scholarly journals Drake Passage Oceanic pCO2: Evaluating CMIP5 Coupled Carbon–Climate Models Using in situ Observations

2014 ◽  
Vol 27 (1) ◽  
pp. 76-100 ◽  
Author(s):  
ChuanLi Jiang ◽  
Sarah T. Gille ◽  
Janet Sprintall ◽  
Colm Sweeney
Keyword(s):  
Seasonal Cycle ◽  
Climate Models ◽  
Dissolved Inorganic Carbon ◽  
Coupled Model ◽  
Co2 Flux ◽  
Drake Passage ◽  
Polar Front ◽  
Intercomparison Project ◽  
Opposing Effects ◽  
Mixed Layers

Abstract Surface water partial pressure of CO2 (pCO2) variations in Drake Passage are examined using decade-long underway shipboard measurements. North of the Polar Front (PF), the observed pCO2 shows a seasonal cycle that peaks annually in August and dissolved inorganic carbon (DIC)–forced variations are significant. Just south of the PF, pCO2 shows a small seasonal cycle that peaks annually in February, reflecting the opposing effects of changes in SST and DIC in the surface waters. At the PF, the wintertime pCO2 is nearly in equilibrium with the atmosphere, leading to a small sea-to-air CO2 flux. These observations are used to evaluate eight available Coupled Model Intercomparison Project, phase 5 (CMIP5), Earth system models (ESMs). Six ESMs reproduce the observed annual-mean pCO2 values averaged over the Drake Passage region. However, the model amplitude of the pCO2 seasonal cycle exceeds the observed amplitude of the pCO2 seasonal cycle because of the model biases in SST and surface DIC. North of the PF, deep winter mixed layers play a larger role in pCO2 variations in the models than they do in observations. Four ESMs show elevated wintertime pCO2 near the PF, causing a significant sea-to-air CO2 flux. Wintertime winds in these models are generally stronger than the satellite-derived winds. This not only magnifies the sea-to-air CO2 flux but also upwells DIC-rich water to the surface and drives strong equatorward Ekman currents. These strong model currents likely advect the upwelled DIC farther equatorward, as strong stratification in the models precludes subduction below the mixed layer.

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 Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Suchada Kamworapan ◽  
Chinnawat Surussavadee
Keyword(s):  
Southeast Asia ◽  
Climate Models ◽  
Performance Metrics ◽  
Global Climate ◽  
Total Error ◽  
Coupled Model ◽  
Global Climate Models ◽  
Ensemble Average ◽  
Temperature And Precipitation ◽  
Intercomparison Project

This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

The Double-ITCZ Bias in CMIP6 Models

2020 ◽  
Author(s):  
Baijun Tian
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Tropical Precipitation ◽  
Double Itcz ◽  
Intercomparison Project ◽  
Fully Coupled

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>

Constraining the Ratio of Global Warming to Cumulative CO2 Emissions Using CMIP5 Simulations*

2013 ◽  
Vol 26 (18) ◽  
pp. 6844-6858 ◽  
Author(s):  
Nathan P. Gillett ◽  
Vivek K. Arora ◽  
Damon Matthews ◽  
Myles R. Allen
Keyword(s):  
Carbon Emissions ◽  
Climate Models ◽  
Coupled Model ◽  
Climate Mitigation ◽  
Mitigation Policy ◽  
Transient Climate Response ◽  
Emissions Scenarios ◽  
Intercomparison Project ◽  
Cumulative Co2 Emissions ◽  
Global Mean

Abstract The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2 doubling in a 1% yr−1 CO2 increase experiment, ranges from 0.8 to 2.4 K EgC−1 in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)—a somewhat broader range than that found in a previous generation of carbon–climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%–95% range of 0.7–2.0 K EgC−1.

scholarly journals Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations

2020 ◽  
Author(s):  
Zebedee R. J. Nicholls ◽  
Malte Meinshausen ◽  
Jared Lewis ◽  
Robert Gieseke ◽  
Dietmar Dommenget ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Phase 1 ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
Second Phase ◽  
Model Intercomparison ◽  
Intercomparison Project ◽  
Reduced Complexity

Abstract. Here we present results from the first phase of the Reduced Complexity Model Intercomparison Project (RCMIP). RCMIP is a systematic examination of reduced complexity climate models (RCMs), which are used to complement and extend the insights from more complex Earth System Models (ESMs), in particular those participating in the Sixth Coupled Model Intercomparison Project (CMIP6). In Phase 1 of RCMIP, with 14 participating models namely ACC2, AR5IR (2 and 3 box versions), CICERO-SCM, ESCIMO, FaIR, GIR, GREB, Hector, Held et al. two layer model, MAGICC, MCE, OSCAR and WASP, we highlight the structural differences across various RCMs and show that RCMs are capable of reproducing global-mean surface air temperature (GSAT) changes of ESMs and historical observations. We find that some RCMs are capable of emulating the GSAT response of CMIP6 models to within a root-mean square error of 0.2 °C (of the same order of magnitude as ESM internal variability) over a range of scenarios. Running the same model configurations for both RCP and SSP scenarios, we see that the SSPs exhibit higher effective radiative forcing throughout the second half of the 21st Century. Comparing our results to the difference between CMIP5 and CMIP6 output, we find that the change in scenario explains approximately 46 % of the increase in higher end projected warming between CMIP5 and CMIP6. This suggests that changes in ESMs from CMIP5 to CMIP6 explain the rest of the increase, hence the higher climate sensitivities of available CMIP6 models may not be having as large an impact on GSAT projections as first anticipated. A second phase of RCMIP will complement RCMIP Phase 1 by exploring probabilistic results and emulation in more depth to provide results available for the IPCC's Sixth Assessment Report author teams.

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.

Sign in / Sign up

Export Citation Format

Share Document