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 Tropical Intraseasonal Variability in 14 IPCC AR4 Climate Models. Part I: Convective Signals

2006 ◽  
Vol 19 (12) ◽  
pp. 2665-2690 ◽  
Author(s):  
Jia-Lin Lin ◽  
George N. Kiladis ◽  
Brian E. Mapes ◽  
Klaus M. Weickmann ◽  
Kenneth R. Sperber ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Intraseasonal Variability ◽  
Phase Speed ◽  
General Circulation Models ◽  
Climate Simulation ◽  
Equatorial Waves ◽  
Intergovernmental Panel ◽  
Convectively Coupled Equatorial Waves ◽  
Wide Range

Abstract This study evaluates the tropical intraseasonal variability, especially the fidelity of Madden–Julian oscillation (MJO) simulations, in 14 coupled general circulation models (GCMs) participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). Eight years of daily precipitation from each model’s twentieth-century climate simulation are analyzed and compared with daily satellite-retrieved precipitation. Space–time spectral analysis is used to obtain the variance and phase speed of dominant convectively coupled equatorial waves, including the MJO, Kelvin, equatorial Rossby (ER), mixed Rossby–gravity (MRG), and eastward inertio–gravity (EIG) and westward inertio–gravity (WIG) waves. The variance and propagation of the MJO, defined as the eastward wavenumbers 1–6, 30–70-day mode, are examined in detail. The results show that current state-of-the-art GCMs still have significant problems and display a wide range of skill in simulating the tropical intraseasonal variability. The total intraseasonal (2–128 day) variance of precipitation is too weak in most of the models. About half of the models have signals of convectively coupled equatorial waves, with Kelvin and MRG–EIG waves especially prominent. However, the variances are generally too weak for all wave modes except the EIG wave, and the phase speeds are generally too fast, being scaled to excessively deep equivalent depths. An interesting result is that this scaling is consistent within a given model across modes, in that both the symmetric and antisymmetric modes scale similarly to a certain equivalent depth. Excessively deep equivalent depths suggest that these models may not have a large enough reduction in their “effective static stability” by diabatic heating. The MJO variance approaches the observed value in only 2 of the 14 models, but is less than half of the observed value in the other 12 models. The ratio between the eastward MJO variance and the variance of its westward counterpart is too small in most of the models, which is consistent with the lack of highly coherent eastward propagation of the MJO in many models. Moreover, the MJO variance in 13 of the 14 models does not come from a pronounced spectral peak, but usually comes from part of an overreddened spectrum, which in turn is associated with too strong persistence of equatorial precipitation. The two models that arguably do best at simulating the MJO are the only ones having convective closures/triggers linked in some way to moisture convergence.

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>

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 Effective radiative forcing and adjustments in CMIP6 models

2020 ◽  
Vol 20 (16) ◽  
pp. 9591-9618 ◽  
Author(s):  
Christopher J. Smith ◽  
Ryan J. Kramer ◽  
Gunnar Myhre ◽  
Kari Alterskjær ◽  
William Collins ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Aerosol Forcing ◽  
Intercomparison Project ◽  
Effective Radiative Forcing

Abstract. The effective radiative forcing, which includes the instantaneous forcing plus adjustments from the atmosphere and surface, has emerged as the key metric of evaluating human and natural influence on the climate. We evaluate effective radiative forcing and adjustments in 17 contemporary climate models that are participating in the Coupled Model Intercomparison Project (CMIP6) and have contributed to the Radiative Forcing Model Intercomparison Project (RFMIP). Present-day (2014) global-mean anthropogenic forcing relative to pre-industrial (1850) levels from climate models stands at 2.00 (±0.23) W m−2, comprised of 1.81 (±0.09) W m−2 from CO2, 1.08 (± 0.21) W m−2 from other well-mixed greenhouse gases, −1.01 (± 0.23) W m−2 from aerosols and −0.09 (±0.13) W m−2 from land use change. Quoted uncertainties are 1 standard deviation across model best estimates, and 90 % confidence in the reported forcings, due to internal variability, is typically within 0.1 W m−2. The majority of the remaining 0.21 W m−2 is likely to be from ozone. In most cases, the largest contributors to the spread in effective radiative forcing (ERF) is from the instantaneous radiative forcing (IRF) and from cloud responses, particularly aerosol–cloud interactions to aerosol forcing. As determined in previous studies, cancellation of tropospheric and surface adjustments means that the stratospherically adjusted radiative forcing is approximately equal to ERF for greenhouse gas forcing but not for aerosols, and consequentially, not for the anthropogenic total. The spread of aerosol forcing ranges from −0.63 to −1.37 W m−2, exhibiting a less negative mean and narrower range compared to 10 CMIP5 models. The spread in 4×CO2 forcing has also narrowed in CMIP6 compared to 13 CMIP5 models. Aerosol forcing is uncorrelated with climate sensitivity. Therefore, there is no evidence to suggest that the increasing spread in climate sensitivity in CMIP6 models, particularly related to high-sensitivity models, is a consequence of a stronger negative present-day aerosol forcing and little evidence that modelling groups are systematically tuning climate sensitivity or aerosol forcing to recreate observed historical warming.

