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BackgroundWest Nile virus (WNV) is the leading cause of mosquito-borne disease in humans in the United States. Since the introduction of the disease in 1999, incidence levels have stabilized in many regions, allowing for analysis of climate conditions that shape the spatial structure of disease incidence.ObjectivesOur goal was to identify the seasonal climate variables that influence the spatial extent and magnitude of WNV incidence in humans.MethodsWe developed a predictive model of contemporary mean annual WNV incidence using U.S. county-level case reports from 2005 to 2019 and seasonally averaged climate variables. We used a random forest model that had an out-of-sample model performance of R2=0.61.ResultsOur model accurately captured the V-shaped area of higher WNV incidence that extends from states on the Canadian border south through the middle of the Great Plains. It also captured a region of moderate WNV incidence in the southern Mississippi Valley. The highest levels of WNV incidence were in regions with dry and cold winters and wet and mild summers. The random forest model classified counties with average winter precipitation levels

This paper provides an overview of the United States (US) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2 (E3SMv2) fully coupled regionally refined model (RRM) and documents the overall atmosphere, land, and river results from the Coupled Model Intercomparison Project 6 (CMIP6) DECK (Diagnosis, Evaluation, and Characterization of Klima) and historical simulations - a first-of-its-kind set of climate production simulations using RRM. The North American (NA) RRM (NARRM) is developed as the high-resolution configuration of E3SMv2 with the primary goal of more explicitly addressing DOE's mission needs regarding impacts to the US energy sector facing Earth system changes. The NARRM features finer horizontal resolution grids centered over NA, consisting of 25→100¯km atmosphere and land, a 0.125 river-routing model, and 14→60¯km ocean and sea ice. By design, the computational cost of NARRM is 1/43× of the uniform low-resolution (LR) model at 100¯km but only 1/4¯10¯%-20¯% of a globally uniform high-resolution model at 25¯km. A novel hybrid time step strategy for the atmosphere is key for NARRM to achieve improved climate simulation fidelity within the high-resolution patch without sacrificing the overall global performance. The global climate, including climatology, time series, sensitivity, and feedback, is confirmed to be largely identical between NARRM and LR as quantified with typical climate metrics. Over the refined NA area, NARRM is generally superior to LR, including for precipitation and clouds over the contiguous US (CONUS), summertime marine stratocumulus clouds off the coast of California, liquid and ice phase clouds near the North Pole region, extratropical cyclones, and spatial variability in land hydrological processes. The improvements over land are related to the better-resolved topography in NARRM, whereas those over ocean are attributable to the improved air-sea interactions with finer grids for both atmosphere and ocean and sea ice. Some features appear insensitive to the resolution change analyzed here, for instance the diurnal propagation of organized mesoscale convective systems over CONUS and the warm-season land-atmosphere coupling at the southern Great Plains. In summary, our study presents a realistically efficient approach to leverage the fully coupled RRM framework for a standard Earth system model release and high-resolution climate production simulations.

This work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid-latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single-forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol-related forcing.

Earth system models account for seasonal snow cover, but many do not accommodate the deeper snowpack on ice sheets (aka firn) that slowly transforms to ice under accumulating snowfall. To accommodate and resolve firn depths of up to 60 m in the Energy Exascale Earth System Model's land surface model (ELM), we add 11 layers to its snowpack and evaluate three dry snow compaction equations in multi-century simulations. After comparing results from ELM simulations (forced with atmospheric reanalysis) with empirical data, we find that implementing into ELM a two-stage firn densification model produces more accurate dry firn densities at intermediate depths of 20–60 m. Compared to modeling firn using the equations in the (12 layer) Community Land Model (version 5), switching to the two-stage firn densification model (with 16 layers) significantly decreases root-mean-square errors in upper 60 m dry firn densities by an average of 41 kg m−3 (31%). Simulations with three different firn density parameterizations show that the two-stage firn densification model should be used for applications that prioritize accurate upper 60 m firn air content (FAC) in regions where the mean annual surface temperature is greater than roughly −31°C. Because snow metamorphism, firn density, and FAC are major components in modeling ice sheet surface albedo, melt water retention, and climatic mass balance, these developments advance broader efforts to simulate the response of land ice to atmospheric forcing in Earth system models.

Coccidioidomycosis (Valley fever) is a fungal disease endemic to the southwestern United States. Across this region, temperature and precipitation influence the extent of the endemic region and number of Valley fever cases. Climate projections for the western United States indicate that temperatures will increase and precipitation patterns will shift, which may alter disease dynamics. We estimated the area potentially endemic to Valley fever using a climate niche model derived from contemporary climate and disease incidence data. We then used our model with projections of climate from Earth system models to assess how endemic areas will change during the 21st century. By 2100 in a high warming scenario, our model predicts that the area of climate-limited endemicity will more than double, the number of affected states will increase from 12 to 17, and the number of Valley fever cases will increase by 50%. The Valley fever endemic region will expand north into dry western states, including Idaho, Wyoming, Montana, Nebraska, South Dakota, and North Dakota. Precipitation will limit the disease from spreading into states farther east and along the central and northern Pacific coast. This is the first quantitative estimate of how climate change may influence Valley fever in the United States. Our predictive model of Valley fever endemicity may provide guidance to public health officials to establish disease surveillance programs and design mitigation efforts to limit the impacts of this disease.

