ALBERT

Description: A 250-km-long broadband and long-period (0.01–20,000 s periods) magnetotelluric (MT) line along latitude 401/2°N spans from the California-Nevada border at longitude 120°W across the buried extension of the northern Sierra Nevada, the southern Cascades arc, and the subducting Gorda plate to longitude 123°W. The resulting resistivity cross section reveals conductors at the locations of dewatering of the subducting metasomatized crust at 100 km depth, partial melting of the mantle wedge at 40–60 km depth, and melting in the crust shallower than 40 km. The conductor at 100 km is too conductive (8 S/m) to result solely from magma; very conductive fluids must be present either separately or incorporated into hydrous melts. A melt fraction of 7% is estimated for the mantle wedge (average conductivity is 0.06 S/m). Sites of Pleistocene and Holocene volcanism at Lassen Peak, Hat Creek, and Poison Lake are closely associated with crustal conductors inferred to be shallow magma. The MT study also reveals presumably basaltic magma at 40–50 km depth in the upper mantle where the eastern end of the profile extends into the Basin and Range province.

Description: This study examines the uncertainty in calculating the fundamental climatological characteristics of precipitation in the East Asia region from multiple fine-resolution gridded analysis datasets based on in-situ rain gauge observations. Five observation-based gridded precipitation datasets are used to derive the long-term means, standard deviations in lieu of interannual variability and linear trends over the 28-year period from 1980 to 2007. Both the annual and summer (June–July–August) mean precipitation is examined. The agreement amongst these precipitation datasets are examined using multiple metrics including the signal-to-noise ratio (SNR) defined as the ratio between long-term means and the corresponding standard deviations, and Taylor diagrams which allows examinations of the pattern correlation, the standard deviation, and the centered root mean square error. It is found that the five gauge-based precipitation analysis datasets agree well in the long-term mean and interannual variability in most of the East Asia region including eastern China, Manchuria, South Korea, and Japan, which are densely populated and have fairly high density observation networks. The regions of large inter-dataset variations include Tibetan Plateau, Mongolia, northern Indo-China, and North Korea. The regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or extreme high elevations. Unlike the long-term mean and interannual variability, agreements between datasets in the linear trend is weak, both for the annual and summer mean values. In most of the East Asia region, the SNR for the linear trend is below 0.5, i.e., the inter-dataset variability exceeds the multi-data ensemble mean. The uncertainty in the spatial distribution of long-term means among these datasets occurs both in the spatial pattern and variability, but the uncertainty for the interannual variability and time trend is much larger in the variability than in the pattern correlation. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over the East Asia region.

Description: This study examines the uncertainty in calculating the fundamental climatological characteristics of precipitation in the East Asia region from multiple fine-resolution gridded analysis data sets based on in situ rain gauge observations and data assimilations. Five observation-based gridded precipitation data sets are used to derive the long-term means, standard deviations in lieu of interannual variability and linear trends over the 28-year period from 1980 to 2007. Both the annual and summer (June–July–August) mean precipitation is examined. The agreement amongst these precipitation data sets is examined using two metrics including the signal-to-noise ratio (SNR) defined as the ratio between long-term means and the corresponding standard deviations, and Taylor diagrams, which allow examinations of the pattern correlation, the standard deviation, and the centered root mean square error. It is found that the five gauge-based precipitation analysis data sets agree well in the long-term mean and interannual variability in most of the East Asia region including eastern China, Manchuria, South Korea, and Japan, which are densely populated and have fairly high-density observation networks. The regions of large inter-data-set variations include Tibetan Plateau, Mongolia, northern Indo-China, and North Korea. The regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or extremely high elevations. Unlike the long-term mean and interannual variability, agreement between data sets in the linear trend is weak, both for the annual and summer mean values. In most of the East Asia region, the SNR for the linear trend is below 0.5: the inter-data-set variability exceeds the multi-data ensemble mean. The uncertainty in the spatial distribution of long-term means among these data sets occurs both in the spatial pattern and variability, but the uncertainty for the interannual variability and time trend is much larger in the variability than in the pattern correlation. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over the East Asia region.

Description: Since the air quality forecast is related to both chemistry and meteorology, the coupled atmosphere–chemistry data assimilation (DA) system is essential to air quality forecasting. Ozone (O3) plays an important role in chemical reactions and is usually assimilated in chemical DA. In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting atmospheric as well as chemical variables. To identify the impact of O3 observations on TC structure, including atmospheric and chemical information, we employed the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) with an ensemble-based DA algorithm – the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over the East Asia, our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts atmospheric and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on atmospheric variables was similar in both over China and near the TC. The analysis results are validated using several measures that include the cost function, root-mean-squared error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis – the cost function and root mean square error have decreased by 16.9 and 8.87%, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeast China.

