An Assessment of Climate Feedbacks in Observations and Climate Models Using Different Energy Balance Frameworks

2021 ◽  
pp. 1-30
Author(s):  
Li-Wei Chao ◽  
Andrew E. Dessler
Keyword(s):  
Energy Balance ◽  
Climate Models ◽  
Coupled Model ◽  
Structural Difference ◽  
Data Set ◽  
Pattern Effect ◽  
Alternative Framework ◽  
Wide Range ◽  
Intercomparison Project ◽  
Model Ensembles

AbstractThis study evaluates the performance of Coupled Model Intercomparison Project (CMIP) phase 5 and phase 6 models by comparing feedbacks in models to those inferred from observations. Overall, we find no systematic disagreements between the feedbacks in the model ensembles and feedbacks inferred from observations, although there is a wide range in the ability of individual models to reproduce the observations. In particular, 40 of 52 models have best estimates that fall within the uncertainty of the observed total feedback. We quantify two sources of uncertainty in the model ensembles: (1) the structural difference, due to the differences in model parameterizations, and (2) the unforced pattern effect, due to unforced variability, and find that both are important when comparing to an 18-year observational data set. We perform the comparison using two energy balance frameworks: the traditional energy balance framework, in which it is assumed that changes in energy balance are controlled by changes in global average surface temperatures, and an alternative framework that assumes the changes in energy balance are controlled by tropical atmospheric temperatures. We find that the alternative framework provides a more robust way of comparing the models to observations, with both smaller structural differences and smaller unforced pattern effect. However, when considering the relation of feedbacks in response to interannual variability and long-term warming, the traditional framework has advantages. There are no great differences between the CMIP5 and CMIP6 ensembles’ ability to reproduce the observed feedbacks.

scholarly journals On the Climate Sensitivity and Historical Warming Evolution in Recent Coupled Model Ensembles

2020 ◽  
Author(s):  
Clare Marie Flynn ◽  
Thorsten Mauritsen
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
Future Warming ◽  
Model Treatment ◽  
Systematic Shift ◽  
Model Ensembles

Abstract. The Earth's equilibrium climate sensitivity (ECS) to a doubling of atmospheric CO2, along with the transient 35 climate response (TCR) and greenhouse gas emissions pathways, determines the amount of future warming. Coupled climate models have in the past been important tools to estimate and understand ECS. ECS estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) models lies between 2.0 and 4.7 K (mean of 3.2 K), whereas in the latest CMIP6 the spread has increased: 1.8–5.5 K (mean of 3.7 K), with 5 out of 25 models exceeding 5 K. It is thus pertinent to understand the causes underlying this shift. Here we compare the CMIP5 and CMIP6 model ensembles, and find a systematic shift between CMIP eras to be unexplained as a process of random sampling from modeled forcing and feedback distributions. Instead, shortwave feedbacks shift towards more positive values, in particular over the Southern Ocean, driving the shift towards larger ECS values in many of the models. These results suggest that changes in model treatment of mixed-phase cloud processes and changes to Antarctic sea ice representation are likely causes of the shift towards larger ECS. Somewhat surprisingly, CMIP6 models exhibit less historical warming than CMIP5 models; the evolution of the warming suggests, however, that several of the models apply too strong aerosol cooling resulting in too weak mid 20th Century warming compared to the instrumental record.

scholarly journals On the climate sensitivity and historical warming evolution in recent coupled model ensembles

2020 ◽  
Vol 20 (13) ◽  
pp. 7829-7842 ◽  
Author(s):  
Clare Marie Flynn ◽  
Thorsten Mauritsen
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Transient Climate Response ◽  
Antarctic Sea Ice ◽  
Intercomparison Project ◽  
Future Warming ◽  
Model Treatment ◽  
Model Ensembles

