Contact — Julia Hargreaves 2013/04/26 06:53 to contribute to this page, or leave a comment in the discussion box.

Introduction

Climate models combine our understanding of different components the climate, gained largely from observation of processes in the modern climate. The models, designed and tuned to some extent to look like the modern climate, are then used to estimate future climate changes such as those caused by anthropogenic emission of greenhouse gases. The multi-model ensemble of these models gives some sort of estimate of our uncertainty about future climate. It remains possible, however, that this estimate may be further refined by increasing understanding about the way the climate reacts to particular forcings.

The climate history of the earth includes a wide range of different climates, and climate variability on a wide range of time scales. Using this information from the past to learn more about the climate and thus further improve climate models is one of the goals of PMIP. In the Past to Future working group we focus directly on the ways that we may combine information from the past with the multi-model climate ensembles, to provide improved forecasts for future climate change.

The basic idea is that, if relationships exist between past and future climates in model ensembles, then data from the past may be used to inform and even improve the ensemble projections of future climate. This idea may be implemented with various degrees of statistical formalism, and overall the development of quantitative methods taking advantage of experiment ensembles (however small) are still in its early stages, and this is what this page is about.

The method most commonly used to date consists of finding a linear relationship between past and future climates in the ensemble and then using information derived from proxy data from past climate to constrain the ensemble. The figure shown below indicates this procedure for LGM tropical temperature change and climate sensitivity (based on Hargreaves et al GRL 2012). A significant correlation is found in the ensemble between these two variables. The uncertainty in the correlation and in the observational constraint are sampled by the red dots, from which a distribution for climate sensitivity can be derived (red arrows). In principle, however, the relationship between past and future climate may not be linear or so simple. It may indeed be a temporal or spatial pattern of climate change, in which case alternative methods are required. Bayesian methods may equally well be applied; a prior belief for climate sensitivity is updated by a likelihood function in which the models are weighted according to how well they agree with the observational constraint. One important challenge is to identify and quantify the different sources of uncertainties in this process.

We hope this page will evolve in future. Initially, rather than being prescriptive about methods, we focus on providing a list of references. With each we provide a short description, or quote from the paper, which highlights the relevance of the work to Past to Future. We include some work which focusses on recent changes, as the methods are fundamentally the same (for recent observations, the data quality is generally higher, but the signal to noise ratio may be low).

Keywords

To aid comprehension of the following overview we have defined a number of ‘keywords’ that characterise current research on P2F.

_ ensemble : refers to an article focused to the analysis of a series of experiments. The current literature sometimes ‘ensembles of opportunity’, that is, a series of experiments that have not been designed with the idea of being analysed ‘as en ensemble’, as opposed to ensemble of experiments designed with a carefully chosen sampling scheme (latin hypercube etc. )

_ Model ensemble : article making explicit use of several ‘structurally distinct’ climate simulators (as opposed to one simulator with various parameter configurations)

_ Bayesian : refers to an article featuring an inference process based on the Bayesian paradigm, with explicit references to a prior, a likelihood and a posterior

_ Evaluation : a fairly broad concept referring to the use of model performance indicators, either presented as quantitative metrics, or yes/no pass tests, the main idea being that models which compare well with data for past climates may be more reliable for predicting future climate change.

_ Emulator : statistical technique consisting in calibrating a statistical model (generally a Gaussian process) for use as as surrogate to an actual climate simulator, in order to sample efficiently large input spaces, generally in the context of Bayesian inference or global sensitivity analysis

_ climate sensitivity : papers with attempting to contribute to the quantification off ‘climate sensitivity’ defined as change in the global average of surface air temperature in response to a doubling in CO2 concentration for pre-industrial.

_ review / prospective : explicit enough.

_ LGM, mid-Holocene, Eocene, Past Millennia, Arctic, past century, modern : epoch tags _ Europe, Global, Northern Hemisphere, Southern Hemisphere : regional tags _ Ocean, Atmosphere, Sea-ice, Monsoon, … : climate process tag _ CMIP, PMIP : ‘Project’ tag

_ detection - attribution : in the climate literature refers to the process of identifying and quantifying forcing agents based on an analysis of the spatio-temporal evolution of both model outputs and observations. A statistical model is generally explicitly defined.

