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Paleoclimatological field reconstructions are valuable for understanding past hydroclimatic variability, which is crucial for assessing potential future hydroclimate changes. Despite being as impactful on societies as temperature variability, hydroclimatic variability – particularly beyond the instrumental record – has received less attention. The reconstruction of globally complete fields of climate variables lacks adequate proxy data from tropical regions like South America, limiting our understanding of past hydroclimatic changes in these areas. This study addresses this gap using low-resolution climate archives, including speleothems, previously omitted from reconstructions. Speleothems record climate variations on decadal to centennial timescales and provide a rich dataset for the otherwise proxy-data-scarce region of tropical South America. By employing a multi-timescale paleoclimate data assimilation approach, we synthesize climate proxy records and climate model simulations capable of simulating water isotopologs in the atmosphere to reconstruct 2000 years of South American climate. This includes surface air temperature, precipitation amount, drought index, isotopic composition of precipitation amount and the intensity of the South American Summer Monsoon. The reconstruction reveals anomalous climate periods: a wetter and colder phase during the Little Ice Age (∼ 1500–1850 CE) and a drier, warmer period corresponding to the early Medieval Climate Anomaly (∼ 600–900 CE). However, these patterns are not uniform across the continent, with climate trends in northeastern Brazil and the Southern Cone not following the patterns of the rest of the continent, indicating regional variability. The anomalies are more pronounced than in previous reconstructions but match trends found in local proxy record studies, thus highlighting the importance of including speleothem proxies. The multi-timescale approach is essential for reconstructing multi-decadal and centennial climate variability. Despite methodological uncertainties regarding climate model biases and proxy record interpretations, this study marks a crucial first step in incorporating low-resolution proxy records such as speleothems into climate field reconstructions using a multi-timescale approach. Adequately extracting and using the information from speleothems potentially enhances insights into past hydroclimatic variability and hydroclimate projections.

An increasing number of climate model simulations is becoming available for the transition from the Last Glacial Maximum to the Holocene. Assessing the simulations' reliability requires benchmarking against environmental proxy records. To date, no established method exists to compare these two data sources in space and time over a period with changing background conditions. Here, we develop a new algorithm to rank simulations according to their deviation from reconstructed magnitudes and temporal patterns of orbital and millennial-scale temperature variations. The use of proxy forward modeling allows for accounting for non-climatic processes that affect the temperature reconstructions. It further avoids the need to reconstruct gridded fields or regional mean temperature time series from sparse and uncertain proxy data. First, we test the reliability and robustness of our algorithm in idealized experiments with prescribed deglacial temperature histories. We quantify the influence of limited temporal resolution, chronological uncertainties, and non-climatic processes by constructing noisy pseudo-proxies. While model–data comparison results become less reliable with increasing uncertainties, we find that the algorithm discriminates well between simulations under realistic non-climatic noise levels. To obtain reliable and robust rankings, we advise spatial averaging of the results for individual proxy records. Second, we demonstrate our method by quantifying the deviations between an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. The ranking of the simulations differs substantially between the considered regions and timescales, which suggests that optimizing for agreement with the temporal patterns of a small set of proxies might be insufficient for capturing the spatial structure of the deglacial temperature variability. We attribute the diversity in the rankings to more regionally confined temperature variations in reconstructions than in simulations, which could be the result of uncertainties in boundary conditions, shortcomings in models, or regionally varying characteristics of reconstructions such as recording seasons and depths. Future work towards disentangling these potential reasons can leverage the flexible design of our algorithm and its demonstrated ability to identify varying levels of model–data agreement. Additionally, the algorithm can be applied to variables like oxygen isotopes and climate transitions such as the penultimate deglaciation and the last glacial inception.

