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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The E3SM Diagnostics Package (E3SM Diags v2.7): a Python-based diagnostics package for Earth system model evaluation

Abstract. The E3SM Diagnostics Package (E3SM Diags) is a modern, Python-based Earth system model (ESM) evaluation tool (with Python module name e3sm_diags), developed to support the Department of Energy (DOE) Energy Exascale Earth System Model (E3SM). E3SM Diags provides a wide suite of tools for evaluating native E3SM output, as well as ESM data on regular latitude–longitude grids, including output from Coupled Model Intercomparison Project (CMIP) class models. E3SM Diags is modeled after the National Center for Atmospheric Research (NCAR) Atmosphere Model Working Group (AMWG, 2022) diagnostics package. In its version 1 release, E3SM Diags included a set of core essential diagnostics to evaluate the mean physical climate from model simulations. As of version 2.7, more process-oriented and phenomenon-based evaluation diagnostics have been implemented, such as analysis of the quasi-biennial oscillation (QBO), the El Niño–Southern Oscillation (ENSO), streamflow, the diurnal cycle of precipitation, tropical cyclones, ozone and aerosol properties. An in situ dataset from DOE's Atmospheric Radiation Measurement (ARM) program has been integrated into the package for evaluating the representation of simulated cloud and precipitation processes. This tool is designed with enough flexibility to allow for the addition of new observational datasets and new diagnostic algorithms. Additional features include customizable figures; streamlined installation, configuration and execution; and multiprocessing for fast computation. The package uses an up-to-date observational data repository maintained by its developers, where recent datasets are added to the repository as they become available. Finally, several applications for the E3SM Diags module were introduced to fit a diverse set of use cases from the scientific community.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Intercomparison of Models Representing Direct Shortwave Radiative Forcing by Sulfate Aerosols

The importance of aerosols as agents of climate change has recently been highlighted. However, the magnitude of aerosol forcing by scattering of shortwave radiation (direct forcing) is still very uncertain even for the relatively well characterized sulfate aerosol. A potential source of uncertainty is in the model representation of aerosol optical properties and aerosol influences on radiative transfer in the atmosphere. Although radiative transfer methods and codes have been compared in the past, these comparisons have not focused on aerosol forcing (change in net radiative flux at the top of the atmosphere). Here we report results of a project involving 12 groups using 15 models to examine radiative forcing by sulfate aerosol for a wide range of values of particle radius, aerosol optical depth, surface albedo, and solar zenith angle. Among the models that were employed were high and low spectral resolution models incorporating a variety of radiative transfer approximations as well as a line-by-line model. The normalized forcings (forcing per sulfate column burden) obtained with the several radiative transfer models were examined, and the discrepancies were characterized. All models simulate forcings of comparable amplitude and exhibit a similar dependence on input parameters. As expected for a non-light-absorbing aerosol, forcings were negative (cooling influence) except at high surface albedo combined with small solar zenith angle. The relative standard deviation of the zenith-angle-averaged normalized broadband forcing for 15 models-was 8% for particle radius near the maximum in this forcing (approx. 0.2 microns) and at low surface albedo. Somewhat greater model-to-model discrepancies were exhibited at specific solar zenith angles. Still greater discrepancies were exhibited at small particle radii and much greater discrepancies were exhibited at high surface albedos, at which the forcing changes sign; in these situations, however, the normalized forcing is quite small quite small. Discrepancies among the models arise from inaccuracies in Mie calculations, differing treatment of the angular scattering phase function, differing wavelength and angular resolution, and differing treatment of multiple scattering. These results imply the need for standardized radiative transfer methods tailored to the direct aerosol forcing problem. However, the relatively small spread in these results suggests that the uncertainty in forcing arising from the treatment of radiative forcing of a well-characterized aerosol at well-specified surface albedo is smaller than some of the other sources of uncertainty in estimates of direct forcing by anthropogenic sulfate aerosols and anthropogenic aerosols generally.

