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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| 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 | ||
| + | |||
| + | * **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. | ||
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| Chronological by publication date, most recent first: | 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, 2013, 10.1073/pnas.1219716110/-/DCSupplemental | + | ==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]] | ||
| + | |||
| + | ''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]]'' | ||
| + | |||
| + | == 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. | ||
| //keywords: CMIP, model ensemble, Arctic, Benchmark, past century// | //keywords: CMIP, model ensemble, Arctic, Benchmark, past century// | ||
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| 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." '' | 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, 2013. | + | ==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. | ||
| //keywords: CMIP, PMIP, model ensemble, LGM// | //keywords: CMIP, PMIP, model ensemble, LGM// | ||
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| '' | '' | ||
| - | **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 | + | ==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 | ||
| //keywords: review/prospective, evaluation, PMIP, CMIP// | //keywords: review/prospective, evaluation, PMIP, CMIP// | ||
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| * The relationship between metrics and targets in the past and future must be examined, and not simply assumed." | * 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, 2012. | + | ==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. | ||
| - | keywords: prospective/review, Bayesian, evaluation | + | //keywords: prospective/review, Bayesian, evaluation// |
| - | Some background with links to other papers on probabilistic prediction, single model ensembles, metrics and the like. | + | ''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, [[http://onlinelibrary.wiley.com/doi/10.1029/2012GL053872/full|open access]], 2012. | ||
| + | //keywords: PMIP, climate sensitiivity, model ensemble// | ||
| - | 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. | + | ''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: PMIP, climate sensitiivity, model ensemble | + | ==Statistical framework for evaluation of climate model simulations by use of climate proxy data from the last millennium == |
| - | 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. | + | **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. |
| + | **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]] | ||
| - | 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. | + | //keywords : last millennium, test statistics, evaluation, detection / attribution// |
| - | keywords : last millennium, test statistics, 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 | + | ==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, doi:10.1038/nclimate1456, 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, [[http://www.nature.com/nclimate/journal/v2/n6/full/nclimate1456.html|paywall]], doi:10.1038/nclimate1456, 2012 |
| - | keywords: review/prospective, PMIP, evaluation | + | //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. | + | ''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. | ||
| - | Sensitivity of tropical precipitation extremes to climate change, Nature Geosci, O'Gorman, P. A., 5(10), 697–700, doi:doi:10.1038/ngeo1568, 2012. | + | //keywords: model ensemble, CMIP3, modern, precipitation// |
| - | 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. '' |
| - | 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. | + | ==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// | ||
| - | 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. | + | ''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]]. |
| - | 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) | ||
| - | 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. | + | ==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 | ||
| + | //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. '' | ||
| - | 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 | + | ==A probabilistic calibration of climate sensitivity and terrestrial carbon change in GENIE-1== |
| - | Clim Dyn DOI 10.1007/s00382-009-0630-8, 2010. | + | 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: Bayesian, emulator, terrestrial carbon, LGM | + | //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. | + | ''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. | ||
| - | 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. | + | //keywords : model ensemble, past century, sea-ice, Arctic// |
| - | 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."'' |
| - | 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 // | ||
| - | 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. | + | '' 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.'' |
| - | keywords: evaluation, past century, CMIP3, model ensemble | + | ==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 | ||
| - | 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. | + | //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.'' | ||
| - | Information on the early Holocene climate constrains the summer sea ice projections for the 21st century | + | ==Assessment of the use of current climate patterns to evaluate regional enhanced greenhouse response patterns of climate models== |
| - | H. Goosse, E. Driesschaert, T. Fichefet, and M.-F. Loutre, Clim. Past, 3, 683-692, 2007 | + | 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: parameter ensemble, Holocene, sea-ice | + | //keywords: evaluation, CMIP3, regional climate // |
| - | 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. | + | ''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. | ||
| - | 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: PMIP, climate sensitivity, (small) model ensemble// |
| - | keywords: evaluation, CMIP (need help: no access to paper) | + | ''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. '' |
| - | 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. | + | ==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// | ||
| - | Does the Last Glacial Maximum constrain climate sensitivity? | + | ''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.'' |
| - | M Crucifix, GRL, doi:10.1029/2006GL027137, 2006. | + | |
| - | keywords: PMIP, climate sensitivity, (small) model ensemble | + | ==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 | ||
| - | 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. | + | //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 past to constrain the future: how the palaeorecord can improve estimates of global warming, Tamsin L. Edwards, Michel Crucifix and Sandy P. Harrison | + | ==Climate sensitivity estimated from ensemble simulations of glacial climate== |
| - | Progress in Physical Geography; 31; 481 DOI: 10.1177/0309133307083295. 2007. | + | 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: review/prospective, climate sensitivity, Bayesian | + | //keywords: parameter ensemble, climate sensitivity, LGM, dust// |
| - | 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. | + | ''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.'' |
| - | 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 | + | ==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: Northern hemisphere, PMIP, modern, model ensemble | + | //keywords: parameter ensemble, climate sensitivity, LGM, Bayesian// |
| - | From the abstract: | + | ''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.'' |
| - | "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." | + | ====Authors==== |
| + | Contributors to this page: | ||
| + | James Annan, | ||
| + | Michel Crucifix, | ||
| + | Julia Hargreaves. | ||
| - | 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. | ||
| - | ===== 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
