ALBERT

Description: ABSTRACT In this study, we examined the annual precipitation amounts, the seasonality over global land and their linear trends, as well as the uncertainties in two observations (precipitation reconstruction and Global Precipitation Climatology Centre), and then compared them with historical runs by multiple models. Overall, the large-scale patterns of both the climatology of the annual precipitation amount and the seasonality are consistent between the two observations. Nevertheless, some noticeable differences existed, particularly in the regions with fewer gauge observations, such as northern Africa and the Tibetan Plateau. For long-term changes, significant drying trends during 1948–2005 were observed in the tropical areas of northern Africa, accompanied by significant wetting trends in the polar region of Canada. The seasonality change during the period was dominated by a decreasing trend in precipitation, especially in the western portion of Russia. The model simulations of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) reproduced the climatological mean state of annual precipitation and its seasonality in the observations, as well as to some extent the zonal mean trends of precipitation amounts, but did not reproduce the zonal mean trends of seasonality. The two-dimensional distribution of linear trends of annual precipitation and seasonality simulated by CMIP5 models showed little consistency with their observational counterparts. One possibility for the inconsistencies was that they were largely determined by internal variations of the climate system rather than external forcings. In contrast, it might also suggest a challenge for state-of-the-art climate models to correctly simulate the spatial distribution of responses of annual precipitation amounts and seasonality to the evolution of external forcings. Our results suggest that, in addition to the precipitation amount, seasonality should be used as a metric to assess the ability of a climate model to simulate current climate conditions and project future climate change.

Description: The strength of El Niño-Southern Oscillation (ENSO) is often measured using a single, discrete value of the Niño index. However, this method does not consider the sea surface temperature (SST) uncertainty associated with the observations and data processing. On the basis of the Niño3.4 index and its uncertainty, we find that the strength of the three strongest ENSO events is not separable at 95% confidence level. The monthly peak SST anomalies in the most recent 2015-16 El Niño is tied with 1997-98 and 1982-83 El Niño as the strongest. The three most negative monthly Niño values occur within the 1955-56, 1973-74, and 1975-76 La Niña events, which cannot be discriminated by rank. The histograms of 1000-member ensemble analysis support the conclusion that the strength of the three strongest ENSO events is not separable. These results highlight that the ENSO ranking has to include the SST uncertainty.

Description: ABSTRACT This study performs the spatial and temporal trend analysis of the annual, seasonal and monthly time series of a set of uniformly distributed 36 stations precipitation data in Sichuan Province during 1960–2013. The result indicates that the annual precipitation amount and precipitation days across the region have decreasing trends, but the last is significant. The annual precipitation intensity and maximum 1-day precipitation have slightly increasing trends. Although annual precipitation amount and precipitation days decline, annual precipitation intensity and maximum 1-day precipitation rise. This reflects that droughts and floods increase in Sichuan Province in recent years. Seasonally, precipitation amount has decreased in all seasons, while autumn precipitation amount is statistically significant. Regionally average trends in precipitation days also show large differences in all seasons. The temporal variability in regional precipitation intensity increases except for autumn, but significant increases are found in spring and summer. Regional maximum 1-day precipitation has significantly increasing trends in spring and summer, but slightly decreasing trend in autumn and winter. On a monthly basis, trends of precipitation amount show tremendous differences. Spatial changes in annual, seasonal and monthly precipitation events are not the same, and they are not clustered, either. Additionally, the correlations between elevation and the magnitudes of the trends in annual precipitation amount and precipitation days are statistically significant whereas the correlations are not perfect in precipitation intensity and maximum 1-day precipitation. As can be seen from the seasonal and monthly precipitation amount, the correlations between elevation and the magnitudes of the trends in precipitation amount are higher in spring and autumn, but not perfect in summer and winter. April, October and December precipitation amount shows the strongest positive correlations with elevation. On the whole, the increasing trend of precipitation is relatively prominent at higher elevations, while the decrease trend is more significant at low elevation stations.

