TOAD v1.0: A Python Framework for Detecting Abrupt Shifts and Coherent Spatial Domains in Earth-System Data

<p>Internal climate variability plays an important role in the abundance and distribution of phytoplankton in the global ocean. Previous studies using large ensembles of Earth system models (ESMs) have demonstrated their utility in the study of marine phytoplankton variability. These ESM large ensembles simulate the evolution of multiple alternate realities, each with a different phasing of internal climate variability. However, ESMs may not accurately represent real world variability as recorded via satellite and in situ observations of ocean chlorophyll over the past few decades. Observational records of surface ocean chlorophyll equate to a single ensemble member in the large ensemble framework, and this can cloud the interpretation of long-term trends: are they externally forced, caused by the phasing of internal variability, or both? Here, we use a novel statistical emulation technique to place the observational record of surface ocean chlorophyll into the large ensemble framework. Much like a large initial condition ensemble generated with an ESM, the resulting synthetic ensemble represents multiple possible evolutions of ocean chlorophyll concentration, each with a different phasing of internal climate variability. We further demonstrate the validity of our statistical approach by recreating a ESM ensemble of chlorophyll using only a single ESM ensemble member. We use the synthetic ensemble to explore the interpretation of long-term trends in the presence of internal variability. Our results suggest the potential to explore this approach for other ocean biogeochemical variables.</p>

Elaboration of a modern Earth system model (ESM) requires incorporation of ice sheet dynamics. Coupling of an ice sheet model (ICM) to an AOGCM is complicated by essential differences in spatial and temporal scales of cryospheric, atmospheric and oceanic components. To overcome this difficulty, we apply two different approaches for the incorporation of ice sheets into an ESM. Coupling of the Antarctic ice sheet model (AISM) to the AOGCM is accomplished via using procedures of resampling, interpolation and assigning to the AISM grid points annually averaged meanings of air surface temperature and precipitation fields generated by the AOGCM. Surface melting, which takes place mainly on the margins of the Antarctic peninsula and on ice shelves fringing the continent, is currently ignored. AISM returns anomalies of surface topography back to the AOGCM. To couple the Greenland ice sheet model (GrISM) to the AOGCM, we use a simple buffer energy- and water-balance model (EWBM-G) to account for orographically-driven precipitation and other sub-grid AOGCM-generated quantities. The output of the EWBM-G consists of surface mass balance and air surface temperature to force the GrISM, and freshwater run-off to force thermohaline circulation in the oceanic block of the AOGCM. Because of a rather complex coupling procedure of GrIS compared to AIS, the paper mostly focuses on Greenland.

Abstract. The ocean plays a key role in modulating the climate of the Earth system (ES). At the present time it is also a major sink both for the carbon dioxide (CO2) released by human activities and for the excess heat driven by the resulting atmospheric greenhouse effect. Understanding the ocean's role in these processes is critical for model projections of future change and its potential impacts on human societies. A necessary first step in assessing the credibility of such future projections is an evaluation of their performance against the present state of the ocean. Here we use a range of observational fields to validate the physical and biogeochemical performance of the ocean component of UKESM1, a new Earth system model (ESM) for CMIP6 built upon the HadGEM3-GC3.1 physical climate model. Analysis focuses on the realism of the ocean's physical state and circulation, its key elemental cycles, and its marine productivity. UKESM1 generally performs well across a broad spectrum of properties, but it exhibits a number of notable biases. Physically, these include a global warm bias inherited from model spin-up, excess northern sea ice but insufficient southern sea ice and sluggish interior circulation. Biogeochemical biases found include shallow remineralization of sinking organic matter, excessive iron stress in regions such as the equatorial Pacific, and generally lower surface alkalinity that results in decreased surface and interior dissolved inorganic carbon (DIC) concentrations. The mechanisms driving these biases are explored to identify consequences for the behaviour of UKESM1 under future climate change scenarios and avenues for model improvement. Finally, across key biogeochemical properties, UKESM1 improves in performance relative to its CMIP5 precursor and performs well alongside its fellow members of the CMIP6 ensemble.

