Global Airborne Observations of Nitrogen Oxides and Ozone from the Atmospheric Tomography Mission

NASA Astrophysics Data System (ADS)

Thompson, C. R.; Peischl, J.; Ryerson, T. B.

2016-12-01

The Atmospheric Tomography (ATom) Mission is an ambitious airborne field campaign that will conduct measurements of an extensive suite of trace gases and aerosols from the NASA DC-8 platform over three years and four seasons. Flights will travel nearly pole-to-pole, traversing both the Pacific and Atlantic Oceans, while profiling continuously from 0.2 to 12 km altitude to provide nearly global-scale observations of greenhouse gases and reactive species. Measurements from ATom will provide an unprecedented test for current global chemistry-climate models (CCMs) and will inform further improvements to these models. In particular, reactive species, such as ozone, are difficult to represent accurately in CCMs. We will present global observations of reactive nitrogen compounds (NO, NO2, NOy) and ozone from the first deployment of the ATom Mission in August of 2016. Flights will intercept a wide variety of air masses with different pollution signatures, including aged biomass burning emissions from Siberian wildfires and anthropogenic outflow from the Asian mainland, which will contrast the relatively clean background atmosphere encountered in the high latitudes of the Northern and Southern Hemispheres. We will compare the composition of the NOy budgets across these variety of air masses and impacts on ozone.

SPARC's Stratospheric Sulfur and its Role in Climate Activity (SSiRC)

NASA Technical Reports Server (NTRS)

Thomason, Larry

2015-01-01

The stratospheric aerosol layer is a key component in the climate system. It affects the radiative balance of the atmosphere directly through interactions with solar and terrestrial radiation, and indirectly through its effect on stratospheric ozone. Because the stratospheric aerosol layer is prescribed in many climate models and Chemistry-Climate Models (CCMs), model simulations of future atmospheric conditions and climate generally do not account for the interaction between the aerosol-sulfur cycle and changes in the climate system. The present understanding of how the stratospheric aerosol layer may be affected by future climate change and how the stratospheric aerosol layer may drive climate change is, therefore, very limited. The purposes of SSiRC (Stratospheric Sulfur and its Role in Climate) include: (i) providing a coordinating structure for the various individual activities already underway in different research centers; (ii) encouraging and supporting new instrumentation and measurements of sulfur containing compounds, such as COS, DMS, and non-volcanic SO2 in the UT/LS globally; and (iii) initiating new model/data inter-comparisons. SSiRC is developing collaborations with a number of other SPARC activities including CCMI and ACAM. This presentation will highlight the scientific goals of this project and on-going activities and propose potential interactions between SSiRC and ACAM.

Functional Traits for Carbon Access in Macrophytes

PubMed Central

Pfister, Catherine A.; Wootton, J. Timothy

2016-01-01

Understanding functional trait distributions among organisms can inform impacts on and responses to environmental change. In marine systems, only 1% of dissolved inorganic carbon in seawater exists as CO2. Thus the majority of marine macrophytes not only passively access CO2 for photosynthesis, but also actively transport CO2 and the more common bicarbonate (HCO3-, 92% of seawater dissolved inorganic carbon) into their cells. Because species with these carbon concentrating mechanisms (CCMs) are non-randomly distributed in ecosystems, we ask whether there is a phylogenetic pattern to the distribution of CCMs among algal species. To determine macrophyte traits that influence carbon uptake, we assessed 40 common macrophyte species from the rocky intertidal community of the Northeast Pacific Ocean to a) query whether macrophytes have a CCM and b) determine the evolutionary history of CCMs, using ancestral state reconstructions and stochastic character mapping based on previously published data. Thirty-two species not only depleted CO2, but also concentrated and depleted HCO3-, indicative of a CCM. While analysis of CCMs as a continuous trait in 30 families within Phylum Rhodophyta showed a significant phylogenetic signal under a Brownian motion model, analysis of CCMs as a discrete trait (presence or absence) indicated that red algal families are more divergent than expected in their CCM presence or absence; CCMs are a labile trait within the Rhodophyta. In contrast, CCMs were present in each of 18 Ochrophyta families surveyed, indicating that CCMs are highly conserved in the brown algae. The trait of CCM presence or absence was largely conserved within Families. Fifteen of 23 species tested also changed the seawater buffering capacity, or Total Alkalinity (TA), shifting DIC composition towards increasing concentrations of HCO3- and CO2 for photosynthesis. Manipulating the external TA of the local environment may influence carbon availability in boundary layers and areas of low water mixing, offering an additional mechanism to increase CO2 availability. PMID:27415005

Dynamic contrast-enhanced MRI evaluation of cerebral cavernous malformations.

PubMed

Hart, Blaine L; Taheri, Saeid; Rosenberg, Gary A; Morrison, Leslie A

2013-10-01

The aim of this study is to quantitatively evaluate the behavior of CNS cavernous malformations (CCMs) using a dynamic contrast-enhanced MRI (DCEMRI) technique sensitive for slow transfer rates of gadolinium. The prospective study was approved by the institutional review board and was HIPPA compliant. Written informed consent was obtained from 14 subjects with familial CCMs (4 men and 10 women, ages 22-76 years, mean 48.1 years). Following routine anatomic MRI of the brain, DCEMRI was performed for six slices, using T1 mapping with partial inversion recovery (TAPIR) to calculate T1 values, following administration of 0.025 mmol/kg gadolinium DTPA. The transfer rate (Ki) was calculated using the Patlak model, and Ki within CCMs was compared to normal-appearing white matter as well as to 17 normal control subjects previously studied. All subjects had typical MRI appearance of CCMs. Thirty-nine CCMs were studied using DCEMRI. Ki was low or normal in 12 lesions and elevated from 1.4 to 12 times higher than background in the remaining 27 lesions. Ki ranged from 2.1E-6 to 9.63E-4 min(-1), mean 3.55E-4. Normal-appearing white matter in the CCM patients had a mean Ki of 1.57E-4, not statistically different from mean WM Ki of 1.47E-4 in controls. TAPIR-based DCEMRI technique permits quantifiable assessment of CCMs in vivo and reveals considerable differences not seen with conventional MRI. Potential applications include correlation with biologic behavior such as lesion growth or hemorrage, and measurement of drug effects.

Dynamic Contrast-Enhanced MRI Evaluation of Cerebral Cavernous Malformations

PubMed Central

Hart, B. L.; Taheri, S.; Rosenberg, G. A.; Morrison, L. A.

2013-01-01

The aim of this study is to quantitatively evaluate the behavior of CNS cavernous malformations (CCMs) using a dynamic contrast-enhanced MRI (DCEMRI) technique sensitive for slow transfer rates of gadolinium. The prospective study was approved by the institutional review board and was HIPPA compliant. Written informed consent was obtained from 14 subjects with familial CCMs (4 men and 10 women, ages 22–76 years, mean 48.1 years). Following routine anatomic MRI of the brain, DCEMRI was performed for six slices, using T1 mapping with partial inversion recovery (TAPIR) to calculate T1 values, following administration of 0.025 mmol/kg gadolinium DTPA. The transfer rate (Ki) was calculated using the Patlak model, and Ki within CCMs was compared to normal-appearing white matter as well as to 17 normal control subjects previously studied. All subjects had typical MRI appearance of CCMs. Thirty-nine CCMs were studied using DCEMRI. Ki was low or normal in 12 lesions and elevated from 1.4 to 12 times higher than background in the remaining 27 lesions. Ki ranged from 2.1E–6 to 9.63E–4 min−1, mean 3.55E–4. Normal-appearing white matter in the CCM patients had a mean Ki of 1.57E–4, not statistically different from mean WM Ki of 1.47E–4 in controls. TAPIR-based DCEMRI technique permits quantifiable assessment of CCMs in vivo and reveals considerable differences not seen with conventional MRI. Potential applications include correlation with biologic behavior such as lesion growth or hemorrage, and measurement of drug effects. PMID:24323376

Using Photogrammetry to Estimate Tank Waste Volumes from Video

DOE Office of Scientific and Technical Information (OSTI.GOV)

Field, Jim G.

Washington River Protection Solutions (WRPS) contracted with HiLine Engineering & Fabrication, Inc. to assess the accuracy of photogrammetry tools as compared to video Camera/CAD Modeling System (CCMS) estimates. This test report documents the results of using photogrammetry to estimate the volume of waste in tank 241-C-I04 from post-retrieval videos and results using photogrammetry to estimate the volume of waste piles in the CCMS test video.

3D Viewer Platform of Cloud Clustering Management System: Google Map 3D

NASA Astrophysics Data System (ADS)

Choi, Sung-Ja; Lee, Gang-Soo

The new management system of framework for cloud envrionemnt is needed by the platfrom of convergence according to computing environments of changes. A ISV and small business model is hard to adapt management system of platform which is offered from super business. This article suggest the clustering management system of cloud computing envirionments for ISV and a man of enterprise in small business model. It applies the 3D viewer adapt from map3D & earth of google. It is called 3DV_CCMS as expand the CCMS[1].

Multimodel assessment of the upper troposphere and lower stratosphere: Tropics and global trends

NASA Astrophysics Data System (ADS)

Gettelman, A.; Hegglin, M. I.; Son, S.-W.; Kim, J.; Fujiwara, M.; Birner, T.; Kremser, S.; Rex, M.; AñEl, J. A.; Akiyoshi, H.; Austin, J.; Bekki, S.; Braesike, P.; Brühl, C.; Butchart, N.; Chipperfield, M.; Dameris, M.; Dhomse, S.; Garny, H.; Hardiman, S. C.; JöCkel, P.; Kinnison, D. E.; Lamarque, J. F.; Mancini, E.; Marchand, M.; Michou, M.; Morgenstern, O.; Pawson, S.; Pitari, G.; Plummer, D.; Pyle, J. A.; Rozanov, E.; Scinocca, J.; Shepherd, T. G.; Shibata, K.; Smale, D.; TeyssèDre, H.; Tian, W.

2010-01-01

The performance of 18 coupled Chemistry Climate Models (CCMs) in the Tropical Tropopause Layer (TTL) is evaluated using qualitative and quantitative diagnostics. Trends in tropopause quantities in the tropics and the extratropical Upper Troposphere and Lower Stratosphere (UTLS) are analyzed. A quantitative grading methodology for evaluating CCMs is extended to include variability and used to develop four different grades for tropical tropopause temperature and pressure, water vapor and ozone. Four of the 18 models and the multi-model mean meet quantitative and qualitative standards for reproducing key processes in the TTL. Several diagnostics are performed on a subset of the models analyzing the Tropopause Inversion Layer (TIL), Lagrangian cold point and TTL transit time. Historical decreases in tropical tropopause pressure and decreases in water vapor are simulated, lending confidence to future projections. The models simulate continued decreases in tropopause pressure in the 21st century, along with ˜1K increases per century in cold point tropopause temperature and 0.5-1 ppmv per century increases in water vapor above the tropical tropopause. TTL water vapor increases below the cold point. In two models, these trends are associated with 35% increases in TTL cloud fraction. These changes indicate significant perturbations to TTL processes, specifically to deep convective heating and humidity transport. Ozone in the extratropical lowermost stratosphere has significant and hemispheric asymmetric trends. O3 is projected to increase by nearly 30% due to ozone recovery in the Southern Hemisphere (SH) and due to enhancements in the stratospheric circulation. These UTLS ozone trends may have significant effects in the TTL and the troposphere.

NATO CCMS PILOT STUDY - CLEAN PRODUCTS AND PROCESSES, NATIONAL RISK MANAGEMENT RESEARCH LABORATORY

EPA Science Inventory

The Committee on the Challenges to Modern Society (CCMS) was established by the North Atlantic Treaty Organization (NATO) in 1969. The mission of CCMS is to develop meaningful programs to share information among countries on important environmental and societal issues that comple...

NATO/CCMS PILOT STUDY - EVALUATION OF DEMONSTRATED AND EMERGING TECHNOLOGIES FOR THE TREATMENT OF CONTAMINATED LAND AND GROUNDWATER (PHASE III)

EPA Science Inventory

The Council of the North Atlantic Treaty Organization (NATO) established the Committee on the Challenges of Modern Society (CCMS) in 1969. CCMS was charged with developing meaningful programs to share information among countries on environmental and societal issues that complemen...

A case of severe type of cerebro-costo-mandibular syndrome.

PubMed

Matić, Aleksandra; Velisavljev-Filipović, Gordana; Lovrenski, Jovan; Gajdobranski, Djordje

2016-01-01

Cerebro-costo-mandibular syndrome (CCMS) is a rare disorder, with only 75 cases described in the literature to date. CCMS is characterized by association of micrognathia and specific multiple rib defects. It is accompanied by mental deficiency in considerable number of cases. Sometimes, there are associated anomalies and problems, such as spine deformities, brain, heart, kidney or ear anomalies, feeding difficulties, delayed psychomotor development, and growth impairment. Depending on severity of deformities and consecutive respiratory insufficiency, in about 35–50% of CCMS cases, death occurs during the first year of life. These cases are referred to as severe types of CCMS. In this paper we present a female infant with severe type of CCMS. Diagnosis was established in the first day of life, based on micrognathia and findings of posterior rib-gap defects on the chest X-ray, accompanied by dyspnea. Progressive severe respiratory insufficiency caused by chest and air-way deformities and exacerbated by episodes of pneumonia, led to respiratory failure and death at the age of 7.5 months. CCMS should be considered in every infant with micrognathia and rib-gap defects on chest X-ray.

