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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 over across timescales, we present here an ensemble reconstruction of volcanic stratospheric sulfur injection (VSSI) over the last 130,000 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, Schindlbeck-Belo, 2023).

Speleothems (cave carbonates) are widely distributed in terrestrial regions, and provide highly resolved records of past changes in climate and ecosystem conditions, encoded in the oxygen and carbon isotope proxies. The SISALv2 database, created by the PAGES-SISAL Phase 1 Working Group, provided 700 speleothem records from 293 cave sites, 500 of which have standardized chronologies. The database provided access to records that were hitherto unavailable in the original publications and/or repositories, and enabled regional-to-global scale analysis of climatic patterns using a variety of approaches such as data-model comparisons. During the three year run of SISAL Phase 2, the working group members have: (i) explored ways to synthesize modern cave monitoring data to provide robust modern baselines and improve proxy interpretations(ii) added trace element proxies of Mg, Sr, Ba, and U concentrations, and Sr isotopes to a new SISAL database version to increase our understanding of regional climatic variability.(iii) updated the SISAL database to incorporate an additional ~100 speleothem stable isotope datasets (iv) and created an online interface web app (The SISAL App) with a user-friendly GUI to increase SISAL data accessibility.Here, we present ongoing work synthesizing cave monitoring data, a summary of speleothem proxy records available in the SISALv3 database update and of ongoing Working Group research projects and a simple use case of The SISAL App. We briefly present a synopsis of the SISAL-community level discussions on best practices for reporting trace element data, and reducing data measured with high resolution laser ablation methods. We conclude with a short discussion on research projects based on the latest SISAL database update and discuss ideas for potential future SISAL phases and projects. For this, we encourage participation and collaboration from researchers in different stages of their academic career and working in different geographical regions and allied disciplines interested in exploiting the new SISAL database version.

Reliable projections of future climate change are vital for mitigation and adaptation efforts. Such efforts require not only projections of mean changes but of changes in variability, too, since those directly affect the occurrence of extremes. The evaluation of climate models regarding their ability to simulate expected changes in variability of temperature and precipitation relies on the comparison of observations with simulations of past and present-day climate. As such, studying past periods of warming furthers the understanding of the climate system and its projected changes. However, the response of the climate system to forcings depends on the background state. Thus, understanding how insights from studies of the past transfer to future projections and the limitations of this transfer is vital.Here, we present an analysis of temperature and precipitation variability in transient simulations of the Last Deglaciation and projected future climate. To this end, we analyze how the distributions of temperature and precipitation change as exemplified by the moments of the distribution, i.e. variance, skewness and kurtosis. We identify trends in the projections and compare them to results for the Last Deglaciation and present commonalities and differences between the responses in these climate states. We further present how these changes relate to differences in the background state, forcings, and the timescales on which these forcings act as well as the limitations imposed by these differences. Based on this analysis of the state-dependency of variability and its change with a warming mean state, we present conclusions on how past climates can inform and support studies of future climate change.

As we observe and expect severe changes in the Earth’ climate, the analyses of past climate state transitions is of major value for improving our Earth system understanding. Under this objective, the last deglaciation (~ 21 ka to 9 ka before present), the transition from the Last Glacial Maximum (LGM) to the Holocene, is an ideal case study. During this transition, the orbital configuration gradually changed and greenhouse gases have risen, which caused a sharp decline in northern hemisphere ice sheets and an increase in the global mean surface temperature.We create an ensemble of deglaciation simulations with a modified version of the Planet Simulator, an Earth system model of intermediate complexity (EMIC). We produce single and combined forcing simulations for further investigation from a thermodynamic perspective. The response to the transiently changing radiative forcing is investigated in terms of energy and entropy budgets of the atmosphere. Here, we focus on the deglacial evolution of the material entropy production (MEP). Its contributions represent the strength of major climate features such as the kinetic energy generation rate, vertical and horizontal heat transport and the hydrological cycle. Preliminary results show an increase of the global mean MEP from the LGM to the Holocene because of a strengthening of the hydrological contribution. In contrast, the relative importance of kinetic energy dissipation and turbulent heat diffusion in the boundary layer decrease. Our work can provide the basis for investigating the MEP as a diagnostic quantity with other models and for other climate state transitions.

Dansgaard-Oeschger (DO) events are the most iconic mode of millennial-scale variability during the last glacial period. The manifestation of DO events outside the North Atlantic region and mechanisms responsible for the propagation of the North Atlantic signal across the globe are still little understood. Propagation of DO events to the Southern Hemisphere (SH) has first been explained by oceanic processes, that result in a muted and delayed signal in the Antarctic ice core record, known as Antarctic Isotope Maxima (AIM). Recent ice core-based reconstructions found an additional short-timescale response (years-to-decades, compared to centuries for the oceanic processes) in phase with the climate changes in Greenland. This fast response has been interpreted as the result of atmospheric transport processes. Shifts in the intertropical convergence zone and SH mid-latitude westerlies are seen as mediators of this response.Here, we investigate the propagation of abrupt climate changes in the North Atlantic region to the SH in general circulation model simulations with spontaneous DO-like oscillations under glacial conditions. We study the relative timing of changes in temperature, hydroclimate, and atmospheric circulation and compare our results with ice core and speleothem based reconstructions. In the simulations, the timing of changes in different elements of the climate system varies on a continuum of timescales from months to centuries. This indicates the existence of more complex propagation mechanisms than the simple separation into an atmospheric and an oceanic mode. Our work emphasizes that future analysis of simulations of DO-like events should focus not just on the mechanisms responsible for the spontaneous oscillations but also on the spatio-temporal fingerprint of the oscillations across the globe.

Data Assimilation in paleoclimatology (PaleoDA) is a method that has been used in several climate reconstructions for the last millennium. By fusing information from both climate proxies and general circulation models (GCMs), PaleoDA provides statistical estimates of climate fields that are dynamically consistent. However, existing reconstructions mostly rely on calibrated tree ring data and assimilate proxy records on a single, annual time scale. Ice cores and speleothems, which record past variations in the oxygen isotope ratio of precipitation, often have a lower and irregular time resolution, but reliably record climate variations on decadal to centennial time scales. Here, we implemented a computationally efficient DA algorithm that enables the assimilation of proxy records on multiple timescales. The algorithm has been applied to speleothem and ice core records from the SISALv2 and Iso2k database and five isotope-enabled GCMs. Reconstructions of global mean temperature changes during the last millennium compare well in both amplitude and uncertainty to recent studies. The potential of incorporating speleothems is shown with a reconstruction of hydroclimatic changes in tropical South America, where speleothems represent the most abundant type of hydroclimate archive. The experiments performed suggest an increased reconstructed decadal to centennial variability by using proxy records on multiple timescales. Making use of different climate models shows the influence of model biases on the reconstructions. Future PaleoDA reconstructions could be improved from more proxy records and the multiple time scale approach to provide a globally complete picture of past climate changes.