Overview of papers

Chronological by publication date, most recent first:

Reducing spread in climate model projections of a September ice-free Arctic]]

Jiping Liu, Mirong Song, Radley M. Horton, and Yongyun Hu PNAS, 10.1073/pnas.1219716110/-/DCSupplemental, paywall, 2013.

keywords: CMIP, model ensemble, Arctic, Benchmark, past century

In CMIP3, all sea-ice trends were less than observed. In CMIP5 there are models with both greater and lesser trends. Thus the result obtained by Liu et al is less far from the (new) ensemble mean than was the case for Boé et al 2009. From the abstract: “Here we reduce the spread in the timing of an ice-free state using two different approaches for the 30 CMIP5 models: (i) model selection based on the ability to reproduce the observed sea ice climatology and variability since 1979 and (ii) constrained estimation based on the strong and persistent relationship between present and future sea ice conditions. Results from the two approaches show good agreement. Under a high-emission scenario both approaches project that September ice extent will drop to ∼1.7 million km2 in the mid 2040s and reach the ice-free state (defined as 1 million km2) in 2054–2058. Under a medium-mitigation scenario, both approaches project a decrease to ∼1.7 million km2 in the early 2060s, followed by a leveling off in the ice extent.”

Precipitation scaling with temperature in warm and cold climates: an analysis of CMIP5 simulations.

Li, G., Harrison, S. P., Bartlein, P. J., Izumi, K., & Prentice, I. C. Geophysical Research Letters. doi:10.1002/grl.50730, open access, 2013.

keywords: CMIP, PMIP, model ensemble, LGM

Abstract, “We investigate the scaling between precipitation and temperature changes in warm and cold climates using six models that have simulated the response to both increased CO2 and Last Glacial Maximum (LGM) boundary conditions. Globally, precipitation increases in warm and decreases in cold climates by between 1.5 to 3%/ ̊C. Precipitation sensitivity to temperature changes are lower over land than ocean and lower over tropical land compared to extratropical land, reflecting the constraint of water availability. The wet tropics get wetter in warm and drier in cold climates, but the changes in dry areas differ among models. Seasonal changes of tropical precipitation in a warmer world also reflect this “rich get richer” syndrome. Precipitation seasonality is decreased in the cold-climate state. The simulated changes in precipitation per degree temperature change are comparable to the observed changes in both the historical period and the LGM.”

Using paleo-climate comparisons to constrain future projections in CMIP5

G. A. Schmidt, J. D. Annan, P. J. Bartlein, B. I. Cook, E. Guilyardi, J. C. Hargreaves, S. P. Harrison, M. Kageyama, A. N. LeGrande, B. Konecky, S. Lovejoy, M. E. Mann, V. Masson-Delmotte, C. Risi, D. Thompson13, A. Timmermann, L.-B. Tremblay, and P. Yiou, Clim. Past Discuss., 9, 775-835, open access, doi:10.5194/cpd-9-775-2013, 2013

keywords: review/prospective, evaluation, PMIP, CMIP

A 2013 discussion of recent progress in the field. Overview of general methods, and some examples, which include direct constraint of the multi-model ensemble as well slightly more qualitative examples, for example, looking at common patterns of precipitation changes in the models for past and future. Recommendations for ways to tackle the problem are also included, “These examples illustrate some general points that should be required in any attempts to use the paleo-climate simulations to constrain future projections:

The chosen metrics should be robust to uncertainties in external forcing,

They should not be overly sensitive to the model representation of key phenomena, and are within the scope of the modelled system.

A spatially diverse and, preferably multi-proxy, paleo-data synthesis is available for comparison.

The relationship between metrics and targets in the past and future must be examined, and not simply assumed.”

Quantifying future climate change

Collins, M., Chandler, R. E., Cox, P. M., Huthnance, J. M., Rougier, J., & Stephenson, D. B. Nature Climate Change, 2(6), 403–409. doi:10.1038/nclimate1414, paywall, 2012.

keywords: prospective/review, Bayesian, evaluation

Some background with links to other papers on probabilistic prediction, single model ensembles, metrics and the like.

Can the Last Glacial Maximum constrain climate sensitivity?

J. C. Hargreaves, J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L24702, doi:10.1029/2012GL053872, open access, 2012.

keywords: PMIP, climate sensitiivity, model ensemble

Using the PMIP2 models and a reconstruction of LGM temperatures (Annan and Hargreaves 2013), to provide a constraint on climate sensitivity. Two different methods for constraining the ensemble were compared, which relied on an apparent correlation between tropical LGM temperature anomaly, and equilibrium climate sensitivity.

Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 1: Theory

Sundberg, R., A. Moberg and A. Hind, Clim. Past, 8, 1339-1353, open access, doi:10.5194/cp-8-1339-2012, 2012. and Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium – Part 2: A pseudo-proxy study addressing the amplitude of solar forcing A. Hind, A. Moberg, and R. Sundberg, Clim. Past, 8, 1355–1365, open access

keywords : last millennium, test statistics, evaluation, detection / attribution

Pseudo-proxy experiment to distinguish between high and low solar forcings from model output run over the Last Millennium

Evaluation of climate models using palaeoclimatic data

Pascale Braconnot, Sandy P. Harrison, Masa Kageyama, Patrick J. Bartlein, Valerie Masson-Delmotte, Ayako Abe-Ouchi, Bette Otto-Bliesner & Yan Zhao, Nature Climate Change 2, 417–424, paywall, doi:10.1038/nclimate1456, 2012

keywords: review/prospective, PMIP, evaluation

Review paper focused on PMIP efforts, displaying relationships between (a) land temperature change and ocean temperature change and (b) global and regional changes and elaborating on how carefullyevaluation ofmodelling of past climates may provide insights / constraints on future climate change.

