publications

Journal Articles & Pre-prints

2026

1. The implications of overshooting 1.5 °C on Earth system tipping elements—a review

   Paul D L Ritchie, Norman J Steinert, Jesse F Abrams, and 25 more authors

   Environmental Research Letters, Feb 2026

   Abs DOI

   Due to insufficient emission reductions in recent years, it is increasingly likely that global warming will exceed the 1.5 °C temperature limit in the late 2020s or 2030s. As a result, several Earth system tipping elements could, at least temporarily, have their tipping points surpassed, posing risks of large-scale and profound structural change. Tipping does not always occur immediately upon crossing such a critical threshold. If the length of time the driver is beyond the critical level is short enough, tipping could still be avoided for some slow-responding elements of the climate system. An improved understanding is therefore needed of whether tipping remains avoidable, for which systems, and under what conditions. Here, we review how minimising the magnitude and duration of any temperature overshoot beyond 1.5 °C could decrease tipping risks. Tipping elements with fast response times, such as warm-water coral reefs, are especially vulnerable to overshoot. In contrast, those with slow response times, such as polar ice sheets, may be less sensitive to temporary overshoot. Potential interactions between tipping elements and additional human pressures, such as deforestation in the Amazon or pollution and overfishing of coral reef habitats, may further lower tipping points, narrowing the range of overshoot trajectories that can still avoid it. The vulnerability of many tipping elements, even under shorter overshoot conditions, underscores that global warming must peak below 2 °C above pre-industrial levels, return to below 1.5 °C as quickly as possible (i.e. within this century), and to around 1 °C thereafter to limit tipping point risks.

2. Generative deep learning improves reconstruction of global historical climate records

   Zhen Qian, Teng Liu, Sebastian Bathiany, and 7 more authors

   Feb 2026

   arXiv:2602.16515 [physics]

   Abs DOI

   Accurate assessment of anthropogenic climate change relies on historical instrumental data, yet observations from the early 20th century are sparse, fragmented, and uncertain. Conventional reconstructions rely on disparate statistical interpolation, which excessively smooths local features and creates unphysical artifacts, leading to systematic underestimation of intrinsic variability and extremes. Here, we present a unified, probabilistic generative deep learning framework that overcomes these limitations and reveals previously unresolved historical climate variability back to 1850. Leveraging a learned generative prior of Earth system dynamics, our model performs probabilistic inference to recover spatiotemporally consistent historical temperature and precipitation fields from sparse observations. Our approach preserves the higher-order statistics of climate dynamics, transforming reconstruction into a robust uncertainty-aware assessment. We demonstrate that our reconstruction overcomes pronounced biases in widely used historical reference products, including those underlying IPCC assessments, especially regarding extreme weather events. Notably, we uncover higher early 20th-century global warming levels compared to existing reconstructions, primarily driven by more pronounced polar warming, with mean Arctic warming trends exceeding established benchmarks by 0.15–0.29°C per decade for 1900–1980. Conversely, for the modern era, our reconstruction indicates that the broad Arctic warming trend is likely overestimated in recent assessments, yet explicitly resolves previously unrecognized intense, localized hotspots in the Barents Sea and Northeastern Greenland. Furthermore, based on our seamless global reconstruction that recovers precipitation variability across the oceans and under-monitored regions, we uncover an intensification of the global hydrological cycle.

3. Informing low-order models of climate tipping elements using outputs from higher-complexity Earth system models

   Nils Bochow, Jonathan Krönke, Julius Garbe, and 1 more author

   EGUsphere, Feb 2026

   Abs DOI

   \textlessp\textgreater\textlessstrong class="journal-contentHeaderColor"\textgreaterAbstract.\textless/strong\textgreater Crossing climate tipping points poses a rising risk under continued global warming. Yet quantitative tipping risk assessments often rely on idealised system dynamics and do not take into account Earth system model (ESM) processes. Here, we present a process-informed, updatable framework that links systematic stability assessments from comprehensive models to transparent low-order dynamical systems for three high-impact climate tipping elements (TEs): the Greenland Ice Sheet (GrIS), the West Antarctic Ice Sheet (WAIS) and the Atlantic Meridional Overturning Circulation (AMOC). We assemble TE experiments from Earth system and Earth system component models, fit element-specific dynamical systems with saddle-node bifurcations that map external forcing to state transitions, and run idealised instantaneous-forcing experiments to show the application of our framework. A simple, modular update protocol allows tipping thresholds and timescales to be revised as new simulations from ESMs become available without refitting the full framework. Applied to current ESM simulations, our emulators reproduce multistability of the GrIS and WAIS and a freshwater-forced weakening of the AMOC, yielding decision-relevant transient and equilibrium behaviour consistent with the underlying ESMs. Our approach provides a transparent bridge between comprehensive simulations and simple dynamical systems, and can be extended to additional climate tipping elements as suitable experiments become available.\textless/p\textgreater

2025

1. 

