17 research outputs found
D3.2. End-of-Project KGSIR on interaction of ocean heat uptake and radiative feedback change via SST-pattern change
No full text<p><strong>Work Package:</strong> WP3</p><p><strong>Deliverable number:</strong> D3.2</p><p><strong>Deliverable title:</strong> End-of-Project KGSIR on interaction of ocean heat uptake and radiative feedback change via SST-pattern change</p><p><strong>Key messages:</strong></p><ul><li>Post 1980 the Earth warmed with a configuration of SST patterns (cooling in the eastern Pacific and Southern Ocean) that results in feedbacks that are uncorrelated with – and indicating much lower equilibrium climate sensitivity than—that expected for long-term CO2 increase.</li><li>Satellite observations of changes in top-of-atmosphere radiative fluxes since 1985 are in agreement with Atmospheric General Circulation Model (AGCM) simulations forced with observed SST and sea-ice, and are suggestive of a relationship between the pattern effect and ocean heat uptake efficiency.</li><li>The 2015/16 El-Nino had a substantial impact on the Earth's diagnosed feedback parameter, reducing it by ~25% due to a large warming of the eastern Pacific. Since then three La Nina's in a row have been observed. The impact of this on radiative feedback is yet to be assessed. Continuity of satellite record radiation budget crucial to monitoring this.</li></ul><p><strong>Cite as:</strong></p><p>Andrews T., Mauritsen T., Olonscheck D., Smith D. and Toniazzo T. 2022. Interaction of ocean heat uptake and radiative feedback change via SST-pattern change over recent decades. Knowledge Gains: Summary and Implication Report. The CONSTRAIN Project. DOI: <a href="https://doi.org/10.5281/zenodo.10125154">10.5281/zenodo.10125154</a>.</p>
D4.8. KGSIR on knowledge gained in understanding of climate sensitivity and its role in projections
No full text<p><strong>Work Package:</strong> WP4</p><p><strong>Deliverable number:</strong> D4.8</p><p><strong>Deliverable title:</strong> KGSIR on knowledge gained in understanding of climate sensitivity and its role in projections</p><p><strong>Key messages:</strong></p><ul><li>Both CO2 and non-CO2 greenhouse gas emissions must be decreased as quickly as possible to maintain a chance of not exceeding the global temperature limits in the Paris Agreement.</li><li>Using historical warming to constrain future climate projections, we narrow down the uncertainty in climate sensitivity and projected warming by 30-50%.</li><li>We provide a framework to estimate the remaining carbon budget that enables taking into account uncertainties in climate sensitivity and other feedbacks.</li><li>The median estimate for the remaining carbon budget given a 1.5°C limit was 440 GtCO2 from 2020 onwards, which will be surpassed in 2032 at the current levels of emissions.</li><li>Some climate models of the latest generation (CMIP6) have projected very strong warming, however these models represent unlikely futures as they simulate implausibly strong reductions in shallow cloud coverage and are difficult to reconcile with historical data.</li><li>Due to decadal changes in the spatial pattern of warming (the "pattern effect"), climate sensitivity as derived from historical data is probably underestimated. Once this effect is accounted for, "observed" climate sensitivity is in better agreement with other lines of evidence.</li></ul><p><strong>Cite as:</strong></p><p>Humphrey V., Merrifield A.L., Rogelj J., Lamboll R., Olonscheck D., Mauritsen T., Ribes A. and Knutti R. 2023. Climate sensitivity, TCRE, and its role in projections. Knowledge Gains: Summary and Implication Report. The CONSTRAIN Project. DOI: <a href="https://doi.org/10.5281/zenodo.10159472">10.5281/zenodo.10159472</a>.</p>
Consistently estimating internal climate variability from climate model simulations
No full textAbstractThis paper introduces and applies a new method to consistently estimate internal climate variability for all models within a multi-model ensemble. The method regresses each model?s estimate of internal variability from the preindustrial control simulation on the variability derived from a model?s ensemble simulations, thus providing practical evidence of the quasi-ergodic assumption. The method allows one to test in a multi-model consensus view how the internal variability of a variable changes for different forcing scenarios. Applying the method to the CMIP5 model ensemble shows that the internal variability of global-mean surface air temperature remains largely unchanged for historical simulations and might decrease for future simulations with a large CO2 forcing. Regionally, the projected changes reveal likely increases in temperature variability in the tropics, subtropics and polar regions and extremely likely decreases in mid-latitudes. Applying the method to sea-ice volume and area shows that their internal variability decreases extremely likely or likely and proportionally to their mean state, except for Arctic sea-ice area, which shows no consistent change across models. For the evaluation of CMIP5 simulations of Arctic and Antarctic sea ice the method confirms that internal variability can explain most of the models? deviation from observed trends, but often not the models? deviation from the observed mean states. Our method benefits from a large number of models and long pre-industrial control simulations, but requires only a small number of ensemble simulations. The method allows for a consistent consideration of internal variability in multi-model studies and thus fosters our understanding of the role of internal variability in a changing climate
Arctic sea-ice variability is primarily driven by atmospheric temperature fluctuations
No full textThe anthropogenically forced decline of Arctic sea ice is superimposed on strong internal variability. Possible drivers for this variability include fluctuations in surface albedo, clouds and water vapour, surface winds and poleward atmospheric and oceanic energy transport, but their relative contributions have not been quantified. By isolating the impact of the individual drivers in an Earth system model, we here demonstrate that internal variability of sea ice is primarily caused directly by atmospheric temperature fluctuations. The other drivers together explain only 25% of sea-ice variability. The dominating impact of atmospheric temperature fluctuations on sea ice is consistent across observations, reanalyses and simulations from global climate models. Such atmospheric temperature fluctuations occur due to variations in moist-static energy transport or local ocean heat release to the atmosphere. The fact that atmospheric temperature fluctuations are the key driver for sea-ice variability limits prospects of interannual predictions of sea ice, and suggests that observed record lows in Arctic sea-ice area are a direct response to an unusually warm atmosphere