Sensitivity of tropical precipitation extremes to climate change

O'Gorman, P. A., Nature Geosci, 5(10), 697–700, paywall, doi:doi:10.1038/ngeo1568, 2012.

keywords: model ensemble, CMIP3, modern, precipitation

Finds a relationship between interannual variability and change in extremes of tropical precipitation under global warming in models. Uses satellite observations to estimate the response of the tropical extremes to global warming.

Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene

Clim. Past, 9, 811–823, open access doi:10.5194/cp-9-811-2013, J. C. Hargreaves, J. D. Annan1, R. Ohgaito, A. Paul, and A. Abe-Ouchi, 2013. and Are paleoclimate model ensembles consistent with the MARGO data synthesis? J. C. Hargreaves, A. Paul, R. Ohgaito, A. Abe-Ouchi, and J. D. Annan Clim. Past, 7, 917–933, open access doi:10.5194/cp-7-917-2011, 2011

keywords: PMIP, LGM, evaluation, ocean (SST)

Show that PMIP2 and available PMIP3 models are reliable and have skill for air and surface ocean temperatures on broad scales, for the LGM. On the other hand, the MIROC single model ensemble is under-dispersive (a result common for single model ensembles - see Yokohata et al 2010). Additionally the models have no skill and are not reliable for the mid-Holocene interval.

A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1

Philip B. Holden, N. R. Edwards, K. I. C. Oliver, T. M. Lenton & R. D. Wilkinson Clim Dyn free PDF at Open University repository or journal website, DOI 10.1007/s00382-009-0630-8, 2010.

keywords: Bayesian, emulator, terrestrial carbon, LGM

Using an emulator of multiple varied parameters in the GENIE model. The emulated LGM ensemble is constrained with tropical SST data to produce a probabilistic estimate of climate sensitivity.

September sea-ice cover in the Arctic Ocean projected to vanish by 2100

Julien Boé, Alex Hall and Xin Qu, Nature Geoscience 2, 341, free PDF at author's homepage or paywall], ]doi:10.1038/ngeo467, 2009. //keywords : model ensemble, past century, sea-ice, Arctic// ''From the abstract: "Here we analyse the simulated trends in past sea-ice cover in 18 state-of-art-climate models and find a direct relationship between the simulated evolution of September sea-ice cover over the twenty-first century and the magnitude of past trends in sea-ice cover. Using this relationship together with observed trends, we project the evolution of September sea-ice cover over the twenty-first century."'' ==Correlation between Inter-Model Similarities in Spatial Pattern for Present and Projected Future Mean Climate== Manabu Abe, Hideo Shiogama, Julia C. Hargreaves, James D. Annan, Toru Nozawa, and Seita Emori, SOLA, Vol. 5, 133‒136, [[https://www.jstage.jst.go.jp/article/sola/5/0/5_0_133/_article¥open access, doi:10.2151/sola.2009‒034 133 1, 3, 2009.

keywords: evaluation, past century, CMIP3, model ensemble

One of several papers from around 2007-2009, looking for “metrics” that relate to future performance. The idea was that if a relationship may be found in the multi-model ensemble between a measurable quantity in the present and a feature of the climate in the future projections, then this may in principle be use to constrain the ensemble. In this study the globe was split into broad latitude bands. The metric used is a measure of model similarity. Significant correlations for this metric between present and future were found mostly for precipitation, some also for temperature and none for sea level pressure.

Information on the early Holocene climate constrains the summer sea ice projections for the 21st century

H. Goosse, E. Driesschaert, T. Fichefet, and M.-F. Loutre, Clim. Past, 3, 683-692, 2007

keywords: parameter ensemble, Holocene, sea-ice

Abstract. The summer sea ice extent strongly decreased in the Arctic over the last decades. This decline is very likely to continue in the future but uncertainty of projections is very large. An ensemble of experiments with the climate model LOVECLIM using 5 different parameter sets has been performed to show that summer sea ice changes during the early Holocene (8 kyr BP) and the 21st century are strongly linked, allowing for the reduction of this uncertainty. Using the limited number of records presently available for the early Holocene, simulations presenting very large changes over the 21st century could reasonably be rejected. On the other hand, simulations displaying low to moderate changes during the second half of the 20th century (and also over the 21st century) are not consistent with recent observations. Using this very complementary information based on observations during both the early Holocene and the last decades, the most realistic projection with LOVECLIM indicates a nearly disappearance of the sea ice in summer at the end of the 21st century for a moderate increase in atmospheric greenhouse gas concentrations. Our results thus strongly indicate that additional proxy records of the early Holocene sea ice changes, in particular in the central Arctic Basin, would help to improve our projections of summer sea ice evolution and that the simulation at 8 kyr BP should be considered as a standard test for models aiming at simulating those future summer sea ice changes in the Arctic.

