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Smoke aerosols emitted by wildfires can ascend to the free troposphere, travel over long distances and descend to affect local air quality (AQ) in downwind areas. This study investigates the AQ impact of long-range transported (LRT) smoke aerosols from western North America during summer 2017 in New York State (NYS) using observations and numerical products. Analysis of total fine particulate matter (PM2.5) and black carbon measurements at Queens and Buffalo shows that about 38% and 43% of the polluted events are related to the LRT smoke aerosols, respectively. Two LRT smoke events transported from the Pacific Northwest to NYS on Sep. 5 and 16, 2017, are analyzed. The transport path is determined by the large-scale flow (positive tilted trough for the 1st case and stationary front for the 2nd case). During both events, smoke aerosols were entrained into the boundary layer during the growth of the planetary boundary layer (PBL) and accumulated near surface when the PBL collapsed. The enhanced aerosol mass is primarily due to carbonaceous and secondary sulfate aerosols. Although the two events differed in aerosol loadings (150 versus 80 mg m−2 for column mass density), weather conditions (cold front passage versus high-pressure system), and entrainment rates (6 versus 12 cm s−1), the AQ impacts were comparable for two events (about 10 μg m−3 increase of total PM2.5). Our study of two smoke cases indicates that the LRT smoke events even with moderate intensity would degrade local AQ if the underlying meteorological conditions are favorable for downward mixing.

In this paper, we proposed two ways to understand the rank deficiency in the continuous system (boundary integral equation method, BIEM) and discrete system (boundary element method, BEM) for a circular case. The infinite-dimensional degree of freedom for the continuous system can be reduced to finite-dimensional space using the generalized Fourier coordinates. The property of the second-order tensor for the influence matrix under different observers is also examined. On the other hand, the discrete system in the BEM can be analytically studied, thanks to the spectral property of circulant matrix. We adopt the circulant matrix of odd dimension, (2N + 1) by (2N + 1), instead of the previous even one of 2N by 2N to connect the continuous system by using the Fourier bases. Finally, the linkage of influence matrix in the continuous system (BIE) and discrete system (BEM) is constructed. The equivalence of the influence matrix derived by using the generalized coordinates and the circulant matrix are proved by using the eigen systems (eigenvalue and eigenvector). The mechanism of degenerate scale for a circular domain can be analytically explained in the discrete system.

Aqueous chemical processing within cloud and fog water is thought to be a key process in the production and transformation of secondary organic aerosol mass, found abundantly and ubiquitously throughout the troposphere. Yet, significant uncertainty remains regarding the organic chemical reactions taking place within clouds and the conditions under which those reactions occur, owing to the wide variety of organic compounds and their evolution under highly variable conditions when cycled through clouds. Continuous observations from a fixed remote site like Whiteface Mountain (WFM) in New York State and other mountaintop sites have been used to unravel complex multiphase interactions in the past, particularly the conversion of gas-phase emissions of SO2 to sulfuric acid within cloud droplets in the presence of sunlight. These scientific insights led to successful control strategies that reduced aerosol sulfate and cloud water acidity substantially over the following decades. This paper provides an overview of observations obtained during a pilot study that took place at WFM in August 2017 aimed at obtaining a better understanding of Chemical Processing of Organic Compounds within Clouds (CPOC). During the CPOC pilot study, aerosol cloud activation efficiency, particle size distribution, and chemical composition measurements were obtained below-cloud for comparison to routine observations at WFM, including cloud water composition and reactive trace gases. Additional instruments deployed for the CPOC pilot study included a Doppler lidar, sun photometer, and radiosondes to assist in evaluating the meteorological context for the below-cloud and summit observations.