Future temperature variations under greenhouse gas (GHG) emission scenarios are critical to assess possible impacts on human society and make reasonable mitigation policies. Due to the huge running cost, Earth system models (ESMs) may be difficult to flexibly provide the temperature projections following some specific emission pathways for empirical analysis. This study develops the mean and variability filed emulators in the high-resolution land grids to approximate the temperature behavior conditioned on GHG emissions in ESM. The emulator of mean temperature response is modeled as a function of GHG emissions to represent the expected values for ESM output, and the associated high-dimensional spatial dependence across grid points is estimated by the nearest-neighbor Gaussian process. The variability emulator is constructed with the residuals between the mean temperature response and the ESM output, and the associated space-time correlation structure is decomposed by principal component analysis and discrete Fourier transform. The analysis shows that the emulators trained with the runs of ESM only from part of representative concentration pathways can efficiently reproduce the temperature variations under different emission scenarios. The emulated gridded temperatures would be easily taken for climate impact and risk assessment, and be incorporated in the integrated assessment model for climate policy analysis.

Abstract. Human-induced atmospheric composition changes cause a radiative imbalance at the top of the atmosphere which is driving global warming. This Earth energy imbalance (EEI) is the most critical number defining the prospects for continued global warming and climate change. Understanding the heat gain of the Earth system – and particularly how much and where the heat is distributed – is fundamental to understanding how this affects warming ocean, atmosphere and land; rising surface temperature; sea level; and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory and presents an updated assessment of ocean warming estimates as well as new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960–2018. The study obtains a consistent long-term Earth system heat gain over the period 1971–2018, with a total heat gain of 358±37 ZJ, which is equivalent to a global heating rate of 0.47±0.1 W m−2. Over the period 1971–2018 (2010–2018), the majority of heat gain is reported for the global ocean with 89 % (90 %), with 52 % for both periods in the upper 700 m depth, 28 % (30 %) for the 700–2000 m depth layer and 9 % (8 %) below 2000 m depth. Heat gain over land amounts to 6 % (5 %) over these periods, 4 % (3 %) is available for the melting of grounded and floating ice, and 1 % (2 %) is available for atmospheric warming. Our results also show that EEI is not only continuing, but also increasing: the EEI amounts to 0.87±0.12 W m−2 during 2010–2018. Stabilization of climate, the goal of the universally agreed United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Paris Agreement in 2015, requires that EEI be reduced to approximately zero to achieve Earth's system quasi-equilibrium. The amount of CO2 in the atmosphere would need to be reduced from 410 to 353 ppm to increase heat radiation to space by 0.87 W m−2, bringing Earth back towards energy balance. This simple number, EEI, is the most fundamental metric that the scientific community and public must be aware of as the measure of how well the world is doing in the task of bringing climate change under control, and we call for an implementation of the EEI into the global stocktake based on best available science. Continued quantification and reduced uncertainties in the Earth heat inventory can be best achieved through the maintenance of the current global climate observing system, its extension into areas of gaps in the sampling, and the establishment of an international framework for concerted multidisciplinary research of the Earth heat inventory as presented in this study. This Earth heat inventory is published at the German Climate Computing Centre (DKRZ, https://www.dkrz.de/, last access: 7 August 2020) under the DOI https://doi.org/10.26050/WDCC/GCOS_EHI_EXP_v2 (von Schuckmann et al., 2020).

