Journal articles: 'Simple climate model'

1

Garzoli, K. "A SIMPLE GREENHOUSE CLIMATE MODEL." Acta Horticulturae, no. 174 (December 1985): 393–400. http://dx.doi.org/10.17660/actahortic.1985.174.52.

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2

TSUTSUI, Junichi. "SEEPLUS: A SIMPLE ONLINE CLIMATE MODEL." Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 67, no. 3 (2011): 134–49. http://dx.doi.org/10.2208/jscejer.67.134.

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3

Kováč, Eugen, and Robert C. Schmidt. "A simple dynamic climate cooperation model." Journal of Public Economics 194 (February 2021): 104329. http://dx.doi.org/10.1016/j.jpubeco.2020.104329.

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4

Rombouts, J., and M. Ghil. "Oscillations in a simple climate–vegetation model." Nonlinear Processes in Geophysics 22, no. 3 (May 7, 2015): 275–88. http://dx.doi.org/10.5194/npg-22-275-2015.

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Abstract. We formulate and analyze a simple dynamical systems model for climate–vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate–vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various timescales is discussed.

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Rombouts, J., and M. Ghil. "Oscillations in a simple climate–vegetation model." Nonlinear Processes in Geophysics Discussions 2, no. 1 (February 2, 2015): 145–78. http://dx.doi.org/10.5194/npgd-2-145-2015.

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Abstract. We formulate and analyze a simple dynamical systems model for climate–vegetation interaction. The planet we consider consists of a large ocean and a land surface on which vegetation can grow. The temperature affects vegetation growth on land and the amount of sea ice on the ocean. Conversely, vegetation and sea ice change the albedo of the planet, which in turn changes its energy balance and hence the temperature evolution. Our highly idealized, conceptual model is governed by two nonlinear, coupled ordinary differential equations, one for global temperature, the other for vegetation cover. The model exhibits either bistability between a vegetated and a desert state or oscillatory behavior. The oscillations arise through a Hopf bifurcation off the vegetated state, when the death rate of vegetation is low enough. These oscillations are anharmonic and exhibit a sawtooth shape that is characteristic of relaxation oscillations, as well as suggestive of the sharp deglaciations of the Quaternary. Our model's behavior can be compared, on the one hand, with the bistability of even simpler, Daisyworld-style climate–vegetation models. On the other hand, it can be integrated into the hierarchy of models trying to simulate and explain oscillatory behavior in the climate system. Rigorous mathematical results are obtained that link the nature of the feedbacks with the nature and the stability of the solutions. The relevance of model results to climate variability on various time scales is discussed.

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6

Chaumont, Sébastien, Peter Imkeller, Matthias Müller, and Ulrich Horst. "A Simple Model for Trading Climate Risk." Vierteljahrshefte zur Wirtschaftsforschung 74, no. 2 (April 2005): 175–95. http://dx.doi.org/10.3790/vjh.74.2.175.

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7

Emanuel, Kerry. "A simple model of multiple climate regimes." Journal of Geophysical Research: Atmospheres 107, no. D9 (May 8, 2002): ACL 4–1—ACL 4–10. http://dx.doi.org/10.1029/2001jd001002.

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8

Schwarber, Adria K., Steven J. Smith, Corinne A. Hartin, Benjamin Aaron Vega-Westhoff, and Ryan Sriver. "Evaluating climate emulation: fundamental impulse testing of simple climate models." Earth System Dynamics 10, no. 4 (November 13, 2019): 729–39. http://dx.doi.org/10.5194/esd-10-729-2019.

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Abstract. Simple climate models (SCMs) are numerical representations of the Earth's gas cycles and climate system. SCMs are easy to use and computationally inexpensive, making them an ideal tool in both scientific and decision-making contexts (e.g., complex climate model emulation, parameter estimation experiments, climate metric calculations, and probabilistic analyses). Despite their prolific use, the fundamental responses of SCMs are often not directly characterized. In this study, we use fundamental impulse tests of three chemical species (CO2, CH4, and black carbon – BC) to understand the fundamental gas cycle and climate system responses of several comprehensive (Hector v2.0, MAGICC 5.3, MAGICC 6.0) and idealized (FAIR v1.0, AR5-IR) SCMs. We find that while idealized SCMs are widely used, they fail to capture the magnitude and timescales of global mean climate responses under emissions perturbations, which can produce biased temperature results. Comprehensive SCMs, which have physically based nonlinear forcing and carbon cycle representations, show improved responses compared to idealized SCMs. Even the comprehensive SCMs, however, fail to capture the response timescales to BC emission perturbations seen recently in two general circulation models. Some comprehensive SCMs also generally respond faster than more complex models to a 4×CO2 concentration perturbation, although this was not evident for lower perturbation levels. These results suggest where improvements should be made to SCMs. Further, we demonstrate here a set of fundamental tests that we recommend as a standard evaluation suite for any SCM. Fundamental impulse tests allow users to understand differences in model responses and the impact of model selection on results.

