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Net-zero greenhouse gas (GHG) emissions targets are driving interest in opportunities for biomass-based negative emissions and bioenergy, including from marine sources such as seaweed. Yet the biophysical and economic limits to farming seaweed at scales relevant to the global carbon budget have not been assessed in detail. We use coupled seaweed growth and technoeconomic models to estimate the costs of global seaweed production and related climate benefits, systematically testing the relative importance of model parameters. Under our most optimistic assumptions, sinking farmed seaweed to the deep sea to sequester a gigaton of CO2 per year costs as little as US$480 per tCO2 on average, while using farmed seaweed for products that avoid a gigaton of CO2-equivalent GHG emissions annually could return a profit of $50 per tCO2-eq. However, these costs depend on low farming costs, high seaweed yields, and assumptions that almost all carbon in seaweed is removed from the atmosphere (that is, competition between phytoplankton and seaweed is negligible) and that seaweed products can displace products with substantial embodied non-CO2 GHG emissions. Moreover, the gigaton-scale climate benefits we model would require farming very large areas (>90,000 km2)—a >30-fold increase in the area currently farmed. Our results therefore suggest that seaweed-based climate benefits may be feasible, but targeted research and demonstrations are needed to further reduce economic and biophysical uncertainties.

A proposal to develop new labelled array data structures for the scientific Python ecosystem

Climate data from Earth System Models are increasingly being used to study the impacts of climate change on a broad range of biogeophysical (forest fires, fisheries, etc.) and human systems (reservoir operations, urban heat waves, etc.). Before this data can be used to study many of these systems, post-processing steps commonly referred to as bias correction and statistical downscaling must be performed. “Bias correction” is used to correct persistent biases in climate model output and “statistical downscaling” is used to increase the spatiotemporal resolution of the model output (i.e. 1 deg to 1/16th deg grid boxes). For our purposes, we’ll refer to both parts as “downscaling”.In the past few decades, the applications community has developed a plethora of downscaling methods. Many of these methods are ad-hoc collections of post processing routines while others target very specific applications. The proliferation of downscaling methods has left the climate applications community with an overwhelming body of research to sort through without much in the form of synthesis guilding method selection or applicability.Motivated by the pressing socio-environmental challenges of climate change – and with the learnings from previous downscaling efforts in mind – we have begun working on a community-centered open framework for climate downscaling: scikit-downscale. We believe that the community will benefit from the presence of a well-designed open source downscaling toolbox with standard interfaces alongside a repository of benchmark data to test and evaluate new and existing downscaling methods.In this notebook, we provide an overview of the scikit-downscale project, detailing how it can be used to downscale a range of surface climate variables such as air temperature and precipitation. We also highlight how scikit-downscale framework is being used to compare exisiting methods and how it can be extended to support the development of new downscaling methods.

Methodological choices can have strong effects on projections of climate change impacts on hydrology. In this study, we investigate the ways in which four different steps in the modeling chain influence the spread in projected changes of different aspects of hydrology. To form the basis of these analyses, we constructed an ensemble of 160 simulations from permutations of two Representative Concentration Pathways, 10 global climate models, two downscaling methods, and four hydrologic model implementations. The study is situated in the Pacific Northwest of North America, which has relevance to a diverse, multinational cast of stakeholders. We analyze the effects of each modeling decision on changes in gridded hydrologic variables of snow water equivalent and runoff, as well as streamflow at point locations. Results show that the choice of representative concentration pathway or global climate model is the driving contributor to the spread in annual streamflow volume and timing. On the other hand, hydrologic model implementation explains most of the spread in changes in low flows. Finally, by grouping the results by climate region the results have the potential to be generalized beyond the Pacific Northwest. Future hydrologic impact assessments can use these results to better tailor their modeling efforts.

Carbon offsets are widely used by individuals, corporations, and governments to mitigate their greenhouse gas emissions on the assumption that offsets reflect equivalent climate benefits achieved elsewhere. These climate-equivalence claims depend on offsets providing real and additional climate benefits beyond what would have happened, counterfactually, without the offsets project. Here, we evaluate the design of California's prominent forest carbon offsets program and demonstrate that its climate-equivalence claims fall far short on the basis of directly observable evidence. By design, California's program awards large volumes of offset credits to forest projects with carbon stocks that exceed regional averages. This paradigm allows for adverse selection, which could occur if project developers preferentially select forests that are ecologically distinct from unrepresentative regional averages. By digitizing and analyzing comprehensive offset project records alongside detailed forest inventory data, we provide direct evidence that comparing projects against coarse regional carbon averages has led to systematic over-crediting of 30.0 million tCO2 e (90% CI: 20.5-38.6 million tCO2 e) or 29.4% of the credits we analyzed (90% CI: 20.1%-37.8%). These excess credits are worth an estimated $410 million (90% CI: $280-$528 million) at recent market prices. Rather than improve forest management to store additional carbon, California's forest offsets program creates incentives to generate offset credits that do not reflect real climate benefits.

