Search

What: The aim of this workshop was to discuss the needs and challenges in using high-resolution climate model outputs for impacts-relevant modeling. Development of impacts-relevant climate projections in the polar regions requires effective collaboration between regional climate modelers and impacts-relevant modelers in the design stage of high-resolution climate projections for the polar regions. When: 8 November 2021 Where: Online.

It has long been hypothesized that climate can modify both the pattern and magnitude of erosion in mountainous landscapes, thereby controlling morphology, rates of deformation, and potentially modulating global carbon and nutrient cycles through weathering feedbacks. Although conceptually appealing, geologic evidence for a direct climatic control on erosion has remained ambiguous owing to a lack of high-resolution, long-term terrestrial records and suitable field sites. Here we provide direct terrestrial field evidence for long-term synchrony between erosion rates and Milankovitch-driven, 400-kyr eccentricity cycles using a Plio-Pleistocene cosmogenic radionuclide paleo-erosion rate record from the southern Central Andes. The observed climate-erosion coupling across multiple orbital cycles, when combined with results from the intermediate complexity climate model CLIMBER-2, are consistent with the hypothesis that relatively modest fluctuations in precipitation can cause synchronous and nonlinear responses in erosion rates as landscapes adjust to ever-evolving hydrologic boundary conditions imposed by oscillating climate regimes.

We calculate a regional surface “melt potential” index (MPI) over Antarctic ice shelves that describes the frequency (MPI-freq; %) and intensity (MPI-int; K) of daily maximum summer temperatures exceeding a melt threshold of 273.15 K. This is used to determine which ice shelves are vulnerable to melt-induced hydrofracture and is calculated using near-surface temperature output for each summer from 1979/80 to 2018/19 from two high-resolution regional atmospheric model hindcasts (using the MetUM and HIRHAM5). MPI is highest for Antarctic Peninsula ice shelves (MPI-freq 23%–35%, MPI-int 1.2–2.1 K), lowest (2%–3%

Firn simulations are essential for understanding Antarctic ice sheet mass change, as they enable us to convert satellite altimetry observed volume changes to mass changes and column thickness to ice thickness and to quantify the meltwater buffering capacity of firn. Here, we present and evaluate a simulation of the contemporary Antarctic firn layer using the updated semi-empirical IMAU Firn Densification Model (IMAU-FDM) for the period 1979–2020. We have improved previous fresh-snow density and firn compaction parameterizations and used updated atmospheric forcing. In addition, the model has been calibrated and evaluated using 112 firn core density observations across the ice sheet. We found that 62 % of the seasonal and 67 % of the decadal surface height variability are due to variations in firn air content rather than firn mass. Comparison of simulated surface elevation change with a previously published multi-mission altimetry product for the period 2003–2015 shows that performance of the updated model has improved, notably in Dronning Maud Land and Wilkes Land. However, a substantial trend difference (>10 cm yr−1) remains in the Antarctic Peninsula and Ellsworth Land, mainly caused by uncertainties in the spin-up forcing. By estimating previous climatic conditions from ice core data, these trend differences can be reduced by 38 %.

Addressing complex sustainability issues in higher education requires the combination and integration of various disciplines, perspectives and approaches. Challenge-Based Learning (CBL) can support interdisciplinary collaboration on sustainability issues. It requires students to actively explore, discuss, reflect on and integrate information and methods from various disciplines. Online learning could enhance interdisciplinary collaboration since it is associated with greater geographical and educational flexibility and accessibility. Applying an active learning approach such as CBL in an online setting is believed to support interdisciplinary learning and collaboration. We present a case study that took place in a 10-week online interdisciplinary, inter-university undergraduate course on sustainability education. Our research is based on well-known online learning theories “Transactional distance” and “Community of Inquiry” (CoI). The aim of this study was to investigate how transactional distance, presence and (online) interdisciplinary learning are perceived by students. 23 undergraduate students from three universities were enrolled in the course. Quantitative survey data (N = 13) and qualitative data from student reflection papers and interviews (N = 15) were collected. Students perceived low levels of transactional distance and high levels of presence. Unexpectedly, a small increase in perceived distance between students was measured which could be explained by reported limitations of the course design. Students valued the open, interactive and creative character of the course and the online format was not perceived as hindering. Students reflected on interdisciplinary competences that they developed during the course. This study is a first step towards future national as well as international interdisciplinary, inter-university educational collaboration on sustainability issues.

