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Research into the response of several climate models to a rise in greenhouse-gas concentrations indicates that interannual variability of Northern Hemisphere wintertime sea-level pressure is dominated by an almost axisymmetric spatial pattern centred over the Arctic. This trend is reproduced only by models that resolve stratospheric dynamics. Models with no dynamical stratosphere do not simulate the observed trend in the Arctic Oscillation. It appears that the stratosphere influences climate at the surface by modulating energy propagation out of the troposphere.

Coral oxygen isotopes (δ18O) from the central equatorial Pacific provide monthly resolved records of El Niño‐Southern Oscillation activity over past centuries to millennia. However, calibration studies using in situ data to assess the relative contributions of warming and freshening to coral δ18O records are exceedingly rare. Furthermore, the fidelity of coral δ18O records under the most severe thermal stress events is difficult to assess. Here, we present six coral δ18O records and in situ temperature, salinity, and seawater δ18O data from Kiritimati Island (2°N, 157°W) spanning the very strong 2015/16 El Niño event. Local sea surface temperature (SST) anomalies of +2.4 ± 0.4°C and seawater δ18O anomalies of −0.19 ± 0.02‰ contribute to the observed coral δ18O anomalies of −0.58 ± 0.05‰, consistent with a ∼70% contribution from SST and ∼30% from seawater δ18O. Our results demonstrate that Kiritimati coral δ18O records can provide reliable reconstructions even during the largest class of El Niño events. Plain Language Summary: Oxygen isotope anomalies in coral skeletons are a well‐established proxy for changes in tropical Pacific Ocean temperature variations, which have a profound impact on weather extremes around the planet. However, only a handful of calibrations exist that quantify the relationship between ocean temperature and coral oxygen isotopic composition at a given site, especially across extreme events where this relationship may vary most strongly. Here we compare ocean temperature data from loggers installed on the reef at Kiritimati Island (2°N, 157°W) to coral oxygen isotopic records spanning the record‐breaking 2015/16 El Niño event. We find that oxygen isotopes in corals provide accurate reconstructions of ocean temperature extremes during this very strong El Niño event, with ∼70% of the signal originating from ocean temperature and the remainder from increased rainfall. Key Points: An ensemble of coral oxygen isotope timeseries from the central equatorial Pacific tracks the 2015/16 El Niño eventCoral oxygen isotope records reflect ∼70% contribution from warming and ∼30% from freshening during the 2015/16 El Niño eventIn situ seawater oxygen isotope data provide quantitative constraints on temperature versus hydrological contributions to coral records [ABSTRACT FROM AUTHOR]

In recent years, with the rise of social media, many more scientists are becoming public communicators. In politicized fields such as climate science, these communications can attract disproportionate attention. The author argues that public statements in such a situation are inevitably advocacy for some position, view, or outcome. However, rather than suggesting that scientists avoid advocacy in a misplaced attempt to remain objective, he recommends that scientists be explicit about the combination of values and science that drives their views, and discusses the ways scientists can ensure that their advocacy remains responsible. [ABSTRACT FROM PUBLISHER]

Stratospheric radiative damping increases as atmospheric carbon dioxide concentration rises. We use the one-dimensional mechanistic models of the QBO to conduct sensitivity experiments and find that when atmospheric carbon dioxide concentration increases, the simulated QBO period shortens due to the enhancing of radiative damping in the stratosphere. This result suggests that increasing stratospheric radiative damping due to rising CO2 may play a role in determining the QBO period in a warming climate along with wave momentum flux entering the stratosphere and tropical vertical residual velocity, both of which also respond to increasing CO2. [ABSTRACT FROM AUTHOR]

Factorisation is widely used in the analysis of numerical simulations. It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system. There are many possible factorisation methods; here we discuss three previously-proposed factorisations that have been applied in the field of climate modelling: the linear factorisation, the Stein and Alpert (1993) factorisation, and the Lunt et al (2012) factorisation. We show that, when more than two variables are being considered, none of these three methods possess all three properties of uniqueness, symmetry, and completeness. Here, we extend each of these factorisations so that they do possess these properties for any number of variables, resulting in three factorisations - the linear-sum factorisation, the shared-interaction factorisation, and the scaled-total factorisation. We show that the linear-sum factorisation and the shared-interaction factorisation reduce to be identical. We present the results of the factorisations in the context of studies that used the previously-proposed factorisations. This reveals that only the linear-sum/shared-interaction factorisation possesses a fourth property - boundedness, and as such we recommend the use of this factorisation in applications for which these properties are desirable. [ABSTRACT FROM AUTHOR]