Exclusion of MYF5, GSC, RUNX2, and TCOF1 mutation in a case of cerebro-costo-mandibular syndrome.

PubMed

Su, Pen-Hua; Chen, Jia-Yuh; Chiang, Chin-Lung; Ng, Yan-Yan; Chen, Suh-Jen

2010-04-01

Cerebro-costo-mandibular syndrome (CCMS) is an uncommon multiple congenital anomaly syndrome characterized by severe micrognathia, posterior rib-gap defects, and developmental delay. The cause of CCMS is unknown. Genes hypothesized to have a causal role in CCMS, include myogenic factor 5 (MYF5), goosecoid homeobox (GSC) and runt-related transcription factor 2 (RUNX2) [formerly known as core-binding factor (CBFA1)]. We report an infant with typical features of CCMS who, on prenatal ultrasound, was found to have severe micrognathia. We present the first image by three-dimensional computed tomography of posterior rib-defect, and we exclude mutations of the MYF5, GSC, RUNX2, and TCOF1 genes in our patient. Further molecular studies are needed to evaluate the cause of CCMS.

Ex utero intrapartum treatment for an infant with cerebro-costo-mandibular syndrome.

PubMed

Ogasawara, Kei; Honda, Yoshinobu; Hosoya, Mitsuaki

2014-08-01

Cerebro-costo-mandibular syndrome (CCMS) is a rare disorder characterized by multiple rib abnormalities, micrognathia described as Pierre-Robin sequence, and cerebral involvement. Appropriate management of respiratory distress immediately after birth is crucial to rescue these patients. A boy, having a mother with Pierre-Robin sequence and a sister with CCMS, was diagnosed prenatally with CCMS and successfully treated with ex utero intrapartum treatment (EXIT) at 36 weeks 6 days of gestation. EXIT would be an effective option for rescuing patients with prenatally diagnosed CCMS and preventing neonatal hypoxia. © 2014 Japan Pediatric Society.

Quantifying Uncertainty in Projections of Stratospheric Ozone Over the 21st Century

NASA Technical Reports Server (NTRS)

Charlton-Perez, A. J.; Hawkins, E.; Eyring, V.; Cionni, I.; Bodeker, G. E.; Kinnison, D. E.; Akiyoshi, H.; Frith, S. M.; Garcia, R.; Gettelman, A.;

Predicting Culex pipiens/restuans population dynamics by interval lagged weather data

PubMed Central

2013-01-01

Background Culex pipiens/restuans mosquitoes are important vectors for a variety of arthropod borne viral infections. In this study, the associations between 20 years of mosquito capture data and the time lagged environmental quantities daytime length, temperature, precipitation, relative humidity and wind speed were used to generate a predictive model for the population dynamics of this vector species. Methods Mosquito population in the study area was represented by averaged time series of mosquitos counts captured at 6 sites in Cook County (Illinois, USA). Cross-correlation maps (CCMs) were compiled to investigate the association between mosquito abundances and environmental quantities. The results obtained from the CCMs were incorporated into a Poisson regression to generate a predictive model. To optimize the predictive model the time lags obtained from the CCMs were adjusted using a genetic algorithm. Results CCMs for weekly data showed a highly positive correlation of mosquito abundances with daytime length 4 to 5 weeks prior to capture (quantified by a Spearman rank order correlation of rS = 0.898) and with temperature during 2 weeks prior to capture (rS = 0.870). Maximal negative correlations were found for wind speed averaged over 3 week prior to capture (rS = −0.621). Cx. pipiens/restuans population dynamics was predicted by integrating the CCM results in Poisson regression models. They were used to simulate the average seasonal cycle of the mosquito abundance. Verification with observations resulted in a correlation of rS = 0.899 for daily and rS = 0.917 for weekly data. Applying the optimized models to the entire 20-years time series also resulted in a suitable fit with rS = 0.876 for daily and rS = 0.899 for weekly data. Conclusions The study demonstrates the application of interval lagged weather data to predict mosquito abundances with a feasible accuracy, especially when related to weekly Cx. pipiens/restuans populations. PMID:23634763

In vitro inhibition of lipid accumulation induced by oleic acid and in vivo pharmacokinetics of chitosan microspheres (CTMS) and chitosan-capsaicin microspheres (CCMS)

PubMed Central

Wu, Sihui; Pan, Haitao; Tan, Sirong; Ding, Chen; Huang, Guidong; Liu, Guihua; Guo, Jiao; Su, Zhengquan

2017-01-01

ABSTRACT Chitosan and capsaicin are compounds extracted from natural products and have been indicated to lower body weight and prevent fatty liver. However, their applications are limited by poor oral bioavailability, low compliance and some serious side effects. To solve these problems, we successfully prepared chitosan microspheres (CTMS) and chitosan-capsaicin microspheres (CCMS) in previous study. Therefore, in the present study, we evaluated the ability of CTMS and CCMS to eliminate lipid accumulation in hepatocytesand also characterized their pharmacokinetic parameters after administration. The results showed that the two microspheres could significantly reduce intracellular lipid accumulation and dose-dependently improve the triglyceride (TG) content in HepG2 cells. A pharmacokinetic study indicated that CTMS and CCMS were distributed in almost all of the measured tissues, especially liver and kidney, and that their absorption was better than those of chitosan and capsaicin. Simultaneously, the prolonged circulating half-lives, the lower clearance and higher plasma concentration of CTMS and CCMS showed that their bioavailability was effectively enhanced. All of the results indicated that the lipid accumulation inhibition of CTMS and CCMS was better than that of chitosan and capsaicin, and that these microspheres can be developed as preventive agents for fatty liver or obesity. PMID:28659743

Challenges to producing a long-term stratospheric aerosol climatology for chemistry and climate

NASA Astrophysics Data System (ADS)

Thomason, Larry; Vernier, Jean-Paul; Bourassa, Adam; Rieger, Landon; Luo, Beiping; Peter, Thomas; Arfeuille, Florian

2016-04-01

Stratospheric aerosol data sets are key inputs for climate models (GCMs, CCMs) particularly for understanding the role of volcanoes on climate and as a surrogate for understanding the potential of human-derived stratospheric aerosol as mitigation for global warming. In addition to supporting activities of individual climate models, the data sets also act as a historical input to the activities of SPARC's Chemistry-Climate Model Initiative (CCMI) and the World Climate Research Programme's Coupled Model Intercomparison Project (CMIP). One such data set was produced in 2004 as a part of the SPARC Assessment of Stratospheric Aerosol Properties (ASAP), extending from 1979 and 2004. It was primarily constructed from the Stratospheric Aerosol and Gas Experiment series of instruments but supplemented by data from other space-based sources and a number of ground-based and airborne instruments. Updates to this data set have expanded the timeframe to span from 1850 through 2014 through the inclusion of data from additional sources, such as photometer data and ice core analyses. Fundamentally, there are limitations to the reliability of the optical properties of aerosol inferred from even the most complete single instrument data sets. At the same time, the heterogeneous nature of the underlying data to this historical data set produces considerable challenges to the production of a climate data set which is both homogeneous and reliable throughout its timespan. In this presentation, we will discuss the impact of this heterogeneity showing specific examples such as the SAGE II to OSIRIS/CALIPSO transition in 2005. Potential solutions to these issues will also be discussed.

GRACILE: a comprehensive climatology of atmospheric gravity wave parameters based on satellite limb soundings

NASA Astrophysics Data System (ADS)

Ern, Manfred; Trinh, Quang Thai; Preusse, Peter; Gille, John C.; Mlynczak, Martin G.; Russell, James M., III; Riese, Martin

2018-04-01

Gravity waves are one of the main drivers of atmospheric dynamics. The spatial resolution of most global atmospheric models, however, is too coarse to properly resolve the small scales of gravity waves, which range from tens to a few thousand kilometers horizontally, and from below 1 km to tens of kilometers vertically. Gravity wave source processes involve even smaller scales. Therefore, general circulation models (GCMs) and chemistry climate models (CCMs) usually parametrize the effect of gravity waves on the global circulation. These parametrizations are very simplified. For this reason, comparisons with global observations of gravity waves are needed for an improvement of parametrizations and an alleviation of model biases. We present a gravity wave climatology based on atmospheric infrared limb emissions observed by satellite (GRACILE). GRACILE is a global data set of gravity wave distributions observed in the stratosphere and the mesosphere by the infrared limb sounding satellite instruments High Resolution Dynamics Limb Sounder (HIRDLS) and Sounding of the Atmosphere using Broadband Emission Radiometry (SABER). Typical distributions (zonal averages and global maps) of gravity wave vertical wavelengths and along-track horizontal wavenumbers are provided, as well as gravity wave temperature variances, potential energies and absolute momentum fluxes. This global data set captures the typical seasonal variations of these parameters, as well as their spatial variations. The GRACILE data set is suitable for scientific studies, and it can serve for comparison with other instruments (ground-based, airborne, or other satellite instruments) and for comparison with gravity wave distributions, both resolved and parametrized, in GCMs and CCMs. The GRACILE data set is available as supplementary data at https://doi.org/10.1594/PANGAEA.879658.

Ocular Features of Cerebro-Costo-Mandibular Syndrome.

PubMed

Hameed, Zoya; Taylor, Simon; Lindfield, Dan

2018-01-01

Cerebro-costo-mandibular syndrome (CCMS) is a rare hereditary disorder characterized by micrognathia, posterior rib gaps, and secondary developmental delay. Patients often require ventilation and feeding support throughout life. We describe the first reported ophthalmic findings of CCMS and propose that defects in choroidal permeability lead to chronic macular edema and refractory aqueous misdirection syndrome. Here we discuss the medical and surgical management concerns of recurrent angle closure and raised intraocular pressure in a CCMS patient.

Satellite Observations and Chemistry Climate Models - A Meandering Path Towards Better Predictions

NASA Technical Reports Server (NTRS)

Douglass, Anne R.

2011-01-01

Knowledge of the chemical and dynamical processes that control the stratospheric ozone layer has grown rapidly since the 1970s, when ideas that depletion of the ozone layer due to human activity were put forth. The concept of ozone depletion due to anthropogenic chlorine increase is simple; quantification of the effect is much more difficult. The future of stratospheric ozone is complicated because ozone is expected to increase for two reasons: the slow decrease in anthropogenic chlorine due to the Montreal Protocol and its amendments and stratospheric cooling caused by increases in carbon dioxide and other greenhouse gases. Prediction of future ozone levels requires three-dimensional models that represent physical, photochemical and radiative processes, i.e., chemistry climate models (CCMs). While laboratory kinetic and photochemical data are necessary inputs for a CCM, atmospheric measurements are needed both to reveal physical and chemical processes and for comparison with simulations to test the conceptual model that CCMs represent. Global measurements are available from various satellites including but not limited to the LIMS and TOMS instruments on Nimbus 7 (1979 - 1993), and various instruments on the Upper Atmosphere Research Satellite (1991 - 2005), Envisat (2002 - ongoing), Sci-Sat (2003 - ongoing) and Aura (2004 - ongoing). Every successful satellite instrument requires a physical concept for the measurement, knowledge of physical chemical properties of the molecules to be measured, and stellar engineering to design an instrument that will survive launch and operate for years with no opportunity for repair but providing enough information that trend information can be separated from any instrument change. The on-going challenge is to use observations to decrease uncertainty in prediction. This talk will focus on two applications. The first considers transport diagnostics and implications for prediction of the eventual demise of the Antarctic ozone hole. The second focuses on the upper stratosphere, where ozone is predicted to increase both due to chlorine decrease and due to temperature decrease expected as a result of increased concentrations Of CO2 and other greenhouse gases. Both applications show how diagnostics developed from global observations are being used to explain why the ozone response varies among CCM predictions for stratospheric ozone in the 21st century.

Staying Connected: Sustaining Collaborative Care Models with Limited Funding.

PubMed

Johnston, Brenda J; Peppard, Lora; Newton, Marian

2015-08-01

Providing psychiatric services in the primary care setting is challenging. The multidisciplinary, coordinated approach of collaborative care models (CCMs) addresses these challenges. The purpose of the current article is to discuss the implementation of a CCM at a free medical clinic (FMC) where volunteer staff provide the majority of services. Essential components of CCMs include (a) comprehensive screening and assessment, (b) shared development and communication of care plans among providers and the patient, and (c) care coordination and management. Challenges to implementing and sustaining a CCM at a FMC in Virginia attempting to meet the medical and psychiatric needs of the underserved are addressed. Although the CCM produced favorable outcomes, sustaining the model long-term presented many challenges. Strategies for addressing these challenges are discussed. Copyright 2015, SLACK Incorporated.

Mutations in SNRPB, encoding components of the core splicing machinery, cause cerebro-costo-mandibular syndrome.

PubMed

Bacrot, Séverine; Doyard, Mathilde; Huber, Céline; Alibeu, Olivier; Feldhahn, Niklas; Lehalle, Daphné; Lacombe, Didier; Marlin, Sandrine; Nitschke, Patrick; Petit, Florence; Vazquez, Marie-Paule; Munnich, Arnold; Cormier-Daire, Valérie

2015-02-01

Cerebro-costo-mandibular syndrome (CCMS) is a developmental disorder characterized by the association of Pierre Robin sequence and posterior rib defects. Exome sequencing and Sanger sequencing in five unrelated CCMS patients revealed five heterozygous variants in the small nuclear ribonucleoprotein polypeptides B and B1 (SNRPB) gene. This gene includes three transcripts, namely transcripts 1 and 2, encoding components of the core spliceosomal machinery (SmB' and SmB) and transcript 3 undergoing nonsense-mediated mRNA decay. All variants were located in the premature termination codon (PTC)-introducing alternative exon of transcript 3. Quantitative RT-PCR analysis revealed a significant increase in transcript 3 levels in leukocytes of CCMS individuals compared to controls. We conclude that CCMS is due to heterozygous mutations in SNRPB, enhancing inclusion of a SNRPB PTC-introducing alternative exon, and show that this developmental disease is caused by defects in the splicing machinery. Our finding confirms the report of SNRPB mutations in CCMS patients by Lynch et al. (2014) and further extends the clinical and molecular observations. © 2014 WILEY PERIODICALS, INC.

