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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.

Multiple Earth system models (ESMs) discretize surface albedo into two semi‐broadbands comprising the UV/visible and near‐infrared (NIR) wavelengths. Here, we use an offline single‐column radiative transfer model to investigate the radiative effects of spectrally resolving the surface albedo. We use the Snow, Ice, and Aerosol Radiative model, extended to simulate liquid water, to calculate snow, ice, and liquid water albedo. We flux‐weight the hyperspectral albedo into the coarser spectral bands used by the atmospheric shortwave radiative transfer model. We establish representative atmospheric profiles for the three surface types and compare their shortwave fluxes and atmospheric warming rates with the spectrally resolved albedo to those calculated with the semi‐broadband approximation. Spectrally resolved surface albedo over snow and ice reduces atmospheric warming by darkening the albedo of NIR bands, correcting the too‐strong surface absorption in visible bands, and too‐weak surface absorption in shortwave infrared bands caused by the semi‐broadband approximation. We explore the effects on surface and atmospheric warming rates of varying solar zenith angle, cloud cover, relative humidity, and snow grain/air bubble radii. The semi‐broadband albedo biases can exceed 10% and 2% for the surface and atmospheric net flux respectively, being particularly strong under conditions which alter the distributions of surface insolation (i.e., cloud cover or increased atmospheric water vapor). These results show that transmitting a higher resolution spectral radiation field between the atmosphere and surface reduces biases in surface absorption and atmospheric heating present in ESMs that currently use the semi‐broadband approximation. Plain Language Summary: Snow, ice, and (to a lesser extent) liquid water reflectances depend on the wavelength of light. Snow, for example, reflects almost all light at shorter, or visible, wavelengths, while absorbing longer, near‐infrared wavelengths. Some Earth System Models (ESMs) approximate this spectrally varying reflectance with two bands. This eases the computational burden, yet fails to accurately capture the spectrally structured absorption of the surface, and changes the distribution of flux that is reflected back to the atmosphere. Here we use a single‐column radiative transfer model to expand the spectral representation of snow, ice, and liquid water albedo to 14 bands (matching those used by the atmospheric model in ESMs). By implementing this moderately resolved albedo, we change the radiation absorbed by the surface, as well as the atmosphere. Depending on environmental conditions, implementing a 14 band albedo over snow changes surface absorption by over 10%, relative to the semi‐broadband approximation, which increases or reduces the susceptibility of snow to melting. Furthermore, changes to the spectral distribution of sunlight reduce atmospheric absorption by nearly 2%. These results highlight the importance of accurately representing the spectral albedo for snow and ice‐covered surfaces in ESMs that adopt the semi‐broadband albedo approximation. Key Points: Spectrally varying surface albedo and cloud extinction cause the largest biases in semi‐broadband albedo approximationsSuch approximations simultaneously overestimate surface and atmospheric heating under clear skies, and destabilize the lower atmosphereSpectrally resolved albedo of snowpack can change clear and cloudy sky surface absorption by up to 1% and 10%, respectively [ABSTRACT FROM AUTHOR]

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.

Geoscientific models and measurements generate false precision (scientifically meaningless data bits) that wastes storage space. False precision can mislead (by implying noise is signal) and be scientifically pointless, especially for measurements. By contrast, lossy compression can be both economical (save space) and heuristic (clarify data limitations) without compromising the scientific integrity of data. Data quantization can thus be appropriate regardless of whether space limitations are a concern. We introduce, implement, and characterize a new lossy compression scheme suitable for IEEE floating-point data. Our new Bit Grooming algorithm alternately shaves (to zero) and sets (to one) the least significant bits of consecutive values to preserve a desired precision. This is a symmetric, two-sided variant of an algorithm sometimes called Bit Shaving that quantizes values solely by zeroing bits. Our variation eliminates the artificial low bias produced by always zeroing bits, and makes Bit Grooming more suitable for arrays and multi-dimensional fields whose mean statistics are important. Bit Grooming relies on standard lossless compression to achieve the actual reduction in storage space, so we tested Bit Grooming by applying the DEFLATE compression algorithm to bit-groomed and full-precision climate data stored in netCDF3, netCDF4, HDF4, and HDF5 formats. Bit Grooming reduces the storage space required by initially uncompressed and compressed climate data by 25-80 and 5-65%, respectively, for single-precision values (the most common case for climate data) quantized to retain 1-5 decimal digits of precision. The potential reduction is greater for double-precision datasets. When used aggressively (i.e., preserving only 1-2 digits), Bit Grooming produces storage reductions comparable to other quantization techniques such as Linear Packing. Unlike Linear Packing, whose guaranteed precision rapidly degrades within the relatively narrow dynamic range of values that it can compress, Bit Grooming guarantees the specified precision throughout the full floating-point range. Data quantization by Bit Grooming is irreversible (i.e., lossy) yet transparent, meaning that no extra processing is required by data users/readers. Hence Bit Grooming can easily reduce data storage volume without sacrificing scientific precision or imposing extra burdens on users.

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.

Snowpacks absorb more sunlight as they warm. The Antarctic Plateau may buck this trend over the 21st century sinceincreased snowfall there inhibits the snowpack from dimming.

