scholarly journals Intraseasonal Convective Moistening in CMIP3 Models

2012 ◽  
Vol 25 (8) ◽  
pp. 2569-2577 ◽  
Author(s):  
Prince K. Xavier
Keyword(s):  
Climate Models ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Specific Humidity ◽  
Rainfall Regime ◽  
Physical Relationship ◽  
Intercomparison Project ◽  
Precise Relationship ◽  
The Relationship

Abstract A precise relationship between tropospheric moisture and convection is thought to be a key to the accurate simulation of tropical intraseasonal variability. An evaluation of the precipitation distribution and its fundamental physical relationship with relative humidity (RH) in the 14 climate models that participated in the Coupled Model Intercomparison Project phase 3 (CMIP3) is presented here. Most models tend to reside in a light rainfall regime that largely determines the models’ basic state, and the intraseasonal transition toward heavy precipitation is not as gradual as in the observations. Some of the precipitation biases are related to the deficiencies in the representation of the relationship between the precipitation and RH, and the moisture preconditioning ahead of intraseasonal convection. It is also shown that even for models with reasonable baroclinic temperature anomaly structures of the MJO, there are large biases in the intraseasonal specific humidity anomalies, some of which may be related to the uncertainties in representing shallow cumulus, convective downdrafts, and convective detrainment.

scholarly journals An Evaluation Metric for Intraseasonal Variability and its Application to CMIP3 Twentieth-Century Simulations

2010 ◽  
Vol 23 (13) ◽  
pp. 3497-3508 ◽  
Author(s):  
Prince K. Xavier ◽  
Jean-Philippe Duvel ◽  
Pascale Braconnot ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Climate Models ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Summer Monsoon Rainfall ◽  
Boreal Summer ◽  
Phase 3 ◽  
Weather And Climate ◽  
Intercomparison Project ◽  
The Indian Ocean ◽  
Evaluation Metric

Abstract The intraseasonal variability (ISV) is an intermittent phenomenon with variable perturbation patterns. To assess the robustness of the simulated ISV in climate models, it is thus interesting to consider the distribution of perturbation patterns rather than only one average pattern. To inspect this distribution, the authors first introduce a distance that measures the similarity between two patterns. The reproducibility (realism) of the simulated intraseasonal patterns is then defined as the distribution of distances between each pattern and the average simulated (observed) pattern. A good reproducibility is required to analyze the physical source of the simulated disturbances. The realism distribution is required to estimate the proportion of simulated events that have a perturbation pattern similar to observed patterns. The median value of this realism distribution is introduced as an ISV metric. The reproducibility and realism distributions are used to evaluate boreal summer ISV of precipitations over the Indian Ocean for 19 phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. The 19 models are classified in increasing ISV metric order. In agreement with previous studies, the four best ISV metrics are obtained for models having a convective closure totally or partly based on the moisture convergence. Models with high metric values (poorly realistic) tend to give (i) poorly reproducible intraseasonal patterns, (ii) rainfall perturbations poorly organized at large scales, (iii) small day-to-day variability with overly red temporal spectra, and (iv) less accurate summer monsoon rainfall distribution. This confirms that the ISV is an important link in the seamless system that connects weather and climate.

scholarly journals Observations and climate models confirm precipitation pattern is changing over Nepal

Jalawaayu ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 25-46
Author(s):  
Rocky Talchabhadel
Keyword(s):  
Climate Models ◽  
Daily Precipitation ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Precipitation Pattern ◽  
Daily Precipitation Data ◽  
Comprehensive Picture ◽  
Intercomparison Project ◽  
Precipitation Indices

This paper presents a comprehensive picture of precipitation variability across Nepal over the present (1985-2014) and future (2021-2050) based on gauge-based observations from 28 precipitation stations distributed throughout the country and thirteen climate models of the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP 245 and SSP 585). Seventeen different precipitation indices are computed using daily precipitation data based on gauge-based observations and climate models. Along with absolute extreme precipitation indices, such as maximum 1-day, maximum consecutive 3-day, 5-day, and 7-day precipitation amounts, this study also computes the contribution of such instances to the annual precipitation. The selected precipitation indices not only allow for the analyses of heavy precipitation-related extremes but also guide the evaluation of agricultural productivity and drought indications, such as consecutive dry and wet days (CDD and CWD). The number of wet days and average precipitation during those wet days, along with the information of the number of days with daily precipitation ≥ 10, 20, 50, and 100 mm, summarize the distribution of total precipitation. This study emphasizes changing precipitation patterns by looking at these indices over the present and future periods. Observations and climate models show a changing nature of precipitation over Nepal. However, different climate models exhibit a different severity of changes. Though the yearly precipitation amount is not altered noticeably, this study finds that the extremes are expected to alter significantly than the averages. It is also to be noted that climate models are unable to capture localized extremes in Nepal Himalayas.