Boucher, O.↗
NASA NTRS2002

Intercomparison of Model Simulations of the Impact of 1997/98 El Nino on South American Summer Monsoon

The simulations of climatology and response of the South American summer monsoon (SASM) to the 1997/98 El Nino are investigated using six atmospheric general circulation models. Results show all models simulate the large-scale features of the SASM reasonably well. However, both stationary and seasonal components of the surface pressure are overestimated, resulting in an excessively strong SASM in the model climatology. The low-level northwesterly jet over eastern foothills of the Andes is not well resolved because of the coarse resolution of the models. Large rainfall simulation biases are found in association with the Andes and the Atlantic ITCZ, indicating model problems in handling steep mountains and parameterization of convective processes. The simulation of the 1997/98 El Nino impact on SASM is examined based on an ensemble of ten two-year (September 1996 - August 1998) integration. Results show that most models can simulate the large-scale tropospheric warming response over the tropical central Pacific, including the dynamic response of Rossby wave propagation of the Pacific-South America (PSA) pattern that influences remote areas. Deficiencies are found in simulating the regional impacts over South America. Model simulation fails to capture the southeastward expansion of anomalously warm tropospheric air. As a result, the upper tropospheric anomalous high over the subtropical Andes is less pronounced, and the enhancement of subtropical westerly jet is displaced 5deg-10deg equatorward compared to the observed. Over the Amazon basin, the shift of Walker cell induced by El Nino is not well represented, showing anomalous easterlies in both upper and lower troposphere.

Zhou, Jiayu↗
NASA NTRS2004

EOS Aqua AMSR-E Arctic Sea Ice Validation Program: Intercomparison Between Modeled and Measured Sea Ice Brightness Temperatures

During March 2003, an extensive field campaign was conducted near Barrow, Alaska to validate AQUA Advanced Microwave Scanning Radiometer (AMSR) sea ice products. Field, airborne and satellite data were collected over three different types of sea ice: 1) first year ice with little deformation, 2) first year ice with various amounts of deformation and 3) mixed first year ice and multi-year ice with various degrees of deformation. The validation plan relies primarily on comparisons between satellite, aircraft flights and ground-based measurements. Although these efforts are important, key aspects such as the effects of atmospheric conditions, snow properties, surface roughness, melt processes, etc on the sea ice algorithms are not sufficiently well understood or documented. To improve our understanding of these effects, we combined the detailed, in-situ data collection from the 2003 field campaign with radiance modeling using a radiative transfer model to simulate the top of the atmosphere AMSR brightness temperatures. This study reports on the results of the simulations for a variety of snow and ice types and compares the results with the National Oceanographic and Atmospheric Administration Environmental Technology Laboratory Polarimetric Scanning Radiometer (NOAA) (ETL) (PSR) microwave radiometer that was flown on the NASA P-3.

Stroeve, J.↗
NASA NTRS2015

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

terrain↗
NASA NTRS2015

The ENSO Effects on Tropical Clouds and Top-of-Atmosphere Cloud Radiative Effects in CMIP5 Models

The El Nino-Southern Oscillation (ENSO) effects on tropical clouds and top-of-atmosphere (TOA) cloud radiative effects (CREs) in Coupled Model Intercomparison Project Phase5 (CMIP5) models are evaluated using satellite-based observations and International Satellite Cloud Climatology Project satellite simulator output. Climatologically, most CMIP5 models produce considerably less total cloud amount with higher cloud top and notably larger reflectivity than observations in tropical Indo-Pacific (60 degrees East - 200 degrees East; 10 degrees South - 10 degrees North). During ENSO, most CMIP5 models considerably underestimate TOA CRE and cloud changes over western tropical Pacific. Over central tropical Pacific, while the multi-model mean resembles observations in TOA CRE and cloud amount anomalies, it notably overestimates cloud top pressure (CTP) decreases; there are also substantial inter-model variations. The relative effects of changes in cloud properties, temperature and humidity on TOA CRE anomalies during ENSO in the CMIP5 models are assessed using cloud radiative kernels. The CMIP5 models agree with observations in that their TOA shortwave CRE anomalies are primarily contributed by total cloud amount changes, and their TOA longwave CRE anomalies are mostly contributed by changes in both total cloud amount and CTP. The model biases in TOA CRE anomalies particularly the strong underestimations over western tropical Pacific are, however, mainly explained by model biases in CTP and cloud optical thickness (tau) changes. Despite the distinct model cloud biases particularly in tau regime, the TOA CRE anomalies from cloud amount changes are comparable between the CMIP5 models and observations, because of the strong compensations between model underestimation of TOA CRE anomalies from thin clouds and overestimation from medium and thick clouds.

