pmip3:wg:p2f:methods
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| ~~DISCUSSION~~ | ~~DISCUSSION~~ | ||
| - | Contact --- //[[jules@jamstec.go.jp|Julia Hargreaves]] 2013/04/26 06:53// to contribute to this page, or leave a comment in the discussion box. | + | Contact --- //[[jules@blueskiesresearch.org.uk|Julia Hargreaves]] 2013/04/26 06:53// to contribute to this page, or leave a comment in the discussion box. |
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| - | ====== New PMIP3 page template ====== | ||
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| - | <note info>Welcome Julia Hargreaves ! | ||
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| - | Thank you for creating this new page: ''pmip3:wg:p2f:methods'' | ||
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| - | Please make sure that you **read the instructions below** before proceeding with the page creation...</note> | ||
| ====== Introduction ====== | ====== Introduction ====== | ||
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| 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. | 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. | ||
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| 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). | 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). | ||
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| To aid comprehension of the following overview we have defined a number of ‘keywords’ that characterise current research on P2F. | 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. ) | + | * **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) | + | * **Single-model ensemble**, **Multi-model ensemble** : article making explicit use of one (for single-model) or several //structurally distinct// (for multi-model) climate models (as opposed to one model 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 | + | * **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. | + | * **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 | + | * **Emulator** : statistical technique consisting in calibrating a statistical model (generally a Gaussian process) for use as as surrogate to an actual climate simulator (more commonly known as "a climate model"), 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 paper | + | * **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. | + | * **Review / prospective** : explicit enough. |
| - | _ LGM, mid-Holocene, Eocene, Past Millennia, Arctic, past century, modern : epoch tags | + | * **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. | + | * **Europe, Global, Northern Hemisphere, Southern Hemisphere ** : regional tags |
| + | * **Ocean, Atmosphere, Sea-ice, Monsoon, ...** : climate process tag | ||
| - | ====== Overview of papers ====== | + | * **CMIP, PMIP** : //Project// tag |
| - | Chronological by publication date, most recent first: | + | * **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. |
| - | Reducing spread in climate model projections of a September ice-free Arctic, Jiping Liu, Mirong Song, Radley M. Horton, and Yongyun Hu PNAS, 2013, 10.1073/pnas.1219716110/-/DCSupplemental | ||
| - | //keywords: CMIP, model ensemble, Arctic, Benchmark, past century | + | ====== Overview of papers ====== |
| - | // | + | |
| - | 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." | + | Chronological by publication date, most recent first: |
| + | ==Introduction: Warm climates of the past—a lesson for the future?== | ||
| + | D. J. Lunt, H. Elderfield, R. Pancost, A. Ridgwell, G. L. Foster, A. Haywood, J. Kiehl, N. Sagoo, C. Shields, E. J. Stone, and P. Valdes, Phil. Trans. R. Soc. A. 2013 371 20130146; doi:10.1098/rsta.2013.0146 (published 16 September 2013) [[http://rsta.royalsocietypublishing.org/content/371/2001/20130146.full.pdf+html|open access]] | ||
| - | 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, 2013. | + | ''An introduction to a special issue related to the Discussion Meeting ‘Warm climates of the past—a lesson for the future?’ compiled and edited by Daniel J. Lunt, Harry Elderfield, Richard Pancost and Andy Ridgwell. It is focussed towards emphasising the potential usefulness of the warm climates of the past. Most of the papers seem (I can't read most of them, as only a few are open access and it seems that even mighty JAMSTEC does not subscribe to Phil Trans) focussed towards understanding the past, but there is also one on climate sensitivity by J. Hansen et al, [[http://rsta.royalsocietypublishing.org/content/371/2001/20120294.full.pdf+html|open access]]'' |