Description: In this study, the impact of ocean initial conditions (OIC) on the prediction skill in the tropical Pacific Ocean is examined. Four sets of OIC are used to initialize the 12-month hindcasts of the tropical climate from 1979 to 2007, using the Climate Forecast System, version 2 (CFSv2), the current operational climate prediction model at the National Centers for Environmental Predictions (NCEP). These OICs are chosen from four ocean analyses produced by the NCEP and the European Center for Medium Range Weather Forecasts (ECMWF). For each hindcast starting from a given OIC, four ensemble members are generated with different atmosphere and land initial states. The predictive skill in the tropical Pacific Ocean is assessed based on the ensemble mean hindcasts from each individual as well as multiple oceanic analyses. To reduce the climate drift from various oceanic analyses, an anomaly initialization strategy is used for all hindcasts. The results indicate that there exists a substantial spread in the sea surface temperature (SST) prediction skill with different ocean analyses. Specifically, the ENSO prediction skill in terms of the anomaly correlation of Niño-3.4 index can differ by as much as 0.1–0.2 at lead times longer than 2 months. The ensemble mean of the predictions initialized from all four ocean analyses gives prediction skill equivalent to the best one derived from the individual ocean analysis. It is suggested that more accurate OIC can improve the ENSO prediction skill and an ensemble ocean initialization has the potential of enhancing the skill at the present stage.

Description: The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO 2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAI GS ) estimated by satellite datasets (average trend of 0.0070 yr −1 , ranging from 0.0035 yr −1 to 0.0127 yr −1 ), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAI GS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAI GS (P〈0.05), and marginally significantly (P=0.07) correlated with the residual of LAI GS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO 2 concentration, and nitrogen deposition, across different provinces. This result highlights the important role of China's afforestation program in explaining the spatial patterns of trend in vegetation growth. This article is protected by copyright. All rights reserved.

Description: The hysteresis behaviors and phase characteristics of Pb 0.97 La 0.02 (Zr 0.90 Sn 0.025 Ti 0.075 )O 3 (PLZST) ceramics were investigated in this work. A single mini hysteresis loop at 3 kV/mm with the maximum polarization ( P max ) of 8.3 μC/cm 2 and triple hysteresis loops at 6.6 kV/mm were observed, which indicates the coexistence of rhombohedral ferroelectric phase and tetragonal antiferroelectric phase. The X-ray Diffraction patterns and dielectric temperature spectra both demonstrate this coexistence. Moreover, the hysteresis loops with increasing temperature indicated that a ferroelectric–antiferroelectric phase transition occurred at about 60°C. These phenomena would be useful for understanding the domain evolution during ferroelectric and antiferroelectric phase transition.

Description: Cezanne, a deubiquitinating cysteine protease (DUB) belonging to A20 subgroup of ovarian tumor (OTU) protein superfamily, functions as a negative regulator of NF-κB to attenuate NF-κB activation and to restrain pro-inflammatory transcription in response to TNF receptor (TNFR) signaling. It is the first documented OTU DUB that preferably disassembles Lys11-linked polyubiquitin chains and has been shown to regulate multiple cellular events including immune signaling, cell survival and tumor progression. Previous studies showed that in response to TNF stimulation, Cezanne is recruited to the activated TNFR complex to suppress the build-up of polyubiquitinated RIP1 signal by removing Lys63 polyubiquitin from RIP1. However, how is Cezanne recognized and recruited to TNFR complex is not clear yet. In this study, we characterized a ubiquitin-associated (UBA) domain in the N-terminal region of Cezanne and proved its activity to bind Lys63 polyubiquitin chain. By constructing a series of truncated and site-specific point mutants, we further localized the crucial binding sites for Lys63 polyubiquitin chains at Leu9 and Ser10 sites of Cezanne UBA domain. Mutation at these sites disrupted the recruitment of Cezanne to activated TNFR complex and dramatically reduced the inhibition of NF-κB activation by Cezanne. Our study demonstrated that the N-terminal UBA domain is crucial for the function of Cezanne during NF-κB activation. Cezanne is recognized and recruited into activated TNFR complex by specifically binding to polyubiquitinated signaling proteins after TNF stimulation through its N-terminal polyubiquitin binding site. This study sheds light on the molecular mechanism of negative regulation of NF-κB activation by Cezanne. This article is protected by copyright. All rights reserved

