Sub-annual moraine formation at an active temperate Icelandic glacier

This paper presents detailed geomorphological and sedimentological investigations of small recessional moraines at Fjallsjökull, an active temperate outlet of Öræfajökull, southeast Iceland. The moraines are characterized by striking sawtooth or hairpin planforms, which are locally superimposed, giving rise to a complex spatial pattern. We recognize two distinct populations of moraines, namely a group of relatively prominent moraine ridges (mean height ~1.2 m) and a group of comparatively low-relief moraines (mean height ~0.4 m). These two groups often occur in sets/systems, comprising one pronounced outer ridge and several inset smaller moraines. Using a representative subsample of the moraines, we establish that they form by either (i) submarginal deformation and squeezing of subglacial till or (ii) pushing of extruded tills. Locally, proglacial (glaciofluvial) sediments are also incorporated within the moraines during pushing. For the first time, to our knowledge, we demonstrate categorically that these moraines formed sub-annually using repeat uncrewed aerial vehicle (UAV) imagery. We present a conceptual model for sub-annual moraine formation at Fjallsjökull that proposes the sawtooth moraine sequence comprises (i) sets of small squeeze moraines formed during melt-driven squeeze events and (ii) larger push moraines formed during winter re-advances. We suggest the development of this process-form regime is linked to a combination of elevated temperatures, high surface meltwater fluxes to the bed and emerging basal topography (a depositional overdeepening). These factors result in highly saturated subglacial sediments and high porewater pressures, which induces submarginal deformation and ice-marginal squeezing during the melt season. Strong glacier recession during the summer, driven by elevated temperatures, allows several squeeze moraines to be emplaced. This process-form regime may be characteristic of active temperate glaciers receding into overdeepenings during phases of elevated temperatures, especially where their englacial drainage systems allow efficient transfer of surface meltwater to the glacier bed near the snout margin. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd     

Emergent characteristics of rockfall inventories captured at a regional scale

High-resolution rockfall inventories captured at a regional scale are scarce. This is partly owing to difficulties in measuring the range of possible rockfall volumes with sufficient accuracy and completeness, and at a scale exceeding the influence of localized controls. This paucity of data restricts our ability to abstract patterns of erosion, identify long-term changes in behaviour and assess how rockfalls respond to changes in rock mass structural and environmental conditions. We have addressed this by developing a workflow that is tailored to monitoring rockfalls and the resulting cliff retreat continuously (in space), in three-dimensional (3D) and over large spatial scales (>10⁴ m). We tested our approach by analysing rockfall activity along 20.5 km of coastal cliffs in North Yorkshire (UK), in what we understand to be the first multi-temporal detection of rockfalls at a regional scale. We show that rockfall magnitude–frequency relationships, which often underpin predictive models of erosion, are highly sensitive to the spatial extent of monitoring. Variations in rockfall shape with volume also imply a systemic shift in the underlying mechanisms of detachment with scale, leading us to question the validity of applying a single probabilistic model to the full range of rockfalls observed here. Finally, our data emphasize the importance of cliff retreat as an episodic process. Going forwards, there will a pressing need to understand and model the erosional response of such coastlines to rising global sea levels as well as projected changes to winds, tides, wave climates, precipitation and storm events. The methodologies and data presented here are fundamental to achieving this, marking a step-change in our ability to understand the competing effects of different processes in determining the magnitude and frequency of rockfall activity and ultimately meaning that we are better placed to investigate relationships between process and form/erosion at critical, regional scales. © 2020 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd     

On the small-scale fractal geometrical structure of a living coral reef barrier

The topographical complexity of coral reefs is of primary importance for a number of hydrodynamical and ecological processes. The present study is based on a series of high-resolution seabottom elevation measurements along the Maupiti Barrier Reef, French Polynesia. Several statistical metrics and spectral analysis are used to characterize the spatial evolution of the coral geometrical structure from the reef crest to the backreef. A consistent fractal-like power law exists in the spectral density of bottom elevation for length scales between 0.1 and 7 m, while at larger scale, the reef structure shows a different pattern. Such a fine characterization of the reef geometrical structure provides key elements to reconstruct the reef history, to improve the representation of reef roughness in hydrodynamical models and to monitor the evolution of coral reef systems in the context of global change. © 2020 John Wiley & Sons, Ltd.     

Predicting gully densities at sub-continental scales: a case study for the Horn of Africa

Despite its environmental and scientific significance, predicting gully erosion remains problematic. This is especially so in strongly contrasting and degraded regions such as the Horn of Africa. Machine learning algorithms such as random forests (RF) offer great potential to deal with the complex, often non-linear, nature of factors controlling gully erosion. Nonetheless, their applicability at regional to continental scales remains largely untested. Moreover, such algorithms require large amounts of observations for model training and testing. Collecting such data remains an important bottleneck. Here we help to address these gaps by developing and testing a methodology to simulate gully densities across Ethiopia, Eritrea and Djibouti (total area: 1.2 million km²). We propose a methodology to quickly assess the gully head density (GHD) for representative 1 km² study sites by visually scoring the presence of gullies in Google Earth and then converting these scores to realistic estimates of GHD. Based on this approach, we compiled GHD observations for 1,700 sites. We used these data to train sets of RF regression models that simulate GHD at a 1 km² resolution, based on topographic/geomorphic, land cover, soil and rainfall conditions. Our approach also accounts for uncertainties in GHD observations. Independent validations showed generally acceptable simulations of regional GHD patterns. We further show that: (i) model performance strongly depends on the amount of training data used, (ii) large prediction errors mainly occur in areas where also the predicted uncertainty is large and (iii) collecting additional training data for these areas results in more drastic model performance improvements. Analyses of the feature importance of predictor variables further showed that patterns of GHD across the Horn of Africa strongly depend on NDVI and annual rainfall, but also on normalized steepness index (k_sn) and distance to rivers. Overall, our work opens promising perspectives to assess gully densities at continental scales. © 2020 John Wiley & Sons, Ltd.     