This study incorporates aerosol effects into satellite radiance calculations within the Global Data Assimilation System (GDAS) to investigate its impact on the analyses and forecasts of African easterly waves (AEWs). Analysis fields from the aerosol-aware assimilation experiment were compared to an aerosol-blind control during August 2017. The results showed that the aerosol-aware assimilation warmed the Saharan boundary layer, accelerated the African easterly jet, and modified the time-averaged AEWs by enhancing the northern track and reducing the southern track. The changes to the tracks are qualitatively consistent with arguments of baroclinic and barotropic instability. During the time period, we also examined two AEWs that developed hurricanes Gert and Harvey over the Atlantic but were structurally different over Africa; the AEW for Gert consisted of a southern vortex, while the AEW for Harvey consisted of a northern and southern vortex. Analysis differences of the cases showed stronger vorticity changes for the AEW that developed Harvey, which we attribute to the aerosol-aware assimilation capturing the radiative effects of a large-scale Saharan dust plume interacting with the northern vortex of the wave. Subsequent forecasts for the AEW cases using the Global Forecast System (GFS, v14) showed that the aerosol-aware assimilation reduced errors in the downstream vorticity structure for the AEW that developed Harvey; neutral improvement was found for the AEW that developed Gert. Thus, aerosol-affected radiances in the assimilation system have the ability to account for dust radiative effects on the analyzed AEWs, which, in turn, can improve the forecasting of AEWs downstream.

This study incorporates time-varying aerosols into satellite radiance calculations within the Global Data Assimilation System (GDAS) to investigate its impact on African easterly waves (AEWs) and their environment. Comparison of analysis fields from the aerosol-aware experiment and an aerosol-blind control during August 2017 showed that the aerosol-affected radiances accelerated the African easterly jet and West African monsoon flow; warmed the Saharan boundary layer; and modified the AEW vorticity structure, with increases in the northern circulation and decreases in the southern circulation. Analysis fields from each experiment were used in the Global Forecast System (GFS) to examine differences in forecasting two AEW cases that developed hurricanes over the Atlantic, but were structurally different over North Africa. The aerosol-aware experiment reduced errors in forecasting the AEW case whose northern circulation interacted with a large-scale Saharan dust plume; neutral improvement was found for the other AEW, which did not contain a northern circulation nor interacted with a dust plume. The changes to the analysis fields by the aerosol-aware assimilation are reminiscent of dust radiative effects that operate on AEWs and their environment. That is, the aerosol-affected radiances produce corrections to the brightness temperatures that modify the analysis fields like dust aerosols that are radiatively coupled to the atmospheric variables in the forecast model. We show qualitatively that dust radiative effects are captured by the aerosol-affected radiances for the AEW case that interacted with a dust plume, which served to improve forecasts of the wave downstream.

Twenty-six balloon-borne ozonesondes were launched near the north shore of central Long Island in the summers of 2018 and 2019 as part of the Long Island Sound Tropospheric Ozone Study (LISTOS). Wh...

Aerosol radiative effects have been studied extensively by climate and weather research communities. However, aerosol impacts on radiance in the context of data assimilation (DA) have received little research attention. In this study, we investigated the aerosol impacts on the assimilation of satellite radiances by incorporating time-varying three-dimensional aerosol distributions into the radiance observation operator. A series of DA experiments was conducted for August 2017. We assessed the aerosol impacts on the simulated brightness temperatures (BTs), bias correction and quality control (QC) algorithms for the assimilated infrared sensors, and analyzed temperature fields. We found that taking the aerosols into account reduces simulated BT in thermal window channels (8 to 13μm) by up to 4 K over dust-dominant regions. The cooler simulated BTs result in more positive first-guess departures, produce more negative biases, and alter the QC checks about 20%/40% of total/assimilated observations at the wavelength of 10.39μm. As a result, assimilating aerosol-affected BTs produces a warmer analyzed lower atmosphere and sea surface temperature which have better agreement with measurements over the trans-Atlantic region.

