. The Scenario Model Intercomparison Project (ScenarioMIP) defines and coordinates the primary future climate projections within the Coupled Model Intercomparison Project Phase 6 (CMIP6). This paper presents a range of its outcomes by synthesizing results from the participating global coupled Earth system models for concentration driven simulations. We limit our scope to the analysis of strictly geophysical outcomes: mainly global averages and spatial patterns of change for surface air temperature and precipitation. We also compare CMIP6 projections to CMIP5 results, especially for those scenarios that were designed to provide continuity across the CMIP phases, at the same time highlighting important differences in forcing composition, as well as in results. The range of future temperature and precipitation changes by the end of the century encompassing the Tier 1 experiments (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) and SSP1-1.9 spans a larger range of outcomes compared to CMIP5, due to higher warming (by 1.15 °C) reached at the upper end of the 5–95 % envelope of the highest scenario, SSP5-8.5. This is due to both the wider range of radiative forcing that the new scenarios cover and to higher climate sensitivities in some of the new models compared to their CMIP5 predecessors. Spatial patterns of change for temperature and precipitation averaged over models and scenarios have familiar features, and an analysis of their variations confirms model structural differences to be the dominant source of uncertainty. Models also differ with respect to the size and evolution of internal variability as measured by individual models' initial condition ensembles' spread, according to a set of initial condition ensemble simulations available under SSP3-7.0. The same experiments suggest a tendency for internal variability to decrease along the course of the century, a new result that will benefit from further analysis over a larger set of models. Benefits of mitigation, all else being equal in terms of societal drivers, appear clearly when comparing scenarios developed under the same SSP, but to which different degrees of mitigation have been applied. It is also found that a mild overshoot in temperature of a few decades in mid-century, as represented in SSP5-3.4OS, does not affect the end outcome in terms of temperature and precipitation changes by 2100, which return to the same level as those reached by the gradually increasing SSP4-3.4. Central estimates of the time at which the ensemble means of the different scenarios reach a given warming level show all scenarios reaching 1.5 °C of warming compared to the 1850–1900 baseline in the second half of the current decade, with the time span between slow and fast warming covering 20–28 years from present. 2 °C of warming is reached as early as the late '30s by the ensemble mean under SSP5-8.5, but as late as the late '50s under SSP1-2.6. The highest warming level considered, 5 °C, is reached only by the ensemble mean under SSP5-8.5, and not until the mid-90s.

/pdf/climate-model-projections-from-the-scenario-model-3mmuduzuwg.pdf

Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6

Cholesterol is an integral component of eukaryotic cell membranes and a key molecule in controlling membrane fluidity, organization, and other physicochemical parameters. It also plays a regulatory function in antibiotic drug resistance and the immune response of cells against viruses, by stabilizing the membrane against structural damage. While it is well understood that, structurally, cholesterol exhibits a densification effect on fluid lipid membranes, its effects on membrane bending rigidity are assumed to be nonuniversal; i.e., cholesterol stiffens saturated lipid membranes, but has no stiffening effect on membranes populated by unsaturated lipids, such as 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC). This observation presents a clear challenge to structure-property relationships and to our understanding of cholesterol-mediated biological functions. Here, using a comprehensive approach-combining neutron spin-echo (NSE) spectroscopy, solid-state deuterium NMR (2H NMR) spectroscopy, and molecular dynamics (MD) simulations-we report that cholesterol locally increases the bending rigidity of DOPC membranes, similar to saturated membranes, by increasing the bilayer's packing density. All three techniques, inherently sensitive to mesoscale bending fluctuations, show up to a threefold increase in effective bending rigidity with increasing cholesterol content approaching a mole fraction of 50%. Our observations are in good agreement with the known effects of cholesterol on the area-compressibility modulus and membrane structure, reaffirming membrane structure-property relationships. The current findings point to a scale-dependent manifestation of membrane properties, highlighting the need to reassess cholesterol's role in controlling membrane bending rigidity over mesoscopic length and time scales of important biological functions, such as viral budding and lipid-protein interactions.

https://www.pnas.org/content/pnas/117/36/21896.full.pdf

How cholesterol stiffens unsaturated lipid membranes

 Climate sensitivity to CO2 remains the key uncertainty in projections of future climate change Transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets Equilibrium climate sensitivity (ECS) is vital for understanding longer-term climate change and stabilisation targets In the IPCC 5th Assessment Report (AR5), the stated “likely” ranges (16 %–84 % confidence) of TCR (10–25 K) and ECS (15–45 K) were broadly consistent with the ensemble of CMIP5 Earth system models (ESMs) available at the time However, many of the latest CMIP6 ESMs have larger climate sensitivities, with 5 of 34 models having TCR values above 25 K and an ensemble mean TCR of 20±04  K Even starker, 12 of 34 models have an ECS value above 45 K On the face of it, these latest ESM results suggest that the IPCC likely ranges may need revising upwards, which would cast further doubt on the feasibility of the Paris targets Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to constrain the likely range of TCR to 13–21 K, with a central estimate of 168 K We reach this conclusion through an emergent constraint approach which relates the value of TCR linearly to the global warming from 1975 onwards This is a period when the signal-to-noise ratio of the net radiative forcing increases strongly, so that uncertainties in aerosol forcing become progressively less problematic We find a consistent emergent constraint on TCR when we apply the same method to CMIP5 models Our constraints on TCR are in good agreement with other recent studies which analysed CMIP ensembles The relationship between ECS and the post-1975 warming trend is less direct and also non-linear However, we are able to derive a likely range of ECS of 19–34 K from the CMIP6 models by assuming an underlying emergent relationship based on a two-box energy balance model Despite some methodological differences; this is consistent with a previously published ECS constraint derived from warming trends in CMIP5 models to 2005 Our results seem to be part of a growing consensus amongst studies that have applied the emergent constraint approach to different model ensembles and to different aspects of the record of global warming