Stratiform anvils in the upper tropical troposphere were simulated to determine the sensitivities of their radiative properties to the presence of small ice crystals. Cloud evolution was modeled in a one-dimensional (vertical) framework incorporating an updraft, deposition, sublimation, sedimentation, nucleation, and radiation. The sensitivities of cloud radiative forcing, albedo, emissivity, and heating rate were derived from a test that included and then excluded the presence of numerous small crystals. These crystals sizes (3 < L < 20 μm) have been measured in recent observations but are smaller than the detection limit of most past observations. The shortwave forcing and albedo were very sensitive to the presence of the small crystals, even though these crystals accounted for less than 2% of total cloud mass. For optically thick anvils the longwave forcing and emissivity were, in general, much less sensitive to the small ice crystals than their shortwave counterparts. Radiative treatments assuming a hexagonal crystal habit yielded the same sensitivities as the spherical habit. The results agreed with previous studies in that the increased backscatter from hexagonal crystals enhanced the planetary albedo by ∼10–15%. The heating rate sensitivity to the small crystals depended on vertical location within the cloud and showed cancelation between the longwave and the shortwave heating perturbations. The small crystals changed heating rates by up to 50% at cloud top and base.

Mineral aerosol deposition is recognized as the dominant source of iron to the open ocean and the solubility of iron in the dust aerosol is highly variable, with measurements ranging from 0.01-80%. Global models have difficulty capturing the observed variations in solubility, and have ignored the solubility dependence on aerosol size. We introduce two idealized physical models to estimate the size dependence of mineral aerosol solubility: a diffusion-controlled model and a surface-area-controlled model. These models produce differing time- and space-varying solubility maps for aerosol Fe and Al given the dust age at deposition, size-resolved dust entrainment fields, and the aerosol acidity. The resulting soluble iron deposition fluxes are substantially different, and more realistic, than a globally uniform solubility approximation. The surface-area-controlled solubility varies more than the diffusion-controlled solubility and better captures the spatial pattern of observed solubility in the Atlantic. However, neither of these two models explains the large solubility variation observed in the Pacific. We then examine the impacts of spatially variable, size-dependent solubility on marine biogeochemistry with the Biogeochemical Elemental Cycling (BEC) ocean model by comparing the modeled surface ocean dissolved Fe and Al with observations. The diffusion-based variable solubility does not significantly improve the simulation of dissolved Fe relative to a 5% globally uniform solubility, while the surface-area-based variable solubility improves the simulation in the North Atlantic but worsens it in the Pacific and Indian Oceans.

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.

Throughout much of the latter half of the twentieth century, the dominant mode of Northern Hemisphere (NH) extratropical wintertime circulation variability-the Arctic Oscillation (AO)-exhibited a positive trend, with decreasing high-latitude sea level pressure (SLP) and increasing midlatitude SLP. General circulation models (GCMs) show that this trend is related to several factors, including North Atlantic SSTs, greenhouse gas/ozone-induced stratospheric cooling, and warming of the Indo-Pacific warm pool. Over the last approximately two decades, however, the AO has been decreasing, with 2009/10 featuring the most negative AO since 1900. Observational and idealized modeling studies suggest that snow cover, particularly over Eurasia, may be important. An observed snow-AO mechanism also exists, involving the vertical propagation of a Rossby wave train into the stratosphere, which induces a negative AO response that couples to the troposphere. Similar to other GCMs, the authors show that transient simulations with the Community Atmosphere Model, version 3 (CAM3) yield a snow-AO relationship inconsistent with observations and dissimilar AO trends. However, Eurasian snow cover and its interannual variability are significantly underestimated. When the albedo effects of snow cover are prescribed in CAM3 (CAMPS) using satellite-based snow cover fraction data, a snow-AO relationship similar to observations develops. Furthermore, the late-twentieth-century increase in the AO, and particularly the recent decrease, is reproduced by CAM PS. The authors therefore conclude that snow cover has helped force the observed AO trends and that it may play an important role in future AO trends.

Water impoundment in the Three Gorges Reservoir (TGR) of China caused a large mass redistribution from the oceans to a concentrated land area in a short time period. We show that this mass shift is captured by the Gravity Recovery and Climate Experiment (GRACE) unconstrained global solutions at a 400 km spatial resolution after removing correlated errors. The WaterGAP Global Hydrology Model (WGHM) is selected to isolate the TGR contribution from regional water storage changes. For the first time, this study compares the GRACE (minus WGHM) estimated TGR volume changes with in situ measurements from April 2002 to May 2010 at a monthly time scale. During the 8 year study period, GRACE-WGHM estimated TGR volume changes show an increasing trend consistent with the TGR in situ measurements and lead to similar estimates of impounded water volume. GRACE-WGHM estimated total volume increase agrees to within 14% (3.2 km(3)) of the in situ measurements. This indicates that GRACE can retrieve the true amplitudes of large surface water storage changes in a concentrated area that is much smaller than the spatial resolution of its global harmonic solutions. The GRACE-WGHM estimated TGR monthly volume changes explain 76% (r(2) = 0.76) of in situ measurement monthly variability and have an uncertainty of 4.62 km(3). Our results also indicate reservoir leakage and groundwater recharge due to TGR filling and contamination from neighboring lakes are nonnegligible in the GRACE total water storage changes. Moreover, GRACE observations could provide a relatively accurate estimate of global water volume withheld by newly constructed large reservoirs and their impacts on global sea level rise since 2002.