Forests are currently a substantial carbon sink globally. Many climate change mitigation strategies rely on forest preservation and expansion, but the effectiveness of these approaches hinges on forests sequestering carbon for centuries despite anthropogenic climate change. Yet climate-driven disturbances pose critical risks to the long-term stability of forest carbon. We quantify the key climate drivers that fuel wildfire, drought, and insects, for the United States over 1984-2018 and project future disturbance risks over the 21st century. We find that current risks are widespread and projected to increase across different emission scenarios by a factor of 4-14 for fire and 1.3-1.8 for drought and insects. Our results provide insights for carbon cycle modeling, conservation, and climate policy, underscoring the escalating climate risks facing forests and the need for emissions reductions to mitigate climate change.

Carbon offsets are widely used by individuals, corporations, and governments to mitigate their greenhouse gas emissions on the assumption that offsets reflect equivalent climate benefits achieved elsewhere. These climate-equivalence claims depend on offsets providing “additional” climate benefits beyond what would have happened, counterfactually, without the offsets project. Here, we evaluate the design of California’s prominent forest carbon offsets program and demonstrate that its climate-equivalence claims fall far short on the basis of directly observable evidence. By design, California’s program awards large volumes of offset credits to forest projects with carbon stocks that exceed regional averages. This paradigm allows for adverse selection, which could occur if project developers preferentially select forests that are ecologically distinct from unrepresentative regional averages. By digitizing and analyzing comprehensive offset project records alongside detailed forest inventory data, we provide direct evidence that comparing projects against coarse regional carbon averages has led to systematic over-crediting of 30.0 million tCO2e (90% CI: 20.5 to 38.6 million tCO2e) or 29.4% of the credits we analyzed (90% CI: 20.1 to 37.8%). These excess credits are worth an estimated $410 million (90% CI: $280 to $528 million) at recent market prices. Rather than improve forest management to store additional carbon, California’s offsets program creates incentives to generate offset credits that do not reflect real climate benefits.Significance StatementForest carbon offsets are increasingly prominent in corporate and government “net zero” emission strategies, but face growing criticism about their efficacy. California’s forest offsets program is frequently promoted as a high-quality approach that improves on the failures of earlier efforts. Our analysis demonstrates, however, that substantial ecological and statistical shortcomings in the design of California’s forest offset protocol generate offset credits that do not reflect real climate benefits. Looking globally, our results illustrate how protocol designs with easily exploitable rules can undermine policy objectives and highlight the need for stronger governance in carbon offset markets.

The Arctic has warmed during the recent observational record and is projected to keep warming through the end of the 21st century in nearly every future emissions scenario and global climate model. This will drive continued thawing of permafrost-rich soils, alter the partitioning of rain versus snow events, and greatly affectthe water cycle and land-surface processes across the Arctic. However, previous analyses of these impacts using dynamical models have relied on global climate model output or relatively coarse regional climate model simulations. In the coarse simulations, projections of changes to the water cycle and land-surface processes in areas of complex orography and high land-surface heterogeneity, which are characteristic of many regions in the Arctic, may thus be limited. Here, we discuss recent work examining high-resolution regional climate simulations over Alaska and NW Canada. Completed and upcoming simulations have been and will be run at a 4 km grid spacing, which is sufficient to resolve orography across this region’s mountain ranges. The initial simulation results are very encouraging and show the regional climate model yields a realistic representation of the seasonal and spatial evolution of precipitation, temperature, and snowpack compared to previous studies across Alaska and other Arctic regions. A paired future climate simulation uses the Pseudo-Global Warming (PGW) approach, where the end of century ensemble mean monthly climate perturbations (CMIP5 RCP8.5) are used to incorporate the thermodynamic effects of future warming into the present-day climate as represented by ERA-Interim reanalysis data. Changes in major components of the hydroclimate (e.g. precipitation, temperature, snowfall, snowpack) are projected to sometimes be significant in this future scenario. For example, the seasonal snow cover in some regions is projected to mostly disappear. However, there are also projected increases in snowpack in historically very cold areas (e.g. high elevations) that are able to stay cold enough in the future to support snowfall and snowpack.Finally, we will present a new effort to couple an advanced land-surface model, the Community Terrestrial Systems Model (CTSM), within the Regional Arctic Systems Model (RASM) in an effort to better represent complex land-surface and subsurface (e.g. permafrost, streamflow availability timing and temperatures) processes for climate change impact studies. CTSM is a complex physically based land-surface model that is able to represent multiple snow layers, a complex canopy, and multiple soil layers including organic matter and frozen soils, which enables us to explicitly represent spatial variability in the regional hydroclimate and land states (e.g. permafrost) at relatively high spatial resolutions relative to other simulations (4 km land and atmosphere grids). Successful coupling of CTSM within RASM has been completed and we will discuss some preliminary land-atmosphere coupled test results.

Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24th degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.

Scientific data has traditionally been distributed via downloads from data server to local computer. This way of working suffers from limitations as scientific datasets grow towards the petabyte scale. A “cloud-native data repository,” as defined in this paper, offers several advantages over traditional data repositories—performance, reliability, cost-effectiveness, collaboration, reproducibility, creativity, downstream impacts, and access & inclusion. These objectives motivate a set of best practices for cloud-native data repositories: analysis-ready data, cloud-optimized (ARCO) formats, and loose coupling with data-proximate computing. The Pangeo Project has developed a prototype implementation of these principles by using open-source scientific Python tools. By providing an ARCO data catalog together with on-demand, scalable distributed computing, Pangeo enables users to process big data at rates exceeding 10 GB/s. Several challenges must be resolved in order to realize cloud computing’s full potential for scientific research, such as organizing funding, training users, and enforcing data privacy requirements.

Early work in the field of Machine Learning (ML) for hydrologic prediction is showing significant potential. Indeed, it has provided important and measurable advances toward prediction in ungauged basins (PUB). At the same time, it has motivated a new research targeting important ML topics such as uncertainty attribution and physical constrains. It has also brought into question how to best harness the wide variety of climatic and hydrologic data available today. In this work, we present initial results employing transfer learning to combine information about meteorology, streamflow, surface fluxes (FluxNet), and snow (SNOTEL) into a state of the art ML-based hydrologic model. Specifically, we will present early work demonstrating how relatively simple implementations of transfer learning can be used to enhance predictions of streamflow by transferring learning from flux and snow station observations to the watershed scale. Our work is shown to extend recently published results from Kratzert et al. (2018) using the CAMELS data set (Newman et al. 2014) for streamflow prediction in North America.Kratzert, F., Klotz, D., Brenner, C., Schulz, K., and Herrnegger, M.: Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks, Hydrol. Earth Syst. Sci., 22, 6005-6022, https://doi.org/10.5194/hess-22-6005-2018, 2018a. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D

In this presentation, we will describe the [Pangeo Project](http://pangeo.io), a coordinated community effort with support from NASA, NSF, AWS, Microsoft Azure and Google Cloud, to develop interact...

Water managers are actively incorporating climate change information into their long- and short-term planning processes. This is generally seen as a step in the right direction because it supplements traditional methods, providing new insights that can help in planning for a non-stationary climate. However, the continuous evolution of climate change information can make it challenging to use available information appropriately. Advice on how to use the information is not always straightforward and typically requires extended dialogue between information producers and users, which is not always feasible. To help navigate better the ever-changing climate science landscape, this review is organized as a set of nine guidelines for water managers and planners that highlight better practices for incorporating climate change information into water resource planning and management. Each DOs and DON'Ts recommendation is given with context on why certain strategies are preferable and addresses frequently asked questions by exploring past studies and documents that provide guidance, including real-world examples mainly, though not exclusively, from the United States. This paper is intended to provide a foundation that can expand through continued dialogue within and between the climate science and application communities worldwide, a two-way information sharing that can increase the actionable nature of the information produced and promote greater utility and appropriate use.

The Variable Infiltration Capacity (VIC) model is a macroscale semi-distributed hydrologic model. VIC development began in the early 1990s and the model has since been used extensively for basin- to global-scale applications that include hydrologic dataset construction, trend analysis of hydrologic fluxes and states, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact assessment. Ongoing operational applications of the VIC model include the University of Washington's drought monitoring and forecasting systems and NASA's Land Data Assimilation System. This paper documents the development of VIC version 5 (VIC-5), which includes a major reconfiguration of the legacy VIC source code to support a wider range of modern hydrologic modeling applications. The VIC source code has been moved to a public GitHub repository to encourage participation by the broader user and developer communities. The reconfiguration has separated the core physics of the model from the driver source code, whereby the latter is responsible for memory allocation, preprocessing and post-processing, and input–output (I–O). VIC-5 includes four drivers that use the same core physics modules, but which allow for different methods for accessing this core to enable different model applications. Finally, VIC-5 is distributed with robust test infrastructure, components of which routinely run during development using cloud-hosted continuous integration. The work described here provides an example to the model development community for extending the life of a legacy model that is being used extensively. The development and release of VIC-5 represents a significant step forward for the VIC user community in terms of support for existing and new model applications, reproducibility, and scientific robustness.