| - | keywords: CMIP, PMIP, model ensemble, LGM | + | == 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, [[http://www.pnas.org/content/110/31/12571.short|paywall]], 2013. | ||
| - | 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." | + | //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. | ||
| - | Using paleo-climate comparisons to constrain future projections in CMIP5, G. A. Schmidt1, 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, www.clim-past-discuss.net/9/775/2013/, doi:10.5194/cpd-9-775-2013, 2013 | + | 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." '' |
| - | keywords: review/prospective, evaluation, PMIP, CMIP | + | ==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, [[http://onlinelibrary.wiley.com/doi/10.1002/grl.50730/full|open access]], 2013. | ||
| - | 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. | + | //keywords: CMIP, PMIP, model ensemble, LGM// |
| - | Recommendations for ways to tackle the problem are also included, | + | ''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." |
| - | "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." | + | |
| + | ==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, [[http://www.clim-past-discuss.net/9/775/2013/|open access]], doi:10.5194/cpd-9-775-2013, 2013 | ||
| - | 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, 2012. | + | //keywords: review/prospective, evaluation, PMIP, CMIP// |
| - | keywords: prospective/review, Bayesian, evaluation | + | ''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. |
| - | Some background with links to other papers on probabilistic prediction, single model ensembles, metrics and the like. | + | 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, [[http://www.nature.com/nclimate/journal/v2/n6/full/nclimate1414.html|paywall]], 2012. | ||
| - | 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, 2012. | + | //keywords: prospective/review, Bayesian, evaluation// |
| - | keywords: PMIP, climate sensitiivity, model ensemble | + | ''Some background with links to other papers on probabilistic prediction, single model ensembles, metrics and the like.'' |
| - | 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. | + | ==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, [[http://onlinelibrary.wiley.com/doi/10.1029/2012GL053872/full|open access]], 2012. | ||
| + | //keywords: PMIP, climate sensitiivity, model ensemble// | ||
| - | 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, doi:10.5194/cp-8-1339-2012, 2012. | + | ''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. '' |
| - | keywords : last millennium, test statistics, detection / attribution | + | ==Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium == |
| - | Evaluation of climate models using palaeoclimatic data | + | **Part 1: Theory**, Sundberg, R., A. Moberg and A. Hind, Clim. Past, 8, 1339-1353, [[http://www.clim-past.net/8/1355/2012/cp-8-1355-2012.html|open access]], doi:10.5194/cp-8-1339-2012, 2012. |
| - | 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, doi:10.1038/nclimate1456, 2012 | + | |
| - | keywords: review/prospective, PMIP, evaluation | + | **Part 2: A pseudo-proxy study addressing the amplitude of solar forcing** A. Hind, A. Moberg, and R. Sundberg, Clim. Past, 8, 1355–1365, [[http://www.clim-past.net/8/1355/2012/, doi:10.5194/cp-8-1355-2012, 2012 |open access]] |
| - | 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. | + | //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'' | ||
| - | Sensitivity of tropical precipitation extremes to climate change, Nature Geosci, O'Gorman, P. A., 5(10), 697–700, doi:doi:10.1038/ngeo1568, 2012. | + | ==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, [[http://www.nature.com/nclimate/journal/v2/n6/full/nclimate1456.html|paywall]], doi:10.1038/nclimate1456, 2012 | ||
| - | keywords: model ensemble, CMIP3, modern, precipitation | + | //keywords: review/prospective, PMIP, evaluation // |
| - | 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. | + | ''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, [[http://www.nature.com/ngeo/journal/v5/n10/full/ngeo1568.html|paywall]], doi:doi:10.1038/ngeo1568, 2012. | ||
| - | Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene, Clim. Past, 9, 811–823, www.clim-past.net/9/811/2013/ doi:10.5194/cp-9-811-2013, J. C. Hargreaves, J. D. Annan1, R. Ohgaito, A. Paul, and A. Abe-Ouchi, 2013. | + | //keywords: model ensemble, CMIP3, modern, precipitation// |