To characterize Earth’s temperature variability, it is necessary to better understand underlying mechanisms and contributions from internal and externally forced components. Here, we utilize a stochastic two-box energy balance model to emulate internal and forced global mean surface temperature (GMST) variability [1]. As target data for the emulation, we employ observations and 20 last millennium simulations from climate models of intermediate to high complexity. We infer the parameters of the stochastic EBM using the target data and a Bayesian approach, as implemented with a Markov Chain Monte Carlo algorithm in our “ClimBayes” software package [2]. This yields the best estimates of the EBM’s forced and forced + internal response. Applying spectral analysis, we contrast timescale-dependent variances of the EBM’s forced and forced + internal variance with that of the GMST target. Our findings show that the simple two-box stochastic EBM reproduces the characteristics of simulated global temperature fluctuations, even from comprehensive climate models. Minor deviations occur mainly at interannual timescales and are related to the simplistic representation of internal variability in the EBM. Furthermore, the relative contribution of internal dynamics increases with model complexity and decreases with timescale. Altogether, we demonstrate that the combined use of simple stochastic climate models and Bayesian inference provides a valuable tool to emulate climate variability across timescales. [1] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld: “Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework,” Chaos, https://doi.org/10.1063/5.0106123 (2022). [2] M. Schillinger, B. Ellerhoff, R. Scheichl, and K. Rehfeld, “The ClimBayes package in R,” Zenodo, V. 0.1.1, https://doi.org/10.5281/zenodo.7317984 (2022).

Hydrogen as a versatile, greenhouse gas-free energy carrier will play an important role in our future economy. Yet sustainable, competitive production and distribution of hydrogen remains a challenge. Highly integrated solar water splitting systems aim to combine solar energy harvesting and electrolysis in a single device, similar to a photovoltaic module.[1] Such a system can produce hydrogen locally without the requirement to be connected to the electricity grid. Unlike large electrolysis that draws power from the grid, the power density of such a device is reduced so far that it does not require active cooling, but its operating temperature will closely follow outdoor conditions.Here, we present our work on high-efficiency integrated solar water splitting devices based on multi-junction solar absorbers. The light-absorbing component is sensitive to the shape of the solar spectrum and generally becomes more efficient at lower temperatures. Catalysis, on the other hand, benefits from higher temperatures. These conflicting trends wih respect to the temperature impact the design of the solar hydrogen production system. We analyse how the local climate affects production efficiency[2] and show in a lab study that adequate system design allows efficient operation at temperatures as low as -20°C.[3] These insights can help to design small-scale distributed solar hydrogen production for both temperate regions, but also more extreme climatic conditions.[1] M.M. May et al., Nature Communications 6 (2015), 8286.[2] M. Kölbach et al, Sustainable Energy & Fuels 6 (2022), 4062.[3] M. Kölbach, K. Rehfeld, M.M. May, Energy & Environmental Sciences 14 (2021), 4410-4417.

Solar panels of utility scale are a rapidly growing contribution to the renewable energy supply with increasing efficiency and steeply reducing costs of photovoltaics (PV). Although there is consensus on long term reduction of greenhouse gas emissions and advantages of avoided emissions by using PV systems, there is need to understand the climatic effects of land surface modifications of such systems.We first review studies focusing on effects of utility-scale solar farms and compare their results in accordance to their characterization of a photovoltaic and the type of model used to evaluate effects. Then, we perform simulations of larger than current utility-scale solar farms but also comparatively small localized farms which are both characterized by modified land surface properties. These regions are either identified on the basis of solar insolation and desert-like criteria (low precipitation) or are proposed in existing studies for potential deployment of solar farms. The solar farm deployments are characterized by static or gradual changes in respective boundary conditions of albedo, soil roughness and outgoing longwave thermal properties. Separate experiments are conducted with non-dynamic and dynamic vegetation components to investigate potential feedbacks originating from the interplay of vegetation and precipitation. To this end, we use a comprehensive model (MPI-ESM-LR; Mauritsen et al., 2019), and the Earth System Model of Intermediate Complexity PlaSim (Fraedrich et al., 2005) to understand, and cross-validate, the effects in two models of different complexity model.We examine the results considering changes in radiative forces and surface energy budget and, therefore, the surface temperatures which can likely lead to atmospheric circulation patterns, precipitation and vegetation feedbacks. If lower complexity models provide results in an acceptable ballpark of comprehensive ESMs, then they could be further used for future quick compute simulations with various deployment scenarios and experimentation with variable technical aspects. Spatially explicit deployment of PV with focus on parameterized thermal properties dependent on the simulated climate would also allows us to look at the complementary effect of climate on panels and could help constrain the effect of different pathways on PV technology in the future.

The Pantanal is a large region located in the central parts of South America (140,000 km2) with a unique climate and vegetation setting. This region is subjected to seasonal floods, which makes the Pantanal one of the most important wetlands on the planet. In this region occur transitions between different biomes, such as the Amazon Forest, Cerrado (Brazilian Savanna), and Atlantic Forest, located to the North, East, and South of this region, respectively. The area also serves as a moisture pathway for the South American Summer Monsoon (SASM), which connects the Amazon Basin with the La Plata Basin. The two major drainage basins of South America. Due to the complex hydrology of the rivers and lakes of this region, it is necessary to combine multiple proxy archives from different parts of the Pantanal basin to understand its climate and vegetation evolution during the Holocene.Here we present isotope records from stalagmites collected at sites located at the northern and southern borders of the Pantanal. Hiatuses in speleothem deposition during the mid-Holocene identified in several stalagmites indicate overall dry conditions in the region at this period. However, the drier conditions recorded in the northern portion of the basin precede similar conditions in the South by approximately two thousand years. Furthermore, summer insolation seems to drive the intensity of the SASM at the North Pantanal, while its influence is weaker in the southern part. During the late Holocene, this establishes a moisture gradient between a wetter North and drier South. Our record also shows a strong multidecadal to centennial variability, which was probably responsible for the high hydrology complexity of the rivers of the Pantanal, which are subject to seasonal floods and migration of its channels and tributaries.

At the current decarbonization rate, we are set on a path towards re-shaping a substantial share of land for carbon dioxide removal (CDR) over the following decades. However, existing Earth system models which could help to quantify the character of resulting CDR side effects and their consequences for the cumulative CO2 removal do not yet resolve dynamic CDR cover in space. Here, we embark on shedding light on this CDR uncertainty space, scrutinizing CDR impacts in spatial simulations with a comprehensive Earth system model. Assuming CDR to be driven by solar irradiation in the style of photovoltaics, our model is the first to simulate an idealized approach to land-based CDR with its physical, biospheric, and land use couplings on a grid box scale. We analyze dynamic CDR simulations for spatial deployment scenarios according to the country-wise burden of past CO2 emissions, to livelihood constraints, and to optimal irradiation conditions. Shared socio-economic pathways drive the overall global CDR use for a range of potential future emission scenarios. Aside from these spatio-temporal scenarios, the simulations also cover different ways of releasing excess energy from the solar-to-carbon conversion, permitting either local cooling through carbon storage, heat dissipation resulting from system losses or co-benefits for energy production. Based on simulation ensembles for the different scenarios, we quantify Earth system impacts of CDR and their consequences for CO2 removal if grid-scale feedbacks are properly resolved. With new spatially resolved CDR representations in Earth system models we will be able to test CDR-induced Earth system dynamics and CDR promises in greater detail than with existing globally forced projections. This spatially explicit modeling strategy could also open a way toward more comprehensive modeling strategies which include consequences for land use decisions on CDR.