The near-surface climate, including the atmosphere, ocean, sea ice, and land state and fluxes, in the initial version of the Regional Arctic System Model (RASM) are presented. The sensitivity of the RASM near-surface climate to changes in atmosphere, ocean, and sea ice parameters and physics is evaluated in four simulations. The near-surface atmospheric circulation is well simulated in all four RASM simulations but biases in surface temperature are caused by biases in downward surface radiative fluxes. Errors in radiative fluxes are due to biases in simulated clouds with different versions of RASM simulating either too much or too little cloud radiative impact over open ocean regions and all versions simulating too little cloud radiative impact over land areas. Cold surface temperature biases in the central Arctic in winter are likely due to too few or too radiatively thin clouds. The precipitation simulated by RASM is sensitive to changes in evaporation that were linked to sea surface temperature biases. Future work will explore changes in model microphysics aimed at minimizing the cloud and radiation biases identified in this work.

MetSim is a freely available, open source Python based model for simulation and disaggregation of meteorological variables with applications in the environmental and Earth sciences. MetSim can be used to generate spatially distributed sub-daily timeseries of incoming shortwave radiation, outgoing longwave radiation, air pressure, specific humidity, relative humidity, vapor pressure, precipitation, and air temperature given daily timeseries of minimum temperature, maximum temperature, and precipitation. Based on previously developed algorithms, we demonstrate that MetSim is able to closely reproduce their results while providing a number of advantages and improvements. We implemented automated testing to decrease errors during development and to improve reproducibility, modularized the algorithms to allow for extensions and modifications, and implemented robust single and multi-node parallelism. We describe the overall architecture, algorithms, and capabilities of MetSim by describing its four major modules. These are split into a model driver, solar geometry module, meteorological simulation module, and temporal disaggregation module. We also describe the available options and parameters that MetSim exposes to its users and analyze MetSim's scalability for large datasets.

The Regional Arctic System Model (RASM) is a fully coupled, regional Earth system model applied over the pan-Arctic domain. This paper discusses the implementation of the Variable Infiltration Capacity land surface model (VIC) in RASM and evaluates the ability of RASM, version 1.0, to capture key features of the land surface climate and hydrologic cycle for the period 1979–2014 in comparison with uncoupled VIC simulations, reanalysis datasets, satellite measurements, and in situ observations. RASM reproduces the dominant features of the land surface climatology in the Arctic, such as the amount and regional distribution of precipitation, the partitioning of precipitation between runoff and evapotranspiration, the effects of snow on the water and energy balance, and the differences in turbulent fluxes between the tundra and taiga biomes. Surface air temperature biases in RASM, compared to reanalysis datasets ERA-Interim and MERRA, are generally less than 2°C; however, in the cold seasons there are local biases that exceed 6°C. Compared to satellite observations, RASM captures the annual cycle of snow-covered area well, although melt progresses about two weeks faster than observations in the late spring at high latitudes. With respect to derived fluxes, such as latent heat or runoff, RASM is shown to have similar performance statistics as ERA-Interim while differing substantially from MERRA, which consistently overestimates the evaporative flux across the Arctic region.

Strong, mesoscale tip jets and barrier winds that occur along the southeastern Greenland coast have the potential to impact deep convection in the Irminger Sea. The self-organizing map (SOM) training algorithm was used to identify 12 wind patterns that represent the range of winter [November–March (NDJFM)] wind regimes identified in the fully coupled Regional Arctic System Model (RASM) during 1990–2010. For all wind patterns, the ocean loses buoyancy, primarily through the turbulent sensible and latent heat fluxes; haline contributions to buoyancy change were found to be insignificant compared to the thermal contributions. Patterns with westerly winds at the Cape Farewell area had the largest buoyancy loss over the Irminger and Labrador Seas due to large turbulent fluxes from strong winds and the advection of anomalously cold, dry air over the warmer ocean. Similar to observations, RASM simulated typical ocean mixed layer depths (MLD) of approximately 400 m throughout the Irminger basin, with individual years experiencing MLDs of 800 m or greater. The ocean mixed layer deepens over most of the Irminger Sea following wind events with northerly flow, and the deepening is greater for patterns of longer duration. Seasonal deepest MLD is strongly and positively correlated to the frequency of westerly tip jets with northerly flow.

