Effects of forcing differences and initial conditions on inter-model agreement in the VolMIP volc-pinatubo-full experiment

International audienceThis paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991-1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed pre-industrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1).The results show overall good agreement between the different models on the global and hemispheric scale concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP’s experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes especially in the tropics, that preliminary analyses link with minor differences in forcing implementation, model physics, notably aerosol-radiation interactions, the simulation and sampling of El Niño-Southern Oscillation (ENSO) and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modeling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocolwith greater focus on the evolving state of ENSO in the pre-eruption period

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations

Modulation of Near-Inertial Motions On the Mississippi-Alabama Shelf

In this study, we diagnose the time variability and vertical structure of the high- and low-frequency motions on the Mississippi-Alabama Shelf as observed with a bottom-mounted ADCP (Acoustic Doppler Current Profiler) and CTD (Conductivity-Temperature-Depth). The mooring was deployed about 20 km offshore of Mobile Bay for a period from May 17 to August 23, 2018. At this latitude, the diurnal land and sea breeze has the same frequency as the local inertial frequency. Similar to the wind, the observed high-frequency baroclinic velocities (\u3e 30 cm/s) have a broadband diurnal peak and more energy in the clockwise motions. About 60% of the variance in these motions is due to mode 1, which resembles a two-layer structure with surface and bottom velocities that are 180∘ out of phase. These are all characteristics of wind-driven motions that interact with the coastal wall. The month of June features the best conditions for energetic near-inertial motions: upwelling, consistent sea breeze, and a more continuous instead of a two-layer stratification. This causes near-inertial energy to be also projected on a baroclinic mode 2, featuring a subsurface maximum. This maximum may be attributed to the downward propagation of near-inertial internal wave energy. The observed alongshore low-frequency flows and the up- and downwelling are mostly driven by low-frequency winds. About 83% of the variance in the alongshore low-frequency flows is due to mode 1, which eigenfunction resembles a vertically sheared flow. We find that the amplitude of the near-inertial motions is modulated by the up- and downwelling. During downwelling, the near-inertial baroclinic kinetic energy is greatly reduced because of a reduction in stratification and weaker diurnal winds

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research. Hence, the use of efficient methods to share, reproduce, and collaborate on code as well as document research is fundamental. GitHub is an online, cloud-based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols. Despite these benefits, the use of GitHub in ecology and evolution is not widespread. To help researchers in ecology and evolution adopt useful features from GitHub to improve their research workflows, we review 12 practical ways to use the platform. We outline features ranging from low to high technical difficulty, including storing code, managing projects, coding collaboratively, conducting peer review, writing a manuscript, and using automated and continuous integration to streamline analyses. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research

Not just for programmers: How GitHub can accelerate collaborative and reproducible research in ecology and evolution

Abstract Researchers in ecology and evolutionary biology are increasingly dependent on computational code to conduct research. Hence, the use of efficient methods to share, reproduce, and collaborate on code as well as document research is fundamental. GitHub is an online, cloud‐based service that can help researchers track, organize, discuss, share, and collaborate on software and other materials related to research production, including data, code for analyses, and protocols. Despite these benefits, the use of GitHub in ecology and evolution is not widespread. To help researchers in ecology and evolution adopt useful features from GitHub to improve their research workflows, we review 12 practical ways to use the platform. We outline features ranging from low to high technical difficulty, including storing code, managing projects, coding collaboratively, conducting peer review, writing a manuscript, and using automated and continuous integration to streamline analyses. Given that members of a research team may have different technical skills and responsibilities, we describe how the optimal use of GitHub features may vary among members of a research collaboration. As more ecologists and evolutionary biologists establish their workflows using GitHub, the field can continue to push the boundaries of collaborative, transparent, and open research