Abstract. The Earth's equilibrium climate sensitivity (ECS) to a doubling of atmospheric CO2, along with the transient climate response (TCR) and greenhouse gas emissions pathways, determines the amount of future warming. Coupled climate models have in the past been important tools to estimate and understand ECS. ECS estimated from Coupled Model Intercomparison Project Phase 5 (CMIP5) models lies between 2.0 and 4.7 K (mean of 3.2 K), whereas in the latest CMIP6 the spread has increased to 1.8–5.5 K (mean of 3.7 K), with 5 out of 25 models exceeding 5 K. It is thus pertinent to understand the causes underlying this shift. Here we compare the CMIP5 and CMIP6 model ensembles and find a systematic shift between CMIP eras to be unexplained as a process of random sampling from modeled forcing and feedback distributions. Instead, shortwave feedbacks shift towards more positive values, in particular over the Southern Ocean, driving the shift towards larger ECS values in many of the models. These results suggest that changes in model treatment of mixed-phase cloud processes and changes to Antarctic sea ice representation are likely causes of the shift towards larger ECS. Somewhat surprisingly, CMIP6 models exhibit less historical warming than CMIP5 models, despite an increase in TCR between CMIP eras (mean TCR increased from 1.7 to 1.9 K). The evolution of the warming suggests, however, that several of the CMIP6 models apply too strong aerosol cooling, resulting in too weak mid-20th century warming compared to the instrumental record.

scholarly journals Tropospheric ozone in CCMI models and Gaussian emulation to understand biases in the SOCOLv3 chemistry-climate model

2018 ◽  
Author(s):  
Laura E. Revell ◽  
Andrea Stenke ◽  
Fiona Tummon ◽  
Aryeh Feinberg ◽  
Eugene Rozanov ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Tropospheric Ozone ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Gaussian Process Emulation ◽  
Intercomparison Project ◽  
Column Ozone

Abstract. Previous multi-model intercomparisons have shown that chemistry-climate models exhibit significant biases in tropospheric ozone compared with observations. We investigate annual-mean tropospheric column ozone in 15 models participating in the SPARC/IGAC (Stratosphere-troposphere Processes and their Role in Climate/International Global Atmospheric Chemistry) Chemistry-Climate Model Initiative (CCMI). These models exhibit a positive bias, on average, of up to 40–50 % in the Northern Hemisphere compared with observations derived from the Ozone Monitoring Instrument and Microwave Limb Sounder (OMI/MLS), and a negative bias of up to ~ 30 % in the Southern Hemisphere. SOCOLv3.0 (version 3 of the Solar-Climate Ozone Links CCM), which participated in CCMI, simulates global-mean tropospheric ozone columns of 40.2 DU – approximately 33 % larger than the CCMI multi-model mean. Here we introduce an updated version of SOCOLv3.0, SOCOLv3.1, which includes an improved treatment of ozone sink processes, and results in a reduction in the tropospheric column ozone bias of up to 8 DU, mostly due to the inclusion of N2O5 hydrolysis on tropospheric aerosols. As a result of these developments, tropospheric column ozone amounts simulated by SOCOLv3.1 are comparable with several other CCMI models. We apply Gaussian process emulation and sensitivity analysis to understand the remaining ozone bias in SOCOLv3.1. This shows that ozone precursors (nitrogen oxides (NOx, carbon monoxide, methane and other volatile organic compounds) are responsible for more than 90 % of the variance in tropospheric ozone. However, it may not be the emissions inventories themselves that result in the bias, but how the emissions are handled in SOCOLv3.1, and we discuss this in the wider context of the other CCMI models. Given that the emissions data set to be used for phase 6 of the Coupled Model Intercomparison Project includes approximately 20 % more NOx than the data set used for CCMI, further work is urgently needed to address the challenges of simulating sub-grid processes of importance to tropospheric ozone in the current generation of chemistry-climate models.

scholarly journals Future Changes in Simulated Evapotranspiration across Continental Africa Based on CMIP6 CNRM-CM6

2021 ◽  
Vol 18 (13) ◽  
pp. 6760
Author(s):  
Isaac Kwesi Nooni ◽  
Daniel Fiifi T. Hagan ◽  
Guojie Wang ◽  
Waheed Ullah ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Extreme Events ◽  
Coupled Model ◽  
Baseline Period ◽  
Model Intercomparison ◽  
Additional Information ◽  
Intercomparison Project ◽  
Key Drivers ◽  
Model Ensembles ◽  
Projected Changes ◽  
Near Future

