Global reorganization of atmospheric circulation during Dansgaard-Oeschger cycles.

Ice core records from Greenland provide evidence for multiple abrupt cold-warm-cold events recurring at millennial time scales during the last glacial interval. Although climate variations resembling Dansgaard-Oeschger (DO) oscillations have been identified in climate archives across the globe, our understanding of the climate and ecosystem impacts of the Greenland warming events in lower latitudes remains incomplete. Here, we investigate the influence of DO-cold-to-warm transitions on the global atmospheric circulation pattern. We comprehensively analyze δ18O changes during DO transitions in a globally distributed dataset of speleothems and set those in context with simulations of a comprehensive high-resolution climate model featuring internal millennial-scale variations of similar magnitude. Across the globe, speleothem δ18O signals and model results indicate consistent large-scale changes in precipitation amount, moisture source, or seasonality of precipitation associated with the DO transitions, in agreement with northward shifts of the Hadley circulation. Furthermore, we identify a decreasing trend in the amplitude of DO transitions with increasing distances from the North Atlantic region. This provides quantitative observational evidence for previous suggestions of the North Atlantic region being the focal point for these archetypes of past abrupt climate changes.

Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework.

Earth's temperature variability can be partitioned into internal and externally forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently understood, especially on decadal to centennial timescales. Important reasons for this are difficulties in isolating internal and externally forced variability. Here, we provide a physically motivated emulation of global mean surface temperature (GMST) variability, which allows for the separation of internal and external variations. To this end, we introduce the "ClimBayes" software package, which infers climate parameters from a stochastic energy balance model (EBM) with a Bayesian approach. We apply our method to GMST data from temperature observations and 20 last millennium simulations from climate models of intermediate to high complexity. This yields the best estimates of the EBM's forced and forced + internal response, which we refer to as emulated variability. The timescale-dependent variance is obtained from spectral analysis. In particular, we contrast the emulated forced and forced + internal variance on interannual to centennial timescales with that of the GMST target. Our findings show that a stochastic EBM closely approximates the power spectrum and timescale-dependent variance of GMST as simulated by modern climate models. Small deviations at interannual timescales can be attributed to the simplified representation of internal variability and, in particular, the absence of (pseudo-)oscillatory modes in the stochastic EBM. Altogether, we demonstrate the potential of combining Bayesian inference with conceptual climate models to emulate statistics of climate variables across timescales.

Probing the timescale dependency of local and global variations in surface air temperature from climate simulations and reconstructions of the last millennia.

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. Essential climate variables, such as surface air temperature, describe this dynamics. Our current interglacial, the Holocene (11 700 yr ago to today), has been characterized by small variations in global mean temperature prior to anthropogenic warming. However, the mechanisms and spatiotemporal patterns of fluctuations around this mean, called temperature variability, are poorly understood despite their socioeconomic relevance for climate change mitigation and adaptation. Here we examine discrepancies between temperature variability from model simulations and paleoclimate reconstructions by categorizing the scaling behavior of local and global surface air temperature on the timescale of years to centuries. To this end, we contrast power spectral densities (PSD) and their power-law scaling using simulated and observation-based temperature series of the last 6000 yr. We further introduce the spectral gain to disentangle the externally forced and internally generated variability as a function of timescale. It is based on our estimate of the joint PSD of radiative forcing, which exhibits a scale break around the period of 7 yr. We find that local temperature series from paleoclimate reconstructions show a different scaling behavior than simulated ones, with a tendency towards stronger persistence (i.e., correlation between successive values within a time series) on periods of 10 to 200 yr. Conversely, the PSD and spectral gain of global mean temperature are consistent across data sets. Our results point to the limitation of climate models to fully represent local temperature statistics over decades to centuries. By highlighting the key characteristics of temperature variability, we pave a way to better constrain possible changes in temperature variability with global warming and assess future climate risks.

Publisher Correction: A global database of Holocene paleotemperature records.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: A global database of Holocene paleotemperature records.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: A global database of Holocene paleotemperature records.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

A global database of Holocene paleotemperature records.

A comprehensive database of paleoclimate records is needed to place recent warming into the longer-term context of natural climate variability. We present a global compilation of quality-controlled, published, temperature-sensitive proxy records extending back 12,000 years through the Holocene. Data were compiled from 679 sites where time series cover at least 4000 years, are resolved at sub-millennial scale (median spacing of 400 years or finer) and have at least one age control point every 3000 years, with cut-off values slackened in data-sparse regions. The data derive from lake sediment (51%), marine sediment (31%), peat (11%), glacier ice (3%), and other natural archives. The database contains 1319 records, including 157 from the Southern Hemisphere. The multi-proxy database comprises paleotemperature time series based on ecological assemblages, as well as biophysical and geochemical indicators that reflect mean annual or seasonal temperatures, as encoded in the database. This database can be used to reconstruct the spatiotemporal evolution of Holocene temperature at global to regional scales, and is publicly available in Linked Paleo Data (LiPD) format.