Palaeoclimate information on multiple climate variables at different spatiotemporal scales is becoming increasingly important to understand environmental and societal responses to climate change. A lack of high-quality reconstructions of past hydroclimate has recently been identified as a critical research gap. Speleothems, with their precise chronologies, widespread distribution, and ability to record changes in local to regional hydroclimate variability, are an ideal source of such information. Here, we present a new version of the Speleothem Isotopes Synthesis and AnaLysis database (SISALv3), which has been expanded to include trace element ratios and Sr isotopes as additional, hydroclimate-sensitive geochemical proxies. The oxygen and carbon isotope data included in previous versions of the database have been substantially expanded. SISALv3 contains speleothem data from 365 sites from across the globe, including 95 Mg/Ca, 85 Sr/Ca, 52 Ba/Ca, 25 U/Ca, 29 P/Ca, and 14 Sr-isotope records. The database also has increased spatiotemporal coverage for stable oxygen (892) and carbon (620) isotope records compared with SISALv2 (which consists of 673 and 430 stable oxygen and carbon records, respectively). Additional meta information has been added to improve the machine-readability and filtering of data. Standardized chronologies are included for all new entities along with the originally published chronologies. Thus, the SISALv3 database constitutes a unique resource of speleothem palaeoclimate information that allows regional to global palaeoclimate analyses based on multiple geochemical proxies, permitting more robust interpretations of past hydroclimate and comparisons with isotope-enabled climate models and other Earth system and hydrological models. The database can be accessed at https://doi.org/10.5287/ora-2nanwp4rk (Kaushal et al., 2024).

Perturbations in stratospheric aerosol due to explosive volcanic eruptions are a primary contributor to natural climate variability. Observations of stratospheric aerosol are available for the past decades, and information from ice cores has been used to derive estimates of stratospheric sulfur injections and aerosol optical depth over the Holocene (approximately 10 000 BP to present) and into the last glacial period, extending back to 60 000 BP. Tephra records of past volcanism, compared to ice cores, are less complete but extend much further into the past. To support model studies of the potential impacts of explosive volcanism on climate variability across timescales, we present here an ensemble reconstruction of volcanic stratospheric sulfur injection (VSSI) over the last 140 000 years that is based primarily on terrestrial and marine tephra records. VSSI values are computed as a simple function of eruption magnitude based on VSSI estimates from ice cores and satellite observations for identified eruptions. To correct for the incompleteness of the tephra record, we include stochastically generated synthetic eruptions assuming a constant background eruption frequency from the ice core Holocene record. While the reconstruction often differs from ice core estimates for specific eruptions due to uncertainties in the data used and reconstruction method, it shows good agreement with an ice-core-based VSSI reconstruction in terms of millennial-scale cumulative VSSI variations over the Holocene. The PalVol reconstruction provides a new basis to test the contributions of forced vs. unforced natural variability to the spectrum of climate and the mechanisms leading to abrupt transitions in the palaeoclimate record with low- to high-complexity climate models. The PalVol volcanic forcing reconstruction is available at https://doi.org/10.26050/WDCC/PalVolv1 (Toohey and Schindlbeck-Belo, 2023).

In this paper we present EMO-5 (“European Meteorological Observations”, spatial resolution of 5 km), a European high-resolution, (sub-)daily, multi-variable meteorological dataset built on historical and real-time observations obtained by integrating data from 18 964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG – INCA, EURO4M-APGD, and CarpatClim), and one global reanalysis (ERA-Interim/Land). EMO-5 includes the following at daily resolution: total precipitation, temperatures (minimum and maximum), wind speed, solar radiation, and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature data. The raw observations from the ground weather stations underwent a set of quality controls before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5×5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high-resolution datasets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high-resolution dataset. The availability of the uncertainty fields increases the transparency of the dataset and hence the possible usage. EMO-5 (version 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen with version 2. As a product of Copernicus, the EU's Earth Observation Programme, the EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2020).