Speleothems are terrestrial paleoclimate archives that occur abundantly in the low and mid latitudes. They archive changes in the past hydroclimate in many ways, including the rate of calcium carbonate accumulation – their growth rate. However, determining speleothem growth rates, and in particular growth rate changes, is challenging due to speleothem inherent features such as growth hiatuses, and large and abrupt growth rate changes. Low dating resolution poses an additional problem, as the U/Th measurements that allow for precise dating are time-consuming and expensive.Here, we analyze speleothem growth rates during the Holocene – an ideal period for method testing due to the high abundance of speleothem records in the SISALv2 database. In particular, we compare speleothem growth rates in the early (12-8 kyr BP), mid (8-4 kyr BP) and the late Holocene (4-0 kyr BP). Using synthetically-modelled stalagmites, we test the strengths and weaknesses of state-of-the-art age-depth modelling methods to determine a set of necessary requirements to quantify speleothem growth rates and growth rate changes. Using these, we find slightly higher growth rates in the early Holocene within speleothems that cover at all periods. Comparing growth rates of speleothems that cover only one of the respective periods in the Holocene did not distinguish any period of highest or lowest growth rate. Detailed regional studies and comparison to model data are used to further interpret these results. Reliably determining growth rate changes in the Holocene may help in further understanding and characterizing hydroclimate changes as archived in speleothems also beyond the Holocene.

Proxy-based reconstructions and climate model simulations of surface temperature trends during the Holocene disagree: While reconstructions show a cooling during the mid- and late Holocene, climate models show a continuous warming – a contradiction known as the Holocene temperature conundrum. Despite extensive research, the reason for the disagreement remains unclear. Both, missing processes in the models as well as biases in the proxies and the resulting reconstructions are possible sources of the conundrum. Here we compare our TransEBM v1.2 climate simulation as well as additional climate models of different complexity and Holocene temperature trends from the Temperature12k dataset (Kaufman et al., 2020b), with regards to model-data and model-model agreement. We show that models of all complexities disagree with mid-Holocene temperature trends in reconstructions and that this disagreement is almost independent of proxy and archive type. While, models show the highest agreement with summer temperature trends in reconstructions, our study shows that a trivial summer bias in proxies is not sufficient to explain the conundrum. Further effort to disentangle seasonal biases in proxies and the testing of potential misrepresentations in climate models, like anthropogenic land-use, in form of sensitivity experiments are needed to resolve the Holocene conundrum.

Integrated solar water splitting devices that produce hydrogen without the use of power inverters operate outdoors and are hence exposed to varying weather conditions. As a result, they might sometimes work at non-optimal operation points below or above the maximum power point of the photovoltaic component, which would directly translate into efficiency losses. Up until now, however, no common parameter describing and quantifying this and other real-life operating related losses (e.g. spectral mismatch) exists in the community. Therefore, the annual-hydrogen-yield-climatic-response (AHYCR) ratio is introduced as a figure of merit to evaluate the outdoor performance of integrated solar water splitting devices. This value is defined as the ratio between the real annual hydrogen yield and the theoretical yield assuming the solar-to-hydrogen device efficiency at standard conditions. This parameter is derived for an exemplary system based on state-of-the-art AlGaAs//Si dual-junction solar cells and an anion exchange membrane electrolyzer using hourly resolved climate data from a location in southern California and from reanalysis data of Antarctica. Moreover, the advantage of devices operating at low current densities over completely decoupled PV-electrolysis is discussed. This work will help to evaluate, compare and optimize the climatic response of solar water splitting devices in different climate zones.

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., Chaos, 32 (11), ISSN:1054-1500, ISSN:1089-7682

Hydrogen produced from water using solar energy constitutes a sustainable alternative to fossil fuels, but solar hydrogen is not yet economically competitive. A major question is whether the approach of coupling photovoltaics via the electricity grid to electrolysis is preferential to higher levels of device integration in ���artificial leaf��� designs. Here, we scrutinise the effects of thermally coupled solar water splitting on device efficiencies and catalyst footprint for sub-freezing ambient temperatures of ���20 ��C. These conditions are found for a significant fraction of the year in many world regions. Using a combination of electrochemical experiments and modelling, we demonstrate that thermal coupling broadens the operating window and significantly reduces the required catalyst loading when compared to electrolysis decoupled from photovoltaics. Efficiency benefits differ qualitatively for dual- and triple-junction solar absorbers, which has implications for the general design of outdoor-located photoelectochemical devices. Similar to high-efficiency photovoltaics that reached technological maturity in space, application cases in polar or alpine climates could support the scale-up of solar hydrogen at the global scale., publishedVersion

Hydrogen is a versatile energy carrier. When produced with renewable energy by water splitting, it is a carbon neutral alternative to fossil fuels. The industrialization process of this technology is currently dominated by electrolyzers powered by solar or wind energy. For small scale applications, however, more integrated device designs for water splitting using solar energy might optimize hydrogen production due to lower balance of system costs and a smarter thermal management. Such devices offer the opportunity to thermally couple the solar cell and the electrochemical compartment. In this way, heat losses in the absorber can be turned into an efficiency boost for the device via simultaneously enhancing the catalytic performance of the water splitting reactions, cooling the absorber, and decreasing the ohmic losses.[1,2] However,integrated devices (sometimes also referred to as “artificial leaves”), currently suffer from a lower technology readiness level (TRL) than the completely decoupled approach.Here, we describe our progress in designing integrated solar water splitting devices to power research stations in Antarctica as a first potentially economic competitive implementation of this technology.[3] In such remote world regions, local and small-scale hydrogen production can become both economically and environmentally favorable, since the logistics for fossil fuels are expensive and environmentally hazardous. One reason for the low TRL of integrated devices is the complex and poorly understood influence of different weather/climate conditions and changes in the solar spectra on their efficiency.[4] Therefore, we introduce an open-source Python-based model that combines solar cell physics, optical simulations, electrochemistry, as well as atmospheric and climate data as part of the “YaSoFo” environment.[5] We model and analyze the climatic response of a device based on state-of-the-art AlGaAs/Si dual-junction photoabsorbers in Antarctica. Furthermore, we present a first prototype demonstrating solar water splitting at temperatures as low as -20°C. [3] Our work gives important insights into the chances and challenges for thermally coupled solar water splitting and lays the foundation for our goal of using these devices in remote world regions with cold climates. [1] R. van de Krol, B. Parkinson, MRS Energy Sustain., 2017, 4(e13), 1-11[2] S. Tembhurne, F. Nandjou, S. Haussener, Nat. Energy, 2019, 4, 399–407[3] M. Kölbach, K. Rehfeld, M.M. May, Energy Environ. Sci., 2021,14, 4410-4417[4] M. Reuß, J. Reul, T. Grube, M. Langemann, S. Calnan, M. Robinius, R. Schlatmann, U. Rau, D. Stolten, Sustain. Energy Fuels, 2019, 3, 801-813[5] M. M. May, D. Lackner, J. Ohlmann, F. Dimroth, R. van de Krol, T. Hannappel, K. Schwarzburg, Sustain. Energy Fuels, 2017, 1, 492-503

Climate variability, resulting from natural radiative forcing and interactions within the climate system, is a major source of uncertainty for regional climate projections. Constraining the amplitude of these natural variations is fundamental to assess the range of plausible future scenarios. As the instrumental record is limited to the last two centuries, information about climate variations on multi-decadal to millennial timescales relies on the analysis of climate proxy records and climate model simulations. However, current results from systematic model-proxy comparisons of natural variability seem contradictory. Several studies suggest that simulated local temperature variability is consistently smaller than proxy-based reconstructions and conclude that climate models might have major deficiencies. Other studies find agreement in global temperature variability across timescales and argue that current models can faithfully simulate climate variability. Here, we review the evidence on the strength of natural temperature variability during recent millennia. We identify systematic biases in the reconstructions that may contribute to the model-proxy discrepancy but are likely not sufficient to reach consistency. Instead, we propose that the seemingly contradictory findings on the (dis)agreement between proxies and simulations can be reconciled assuming that regional climate variations persist on longer time scales than currently simulated by climate models. The combined evidence argues for deficiencies in the simulation of internal variability but a faithful response of climate models to natural radiative forcing. We propose a strategy to test our hypothesis and discuss the implications for future climate projections.

