ENSO amplitude uncertainty under global warming in CMIP5 models

2020 ◽  
Author(s):  
Goratz Beobide ◽  
Tobias Bayr ◽  
Annika Reintges ◽  
Mojib Latif
Keyword(s):  
Global Warming ◽  
Zonal Wind ◽  
Coupled Model ◽  
Correlation Coefficients ◽  
Internal Variability ◽  
Mean Amplitude ◽  
Cmip5 Models ◽  
Amplitude Change ◽  
Uncertainty Model ◽  
Scenario Uncertainty

<p>The possible change of ENSO amplitude during the 21<sup>st</sup> century in response to global warming has been analyzed in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5). Three types of uncertainties are investigated: scenario uncertainty, model uncertainty, and uncertainty due to internal variability.</p><p>The ENSO response obtained from the CMIP5 models is highly uncertain, leading to an ensemble-mean amplitude change of close to zero until the end of the 21<sup>st</sup> century, with an uncertainty exceeding 0.3 °C. The internal variability is the main contributor to the uncertainty during the first two decades of the projections. The inter-model differences dominate thereafter, while scenario uncertainty is relatively small throughout the whole 21<sup>st</sup> century. The zonal wind-SST feedback has been identified as an important factor of ENSO amplitude change: the global warming signal in the ENSO amplitude and zonal wind-SST feedback are highly correlated across the CMIP5 models, with correlation coefficients of 0.87, 0.84 and 0.78 for the RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively.</p><p>The CMIP5 models with realistic ENSO dynamics have been analyzed separately. In this sub-ensemble, the global warming signal is strengthened with a mean ENSO amplitude decrease of approximately 0.1°C by the end of the 21<sup>st</sup> century. When only considering models with large decadal ENSO amplitude variability, the decrease in ENSO amplitude amounts to 0.1°C and 0.2°C for the RCP4.5 and RCP8.5 scenarios, respectively.</p>

scholarly journals Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models

2021 ◽  
Author(s):  
Goratz Beobide-Arsuaga ◽  
Tobias Bayr ◽  
Annika Reintges ◽  
Mojib Latif
Keyword(s):  
Global Warming ◽  
Standard Deviation ◽  
Zonal Wind ◽  
Model Uncertainty ◽  
Internal Variability ◽  
First Century ◽  
Amplitude Change ◽  
Enso Dynamics ◽  
Scenario Uncertainty ◽  
Twenty First Century

AbstractThere is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models.

Multi-model superensemble projection of seasonal soil drought in the midst of various uncertainties

2021 ◽  
Author(s):  
Sisi Chen ◽  
Xing Yuan
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
Hydrological Cycle ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Soil Drought ◽  
Human Society

<p>Seasonal drought has a serious impact on nature and human society, especially during vegetation growing periods. As climate change alters terrestrial hydrological cycle significantly, it is imperative to assess drought changes and develop corresponding risk management measures for adaptation. According to a series of warming targets proposed by IPCC, researchers have focused on the response of regional droughts to global warming, but with inconsistent conclusions due to the large uncertainties in soil moisture simulation by the climate models, and the difficulty in representing the internal variability of climate system by using multi-model ensemble, etc. As compared with Coupled Model Intercomparison Project Phase 5 (CMIP5) models, the future projection of soil moisture based on the latest CMIP6 shows opposite trends over parts of China. Therefore, we project seasonal soil drought over China by using the superensemble that includes a set of CMIP5 and CMIP6 soil moisture data, high resolution land surface simulations driven by bias-corrected CMIP5 climate forcings, as wells large ensemble (LE) simulation data. We also investigate the influences from internal variability, and model uncertainties in responding to global warming at different levels.</p>

scholarly journals A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions

2011 ◽  
Vol 24 (17) ◽  
pp. 4634-4643 ◽  
Author(s):  
Stan Yip ◽  
Christopher A. T. Ferro ◽  
David B. Stephenson ◽  
Ed Hawkins
Keyword(s):  
Model Uncertainty ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
First Century ◽  
Uncertainty Model ◽  
Ensemble Mean ◽  
Scenario Uncertainty ◽  
Twenty First Century ◽  
Multimodel Ensembles