The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modeled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modeled water isotopologues, as well as the diversity of their representation in different models, are not well constrained. Improving our understanding of this variability in past and present climates will help to better constrain future climate change projections and decrease their range of uncertainty. Speleothems are a precisely datable terrestrial paleoclimate archives and provide well-preserved (semi-)continuous multivariate isotope time series in the lower latitudes and mid-latitudes and are therefore well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationships of speleothem oxygen and carbon isotopes to climate variables are influenced by site-specific parameters, and their comparison to GCMs is not always straightforward. Here we compare speleothem oxygen and carbon isotopic signatures from the Speleothem Isotopes Synthesis and Analysis database version 2 (SISALv2) to the output of five different water-isotope-enabled GCMs (ECHAM5-wiso, GISS-E2-R, iCESM, iHadCM3, and isoGSM) over the last millennium (850–1850 CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for modeled isotopes and compare them to those of measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modeled surface temperature. At low latitudes, precipitation amount is the dominant driver for stable water isotope variability; however, at cave locations the agreement between modeled temperature variability is higher than for precipitation variability. While modeled isotopic signatures at cave locations exhibited extreme events coinciding with changes in volcanic and solar forcing, such fingerprints are not apparent in the speleothem isotopes. This may be attributed to the lower temporal resolution of speleothem records compared to the events that are to be detected. Using spectral analysis, we can show that all models underestimate decadal and longer variability compared to speleothems (albeit to varying extents). We found that no model excels in all analyzed comparisons, although some perform better than the others in either mean or variability. Therefore, we advise a multi-model approach whenever comparing proxy data to modeled data. Considering karst and cave internal processes, e.g., through isotope-enabled karst models, may alter the variability in speleothem isotopes and play an important role in determining the most appropriate model. By exploring new ways of analyzing the relationship between the oxygen and carbon isotopes, their variability, and co-variability across timescales, we provide methods that may serve as a baseline for future studies with different models using, e.g., different isotopes, different climate archives, or different time periods.

Characterizing the variability across timescales is important for understanding the underlying dynamics of the Earth system. It remains challenging to do so from palaeoclimate archives since they are more often than not irregular, and traditional methods for producing timescale-dependent estimates of variability, such as the classical periodogram and the multitaper spectrum, generally require regular time sampling. We have compared those traditional methods using interpolation with interpolation-free methods, namely the Lomb–Scargle periodogram and the first-order Haar structure function. The ability of those methods to produce timescale-dependent estimates of variability when applied to irregular data was evaluated in a comparative framework, using surrogate palaeo-proxy data generated with realistic sampling. The metric we chose to compare them is the scaling exponent, i.e. the linear slope in log-transformed coordinates, since it summarizes the behaviour of the variability across timescales. We found that, for scaling estimates in irregular time series, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescales which are particularly affected by the irregularity. In addition, our results suggest that the Haar structure function is the safer choice of interpolation-free method since the Lomb–Scargle periodogram is unreliable when the underlying process generating the time series is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a palaeoclimate time series, and that it is also possible that this changes as a function of timescale, it is a desirable characteristic for the method to handle both stationary and non-stationary cases alike.

Modeling the long-term transient evolution of climate remains a technical and scientific challenge. However, understanding and improving modeling of the long-term behavior of the climate system increases confidence in projected changes in the mid- to long-term future. Energy balance models (EBMs) provide simplified and computationally efficient descriptions of long timescales and allow large ensemble runs by parameterizing energy fluxes. In this way, they can be used to pinpoint periods and phenomena of interest. Here, we present TransEBM, an extended version of the two-dimensional energy balance model by Zhuang et al. (2017a). Transient CO2, solar insolation, orbital configuration, fixed ice coverage, and land–sea distribution are implemented as effective radiative forcings at the land surface. We show that the model is most sensitive to changes in CO2 and ice distribution, but the obliquity and land–sea mask have significant influence on modeled temperatures as well. We tune TransEBM to reproduce the 1960–1989 CE global mean temperature and the Equator-to-pole and seasonal temperature gradients of ERA-20CM reanalysis (Hersbach et al., 2015). The resulting latitudinal and seasonal temperature distributions agree well with reanalysis and the general circulation model (GCM) HadCM3 for a simulation of the past millennium (Bühler et al., 2020). TransEBM does not represent the internal variability of the ocean–atmosphere system, but non-deterministic elements and nonlinearity can be introduced through model restarts and randomized forcing. As the model facilitates long transient simulations, we envisage its use in exploratory studies of stochastic forcing and perturbed parameterizations, thus complementing studies with comprehensive GCMs.

