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Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. Key Points: Parametric controls on runoff sensitivity to climate in the land component of an Earth System Model are investigated in a snowmelt runoff regimeRunoff sensitivities are dominated by interactions between certain parameters of subsurface, snow, and plant processesHigh sensitivity is found for soil porosity and depth, saturated fraction, snow cover and densification, stomatal conductance, and photosynthetic capacity [ABSTRACT FROM AUTHOR]

Background: The incidence of multiple primary cancers (MPC), especially involving primary lung cancer (PLC) and primary hematologic malignancies (PHM), is rising. This study aims to analyze clinicopathological features, gene abnormalities, and prognostic outcomes in individuals diagnosed with PLC-PHM MPC. Methods: A retrospective analysis included 89 patients diagnosed with PLC-PHM MPC at the Respiratory or Hematology Departments of Ruijin Hospital from 2003 to 2022 (a total of 842,047 people). Next-generation sequencing (NGS) assessed lung cancer specimens, while Polymerase Chain Reaction (PCR) and NGS were used for hematologic malignancy specimens. Statistical analysis involved survival analysis and Cox regression. Results: PLC-PHM MPC incidence surged from 1.67 per year (2011–2013) to 16.3 per year (2020–2022). The primary demographic for PLC-PHM MPC consists predominantly of elderly (average age 66 years) males (59.6%), with a high prevalence of metachronous MPC (89.9%). The prevailing histological types were lung adenocarcinoma (70.8%) in lung cancer (LC) and mature B-cell lymphomas (50.6%) in hematologic malignancies (HM). Notably, in a molecular testing cohort of 38 LC patients, 84.2% of lung cancer cases exhibited driver mutations, in which EGFR mutations frequence prevalent was 74.2%. In total group of 85 cases achieved a median overall survival (mOS) of 46.2 months, with a 5-year survival rate of 37.9% and advanced LC patients with LC gene mutations achieved a mOS was 52.6 months, with a 5-year OS rate of 30.6%. The median progression-free survival (PFS) following first-line treatment of 11 advanced patients with lung cancer-associated driver gene mutations is 26.6 months. Multivariate Cox regression revealed a favorable OS associated with surgery for LC, favorable PS score, adenocarcinoma pathology of LC, and the presence of genetic abnormalities associated with HM. Conclusion: PLC-PHM MPC incidence is rising, characterized by a significant proportion of lung adenocarcinoma and a high prevalence of positive driver genes, especially in EGFR. Despite suffering from two primary tumors, the PLC-PHM MPC patients had superior data of both PFS and OS, suggesting an inherently intricate background of genetic abnormalities between the two kinds of tumors. [ABSTRACT FROM AUTHOR]

Multisource meteorological re-analyses provide the most reliable forcing data for driving hydrological models to simulate streamflow. We aimed to assess different hydrological responses through hydrological modeling in the upper Lancang-Mekong River Basin (LMRB) using two gridded meteorological datasets, Climate Forecast System Re-analysis (CFSR) and the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS). We selected the Pearson's correlation coefficient (R), percent bias (PBIAS), and root mean square error (RMSE) indices to compare the six meteorological variables of the two datasets. The spatial distributions of the statistical indicators in CFSR and CMADS, namely, the R, PBIAS, and RMSE values, were different. Furthermore, the soil and water assessment tool plus (SWAT+) model was used to perform hydrological modeling based on CFSR and CMADS meteorological re-analyses in the upper LMRB. The different meteorological datasets resulted in significant differences in hydrological responses, reflected by variations in the sensitive parameters and their optimal values. The differences in the calibrated optimal values for the sensitive parameters led to differences in the simulated water balance components between the CFSR- and CMADS-based SWAT+ models. These findings could help improve the understanding of the strengths and weaknesses of different meteorological re-analysis datasets and their roles in hydrological modeling. [ABSTRACT FROM AUTHOR]

