Metrics for the Diurnal Cycle of Precipitation: Toward Routine Benchmarks for Climate Models

Abstract Metrics are proposed—that is, a few summary statistics that condense large amounts of data from observations or model simulations—encapsulating the diurnal cycle of precipitation. Vector area averaging of Fourier amplitude and phase produces useful information in a reasonably small number of harmonic dial plots, a procedure familiar from atmospheric tide research. The metrics cover most of the globe but down-weight high-latitude wintertime ocean areas where baroclinic waves are most prominent. This enables intercomparison of a large number of climate models with observations and with each other. The diurnal cycle of precipitation has features not encountered in typical climate model intercomparisons, notably the absence of meaningful “average model” results that can be displayed in a single two-dimensional map. Displaying one map per model guides development of the metrics proposed here by making it clear that land and ocean areas must be averaged separately, but interpreting maps from all models becomes problematic as the size of a multimodel ensemble increases. Global diurnal metrics provide quick comparisons with observations and among models, using the most recent version of the Coupled Model Intercomparison Project (CMIP). This includes, for the first time in CMIP, spatial resolutions comparable to global satellite observations. Consistent with earlier studies of resolution versus parameterization of the diurnal cycle, the longstanding tendency of models to produce rainfall too early in the day persists in the high-resolution simulations, as expected if the error is due to subgrid-scale physics.

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How to combine climate models and observations in event attribution?

10.5194/egusphere-egu21-14636 ◽

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

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) 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 both the probability and the intensity of that event have increased significantly in response to human influence. Remarkably, we find that the heat wave considered might not have been possible without climate change. Our results also suggest that combining model data with observations can improve the description of hot temperature distribution.

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

10.5194/egusphere-egu2020-16407 ◽

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

Extreme climate and weather events have a great influence on society and natural systems. That&#8217;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&#8211;2010 in order to estimate their trends. 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. 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.

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Indian Ocean Feedback to the ENSO Transition in a Multimodel Ensemble

Journal of Climate ◽

10.1175/jcli-d-12-00078.1 ◽

2012 ◽

Vol 25 (20) ◽

pp. 6942-6957 ◽

Cited By ~ 10

Author(s):

Jong-Seong Kug ◽

Yoo-Geun Ham

Keyword(s):

Indian Ocean ◽

El Niño ◽

Climate Models ◽

El Nino ◽

Coupled Model ◽

Warm Pool ◽

Multimodel Ensemble ◽

Phase 3 ◽

Intercomparison Project ◽

The Western Pacific

Abstract Observational studies hypothesized that Indian Ocean (IO) feedback plays a role in leading to a fast transition of El Niño. When El Niño accompanies IO warming, IO warming induces the equatorial easterlies over the western Pacific (WP), leading to a rapid termination of El Niño via an oceanic adjust process. In this study, this IO feedback is reinvestigated using the Coupled Model Intercomparison Project phase 3 (CMIP3) coupled GCM simulations. It is found that most of the climate models mimic this IO feedback reasonably, supporting the observational hypothesis. However, most climate models tend to underestimate the strength of the IO feedback, which means the phase transition of ENSO due to the IO feedback is less effective than the observed one. Furthermore, there is great intermodel diversity in simulating the strength of the IO feedback. It is shown that the strength of the IO feedback is related to the precipitation responses to El Niño and IO SST forcings over the warm-pool regions. Moreover, the authors suggest that the distribution of climatological precipitation is one important component in controlling the strength of the IO feedback.

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Defining Sudden Stratospheric Warming in Climate Models: Accounting for Biases in Model Climatologies

Journal of Climate ◽

10.1175/jcli-d-16-0465.1 ◽

2017 ◽

Vol 30 (14) ◽

pp. 5529-5546 ◽

Cited By ~ 17

Author(s):

Junsu Kim ◽

Seok-Woo Son ◽

Edwin P. Gerber ◽

Hyo-Seok Park

Keyword(s):

Climate Models ◽

Climate Model ◽

Coupled Model ◽

Polar Vortex ◽

Reanalysis Data ◽

Cmip5 Models ◽

Stratospheric Warming ◽

Sudden Stratospheric Warming ◽

Intercomparison Project ◽

Wave Flux

A sudden stratospheric warming (SSW) is often defined as zonal-mean zonal wind reversal at 10 hPa and 60°N. This simple definition has been applied not only to the reanalysis data but also to climate model output. In the present study, it is shown that the application of this definition to models can be significantly influenced by model mean biases (i.e., more frequent SSWs appear to occur in models with a weaker climatological polar vortex). To overcome this deficiency, a tendency-based definition is proposed and applied to the multimodel datasets archived for phase 5 of the Coupled Model Intercomparison Project (CMIP5). In this definition, SSW-like events are defined by sufficiently strong vortex deceleration. This approach removes a linear relationship between SSW frequency and intensity of the climatological polar vortex in the CMIP5 models. The models’ SSW frequency instead becomes significantly correlated with the climatological upward wave flux at 100 hPa, a measure of interaction between the troposphere and stratosphere. Lower stratospheric wave activity and downward propagation of stratospheric anomalies to the troposphere are also reasonably well captured. However, in both definitions, the high-top models generally exhibit more frequent SSWs than the low-top models. Moreover, a hint of more frequent SSWs in a warm climate is found in both definitions.

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A community diagnostic tool for Chemistry Climate Model Validation

Geoscientific Model Development Discussions ◽

10.5194/gmdd-5-1229-2012 ◽

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.