Improving the understanding of changes in the mean and variability of climate variables as well as their interrelation is crucial for reliable climate change projections. Comparisons between general circulation models and paleoclimate archives using indirect proxies for temperature or precipitation have been used to test and validate the capability of climate models to represent climate changes. The oxygen isotopic ratio δ18O, a proxy for many different climate variables, is routinely measured in speleothem samples at decadal or higher resolution, and single specimens can cover full glacial–interglacial cycles. The calcium carbonate cave deposits are precisely dateable and provide well preserved (semi-)continuous albeit multivariate climate signals in the lower and mid-latitudes, where the measured δ18O in the mineral does not directly represent temperature or precipitation. Therefore, speleothems represent suitable archives to assess climate model abilities to simulate climate variability beyond the timescales covered by meteorological observations (101–102 years). Here, we present three transient isotope-enabled simulations from the Hadley Center Climate Model version 3 (iHadCM3) covering the last millennium (850–1850 CE) and compare them to a large global dataset of speleothem δ18O records from the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database version 2 (Comas-Bru et al., 2020b). We systematically evaluate offsets in mean and variance of simulated δ18O and test for the main climate drivers recorded in δ18O for individual records or regions. The time-mean spatial offsets between the simulated δ18O and the speleothem data are fairly small. However, using robust filters and spectral analysis, we show that the observed archive-based variability of δ18O is lower than simulated by iHadCM3 on decadal and higher on centennial timescales. Most of this difference can likely be attributed to the records' lower temporal resolution and averaging or smoothing processes affecting the δ18O signal, e.g., through soil water residence times. Using cross-correlation analyses at site level and modeled grid-box level, we find evidence for highly variable but generally low signal-to-noise ratios in the proxy data. This points to a high influence of cave-internal processes and regional climate particularities and could suggest low regional representativity of individual sites. Long-range strong positive correlations dominate the speleothem correlation network but are much weaker in the simulation. One reason for this could lie in a lack of long-term internal climate variability in these model simulations, which could be tested by repeating similar comparisons with other isotope-enabled climate models and paleoclimate databases.

Characterizing the temporal uncertainty in palaeoclimate records is crucial for analysing past climate change, correlating climate events between records, assessing climate periodicities, identifying potential triggers and evaluating climate model simulations. The first global compilation of speleothem isotope records by the SISAL (Speleothem Isotope Synthesis and Analysis) working group showed that age model uncertainties are not systematically reported in the published literature, and these are only available for a limited number of records (ca. 15 %, n=107/691). To improve the usefulness of the SISAL database, we have (i) improved the database's spatio-temporal coverage and (ii) created new chronologies using seven different approaches for age–depth modelling. We have applied these alternative chronologies to the records from the first version of the SISAL database (SISALv1) and to new records compiled since the release of SISALv1. This paper documents the necessary changes in the structure of the SISAL database to accommodate the inclusion of the new age models and their uncertainties as well as the expansion of the database to include new records and the quality-control measures applied. This paper also documents the age–depth model approaches used to calculate the new chronologies. The updated version of the SISAL database (SISALv2) contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including age–depth temporal uncertainties for 512 speleothems. SISALv2 is available at https://doi.org/10.17864/1947.256 (Comas-Bru et al., 2020a).

