scholarly journals A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the community atmosphere model CAM5

2013 ◽  
Vol 13 (21) ◽  
pp. 10969-10987 ◽  
Author(s):  
C. Zhao ◽  
X. Liu ◽  
Y. Qian ◽  
J. Yoon ◽  
Z. Hou ◽  
...  
Keyword(s):  
Regional Scale ◽  
Cloud Microphysics ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Dimensional Parameter ◽  
Individual Parameter ◽  
Radiative Fluxes ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Global Mean

Abstract. In this study, we investigated the sensitivity of net radiative fluxes (FNET) at the top of atmosphere (TOA) to 16 selected uncertain parameters mainly related to the cloud microphysics and aerosol schemes in the Community Atmosphere Model version 5 (CAM5). We adopted a quasi-Monte Carlo (QMC) sampling approach to effectively explore the high-dimensional parameter space. The output response variables (e.g., FNET) are simulated using CAM5 for each parameter set, and then evaluated using the generalized linear model analysis. In response to the perturbations of these 16 parameters, the CAM5-simulated global annual mean FNET ranges from −9.8 to 3.5 W m−2 compared to 1.9 W m−2 with the default parameter values. Variance-based sensitivity analysis is conducted to show the relative contributions of individual parameter perturbations to the global FNET variance. The results indicate that the changes in the global mean FNET are dominated by changes in net cloud forcing (CF) within the parameter ranges being investigated. The threshold size parameter related to auto-conversion of cloud ice to snow is identified as one of the most influential parameters for FNET in CAM5 simulations. The strong heterogeneous geographic distribution of FNET variance shows that parameters have a clear localized effect over regions where they are acting. However, some parameters also have non-local impacts on FNET variance. Although external factors, such as perturbations of anthropogenic and natural emissions, largely affect FNET variance at the regional scale, their impact is weaker than that of model internal parameters in terms of simulating global mean FNET. The interactions among the 16 selected parameters contribute a relatively small portion to the total FNET variance over most regions of the globe. This study helps us better understand the parameter uncertainties in the CAM5 model, and thus provides information for further calibrating uncertain model parameters with the largest sensitivity.

scholarly journals A sensitivity study of radiative fluxes at the top of atmosphere to cloud-microphysics and aerosol parameters in the Community Atmosphere Model CAM5

2013 ◽  
Vol 13 (5) ◽  
pp. 12135-12176 ◽  
Author(s):  
C. Zhao ◽  
X. Liu ◽  
Y. Qian ◽  
J. Yoon ◽  
Z. Hou ◽  
...  
Keyword(s):  
Regional Scale ◽  
Cloud Microphysics ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Dimensional Parameter ◽  
Individual Parameter ◽  
Radiative Fluxes ◽  
Community Atmosphere Model ◽  
Atmosphere Model ◽  
Global Mean

Abstract. In this study, we investigated the sensitivity of net radiative fluxes (FNET) at the top of atmosphere (TOA) to 16 selected uncertain parameters mainly related to the cloud microphysics and aerosol schemes in the Community Atmosphere Model version 5 (CAM5). We adopted a quasi-Monte Carlo (QMC) sampling approach to effectively explore the high dimensional parameter space. The output response variables (e.g., FNET) were simulated using CAM5 for each parameter set, and then evaluated using the generalized linear model analysis. In response to the perturbations of these 16 parameters, the CAM5-simulated global annual mean FNET ranges from −9.8 to 3.5 W m−2 compared to the CAM5-simulated FNET of 1.9 W m−2 with the default parameter values. Variance-based sensitivity analysis was conducted to show the relative contributions of individual parameter perturbation to the global FNET variance. The results indicate that the changes in the global mean FNET are dominated by those of net cloud forcing (CF) within the parameter ranges being investigated. The threshold size parameter related to auto-conversion of cloud ice to snow is identified as one of the most influential parameters for FNET in CAM5 simulations. The strong heterogeneous geographic distribution of FNET variance shows parameters have a clear localized effect over regions where they are acting. However, some parameters also have non-local impacts on FNET variance. Although external factors, such as perturbations of anthropogenic and natural emissions, largely affect FNET variance at the regional scale, their impact is weaker than that of model internal parameters in terms of simulating global mean FNET. The interactions among the 16 selected parameters contribute a relatively small portion to the total FNET variance over most regions of the globe. This study helps us better understand the parameter uncertainties in the CAM5 model, and thus provides information for further calibrating uncertain model parameters with the largest sensitivity.

scholarly journals Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

2018 ◽  
Vol 18 (13) ◽  
pp. 9975-10006 ◽  
Author(s):  
Leighton A. Regayre ◽  
Jill S. Johnson ◽  
Masaru Yoshioka ◽  
Kirsty J. Pringle ◽  
David M. H. Sexton ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Radiative Forcing ◽  
Computational Cost ◽  
Short Wave ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Large Set ◽  
Individual Parameter ◽  
Cloud Water Content ◽  
Atmosphere Model

