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The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

 

Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations. 

Biological molecules such as DNA, proteins, and lipids can be assembled into naturally existing nanoparticles, such as bacterial viruses (also called bacteriophages or phages), plant viruses, nucleic acid nanoparticles (e.g., DNA origami), protein nanoparticles, and exosomes. These bionanoparticles have their own distinct properties (including compositions, structures, shapes, and functions), laying the foundation for their unique applications as probes for cancer imaging, as detectors for cancer diagnosis, or as therapeutics for cancer therapy. To highlight how the distinct properties of different bionanoparticles can be explored in cancer nanotheranostics, this review critically analyzed the use of bionanoparticles in cancer imaging, diagnosis, and treatment. Specifically, for each of these representative bionanoparticles, we describe its unique properties that render it powerful in cancer theranostics compared with synthetic inorganic nanoparticles. We also summarize how to genetically or chemically modify or redesign the bionanoparticles so that they gain new functions desired for cancer theranostics, such as tumor‐seeking or tumor‐destructive capabilities. Finally, we discussed the challenges in this exciting field. The bionanoparticles covered in this review represent different biomolecular assemblies with unique theranostic applications, showcasing the power of bionanoparticles in disease diagnosis and treatment.

 

Cloud diurnal variation (CDV) affects cloud radiative effects significantly as clouds reflect shortwave radiation only during the daytime but trap outgoing longwave radiation in both daytime and nighttime. Meanwhile, CDV also rectifies atmospheric variations of longer time scales via interactions with other physical and dynamic processes. These make CDV a valuable aspect for diagnosing climate model performance. Here, we evaluate the accuracy of simulated CDV in state‐of‐the‐art global climate models (GCMs) by comparing CDV in the historical simulation of 32 GCMs from 20 institutes participating the Coupled Model Intercomparison Project Phase 6 (CMIP6) with observations from the International Satellite and Cloud Climatology Project‐H product. While good agreement is found over the oceans, significant biases exist over land (notably deserts and plateaus), where the models simulate excessive nighttime clouds and insufficient daytime clouds and miss the observed peak of cloud fraction in the early afternoon. These biases persist throughout the year. It is illustrated that correcting the CDV biases tends to reduce the known model biases of smaller shortwave cloud radiative effect over the midlatitude Africa‐Europe‐Asia continent, South America, and vast ocean areas. Inter‐model comparisons show that the CDV biases vary significantly among models from different institutes and present similar characteristics among models from the same institutes, and suggests that the biases are more likely to be attributed to deficiencies in cloud‐related physical parameterizations rather than the model treatment of resolution, ocean, and chemistry. The improvement of CMIP6 models against their CMIP5 counterparts in simulating CDV is also discussed.

 

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges(GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiativecalled “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature(LST)/subsurface temperature (SUBT) anomalies over high mountain areas as acrucial factor that can lead to significant improvement in precipitationprediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is differentfrom, and complements, other international projects that focus on theoperational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regionalclimate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect ofthe Tibetan Plateau, discusses the LST/SUBT initialization, and presents thepreliminary results. Multi-model ensemble experiments and analyses ofobservational data have revealed that the hydroclimatic effect of the springLST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond EastAsia and its S2S prediction. Preliminary studies and analysis have alsoshown that LS4P models are unable to preserve the initialized LST anomaliesin producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are tooshallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state andanomalies of LST over the Tibetan Plateau. Innovative approaches have beendeveloped to largely overcome these problems.