Land–atmosphere coupling (LAC) is a critical process in the climate system, and has great effects on surface air temperature over the humid–dry transition zones. Using ERA5/ERA5‐land reanalysis datasets, we show that the summer temperature anomalies over Northwest China (NWC), known as one of the most important LAC hotpots in China, are significantly correlated with the LAC strength during 1981–2020. The stronger the coupling strength between air temperature and land surface, the more pronounced decrease in air temperature and extreme hot days over NWC. Among the four pathways of land–air temperature coupling, including soil moisture (SM)/soil temperature–latent heat flux/sensible heat flux–air temperature coupling, SM–sensible heat flux–air temperature pathway corresponds most closely with the temperature changes in summer. By analyzing the possible local and non‐local physical links in this relationship, we show that the wave trains composed of the intensification of Ural ridge and Central–Asia trough stimulate a meridional fluctuation of westerly airflow over the mid–high latitudes of Eurasia. Large moisture, therefore, converges into NWC, leading to excessive precipitation and deficient incident solar radiation reaching the land surface. In such case, wetter and cooler soil states accelerate the cooling of the environment through feedbacks between the lands, land–atmosphere exchanging fluxes and air temperature locally, accompanied by a significant enhancement in LAC strength. Our study provides insight into the physical processes causing variations in summer temperature over a typical LAC hotspot and suggests that the role of land surface–atmosphere interactions needs to be considered when studying temperature extremes under climate change. Plain Language Summary: There exist strong interactions between land surface and air temperature over Northwest China (NWC) in summer. In the meantime, NWC region (especially Xinjiang province) has undergone severe hot extremes recently and suffered great loss of life and property. In this case, this study is presented to investigate the relationship of variation in surface air temperature with the land–air temperature coupling strength using reanalysis‐based datasets. We revealed that the stronger the coupling strength, the more pronounced decrease in air temperature and extreme hot days there. Both local and non‐local factors contribute to this process. The large‐scale anomalous Arctic Oscillation–East Atlantic/West Russia‐like patterns are conducive to extra water vapor convergence in southern Xinjiang, accompanied by reduced net solar radiation. Such changes further result in significant variations in land states, and local land–air temperature interactions had also been exacerbated, collectively leading to air temperature decreases. These findings are expected to deepen our understanding of the variation of surface air temperature and related extreme temperature events in NWC. Key Points: The stronger the land–atmosphere coupling (LAC), the more decrease of air temperature and extreme hot days (HDs) during summer in Northwest China (NWC)Anomalous circulation patterns correlate with the LAC strength and air temperature variations in NWCThe local land–air temperature interactions enhance the impacts from non‐local atmospheric forcings [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study is to evaluate whether PIV (Pan-Immune-Inflammation Value) and PILE [a score derived from PIV, lactate dehydrogenase (LDH), and Eastern Cooperative Oncology Group Performance Status (ECOG PS)] can predict clinical outcome of anti-PD-1/PD-L1 inhibitor combined with chemotherapy in patients with extensive-stage (ES) small cell lung cancer (SCLC). Methods: A total of 53 patients with ES-SCLC in the control group of clinical trial (NCT03041311) were included in this study. PIV was calculated as follows: (neutrophil count × platelet count × monocyte count)/lymphocyte count. The PILE scores were composited based on PIV, LDH levels, and ECOG PS. The Kaplan–Meier method and Cox hazards regression models were used for survival analyses. Moreover, the predictive ability of PIV and PILE was validated in an independent real-world group consisting of 84 patients. Results: Patients in the low PIV group (PIV < median) had longer progression-free survival (PFS) and overall survival (OS) than those in the high PIV group (PIV ≥ median), along with the HR, which was 2.157 and 2.359, respectively (PFS HR 95% CI: 1.181–3.940, p = 0.012; OS HR 95% CI: 1.168–4.762, p = 0.020). High PILE score was observed relating to worse treatment efficacy (disease control rate (DCR): 84.21% vs. 100%, p = 0.047; durable clinical benefit (DCB) rate: 10% vs. 48.5%, p = 0.060) and poor clinical outcome (median PFS: 4.75 vs. 5.53 m, p = 0.043; median OS: 7.13 vs. 15.93 m, p = 0.002). Similar results were obtained about the predictive and prognostic abilities of PIV and PILE scores in the validation group. Conclusions: High PIV and high PILE were correlated with worse clinical outcomes in ES-SCLC patients treated with anti-PD-1/PD-L1 inhibitor combined with chemotherapy, reflecting that PIV and PILE might be useful to identify patients unlikely to benefit from anti-PD-1/PD-L1 therapy. [ABSTRACT FROM AUTHOR]