Our current understanding of global mean near-surface (land and sea) air temperature (GMSAT) during the Cenozoic era relies on paleo-proxy estimates of deep-sea temperature combined with assumed relationships between global mean deep-sea temperature (GMDST), global mean sea-surface temperature (GMSST), and GMSAT. The validity of these assumptions is essential in our understanding of past climate states such as the Early Eocene Climate Optimum hothouse climate (EECO, 56–48 Ma). The EECO remains relevant today, because EECO-like CO2 levels are possible in the 22ndcentury under continued high CO2 emissions. We analyze the relationship between the three global temperature indicators for the EECO using 25 different millennia-long model simulations with varying CO2 levels from the Deep-Time Model Intercomparison Project (DeepMIP). The model simulations show limited spatial variability in deep-sea temperature, indicating that local temperature estimates can be regarded representative of GMDST. Linear regression analysis indicates that compared to GMSST, both GMDST and GMSAT respond more strongly to changes in atmospheric CO2 by factors of 1.18 and 1.17, respectively. Consequently, this model-based analysis validates the assumption that changes in GMDST can be used to estimate changes in GMSAT during the EECO. Paleo-proxies of GMDST, GMSST, and GMSAT during EECO show the best fit with model simulations having an atmospheric CO2 level of 1,680 ppm, which matches paleo-proxies of atmospheric CO2 during EECO. Similar analyses of other past climate states are needed to examine whether these results are robust throughout the Cenozoic, providing insight into the long-term future warming under various shared socioeconomic pathways.

During the last glacial cycle (LGC), ice sheets covered large parts of Eurasia and North America, which resulted in ∼120 m of sea level change. Ice sheet–climate interactions have considerable influence on temperature and precipitation patterns and therefore need to be included when simulating this time period. Ideally, ice sheet–climate interactions are simulated by a high-resolution Earth system model. While these models are capable of simulating climates at a certain point in time, such as the pre-industrial (PI) or the Last Glacial Maximum (LGM; 21 000 years ago), a full transient glacial cycle is currently computationally unfeasible as it requires a too-large amount of computation time. Nevertheless, ice sheet models require forcing that captures the gradual change in climate over time to calculate the accumulation and melt of ice and its effect on ice sheet extent and volume changes. Here we simulate the LGC using an ice sheet model forced by LGM and PI climates. The gradual change in climate is modelled by transiently interpolating between pre-calculated results from a climate model for the LGM and the PI. To assess the influence of ice sheet–climate interactions, we use two different interpolation methods: the climate matrix method, which includes a temperature–albedo and precipitation–topography feedback, and the glacial index method, which does not. To investigate the sensitivity of the results to the prescribed climate forcing, we use the output of several models that are part of the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3). In these simulations, ice volume is prescribed, and the climate is reconstructed with a general circulation model (GCM). Here we test those models by using their climate to drive an ice sheet model over the LGC. We find that the ice volume differences caused by the climate forcing exceed the differences caused by the interpolation method. Some GCMs produced unrealistic LGM volumes, and only four resulted in reasonable ice sheets, with LGM Northern Hemisphere sea level contribution ranging between 74–113 m with respect to the present day. The glacial index and climate matrix methods result in similar ice volumes at the LGM but yield a different ice evolution with different ice domes during the inception phase of the glacial cycle and different sea level rates during the deglaciation phase. The temperature–albedo feedback is the main cause of differences between the glacial index and climate matrix methods.

