As climate impacts accelerate, ‘climate change hotspots’ (CCHs) are increasingly used to prioritise limited adaptation resources. However, hotspot conceptualisation remains fragmented. To address this gap, this study first conducts an evidence synthesis of 353 studies to investigate how CCHs are conceptually defined and analytically identified across four major climate hazards (heat, drought, flood and snow). This synthesis shows that most existing hotspot methodologies are dominated by potential hazard intensity, while socio-economic dimensions, stakeholder knowledge and impact-based validation are rarely integrated. To address this knowledge gap, we develop a bottom-up integrated framework for CCHs identification. Transitioning away from a top–down hazard-centric approach, our bottom-up approach integrates biophysical and socio-economic data with event-based storylines and stakeholder consultation. The transferability of our framework is demonstrated through two European case studies, snow avalanches in Alps and Carpathians, and flash flood in Trentino-Alto Adige, Italy. The case studies identify hotspots by intersecting climate hazard data with local geographic characteristics and socio-economic information. In both applications, historical event-based storylines ground quantitative risk classifications in real-world disaster dynamics, while stakeholder consultations refine the localised exposure. The results reveal that Alpine and Carpathian snow hotspots are projected to persist and intensify by 2100. The flash flood hotspots remain concentrated in alluvial valley bottoms, where the hazard converges with dense socio-economic and infrastructural exposure. Our integrated framework provides an adaptable and transferable approach for identifying CCHs. It can serve as good practice and can support the prioritisation of adaptation effort and planning across administrative regions and sectors.

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.

This supplement contains -Additional information on the ensemble of transient simulations of the last deglaciation -Additional information on the proxy record pre-processing -Details on the approximation of the Integrated Quadratic Distance using Monte Carlo samples and the sensitivity of 5 results on the number of Monte Carlo samples -Exemplary plots of time series decompositions

Knowledge of the characteristics of natural climate variability is vital when assessing the range of plausible future climate trajectories in the next decades to centuries. The reliable detection of climate fluctuations on multidecadal to centennial timescales depends on proxy reconstructions and model simulations, as the instrumental record extends back only a few decades in most parts of the world. Systematic comparisons between model-simulated and proxy-based inferences of natural variability, however, often seem contradictory. Locally, simulated temperature variability is consistently smaller on multidecadal and longer timescales than is indicated by proxy-based reconstructions, implying that climate models or proxy interpretations might have deficiencies. In contrast, at global scales, studies found agreement between simulated and proxy reconstructed temperature variations. Here we review the evidence regarding the scale of natural temperature variability during recent millennia. We identify systematic reconstruction deficiencies that may contribute to differing local and global model–proxy agreement but conclude that they are probably insufficient to resolve such discrepancies. Instead, we argue that regional climate variations persisted for longer timescales than climate models simulating past climate states are able to reproduce. This would imply an underestimation of the regional variability on multidecadal and longer timescales and would bias climate projections and attribution studies. Thus, efforts are needed to improve the simulation of natural variability in climate models accompanied by further refining proxy-based inferences of variability.

Abstract. 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, 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.

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.

An increasing number of Earth System Models has been used to simulate the climatic transition from the Last Glacial Maximum to the Holocene. This creates a demand for benchmarking against environmental proxy records, which have been synthesized for the same time period. Comparing these two data sources in space and time over a period with changing background conditions requires new methods. We employ proxy system modeling for probabilistic quantification of the deviation between temperature reconstructions and transient simulations. Regional and global scores quantify the mismatch in the pattern and magnitude of orbital- as well as millennial-scale temperature variations. In pseudo-proxy experiments, we test the ability of our algorithm to accurately rank an ensemble of simulations according to their deviation from a prescribed temperature history, dependent upon the amount of added non-climatic noise. To this purpose, noisy pseudo-proxies are constructed by perturbing a reference simulation. We show that the algorithm detects the main features separating the ensemble members. When scores are aggregated spatially, the algorithm ranks simulations robustly and accurately in the presence of uncertainties. In contrast, erroneous rankings occur more often if only a single location is assessed. Having established the effectiveness of the algorithm in idealized experiments, we apply our method to quantify the deviation between data from the PalMod project: an ensemble of transient deglacial simulations and a global compilation of sea surface temperature reconstructions. No simulation performs consistently well across different regions and components of the temperature evolution which we attribute to the larger spatial heterogeneity in reconstructions. Our work provides a basis for a standardized model-data comparison workflow, which can be extended subsequently with additional proxy data, new simulations, and improved representations of uncertainties.