In the past decades, data-driven machine-learning (ML) models have emerged as promising tools for short-term streamflow forecasting. Among other qualities, the popularity of ML models for such applications is due to their relative ease in implementation, less strict distributional assumption, and competitive computational and predictive performance. Despite the encouraging results, most applications of ML for streamflow forecasting have been limited to watersheds in which rainfall is the major source of runoff. In this study, we evaluate the potential of random forests (RFs), a popular ML method, to make streamflow forecasts at 1 d of lead time at 86 watersheds in the Pacific Northwest. These watersheds cover diverse climatic conditions and physiographic settings and exhibit varied contributions of rainfall and snowmelt to their streamflow. Watersheds are classified into three hydrologic regimes based on the timing of center-of-annual flow volume: rainfall-dominated, transient, and snowmelt-dominated. RF performance is benchmarked against naïve and multiple linear regression (MLR) models and evaluated using four criteria: coefficient of determination, root mean squared error, mean absolute error, and Kling–Gupta efficiency (KGE). Model evaluation scores suggest that the RF performs better in snowmelt-driven watersheds compared to rainfall-driven watersheds. The largest improvements in forecasts compared to benchmark models are found among rainfall-driven watersheds. RF performance deteriorates with increases in catchment slope and soil sandiness. We note disagreement between two popular measures of RF variable importance and recommend jointly considering these measures with the physical processes under study. These and other results presented provide new insights for effective application of RF-based streamflow forecasting. [ABSTRACT FROM AUTHOR]

Predictive modeling of large-scale spatio-temporal data is an important but challenging problem as it requires training models that can simultaneously predict the target variables of interest at multiple locations while preserving the spatial and temporal dependencies of the data. In this paper, we investigate the effectiveness of applying a multi-task learning approach based on supervised tensor decomposition to the spatio-temporal prediction problem. Our proposed framework, known as SMART, encodes the data as a third-order tensor and extracts a set of interpretable, spatial and temporal latent factors from the data. An ensemble of spatial and temporal prediction models are trained using the latent factors as their predictor variables. Outputs from the ensemble model are aggregated to make predictions on test instances. The framework also allows known patterns from the domain to be incorporated as constraints to guide the tensor decomposition and ensemble learning processes. As the data may grow over space and time, an incremental learning version of the framework is given to efficiently update the models. We perform extensive experiments using a global-scale climate dataset to evaluate the accuracy and efficiency of the models as well as interpretability of the latent factors. [ABSTRACT FROM AUTHOR]

El Niño‐Southern Oscillation (ENSO) is an important driver of interannual climate variability with increasing attention for its impacts on water and flood management. The impact of ENSO on basin‐scale floods during the TRMM period (1998–2013) is examined by using the streamflow outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE). Significant simultaneous correlations between flood indices and Niño 3.4 appear in many flood‐prone river basins during peak flood months across both the tropics and midlatitudes especially for flood frequency and flood duration. Gauged by significant lag‐correlations between floods and Niño 3.4, significant ENSO‐leading‐floods relations are found as well in many river basins in South America, south and southeastern Asia, and northern Africa. These ENSO‐floods‐relations can greatly enhance understanding of physical mechanisms relevant to the ENSO impact and may also improve the skills of basin‐scale monthly‐to‐seasonal flood forecast, thus allowing for better preparedness and management of flood risks. Plain Language Summary: As a naturally occurring phenomenon involving quasi‐periodic variation of air pressure and sea surface temperature, the ENSO impacts global floods through its modulation of atmospheric circulations, weather, and precipitation. Previous studies on ENSO‐floods‐relations are often limited in supporting practical flood disaster preparedness and risk analyses because of the lack of a "complete" reconstruction of past flood events likely due to scattered gauge‐based streamflow data and/or coarse‐resolution retrospective simulations. Here flood events at model grids and for individual river basins are identified using the outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE) with a high spatial resolution (0.125° × 0.125°) driven by the state‐of‐the‐art satellite precipitation product TMPA. Simultaneous and lagged responses of basin‐scale floods to ENSO are explored to provide an improved knowledge of the ENSO effect on global floods and a guidance for flood disaster planning and risk management for the regions consistently impacted by ENSO. Key Points: Simultaneous correlations between flood indices and Nino 3.4 appear in many flood‐prone river basins in the tropics and midlatitudesENSO‐leading‐floods relations gauged by significant lag‐correlations between floods and Nino 3.4 can also be seen in many river basinsThese ENSO‐floods‐relations can greatly help our efforts of exploring basin‐scale monthly‐to‐seasonal flood predictability [ABSTRACT FROM AUTHOR]

