Description: Surface ocean pH is declining due to anthropogenic atmospheric CO2 uptake with a global decline of ~0.3 possible by 2100. Extracellular pH influences a range of biological processes, including nutrient uptake, calcification and silicification. However, there are poor constraints on how pH levels in the extracellular microenvironment surrounding phytoplankton cells (the phycosphere) differ from bulk seawater. This adds uncertainty to biological impacts of environmental change. Furthermore, previous modelling work suggests that phycosphere pH of small cells is close to bulk seawater, and this has not been experimentally verified. Here we observe under 140 μmol photons/m**2/s the phycosphere pH of Chlamydomonas concordia (5 µm diameter), Emiliania huxleyi (5 µm), Coscinodiscus radiatus (50 µm) and C. wailesii (100 µm) are 0.11 ± 0.07, 0.20 ± 0.09, 0.41 ± 0.04 and 0.15 ± 0.20 (mean ± SD) higher than bulk seawater (pH 8.00), respectively. Thickness of the pH boundary layer of C. wailesii increases from 18 ± 4 to 122 ± 17 µm when bulk seawater pH decreases from 8.00 to 7.78. Phycosphere pH is regulated by photosynthesis and extracellular enzymatic transformation of bicarbonate, as well as being influenced by light intensity and seawater pH and buffering capacity. The pH change alters Fe speciation in the phycosphere, and hence Fe availability to phytoplankton is likely better predicted by the phycosphere, rather than bulk seawater. Overall, the precise quantification of chemical conditions in the phycosphere is crucial for assessing the sensitivity of marine phytoplankton to ongoing ocean acidification and Fe limitation in surface oceans.

Description: The main component of this data set comprises calculated inorganic iron concentrations (Fe' = sum of iron hydroxide species). Inorganic iron is the most bioavailable chemical form of Fe in the ocean. Concentrations of Fe' were calculated according to two models, which we refer to as the discrete ligand model and the continuous binding site model. The discrete ligand model, which is currently applied to calculate Fe speciation in global biogeochemical models, combines dissolved Fe concentrations, conditional stability constants and ligand concentrations to obtain inorganic iron, whilst the continuous distribution model uses the NICA-Donnan model to obtain Fe'. The data supports the manuscript "Climate change decreases biologically available iron pool in the surface ocean." In this manuscript we use the continuous binding site model to show that surface ocean Fe' is sufficient for Fe-replete phytoplankton. We apply new estimates of Fe' to a simple phytoplankton growth model to show that both Fe' and relative growth rates will decrease under the high-end future climate scenario (SSP5-8.5) in all Fe-limited ocean regions, and will mitigate current projections of increased primary productivity in Fe-limited high latitudes regions such as the Southern Ocean. Overall, we demonstrate that Fe-binding site heterogeneity is critical for iron speciation, and must be considered when predicting the response of marine primary producers to ongoing changes in ocean chemistry.

Description: In this paper we introduce a Bayesian framework, which is explicit about prior assumptions, for using model ensembles and observations together to constrain future climate change. The emergent constraint approach has seen broad application in recent years, including studies constraining the equilibrium climate sensitivity (ECS) using the Last Glacial Maximum (LGM) and the mid-Pliocene Warm Period (mPWP). Most of these studies were based on ordinary least squares (OLS) fits between a variable of the climate state, such as tropical temperature, and climate sensitivity. Using our Bayesian method, and considering the LGM and mPWP separately, we obtain values of ECS of 2.7 K (0.6–5.2, 5th–95th percentiles) using the PMIP2, PMIP3, and PMIP4 datasets for the LGM and 2.3 K (0.5–4.4) with the PlioMIP1 and PlioMIP2 datasets for the mPWP. Restricting the ensembles to include only the most recent version of each model, we obtain 2.7 K (0.7–5.2) using the LGM and 2.3 K (0.4–4.5) using the mPWP. An advantage of the Bayesian framework is that it is possible to combine the two periods assuming they are independent, whereby we obtain a tighter constraint of 2.5 K (0.8–4.0) using the restricted ensemble. We have explored the sensitivity to our assumptions in the method, including considering structural uncertainty, and in the choice of models, and this leads to 95 % probability of climate sensitivity mostly below 5 K and only exceeding 6 K in a single and most uncertain case assuming a large structural uncertainty. The approach is compared with other approaches based on OLS, a Kalman filter method, and an alternative Bayesian method. An interesting implication of this work is that OLS-based emergent constraints on ECS generate tighter uncertainty estimates, in particular at the lower end, an artefact due to a flatter regression line in the case of lack of correlation. Although some fundamental challenges related to the use of emergent constraints remain, this paper provides a step towards a better foundation for their potential use in future probabilistic estimations of climate sensitivity.

Description: Surface ocean pH is declining due to anthropogenic atmospheric CO2 uptake with a global decline of ~0.3 possible by 2100. Extracellular pH influences a range of biological processes, including nutrient uptake, calcification and silicification. However, there are poor constraints on how pH levels in the extracellular microenvironment surrounding phytoplankton cells (the phycosphere) differ from bulk seawater. This adds uncertainty to biological impacts of environmental change. Furthermore, previous modelling work suggests that phycosphere pH of small cells is close to bulk seawater, and this has not been experimentally verified. Here we observe under 140 μmol photons·m−2·s−1 the phycosphere pH of Chlamydomonas concordia (5 µm diameter), Emiliania huxleyi (5 µm), Coscinodiscus radiatus (50 µm) and C. wailesii (100 µm) are 0.11 ± 0.07, 0.20 ± 0.09, 0.41 ± 0.04 and 0.15 ± 0.20 (mean ± SD) higher than bulk seawater (pH 8.00), respectively. Thickness of the pH boundary layer of C. wailesii increases from 18 ± 4 to 122 ± 17 µm when bulk seawater pH decreases from 8.00 to 7.78. Phycosphere pH is regulated by photosynthesis and extracellular enzymatic transformation of bicarbonate, as well as being influenced by light intensity and seawater pH and buffering capacity. The pH change alters Fe speciation in the phycosphere, and hence Fe availability to phytoplankton is likely better predicted by the phycosphere, rather than bulk seawater. Overall, the precise quantification of chemical conditions in the phycosphere is crucial for assessing the sensitivity of marine phytoplankton to ongoing ocean acidification and Fe limitation in surface oceans.