It is virtually certain that the mean surface temperature of the Earth will continue to increase under realistic emission scenarios, yet comparatively little is known about future changes in climate variability. This study explores changes in climate variability over the large range of climates simulated by the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5/6) and the Paleoclimate Modeling Intercomparison Project Phase 3 (PMIP3), including time slices of the Last Glacial Maximum, the mid-Holocene, and idealized experiments (1 % CO2 and abrupt4×CO2). These states encompass climates within a range of 12 ∘C in global mean temperature change. We examine climate variability from the perspectives of local interannual change, coherent climate modes, and through compositing extremes. The change in the interannual variability of precipitation is strongly dependent upon the local change in the total amount of precipitation. At the global scale, temperature variability is inversely related to mean temperature change on intra-seasonal to multidecadal timescales. This decrease is stronger over the oceans, while there is increased temperature variability over subtropical land areas (40∘ S–40∘ N) in warmer simulations. We systematically investigate changes in the standard deviation of modes of climate variability, including the North Atlantic Oscillation, the El Niño–Southern Oscillation, and the Southern Annular Mode, with global mean temperature change. While several climate modes do show consistent relationships (most notably the Atlantic Zonal Mode), no generalizable pattern emerges. By compositing extreme precipitation years across the ensemble, we demonstrate that the same large-scale modes influencing rainfall variability in Mediterranean climates persist throughout paleoclimate and future simulations. The robust nature of the response of climate variability, between cold and warm climates as well as across multiple timescales, suggests that observations and proxy reconstructions could provide a meaningful constraint on climate variability in future projections.

Selective laser sintering (SLS) is one of the hot topics in the manufacturing research community. The SLS printers developed by the researchers have shown a great potential, however, it needs a lot of improvements to be considered for commercial usage. Here, we report an improved design of the SLS printer with a unique heating mechanism to ensure enhanced mechanical properties of the final part. These improvements came in the form of enhanced heating mechanism for the printer once the reverse engineering concepts were put together to come up with an innovative yet scalable design. The design included two axes movement of the laser via computer numerical control (CNC) router as opposed to traditional complex controlled reflecting mirrors. Complete models were developed regarding the laser power, motor torque and determining the specific layer height for layer by layer sintering based on the nylon powder as the raw material. Static structural analysis was carried out on the printer to identify the material to be used for required strength. Complete electrical control of the printer was developed using Raspberry Pi which was coded via python to control the motors of each axis in the printer. Successful testing of the electronic and control system of the proposed SLS printer is also illustrated in this work. Keywords: SLS, Printer, Lightweight, Heating mechanism, Design

Although quantitative isotope data from speleothems has been used to evaluate isotope-enabled model simulations, currently no consensus exists regarding the most appropriate methodology through which to achieve this. A number of modelling groups will be running isotope-enabled palaeoclimate simulations in the framework of the Coupled Model Intercomparison Project Phase 6, so it is timely to evaluate different approaches to using the speleothem data for data–model comparisons. Here, we illustrate this using 456 globally distributed speleothem δ18O records from an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled atmospheric circulation model. We show that the SISAL records reproduce the first-order spatial patterns of isotopic variability in the modern day, strongly supporting the application of this dataset for evaluating model-derived isotope variability into the past. However, the discontinuous nature of many speleothem records complicates the process of procuring large numbers of records if data–model comparisons are made using the traditional approach of comparing anomalies between a control period and a given palaeoclimate experiment. To circumvent this issue, we illustrate techniques through which the absolute isotope values during any time period could be used for model evaluation. Specifically, we show that speleothem isotope records allow an assessment of a model's ability to simulate spatial isotopic trends. Our analyses provide a protocol for using speleothem isotope data for model evaluation, including screening the observations to take into account the impact of speleothem mineralogy on δ18O values, the optimum period for the modern observational baseline and the selection of an appropriate time window for creating means of the isotope data for palaeo-time-slices.

Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPI-OM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low (R), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the assumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncertainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correlation structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the signal content of specific proxy types should become feasible in the near future. The estimated low signal content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite syntheses until further studies are able to facilitate a better characterisation of the signal content in paleoclimate records.

The pace of the transition to a low-carbon economy – especially in the fuels sector – is not high enough to achieve the 2 °C target limit for global warming by only cutting emissions. Most political roadmaps to tackle global warming implicitly rely on the timely availability of mature negative emission technologies, which actively invest energy to remove CO2 from the atmosphere and store it permanently. The models used as a basis for decarbonization policies typically assume an implementation of such large-scale negative emission technologies starting around the year 2030, ramped up to cause net negative emissions in the second half of the century and balancing earlier CO2 release. On average, a contribution of −10 Gt CO2 yr−1 is expected by 2050 (Anderson and Peters, 2016). A viable approach for negative emissions should (i) rely on a scalable and sustainable source of energy (solar), (ii) result in a safely storable product, (iii) be highly efficient in terms of water and energy use, to reduce the required land area and competition with water and food demands of a growing world population, and (iv) feature large-scale feasibility and affordability.