Reliable climate projections in face of global warming require a firm and detailed understanding of climate variability. Variations in climate can be externally-forced, for example by anthropogenic emissions, or internally-generated, for example from chaotic atmosphere and ocean dynamics. To investigate the climatic response to radiative forcing, a common concept is the equilibrium climate sensitivity (ECS). Many studies estimate the ECS by fitting simple energy balance models (EBMs) to observational data. This approach has benefitted from advances in numerical analysis and statistics, enabling a fully Bayesian analysis. Via Bayes theorem, it quantifies the probability of certain climate parameters given observations, for example of surface temperature. To this end, it combines the goodness of the model fit with assumptions on measurement errors and climate variability as well as prior information. Here, we analyse and discuss Bayesian inference of climate parameters such as ECS from global mean temperatures using multibox EBMs. We therefore present an R package which relies on the Markov Chain Monte Carlo algorithm and includes an extension of the one-box model with a time-dependent feedback parameter. Using measurements from the instrumental period as well as temperature reconstructions and model data from the last millennium, we validate and demonstrate the package. We find that the two-box model performs significantly better in fitting the observations than the one-box model, and generates 21st century projections that agree more closely with AR5 estimates. Further, we evaluate the robustness of the estimate against uncertainties in temperature and forcing data through synthetic experiments. To this end, we quantify how estimation errors depend on the strength of noise in temperature data and compare the influence of dating and amplitude uncertainties in forcing reconstructions. In summary, we provide an effective tool for Bayesian estimation of climate parameters and elaborate its potential for studying the response to external forcing.

Projections of anthropogenic climate change suggest possible surface temperature increases similar to those during past major shifts of the mean climate like the Last Deglaciation. Such shifts do not only affect the mean but rather the full probability distributions of climatic variables such as temperature and precipitation. Changes to their distributions can thus be expected for the future as well and need to be constrained. To this end, we examine transient simulations of the Last Deglaciation from a hierarchy of climate models, ranging from an energy balance model to state-of-the-art Earth System Models. Besides the mean, we use the higher moments of variability – variance, skewness, and kurtosis – to characterize changes of the distribution. The analysis covers annual to millennial timescales and examines how patterns vary with timescale and region in response to warming. Furthermore, we evaluate how the changes of the distributions affect the occurrence of extremes. To analyze the influence of forcings on the distributions, we compare the patterns of the fully-forced simulations to those in sensitivity experiments that isolate the effects of individual forcings. In particular, the effect of volcanism is examined across the hierarchy, as well as changes in ice cover, freshwater input, CO2, and orbit. While large-scale global patterns can be found, there are significant regional differences as well as differences between simulations, relating for example to differences in the modelling of ice cover changes and freshwater input. Finally, we investigate whether climate model projections show the same trends with respect to the change in moments as those found in the deglacial simulations and thus whether the patterns found might hold for future climate.

The Last Glacial Maximum (LGM, ~21.000 years before present) was a period with significantly lower global mean temperature, large Northern Hemisphere ice sheets, and low CO2 concentrations. Unlike other high-latitude areas, Siberia was not covered by a terrestrial ice sheet. Climate simulations with LGM boundary conditions show large inter-model differences especially in Northern Siberia, which impede a direct analysis of Siberian LGM climate from simulations. Thus, constraints from proxy data are needed. Here, we reconstruct summer temperature and moisture availability from a network of Siberian pollen records using statistical transfer functions. The estimates are corrected for the lower CO2 concentrations during the LGM with an a posteriori method.Using a Bayesian framework, we combine the pollen-based reconstructions with climate simulations to obtain the first regional state estimate of LGM climate constrained by proxies from Central and Eastern Siberia. Our reconstruction shows higher summer temperature anomalies than most high-latitude areas and simulations. Reconstructed moisture availability is similar to present-day values, consistent with most simulations. Analyses of the simulations show that anomalous circulation patterns and reduced cloud cover contribute particularly to the relatively high summer temperatures, supporting previous studies based solely on climate simulations. Our data-constrained state estimate is well-suited for future analyses of Siberian LGM climate such as investigations of the reasons for the absence of a Siberian ice sheet during the LGM.

Efforts towards standardizing biogeochemical data from palaeoclimate archives such as speleothems, ice cores, corals, trees or marine sediments allow to tackle global-scale changes in palaeoclimate dynamics. These endeavours are sometimes initiated for very specific research questions. One such example is the multi-archive, multi-proxy dataset used in a characterization of changes in temperature variability from the last Glacial Maximum to the current Interglacial [1]. Here, we focused on collecting all published proxy time series for temperature that fulfilled sampling criteria, but we did not include a lot of metadata.Another, quite prominent, example is the database that grew out of the working group on Speleothem synthesis and analysis (SISAL) in the Past Global Changes (PAGES) network. In its construction, researchers from all over the world collaborated, producing a quality-controlled data product with rich metadata. SISAL v2 [2] contains data from 691 speleothem records published over the decades, for more than 500 standardized age models were established. The design and data collection in the community allowed to draw together metadata and observations to reproduce the age modeling process of individual studies. This database has a rich set of purposes, ranging from the evaluation of monsoon dynamics, to that of isotope-enabled climate models [3].Contrasting these two approaches I will discuss the challenges arising when multiple proxies, archives, modeling purposes and community standards need to be considered. I argue that careful design of standardized data products allows for a new type of geoscience work, further catalyzed by digitization, forming a basis for tackling future-relevant palaeoclimatic and palaeoenvironmental questions at the global scale. [1] Rehfeld, K., et al. "Global patterns of declining temperature variability from the Last Glacial Maximum to the Holocene." Nature 554.7692: 356-359, https://doi.org/10.1038/nature25454, 2018[2] Comas-Bru, L., et al. (incl. SISAL Working Group members): SISALv2: a comprehensive speleothem isotope database with multiple age–depth models, Earth Syst. Sci. Data, 12, 2579–2606, https://doi.org/10.5194/essd-12-2579-2020, 2020.[3] Bühler, J. C. et al: Comparison of the oxygen isotope signatures in speleothem records and iHadCM3 model simulations for the last millennium, Clim. Past, 17, 985–1004, https://doi.org/10.5194/cp-17-985-2021, 2021.