Purpose: Etoposide is one of the principal chemotherapeutic agents used for the treatment of small cell lung cancer (SCLC). There are some disadvantages of currently available etoposide injections (EI) such as low LD50, necessary dilution before clinical application, thus, etoposide lipid emulsion (ELE) was developed and expected to have a comparable or better effect on SCLC. Methods: ELE was prepared through high-pressure homogenization method, and a series of evaluations such as encapsulation efficiency (EE%), in vitro release, stability studies, pharmacokinetics study, safety assessment and pharmacodynamic study were systematically performed. Results: ELE had high EE% and good stability. Pharmacokinetics study revealed ELE had a longer T1/2 F compared with EI, which is in agreement with in vitro release in which ELE released slower than EI (EI released over 80% within 12 h, while ELE released 50%). Safety tests showed there was no hematology or significant tendency of accumulated toxicity, and LD50 of ELE was higher than EI. Furthermore, percentage of tumor inhibition (TI%) of ELE was comparable with EI in the same dose. Conclusions: Unlike EI, ELE could further increase the dose, which endowed etoposide with a greater potential for cytotoxic agent. LE is a promising delivery system for etoposide. [ABSTRACT FROM AUTHOR]

Meteorological and hydrological droughts can bring different socioeconomic impacts. In this study, we investigated meteorological and hydrological drought characteristics and propagation using the standardized precipitation index (SPI) and standardized streamflow index (SSI), over the upstream and midstream of the Heihe River basin (UHRB and MHRB, respectively). The correlation analysis and cross-wavelet transform were adopted to explore the relationship between meteorological and hydrological droughts in the basin. Three modeling experiments were performed to quantitatively understand how climate change and human activities influence hydrological drought and propagation. Results showed that meteorological drought characteristics presented little difference between UHRB and MHRB, while hydrological drought events are more frequent in the MHRB. In the UHRB, there were positive relationships between meteorological and hydrological droughts, whereas drought events became less frequent but longer when meteorological drought propagated into hydrological drought. Human activities have obviously changed the positive correlation to negative in the MHRB, especially during warm and irrigation seasons. The propagation time varied with seasonal climate characteristics and human activities, showing shorter values due to higher evapotranspiration, reservoir filling, and irrigation. Quantitative evaluation showed that climate change was inclined to increase streamflow and propagation time, contributing from −57% to 63%. However, more hydrological droughts and shorter propagation time were detected in the MHRB because human activities play a dominant role in water consumption with contribution rate greater than (−)89%. This study provides a basis for understanding the mechanism of hydrological drought and for the development of improved hydrological drought warning and forecasting system in the HRB. [ABSTRACT FROM AUTHOR]

Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model" assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3-6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB. [ABSTRACT FROM AUTHOR]

Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates. [ABSTRACT FROM AUTHOR]

Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of NumericalWeather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0-96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well. [ABSTRACT FROM AUTHOR]