Abstract. The second version of the coupled Norwegian Earth System Model (NorESM2) is presented and evaluated. NorESM2 is based on the second version of the Community Earth System Model (CESM2) and shares with CESM2 the computer code infrastructure and many Earth system model components. However, NorESM2 employs entirely different ocean and ocean biogeochemistry models. The atmosphere component of NorESM2 (CAM-Nor) includes a different module for aerosol physics and chemistry, including interactions with cloud and radiation; additionally, CAM-Nor includes improvements in the formulation of local dry and moist energy conservation, in local and global angular momentum conservation, and in the computations for deep convection and air–sea fluxes. The surface components of NorESM2 have minor changes in the albedo calculations and to land and sea-ice models. We present results from simulations with NorESM2 that were carried out for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Two versions of the model are used: one with lower (∼ 2∘) atmosphere–land resolution and one with medium (∼ 1∘) atmosphere–land resolution. The stability of the pre-industrial climate and the sensitivity of the model to abrupt and gradual quadrupling of CO2 are assessed, along with the ability of the model to simulate the historical climate under the CMIP6 forcings. Compared to observations and reanalyses, NorESM2 represents an improvement over previous versions of NorESM in most aspects. NorESM2 appears less sensitive to greenhouse gas forcing than its predecessors, with an estimated equilibrium climate sensitivity of 2.5 K in both resolutions on a 150-year time frame; however, this estimate increases with the time window and the climate sensitivity at equilibration is much higher. We also consider the model response to future scenarios as defined by selected Shared Socioeconomic Pathways (SSPs) from the Scenario Model Intercomparison Project defined under CMIP6. Under the four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), the warming in the period 2090–2099 compared to 1850–1879 reaches 1.3, 2.2, 3.0, and 3.9 K in NorESM2-LM, and 1.3, 2.1, 3.1, and 3.9 K in NorESM-MM, robustly similar in both resolutions. NorESM2-LM shows a rather satisfactory evolution of recent sea-ice area. In NorESM2-LM, an ice-free Arctic Ocean is only avoided in the SSP1-2.6 scenario.

Despite decades of research, large multi-model uncertainty remains about the Earth’s equilibrium climate sensitivity to carbon dioxide forcing as inferred from state-of-the-art Earth system models (ESMs). Statistical treatments of multi-model uncertainties are often limited to simple ESM averaging approaches. Sometimes models are weighted by how well they reproduce historical climate observations. Here, we propose a novel approach to multi-model combination and uncertainty quantification. Rather than averaging a discrete set of models, our approach samples from a continuous distribution over a reduced space of simple model parameters. We fit the free parameters of a reduced-order climate model to the output of each member of the multi-model ensemble. The reduced-order parameter estimates are then combined using a hierarchical Bayesian statistical model. The result is a multi-model distribution of reduced-model parameters, including climate sensitivity. In effect, the multi-model uncertainty problem within an ensemble of ESMs is converted to a parametric uncertainty problem within a reduced model. The multi-model distribution can then be updated with observational data, combining two independent lines of evidence. We apply this approach to 24 model simulations of global surface temperature and net top-of-atmosphere radiation response to abrupt quadrupling of carbon dioxide, and four historical temperature data sets. Our reduced order model is a 2-layer energy balance model. We present probability distributions of climate sensitivity based on (1) the multi-model ensemble alone and (2) the multi-model ensemble and observations.

A good understanding of uncertainty is of paramount importance in the hydrological sciences, notably in streamflow prediction. Over recent decades, hydroinformatics has played a key role in advancing hydrological prediction through the exploration of physically inspired and conceptual models and data-driven approaches. In particular, machine learning (ML) models demonstrate strong predictive skills. Despite this, limited interpretability and potentially weak extrapolation under extreme conditions remain major disadvantages of ML applications [1,2].To address these limitations, a hybrid framework that combines conceptual hydrological modelling with machine learning–based probabilistic forecasting is proposed. The so-called Generalized Pareto Uncertainty (GPU) framework can be used to train an ensemble of models (potentially physically based) so that it reliably reproduces the predictive uncertainty of the output [3]. In this case, GPU is employed with the conceptual HYdrological Predictions for the Environment (HYPE) model. By embedding hydrological knowledge into a data-driven uncertainty framework, the proposed approach seeks to improve robustness, generalization, and physical consistency of streamflow forecasts.GPU relies on finding a multi-objective optimal surface (something akin to a double Pareto surface) that selects model parameters that span the full range of exceedance of simulations—at the extremes, forcing some models to always underpredict and others to always overpredict—while simultaneously searching for optimal error metrics (e.g., Nash-Sutcliffe efficiency, King-Gupta efficiency, mean absolute error, etc.). One promising feature of the framework is that it is not constrained to one type of error metric or even two dimensions (exceedance and error metric), potentially even opening avenues for addressing equifinality challenges.The methodology is applied to the Nabão and Douro river basin in Portugal, one basin in Sweden, and one in Ireland. The performance of three modelling strategies is compared: (i) a standalone conceptual model (HYPE), (ii) GPU combined with artificial neural networks (missing indirect foreknowledge about hydrological processes), and (iii) a hybrid approach that incorporates HYPE models as ensemble members. Results show that the inclusion of conceptual hydrological information leads to clear improvements in the quality of the predictive uncertainty estimates, including its resolution, reliability, and aggregate metrics (e.g., CRPS).In this work, we clarify the concept behind GPU, demonstrate its results, address challenges, and discuss potential innovative applications.[1] Baste, S., Klotz, D., Acuña Espinoza, E., Bardossy, A., & Loritz, R. (2025). Unveiling the limits of deep learning models in hydrological extrapolation tasks. Hydrology and Earth System Sciences, 29(21), 5871–5891. https://doi.org/10.5194/hess-29-5871-2025[2] Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D., Frame, J. M., Prieto, C., & Gupta, H. v. (2021). What Role Does Hydrological Science Play in the Age of Machine Learning? In Water Resources Research (Vol. 57, Issue 3). Blackwell Publishing Ltd. https://doi.org/10.1029/2020WR028091[3] Matos, J. P., Hassan, M. A., Lu, X. X., & Franca, M. J. (2018). Probabilistic Prediction and Forecast of Daily Suspended Sediment Concentration on the Upper Yangtze River. Journal of Geophysical Research: Earth Surface, 123(8), 1982–2003. https://doi.org/10.1029/2017JF00424