/pdf/emergent-constraints-on-transient-climate-response-tcr-and-4csw52k6tx.pdf

Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models

The elastic and viscous properties of self-assembled amphiphilic membranes dictate the intricate hierarchy of their structure and dynamics ranging from the diffusion of individual molecules to the large-scale deformation of the membrane. We previously demonstrated that neutron spin echo spectroscopy measurements of model amphiphilic membranes can access the naturally occurring submicrosecond membrane motions, such as bending and thickness fluctuations. Here we show how the experimentally measured fluctuation parameters can be used to determine the inherent membrane properties and demonstrate how membrane viscosity and compressibility modulus are influenced by lipid composition in a series of simple phosphatidylcholine bilayers with different tail lengths as a function of temperature. This approach highlights the interdependence of the bilayer elastic and viscous properties and the collective membrane dynamics and opens new avenues to investigating the mechanical properties of more complex and biologically...

https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=923704

Probing Elastic and Viscous Properties of Phospholipid Bilayers Using Neutron Spin Echo Spectroscopy

Diffusion dynamics of charged nanoparticles on the lipid membrane is of essential importance to cellular functioning. Yet a fundamental insight into electrostatics-mediated diffusion dynamics of charged nanoparticles on the membrane is lacking and remains to be an urgent issue. Here we present the computational investigation to uncover the pivotal role of electrostatics in the diffusion dynamics of charged nanoparticles on the lipid membrane. Our results demonstrate diffusive behaviors and directional transport of a charged nanoparticle, significantly depending on the sign and spatial distribution of charges on its surface. In contrast to the Fickian diffusion of neutral nanoparticles, randomly charged nanoparticles undergo superdiffusive transport with directionality. However, the dynamics of uniformly charged nanoparticles favors Fickian diffusion that is significantly enhanced. Such observations can be explained in term of electrostatics-induced surface reconstruction and fluctuation of lipid membrane....

Diffusion and Directionality of Charged Nanoparticles on Lipid Bilayer Membrane

The two-dimensional fluidity of lipid bilayers enables the motion of membrane-bound macromolecules and is therefore crucial to biological function. Microrheological methods that measure fluid viscosity via the translational diffusion of tracer particles are challenging to apply and interpret for membranes, due to uncertainty about the local environment of the tracers. Here, we demonstrate a new technique in which determination of both the rotational and translational diffusion coefficients of membrane-linked particles enables quantification of viscosity, measurement of the effective radii of the tracers, and assessment of theoretical models of membrane hydrodynamics. Surprisingly, we find a wide distribution of effective tracer radii, presumably due to a variable number of lipids linked to each tracer particle. Furthermore, we show for the first time that a protein involved in generating membrane curvature, the vesicle trafficking protein Sar1p, dramatically increases membrane viscosity. Using the rheological method presented here, therefore, we are able to reveal a class of previously unknown couplings between protein activity and membrane mechanics.

Measuring lipid membrane viscosity using rotational and translational probe diffusion.

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions. Plain Language Summary Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data.

Magnitudes and Spatial Patterns of Interdecadal Temperature Variability in CMIP6

Arctic sea-ice extent (SIE) has declined drastically in recent decades, yet its evolution prior to the satellite era is highly uncertain. Studies using SIE observations find little variability prio...

/pdf/arctic-sea-ice-variability-during-the-instrumental-era-4exctensm5.pdf

Arctic sea ice variability during the Instrumental Era

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2020EA001467

Do Multi-Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?

Policy makers need information about future climate change on spatial scales much finer than is available from typical climate model grids. New and creative methods are being advanced to downscale climate change projections with statistical methods. Important requirements are to reliably downscale the climate parameter means, variability, extremes and trends, while preserving spatial and temporal correlations and permitting uncertainty quantification. In this proof-of-concept study, datasets derived from both observations and climate models were used together to train and test statistical methods. Two machine learning techniques—artificial neural networks and random forests—were tested on the problem of using coarse-scale climate projections (here represented by ERA-Interim reanalyses) to create temperature predictions at specific locations in areas of complex terrain. The methods are trained on and validated by temperature readings from mesonet weather stations in Colorado. This work has implications for fire prevention and water resources management, among other applications.

M. Kathleen Brennan

Papers

Measuring lipid membrane viscosity using rotational and translational probe diffusion.

Magnitudes and Spatial Patterns of Interdecadal Temperature Variability in CMIP6

Arctic sea ice variability during the Instrumental Era

Do Multi-Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?

Exploring the Application of Machine Learning for Downscaling Climate Projections