   Destabilization of Earth system tipping elements

   Niklas Boers, Teng Liu, Sebastian Bathiany, and 9 more authors

   Nature Geoscience, Oct 2025

   Publisher: Nature Publishing Group

   Abs DOI PDF

   

   There is rising concern that several parts of the Earth system may abruptly transition to alternative stable states in response to anthropogenic climate and land-use change. Key candidates of such tipping elements include the Greenland Ice Sheet, the Atlantic Meridional Overturning Circulation, the South American monsoon system and the Amazon rainforest. Owing to the complex dynamics and feedbacks between them via oceanic and atmospheric coupling, the levels of anthropogenic forcing at which transitions to alternative states can be expected remain uncertain. Here we demonstrate how such interactions can generate spurious signals and potentially mask genuine signs of destabilization. We further review and present observation-based evidence that the stability of these four tipping elements has declined in recent decades, suggesting that they have moved towards their critical thresholds, which may be crossed within the range of unmitigated anthropogenic warming. Our results call for better monitoring of these tipping elements and for increased efforts to stop greenhouse gas emissions and land-use change.

2. 

   Reconstructing historical climate fields with deep learning

   Nils Bochow, Anna Poltronieri, Martin Rypdal, and 1 more author

   Science Advances, Apr 2025

   Publisher: American Association for the Advancement of Science

   Abs DOI PDF

   

   Historical records of climate fields are often sparse because of missing measurements, especially before the introduction of large-scale satellite missions. Several statistical and model-based methods have been introduced to fill gaps and reconstruct historical records. Here, we use a recently introduced deep learning approach based on Fourier convolutions, trained on numerical climate model output, to reconstruct historical climate fields. Using this approach, we are able to realistically reconstruct large and irregular areas of missing data and to reproduce known historical events, such as strong El Niño or La Niña events, with very little given information. Our method outperforms the widely used statistical kriging method, as well as other recent machine learning approaches. The model generalizes to higher resolutions than the ones it was trained on and can be used on a variety of climate fields. Moreover, it allows inpainting of masks never seen before during the model training.

3. 

   Physics-constrained generative machine learning-based high-resolution downscaling of Greenland’s surface mass balance and surface temperature

   Nils Bochow, Philipp Hess, and Alexander Robinson

   Jul 2025

   arXiv:2507.22485 [physics]

   Abs DOI PDF

   

   Accurate, high-resolution projections of the Greenland ice sheet’s surface mass balance (SMB) and surface temperature are essential for understanding future sea-level rise, yet current approaches are either computationally demanding or limited to coarse spatial scales. Here, we introduce a novel physics-constrained generative modeling framework based on a consistency model (CM) to downscale low-resolution SMB and surface temperature fields by a factor of up to 32 (from 160 km to 5 km grid spacing) in a few sampling steps. The CM is trained on monthly outputs of the regional climate model MARv3.12 and conditioned on ice-sheet topography and insolation. By enforcing a hard conservation constraint during inference, we ensure approximate preservation of SMB and temperature sums on the coarse spatial scale as well as robust generalization to extreme climate states without retraining. On the test set, our constrained CM achieves a continued ranked probability score of 6.31 mmWE for the SMB and 0.1 K for the surface temperature, outperforming interpolation-based downscaling. Together with spatial power-spectral analysis, we demonstrate that the CM faithfully reproduces variability across spatial scales. We further apply bias-corrected outputs of the NorESM2 Earth System Model as inputs to our CM, to demonstrate the potential of our model to directly downscale ESM fields. Our approach delivers realistic, high-resolution climate forcing for ice-sheet simulations with fast inference and can be readily integrated into Earth-system and ice-sheet model workflows to improve projections of the future contribution to sea-level rise from Greenland and potentially other ice sheets and glaciers too.

2024

1. 