Much effort goes into studying the causes of systematic errors in Earth System Models (ESMs). Reducing them is often seen as a high priority. Indeed, the development of Digital Twin approaches in climate research is founded on the idea that a sufficiently good model would be able to provide reliable and robust, conditional predictions of climate change (predictions conditioned on scenarios of future greenhouse gas emissions). Here, “reliable” encapsulates the idea that the predictions are suitable for use by society in anticipating and planning for future climate change, and “robust” encapsulates the idea that they are unlikely to change as the models are improved and developed.Such an approach, however, begs the question, when is a model sufficiently realistic to be able to provide reliable, detailed predictions? A physical processes view of current ESMs suggests that they are not close to this level of realism while a nonlinear dynamical systems perspective raises questions over whether it will ever be possible to achieve such reliability for the types of regionally-specific, extrapolatory, climate change predictions that we may think society seeks.Given this context, multi-model and perturbed-physics ensembles are often seen as a means to quantify uncertainty in conditional, climate change predictions (commonly referred to as “projections” in the scientific community). In the IPCC atlas (https://interactive-atlas.ipcc.ch/) the most easily accessible output is the multi-model median with the 10th, 25th, 75th and 90th percentiles of the multi-model distribution also prominent. This presentation in terms of probabilities implies that the probabilities themselves have meaning to the users of the data - most users are likely to take them as probabilities of different outcomes in reality. Unfortunately multi-model ensembles cannot be interpreted that way because we have no metric for the shape of model space nor any idea of how to explore it, so the ensemble members cannot be taken as independent samples of possible models. Perturbed-parameter ensembles work in a more defined space of possible model-versions but the shape of that space is also undefined and as a result the ensemble-based probabilities are again arbitrary.When seeking the best possible information for society, multi-model and perturbed physics ensembles would benefit from targeting diversity: the greatest possible range of responses given a particular model structure. Model emulators could be used to systematise this process. Such an approach would provide more reliable information. It changes the question, however, from “when is a model sufficiently realistic” to “how unrealistic does a model have to be to be uninformative about extrapolatory future climatic behaviour?”In this presentation I will discuss and elaborate on these issues. 