Description: The Junggar–Balkhash Ocean was a major branch of the southern Paleo-Asian Ocean. The timing of its closure is important for understanding the history of the Central Asian Orogenic Belt. New sedimentological and geochronological data from the Late Paleozoic volcano-sedimentary sequences in the Barleik Mountains of West Junggar, NW China, help to constrain the closure time of the Junggar–Balkhash Ocean. Tielieketi Formation (Fm) is dominated by littoral sediments, but its upper glauconite-bearing sandstone is interpreted to deposit rapidly in a shallow-water shelf setting. By contrast, Heishantou Fm consists chiefly of volcanic rocks, conformably overlying or in fault contact with Tielieketi Fm. Molaoba Fm is composed of parallel-stratified fine sandstone and sandy conglomerate with graded bedding, typical of non-marine, fluvial deposition. This formation unconformably overlies the Tielieketi and Heishantou formations and is conformably covered by Kalagang Fm characterized by a continental bimodal volcanic association. The youngest U–Pb ages of detrital zircons from sandstones and zircon U–Pb ages from volcanic rocks suggest that the Tielieketi, Heishantou, Molaoba, and Kalagang formations were deposited during the Famennian–Tournaisian, Tournaisian–early Bashkirian, Gzhelian, and Asselian–Sakmarian, respectively. The absence of upper Bashkirian to Kasimovian was likely caused by tectonic uplifting of the West Junggar terrane. This is compatible with the occurrence of coeval stitching plutons in the West Junggar and adjacent areas. The Junggar–Balkhash Ocean should be finally closed before the Gzhelian, slightly later or concurrent with that of other ocean domains of the southern Paleo-Asian Ocean.

Description: This letter explored the influence of atmospheric convection scheme on El Niño-Southern Oscillation (ENSO) predictions using a set of hindcast experiments. Specifically, a low-resolution version of the Climate Forecast System, version 2 (CFSv2L) is used for 12-month hindcasts starting from each April during 1982-2011. The hindcast experiments are repeated with three atmospheric convection schemes. All three hindcasts apply the identical initialization with ocean initial conditions taken from the European Center for Medium Range Weather Forecasts (ECMWF) and atmosphere/land initial states from the National Centers for Environmental Predictions (NCEP). Assessments indicate a substantial sensitivity of the SST prediction skill to the different convection schemes, particularly over the eastern tropical Pacific. For the Niño-3.4 index, the anomaly correlation skill can differ by 0.1-0.2 at lead times longer than 2 months. Long-term simulations are further conducted with the three convection schemes to understand the differences in prediction skill. By conducting heat budget analyses for the mixed-layer temperature anomalies, it is suggested that the convection scheme having the highest skill simulates stronger and more realistic coupled feedbacks related to ENSO. Particularly, the strength of the Ekman pumping feedback is better represented, which is traced to more realistic simulation of surface wind stress. Our results imply that improving the mean state simulations in CGCMs (e.g., ameliorating the ITCZ simulation) might further improve our ENSO prediction capability.

Description: It is reasonable to assume that the quality of an ocean reanalysis will depend not only on the ocean data assimilation system, but also on the availability of observational data. Among the data assimilated in the tropical Pacific, observations from the Tropical Atmosphere Ocean (TAO) moored array are an important contributor. Due to various reasons, in the last couple of years, TAO had a very low data delivery rate at some of the mooring sites. In this work, we examined the influence of the availability of in-situ TAO data from all ten equatorial mooring sites in 1999-2014 by comparing ocean temperature from two data assimilation systems at National Centers for Environmental Prediction ( NCEP ) : the Global Ocean Data Assimilation System (GODAS) and the Climate Forecast System Reanalysis (CFSR). Our working hypothesis is that missing of in-situ observational data should lead to less observational constraint, and consequently, larger divergence between ocean reanalyses. Compared with periods of no in-situ TAO observations, the disagreements of ocean temperature between CFSR and GODAS are indeed smaller when in-situ TAO observations are available. The disagreements are largest along the thermocline due to its being the region of strongest variability. Thus, it is suggested that without the constraint of in-situ TAO observations, the inconsistency between the ocean reanalyses from two NCEP data assimilation systems is larger, highlighting the contribution of TAO observations. Moreover, it is also suggested that the disagreement of ocean temperature between GODAS and CFSR is mainly caused by the difference of the models used in the assimilation systems and the impact of in-situ TAO availability plays a secondary, but important role. To improve the quality of the assimilation products, it is necessary both to reduce model bias and to assimilate more high quality data. This article is protected by copyright. All rights reserved.