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9

SZILDER, Krzysztof, Kimiteru SADO, and Edward P. LOZOWSKI. "Climate Stability in a Simple Climate Model with a Hydrological Cycle." Journal of Japan Society of Hydrology and Water Resources 9, no. 1 (1996): 68–76. http://dx.doi.org/10.3178/jjshwr.9.68.

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10

Monckton of Brenchley, Christopher, Willie W. H. Soon, David R. Legates, and William M. Briggs. "Keeping it simple: the value of an irreducibly simple climate model." Science Bulletin 60, no. 15 (August 2015): 1378–90. http://dx.doi.org/10.1007/s11434-015-0856-2.

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11

Rahmstorf, Stefan, and Andrey Ganopolski. "Simple Theoretical Model May Explain Apparent Climate Instability." Journal of Climate 12, no. 5 (May 1999): 1349–52. http://dx.doi.org/10.1175/1520-0442(1999)012<1349:stmmea>2.0.co;2.

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12

Sherwood, Steven C. "Feedbacks in a Simple Prognostic Tropical Climate Model." Journal of the Atmospheric Sciences 56, no. 13 (July 1999): 2178–200. http://dx.doi.org/10.1175/1520-0469(1999)056<2178:fiaspt>2.0.co;2.

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13

Krueger, Oliver, and Jin-Song Von Storch. "A Simple Empirical Model for Decadal Climate Prediction." Journal of Climate 24, no. 4 (February 15, 2011): 1276–83. http://dx.doi.org/10.1175/2010jcli3726.1.

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Abstract Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.

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14

Bossy, Thomas, Thomas Gasser, and Philippe Ciais. "Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios." Geoscientific Model Development 15, no. 23 (December 12, 2022): 8831–68. http://dx.doi.org/10.5194/gmd-15-8831-2022.

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Abstract. The Pathfinder model was developed to fill a perceived gap within the range of existing simple climate models. Pathfinder is a compilation of existing formulations describing the climate and carbon cycle systems, chosen for their balance between mathematical simplicity and physical accuracy. The resulting model is simple enough to be used with Bayesian inference algorithms for calibration, which enables assimilation of the latest data from complex Earth system models and the IPCC sixth assessment report, as well as a yearly update based on observations of global temperature and atmospheric CO2. The model's simplicity also enables coupling with integrated assessment models and their optimization algorithms or running the model in a backward temperature-driven fashion. In spite of this simplicity, the model accurately reproduces behaviours and results from complex models – including several uncertainty ranges – when run following standardized diagnostic experiments. Pathfinder is an open-source model, and this is its first comprehensive description.

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15

Barsugli, Joseph, Sang-Ik Shin, and Prashant D. Sardeshmukh. "Tropical Climate Regimes and Global Climate Sensitivity in a Simple Setting." Journal of the Atmospheric Sciences 62, no. 4 (April 1, 2005): 1226–40. http://dx.doi.org/10.1175/jas3404.1.

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Abstract Multiple tropical climate regimes are found in an atmospheric general circulation model (AGCM) coupled to a global slab ocean when the model is forced by different values of globally uniform insolation. Even in this simple setting, convection organizes into an intertropical convergence zone (ITCZ) solely due to the effect of planetary rotation, as was found in Kirtman and Schneider, for a single value of insolation. Here the response to a range of insolation values is explored, and surprisingly, multiple climate regimes characterized by radically different ITCZ structures are found. In order from the coldest to warmest climates, these are a symmetric double ITCZ, a near-symmetric equatorial ITCZ, a transient asymmetric ITCZ, and a stable, strongly asymmetric ITCZ. The model exhibits hysteresis in the transition from the near-symmetric to the strongly asymmetric ITCZ regimes when insolation is increased and then decreased. The initial transition away from symmetry can occur in the absence of air–sea coupling; however, the coupling is essential for the establishment and maintenance of the strongly asymmetric ITCZ. Wind–evaporation–SST feedback as well as the longwave radiative effects of clouds and water vapor on SSTs appear to be important in maintaining the asymmetric regime. The existence of multiple regimes in a single AGCM, and the dependence of these regimes on SST feedbacks, may have a bearing on the ITCZ simulation errors of current coupled climate models. The sensitivity of the global mean surface temperature generally decreases with increasing insolation, a consequence primarily of increasingly negative shortwave cloud forcing. Climate sensitivity measured across a regime transition can be much larger than the sensitivity within a single regime.