Cerebral Cavernous Malformation

MedlinePlus

... and individuals frequently have multiple CCMs found via magnetic resonance imaging. Individuals with CCM are faced with a diagnosis ... and individuals frequently have multiple CCMs found via magnetic resonance imaging. Individuals with CCM are faced with a diagnosis ...

Algal and aquatic plant carbon concentrating mechanisms in relation to environmental change.

PubMed

Raven, John A; Giordano, Mario; Beardall, John; Maberly, Stephen C

2011-09-01

Carbon dioxide concentrating mechanisms (also known as inorganic carbon concentrating mechanisms; both abbreviated as CCMs) presumably evolved under conditions of low CO(2) availability. However, the timing of their origin is unclear since there are no sound estimates from molecular clocks, and even if there were, there are no proxies for the functioning of CCMs. Accordingly, we cannot use previous episodes of high CO(2) (e.g. the Palaeocene-Eocene Thermal Maximum) to indicate how organisms with CCMs responded. Present and predicted environmental change in terms of increased CO(2) and temperature are leading to increased CO(2) and HCO(3)(-) and decreased CO(3)(2-) and pH in surface seawater, as well as decreasing the depth of the upper mixed layer and increasing the degree of isolation of this layer with respect to nutrient flux from deeper waters. The outcome of these forcing factors is to increase the availability of inorganic carbon, photosynthetic active radiation (PAR) and ultraviolet B radiation (UVB) to aquatic photolithotrophs and to decrease the supply of the nutrients (combined) nitrogen and phosphorus and of any non-aeolian iron. The influence of these variations on CCM expression has been examined to varying degrees as acclimation by extant organisms. Increased PAR increases CCM expression in terms of CO(2) affinity, whilst increased UVB has a range of effects in the organisms examined; little relevant information is available on increased temperature. Decreased combined nitrogen supply generally increases CO(2) affinity, decreased iron availability increases CO(2) affinity, and decreased phosphorus supply has varying effects on the organisms examined. There are few data sets showing interactions amongst the observed changes, and even less information on genetic (adaptation) changes in response to the forcing factors. In freshwaters, changes in phytoplankton species composition may alter with environmental change with consequences for frequency of species with or without CCMs. The information available permits less predictive power as to the effect of the forcing factors on CCM expression than for their overall effects on growth. CCMs are currently not part of models as to how global environmental change has altered, and is likely to further alter, algal and aquatic plant primary productivity.

NATO/CCMS PILOT STUDY - CLEAN PRODUCTS AND PROCESSES (PHASE I) 2000 ANNUAL REPORT, NUMBER 242

EPA Science Inventory

This annual report presents the proceedings of the Third Annual NATO/CCMS pilot study meeting in Copenhagen, Denmark. Guest speakers focused on efforts in the area of research of clean products and processes, life cycle analysis, computer tools and pollution prevention.

NATO CCMS PILOT STUDY ON CLEAN PRODUCTS AND PROCESSES -(PHASE I) - 2002 ANNUAL REPORT

EPA Science Inventory

The annual report summarizes the activities of the NATO CCMS Pilot Study on clean products and processes for 2002, including the proceedings of the 2002 annual meeting held in Vilnius, Lithuania. The report presents a wealth of information on cleaner production activities in ove...

78 FR 44895 - Energy Conservation Program for Consumer Products: First Co. Petition for Reconsideration

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-25

... lead times. Architects, engineers and builders often select HVAC systems for such projects up to 9-12... through the Compliance, Certification Management System (``CCMS'') as part of annual certification... System (``CCMS'') as part of annual certification reporting requirements; and (ii) publish the...

Collaborative Chronic Care Models for Mental Health Conditions: Cumulative Meta-Analysis and Meta-Regression to Guide Future Research and Implementation

PubMed Central

Grogan-Kaylor, Andrew; Perron, Brian E.; Kilbourne, Amy M.; Woltmann, Emily; Bauer, Mark S.

2013-01-01

Objective Prior meta-analysis indicates that collaborative chronic care models (CCMs) improve mental and physical health outcomes for individuals with mental disorders. This study aimed to investigate the stability of evidence over time and identify patient and intervention factors associated with CCM effects in order to facilitate implementation and sustainability of CCMs in clinical practice. Method We reviewed 53 CCM trials that analyzed depression, mental quality of life (QOL), or physical QOL outcomes. Cumulative meta-analysis and meta-regression were supplemented by descriptive investigations across and within trials. Results Most trials targeted depression in the primary care setting, and cumulative meta-analysis indicated that effect sizes favoring CCM quickly achieved significance for depression outcomes, and more recently achieved significance for mental and physical QOL. Four of six CCM elements (patient self-management support, clinical information systems, system redesign, and provider decision support) were common among reviewed trials, while two elements (healthcare organization support and linkages to community resources) were rare. No single CCM element was statistically associated with the success of the model. Similarly, meta-regression did not identify specific factors associated with CCM effectiveness. Nonetheless, results within individual trials suggest that increased illness severity predicts CCM outcomes. Conclusions Significant CCM trials have been derived primarily from four original CCM elements. Nonetheless, implementing and sustaining this established model will require healthcare organization support. While CCMs have typically been tested as population-based interventions, evidence supports stepped care application to more severely ill individuals. Future priorities include developing implementation strategies to support adoption and sustainability of the model in clinical settings while maximizing fit of this multi-component framework to local contextual factors. PMID:23938600

The possible evolution and future of CO2-concentrating mechanisms.

PubMed

Raven, John A; Beardall, John; Sánchez-Baracaldo, Patricia

2017-06-01

CO2-concentrating mechanisms (CCMs), based either on active transport of inorganic carbon (biophysical CCMs) or on biochemistry involving supplementary carbon fixation into C4 acids (C4 and CAM), play a major role in global primary productivity. However, the ubiquitous CO2-fixing enzyme in autotrophs, Rubisco, evolved at a time when atmospheric CO2 levels were very much higher than today and O2 was very low and, as CO2 and O2 approached (by no means monotonically), today's levels, at some time subsequently many organisms evolved a CCM that increased the supply of CO2 and decreased Rubisco oxygenase activity. Given that CO2 levels and other environmental factors have altered considerably between when autotrophs evolved and the present day, and are predicted to continue to change into the future, we here examine the drivers for, and possible timing of, evolution of CCMs. CCMs probably evolved when CO2 fell to 2-16 times the present atmospheric level, depending on Rubisco kinetics. We also assess the effects of other key environmental factors such as temperature and nutrient levels on CCM activity and examine the evidence for evolutionary changes in CCM activity and related cellular processes as well as limitations on continuity of CCMs through environmental variations. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

PROCEEDINGS FROM A NATO/CCMS PILOT STUDY MEETING AT LECCE, ITALY: LINKAGES AMONG LANDSCAPE ASSESSMENT, QUALITY OF LIFE AND ENVIRONMENTAL SECURITY

EPA Science Inventory

From 5-9 September 2004 the international working group of the NATO/CCMS Pilot Study on the "Use of Landscape Sciences for Environmental Assessment" met in Lecce, Italy. The results of this meeting are documented and the scientific presentations and results are published in EcoSy...

Cerebro-costo-mandibular syndrome: Report of two cases.

PubMed

Abdalla, Wael; Panigrahy, Ashok; Bartoletti, Stefano C

2011-01-01

Cerebro-costo-mandibular syndrome (CCMS) is a rare syndrome that includes a constellation of mandibular hypoplasia and posterior rib defects as its basic features. Additional features can include hearing loss, tracheal cartilage abnormalities, scoliosis, elbow hypoplasia, and spina bifida. Here we report two cases of CCMS and discuss the reported long-term outcome of the disease.

NATO/CCMS PILOT STUDY CLEAN PRODUCTS AND PROCESSES (PHASE II) 2003 ANNUAL REPORT

EPA Science Inventory

The 6th annual meeting of the NATO CCMS Pilot Study, Clean Products and Processes, was held in Cetraro, Italy, from May 11 to 15, 2003. This was also the first meeting of its Phase II study. 24 country representatives attended this meeting. This meeting was very ably run by th...

CDAC Student Report: Summary of LLNL Internship

DOE Office of Scientific and Technical Information (OSTI.GOV)

Herriman, Jane E.

Multiple objectives motivated me to apply for an internship at LLNL: I wanted to experience the work environment at a national lab, to learn about research and job opportunities at LLNL in particular, and to gain greater experience with code development, particularly within the realm of high performance computing (HPC). This summer I was selected to participate in LLNL's Computational Chemistry and Material Science Summer Institute (CCMS). CCMS is a 10 week program hosted by the Quantum Simulations group leader, Dr. Eric Schwegler. CCMS connects graduate students to mentors at LLNL involved in similar re- search and provides weekly seminarsmore » on a broad array of topics from within chemistry and materials science. Dr. Xavier Andrade and Dr. Erik Draeger served as my co-mentors over the summer, and Dr. Andrade continues to mentor me now that CCMS has concluded. Dr. Andrade is a member of the Quantum Simulations group within the Physical and Life Sciences at LLNL, and Dr. Draeger leads the HPC group within the Center for Applied Scientific Computing (CASC). The two have worked together to develop Qb@ll, an open-source first principles molecular dynamics code that was the platform for my summer research project.« less

Model-data integration for developing the Cropland Carbon Monitoring System (CCMS)

NASA Astrophysics Data System (ADS)

Jones, C. D.; Bandaru, V.; Pnvr, K.; Jin, H.; Reddy, A.; Sahajpal, R.; Sedano, F.; Skakun, S.; Wagle, P.; Gowda, P. H.; Hurtt, G. C.; Izaurralde, R. C.

2017-12-01

The Cropland Carbon Monitoring System (CCMS) has been initiated to improve regional estimates of carbon fluxes from croplands in the conterminous United States through integration of terrestrial ecosystem modeling, use of remote-sensing products and publically available datasets, and development of improved landscape and management databases. In order to develop these improved carbon flux estimates, experimental datasets are essential for evaluating the skill of estimates, characterizing the uncertainty of these estimates, characterizing parameter sensitivities, and calibrating specific modeling components. Experiments were sought that included flux tower measurement of CO2 fluxes under production of major agronomic crops. Currently data has been collected from 17 experiments comprising 117 site-years from 12 unique locations. Calibration of terrestrial ecosystem model parameters using available crop productivity and net ecosystem exchange (NEE) measurements resulted in improvements in RMSE of NEE predictions of between 3.78% to 7.67%, while improvements in RMSE for yield ranged from -1.85% to 14.79%. Model sensitivities were dominated by parameters related to leaf area index (LAI) and spring growth, demonstrating considerable capacity for model improvement through development and integration of remote-sensing products. Subsequent analyses will assess the impact of such integrated approaches on skill of cropland carbon flux estimates.

An Ocean Acidification Acclimatised Green Tide Alga Is Robust to Changes of Seawater Carbon Chemistry but Vulnerable to Light Stress

PubMed Central

Li, Xinshu; Feng, Zhihua; Xu, Juntian

2016-01-01

Ulva is the dominant genus in the green tide events and is considered to have efficient CO2 concentrating mechanisms (CCMs). However, little is understood regarding the impacts of ocean acidification on the CCMs of Ulva and the consequences of thalli’s acclimation to ocean acidification in terms of responding to environmental factors. Here, we grew a cosmopolitan green alga, Ulva linza at ambient (LC) and elevated (HC) CO2 levels and investigated the alteration of CCMs in U. linza grown at HC and its responses to the changed seawater carbon chemistry and light intensity. The inhibitors experiment for photosynthetic inorganic carbon utilization demonstrated that acidic compartments, extracellular carbonic anhydrase (CA) and intracellular CA worked together in the thalli grown at LC and the acquisition of exogenous carbon source in the thalli could be attributed to the collaboration of acidic compartments and extracellular CA. Contrastingly, when U. linza was grown at HC, extracellular CA was completely inhibited, acidic compartments and intracellular CA were also down-regulated to different extents and thus the acquisition of exogenous carbon source solely relied on acidic compartments. The down-regulated CCMs in U. linza did not affect its responses to changes of seawater carbon chemistry but led to a decrease of net photosynthetic rate when thalli were exposed to increased light intensity. This decrease could be attributed to photodamage caused by the combination of the saved energy due to the down-regulated CCMs and high light intensity. Our findings suggest future ocean acidification might impose depressing effects on green tide events when combined with increased light exposure. PMID:28033367

Susceptibility-weighted imaging at 7 T: Improved diagnosis of cerebral cavernous malformations and associated developmental venous anomalies☆☆☆

PubMed Central

Frischer, Josa M.; Göd, Sabine; Gruber, Andreas; Saringer, Walter; Grabner, Günther; Gatterbauer, Brigitte; Kitz, Klaus; Holzer, Sabrina; Kronnerwetter, Claudia; Hainfellner, Johannes A.; Knosp, Engelbert; Trattnig, Siegfried

2012-01-01

Background and aim In the diagnosis of cerebral cavernous malformations (CCMs) magnetic resonance imaging is established as the gold standard. Conventional MRI techniques have their drawbacks in the diagnosis of CCMs and associated venous malformations (DVAs). The aim of our study was to evaluate susceptibility weighted imaging SWI for the detection of CCM and associated DVAs at 7 T in comparison with 3 T. Patients and methods 24 patients (14 female, 10 male; median age: 38.3 y (21.1 y–69.1 y) were included in the study. Patients enrolled in the study received a 3 T and a 7 T MRI on the same day. The following sequences were applied on both field strengths: a T1 weighted 3D GRE sequence (MP-RAGE) and a SWI sequence. After obtaining the study MRIs, eleven patients underwent surgery and 13 patients were followed conservatively or were treated radio-surgically. Results Patients initially presented with haemorrhage (n = 4, 16.7%), seizures (n = 2, 8.3%) or other neurology (n = 18, 75.0%). For surgical resected lesions histopathological findings verified the diagnosis of CCMs. A significantly higher number of CCMs was diagnosed at 7 T SWI sequences compared with 3 T SWI (p < 0.05). Additionally diagnosed lesions on 7 T MRI were significantly smaller compared to the initial lesions on 3 T MRIs (p < 0.001). Further, more associated DVAs were diagnosed at 7 T MRI compared to 3 T MRI. Conclusion SWI sequences at ultra-high-field MRI improve the diagnosis of CCMs and associated DVAs and therefore add important pre-operative information. PMID:24179744

An Ocean Acidification Acclimatised Green Tide Alga Is Robust to Changes of Seawater Carbon Chemistry but Vulnerable to Light Stress.