Quantifying the role of karstic groundwater in a snowmelt-dominated hydrologic system

River discharge in mountainous regions of the world is often dominated by snowmelt, but base flows are sustained primarily by groundwater storage and discharge. Although numerous recent studies have focused on base-flow discharge in mountain systems, almost no work has explicitly investigated the role of karst groundwater in these systems across a full range of flow conditions. We directly measured groundwater discharge from 48 karst springs in the Kaweah River and its five forks in the Sierra Nevada mountains, California, United States. Relationships between spring and river discharge showed that karst aquifers and springs provide significant storage and delayed discharge to the river. Regression models showed that, of all potential seasonal groundwater storage compartments in the river basin, the area of karst (0.1–4.4%) present provides the best explanation of base-flow recession in each fork of the Kaweah River (directly measured contributions from karst springs ranged from 3.5 to 16% during high-flow to 20 to 65% during base-flow conditions). These results show that, even in settings where karst represents a small portion of basin area, it may play an over-sized role in seasonal storage and water resources in mountain systems. Karst aquifers are the single most important non-snow storage component in the Kaweah River basin, and likely provide similar water storage capacities and higher base flows in other mountain river systems with karst when compared with systems without karst.     

Rapid vegetation responses and feedbacks amplify climate model response to snow cover changes

We investigate the response of a climate system model to two different methods for estimating snow cover fraction. In the control case, snow cover fraction changes gradually with snow depth; in the alternative scenarios (one with prescribed vegetation and one with dynamic vegetation), snow cover fraction initially increases with snow depth almost twice as fast as the control method. In cases where the vegetation was fixed (prescribed), the choice of snow cover parameterization resulted in a limited model response. Increased albedo associated with the high snow caused some moderate localized cooling (3–5°C), mostly at very high latitudes (>70°N) and during the spring season. During the other seasons, however, the cooling was not very extensive. With dynamic vegetation the change is much more dramatic. The initial increases in snow cover fraction with the new parameterization lead to a large-scale southward retreat of boreal vegetation, widespread cooling, and persistent snow cover over much of the boreal region during the boreal summer. Large cold anomalies of up to 15°C cover much of northern Eurasia and North America and the cooling is geographically extensive in the northern hemisphere extratropics, especially during the spring and summer seasons. This study demonstrates the potential for dynamic vegetation within climate models to be quite sensitive to modest forcing. This highlights the importance of dynamic vegetation, both as an amplifier of feedbacks in the climate system and as an essential consideration when implementing adjustments to existing model parameters and algorithms.     

Origin of regional climate differences: role of boundary conditions and model formulation in two GCMs

Model differences in projections of extratropical regional climate change due to increasing greenhouse gases are investigated using two atmospheric general circulation models (AGCMs): ECHAM4 (Max Planck Institute, version 4) and CCM3 (National Center for Atmospheric Research Community Climate Model version 3). Sea-surface temperature (SST) fields calculated from observations and coupled versions of the two models are used to force each AGCM in experiments based on time-slice methodology. Results from the forced AGCMs are then compared to coupled model results from the Coupled Model Intercomparison Project 2 (CMIP2) database. The time-slice methodology is verified by showing that the response of each model to doubled CO₂ and SST forcing from the CMIP2 experiments is consistent with the results of the coupled GCMs. The differences in the responses of the models are attributed to (1) the different tropical SST warmings in the coupled simulations and (2) the different atmospheric model responses to the same tropical SST warmings. Both are found to have important contributions to differences in implied Northern Hemisphere (NH) winter extratropical regional 500 mb height and tropical precipitation climate changes. Forced teleconnection patterns from tropical SST differences are primarily responsible for sensitivity differences in the extratropical North Pacific, but have relatively little impact on the North Atlantic. There are also significant differences in the extratropical response of the models to the same tropical SST anomalies due to differences in numerical and physical parameterizations. Differences due to parameterizations dominate in the North Atlantic. Differences in the control climates of the two coupled models from the current climate, in particular for the coupled model containing CCM3, are also demonstrated to be important in leading to differences in extratropical regional sensitivity.     

Uncertainty, learning and ambiguity in economic models on climate policy: some classical results and new directions

We present how uncertainty and learning are classically studied in economic models. Specifically, we study a standard expected utility model with two sequential decisions, and consider two particular cases of this model to illustrate how uncertainty and learning may affect climate policy. While uncertainty has generally a negative effect on welfare, learning has always a positive, and thus opposite, effect. The effects of both uncertainty and learning on decisions are less clear. Neither uncertainty nor learning can be used as a general argument to increase or reduce emissions today without studying the specific intertemporal costs and benefits. Considering limits in applying the expected utility framework to climate change problems, we then consider a more recent framework with ambiguity-aversion which accounts for situations of imprecise or multiple probability distributions. We discuss both the impact of ambiguity-aversion on decisions and difficulties in applying such a non-expected utility framework to a dynamic context.     

On the estimation of systematic error in regression-based predictions of climate sensitivity

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.