   Projections of precipitation and temperatures in Greenland and the impact of spatially uniform anomalies on the evolution of the ice sheet

   Nils Bochow, Anna Poltronieri, and Niklas Boers

   The Cryosphere, Dec 2024

   Publisher: Copernicus GmbH

   Abs DOI PDF

   

   Simulations of the Greenland ice sheet (GrIS) at millennial timescales and beyond often assume spatially and temporally uniform temperature anomalies and precipitation sensitivities over these timescales or rely on simple parameterisation schemes for the precipitation rates. However, there is no a priori reason to expect spatially and temporally uniform sensitivities across the whole GrIS. Precipitation is frequently modelled to increase with the standard thermodynamic scaling of ∼7 % K−1 derived from the Clausius–Clapeyron relation and often based on older model generations. Here, we update the commonly used parameters for long-term modelling of the GrIS, based on the output of the latest generation of coupled Earth system models (CMIP6), using the historical time period and four different future emission scenarios. We show that the precipitation sensitivities in Greenland have a strong spatial dependence, with values ranging from −3 % K−1 in southern Greenland to 13 % K−1 in northeastern Greenland relative to the local annual mean near-surface temperature in the CMIP6 ensemble mean. Additionally, we show that the annual mean temperatures in Greenland increase between 1.29 and 1.53 times faster than the global mean temperature (GMT), with northern Greenland warming up to 2 times faster than southern Greenland in all emission scenarios. However, we also show that there is a considerable spread in the model responses that can, at least partially, be attributed to differences in the Atlantic meridional overturning circulation (AMOC) response across models. Finally, using the Parallel Ice Sheet Model (PISM), we show that assuming uniform temperature and precipitation anomalies and sensitivities leads to overestimation of near-surface temperatures and underestimation of precipitation in key regions of the GrIS, such as southwestern Greenland. This, in turn, can result in substantial overestimation of ice loss in the long-term evolution of the GrIS.

2. 

   Increasing fluctuations in the Arctic summer sea ice cover are expected with future global warming

   Anna Poltronieri, Nils Bochow, Niklas Boers, and 1 more author

   Environmental Research: Climate, Jun 2024

   Publisher: IOP Publishing

   Abs DOI PDF

   

   The loss of Arctic sea ice (ASI) represents a major transformation in the Arctic region, impacting regional and global climate, ecosystems, and socio-economic structures. Observational and reanalysis data have consistently shown a notable shift in polar environmental conditions over recent decades, marked by a substantial reduction in the ASI area and a rise in the variability in its coverage and distribution. Utilizing data from the latest Coupled Model Intercomparison Project phase, our study reveals a consistent pattern highlighting a fundamental shift in ASI dynamics preceding total loss. We observe increasing fluctuations in the September ASI area as the threshold for an ice-free Arctic is approached across various scenarios and models. This pattern is particularly concentrated in the Central Arctic (CA) sub-region. Spatial analyses reveal increasing variance along the CA’s northern coastlines, accompanied by a substantial increase in open water coverage, underscoring the shift from stable to highly variable ice conditions in this region. Additionally, our findings suggest a potential link between increased ASI fluctuations and variability in surface wind speeds. These specific results underscore the urgency of multidisciplinary approaches in addressing the challenges posed by ASI variability, with implications for marine ecosystems, Indigenous communities, and navigational safety.

3. 

   Arctic summer sea ice loss will accelerate in coming decades

   Anna Poltronieri, Nils Bochow, Nikolas Olson Aksamit, and 3 more authors

   Environmental Research Letters, Jun 2024

   Publisher: IOP Publishing

   Abs DOI PDF

   

   The Arctic sea ice (ASI) is expected to decrease with further global warming. However, considerable uncertainty remains regarding the temperature range that would lead to a completely ice-free Arctic. Here, we combine satellite data and a large suite of models from the latest phase of the Coupled Model Intercomparison Project (CMIP6) to develop an empirical, observation-based projection of the September ASI area for increasing global mean surface temperature (GMST) values. This projection harnesses two simple linear relationships that are statistically supported by both observations and model data. First, we show that the September ASI area is linearly proportional to the area inside a specific northern hemisphere January–September mean temperature contour T c . Second, we use observational data to show how zonally averaged temperatures have followed a positive linear trend relative to the GMST, consistent with Arctic amplification. To ensure the reliability of these observations throughout the rest of the century, we validate this trend by employing the CMIP6 ensemble. Combining these two linear relationships, we show that the September ASI area decrease will accelerate with respect to the GMST increase. Our analysis of observations and CMIP6 model data suggests a complete loss of the September ASI (area below 10 km) for global warming between C and C above pre-industrial GMST levels.

2023

1. 

   Overshooting the critical threshold for the Greenland ice sheet

   Nils Bochow, Anna Poltronieri, Alexander Robinson, and 3 more authors

   Nature, Oct 2023

   Number: 7983 Publisher: Nature Publishing Group

   Abs DOI PDF

   