It has been argued that the −5 °C annual mean 2 m air temperature isotherm defines a limit of ice shelf viability on the Antarctic Peninsula as melt ponding increases at higher temperatures. It is, however, presently unknown whether this threshold can also be applied to other Antarctic ice shelves. Here we use two present-day and three future high-resolution Antarctic climate simulations to predict warming thresholds for Antarctic ice shelf melt pond formation on the basis of the melt-over-accumulation ratio. The associated warming thresholds match well with observed melt pond volumes and are found to be spatially highly variable and controlled by snow accumulation. For relatively wet ice shelves, the −5 °C temperature threshold was confirmed; but cold and dry ice shelves such as Amery, Ross and Filchner-Ronne are more vulnerable than previously thought, with threshold temperatures well below −15 °C. Coupled Model Intercomparison Project Phase 6 models predict that towards the end of this century these thresholds can be reached on many ice shelves, even on cold ice shelves and under moderate warming scenarios.

Turbulent heat fluxes, that is, the sensible heat flux and latent heat flux, are important sources/sinks of energy for surface melt over glaciers and ice sheets. Therefore, credible simulations of for example, future Greenland Ice Sheet mass loss need an accurate description of these fluxes. However, the parameterization of surface turbulent heat fluxes in climate models requires knowledge about the surface roughness lengths for momentum, heat and moisture, which are currently either unknown or tuned to indirect observations. In this study we take advantage of a large data set of eddy covariance observations acquired during multiple years and at multiple sites over the Greenland Ice Sheet. These in-situ observations are used to develop an improved parameterization for the roughness length for momentum, and update the parameterization for the roughness lengths for heat and moisture over rough ice surfaces. The newly derived parameterizations are implemented in a surface energy balance model that is used to compute surface melt. Sensitivity experiments confirm the high sensitivity of surface melt to the chosen roughness length models. The new parameterization models the sensible heat flux to within 10 W m−2, and the cumulative ice ablation within 10% at three out of four sites. Two case studies demonstrate the important contribution of the turbulent heat fluxes to surface ablation. The presented roughness parameterizations can be implemented in climate models to improve the physical representation of surface roughness over rough snow and ice surfaces, which is expected to improve the modeled turbulent heat fluxes and thus surface melt.

A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

The mid-Pliocene warm period (3.264-3.025 Ma) is the most recent geological period in which the atmospheric CO2 concentration was approximately equal to the concentration we measure today (ca. 400 ppm). Sea surface temperature (SST) proxies indicate above-average warming over the North Atlantic in the mid-Pliocene with respect to the pre-industrial period, which may be linked to an intensified Atlantic Meridional Overturning Circulation (AMOC). Earlier results from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) show that the ensemble simulates a stronger AMOC in the mid-Pliocene than in the pre-industrial. However, no consistent relationship between the stronger mid-Pliocene AMOC and either the Atlantic northward ocean heat transport (OHT) or average North Atlantic SSTs has been found. In this study, we look further into the drivers and consequences of a stronger AMOC in mid-Pliocene compared to pre-industrial simulations in PlioMIP2. We find that all model simulations with a closed Bering Strait and Canadian Archipelago show reduced freshwater transport from the Arctic Ocean into the North Atlantic. This contributes to an increase in salinity in the subpolar North Atlantic and Labrador Sea that can be linked to the stronger AMOC in the mid-Pliocene. To investigate the dynamics behind the ensembles variable response of the total Atlantic OHT to the stronger AMOC, we separate the Atlantic OHT into two components associated with either the overturning circulation or the wind-driven gyre circulation. While the ensemble mean of the overturning component is increased significantly in magnitude in the mid-Pliocene, it is partly compensated by a reduction in the gyre component in the northern subtropical gyre region. This indicates that the lack of relationship between the total OHT and AMOC is due to changes in OHT by the subtropical gyre. The overturning and gyre components should therefore be considered separately to gain a more complete understanding of the OHT response to a stronger mid-Pliocene AMOC. In addition, we show that the AMOC exerts a stronger influence on North Atlantic SSTs in the mid-Pliocene than in the pre-industrial, providing a possible explanation for the improved agreement of the PlioMIP2 ensemble mean SSTs with reconstructions in the North Atlantic. Funding Agencies|Netherlands Earth System Science Centre (OCW grant) [024.002.001]