PubMed

Gao, Guang; Liu, Yameng; Li, Xinshu; Feng, Zhihua; Xu, Juntian

2016-01-01

Ulva is the dominant genus in the green tide events and is considered to have efficient CO2 concentrating mechanisms (CCMs). However, little is understood regarding the impacts of ocean acidification on the CCMs of Ulva and the consequences of thalli's acclimation to ocean acidification in terms of responding to environmental factors. Here, we grew a cosmopolitan green alga, Ulva linza at ambient (LC) and elevated (HC) CO2 levels and investigated the alteration of CCMs in U. linza grown at HC and its responses to the changed seawater carbon chemistry and light intensity. The inhibitors experiment for photosynthetic inorganic carbon utilization demonstrated that acidic compartments, extracellular carbonic anhydrase (CA) and intracellular CA worked together in the thalli grown at LC and the acquisition of exogenous carbon source in the thalli could be attributed to the collaboration of acidic compartments and extracellular CA. Contrastingly, when U. linza was grown at HC, extracellular CA was completely inhibited, acidic compartments and intracellular CA were also down-regulated to different extents and thus the acquisition of exogenous carbon source solely relied on acidic compartments. The down-regulated CCMs in U. linza did not affect its responses to changes of seawater carbon chemistry but led to a decrease of net photosynthetic rate when thalli were exposed to increased light intensity. This decrease could be attributed to photodamage caused by the combination of the saved energy due to the down-regulated CCMs and high light intensity. Our findings suggest future ocean acidification might impose depressing effects on green tide events when combined with increased light exposure.

The Preparation of Capsaicin-Chitosan Microspheres (CCMS) Enteric Coated Tablets

PubMed Central

Chen, Jian; Huang, Gui-Dong; Tan, Si-Rong; Guo, Jiao; Su, Zheng-Quan

2013-01-01

This study aimed to research the preparation and content determination of capsaicin-chitosan microspheres (CCMS) enteric coated tablets. The core tablets were prepared with the method of wet granulation. Nine formulae were designed to determine the optimal formula of the core tablet. Eudragit L100 was used to prepare the CCMS enteric-coated tablets. The effect of enteric coated formulation variables such as content of talc (10%, 25% and 40%), plasticisers (TEC and DBS), dosage of plasticiser (10%, 20% and 30%) and coating weight (2%, 3% and 5%) were evaluated for drug release characteristics. The in vitro release was studied using 0.1 N HCl and pH 6.8 phosphate buffer. Enteric coated tablets without ruptures or swelling behaviour over 2 h in 0.1 N HCl indicated that these tablets showed acid resistance. The accumulated release rate in phosphate buffer (pH 6.8) revealed that the prepared tablets were able to sustain drug release into the intestine and a first-order release was obtained for capsaicin. This research is the first report of the preparation and content determination of CCMS enteric coated tablets. The sustained release behavior of enteric coated formulations in pH 6.8 phosphate buffer demonstrated that it would be a potential drug delivery platform for sustained delivery of gastric irritant drugs. PMID:24351818

Skin cancer risks avoided by the Montreal Protocol--worldwide modeling integrating coupled climate-chemistry models with a risk model for UV.

PubMed

van Dijk, Arjan; Slaper, Harry; den Outer, Peter N; Morgenstern, Olaf; Braesicke, Peter; Pyle, John A; Garny, Hella; Stenke, Andrea; Dameris, Martin; Kazantzidis, Andreas; Tourpali, Kleareti; Bais, Alkiviadis F

2013-01-01

The assessment model for ultraviolet radiation and risk "AMOUR" is applied to output from two chemistry-climate models (CCMs). Results from the UK Chemistry and Aerosols CCM are used to quantify the worldwide skin cancer risk avoided by the Montreal Protocol and its amendments: by the year 2030, two million cases of skin cancer have been prevented yearly, which is 14% fewer skin cancer cases per year. In the "World Avoided," excess skin cancer incidence will continue to grow dramatically after 2030. Results from the CCM E39C-A are used to estimate skin cancer risk that had already been inevitably committed once ozone depletion was recognized: excess incidence will peak mid 21st century and then recover or even super-recover at the end of the century. When compared with a "No Depletion" scenario, with ozone undepleted and cloud characteristics as in the 1960s throughout, excess incidence (extra yearly cases skin cancer per million people) of the "Full Compliance with Montreal Protocol" scenario is in the ranges: New Zealand: 100-150, Congo: -10-0, Patagonia: 20-50, Western Europe: 30-40, China: 90-120, South-West USA: 80-110, Mediterranean: 90-100 and North-East Australia: 170-200. This is up to 4% of total local incidence in the Full Compliance scenario in the peak year. © 2012 Wiley Periodicals, Inc. Photochemistry and Photobiology © 2012 The American Society of Photobiology.

MODELING NUTRIENT LOADS AND RESPONSE IN RIVER AND ESTUARY SYSTEMS

EPA Science Inventory

This Pilot Study was proposed by the United States and approved at the Committee on the Challenges of Modern Society's (CCMS) plenary session 27-31 March 1999 in Brussels. The proposal was finalized on 12 April 1999. The pilot study intended to address a major and widespread aqu...

Modifying dementia risk and trajectories of cognitive decline in aging: the Cache County Memory Study.

PubMed

Welsh-Bohmer, Kathleen A; Breitner, John C S; Hayden, Kathleen M; Lyketsos, Constantine; Zandi, Peter P; Tschanz, Joann T; Norton, Maria C; Munger, Ron

2006-07-01

The Cache County Study of Memory, Health, and Aging, more commonly referred to as the "Cache County Memory Study (CCMS)" is a longitudinal investigation of aging and Alzheimer's disease (AD) based in an exceptionally long-lived population residing in northern Utah. The study begun in 1994 has followed an initial cohort of 5,092 older individuals (many over age 84) and has examined the development of cognitive impairment and dementia in relation to genetic and environmental antecedents. This article summarizes the major contributions of the CCMS towards the understanding of mild cognitive disorders and AD across the lifespan, underscoring the role of common health exposures in modifying dementia risk and trajectories of cognitive change. The study now in its fourth wave of ascertainment illustrates the role of population-based approaches in informing testable models of cognitive aging and Alzheimer's disease.

Cerebro-costo-mandibular syndrome: Clinical, radiological, and genetic findings.

PubMed

Tooley, Madeleine; Lynch, Danielle; Bernier, Francois; Parboosingh, Jillian; Bhoj, Elizabeth; Zackai, Elaine; Calder, Alistair; Itasaki, Nobue; Wakeling, Emma; Scott, Richard; Lees, Melissa; Clayton-Smith, Jill; Blyth, Moira; Morton, Jenny; Shears, Debbie; Kini, Usha; Homfray, Tessa; Clarke, Angus; Barnicoat, Angela; Wallis, Colin; Hewitson, Rebecca; Offiah, Amaka; Saunders, Michael; Langton-Hewer, Simon; Hilliard, Tom; Davis, Peter; Smithson, Sarah

2016-05-01

Cerebro-Costo-Mandibular syndrome (CCMS) is a rare autosomal dominant condition comprising branchial arch-derivative malformations with striking rib-gaps. Affected patients often have respiratory difficulties, associated with upper airway obstruction, reduced thoracic capacity, and scoliosis. We describe a series of 12 sporadic and 4 familial patients including 13 infants/children and 3 adults. Severe micrognathia and reduced numbers of ribs with gaps are consistent findings. Cleft palate, feeding difficulties, respiratory distress, tracheostomy requirement, and scoliosis are common. Additional malformations such as horseshoe kidney, hypospadias, and septal heart defect may occur. Microcephaly and significant developmental delay are present in a small minority of patients. Key radiological findings are of a narrow thorax, multiple posterior rib gaps and abnormal costo-transverse articulation. A novel finding in 2 patients is bilateral accessory ossicles arising from the hyoid bone. Recently, specific mutations in SNRPB, which encodes components of the major spliceosome, have been found to cause CCMS. These mutations cluster in an alternatively spliced regulatory exon and result in altered SNRPB expression. DNA was available from 14 patients and SNRPB mutations were identified in 12 (4 previously reported). Eleven had recurrent mutations previously described in patients with CCMS and one had a novel mutation in the alternative exon. These results confirm the specificity of SNRPB mutations in CCMS and provide further evidence for the role of spliceosomal proteins in craniofacial and thoracic development. © 2016 Wiley Periodicals, Inc.

Quantifying the effect of mixing on the mean age of air in CCMVal-2 and CCMI-1 models

NASA Astrophysics Data System (ADS)

Dietmüller, Simone; Eichinger, Roland; Garny, Hella; Birner, Thomas; Boenisch, Harald; Pitari, Giovanni; Mancini, Eva; Visioni, Daniele; Stenke, Andrea; Revell, Laura; Rozanov, Eugene; Plummer, David A.; Scinocca, John; Jöckel, Patrick; Oman, Luke; Deushi, Makoto; Kiyotaka, Shibata; Kinnison, Douglas E.; Garcia, Rolando; Morgenstern, Olaf; Zeng, Guang; Stone, Kane Adam; Schofield, Robyn

2018-05-01

The stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry-climate models (CCMs). Compared to observational estimates, simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both mean transport by the residual circulation and two-way mixing; we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry-Climate Model Validation Activity 2) and CCMI-1 (Chemistry-Climate Model Initiative, phase 1). Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase in AoA by mixing. The mixing efficiency varies strongly between the models from 0.24 to 1.02. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid-scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parameterized wave forcing do not appear to be related to differences in mixing efficiency or AoA.

Using Transport Diagnostics to Understand Chemistry Climate Model Ozone Simulations

NASA Technical Reports Server (NTRS)

Strahan, S. E.; Douglass, A. R.; Stolarski, R. S.; Akiyoshi, H.; Bekki, S.; Braesicke, P.; Butchart, N.; Chipperfield, M. P.; Cugnet, D.; Dhomse, S.;

Cerebro-costo-mandibular syndrome: prognosis and proposal for classification.

PubMed

Nagasawa, Hiroyuki; Yamamoto, Yutaka; Kohno, Yoshinori

2010-09-01

Cerebro-costo-mandibular syndrome (CCMS) is a very rare syndrome characterized by micrognathia and posterior rib gap, with a poor prognosis. To date, only 75 cases have been reported worldwide. The overall survival rate for patients with this disorder has not been reported, and a classification of the patients on the basis of the prognosis is not yet available. The present study analyzed the figures and prognoses of past patients and documented a new case of CCMS. Formerly published case reports and personal communications were used to reveal the prognosis and classification of CCMS. The occurrence ratios of rib gap defects and of missing ribs were examined. Patients were divided into the following three groups according to their life span: lethal type, where the patients died before 1 month; severe type, where the patients lived for 1-12 months; and mild type, where they survived for more than 1 year. A comparison was made of the number of rib gaps, missing ribs, and the rib gap ratio (defined as the number of rib gaps divided by the number of all existing ribs) among these three groups. A significant difference in the number of rib defects between the lethal type and other types was noted. Short life span of severe type patients, compared to mild type, was attributed to their subjection to severe respiratory infection. CCMS can be classified into three categories--lethal, severe, and mild--according to the severity of the symptoms and prognosis.

Evaluation of the reactive nitrogen budget of the remote atmosphere in global models using airborne measurements

NASA Astrophysics Data System (ADS)

Murray, L. T.; Strode, S. A.; Fiore, A. M.; Lamarque, J. F.; Prather, M. J.; Thompson, C. R.; Peischl, J.; Ryerson, T. B.; Allen, H.; Blake, D. R.; Crounse, J. D.; Brune, W. H.; Elkins, J. W.; Hall, S. R.; Hintsa, E. J.; Huey, L. G.; Kim, M. J.; Moore, F. L.; Ullmann, K.; Wennberg, P. O.; Wofsy, S. C.