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Global exposure to flooding from the new CMIP6 climate model projections

Scientific Reports ◽

10.1038/s41598-021-83279-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yukiko Hirabayashi ◽

Masahiro Tanoue ◽

Orie Sasaki ◽

Xudong Zhou ◽

Dai Yamazaki

Keyword(s):

Spatial Patterns ◽

Flood Risk ◽

Climate Models ◽

Climate Model ◽

Model Development ◽

Coupled Model ◽

Model Intercomparison ◽

Internal Climate Variability ◽

Intercomparison Project ◽

New Generation

AbstractEstimates of future flood risk rely on projections from climate models. The relatively few climate models used to analyze future flood risk cannot easily quantify of their associated uncertainties. In this study, we demonstrated that the projected fluvial flood changes estimated by a new generation of climate models, the collectively known as Coupled Model Intercomparison Project Phase 6 (CMIP6), are similar to those estimated by CMIP5. The spatial patterns of the multi-model median signs of change (+ or −) were also very consistent, implying greater confidence in the projections. The model spread changed little over the course of model development, suggesting irreducibility of the model spread due to internal climate variability, and the consistent projections of models from the same institute suggest the potential to reduce uncertainties caused by model differences. Potential global exposure to flooding is projected to be proportional to the degree of warming, and a greater threat is anticipated as populations increase, demonstrating the need for immediate decisions.

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Could the Pliocene constrain the equilibrium climate sensitivity?

Climate of the Past ◽

10.5194/cp-12-1591-2016 ◽

2016 ◽

Vol 12 (8) ◽

pp. 1591-1599 ◽

Cited By ~ 8

Author(s):

J. C. Hargreaves ◽

J. D. Annan

Keyword(s):

Climate Sensitivity ◽

Atmospheric Carbon Dioxide ◽

Climate Models ◽

Climate Model ◽

Temperature Anomaly ◽

Model Intercomparison ◽

Equilibrium Climate Sensitivity ◽

Intercomparison Project ◽

First Time ◽

Industrial State

Abstract. The mid-Pliocene Warm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene Model Intercomparison Project (PlioMIP) presented boundary conditions for the mPWP and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We observe a correlation in the ensemble between their tropical temperature anomalies at the mPWP and their equilibrium sensitivities. If the real world is assumed to also obey this relationship, then the reconstructed tropical temperature anomaly at the mPWP can in principle generate a constraint on the true sensitivity. Directly applying this methodology using available data yields a range for the equilibrium sensitivity of 1.9–3.7 °C, but there are considerable additional uncertainties surrounding the analysis which are not included in this estimate. We consider the extent to which these uncertainties may be better quantified and perhaps lessened in the next few years.

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Northern Hemisphere Snow-Cover Trends (1967–2018): A Comparison between Climate Models and Observations

Geosciences ◽

10.3390/geosciences9030135 ◽

2019 ◽

Vol 9 (3) ◽

pp. 135 ◽

Cited By ~ 8

Author(s):

◽

...

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Climate Models ◽

Climate Model ◽

Coupled Model ◽

Poor Performance ◽

Four Seasons ◽

Intercomparison Project ◽

Autumn And Winter

Observed changes in Northern Hemisphere snow cover from satellite records were compared to those predicted by all available Coupled Model Intercomparison Project Phase 5 (“CMIP5”) climate models over the duration of the satellite’s records, i.e., 1967–2018. A total of 196 climate model runs were analyzed (taken from 24 climate models). Separate analyses were conducted for the annual averages and for each of the seasons (winter, spring, summer, and autumn/fall). A longer record (1922–2018) for the spring season which combines ground-based measurements with satellite measurements was also compared to the model outputs. The climate models were found to poorly explain the observed trends. While the models suggest snow cover should have steadily decreased for all four seasons, only spring and summer exhibited a long-term decrease, and the pattern of the observed decreases for these seasons was quite different from the modelled predictions. Moreover, the observed trends for autumn and winter suggest a long-term increase, although these trends were not statistically significant. Possible explanations for the poor performance of the climate models are discussed.

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Set-up of the PMIP3 paleoclimate experiments conducted using an Earth system model, MIROC-ESM

Geoscientific Model Development ◽

10.5194/gmd-6-819-2013 ◽

2013 ◽

Vol 6 (3) ◽

pp. 819-836 ◽

Cited By ~ 49

Author(s):

T. Sueyoshi ◽

R. Ohgaito ◽

A. Yamamoto ◽

M. O. Chikamoto ◽

T. Hajima ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Coupled Model ◽

Volcanic Eruptions ◽

Earth System ◽

Good Test ◽

Effective Measure ◽

Intercomparison Project

Abstract. Paleoclimate experiments using contemporary climate models are an effective measure to evaluate climate models. In recent years, Earth system models (ESMs) were developed to investigate carbon cycle climate feedbacks, as well as to project the future climate. Paleoclimate events can be suitable benchmarks to evaluate ESMs. The variation in aerosols associated with the volcanic eruptions provide a clear signal in forcing, which can be a good test to check the response of a climate model to the radiation changes. The variations in atmospheric CO2 level or changes in ice sheet extent can be used for evaluation as well. Here we present implementations of the paleoclimate experiments proposed by the Coupled Model Intercomparison Project phase 5/Paleoclimate Modelling Intercomparison Project phase 3 (CMIP5/PMIP3) using MIROC-ESM, an ESM based on the global climate model MIROC (Model for Interdisciplinary Research on Climate). In this paper, experimental settings and spin-up procedures of the mid-Holocene, the Last Glacial Maximum, and the Last Millennium experiments are explained. The first two experiments are time slice experiments and the last one is a transient experiment. The complexity of the model requires various steps to correctly configure the experiments. Several basic outputs are also shown.

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A community diagnostic tool for chemistry climate model validation

Geoscientific Model Development ◽

10.5194/gmd-5-1061-2012 ◽

2012 ◽

Vol 5 (5) ◽

pp. 1061-1073 ◽

Cited By ~ 9

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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