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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The June 2012 North American Derecho: A Testbed for Evaluating Regional and Global Climate Modeling Systems at Cloud‐Resolving Scales

Abstract In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%.

2012 North American derecho↗
DOE OSTI2023

Weather Systems Connecting Modes of Climate Variability to Regional Hydroclimate Extremes

Abstract Weather system clustering provides a high‐level summary of regional meteorological conditions. Most quantitative clustering schemes focus on precipitation alone, which does not sufficiently describe the meteorological conditions driving hydroclimate variability. This study presents the Weather Anomaly Clustering (WAC‐hydro), which extends the existing capability of predicting weather systems to predicting hydroclimate variability. Focusing on both precipitation and temperature predictions, WAC‐hydro identifies 12 clusters of daily weather anomaly modes in the US Pacific Northwest Puget Sound region during 1981–2020. The influence of El Niño‐Southern Oscillation and Madden‐Julian Oscillation on regional precipitation can be well approximated by their modulation on the weather clusters. Within each weather cluster, local factors such as topography only play a secondary role in the hydrologic variability. The weather clusters highlight two types of flood‐inducing regional weather conditions, one causing floods by inducing positive precipitation anomalies and the other causing floods through combined precipitation and temperature‐induced rain‐on‐snow effect.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

“Which Projections Do I Use?” Strategies for Climate Model Ensemble Subset Selection Based on Regional Stakeholder Needs

Climate model (or earth system model) projections are increasingly used for climate adaptation planning and impact assessments. As part of this process, many end‐users evaluate a subset of downscaled climate projections without being aware of the implications of downscaling methodology for statistics or event outcomes. Approaches for determining a subset of global climate models to use often focus on values from the raw models, rather than from their downscaled counterparts, in other words assuming that the statistical distribution of the multi‐model ensemble does not change post downscaling. This study demonstrates that a downscaled ensemble will typically retain the change distribution as a raw ensemble, but individual models can differ dramatically post‐downscaling. We recommend that subset‐selection methods account for this possibility and that decision‐relevant downscaled climate projections provide proper descriptions of fitness‐for‐purpose and essential caveats, so that non‐specialists can interpret the results with an appropriate level of confidence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Spatial controls of methane uptake in upland soils across climatic and geological regions in Greenland

Abstract In the Arctic, the spatiotemporal variation of net methane uptake in upland soils depends on unresolved interactive controls between edaphic and microbial factors not yet included in current models, underpinning the uncertainty of upscaling the Arctic methane budget. Here we show that upland soils in Greenland are consistent methane sinks (−1.83 ± 0.19 nmol methane g −1 dw d −1 ) across a N-S (64–83 °N) pedoclimatic transect. We demonstrate that methane oxidizers abundance, soil pH, and available soil copper are important controls on the spatial variation in methane oxidation. We revised a soil biogeochemical model with a high-resolution land classification and meteorological data for Greenland and tested it against our methane uptake measurements. The model simulated well the magnitudes of observed methane uptake but not the spatial variation across all sites. This work provides novel insights into the controls of methane uptake, which are critical for the accuracy of methane budgets.

Environmental Sciences & Ecology↗
DOE OSTI2025

Influence of Lake Ice Biases in Reanalysis Data on Downscaled Climate Simulations over the Great Lakes Region

This data package contains observation-based and model-simulated datasets (all provided in NetCDF format) for evaluating how wintertime lake-ice representation affects regional weather and climate over the Laurentian Great Lakes (freshwater lake ecosystem) during the high–ice-cover winter of 2009. The observational component includes: (1) Stage IV gridded precipitation at 4 km, hourly resolution for January–February 2009 over the Great Lakes region (radar–gauge multisensor precipitation analyses); (2) Great Lakes Surface Environmental Analysis (GLSEA) satellite-derived lake-ice coverage at 1.3 km, daily resolution for the 2009 winter months, providing ice coverage over Lakes Superior, Michigan, Huron, Erie, and Ontario; and (3) in situ measurements at the Standard Rock site on Lake Superior from the Great Lakes Evaporation Network (GLEN) at hourly resolution, including near-surface atmospheric variables and sensible and latent heat fluxes (air–lake exchange) at a fixed point location. The modeling component provides corresponding fields from two simulations, both archived at 4 km, hourly resolution: a standalone Weather Research Forecasting model (WRF) run driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), and a two-way coupled model using WRF and the Finite Volume Community Ocean Model (WRF-FVCOM, a 3-D hydrodynamic lake model). These outputs include variables relevant to air–lake interaction and lake-effect processes (e.g., near-surface temperature, humidity, wind, precipitation, and surface turbulent fluxes), enabling direct comparison with the observational datasets. Users can analyze and visualize these NetCDF files with common tools such as Python (e.g., xarray, netCDF4, numpy, pandas), NCO/CDO, Panoply, or ncview; NetCDF variables can also be converted to other formats (e.g., CSV, GeoTIFF) using these utilities.