The Energy Exascale Earth System Model (E3SM) Project is an ongoing, state-of-the-science Earth system modeling, simulation, and prediction project developed by the U.S. Department of Energy (DOE). With an emphasis on supporting DOE's energy mission, understanding and quantifying how well the model simulates water cycle processes is of particular importance. Here, we evaluate E3SM version v1.0 for its ability to represent atmospheric rivers (ARs), which play significant roles in water vapor transport and precipitation. The characteristics and precipitation associated with global ARs in E3SM at standard resolution (1° × 1°) are compared to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2). Global pattern of AR frequencies in E3SM show high degrees of correlation (>= 0.97) with MERRA2 and low mean absolute errors (< 1 %) annually, seasonally, and across different ensemble members. However, some large-scale condition biases exist leading to AR biases - most significant of which are: the double-ITCZ, a stronger and/or equatorward shifted subtropical jet during boreal and austral winter, and enhanced northern hemisphere westerlies during summer. By comparing atmosphere-only and fully coupled simulations, we attribute the sources of the biases to the atmospheric component or to a coupling response. Using relationships revealed in Dong et al. (2021), we provide evidence showing the stronger north Pacific jet in winter and enhanced northern hemisphere westerlies during summer associated with E3SM's double-ITCZ and related weaker AMOC, respectively, are the sources of much of the AR biases found in the coupled simulations.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observational data, and connects them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. The NASA Advanced Information Systems Technology (AIST) program&amp;#8217;s Integrated Digital Earth Analysis System (IDEAS) project partners with the Space for Climate Observatory (SCO) (https://www.spaceclimateobservatory.org/) FloodDAM Digital Twin effort led by CNES to establish an extensible open-source framework to develop digital twins of our physical environment for Earth Science with an initial focus on surface water hydrology in Earth&amp;#8217;s rivers and lakes. The joint effort delivers an open-source system architecture with mechanisms for the outputs of one model to feed into others, for driving models with observation data, and for harmonizing observation data and model outputs for analysis. Water resource science is multidisciplinary in nature, and it not only assesses the impact from our changing climate using measurements and modeling, but it also offers opportunities for science-guided, data-driven decision support. The joint effort uses flood prediction and analysis as its primary use case. The work presents a multi-agency joint effort to define and develop a federated Earth System Digital Twin solution between NASA and CNES that powers advanced immersive science and custom user applications for scenario-based analysis.

An Earth System Digital Twin (ESDT) is a dynamic, interactive, digital replica of the state and temporal evolution of Earth systems. It integrates multiple models along with observations, and connects them with analysis, AI, and visualization tools. Together, these enable users to explore the current state of the Earth system, predict future conditions, and run hypothetical scenarios to understand how the system would evolve under various assumptions. To establish a sustainable, extensible, and expandable ESDT solution, a formal software architecture is needed. Through the collaboration across multiple NASA centers, the teams partnered with the Apache Software Foundation to establish the professional open-source Integrated Digital Exploratory Analysis System (IDEAS) framework for digital twins. IDEAS is designed with three basic concepts.&amp;#160;Design to Exploit&amp;#8211; ESDT architect needs to be aware of existing freely available provisioned data, model, and visualization services that the ESDT could benefit from. In computer science, we instructed our students the simple Don&amp;#8217;t Repeat Yourself (DRY) principle. The is especially true for ESDT, because ESDT is aiming for operational big data solutions, not just a quick demonstration. If an organization is already actively making their curated data, analysis, or model outputs available, the ESDT should find ways to exploit them. Software reuse is another form of exploitation. Why reinvent? Exploiting existing services and software reuse would significantly reduce future operation costs. An ESDT should also be prepared to be exploited by other ESDTs. It will be discussed in the Design to Expand principle.&amp;#160; Design to Extend&amp;#8211; An ESDT needs to be extensible to support new measurements, models (both numeric and AI-based), and interfaces. As we are connecting our digital assets, we will encounter gaps and limitations, both in data and technology. As we identify new climate phenomena or scientific needs, the ESDT needs to be prepared for these changes in technology and requirements.&amp;#160; Design to Expand&amp;#8211; It would be unrealistic to expect a single ESDT is capable to replicate the complex Earth System that is equipped with all the past and present observations, to drive all possible global and regional numerical models, and process all AI capabilities concerning instruments, data, and predictions. We could try to acquire all publicly available data, but an ESDT should also be able to integrate private, commercial data. The Babel-like ESDT would require significant computing and store, and well as staff (science and technical) to operate. This is the motivation behind the open-source IDEAS framework, to encourage collaboration to set up common public-facing architecture. Imagine being able to orchestrate a federation of ESDTs using exploration tools like a Jupyter notebook without having to setting up a local ESDT instance. We think federated ESTDs is the answer to making actionable science available to Disadvantaged Communities.&amp;#160; The presentation will discuss IDEAS architecture, its latest progress, and its growing portfolio of digital twins for the Earth including air quality, wildfire, hydrology, and coastal zone.

