scholarly journals Technical Note: Simultaneous fully dynamic characterization of multiple input-output relationships in climate models

2016 ◽  
Author(s):  
Ben Kravitz ◽  
Douglas G. MacMartin ◽  
Philip J. Rasch ◽  
Hailong Wang
Keyword(s):  
Steady State ◽  
Climate Models ◽  
Climate Model ◽  
Climate Science ◽  
Technical Note ◽  
Input Output ◽  
Response Characteristics ◽  
Input Field ◽  
Low Cloud

Abstract. We introduce system identification techniques to climate science wherein multiple dynamic input-output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to those of step-change simulations, but in this new method, the responses for 22 regions can be characterized simultaneously. Furthermore, we can estimate the timescale over which the steady state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.

scholarly journals Technical note: Simultaneous fully dynamic characterization of multiple input–output relationships in climate models

2017 ◽  
Vol 17 (4) ◽  
pp. 2525-2541 ◽  
Author(s):  
Ben Kravitz ◽  
Douglas G. MacMartin ◽  
Philip J. Rasch ◽  
Hailong Wang
Keyword(s):  
Steady State ◽  
Climate Models ◽  
Climate Model ◽  
Climate Science ◽  
Technical Note ◽  
Input Output ◽  
Response Characteristics ◽  
Input Field ◽  
Low Cloud

Abstract. We introduce system identification techniques to climate science wherein multiple dynamic input–output relationships can be simultaneously characterized in a single simulation. This method, involving multiple small perturbations (in space and time) of an input field while monitoring output fields to quantify responses, allows for identification of different timescales of climate response to forcing without substantially pushing the climate far away from a steady state. We use this technique to determine the steady-state responses of low cloud fraction and latent heat flux to heating perturbations over 22 regions spanning Earth's oceans. We show that the response characteristics are similar to those of step-change simulations, but in this new method the responses for 22 regions can be characterized simultaneously. Furthermore, we can estimate the timescale over which the steady-state response emerges. The proposed methodology could be useful for a wide variety of purposes in climate science, including characterization of teleconnections and uncertainty quantification to identify the effects of climate model tuning parameters.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Systematic Climate Model Rainfall Biases over Southern Africa: Links to Moisture Circulation and Topography

2018 ◽  
Vol 31 (18) ◽  
pp. 7533-7548 ◽  
Author(s):  
C. Munday ◽  
R. Washington
Keyword(s):  
Southern Africa ◽  
Climate Models ◽  
Climate Model ◽  
Regional Scale ◽  
Model Development ◽  
Coupled Model ◽  
Climate Science ◽  
Summer Rainfall ◽  
Southwest Indian Ocean ◽  
Low Level

An important challenge for climate science is to understand the regional circulation and rainfall response to global warming. Unfortunately, the climate models used to project future changes struggle to represent present-day rainfall and circulation, especially at a regional scale. This is the case in southern Africa, where models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) overestimate summer rainfall by as much as 300% compared to observations and tend to underestimate rainfall in Madagascar and the southwest Indian Ocean. In this paper, we explore the climate processes associated with the rainfall bias, with the aim of assessing the reliability of the CMIP5 ensemble and highlighting important areas for model development. We find that the high precipitation rates in models that are wet over southern Africa are associated with an anomalous northeasterly moisture transport (~10–30 g kg−1 s−1) that penetrates across the high topography of Tanzania and Malawi and into subtropical southern Africa. This transport occurs in preference to a southeasterly recurvature toward Madagascar that is seen in drier models and reanalysis data. We demonstrate that topographically related model biases in low-level flow are important for explaining the intermodel spread in rainfall; wetter models have a reduced tendency to block the oncoming northeasterly flow compared to dry models. The differences in low-level flow among models are related to upstream wind speed and model representation of topography, both of which should be foci for model development.

scholarly journals Evaluating Models' Response Of Tropical Low Clouds to SST Forcings Using CALIPSO Observations

2018 ◽  
Author(s):  
Gregory Cesana ◽  
Anthony D. Del Genio ◽  
Andrew S. Ackerman ◽  
Maxwell Kelley ◽  
Gregory Elsaesser ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Vertical Structure ◽  
Climate Models ◽  
Climate Model ◽  
Cloud Fraction ◽  
Surface Warming ◽  
Cloud Radiative Effect ◽  
Low Cloud ◽  
Moist Processes ◽  
Low Cloud Cover