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2023

Insights on Simulating Summer Warming of the Great Lakes: Understanding the Behavior of a Newly Developed Coupled Lake-Atmosphere Modeling System

The Laurentian Great Lakes are the world's largest freshwater system and regulate the climate of the Great Lakes region, which has been increasingly experiencing climatic, hydrological, and ecological changes. An accurate mechanistic representation of the Great Lakes thermal structure in Regional Climate Models (RCMs) is paramount to studying the climate of this region. Currently, RCMs have primarily represented the Great Lakes through coupled one-dimensional (1D) column lake models; this approach works well for small inland lakes but is unable to resolve the realistic hydrodynamics of the Great Lakes and leads to inaccurate representations of lake surface temperature (LST) that influence regional climate and weather patterns. This work overcomes this limitation by developing a fully two-way coupled modeling system using the Weather Research and Forecasting model and a three-dimensional (3D) hydrodynamic model. The coupled model system resolves the interactive physical processes between the atmosphere, lake, and surrounding watersheds; and validated against a range of observational data. The model is then used to investigate the potential impacts of lake-atmosphere coupling on the simulated summer LST of Lake Superior. By evaluating the difference between our two-way coupled modeling system and our observation-driven modeling system, we find that coupled-lake atmosphere dynamics can lead to a higher LST during June-September through higher net surface heat flux entering the lake in June and July and a lower net surface heat flux entering the lake in August and September. The unstratified water in June distributes the entering surface heat flux throughout the water column leading to a minor LST increase, while the stratified waters of July create a conducive thermal structure for the water surface to warm rapidly under the higher incoming surface heat flux. This research provides insight into the coupled modeling system behavior, which is critical for enhancing our predictive understanding of the Great Lakes climate system.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

The surface of the Great Lakes interacts with the atmosphere, influencing the weather and climate over the Great Lakes Region (GLR). However, most climate models were not designed with sufficient emphasis on lake–atmosphere interactions, which could potentially cause model biases over the GLR. To understand how lake surface temperature (LST) affects the regional summer climate over the GLR, we conducted twin experiments using the Weather Research and Forecasting model at a spatial resolution of 4 km using two different LST data sets as the bottom boundary condition over the Great Lakes. In this work, our simulations include 10 ensemble members for the summer of 2018 and a single multiyear run for the summers of 2014–2020. Results show that variations in LST influence atmospheric temperature and moisture at a local scale—while affecting the convective environment and precipitation processes over a much larger spatial scale. In particular, an LST that is only 1°C–3°C warmer (depending on the lake) increases near-surface air temperature by 1.93°C and 0.97°C over Lake Superior and Lake Erie, respectively, and increases evaporation over the lakes by 0.23 and 1.1 mm day –1 . The warmer LST reduces mesoscale convective precipitation upstream of the GLR; however, it increases isolated deep convective precipitation and nonconvective precipitation downstream of GLR due to increased local instability and enhancement of moisture transport. Our analyses confirm the robustness of these impacts, which is at least 2 times larger than the model internal variability and is seen across all simulated summer seasons.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER-VOC: Speciated Volatile Organic Compounds at the Tracking Aerosol Convection Interactions Experiment Field Campaign Report