Abstract. Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol–climate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m−2. This suggests the strongest forcings (below around −2.4 W m−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

Initial evaluation of a simple coupled surface and ground water hydrological model to assess sustainable ground water abstractions at the regional scale

2009 ◽  
Vol 41 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Denis A. Hughes ◽  
Evison Kapangaziwiri ◽  
Kathleen Baker
Keyword(s):  
Ground Water ◽  
Regional Scale ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Ungauged Catchments ◽  
Model Configuration ◽  
Data Deficient ◽  
Rainfall Runoff Model ◽  
Available Information ◽  
Runoff Model

Additional surface–ground water interaction routines were recently added to the Pitman monthly rainfall–runoff model, widely used in South Africa for quantifying water resources in ungauged catchments. Some evaluations of the model have demonstrated that it can realistically simulate interactions between surface and ground water at catchment scales of approximately 100 to 5,000 km2. The model allows ground water abstractions to be simulated, but no reported evaluations of this component are available. This study uses the model to estimate sustainable abstraction volumes in a semi-arid catchment and includes an assessment of model parameter uncertainties. In recognition of potential spatial scale issues related to the model structure an alternative model configuration, based on splitting the total catchment into recharge and abstraction sub-catchments, was also tested. While the results appear to be conceptually appropriate, there is insufficient available information to quantitatively confirm the model parameters and results. The same would apply regardless of the type of model being applied in such a data-deficient area. Additional geo-hydrological information is required to resolve the model uncertainties and improve the parameter estimation process. This pilot study has highlighted the type of information required, but further work is needed to identify how best to obtain that information.

scholarly journals The Ocean–Land–Atmosphere Model: Optimization and Evaluation of Simulated Radiative Fluxes and Precipitation

2010 ◽  
Vol 138 (5) ◽  
pp. 1923-1939 ◽  
Author(s):  
David Medvigy ◽  
Robert L. Walko ◽  
Martin J. Otte ◽  
Roni Avissar
Keyword(s):  
Large Scale ◽  
Likelihood Function ◽  
Droplet Diameter ◽  
Radiant Energy ◽  
Diameter Distribution ◽  
Precipitation Pattern ◽  
Parameter Uncertainties ◽  
Radiative Fluxes ◽  
Atmosphere Model ◽  
The Impact

Abstract This work continues the presentation and evaluation of the Ocean–Land–Atmosphere Model (OLAM), focusing on the model’s ability to represent radiation and precipitation. OLAM is a new, state-of-the-art earth system model, capable of user-specified grid resolution and local mesh refinement. An objective optimization of the microphysics parameterization is carried out. Data products from the Clouds and the Earth’s Radiant Energy System (CERES) and the Global Precipitation Climatology Project (GPCP) are used to construct a maximum likelihood function, and thousands of simulations using different values for key parameters are carried out. Shortwave fluxes are found to be highly sensitive to both the density of cloud droplets and the assumed shape of the cloud droplet diameter distribution function. Because there is considerable uncertainty in which values for these parameters to use in climate models, they are targeted as the tunable parameters of the objective optimization procedure, which identified high-likelihood volumes of parameter space as well as parameter uncertainties and covariances. Once optimized, the model closely matches observed large-scale radiative fluxes and precipitation. The impact of model resolution is also tested. At finer characteristic length scales (CLS), smaller-scale features such as the ITCZ are better resolved. It is also found that the Amazon was much better simulated at 100- than 200-km CLS. Furthermore, a simulation using OLAM’s variable resolution functionality to cover South America with 100-km CLS and the rest of the world with 200-km CLS generates a precipitation pattern in the Amazon similar to the global 100-km CLS run.

scholarly journals DeepMIP: Model intercomparison of early Eocene climatic optimum (EECO) large-scale climate features and comparison with proxy data

2021 ◽  
Author(s):  
Dan Lunt ◽  
Keyword(s):  
Boundary Conditions ◽  
Air Temperature ◽  
Ocean Circulation ◽  
Large Scale ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Surface Air Temperature ◽  
Model Parameters ◽  
Global Mean