Abstract. Recent studies have shown that in response to a surface warming, the marine tropical low-cloud cover (LCC) as observed by passive sensor satellites substantially decreases, therefore generating a smaller negative value of the top-of-the-atmosphere cloud radiative effect (CRE). Here we study the LCC and CRE interannual changes in response to sea surface temperature (SST) forcings in the GISS Model E2 climate model, a developmental version of the GISS Model E3 climate model, and in 12 other climate models, as a function of their ability to represent the vertical structure of the cloud response to SST change against 10 years of CALIPSO observations. The more realistic models (those that satisfy the observational constraint) capture the observed interannual LCC change quite well (ΔLCC/ΔSST = −3.49 ± 1.01 % K−1 vs. ΔLCC/ΔSSTobs = −3.59 ± 0.28 % K−1) while the others largely underestimate it (ΔLCC/ΔSST = −1.32 ± 1.28 % K−1). Consequently, the more realistic models simulate more positive shortwave feedback (ΔCRE/ΔSST = 2.60 ± 1.13 W m−2 K−1) than the less realistic models (ΔCRE/ΔSST = 0.87 ± 2.63 W m−2 K−1), in better agreement with the observations (ΔCRE/ΔSSTobs = 3.05 ± 0.28 W m−2 K−1), although slightly underestimated. The ability of the models to represent moist processes within the planetary boundary layer and produce persistent stratocumulus decks appears crucial to replicating the observed relationship between clouds, radiation and surface temperature. This relationship is different depending on the type of low cloud in the observations. Over stratocumulus regions, cloud top height increases slightly with SST, accompanied by a large decrease of cloud fraction, whereas over trade cumulus regions, cloud fraction decreases everywhere, to a smaller extent.

scholarly journals Technical Note: A simple procedure for removing temporal discontinuities in ERA-Interim upper stratospheric temperatures for use in nudged chemistry-climate model simulations

2014 ◽  
Vol 14 (3) ◽  
pp. 1547-1555 ◽  
Author(s):  
C. McLandress ◽  
D. A. Plummer ◽  
T. G. Shepherd
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Middle Atmosphere ◽  
Simple Procedure ◽  
Stratospheric Ozone ◽  
Technical Note ◽  
Strong Temperature Dependence ◽  
Transient Effects ◽  
Climate Model Simulations ◽  
The Times

Abstract. This note describes a simple procedure for removing unphysical temporal discontinuities in ERA-Interim upper stratospheric global mean temperatures in March 1985 and August 1998 that have arisen due to changes in satellite radiance data used in the assimilation. The derived temperature adjustments (offsets) are suitable for use in stratosphere-resolving chemistry-climate models that are nudged (relaxed) to ERA-Interim winds and temperatures. Simulations using a nudged version of the Canadian Middle Atmosphere Model (CMAM) show that the inclusion of the temperature adjustments produces temperature time series that are devoid of the large jumps in 1985 and 1998. Due to its strong temperature dependence, the simulated upper stratospheric ozone is also shown to vary smoothly in time, unlike in a nudged simulation without the adjustments where abrupt changes in ozone occur at the times of the temperature jumps. While the adjustments to the ERA-Interim temperatures remove significant artefacts in the nudged CMAM simulation, spurious transient effects that arise due to water vapour and persist for about 5 yr after the 1979 switch to ERA-Interim data are identified, underlining the need for caution when analysing trends in runs nudged to reanalyses.

scholarly journals Satellite Observations for CMIP5: The Genesis of Obs4MIPs

2014 ◽  
Vol 95 (9) ◽  
pp. 1329-1334 ◽  
Author(s):  
Joao Teixeira ◽  
Duane Waliser ◽  
Robert Ferraro ◽  
Peter Gleckler ◽  
Tsengdar Lee ◽  
...  
Keyword(s):  
Observational Data ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Climate Science ◽  
Satellite Observations ◽  
Model Intercomparison ◽  
Uncertainty Estimates ◽  
Science Community ◽  
Climate Model Simulations

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

scholarly journals A community diagnostic tool for Chemistry Climate Model Validation

2012 ◽  
Vol 5 (2) ◽  
pp. 1229-1261
Author(s):  
A. Gettelman ◽  
V. Eyring ◽  
C. Fischer ◽  
H. Shiona ◽  
I. Cionni ◽  
...  
Keyword(s):  
Model Validation ◽  
Climate Models ◽  
Performance Metrics ◽  
Climate Model ◽  
Model Development ◽  
Coupled Model ◽  
Technical Note ◽  
Complex Evaluation ◽  
System User ◽  
Intercomparison Project