The main goal of this study was to assess the interannual variations and spatial patterns of projected changes in simulated evapotranspiration (ET) in the 21st century over continental Africa based on the latest Shared Socioeconomic Pathways and the Representative Concentration Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) provided by the France Centre National de Recherches Météorologiques (CNRM-CM) model in the Sixth Phase of Coupled Model Intercomparison Project (CMIP6) framework. The projected spatial and temporal changes were computed for three time slices: 2020–2039 (near future), 2040–2069 (mid-century), and 2080–2099 (end-of-the-century), relative to the baseline period (1995–2014). The results show that the spatial pattern of the projected ET was not uniform and varied across the climate region and under the SSP-RCPs scenarios. Although the trends varied, they were statistically significant for all SSP-RCPs. The SSP5-8.5 and SSP3-7.0 projected higher ET seasonality than SSP1-2.6 and SSP2-4.5. In general, we suggest the need for modelers and forecasters to pay more attention to changes in the simulated ET and their impact on extreme events. The findings provide useful information for water resources managers to develop specific measures to mitigate extreme events in the regions most affected by possible changes in the region’s climate. However, readers are advised to treat the results with caution as they are based on a single GCM model. Further research on multi-model ensembles (as more models’ outputs become available) and possible key drivers may provide additional information on CMIP6 ET projections in the region.

scholarly journals Advances in Collaborative Documentation Support for CMIP6

2020 ◽  
Author(s):  
Charlotte Pascoe ◽  
David Hassell ◽  
Martina Stockhause ◽  
Mark Greenslade
Keyword(s):  
Science Communication ◽  
Scientific Community ◽  
Climate Models ◽  
Coupled Model ◽  
Automatic Generation ◽  
Earth System ◽  
The Earth ◽  
Data Citation ◽  
On Line ◽  
Intercomparison Project

<div>The Earth System Documentation (ES-DOC) project aims to nurture an ecosystem of tools & services in support of Earth System documentation creation, analysis and dissemination. Such an ecosystem enables the scientific community to better understand and utilise Earth system model data.</div><div>The ES-DOC infrastructure for the Coupled Model Intercomparison Project Phase 6 (CMIP6) modelling groups to describe their climate models and make the documentation available on-line has been available for 18 months, and more recently the automatic generation of documentation of every published simulation has meant that every CMIP6 dataset within the Earth System Grid Federation (ESGF) is now immediately connected to the ES-DOC description of the entire workflow that created it, via a “further info URL”.</div><div>The further info URL is a landing page from which all of the relevant CMIP6 documentation relevant to the data may be accessed, including experimental design, model formulation and ensemble description, as well as providing links to the data citation information.</div><div>These DOI landing pages are part of the Citation Service, provided by DKRZ. Data citation information is also available independently through the ESGF Search portal or in the DataCite search or Google’s dataset search. It provides users of CMIP6 data with the formal citation that should accompany any use of the datasets that comprise their analysis.</div><div>ES-DOC services and the Citation Service form a CMIP6 project  collaboration, and depend upon structured documentation provided by the scientific community. Structured scientific metadata has an important role in science communication, however it’s creation and collation exacts a cost in time, energy and attention.  We discuss progress towards a balance between the ease of information collection and the complexity of our information handling structures.</div><div> </div><div>CMIP6: https://pcmdi.llnl.gov/CMIP6/</div><div>ES-DOC: https://es-doc.org/</div><div>Further Info URL: https://es-doc.org/cmip6-ensembles-further-info-url</div><div> <p>Citation Service: http://cmip6cite.wdc-climate.de</p> </div>

scholarly journals Experimental protocol for sealevel projections from ISMIP6 standalone ice sheet models