The socio-economic impacts of rare extreme events, such as droughts, are one of the main ways in which climate affects humanity. A key challenge is to quantify the changing risk of once-in-a-decade or even once-in-a-century events under global warming, while leaning heavily on comparatively short observation spans. The predictive power of classical statistical methods from extreme value theory (EVT) often remains limited to uncorrelated events with short return periods. This is mainly due to their strong assumption of an underlying exponential family distribution of the variable in question. Standard EVT is therefore at odds with the rich and large-scale correlations found in various surface climate parameters such as local temperatures, as well as the more complex shape of empirical distributions. Here, we turn to recent developments in machine learning, namely to conditional normalizing flows, which are flexible neural networks for modeling highly-correlated unknown distributions. Given a short time series, we show how such networks can model the posterior probability of events whose return periods are much longer than the observation span. The necessary correlations and patterns can be extracted from a paired set of inputs, i.e. time series, and outputs, i.e. return periods. To evaluate this approach in a controlled setting, we generate synthetic training data by sampling temporally autoregressive processes with a non-trivial covariance structure. We compare the results to a baseline analysis using EVT. In this work, we focus on the prediction of return periods of rare statistical events. However, we expect the same potential for a wide range of statistical measures, such as the power spectrum and rate functions. Future work should also investigate its applicability to compound and spatially extended events, as well as changing conditions under warming scenarios.

Recent progress in modelling the Earth system has made it possible to produce transient climate simulations longer than 10.000 years with comprehensive ESMs. These simulations improve our understanding of slow climatic feedbacks, climate state transitions, and abrupt climate changes. However, assessing the quality and reliability of such paleoclimate simulations is particularly challenging due to the inherent characteristic differences between model data and the climate reconstructions used to validate them.Here, we present a collection of software packages for inter-model and model-data comparisons called Palaeo ToolBox (PTBox). Its first intent is to evaluate transient simulations of the PalMod project (deglaciation, glacial inception, MIS3) using several proxy data syntheses. Various variables are evaluated (including temperature, precipitation, oxygen isotopes, vegetation, carbon storages and fluxes), across a range of timescales (from decadal to multi-millenial). PTBox provides integrated model-data workflows, from data pre-processing to visualisations, organised into a series of (mostly R) packages. So far, PTBox includes 1) tools for pre-processing simulations and proxy data, 2) ensemble and pseudo-proxy methods to bridge the gap between simulations and proxies and to quantify uncertainties, 3) spectral methods to analyse timescale-dependent climate variability, and 4) newly developed metrics for spatio-temporal model-data comparisons.Finally, PTBox is accompanied by a website (http://palmodapp.cloud.dkrz.de/) with examples on how to use PTBox and interactive visualisations of the datasets produced in the PalMod project.

The Last Glacial Maximum (LGM, around 21.000 years before present) was a period with significantly colder global mean temperature, large Northern Hemisphere ice sheets, and lower CO2 concentrations. Siberia was affected by a lower sea level which led to a closed Bering strait and a northward shift of the Arctic Ocean coastline. However, unlike other high-latitude areas, Siberia was not covered by a terrestrial ice sheet at that time. Climate simulations with LGM boundary conditions show large inter-model differences especially in Northern Siberia, which impede a direct analysis of Siberian LGM climate from simulations. Preserved pollen samples provide information on the LGM vegetation and climate state.We reconstruct mean temperature of the warmest month and mean annual precipitation from a network of pollen samples using weighted averaging partial least squares transfer functions. Combining these local reconstructions with a multi-model ensemble of PMIP3 climate simulations in a Bayesian framework, we obtain the first probabilistic spatial reconstruction of Siberian LGM climate. Our reconstruction shows less cold summer temperature anomalies along the coastlines than in Western Siberia, and a weaker precipitation reduction in Eastern Siberia compared to Western Siberia. Finally, we quantify the mismatch between each ensemble member and the proxy data. By examining common features among similarly performing simulations, we find that most simulations with a comparatively warm and wet Siberian climate score better.Our data constrained state estimate is well-suited for future analyses of Siberian LGM climate. Potential applications include the investigation of reasons for the absence of a Siberian ice sheet during the LGM, the quantification of land-atmosphere carbon fluxes, and the estimation of past afforestation rates.

Without substantial decarbonization of the global economy, rising atmospheric carbon dioxide (CO2) levels are projected to lead to severe impacts on ecosystems and human livelihoods. Integrated assessments of economy and climate therefore favour large-scale CO2 removal to reach ambitious temperature-stabilization targets. However, most of the proposed approaches to artificially remove CO2 from the atmosphere are in conflict with planetary boundaries due to land-use needs and they may come with unintended climatic side-effects. Long-term draw-down of CO2 by photoelectrochemical (PEC) reduction is a recent and promising approach that potentially entails a very low water footprint and could offer a variety of carbon sink products for safe geological storage. For renewable hydrogen fuel production, PEC devices have already been demonstrated to deliver high solar-to-fuel efficiencies. If such devices are adjusted to deliver high solar-to-carbon efficiencies for carbon dioxide removal, they would require comparably little land for achieving annual sequestration rates that are compatible with limiting global warming to 2°C or below. Yet, no production-scale prototype exists and the climatic side-effects of such an "artificial photosynthesis'' approach for negative emissions are unknown. Here, we discuss our work towards investigating potential impacts of PEC CO2 removal on the climate and the carbon cycle in simulations with the comprehensive Earth System Model MPI-ESM. We designed a scheme to represent hypothetical PEC devices as a land surface type which is influencing land-atmosphere energy and moisture fluxes. We parameterize the irradiation-driven carbon sequestration of the devices and interactively couple their deployment area and location to a negative emission target. We plan to compare the potential side-effects between scenarios of dense, localized deployment and spread-out, decentralized application. These scenarios represent different guiding objectives for deploying hypothetical PEC systems such as maximizing the insolation per module area, or mitigating the overall impacts on climate and on carbon stocks. For the different scenarios, we intend to investigate changes in the surface balances, which could impact atmospheric circulations patterns. We further plan to quantify the amount of land-stored carbon that is relocated due to land-use change, as this affects the amount of CO2 that can effectively be withdrawn from the atmosphere. Finally, we relate theoretical expectations for area requirements and CO2 withdrawal with results from the coupled simulations which could inform the technological development. While ambitious emission reductions remain the only appropriate measure for stabilizing anthropogenic warming, our work could advance the understanding of possible benefits and side-effects of hypothetical PEC CO2 removal.M. M. May & K. Rehfeld, ESD Ideas: Photoelectrochemical carbon removal as negative emission technology. Earth Syst. Dynam. 10, 1–7 (2019).

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- as well as millennial-scale temperature variations. The use of proxy forward modeling avoids the need to reconstruct gridded or regional mean temperatures 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. We attribute this 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.

Climate variability is the primary influence on climate extremes and affected by natural forcing from solar irradiance and volcanic eruptions. Global warming impacts climate variability, but there is contradictory and incomplete evidence on the spatio-temporal patterns. Strong volcanic eruptions have been suggested to reduce temperatures less in warmer climate states. However, the underlying question of state-dependent effect of natural forcing on local and global variability remains open. Moreover, there are uncertainties about the role of natural forcing in the mismatch between simulated and reconstructed local, long-term variability. Using a 12-member GCM ensemble with targeted boundary conditions, we present naturally-forced and equilibrium, millennium-length simulations for the Last Glacial Maximum (LGM) and the Pre-Industrial (PI). We quantify the local and global climate response to solar and volcanic forcing in the LGM and PI, and contrast variability from forced and control simulations on annual-to-multicentennial scales. We differentiate various contributions from the atmosphere, oceans, and particularly that of sea ice using a 2D energy balance model (EBM). Spectral analysis of simulated temperatures shows that global variability is predominately determined by natural forcing. Local mean spectra are more characteristic for the mean climate state and reveal a decrease in local variability with warming. The global and local response to natural forcing is robust against changes in the mean climate. Particularly, the spatial patterns of the surface climate's response to volcanic eruptions widely agree across states. Weak local differences resulted primarily from sea ice dynamics. The sea ice contribution is the strongest on interannual scales. It remains significant on decadal scales and longer, providing a key mechanism of long-term variability. We validate the simulated variability against observational and paleoclimate data. The variance obtained from proxies is increasingly larger on longer timescales compared to that from simulated time series. The inclusion of natural forcing reduces the model-data mismatch on decadal-to-multicentennial scales and, thus, provides a more accurate representation of climate variability. Consideration of natural forcing is therefore paramount for model-data comparison and future projections. The robust temperature response suggests that findings on the ability of models to simulate past variability should translate to future climates, and can thus help constrain variability.