We outline a new and improved uncertainty analysis for the Goddard Institute for Space Studies Surface Temperature product version 4 (GISTEMP v4). Historical spatial variations in surface temperature anomalies are derived from historical weather station data and ocean data from ships, buoys, and other sensors. Uncertainties arise from measurement uncertainty, changes in spatial coverage of the station record, and systematic biases due to technology shifts and land cover changes. Previously published uncertainty estimates for GISTEMP included only the effect of incomplete station coverage. Here, we update this term using currently available spatial distributions of source data, state‐of‐the‐art reanalyses, and incorporate independently derived estimates for ocean data processing, station homogenization, and other structural biases. The resulting 95% uncertainties are near 0.05 °C in the global annual mean for the last 50 years and increase going back further in time reaching 0.15 °C in 1880. In addition, we quantify the benefits and inherent uncertainty due to the GISTEMP interpolation and averaging method. We use the total uncertainties to estimate the probability for each record year in the GISTEMP to actually be the true record year (to that date) and conclude with 86% likelihood that 2016 was indeed the hottest year of the instrumental period (so far). Key Points: A total uncertainty analysis for GISTEMP is presented for the first timeUncertainty in global mean surface temperature is roughly 0.05 degrees Celsius in recent decades increasing to 0.15 degrees Celsius in the nineteenth centuryAnnual mean uncertainties are small relative to the long‐term trend [ABSTRACT FROM AUTHOR]

In the early Pleistocene, global temperature cycles predominantly varied with ~41‐kyr (obliquity‐scale) periodicity. Atmospheric greenhouse gas concentrations likely played a role in these climate cycles; marine sediments provide an indirect geochemical means to estimate early Pleistocene CO2. Here we present a boron isotope‐based record of continuous high‐resolution surface ocean pH and inferred atmospheric CO2 changes. Our results show that, within a window of time in the early Pleistocene (1.38–1.54 Ma), pCO2 varied with obliquity, confirming that, analogous to late Pleistocene conditions, the carbon cycle and climate covaried at ~1.5 Ma. Pairing the reconstructed early Pleistocene pCO2 amplitude (92 ± 13 μatm) with a comparably smaller global surface temperature glacial/interglacial amplitude (3.0 ± 0.5 K) yields a surface temperature change to CO2 radiative forcing ratio of S[CO2]~0.75 (±0.5) °C−1·W−1·m−2, as compared to the late Pleistocene S[CO2] value of ~1.75 (±0.6) °C−1·W−1·m−2. This direct comparison of pCO2 and temperature implicitly incorporates the large ice sheet forcing as an internal feedback and is not directly applicable to future warming. We evaluate this result with a simple climate model and show that the presumably thinner, though extensive, northern hemisphere ice sheets would increase surface temperature sensitivity to radiative forcing. Thus, the mechanism to dampen actual temperature variability in the early Pleistocene more likely lies with Southern Ocean circulation dynamics or antiphase hemispheric forcing. We also compile this new carbon dioxide record with published Plio‐Pleistocene δ11B records using consistent boundary conditions and explore potential reasons for the discrepancy between Pliocene pCO2 based on different planktic foraminifera. Key Points: Early Pleistocene pCO2 roughly varied with obliquity cyclesInterglacial pCO2 was similar in the early and late Pleistocene; glacial pCO2 declined over the mid‐Pleistocene transitionDiscrepancies between δ11B values and corresponding pCO2 estimates from G. ruber and T. sacculifer are observed and may indicate evolving vital effects [ABSTRACT FROM AUTHOR]

The rationale for using multi-model ensembles in climate change projections and impacts research is often based on the expectation that different models constitute independent estimates, so that a range of models allows a better characterisation of the uncertainties in the representation of the climate system than a single model. However, it is known that research groups share literature, ideas for representations of processes, parameterisations, evaluation data sets and even sections of model code. Thus, nominally different models might have similar biases because of similarities in the way they represent a subset of processes, or even be near duplicates of others, weakening the assumption that they constitute independent estimates. If there are near-replicates of some models, then treating all models equally is likely to bias the inferences made using these ensembles. The challenge is to establish the degree to which this might be true for any given application. While this issue is recognized by many in the community, quantifying and accounting for model dependence in anything other than an ad-hoc way is challenging. Here we present a synthesis of the range of disparate attempts to define, quantify and address model dependence in multi-model climate ensembles in a common conceptual framework, and provide guidance on how users can test the efficacy of approaches that move beyond the equally weighted ensemble. In the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6), several new models that are closely related to existing models are anticipated, as well as large ensembles from some models. We argue that quantitatively accounting for dependence in addition to model performance, and thoroughly testing the effectiveness of the approach used will be key to a sound interpretation of the CMIP ensembles in future scientific studies. [ABSTRACT FROM AUTHOR]

A study demonstrates the way in which the climate models can be used to study the potential dangers of anthropogenic interference with the earth's climate system, though it is proven that climate is a phenomenon that emerges from various complicated interactions among small-scale physical systems. The models used are capable of accurately capturing the fundamental aspects of air, ocean and sea-ice circulations and hence can be used to estimate the consequences of humankind's ongoing planetary experiment.