Comment on gmd-2021-290

The mission of the Earth System Grid Federation (ESGF) is to provide the worldwide climate-research community with access to the data, information, model codes, analysis tools, and intercomparison capabilities required to make sense of enormous climate data sets. Its specific goals are to (1) provide an easy-to-use and secure web-based data access environment for data sets; (2) add value to individual data sets by presenting them in the context of other data sets and tools for comparative analysis; (3) address the specific requirements of participating organizations with respect to bandwidth, access restrictions, and replication; (4) ensure that the data are readily accessible through the analysis and visualization tools used by the climate research community; and (5) transfer infrastructure advances to other domain areas. For the ESGF, the U.S. Department of Energy's (DOE's) Earth System Grid Center for Enabling Technologies (ESG-CET) team has led international development and delivered a production environment for managing and accessing ultra-scale climate data. This production environment includes multiple national and international climate projects (such as the Community Earth System Model and the Coupled Model Intercomparison Project), ocean model data (such as the Parallel Ocean Program), observation data (Atmospheric Radiation Measurement Best Estimate, Carbon Dioxide Information and Analysis Center, Atmospheric Infrared Sounder, etc.), and analysis and visualization tools, all serving a diverse user community. These data holdings and services are distributed across multiple ESG-CET sites (such as ANL, LANL, LBNL/NERSC, LLNL/PCMDI, NCAR, and ORNL) and at unfunded partner sites, such as the Australian National University National Computational Infrastructure, the British Atmospheric Data Centre, the National Oceanic and Atmospheric Administration Geophysical Fluid Dynamics Laboratory, the Max Planck Institute for Meteorology, the German Climate Computing Centre, the National Aeronautics and Space Administration Jet Propulsion Laboratory, and the National Oceanic and Atmospheric Administration. The ESGF software is distinguished from other collaborative knowledge systems in the climate community by its widespread adoption, federation capabilities, and broad developer base. It is the leading source for present climate data holdings, including the most important and largest data sets in the global-climate community, and - assuming its development continues - we expect it to be the leading source for future climate data holdings as well. Recently, ESG-CET extended its services beyond data-file access and delivery to include more detailed information products (scientific graphics, animations, etc.), secure binary data-access services (based upon the OPeNDAP protocol), and server-side analysis. The latter capabilities allow users to request data subsets transformed through commonly used analysis and intercomparison procedures. As we transition from development activities to production and operations, the ESG-CET team is tasked with making data available to all users seeking to understand, process, extract value from, visualize, and/or communicate it to others. This ongoing effort, though daunting in scope and complexity, will greatly magnify the value of numerical climate model outputs and climate observations for future national and international climate-assessment reports. The ESG-CET team also faces substantial technical challenges due to the rapidly increasing scale of climate simulation and observational data, which will grow, for example, from less than 50 terabytes for the last Intergovernmental Panel on Climate Change (IPCC) assessment to multiple Petabytes for the next IPCC assessment. In a world of exponential technological change and rapidly growing sophistication in climate data analysis, an infrastructure such as ESGF must constantly evolve if it is to remain relevant and useful. Regretfully, we submit our final report at the end of project funding. To continue to serve the climate-science community, we are currently seeking additional funding. Such funding would allow us to maintain and enhance ESGF production and operation of this vital endeavor of cataloging, serving, and analyzing ultra-scale climate science data. At this time, the entire ESG-CET team would like to take this opportunity to sincerely thank our funding agencies in the DOE Scientific Discovery through Advanced Computing (SciDAC) program and the Office of Biological and Environmental Research (OBER) - as well as our national and international collaborators, stakeholders, and partners - for allowing us to work with you and serve the community these past several years.

<p>The last few decades have seen a range of advances in climate science and consequential policy initiatives at both national and international levels. These advances have been built on the back of progress in modelling and in part been enabled by the global data sharing initiative - the Earth System Grid Federation (ESGF) - which has underpinned recent phases of the World Climate Research Programme's Coupled Model Intercomparison Projects.  </p><p>The ESGF itself consists of data nodes deployed by individual modelling centres and a backbone of software development and services delivered by a few core institutions. Within Europe, along with some shared development of model components, these core ESGF software development and services are coordinated by the European Network on Earth System Modelling (ENES) and supported by the H2020 IS-ENES Phase 3 research infrastructure project.  </p><p>We provide an historical overview on advances in policy-relevant science, such as the Intergovernmental Panel for Climate Change (IPCC), that have been enabled by long-term underpinning development and funding of the ENES and ESGF infrastructure. We illustrate the recent shift of research funding from physical science objectives alone towards funding services to society (and the necessary underpinning research). We stress the potential dangers of underfunding research infrastructures that need to be simultaneously flexible and reliable enough to serve both ongoing basic research and the growing societal objectives, as emphasised by the development of climate services such as Copernicus Climate Change Service. We conclude by presenting some steps towards sustaining such research infrastructure in the context of the ENES and the possible futures of climate science.</p>

