The role of dynamic sea ice in a simplified general circulation model used for palaeoclimate studies

[EN] Observational records provide a strong basis for constraining sea ice models within a narrow range of climate conditions. Given current trends away from these conditions, models need to be tested over a wider range of climate states. The past provides many such examples based on paleoclimate data, including abrupt, large-amplitude climate events. However, the millennial-duration of typical paleoclimate simulations necessitates balancing the inclusion and sophistication of model processes against computational cost. This is why many simplified models used for multi-millennial simulation only feature representations of thermodynamic sea ice processes, while representing sea ice dynamics is essential for more complex general circulation models. We investigate the impact on climate mean states and variability of introducing sea ice dynamics into the simplified general circulation model PlaSim-LSG. We extend the default thermodynamic sea ice component in PlaSim-LSG with one that includes also dynamic sea ice processes. We adapt the structure and parallelization scheme of this new submodel originating from the MITgcm, a more complex state–of–the–art general circulation model. Then, we evaluate the impact of sea ice dynamics on the simulated climate. Comparing climatologies and the variability of the extended model to control simulations of the pre-existing setup, we find that the standard model overestimates sea ice extent, concentration and thickness. The extended model, however, is biased towards low sea ice amounts and extent. Modifying individual parameters in initial tests of the newly added component is not sufficient to compensate for this bias. Still, the general ability of the model to represent positive and negative biases of the sea ice cover provides a promising starting point for the tuning of PlaSim-LSG with sea ice dynamics. Eventually, the extended model can be used to investigate the role of sea ice for past climate oscillations.This research has been funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project no. 395588486, and contributes to the PalMod project (https://www.palmod.de).Adam, M.; Andres, H.; Rehfeld, K. (2022). The role of dynamic sea ice in a simplified general circulation model used for palaeoclimate studies. En Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. 386-395. https://doi.org/10.4995/YIC2021.2021.12383OCS38639

Separating internal and externally-forced contributions to global temperature variability using a Bayesian stochastic energy balance framework

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. This demonstrates the potential of combining Bayesian inference with conceptual climate models to emulate statistics of climate variables across timescales.Comment: The following article has been submitted to Chaos: An Interdisciplinary Journal of Nonlinear Science. After it is published, it will be found at https://aip.scitation.org/journal/ch

ESD Ideas: Photoelectrochemical carbon removal as negative emission technology

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

Empirical estimate of the signal content of Holocene temperature proxy records

Proxy records from climate archives provide evidence about past climate changes, but the recorded signal is affected by non-climate-related effects as well as time uncertainty. As proxy-based climate reconstructions are frequently used to test climate models and to quantitatively infer past climate, we need to improve our understanding of the proxy record signal content as well as the uncertainties involved. In this study, we empirically estimate signal-to-noise ratios (SNRs) of temperature proxy records used in global compilations of the middle to late Holocene (last 6000 years). This is achieved through a comparison of the correlation of proxy time series from nearby sites of three compilations and model time series extracted at the proxy sites from two transient climate model simulations: a Holocene simulation of the ECHAM5/MPIOM model and the Holocene part of the TraCE-21ka simulation. In all comparisons, we found the mean correlations of the proxy time series on centennial to millennial timescales to be low (R < 0.2), even for nearby sites, which resulted in low SNR estimates. The estimated SNRs depend on the as- sumed time uncertainty of the proxy records, the timescale analysed, and the model simulation used. Using the spatial correlation structure of the ECHAM5/MPI-OM simulation, the estimated SNRs on centennial timescales ranged from 0.05 – assuming no time uncertainty – to 0.5 for a time uncer- tainty of 400 years. On millennial timescales, the estimated SNRs were generally higher. Use of the TraCE-21ka correla- tion structure generally resulted in lower SNR estimates than for ECHAM5/MPI-OM. As the number of available high-resolution proxy records continues to grow, a more detailed analysis of the signal content of specific proxy types should become feasible in the near future. The estimated low signal content of Holocene temperature compilations should caution against over-interpretation of these multi-proxy and multisite synthe- ses until further studies are able to facilitate a better characterisation of the signal content in paleoclimate records

Variability of surface climate in simulations of past and future

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

Towards Learned Emulation of Interannual Water Isotopologue Variations in General Circulation Models

Simulating abundances of stable water isotopologues, i.e. molecules differing in their isotopic composition, within climate models allows for comparisons with proxy data and, thus, for testing hypotheses about past climate and validating climate models under varying climatic conditions. However, many models are run without explicitly simulating water isotopologues. We investigate the possibility to replace the explicit physics-based simulation of oxygen isotopic composition in precipitation using machine learning methods. These methods estimate isotopic composition at each time step for given fields of surface temperature and precipitation amount. We implement convolutional neural networks (CNNs) based on the successful UNet architecture and test whether a spherical network architecture outperforms the naive approach of treating Earth's latitude-longitude grid as a flat image. Conducting a case study on a last millennium run with the iHadCM3 climate model, we find that roughly 40\% of the temporal variance in the isotopic composition is explained by the emulations on interannual and monthly timescale, with spatially varying emulation quality. A modified version of the standard UNet architecture for flat images yields results that are equally good as the predictions by the spherical CNN. We test generalization to last millennium runs of other climate models and find that while the tested deep learning methods yield the best results on iHadCM3 data, the performance drops when predicting on other models and is comparable to simple pixel-wise linear regression. An extended choice of predictor variables and improving the robustness of learned climate--oxygen isotope relationships should be explored in future work