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16

Lin, R. Q., and G. R. North. "A study of abrupt climate change in a simple nonlinear climate model." Climate Dynamics 4, no. 4 (October 1990): 253–61. http://dx.doi.org/10.1007/bf00211062.

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17

Monckton, Christopher, Willie W. H. Soon, David R. Legates, and William M. Briggs. "Why models run hot: results from an irreducibly simple climate model." Science Bulletin 60, no. 1 (January 2015): 122–35. http://dx.doi.org/10.1007/s11434-014-0699-2.

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18

Davis, Nicholas A., Dian J. Seidel, Thomas Birner, Sean M. Davis, and Simone Tilmes. "Changes in the width of the tropical belt due to simple radiative forcing changes in the GeoMIP simulations." Atmospheric Chemistry and Physics 16, no. 15 (August 11, 2016): 10083–95. http://dx.doi.org/10.5194/acp-16-10083-2016.

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Abstract. Model simulations of future climates predict a poleward expansion of subtropical arid climates at the edges of Earth's tropical belt, which would have significant environmental and societal impacts. This expansion may be related to the poleward shift of the Hadley cell edges, where subsidence stabilizes the atmosphere and suppresses precipitation. Understanding the primary drivers of tropical expansion is hampered by the myriad forcing agents in most model projections of future climate. While many previous studies have examined the response of idealized models to simplified climate forcings and the response of comprehensive climate models to more complex climate forcings, few have examined how comprehensive climate models respond to simplified climate forcings. To shed light on robust processes associated with tropical expansion, here we examine how the tropical belt width, as measured by the Hadley cell edges, responds to simplified forcings in the Geoengineering Model Intercomparison Project (GeoMIP). The tropical belt expands in response to a quadrupling of atmospheric carbon dioxide concentrations and contracts in response to a reduction in the solar constant, with a range of a factor of 3 in the response among nine models. Models with more surface warming and an overall stronger temperature response to quadrupled carbon dioxide exhibit greater tropical expansion, a robust result in spite of inter-model differences in the mean Hadley cell width, parameterizations, and numerical schemes. Under a scenario where the solar constant is reduced to offset an instantaneous quadrupling of carbon dioxide, the Hadley cells remain at their preindustrial width, despite the residual stratospheric cooling associated with elevated carbon dioxide levels. Quadrupled carbon dioxide produces greater tropical belt expansion in the Southern Hemisphere than in the Northern Hemisphere. This expansion is strongest in austral summer and autumn. Ozone depletion has been argued to cause this pattern of changes in observations and model experiments, but the results here indicate that seasonally and hemispherically asymmetric tropical expansion can be a basic response of the general circulation to climate forcings.

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19

Körner, O., and K. L. Nielsen. "A SIMPLE KALANCHOË PHOTOSYNTHESIS MODEL FOR GREENHOUSE CLIMATE CONTROL." Acta Horticulturae, no. 952 (June 2012): 111–18. http://dx.doi.org/10.17660/actahortic.2012.952.12.

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20

Kroll, John. "Fire, ice, water, and dirt: A simple climate model." Chaos: An Interdisciplinary Journal of Nonlinear Science 27, no. 7 (July 2017): 073101. http://dx.doi.org/10.1063/1.4991383.

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21

Braun, H., A. Ganopolski, M. Christl, and D. R. Chialvo. "A simple conceptual model of abrupt glacial climate events." Nonlinear Processes in Geophysics 14, no. 6 (November 23, 2007): 709–21. http://dx.doi.org/10.5194/npg-14-709-2007.