   Melting of the Greenland ice sheet (GrIS) in response to anthropogenic global warming poses a severe threat in terms of global sea-level rise (SLR)1. Modelling and palaeoclimate evidence suggest that rapidly increasing temperatures in the Arctic can trigger positive feedback mechanisms for the GrIS, leading to self-sustained melting2–4, and the GrIS has been shown to permit several stable states5. Critical transitions are expected when the global mean temperature (GMT) crosses specific thresholds, with substantial hysteresis between the stable states6. Here we use two independent ice-sheet models to investigate the impact of different overshoot scenarios with varying peak and convergence temperatures for a broad range of warming and subsequent cooling rates. Our results show that the maximum GMT and the time span of overshooting given GMT targets are critical in determining GrIS stability. We find a threshold GMT between 1.7 °C and 2.3 °C above preindustrial levels for an abrupt ice-sheet loss. GrIS loss can be substantially mitigated, even for maximum GMTs of 6 °C or more above preindustrial levels, if the GMT is subsequently reduced to less than 1.5 °C above preindustrial levels within a few centuries. However, our results also show that even temporarily overshooting the temperature threshold, without a transition to a new ice-sheet state, still leads to a peak in SLR of up to several metres.

2. 

   The South American monsoon approaches a critical transition in response to deforestation

   Nils Bochow and Niklas Boers

   Science Advances, Oct 2023

   Publisher: American Association for the Advancement of Science

   Abs DOI PDF

   

   The Amazon rainforest is threatened by land-use change and increasing drought and fire frequency. Studies suggest an abrupt dieback of large parts of the rainforest after partial forest loss, but the critical threshold, underlying mechanisms, and possible impacts of forest degradation on the monsoon circulation remain uncertain. Here, we use a nonlinear dynamical model of the moisture transport and recycling across the Amazon to identify several precursor signals for a critical transition in the coupled atmosphere-vegetation dynamics. Guided by our simulations, we reveal both statistical and physical precursor signals of an approaching critical transition in reanalysis and observational data. In accordance with our model results, we attribute these characteristic precursor signals to the nearing of a critical transition of the coupled Amazon atmosphere-vegetation system induced by forest loss due to deforestation, droughts, and fires. The transition would lead to substantially drier conditions, under which the rainforest could likely not be maintained.

Data & Software

2025

1. Reconstructing Historical Climate Fields With Deep Learning

   Nils Bochow, Anna Poltronieri, Martin Rypdal, and 1 more author

   Mar 2025

   Abs DOI

   Supplement to the article "Reconstructing Historical Climate Fields With Deep Learning". Model checkpoints for LaMa, LaMa random, LaMa(years) on tas and LaMa trained on the sea ice concentration  are provided.  Netcdf files for the sitewise and spatial RMSE of the different models on the test set are provided. Additionally, we provide the reconstructed fields shown in the paper as well as an archived version of the original LaMa source code and our modified version.  For questions contact Nils Bochow (nils.bochow@uit.no)

2024

1. Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet

   Nils Bochow, Anna Poltronieri, and Niklas Boers

   Sep 2024

   Abs DOI

   Supplementary data used in the article Projections of Precipitation and Temperatures in Greenland and the Impact of Spatially Uniform Anomalies on the Evolution of the Ice Sheet submitted to The Cryosphere. Please cite the corresponding paper if you use this data.  We supply the regridded CMIP6 precipitation (pr) and temperature (tas) model output for Greenland. local precipitation-near surface temperature sensitivities for each model in the folder local_slopes_precipitation The monthly anomalies for near-surface temperature and precipitation as well as the local change in precipitation (compare to the climatology, in %) with respect to the climatology (year 1980-200) in the folder 2100_anomalies The CMIP6 monthly climatology from the years 1980-2000 (precipitation & near-surface temperature)  The monthly precipitation and near-surface temperature anomalies from 2015-2100 in the folder 2100_85years   License of CMIP6 model output:  CMIP6 model data produced is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0; https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.

2023

1. The South American monsoon approaches a critical transition in response to deforestation

   Nils Bochow and Niklas Boers

   Oct 2023

   Language: eng

   Abs DOI

   The South American monsoon approaches a critical transition in response to deforestation This repository contains the supplementary code for the article The South American monsoon approaches a critical transition in response to deforestation by Bochow and Boers (2023). The code for analysis is located in the folder Analysis. Build the Python environment with all required packages via conda env create -f environment.yml. It will create a conda environment called myclone. The model is written in Julia with Python snippets. The model was written using Julia 1.3.0. For questions, problems or comments contact Nils Bochow (nils.bochow@uit.no).

2. Overshooting the critical threshold for the Greenland ice sheet

   Nils Bochow, Anna Poltronieri, Alexander Robinson, and 3 more authors

   Oct 2023

   Abs DOI

   Model output of PISM-dEBM-simple and Yelmo-REMBO used in the paper Overshooting the critical threshold for the Greenland ice sheet. Code for analysis/recreating the main figures of the paper is provided as well as an example script of how to run PISM-dEBM-simple. The models, methods and the used parameters are described in the paper. Contact: nils.bochow@uit.no