Jet streams control hydroclimate variability in the mid-latitudes with important impacts on water availability and human societies. According to future projections, global warming will change jet stream characteristics, including its mean position. Variability of these characteristics on hourly-to-daily timescales is key to understanding the mid-latitudes circulation. Therefore, most analysis methods of present-day jet streams are designed for 6-hourly data. By modelling the climate since the Last Glacial Maximum, we can investigate the long-term drivers of jet stream characteristics. However, for transient simulations of the last deglaciation, 3d wind fields are only archived with a monthly resolution due to storage limitations. Hence, jet variability at shorter timescales cannot be identified, and established methods can’t be used.Here, we study to what extent changes of jet stream characteristics can be inferred from monthly wind fields. Therefore, we compare latitudinal jet stream positions, strength, tilt and their variability from daily and monthly wind fields in reanalysis data and for LGM and PI simulations. We test three different methods to construct jet stream typologies and metrics. This comparison identifies to which extend these jet stream characteristics can be robustly studied from monthly wind fields. In addition, our analysis assesses the added value of archived daily data for future research. Once the limitations of monthly wind output are known, jet stream characteristics in transient simulations of the last deglaciation can be analysed. This analysis provides new insights on jet stream changes on decadal-to-orbital timescales and identifies the factors controlling these changes.

To reliably predict future changes, it is crucial to understand the response of the climate system to past changes in radiative forcing, which are investigated using climate models as well as information extracted from paleoclimate archives such as speleothems. Hydrological changes in past, present, and future are, however, far less understood and more uncertain than changes in temperature.Speleothems are terrestrial archives in the low to mid latitudes. Their growth rate changes are hypothesized to reflect local changes in precipitation amount, albeit the response may be non-linear and subject to karst specific processes. Full coverage of glacial-interglacial cycles and high precision dating through U/Th measurements makes them a suitable archive to assess and constrain state-dependent precipitation changes. However, speleothem inherent features, such as growth hiatuses or large and abrupt changes in growth rates, are a challenge for current age-depth modelling methods.Here, we compare modelled precipitation amount from the Paleoclimate Modeling Intercomparison Project (PMIP), in particular time slices of the Last Glacial Maximum (around 21.000 years before present) and the Mid-Holocene (around 6.000 years before present) to growth rate changes of speleothems from the global speleothem database SISALv2. We perform case studies on a large ensemble of synthetic speleothems to systematically assess the resolution of age measurements necessary to reliably detect and model growth rate changes. These synthetic speleothems cover a large range of characteristic speleothem features observed in the SISALv2 database and are analyzed by six different age-depth modeling methods (linear regression, linear interpolation, copRa, StalAge, Bacon, and Bchron). Comparing the simulated changes with speleothems selected from SISALv2 according to these criteria can thus help to constrain past precipitation changes and subsequently confine uncertainty of future changes.

Here we provide data of twelve climate model simulations in supplement to Ellerhoff et al., 2022. More information on the simulations can be found in the file README.txt and the preprint this upload supplements., Financial support for this study was given by the Deutsche Forschungsgemeinschaft (DFG, projects 316076679 and 395588486), the German Federal Ministry of Education and Research (BMBF, grant no. 01LP1926C) and the Heinrich B��ll Stiftung. Simulations were carried out using the ARCHER UK National Supercomputing Service (\url{http://www.archer.ac.uk}).

Ice core records from Greenland provide evidence for multiple abrupt warming events recurring at millennial time scales during the last glacial interval. Although climate transitions strongly resembling these Dansgaard-Oeschger (DO) transitions have been identified in several speleothem records, 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 transitions on the global atmospheric circulation pattern. We comprehensively analyse d18O changes during DO transitions in a globally distributed dataset of speleothems (SISALv2; Comas-Bru et al., 2020). Speleothem d18O signals mostly reflect changes in precipitation amount and moisture source. Thereby this proxy allows us to infer spatially resolved changes in global atmospheric dynamics that are characteristically linked to DO transitions. We confirm the previously proposed shift of the Intertropical Convergence Zone towards more northerly positions. In addition, we find evidence for a similar northward shift of the westerly winds of the Northern Hemisphere. Furthermore, we identify a decreasing trend in the transition amplitudes with increasing distances from the North Atlantic region. This confirms previous suggestions of this region being the core and origin of these past abrupt climate changes. References:Comas-Bru et al., 2020, Earth System Science Data 12, 2579–2606

The incorporation of water isotopologues into the hydrology of general circulation models (GCMs) facilitates the comparison between modelled and measured proxy data in paleoclimate archives. However, the variability and drivers of measured and modelled water isotopologues, and indeed 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 paleoclimate archive and provide well preserved (semi-)continuous multivariate isotope time series in the lower and mid-latitudes, and are, therefore, well suited to assess climate and isotope variability on decadal and longer timescales. However, the relationship between speleothem oxygen and carbon isotopes to climate variables also depends on 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 common era, CE). We systematically evaluate differences and commonalities between the standardized model simulation outputs. The goal is to distinguish climatic drivers of variability for both modelled and measured isotopes. We find strong regional differences in the oxygen isotope signatures between models that can partly be attributed to differences in modelled temperatures. At low latitudes, precipitation amount is the dominant driver for water isotope variability, however, at cave locations the agreement between modelled temperature variability is higher than for precipitation variability. While modelled 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, and 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, although to varying extent. 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 modelled data. Considering karst and cave internal processes through e.g. 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 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.

This study presents a long-term, multi-proxy reconstruction of the Asian southwest monsoon during the Tortonian to Piacenzian, based on a 4.78 Ma record from Coastal Makran, northwestern Gulf of Oman, southeast Iran. The integration of humidity proxies (clay minerals, Th/K, volume magnetic susceptibility, and grain size analysis), marine redox sensitivity (Th/U), total organic matter, carbonate content, 87Sr/86Sr ratio, and spectral gamma-ray data conducted here provide valuable information that fill the existing gap in marine palaeoclimate records. The results show that a strong winter monsoon condition associated with relatively low precipitation and subsequently low physical and chemical weathering dominated the region during late Tortonian – late Messinian (7.65–5.83 Ma). However, a few episodes of intense physical and chemical weathering related to high precipitation are observed during this period (6.23–6.01 Ma), which is consistent with increased organic matter input from continental reservoirs to the oceans. In addition, the data indicate that from the latest Messinian (5.82–5.33 Ma) to Zanclean–Piacenzian (5.33–2.87 Ma), a strong summer monsoon accompanied by a relatively wetter condition and higher physical and chemical weathering resulted in a high detrital input into the basin. This higher weathering period is associated with the highest rate of Himalayan uplift, causing enhanced precipitation. Wavelet analysis of spectral gamma-ray data revealed notable periodicities at 750 Ka and 1.7 Ma, with significant periodicities centered around 5.75–6.03 Ma over the latest Messinian – Zanclean. Comparison with palaeoclimate records from other sites indicates a teleconnection with respect to precipitation, weathering, and productivity, especially during the Messinian–Zanclean transition.