Earth system science research depends on large and complex datasets, yet accessing them often requires substantial technical expertise. The Earth System Grid Federation (ESGF) provides distributed access to petabytes of observational and model simulation data, but users unfamiliar with metadata structures or programming face steep barriers. General-purpose large language models (lLMs), while powerful, have shown limited accuracy and reliability for ESGF-specific queries, frequently misinterpreting metadata or producing hallucinated results. To address this gap, we developed a domain-specific LLM trained on curated ESGF instruction-response pairs reflecting realistic user workflows, particularly for intake-esgf code generation. Our approach fine-tunes the LLaMA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3.1(8 B)$</tex> model using parameterefficient methods, integrates retrieval-augmented generation to ground responses in ESGF and Coupled Model Intercomparison Project (CMIP) metadata, and deploys the assistant through a lightweight, browser-based interface for interactive use. Evaluation using expert review and BERTScore shows that the finetuned model significantly outperforms its untuned counterpart in accuracy, contextual relevance, and usability. This work offers a generalizable framework for applying domain-specific LLMs to complex scientific data infrastructures.

Abstract. A community diagnostics and performance metrics tool for the evaluation of Earth system models (ESMs) has been developed that allows for routine comparison of single or multiple models, either against predecessor versions or against observations. The priority of the effort so far has been to target specific scientific themes focusing on selected essential climate variables (ECVs), a range of known systematic biases common to ESMs, such as coupled tropical climate variability, monsoons, Southern Ocean processes, continental dry biases, and soil hydrology–climate interactions, as well as atmospheric CO2 budgets, tropospheric and stratospheric ozone, and tropospheric aerosols. The tool is being developed in such a way that additional analyses can easily be added. A set of standard namelists for each scientific topic reproduces specific sets of diagnostics or performance metrics that have demonstrated their importance in ESM evaluation in the peer-reviewed literature. The Earth System Model Evaluation Tool (ESMValTool) is a community effort open to both users and developers encouraging open exchange of diagnostic source code and evaluation results from the Coupled Model Intercomparison Project (CMIP) ensemble. This will facilitate and improve ESM evaluation beyond the state-of-the-art and aims at supporting such activities within CMIP and at individual modelling centres. Ultimately, we envisage running the ESMValTool alongside the Earth System Grid Federation (ESGF) as part of a more routine evaluation of CMIP model simulations while utilizing observations available in standard formats (obs4MIPs) or provided by the user.

&amp;lt;p&amp;gt;The Earth System Grid Federation (ESGF) is a globally distributed e-infrastructure for the hosting and dissemination of climate-related data.&amp;amp;#160; ESGF was originally developed to support the community in the analysis of CMIP5 (5th Coupled Model Intercomparison Project) data in support of the 5th Assessment report made by the IPCC (Intergovernmental Panel on Climate Change).&amp;amp;#160; Recognising the challenge of the large volumes of data concerned and the international nature of the work, a federated system was developed linking together a network of collaborating data providers around the world. This enables users to discover, download and access data through a single unified system such that they can seamlessly pull data from these multiple hosting centres via a common set of APIs.&amp;amp;#160; ESGF has grown to support over 16000 registered users and besides the CMIPs, supports a range of other projects such as the Energy Exascale Earth System Model, Obs4MIPS, CORDEX and the European Space Agency&amp;amp;#8217;s Climate Change Initiative Open Data Portal.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Over the course of its evolution, ESGF has pioneered technologies and operational practice for distributed systems including solutions for federated search, metadata modelling and capture, identity management and large scale replication of data.&amp;amp;#160; Now in its tenth year of operation, a major review of the system architecture is underway. For this next generation system, we will be drawing from our experience and lessons learnt running an operational e-infrastructure but also considering other similar systems and initiatives.&amp;amp;#160; These include for example, ESA&amp;amp;#8217;s Earth Observation Exploitation Platform Common Architecture, outputs from recent OGC Testbeds and Pangeo (https://pangeo.io/), a community and software stack for the geosciences. &amp;amp;#160; Drawing from our own recent pilot work, we look at the role of cloud computing with its impact on deployment practice and hosting architecture but also new paradigms for massively parallel data storage and access, such as object store. The cloud also offers a potential point of entry for scientists without access to large-scale computing, analysis, and network resources.&amp;amp;#160; As trusted international repositories, the major national computing centres that host and replicate large corpuses of ESGF have increasingly been supporting a broader range of domains and communities in the Earth sciences. We explore the critical role of standards for connecting data and the application of FAIR data principles to ensure free and open access and interoperability with other similar systems in the Earth Sciences.&amp;lt;/p&amp;gt;