Understanding the climate of the past two millennia (2k) remains vital for developing our wider comprehension of the climate system, including the nature and scale of recent and future anthropogenic change. Phase 4 of the PAGES 2k network will build on previous phases and take us to a new level of understanding and science-policy integration. During previous phases, PAGES 2k members compiled global networks of proxy measurements, extending records beyond the instrumental period by more than an order of magnitude, reconstructing past climate and developing new knowledge of past variability and processes. Through data-model integration with state-of-the-art Earth systems models, proxy system modelling and data assimilation, we took key steps towards a more comprehensive understanding of climate dynamics. Phase 4 will take us even further, challenging our community to turn its focus primarily towards the hydroclimate of the Common Era: performing new reconstructions and improving the interoperability, extent and scope of our data and model products. In doing so, we also seek to facilitate the translation of our science into evidence-based policy outcomes. Our overarching aim is to reconstruct hydroclimate variability over the Common Era from local to global spatial scales, at sub-annual to multi-centennial time scales. We propose to achieve this through new community-led data curation efforts and the development of new data-driven tools and practices to maximise the interoperability of convenient, efficient and widespread model/data products. We will aim for a process-level understanding of past hydroclimate events and variability by evaluating and constraining Earth system models and through data assimilation. Our coordination team places a strong emphasis on respect and inclusion, aiming to foster a diverse and equitable community. Through a ‘hub and spoke’ structure, our team will provide a facilitation, coordination and support role (the hub) for Pages 2k working groups (the spokes). We are actively seeking participation of those engaging in climate policy and climate services. Welcome to Phase 4! We warmly invite your collaborations and contributions!

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.

Inferences about climate states and climate variability of the Holocene and the deglaciation rely on sparse paleo-observational proxy data. Combining these proxies with output from climate simulations is a means for increasing the understanding of the climate throughout the last tens of thousands of years. The analogue method is one approach to do this. The method takes a number of sparse proxy records and then searches within a pool of more complete information (e.g., model simulations) for analogues according to a similarity criterion. The analogue method is non-linear and allows considering the spatial covariance among proxy records. Beyond the last two millennia, we have to rely on proxies that are not only sparse in space but also irregular in time and with considerably uncertain dating. This poses additional challenges for the analogue method, which have seldom been addressed previously. The method has to address the uncertainty of the proxy-inferred variables as well as the uncertain dating. It has to cope with the irregular and non-synchronous sampling of different proxies. Here, we describe an implementation of the analogue method including a specific way of addressing these obstacles. We include the uncertainty in our proxy estimates by using "ellipses of tolerance" for tuples of individual proxy values and dates. These ellipses are central to our approach. They describe a region in the plane spanned by proxy dimension and time dimension for which a model analogue is considered to be acceptable. They allow us to consider the dating as well as the data uncertainty. They therefore form the basic criterion for selecting valid analogues. We discuss the benefits and limitations of this approach. The results highlight the potential of the analogue method to reconstruct the climate from the deglaciation up to the late Holocene. However, in the present case, the reconstructions show little variability of their central estimates but large uncertainty ranges. The reconstruction by analogue provides not only a regional average record but also allows assessing the spatial climate field compliant with the used proxy predictors. These fields reveal that uncertainties are also locally large. Our results emphasize the ambiguity of reconstructions from spatially sparse and temporally uncertain, irregularly sampled proxies.

The past two thousand years is a key interval for climate science. This period encompasses both the era of human-induced global warming and a much longer interval when changes in Earth’s climate were governed principally by natural drivers and unforced variability. Since 2009, the Past Global Changes (PAGES) 2k Network has brought together hundreds of scientists from around the world to reconstruct and understand the climate of the Common Era using open and collaborative approaches to palaeoclimate science, including virtual meetings. The third phase of the network will end in December 2021. Here we highlight some key outputs of PAGES 2k and present the major themes and scientific questions emerging from recent surveys of the community. We explore how these might boost a new phase of PAGES 2k or a successor project(s). This year we will further reach out to the community through Town Hall consultations, vEGU and other meetings, and a PAGES 2k global webinar series. The aim of these activities is to foster development of post-2021 community-led PAGES initiatives that connect past and present climate.

Data is an important foundation of scientific progress.It allows us to contrast hypotheses with observational evidence.

The increasing number of Earth system model simulations that try to simulate the climate during the last deglaciation (ca 20 to 10 thousand years ago) creates a demand for benchmarking against environmental proxy records synthesized for the same time period. Comparing these two data sources over a period with changing background conditions requires new methods for model-data comparison that incorporate multiple types and sources of uncertainty. Natural archives of past reality are distributed sparsely and non-uniformly in space and time. Signals that can be obtained are in addition perturbed by uncertainties related to dating, the relationship between the proxy sensor and environmental fields, the archive build-up, and measurement. On the other hand, paleoclimate simulations are four-dimensional, complete, and physically consistent representations of the climate. However, they are subject to errors due to model inadequacies and sensitivity to the forcing protocol, and will not reproduce any particular history of unforced variability. We present a method for probabilistic, multivariate quantification of the deviation between paleo-data and paleoclimate simulations that draws on the strengths of both sources of information and accounts for the aforementioned uncertainties. We compare the shape and magnitude of orbital- and millennial-scale temperature fluctuations during the last deglaciation and compute metrics of regional and global model-data mismatches. We test our algorithm with an ensemble of published simulations of the deglaciation and simulations from the ongoing PalMod project, which aims at the simulation of the last glacial cycle with comprehensive Earth system models. These are evaluated against a compilation of temperature reconstructions from multiple archives. Our work aims for a standardized model-data comparison workflow that will be used in PalMod. This workflow can be extended subsequently with additional proxy data, new simulations, and improved representations of proxy uncertainties.