Formation of secondary organic aerosol (SOA) is responsible for the growth of particles into cloud condensation nuclei (CCN)-relevant sizes and accounts for a substantial fraction of aerosol mass in the troposphere. Despite its wide abundance and importance, capturing SOA in regional and climate models is still challenging due to complex chemical transformations involved in production of SOA. Oxidation products of biogenic hydrocarbons, such as isoprene, monoterpenes, and sesquiterpenes account for the largest fraction of SOA mass over the continents. As such, biogenic SOA is an important source of continental CCN, and has been recently shown to nucleate ice in the deposition mode. To better account for the processes involved in SOA formation in global and regional climate models, we need better measurements of sources and transformations of volatile organic compounds (VOCs), the precursors to SOA formation in the atmosphere. Understanding aerosol-deep convection interactions is an important science driver of the Tracking Aerosol Convection Interactions Experiment (TRACER), requiring a characterization of the critical controls on aerosol properties and processes, and how those properties and processes vary spatially and temporally in the Houston area. In that environment, the atmospheric photo-oxidative chemistry that drives the evolution and transformation of aerosol properties and processes is strongly impacted by the emission of VOCs, which have both anthropogenic (e.g., industrial, urban, transportation, biomass burning) and biogenic (e.g., marine) sources in the region. Understanding (and disentangling) the complex role of VOCs in such a heterogenous region requires high-time-resolution, speciated VOC measurements that are spatially resolved. To meet these measurement requirements, the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s time-of-flight proton transfer reaction-mass spectrometer (ToF-PTR-MS) was deployed during the TRACER intensive operational period (IOP) from June to September, 2022 at the ancillary site (ANC), which was sited to provide a characterization of the rural atmospheric environment in contrast to the main site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Episodes of Early Pleistocene West Antarctic Ice Sheet Retreat Recorded by Iceberg Alley Sediments

Abstract Ice loss in the Southern Hemisphere has been greatest over the past 30 years in West Antarctica. The high sensitivity of this region to climate change has motivated geologists to examine marine sedimentary records for evidence of past episodes of West Antarctic Ice Sheet (WAIS) instability. Sediments accumulating in the Scotia Sea are useful to examine for this purpose because they receive iceberg‐rafted debris (IBRD) sourced from the Pacific‐ and Atlantic‐facing sectors of West Antarctica. Here we report on the sedimentology and provenance of the oldest of three cm‐scale coarse‐grained layers recovered from this sea at International Ocean Discovery Program Site U1538. These layers are preserved in opal‐rich sediments deposited ∼1.2 Ma during a relatively warm regional climate. Our microCT‐based analysis of the layer's in‐situ fabric confirms its ice‐rafted origin. We further infer that it is the product of an intense but short‐lived episode of IBRD deposition. Based on the petrography of its sand fraction and the Phanerozoic 40 Ar/ 39 Ar ages of hornblende and mica it contains, we conclude that the IBRD it contains was likely sourced from the Weddell Sea and/or Amundsen Sea embayment(s) of West Antarctica. We attribute the high concentrations of IBRD in these layers to “dirty” icebergs calved from the WAIS following its retreat inland from its modern grounding line. These layers also sit at the top of a ∼366‐m thick Pliocene and early Pleistocene sequence that is much more dropstone‐rich than its overlying sediments. We speculate this fact may reflect that WAIS mass‐balance was highly dynamic during the ∼41‐kyr (inter)glacial world.

Bailey, Ian↗
DOE OSTI2023

Applying NIR and MIR spectroscopy for C and soil property prediction in northern cold-region ecosystems. Which approach works better?

Here, developing reliable predictions of soil attributes is necessary to understand northern cold-region climate-soil feedback. Calibration models using near-infrared (NIR) and mid-infrared (MIR) spectroscopy were developed to predict eight commonly measured soil properties for 119 soil samples representing a range of vegetation types, parent materials, and soil types spanning >23° of latitude from southeast Alaska to the Canadian high Arctic. In order to obtain a more accurate prediction, this study compared the performance of linear and non-linear calibration techniques, including lasso regression (Lasso), support vector machine (SVM), random forest (RF) and classic partial least squares (PLS) to predict different soil properties of these soils. Comparing the four models, we noticed that their performance was quite similar for MIR overall, while NIR achieved better results with a PLS model for our dataset. PLS coupled with MIR showed a better performance for soil parameters, such as total organic carbon (TOC), total nitrogen (TN), cation exchange capacity (CEC) and clay (R-squared of 0.9, 0.81, 0.80, and 0.84) when compared with NIR (R-squared of 0.85, 0.72, 0.81 and 0.68). However, using either MIR or NIR spectroscopy, PLS predictions for bulk density (BD) and sand content were not accurate. The variable importance analysis based on the PLS model successfully estimated the relative contribution of wavelengths influencing soil property predictions most. Overall, TOC, TN, CEC and clay mineral predictions are closely related to the occurrence of specific spectral bands in the MIR region. For example, wavelengths at 2978 and 1761 cm -1 for TOC and TN, as well as at 3064 cm -1 for CEC, were selected as the most influential predictor variables. We demonstrated that MIR spectroscopy is a powerful tool for more extensive monitoring in soils of the northern cold climate region; however, NIR could be utilized for rapid estimates when the highest accuracy is not essential.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Bias Correction and Statistical Downscaling of Solar Radiation Using NA-CORDEX and the NSRDB