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 variability across timescales is important to understand the underlying dynamics of the Earth system. It remains challenging to do so from paleoclimate archives since they are more than often irregular and traditional methods to produce 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 paleo-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 timeseries, the interpolation-free methods are to be preferred over the methods requiring interpolation as they allow for the utilization of the information from shorter timescale 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 timeseries is not stationary. Given that we cannot know a priori what kind of scaling behaviour is contained in a paleoclimate timeseries, 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.

Stable water isotope ratios (δ18O) measured in e.g. ice-cores or speleothems have long been established as temperature proxies and are used to reconstruct past climate variability but still require more quantification on spatial and temporal scales. The high resolution ice-core archives are mainly found in polar and alpine regions, whereas the speleothem records mostly grow in caves in low to mid-latitudes. To bridge between the archives, models are needed to compare the climate variability stored in both ice-cores and speleothems, which will help to evaluate future projections of climate variability. Here, we compare a transient isotope enabled simulation from the Hadley Center Climate Model version 3 (iHadCM3) [1, 2] to polar ice-core records from the iso2k database [3] for the last millennium (LM, 850-1850 CE). We analyze time-averaged isotope ratios and their variability on decadal to centennial timescales to systematically evaluate the offsets and correlation patterns between simulated and recorded isotopes to specific climatic drivers. For better comparability between speleothem and ice core-archives, we also include non-polar ice core records, as well as monitored precipitation δ18O from a global database. We find the time-averaged δ18O offsets between the simulation and ice-core records to be fairly small for most of the polar ice-core sites, indicating a low simulation climate offset.As expected, we find the simulated δ18O variability to be higher in the polar regions of ice-core locations, compared to the simulated variability at speleothem cave locations. Recorded δ18O variability is also generally higher as stored in ice-cores, compared to that stored in speleothems. Both speleothems and ice-core records show damping effects on decadal time scales, which can in part be attributed to the temporal resolution of the individual records. This comparison of different proxy archives to isotope-enabled GCM output shows a promising way to evaluate the model’s capability to resolve δ18O variability. [1] Bühler, J. C. et al. Comparison of the oxygen isotope signatures in speleothem records and iHadCM3 model simulations for the last millennium. Climate of the Past: Discussions 1–30 (2020). [2] Tindall, J. C., Valdes, P. J. & Sime, L. C. Stable water isotopes in HadCM3: Isotopic signature of El Niño-Southern Oscillation and the tropical amount effect. Journal of Geophysical Research Atmospheres 114, 1–12 (2009). [3] Konecky, B. L. et al. The Iso2k database: A global compilation of paleo-δ18O and δ2H records to aid understanding of Common Era climate. Earth System Science Data 12, 2261–2288 (2020).

Comparing simulations and data from paleoclimate archives such as speleothems can test the capability of climate models to capture past climate changes. In past, present, and future, the hydrologic response to radiative forcing changes is far less understood and more uncertain than thermal changes. Speleothems store terrestrial climate information in the form of isotopic oxygen in mineral and are found mostly in the low-to mid-latitudes of the landmasses. Their usually well preserved (semi-)continuous time series of oxygen isotope ratio δ18O can cover full Glacial-Interglacial cycles and are used for past climate reconstructions. However, the measured δ18O in the mineral is influenced by multiple climate and cave-related variables and does, therefore, not directly represent past temperature or precipitation. We assess the capability of the isotope-enabled models HadCM3 and ECHAM5-MPI/OM to simulate decadal to centennial climate variability beyond the instrumental period. In particular, we investigate the relationship between simulated δ18O and precipitation variability under different background conditions. By comparing simulated δ18O values at cave locations to the large global speleothem database SISALv2 (Comas-Bru et al. 2020), we also examine the consistency between modeled and archived temporal changes in δ18O in the mean state and variability. Our strategy involves forward-modeling of cave processes such as temperature-dependent fractionation and transit times to constrain a simple speleothem proxy model for the simulation output. For the late Holocene, we observe a strongly underestimated simulated isotopic variability on decadal to centennial timescales. We further test how much this underestimation depends on the background radiative forcing conditions by comparing the Last Glacial Maximum, the mid-Holocene, and the late Holocene. This provides deeper insight on low to mid-latitude state-dependent climate variability on decadal to centennial time scales. Reference:Comas-Bru, L., et. al. SISALv2: a comprehensive speleothem isotope database with multiple age-depth models. Earth System Science Data 12, 2579-2606 (2020) https://essd.copernicus.org/articles/12/2579/2020/

Much about the response of temperature variability to a change in the climate's mean state, as the one projected for the current century, remains uncertain. These uncertainties include spatiotemporal patterns, the magnitude, and, in some cases, even the sign. For the last Deglaciation, - the last change in global mean temperature of a similar degree to that expected in projections - variability analyses of climate model simulations and temperature proxies produce conflicting results. Here, we build a hierarchy of transient simulations covering the period since the Last Glacial Maximum about 26k years ago. We include a range of climate models, from conceptual to complex Earth System Models. The simulations cover a variety of temporal and spatial resolutions, parameterizations, and modeled processes. For annual to multi-millennial temporal as well as regional to global spatial scales, we compare variability patterns and power spectra and analyze how they relate to model properties and the background state of Earth's climate. This allows for the examination of regional temperature differences between low, middle, and high latitudes and at locations of available paleoclimate proxy records. For sets of sensitivity experiments, we investigate effects of changes to ice sheets, sea ice, and in volcanic, solar, greenhouse, and orbital forcing on modeled climate variability. Thus, our analysis provides insights into when and how models disagree with each other and with proxies, and what differences arise due to specific models, simulation setups, and boundary conditions. Based on these results, we can then gauge the degree of complexity which is required to reproduce past temperature variability and predict its changes in the future.

Modelling studies are evaluated by comparing the simulation outputs to an observational reference. In climate science, the number and complexity of the models and the mass of data have led the community to develop standardised methods and automated tools, such as the Climate Variability Diagnostic Package or the ESMValTool. However, these tools are mostly designed to evaluate simulations of the instrumental period. Different methods are required to compare paleoclimate simulations to palaeodata. For example, new variables are being modelled, such as vegetation, ice sheet extent, or isotopic ratio, and are used for the evaluation. Changing boundary conditions in transient simulations further complicate the evaluation process: traditional indices that characterise circulation (e.g. monsoon) or modes of variability (e.g. NAO, ENSO) need to be adapted, while new ones are needed to investigate modes of longer timescale and abrupt events. Finally, the palaeodata also present challenges: various type of uncertainty, complex relation to climate variables, and different spatio-temporal representativeness compared to model outputs. Here, we summarise the challenges of model-data comparison in paleo-climate studies. We then review some of the different methods and tools already developed by the community, such as biome comparison and Bayesian approaches to quantify model-data deviation. We finally discuss the implementation of an evaluation framework which aims to provide both adaptable tools to the community and automated standardised analyses.

