ALBERT

Description: Irrigation is one of the land managements that can affect the local climate. Recent literature shows that it affects mostly the near-surface variables and it is associated with an irrigation cooling effect. However, there is no common parameterization that also accounts for a realistic water amount, and these factors could be ascribed as causes of different impacts found in previous studies. This work aims to develop three new surface irrigation parameterizations within the WRF-ARW model (V3.8.1) that consider different evaporative processes. The parameterizations are tested on one of the regions where global studies disagree on the signal of irrigation: the Mediterranean area, and in particular the Po Valley. Three sets of experiments are performed using the same irrigation water amount of 5.7 mm/d, derived from Eurostat data. Two complementary validations are performed for July 2015: monthly mean, minimum and maximum temperature with ground stations and potential evapotranspiration with the MODIS product. All tests show that both mean and maximum temperature, as well as potential evapotranspiration, simulated fields approximate better the measures when using the irrigation parameterizations. This study addresses the sensitivity of the results to the parameterizations' human-decision assumptions: start time, length and frequency. The main impact of irrigation on surface variables such as soil moisture is due to the parameterization choice itself, rather than the timing. Moreover, on average, the atmosphere and soil variables are not very sensitive to the parameterizations assumptions for realistic timing and length.

Description: Geostatistical inverse modeling (GIM) has become a common approach to estimating greenhouse gas fluxes at the Earth's surface using atmospheric observations. GIMs are unique relative to other commonly-used approaches because they do not require a single emissions inventory or a bottom-up model to serve as an initial guess of the fluxes. Instead, a modeler can incorporate a wide range of environmental, economic, and/or land use data to estimate the fluxes. Traditionally, GIMs have been paired with in situ observations that number in the thousands or tens of thousands. However, the number of available atmospheric greenhouse gas observations has been increasing enormously as the number of satellites, airborne measurement campaigns, and in situ monitoring stations continues to increase. This era of prolific greenhouse gas observations presents computational and statistical challenges for inverse modeling frameworks that have traditionally been paired with a limited number of in situ monitoring sites. In this article, we discuss the challenges of estimating greenhouse gas fluxes using large atmospheric datasets with a particular focus on GIMs. We subsequently discuss several strategies for estimating the fluxes and quantifying uncertainties, strategies that are adapted from hydrology, applied math, or other academic fields and are compatible with a wide variety of atmospheric models. We further evaluate the accuracy and computational burden of each strategy using CO2 observations from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. Specifically, we simultaneously estimate a full year of 3-hourly CO2 fluxes across North America in one case study – a total of 9.4 × 106 unknown fluxes using 9.9 × 104 observations. The strategies discussed here provide accurate estimates of CO2 fluxes that are comparable to fluxes calculated directly or analytically. We are also able to approximate posterior uncertainties in the fluxes, but these approximation are typically an over- or underestimate depending upon the strategy employed and the degree of approximation required to make the calculations manageable.

Description: This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of the ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of three. The performance can be further improved, up to a factor of more than 30, when the newly-introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, setting of user configurable options based on modern language formats, and high code quality standards following the best practices for software development.

Description: Here, we present the implementation of the freshwater carbon (C) cycle in the Dynamic In-stream Chemistry module (CARBON-DISC), which is part of the Integrated Model to Assess the Global Environment-Dynamic Global Nutrient Model (IMAGE-DGNM). A coupled hydrology-biogeochemistry approach with 0.5 by 0.5-degree resolution accounts for the spatial and temporal variability in dynamic conditions in the aquatic continuum using independent global databases. This process-based model resolves the concentrations, transformations and transfer fluxes of dissolved inorganic carbon (DIC), dissolved organic carbon (DOC) and terrestrial and autochthonous particulate organic carbon (POC) from headwaters to river mouth with a time step of 1 month for the period 1950–2000. This is a major step forward in basin scale modelling of the C processing in freshwater systems, since simulated results can be validated at every location and point in time, and the model can be applied for retrodiction and to analyse future scenarios. Validation of the model with long-term measurement data shows a fair agreement, considering that this is a global model. To analyse the performance of the full production-respiration DISC module, two other schemes are presented, including an abiotic system excluding any in-stream processing of DOC and allochthonous production, and an extended abiotic system including heterotrophic respiration, but excluding production. Furthermore, a sensitivity analysis shows that many parameters, such as temperature, solar radiation, organic sediment mineralization rate and C inputs, including particulate organic carbon from terrestrial vegetation and dissolved inorganic carbon from groundwater, strongly affect atmosphere-freshwater exchange of CO2.