The Irminger Sea is one of the few regions in the ocean where deep (>1,000 m) convection occurs. Convection is followed by restratification during summer, when the stratification of the water column is reestablished and the convectively formed water is exported at depth. There are currently no descriptions of interannual variability and physical drivers of restratification in the Irminger Sea. We investigate restratification in the upper 600 m of the central Irminger Sea using reanalysis data for the years 1993–2019. We find distinctly different restratification processes in the upper 100 m (the upper layer) and the water below it (the lower layer). In the upper layer, the stratification is dominated by a seasonal cycle that matches the cycle of the surface heat flux. In 2010 and 2019, there were peaks in upper layer restratification, which could partly be related to strong atmospheric heat and freshwater fluxes. Greenland runoff likely also contributed to the high restratification, although this contribution could not be quantified in the present study. In the lower layer there is strong interannual variability in stratification, caused by variability both in the convection and the restratification strength. The restratification strength is strongly correlated with the eddy kinetic energy in the eastern Irminger Sea, suggesting that lower layer restratification is driven by lateral advection of warm, saline waters through Irminger Current eddies. In the future, surface warming and freshening of the Irminger Sea due to anthropogenic climate change are expected to increase upper layer stratification, potentially inhibiting convection.

Firn (compressed snow) covers approximately 90$$\%$$%of the Greenland ice sheet (GrIS) and currently retains about half of rain and meltwater through refreezing, reducing runoff and subsequent mass loss. The loss of firn could mark a tipping point for sustained GrIS mass loss, since decades to centuries of cold summers would be required to rebuild the firn buffer. Here we estimate the warming required for GrIS firn to reach peak refreezing, using 51 climate simulations statistically downscaled to 1 km resolution, that project the long-term firn layer evolution under multiple emission scenarios (1850–2300). We predict that refreezing stabilises under low warming scenarios, whereas under extreme warming, refreezing could peak and permanently decline starting in southwest Greenland by 2100, and further expanding GrIS-wide in the early 22$${}^{{nd}}$$ndcentury. After passing this peak, the GrIS contribution to global sea level rise would increase over twenty-fold compared to the last three decades.

Two machine learning (ML) models are investigated for retrieving sea surface temperature (SST) from passive microwave (PMW) satellite observations from the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) and auxiliary data, such as ERA5 reanalysis data. The first model is the Extreme Gradient Boosting (XBG) model and the second is a multilayer perceptron neural network (NN). The performance of the two ML algorithms is compared to that of an existing state-of-the-art regression (RE) retrieval algorithm. The performance of the three algorithms is assessed using independent in situ SSTs from drifting buoys. Overall, the three models have similar biases; 0.01, 0.01 and −0.02 K for the XGB, NN and RE, respectively. The XGB model performs best with respect to standard deviation; 0.36 K. While the NN model performs slightly better than the RE model with respect to standard deviation, 0.50 and 0.55 K, respectively, the RE model is found to be more sensitive to changes in the in situ SST. Moreover, the XGB model is the least sensitive with an overall sensitivity of 0.78, compared to 0.90 for the RE model and 0.88 for the NN model. The good performance of the two ML algorithms compared to the state-of-the-art RE algorithm in this initial study demonstrates that there is a large potential in the use of ML algorithms for the retrieval of SST from PMW satellite observations.

Sea level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate for the deep ocean and ice sheets. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientists and practitioners builds on a framework of discussing physical evidence to quantify high-end global SLR for practitioners. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2°C in 2100 (RCP2.6/SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for a (RCP8.5/SSP5-8.5), we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2°C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while here we emphasize the importance of the timing of ice shelf collapse around Antarctica. This is highly uncertain due to low understanding of the driving processes. Hence both process understanding and emission scenario control high-end SLR. publishedVersion