Earth's climate can be understood as a dynamical system that changes due to external forcing and internal couplings. It can be characterized from the evolution of essential climate variables, such as surface air temperature. Yet, the mechanisms, amplitudes, and spatiotemporal patterns of global and local temperature fluctuations around its mean, called temperature variability, are insufficiently understood. Discrepancies exist between temperature variability from model and paleoclimate data at the temporal scale of years to centuries and at the local scale, both of which are important socio-economic scales for long-term planning. Here, we clarify whether global and local temperature signals from the last millennia show a stationary variance on these timescales and thus behave in a stable manner or not. Therefore, we contrast power spectral densities and their scaling behaviors using simulated, observed, and reconstructed temperatures on periods between 10 and 200 years. Despite careful consideration of possible spectral biases, we find that local temperatures from paleoclimate data tend to show unstable behavior, while simulated temperatures almost exclusively show stable behavior. Conversely, the global mean temperature tends to be stable. We explain this by introducing the gain as a powerful tool to analyze the forced temperature response, based on a novel estimate of the joint power spectrum of radiative forcing. Our analysis identifies main deficiencies in the properties of temperature variability and offers new insights into the linkage between raditative forcing and temperature response, relevant to the understanding of Earth’s dynamics and the assessment of climate risks.

The global hydrological cycle is of crucial importance for life on Earth. Hence, it is a focus of both future climate projections and paleoclimate modeling. The latter typically requires long integrations or large ensembles of simulations, and therefore models of reduced complexity are needed to reduce the computational cost. Here, we study the hydrological cycle of the the Planet Simulator (PlaSim) [1], a general circulation model (GCM) of intermediate complexity, which includes evaporation, precipitation, soil hydrology, and river advection.Using published parameter configurations for T21 resolution [2, 3], PlaSim strongly underestimates precipitation in the mid-latitudes as well as global atmospheric water compared to ERA5 reanalysis data [4]. However, the tuning of PlaSim has been limited to optimizing atmospheric temperatures and net radiative fluxes so far [3].Here, we present a different approach by tuning the model’s atmospheric energy balance and water budget simultaneously. We argue for the use of the globally averaged mean absolute error (MAE) for 2 m temperature, net radiation, and evaporation in the objective function. To select relevant model parameters, especially with respect to radiation and the hydrological cycle, we perform a sensitivity analysis and evaluate the feature importance using a Random Forest regressor. An optimal set of parameters is obtained via Bayesian optimization.Using the optimized set of parameters, the mean absolute error of temperature and cloud cover is reduced on most model levels, and mid-latitude precipitation patterns are improved. In addition to annual zonal-mean patterns, we examine the agreement with the seasonal cycle and discuss regions in which the bias remains considerable, such as the monsoon region over the Pacific.We discuss the robustness of this tuning with regards to resolution (T21, T31, and T42), and compare the atmosphere-only results to simulations with a mixed-layer ocean. Finally, we provide an outlook on the applicability of our parametrization to climate states other than present-day conditions.[1] K. Fraedrich et al., Meteorol. Z. 14, 299–304 (2005)[2] F. Lunkeit et al., Planet Simulator User’s Guide Version 16.0 (University of Hamburg, 2016)[3] G. Lyu et al., J. Adv. Model. Earth Syst. 10, 207–222 (2018)[4] H. Hersbach et al., Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020)

Global mean surface temperature is a fundamental measure for climate evolution in both past and present and a key quantity to evaluate climate simulations. However, for paleoclimate periods, its calculation hinges on proxy data distributed sparsely and inhomogeneously in both space and time. Thus, large sets of different proxy records need to be combined in order to obtain global mean temperature reconstructions, but there is no widely accepted method to perform this task. Building on the work of [1], we suggest and evaluate an algorithm to reconstruct spatially averaged surface temperatures on centennial to orbital timescales. As the most abundant archive for continuous temperature reconstructions, we focus on marine sediment records as input data. Our implementation is applicable to any compilation of sea-surface temperature reconstructions and capable of calculating global, hemispherical and regional temperature. Major steps of the reconstruction algorithm are interpolation to a common timescale, zonal normalization and calculation of spatially weighted sums, including uncertainty propagation via Monte Carlo methods. We assess the applicability of the algorithm by employing it to the PalMod130k marine palaeoclimate data synthesis [2] and to pseudo-proxy data generated from transient simulations of the last glacial cycle. Our results suggest that the algorithm is capable of calculating average temperatures mostly consistent with expectations, however capturing centennial-scale variability is limited due to the low spatio-temporal distribution of the input data. This underlines the importance of both increasing the amount, resolution and age control of proxy data as well as extending the algorithm such that it also incorporates other types of paleoclimate archives. References:[1] C. W. Snyder, “Evolution of global temperature over the past two million years,” Nature, vol. 538, no. 7624, pp. 226–228, 2016[2] L. Jonkers, O. Cartapanis, M. Langner, N. McKay, S. Mulitza, A. Strack, and M. Kucera, “Integrating palaeoclimate time series with rich metadata for uncertainty modelling: Strategy and documentation of the PALMOD 130k marine palaeoclimate data synthesis,” Earth System Science Data, vol. 12, no. 2, pp. 1053–1081, 2020

Tracing the spatio-temporal distribution of water isotopologues (e.g., H216O, H218O,HD16O, D216O), in the atmosphere allows insights in to the hydrological cycle and surface-atmosphere interactions. Strong relationships between atmospheric circulation and isotopologue variability exist, mitigated by fractionation during phase transitions of water. Isotopic gradients correlate with precipitation amount, temperature, with distance to source areas of evaporation and often follow topographic features. Isotope-enabled general circulation models (iGCMs) have been established to explicitly simulate the processes that lead to these distributions, in response to the changes in radiative forcing, boundary conditions, and including effects of internal variability of the climate system. However, few of these iGCMs1,2 of varying complexity exist to date and isotopic tracers decrease their computational efficiency.Here, we evaluate the potential of replacing the explicit simulation of the isotopic component in the water cycle by statistical learning for offline model evaluation at interannual to multi-millennial timescales. This is challenging. While the relevant fractionation processes are well understood, the climate system is a chaotic, nonstationary system of high dimensionality. Therefore, successful statistical prediction requires the (so far elusive) understanding of the timescale-dependent relationships in the climate system. We present a case study on the feasibility of this approach.We focus on the impact of variable selection (primarily surface temperature, precipitation and sea-level pressure) and boundary conditions (CO2 concentrations, ice sheet distribution). We also compare different approaches to dimensionality reduction, and compare the performance of different machine-learning approaches including simple linear regression, random forests, Gaussian Processes and different types of neural networks. The accuracy of the predictions is evaluated using regional and global area-weighted mean squared errors across training and evaluation data from individual GCM simulations and across climatic states. We find a high spatial variability of prediction accuracy, modest in many locations with the presently employed approaches. We obtain encouraging results for the prediction of isotope variability in Greenland and the Antarctic.References[1] Tindall, J. C., P. J. Valdes, and Louise C. Sime. "Stable water isotopes in HadCM3: Isotopic signature of El Niño–Southern Oscillation and the tropical amount effect." Journal of Geophysical Research: Atmospheres 114.D4 (2009)[2] Werner, Martin, et al. "Glacial–interglacial changes in H 2 18 O, HDO and deuterium excess–results from the fully coupled ECHAM5/MPI-OM Earth system model." Geoscientific Model Development 9.2 (2016): 647-670.