Search

Estimation of climate sensitivity is fundamental to assessing how global climate will warm as atmospheric CO2 ${\mathrm{C}\mathrm{O}}_{2}$ concentration increases. Geological archives of environmental change provide insights into Earth's past climate, but the incomplete nature of paleoclimate reconstructions and their inherent uncertainties make estimation of climate sensitivity challenging. Thus, quantifying climate sensitivity and assessing how it changed through geological time requires statistical frameworks that can handle data uncertainties in a principled fashion. Here we demonstrate some of the hurdles to estimating climate sensitivity, with a focus on current statistical techniques that may underestimate both climate sensitivity and its associated uncertainty. To solve these issues, we present a Bayesian error‐in‐variables regression model, which can yield estimates of climate sensitivity without bias. The regression model is flexible and can account for data point uncertainties with a known parametric form. The utility of this approach is demonstrated by estimating specific climate sensitivity with uncertainty for the Eocene. Plain Language Summary: As atmospheric CO2 ${\mathrm{C}\mathrm{O}}_{2}$ increases due to human activities, the Earth will warm. But how much warming can be expected? Climate sensitivity describes how much global average surface temperature will warm with a given increase in atmospheric CO2 ${\mathrm{C}\mathrm{O}}_{2}$. While this is a simple definition, estimating climate sensitivity is difficult because Earth's climate system is complex with a number of poorly understood interacting parts. One approach to estimating climate sensitivity is to quantify how Earth's climate changed as a result of variations in atmospheric CO2 ${\mathrm{C}\mathrm{O}}_{2}$ through geological time. This information is invaluable, but it is patchy and has large uncertainties that make estimating climate sensitivity challenging. In particular, existing statistical techniques may underestimate climate sensitivity and, thus, underestimate future warming. In this paper we present an alternative approach to determining climate sensitivity that overcomes the underestimation problem and demonstrate its performance using geological data from the Eocene epoch. Key Points: We demonstrate that regression‐based estimates of specific climate sensitivity may be biased toward zero because of data uncertaintiesA Bayesian error‐in‐variables approach is developed that accounts for data uncertainties in regression‐based climate sensitivity estimatesEocene specific climate sensitivity is estimated to demonstrate the utility of Bayesian error‐in‐variables regression [ABSTRACT FROM AUTHOR]

This paper addresses one of the critical questions of scientific inquiry: How do we know when a given data set is representative of the phenomenon being examined? For paleomagnetists, the question is often whether a particular data set sufficiently averaged paleosecular variation (PSV). To this aim, we updated an existing PSV data set that now comprises 2,441 site mean directions from 94 individual studies (PSV10‐24). Minimal filtering for data quality resulted in 1,619 sites from 90 publications. Fitting PSV10‐24 with two newly defined parameters as well as two existing ones form the basis of a Giant Gaussian Process field model (THG24) consistent with the data. Drawing directions from THG24 yields directional distributions predicted for a given latitude allowing a comparison between empirical distributions and the cumulative distribution function generated by the model. This tests whether the observed data adequately averaged out PSV according to THG24. We applied these tests to five data sets from Large Igneous Provinces from the last billion years and find that they are consistent with the THG24 model as well. Sedimentary data sets that may have experienced inclination shallowing can be corrected using an (un)flattening factor that yields directions satisfying THG24 in a newly‐defined, four‐parameter space. This approach builds on the Elongation‐Inclination (E/I) method of Tauxe and Kent (2004), https://doi.org/10.1029/145gm08, so the approach introduced here is called SVEI. We show examples of the use of SVEI and explain how to use this newly developed Python code that is publicly available in the PmagPy GitHub repository. Plain Language Summary: Paleomagnetic vectors recorded by rocks and archeological materials yield information on the structure of the magnetic field through Earth's history. The geomagnetic field mostly resembles a geocentric dipole aligned with the spin axis, but the directions at any given time and place generally deviate from this simple model. Data sets produced over several decades help define global magnetic field behavior. Here, we updated a compilation of paleomagnetic data and used it to establish a new field model representative of the last 10 million years. Prior or emerging data sets can be tested against the model to see whether the two agree in terms of field structure and variability. We tested data sets as old as 1.1 billion years and found them compatible with the model. Moreover, this model can also assess, and potentially correct data from sedimentary rocks that may have suffered from inclination shallowing. Although other correction methods exist, our approach employs a more complete description of the geometry of directional data, thereby allowing a more quantitative comparison of empirical and predicted distributions. Corrected inclinations and their uncertainties define paleolatitudes more accurately, key for plate tectonic and paleoclimatic reconstructions. Key Points: Update of the paleosecular variation database from lava flows (PSV10‐24) for the last 10 MaDefinition of a new Giant Gaussian Process (GGP) model (THG24), compatible with PSV10‐24 matching VGP scatter including data sets as old as 1.1 GaDescription of the SVEI method (an update of E/I) to assess secular variation records and to correct for inclination shallowing in sediments [ABSTRACT FROM AUTHOR]