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Abstract. Here we use a very simple conceptual model in an attempt to reduce essential parts of the complex nonlinearity of abrupt glacial climate changes (the so-called Dansgaard-Oeschger events) to a few simple principles, namely (i) the existence of two different climate states, (ii) a threshold process and (iii) an overshooting in the stability of the system at the start and the end of the events, which is followed by a millennial-scale relaxation. By comparison with a so-called Earth system model of intermediate complexity (CLIMBER-2), in which the events represent oscillations between two climate states corresponding to two fundamentally different modes of deep-water formation in the North Atlantic, we demonstrate that the conceptual model captures fundamental aspects of the nonlinearity of the events in that model. We use the conceptual model in order to reproduce and reanalyse nonlinear resonance mechanisms that were already suggested in order to explain the characteristic time scale of Dansgaard-Oeschger events. In doing so we identify a new form of stochastic resonance (i.e. an overshooting stochastic resonance) and provide the first explicitly reported manifestation of ghost resonance in a geosystem, i.e. of a mechanism which could be relevant for other systems with thresholds and with multiple states of operation. Our work enables us to explicitly simulate realistic probability measures of Dansgaard-Oeschger events (e.g. waiting time distributions, which are a prerequisite for statistical analyses on the regularity of the events by means of Monte-Carlo simulations). We thus think that our study is an important advance in order to develop more adequate methods to test the statistical significance and the origin of the proposed glacial 1470-year climate cycle.

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22

Salazar, JM, and C. Nicolis. "Self-generated aperiodic behaviour in a simple climate model." Climate Dynamics 3, no. 2 (October 1988): 105–14. http://dx.doi.org/10.1007/bf01080904.

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23

Fan, X., J. F. Chou, B. R. Guo, and M. D. Shulski. "A coupled simple climate model and its global analysis." Theoretical and Applied Climatology 79, no. 1-2 (July 29, 2004): 31–43. http://dx.doi.org/10.1007/s00704-004-0071-6.

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24

Ruzmaikin, Alexander, John K. Lawrence, and Ana Cristina Cadavid. "A simple model of solar variability influence on climate." Advances in Space Research 34, no. 2 (January 2004): 349–54. http://dx.doi.org/10.1016/j.asr.2003.02.048.

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Bendtsen, J. "Climate sensitivity to changes in solar insolation in a simple coupled climate model." Climate Dynamics 18, no. 7 (March 2002): 595–609. http://dx.doi.org/10.1007/s00382-001-0198-4.

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26

Blyth, E. M., and C. C. Daamen. "The accuracy of simple soil water models in climate forecasting." Hydrology and Earth System Sciences 1, no. 2 (June 30, 1997): 241–48. http://dx.doi.org/10.5194/hess-1-241-1997.

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Abstract. Several simple soil water models with four layers or less, typical of those used in GCMS, are compared to a complex multilayered model. They are tested by applying a repeating wetting/drying cycle at different frequencies, and run to equilibrium. The ability of the simple soil models to reproduce the results of the multilayer model vary according to the frequency of the forcing cycle, the soil type, the number of layers and the depth of the top layer of the model. The best overall performance was from the four layer model. The two layer model with a thin top layer (0.1 m) modelled sandy soils well while the two layer model with a thick top layer (0.5 m) modelled clay soils well. The model with just one layer overestimated evaporation during long drying periods for all soil types.

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27

Forster, Piers Mde F., and Karl E. Taylor. "Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations." Journal of Climate 19, no. 23 (December 1, 2006): 6181–94. http://dx.doi.org/10.1175/jcli3974.1.

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Abstract A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.

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Smith, Christopher J., Piers M. Forster, Myles Allen, Nicholas Leach, Richard J. Millar, Giovanni A. Passerello, and Leighton A. Regayre. "FAIR v1.3: a simple emissions-based impulse response and carbon cycle model." Geoscientific Model Development 11, no. 6 (June 18, 2018): 2273–97. http://dx.doi.org/10.5194/gmd-11-2273-2018.

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Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

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Mueller, Eli A., and Samuel N. Stechmann. "Shallow-cloud impact on climate and uncertainty: A simple stochastic model." Mathematics of Climate and Weather Forecasting 6, no. 1 (March 20, 2020): 16–37. http://dx.doi.org/10.1515/mcwf-2020-0002.

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AbstractShallow clouds are a major source of uncertainty in climate predictions. Several different sources of the uncertainty are possible—e.g., from different models of shallow cloud behavior, which could produce differing predictions and ensemble spread within an ensemble of models, or from inherent, natural variability of shallow clouds. Here, the latter (inherent variability) is investigated, using a simple model of radiative statistical equilibrium, with oceanic and atmospheric boundary layer temperatures, To and Ta, and with moisture q and basic cloud processes. Stochastic variability is used to generate a statistical equilibrium with climate variability. The results show that the intrinsic variability of the climate is enhanced due to the presence of shallow clouds. In particular, the on-and-off switching of cloud formation and decay is a source of additional climate variability and uncertainty, beyond the variability of a cloud-free climate. Furthermore, a sharp transition in the mean climate occurs as environmental parameters are changed, and the sharp transition in the mean is also accompanied by a substantial enhancement of climate sensitivity and uncertainty. Two viewpoints of this behavior are described, based on bifurcations and phase transitions/statistical physics. The sharp regime transitions are associated with changes in several parameters, including cloud albedo and longwave absorptivity/carbon dioxide concentration, and the climate state transitions between a partially cloudy state and a state of full cloud cover like closed-cell stratocumulus clouds. Ideas of statistical physics can provide a conceptual perspective to link the climate state transitions, increased climate uncertainty, and other related behavior.