The existing National Air Quality Forecast Capability (NAQFC) operated at NOAA provides operational forecast guidance for ozone and particle matter with aerodynamic diameter less than 2.5 μm (PM2.5) over the contiguous 48 U.S. states (CONUS) using the Community Multi-scale Air Quality (CMAQ) model. Currently NAQFC is using chemical lateral boundary conditions (CLBCs) from a monthly climatology, which cannot capture pollutant intrusion events originated outside of the model domain. In this study, we developed a model framework to introduce the time-varying chemical simulation from the Goddard Earth Observing System Model, version 5 (GEOS) as the CLBCs to drive NAQFC. The method of mapping GEOS chemical species to CMAQ CB05-Aero6 species was also developed. We then evaluated NAQFC's performance using the new CLBCs from GEOS. The utilization of the GEOS dynamic CLBCs showed an overall best score when comparing the NAQFC simulation with the surface observations during the Saharan dust intrusion and Canadian wildfire events in summer 2015: the PM2.5 correlation coefficient R was improved from 0.18 to 0.37 and the mean bias was narrowed from −6.74 μg/m3 to −2.96 μg/m3 over CONUS. The CLBCs' influences depended on not only the distance from the inflow boundary, but also species and their regional characteristics. For the PM2.5 prediction, the CLBC's effect on the correlations was mainly near the inflow boundary, and its impact on the background could reach farther inside the domain. The CLBCs also altered background ozone through the inflows of ozone itself and its precursors. It was further found that aerosol optical thickness (AOT) from VIIRS retrieval correlated well to the column CO and elemental carbon from GEOS, based on which the new CLBCs for wildfire intrusion event was derived. The AOT derived CLBCs successfully captured the wildfire intrusion events in our case study for summer 2018. It can be a useful alternative in case the CLBCs of GEOS are not available.

A methodology utilizing multiple aerosol measurements and reanalysis was developed to analyze the long-range transport (LRT) of North African and Asian dust events and their effect on contiguous U.S. (CONUS) air quality. Two events in 2015, that originated inland from North Africa and Asia, were detected with aerosol optical depth (AOD) greater than 0.8 over the Gulf of Mexico (June 21) and west coast of Oregon and Washington (April 19). Large amounts of dust were rising over Sahara-Sahel and Taklamakan deserts during these events. For the North African dust event, dust remained the dominant aerosol species transporting from North Africa to CONUS. For Asian dust, before the Taklamakan dust outbreak traveled across the Pacific Ocean, it mixed with sulfate from East China and organic carbons from Siberia. The trans-Atlantic North African dust emerged and compressed below 5 km over the Azores High, whereas the trans-Pacific Asian dust entered the westerlies (5–10 km) along with the mid-latitude frontal system. For the two cases examined, the North African dust event tended to have more influence on CONUS air quality. During the LRT periods, there were 4 of 17 rural and suburban stations (with AOD > 0.5 as the LRT dust approached) in Texas and Louisiana reporting PM2.5 daily maximum observations greater than 36.3 μg m−3. In contrast, the Asian dust stayed above ground level, and all of the 4 rural and suburban stations (with AOD > 0.5 as the LRT dust approached) in Washington and Oregon observed daily maximum PM2.5 below 22.4 μg m−3. The integration of measurements and modeling was able to successfully characterize the sources and transport patterns of the LRT dust events, and differentiate their influence on CONUS air quality. Even though there was high AOD from the Asian LRT dust detected over the western CONUS, significant impacts on surface air quality was not observed as the dust remained aloft.

An accurate representation of aerosols in global numerical weather prediction (NWP) models is important to predict major air pollution events and to also understand aerosol effects on short-term weather forecasts. Recently the global aerosol forecast model at NOAA, the NOAA Environmental Modeling System (NEMS) GFS Aerosol Component (NGAC), was upgraded from its dust-only version 1 to include five species of aerosols (black carbon, organic carbon, sulfate, sea salt and dust). This latest upgrade, now called NGACv2, is an in-line aerosol forecast system providing three-dimensional aerosol mixing ratios along with aerosol optical properties, including aerosol optical thickness (AOT), every 3 h up to 5 days at global 1∘×1∘ resolution. In this paper, we evaluated nearly 1.5 years of model AOT at 550 nm with available satellite retrievals, multi-model ensembles and surface observations over different aerosol regimes. Evaluation results show that NGACv2 has high correlations and low root mean square errors associated with African dust and also accurately represented the seasonal shift in aerosol plumes from Africa. Also, the model represented southern African and Canadian forest fires, dust from Asia, and AOT within the US with some degree of success. We have identified model underestimation for some of the aerosol regimes (particularly over Asia) and will investigate this further to improve the model forecast. The addition of a data assimilation capability to NGAC in the near future is expected to provide a positive impact in aerosol forecast by the model.