2020 ◽  
Author(s):  
Sophie Nowicki ◽  
Antony J. Payne ◽  
Heiko Goelzer ◽  
Helene Seroussi ◽  
William H. Lipscomb ◽  
...  
Keyword(s):  
Sea Level ◽  
Climate Models ◽  
Ice Sheet ◽  
Coupled Model ◽  
Sea Level Change ◽  
Model Intercomparison ◽  
Level Change ◽  
Ice Sheet Modeling ◽  
Emissions Scenarios ◽  
Intercomparison Project

Abstract. Projection of the contribution of ice sheets to sea-level change as part of the Coupled Model Intercomparison Project – phase 6 (CMIP6) takes the form of simulations from coupled ice-sheet-climate models and standalone ice sheet models, overseen by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). This paper describes the experimental setup for process-based sea-level change projections to be performed with standalone Greenland and Antarctic ice sheet models in the context of ISMIP6. The ISMIP6 protocol relies on a suite of polar atmospheric and oceanic CMIP-based forcing for ice sheet models, in order to explore the uncertainty in projected sea-level change due to future emissions scenarios, CMIP models, ice sheet models, and parameterizations for ice-ocean interactions. We describe here the approach taken for defining the suite of ISMIP6 standalone ice sheet simulations, document the experimental framework and implementation, as well as present an overview of the ISMIP6 forcing to be used by participating ice sheet modeling groups.

scholarly journals Diagnosis of Multiyear Predictability on Continental Scales

2011 ◽  
Vol 24 (19) ◽  
pp. 5108-5124 ◽  
Author(s):  
Liwei Jia ◽  
Timothy DelSole
Keyword(s):  
Time Scales ◽  
Air Temperature ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Optimization Method ◽  
First Year ◽  
Temperature And Precipitation ◽  
Regional Prediction ◽  
Intercomparison Project

A new statistical optimization method is used to identify components of surface air temperature and precipitation on six continents that are predictable in multiple climate models on multiyear time scales. The components are identified from unforced “control runs” of the Coupled Model Intercomparison Project phase 3 dataset. The leading predictable components can be calculated in independent control runs with statistically significant skill for 3–6 yr for surface air temperature and 1–3 yr for precipitation, depending on the continent, using a linear regression model with global sea surface temperature (SST) as a predictor. Typically, lag-correlation maps reveal that the leading predictable components of surface air temperature are related to two types of SST patterns: persistent patterns near the continent itself and an oscillatory ENSO-like pattern. The only exception is Europe, which has no significant ENSO relation. The leading predictable components of precipitation are significantly correlated with an ENSO-like SST pattern. No multiyear predictability of land precipitation could be verified in Europe. The squared multiple correlations of surface air temperature and precipitation for nonzero lags on each continent are less than 0.4 in the first year, implying that less than 40% of variations of the leading predictable component can be predicted from global SST. The predictable components describe the spatial structures that can be predicted on multiyear time scales in the absence of anthropogenic and natural forcing, and thus provide a scientific rationale for regional prediction on multiyear time scales.

How to combine climate models and observations in event attribution?

2021 ◽  
Author(s):  
Yoann Robin ◽  
Aurélien Ribes
Keyword(s):  
Bayesian Method ◽  
Climate Models ◽  
Climate Model ◽  
Coupled Model ◽  
Model Data ◽  
Human Influence ◽  
Scale Parameters ◽  
Event Attribution ◽  
Intercomparison Project ◽  
Climate Model Simulations

<p>We describe a statistical method to derive event attribution diagnoses combining climate model simulations and observations. We fit nonstationary Generalized Extreme Value (GEV) distributions to extremely hot temperatures from an ensemble of Coupled Model Intercomparison Project phase 5 (CMIP)<br>models. In order to select a common statistical model, we discuss which GEV parameters have to be nonstationary and which do not. Our tests suggest that the location and scale parameters of GEV distributions should be considered nonstationary. Then, a multimodel distribution is constructed and constrained by observations using a Bayesian method. This new method is applied to the July 2019 French heatwave. Our results show that<br>both the probability and the intensity of that event have increased significantly in response to human influence.<br>Remarkably, we find that the heat wave considered might not have been possible without climate change. Our<br>results also suggest that combining model data with observations can improve the description of hot temperature<br>distribution.</p>

scholarly journals AerChemMIP: quantifying the effects of chemistry and aerosols in CMIP6