The past two thousand years is a key interval for climate science because this period encompasses both the era of human-induced global warming and a much longer interval when changes in Earth's climate were governed principally by natural drivers. This earlier 'pre-industrial' period is particularly important for two reasons. Firstly, we now have a growing number of well-dated, climate sensitive proxy data with high temporal resolution that spans the full period. Secondly, the pre-industrial climate provides context for present-day climate change, sets real-world targets against which to evaluate the performance of climate models, and allows us to address other questions of Earth sciences that cannot be answered using only a century and a half of observational data. Here, we first provide several perspectives on the concept of a 'pre-industrial climate'. Then, we highlight the activities of the PAGES 2k Network, an international collaborative effort focused on global climate change during the past two thousand years. We highlight those aspects of pre-industrial conditions (including both past climate changes and past climate drivers) that are not yet well constrained, and suggest potential areas for research during this period that would be relevant to the evolution of Earth's future climate.

Combining proxy information and climate model simulations reconciles these sources of information about past climates. This, in turn, strengthens our understanding of past climatic changes. The analogue or proxy surrogate reconstruction method is a computationally cheap data assimilation approach, which searches in a pool of simulated climate states the best fit to proxy data. We use the approach to reconstruct European summer mean temperature from the 13th century until present using the Euro 2k set of proxy records and a pool of global climate simulation output fields. Our focus is on quantifying the uncertainty of the reconstruction, because previous applications of the analogue method rarely provided uncertainty ranges. We show several ways of estimating reconstruction uncertainty for the analogue method, which take into account the non-climate part of the variability in each proxy record. In general, our reconstruction agrees well at multi-decadal timescales with the Euro 2k reconstruction, which was conducted with two different statistical methods and no information from model simulations. In both methodological approaches, the decades around the year 1600 CE were the coldest. However, the approaches disagree on the warmest pre-industrial periods. The reconstructions from the analogue method also represent the local variations of the observed proxies. The diverse uncertainty estimates obtained from our analogue approaches can be locally larger or smaller than the estimates from the Euro 2k effort. Local uncertainties of the temperature reconstructions tend to be large in areas that are poorly covered by the proxy records. Uncertainties highlight the ambiguity of field-based reconstructions constrained by a limited set of proxies.

The paper discusses an analogue method of paleoclimate reconstruction. In this method, the researcher starts with a set of paleoclimate records (here, temperaturesensitive records in or near Europe) and searches for similar climate states within a pool of climate simulation outputs. By finding modeled states with match the proxy records, this method can be used to estimate the state of the climate system at locations which do not have local data. This method has been used in previous research, so the main focus of this paper is on the treatment of temporal and magnitude uncertainty of the proxy records.

The project PalMod II is the second phase of Germany’s national paleoclimate modelling initiative (www.palmod.de) whose aim is to model the transient climate evolution from the last interglacial to the anthropocene with state of the art earth system models. The second phase more precisely wants to perform simulations for the last glacial inception, the marine isotope stage 3, and the last deglaciation. It further plans to compile paleo-observational proxy data over the full glacial cycle from about 130,000 years before present until today. Models of differing complexity (fully-coupled earth system models and models of intermediate complexity) will be used to assess the scientific questions posed in PalMod II. Model output will be combined with the compiled paleo-proxy data for validation purposes. The sheer data amount in excess of several petabytes and different data handling practices of the participating communities require dedicated management of the data workflow both in- and outside of the immediate PalMod community. The PalMod II data management takes place in close collaboration between data management specialists and the scientists. The objectives include the standardisation of each simulation and proxy dataset, the facilitation of data sharing and data reuse between work packages, the access channels for external collaborators, and the long-term preservation of the data. The data management follows the concept of the "Active Data Management Plan", which foresees a continuous development of the data management plan (DMP), starting with an initial basic version. The DMP covers the entire life cycle of the research data generated in the project, from generation and analysis to data publication and archiving. This includes aspects such as data formats, metadata standards and data usage licenses. Ownership and responsibilities for simulation and paleo data sets as well as the input data during and after the end of the project will also be considered. This contribution will present the initial DMP for PalMod II. It will describe the amount of data produced in the project, highlight how the above mentioned aspects will be dealt with, and present how the project aims to ensure the Findability, Accessibility, Interoperability, and Reusability, i.e. the FAIR data principles, of simulation output, post-processed model data, and paleo-proxy data from PalMod II. * This contribution presents results of the full PalMod II initiative, the authors present them on behalf of the initiative.