This study analyzes diurnal and seasonal variations of snow albedo at four Baseline Surface Radiation Network stations in the Arctic and Antarctica from 2003 to 2008 to elucidate similarities and differences in snow albedo diurnal cycles across geographic zones and to assess how diurnal changes in snow albedo affect the surface energy budget. At the seasonal scale, the daily albedo for the perennial snow at stations South Pole and Georg von Neumayer in Antarctica has a similar symmetric variation with solar zenith angle (SZA) around the austral summer; whereas the daily albedo for the seasonal snow at stations Barrow, Alaska, and Ny-Ålesund, Spitsbergen, in the Arctic tends to decrease with SZA decrease from winter to spring before snow starts melting. At the hourly scale, each station shows unique diurnal cycles due to different processes that affect snow albedo such as cloud cover, snow metamorphism, surface hoar formation, SZA, solar azimuth angle, and surface features. Cloud escalates the snow albedo at all four stations by shifting solar radiation to visible wavelengths and diminishes the diurnal variation by diffusing incident solar radiation. The 24 h mean snow albedo is higher on cloudy than clear days by 0.02 at the South Pole (December) and Barrow (May), 0.05 at Neumayer (December), and 0.07 at Ny-Ålesund (April). Snow surface structures, for example, wind-channeled sastrugi, appear to be a controlling factor in the diurnal variation of clear-sky snow albedo at the South Pole and Ny-Ålesund. The surface hoar formation cycles and snow metamorphism are consistent with the asymmetric diurnal variation of snow albedo at Neumayer and Barrow. Near the melting point temperature, melt-freeze cycles exceed cloud and surface structure impacts and dominate the diurnal variation of snow albedo at stations Barrow and Ny-Ålesund. The satellite-measured clear-sky snow albedo usually underestimates the average all-sky snow albedo at these stations. These results illustrate the potential biases in daily and monthly albedo products constructed from sun-synchronous satellite daily instantaneous observations which inevitably undersample the diurnal variation of snow albedo.

We introduce and apply to dust aerosols an efficient method to track tracer age (time since emission) as a function of space and time in large-scale geophysical models. Our mass-age tracking (MAT) method follows the full tracer lifecycles directly and does not depend on proxy, ensemble, or Green's function techniques. MAT sends a mass-age tracer through the same algorithms that the host models use to predict tracer mass processes and then estimates age as the ratio of mass-age to mass. We apply MAT to size-resolved dust aerosol tracers to study the age of dust that remains in the atmosphere and the age of dust at deposition. The results include the first global distribution maps of aerosol age. Dust age varies with location, time, and particle size and is strongly sensitive to climate, wind and precipitation in particular. The global average age of dust at deposition agrees with residence time at ∼2.7 days, while dust in the atmosphere is, on average, twice as old. As expected, older dust prevails far from sources, at higher altitudes and in smaller sizes. Dust age exhibits a seasonal cycle, stronger for larger dust particles, that peaks in April–June, the period of maximum Asian and North African emissions. The oldest dust at deposition falls in the Antarctic and South Pacific Convergence Zone about 1 month after emission. The mass-weighted ages provided by MAT are useful for investigating and parameterizing the evolution of aerosol physical and chemical properties.

An understanding of the surface albedo of high latitudes is crucial for climate change studies. MODIS albedo retrievals flagged as high-quality compare well with in situ Greenland Climate Network (GC-Net) measurements but cover too small an area to fully characterize Greenland's albedo in nonsummer months. In contrast, poor quality MODIS retrievals provide adequate spatiotemporal coverage, but are not recommended for use at large solar zenith angles (SZAs) where they have a systematic low bias. We introduce an empirical adjustment to the poor quality data based on high-quality reference albedos and constrained by GC-Net data and theory, and use the adjusted data to improve estimates and fill in gaps of the year-round, Greenland-wide, albedo and surface energy budget. For observations made with SZAs between 55° and 75°, the mean differences (MODIS minus GC-Net) between our adjusted MODIS albedo and GC-Net measurements are −0.02 and −0.03 at Saddle and Summit, respectively, compared to −0.05 and −0.08 between the unadjusted MODIS albedo and GC-Net measurements. The adjusted MODIS snow albedos are usually between 0.75 and 0.87 over dry snow when SZA is larger than 55°, and they reduce unrealistic seasonal and meridional trends associated with MODIS retrievals at large SZA, defined as SZA > 55° and 70°, respectively, for low- and high-quality retrievals. The impact of the adjusted albedo on the surface energy budget, relative to the unadjusted albedo from all MODIS data, is smallest (−0.7 ± 0.1W/m2) in June, and largest (−6.2 ± 0.9 W/m2) in September for the black-sky albedo (BSA). The mean annual absorbed solar radiation (ASR) reduction by the adjusted MODIS albedo in Greenland from 2003 to 2005 is 3.1 ± 0.2 and 4.3 ± 0.2 W/m2 for BSA and white-sky albedo (WSA), respectively, about 8.0 ± 0.5% and 10.8 ± 0.4% of ASR based on the raw BSA and WSA. The ASR reduction by the adjusted blue-sky (actual) albedo is between 2.9 and 4.5 W/m2, enough to annually melt 27.1 to 41.7 cm snow water equivalent (SWE), or to sublimate 3.2 to 4.9 cm SWE. The ASR difference between the adjusted MODIS BSA and CERES albedo in March from 2003 to 2005 is only −0.1 ± 0.9 W/m2, much less than the difference (4.9 ± 1.4 W/m2) between the unadjusted MODIS BSA and CERES. The albedo adjustments exceed the likely direct anthropogenic radiative forcing experienced by Greenland due to greenhouse gases or aerosols. The proposed adjustment preserves most of the zonal and meridional structure of raw MODIS albedo, and extends its usefulness as a cryospheric climate record in times and regions of Greenland with large SZA.