The NEMS GFS Aerosol Component Version 2.0 (NGACv2) for global multispecies aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), the NASA Goddard Space Flight Center (GSFC), and the University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0, NGACv1). With NGACv2, additional sea salt, sulfate, organic carbon, and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar-orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite sea surface temperature (SST) physical retrievals, and the global solar insolation estimation. Positive impacts have been seen in these applications.

Smoke aerosols emitted from wildfires can transport across long distances and affect the local air quality in downwind regions. In New York State (NYS), the local air quality has significantly improved due to reductions in anthropogenic emission over the past decades. As the intensity and frequency of wildfires are continuously increasing under changing climate, smoke aerosols are predicted to become the dominant source of fine particulate matter (PM2.5) concentration in NYS in the future. In this study, smoke and non-smoke cases in NYS during the summer seasons of 2012–2019 were identified using satellite measurements and aerosol reanalysis products. Overall, smoke cases showed higher PM2.5 concentrations than non-smoke cases with average PM2.5 concentrations of 11.5 ± 5.9 μg m−3 and 6.6 ± 4.6 μg m−3, respectively. PM2.5 concentrations exceeding 20 μg m−3 mainly occurred during smoke cases. In addition, an artificial neural network (ANN) algorithm was used to estimate surface PM2.5 mass concentrations at 21 air quality monitoring sites in NYS. Results showed that, for smoke cases, the application of predictors designed as indicators of vertical transport mechanisms and smoke inflow from the fire source regions generally improved the model performance by reducing the model errors. Also, analysis of the variable correlations and variable importance indicated that synoptic subsidence, entrainment process, and turbulent mixing within PBL collectively contributed to PM2.5 concentrations for smoke cases. Machine learning techniques showed the capabilities of learning the general air quality features, characterizing the key contributors to PM2.5 concentrations, and distinguishing the vertical transport processes of smoke aerosols.

The NOAA National Centers for Environmental Prediction (NCEP) implemented the NOAA Environmental Modeling System (NEMS) Global Forecast System (GFS) Aerosol Component (NGAC) for global dust forecasting in collaboration with NASA Goddard Space Flight Center (GSFC). NGAC Version 1.0 has been providing 5-day dust forecasts at 1° × 1° resolution on a global scale, once per day at 00:00 Coordinated Universal Time (UTC), since September 2012. This is the first global system capable of interactive atmosphere aerosol forecasting at NCEP. The implementation of NGAC V1.0 reflects an effective and efficient transitioning of NASA research advances to NCEP operations, paving the way for NCEP to provide global aerosol products serving a wide range of stakeholders, as well as to allow the effects of aerosols on weather forecasts and climate prediction to be considered.

The NEMS GFS Aerosol component (NGAC) version 2.0 for global multi-species aerosol forecast has been developed at the National Centers of Environment Prediction (NCEP) in collaboration with the NESDIS Center for Satellite Applications and Research (STAR), NASA Goddard Space Flight Center (GSFC), and University at Albany, State University of New York (SUNYA). This paper describes the continuous development of the NGAC system at NCEP after the initial global dust-only forecast implementation (NGAC version 1.0). With version 2, additional sea salt, sulfate, organic carbon and black carbon aerosol species were included. The smoke emissions are from the NESDIS STAR's Global Biomass Burning Product (GBBEPx), blended from the global biomass burning emission product from a constellation of geostationary satellites (GBBEP-Geo) and GSFC's Quick Fire Emission Data Version 2 from a polar orbiting sensor (QFED2). This implementation advanced the global aerosol forecast capability and made a step forward toward developing a global aerosol data assimilation system. The aerosol products from this system have been used by many applications such as for regional air quality model lateral boundary conditions, satellite SST physical retrievals and the global solar insolation estimation. Positive impacts have been seen in these applications.