A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

scholarly journals Climate Drift in the CMIP5 Models*

2013 ◽  
Vol 26 (21) ◽  
pp. 8597-8615 ◽  
Author(s):  
Alexander Sen Gupta ◽  
Nicolas C. Jourdain ◽  
Jaclyn N. Brown ◽  
Didier Monselesan
Keyword(s):  
Climate Models ◽  
Coupled Model ◽  
Deep Ocean ◽  
Internal Variability ◽  
Correction Method ◽  
Physical Models ◽  
Cmip5 Models ◽  
Steric Sea Level ◽  
Biogeochemical Models ◽  
The Difference

Abstract Climate models often exhibit spurious long-term changes independent of either internal variability or changes to external forcing. Such changes, referred to as model “drift,” may distort the estimate of forced change in transient climate simulations. The importance of drift is examined in comparison to historical trends over recent decades in the Coupled Model Intercomparison Project (CMIP). Comparison based on a selection of metrics suggests a significant overall reduction in the magnitude of drift from phase 3 of CMIP (CMIP3) to phase 5 of CMIP (CMIP5). The direction of both ocean and atmospheric drift is systematically biased in some models introducing statistically significant drift in globally averaged metrics. Nevertheless, for most models globally averaged drift remains weak compared to the associated forced trends and is often smaller than the difference between trends derived from different ensemble members or the error introduced by the aliasing of natural variability. An exception to this is metrics that include the deep ocean (e.g., steric sea level) where drift can dominate in forced simulations. In such circumstances drift must be corrected for using information from concurrent control experiments. Many CMIP5 models now include ocean biogeochemistry. Like physical models, biogeochemical models generally undergo long spinup integrations to minimize drift. Nevertheless, based on a limited subset of models, it is found that drift is an important consideration and must be accounted for. For properties or regions where drift is important, the drift correction method must be carefully considered. The use of a drift estimate based on the full control time series is recommended to minimize the contamination of the drift estimate by internal variability.

Challenges in Constraining Future Change of Global Land Precipitation in CMIP6 Models

2020 ◽  
Author(s):  
June-Yi Lee ◽  
Kyung-Sook Yun ◽  
Arjun Babu ◽  
Young-Min Yang ◽  
Eui-Seok Chung ◽  
...  
Keyword(s):  
Global Warming ◽  
Emission Scenario ◽  
Coupled Model ◽  
Precipitation Change ◽  
Cmip5 Models ◽  
Model Intercomparison ◽  
Projected Change ◽  
Global Land ◽  
Intercomparison Project ◽  
Global Mean

<p><span>The Coupled Model Intercomparison Project Phase 5 (CMIP5) models have showed substantial inter-model spread in estimating annual global-mean precipitation change per one-degree greenhouse-gas-induced warming (precipitation sensitivity), ranging from -4.5</span><span>–4.2</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the Representative Concentration Pathway (RCP) 2.6, the lowest emission scenario, to 0.2–4.0</span><span>%</span><sup><span>o</span></sup><span>C<sup>-1</sup>in the RCP 8.5, the highest emission scenario. The observed-based estimations in the global-mean land precipitation sensitivity during last few decades even show much larger spread due to the considerable natural interdecadal variability, role of anthropogenic aerosol forcing, and uncertainties in observation. This study tackles to better quantify and constrain global land precipitation change in response to global warming by analyzing the new range of Shared Socio-economic Pathway (SSP) scenarios in the </span><span>Coupled Model Intercomparison Project Phase 6 (CMIP6) compared with RCP scenarios in the CMIP5. We show that the range of projected change in annual global-mean land (ocean) precipitation by the end of the 21<sup>st</sup>century relative to the recent past (1995-2014) in the 23 CMIP6 models is over 50% (20%) larger than that in corresponding scenarios of the 40 CMIP5 models. The estimated ranges of precipitation sensitivity in four Tier-1 SSPs are also larger than those in corresponding CMIP5 RCPs. The large increase in projected precipitation change in the highest quartile over ocean is mainly due to the increased number of high equilibrium climate sensitivity (ECS) models in CMIP6 compared to CMIP5, but not over land due to different response of thermodynamic moisture convergence and dynamic processes to global warming. We further discuss key challenges in constraining future precipitation change and source of uncertainties in land precipitation change.</span></p>

