scholarly journals FaIRv2.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration

2020 ◽  
Author(s):  
Nicholas J. Leach ◽  
Stuart Jenkins ◽  
Zebedee Nicholls ◽  
Christopher J. Smith ◽  
John Lynch ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Impulse Response ◽  
Climate Models ◽  
Climate System ◽  
System Response ◽  
Common Denominator ◽  
Response Model ◽  
Wide Range ◽  
Complex Models ◽  
Additional Equation

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard Impulse Response model used for greenhouse gas (GHG) metric calculations in the IPCC's fifth assessment report, plus one additional physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth System Models and observations. These six equations are transparent and sufficiently simple that the model is able to be written in standard tabular data analysis packages, such as Excel; increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a one million member ensemble, using the constrained ensemble to make scenario dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased hot or cold: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications; and therefore could be used as a lowest common denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

scholarly journals FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration

2021 ◽  
Vol 14 (5) ◽  
pp. 3007-3036
Author(s):  
Nicholas J. Leach ◽  
Stuart Jenkins ◽  
Zebedee Nicholls ◽  
Christopher J. Smith ◽  
John Lynch ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Impulse Response ◽  
Climate Models ◽  
Global Climate ◽  
Climate System ◽  
System Response ◽  
Common Denominator ◽  
Response Model ◽  
Wide Range ◽  
Complex Models

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis, and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard impulse response model used for greenhouse gas (GHG) metric calculations in the IPCC's Fifth Assessment Report, plus one additional physically motivated equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth system models and observations. These six equations are transparent and sufficiently simple that the model is able to be ported into standard tabular data analysis packages, such as Excel, increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a million-member ensemble, using the constrained ensemble to make scenario-dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased “hot” or “cold”: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications and therefore could be used as a lowest-common-denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

scholarly journals GIR v1.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration

2020 ◽  
Author(s):  
Nicholas James Leach ◽  
Zebedee Nicholls ◽  
Stuart Jenkins ◽  
Christopher J. Smith ◽  
John Lynch ◽  
...  
Keyword(s):  
Greenhouse Gas ◽  
Impulse Response ◽  
Radiative Forcing ◽  
Climate Models ◽  
Temperature Response ◽  
Structural Complexity ◽  
Common Denominator ◽  
Response Model ◽  
State Dependent ◽  
Effective Radiative Forcing

Abstract. Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

scholarly journals Developing the next-generation climate system models: challenges and achievements

2008 ◽  
Vol 367 (1890) ◽  
pp. 815-831 ◽  
Author(s):  
Julia Slingo ◽  
Kevin Bates ◽  
Nikos Nikiforakis ◽  
Matthew Piggott ◽  
Malcolm Roberts ◽  
...  
Keyword(s):  
Adaptive Mesh Refinement ◽  
Climate Models ◽  
Spatial Scales ◽  
Climate System ◽  
System Modelling ◽  
Earth System ◽  
System Models ◽  
Wide Range ◽  
Petascale Computing ◽  
Climate System Models

Although climate models have been improving in accuracy and efficiency over the past few decades, it now seems that these incremental improvements may be slowing. As tera/petascale computing becomes massively parallel, our legacy codes are less suitable, and even with the increased resolution that we are now beginning to use, these models cannot represent the multiscale nature of the climate system. This paper argues that it may be time to reconsider the use of adaptive mesh refinement for weather and climate forecasting in order to achieve good scaling and representation of the wide range of spatial scales in the atmosphere and ocean. Furthermore, the challenge of introducing living organisms and human responses into climate system models is only just beginning to be tackled. We do not yet have a clear framework in which to approach the problem, but it is likely to cover such a huge number of different scales and processes that radically different methods may have to be considered. The challenges of multiscale modelling and petascale computing provide an opportunity to consider a fresh approach to numerical modelling of the climate (or Earth) system, which takes advantage of the computational fluid dynamics developments in other fields and brings new perspectives on how to incorporate Earth system processes. This paper reviews some of the current issues in climate (and, by implication, Earth) system modelling, and asks the question whether a new generation of models is needed to tackle these problems.

scholarly journals PDRMIP: A Precipitation Driver and Response Model Intercomparison Project—Protocol and Preliminary Results

2017 ◽  
Vol 98 (6) ◽  
pp. 1185-1198 ◽  
Author(s):  
G. Myhre ◽  
P. M. Forster ◽  
B. H. Samset ◽  
Ø. Hodnebrog ◽  
J. Sillmann ◽  
...  
Keyword(s):  
Climate Models ◽  
Large Fraction ◽  
Large Degree ◽  
Climate Projections ◽  
Large Set ◽  
Response Model ◽  
Model Intercomparison ◽  
Wide Range ◽  
Intercomparison Project ◽  
Climate Forcings