Su, Wenying↗
NASA NTRS2017

Assessing Inter-Sectoral Climate Change Risks: The Role of ISIMIP

The aims of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) are to provide a framework for the intercomparison of global and regional-scale risk models within and across multiple sectors and to enable coordinated multi-sectoral assessments of different risks and their aggregated effects. The overarching goal is to use the knowledge gained to support adaptation and mitigation decisions that require regional or global perspectives within the context of facilitating transformations to enable sustainable development, despite inevitable climate shifts and disruptions. ISIMIP uses community-agreed sets of scenarios with standardized climate variables and socioeconomic projections as inputs for projecting future risks and associated uncertainties, within and across sectors. The results are consistent multi-model assessments of sectoral risks and opportunities that enable studies that integrate across sectors, providing support for implementation of the Paris Agreement under the United Nations Framework Convention on Climate Change.

Regional-scale risk models↗
NASA NTRS2016

Evaluation of Modeled Vertical Distribution of Atmospheric SO2 and Sulfate in UTLS

We evaluated the vertical profiles of both SO2 and sulfate in the AEROCOM (Aerosol Model Intercomparison) Phase II participating models. SO2 and sulfate show significant concentration gradient in both horizontal and vertical directions. Both online and offline aerosol transport models show large difference in the vertical distribution of sulfur species from surface all the way up to lower stratosphere. Comparison with available aircraft measurements suggests models agree with observations well when SO2 concentration is high. For the volcanic plumes, the injection height and magnitude determines initial SO2 plume distribution and following transport pattern. At high altitude, where the background concentration of SO2 is often below the detection limit of the current aircraft instruments and satellite retrievals, modeled SO2 and sulfate concentration, lifetime, and budget, as well as their uncertainties can be difficult to be accurately quantified.

volcanic plumes↗
NASA NTRS2020

Bias in CMIP6 Models as Compared to Observed Regional Dimming and Brightening

Anthropogenic aerosol emissions have increased considerably over the last century, but climate effects and quantification of the emissions are highly uncertain as one goes back in time. This uncertainty is partly due to a lack of observations in the pre-satellite era, making the observations we do have before 1990 additionally valuable. Aerosols suspended in the atmosphere scatter and absorb incoming solar radiation and thereby alter the Earth's surface energy balance. Previous studies show that Earth system models (ESMs) do not adequately represent surface energy fluxes over the historical era. We investigated global and regional aerosol effects over the time period 1961–2014 by looking at surface downwelling shortwave radiation (SDSR). We used observations from ground stations as well as multiple experiments from eight ESMs participating in the Coupled Model Intercomparison Project Version 6 (CMIP6). Our results show that this subset of models reproduces the observed transient SDSR well in Europe but poorly in China. We suggest that this may be attributed to missing emissions of sulfur dioxide in China, sulfur dioxide being a precursor to sulfate, which is a highly reflective aerosol and responsible for more reflective clouds. The emissions of sulfur dioxide used in the models do not show a temporal pattern that could explain observed SDSR evolution over China. The results from various aerosol emission perturbation experiments from DAMIP, RFMIP and AerChemMIP show that only simulations containing anthropogenic aerosol emissions show dimming, even if the dimming is underestimated. Simulated clear-sky and all-sky SDSR do not differ greatly, suggesting that cloud cover changes are not a dominant cause of the biased SDSR evolution in the simulations. Therefore we suggest that the discrepancy between modeled and observed SDSR evolution is partly caused by erroneous aerosol and aerosol precursor emission inventories. This is an important finding as it may help interpret whether ESMs reproduce the historical climate evolution for the right or wrong reason.