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Yip, Stan, Christopher A. T. Ferro, David B. Stephenson, and Ed Hawkins. "A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions." Journal of Climate 24, no. 17 (September 2011): 4634–43. http://dx.doi.org/10.1175/2011jcli4085.1.

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A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

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31

Oerlemans, Johannes. "Simulation of Historic Glacier Variations with a Simple Climate-Glacier Model." Journal of Glaciology 34, no. 118 (1988): 333–41. http://dx.doi.org/10.1017/s0022143000007103.

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Abstract Glacier variations during the last few centuries have shown a marked coherence over the globe. Characteristic features are the maximum stand somewhere in the middle of the nineteenth century, and the steady retreat afterwards (with some minor interruptions depending on the particular region). In many papers, qualitative statements have been made about the causes of these fluctuations. Lower temperatures associated with solar variability and/or volcanic activity are the most popular explanations. In particular, the statistical relation between glacier activity and major volcanic eruptions appears to be strong. In this paper, an attempt is made to simulate recent glacier fluctations with a physics-based model. A simple climate model, calculating perturbations of surface-radiation balance and air temperature (not necessarily in phase!), is coupled to a schematic time-dependent glacier model. The climate model is forced by volcanic activity (Greenland acidity and/or Lamb’s dust-veil index) and greenhouse warming. Solar variability was not considered, because its effect on climate has still not been demonstrated in a convincing way. The output is translated into variations in equilibrium-line altitude, driving the glacier model. The simulated variations in glacier length show good agreement with the observed record, but the amplitude is too small. This is improved when mass-balance gradients are assumed to be larger in warmer climates. Compared to recently published modelling studies of particular glaciers, in which series of local parameters (e.g. tree-ring width and temperature) were used as forcing, the present simulation is better. This suggests that the radiation balance is a decisive factor with regard to glacier variations on longer time-scales. The model experiments lend support to the results of Porter (1986), who concluded from a more qualitative study that a strong relation exists between periods of increased volcanic activity and glacier advances. A comparison of some selected runs shows that, according to the present model, the greenhouse warming would be responsible for about 50% of the glacier retreat observed over the last 100 years.

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32

Oerlemans, Johannes. "Simulation of Historic Glacier Variations with a Simple Climate-Glacier Model." Journal of Glaciology 34, no. 118 (1988): 333–41. http://dx.doi.org/10.3189/s0022143000007103.

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AbstractGlacier variations during the last few centuries have shown a marked coherence over the globe. Characteristic features are the maximum stand somewhere in the middle of the nineteenth century, and the steady retreat afterwards (with some minor interruptions depending on the particular region). In many papers, qualitative statements have been made about the causes of these fluctuations. Lower temperatures associated with solar variability and/or volcanic activity are the most popular explanations. In particular, the statistical relation between glacier activity and major volcanic eruptions appears to be strong.In this paper, an attempt is made to simulate recent glacier fluctations with a physics-based model. A simple climate model, calculating perturbations of surface-radiation balance and air temperature (not necessarily in phase!), is coupled to a schematic time-dependent glacier model. The climate model is forced by volcanic activity (Greenland acidity and/or Lamb’s dust-veil index) and greenhouse warming. Solar variability was not considered, because its effect on climate has still not been demonstrated in a convincing way. The output is translated into variations in equilibrium-line altitude, driving the glacier model.The simulated variations in glacier length show good agreement with the observed record, but the amplitude is too small. This is improved when mass-balance gradients are assumed to be larger in warmer climates. Compared to recently published modelling studies of particular glaciers, in which series of local parameters (e.g. tree-ring width and temperature) were used as forcing, the present simulation is better. This suggests that the radiation balance is a decisive factor with regard to glacier variations on longer time-scales. The model experiments lend support to the results of Porter (1986), who concluded from a more qualitative study that a strong relation exists between periods of increased volcanic activity and glacier advances.A comparison of some selected runs shows that, according to the present model, the greenhouse warming would be responsible for about 50% of the glacier retreat observed over the last 100 years.