Consistent multi-decadal variability in global temperature reconstructions and simulations over the Common Era.

Multi-decadal surface temperature changes may be forced by natural as well as anthropogenic factors, or arise unforced from the climate system. Distinguishing these factors is essential for estimating sensitivity to multiple climatic forcings and the amplitude of the unforced variability. Here we present 2,000-year-long global mean temperature reconstructions using seven different statistical methods that draw from a global collection of temperature-sensitive paleoclimate records. Our reconstructions display synchronous multi-decadal temperature fluctuations, which are coherent with one another and with fully forced CMIP5 millennial model simulations across the Common Era. The most significant attribution of pre-industrial (1300-1800 CE) variability at multi-decadal timescales is to volcanic aerosol forcing. Reconstructions and simulations qualitatively agree on the amplitude of the unforced global mean multi-decadal temperature variability, thereby increasing confidence in future projections of climate change on these timescales. The largest warming trends at timescales of 20 years and longer occur during the second half of the 20th century, highlighting the unusual character of the warming in recent decades.

Corrigendum: Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene.

This corrects the article DOI: 10.1038/nature25454.

Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene.

Changes in climate variability are as important for society to address as are changes in mean climate. Contrasting temperature variability during the Last Glacial Maximum and the Holocene can provide insights into the relationship between the mean state of the climate and its variability. However, although glacial-interglacial changes in variability have been quantified for Greenland, a global view remains elusive. Here we use a network of marine and terrestrial temperature proxies to show that temperature variability decreased globally by a factor of four as the climate warmed by 3-8 degrees Celsius from the Last Glacial Maximum (around 21,000 years ago) to the Holocene epoch (the past 11,500 years). This decrease had a clear zonal pattern, with little change in the tropics (by a factor of only 1.6-2.8) and greater change in the mid-latitudes of both hemispheres (by a factor of 3.3-14). By contrast, Greenland ice-core records show a reduction in temperature variability by a factor of 73, suggesting influences beyond local temperature or a decoupling of atmospheric and global surface temperature variability for Greenland. The overall pattern of reduced variability can be explained by changes in the meridional temperature gradient, a mechanism that points to further decreases in temperature variability in a warmer future.

Tropical rainfall over the last two millennia: evidence for a low-latitude hydrologic seesaw.

The presence of a low- to mid-latitude interhemispheric hydrologic seesaw is apparent over orbital and glacial-interglacial timescales, but its existence over the most recent past remains unclear. Here we investigate, based on climate proxy reconstructions from both hemispheres, the inter-hemispherical phasing of the Intertropical Convergence Zone (ITCZ) and the low- to mid-latitude teleconnections in the Northern Hemisphere over the past 2000 years. A clear feature is a persistent southward shift of the ITCZ during the Little Ice Age until the beginning of the 19th Century. Strong covariation between our new composite ITCZ-stack and North Atlantic Oscillation (NAO) records reveals a tight coupling between these two synoptic weather and climate phenomena over decadal-to-centennial timescales. This relationship becomes most apparent when comparing two precisely dated, high-resolution paleorainfall records from Belize and Scotland, indicating that the low- to mid-latitude teleconnection was also active over annual-decadal timescales. It is likely a combination of external forcing, i.e., solar and volcanic, and internal feedbacks, that drives the synchronous ITCZ and NAO shifts via energy flux perturbations in the tropics.

Networks from flows--from dynamics to topology.

Complex network approaches have recently been applied to continuous spatial dynamical systems, like climate, successfully uncovering the system's interaction structure. However the relationship between the underlying atmospheric or oceanic flow's dynamics and the estimated network measures have remained largely unclear. We bridge this crucial gap in a bottom-up approach and define a continuous analytical analogue of Pearson correlation networks for advection-diffusion dynamics on a background flow. Analysing complex networks of prototypical flows and from time series data of the equatorial Pacific, we find that our analytical model reproduces the most salient features of these networks and thus provides a general foundation of climate networks. The relationships we obtain between velocity field and network measures show that line-like structures of high betweenness mark transition zones in the flow rather than, as previously thought, the propagation of dynamical information.