Description: Optimization of land surface models has been very challenging due to the increasing complexity of such models. Typical parameter calibration techniques often limit the solution of the spatiotemporal discrepancy in the modeling performance levels especially for regional applications. Thus, in this study, an attempt was made to perform scheme-based model optimization by designing a framework for coupling a micro-genetic algorithm (micro-GA) with the Noah land surface model that has multiple physics options (Noah-MP). Micro-GA controls the scheme selections in 10 different land surface parameterization fields in Noah-MP in order to extract the optimal scheme combination for a certain region. This coupling framework was successfully applied to the optimization of the surface water partitioning in the Korean Peninsula, promising not only the effectiveness of the scheme-based optimization but also model diagnosis capability by exploring the scheme sensitivity during the micro-GA evolution process. Then, the method was applied to four different regions in East Asia that have different climatic characteristics. The results indicate that (1) the optimal scheme combinations vary with the regions, (2) schemes related to the surface water partitioning are important for the modeling accuracy, and (3) specialized post-parameter optimization for each region may be required.

Description: Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, the vegetation effect rarely exerts on the surface albedo in winter in East Asia with only these parameters. Thus, we investigated the vegetation effects on the snow-covered albedo from observations and evaluated the model improvement by considering such effect. We found that calculation of albedo without proper reflection of the vegetation effect is mainly responsible for the large positive bias in winter. Therefore, we developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the winter albedo. As a result, the Noah-MP's performance in albedo has been significantly improved – RMSE is reduced by approximately 73%.

Description: In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed, e.g., atmospheric observations can exert impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.

Description: In this study, we examined the structure of an ensemble-based coupled atmosphere–chemistry forecast error covariance. The Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem), a coupled atmosphere–chemistry model, was used to create an ensemble error covariance. The control variable includes both the dynamical and chemistry model variables. A synthetic single observation experiment was designed in order to evaluate the cross-variable components of a coupled error covariance. The results indicate that the coupled error covariance has important cross-variable components that allow a physically meaningful adjustment of all control variables. The additional benefit of the coupled error covariance is that a cross-component impact is allowed; e.g., atmospheric observations can exert an impact on chemistry analysis, and vice versa. Given the realistic structure of ensemble forecast error covariance produced by the WRF-Chem, we anticipate that the ensemble-based coupled atmosphere–chemistry data assimilation will respond similarly to assimilation of real observations.

Description: Optimization of land surface models has been challenging due to the model complexity and uncertainty. In this study, we performed scheme-based model optimizations by designing a framework for coupling "the micro-genetic algorithm" (micro-GA) and "the Noah land surface model with multiple physics options" (Noah-MP). Micro-GA controls the scheme selections among eight different land surface parameterization categories, each containing 2–4 schemes, in Noah-MP in order to extract the optimal scheme combination that achieves the best skill score. This coupling framework was successfully applied to the optimizations of evapotranspiration and runoff simulations in terms of surface water balance over the Han River basin in Korea, showing outstanding speeds in searching for the optimal scheme combination. Taking advantage of the natural selection mechanism in micro-GA, we explored the model sensitivity to scheme selections and the scheme interrelationship during the micro-GA evolution process. This information is helpful for better understanding physical parameterizations and hence it is expected to be effectively used for further optimizations with uncertain parameters in a specific set of schemes.

Description: Ozone (O3) plays an important role in chemical reactions and is usually incorporated in chemical data assimilation (DA). In tropical cyclones (TCs), O3 usually shows a lower concentration inside the eyewall and an elevated concentration around the eye, impacting meteorological as well as chemical variables. To identify the impact of O3 observations on TC structure, including meteorological and chemical information, we developed a coupled meteorology–chemistry DA system by employing the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and an ensemble-based DA algorithm – the maximum likelihood ensemble filter (MLEF). For a TC case that occurred over East Asia, Typhoon Nabi (2005), our results indicate that the ensemble forecast is reasonable, accompanied with larger background state uncertainty over the TC, and also over eastern China. Similarly, the assimilation of O3 observations impacts meteorological and chemical variables near the TC and over eastern China. The strongest impact on air quality in the lower troposphere was over China, likely due to the pollution advection. In the vicinity of the TC, however, the strongest impact on chemical variables adjustment was at higher levels. The impact on meteorological variables was similar in both over China and near the TC. The analysis results are verified using several measures that include the cost function, root mean square (RMS) error with respect to observations, and degrees of freedom for signal (DFS). All measures indicate a positive impact of DA on the analysis – the cost function and RMS error have decreased by 16.9 and 8.87 %, respectively. In particular, the DFS indicates a strong positive impact of observations in the TC area, with a weaker maximum over northeastern China.