This study depend on the results of the study prepared by the World Bank (WB), the United Nations Development Program (UNDP) and the Food and Agriculture Organization of the United Nations (FAO) in order to indicate and analyze the average individual cereal consumption in Egypt and some neighboring countries. The consumption of wheat is ranked first in all countries of the study sample. Consumption of maize comes in second place after consumption of wheat in the countries of the study sample in Africa continent. Consumption of rice is also comes second only to consumption of wheat in the sample countries in Asia continent. The difference between the average cereal consumption in Egypt and the average cereal consumption in Algeria was about 35.2 kg. This difference is attributed to the total determinants studied as follows: 6.2% due to the percentage of average years of education, 87.3% 17.8% attributable to the percentage of average per capita consumption of maize, -27.5% attributable to the percentage of average per capita consumption of wheat, 11.2% attributed to the percentage of corn / rice import price. In general, it is found that the percentage of rural / urban population is primarily responsible for the difference between the average cereal consumption in Egypt and the average cereal consumption in Egypt's neighboring countries, followed by average per capita consumption of wheat and average per capita consumption of maize.

Reconstructions of summer, winter or annual mean temperatures based on the species composition of bio-indicators such as pollen, foraminifera or chironomids are routinely used in climate model–proxy data comparison studies. Most reconstruction algorithms exploit the joint distribution of modern spatial climate and species distribution for the development of the reconstructions. They rely on the space-for-time substitution and the specific assumption that environmental variables other than those reconstructed are not important or that their relationship with the reconstructed variable(s) should be the same in the past as in the modern spatial calibration dataset. Here we test the implications of this “correlative uniformitarianism” assumption on climate reconstructions in an ideal model world, in which climate and vegetation are known at all times. The alternate reality is a climate simulation of the last 6000 years with dynamic vegetation. Transient changes of plant functional types are considered as surrogate pollen counts and allow us to establish, apply and evaluate transfer functions in the modeled world. We find that in our model experiments the transfer function cross validation r2 is of limited use to identify reconstructible climate variables, as it only relies on the modern spatial climate–vegetation relationship. However, ordination approaches that assess the amount of fossil vegetation variance explained by the reconstructions are promising. We furthermore show that correlations between climate variables in the modern climate–vegetation relationship are systematically extended into the reconstructions. Summer temperatures, the most prominent driving variable for modeled vegetation change in the Northern Hemisphere, are accurately reconstructed. However, the amplitude of the model winter and mean annual temperature cooling between the mid-Holocene and present day is overestimated and similar to the summer trend in magnitude. This effect occurs because temporal changes of a dominant climate variable, such as summer temperatures in the model's Arctic, are imprinted on a less important variable, leading to reconstructions biased towards the dominant variable's trends. Our results, although based on a model vegetation that is inevitably simpler than reality, indicate that reconstructions of multiple climate variables based on modern spatial bio-indicator datasets should be treated with caution. Expert knowledge on the ecophysiological drivers of the proxies, as well as statistical methods that go beyond the cross validation on modern calibration datasets, are crucial to avoid misinterpretation.

The quantitative measure of disorder or randomness based on the entropy production characterizes thermodynamical irreversibility, which is relevant to the conventional second law of thermodynamics. Here we report, in a quantum mechanical fashion, the first theoretical prediction and experimental exploration of an information-theoretical bound on the entropy production. Our theoretical model consists of a simplest two-level dissipative system driven by a purely classical field, and under the Markovian dissipation, we find that such an information-theoretical bound, not fully validating quantum relaxation processes, strongly depends on the drive-to-decay ratio and the initial state. Furthermore, we carry out experimental verification of this information-theoretical bound by means of a single spin embedded in an ultracold trapped ^{40}Ca^{+} ion. Our finding, based on a two-level model, is fundamental to any quantum thermodynamical process and indicates much difference and complexity in quantum thermodynamics with respect to the conventionally classical counterpart.