For the first time, global ocean usable wind power is evaluated for modern offshore turbine characteristics including hub height, usable portion of the wind speed distribution, and siting depth. Mean wind power increases by 30%, 69%, and 73% within the tropics and Northern and Southern Hemisphere extratropics, respectively, between hub heights of 10 m and 100 m. A turbine with a cut-out speed of 25 m s−1 (30 m s−1) within the Northern Hemisphere storm track harvests between 55% (82%) and 85% (>98%) of available power. Within this region, a 2–3 m s−1 change in cut-out speed can result in a 5–7% change in usable power. Eighty meter wind power accumulates at a rate of 20–45 MW km2 m−2 per meter depth increase from the shore to the shelf break. Beyond the shelf break, wind power accumulates at a slower rate (

In situ measurements of snow albedo at five stations along a north–south transect in the dry-snow facies of the interior of Greenland follow the theoretically expected dependence of snow albedo with solar zenith angle (SZA). Greenland Climate Network (GC-Net) measurements from 1997 through 2007 exhibit the trend of modest surface brightening with increasing SZA on both diurnal and seasonal timescales. SZA explains up to 50% of seasonal albedo variability. The two other environmental factors considered, temperature and cloudiness, play much less significant roles in seasonal albedo variability at the five stations studied. Compared to the 10-year record of these GC-Net measurements, the five-year record of MODIS satellite-retrieved snow albedo shows a systematic negative bias for SZA larger than about 55°. Larger bias of MODIS snow albedo exists at more northerly stations. MODIS albedos successfully capture the snow albedo dependence on SZA and have a relatively good agreement with GC-Net measurements for SZA  55° and often becomes physically unrealistic for SZA > 65°. This unphysical behavior biases parameterizations of surface albedo and restricts the range of usefulness of the MODIS albedo products. Seasonal-to-interannual trends in surface brightness in Greenland, and in polar (i.e., large SZA) regions in general, and model simulations of these trends, should be evaluated in light of these limitations.

The primary nutrients that limit marine phytoplankton growth rates include nitrogen (N), phosphorus (P), iron (Fe), and silicon (Si). Atmospheric transport and deposition provides a source for each of these nutrients to the oceans. We utilize an ocean biogeochemical model to examine the relative importance of these atmospheric inputs for ocean biogeochemistry and export production. In the current era, simulations with the biogeochemical elemental cycling ocean model suggest that globally, atmospheric Fe inputs could support roughly 50% of the Fe exported from the euphotic zone by sinking organic and inorganic particles. Variations in atmospheric iron inputs strongly impact spatial patterns of phytoplankton growth limitation and the areal extent of the high-nutrient, low-chlorophyll regions. Atmospheric inputs of N, Si, and P have smaller impacts, potentially accounting for 5.1%, 0.21%, and 0.12% of the biogenic export of these elements from the euphotic zone, respectively. Soluble Fe input from the atmosphere is sufficient to support most of the export production in many ocean regions, whether we use a spatially variable aerosol Fe solubility, or a globally constant 2% solubility. Regionally atmospheric N inputs can have significant impacts on marine biogeochemistry, potentially supporting >25% of the export production, an impact that is increasing due to human activities. Atmospheric Si and P inputs have only minimal impacts on marine ecosystem productivity and biogeochemistry, as these inputs are typically quite small relative to the flux of these nutrients from below the euphotic zone.

We present results from transient sensitivity studies with the Biogeochemical Elemental Cycling (BEC) ocean model to increasing anthropogenic atmospheric inorganic nitrogen (N) and soluble iron (Fe) deposition over the industrial era. Elevated N deposition results from fossil fuel combustion and agriculture, and elevated soluble Fe deposition results from increased atmospheric processing in the presence of anthropogenic pollutants and soluble Fe from combustion sources. Simulations with increasing Fe and increasing Fe and N inputs raised simulated marine nitrogen fixation, with the majority of the increase in the subtropical North and South Pacific, and raised primary production and export in the high-nutrient low-chlorophyll (HNLC) regions. Increasing N inputs alone elevated small phytoplankton and diatom production, resulting in increased phosphorus (P) and Fe limitation for diazotrophs, hence reducing nitrogen fixation (∼6%). Globally, the simulated primary production, sinking particulate organic carbon (POC) export. and atmospheric CO2 uptake were highest under combined increase in Fe and N inputs compared to preindustrial control. Our results suggest that increasing combustion iron sources and aerosol Fe solubility along with atmospheric anthropogenic nitrogen deposition are perturbing marine biogeochemical cycling and could partially explain the observed trend toward increased P limitation at station ALOHA in the subtropical North Pacific. Excess inorganic nitrogen ([NO3 −] + [NH4 +] − 16[PO4 3−]) distributions may offer useful insights for understanding changing ocean circulation and biogeochemistry.