Current understanding of the combined effects of sea level rise (SLR), storm surge, and changes in river flooding on near-coastal environments is very limited. This project uses a suite of numerical models to examine the combined effects of projected future climate change on flooding in the Skagit floodplain and estuary. Statistically and dynamically downscaled global climate model scenarios from the ECHAM-5 GCM were used as the climate forcings. Unregulated daily river flows were simulated using the VIC hydrology model, and regulated river flows were simulated using the SkagitSim reservoir operations model. Daily tidal anomalies (TA) were calculated using a regression approach based on ENSO and atmospheric pressure forcing simulated by the WRF regional climate model. A 2-D hydrodynamic model was used to estimate water surface elevations in the Skagit floodplain using resampled hourly hydrographs keyed to regulated daily flood flows produced by the reservoir simulation model, and tide predictions...

The article of record as published may be found at https://doi.org/10.5194/gmd-11-4817-2018 Includes supplementary material The Regional Arctic System Model version 1 (RASM1) has been developed to provide high-resolution simulations of the Arctic atmosphere–ocean–sea ice–land system. Here, we provide a baseline for the capability of RASM to simulate interface processes by comparing retrospective simulations from RASM1 for 1990–2014 with the Community Earth System Model version 1 (CESM1) and the spread across three recent reanalyses. Evaluations of surface and 2 m air temperature, surface radiative and turbulent fluxes, precipitation, and snow depth in the various models and reanalyses are performed using global and regional datasets and a variety of in situ datasets, including flux towers over land, ship cruises over oceans, and a field experiment over sea ice. These evaluations reveal that RASM1 simulates precipitation that is similar to CESM1, reanalyses, and satellite gauge combined precipitation datasets over all river basins within the RASM domain. Snow depth in RASM is closer to upscaled surface observations over a flatter region than in more mountainous terrain in Alaska. The sea ice–atmosphere interface is well simulated in regards to radiation fluxes, which generally fall within observational uncertainty. RASM1 monthly mean surface temperature and radiation biases are shown to be due to biases in the simulated mean diurnal cycle. At some locations, a minimal monthly mean bias is shown to be due to the compensation of roughly equal but opposite biases between daytime and nighttime, whereas this is not the case at locations where the monthly mean bias is higher in magnitude. These biases are derived from errors in the diurnal cycle of the energy balance (radiative and turbulent flux) components. Therefore, the key to advancing the simulation of SAT and the surface energy budget would be to improve the representation of the diurnal cycle of radiative and turbulent fluxes. The development of RASM2 aims to address these biases. Still, an advantage of RASM1 is that it captures the interannual and interdecadal variability in the climate of the Arctic region, which global models like CESM cannot do. This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP). This multi-institutional work was funded by the U.S. Department of Energy (DE-SC0006693, DE-SC0006178, DE-SC0006643, DE-FG02-07ER64460, DE-SC0006856, DE- SC0005783, and DE-SC0005522), by the U.S. National Science Foundation (PLR-1107788, PLR-1417818, and ARC1023369), and by the National Aeronautics and Space Administration (NNX14AM02G). Computing resources were provided via a Chal- lenge Grant from the U.S. Department of Defense (DoD) High Performance Computing Modernization Program (HPCMP).

xarray is an open source project and Python package that provides a toolkit and data structures for N-dimensional labeled arrays. Our approach combines an application programing interface (API) inspired by pandas with the Common Data Model for self-described scientific data. Key features of the xarray package include label-based indexing and arithmetic, interoperability with the core scientific Python packages (e.g., pandas, NumPy, Matplotlib), out-of-core computation on datasets that don’t fit into memory, a wide range of serialization and input/output (I/O) options, and advanced multi-dimensional data manipulation tools such as group-by and resampling. xarray, as a data model and analytics toolkit, has been widely adopted in the geoscience community but is also used more broadly for multi-dimensional data analysis in physics, machine learning and finance.

ReliaSofts XFRACAS is a tool chosen by Sandia National Laboratories (SNL) for the purpose of collecting and organizing photovoltaic (PV) system field data that is to be used in assessing PV system reliability. It is a Web-based, closed-loop, incident (failure) reporting, analysis, and corrective action system software package designed for the acquisition, management and analysis of quality and reliability data from multiple sources. The ability to export PV system times-to-failure and times-to-suspension for ready analysis by ReliaSofts Weibull++ and RGA was a primary consideration in choosing XFRACAS as a reliability data collection tool.