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33

Flato, G. M., and W. D. Hibler. "On a Simple Sea-Ice Dynamics Model for Climate Studies." Annals of Glaciology 14 (1990): 72–77. http://dx.doi.org/10.3189/s0260305500008296.

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Sea-ice motion and dynamic thickness build-up play an important role in the transfer of heat between the ocean and the atmosphere and so must be included in large-scale climate studies. A “cavitating-fluid” approximation allows these dynamic processes to be parameterized in a simple way by ignoring shear and tensile strength yet retaining compressive strength. A simple procedure for approximating a cavitating fluid is presented here and is compared to the more complete viscous-plastic sea-ice model by performing several three year simulations with daily varying and monthly average wind forcing. Although differences exist on a monthly basis, the two models compare favourably over a seasonal cycle, particularly when compared to a thermodynamics only model in which ice motion is ignored. The lack of shear strength in a cavitating-fluid approximation makes it less sensitive to smoothing of the wind fields (as demonstrated by the monthly average wind simulations); however it also changes the detailed circulation and thickness build-up patterns somewhat. Overall, the cavitating-fluid approximation shows considerable promise for including sea-ice dynamics in large-scale climate models, especially where averaged wind fields are employed.

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Flato, G. M., and W. D. Hibler. "On a Simple Sea-Ice Dynamics Model for Climate Studies." Annals of Glaciology 14 (1990): 72–77. http://dx.doi.org/10.1017/s0260305500008296.

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Sea-ice motion and dynamic thickness build-up play an important role in the transfer of heat between the ocean and the atmosphere and so must be included in large-scale climate studies. A “cavitating-fluid” approximation allows these dynamic processes to be parameterized in a simple way by ignoring shear and tensile strength yet retaining compressive strength. A simple procedure for approximating a cavitating fluid is presented here and is compared to the more complete viscous-plastic sea-ice model by performing several three year simulations with daily varying and monthly average wind forcing. Although differences exist on a monthly basis, the two models compare favourably over a seasonal cycle, particularly when compared to a thermodynamics only model in which ice motion is ignored. The lack of shear strength in a cavitating-fluid approximation makes it less sensitive to smoothing of the wind fields (as demonstrated by the monthly average wind simulations); however it also changes the detailed circulation and thickness build-up patterns somewhat. Overall, the cavitating-fluid approximation shows considerable promise for including sea-ice dynamics in large-scale climate models, especially where averaged wind fields are employed.

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Kirk-Davidoff, D. B. "On the diagnosis of climate sensitivity using observations of fluctuations." Atmospheric Chemistry and Physics 9, no. 3 (February 2, 2009): 813–22. http://dx.doi.org/10.5194/acp-9-813-2009.

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Abstract. It has been shown that lag-covariance based statistical measures, suggested by the Fluctuation Dissipation Theorem (FDT), may allow estimation of climate sensitivity in a climate model. Recently Schwartz (2007) has used measures of the decay of autocorrelation in a global surface temperature time series to estimate the real world climate sensitivity. Here we use a simple climate model, and analysis of archived coupled climate model output from the IPCC AR4 runs, for which the climate sensitivity is known, to test the utility of this approach. Our analysis of these archived model output data show that estimates of climate sensitivity derived from century-long time scales typically grossly underestimate the models' true climate sensitivity. We analyze the behavior of the simple model with adjustable heat capacity in two surface layers, subject to various stochastic forcings and for various climate sensitivities, modulated by albedo and water vapor feedbacks. We use our simple climate model to demonstrate: 1. that a much longer time series would be required to accurately diagnose the earth's climate sensitivity than is presently available 2. that for shorter time series there is a systematic bias towards underpredicting climate sensitivity, 3. that the addition of a second heat reservoir weakly coupled to the first greatly reduces the decorrelation timescale of short temperature time series produced by the model, aggravating the tendency to underestimate climate sensitivity, and 4. that because of this it is possible to have a selection of models in which the climate sensitivity is inversely related to the decorrelation time scale, as is true for the IPCC models.

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Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration." Atmospheric Chemistry and Physics 11, no. 4 (February 16, 2011): 1417–56. http://dx.doi.org/10.5194/acp-11-1417-2011.

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Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 °C, about 0.33 °C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.

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Lin, R. Q., H. Kreiss, W. J. Kuang, and L. Y. Leung. "A study of long-term climate change in a simple seasonal nonlinear climate model." Climate Dynamics 6, no. 1 (July 1991): 35–41. http://dx.doi.org/10.1007/bf00210580.