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.RFMIP.MIROC.MIROC6.piClim-histaer' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Earth System Model MIROC6, released in 2017, includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 100 km, atmos: 250 km, land: 100 km, ocean: 100 km, seaIce: 100 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ. The project includes simulations from about 90 global climate models and around 40 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

The National Institute of Meteorological Sciences-Korea Meteorological Administration (NIMS-KMA) has participated in the Coupled Model Inter-comparison Project (CMIP) and provided long-term simulations using the coupled climate model. The NIMS-KMA produces new future projections using the ensemble mean of KMA Advanced Community Earth system model (K-ACE) and UK Earth System Model version1 (UKESM1) simulations to provide scientific information of future climate changes. In this study, we analyze four experiments those conducted following the new shared socioeconomic pathway (SSP) based scenarios to examine projected climate change in the twenty-first century. Present day (PD) simulations show high performance skill in both climate mean and variability, which provide a reliability of the climate models and reduces the uncertainty in response to future forcing. In future projections, global temperature increases from 1.92 °C to 5.20 °C relative to the PD level (1995–2014). Global mean precipitation increases from 5.1% to 10.1% and sea ice extent decreases from 19% to 62% in the Arctic and from 18% to 54% in the Antarctic. In addition, climate changes are accelerating toward the late twenty-first century. Our CMIP6 simulations are released to the public through the Earth System Grid Federation (ESGF) international data sharing portal and are used to support the establishment of the national adaptation plan for climate change in South Korea.

CMIP6 Forcing Datasets (input4MIPs): The forcing datasets (and boundary conditions) needed for CMIP6 experiments are being prepared by a number of different experts. Initially many of these datasets may only be available from those experts, but over time as part of the 'input4MIPs' activity most of them will be archived by PCMDI and served by the Earth System Grid Federation (https://esgf-node.llnl.gov/search/input4mips/ ). More information is available in the living document: http://goo.gl/r8up31 - This data version 2016-07-26-sectorDim is deprecated. Please use the current version(s) 2017-05-18,2017-08-30,2017-10-05; see http://goo.gl/r8up31 for more information

This article describes the Earth System Grid Federation (ESGF) mission and an international integration strategy for data, database and computational architecture, and stable infrastructure highlighted by the authors (the ESGF Executive Committee). These highlights are key developments needed over the next five to seven years in response to large-scale national and international climate community projects that depend on ESGF for success. Quality assurance and baseline performance from laptop to high performance computing characterizes available and potential data streams and strategies. These are required for interactive data collections to remedy gaps in handling enormous international federated climate data archives. Appropriate cyber security ensures protection of data according to projects but still allows access and portability to different ESGF and individual groups and users. A timeline and plan for forecasting interoperable tools takes ESGF from a federated database archive to a robust virtual laboratory and concludes the article.

AI foundation models have already demonstrated their usefulness in harnessing their potential in a wide range of science application domains. They derive their power from the large volumes of data, along with the computational methods used to exploit them using unprecedented amounts of compute power. We are inundated with data but have managed to exploit only a small fraction of the available data.&amp;#160; The Earth System Grid Federation (ESGF) is hosting nearly 16 PB of data collection from the Coupled Model Intercomparison Project (CMIP6), expected to grow to 5 - 10 more in the CMIP7 era. The NASA Earth Observation Data and Information System (EOSDIS) archive is expected to exceed 600 PB by 2030.&amp;#160;AI-enabled solutions will require integrating multimodal data while being cognizant of the energy footprint introduced by the data and the computational methods. Currently, the energy consumption of transformer-based foundation models scale with the amount of data and corresponding model sizes. This impediment needs to be mitigated by developing data-efficient methods that lead to energy efficiency as well across all scales. There is little guidance in the research community on developing a computational plan for the optimal use of the resources for developing foundation models using multimodal scientific data. The benchmarks based on LLM scaling are still insufficient for vision transformers (ViTs), commonly adopted for geoscientific applications. We need a suite of community benchmarks based on ViT backbones and other methods at different scales to understand energy efficient methods for different classes of science problems.Relatively few studies have focused on the issue of data efficiency for training science foundation models. We have adopted a smart sampling approach to extract the most informative samples is an effective means of significantly reducing the training data. We trained two ViT models, one with all available MODIS data over the ocean and another using an intelligently-sampled subset. We applied the models to classify clouds over the ocean.&amp;#160; Our preliminary results indicate that reasonably accurate models can be trained with only a fraction of total training data. Improvements in reduction of data translate directly into improvements for energy efficiency.&amp;#160;