This work documents the first version of the U.S. Department of Energy (DOE) new Energy Exascale Earth System Model (E3SMv1). We focus on the standard resolution of the fully coupled physical model designed to address DOE mission-relevant water cycle questions. Its components include atmosphere and land (110-km grid spacing), ocean and sea ice (60 km in the midlatitudes and 30 km at the equator and poles), and river transport (55 km) models. This base configuration will also serve as a foundation for additional configurations exploring higher horizontal resolution as well as augmented capabilities in the form of biogeochemistry and cryosphere configurations. The performance of E3SMv1 is evaluated by means of a standard set of Coupled Model Intercomparison Project Phase 6 (CMIP6) Diagnosis, Evaluation, and Characterization of Klima simulations consisting of a long preindustrial control, historical simulations (ensembles of fully coupled and prescribed SSTs) as well as idealized CO2 forcing simulations. The model performs well overall with biases typical of other CMIP-class models, although the simulated Atlantic Meridional Overturning Circulation is weaker than many CMIP-class models. While the E3SMv1 historical ensemble captures the bulk of the observed warming between preindustrial (1850) and present day, the trajectory of the warming diverges from observations in the second half of the twentieth century with a period of delayed warming followed by an excessive warming trend. Using a two-layer energy balance model, we attribute this divergence to the model's strong aerosol-related effective radiative forcing (ERFari+aci = −1.65 W/m2) and high equilibrium climate sensitivity (ECS = 5.3 K).

As global temperatures increase, sea ice loss will increasingly enable commercial shipping traffic to cross the Arctic Ocean, where the ships' gas and particulate emissions may have strong regional effects. Here we investigate impacts of shipping emissions on Arctic climate using a fully coupled Earth system model (CESM 1.2.2) and a suite of newly developed projections of 21st-century trans-Arctic shipping emissions. We find that trans-Arctic shipping will reduce Arctic warming by nearly 1 °C by 2099, due to sulfate-driven liquid water cloud formation. Cloud fraction and liquid water path exhibit significant positive trends, cooling the lower atmosphere and surface. Positive feedbacks from sea ice growth-induced albedo increases and decreased downwelling longwave radiation due to reduced water vapor content amplify the cooling relative to the shipping-free Arctic. Our findings thus point to the complexity in Arctic climate responses to increased shipping traffic, justifying further study and policy considerations as trade routes open.

Surface melt and mass loss of the Greenland Ice Sheet may play crucial roles in global climate change due to their positive feedbacks and large fresh-water storage. With few other regular meteorological observations available in this extreme environment, measurements from automatic weather stations (AWS) are the primary data source for studying surface energy budgets, and for validating satellite observations and model simulations. Station tilt, due to irregular surface melt, compaction and glacier dynamics, causes considerable biases in the AWS shortwave radiation measurements. In this study, we identify tilt-induced biases in the climatology of surface shortwave radiative flux and albedo, and retrospectively correct these by iterative application of solar geometric principles. We found, over all the AWS from the Greenland Climate Network (GC-Net), the Kangerlussuaq transect (K-transect) and the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) networks, insolation on fewer than 40% of clear days peaks within ±0.5h of solar noon time, with the largest shift exceeding 3h due to tilt. Hourly absolute biases in the magnitude of surface insolation can reach up to 200W m-2, with respect to the well-understood clear-day insolation. We estimate the tilt angles and their directions based on the solar geometric relationship between the simulated insolation at a horizontal surface and the observed insolation by these tilted AWS under clear-sky conditions. Our adjustment reduces the root mean square error (RMSE) against references from both satellite observation and reanalysis by 16W m-2 (24%), and raises the correlation coefficients with them to above 0.95. Averaged over the whole Greenland Ice Sheet in the melt season, the adjustment in insolation to compensate station tilt is ∼ 11W m-2, enough to melt 0.24m of snow water equivalent. The adjusted diurnal cycles of albedo are smoother, with consistent semi-smiling patterns. The seasonal cycles and inter-annual variabilities of albedo agree better with previous studies. This tilt-corrected shortwave radiation data set derived using the Retrospective, Iterative, Geometry-Based (RIGB) method provide more accurate observations and validations for surface energy budgets studies on the Greenland Ice Sheet, including albedo variations, surface melt simulations and cloud radiative forcing estimates.

