Porting the WAVEWATCH III (v6.07) wave action source terms to GPU Geoscientific Model Development DOI 10.5194/gmd-16-1445-2023 3 May 2023 Wind-generated waves play an important role in modifying physical processes at the air–sea interface, but they have been traditionally excluded from climate models due to the high computational cost of running spectral wave models for climate simulations. To address this, our work identified and accelerated the computationally intensive section of WAVEWATCH III on GPU using OpenACC. This allows for high-resolution modelling of atmosphere–wave–ocean feedbacks in century-scale climate integrations. Read more
Reproducible and relocatable regional ocean modelling: fundamentals and practices Geoscientific Model Development DOI 10.5194/gmd-16-1481-2023 10 April 2023 The aim is to increase the capacity of the modelling community to respond to societally important questions that require ocean modelling. The concept of reproducibility for regional ocean modelling is developed: advocating methods for reproducible workflows and standardised methods of assessment. Then, targeting the NEMO framework, we give practical advice and worked examples, highlighting key considerations that will the expedite development cycle and upskill the user community. Read more
Introducing CRYOWRF v1.0: multiscale atmospheric flow simulations with advanced snow cover modelling Geoscientific Model Development DOI 10.5194/gmd-16-719-2023 10 March 2023 Most current generation climate and weather models have a relatively simplistic description of snow and snow–atmosphere interaction. One reason for this is the belief that including an advanced snow model would make the simulations too computationally demanding. In this study, we bring together two state-of-the-art models for atmosphere (WRF) and snow cover (SNOWPACK) and highlight both the feasibility and necessity of such coupled models to explore underexplored phenomena in the cryosphere. Read more
The IPCC Sixth Assessment Report WGIII climate assessment of mitigation pathways: from emissions to global temperatures Geoscientific Model Development DOI 10.5194/gmd-15-9075-2022 22 February 2023 Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source “climate-assessment” workflow that was used in the IPCC AR6 Working Group III report. The paper provides key insight for anyone wishing to understand the assessment of climate outcomes of mitigation pathways in the context of the Paris Agreement. Read more
Global biomass burning fuel consumption and emissions at 500m spatial resolution based on the Global Fire Emissions Database (GFED) Geoscientific Model Development DOI 10.5194/gmd-15-8411-2022 23 January 2023 We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial. Read more
HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor Geoscientific Model Development DOI 10.5194/gmd-15-6817-2022 28 November 2022 Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain. Read more
Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not Geoscientific Model Development DOI 10.5194/gmd-15-5481-2022 24 October 2022 The task of evaluating competing models is fundamental to science. Models are evaluated based on an objective function, the choice of which ultimately influences what scientists learn from their observations. The mean absolute error (MAE) and root-mean-squared error (RMSE) are two such functions. Both are widely used, yet there remains enduring confusion over their use. This article reviews the theoretical justification behind their usage, as well as alternatives for when they are not suitable. Read more
The eWaterCycle platform for open and FAIR hydrological collaboration Geoscientific Model Development DOI 10.5194/gmd-15-5371-2022 21 October 2022 With the eWaterCycle platform, we are providing the hydrological community with a platform to conduct their research that is fully compatible with the principles of both open science and FAIR science. The eWatercyle platform gives easy access to well-known hydrological models, big datasets and example experiments. Using eWaterCycle hydrologists can easily compare the results from different models, couple models and do more complex hydrological computational research. Read more
Towards automatic finite-element methods for geodynamics via Firedrake Geoscientific Model Development DOI 10.5194/gmd-15-5127-2022 23 September 2022 Firedrake is a state-of-the-art system that automatically generates highly optimised code for simulating finite-element (FE) problems in geophysical fluid dynamics. It creates a separation of concerns between employing the FE method and implementing it. Here, we demonstrate the applicability and benefits of Firedrake for simulating geodynamical flows, with a focus on the slow creeping motion of Earth’s mantle over geological timescales, which is ultimately the engine driving our dynamic Earth. Read more
Training a supermodel with noisy and sparse observations: a case study with CPT and the synch rule on SPEEDO – v.1 Geoscientific Model Development DOI 10.5194/gmd-15-3831-2022 8 August 2022 In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models. Read more