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Strong increase in thawing of subsea permafrost in the 22nd century caused by anthropogenic climate change

Thawing permafrost releases carbon to the atmosphere, enhancing global warming. Part of the permafrost soils have been flooded by rising sea levels since the last ice age, becoming subsea permafrost (SSPF). The SSPF is less studied than the part on land. In this study we use a global model to obtain rates of thawing of SSPF under different future climate scenarios until the year 3000. After the year 2100 the scenarios strongly diverge, closely connected to the eventual disappearance of sea ice.

Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models

It can be challenging to understand why Earth system models (ESMs) produce specific results because one can arrive at the same result simply by changing the values of the parameters. In our paper, we demonstrate that it is possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) affect the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.

A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach

Surging glaciers show cyclical changes in flow behaviour – between slow and fast flow – and can have drastic impacts on settlements in their vicinity. One of the clusters of surging glaciers worldwide is High Mountain Asia (HMA). We present an inventory of surging glaciers in HMA, identified from satellite imagery. We show that the number of surging glaciers was underestimated and that they represent 20% of the area covered by glaciers in HMA, before discussing new physics for glacier surges.

The Whole Antarctic Ocean Model (WAOM v1.0): development and evaluation

Here we present an improved model of the Antarctic continental shelf ocean and demonstrate that it is capable of reproducing present-day conditions. The improvements are fundamental and regard the inclusion of tides and ocean eddies. We conclude that the model is well suited to gain new insights into processes that are important for Antarctic ice sheet retreat and global ocean changes. The model will ultimately help to improve projections of sea level rise and climate change.

Impact of increased resolution on long-standing biases in HighResMIP-PRIMAVERA climate models

Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.

Impact of the melt–albedo feedback on the future evolution of the Greenland Ice Sheet with PISM-dEBM-simple

With the increasing melt of the Greenland Ice Sheet, which contributes to sea level rise, the surface of the ice darkens. The dark surfaces absorb more radiation and thus experience increased melt, resulting in the melt–albedo feedback. Using a simple surface melt model, we estimate that this positive feedback contributes to an additional 60 % ice loss in a high-warming scenario and additional 90 % ice loss for moderate warming. Albedo changes are important for Greenland’s future ice loss.

Brief communication: A roadmap towards credible projections of ice sheet contribution to sea level

Estimating how much ice loss from Greenland and Antarctica will contribute to sea level rise is of critical societal importance. However, our analysis shows that recent efforts are not trustworthy because the models fail at reproducing contemporary ice melt. Here we present a roadmap towards making more credible estimates of ice sheet melt.

SELF v1.0: a minimal physical model for predicting time of freeze-up in lakes

The time when lakes freeze varies considerably from year to year. A common way to predict it is to use negative degree days, i.e., the sum of air temperatures below 0°C, a proxy for the heat lost to the atmosphere. Here, we show that this is insufficient as the mixing of the surface layer induced by wind tends to delay the formation of ice. To do so, we developed a minimal model based on a simplified energy balance, which can be used both for large-scale analyses and short-term predictions.

Assessment of the ParFlow–CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

Modeling the hydrologic cycle at high resolution and at large spatial scales is an incredible opportunity and challenge for hydrologists. In this paper, we present the results of a high-resolution hydrologic simulation configured over the contiguous United States. We discuss simulated water fluxes through groundwater, soil, plants, and over land, and we compare model results to in situ observations and satellite products in order to build confidence and guide future model development.

Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

We examined the spatiotemporal distribution of stored water in the seasonal snowpack over High Mountain Asia, based on a new snow reanalysis dataset. The dataset was derived utilizing satellite-observed snow information, which spans across 18 water years, at a high spatial (~ 500 m) and temporal (daily) resolution. Snow mass and snow storage distribution over space and time are analysed in this paper, which brings new insights into understanding the snowpack variability over this region.

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