Deep Learning (DL) has recently emerged as an innovative tool to downscale climate variables from large-scale atmospheric fields under the perfect prognosis (PP) approach. Different Convolutional Neural Networks (CNN) have been applied under present-day conditions with promising results, but little is known about their suitability for extrapolating future climate change conditions. Here, we analyze this problem from a multi-model perspective, developing and evaluating an ensemble of CNN-based downscaled projections (DeepESD) for temperature and precipitation over the European EUR-44i (0.5º) domain, based on eight GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5). To our knowledge, this is the first time that CNNs have been used to produce multi-model ensembles of downscaled projections, allowing to quantify inter-model uncertainty in climate change signals. The results are compared with those corresponding to an EUR-44 ensemble of regional climate models (RCMs) showing that DeepESD reduces distributional biases in the historical period. Moreover, the resulting climate change signals are broadly comparable to those obtained with the RCMs, with similar spatial structures. As for the uncertainty of the climate change signal (measured on the basis of inter-model spread), DeepESD yields a smaller uncertainty for precipitation, but a similar uncertainty for temperature. To facilitate further studies of this downscaling approach we follow FAIR principles and make publicly available the code (a Jupyter notebook) and the DeepESD dataset. In particular, DeepESD is published at the Earth System Grid Federation (ESGF), as the first continental-wide PP dataset contributing to CORDEX (EUR-44).

&amp;lt;p&amp;gt;The complexity of the climate system calls for a combined approach of different knowledge areas. For that, increasingly larger projects need a coordinate effort that fosters an active collaboration between members. On the other hand, although the continuous improvement of numerical models and larger observational data availability provides researchers with a growing amount of data to analyze, the need for greater resources to host, access, and evaluate them efficiently through High Performance Computing (HPC) infrastructures is growing more than ever. Finally, the thriving emphasis on FAIR data principles [1] and the easy reproducibility of evaluation workflows also requires a framework that facilitates these tasks. Freva (Free Evaluation System Framework [2, 3]) is an efficient solution to handle customizable evaluation systems of large research projects, institutes or universities in the Earth system community [4-6] over the HPC environment and in a centralized manner.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Freva is a scientific software infrastructure for standardized data and analysis tools (plugins) that provides all its available features both in a shell and web environment. Written in python, is equipped with a standardized model database, an application-programming interface (API) and a history of evaluations, among others:&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;An implemented metadata system in SOLR with its own search tool allows scientists and their plugins to retrieve the required information from a centralized database. The databrowser interface satisfies the international standards provided by the Earth System Grid Federation (ESGF, e.g. [7]).&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;An API allows scientific developers to connect their plugins with the evaluation system independently of the programming language. The connected plugins are able to access from and integrate their results back to the database, allowing for a concatenation of plugins as well. This ecosystem increases the number of scientists involved in the studies, boosting the interchange of results and ideas. It also fosters an active collaboration between plugin developers.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;The history and configuration sub-system stores every analysis performed with Freva in a MySQL database. Analysis configurations and results can be searched and shared among the scientists, offering transparency and reproducibility, and saving CPU hours, I/O, disk space and time.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;&amp;lt;p&amp;gt;Freva efficiently frames the interaction between different technologies thus improving the Earth system modeling science.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This framework has undergone major refactoring and restructuring of the core that will also be discussed. Among others:&amp;lt;/p&amp;gt;&amp;lt;ul&amp;gt;&amp;lt;li&amp;gt;Major core Python update (2.7 to 3.9).&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Easier deployment and containerization of the framework via Docker.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;More secure system configuration via Vault integration.&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Direct Freva function calls via python client (e.g. for jupyter notebooks).&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Improvements in the dataset incorporation.&amp;lt;/li&amp;gt; &amp;lt;/ul&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;&amp;amp;#160;&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;