<div> <div> <div>We present results from an ensemble of eight climate models, each of which has carried out simulations of theearly Eocene climate optimum (EECO, ∼50 million years ago). These simulations have been carried out in the framework of DeepMIP (www.deepmip.org), and as such all models have been configured with the same paleogeographic and vegetation boundary conditions. The results indicate that these non-CO<sub>2</sub> boundary conditions contribute between 3 and 5<sup>o</sup>C to Eocene warmth. Compared to results from previous studies, the DeepMIP simulations show in general reduced spread of global mean surface temperature response across the ensemble for a given atmospheric CO<sub>2</sub> concentration, and an increased climate sensitivity on average. An energy balance analysis of the model ensemble indicates that global mean warming in the Eocene compared with preindustrial arises mostly from decreases in emissivity due to the elevated CO<sub>2</sub> (and associated water vapour and long-wave cloud feedbacks), whereas in terms of the meridional temperature gradient, the reduction in the Eocene is primarily due to emissivity and albedo changes due to the non-CO<sub>2</sub> boundary conditions (i.e. removal of the Antarctic ice sheet and changes in vegetation). Three of the models (CESM, GFDL, and NorESM) show results that are consistent with the proxies in terms of global mean temperature, meridional SST gradient, and CO<sub>2</sub>, without prescribing changes to model parameters. In addition, many of the models agree well with the first-order spatial patterns in the SST proxies. However, at a more regional scale the models lack skill. In particular, in the southwest Pacific, the modelled anomalies are substantially less than indicated by the proxies; here, modelled continental surface air temperature anomalies are more consistent with surface air temperature proxies, implying a possible inconsistency between marine and terrestrial temperatures in either the proxiesor models in this region. Our aim is that the documentation of the large scale features and model-data comparison presented herein will pave the way to further studies that explore aspects of the model simulations in more detail, for example the ocean circulation, hydrological cycle, and modes of variability; and encourage sensitivity studies to aspects such as paleogeography, orbital configuration, and aerosols</div> </div> </div>

scholarly journals Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

2018 ◽  
Author(s):  
Leighton Regayre ◽  
Jill Johnson ◽  
Masaru Yoshioka ◽  
Kirsty Pringle ◽  
David Sexton ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Radiative Forcing ◽  
Computational Cost ◽  
Short Wave ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Large Set ◽  
Individual Parameter ◽  
Cloud Water Content ◽  
Atmosphere Model

Abstract. Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes, rapid adjustments in clouds, water vapour and temperature, and cause an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol-climate model variants that span the model uncertainty due to twenty-seven parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four of the atmospheric and aerosol parameters that cause uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations) also account for around 60% of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 Wm−2 to −2.37 Wm−2. This suggests the strongest forcings (below around −2.4 Wm−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is an order of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

Model-Specific Radiative Kernels for Calculating Cloud and Noncloud Climate Feedbacks

2012 ◽  
Vol 25 (21) ◽  
pp. 7607-7624 ◽  
Author(s):  
Benjamin M. Sanderson ◽  
Karen M. Shell
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
General Circulation Models ◽  
Climate Feedbacks ◽  
Radiative Fluxes ◽  
Community Atmosphere Model ◽  
Radiative Feedbacks ◽  
Successive Generations ◽  
The Tropics ◽  
Global Mean

Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates. This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.

scholarly journals Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1

2021 ◽  
Author(s):  
Po-Lun Ma ◽  
Bryce E. Harrop ◽  
Vincent E. Larson ◽  
Richard Neale ◽  
Andrew Gettelman ◽  
...  
Keyword(s):  
Climate Change ◽  
Surface Temperature ◽  
Cloud Microphysics ◽  
Future Climate ◽  
Future Climate Change ◽  
Physical Mechanisms ◽  
The Past ◽  
Model Version ◽  
Atmosphere Model ◽  
Global Mean

Abstract. Realistic simulation of the Earth’s mean state climate remains a major challenge and yet it is crucial for predicting the climate system in transition. Deficiencies in models’ process representations, propagation of errors from one process to another, and associated compensating errors can often confound the interpretation and improvement of model simulations. These errors and biases can also lead to unrealistic climate projections as well as incorrect attribution of the physical mechanisms governing the past and future climate change. Here we show that a significantly improved global atmospheric simulation can be achieved by focusing on the realism of process assumptions in cloud calibration and subgrid effects using the Energy Exascale Earth System Model (E3SM) Atmosphere Model version 1 (EAMv1). The calibration of clouds and subgrid effects informed by our understanding of physical mechanisms leads to significant improvements in clouds and precipitation climatology, reducing common and longstanding biases across cloud regimes in the model. The improved cloud fidelity in turn reduces biases in other aspects of the system. Furthermore, even though the recalibration does not change the global mean aerosol and total anthropogenic effective radiative forcings (ERFs), the sensitivity of clouds, precipitation, and surface temperature to aerosol perturbations is significantly reduced. This suggests that it is possible to achieve improvements to the historical evolution of surface temperature over EAMv1 and that precise knowledge of global mean ERFs is not enough to constrain historical or future climate change. Cloud feedbacks are also significantly reduced in the recalibrated model, suggesting that there would be a lower climate sensitivity when running as part of the fully coupled E3SM. This study also compares results from incremental changes to cloud microphysics, turbulent mixing, deep convection, and subgrid effects to understand how assumptions in the representation of these processes affect different aspects of the simulated atmosphere as well as its response to forcings. We conclude that the spectral composition and geographical distribution of the ERFs and cloud feedback as well as the fidelity of the simulated base climate state are important for constraining the climate in the past and future.

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