The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500 Geoscientific Model Development DOI 10.5194/gmd-13-3571-2020 27 August 2020 This study provides the future greenhouse gas (GHG) concentrations under the new set of so-called SSP scenarios (the successors of the IPCC SRES and previous representative concentration pathway (RCP) scenarios). The projected CO 2 concentrations range from 350 ppm for low-emission scenarios by 2150 to more than 2000 ppm under the high-emission scenarios. We also provide concentrations, latitudinal gradients, and seasonality for most of the other 42 considered GHGs. Read more
Towards an objective assessment of climate multi-model ensembles – a casestudy: the Senegalo-Mauritanian upwelling region Geoscientific Model Development DOI 10.5194/gmd-13-2723-2020 30 June 2020 The most robust representation of climate is usually obtained by averaging a large number of simulations, thereby cancelling individual model errors. Here, we work towards an objective way of selecting the least biased models over a certain region, based on physical parameters. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the Senegalo-Mauritanian region, but it could potentially be developed for any other region or process. Read more
RainNet v1.0: a convolutional neural network for radar-based precipitation nowcasting Geoscientific Model Development DOI 10.5194/gmd-13-2631-2020 23 June 2020 In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting, which was trained to predict continuous precipitation intensities at a lead time of 5 min. RainNet significantly outperformed the benchmark models at all lead times up to 60 min. Yet, an undesirable property of RainNet predictions is the level of spatial smoothing. Obviously, RainNet learned an optimal level of smoothing to produce a nowcast at 5 min lead time. Read more
TRAPPIST-1 Habitable Atmosphere Intercomparison (THAI):motivations and protocol version 1.0 Geoscientific Model Development DOI 10.5194/gmd-13-707-2020 3 March 2020 Atmospheric characterization of rocky exoplanets orbiting within the habitable zone of nearby M dwarf stars is around the corner with the James Webb Space Telescope (JWST), expected to be launch in 2021. Global climate models (GCMs) are powerful tools to model exoplanet atmospheres and to predict their habitability. However, intrinsic differences between the models can lead to various predictions. This paper presents an experiment protocol to evaluate these differences. Read more
An urban ecohydrological model to quantify the effect of vegetation on urban climate and hydrology (UT&C v1.0) Geoscientific Model Development DOI 10.5194/gmd-13-335-2020 6 February 2020 We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover. Read more
A comparative assessment of the uncertainties of global surface ocean CO2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall? Geoscientific Model Development DOI 10.5194/gmd-12-5113-2019 17 December 2019 The ocean plays a vital role in mitigating climate change by taking up atmospheric carbon dioxide (CO2). Historically sparse ship-based measurements of surface ocean CO2make direct estimates of CO2exchange changes unreliable. We introduce a machine-learning ensemble approach to fill these observational gaps. Our method performs incrementally better relative to past methods, leading to our hypothesis that we are perhaps reaching the limitation of machine-learning algorithms’ capability. Read more
The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description Geoscientific Model Development DOI 10.5194/gmd-12-4309-2019 8 November 2019 Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere. Read more
The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 2: Model evaluation for tropical South America Geoscientific Model Development DOI 10.5194/gmd-12-4347-2019 29 October 2019 The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose. Read more
Improved methodologies for Earth system modelling of atmospheric soluble iron and observation comparisons using the Mechanism of Intermediate complexity for Modelling Iron (MIMI v1.0) Geoscientific Model Development DOI 10.5194/gmd-12-3835-2019 2 September 2019 MIMI v1.0 was designed for use within Earth system models to simulate the 3-D emission, atmospheric processing, and deposition of iron and its soluble fraction. Understanding the iron cycle is important due to its role as an essential micronutrient for ocean phytoplankton; its supply limits primary productivity in many of the world’s oceans. Human activity has perturbed the iron cycle, and MIMI is capable of diagnosing many of these impacts; hence, it is important for future climate studies. Read more
Terrainbento 1.0: a Python package for multi-model analysis in long-term drainage basin evolution Geoscientific Model Development DOI 10.5194/gmd-12-1267-2019 3 April 2019 Terrainbento 1.0 is a Python package for modeling the evolution of the surface of the Earth over geologic time (e.g., thousands to millions of years). Despite many decades of effort by the geomorphology community, there is no one established governing equation for the evolution of topography. Terrainbento 1.0 thus provides 28 alternative models that support hypothesis testing and multi-model analysis in landscape evolution. Read more