The Asian monsoon system is an important tipping element in Earth's climate with a large impact on human societies in the past and present. In light of the potentially severe impacts of present and future anthropogenic climate change on Asian hydrology, it is vital to understand the forcing mechanisms of past climatic regime shifts in the Asian monsoon domain. Here we use novel recurrence network analysis techniques for detecting episodes with pronounced non-linear changes in Holocene Asian monsoon dynamics recorded in speleothems from caves distributed throughout the major branches of the Asian monsoon system. A newly developed multi-proxy methodology explicitly considers dating uncertainties with the COPRA (COnstructing Proxy Records from Age models) approach and allows for detection of continental-scale regime shifts in the complexity of monsoon dynamics. Several epochs are characterised by non-linear regime shifts in Asian monsoon variability, including the periods around 8.5–7.9, 5.7–5.0, 4.1–3.7, and 3.0–2.4 ka BP. The timing of these regime shifts is consistent with known episodes of Holocene rapid climate change (RCC) and high-latitude Bond events. Additionally, we observe a previously rarely reported non-linear regime shift around 7.3 ka BP, a timing that matches the typical 1.0–1.5 ky return intervals of Bond events. A detailed review of previously suggested links between Holocene climatic changes in the Asian monsoon domain and the archaeological record indicates that, in addition to previously considered longer-term changes in mean monsoon intensity and other climatic parameters, regime shifts in monsoon complexity might have played an important role as drivers of migration, pronounced cultural changes, and the collapse of ancient human societies.

Sedimentary proxy records constitute a significant portion of the recorded evidence that allows us to investigate paleoclimatic conditions and variability. However, uncertainties in the dating of proxy archives limit our ability to fix the timing of past events and interpret proxy record intercomparisons. While there are various age-modeling approaches to improve the estimation of the age–depth relations of archives, relatively little focus has been placed on the propagation of the age (and radiocarbon calibration) uncertainties into the final proxy record. We present a generic Bayesian framework to estimate proxy records along with their associated uncertainty, starting with the radiometric age–depth and proxy–depth measurements, and a radiometric calibration curve if required. We provide analytical expressions for the posterior proxy probability distributions at any given calendar age, from which the expected proxy values and their uncertainty can be estimated. We illustrate our method using two synthetic data sets and then use it to construct the proxy records for groundwater inflow and surface erosion from Lonar lake in central India. Our analysis reveals interrelations between the uncertainty of the proxy record over time and the variance of proxies along the depth of the archive. For the Lonar lake proxies, we show that, rather than the age uncertainties, it is the proxy variance combined with calibration uncertainty that accounts for most of the final uncertainty. We represent the proxy records as probability distributions on a precise, error-free timescale that makes further time series analyses and intercomparisons of proxies relatively simple and clear. Our approach provides a coherent understanding of age uncertainties within sedimentary proxy records that involve radiometric dating. It can be potentially used within existing age modeling structures to bring forth a reliable and consistent framework for proxy record estimation.

A critical challenge in paleoclimate data analysis is the fact that the proxy data are heterogeneously distributed in space, which affects statistical methods that rely on spatial embedding of data. In the paleoclimate network approach nodes represent paleoclimate proxy time series, and links in the network are given by statistically significant similarities between them. Their location in space, proxy and archive type is coded in the node attributes. We develop a semi-empirical model for Spatio-Temporally AutocoRrelated Time series, inspired by the interplay of different Asian Summer Monsoon (ASM) systems. We use an ensemble of transition runs of this START model to test whether and how spatio–temporal climate transitions could be detectable from (paleo)climate networks. We sample model time series both on a grid and at locations at which paleoclimate data are available to investigate the effect of the spatially heterogeneous availability of data. Node betweenness centrality, averaged over the transition region, does not respond to the transition displayed by the START model, neither in the grid-based nor in the scattered sampling arrangement. The regionally defined measures of regional node degree and cross link ratio, however, are indicative of the changes in both scenarios, although the magnitude of the changes differs according to the sampling. We find that the START model is particularly suitable for pseudo-proxy experiments to test the technical reconstruction limits of paleoclimate data based on their location, and we conclude that (paleo)climate networks are suitable for investigating spatio–temporal transitions in the dependence structure of underlying climatic fields.

Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős–Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970–2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.

Climate networks are constructed from climate time series data using correlation measures. It is widely accepted that the geographical proximity, as well as other geographical features such as ocean and atmospheric currents, have a large impact on the observable time-series similarity. Therefore it is to be expected that the spatial sampling will influence the reconstructed network. Here we investigate this by comparing analytical flow networks, networks generated with the START model and networks from temperature data from the Asian monsoon domain. We evaluate them on a regular grid, a grid with added random jittering and two variations of clustered sampling. We find that the impact of the spatial sampling on most network measures only distorts the plots if the node distribution is significantly inhomogeneous. As a simple diagnostic measure for the detection of inhomogeneous sampling we suggest the Voronoi cell size distribution.

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60–55% (in the linear case) to 53–42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity contributes less, particularly for the adapted Gaussian-kernel-based estimators and the event synchronization function. The introduced link strength concept summarizes the hypothesis test results and balances the individual strengths of the estimators: while gXCF is particularly suitable for short and irregular time series, gMI and the ESF can identify nonlinear dependencies. ESF could, in particular, be suitable to study extreme event dynamics in paleoclimate records. Programs to analyze paleoclimatic time series for significant dependencies are included in a freely available software toolbox.

Gastric dilatation and volvulus syndrome is associated with changes in haemostatic profiles. The aims of this study were to compare selected haemostatic and fibrinolytic parameters between healthy dogs and dogs with gastric dilatation and volvulus syndrome, estimate the incidence of disseminated intravascular coagulation (DIC), and determine the most sensitive test for detection of DIC in these patients. Blood was collected from 22 dogs with gastric dilatation and volvulus syndrome, and nine healthy control dogs. Platelet counts, prothrombin time, activated partial thromboplastin time, fibrinogen concentrations and fibrin/fibrinogen degradation products were measured in all control dogs and patients with gastric dilatation and volvulus syndrome, before and after surgery. Significant differences between control dogs and patients were seen in activated partial thromboplastin time and fibrin/fibrinogen degradation products before surgery and all measured parameters after surgery. The incidence of DIC was 59%. The most sensitive tests for detection of DIC before surgery were those for activated partial thromboplastin time and fibrin/fibrinogen degradation products.

Reliable age models are fundamental for any palaeoclimate reconstruction. Available interpolation procedures between age control points are often inadequately reported, and very few translate age uncertainties to proxy uncertainties. Most available modeling algorithms do not allow incorporation of layer counted intervals to improve the confidence limits of the age model in question. We present a framework that allows detection and interactive handling of age reversals and hiatuses, depth-age modeling, and proxy-record reconstruction. Monte Carlo simulation and a translation procedure are used to assign a precise time scale to climate proxies and to translate dating uncertainties to uncertainties in the proxy values. The presented framework allows integration of incremental relative dating information to improve the final age model. The free software package COPRA1.0 facilitates easy interactive usage.

The aim of this study was to determine total antioxidant capacity in dogs with gastric dilatation-volvulus syndrome (GDV) and its correlations with high mobility group box 1 protein (HMGB1) and lactate concentrations. Correlation analyses between the measured parameters and disease severity were also performed. Fourteen dogs with GDV and six control dogs were used in this study. Blood was collected at the time of admission and again in the early reperfusion period. To assess antioxidant capacity, total radical-trapping antioxidant parameter (TRAP) analysis was performed. No significant difference in TRAP values existed between healthy dogs and dogs with GDV at admission. In the reperfusion period, TRAP values decreased in six dogs and increased in eight dogs. Changes in TRAP values strongly correlated with HMGB1 values (r = -0.83, P < 0.01) in the reperfusion period. Strong correlations between disease severity and TRAP values, HMGB1 and lactate levels were also found.