The Program for climate Model diagnosis and Intercomparison: 20-th anniversary Symposium

Twenty years ago, W. Lawrence (Larry) Gates approached the U.S. Department of Energy (DOE) Office of Energy Research (now the Office of Science) with a plan to coordinate the comparison and documentation of climate model differences. This effort would help improve our understanding of climate change through a systematic approach to model intercomparison. Early attempts at comparing results showed a surprisingly large range in control climate from such parameters as cloud cover, precipitation, and even atmospheric temperature. The DOE agreed to fund the effort at the Lawrence Livermore National Laboratory (LLNL), in part because of the existing computing environment and because of a preexisting atmospheric science group that contained a wide variety of expertise. The project was named the Program for Climate Model Diagnosis and Intercomparison (PCMDI), and it has changed the international landscape of climate modeling over the past 20 years. In spring 2009 the DOE hosted a 1-day symposium to celebrate the twentieth anniversary of PCMDI and to honor its founder, Larry Gates. Through their personal experiences, the morning presenters painted an image of climate science in the 1970s and 1980s, that generated early support from the international community for model intercomparison, thereby bringing PCMDI into existence. Four talks covered GatesÃÂâÃÂÃÂÃÂÃÂs early contributions to climate research at the University of California, Los Angeles (UCLA), the RAND Corporation, and Oregon State University through the founding of PCMDI to coordinate the Atmospheric Model Intercomparison Project (AMIP). The speakers were, in order of presentation, Warren Washington [National Center for Atmospheric Research (NCAR)], Kelly Redmond (Western Regional Climate Center), George Boer (Canadian Centre for Climate Modelling and Analysis), and Lennart Bengtsson [University of Reading, former director of the European Centre for Medium-Range Weather Forecasts (ECMWF)]. The afternoon session emphasized the scientific ideas that are the basis of PCMDIÃÂâÃÂÃÂÃÂÃÂs success, summarizing their evolution and impact. Four speakers followed the various PCMDI-supported climate model intercomparison projects, beginning with early work on cloud representations in models, presented by Robert D. Cess (Distinguished Professor Emeritus, Stony Brook University), and then the latest Cloud Feedback Model Intercomparison Projects (CFMIPs) led by Sandrine Bony (Laboratoire de MÃÂÃÂÃÂétÃÂÃÂÃÂéorologie Dynamique). Benjamin Santer (LLNL) presented a review of the climate change detection and attribution (D & A) work pioneered at PCMDI, and Gerald A. Meehl (NCAR) ended the day with a look toward the future of climate change research

Longwave Band-by-band Cloud Radiative Effect and its Application in GCM Evaluation

The cloud radiative effect (CRE) of each longwave (LW) absorption band of a GCM fs radiation code is uniquely valuable for GCM evaluation because (1) comparing band-by-band CRE avoids the compensating biases in the broadband CRE comparison and (2) the fractional contribution of each band to the LW broadband CRE (f(sub CRE)) is sensitive to cloud top height but largely insensitive to cloud fraction, presenting thus a diagnostic metric to separate the two macroscopic properties of clouds. Recent studies led by the first author have established methods to derive such band ]by ]band quantities from collocated AIRS and CERES observations. We present here a study that compares the observed band-by-band CRE over the tropical oceans with those simulated by three different atmospheric GCMs (GFDL AM2, NASA GEOS-5, and CCCma CanAM4) forced by observed SST. The models agree with observation on the annual ]mean LW broadband CRE over the tropical oceans within +/-1W/sq m. However, the differences among these three GCMs in some bands can be as large as or even larger than +/-1W/sq m. Observed seasonal cycles of f(sub CRE) in major bands are shown to be consistent with the seasonal cycle of cloud top pressure for both the amplitude and the phase. However, while the three simulated seasonal cycles of f(sub CRE) agree with observations on the phase, the amplitudes are underestimated. Simulated interannual anomalies from GFDL AM2 and CCCma CanAM4 are in phase with observed anomalies. The spatial distribution of f(sub CRE) highlights the discrepancies between models and observation over the low-cloud regions and the compensating biases from different bands

Decomposition of radiational effects of model feedbacks

Three separate doubled CO/sub 2/ experiments with the statistical dynamic model are used to illustrate efforts to study the climate dynamics, feedbacks, and interrelationships of meteorological parameters by decomposing and isolating their individual effects on radiation transport

Big Data Challenges in Climate Science: Improving the Next-Generation Cyberinfrastructure

The knowledge we gain from research in climate science depends on the generation, dissemination, and analysis of high-quality data. This work comprises technical practice as well as social practice, both of which are distinguished by their massive scale and global reach. As a result, the amount of data involved in climate research is growing at an unprecedented rate. Climate model intercomparison (CMIP) experiments, the integration of observational data and climate reanalysis data with climate model outputs, as seen in the Obs4MIPs, Ana4MIPs, and CREATE-IP activities, and the collaborative work of the Intergovernmental Panel on Climate Change (IPCC) provide examples of the types of activities that increasingly require an improved cyberinfrastructure for dealing with large amounts of critical scientific data. This paper provides an overview of some of climate science's big data problems and the technical solutions being developed to advance data publication, climate analytics as a service, and interoperability within the Earth System Grid Federation (ESGF), the primary cyberinfrastructure currently supporting global climate research activities

Pathogenic Roles of CD14, Galectin-3, and OX40 during Experimental Cerebral Malaria in Mice

An in-depth knowledge of the host molecules and biological pathways that contribute towards the pathogenesis of cerebral malaria would help guide the development of novel prognostics and therapeutics. Genome-wide transcriptional profiling of the brain tissue during experimental cerebral malaria (ECM ) caused by Plasmodium berghei ANKA parasites in mice, a well established surrogate of human cerebral malaria, has been useful in predicting the functional classes of genes involved and pathways altered during the course of disease. To further understand the contribution of individual genes to the pathogenesis of ECM, we examined the biological relevance of three molecules – CD14, galectin-3, and OX40 that were previously shown to be overexpressed during ECM. We find that CD14 plays a predominant role in the induction of ECM and regulation of parasite density; deletion of the CD14 gene not only prevented the onset of disease in a majority of susceptible mice (only 21% of CD14-deficient compared to 80% of wildtype mice developed ECM, p<0.0004) but also had an ameliorating effect on parasitemia (a 2 fold reduction during the cerebral phase). Furthermore, deletion of the galectin-3 gene in susceptible C57BL/6 mice resulted in partial protection from ECM (47% of galectin-3-deficient versus 93% of wildtype mice developed ECM, p<0.0073). Subsequent adherence assays suggest that galectin-3 induced pathogenesis of ECM is not mediated by the recognition and binding of galectin-3 to P. berghei ANKA parasites. A previous study of ECM has demonstrated that brain infiltrating T cells are strongly activated and are CD44+CD62L− differentiated memory T cells [1]. We find that OX40, a marker of both T cell activation and memory, is selectively upregulated in the brain during ECM and its distribution among CD4+ and CD8+ T cells accumulated in the brain vasculature is approximately equal

International Nonregimes: A Research Agenda1

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146934/1/j.1468-2486.2007.00672.x.pd

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms