scholarly journals Minimal CMIP Emulator (MCE v1.2): A new simplified method for probabilistic climate projections

2021 ◽  
Author(s):  
Junichi Tsutsui
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Forced Response ◽  
System Response ◽  
Model Parameters ◽  
Climate Projections ◽  
Impulse Response Functions ◽  
Model Intercomparison ◽  
Probabilistic Climate Projections

Abstract. Climate model emulators have a crucial role in assessing warming levels of many emission scenarios from probabilistic climate projections, based on new insights into Earth system response to CO2 and other forcing factors. This article describes one such tool, MCE, from model formulation to application examples associated with a recent model intercomparison study. The MCE is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters for probabilistic projections are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A part of the model parameters associated with CO2-induced warming have a covariance structure, as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). Although perturbed ensembles can cover the diversity of CMIP models effectively, they need to be constrained toward substantially lower climate sensitivity for the resulting historical warming to agree with the observed trends over recent decades. The model's simplicity and resulting successful calibration imply that a method with less complicated structures and fewer control parameters offers advantages when building reasonable perturbed ensembles in a transparent way. Experimental results for future scenarios show distinct differences between CMIP- and observation-consistent ensembles, suggesting that perturbed ensembles for scenario assessment need to be properly constrained with new insights into forced response over historical periods.

Probabilistic climate projections with Minimal CMIP Emulator (MCE)

2021 ◽  
Author(s):  
Junichi Tsutsui
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Model Parameters ◽  
Climate Projections ◽  
Second Phase ◽  
Impulse Response Functions ◽  
Model Intercomparison ◽  
Probabilistic Climate Projections ◽  
Intercomparison Project

<p>One of the key applications of simple climate models is probabilistic climate projections to assess a variety of emission scenarios in terms of their compatibility with global warming mitigation goals. The second phase of the Reduced Complexity Model Intercomparison Project (RCMIP) compares nine participating models for their probabilistic projection methods through scenario experiments, focusing on consistency with given constraints for climate indicators including radiative forcing, carbon budget, warming trends, and climate sensitivity. The MCE is one of the nine models, recently developed by the author, and has produced results that well match the ranges of the constraints. The model is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters are generated from statistical models and constrained with a Metropolis-Hastings independence sampler. A parameter subset associated with CO<sub>2</sub>-induced warming is assured to have a covariance structure as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). The model's simplicity and the successful results imply that a method with less complicated structures and fewer control parameters has an advantage when building reasonable perturbed ensembles in a transparent way despite less capacity to emulate detailed Earth system components. Experimental results for future scenarios show that the climate sensitivity of CMIP models is overestimated overall, suggesting that probabilistic climate projections need to be constrained with observed warming trends.</p>

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 Observational constraints on ozone radiative forcing from the Atmospheric Chemistry Climate Model Intercomparison Project (ACCMIP)

2012 ◽  
Vol 12 (9) ◽  
pp. 23603-23644 ◽  
Author(s):  
K. Bowman ◽  
D. Shindell ◽  
H. Worden ◽  
J. F. Lamarque ◽  
P. J. Young ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Longwave Radiation ◽  
Model Intercomparison ◽  
Ensemble Mean ◽  
Intercomparison Project

Abstract. We use simultaneous observations of ozone and outgoing longwave radiation (OLR) from the Tropospheric Emission Spectrometer (TES) to evaluate ozone distributions and radiative forcing simulated by a suite of chemistry-climate models that participated in the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP). The ensemble mean of ACCMIP models show a persistent but modest tropospheric ozone low bias (5–20 ppb) in the Southern Hemisphere (SH) and modest high bias (5–10 ppb) in the Northern Hemisphere (NH) relative to TES for 2005–2010. These biases lead to substantial differences in ozone instantaneous radiative forcing between TES and the ACCMIP simulations. Using TES instantaneous radiative kernels (IRK), we show that the ACCMIP ensemble mean has a low bias in the SH tropics of up to 100 m W m−2 locally and a global low bias of 35 ± 44 m W m−2 relative to TES. Combining ACCMIP preindustrial ozone and the TES present-day ozone, we calculate an observationally constrained estimate of tropospheric ozone radiative forcing (RF) of 399 ± 70 m W m−2, which is about 7% higher than using the ACCMIP models alone but with the same standard deviation (Stevenson et al., 2012). In addition, we explore an alternate approach to constraining radiative forcing estimates by choosing a subset of models that best match TES ozone, which leads to an ozone RF of 369 ± 42 m W m−2. This estimate is closer to the ACCMIP ensemble mean RF but about a 40% reduction in standard deviation. These results point towards a profitable direction of combining observations and chemistry-climate model simulations to reduce uncertainty in ozone radiative forcing.

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 Radiative forcing in the ACCMIP historical and future climate simulations

2013 ◽  
Vol 13 (6) ◽  
pp. 2939-2974 ◽  
Author(s):  
D. T. Shindell ◽  
J.-F. Lamarque ◽  
M. Schulz ◽  
M. Flanner ◽  
C. Jiao ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
The Arctic ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Early 21St Century ◽  
Best Estimate ◽  
Intercomparison Project

Abstract. The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) examined the short-lived drivers of climate change in current climate models. Here we evaluate the 10 ACCMIP models that included aerosols, 8 of which also participated in the Coupled Model Intercomparison Project phase 5 (CMIP5). The models reproduce present-day total aerosol optical depth (AOD) relatively well, though many are biased low. Contributions from individual aerosol components are quite different, however, and most models underestimate east Asian AOD. The models capture most 1980–2000 AOD trends well, but underpredict increases over the Yellow/Eastern Sea. They strongly underestimate absorbing AOD in many regions. We examine both the direct radiative forcing (RF) and the forcing including rapid adjustments (effective radiative forcing; ERF, including direct and indirect effects). The models' all-sky 1850 to 2000 global mean annual average total aerosol RF is (mean; range) −0.26 W m−2; −0.06 to −0.49 W m−2. Screening based on model skill in capturing observed AOD yields a best estimate of −0.42 W m−2; −0.33 to −0.50 W m−2, including adjustment for missing aerosol components in some models. Many ACCMIP and CMIP5 models appear to produce substantially smaller aerosol RF than this best estimate. Climate feedbacks contribute substantially (35 to −58%) to modeled historical aerosol RF. The 1850 to 2000 aerosol ERF is −1.17 W m−2; −0.71 to −1.44 W m−2. Thus adjustments, including clouds, typically cause greater forcing than direct RF. Despite this, the multi-model spread relative to the mean is typically the same for ERF as it is for RF, or even smaller, over areas with substantial forcing. The largest 1850 to 2000 negative aerosol RF and ERF values are over and near Europe, south and east Asia and North America. ERF, however, is positive over the Sahara, the Karakoram, high Southern latitudes and especially the Arctic. Global aerosol RF peaks in most models around 1980, declining thereafter with only weak sensitivity to the Representative Concentration Pathway (RCP). One model, however, projects approximately stable RF levels, while two show increasingly negative RF due to nitrate (not included in most models). Aerosol ERF, in contrast, becomes more negative during 1980 to 2000. During this period, increased Asian emissions appear to have a larger impact on aerosol ERF than European and North American decreases due to their being upwind of the large, relatively pristine Pacific Ocean. There is no clear relationship between historical aerosol ERF and climate sensitivity in the CMIP5 subset of ACCMIP models. In the ACCMIP/CMIP5 models, historical aerosol ERF of about −0.8 to −1.5 W m−2 is most consistent with observed historical warming. Aerosol ERF masks a large portion of greenhouse forcing during the late 20th and early 21st century at the global scale. Regionally, aerosol ERF is so large that net forcing is negative over most industrialized and biomass burning regions through 1980, but remains strongly negative only over east and southeast Asia by 2000. Net forcing is strongly positive by 1980 over most deserts, the Arctic, Australia, and most tropical oceans. Both the magnitude of and area covered by positive forcing expand steadily thereafter.

scholarly journals Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Seshagiri Rao Kolusu ◽  
Christian Siderius ◽  
Martin C. Todd ◽  
Ajay Bhave ◽  
Declan Conway ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Investment Decisions ◽  
Climate Impacts ◽  
Future Climate ◽  
Eastern Africa ◽  
Climate Projections ◽  
Systematic Effect ◽  
Risk Profiles ◽  
Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

A new ensemble-based statistical methodology to verify changes in weather and climate models

2021 ◽  
Author(s):  
Christian Zeman ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Numerical Models ◽  
Hypothesis Test ◽  
Model Development ◽  
Atmospheric Model ◽  
Statistical Hypothesis ◽  
Model Parameters ◽  
Rounding Errors ◽  
Weather And Climate