Description: Highlights: • Multi-centennial oscillation with 100–200 years periods is evident in proxy data and model simulations during the Holocene. • Multi-centennial oscillation is a global signal and is more significant in the Northern Hemisphere high latitudes. • None of the external forcings is found to be the sole driver of the multi-centennial variability. • It indicates the multi-centennial oscillation may be due to potential internal drivers and essential feedbacks. Abstract: Variability on centennial to multi-centennial timescales is mentioned as a feature in reconstructions of the Holocene climate. As more long transient model simulations with complex climate models become available and efforts have been made to compile large proxy databases, there is now a unique opportunity to study multi-centennial variability with greater detail and a large amount of data than earlier. This paper presents a spectral analysis of transient Holocene simulations from 9 models and 120 proxy records to find the common signals related to oscillation periods and geographic dependencies and discuss the implications for the potential driving mechanisms. Multi-centennial variability is significant in most proxy records, with the dominant oscillation periods around 120–130 years and an average of 240 years. Spectra of model-based global mean temperature (GMT) agree well with proxy evidence with significant multi-centennial variability in all simulations with the dominant oscillation periods around 120–150 years. It indicates a comparatively good agreement between model and proxy data. A lack of latitudinal dependencies in terms of oscillation period is found in both the model and proxy data. However, all model simulations have the highest spectral density distributed over the Northern hemisphere high latitudes, which could indicate a particular variability sensitivity or potential driving mechanisms in this region. Five models also have differentiated forcings simulations with various combinations of forcing agents. Significant multi-centennial variability with oscillation periods between 100 and 200 years is found in all forcing scenarios, including those with only orbital forcing. The different forcings induce some variability in the system. Yet, none appear to be the predominant driver based on the spectral analysis. Solar irradiance has long been hypothesized to be a primary driver of multi-centennial variability. However, all the simulations without this forcing have shown significant multi-centennial variability. The results then indicate that internal mechanisms operate on multi-centennial timescales, and the North Atlantic-Arctic is a region of interest for this aspect.

Description: Numerical modeling enables a comprehensive understanding not only of the Earth's system today, but also of the past. To date, a significant amount of time and effort has been devoted to paleoclimate modeling and analysis, which involves the latest and most advanced Paleoclimate Modelling Intercomparison Project phase 4 (PMIP4). The definition of seasonality, which is influenced by slow variations in the Earth's orbital parameters, plays a key role in determining the calculated seasonal cycle of the climate. In contrast to the classical calendar used today, where the lengths of the months and seasons are fixed, the angular calendar calculates the lengths of the months and seasons according to a fixed number of degrees along the Earth's orbit. When comparing simulation results for different time intervals, it is essential to account for the angular calendar to ensure that the data for comparison are from the same position along the Earth's orbit. Most models use the classical calendar, which can lead to strong distortions of the monthly and seasonal values, especially for the climate of the past. Here, by analyzing daily outputs from multiple PMIP4 model simulations, we examine calendar effects on surface air temperature and precipitation under mid-Holocene, Last Interglacial, and pre-industrial climate conditions. We came to the following conclusions. (a) The largest cooling bias occurs in boreal autumn when the classical calendar is applied for the mid-Holocene and Last Interglacial, due to the fact that the vernal equinox is fixed on 21 March. (b) The sign of the temperature anomalies between the Last Interglacial and pre-industrial in boreal autumn can be reversed after the switch from the classical to angular calendar, particularly over the Northern Hemisphere continents. (c) Precipitation over West Africa is overestimated in boreal summer and underestimated in boreal autumn when the classical seasonal cycle is applied. (d) Finally, month-length adjusted values for surface air temperature and precipitation are very similar to the day-length adjusted values, and therefore correcting the calendar based on the monthly model results can largely reduce the artificial bias. In addition, we examine the calendar effects in three transient simulations for 6–0 ka by AWI-ESM, MPI-ESM, and IPSL-CM. We find significant discrepancies between adjusted and unadjusted temperature values over continents for both hemispheres in boreal autumn, while for other seasons the deviations are relatively small. A drying bias can be found in the summer monsoon precipitation in Africa (in the classical calendar), whereby the magnitude of bias becomes smaller over time. Overall, our study underlines the importance of the application of calendar transformation in the analysis of climate simulations. Neglecting the calendar effects could lead to a profound artificial distortion of the calculated seasonal cycle of surface air temperature and precipitation.

Description: The Atlantic Meridional Overturning Circulation (AMOC) is a key feature of the North Atlantic with global ocean impacts. The AMOC's response to past changes in forcings during the Holocene provides important context for the coming centuries. Here, we investigate AMOC trends using an emerging set of transient simulations using multiple global climate models for the past 6,000 years. Although some models show changes, no consistent trend in overall AMOC strength during the mid-to-late Holocene emerges from the ensemble. We interpret this result to suggest no overall change in AMOC, which fits with our assessment of available proxy reconstructions. The decadal variability of the AMOC does not change in ensemble during the mid- and late-Holocene. There are interesting AMOC changes seen in the early Holocene, but their nature depends a lot on which inputs are used to drive the experiment.