Simulation of wind damages associated with the PRIMAVERA European windstorm event set

2021 ◽  
Author(s):  
Tristan Perotin
Keyword(s):  
Climate Models ◽  
Dynamical Downscaling ◽  
Insurance Industry ◽  
Coupled Model ◽  
Correction Method ◽  
Weather Data ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Insurance Sector ◽  
Intercomparison Project

<p>Winter windstorms are one of the major natural hazards affecting Europe, potentially causing large damages. The study of windstorm risks is therefore particularly important for the insurance industry. Physical natural catastrophe models for the insurance industry appeared in the 1980s and enable a fine analysis of the risk by taking into account all of its components (hazard, vulnerability and exposure). One main aspect of this catastrophe modeling is the production and validation of extreme hazard scenarios. As observational weather data is very sparse before the 1980s, estimates of extreme windstorm risks are usually based on climate models, despite the limited resolution of these models. Even though this limitation can be partially corrected by statistical or dynamical downscaling and calibration techniques, new generations of climate models can bring new understanding of windstorm risks.</p><p>In that context, PRIMAVERA, a European Union Horizon2020 project, made available a windstorm event set based on 21 tier 1 (1950-2014) highresSST-present simulations of the High Resolution Model Intercomparison Project (HighResMIP) component of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The events were identified with a storm tracking algorithm, footprints were defined for each event as maximum gusts over a 72 hour period, and the footprints were re-gridded to the ERA5 grid and calibrated with a quantile mapping correction method. The native resolution of these simulations ranges from 150km (typical resolution of the CMIP5 models) to 25km.</p><p>We have studied the applicability of the PRIMAVERA European windstorm event set for the modeling of European windstorm risks for the insurance sector. Preliminary results show that losses simulated from the event set appear to be consistent with historical data for all of the included simulations. The event set enables a better representation of attritional events and storm clustering than other existing event sets. An alternative calibration technique for extreme gusts and potential future developments of the event set will be proposed.</p>

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

2017 ◽  
Vol 30 (14) ◽  
pp. 5529-5546 ◽  
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.

scholarly journals Evaluation of snow cover and snow water equivalent in the continental Arctic in CMIP5 models

2020 ◽  
Vol 55 (11-12) ◽  
pp. 2993-3016
Author(s):  
María Santolaria-Otín ◽  
Olga Zolina
Keyword(s):  
Snow Cover ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Spatial And Temporal Patterns ◽  
Multiple Data ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Intercomparison Project

Abstract Spatial and temporal patterns of snow cover extent (SCE) and snow water equivalent (SWE) over the terrestrial Arctic are analyzed based on multiple observational datasets and an ensemble of CMIP5 models during 1979–2005. For evaluation of historical simulations of the Coupled Model Intercomparison Project (CMIP5) ensemble, we used two reanalysis products, one satellite-observed product and an ensemble of different datasets. The CMIP5 models tend to significantly underestimate the observed SCE in spring but are in better agreement with observations in autumn; overall, the observed annual SCE cycle is well captured by the CMIP5 ensemble. In contrast, for SWE, the annual cycle is significantly biased, especially over North America, where some models retain snow even in summer, in disagreement with observations. The snow margin position (SMP) in the CMIP5 historical simulations is in better agreement with observations in spring than in autumn, when close agreement across the CMIP5 models is only found in central Siberia. Historical experiments from most CMIP5 models show negative pan-Arctic trends in SCE and SWE. These trends are, however, considerably weaker (and less statistically significant) than those reported from observations. Most CMIP5 models can more accurately capture the trend pattern of SCE than that of SWE, which shows quantitative and qualitative differences with the observed trends over Eurasia. Our results demonstrate the importance of using multiple data sources for the evaluation of snow characteristics in climate models. Further developments should focus on the improvement of both dataset quality and snow representation in climate models, especially ESM-SnowMIP.

scholarly journals Past warming trend constrains future warming in CMIP6 models

2020 ◽  
Author(s):  
Martin Stolpe ◽  
Katarzyna Tokarska ◽  
Sebastian Sippel ◽  
Erich Fischer ◽  
Christopher Smith ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Warming Trend ◽  
Paris Agreement ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Mitigation Scenario ◽  
Future Global Warming ◽  
Intercomparison Project ◽  
Future Warming

<div>Future global warming estimates have been similar across past assessments, but several climate models of the latest Sixth Coupled Model Intercomparison Project (CMIP6) simulate much stronger warming, apparently inconsistent with past assessments. Here we show that projected future warming is correlated with the simulated warming trend during recent decades across CMIP5 and CMIP6 models, enabling us to constrain future warming based on consistency with the observed warming. These findings carry important policy-relevant implications: the observationally-constrained CMIP6 median warming in high emissions and ambitious mitigation scenarios is over 16% and 14% lower by 2050 compared to the raw CMIP6 median, respectively, and over 14% and 8% lower by 2090, relative to 1995-2014. Observationally-constrained CMIP6 warming is consistent with previous assessments based on CMIP5 models, and in an ambitious mitigation scenario, the likely range is consistent with reaching the Paris Agreement target.</div><div> </div><div>Reference: </div><div>Tokarska, K.B.<sup>†</sup>, Stolpe, M.B.<sup>†</sup>, Sippel, S., Fischer, E.M., Smith, C.J., Lehner, F., and Knutti, R. (2020). Past warming trend constrains future warming in CMIP6 models. <em>Science Advances</em>  (accepted).</div><div><sup>†</sup>equal first authors</div>

scholarly journals Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on U.S. maize yields

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David C. Lafferty ◽  
Ryan L. Sriver ◽  
Iman Haqiqi ◽  
Thomas W. Hertel ◽  
Klaus Keller ◽  
...  
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Cmip5 Models ◽  
Climate Information ◽  
Model Intercomparison ◽  
Temperature And Precipitation ◽  
Panel Model ◽  
Trade Offs ◽  
Intercomparison Project ◽  
Maize Yields

AbstractEfforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence.

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