<p>Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather and climate prediction. As such, they are a constant subject to changes, thanks to advances in computer systems, numerical methods, and the ever increasing knowledge about the atmosphere of Earth. Many of the changes in today's models relate to seemingly unsuspicious modifications, associated with minor code rearrangements, changes in hardware infrastructure, or software upgrades. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere - any small change, even rounding errors, can have a big impact on individual simulations. Overall this represents a serious challenge to a consistent model development and maintenance framework.</p><p>Here we propose a new methodology for quantifying and verifying the impacts of minor atmospheric model changes, or its underlying hardware/software system, by using ensemble simulations in combination with a statistical hypothesis test. The methodology can assess effects of model changes on almost any output variable over time, and can also be used with different hypothesis tests.</p><p>We present first applications of the methodology with the regional weather and climate model COSMO. The changes considered include a major system upgrade of the supercomputer used, the change from double to single precision floating-point representation, changes in the update frequency of the lateral boundary conditions, and tiny changes to selected model parameters. While providing very robust results, the methodology also shows a large sensitivity to more significant model changes, making it a good candidate for an automated tool to guarantee model consistency in the development cycle.</p>

scholarly journals Atmospheric circulation and hydroclimate impacts of alternative warming scenarios for the Eocene

2017 ◽  
Vol 13 (8) ◽  
pp. 1037-1048 ◽  
Author(s):  
Henrik Carlson ◽  
Rodrigo Caballero
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Cloud Albedo ◽  
Cloud Properties ◽  
Atmospheric Co2 Concentrations ◽  
Global Mean

Abstract. Recent work in modelling the warm climates of the early Eocene shows that it is possible to obtain a reasonable global match between model surface temperature and proxy reconstructions, but only by using extremely high atmospheric CO2 concentrations or more modest CO2 levels complemented by a reduction in global cloud albedo. Understanding the mix of radiative forcing that gave rise to Eocene warmth has important implications for constraining Earth's climate sensitivity, but progress in this direction is hampered by the lack of direct proxy constraints on cloud properties. Here, we explore the potential for distinguishing among different radiative forcing scenarios via their impact on regional climate changes. We do this by comparing climate model simulations of two end-member scenarios: one in which the climate is warmed entirely by CO2 (which we refer to as the greenhouse gas (GHG) scenario) and another in which it is warmed entirely by reduced cloud albedo (which we refer to as the low CO2–thin clouds or LCTC scenario) . The two simulations have an almost identical global-mean surface temperature and equator-to-pole temperature difference, but the LCTC scenario has  ∼  11 % greater global-mean precipitation than the GHG scenario. The LCTC scenario also has cooler midlatitude continents and warmer oceans than the GHG scenario and a tropical climate which is significantly more El Niño-like. Extremely high warm-season temperatures in the subtropics are mitigated in the LCTC scenario, while cool-season temperatures are lower at all latitudes. These changes appear large enough to motivate further, more detailed study using other climate models and a more realistic set of modelling assumptions.

scholarly journals The Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP): Experimental design and forcing input data

2016 ◽  
Author(s):  
Davide Zanchettin ◽  
Myriam Khodri ◽  
Claudia Timmreck ◽  
Matthew Toohey ◽  
Anja Schmidt ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Initial Conditions ◽  
Volcanic Eruptions ◽  
Stratospheric Aerosol ◽  
Aerosol Layer ◽  
Model Intercomparison ◽  
Climatic Response ◽  
Volcanic Forcing ◽  
Intercomparison Project

Abstract. The enhancement of the stratospheric aerosol layer by volcanic eruptions induces a complex set of responses causing global and regional climate effects on a broad range of timescales. Uncertainties exist regarding the climatic response to strong volcanic forcing identified in coupled climate simulations that contributed to the fifth phase of the Climate Model Intercomparison Project (CMIP5). In order to better understand the sources of these model diversities, the model intercomparison project on the climate response to volcanic forcing (VolMIP) has defined a coordinated set of idealized volcanic perturbation experiments to be carried out in alignment with the CMIP6 protocol. VolMIP provides a common stratospheric aerosol dataset for each experiment to eliminate differences in the applied volcanic forcing, and defines a set of initial conditions to determine how internal climate variability contributes to determining the response. VolMIP will assess to what extent volcanically-forced responses of the coupled ocean-atmosphere system are robustly simulated by state-of-the-art coupled climate models and identify the causes that limit robust simulated behavior, especially differences in the treatment of physical processes. This paper illustrates the design of the idealized volcanic perturbation experiments in the VolMIP protocol and describes the common aerosol forcing input datasets to be used.

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