Abstract As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of intermodel differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere, leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and better quantifications are needed to improve precipitation predictions. Here, the authors introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve understanding of the causes of the present diversity in future climate projections.

scholarly journals Temperature increase of 21st century mitigation scenarios

2008 ◽  
Vol 105 (40) ◽  
pp. 15258-15262 ◽  
Author(s):  
D. P. Van Vuuren ◽  
M. Meinshausen ◽  
G.-K. Plattner ◽  
F. Joos ◽  
K. M. Strassmann ◽  
...  
Keyword(s):  
21St Century ◽  
Temperature Increase ◽  
Radiative Forcing ◽  
Climate Models ◽  
Full Range ◽  
Climate System ◽  
Mitigation Scenarios ◽  
Climate Policies ◽  
Wide Range ◽  
The Impact

Estimates of 21st Century global-mean surface temperature increase have generally been based on scenarios that do not include climate policies. Newly developed multigas mitigation scenarios, based on a wide range of modeling approaches and socioeconomic assumptions, now allow the assessment of possible impacts of climate policies on projected warming ranges. This article assesses the atmospheric CO2 concentrations, radiative forcing, and temperature increase for these new scenarios using two reduced-complexity climate models. These scenarios result in temperature increase of 0.5–4.4°C over 1990 levels or 0.3–3.4°C less than the no-policy cases. The range results from differences in the assumed stringency of climate policy and uncertainty in our understanding of the climate system. Notably, an average minimum warming of ≈1.4°C (with a full range of 0.5–2.8°C) remains for even the most stringent stabilization scenarios analyzed here. This value is substantially above previously estimated committed warming based on climate system inertia alone. The results show that, although ambitious mitigation efforts can significantly reduce global warming, adaptation measures will be needed in addition to mitigation to reduce the impact of the residual warming.

scholarly journals HIRM v1.0: A hybrid impulse response model for climate modeling and uncertainty analyses

2020 ◽  
Author(s):  
Kalyn Dorheim ◽  
Steven Smith ◽  
Ben Bond-Lamberty
Keyword(s):  
Impulse Response ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Computational Cost ◽  
Global Temperature ◽  
Impulse Response Functions ◽  
Response Model ◽  
Modeling Framework ◽  
Uncertainty Analyses

Abstract. Simple climate models (SCMs) are frequently used in research and decision-making communities because of their flexibility, tractability, and low computational cost. SCMs can be idealized, flexibly representing major climate dynamics as impulse response functions, or process-based, using explicit equations to model possibly nonlinear climate and earth system dynamics. Each of these approaches has strengths and limitations. Here we present and test a hybrid impulse response modeling framework (HIRM) that combines the strengths of process-based SCMs in an idealized impulse response model, with HIRM’s input derived from the output of a process-based model. This structure allows it to capture the crucial nonlinear dynamics frequently encountered in going from greenhouse gas emissions to atmospheric concentration to radiative forcing to climate change. As a test, the HIRM framework was configured to emulate total temperature of the simple climate model Hector 2.0 under the four Representative Concentration Pathways and the temperature response of an abrupt four times CO2 concentration step. HIRM was able to reproduce near-term and long-term Hector global temperature with a high degree of fidelity. Additionally, we conducted two case studies to demonstrate potential applications for this hybrid model: examining the effect of aerosol forcing uncertainty on global temperature, and incorporating more process-based representations of black carbon into a SCM. The open-source HIRM framework has a range of applications including complex climate model emulation, uncertainty analyses of radiative forcing, attribution studies, and climate model development.

scholarly journals A nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS)

2001 ◽  
Vol 18 (3-4) ◽  
pp. 189-202 ◽  
Author(s):  
G. Hooss ◽  
R. Voss ◽  
K. Hasselmann ◽  
E. Maier-Reimer ◽  
F. Joos
Keyword(s):  
Carbon Cycle ◽  
Impulse Response ◽  
Climate System ◽  
Response Model

Numerical aspects of applying the fluctuation dissipation theorem to study climate system sensitivity to external forcings

2016 ◽  
Vol 31 (6) ◽  
Author(s):  
Andrey Gritsun ◽  
Grant Branstator
Keyword(s):  
Climate Models ◽  
Difficult Problem ◽  
Climate System ◽  
System Response ◽  
Climate System Model ◽  
System Sensitivity ◽  
The North ◽  
Fluctuation Dissipation ◽  
Fluctuation Dissipation Theorem ◽  
External Forcings