Description: The biogeochemical dynamics of Hg, and specifically of its three species Hg0, HgII, and MeHg (elemental, inorganic, and organic, respectively) in the marine coastal area of Augusta Bay (southern Italy) have been explored by the high resolution 3D Hg (HR3DHG) model, namely an advection-diffusion-reaction model for the dissolved mercury in the seawater compartment coupled with i) a diffusion-reaction model for dissolved mercury in the pore water of sediments and ii) a sorption/de-sorption model for total mercury in the sediments. The spatio-temporal variability of dissolved and total mercury concentration both in seawater ([HgD] and [HgT]) first layers of bottom sediments ([HgsedD] and [HgsedT]), and the Hg fluxes at the boundaries of the 3D model domain have been theoretically reproduced, showing an excellent agreement with the experimental data, collected in multiple field observations during six different oceanographic cruises. The mass-balance of the different Hg species in seawater has been calculated for the Augusta Harbor, improving previous estimations. The HR3DHG model includes modules that can be implemented for specific and detailed exploration of the effects of climate change on the spatio-temporal distribution of Hg in highly contaminated coastal-marine areas.

Description: The full Stokes equations are solved by a finite element method for simulation of large ice sheets and glaciers. The simulation is particularly sensitive to the discretization of the grounding line which separates the ice resting on the bedrock and the ice floating on water and is moving in time. The boundary conditions at the ice base are enforced by Nitsche's method and a subgrid treatment of the elements in the discretization close to the grounding line. Simulations with the method in two dimensions for an advancing and a retreating grounding line illustrate the performance of the method. It is implemented in the two dimensional version of the open source code Elmer/ICE.

Description: The increasing demand for high-resolution climate information has attracted a growing attention for statistical downscaling methods (SD), due in part to their relative advantages and merits as compared to dynamical approaches (based on regional climate model simulations), such as their much lower computational cost and their fitness-for-purpose for many local-scale applications. As a result, a plethora of SD methods is nowadays available for climate scientists, which has motivated recent efforts for their comprehensive evaluation, like the VALUE Project (http://www.value-cost.eu). The systematic intercomparison of a large number of SD techniques undertaken in VALUE, many of them independently developed by different authors and modeling centers in a variety of languages/environments, has shown a compelling need for new tools allowing for their application within an integrated framework. With this regard, downscaleR is an R package for statistical downscaling of climate information which covers the most popular approaches (Model Output Statistics – including the so called 'bias correction' methods – and Perfect Prognosis) and state-of-the-art techniques. It has been conceived to work primarily with daily data and can be used in the framework of both seasonal forecasting and climate change studies. Its full integration within the climate4R framework (Iturbide et al. 2019) makes possible the development of end-to-end downscaling applications, from data retrieval to model building, validation and prediction, bringing to climate scientists and practitioners a unique comprehensive framework for SD model development. In this article the main features of downscaleR are showcased through the replication of some of the results obtained in the VALUE Project, making an emphasis in the most technically complex stages of perfect-prog model calibration (predictor screening, cross-validation and model selection) that are accomplished through simple commands allowing for extremely flexible model tuning, tailored to the needs of users requiring an easy interface for different levels of experimental complexity. As part of the open-source climate4R framework, downscaleR is freely available and the necessary data and R scripts to fully replicate the experiments included in this paper are also provided as a companion notebook.

