Trend analysis and transient climate sensitivity revealed by CMIP6

2020 ◽  
Author(s):  
Menghan Yuan ◽  
Thomas Leirvik
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Published Data ◽  
The Difference ◽  
Climate Forcings ◽  
The Moment

<p>CMIP6 (Coupled Model Intercomparison Project Version 6) is currently publishing updates on simulations for Global Climate Models (GCMs). In this paper, we focus on analyzing surface temperature and downward solar radiation (SDSR), which are two essential variables in estimating the transient climate sensitivity (TCS). We carry out the analysis for five GCMs that have published data at the moment. More GCMs will be included in the analysis when data is available. The research period dates from 1960 to 2014, providing the latest available projection for climate forcings. Temperature projections accord reasonably well with observations. This is no surprise, as data for CMIP5 was also aligned with observations.  On the other hand, a striking improvement has been observed with respect to SDSR. According to Storelvmo et al. (2018), CMIP5 models showed no statistically significant trend over time and revealed egregious mismatch with observations, casting major concerns about their fidelity. The data from CMIP6 models, however, this mismatch between simulations and observations is substantially alleviated. Not only is a negative trend recorded, but the significant fall around the beginning of the 1990s, due to the Mount Pinatubo eruption, is also reproduced, though with a slightly smaller scale compared to the observations in that period.<br>Based on the econometric framework from Phillips et al. (2019), we estimate the TCS for five GCMs. We find that the TCS estimates range from 2.03K to 2.65K. Each reported TCS for the five GCM’s are within it’s corresponding 95% confidence interval for the estimated TCS. It is worth noticing that a 25-year rolling window estimation indicates that average TCS for the GCMs varies greatly along time, though it has a significant upward trend from the beginning of the 1990s until 2009, and flattens, or even decreases, afterward.<br>We also compute the sample average of the TCS estimates. We find that for the period 1964-2005, which is used in Phillips et al. (2019), the average TCS is 1.82 for the CMIP5 models, and 2.07 for CMIP6. The difference is not significant. For the 1964-2014 period, however, the average TCS estimate for CMIP6 is 2.38, which is significantly higher than the average CMIP5 estimates. Since we find that the CMIP6 simulations reproduce observed trends in RSDS much better than the CMIP5 simulations, when compared to observations, this indicates both that the econometric framework of Phillips et al.(2019) is working very well and captures key drivers of the climate, and that the true TCS is most likely closer to the estimated TCS for observations.</p>

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>

Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models

2015 ◽  
Vol 28 (23) ◽  
pp. 9298-9312 ◽  
Author(s):  
Kevin M. Grise ◽  
Lorenzo M. Polvani ◽  
John T. Fasullo
Keyword(s):  
Southern Ocean ◽  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Radiation Budget ◽  
Cmip5 Models ◽  
Net Radiation ◽  
Subtropical Clouds

Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

scholarly journals The Climatology of the Atmospheric Boundary Layer in Contemporary Global Climate Models

2018 ◽  
Vol 31 (22) ◽  
pp. 9151-9173 ◽  
Author(s):  
Richard Davy
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
General Circulation Models ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Seasonal Cycles ◽  
Layer Depth

Here, we present the climatology of the planetary boundary layer depth in 18 contemporary general circulation models (GCMs) in simulations of the late-twentieth-century climate that were part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). We used a bulk Richardson methodology to establish the boundary layer depth from the 6-hourly synoptic-snapshot data available in the CMIP5 archives. We present an ensemble analysis of the climatological mean, diurnal cycle, and seasonal cycle of the boundary layer depth in these models and compare it to the climatologies from the ECMWF ERA-Interim reanalysis. Overall, we find that the CMIP5 models do a reasonably good job of reproducing the distribution of mean boundary layer depth, although the geographical patterns vary considerably between models. However, the models are biased toward weaker diurnal and seasonal cycles in the boundary layer depth and generally produce much deeper boundary layers at night and during the winter than are found in the reanalysis. These biases are likely to reduce the ability of these models to accurately represent other properties of the diurnal and seasonal cycles, and the sensitivity of these cycles to climate change.

scholarly journals Brief communication: Recent changes in summer Greenland blocking captured by none of the CMIP5 models

2018 ◽  
Vol 12 (10) ◽  
pp. 3287-3292 ◽  
Author(s):  
Edward Hanna ◽  
Xavier Fettweis ◽  
Richard J. Hall
Keyword(s):  
North Atlantic ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Coupled Model ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Cmip5 Models