Abstract. This technical note presents an overview of the Chemistry-Climate Model Validation Diagnostic (CCMVal-Diag) tool for model evaluation. The CCMVal-Diag tool is a flexible and extensible open source package that facilitates the complex evaluation of global models. Models can be compared to other models, ensemble members (simulations with the same model), and/or many types of observations. The tool can also compute quantitative performance metrics. The initial construction and application is to coupled Chemistry-Climate Models (CCMs) participating in CCMVal, but the evaluation of climate models that submitted output to the Coupled Model Intercomparison Project (CMIP) is also possible. The package has been used to assist with analysis of simulations for the 2010 WMO/UNEP Scientific Ozone Assessment and the SPARC Report on the Evaluation of CCMs. The CCMVal-Diag tool is described and examples of how it functions are presented, along with links to detailed descriptions, instructions and source code. The CCMVal-Diag tool is supporting model development as well as quantifying model improvements, both for different versions of individual models and for different generations of community-wide collections of models used in international assessments. The code allows further extensions by different users for different applications and types, e.g. to other components of the Earth System. User modifications are encouraged and easy to perform with a minimum of coding.

scholarly journals Microphysical process rates and global aerosol–cloud interactions

2013 ◽  
Vol 13 (19) ◽  
pp. 9855-9867 ◽  
Author(s):  
A. Gettelman ◽  
H. Morrison ◽  
C. R. Terai ◽  
R. Wood
Keyword(s):  
Steady State ◽  
Climate Models ◽  
Climate Model ◽  
State Model ◽  
Precipitation Process ◽  
Microphysical Process ◽  
Aerosol Cloud ◽  
Steady State Model ◽  
Aerosol Cloud Interactions ◽  
Rain Formation

Abstract. Cloud microphysical process rates control the amount of condensed water in clouds and impact the susceptibility of precipitation to cloud-drop number and aerosols. The relative importance of different microphysical processes in a climate model is analyzed, and the autoconversion and accretion processes are found to be critical to the condensate budget in most regions. A simple steady-state model of warm rain formation is used to illustrate that the diagnostic rain formulations typical of climate models may result in excessive contributions from autoconversion, compared to observations and large eddy simulation models with explicit bin-resolved microphysics and rain formation processes. The behavior does not appear to be caused by the bulk process rate formulations themselves, because the steady-state model with the same bulk accretion and autoconversion has reduced contributions from autoconversion. Sensitivity tests are conducted to analyze how perturbations to the precipitation microphysics for stratiform clouds impact process rates, precipitation susceptibility and aerosol–cloud interactions (ACI). With similar liquid water path, corrections for the diagnostic rain assumptions in the GCM based on the steady-state model to boost accretion indicate that the radiative effects of ACI may decrease by 20% in the GCM. Links between process rates, susceptibility and ACI are not always clear in the GCM. Better representation of the precipitation process, for example by prognosticating precipitation mass and number, may help better constrain these effects in global models with bulk microphysics schemes.

scholarly journals Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

2016 ◽  
Vol 9 (5) ◽  
pp. 1937-1958 ◽  
Author(s):  
Veronika Eyring ◽  
Sandrine Bony ◽  
Gerald A. Meehl ◽  
Catherine A. Senior ◽  
Bjorn Stevens ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Coupled Model ◽  
Research Programme ◽  
Climate Science ◽  
Future Climate ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.

scholarly journals A community diagnostic tool for chemistry climate model validation

2012 ◽  
Vol 5 (5) ◽  
pp. 1061-1073 ◽  
Author(s):  
A. Gettelman ◽  
V. Eyring ◽  
C. Fischer ◽  
H. Shiona ◽  
I. Cionni ◽  
...  
Keyword(s):  
Model Validation ◽  
Climate Models ◽  
Climate Model ◽  
Model Development ◽  
Coupled Model ◽  
Technical Note ◽  
The Earth ◽  
Complex Evaluation ◽  
System User ◽  
Intercomparison Project

Abstract. This technical note presents an overview of the Chemistry-Climate Model Validation Diagnostic (CCMVal-Diag) tool for model evaluation. The CCMVal-Diag tool is a flexible and extensible open source package that facilitates the complex evaluation of global models. Models can be compared to other models, ensemble members (simulations with the same model), and/or many types of observations. The initial construction and application is to coupled chemistry-climate models (CCMs) participating in CCMVal, but the evaluation of climate models that submitted output to the Coupled Model Intercomparison Project (CMIP) is also possible. The package has been used to assist with analysis of simulations for the 2010 WMO/UNEP Scientific Ozone Assessment and the SPARC Report on the Evaluation of CCMs. The CCMVal-Diag tool is described and examples of how it functions are presented, along with links to detailed descriptions, instructions and source code. The CCMVal-Diag tool supports model development as well as quantifies model changes, both for different versions of individual models and for different generations of community-wide collections of models used in international assessments. The code allows further extensions by different users for different applications and types, e.g. to other components of the Earth system. User modifications are encouraged and easy to perform with minimum coding.

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