2017-12-01

Nitrogen oxides (NOx ≡ NO + NO2) in the background atmosphere are critical precursors for the formation of tropospheric ozone and OH, thereby exerting strong influence on surface air quality, reactive greenhouse gases, and ecosystem health. The impact of NOx on atmospheric composition and climate is sensitive to the relative partitioning of reactive nitrogen between NOx and longer-lived reservoir species of the total reactive nitrogen family (NOy) such as HNO3, HNO4, PAN and organic nitrates (RONO2). Unfortunately, global chemistry-climate models (CCMs) and chemistry-transport models (CTMs) have historically disagreed in their reactive nitrogen budgets outside of polluted continental regions, and we have lacked in situ observations with which to evaluate them. Here, we compare and evaluate the NOy budget of six global models (GEOS-Chem CTM, GFDL AM3 CCM, GISS E2.1 CCM, GMI CTM, NCAR CAM CCM, and UCI CTM) using new observations of total reactive nitrogen and its member species from the NASA Atmospheric Tomography (ATom) mission. ATom has now completed two of its four planned deployments sampling the remote Pacific and Atlantic basins of both hemispheres with a comprehensive suite of measurements for constraining reactive photochemistry. All six models have simulated conditions climatologically similar to the deployments. The GMI and GEOS-Chem CTMs have in addition performed hindcast simulations using the MERRA-2 reanalysis, and have been sampled along the flight tracks. We evaluate the performance of the models relative to the observations, and identify factors contributing to their disparate behavior using known differences in model oxidation mechanisms, heterogeneous loss pathways, lightning and surface emissions, and physical loss processes.

Probing platinum degradation in polymer electrolyte membrane fuel cells by synchrotron X-ray microscopy.

PubMed

Berejnov, Viatcheslav; Martin, Zulima; West, Marcia; Kundu, Sumit; Bessarabov, Dmitri; Stumper, Jürgen; Susac, Darija; Hitchcock, Adam P

2012-04-14

Synchrotron-based scanning transmission X-ray spectromicroscopy (STXM) was used to characterize the local chemical environment at and around the platinum particles in the membrane (PTIM) which form in operationally tested (end-of-life, EOL) catalyst coated membranes (CCMs) of polymer electrolyte membrane fuel cells (PEM-FC). The band of metallic Pt particles in operationally tested CCM membranes was imaged using transmission electron microscopy (TEM). The cathode catalyst layer in the beginning-of-life (BOL) CCMs was fabricated using commercially available catalysts created from Pt precursors with and without nitrogen containing ligands. The surface composition of these catalyst powders was measured by X-ray Photoelectron Spectroscopy (XPS). The local chemical environment of the PTIM in EOL CCMs was found to be directly related to the Pt precursor used in CCM fabrication. STXM chemical mapping at the N 1s edge revealed a characteristic spectrum at and around the dendritic Pt particles in CCMs fabricated with nitrogen containing Pt-precursors. This N 1s spectrum was identical to that of the cathode and different from the membrane. For CCM samples fabricated without nitrogen containing Pt-precursors the N 1s spectrum at the Pt particles was indistinguishable from that of the adjacent membrane. We interpret these observations to indicate that nitrogenous ligands in the nitrogen containing precursors, or decomposition product(s) from that source, are transported together with the dissolved Pt from the cathode into the membrane as a result of the catalyst degradation process. This places constraints on possible mechanisms for the PTIM band formation process.

Modelling an advanced ManPAD with dual band detectors and a rosette scanning seeker head

NASA Astrophysics Data System (ADS)

Birchenall, Richard P.; Richardson, Mark A.; Butters, Brian; Walmsley, Roy

2012-01-01

Man Portable Air Defence Systems (ManPADs) have been a favoured anti aircraft weapon since their appearance on the military proliferation scene in the mid 1960s. Since this introduction there has been a 'cat and mouse' game of Missile Countermeasures (CMs) and the aircraft protection counter counter measures (CCMs) as missile designers attempt to defeat the aircraft platform protection equipment. Magnesium Teflon Viton (MTV) flares protected the target aircraft until the missile engineers discovered the art of flare rejection using techniques including track memory and track angle bias. These early CCMs relied upon CCM triggering techniques such as the rise rate method which would just sense a sudden increase in target energy and assume that a flare CM had been released by the target aircraft. This was not as reliable as was first thought as aspect changes (bringing another engine into the field of view) or glint from the sun could inadvertently trigger a CCM when not needed. The introduction of dual band detectors in the 1980s saw a major advance in CCM capability allowing comparisons between two distinct IR bands to be made thus allowing the recognition of an MTV flare to occur with minimal false alarms. The development of the rosette scan seeker in the 1980s complemented this advancement allowing the scene in the missile field of view (FOV) to be scanned by a much smaller (1/25) instantaneous FOV (IFOV) with the spectral comparisons being made at each scan point. This took the ManPAD from a basic IR energy detector to a pseudo imaging system capable of analysing individual elements of its overall FOV allowing more complex and robust CCM to be developed. This paper continues the work published in [1,2] and describes the method used to model an advanced ManPAD with a rosette scanning seeker head and robust CCMs similar to the Raytheon Stinger RMP.

A Model and Satellite-Based Analysis of the Tropospheric Ozone Distribution in Clear Versus Convectively Cloudy Conditions

NASA Technical Reports Server (NTRS)

Strode, Sarah A.; Douglass, Anne R.; Ziemke, Jerald R.; Manyin, Michael; Nielsen, J. Eric; Oman, Luke D.

2017-01-01

Satellite observations of in-cloud ozone concentrations from the Ozone Monitoring Instrument and Microwave Limb Sounder instruments show substantial differences from background ozone concentrations. We develop a method for comparing a free-running chemistry-climate model (CCM) to in-cloud and background ozone observations using a simple criterion based on cloud fraction to separate cloudy and clear-sky days. We demonstrate that the CCM simulates key features of the in-cloud versus background ozone differences and of the geographic distribution of in-cloud ozone. Since the agreement is not dependent on matching the meteorological conditions of a specific day, this is a promising method for diagnosing how accurately CCMs represent the relationships between ozone and clouds, including the lower ozone concentrations shown by in-cloud satellite observations. Since clouds are associated with convection as well as changes in chemistry, we diagnose the tendency of tropical ozone at 400 hPa due to chemistry, convection and turbulence, and large-scale dynamics. While convection acts to reduce ozone concentrations at 400 hPa throughout much of the tropics, it has the opposite effect over highly polluted regions of South and East Asia.

Geothermal pilot study final report: creating an international geothermal energy community

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bresee, J.C.; Yen, W.W.S.; Metzler, J.E.

The Geothermal Pilot Study under the auspices of the Committee on the Challenges of Modern Society (CCMS) was established in 1973 to apply an action-oriented approach to international geothermal research and development, taking advantage of the established channels of governmental communication provided by the North Atlantic Treaty Organization (NATO). The Pilot Study was composed of five substudies. They included: computer-based information systems; direct application of geothermal energy; reservoir assessment; small geothermal power plants; and hot dry rock concepts. The most significant overall result of the CCMS Geothermal Pilot Study, which is now complete, is the establishment of an identifiable communitymore » of geothermal experts in a dozen or more countries active in development programs. Specific accomplishments include the creation of an international computer file of technical information on geothermal wells and fields, the development of studies and reports on direct applications, geothermal fluid injection and small power plants, and the operation of the visiting scientist program. In the United States, the computer file has aready proven useful in the development of reservoir models and of chemical geothermometers. The state-of-the-art report on direct uses of geothermal energy is proving to be a valuable resource document for laypersons and experts in an area of increasing interest to many countries. Geothermal fluid injection studies in El Salvador, New Zealand, and the United States have been assisted by the Reservoir Assessment Substudy and have led to long-range reservoir engineering studies in Mexico. At least seven small geothermal power plants are in use or have been planned for construction around the world since the Small Power Plant Substudy was instituted--at least partial credit for this increased application can be assigned to the CCMS Geothermal Pilot Study. (JGB)« less

The ins and outs of CO2

PubMed Central

Raven, John A.; Beardall, John

2016-01-01

It is difficult to distinguish influx and efflux of inorganic C in photosynthesizing tissues; this article examines what is known and where there are gaps in knowledge. Irreversible decarboxylases produce CO2, and CO2 is the substrate/product of enzymes that act as carboxylases and decarboxylases. Some irreversible carboxylases use CO2; others use HCO3 –. The relative role of permeation through the lipid bilayer versus movement through CO2-selective membrane proteins in the downhill, non-energized, movement of CO2 is not clear. Passive permeation explains most CO2 entry, including terrestrial and aquatic organisms with C3 physiology and biochemistry, terrestrial C4 plants and all crassulacean acid metabolism (CAM) plants, as well as being part of some mechanisms of HCO3 – use in CO2 concentrating mechanism (CCM) function, although further work is needed to test the mechanism in some cases. However, there is some evidence of active CO2 influx at the plasmalemma of algae. HCO3 – active influx at the plasmalemma underlies all cyanobacterial and some algal CCMs. HCO3 – can also enter some algal chloroplasts, probably as part of a CCM. The high intracellular CO2 and HCO3 – pools consequent upon CCMs result in leakage involving CO2, and occasionally HCO3 –. Leakage from cyanobacterial and microalgal CCMs involves up to half, but sometimes more, of the gross inorganic C entering in the CCM; leakage from terrestrial C4 plants is lower in most environments. Little is known of leakage from other organisms with CCMs, though given the leakage better-examined organisms, leakage occurs and increases the energetic cost of net carbon assimilation. PMID:26466660

Evolution of photorespiration from cyanobacteria to land plants, considering protein phylogenies and acquisition of carbon concentrating mechanisms.

PubMed

Hagemann, Martin; Kern, Ramona; Maurino, Veronica G; Hanson, David T; Weber, Andreas P M; Sage, Rowan F; Bauwe, Hermann

2016-05-01

Photorespiration and oxygenic photosynthesis are intimately linked processes. It has been shown that under the present day atmospheric conditions cyanobacteria and all eukaryotic phototrophs need functional photorespiration to grow autotrophically. The question arises as to when this essential partnership evolved, i.e. can we assume a coevolution of both processes from the beginning or did photorespiration evolve later to compensate for the generation of 2-phosphoglycolate (2PG) due to Rubisco's oxygenase reaction? This question is mainly discussed here using phylogenetic analysis of proteins involved in the 2PG metabolism and the acquisition of different carbon concentrating mechanisms (CCMs). The phylogenies revealed that the enzymes involved in the photorespiration of vascular plants have diverse origins, with some proteins acquired from cyanobacteria as ancestors of the chloroplasts and others from heterotrophic bacteria as ancestors of mitochondria in the plant cell. Only phosphoglycolate phosphatase was found to originate from Archaea. Notably glaucophyte algae, the earliest branching lineage of Archaeplastida, contain more photorespiratory enzymes of cyanobacterial origin than other algal lineages or land plants indicating a larger initial contribution of cyanobacterial-derived proteins to eukaryotic photorespiration. The acquisition of CCMs is discussed as a proxy for assessing the timing of periods when photorespiratory activity may have been enhanced. The existence of CCMs also had marked influence on the structure and function of photorespiration. Here, we discuss evidence for an early and continuous coevolution of photorespiration, CCMs and photosynthesis starting from cyanobacteria via algae, to land plants. © The Author 2016. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Impact of the voluntary withdrawal of over-the-counter cough and cold medications on pediatric ingestions reported to poison centers†

PubMed Central

Klein-Schwartz, Wendy; Sorkin, John David; Doyon, Suzanne

2015-01-01

SUMMARY Purpose To assess the impact of a voluntary withdrawal of over-the-counter cough and cold medications (OTC CCMs) labeled for children under age 2 years on pediatric ingestions reported to the American Association of Poison Control Centers. Methods Trend analysis of OTC CCMs ingestions in children under the age 6 years resulting from therapeutic errors or unintentional poisonings for 27 months before (pre-) and 15 months after (post-) the October 2007 voluntary withdrawal was conducted. The rates and outcome severity were examined. Results The mean annual rate of therapeutic errors involving OTC CCMs post-withdrawal, in children less than 2-years of age, 45.2/100 000 (95%CI 30.7–66.6) was 54% of the rate pre-withdrawal, 83.8/100 000 (95%CI 67.6–104.0). The decrease was statistically significant p < 0.02. In this age group, there was no difference in the frequency of severe outcomes resulting from therapeutic errors post-withdrawal. There was no significant difference in unintentional poisoning rates post-withdrawal 82.1/100 000 (66.0–102.2) vs. pre-withdrawal 98.3/100 000 (84.4–114.3) (p < 0.21) in children less than 2-years of age. There were no significant reductions in rates of therapeutic errors and unintentional poisonings in children ages 2–5 years, who were not targeted by the withdrawal. Conclusions A significant decrease in annual rates of therapeutic errors in children under 2-years reported to Poison Centers followed the voluntary withdrawal of OTC CCMs for children under age 2-years. Concerns that withdrawal of pediatric medications would paradoxically increase poisonings from parents giving products intended for older age groups to young children are not supported. PMID:20533537

Multi-detector thoracic CT findings in cerebro-costo-mandibular syndrome: rib gaps and failure of costo-vertebral separation.