Global ocean wind power has recently been assessed (W. T. Liu et al., 2008) using scatterometry-based 10 m winds. We characterize, for the first time, wind power at 80 m (typical wind turbine hub height) above the global ocean surface, and account for the effects of surface layer stability. Accounting for realistic turbine height and atmospheric stability increases mean global ocean wind power by +58% and −4%, respectively. Our best estimate of mean global ocean wind power is 731 W m−2, about 50% greater than the 487 W m−2 based on previous methods. 80 m wind power is 1.2–1.5 times 10 m power equatorward of 30° latitude, between 1.4 and 1.7 times 10 m power in wintertime storm track regions and >6 times 10 m power in stable regimes east of continents. These results are relatively insensitive to methodology as wind power calculated using a fitted Weibull probability density function is within 10% of power calculated from discrete wind speed measurements over most of the global oceans.

Technology continues to enable scientists to set new records in data collection and production, intensifying a need for large scale tools to efficiently process and analyze the growing mountain of data. To complement growth in the number of data centers and the volume of data they store, we introduce our Script Workflow Analysis for MultiProcessing (SWAMP) system. Our system provides safe server-side processing capabilities that allow scientists to reuse familiar desktop-based analysis methods represented in shell-scripts. Built-in script compilation isolates file accesses and generates workflows, while a cluster-capable execution engine partitions and executes the resulting workflow. Benchmarks illustrate up to 20X performance gains, as well as the importance of I/O considerations which make other computation systems less effective at geoscience data reduction.

Climatological surface wind speed probability density functions (PDFs) estimated from observations are characterized and used to evaluate, for the first time, contemporaneous wind PDFs predicted by a GCM. The observations include NASA’s global Quick Scatterometer (QuikSCAT) dataset, the NCEP/Department of Energy Global Reanalysis 2 (NCEP-2) 6-hourly reanalysis, and the Tropical Atmosphere Ocean (TAO)/Triangle Trans-Ocean Buoy Network (TRITON) moored buoy data, all from 2000 to 2005. Wind speed mean, 90th percentile, standard deviation, and Weibull shape parameter climatologies are constructed from these data. New features that emerge from the analysis include the identification of a stationary pattern to the wind speed variance in the equatorial Pacific. Interestingly, a distinct wind speed shape anomaly migrates with the ITCZ across this stationary background.The GCM despite its coarser spatial and temporal resolution predicts wind speed PDFs in general agreement with observations. Relative to QuikSCAT, the NCAR Community Atmosphere Model, version 3 (CAM3) GCM has a globally averaged positive mean wind speed bias of about 0.2 m s−1 originating primarily within the trades and Southern Hemisphere storm track. Global standard deviation biases are largest in the winter hemisphere storm tracks. The largest shape biases occur along the equatorial peripheries of the Northern Hemisphere and southern Indian Ocean anticyclones. Year-round negative shape and mean wind speed biases persist along the ITCZ. The GCM’s overactive tropical convection and slight subtropical anticyclone displacement contribute to positive mean speed, standard deviation, and shape trade biases.Surface heat and energy fluxes depend nonlinearly on wind speed magnitude, are sensitive to the tails of the wind distribution, and hence vary significantly on spatiotemporal scales not resolved by GCMs. Limited computing resources force the use of coarse-resolution GCMs, which do not resolve finer-scale wind speed fluctuations. Rather, surface fluxes are determined from the mean wind speed computed by averaging spatially and temporally over subgrid-scale features. Some surface flux routines account for gustiness during low mean winds resulting from thermally driven convection. The authors hypothesize that GCMs systematically underestimate surface momentum flux nonlinearities and that this biases surface wind predictions most in regions of strong winds with high variability. To test this, climate simulations that account for surface fluxes due to subgrid-scale GCM winds are performed. This significantly improves climatological surface wind speed statistics, particularly in the Southern Hemisphere storm track, consistent with the hypothesis. These wind speed improvements can be attributed to a reduction in GCM sea level pressure biases throughout the globe.

The netCDF Operator (NCO) software facilitates manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format. NCO is optimized to efficiently analyze large multi-dimensional data sets spanning many files. Researchers and data centers often use NCO to analyze and serve observed and modeled geoscience data including satellite observations and weather, air quality, and climate forecasts. NCO's functionality includes shared memory threading, a message-passing interface, network transparency, and an interpreted language parser. NCO treats data files as a high level data type whose contents may be simultaneously manipulated by a single command. Institutions and data portals often use NCO for middleware to hyperslab and aggregate data set requests, while scientific researchers use NCO to perform three general functions: arithmetic operations, data permutation and compression, and metadata editing. We describe NCO's design philosophy and primary features, illustrate techniques to solve common geoscience and environmental data analysis problems, and suggest ways to design gridded data sets that can ease their subsequent analysis. © 2008 Elsevier Ltd. All rights reserved.

Supercomputing advances have enabled computational science data volumes to grow at ever increasing rates, commonly resulting in more data produced than can be practically analyzed. Whole-dataset download costs have grown to impractical heights, even with multi-Gbps networks, forcing scientists to rely on server-side subsetting and limiting the scope of data they can analyze on a workstation. Our system supplements existing scientific data services with lightweight computational capability, providing a means of safely relocating analysis from the desktop to the server where clustered execution can be coordinated, exploiting data locality, reducing unnecessary data transfer, and providing end-users with results several times faster. We show how dataflow and other compiler-inspired analyses of shell scripts of scientists' most common analysis tools enables parallelization and optimizations in disk and network I/O bandwidth. We benchmark using an actual geoscience analysis script, illustrating the crucial performance gains of extracting workflows defined in scripts and optimizing their execution. Current results quantify significant improvements in performance, showing the promise of bringing transparent high-performance analysis to the scientist's desktop. © 2008 IEEE.