Sapropels are dark, organic‐rich layers found in Mediterranean sediments that formed during periods of bottom water anoxia. While various mechanisms have been proposed to have caused anoxic conditions, a primary factor is considered to be water column stratification induced by freshwater runoff related to intensified North African monsoon precipitation during precession minima. Monsoon intensification also induced Green Sahara Periods that may have impacted North African hominin dispersal. In this study, we present a novel regression‐based deconvolution of a high‐resolution planktonic foraminiferal oxygen isotope record to estimate the combination of freshwater runoff reaching the eastern Mediterranean and associated surface warming of the water column over the past 5 million years. Sapropels are known to occur in clusters associated with periods of high orbital eccentricity. Our analysis reveals a consistent influence of orbital eccentricity in modulating the North African monsoon, and a possible shift in runoff source area induced by the initiation of Northern Hemisphere ice sheets. Our findings provide important insights into the role of the North African monsoon in shaping Mediterranean environmental changes over the past 5 million years. Plain Language Summary: We investigate long‐term changes in monsoon rains over North Africa, which annually result in freshwater flowing into the Mediterranean Sea. Over geological time, Earth's orbital variations have played a significant role in shaping the monsoon and, consequently, the quantity of freshwater entering the Mediterranean. Foraminifera, small marine organisms, record the oxygen isotope composition of their environment in their shells. Notably, the oxygen isotope balance in North African monsoon rains and the Mediterranean Sea differ, but eventually mix upon the freshwater entering into the Mediterranean. Our research combines a statistical analysis of oxygen isotope data preserved in foraminifera shells with a numerical model of the Mediterranean Sea, enabling us to estimate changes in monsoon freshwater input into the Mediterranean over the past 5 million years. This information not only enhances our understanding of monsoon evolution but also provides insights into the potential for hominin migrations in a more lush North African landscape characterized by higher rainfall than today. Key Points: We present quantitative estimates of Plio‐Pleistocene North African monsoon activity based on deconvolution of planktonic foraminiferal δ18OThrough the past 5 million years estimated monsoon activity is consistent with the expected pattern of orbital eccentricity modulationApparent monsoon strengthening at ∼2.6 Ma may be due to shifting runoff sources induced by Northern Hemisphere ice sheet initiation [ABSTRACT FROM AUTHOR]

A synthesis of new and existing data allows Heinrich Stadial 11 (HS11), a prominent Northern Hemisphere cold event, to be linked to the timing of peak sea-level rise during glacial termination T-II, whereas rapid sea-level rise in T-I is shown to clearly post-date Heinrich Stadial 1, so fundamentally different mechanisms seem to be at work during glacial terminations. Anatomy of a glacial termination A central goal of palaeoclimate research is that of deciphering the mechanisms responsible for major state shifts in the Earth system, such as between glacial and interglacial conditions. This has proven difficult enough even for the last glacial termination (T-I), much less termination II (T-II), which ended glacial conditions about 130,000 years ago. Gianluca Marino et al. use new and existing data to demonstrate a link, within uncertainties, between Heinrich Stadial 11 (HS11) -- a prominent Northern Hemisphere cold event -- and the timing of peak sea level rise during T-II. A strong Southern Hemisphere warming also occurred during HS11, consistent with the idea of a bipolar seesaw that would probably have promoted Antarctic ice sheet melting. In contrast, rapid sea level rise in T-1 clearly postdated Heinrich Stadial 1. Possibly in response to differing CO.sub.2 and insolation conditions during T-I and T-II, fundamentally different mechanisms seem to be at work in triggering glacial terminations. Our current understanding of ocean-atmosphere-cryosphere interactions at ice-age terminations relies largely on assessments of the most recent (last) glacial-interglacial transition.sup.1,2,3, Termination I (T-I). But the extent to which T-I is representative of previous terminations remains unclear. Testing the consistency of termination processes requires comparison of time series of critical climate parameters with detailed absolute and relative age control. However, such age control has been lacking for even the penultimate glacial termination (T-II), which culminated in a sea-level highstand during the last interglacial period that was several metres above present.sup.4. Here we show that Heinrich Stadial 11 (HS11), a prominent North Atlantic cold episode.sup.5,6, occurred between 135 [plus or minus] 1 and 130 [plus or minus] 2 thousand years ago and was linked with rapid sea-level rise during T-II. Our conclusions are based on new and existing.sup.6,7,8,9 data for T-II and the last interglacial that we collate onto a single, radiometrically constrained chronology. The HS11 cold episode.sup.5,6 punctuated T-II and coincided directly with a major deglacial meltwater pulse, which predominantly entered the North Atlantic Ocean and accounted for about 70 per cent of the glacial-interglacial sea-level rise.sup.8,9. We conclude that, possibly in response to stronger insolation and CO.sub.2 forcing earlier in T-II, the relationship between climate and ice-volume changes differed fundamentally from that of T-I. In T-I, the major sea-level rise clearly post-dates.sup.3,10,11 Heinrich Stadial 1. We also find that HS11 coincided with sustained Antarctic warming, probably through a bipolar seesaw temperature response.sup.12, and propose that this heat gain at high southern latitudes promoted Antarctic ice-sheet melting that fuelled the last interglacial sea-level peak., Author(s): G. Marino [sup.1] , E. J. Rohling [sup.1] [sup.2] , L. Rodríguez-Sanz [sup.1] , K. M. Grant [sup.1] , D. Heslop [sup.1] , A. P. Roberts [sup.1] , J. [...]