PubMed

Watson, Tom Anthony; Arthurs, Owen John; Muthialu, Nagarajan; Calder, Alistair Duncan

2014-02-01

Cerebro-costo-mandibular syndrome (CCMS) describes a triad of mandibular hypoplasia, brain dysfunction and posterior rib defects ("rib gaps"). We present the CT imaging for a 2-year-old girl with CCMS that highlights the rib gap defects and shows absent transverse processes with abnormal fusion of the ribs directly to the vertebral bodies. We argue that this is likely to relate to abnormal lateral sclerotome development in embryology, with the failure of normal costo-vertebral junctions compounding impaired thoracic function. The case also highlights the use of CT for specific indications in skeletal dysplasia.

Photogrammetry and Laser Imagery Tests for Tank Waste Volume Estimates: Summary Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Field, Jim G.

2013-03-27

Feasibility tests were conducted using photogrammetry and laser technologies to estimate the volume of waste in a tank. These technologies were compared with video Camera/CAD Modeling System (CCMS) estimates; the current method used for post-retrieval waste volume estimates. This report summarizes test results and presents recommendations for further development and deployment of technologies to provide more accurate and faster waste volume estimates in support of tank retrieval and closure.

Understanding Differences in Upper Stratospheric Ozone Response to Changes in Chlorine and Temperature as Computed Using CCMVal Models

NASA Technical Reports Server (NTRS)

Douglass, A. R.; Stolarski, R. S.; Strahan, S. E.; Oman, L. D.

2012-01-01

Projections of future ozone levels are made using models that couple a general circulation model with a representation of atmospheric photochemical processes, allowing interactions among photochemical processes, radiation, and dynamics. Such models are known as chemistry and climate models (CCMs). Although developed from common principles and subject to the same boundary conditions, simulated ozone time series vary for projections of changes in ozone depleting substances (ODSs) and greenhouse gases. In the upper stratosphere photochemical processes control ozone level, and ozone increases as ODSs decrease and temperature decreases due to greenhouse gas increase. Simulations agree broadly but there are quantitative differences in the sensitivity of ozone to chlorine and to temperature. We obtain insight into these differences in sensitivity by examining the relationship between the upper stratosphere annual cycle of ozone and temperature as produced by a suite of models. All simulations conform to expectation in that ozone is less sensitive to temperature when chlorine levels are highest because chlorine catalyzed loss is nearly independent of temperature. Differences in sensitivity are traced to differences in simulated temperature, ozone and reactive nitrogen when chlorine levels are close to background. This work shows that differences in the importance of specific processes underlie differences in simulated sensitivity of ozone to composition change. This suggests a) the multi-model mean is not a best estimate of the sensitivity of upper ozone to changes in ODSs and temperature; b) the spread of values is not an appropriate measure of uncertainty.

The carbonic anhydrase CAH1 is an essential component of the carbon-concentrating mechanism in Nannochloropsis oceanica.

PubMed

Gee, Christopher W; Niyogi, Krishna K

2017-04-25

Aquatic photosynthetic organisms cope with low environmental CO 2 concentrations through the action of carbon-concentrating mechanisms (CCMs). Known eukaryotic CCMs consist of inorganic carbon transporters and carbonic anhydrases (and other supporting components) that culminate in elevated [CO 2 ] inside a chloroplastic Rubisco-containing structure called a pyrenoid. We set out to determine the molecular mechanisms underlying the CCM in the emerging model photosynthetic stramenopile, Nannochloropsis oceanica , a unicellular picoplanktonic alga that lacks a pyrenoid. We characterized CARBONIC ANHYDRASE 1 ( CAH1 ) as an essential component of the CCM in N. oceanica CCMP1779. We generated insertions in this gene by directed homologous recombination and found that the cah1 mutant has severe defects in growth and photosynthesis at ambient CO 2 We identified CAH1 as an α-type carbonic anhydrase, providing a biochemical role in CCM function. CAH1 was found to localize to the lumen of the epiplastid endoplasmic reticulum, with its expression regulated by the external inorganic carbon concentration at both the transcript and protein levels. Taken together, these findings show that CAH1 is an indispensable component of what may be a simple but effective and dynamic CCM in N. oceanica .

GLYOXALASE I A111E, PARAOXONASE 1 Q192R AND L55M POLYMORPHISMS IN ITALIAN PATIENTS WITH SPORADIC CEREBRAL CAVERNOUS MALFORMATIONS: A PILOT STUDY.

PubMed

Rinaldi, C; Bramanti, P; Famà, A; Scimone, C; Donato, L; Antognelli, C; Alafaci, C; Tomasello, F; D'Angelo, R; Sidoti, A

2015-01-01

It is already known that the conditions of increased oxidative stress are associated to a greater susceptibility to vascular malformations including cerebral cavernous malformations (CCMs). These are vascular lesions of the CNS characterized by abnormally enlarged capillary cavities that can occur sporadically or as a familial autosomal dominant condition with incomplete penetrance and variable clinical expression attributable to mutations in three different genes: CCM1(Krit1), CCM2 (MGC4607) and CCM3 (PDCD10). Polymorphisms in the genes encoding for enzymes involved in the antioxidant systems such as glyoxalase I (GLO I) and paraoxonase I (PON I) could influence individual susceptibility to the vascular malformations. A single nucleotide polymorphism was identified in the exon 4 of GLO 1 gene that causes an amino acid substitution of Ala for Glu (Ala111Glu). Two common polymorphisms have been described in the coding region of PON1, which lead to glutamine → arginine substitution at 192 (Q192R) and a leucine → methionine substitution at 55 (L55M). The polymorphisms were characterized in 59 patients without mutations in the CCM genes versus 213 healthy controls by PCR/RFLP methods using DNA from lymphocytes. We found that the frequency of patients carrying the GLO1 A/E genotype among the case group (56%) was four-fold higher than among the controls (14.1%). In the cohort of CCM patients, an increase in the frequency of PON192 Q/R genotype was observed (39% in the CCM group versus 3.7% in the healthy controls). Similarly, an increase was observed in the proportion of individuals with the genotype R/R in the disease group (5%) in respect to the normal healthy cohort (0.5%). Finally, the frequency of the PON55 heterozygotes L/M genotype was 29% in patients with CCMs and 4% in the healthy controls. The same trend was observed in PON55 homozygous M/M genotype frequency (CCMs 20% vs controls 10%). The present study aimed to investigate the possible association of GLO1 A111E, PON1 Q192R and L55M polymorphisms with the risk of CCMs. We found that individuals with the GLO1 A /E genotype, PON192/QR-RR genotypes and PON55/LM-MM genotypes had a significantly higher risk of CCMs compared with the other genotypes. However, because CCM is a heterogeneous disease, other additional factors might be involved in the initiation and progression of CCM disease.

Recent variability of the solar spectral irradiance and its impact on climate modelling

NASA Astrophysics Data System (ADS)

Ermolli, I.; Matthes, K.; Dudok de Wit, T.; Krivova, N. A.; Tourpali, K.; Weber, M.; Unruh, Y. C.; Gray, L.; Langematz, U.; Pilewskie, P.; Rozanov, E.; Schmutz, W.; Shapiro, A.; Solanki, S. K.; Woods, T. N.

2013-04-01

The lack of long and reliable time series of solar spectral irradiance (SSI) measurements makes an accurate quantification of solar contributions to recent climate change difficult. Whereas earlier SSI observations and models provided a qualitatively consistent picture of the SSI variability, recent measurements by the SORCE (SOlar Radiation and Climate Experiment) satellite suggest a significantly stronger variability in the ultraviolet (UV) spectral range and changes in the visible and near-infrared (NIR) bands in anti-phase with the solar cycle. A number of recent chemistry-climate model (CCM) simulations have shown that this might have significant implications on the Earth's atmosphere. Motivated by these results, we summarize here our current knowledge of SSI variability and its impact on Earth's climate. We present a detailed overview of existing SSI measurements and provide thorough comparison of models available to date. SSI changes influence the Earth's atmosphere, both directly, through changes in shortwave (SW) heating and therefore, temperature and ozone distributions in the stratosphere, and indirectly, through dynamical feedbacks. We investigate these direct and indirect effects using several state-of-the art CCM simulations forced with measured and modelled SSI changes. A unique asset of this study is the use of a common comprehensive approach for an issue that is usually addressed separately by different communities. We show that the SORCE measurements are difficult to reconcile with earlier observations and with SSI models. Of the five SSI models discussed here, specifically NRLSSI (Naval Research Laboratory Solar Spectral Irradiance), SATIRE-S (Spectral And Total Irradiance REconstructions for the Satellite era), COSI (COde for Solar Irradiance), SRPM (Solar Radiation Physical Modelling), and OAR (Osservatorio Astronomico di Roma), only one shows a behaviour of the UV and visible irradiance qualitatively resembling that of the recent SORCE measurements. However, the integral of the SSI computed with this model over the entire spectral range does not reproduce the measured cyclical changes of the total solar irradiance, which is an essential requisite for realistic evaluations of solar effects on the Earth's climate in CCMs. We show that within the range provided by the recent SSI observations and semi-empirical models discussed here, the NRLSSI model and SORCE observations represent the lower and upper limits in the magnitude of the SSI solar cycle variation. The results of the CCM simulations, forced with the SSI solar cycle variations estimated from the NRLSSI model and from SORCE measurements, show that the direct solar response in the stratosphere is larger for the SORCE than for the NRLSSI data. Correspondingly, larger UV forcing also leads to a larger surface response. Finally, we discuss the reliability of the available data and we propose additional coordinated work, first to build composite SSI data sets out of scattered observations and to refine current SSI models, and second, to run coordinated CCM experiments.

Algal evolution in relation to atmospheric CO2: carboxylases, carbon-concentrating mechanisms and carbon oxidation cycles

PubMed Central

Raven, John A.; Giordano, Mario; Beardall, John; Maberly, Stephen C.

2012-01-01

Oxygenic photosynthesis evolved at least 2.4 Ga; all oxygenic organisms use the ribulose bisphosphate carboxylase-oxygenase (Rubisco)–photosynthetic carbon reduction cycle (PCRC) rather than one of the five other known pathways of autotrophic CO2 assimilation. The high CO2 and (initially) O2-free conditions permitted the use of a Rubisco with a high maximum specific reaction rate. As CO2 decreased and O2 increased, Rubisco oxygenase activity increased and 2-phosphoglycolate was produced, with the evolution of pathways recycling this inhibitory product to sugar phosphates. Changed atmospheric composition also selected for Rubiscos with higher CO2 affinity and CO2/O2 selectivity correlated with decreased CO2-saturated catalytic capacity and/or for CO2-concentrating mechanisms (CCMs). These changes increase the energy, nitrogen, phosphorus, iron, zinc and manganese cost of producing and operating Rubisco–PCRC, while biosphere oxygenation decreased the availability of nitrogen, phosphorus and iron. The majority of algae today have CCMs; the timing of their origins is unclear. If CCMs evolved in a low-CO2 episode followed by one or more lengthy high-CO2 episodes, CCM retention could involve a combination of environmental factors known to favour CCM retention in extant organisms that also occur in a warmer high-CO2 ocean. More investigations, including studies of genetic adaptation, are needed. PMID:22232762

Climate variability related to the 11 year solar cycle as represented in different spectral solar irradiance reconstructions

NASA Astrophysics Data System (ADS)

Kruschke, Tim; Kunze, Markus; Misios, Stergios; Matthes, Katja; Langematz, Ulrike; Tourpali, Kleareti

2016-04-01

Advanced spectral solar irradiance (SSI) reconstructions differ significantly from each other in terms of the mean solar spectrum, that is the spectral distribution of energy, and solar cycle variability. Largest uncertainties - relative to mean irradiance - are found for the ultraviolet range of the spectrum, a spectral region highly important for radiative heating and chemistry in the stratosphere and troposphere. This study systematically analyzes the effects of employing different SSI reconstructions in long-term (40 years) chemistry-climate model (CCM) simulations to estimate related uncertainties of the atmospheric response. These analyses are highly relevant for the next round of CCM studies as well as climate models within the CMIP6 exercise. The simulations are conducted by means of two state-of-the-art CCMs - CESM1(WACCM) and EMAC - run in "atmosphere-only"-mode. These models are quite different with respect to the complexity of the implemented radiation and chemistry schemes. CESM1(WACCM) features a chemistry module with considerably higher spectral resolution of the photolysis scheme while EMAC employs a radiation code with notably higher spectral resolution. For all simulations, concentrations of greenhouse gases and ozone depleting substances, as well as observed sea surface temperatures (SST) are set to average conditions representative for the year 2000 (for SSTs: mean of decade centered over year 2000) to exclude anthropogenic influences and differences due to variable SST forcing. Only the SSI forcing differs for the various simulations. Four different forcing datasets are used: NRLSSI1 (used as a reference in all previous climate modeling intercomparisons, i.e. CMIP5, CCMVal, CCMI), NRLSSI2, SATIRE-S, and the SSI forcing dataset recommended for the CMIP6 exercise. For each dataset, a solar maximum and minimum timeslice is integrated, respectively. The results of these simulations - eight in total - are compared to each other with respect to their shortwave heating rate differences (additionally collated with line-by-line calculations using libradtran), differences in the photolysis rates, as well as atmospheric circulation features (temperature, zonal wind, geopotential height, etc.). It is shown that atmospheric responses to the different SSI datasets differ significantly from each other. This is a result from direct radiative effects as well as indirect effects induced by ozone feedbacks. Differences originating from using different SSI datasets for the same level of solar activity are in the same order of magnitude as those associated with the 11 year solar cycle within a specific dataset. However, the climate signals related to the solar cycle are quite comparable across datasets.

Cerebro-costo-mandibular syndrome in a father and a female fetus: early prenatal ultrasonographic diagnosis and autosomal dominant transmission.