Ensemble forecasting is a widely-used numerical prediction method for modeling the evolution of nonlinear dynamic systems. To predict the future state of such systems, a set of ensemble member forecasts is generated from multiple runs of computer models, where each run is obtained by perturbing the starting condition or using a different model representation of the system. The ensemble mean or median is typically chosen as a point estimate for the ensemble member forecasts. These approaches are limited in that they assume each ensemble member is equally skillful and may not preserve the temporal autocorrelation of the predicted time series. To overcome these limitations, we present an online multi-task learning framework called ORION to estimate the optimal weights for combining the ensemble member forecasts. Unlike other existing formulations, the proposed framework is novel in that its learning algorithm must backtrack and revise its previous forecasts before making future predictions if the earlier forecasts were incorrect when verified against new observation data. We termed this strategy as online learning with restart. Our proposed framework employs a graph Laplacian regularizer to ensure consistency of the predicted time series. It can also accommodate different types of loss functions, including $\epsilon$ -insensitive and quantile loss functions, the latter of which is particularly useful for extreme value prediction. A theoretical proof demonstrating the convergence of our algorithm is also given. Experimental results on seasonal soil moisture forecasts from 12 major river basins in North America demonstrate the superiority of ORION compared to other baseline algorithms. [ABSTRACT FROM PUBLISHER]

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Historically, Michigan's climate had mainly three challenges for grape production: growing season temperatures were too low, the growing season was too short and there was too much rain near harvest. However, climate change in the past decades has led to a vastly different landscape that is evolving to meet the new climate. Recently, there has been a significant move from Vitis labrusca (North American) grape plantings to Vitis vinifera (wine grapes) as a consequence of Michigan's shifting climate. The goal of this study was to analyze the historical shift in climate and its potential future impact on the grape industry. We obtained data climate model projection data from two greenhouse gas (GHG) emission scenarios. First, a multi-linear regression model was built to predict future grape yields (t/ac) using data from the climate model projections. Second, trends in the severity of the three challenges (temperature, season length, precipitation timing) were analyzed. In both GHG scenarios grape yields are seen to improve, but to different extents. The improvement is likely a response to warmer season temperatures canceling out losses to early season frost. Model projections recommend that Michigan's future climate will be more accommodating for all varieties of grapes. This suggests that grape production will continue to grow, but the landscape will continue to evolve with more emphasis on varieties that are more climatically sensitive to cold temperatures. Climate change has greatly affected Michigan's viticultural landscape, and will continue to do so in the coming decades. [ABSTRACT FROM AUTHOR]

Background and Purpose Pro-inflammatory cytokines are important in rheumatoid arthritis ( RA) and their production is mainly regulated by NF- κB and inflammasomes. Carboxyamidotriazole ( CAI) exhibits potent anti-inflammatory activities by decreasing cytokines. Here, we have investigated NACHT, LRR and PYD domains-containing protein ( NALP) inflammasomes in a rat model of RA and explored the therapeutic effects of CAI in this model and the involvement of NF- κB and inflammasomes in the actions of CAI. Experimental Approach The anti-arthritic effects of CAI were assessed in the adjuvant arthritis ( AA) model in rats, using radiological and histological techniques. NALP1 and NALP3 inflammasomes, NF- κB pathway and pro-inflammatory cytokines levels were measured with Western blots, immunohistochemistry and ELISA. Key Results CAI decreased the arthritis index, improved radiological and histological changes, and reduced synovial IL-1 β, IL-6, IL-18 and TNF- α levels in rats with AA. Compared with normal rats, the 70 kDa NALP1 isoform was up-regulated, NALP3 was down-regulated, and levels of the 165 kDa NALP1 isoform and the adaptor protein ASC were unchanged in synovial tissue from AA rats. CAI reduced the 70 kDa NALP1 isoform and restored NALP3 levels in AA rats; CAI inhibited caspase-1 activation in AA synovial tissue, but not its enzymic activity in vitro. In addition, CAI reduced expression of p65 NF- κB subunit and IκBα phosphorylation and degradation in AA rats. Conclusion and Implications NALP1 inflammasomes were activated in synovial tissues from AA rats and appeared to be a novel therapeutic target for RA. CAI could have therapeutic value in RA by inhibiting activation of NF- κB and NALP1 inflammasomes and by decreasing pro-inflammatory cytokines. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the Association of American Geographers is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, rainfall variability in the Beijing metropolitan region and its link to urbanization during the first 10 years of the twenty-first century (2000-09) was examined. Analyses are based on both observations and regional climate model simulations. The study was focused on August, one of the summer months that receive most of the warm season precipitation in Beijing. Observations from surface stations and weather radars in Beijing and its surrounding regions along with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) are used to characterize the spatial and temporal variability of precipitation. It is found that the urban area has fewer rain days and higher rainfall intensity compared to its surrounding region. This suggests a possible impact of urbanization on the spatial variability of rainfall for the region. Regional climate model simulations with the Weather Research and Forecasting (WRF) Model are thus performed with two land use-land cover datasets that represent different stages of urbanization in the Beijing metropolitan region to investigate such an impact. The modeling study demonstrates how urbanization modifies the surface energy budget and the planetary boundary layer, which in turn affects the production of precipitation. [ABSTRACT FROM AUTHOR]