We apply our Snow, Ice, and Aerosol Radiative (SNICAR) model, coupled to a general circulation model with prognostic carbon aerosol transport, to improve understanding of climate forcing and response from black carbon (BC) in snow. Building on two previous studies, we account for interannually varying biomass burning BC emissions, snow aging, and aerosol scavenging by snow meltwater. We assess uncertainty in forcing estimates from these factors, as well as BC optical properties and snow cover fraction. BC emissions are the largest source of uncertainty, followed by snow aging. The rate of snow aging determines snowpack effective radius (r e), which directly controls snow reflectance and the magnitude of albedo change caused by BC. For a reasonable r e range, reflectance reduction from BC varies threefold. Inefficient meltwater scavenging keeps hydrophobic impurities near the surface during melt and enhances forcing. Applying biomass burning BC emission inventories for a strong (1998) and weak (2001) boreal fire year, we estimate global annual mean BC/snow surface radiative forcing from all sources (fossil fuel, biofuel, and biomass burning) of +0.054 (0.007–0.13) and +0.049 (0.007–0.12) W m−2, respectively. Snow forcing from only fossil fuel + biofuel sources is +0.043 W m−2 (forcing from only fossil fuels is +0.033 W m−2), suggesting that the anthropogenic contribution to total forcing is at least 80%. The 1998 global land and sea-ice snowpack absorbed 0.60 and 0.23 W m−2, respectively, because of direct BC/snow forcing. The forcing is maximum coincidentally with snowmelt onset, triggering strong snow-albedo feedback in local springtime. Consequently, the “efficacy” of BC/snow forcing is more than three times greater than forcing by CO2. The 1998 and 2001 land snowmelt rates north of 50°N are 28% and 19% greater in the month preceding maximum melt of control simulations without BC in snow. With climate feedbacks, global annual mean 2-meter air temperature warms 0.15 and 0.10°C, when BC is included in snow, whereas annual arctic warming is 1.61 and 0.50°C. Stronger high-latitude climate response in 1998 than 2001 is at least partially caused by boreal fires, which account for nearly all of the 35% biomass burning contribution to 1998 arctic forcing. Efficacy was anomalously large in this experiment, however, and more research is required to elucidate the role of boreal fires, which we suggest have maximum arctic BC/snow forcing potential during April–June. Model BC concentrations in snow agree reasonably well (r = 0.78) with a set of 23 observations from various locations, spanning nearly 4 orders of magnitude. We predict concentrations in excess of 1000 ng g−1 for snow in northeast China, enough to lower snow albedo by more than 0.13. The greatest instantaneous forcing is over the Tibetan Plateau, exceeding 20 W m−2 in some places during spring. These results indicate that snow darkening is an important component of carbon aerosol climate forcing.

We perform a sensitivity study with the Biogeochemical Elemental Cycling (BEC) ocean model to understand the impact of atmospheric inorganic nitrogen deposition on marine biogeochemistry and air-sea CO2 exchange. Simulations involved examining the response to three different atmospheric inorganic nitrogen deposition scenarios namely, Pre-industrial (22 Tg N/year), 1990s (39 Tg N/year), and an Intergovernmental Panel on Climate Change (IPCC) prediction for 2100, IPCC-A1FI (69 Tg N/year). Globally, the increasing N deposition had widespread, but modest effects on export production and air-sea CO2 exchange. The maximum increase in N deposition was 47 Tg N/year since Pre-industrial control for the IPCC-A1FI case, which had an increase in primary production (0.98 Gt C/year or 2%), export production (0.16 Gt C/year or 3%) and a decrease in atmospheric pCO2 of 1.66 ppm (0.6%) relative to the Pre-industrial control. In some regions, atmospheric N inputs supported >20% of the export production in the current era and >50% of the export production in the IPCC-A1FI case. As nitrogen deposition increased, N fixation decreased because the diazotrophs were outcompeted by diatoms and small phytoplankton under more N-replete conditions. This decrease in N fixation could partially counteract the ongoing increase in new nitrogen inputs via atmospheric N deposition.

We use an inorganic aerosol thermodynamic equilibrium model in a three-dimensional chemical transport model to understand the roles of ammonia chemistry and natural aerosols on the global distribution of aerosols. The thermodynamic equilibrium model partitions gas-phase precursors among modeled aerosol species self-consistently with ambient relative humidity and natural and anthropogenic aerosol emissions during the 1990s.Model simulations show that accounting for aerosol inorganic thermodynamic equilibrium, ammonia chemistry and dust and sea-salt aerosols improve agreement with observed SO4, NO3, and NH4 aerosols especially at North American sites. This study shows that the presence of sea salt, dust aerosol and ammonia chemistry significantly increases sulfate over polluted continental regions. In all regions and seasons, representation of ammonia chemistry is required to obtain reasonable agreement between modeled and observed sulfate and nitrate concentrations. Observed and modeled correlations of sulfate and nitrate with ammonium confirm that the sulfate and nitrate are strongly coupled with ammonium. SO4concentrations over East China peak in winter, while North American SO4 peaks in summer. Seasonal variations of NO3 and SO4 are the same in East China. In North America, the seasonal variation is much stronger for NO3 than SO4 and peaks in winter.Natural sea salt and dust aerosol significantly alter the regional distributions of other aerosols in three main ways. First, they increase sulfate formation by 10–70% in polluted areas. Second, they increase modeled nitrate over oceans and reduce nitrate over Northern hemisphere continents. Third, they reduce ammonium formation over oceans and increase ammonium over Northern Hemisphere continents. Comparisons of SO4, NO3 and NH4 deposition between pre-industrial, present, and year 2100 scenarios show that the present NO3and NH4 deposition are twice pre-industrial deposition and present SO4 deposition is almost five times pre-industrial deposition.