scholarly journals Causes of Strengthening and Weakening of ENSO Amplitude under Global Warming in Four CMIP5 Models*

2015 ◽  
Vol 28 (8) ◽  
pp. 3250-3274 ◽  
Author(s):  
Lin Chen ◽  
Tim Li ◽  
Yongqiang Yu
Keyword(s):  
Global Warming ◽  
Wind Stress ◽  
Zonal Wind ◽  
Southern Oscillation ◽  
Heat Budget ◽  
Meridional Overturning Circulation ◽  
Cmip5 Models ◽  
Zonal Wind Stress ◽  
Equatorial Thermocline ◽  
Change In Mean

Abstract The mechanisms for El Niño–Southern Oscillation (ENSO) amplitude change under global warming are investigated through quantitative assessment of air–sea feedback processes in present-day and future climate simulations of four models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Two models (MPI-ESM-MR and MRI-CGCM3) project strengthened ENSO amplitude, whereas the other two models (CCSM4 and FGOALS-g2) project weakened ENSO amplitude. A mixed layer heat budget diagnosis shows that the major cause of the projected ENSO amplitude difference between the two groups is attributed to the changes of the thermocline and zonal advective feedbacks. A weaker (stronger) equatorial thermocline response to a unit anomalous zonal wind stress forcing in the Niño-4 region is found in CCSM4 and FGOALS-g2 (MPI-ESM-MR and MRI-CGCM3). The cause of the different response arises from the change in the meridional scale of ENSO. A narrower (wider) meridional width of sea surface temperature (SST) and zonal wind stress anomalies causes a strengthening (weakening) of the equatorial thermocline response and thus stronger Bjerknes and zonal advective feedbacks, as the subsurface temperature and zonal current anomalies depend on the thermocline response; consequently, the ENSO amplitude increases (decreases). The change of ENSO meridional width is caused by the change in mean meridional overturning circulation in the equatorial Pacific Ocean, which depends on change of mean wind stress and SST warming patterns under global warming.

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.

scholarly journals Revisiting the Northern Mode of East Asian Winter Monsoon Variation and Its Response to Global Warming

2018 ◽  
Vol 31 (21) ◽  
pp. 9001-9014 ◽  
Author(s):  
Hainan Gong ◽  
Lin Wang ◽  
Wen Zhou ◽  
Wen Chen ◽  
Renguang Wu ◽  
...  
Keyword(s):  
Global Warming ◽  
Radiative Forcing ◽  
East Asian Winter Monsoon ◽  
East Asian ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Winter Monsoon ◽  
Cmip5 Models ◽  
Boreal Winter ◽  
Asian Winter Monsoon

This study revisits the northern mode of East Asian winter monsoon (EAWM) variation and investigates its response to global warming based on the ERA dataset and outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. Results show that the observed variation in East Asian surface air temperature (EAT) is tightly coupled with sea level pressure variation in the expanded Siberian high (SH) region during boreal winter. The first singular value decomposition (SVD) mode of the EAT and SH explains 95% of the squared covariance in observations from 1961 to 2005, which actually represents the northern mode of EAWM variation. Meanwhile, the first SVD mode of the EAT and SH is verified to be equivalent to the first empirical orthogonal function mode (EOF1) of the EAT and SH, respectively. Since the leading mode of the temperature variation is significantly influenced by radiative forcing in a rapidly warming climate, for reliable projection of long-term changes in the northern mode of the EAWM, we further employ the EOF1 mode of the SH to represent the northern mode of EAWM variation. The models can well reproduce this coupling between the EAT and SH in historical simulations. Meanwhile, a robust weakening of the northern mode of the EAWM is found in the RCP4.5 scenario, and with stronger warming in the RCP8.5 scenario, the weakening of the EAWM is more pronounced. It is found that the weakening of the northern mode of the EAWM can contribute 6.7% and 9.4% of the warming trend in northern East Asian temperature under the RCP4.5 and RCP8.5 scenarios, respectively.