Assessment of the use of current climate patterns to evaluate regional enhanced greenhouse response patterns of climate models

Penny Whetton, Ian Macadam, Janice Bathols, and Julian O’Grady GEOPHYSICAL RESEARCH LETTERS, VOL. 34, L14701, doi:10.1029/2007GL030025, 2007

keywords: evaluation, CMIP (need help: no access to paper)

One of several papers from around 2007-2009, looking for “metrics”. The idea was that if a relationship may be found in the multi-model ensemble between a measurable quantity in the present and a feature of the climate in the future projections, then this may in principle be use to constrain the ensemble. In this study the globe was split into the land-based “Giorgi regions”. The metric is a measure of model similarity. Combining temperature, precipitation and sea level pressure seems to provide the best correlations for future performance both regionally and globally.

Does the Last Glacial Maximum constrain climate sensitivity?

M Crucifix, GRL, doi:10.1029/2006GL027137, 2006.

keywords: PMIP, climate sensitivity, (small) model ensemble

Finds no clear relationship past and future in the (then) small PMIP2 ensemble, and argues as a result that the LGM can only weakly constrain climate sensitivity, with the caveat, though, that the range of sensitivities covered by PMIP2 was at the time fairly narrow.

Using the past to constrain the future: how the palaeorecord can improve estimates of global warming

Tamsin L. Edwards, Michel Crucifix and Sandy P. Harrison Progress in Physical Geography; 31; 481 DOI: 10.1177/0309133307083295. 2007.

keywords: review/prospective, climate sensitivity, Bayesian

A 2007 overview of efforts to use models to constrain climate sensitivity. Figure 4 is the most enduring result from this paper. It shows that model ensembles derived from different single models do not always overlap. If the multi-model ensemble is a good representation of uncertainty, then the single model ensemble members may be considered to be lacking in diversity.

Using the current seasonal cycle to constrain snow albedo feedback in future climate change

Hall, A., & Qu, X. Geophysical Research Letters, 33(3), L03502. 2006

keywords: Northern hemisphere, PMIP, modern, model ensemble

From the abstract: “Large intermodel variations in feedback strength in climate change are nearly perfectly correlated with comparably large intermodel variations in feedback strength in the context of the seasonal cycle. Moreover, the feedback strength in the real seasonal cycle can be measured and compared to simulated values. These mostly fall outside the range of the observed estimate, suggesting many models have an unrealistic snow albedo feedback in the seasonal cycle context. Because of the tight correlation between simulated feedback strength in the seasonal cycle and climate change, eliminating the model errors in the seasonal cycle will lead directly to a reduction in the spread of feedback strength in climate change. Though this comparison to observations may put the models in an unduly harsh light because of uncertainties in the observed estimate that are difficult to quantify, our results map out a clear strategy for targeted observation of the seasonal cycle to reduce divergence in simulations of climate sensitivity.”

Climate sensitivity estimated from ensemble simulations of glacial climate

Thomas Schneider von Deimling, Hermann Held, Andrey Ganopolski & Stefan Rahmstorf, Climate Dynamics, DOI 10.1007/s00382-006-0126-8, 2006

keywords: parameter ensemble, climate sensitivity, LGM, dust

Possibly the first attempt to use information derived from data for the LGM to directly constrain a model ensemble and provide a constrained prediction of a variable related to future climate (climate sensitivity in this case). A single model ensemble of the EMIC, CLIMBER was used, with the ensemble constrained with estimates of Tropical Atlantic SST for the LGM (GLAMAP). The influence of dust forcing, not included in most model configurations for the LGM climate, was shown to potentially cause a significant bias in the results.

Efficiently Constraining Climate Sensitivity with Ensembles of Paleoclimate Simulations

J. D. Annan, J. C. Hargreaves, R. Ohgaito, A. Abe-Ouchi and S. Emori, SOLA, Vol. 1, 181‒184, doi: 10.2151/sola. 2005‒047 181, 2005.

keywords: parameter ensemble, climate sensitivity, LGM, Bayesian

In this case, estimates of tropical SST taken from the literature were used to constrain a since model ensemble with varied parameters of the MIROC GCM. This MIROC ensemble has now been shown to be of much lower dispersion than the multi-model ensemble (Hargreaves et al above, and Yokohata et al 2010). Indeed it was found impossible to produce a run with this model climate sensitivity less than 4C, while the data constraint for the LGM data suggested a lower value was entirely plausible.

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