Extreme weather and climate events, especially short-term excessive drought and wet periods over agricultural areas, have received increased attention. The Southern Great Plains (SGP) is one of the largest agricultural regions in North America and features the underlying Ogallala-High Plains Aquifer system worth great economic value in large part due to production gains from groundwater. Climate research over the SGP is needed to better understand complex coupled climate-hydrology-socioeconomic interactions critical to the sustainability of this region, especially under extreme climate scenarios. Here the authors studied growing-season extreme conditions using the Weather Research and Forecasting (WRF) Model. The six most extreme recent years, both wet and dry, were simulated to investigate the impacts of land surface model and cumulus parameterization on the simulated hydroclimate. The results show that under short-term climate extremes, the land surface model plays a more important role modulating the land-atmosphere water budget, and thus the entire regional climate, than the cumulus parameterization under current model configurations. Between the two land surface models tested, the more sophisticated land surface model produced significantly larger wet bias in large part due to overestimation of moisture flux convergence, which is attributed mainly to an overestimation of the surface evapotranspiration during the simulated period. The deficiencies of the cumulus parameterizations resulted in the model's inability to depict the diurnal rainfall variability. Both land surface processes and cumulus parameterizations remain the most challenging parts of regional climate modeling under extreme climates over the SGP, with the former strongly affecting the precipitation amount and the latter strongly affecting the precipitation pattern. [ABSTRACT FROM AUTHOR]

Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July-September (JAS)] to late autumn [October-December (OND)] and from winter [December-February (DJF)] to spring [March-May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June-August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging. [ABSTRACT FROM AUTHOR]

Soil temperature plays an important role in hydrology, agriculture, and meteorology. In order to improve the accuracy of soil temperature simulation, a soil temperature data assimilation system was developed based on the Ensemble Particle Filter (EnPF) and the Common Land Model (CLM), and then applied in the Walnut Gulch Experimental Watershed (WGEW) in Arizona, United States. Surface soil temperature in situ observations and Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST) data were assimilated into the system. In this study, four different assimilation experiments were conducted: (1) assimilating in situ observations of instantaneous surface soil temperature each hour, (2) assimilating in situ observations of instantaneous surface soil temperature once per day, (3) assimilating verified MODIS LST once per day, and (4) assimilating original MODIS LST once per day. These four experiments reflect a transition from high-quality and more frequent in situ observations to lower quality and less frequent remote sensing data in the data assimilation system. The results from these four experiments show that the assimilated results are better than the simulated results without assimilation at all layers except the bottom layer, while the superiority gradually diminishes as the quality and frequency of the observations decrease. This demonstrates that remote sensing data can be assimilated using the ensemble particle filter in poorly gauged catchments to obtain highly accurate soil variables (e.g., soil moisture, soil temperature). Meanwhile, the results also demonstrate that the ensemble particle filter is effective in assimilating soil temperature observations to improve simulations, but the performance of the data assimilation method is affected by the frequency of assimilation and the quality of the input data. [ABSTRACT FROM AUTHOR]