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Kirk-Davidoff, D. "On the diagnosis of climate sensitivity using observations of fluctuations." Atmospheric Chemistry and Physics Discussions 8, no. 3 (June 30, 2008): 12409–34. http://dx.doi.org/10.5194/acpd-8-12409-2008.

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Abstract. It has been shown that lag-covariance based statistical measures, suggested by the Fluctuation Dissipation Theorem (FDT), may allow estimation of climate sensitivity in a climate model. Recently Schwartz (2007) has used measures of the decay of autocorrelation in a global surface temperature time series to estimate the real world climate sensitivity. Here we use a simple climate model, and analysis of archived coupled climate model output from the IPCC AR runs, for which the climate sensitivity is known, to test the utility of this approach. Our analysis of archived data show that estimates of climate sensitivity derived from century-long time scales typically grossly underestimate the models' true climate sensitivity. We analyze the behavior of the simple model with adjustable heat capacity in two surface layers, subject to various stochastic forcings and for various climate sensitivities, modulated by albedo and water vapor feedbacks. We use our simple climate model to demonstrate: 1. that a much longer time series would be required to accurately diagnose the earth's climate sensitivity than is presently available, 2. that for shorter time series there is a systematic bias towards underpredicting climate sensitivity, 3. that the addition of a second heat reservoir weakly coupled to the first greatly reduces the decorrelation timescale of short temperature time series produced by the model, aggravating the tendency to underestimate climate sensitivity, and 4. that because of this it is possible to have a selection of models in which the climate sensitivity is inversely related to the decorrelation time scale, as is true for the IPCC models.

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Overland, James E., Muyin Wang, Nicholas A. Bond, John E. Walsh, Vladimir M. Kattsov, and William L. Chapman. "Considerations in the Selection of Global Climate Models for Regional Climate Projections: The Arctic as a Case Study*." Journal of Climate 24, no. 6 (March 15, 2011): 1583–97. http://dx.doi.org/10.1175/2010jcli3462.1.

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Abstract Climate projections at regional scales are in increased demand from management agencies and other stakeholders. While global atmosphere–ocean climate models provide credible quantitative estimates of future climate at continental scales and above, individual model performance varies for different regions, variables, and evaluation metrics—a less than satisfying situation. Using the high-latitude Northern Hemisphere as a focus, the authors assess strategies for providing regional projections based on global climate models. Starting with a set of model results obtained from an “ensemble of opportunity,” the core of this procedure is to retain a subset of models through comparisons of model simulations with observations at both continental and regional scales. The exercise is more one of model culling than model selection. The continental-scale evaluation is a check on the large-scale climate physics of the models, and the regional-scale evaluation emphasizes variables of ecological or societal relevance. An additional consideration is given to the comprehensiveness of processes included in the models. In many but not all applications, different results are obtained from a reduced set of models compared to relying on the simple mean of all available models. For example, in the Arctic the top-performing models tend to be more sensitive to greenhouse forcing than the poorer-performing models. Because of the mostly unexplained inconsistencies in model performance under different selection criteria, simple and transparent evaluation methods are favored. The use of a single model is not recommended. For some applications, no model may be able to provide a suitable regional projection. The use of model evaluation strategies, as opposed to relying on simple averages of ensembles of opportunity, should be part of future synthesis activities such as the upcoming Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

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Bodman, R., D. Karoly, S. Wijffels, and I. Enting. "Observational constraints on parameter estimates for a simple climate model." Australian Meteorological and Oceanographic Journal 62, no. 4 (December 2013): 277–86. http://dx.doi.org/10.22499/2.6204.007.

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Dickinson, Robert E., and Kimberly J. Schaudt. "Analysis of Timescales of Response of a Simple Climate Model." Journal of Climate 11, no. 1 (January 1998): 97–106. http://dx.doi.org/10.1175/1520-0442(1998)011<0097:aotoro>2.0.co;2.

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Gottinger, Hans. "A Simple Endogenous Model of Economic Activity and Climate Change." Metroeconomica 49, no. 2 (June 1998): 139–68. http://dx.doi.org/10.1111/1467-999x.00047.

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Kurgansky, M. V., K. Dethloff, I. A. Pisnichenko, H. Gernandt, F. M. Chmielewski, and W. Jansen. "Long-term climate variability in a simple, nonlinear atmospheric model." Journal of Geophysical Research: Atmospheres 101, no. D2 (February 1, 1996): 4299–314. http://dx.doi.org/10.1029/95jd02703.

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N Willner, Sven, Corinne Hartin, and Robert Gieseke. "pyhector: A Python interface for the simple climate model Hector." Journal of Open Source Software 2, no. 12 (April 28, 2017): 248. http://dx.doi.org/10.21105/joss.00248.

