scholarly journals ASSESSMENT OF THE QUALITY OF SIMULATION OF CHANGES OF THE DOWNWELLING SHORTWAVE RADIATION IN THE SEVASTOPOL REGION USING THE CMIP6 MODELS

2021 ◽  
pp. 31-37
Author(s):  
О.Yu. Sukhonos ◽  
◽  
А.S. Lubkov ◽  
Е.N. Voskresenskaya ◽  
◽  
...  
Keyword(s):  
Climate Changes ◽  
Climate Models ◽  
Linear Trend ◽  
Coupled Model ◽  
Statistical Characteristics ◽  
Shortwave Radiation ◽  
Energy Potential ◽  
Intercomparison Project ◽  
Climatic Characteristic

Using the data of the Coupled Model Intercomparison Project 6 (CMIP6), the quality of the simulation of the observed climate changes of downwelling shortwave radiation in the Sevastopol region for the period 1983–2012 is assessed. It is shown that the average values of the considered characteristics of solar resources according to the data of climate models are, in general, higher than according to the ob-servational data, whereas the values of the standard deviation are lower. The analysis of linear trends of the downwelling shortwave radiation show that most climate models from the CMIP6 project correctly simulate the process up to the sign of the linear trend. Using a number of statistical characteristics, mod-els have been determined that best simulate the analyzed climatic characteristic and will be suitable for assessing future changes in the solar energy potential in the Sevastopol region.

Simulation of the observed climate extremes trends during 1901–2010 with INMCM5

2020 ◽  
Author(s):  
Maria Tarasevich ◽  
Evgeny Volodin
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Great Influence ◽  
Coupled Model ◽  
Climate Extremes ◽  
Reanalysis Data ◽  
Natural Systems ◽  
Weather Events ◽  
Intercomparison Project

<p>Extreme climate and weather events have a great influence on society and natural systems. That’s why it is important to be able to precisely simulate these events with the climate models. To asses the quality of such simulations 27 climate extremes indices were defined by ETCCDI. In the present work these indices are calculated for the 1901–2010 in order to estimate their trends.<br>Climate extremes trends are studied on the basis of ten historical runs with the up-to-date INM RAS climate model (INMCM5) under the scenario proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Developed by ECMWF ERA-20C and CERA-20C reanalyses are taken as observational data.<br>Trends obtained from the reanalysis data are compared with the simulation results of the INMCM5. The comparison shows that the simulated land-averaged climate extremes trends are in good agreement with the reanalysis data, but their spatial distributions differ significantly even between the reanalyses themselves.</p>

scholarly journals Historical and future trends in wetting and drying in 291 catchments across China

2017 ◽  
Vol 21 (4) ◽  
pp. 2233-2248 ◽  
Author(s):  
Zhongwang Chen ◽  
Huimin Lei ◽  
Hanbo Yang ◽  
Dawen Yang ◽  
Yongqiang Cao
Keyword(s):  
Climate Changes ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Trends ◽  
Wetting And Drying ◽  
Intercomparison Project ◽  
Streamflow Data

Abstract. An increasingly uneven distribution of hydrometeorological factors related to climate change has been detected by global climate models (GCMs) in which the pattern of changes in water availability is commonly described by the phrase dry gets drier, wet gets wetter (DDWW). However, the DDWW pattern is dominated by oceanic areas; recent studies based on both observed and modelled data have failed to verify the DDWW pattern on land. This study confirms the existence of a new DDWW pattern in China after analysing the observed streamflow data from 291 Chinese catchments from 1956 to 2000, which reveal that the distribution of water resources has become increasingly uneven since the 1950s. This pattern can be more accurately described as drier regions are more likely to become drier, whereas wetter regions are more likely to become wetter. Based on a framework derived from the Budyko hypothesis, this study estimates runoff trends via observations of precipitation (P) and potential evapotranspiration (Ep) and predicts the future trends from 2001 to 2050 according to the projections of five GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under three scenarios: RCP2.6, RCP4.5, and RCP8.5. The results show that this framework has a good performance for estimating runoff trends; such changes in P play the most significant role. Most areas of China, including more than 60 % of catchments, will experience water resource shortages under the projected climate changes. Despite the differences among the predicted results of the different models, the DDWW pattern does not hold in the projections regardless of the model used. Nevertheless, this conclusion remains tentative owing to the large uncertainties in the GCM outputs.