The role of direct radiative forcing of desert dust aerosol in the change from wet to dry climate observed in the African Sahel region in the last half of the twentieth century is investigated using simulations with an atmospheric general circulation model. The model simulations are conducted either forced by the observed sea surface temperature (SST) or coupled with the interactive SST using the Slab Ocean Model (SOM). The simulation model uses dust that is less absorbing in the solar wavelengths and has larger particle sizes than other simulation studies. As a result, simulations show less shortwave absorption within the atmosphere and larger longwave radiative forcing by dust. Simulations using SOM show reduced precipitation over the intertropical convergence zone (ITCZ) including the Sahel region and increased precipitation south of the ITCZ when dust radiative forcing is included. In SST-forced simulations, on the other hand, significant precipitation changes are restricted to over North Africa. These changes are considered to be due to the cooling of global tropical oceans as well as the cooling of the troposphere over North Africa in response to dust radiative forcing. The model simulation of dust cannot capture the magnitude of the observed increase of desert dust when allowing dust to respond to changes in simulated climate, even including changes in vegetation, similar to previous studies. If the model is forced to capture observed changes in desert dust, the direct radiative forcing by the increase of North African dust can explain up to 30% of the observed precipitation reduction in the Sahel between wet and dry periods. A large part of this effect comes through atmospheric forcing of dust, and dust forcing on the Atlantic Ocean SST appears to have a smaller impact. The changes in the North and South Atlantic SSTs may account for up to 50% of the Sahel precipitation reduction. Vegetation loss in the Sahel region may explain about 10% of the observed drying, but this effect is statistically insignificant because of the small number of years in the simulation. Greenhouse gas warming seems to have an impact to increase Sahel precipitation that is opposite to the observed change. Although the estimated values of impacts are likely to be model dependent, analyses suggest the importance of direct radiative forcing of dust and feedbacks in modulating Sahel precipitation.

Coccidioidomycosis (valley fever) is a fungal infection found in the southwestern US, northern Mexico, and some places in Central and South America. The fungus that causes it (Coccidioides immitis) is normally soil-dwelling but, if disturbed, becomes air-borne and infects the host when its spores are inhaled. It is thus natural to surmise that weather conditions that foster the growth and dispersal of the fungus must have an effect on the number of cases in the endemic areas. We present here an attempt at the modeling of valley fever incidence in Kern County, California, by the implementation of a generalized auto regressive moving average (GARMA) model. We show that the number of valley fever cases can be predicted mainly by considering only the previous history of incidence rates in the county. The inclusion of weather-related time sequences improves the model only to a relatively minor extent. This suggests that fluctuations of incidence rates (about a seasonally varying background value) are related to biological and/or anthropogenic reasons, and not so much to weather anomalies.

Geoscience analysis is currently limited by cumbersome access and manipulation of large datasets from remote sources. Due to their data-heavy and compute-light nature, these analysis workloads represent a class of applications unsuited to a computational grid optimized for compute-intensive applications. We present the Script Workflow Analysis for Multiprocessing (SWAMP) system, which relocates data-intensive workflows from scientists' workstations to the hosting datacenters in order to reduce data transfer and exploit locality. Our colocation of computation and data leverages the typically reductive characteristics of these workflows, allowing SWAMP to complete workflows in a fraction of the time and with much less data transfer. We describe SWAMP's implementation and interface, which is designed to leverage scientists' existing script-based workflows. Tests with a production geoscience workflow show drastic improvements not only in overall execution time, but in computation time as well. SWAMP's workflow analysis capability allows it to detect dependencies, optimize I/O, and dynamically parallelize execution. Benchmarks quantify the drastic reduction in transfer time, computation time, and end-to-end execution time. © Springer-Verlag Berlin Heidelberg 2007.

Snow aging causes reflectance to vary significantly on timescales of days. This variability influences the strength of snow albedo feedback and can affect the timing of snowmelt. However, climate models have yet to incorporate important controls on snow aging and albedo evolution. We develop a physically based model that predicts evolution of dry, pure snow specific surface area, and apply aspherical ice particle theory to link these results with albedo evolution. This is the first theoretical study to quantify the relative roles of initial size distribution, vertical temperature gradient, and snow density in snow albedo evolution. Vapor diffusion caused by curvature differences causes rapid albedo decay in the first day following snowfall. Vertical temperature gradient generally dominates grain growth processes afterward but is modulated by snow density, irregularity in particle spacing, and temperature. These processes operate as a coupled system, which we uniquely represent without abrupt transitions between regimes. Model results agree very well with measurements of isothermal snow evolution and are within reasonable range of temperature gradient observations. We show that different snow state regimes cause albedo of nonmelting snow surfaces with identical initial albedo to vary by 0.12 or more after 14 days. Lack of quality observational data illuminates the need for well-controlled snow studies that simultaneously monitor specific surface area, temperature gradient, and albedo. Accounting for snow aging processes, especially temperature gradient, will improve understanding and assessment of snow albedo feedback and climate sensitivity. The modeling framework we develop will also be useful for air-snow chemistry studies that consider specific surface area.