| - | 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, www.clim-past.net/7/917/2011/ doi:10.5194/cp-7-917-2011, 2011 | + | |
| - | keywords: PMIP, LGM, evaluation, ocean (SST) | + | ''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. '' |
| - | 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. | + | ==Climate Sensitivity Estimated from Temperature Reconstructions of the Last Glacial Maximum== |
| + | Schmittner, A., Urban N. M., Shakun, J. D., Mahowald, N. M., Clark, P. U., Bartlein, P. J., Mix, A. C., and Rosell-Mele, A., Science, 334, 1385-1388, [[http://www.sciencemag.org/content/334/6061/1385.abstract?keytype=ref&siteid=sci&ijkey=jI1RklqVcZeJ6|paywall]], doi: 10.1126/science.1203513, 2011 | ||
| + | //keywords: single-model ensemble, LGM, Climate Sensitivity// | ||
| + | ''Abstract:Assessing the impact of future anthropogenic carbon emissions is currently impeded by uncertainties in our knowledge of equilibrium climate sensitivity to atmospheric carbon dioxide doubling. Previous studies suggest 3 kelvin (K) as the best estimate, 2 to 4.5 K as the 66% probability range, and nonzero probabilities for much higher values, the latter implying a small chance of high-impact climate changes that would be difficult to avoid. Here, combining extensive sea and land surface temperature reconstructions from the Last Glacial Maximum with climate model simulations, we estimate a lower median (2.3 K) and reduced uncertainty (1.7 to 2.6 K as the 66% probability range, which can be widened using alternate assumptions or data subsets). Assuming that paleoclimatic constraints apply to the future, as predicted by our model, these results imply a lower probability of imminent extreme climatic change than previously thought.'' The data, but not the paper may be downlaoded for free from [[http://people.oregonstate.edu/~schmita2/#y2011|Andreas' website]]. | ||
| - | 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 DOI 10.1007/s00382-009-0630-8, 2010. | ||
| - | keywords: Bayesian, emulator, terrestrial carbon, LGM | + | ==Skill and reliability of climate model ensembles at the Last Glacial Maximum and mid-Holocene== |
| + | J. C. Hargreaves, J. D. Annan1, R. Ohgaito, A. Paul, and A. Abe-Ouchi, Clim. Past, 9, 811–823, [[http://www.clim-past.net/9/811/2013/|open access]] doi:10.5194/cp-9-811-20132013. 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, [[http://www.clim-past.net/7/917/2011/|open access]] doi:10.5194/cp-7-917-2011, 2011 | ||
| - | 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. | + | //keywords: PMIP, LGM, evaluation, temperature (SAT over land, SST for ocean)// |
| + | ''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. '' | ||
| - | September sea-ice cover in the Arctic Ocean projected to vanish by 2100, Julien Boé, Alex Hall and Xin Qu, Nature Geoscience 2, 341, doi:10.1038/ngeo467, 2009. | + | ==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 [[http://oro.open.ac.uk/19191/2/|free PDF at Open University repository]] or [[http://link.springer.com/article/10.1007%2Fs00382-009-0630-8#page-1|journal website]], DOI 10.1007/s00382-009-0630-8, 2010. | ||
| - | keywords : model ensemble, past century, sea-ice, Arctic | + | //keywords: Bayesian, emulator, terrestrial carbon, LGM// |
| - | 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." | + | ''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, [[http://www.atmos.ucla.edu/csrl/publications/Hall/boe_et_al_2009a.pdf|free PDF at author's homepage]] or [[http://www.nature.com/ngeo/journal/v2/n5/abs/ngeo467.html|paywall]], doi:10.1038/ngeo467, 2009. | ||
| - | 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, doi:10.2151/sola.2009‒034 133 1, 3, 2009. | + | //keywords : model ensemble, past century, sea-ice, Arctic// |
| - | keywords: evaluation, past century, CMIP3, model ensemble | + | ''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."'' |
| - | 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. | + | ==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 // | ||
| - | Information on the early Holocene climate constrains the summer sea ice projections for the 21st century | + | '' 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.'' |
| - | H. Goosse, E. Driesschaert, T. Fichefet, and M.-F. Loutre, Clim. Past, 3, 683-692, 2007 | + | |
| - | keywords: parameter ensemble, Holocene, sea-ice | + | ==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, [[http://www.clim-past.net/3/683/2007/cp-3-683-2007.html|open access]], doi:10.5194/cp-3-683-2007, 2007 | ||