2017 ◽  
Vol 10 (2) ◽  
pp. 585-607 ◽  
Author(s):  
William J. Collins ◽  
Jean-François Lamarque ◽  
Michael Schulz ◽  
Olivier Boucher ◽  
Veronika Eyring ◽  
...  
Keyword(s):  
Air Quality ◽  
Atmospheric Chemistry ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Climate Impacts ◽  
Atmospheric Composition ◽  
Historical Period ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract. The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is endorsed by the Coupled-Model Intercomparison Project 6 (CMIP6) and is designed to quantify the climate and air quality impacts of aerosols and chemically reactive gases. These are specifically near-term climate forcers (NTCFs: methane, tropospheric ozone and aerosols, and their precursors), nitrous oxide and ozone-depleting halocarbons. The aim of AerChemMIP is to answer four scientific questions. 1. How have anthropogenic emissions contributed to global radiative forcing and affected regional climate over the historical period? 2. How might future policies (on climate, air quality and land use) affect the abundances of NTCFs and their climate impacts? 3.How do uncertainties in historical NTCF emissions affect radiative forcing estimates? 4. How important are climate feedbacks to natural NTCF emissions, atmospheric composition, and radiative effects? These questions will be addressed through targeted simulations with CMIP6 climate models that include an interactive representation of tropospheric aerosols and atmospheric chemistry. These simulations build on the CMIP6 Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, the CMIP6 historical simulations, and future projections performed elsewhere in CMIP6, allowing the contributions from aerosols and/or chemistry to be quantified. Specific diagnostics are requested as part of the CMIP6 data request to highlight the chemical composition of the atmosphere, to evaluate the performance of the models, and to understand differences in behaviour between them.

scholarly journals Evaluating multi-year, multi-site data on the energy balance closure of eddy-covariance flux measurements at cropland sites in southwestern Germany

2019 ◽  
Vol 16 (2) ◽  
pp. 521-540 ◽  
Author(s):  
Ravshan Eshonkulov ◽  
Arne Poyda ◽  
Joachim Ingwersen ◽  
Hans-Dieter Wizemann ◽  
Tobias K. D. Weber ◽  
...  
Keyword(s):  
Energy Balance ◽  
Eddy Covariance ◽  
Wind Direction ◽  
Climate Models ◽  
Global Climate Models ◽  
Energy Balance Closure ◽  
Prevailing Wind ◽  
Atmospheric Conditions ◽  
Prevailing Wind Direction ◽  
Wide Range

Abstract. The energy balance of eddy-covariance (EC) measurements is typically not closed, resulting in one of the main challenges in evaluating and interpreting EC flux data. Energy balance closure (EBC) is crucial for validating and improving regional and global climate models. To investigate the nature of the gap in EBC for agroecosystems, we analyzed EC measurements from two climatically contrasting regions (Kraichgau – KR – and Swabian Jura – SJ) in southwestern Germany. Data were taken at six fully equipped EC sites from 2010 to 2017. The gap in EBC was quantified by ordinary linear regression, relating the energy balance ratio (EBR), calculated as the quotient of turbulent fluxes and available energy, to the residual energy term. In order to examine potential reasons for differences in EBC, we compared the EBC under varying environmental conditions and investigated a wide range of possible controls. Overall, the variation in EBC was found to be higher during winter than summer. Moreover, we determined that the site had a statistically significant effect on EBC but no significant effect on either crop or region (KR vs SJ). The time-variable footprints of all EC stations were estimated based on data measured in 2015, complimented by micro-topographic analyses along the prevailing wind direction. The smallest mean annual energy balance gap was 17 % in KR and 13 % in SJ. Highest EBRs were mostly found for winds from the prevailing wind direction. The spread of EBRs distinctly narrowed under unstable atmospheric conditions, strong buoyancy, and high friction velocities. Smaller footprint areas led to better EBC due to increasing homogeneity. Flow distortions caused by the back head of the anemometer negatively affected EBC during corresponding wind conditions.

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