scholarly journals Reply to “Comment on ‘Comparison of Low-Frequency Internal Climate Variability in CMIP5 Models and Observations’”

2017 ◽  
Vol 30 (23) ◽  
pp. 9773-9782 ◽  
Author(s):  
Anson H. Cheung ◽  
Michael E. Mann ◽  
Byron A. Steinman ◽  
Leela M. Frankcombe ◽  
Matthew H. England ◽  
...  
Keyword(s):  
Low Frequency ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
Multimodel Ensemble ◽  
Internal Climate Variability ◽  
Instrumental Record ◽  
Ensemble Mean ◽  
Intercomparison Project ◽  
The Individual

In a comment on a 2017 paper by Cheung et al., Kravtsov states that the results of Cheung et al. are invalidated by errors in the method used to estimate internal variability in historical surface temperatures, which involves using the ensemble mean of simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to estimate the forced signal. Kravtsov claims that differences between the forced signals in the individual models and as defined by the multimodel ensemble mean lead to errors in the assessment of internal variability in both model simulations and the instrumental record. Kravtsov proposes a different method, which instead uses CMIP5 models with at least four realizations to define the forced component. Here, it is shown that the conclusions of Cheung et al. are valid regardless of whether the method of Cheung et al. or that of Kravtsov is applied. Furthermore, many of the points raised by Kravtsov are discussed in Cheung et al., and the disagreements of Kravtsov appear to be mainly due to a misunderstanding of the aims of Cheung et al.

scholarly journals Attributing Causes of 2015 Record Minimum Sea-Ice Extent in the Sea of Okhotsk

2017 ◽  
Vol 30 (12) ◽  
pp. 4693-4703 ◽  
Author(s):  
Seungmok Paik ◽  
Seung-Ki Min ◽  
Yeon-Hee Kim ◽  
Baek-Min Kim ◽  
Hideo Shiogama ◽  
...  
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Coupled Model ◽  
Internal Variability ◽  
Cmip5 Models ◽  
External Forcing ◽  
Sea Ice Extent ◽  
The Sea Of Okhotsk ◽  
The North

In 2015, the sea ice extent (SIE) over the Sea of Okhotsk (Okhotsk SIE) hit a record low since 1979 during February–March, the period when the sea ice extent generally reaches its annual maximum. To quantify the role of anthropogenic influences on the changes observed in Okhotsk SIE, this study employed a fraction of attributable risk (FAR) analysis to compare the probability of occurrence of extreme Okhotsk SIE events and long-term SIE trends using phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel simulations performed with and without anthropogenic forcing. It was found that because of anthropogenic influence, both the probability of extreme low Okhotsk SIEs that exceed the 2015 event and the observed long-term trends during 1979–2015 have increased by more than 4 times (FAR = 0.76 to 1). In addition, it is suggested that a strong negative phase of the North Pacific Oscillation (NPO) during midwinter (January–February) 2015 also contributed to the 2015 extreme SIE event. An analysis based on multiple linear regression was conducted to quantify relative contributions of the external forcing (anthropogenic plus natural) and the NPO (internal variability) to the observed SIE changes. About 56.0% and 24.7% of the 2015 SIE anomaly was estimated to be attributable to the external forcing and the strong negative NPO influence, respectively. The external forcing was also found to explain about 86.1% of the observed long-term SIE trend. Further, projections from the CMIP5 models indicate that a sea ice–free condition may occur in the Sea of Okhotsk by the late twenty-first century in some models.

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