one for the land surface and one for the ocean. The land surface scheme was tested against present day and past representations of vegetation and the ocean ensemble was tested against observations of nitrate. An advantage of using a relatively fast climate model is that a large number of simulations can be run and hence the model parameter space (a large source of climate model uncertainty) can be more thoroughly sampled. This has the associated benefit of being able to assess the sensitivity of model results to changes in each parameter. The climatologies of surface and tropospheric air temperature and precipitation are improved relative to previous versions of FAMOUS. The improved representation of upper atmosphere temperatures is driven by improved ozone concentrations near the tropopause and better upper level winds.

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
Author(s):  
Ed Hawkins ◽  
Buwen Dong ◽  
Jon Robson ◽  
Rowan Sutton ◽  
Doug Smith
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Time Dependent ◽  
Start Time ◽  
Transient Climate Response ◽  
Global Mean Temperature ◽  
Decadal Climate ◽  
Highly Correlated ◽  
Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

scholarly journals The DeepMIP contribution to PMIP4: experimental design for model simulations of the EECO, PETM, and pre-PETM (version 1.0)

2017 ◽  
Vol 10 (2) ◽  
pp. 889-901 ◽  
Author(s):  
Daniel J. Lunt ◽  
Matthew Huber ◽  
Eleni Anagnostou ◽  
Michiel L. J. Baatsen ◽  
Rodrigo Caballero ◽  
...  
Keyword(s):  
Experimental Design ◽  
Land Surface ◽  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Geological Data ◽  
Time Model ◽  
Model Simulations ◽  
Intercomparison Project ◽  
Climate Model Simulations

Abstract. Past warm periods provide an opportunity to evaluate climate models under extreme forcing scenarios, in particular high ( >  800 ppmv) atmospheric CO2 concentrations. Although a post hoc intercomparison of Eocene ( ∼  50  Ma) climate model simulations and geological data has been carried out previously, models of past high-CO2 periods have never been evaluated in a consistent framework. Here, we present an experimental design for climate model simulations of three warm periods within the early Eocene and the latest Paleocene (the EECO, PETM, and pre-PETM). Together with the CMIP6 pre-industrial control and abrupt 4 ×  CO2 simulations, and additional sensitivity studies, these form the first phase of DeepMIP – the Deep-time Model Intercomparison Project, itself a group within the wider Paleoclimate Modelling Intercomparison Project (PMIP). The experimental design specifies and provides guidance on boundary conditions associated with palaeogeography, greenhouse gases, astronomical configuration, solar constant, land surface processes, and aerosols. Initial conditions, simulation length, and output variables are also specified. Finally, we explain how the geological data sets, which will be used to evaluate the simulations, will be developed.

scholarly journals Understanding drivers of glacier-length variability over the last millennium

2021 ◽  
Vol 15 (3) ◽  
pp. 1645-1662
Author(s):  
Alan Huston ◽  
Nicholas Siler ◽  
Gerard H. Roe ◽  
Erin Pettit ◽  
Nathan J. Steiger
Keyword(s):  
Climate Variability ◽  
Climate Model ◽  
Volcanic Eruptions ◽  
Internal Variability ◽  
Forced Response ◽  
Last Millennium ◽  
External Forcing ◽  
Climate History ◽  
Temperature And Precipitation ◽  
Glacier Length

Abstract. Changes in glacier length reflect the integrated response to local fluctuations in temperature and precipitation resulting from both external forcing (e.g., volcanic eruptions or anthropogenic CO2) and internal climate variability. In order to interpret the climate history reflected in the glacier moraine record, the influence of both sources of climate variability must therefore be considered. Here we study the last millennium of glacier-length variability across the globe using a simple dynamic glacier model, which we force with temperature and precipitation time series from a 13-member ensemble of simulations from a global climate model. The ensemble allows us to quantify the contributions to glacier-length variability from external forcing (given by the ensemble mean) and internal variability (given by the ensemble spread). Within this framework, we find that internal variability is the predominant source of length fluctuations for glaciers with a shorter response time (less than a few decades). However, for glaciers with longer response timescales (more than a few decades) external forcing has a greater influence than internal variability. We further find that external forcing also dominates when the response of glaciers from widely separated regions is averaged. Single-forcing simulations indicate that, for this climate model, most of the forced response over the last millennium, pre-anthropogenic warming, has been driven by global-scale temperature change associated with volcanic aerosols.

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