The current state-of-art for estimating long-term PV production uses long-term estimates of solar radiation variables, such as global horizontal irradiance (GHI), from previous years. This data is used in models such as the System Advisor Model (SAM) or PYSyst to predict annual production for a PV plant. This information is then used to estimate the production over the next 20 years (a typical plant lifetime) under the assumption that the variability over the current period is representative of the future. As the PV industry moves to extend plant lifetimes to 50 years the current assumptions of representativeness of weather may not be appropriate. This is especially true as our climate changes rapidly. To assess long-term PV production, future projections for solar radiation based on projected carbon emissions are readily available in regional and global climate models. However, climate model projections contain inherent biases that may need to be corrected for accurate analysis of future projections of climate variables. Several studies have analyzed projections of solar radiation for future years, however the accuracy of the model output compared to current and historic data has not been widely studied. Chen (2021) showed that available climate models do not accurately represent solar radiation in some cases, over-projecting GHI at the surface while under-projecting its obstructions, such as clouds and aerosols. This works aims to (1) increase understanding of the accuracy of solar radiation currently available in global and regional climate models and (2) implement bias correction through linear models based on reanalysis data compared to observed solar radiation. The latter aim will be conducted using available observed solar radiation data and modeled data from several regional climate models (RCMs). The bias correction method will be applied to projections of solar radiation resulting in a more accurate representation of the future of solar production.

climate data↗
DOE OSTI2022

Intercomparison of Dynamically and Statistically Downscaled Climate Change Projections over the Midwest and Great Lakes Region

Downscaling of global climate model (GCMs) simulations is a key element of regional-to-local-scale climate change projections that can inform impact assessments, long-term planning, and resource management in different sectors. Here, we conduct an intercomparison between statistically and dynamically downscaled GCMs simulations using the hybrid delta (HD) and the Weather Research and Forecast (WRF) Model, respectively, over the Midwest and Great Lakes region to 1) validate their performance in reproducing extreme daily precipitation (P) and daily maximum temperature (T max ) for summer and winter and 2) evaluate projections of extremes in the future. Our results show the HD statistical downscaling approach, which includes large-scale bias correction of GCM inputs, can reproduce observed extreme P and T max reasonably well for both summer and winter. However, raw historical WRF simulations show significant bias in both extreme P and T max for both seasons. Interestingly, the convection-permitting WRF simulation at 4-km grid spacing does not produce better results for seasonal extremes than the WRF simulation at 12 km using a parameterized convection scheme. Despite a broad similarity for winter extreme P projections, the projected changes in the future summer storms are quite different between downscaling methods; WRF simulations show substantial increases in summer extreme precipitation, while the changes projected by the HD approach exhibit moderate decreases overall. The WRF simulations at 4 km also show a pronounced decoupling effect between seasonal totals and extreme daily P for summer, which suggests that there could be more intense summer extremes at two different time scales, with more severe individual convective storms combined with longer summer droughts at the end of the twenty-first century.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High-resolution Hindcasts for U.S. Wave Energy Resource Characterization

The marine and hydrokinetic (MHK) industry is at an early stage of development and has the potential to play a significant role in diversifying the U.S. energy portfolio and reducing the U.S. carbon footprint. Wave energy is the largest among all the U.S. MHK energy resources, which include wave energy, ocean current, tidal-instream, ocean thermal energy conversion, and river-instream. Wave resource characterization is an essential step for regional wave energy assessments, Wave Energy Converter (WEC) project development, site selection and WEC design. The present paper provides an overview of a joint modelling effort by the Pacific Northwest National Laboratory and Sandia National Laboratories on high-resolution wave hindcasts to support the U.S. Department of Energy’s Water Power Technologies Office’s program of wave resource characterization, assessment and classifications in all US coastal regions. Topics covered include the modelling approach, model input requirements, model validation strategies, high performance computing resource requirements, model outputs and data management strategies. Examples of model setup and validation for different regions are provided along with application to development of classification systems, and analysis of regional wave climates. Lessons learned and technical challenges of the long-term, high-resolution regional wave hindcast are discussed.