surface downwelling shortwave radiation (SDSR)↗
NASA NTRS1994

A three-dimensional transport model for the middle atmosphere

In this paper we describe fundamental properties of an 'off-line' three-dimensional transport model, that is, a model which uses prescribed rather than predicted winds. The model is currently used primarily for studying problems of the middle atmosphere because we have not (yet) incorporated a formulation for the convective transport of trace species, a prerequisite for many tropospheric problems. The off-line model is simpler and less expensive than a model which predicts the wind and mass evolution (an 'on-line' model), but it is more complex than the two-dimensional (2-D) zonally averaged transport models often used in the study of chemistry and transport in the middle atmosphere. It thus serves as a model of intermediate complexity and can fill a useful niche for the study of transport and chemistry. We compare simulations of four tracers, released in the lower stratosphere, in both the on- and off-line models to document the difference resulting from differences in modeling the same problem with this intermediate model. These differences identify the price to be paid in going to a cheaper and simpler calculation. The off-line model transports a tracer in three dimensions. For this reason, it requires fewer approximations than 2-D transport model, which must parameterize the effects of mixing by transient and zonally asymmetric wind features. We compare simulations of the off-line model with simulations of a 2-D model for two problems. First, we compare 2-D and three-dimensional (3-D) models by simulating the emission of an NO(x)-like tracer by a fleet of high-speed aircraft. The off-line model is then used to simulate the transport of C-14 and to contrast its simulation properties to that of the host of 2-D models which participated in an identical simulation in a recent NASA model intercomparison. The off-line model is shown to be somewhat sensitive to the sampling strategy for off-line winds. Simulations with daily averaged winds are in very good qualitative agreement but are less diffusive than when driven with instantaneous winds sampled at half-hour intervals. Simulations with the off-line and 2-D models are quite similar in the middle and upper stratosphere but behave quite differently in the lower stratosphere, where the 3-D model has a substantially more vigorous circulation. The off-line model is quite realistic in its simulation of C-14. While there are still systematic differences between the 3-D calculation and the observations, the differences seem to be substantially reduced when compared with the body of 2-D simulations documented in the above mentioned NASA intercomparison, particularly at 31 deg N.

Rasch, Philip J.↗
NASA NTRSDate not supplied

Regional and Model-Specific Response Types in A Global Gridded Crop Model Ensemble

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

crop models↗
DOE OSTI2024

Concurrent Inter-Model Spread of Boreal Winter Westerly Jet Meridional Positions Between the Northern and Southern Hemispheres in CMIP6 Models

Here, this study investigates the inter-model spread of climatological extratropical westerly jets in boreal winter, using the historical simulation of 52 Coupled Model Intercomparison Project phase 6 (CMIP6) models from 1851 to 2014. The results show that there is a substantial spread in the latitude of the upper-tropospheric westerly jet across models, characterised by large inter-model standard deviations to both the poleward and equatorward sides of the jet axis, although the multi-model ensemble mean (MME) performs well in simulating meridional position of westerly jets. Furthermore, we detect the consistency of inter-model jet position spread between the Northern and Southern Hemispheres, based on the inter-model empirical orthogonal function (EOF) decomposition and correlation of regional-averaged zonal winds. Specifically, the models that simulate the westerly jets poleward/equatorward relative to the MME position in one hemisphere also tend to simulate the jets poleward/equatorward in the other hemisphere. Accordingly, we define a global jet spread index to depict the concurrence of jet shift in the two hemispheres. The results of inter-model regression analyses based on this index indicate that the models positioning the jets poleward than the MME tend to simulate a wider Hadley Cell, a poleward-shifted Ferrel Cell in the Southern Hemisphere, enhanced precipitation in the subtropics and suppressed precipitation in the tropics, and warmer sea surface temperatures in the subtropics and mid-latitudes. The present results suggest that improving the simulation of jet positions in climate models requires a comprehensive consideration of thermal states in the tropics and subtropics/mid latitudes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of precipitation indices in suites of dynamically and statistically downscaled regional climate models over Florida

Abstract The present work evaluates historical precipitation and its indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) in suites of dynamically and statistically downscaled regional climate models (RCMs) against NOAA’s Global Historical Climatology Network Daily (GHCN-Daily) dataset over Florida. The models examined here are: (1) nested RCMs involved in the North American CORDEX (NA-CORDEX) program, (2) variable resolution Community Earth System Models (VR-CESM), (3) Coupled Model Intercomparison Project phase 5 (CMIP5) models statistically downscaled using localized constructed analogs (LOCA) technique. To quantify observational uncertainty, three in situ-based (PRISM, Livneh, CPC) and three reanalysis (ERA5, MERRA2, NARR) datasets are also evaluated against the station data. The reanalyses and dynamically downscaled RCMs generally underestimate the magnitude of the monthly precipitation and the frequency of the extreme rainfall in summer. The models forced with CanESM2 miss the phase of the seasonality of extreme precipitation. All models and reanalyses severely underestimate both the mean and interannual variability of mean wet-day precipitation (SDII), consecutive dry days (CDD), and overestimate consecutive wet days (CWD). Metric analysis suggests large uncertainty across NA-CORDEX models. Both the LOCA and VR-CESM models perform better than the majority of models. Overall, RegCM4 and WRF models perform poorer than the median model performance. The performance uncertainty across models is comparable to that in the reanalyses. Specifically, NARR performs poorer than the median model performance in simulating the mean indices and MERRA2 performs worse than the majority of models in capturing the interannual variability of the indices.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate models project increasing precipitation in the US Southwest in 2024–2100