Description: Atmospheric inversions are commonly used for estimating large-scale (continental to regional) net sources and sinks of CO2 and other stable atmospheric tracers from their observed concentrations. Recently, there has been an increasing demand from stakeholders for robust estimates of greenhouse gases at country-scale (or higher) resolution, in particular in the framework of the Paris agreement. This increase in resolution is in theory enabled by the growing availability of observations from surface in-situ networks (such as ICOS in Europe) and from remote sensing products (OCO-2, GOSAT-2). The increase in the resolution of inversions is also a necessary step to provide efficient feedback to the process-based (bottom-up) modelling community (vegetation models, fossil fuel emission inventories). This, however, calls for new developments in the inverse modelling systems, mainly in terms of diversification of the inversion approaches, shift from global to regional inversions, and improvement in the computational efficiency, We have developed the Lund University Modular Inversion Algorithm (LUMIA) as a tool to address some of these new developments. LUMIA is meant to be a platform for inverse modelling developments at Lund University. It aims at being a flexible, yet simple and easy to maintain set of tools that modellers can combine to build inverse modelling experiments. It is in particular designed to be transport model agnostic, which should facilitate isolating the transport model errors from those introduced by the inversion setup itself. Here, we briefly describe the motivations for developing LUMIA as well as the underlying development principles, current status and future prospects. We present a first LUMIA inversion setup for a regional CO2 inversions over Europe, based on a new coupling between the Lagrangian FLEXPART (high resolution foreground transport) and the global coarse resolution TM5 transport models, using in-situ data from surface and tall tower observation sites.

Description: Accurate predictions of future sea level rise require numerical models that capture the complex thermomechanical feedbacks in rapidly deforming ice. Shear margins, grounding zones and the basal sliding interface are locations of particular interest where the stress-field is complex and fundamentally three-dimensional. These transition zones are prone to thermomechanical localisation, which can be captured numerically only with high temporal and spatial resolution. Thus, better understanding the coupled physical processes that govern these boundaries of localised strain necessitates a non-linear, full Stokes model that affords high resolution and scales well in three dimensions. This paper’s goal is to contribute to the growing toolbox for modelling thermomechanical deformation in ice by levering GPU accelerators’ parallel scalability. We propose a numerical model that relies on pseudo-transient iterations to solve the implicit thermomechanical coupling between ice motion and temperature involving shear-heating and a temperature-dependant ice viscosity. Our method is based on the finite-difference discretisation, and we implement the pseudo-time integration in a matrix-free way. We benchmark the mechanical Stokes solver against the finite-element code Elmer/Ice and report good agreement among the results. We showcase a parallel version of the solver to run on GPU-accelerated distributed memory machines, reaching a parallel efficiency of 93 %. We show that our model is particularly useful for improving our process-based understanding of flow localisation in the complex transition zones bounding rapidly moving ice.

Description: Coasts are among the most intensely used environments on the planet, but they also present dynamic and unique hazards including flooding and erosion. Sea level rise and changing wave climates will alter patterns of erosion and deposition, but some existing coastline evolution models are unable to simulate these effects due to their one-dimensional representation of the systems, or of sediment transport processes. In this paper, the development and application of the Coastline Evolution Model 2D (CEM2D) is presented, that incorporates these influences. The model has been developed from the established CEM model and is capable of simulating fundamental cause-effect relationships in coastal systems. The two-dimensional storage and transport of sediment in CEM2D, which is only done in one-dimension in CEM, means it is also capable of exploring the influence of a variable water level on sediment transport and the formation and evolution of morphological features and landforms at the meso-scale, from 10 to 100 years and over 10 to 100 kilometres. The model sits between one-dimensional and three-dimensional models, with the advantage of increased complexity and detail in model outputs compared to the former, but with more efficiency and less computational expense than the latter.