scholarly journals Simulating the mid-Holocene, Last Interglacial and mid-Pliocene climate with EC-Earth3-LR

2020 ◽  
Author(s):  
Qiong Zhang ◽  
Qiang Li ◽  
Qiang Zhang ◽  
Ellen Berntell ◽  
Josefine Axelsson ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Changes ◽  
Climate Models ◽  
Coupled Model ◽  
Walker Circulation ◽  
Earth Model ◽  
Model Intercomparison ◽  
Test Bed ◽  
Last Interglacial ◽  
Intercomparison Project

Abstract. Paleoclimate modelling has long been regarded as a strong out-of-sample test-bed of the climate models that are used for the projection of future climate changes. For the first time, the EC-Earth model contributes to the Paleoclimate Model Intercomparison Project (PMIP) phase 4, which is part of the current sixth phase of the Coupled Model Intercomparison Project (CMIP6). Here, we document the model setup for PMIP4 experiments with EC-Earth3-LR and present the results on the large-scale features from the completed production simulations for the three past warm periods (mid-Holocene, Last Interglacial, and mid-Pliocene). Using the pre-industrial climate as a reference state, we show the changes in global temperature, large scale Hadley circulation and Walker circulation, polar warming and global monsoons, as well as the climate variability modes (ENSO, PDO, AMO). The EC-Earth3-LR simulates reasonable climate responses during past warm periods as shown in the other PMIP4-CMIP6 model ensemble. The systematic comparison of these climate changes in three past warm periods in an individual model demonstrates the ability of the model to capture the climate response under different climate forcings, providing potential implications for confidence in future projections with EC-Earth model.

scholarly journals The Transpose-AMIP II Experiment and Its Application to the Understanding of Southern Ocean Cloud Biases in Climate Models

2013 ◽  
Vol 26 (10) ◽  
pp. 3258-3274 ◽  
Author(s):  
K. D. Williams ◽  
A. Bodas-Salcedo ◽  
M. Déqué ◽  
S. Fermepin ◽  
B. Medeiros ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Large Scale ◽  
Climate Models ◽  
Layer Structure ◽  
Coupled Model ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Shortwave Radiation ◽  
Model Intercomparison ◽  
Intercomparison Project

Abstract The Transpose-Atmospheric Model Intercomparison Project (AMIP) is an international model intercomparison project in which climate models are run in “weather forecast mode.” The Transpose-AMIP II experiment is run alongside phase 5 of the Coupled Model Intercomparison Project (CMIP5) and allows processes operating in climate models to be evaluated, and the origin of climatological biases to be explored, by examining the evolution of the model from a state in which the large-scale dynamics, temperature, and humidity structures are constrained through use of common analyses. The Transpose-AMIP II experimental design is presented. The project requests participants to submit a comprehensive set of diagnostics to enable detailed investigation of the models to be performed. An example of the type of analysis that may be undertaken using these diagnostics is illustrated through a study of the development of cloud biases over the Southern Ocean, a region that is problematic for many models. Several models share a climatological bias for too little reflected shortwave radiation from cloud across the region. This is found to mainly occur behind cold fronts and/or on the leading side of transient ridges and to be associated with more stable lower-tropospheric profiles. Investigation of a case study that is typical of the bias and associated meteorological conditions reveals the models to typically simulate cloud that is too optically and physically thin with an inversion that is too low. The evolution of the models within the first few hours suggests that these conditions are particularly sensitive and a positive feedback can develop between the thinning of the cloud layer and boundary layer structure.

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.

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