Coupled climate models systematically underestimate radiation response to surface warming
No full textAbstract A realistic representation of top‐of‐the‐atmosphere (TOA) radiation response to surface warming is key for trusting climate model projections. We show that coupled models with freely evolving ocean‐atmosphere interactions systematically underestimate the observed global TOA radiation trend during 2001–2022 in 552 simulations. Locally, even if a simulation spontaneously reproduces observed surface temperature trends, TOA radiation trends are more likely under‐ than overestimated. This response bias stems from the models' inability to reproduce the observed large‐scale surface warming pattern and from errors in the atmospheric physics affecting short‐ and longwave radiation. Models with a better representation of the TOA radiation response to local surface warming have a relatively low equilibrium climate sensitivity. Our bias metric is a novel process‐based approach which links a model's current response to climate change to its behavior in the future
Broad consistency between observed and simulated trends in the sea surface temperature patterns
No full textUsing seven single-model ensembles and the two multimodel ensembles CMIP5 and CMIP6, we show that observed and simulated trends in sea surface temperature (SST) patterns are globally consistent when accounting for internal variability. Some individual ensemble members simulate trends in large-scale SST patterns that closely resemble the observed ones. Observed regional trends that lie at the outer edge of the models' internal variability range allow two nonexclusive interpretations: (a) Observed trends are unusual realizations of the Earth's possible behavior and/or (b) the models are systematically biased but large internal variability leads to some good matches with the observations. The existing range of multidecadal SST trends is influenced more strongly by large internal variability than by differences in the model formulation or the observational data sets. ©2020. The Authors
Arctic marine heatwaves forced by greenhouse gases and triggered by abrupt sea-ice melt
No full textAbstract Since 2007, unprecedented marine heatwave events are occurring over the Arctic Ocean. Here we identify the fraction of the likelihood of Arctic marine heatwaves magnitude that is attributable to greenhouse gas forcing. Results reveal that Arctic marine heatwaves are primarily triggered by an abrupt sea-ice retreat, which coincides with the maximum downward radiative fluxes. Up to 82% of the sea surface temperature variability over the shallow Arctic marginal seas, where marine heatwaves are prone to occur, can be explained by net accumulation of seasonal surface heat flux in the ocean. Event attribution analysis demonstrates that the 103-day long 2020 event – the most intense (4 ∘C) recorded so far in the Arctic – would be exceptionally unlikely in the absence of greenhouse gas forcing in terms of both intensity and duration. Our further results imply that if greenhouse gas emissions continue to rise, along with the expansion of first-year ice extent, moderate marine heatwaves in the Arctic will very likely persistently reoccur
A basic effect of cloud radiative effects on tropical sea surface temperature variability
No full textCloud radiative effects (CREs) are known to play a central role in governing the long-term mean distribution of sea surface temperatures (SSTs). Very recent work suggests that CREs may also play a role in governing the variability of SSTs in the context of El Niño–Southern Oscillation. Here, the authors exploit numerical simulations in the Max Planck Institute Earth System Model with two different representations of CREs to demonstrate that coupling between CREs and the atmospheric circulation has a much more general and widespread effect on tropical climate than that indicated in previous work. The results reveal that coupling between CREs and the atmospheric circulation leads to robust increases in SST variability on time scales longer than a month throughout the tropical oceans. Remarkably, cloud–circulation coupling leads to more than a doubling of the amplitude of decadal-scale variability in tropical-mean SSTs. It is argued that the increases in tropical SST variance derive primarily from the coupling between SSTs and shortwave CREs: Coupling increases the memory in shortwave CREs on hourly and daily time scales and thus reddens the spectrum of shortwave CREs and increases their variance on time scales spanning weeks to decades. Coupling between SSTs and CREs does not noticeably affect the variance of SSTs in the extratropics, where the effects from variability in CREs on the surface energy budget are much smaller than the effects from the turbulent heat fluxes. The results indicate a basic but critical role of CREs in climate variability throughout the tropics
Decomposing the effects of ocean warming on chlorophyll a concentrations into physically and biologically driven contributions
No full textISSN:1748-9326ISSN:1748-9318ISSN:1748-931
Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century
No full textGlobal warming is expected to not only impact mean temperatures but also temperature variability, substantially altering climate extremes. Here we show that human-caused changes in internal year-to-year temperature variability are expected to emerge from the unforced range by the end of the 21st century across climate model initial-condition large ensembles forced with a strong global warming scenario. Different simulated changes in globally averaged regional temperature variability between models can be explained by a trade-off between strong increases in variability on tropical land and substantial decreases in high latitudes, both shown by most models. This latitudinal pattern of temperature variability change is consistent with loss of sea ice in high latitudes and changes in vegetation cover in the tropics. Instrumental records are broadly in line with this emerging pattern, but have data gaps in key regions. Paleoclimate proxy reconstructions support the simulated magnitude and distribution of temperature variability. Our findings strengthen the need for urgent mitigation to avoid unprecedented changes in temperature variability