Yang, Zhaoqing↗
DOE OSTI2021

FEED Study of Carbon Capture Inc. DAC and CarbonCure Utilization Using United States Steel's Gary Works Waste Plant

Direct Air Capture (DAC) has been proposed as a means of reducing atmospheric concentrations of CO2. While DAC has been evaluated through lab-scale, bench-scale, and small-scale pilot units, large-scale deployment has not been achieved. Feasibility studies are one tool to understand the potential design, operation, performance, and impact of commercial-scale DAC. This paper presents the results of a feasibility study for a passive DAC system deployed at >100,000 tpy scale in three different regions across the U.S. and awarded to Carbon Collect by the U.S. Department of Energy National Energy Technology Laboratory (DOE NETL). The MechanicalTree™technology has been designed and engineered by Carbon Collect based on the concept initially developed at Arizona State University Center for Negative Carbon Emissions. It uses a tower of stacked, sorbentcontaining disks supported by a lifting mechanism, exposed to the air to capture CO2 during the adsorption phase. The disks are then lowered into a regeneration chamber for vacuum and steam regeneration to produce CO2 product during the desorption step. The modular tree design allows large installations with repeatable, mass-manufactured units connected to common utilities such as steam supply, vacuum, and CO2 processing for compression and geologic storage. The passive DAC system eliminates the equipment and energy of forced air fans by using natural air circulation to contact the sorbent with the CO2 in the air. Because of this, the performance is dependent on the wind speed in addition to the temperature and relative humidity. Performance and flow rate fluctuations were incorporated into equipment and facility design with considerations for turndown to 10% of maximum rated flow to allow operation in all seasons. The feasibility study was undertaken to evaluate the technology in different regions and climates. Three locations were selected for this study representing different climates: Alabama (hot and humid), California (hot and arid), and Wyoming (continental). For each region, the adsorption/desorption cycle was optimized including tuning the heat integration and cycle timings. The performance of individual trees was then scaled to the full facility with the same design of more than 20,000 trees common between all regions. The installations had expected average CO2 capture rates of between 330,000 and 485,000 tonnes of CO2 per year depending on the climate. The initial engineering design for the facility at each location was performed with cost and performance estimates for the trees, carbon purification and compression, and the balance of plant. To supply thermal and electrical energy for the facility, carbon-free or low-carbon power must be considered. Options for low-carbon, continuous thermal and electrical energy were considered. The best-performing option from those considered was identified to be an electrically-heated molten salt energy storage system, powered by an on-site photovoltaic field. During molten salt thermal discharge, steam is produced from heat exchange with the molten salt and used to generate power in a steam turbine as well as steam for regenerating the DAC carbon trees. The thermal and electrical supply and analysis is presented in the context of low-carbon power for carbon removal. Modelling and economics of transportation and geologic storage of the CO2 is considered and presented for each location. The results of the feasibility study are incorporated into the presented techno-economic and life-cycle assessments. This work is intended to provide an understanding of the performance, cost, and impact of capturing CO2 at the commercial scale and the impact of climate and regional siting on the considered passive DAC system.

42 ENGINEERING↗
DOE OSTI2024

Leveraging regional mesh refinement to simulate future climate projections for California using the Simplified Convection-Permitting E3SM Atmosphere Model Version 0

Abstract. The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations.

58 GEOSCIENCES↗
DOE OSTI2020

CCS Business Development Case Study for CarbonSAFE Illinois - Macon County (Task 4.1)

This report presents components of a commercialize strategy to develop CCS operations in the region of the CarbonSAFE Macon County project. The report describes regional business considerations for specific business plans necessary for successful implementation at commercial scale. This report also provides a brief summary of the regional business climate for CCS in the study region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Performance Evaluation of Weather@home2 Simulations over West African Region

Weather and climate forecasting, using climate models, have become essential tools and life-savers in the West African region; in spite of the fact that climate models do not fully comply with attributes of forecast qualities—RASAP: reliability, association, skill, accuracy, and precision. The objective of this paper is to quantitatively evaluate, in comparison to CRU and ERA5 datasets, the RASAP compliance-level of the weather@home2 modeling system (w@h2). Findings from some statistical evaluations show that, to a moderately significant extent, w@h2 model provides useful information during the monsoon seasons; skills to capture the Little Dry Season over the Guinea zone; predictive skills for the onset season; ability to reproduce all the annual characteristics of the surface maximum air temperature over the region; as well as skill to detect heat waves that usually ravage West Africa during the boreal spring. The model displays traces of attributes that are needed for seasonal climate predictions and applications. Deficiencies in the quantitative reproducibility point to the facts that the model does provide a reliability akin to that of regional climate models. This paper further furnishes a prospective user with information on whether the model might be “useful or not” for a particular application.

West Africa↗