An accurate representation of aerosols in global Numerical Weather Prediction (NWP) models is important to predict major air pollution events and to also understand aerosol effects on short-term weather forecasts. Recently the global aerosol forecast model at NOAA, the NOAA Environmental Modeling System (NEMS) GFS Aerosol Component (NGAC), was upgraded from its dust-only version 1 to include five species of aerosols (black carbon, organic carbon, sulfate, sea-salt and dust). This latest upgrade, now called NGACv2, is an in-line aerosol forecast system providing 3-dimensioanl aerosol mixing ratios along with aerosol optical properties, including aerosol optical thickness (AOT), every 3 hours up to 5 days at global 1° × 1° resolution. In this paper, we evaluated nearly one and half years of model AOT at 550 nm with available satellite retrievals, multi-model ensembles and surface observations over different aerosol regimes. Evaluation results show that NGACv2 has high correlations and low root mean square errors associated with African dust and also accurately represented the seasonal shift of aerosol plumes from Africa. Also, the model represented South African and Canadian forest fires, dust from Asia and AOT within the US with some degree of success. We have identified model underestimation for some of the aerosol regimes (particularly over Asia) and will investigate this further to improve the model forecast. The addition of a data assimilation capability to NGAC in the near future is expected to improve some of the model biases.

In-situ PM2.5 observations and the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol reanalysis were analyzed to characterize long-range transported high PM2.5 events over Northern Taiwan during winter seasons. MERRA-2 aerosol composition was evaluated using independent in-situ observations at the Cape Fuguei (CAFE) site. Results showed that MERRA-2 was able to distinguish the contribution of different species within complicated aerosol mixtures. Fifty transported high PM2.5 events were identified during the winters of 2005–2015. To investigate the transport characteristics associated with Asian continental outflow, these events were further classified into sulfate-dominated, dust-dominated and mixed-composition events. More than 80% of transported events were influenced by Asian dust and 20% of them were dust-dominated. Both sulfate-dominated and dust-dominated events showed similar average PM2.5 concentrations (~44 μg m−3), while dust-dominated events showed a higher average PM10 concentration (114 μg m−3) and thus a lower PM2.5/PM10 ratio (0.41) compared to sulfate-dominated events (94 μg m−3 and 0.46, respectively). Results indicated that the low-level trough at 700 hPa plays a critical role in determining the transport paths of dust aerosols and their impact on local air quality. Therefore, the influence of Asian dust over Northern Taiwan cannot be neglected during winter seasons, and could potentially offset the effectiveness of emission controls and pollutant reductions. Such potential impacts could occur over vast areas affected by Asian continental outflow. The utilization of aerosol reanalysis provided valuable insights for long-range transport studies in the regions with complicated aerosol compositions.

© 2016, Springer-Verlag Berlin Heidelberg. The second West African Monsoon Modeling and Evaluation Project Experiment (WAMME II) is designed to improve understanding of the possible roles and feedbacks of sea surface temperature (SST), land use land cover change (LULCC), and aerosols forcings in the Sahel climate system at seasonal to decadal scales. The project’s strategy is to apply prescribed observationally based anomaly forcing, i.e., “idealized but realistic” forcing, in simulations by climate models. The goal is to assess these forcings’ effects in producing/amplifying seasonal and decadal climate variability in the Sahel between the 1950s and the 1980s, which is selected to characterize the great drought period of the last century. This is the first multi-model experiment specifically designed to simultaneously evaluate such relative contributions. The WAMME II models have consistently demonstrated that SST forcing is a major contributor to the twentieth century Sahel drought. Under the influence of the maximum possible SST forcing, the ensemble mean of WAMME II models can produce up to 60 % of the precipitation difference during the period. The present paper also addresses the role of SSTs in triggering and maintaining the Sahel drought. In this regard, the consensus of WAMME II models is that both Indian and Pacific Ocean SSTs greatly contributed to the drought, with the former producing an anomalous displacement of the Intertropical Convergence Zone before the WAM onset, and the latter mainly contributes to the summer WAM drought. The WAMME II models also show that the impact of LULCC forcing on the Sahel climate system is weaker than that of SST forcing, but still of first order magnitude. According to the results, under LULCC forcing the ensemble mean of WAMME II models can produces about 40 % of the precipitation difference between the 1980s and the 1950s. The role of land surface processes in responding to and amplifying the drought is also identified. The results suggest that catastrophic consequences are likely to occur in the regional Sahel climate when SST anomalies in individual ocean basins and in land conditions combine synergistically to favor drought.