Desert dust simulations generated by the National Center for Atmospheric Research's Community Climate System Model for the current climate are shown to be consistent with present day satellite and deposition data. The response of the dust cycle to last glacial maximum, preindustrial, modern, and doubled-carbon dioxide climates is analyzed. Only natural (non-land use related) dust sources are included in this simulation. Similar to some previous studies, dust production mainly responds to changes in the source areas from vegetation changes, not from winds or soil moisture changes alone. This model simulates a +92%, +33%, and −60% change in dust loading for the last glacial maximum, preindustrial, and doubled-carbon dioxide climate, respectively, when impacts of carbon dioxide fertilization on vegetation are included in the model. Terrestrial sediment records from the last glacial maximum compiled here indicate a large underestimate of deposition in continental regions, probably due to the lack of simulation of glaciogenic dust sources. In order to include the glaciogenic dust sources as a first approximation, we designate the location of these sources, and infer the size of the sources using an inversion method that best matches the available data. The inclusion of these inferred glaciogenic dust sources increases our dust flux in the last glacial maximum from 2.1 to 3.3 times current deposition.

Recent studies suggest that resonant absorption of sunlight by cloud droplets may constitute a significant and unaccounted-for solar energy sink in the atmosphere. We spectrally resolve, for the first time, all solar absorption, including sharp resonances, in typical liquid water clouds. Resolving all sharp resonances requires a resolution in size parameter χ=2π r/λ (r—droplet radius, λ—incident wavelength) of about 10-7. The canonical integration resolution Δχ≈10-1 produces absorption biases up to 70% over 10 nm spectral bands. Hence, neglecting Mie resonances may cause substantial biases in radiance-based retrievals from sensor channels where atmospheric absorption is particle dominated.The canonical resolution produces broadband solar mean and RMS absorption coefficient biases of about 0.02% and 4%, respectively. Self-cancellation of the pseudo-randomly distributed biases explains why the mean bias is much smaller than the RMS bias. Exceeding 1% RMS accuracy in solar absorption requiresΔχ

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.

Time-series analysis of Earth's major dust source regions reveals common traits in responses of wind erosion to climate anomalies. Lag cross-correlations of monthly mean aerosol optical depth, precipitation, vegetation, and wind speed are examined from 1979–1993. The response to monthly climate anomalies can differ greatly from the response to seasonal mean climate. The signs, magnitudes, and lags of highly significant (p < 0.01) correlations show that 14 important mineral dust source areas characterized by Prospero et al. (2002) fall into four response categories. Each category represents distinct mechanisms by which climate anomalies influence subsequent atmospheric dust loading on seasonal to interannual timescales. In most regions, precipitation and vegetation together strongly constrain dust anomalies on multiple timescales. In these regions, dry anomalies increase, and wet anomalies reduce, dust emission. Interestingly, in many other regions the contrary is true: Dust and precipitation anomalies correlate positively, consistent with sediment-supply factors. The response timescales are consistent with loss of surface crusts (less than 1 month) and with alluvial transport and dessication (interannual lags). Supply-limited dust emission appears more prevalent than previously thought and is not accounted for in models. Reproducing these wind erodibility responses in models may help remediate underprediction of observed seasonal to interannual dust variability.

The dominant process in producing fine dust aerosols during saltation is thought to be sandblasting. Recent studies claim that due to competing physical processes, emission efficiencies of dust aerosols oscillate with increasing wind friction speed. These oscillations can result in order of magnitude changes in dust mass emissions. Our work shows that emission efficiencies, and hence emissions of dust aerosols are smooth functions of the wind friction speed for natural soil size distributions. This rules out oscillations as an explanation for scatter in experimental data. We show and explain the reasons for the oscillations.

We describe a model for predicting the size-resolved distribution of atmospheric dust for climate and chemistry-related studies. The dust distribution from 1990 to 1999 is simulated with our mineral aerosol entrainment and deposition module embedded in a chemical transport model. Mobilization processes include entrainment thresholds for saltation, moisture inhibition, drag partitioning, and saltation feedback. For mobilization we assume that soil texture is globally uniform and is replete with saltators. Soil erodibility is prescribed by a new physically based geomorphic index that is proportional to the runoff area upstream of each source region. Dry deposition processes include sedimentation and turbulent mix-out. Nucleation scavenging and size-resolved washout in both stratiform and convective cloud types are represented. Simulations of the 1990s broadly agree with station observations and satellite-inferred dust distributions. Without invoking anthropogenic mechanisms the model captures the seasonal migration of the transatlantic African dust plume, and it captures the spring maximum in Asian dust outflow and concentration over the Pacific. We estimate the 1990s global annual mean and variability ofD < 10 μm dust to be the following: emissions, 1490 ± 160 Tg yr−1; burden, 17 ± 2 Tg; and optical depth at 0.63 μm, 0.030 ± 0.004. This emission, burden, and optical depth are significantly lower than some recent estimates. The model underestimates transport and deposition of East Asian and Australian dust to some regions of the Pacific Ocean. An underestimate of long-range transport of particles larger than 3 μm contributes to this bias. Our experiments support the hypothesis that dust emission “hot spots” exist in regions where alluvial sediments have accumulated and may be disturbed.

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.