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Gieseke, Robert, Sven N Willner, and Matthias Mengel. "Pymagicc: A Python wrapper for the simple climate model MAGICC." Journal of Open Source Software 3, no. 22 (February 4, 2018): 516. http://dx.doi.org/10.21105/joss.00516.

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Ferreira, David, Claude Frankignoul, and John Marshall. "Coupled Ocean–Atmosphere Dynamics in a Simple Midlatitude Climate Model." Journal of Climate 14, no. 17 (September 2001): 3704–23. http://dx.doi.org/10.1175/1520-0442(2001)014<3704:coadia>2.0.co;2.

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Kushnir, Yochanan, Richard Seager, Jennifer Miller, and John C. H. Chiang. "A simple coupled model of tropical Atlantic decadal climate variability." Geophysical Research Letters 29, no. 23 (December 2002): 48–1. http://dx.doi.org/10.1029/2002gl015874.

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CAI, DAVID, KYLE HAVEN, and ANDREW J. MAJDA. "QUANTIFYING PREDICTABILITY IN A SIMPLE MODEL WITH COMPLEX FEATURES." Stochastics and Dynamics 04, no. 04 (December 2004): 547–69. http://dx.doi.org/10.1142/s021949370400122x.

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Here, Kaplan–Yorke type maps are utilized as simplified models to assess new strategies for quantifying predictability through information theory. These models give rise to a wide variety of "climate" distributions from nearly Gaussian to highly non-Gaussian. For complex models, it is almost impossible to compute proposed theoretical measures of predictability directly and alternative methods of estimation must be utilized. Due to the simplicity of the proposed model, accurate approximations of predictability can be computed and compared to various estimation techniques. A recently proposed method for finding a lower bound estimate of the predictability is outlined in the context of the model. Estimates of this type are computed and evaluated for a long-term climate prediction scenario. The factors that control the predictability for this scenario are determined using an ensemble of ensembles approach. In addition, the lower bound estimates are used as a means of assessing the utility of a Gaussian approximation strategy.

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Hartin, C. A., P. Patel, A. Schwarber, R. P. Link, and B. P. Bond-Lamberty. "A simple object-oriented and open source model for scientific and policy analyses of the global carbon cycle – Hector v0.1." Geoscientific Model Development Discussions 7, no. 5 (October 24, 2014): 7075–119. http://dx.doi.org/10.5194/gmdd-7-7075-2014.

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Abstract. Simple climate models play an integral role in policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v0.1, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global scale earth system processes. Hector has three main carbon pools: an atmosphere, land, and ocean. The model's terrestrial carbon cycle includes respiration and primary production, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector's actively solves the inorganic carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO2] and surface temperatures. The model simulates all four Representative Concentration Pathways with high correlations (R>0.7) with current observations, MAGICC (a well-known simple climate model), and the Coupled Model Intercomparison Project version 5. Hector is freely available under an open source license, and its modular design will facilitate a broad range of research in various areas.

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Dommenget, Dietmar, Kerry Nice, Tobias Bayr, Dieter Kasang, Christian Stassen, and Michael Rezny. "The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations." Geoscientific Model Development 12, no. 6 (June 3, 2019): 2155–79. http://dx.doi.org/10.5194/gmd-12-2155-2019.

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Abstract. This study introduces the Monash Simple Climate Model (MSCM) experiment database. The simulations are based on the Globally Resolved Energy Balance (GREB) model to study three different aspects of climate model simulations: (1) understanding processes that control the mean climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external forcing (CO2 concentration and solar radiation). A series of sensitivity experiments in which elements of the climate system are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1300 experiments and has an online web interface for fast analysis and free access to the data. We briefly outline the design of all experiments, give a discussion of some results, put the findings into the context of previously published results from similar experiments, discuss the quality and limitations of the MSCM experiments, and also give an outlook on possible further developments. The GREB model simulation is quite realistic, but the model without flux corrections has a root mean square error in the mean state of the surface temperature of about 10 ∘C, which is larger than those of general circulation models (2 ∘C). It needs to be noted here that the GREB model does not simulate circulation changes or changes in cloud cover (feedbacks). However, the MSCM experiments show good agreement to previously published studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and can be used to quickly try many different sensitivity experiments or scenarios. It builds a basis on which conceptual ideas can be tested to first order and it provides a null hypothesis for understanding complex climate interactions in the context of response to external forcing or interactions in the climate subsystems.

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Journal articles on the topic 'Simple climate model'

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