The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

This chapter presented the latest Community Radiative Transfer Model (CRTM), which is applicable for passive microwave, infrared and visible sensors. The CRTM has been used in operational radiance assimilations in support of weather forecasting and in the generation of satellite products. In the paper we discussed the CRTM applications to assimilate aerosol optical depths derived from satellite measurements. The assimilation improved the analysis of aerosol mass concentrations, and enhanced the forecast skill for aerosol mass concentrations. We also introduced a retrieval algorithm and a retrieval product of carbon monoxide by using satellite measurements.

The Noah land surface model (LSM) has recently been implemented into the Experimental Climate Prediction Center’s (ECPC’s) global Seasonal Forecast Model (SFM). Its performance is compared to the older ECPC SFM with the Oregon State University (OSU) LSM using two sets of 10-member 50-yr Atmospheric Model Intercomparison Project (AMIP) runs. The climatological biases of several fields tend to increase with the Noah LSM. The differences in near-surface temperature bias are traced to changes in the energy budget. In addition to climatology, the variability and skill (anomaly correlation with observations) of the two ensembles are considered. Unlike the climatology, the near-surface temperature skill of the ECPC SFM generally improves with the Noah LSM. Other climatological fields, such as precipitation, show little change in skill. While the global results are mixed, there are however significant regional improvements over Africa both in terms of climatological bias and skill. In the central African Congo River basin, the Noah LSM removed a warm-dry bias and improved upon the near-surface temperature skill of the OSU LSM. In the African Sahel, the Noah LSM greatly enhanced the climatology, variability, and skill of the ECPC SFM as well as improving the location of the African easterly jet.

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

The threshold values of indicator species and ratios delineating the transition between NOx and VOC sensitivity of ozone formation are assumed to be universal by various investigators. However, our previous studies suggested that threshold values might vary according to the locations and conditions. In this study, threshold values derived from various model simulations at two different locations (the area of Switzerland by UAM Model and San Joaquin Valley of Central California by SAQM Model) are examined using a new approach for defining NOx and VOC sensitive regimes. Possible definitions for the distinction of NOx and VOC sensitive ozone production regimes are given. The dependence of the threshold values for indicators and indicator ratios such as NOy, O3/NOz, HCHO/NOy, and H2O2/HNO3 on the definition of NOx and VOC sensitivity is discussed. Then the variations of threshold values under low emission conditions and in two different days are examined in both areas to check whether the models respond consistently to changes in environmental conditions. In both cases, threshold values are shifted similarly when emissions are reduced. Changes in the wind fields and aging of the photochemical oxidants seem to cause the day-to-day variation of the threshold values. O3/NOz and HCHO/NOy indicators are predicted to be unsatisfactory to separate the NOx and VOC sensitive regimes. Although NOy and H2O2/HNO3 provide a good separation of the two regimes, threshold values are affected by changes in the environmental conditions studied in this work.

Trajectory calculations are employed to identify Stratospheric Aerosol and Gas Experiment (SAGE) II flights sampling the same air mass as is observed by a ground-based aerosol lidar at Garmisch-Partenkirchen, Germany (47.5°N, 11.1°E, 735 m above sea level), during the periods of January-April 1993 and January-December 1998. Intercomparisons between lidar-observed and SAGE II-derived backscatters at 532 nm are conducted. Percentage differences between trajectory-tracked SAGE II profiles and ground-based lidar observations with respect to aerosol lidar are generally within 20-40%, though localized discrepancies >50% are found for some cases. In addition, aerosol extinction, aerosol to molecular extinction ratio, and ozone mixing ratio profiles obtained from the SAGE II flights overpassing the vicinity of Garmisch-Partenkirchen during the January-April 1993 period are compared with profiles obtained from corresponding trajectory-tracked SAGE II flights. Percentage discrepancies between SAGE II ozone profiles are generally within 10-20% above the Junge layer. Data comparisons for aerosol profiles show mixed results. While some cases agree within the error bars, there are several cases where percentage discrepancies exceed 50%.