In the Tibetan Plateau, the black carbon (BC) concentration in surface snow and snow pits has received much attention, whereas the seasonal behavior of aerosol-in-snow concentration, vertical profile, melt-scavenging, and enrichment have received relatively little attention. Here we investigate these processes and their impacts on radiative forcing on the Muji glacier in the westernmost Tibetan Plateau during the 2012 snowmelt season. Increasing impurity concentrations were mostly due to post-deposition effects rather than new deposition. On 5 July, BC concentrations in the surface snow were higher than those of fresh snow, implying enrichment via sublimation and/or melting of previous snow. Fresh snow contained 25ngg-1 BC on 27 July; afterward, BC gradually increased, reaching 730.6ngg-1 in September. BC, organic carbon (OC), and dust concentrations co-varied but differed in magnitude. Melt-scavenging efficiencies were estimated at 0.19±0.05 and 0.04±0.01 for OC and BC, respectively, and the BC in surface snow increased by 20-25 times depending on melt intensity. BC-in-snow radiative forcing (RF) was approximately 2.2Wm-2 for fresh snow and 18.1-20.4Wm-2 for aged snow, and was sometimes reduced by the presence of dust.

Observational analyses have shown the width of the tropical belt increasing in recent decades as the world has warmed. This expansion is important because it is associated with shifts in large-scale atmospheric circulation and major climate zones. Although recent studies have attributed tropical expansion in the Southern Hemisphere to ozone depletion, the drivers of Northern Hemisphere expansion are not well known and the expansion has not so far been reproduced by climate models. Here we use a climate model with detailed aerosol physics to show that increases in heterogeneous warming agents--including black carbon aerosols and tropospheric ozone--are noticeably better than greenhouse gases at driving expansion, and can account for the observed summertime maximum in tropical expansion. Mechanistically, atmospheric heating from black carbon and tropospheric ozone has occurred at the mid-latitudes, generating a poleward shift of the tropospheric jet, thereby relocating the main division between tropical and temperate air masses. Although we still underestimate tropical expansion, the true aerosol forcing is poorly known and could also be underestimated. Thus, although the insensitivity of models needs further investigation, black carbon and tropospheric ozone, both of which are strongly influenced by human activities, are the most likely causes of observed Northern Hemisphere tropical expansion.

Coccidiodomycosis (valley fever) is a systemic infection caused by inhalation of airborne spores from Coccidioides immitis, a soil-dwelling fungus found in the southwestern United States, parts of Mexico, and Central and South America. Dust storms help disperse C. immitis so risk factors for valley fever include conditions favorable for fungal growth (moist, warm soil) and for aeolian soil erosion (dry soil and strong winds). Here, we analyze and inter-compare the seasonal and inter-annual behavior of valley fever incidence and climate risk factors for the period 1980-2002 in Kern County, California, the US county with highest reported incidence. We find weak but statistically significant links between disease incidence and antecedent climate conditions. Precipitation anomalies 8 and 20 months antecedent explain only up to 4% of monthly variability in subsequent valley fever incidence during the 23 year period tested. This is consistent with previous studies suggesting that C. immitis tolerates hot, dry periods better than competing soil organisms and, as a result, thrives during wet periods following droughts. Furthermore, the relatively small correlation with climate suggests that the causes of valley fever in Kern County could be largely anthropogenic. Seasonal climate predictors of valley fever in Kern County are similar to, but much weaker than, those in Arizona, where previous studies find precipitation explains up to 75% of incidence. Causes for this discrepancy are not yet understood. Higher resolution temporal and spatial monitoring of soil conditions could improve our understanding of climatic antecedents of severe epidemics.

A system for simulating aerosols has been developed using a chemical transport model together with an assimilation of satellite aerosol retrievals. The methodology and model components are described in this paper, and the modeled distribution of aerosols for the Indian Ocean Experiment (INDOEX) is presented by Rasch et al. [this issue]. The system generated aerosol forecasts to guide deployment of ships and aircraft during INDOEX. The system consists of the Model of Atmospheric Transport and Chemistry (MATCH) combined with an assimilation package developed for applications in atmospheric chemistry. MATCH predicts the evolution of sulfate, carbonaceous, and mineral dust aerosols, and it diagnoses the distribution of sea salt aerosols. The model includes a detailed treatment of the sources, chemical transformation, transport, and deposition of the aerosol species. The aerosol forecasts involve a two-stage process. During the assimilation phase the total column aerosol optical depth (AOD) is estimated from the model aerosol fields. The model state is then adjusted to improve the agreement between the simulated AOD and satellite retrievals of AOD. During the subsequent integration phase the aerosol fields are evolved using meteorological fields from an external model. Comparison of the modeled AOD against estimates of the AOD from INDOEX Sun photometer data show that the differences in daily means are -0.03 ± 0.06. Although the initial application is limited to the Indian Ocean, the methodology could be extended to derive global aerosol analyses combining in situ and remotely sensed aerosol observations. Copyright 2001 by the American Geophysical Union.