This book investigates the billion-year takeover of planet Earth by its organisms and ecosystems. Concluding Chapter 11 focuses on the Anthropocene, which is the period during which human societies had significant impacts on the Earth System, and considers key events that are happening in the twenty-first century and might happen afterwards. The chapter examines four main global anthropogenic perturbations of the Earth System – climate change, ocean acidification, the loss of biodiversity, and water and food insecurity – in terms of: their general mechanisms; the main human activities that contribute to these perturbations; and their feedbacks into the environment and human activities. It also explains the responses provided by adaptation and mitigation approaches to the ongoing global anthropogenic perturbations of the Earth System. The purpose of adaptation is to manage the unavoidable, and that of mitigation to avoid the unmanageable. Adaptation and mitigation are illustrated using sea level rise as example. The text then looks at possible states of the Earth System beyond the twenty-first century, which are: a long interglacial; a very long interglacial; shifting from the present icehouse conditions to those of a greenhouse; and crossing planetary thresholds, caused by runaway warming. The chapter concludes with considerations of what planet Earth would be without some of the key biological innovations that shaped the Earth System. This leads to the idea that humans should become the stewards of their unique planet without delay, taking into account the imperative need to preserve both its habitability and biodiversity.

The Earth system and society's use of ecological resources are tightly coupled through exchanges of water, energy, and nutrients. Terrestrial vegetation transfers materials between the atmosphere, biosphere, and water bodies in the coupled system. Vegetation is also a primary interface between human society and the Earth system through land-cover conversion, cultivation of favorable species, and transfer of organisms between locations. Remote sensing aids analyses of these interactions and, ultimately, contributes information to decision makers for improved management. Multidecadal records from multispectral sensors have been the mainstay for studying terrestrial vegetation at regional and global scales. Representation of vegetation and carbon dynamics is now routine in Earth system models. Challenges remain to incorporate realistic ecological disturbances and human land-use activities based on remote sensing observations. Hyperspectral, LIDAR, and radar systems contribute new capabilities for observing nitrogen fluxes, vegetation stress, canopy structure, and in some cases individual species.

&amp;lt;p&amp;gt;We present new insights from the project PalMod, which started in 2016 and is envisioned to run for a decade. The modelling initiative PalMod aims at filling the long-standing scientific gaps in our understanding of the dynamics and variability of the climate system during the last glacial-interglacial cycle. One of the grand challenges in this context is to quantify the processes that determine the spectrum of climate variability on timescales that range from seasons to millennia. Climatic processes are intimately coupled across these timescales. Understanding variability at any one timescale requires understanding of the whole spectrum. If we could successfully simulate the spectrum of climate variability during the last glacial cycle in Earth system models, would this enable us to more reliably assess the future climate change? Such simulations are necessary to deduce, for example, if a regime shift in climate variability could occur during the next centuries and millennia in response to global warming. PalMod is specifically designed to enhance our understanding of the Earth system dynamics and its variability on timescales up to the multimillennial with complex Earth System Models.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The following major goals were achieved up to now:&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;Full coupling of atmosphere, ocean and ice-sheet models, enabling investigation of Heinrich Events and bi-stability of the AMOC, and millennial-scale transient climate-ice sheet simulations.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Implementation of a coupled ocean and land biogeochemistry enabling simulations with prognostic atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentrations and including improved representation of methane (CH&amp;lt;sub&amp;gt;4&amp;lt;/sub&amp;gt;) in transient deglaciation runs.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Systematic comparison of newly compiled proxy data with model simulations.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;&amp;lt;p&amp;gt;The major goal for the next two years is to set up the fully coupled physical-biogeochemical model which will be tested for three time periods: deglaciation, glacial inception and Marine Isotope Stage 3 (MIS3). This fully coupled model will be eventually used to simulate the complete glacial cycle and project the climate over the next few millennia.&amp;lt;/p&amp;gt;