AbstractThe fluctuation dissipation theorem (FDT), a classical result coming from statistical mechanics, suggests that, under certain conditions, the system response to external forcing can be obtained using the statistics of natural fluctuation of the system. The application of the FDT to the most sophisticated climate models and the real climate system represents a difficult problem due to the huge dimensionality of these systems and the lack of the data available for proper sampling of the system natural variability. As a consequence, one has to use some regularization procedures constraining the form of permitted perturbations. Naturally, the skill of the FDT depends on the type and parameters of the regularization procedure. In the present paper we apply FDT to predict the response of a recent version of the NCAR climate system model (CCSM4) to salinity and temperature forcing anomalies in the North Atlantic. We study the sensitivity of our results to the amount of available data and to key parameters used in our numerical algorithm.

scholarly journals HIRM v1.0: a hybrid impulse response model for climate modeling and uncertainty analyses

2021 ◽  
Vol 14 (1) ◽  
pp. 365-375
Author(s):  
Kalyn Dorheim ◽  
Steven J. Smith ◽  
Ben Bond-Lamberty
Keyword(s):  
Impulse Response ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Computational Cost ◽  
Global Temperature ◽  
Impulse Response Functions ◽  
Response Model ◽  
Modeling Framework ◽  
Uncertainty Analyses

Abstract. Simple climate models (SCMs) are frequently used in research and decision-making communities because of their flexibility, tractability, and low computational cost. SCMs can be idealized, flexibly representing major climate dynamics as impulse response functions, or process-based, using explicit equations to model possibly nonlinear climate and Earth system dynamics. Each of these approaches has strengths and limitations. Here we present and test a hybrid impulse response modeling framework (HIRM) that combines the strengths of process-based SCMs in an idealized impulse response model, with HIRM's input derived from the output of a process-based model. This structure enables the model to capture some of the major nonlinear dynamics that occur in complex climate models as greenhouse gas emissions transform to atmospheric concentration to radiative forcing to climate change. As a test, the HIRM framework was configured to emulate the total temperature of the simple climate model Hector 2.0 under the four Representative Concentration Pathways and the temperature response of an abrupt 4 times CO2 concentration step. HIRM was able to reproduce near-term and long-term Hector global temperature with a high degree of fidelity. Additionally, we conducted two case studies to demonstrate potential applications for this hybrid model: examining the effect of aerosol forcing uncertainty on global temperature and incorporating more process-based representations of black carbon into a SCM. The open-source HIRM framework has a range of applications including complex climate model emulation, uncertainty analyses of radiative forcing, attribution studies, and climate model development.

scholarly journals Extreme sensitivity and climate tipping points

2020 ◽  
Author(s):  
Anna von der Heydt ◽  
Peter Ashwin
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Tipping Point ◽  
Climate System ◽  
Tipping Points ◽  
Equilibrium Climate Sensitivity ◽  
Future Global Warming ◽  
Wide Range ◽  
Extreme Sensitivity ◽  
Global Mean

<p>The equilibrium climate sensitivity (ECS) is widely used as a measure for possible future global warming. It has been determined from a wide range of climate models, observations and palaeoclimate records, however, it still remains relatively unconstrained. In particular, large values of warming as a consequence of atmospheric greenhouse gas increase cannot be excluded, with some of the most recent state-of-the-art climate models (CMIP6) supporting (much) more warming than previous generations of climate models. Moreover, a number of tipping elements have been identified within the climate system, some of which may affect the global mean temperature. Therefore, it is interesting to explore how the climate systems response (e.g. ECS) behaves when the system is close to a tipping point. <br>A climate state close to a tipping point will have a degenerate linear response to perturbations, which can be associated with extreme values of the equilibrium climate sensitivity (ECS). In this talk we contrast linearized ('instantaneous') with fully nonlinear geometric ('two-point') notions of ECS, in both presence and absence of tipping points. For a stochastic energy balance model of the global mean surface temperature with two stable regimes, we confirm that tipping events cause the appearance of extremes in both notions of ECS. Moreover, multiple regimes with different mean sensitivities are visible in the two-point ECS. We confirm some of our findings in a physics-based multi-box model of the climate system.</p><p><strong>Reference</strong><br>P. Ashwin and A. S. von der Heydt (2019), Extreme Sensitivity and Climate Tipping Points, J. Stat. Phys.  <strong>370</strong>, 1166–24. http://doi.org/10.1007/s10955-019-02425-x.</p>

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