| - | 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. | + | //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 | + | ==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 GRL, VOL. 34, L14701, [[http://onlinelibrary.wiley.com/doi/10.1029/2007GL030025/abstract|paywall]], doi:10.1029/2007GL030025, 2007 | ||
| - | keywords: evaluation, CMIP (need help: no access to paper) | + | //keywords: evaluation, CMIP3, regional climate // |
| - | 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. | + | ''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, [[http://onlinelibrary.wiley.com/doi/10.1029/2006GL027137/abstract|paywall]], 2006. | ||
| - | Does the Last Glacial Maximum constrain climate sensitivity? | + | //keywords: PMIP, climate sensitivity, (small) model ensemble// |
| - | 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. '' |
| - | 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 [[http://www.st-andrews.ac.uk/~rjsw/papers/Edwardsetal2007.pdf|free PDF at Uni of St Andrews]] or [[http://ppg.sagepub.com/content/31/5/481|paywall]], DOI: 10.1177/0309133307083295. 2007. | ||
| + | //keywords: review/prospective, climate sensitivity, Bayesian// | ||
| - | 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 | + | ''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.'' |
| - | Progress in Physical Geography; 31; 481 DOI: 10.1177/0309133307083295. 2007. | + | |
| - | keywords: review/prospective, climate sensitivity, Bayesian | + | ==Using the current seasonal cycle to constrain snow albedo feedback in future climate change== |
| + | Hall, A., & Qu, X. Geophysical Research Letters, 33(3), L03502, [[http://www.atmos.ucla.edu/csrl/publications/Hall/Hall_Qu_2006.pdf|free PDF at author's website]] or [[http://onlinelibrary.wiley.com/doi/10.1029/2005GL025127/abstract|paywall]], doi:10.1029/2005GL025127, 2006 | ||
| - | 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. | + | //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." '' | ||
| - | 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 | + | ==Climate sensitivity estimated from ensemble simulations of glacial climate== |
| + | Thomas Schneider von Deimling, Hermann Held, Andrey Ganopolski & Stefan Rahmstorf, Climate Dynamics, [[http://www.pik-potsdam.de/~stefan/Publications/Journals/Schneider_etal_ClimDyn_2006.pdf|free PDF at author's website]] or [[http://link.springer.com/article/10.1007%2Fs00382-006-0126-8|paywall]], DOI 10.1007/s00382-006-0126-8, 2006 | ||
| - | keywords: Northern hemisphere, PMIP, modern, model ensemble | + | //keywords: parameter ensemble, climate sensitivity, LGM, dust// |
| - | From the abstract: | + | ''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.'' |
| - | "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 | + | ==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, [[https://www.jstage.jst.go.jp/article/sola/1/0/1_0_181/_article|open access]], doi: 10.2151/sola. 2005‒047 181, 2005. | ||
| - | keywords: parameter ensemble, climate sensitivity, LGM, dust | + | //keywords: parameter ensemble, climate sensitivity, LGM, Bayesian// |
| - | 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. | + | ''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.'' |
| + | ====Authors==== | ||
| + | Contributors to this page: | ||
| + | James Annan, | ||
| + | Michel Crucifix, | ||
| + | Julia Hargreaves. | ||
| - | 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. | ||
| - | |||
| - | ===== What should I do now? ===== | ||
| - | |||
| - | <code> 1) Create your new page by creating a link to it, following the red (non existing page) | ||
| - | link and editing it. You can also directly type the name of your new page in the | ||
| - | address bar of your browser. | ||
| - | You may want to read at least once the doc about page names | ||
| - | http://www.dokuwiki.org/pagename | ||
| - | and namespaces (ie 'sections') | ||
| - | http://www.dokuwiki.org/namespaces | ||
| - | Remember that the PMIP3 wiki structure should look like | ||
| - | pmip3:index <= Home page | ||
| - | pmip3:section:index <= main page of a section | ||
| - | pmip3:section:my_page <= a page in the same section | ||
| - | pmip3:section:subsection:index <= main page of the subsection | ||
| - | pmip3:section:subsection:my_page <= a page in the same subsection | ||
| - | |||
| - | 2) Edit this page to display its source code | ||
| - | 3) Change the code of the new page and save it. | ||
| - | You should probably also remove most of the comments!</code> | ||
| /* standard page footer */ | /* standard page footer */ | ||
pmip3/wg/p2f/methods.txt · Last modified: 2017/02/10 15:47 by jules