We analyzed the measured precipitation in the southwestern US and found that, from 1900 to 2024, precipitation decreased at an average rate of − 1.2 cm per year per 100 years. The most significant precipitation decreases occurred in the three-month period from February to April. At the same time, precipitation during the southwestern monsoon season (July, August, and September) remained relatively stable. The ensemble mean of all CMIP6 (Coupled Model Intercomparison Project phase 6) climate models, along with the regression model incorporating anthropogenic aerosol (AER) and Pacific Decadal Oscillation (PDO) as predictors, projects precipitation to increase from 2024 to 2100. This projected precipitation increase relies on the anticipated decrease in emissions of anthropogenic aerosols, which is associated with the transition from fossil fuel burning to renewable and nuclear energy sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evaluation of CMIP6 models in simulating the statistics of extreme precipitation over Eastern Africa

We report the Eastern Africa region experiences frequent extreme precipitation events that can cause destruction of property and environment, and loss of lives. Thus, there is a need to understand how these events may change in the future and how well the global climate models that are used to make projections can simulate precipitation extremes in this region before they can be used in downscaling or flood and drought impact assessment studies. In this work, we evaluated the ability of sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models to simulate present-day precipitation extremes over the Eastern Africa region during the two rainy seasons (March–May and September–November). We used nine extreme precipitation indices (including seven (one) indices of wet (dry) extremes) defined by the Expert Team on Climate Change Detection and Indices. The CMIP6 models were evaluated against two gridded observation datasets: Global Precipitation Climatology Project One-Degree Daily Dataset and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis 3B42. Three model performance metrics (percentage bias, normalized root-mean-square error, and pattern correlation coefficient) were employed to further assess the strengths and weakness of the models. Our results show that the multi-model ensemble mean generally provides a better representation of observed precipitation and related extremes compared to individual models when considering all metrics and seasons. Several consistent biases are evident across CMIP6 models, which tend to overestimate the total-wet day precipitation and consecutive wet days, and underestimate very wet days and maximum 5-day precipitation in both seasons. Furthermore, no single model consistently performs best, model performance varies with the season and index under consideration and is generally independent of horizontal resolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evaluation of historical CMIP6 model simulations of extreme precipitation over contiguous US regions

Simulated historical precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices. The model indices are evaluated against corresponding indices from the CPC unified gauge-based analyses of precipitation over seven geographical regions across the contiguous US (CONUS). The regions assessed match those in recent US National Climate Assessment Reports. To estimate observational uncertainty, precipitation indices for three other observational datasets (HadEx2, Livneh and PRISM) are evaluated against the CPC analyses. Both the moderate and extreme mean precipitation intensities are overestimated over the western CONUS and underestimated in the areas of the Central Great Plains (CGP) in most CMIP6 models tested. Most CMIP6 models overestimate the mean and variability of wet spell durations and underestimate the mean and variability of dry spell durations across the CONUS. Biases in interannual variability of most of the indices have similar patterns to those in corresponding mean biases. The median and interquartile model spreads in CMIP6 model biases are clearly smaller than those in CMIP5 model biases for wet spell durations. Multimodel medians of CMIP6 (CMIP6-MMM) and CMIP5 (CMIP5-MMM) have similar biases in climatology and variability but biases tend to be smaller in CMIP6-MMM. Depending on the index, extreme precipitation is slightly better in parts of the eastern half of the CONUS in CMIP6-MMM, otherwise, the biases in climatology and variability are similar to CMIP5-MMM. CMIP6-MMM performs better than individual models and even observational datasets in some cases. Differences between observational datasets for most indices are comparable to the CMIP6 interquartile model spread. The better-performing observational and model datasets are different in different parts of the CONUS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