Previous model studies suggested that ambient measurements of key chemical species and ratios of species could be used to assess the sensitivities of ozone formation to reductions in precursor emissions. Threshold values of these indicator species and ratios, delineating the transition between VOG and NOx sensitivity, were proposed. Subsequently, measurement studies have assumed the universality of these threshold criteria and have compared local observed indicators with previously established criteria to assess ozone sensitivities. In this study the concept of indicator species is extended to combinations of observable species that are consistently associated with different site characteristics (e.g., ozone sensitivities and emissions features). The results of SARMAP Air Quality Model (SAQM) simulations in the San Joaquin Valley, California, are used to investigate the applicability of indicators to assess ozone sensitivities and emissions features. The use of three indicator ratios (O3/(NOy-NOx), HCHO/NO2 and H2O2/HNO3) along with SAQM-derived threshold criteria is found to be effective for identifying VOC- or NOx-sensitive regimes. NOy and (NOy-NOx)NOy are found to be useful in describing emission features and threshold criteria are derived by SAQM prediction. SAQM-derived threshold criteria for assessing ozone sensitivities are found to differ from threshold criteria proposed by previous studies using different models and under different conditions. Such differences suggest that threshold criteria are likely to be dependent on locations and environmental conditions, including emission patterns and rates. Therefore local observed indicator species and ratios can be used to determine ozone sensitivities only if appropriate threshold criteria have been derived for the local conditions.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts.

Coherent structures in turbulent flow above a midlatitude deciduous forest are identified using a wavelet analysis technique. Coupling between motions above the canopy (z/h=1.5, whereh is canopy height) and within the canopy (z/h=0.6) are studied using composite velocity and temperature fields constructed from 85 hours of data. Data are classified into winter and summer cases, for both convective and stable conditions. Vertical velocity fluctuations are in phase at both observation levels. Horizontal motions associated with the structures within the canopy lead those above the canopy, and linear analysis indicates that the horizontal motions deep in the canopy should lead the vertical motions by 90°. On average, coherent structures are responsible for only about 40% of overall turbulent heat and momentum fluxes, much less than previously reported. However, our large data set reveals that this flux fraction comes from a wide distribution that includes much higher fractions in its upper extremes. The separation distanceL s between adjacent coherent structures, 6–10h, is comparable to that obtained in previous observations over short canopies and in the laboratory. Changes in separation between the summer and winter (leafless) conditions are consistent withL s being determined by a local horizontal wind shear scale.

[1] SAGE II and SAGE III are two satellite experiments designed to measure aerosol extinctions and concentrations of trace gases in the atmosphere by using the techniques of solar and/or lunar occultation. SAGE II was launched in October 1984, while SAGE III was launched in December 2001. Four of the nine aerosol channels used by SAGE III are centered at wavelengths very close to that of the four SAGE II aerosol channels. Since 27 February 2002, the first day of SAGE III data set, there are occasions when SAGE II and III measurement locations on the same day are nearly coincident, thereby providing opportunities for a unique measurement comparison. In this paper, the comparison of aerosol extinctions and optical depths at four wavelengths measured by SAGE II and SAGE III in 2002 and 2003 is reported. It was found that in the main aerosol layer, between about 18 to 26 km, differences are less than about 30%. Larger differences are shown at altitudes near the tropopause and around 30 km. In general, SAGE III extinctions at 385 nm are higher than that measured by SAGE II, but the opposite is true for 1020 nm. At 452 nm, SAGE II and SAGE III extinctions are in good agreement and the differences are about 10 to 15%.

[1] Aerosol integral properties inferred from simulated multispectral extinction data at Stratospheric Aerosol and Gas Experiment (SAGE) II and the Halogen Occultation Experiment (HALOE) wavelengths using two different retrieval methods are analyzed and compared. The discrepancy in aerosol integral properties that results from using different retrieval techniques is analyzed, and the differences caused by sampling the same aerosol population at different wavelengths with the same retrieval technique are evaluated. We found that a better agreement was achieved when different methods were applied to the same data set as compared to a case where the same method is applied to data recorded by two different sensors. Our results demonstrate that when comparing aerosol properties derived from different instruments, the particle size information contents from optical instruments have to be taken into consideration together with the propagation of measurement errors and the systematic errors arising from the retrieval methods.