PubMed

Morin, G; Gekas, J; Naepels, P; Gondry, J; Devauchelle, B; Testelin, S; Sevestre, H; Thépôt, F; Mathieu, M

2001-10-01

Ultrasonography in a female fetus revealed cystic cervical hygroma, severe micrognathia, and vertebral and upper limb anomalies suggestive of cerebro-costo-mandibular syndrome (CCMS) which was diagnosed ultrasonographically at 16 weeks' gestation. The father is affected and presents with a Pierre Robin sequence, short stature and typical costovertebral anomalies. CCMS is a rare and severe disorder. The high frequency of sporadic cases, vertical transmission, and the excess of sibs affected via horizontal transmission suggest dominant autosomal mutation with possible germinal mosaicism. The vertical familial case detailed in the present report is a reminder of the high risk when one parent or one sibling is affected and the extreme variability of phenotype and costal ossification. Early prenatal ultrasound diagnosis is possible in a severely affected fetus. Copyright 2001 John Wiley & Sons, Ltd.

Anticancer drug-based multifunctional nanogels through self-assembly of dextran-curcumin conjugates toward cancer theranostics.

PubMed

Nagahama, Koji; Sano, Yoshinori; Kumano, Takayuki

2015-06-15

Curcumin (CCM) has been received much attention in cancer theranostics because CCM exhibits both anticancer activity and strong fluorescence available for bio-imaging. However, CCM has never been utilized in clinical mainly due to its extremely low water solubility and its low cellular uptake into cancer cells. We fabricated novel CCM-based biodegradable nanoparticles through self-assembly of amphiphilic dextran-CCM conjugates. Significantly high CCM loading contents in the nanoparticles and the high water solubility were achieved. Importantly, the dextran-CCMs nanoparticles were effectively delivered into HeLa cells and exhibited strong fluorescence available for live-cell imaging, although the nanoparticles were not delivered into normal cells. Thus, the dextran-CCMs nanoparticles could be a promising for creation of novel CCM-based cancer theranostics with high efficacy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Convective Troposphere-Stratosphere Transport in the Tropics and Hydration by ice Crystals Geysers

NASA Astrophysics Data System (ADS)

Pommereau, J.

2008-12-01

Twenty-five years ago the suggestion was made by Danielsen of direct fast convective penetration of tropospheric air in the stratosphere over land convective systems. Although the existence of the mechanism is accepted, it was thought to be rare and thus its contribution to Troposphere-Stratosphere Transport (TST) of chemical species and water vapour at global scale unimportant at global scale. In contrast to this assumption, observations of temperature, water vapour, ice particles, long-lived tropospheric species during HIBISCUS, TROCCINOX and SCOUT-O3 over Brazil, Australia and Africa and more recently CALIPSO aerosols observations suggest that it is a general feature of tropical land convective regions in the summer. Particularly relevant to stratospheric water vapour is the observation of geyser like ice crystals in the TTL over overshooting events which may result in the moistening of the stratosphere. Although such events successfully captured by small scale Cloud-Resolving Models may have a significant impact on stratospheric ozone chemistry and climate, they are currently totally ignored by NWPs, CTMs and CCMs. Several recent balloon and aircraft observations of overshoots and CRM simulations will be shown illustrating the mechanism, as well as observations from a variety of satellites suggesting a significant impact at global scale.

The potential for co-evolution of CO2-concentrating mechanisms and Rubisco in diatoms.

PubMed

Young, Jodi N; Hopkinson, Brian M

2017-06-01

Diatoms are a diverse group of unicellular algae that contribute significantly to global photosynthetic carbon fixation and export in the modern ocean, and are an important source of microfossils for paleoclimate reconstructions. Because of their importance in the environment, diatoms have been a focus of study on the physiology and ecophysiology of carbon fixation, in particular their CO2-concentrating mechanisms (CCMs) and Rubisco characteristics. While carbon fixation in diatoms is not as well understood as in certain model aquatic photoautotrophs, a greater number of species have been examined in diatoms. Recent work has highlighted a large diversity in the function, physiology, and kinetics of both the CCM and Rubisco between different diatom species. This diversity was unexpected since it has generally been assumed that CCMs and Rubiscos were similar within major algal lineages as the result of selective events deep in evolutionary history, and suggests a more recent co-evolution between the CCM and Rubisco within diatoms. This review explores our current understanding of the diatom CCM and highlights the diversity of both the CCM and Rubisco kinetics. We will suggest possible environmental, physiological, and evolutionary drivers for the co-evolution of the CCM and Rubisco in diatoms. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Regulatory components of carbon concentrating mechanisms in aquatic unicellular photosynthetic organisms.

PubMed

Tomar, Vandana; Sidhu, Gurpreet Kaur; Nogia, Panchsheela; Mehrotra, Rajesh; Mehrotra, Sandhya

2017-11-01

This review provides an insight into the regulation of the carbon concentrating mechanisms (CCMs) in lower organisms like cyanobacteria, proteobacteria, and algae. CCMs evolved as a mechanism to concentrate CO 2 at the site of primary carboxylating enzyme Ribulose-1, 5-bisphosphate carboxylase oxygenase (Rubisco), so that the enzyme could overcome its affinity towards O 2 which leads to wasteful processes like photorespiration. A diverse set of CCMs exist in nature, i.e., carboxysomes in cyanobacteria and proteobacteria; pyrenoids in algae and diatoms, the C 4 system, and Crassulacean acid metabolism in higher plants. Prime regulators of CCM in most of the photosynthetic autotrophs belong to the LysR family of transcriptional regulators, which regulate the activity of the components of CCM depending upon the ambient CO 2 concentrations. Major targets of these regulators are carbonic anhydrase and inorganic carbon uptake systems (CO 2 and HCO 3 - transporters) whose activities are modulated either at transcriptional level or by changes in the levels of their co-regulatory metabolites. The article provides information on the localization of the CCM components as well as their function and participation in the development of an efficient CCM. Signal transduction cascades leading to activation/inactivation of inducible CCM components on perception of low/high CO 2 stimuli have also been brought into picture. A detailed study of the regulatory components can aid in identifying the unraveled aspects of these mechanisms and hence provide information on key molecules that need to be explored to further provide a clear understanding of the mechanism under study.

Cyanobacterial carbon concentrating mechanisms facilitate sustained CO2 depletion in eutrophic lakes

NASA Astrophysics Data System (ADS)

Morales-Williams, Ana M.; Wanamaker, Alan D., Jr.; Downing, John A.

2017-06-01

Phytoplankton blooms are increasing in frequency, intensity, and duration in aquatic ecosystems worldwide. In many eutrophic lakes, these high levels of primary productivity correspond to periods of CO2 depletion in surface waters. Cyanobacteria and other groups of phytoplankton have the ability to actively transport bicarbonate (HCO3-) across their cell membrane when CO2 concentrations are limiting, possibly giving them a competitive advantage over algae not using carbon concentrating mechanisms (CCMs). To investigate whether CCMs can maintain phytoplankton bloom biomass under CO2 depletion, we measured the δ13C signatures of dissolved inorganic carbon (δ13CDIC) and phytoplankton particulate organic carbon (δ13Cphyto) in 16 mesotrophic to hypereutrophic lakes during the ice-free season of 2012. We used mass-balance relationships to determine the dominant inorganic carbon species used by phytoplankton under CO2 stress. We found a significant positive relationship between phytoplankton biomass and phytoplankton δ13C signatures as well as a significant nonlinear negative relationship between water column ρCO2 and isotopic composition of phytoplankton, indicating a shift from diffusive uptake to active uptake by phytoplankton of CO2 or HCO3- during blooms. Calculated photosynthetic fractionation factors indicated that this shift occurs specifically when surface water CO2 drops below atmospheric equilibrium. Our results indicate that active HCO3- uptake via CCMs may be an important mechanism in maintaining phytoplankton blooms when CO2 is depleted. Further increases in anthropogenic pressure, eutrophication, and cyanobacteria blooms are therefore expected to contribute to increased bicarbonate uptake to sustain primary production.

Capture compound mass spectrometry sheds light on the molecular mechanisms of liver toxicity of two Parkinson drugs.

PubMed

Fischer, Jenny J; Michaelis, Simon; Schrey, Anna K; Graebner, Olivia Graebner nee; Glinski, Mirko; Dreger, Mathias; Kroll, Friedrich; Koester, Hubert

2010-01-01

Capture compound mass spectrometry (CCMS) is a novel technology that helps understand the molecular mechanism of the mode of action of small molecules. The Capture Compounds are trifunctional probes: A selectivity function (the drug) interacts with the proteins in a biological sample, a reactivity function (phenylazide) irreversibly forms a covalent bond, and a sorting function (biotin) allows the captured protein(s) to be isolated for mass spectrometric analysis. Tolcapone and entacapone are potent inhibitors of catechol-O-methyltransferase (COMT) for the treatment of Parkinson's disease. We aimed to understand the molecular basis of the difference of both drugs with respect to side effects. Using Capture Compounds with these drugs as selectivity functions, we were able to unambiguously and reproducibly isolate and identify their known target COMT. Tolcapone Capture Compounds captured five times more proteins than entacapone Capture Compounds. Moreover, tolcapone Capture Compounds isolated mitochondrial and peroxisomal proteins. The major tolcapone-protein interactions occurred with components of the respiratory chain and of the fatty acid beta-oxidation. Previously reported symptoms in tolcapone-treated rats suggested that tolcapone might act as decoupling reagent of the respiratory chain (Haasio et al., 2002b). Our results demonstrate that CCMS is an effective tool for the identification of a drug's potential off targets. It fills a gap in currently used in vitro screens for drug profiling that do not contain all the toxicologically relevant proteins. Thereby, CCMS has the potential to fill a technological need in drug safety assessment and helps reengineer or to reject drugs at an early preclinical stage.

NATO/CCMS PILOT STUDY ON CLEAN PRODUCTS & PROCESSES

EPA Science Inventory

Led by the United States, represented by the U.S. Environmental Protection Agency's (EPA's) National Risk Management Research Laboratory, the Pilot Study on Clean Products and Processes was instituted to create an international forum where current trends, developments, and expert...

TOWARDS A CORE DATA SET FOR LANDSCAPE ASSESSMENTS

EPA Science Inventory

One of the primary goals of the NATO Committee on Challenges to Modern Society (CCMS) Landscape Pilot Study is to further develop, apply, and share landscape assessment technologies and spatial databases among participating countries, with the ultimate aim of sustaining environme...

Climate and chemistry effects of a regional scale nuclear conflict

NASA Astrophysics Data System (ADS)

Stenke, A.; Hoyle, C. R.; Luo, B.; Rozanov, E.; Gröbner, J.; Maag, L.; Brönnimann, S.; Peter, T.

2013-05-01

Previous studies have highlighted the severity of detrimental effects for life on Earth after an assumed regionally limited nuclear war. These effects are caused by climatic, chemical and radiative changes persisting for up to one decade. However, so far only a very limited number of climate model simulations have been performed, giving rise to the question how realistic previous computations have been. This study uses the coupled chemistry climate model (CCM) SOCOL, which belongs to a different family of CCMs than previously used, to investigate the consequences of such a hypothetical nuclear conflict. In accordance with previous studies, the present work assumes a scenario of a nuclear conflict between India and Pakistan, each applying 50 warheads with an individual blasting power of 15 kt ("Hiroshima size") against the major population centers, resulting in the emission of tiny soot particles, which are generated in the firestorms expected in the aftermath of the detonations. Substantial uncertainties related to the calculation of likely soot emissions, particularly concerning assumptions of target fuel loading and targeting of weapons, have been addressed by simulating several scenarios, with soot emissions ranging from 1 to 12 Tg. Their high absorptivity with respect to solar radiation leads to a tremendous self-lofting of the soot particles into the strato- and mesosphere, where they remain for several years. Consequently, the model suggests Earth's surface temperatures to drop by several degrees Celsius due to the shielding of solar irradiance by the soot, indicating a major global cooling. In addition, there is a substantial reduction of precipitation lasting 5 to 10 yr after the conflict, depending on the magnitude of the initial soot release. Extreme cold spells associated with massive sea ice formation are found during Northern Hemisphere winter, which expose the continental land masses of Northern America and Eurasia to chilling coldness. In the stratosphere, the strong heating leads to an acceleration of catalytic ozone loss and, consequently, to enhancements of UV radiation at the ground. In contrast to surface temperature and precipitation changes, which show a linear dependence to the soot burden, there is a saturation effect with respect to stratospheric ozone chemistry. Soot emissions of 5 Tg lead to an ozone column reduction of almost 50% in northern high latitudes, while emitting 12 Tg only increases ozone loss by a further 10%. In summary, this study, though using a different chemistry climate model, corroborates the previous investigations with respect to the atmospheric impacts. In addition to these persistent effects, the present study draws attention to episodically cold phases, which would likely add to the severity of human harm worldwide. The best insurance against such a catastrophic development would be the delegitimization of nuclear weapons.

Climate and chemistry effects of a regional scale nuclear conflict

NASA Astrophysics Data System (ADS)

Stenke, A.; Hoyle, C. R.; Luo, B.; Rozanov, E.; Gröbner, J.; Maag, L.; Brönnimann, S.; Peter, T.