&amp;lt;p&amp;gt;The Free Evaluation System Framework (Freva - freva.met.fu-berlin.de) is a software infrastructure for standardized data and tool solutions in Earth system science. Freva runs on high performance computers to handle customizable evaluation systems of research projects, institutes or universities. It combines different software technologies into one common hybrid infrastructure, including all features present in the shell and web environment. The database interface satisfies the international standards provided by the Earth System Grid Federation (ESGF). Freva indexes different data projects into one common search environment by storing the meta data information of the self-describing model, reanalysis and observational data sets in a database. This implemented meta data system with its advanced but easy-to-handle search tool supports users, developers and their plugins to retrieve the required information. A generic application programming interface (API) allows scientific developers to connect their analysis tools with the evaluation system independently of the programming language used. Users of the evaluation techniques benefit from the common interface of the evaluation system without any need to understand the different scripting languages. Facilitation of the provision and usage of tools and climate data automatically increases the number of scientists working with the data sets and identifying discrepancies. The integrated webshell (shellinabox) adds a degree of freedom in the choice of the working environment and can be used as a gate to the research projects HPC. Plugins are able to integrate their e.g. post-processed results into the database of the user. This allows e.g. post-processing plugins to feed statistical analysis plugins, which fosters an active exchange between plugin developers of a research project. Additionally, the history and configuration sub-systemstores every analysis performed with the evaluation system in a database. Configurations and results of the toolscan be shared among scientists via shell or web system. Therefore, plugged-in tools benefit from transparency and reproducibility. Furthermore, if configurations match while starting an evaluation plugin, the system suggests touse results already produced by other users &amp;amp;#8211; saving CPU/h, I/O, disk space and time. The efficient interaction between different technologies improves the Earth system modeling science framed by Freva.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;New Features and aspects of further development and collaboration are discussed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;

&amp;lt;p&amp;gt;Robust hydrological models are critical for the assessment of climate change impacts on hydrological processes. This study analysis the future evolution of the spatiotemporal dynamics of multiple hydrological processes (i.e. streamflow, soil moisture, evaporation and terrestrial water storage) with the fully distributed mesoscale hydrologic Model (mHM), which is constrained with a novel multivariate calibration approach based on the spatial patterns of satellite remote sensing data (Demb&amp;amp;#233;l&amp;amp;#233; et al., 2020). The experiment is done in the large and transboundary Volta River Basin (VRB) in West Africa, which is a hotspot of climate vulnerability. Climate change and land use changes lead to recurrent floods and drought that impact agriculture and affect the lives of the inhabitants.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Based on data availability on the Earth System Grid Federation (ESGF) platform, nine Global Circulation Models (i.e. CanESM2, CNRM-CM5, CSIRO-Mk3-6-0, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR and NorESM1-M) available from the CORDEX-Africa initiative and dynamically downscaled with the latest version of the Rossby Centre's regional atmospheric model (RCA4) are selected for this study. Daily datasets of meteorological variables (i.e. precipitation and air temperature) for the medium and high emission scenarios (RCP4.5 and RCP8.5) are bias-corrected and used to force the mHM model for the reference period 1991-2020, and the near- and long-term future periods 2021-2050 and 2051-2080.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The results show contrasting trends among the hydrological processes as well as among the GCMs. The findings reveal uncertainties in the spatial patterns of hydrological processes (e.g. soil moisture and evaporation), which ultimately have implications for flood and drought predictions. This study highlights the importance of plausible spatial patterns for the assessment of climate change impacts on hydrological processes, and thereby provide valuable information with the potential to reduce the climate vulnerability of the local population.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Reference&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Demb&amp;amp;#233;l&amp;amp;#233;, M., Hrachowitz, M., Savenije, H., Mari&amp;amp;#233;thoz, G., &amp;amp; Schaefli, B. (2020). Improving the predictive skill of a distributed hydrological model by calibration on spatial patterns with multiple satellite datasets. Water Resources Research.&amp;lt;/p&amp;gt;

The Sixth Annual Face-to-Face (F2F) Conference of the Earth System Grid Federation (ESGF), a global consortium of international government agencies, institutions, and companies dedicated to the creation, management, analysis, and distribution of extreme-scale scientific data, was held December 5–9, 2016, in Washington, D.C.