Abstract. Recent studies note a significant increase in high-pressure blocking over the Greenland region (Greenland Blocking Index, GBI) in summer since the 1990s. Such a general circulation change, indicated by a negative trend in the North Atlantic Oscillation (NAO) index, is generally highlighted as a major driver of recent surface melt records observed on the Greenland Ice Sheet (GrIS). Here we compare reanalysis-based GBI records with those from the Coupled Model Intercomparison Project 5 (CMIP5) suite of global climate models over 1950–2100. We find that the recent summer GBI increase lies well outside the range of modelled past reconstructions and future GBI projections (RCP4.5 and RCP8.5). The models consistently project a future decrease in GBI (linked to an increase in NAO), which highlights a likely key deficiency of current climate models if the recently observed circulation changes continue to persist. Given well-established connections between atmospheric pressure over the Greenland region and air temperature and precipitation extremes downstream, e.g. over northwest Europe, this brings into question the accuracy of simulated North Atlantic jet stream changes and resulting climatological anomalies over densely populated regions of northern Europe as well as of future projections of GrIS mass balance produced using global and regional climate models.

scholarly journals Recent changes in summer Greenland blocking captured by none of the CMIP5 models

2018 ◽  
Author(s):  
Edward Hanna ◽  
Xavier Fettweis ◽  
Richard J. Hall
Keyword(s):  
North Atlantic ◽  
General Circulation ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Coupled Model ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Global Climate Models ◽  
Cmip5 Models

Abstract. Recent studies note a significant increase in high-pressure blocking over the Greenland region (Greenland Blocking Index, GBI) in summer since the 1990s. Such a general circulation change, indicated by a negative trend in the North Atlantic Oscillation (NAO) index, is generally highlighted as a major driver of recent surface melt records observed on the Greenland Ice Sheet (GrIS). Here we compare reanalysis-based GBI records with those from the Coupled Model Intercomparison Project 5 (CMIP5) suite of global climate models over 1950–2100. We find that the recent summer GBI increase lies well outside the range of modelled past reconstructions (Historical scenario) and future GBI projections (RCP4.5 and RCP8.5). The models consistently project a future decrease in GBI (linked to an increase in NAO), which highlights a likely key deficiency of current climate models if the recently-observed circulation changes continue to persist. Given well-established connections between atmospheric pressure over the Greenland region and air temperature and precipitation extremes downstream, e.g. over Northwest Europe, this brings into question the accuracy of simulated North Atlantic jet stream changes and resulting climatological anomalies over densely populated regions of northern Europe as well as of future projections of GrIS mass balance produced using global and regional climate models.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models

2021 ◽  
Vol 12 (2) ◽  
pp. 367-386
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Future Climate Change ◽  
Global Mean Temperature

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP6 are of interest. Here, we analyze 32 models of the latest CMIP6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with a high agreement between the models independent of the SSP if global warming is the dominant forcing of the monsoon dynamics as it is in the 21st century; the multi-model mean for JJAS projects an increase of 0.33 mm d−1 and 5.3 % per kelvin of global warming. This is significantly higher than in the CMIP5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP6 simulations largely confirm the findings from CMIP5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

scholarly journals Combining Emergent Constraints for Climate Sensitivity

2020 ◽  
Vol 33 (17) ◽  
pp. 7413-7430 ◽  
Author(s):  
Christopher S. Bretherton ◽  
Peter M. Caldwell
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Climate Feedback ◽  
Feedback Parameter ◽  
Emergent Constraints ◽  
Intercomparison Project ◽  
Special Case

AbstractA method is proposed for combining information from several emergent constraints into a probabilistic estimate for a climate sensitivity proxy Y such as equilibrium climate sensitivity (ECS). The method is based on fitting a multivariate Gaussian PDF for Y and the emergent constraints using an ensemble of global climate models (GCMs); it can be viewed as a form of multiple linear regression of Y on the constraints. The method accounts for uncertainties in sampling this multidimensional PDF with a small number of models, for observational uncertainties in the constraints, and for overconfidence about the correlation of the constraints with the climate sensitivity. Its general form (Method C) accounts for correlations between the constraints. Method C becomes less robust when some constraints are too strongly related to each other; this can be mitigated using regularization approaches such as ridge regression. An illuminating special case, Method U, neglects any correlations between constraints except through their mutual relationship to the climate proxy; it is more robust to small GCM sample size and is appealingly interpretable. These methods are applied to ECS and the climate feedback parameter using a previously published set of 11 possible emergent constraints derived from climate models in the Coupled Model Intercomparison Project (CMIP). The ±2σ posterior range of ECS for Method C with no overconfidence adjustment is 4.3 ± 0.7 K. For Method U with a large overconfidence adjustment, it is 4.0 ± 1.3 K. This study adds confidence to past findings that most constraints predict higher climate sensitivity than the CMIP mean.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

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.

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

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