scholarly journals MJO and Convectively Coupled Equatorial Waves Simulated by CMIP5 Climate Models

2013 ◽  
Vol 26 (17) ◽  
pp. 6185-6214 ◽  
Author(s):  
Meng-Pai Hung ◽  
Jia-Lin Lin ◽  
Wanqiu Wang ◽  
Daehyun Kim ◽  
Toshiaki Shinoda ◽  
...  
Keyword(s):  
Climate Models ◽  
Intraseasonal Variability ◽  
Coupled Model ◽  
Static Stability ◽  
Cmip5 Models ◽  
Equatorial Waves ◽  
Intergovernmental Panel ◽  
Assessment Report ◽  
Convectively Coupled Equatorial Waves ◽  
Intercomparison Project

Abstract This study evaluates the simulation of the Madden–Julian oscillation (MJO) and convectively coupled equatorial waves (CCEWs) in 20 models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and compares the results with the simulation of CMIP phase 3 (CMIP3) models in the IPCC Fourth Assessment Report (AR4). The results show that the CMIP5 models exhibit an overall improvement over the CMIP3 models in the simulation of tropical intraseasonal variability, especially the MJO and several CCEWs. The CMIP5 models generally produce larger total intraseasonal (2–128 day) variance of precipitation than the CMIP3 models, as well as larger variances of Kelvin, equatorial Rossby (ER), and eastward inertio-gravity (EIG) waves. Nearly all models have signals of the CCEWs, with Kelvin and mixed Rossby–gravity (MRG) and EIG waves being especially prominent. The phase speeds, as scaled to equivalent depths, are close to the observed value in 10 of the 20 models, suggesting that these models produce sufficient reduction in their effective static stability by diabatic heating. The CMIP5 models generally produce larger MJO variance than the CMIP3 models, as well as a more realistic ratio between the variance of the eastward MJO and that of its westward counterpart. About one-third of the CMIP5 models generate the spectral peak of MJO precipitation between 30 and 70 days; however, the model MJO period tends to be longer than observations as part of an overreddened spectrum, which in turn is associated with too strong persistence of equatorial precipitation. Only one of the 20 models is able to simulate a realistic eastward propagation of the MJO.

scholarly journals Regional, Very Heavy Daily Precipitation in CMIP5 Simulations

2013 ◽  
Vol 14 (4) ◽  
pp. 1228-1242 ◽  
Author(s):  
Sho Kawazoe ◽  
William J. Gutowski
Keyword(s):  
Climate Models ◽  
Daily Precipitation ◽  
Global Climate ◽  
Coupled Model ◽  
Heavy Precipitation ◽  
Global Climate Models ◽  
Physical Conditions ◽  
Intercomparison Project ◽  
Synoptic Circulation ◽  
Cmip5 Gcms

Abstract The authors analyze the ability of global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble to simulate very heavy daily precipitation and its supporting processes, comparing them with observations. Their analysis focuses on an upper Mississippi region for winter (December–February), when it is assumed that resolved synoptic circulation governs precipitation. CMIP5 GCMs generally reproduce well the precipitation versus intensity spectrum seen in observations to intensities as strong as 20 mm day−1. Most models do not produce the highest precipitation intensities seen in observations. Models show good agreement at the 95th percentile, while the coarsest resolution models generally show lower precipitation at high-intensity thresholds, such as the 99.5th percentile. There is no dominant month for simulated very heavy events to occur, although observed very heavy events occur most frequently in December. Further analysis focuses on precipitation events exceeding the 99.5th percentile that occur simultaneously at several points in the region, yielding so-called “widespread events.” Examination of additional fields during widespread very heavy events shows that the models produce these events under the same physical conditions seen in the observations. The coarsest models generally produce similar behavior, although features have smoother spatial distributions. However, the resolution in itself could not be identified as a major reason that separates one model from another. The capabilities of the CMIP5 GCMs examined here support using them to assess changes in very heavy precipitation under future climate scenarios.

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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