Accurate soil moisture information is useful in agricultural practice, weather forecasting, and various hydrological applications. Although land surface modeling provides a viable approach to simulating soil moisture, many factors such as errors in the precipitation can affect the accuracy of soil moisture simulations. This paper examined how precipitation rate and evapotranspiration rate affect the accuracy of soil moisture simulation using simple biosphere model with and without data assimilation through ensemble Kalman filter (EnKF). For each of the two variables, seven levels of relative errors (−20, −10, −5, 0, 5, 10 and 20 %) were introduced independently, thus a total of 49 combined cases were investigated. Observations from Wudaogou Hydrology Experimental site in the Huaihe River basin, China, were used to drive and verify the simulations. Results indicate that when the error of precipitation rate is within 10 % of the observations, the resulting error in soil moisture simulations is less significant and manageable, thus the simulated precipitation can be used to drive hydrological models in poorly gauged catchments when observations are not available. When the error of evapotranspiration rate is within 20 % of the observations, which is partly caused by model structural and parameterization errors, its impact on soil moisture simulation is less significant and can be acceptable. This study also demonstrated that the EnKF can perform consistently well to improve soil moisture simulation with less sensitivity to precipitation errors. [ABSTRACT FROM AUTHOR]

Wildfires that occurred over the western United States during August 2012 were fewer in number but larger in size when compared with all other Augusts in the twenty-first century. This unique characteristic, along with the tremendous property damage and potential loss of life that occur with large wildfires with erratic behavior, raised the question of whether future climate will favor rapid wildfire growth so that similar wildfire activity may become more frequent as climate changes. This study addresses this question by examining differences in the climatological distribution of the Haines index (HI) between the current and projected future climate over the western United States. The HI, ranging from 2 to 6, was designed to characterize dry, unstable air in the lower atmosphere that may contribute to erratic or extreme fire behavior. A shift in HI distribution from low values (2 and 3) to higher values (5 and 6) would indicate an increased risk for rapid wildfire growth and spread. Distributions of Haines index are calculated from simulations of current (1971-2000) and future (2041-70) climate using multiple regional climate models in the North American Regional Climate Change Assessment Program. Despite some differences among the projections, the simulations indicate that there may be not only more days but also more consecutive days with HI ≥ 5 during August in the future. This result suggests that future atmospheric environments will be more conducive to erratic wildfires in the mountainous regions of the western United States. [ABSTRACT FROM AUTHOR]

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model's ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly. [ABSTRACT FROM AUTHOR]

Soil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979-2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979-2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0-10, 10-40, and 40-100 cm) for annual and monthly time scales. Short-term (1997-99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters. [ABSTRACT FROM AUTHOR]

To assess the influence of global climate change at the regional scale, we examine past and future changes in key climate, hydrological, and biophysical indicators across the US Northeast (NE). We first consider the extent to which simulations of twentieth century climate from nine atmosphere-ocean general circulation models (AOGCMs) are able to reproduce observed changes in these indicators. We then evaluate projected future trends in primary climate characteristics and indicators of change, including seasonal temperatures, rainfall and drought, snow cover, soil moisture, streamflow, and changes in biometeorological indicators that depend on threshold or accumulated temperatures such as growing season, frost days, and Spring Indices (SI). Changes in indicators for which temperature-related signals have already been observed (seasonal warming patterns, advances in high-spring streamflow, decreases in snow depth, extended growing seasons, earlier bloom dates) are generally reproduced by past model simulations and are projected to continue in the future. Other indicators for which trends have not yet been observed also show projected future changes consistent with a warmer climate (shrinking snow cover, more frequent droughts, and extended low-flow periods in summer). The magnitude of temperature-driven trends in the future are generally projected to be higher under the Special Report on Emission Scenarios (SRES) mid-high (A2) and higher (A1FI) emissions scenarios than under the lower (B1) scenario. These results provide confidence regarding the direction of many regional climate trends, and highlight the fundamental role of future emissions in determining the potential magnitude of changes we can expect over the coming century. [ABSTRACT FROM AUTHOR]