2013-10-01

Previous studies have highlighted the severity of detrimental effects for life on earth after an assumed regionally limited nuclear war. These effects are caused by climatic, chemical and radiative changes persisting for up to one decade. However, so far only a very limited number of climate model simulations have been performed, giving rise to the question how realistic previous computations have been. This study uses the coupled chemistry climate model (CCM) SOCOL, which belongs to a different family of CCMs than previously used, to investigate the consequences of such a hypothetical nuclear conflict. In accordance with previous studies, the present work assumes a scenario of a nuclear conflict between India and Pakistan, each applying 50 warheads with an individual blasting power of 15 kt ("Hiroshima size") against the major population centers, resulting in the emission of tiny soot particles, which are generated in the firestorms expected in the aftermath of the detonations. Substantial uncertainties related to the calculation of likely soot emissions, particularly concerning assumptions of target fuel loading and targeting of weapons, have been addressed by simulating several scenarios, with soot emissions ranging from 1 to 12 Tg. Their high absorptivity with respect to solar radiation leads to a rapid self-lofting of the soot particles into the strato- and mesosphere within a few days after emission, where they remain for several years. Consequently, the model suggests earth's surface temperatures to drop by several degrees Celsius due to the shielding of solar irradiance by the soot, indicating a major global cooling. In addition, there is a substantial reduction of precipitation lasting 5 to 10 yr after the conflict, depending on the magnitude of the initial soot release. Extreme cold spells associated with an increase in sea ice formation are found during Northern Hemisphere winter, which expose the continental land masses of North America and Eurasia to a cooling of several degrees. In the stratosphere, the strong heating leads to an acceleration of catalytic ozone loss and, consequently, to enhancements of UV radiation at the ground. In contrast to surface temperature and precipitation changes, which show a linear dependence to the soot burden, there is a saturation effect with respect to stratospheric ozone chemistry. Soot emissions of 5 Tg lead to an ozone column reduction of almost 50% in northern high latitudes, while emitting 12 Tg only increases ozone loss by a further 10%. In summary, this study, though using a different chemistry climate model, corroborates the previous investigations with respect to the atmospheric impacts. In addition to these persistent effects, the present study draws attention to episodically cold phases, which would likely add to the severity of human harm worldwide. The best insurance against such a catastrophic development would be the delegitimization of nuclear weapons.

Intercomparison of spectral irradiance measurements and provision of alternative radiation scheme for CCMs of middle atmosphere

NASA Astrophysics Data System (ADS)

Pagaran, Joseph; Weber, Mark; Burrows, John P.

The Sun's radiative output (total solar irradiance or TSI) determines the thermal structure of the Earth's atmosphere. Its variability is a strong function of wavelength, which drives the photochemistry and general circulation. Contributions to TSI variability from UV wavelengths below 400 nm, i.e. 0.227-day solar rotation or 0.1to be in the 40-60three decades of UV and about a decade of vis-IR observations. Significant progress in UV/vis-IR regions has been achieved with daily monitoring from SCIAMACHY aboard Envisat (ESA) in 2002 and by SIM aboard SORCE (NASA) about a year after. In this contribution, we intercompare SSI measurements from SCIAMACHY and SIM and RGB filters of SPM/VIRGO SoHO: same (a) day and (b) few 27-day time series of spectral measurements in both irradiance and integrated irradiance over selected wavelength intervals. Finally, we show how SSI measurements from GOME, SOLSTICE, in addition to SCIAMACHY and SIM, can be modeled together with solar proxies F10.7 cm, Mg II and sunspot index (PSI) to derive daily SSI variability in the period 1947-2008. The derived variabilities are currently being used as solar input to Bremen's 3D-CTM and are to be recommended as extended alternative to Berlin's FUBRaD radiation scheme. This proxy-based radiation scheme are compared with SATIRE, NRLSSI (or Lean et al.), SUSIM, SSAI (or DeLand et al), and SIP (or Solar2000) models. The use of realistic spectrally resolved solar input to CCMs is to better understand the effects of solar variability on chemistry and temperature in the middle atmosphere over several decades.

A PROPOSAL FOR A CCMS/NATO PILOT STUDY ON THE USE OF LANDSCAPE SCIENCES IN ENVIRONMENTAL ASSESSMENT

EPA Science Inventory

The United States Environmental Protection Agency, in partnership with other Federal and State agencies, has developed through its research programs various landscape science approaches to environmental assessment. These approaches include both landscape characterization (land co...

Hemispheric Differences in Tropical Lower Stratospheric Transport and Tracers Annual Cycle

NASA Technical Reports Server (NTRS)

Tweedy, Olga; Waugh, D.; Stolarski, R.; Oman, L.

2016-01-01

Transport of long-lived tracers (such as O, CO, and N O) in the lower stratosphere largely determines the composition of the entire stratosphere. Stratospheric transport includes the mean residual circulation (with air rising in the tropics and sinking in the polar and middle latitudes), plus two-way isentropic (quasi-horizontal) mixing by eddies. However, the relative importance of two transport components remains uncertain. Previous studies quantified the relative role of these processes based on tropics-wide average characteristics under common assumption of well-mixed tropics. However, multiple instruments provide us with evidence that show significant differences in the seasonal cycle of ozone between the Northern (0-20N) and Southern (0-20S) tropical (NT and ST respectively) lower stratosphere. In this study we investigate these differences in tracer seasonality and quantify transport processes affecting tracers annual cycle amplitude using simulations from Goddard Earth Observing System Chemistry Climate Model (GEOSCCM) and Whole Atmosphere Community Climate Model (WACCM) and compare them to observations from the Microwave Limb Sounder (MLS) on the Aura satellite. We detect the observed contrast between the ST and NT in GEOSCCM and WACCM: annual cycle in ozone and other chemical tracers is larger in the NT than in the ST but opposite is true for the annual cycle in vertical advection. Ozone budgets in the models, analyzed based on the Transformed Eulerian Mean (TEM) framework, demonstrate a major role of quasi-horizontal mixing vertical advection in determining the NTST ozone distribution and behavior. Analysis of zonal variations in the NT and ST ozone annual cycles further suggests important role of North American and Asian Summer Monsoons (associated with strong isentropic mixing) on the lower stratospheric ozone in the NT. Furthermore, multi model comparison shows that most CCMs reproduce the observed characteristic of ozone annual cycle quite well. Thus, latitudinal variations within the tropics have to be considered in order to understand the balance between upwelling and quasi- horizontal mixing in the tropical lower stratosphere and the paradigm of well mixed tropics has to be reconsidered.

75 FR 27182 - Energy Conservation Program: Web-Based Compliance and Certification Management System

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-14

... Conservation Program: Web-Based Compliance and Certification Management System AGENCY: Office of Energy... certification reports to the Department of Energy (DOE) through an electronic Web-based tool, the Compliance and... following means: 1. Compliance and Certification Management System (CCMS)--via the Web portal: http...

Emissions from international shipping: 2. Impact of future technologies on scenarios until 2050

NASA Astrophysics Data System (ADS)

Eyring, V.; KöHler, H. W.; Lauer, A.; Lemper, B.

2005-09-01

In this study the today's fleet-average emission factors of the most important ship exhausts are used to calculate emission scenarios for the future. To develop plausible future technology scenarios, first upcoming regulations and compliance with future regulations through technological improvements are discussed. We present geographically resolved emission inventory scenarios until 2050, based on a mid-term prognosis for 2020 and a long-term prognosis for 2050. The scenarios are based on some very strict assumptions on future ship traffic demands and technological improvements. The four future ship traffic demand scenarios are mainly determined by the economic growth, which follows the IPCC SRES storylines. The resulting fuel consumption is projected through extrapolations of historical trends in economic growth, total seaborne trade and number of ships, as well as the average installed power per ship. For the future technology scenarios we assume a diesel-only fleet in 2020 resulting in fuel consumption between 382 and 409 million metric tons (Mt). For 2050 one technology scenario assumes that 25% of the fuel consumed by a diesel-only fleet can be saved by applying future alternative propulsion plants, resulting in a fuel consumption that varies between 402 and 543 Mt. The other scenario is a business-as-usual scenario for a diesel-only fleet even in 2050 and gives an estimate between 536 and 725 Mt. Dependent on how rapid technology improvements for diesel engines are introduced, possible technology reduction factors are applied to the today's fleet-average emission factors of all important species to estimate future ship emissions. Combining the four traffic demand scenarios with the four technology scenarios, our results suggest emissions between 8.8 and 25.0 Tg (NO2) in 2020, and between 3.1 to 38.8 Tg (NO2) in 2050. The development of forecast scenarios for CO2, NOx, SOx, CO, hydrocarbons, and particulate matter is driven by the requirements for global model studies of the effects of these emissions on the chemical composition of the atmosphere and on climate. The developed scenarios are suitable for use as input for chemical transport models (CTMs) and coupled chemistry-climate models (CCMs).

Implications of Lagrangian Tracer Transport for Coupled Chemistry-Climate Simulations

NASA Astrophysics Data System (ADS)

Stenke, A.

2009-05-01

Today's coupled chemistry-climate models (CCM) consider a large number of trace species and feedback processes. Due to the radiative effect of some species, errors in simulated tracer distributions can feed back to model dynamics. Thus, shortcomings of the applied transport schemes can have severe implications for the overall model performance. Traditional Eulerian approaches show a satisfactory performance in case of homogeneously distributed trace species, but they can lead to severe problems when applied to highly inhomogeneous tracer distributions. In case of sharp gradients many schemes show a considerable numerical diffusion. Lagrangian approaches, on the other hand, combine a number of favourable numerical properties: They are strictly mass-conserving and do not suffer from numerical diffusion. Therefore they are able to maintain steeper gradients. A further advantage is that they allow the transport of a large number of tracers without being prohibitively expensive. A variety of benefits for stratospheric dynamics and chemistry resulting from a Lagrangian transport algorithm are demonstrated by the example of the CCM E39C. In an updated version of E39C, called E39C-A, the operational semi-Lagrangian advection scheme has been replaced with the purely Lagrangian scheme ATTILA. It will be shown that several model deficiencies can be cured by the choice of an appropriate transport algorithm. The most important advancement concerns the reduction of a pronounced wet bias in the extra- tropical lowermost stratosphere. In turn, the associated temperature error ("cold bias") is significantly reduced. Stratospheric wind variations are now in better agreement with observations, e.g. E39C-A is able to reproduce the stratospheric wind reversal in the Southern Hemisphere in summer which was not captured by the previous model version. Resulting changes in wave propagation and dissipation lead to a weakening of the simulated mean meridional circulation and therefore a more realistic representation of tropical upwelling. Simulated distributions of chemical tracers in the stratosphere are clearly improved. For example, the vertical distribution of stratospheric chlorine (Cly) is now in agreement with analyses derived from observations and other CCMs. As a consequence the model realistically covers the altitude of maximum ozone depletion in the stratosphere. Furthermore, the simulated temporal evolution of stratospheric Cly in the past agrees is realistically reproduced which is an important step towards more reliable projections of future changes, especially of stratospheric ozone.

NATO/CCMS Pilot Study Evaluation of Demonstrated and ...

EPA Pesticide Factsheets

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Pilot Study on Demonstration of Remedial Action Technologies for Contaminated Land and Groundwater Volumes 1 and 2 EPA/600/SR-93/012

EPA Science Inventory

This two-volume report presents information on a 5-yr pilot study (1986- 1991) sponsored by the North Atlantic Treaty Organization's (NATO) Committee on the Challenges of Modern Society (CCMS) entitled "Demonstration of Remedial Action Technologies for Contaminated Land and Gr...

Summary of the NATO/CCMS Conference The Demonstration of Remedial Action Technologies for Contaminated Land and GroundWater

EPA Science Inventory

The problem of contamination to land and groundwa- ter from improper handling of hazardous materials/ waste is faced by all countries. Also, the need for reliable, cost-effective technologies to address this problem at contaminated sites exists throughout the world. Many countrie...

Differences in TRICARE Knowledge from Department of Defense Active Duty Family Member Beneficiaries,

DTIC Science & Technology

1998-05-01

becomes most important. Lamb (1994) coined the term "promotion mix" (p.494) as a combination of advertising, personal selling, sales promotion and...interests in the health status of their county residents (i.e., personal selling), patient attitudes quickly improved toward the CCMS. Sales promotion , the

Fabrication of High Performing PEMFC Catalyst-Coated Membranes with a Low Cost Air-Assisted Cylindrical Liquid Jets Spraying System

DOE PAGES

Peng, Xiong; Omasta, Travis; Rigdon, William; ...

2016-11-15

In this paper, a low cost air-assisted cylindrical liquid jets spraying (ACLJS) system was developed to prepare high-performance catalyst-coated membranes (CCMs) for proton exchange membrane fuel cells (PEMFCs). The catalyst ink was flowed from a cylindrical orifice and was atomized by an air stream fed from a coaxial slit and sprayed directly onto the membrane, which was suctioned to a heated aluminum vacuum plate. The CCM pore architecture including size, distribution and volume can be controlled using various flow parameters, and the impact of spraying conditions on electrode structure and PEMFC performance was investigated. CCMs fabricated in the fiber-type break-upmore » regime by ACLJS achieved very high performance during PEMFC testing, with the top-performing cells having a current density greater than 1900 mA/cm 2 at 0.7 V under H 2/O 2 flows and 700 mA/cm 2 under H 2/Air at 1.5 bar(absolute) pressure and 60% gas RH, and 80°C cell temperature.« less

Fabrication of High Performing PEMFC Catalyst-Coated Membranes with a Low Cost Air-Assisted Cylindrical Liquid Jets Spraying System

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peng, Xiong; Omasta, Travis; Rigdon, William

In this paper, a low cost air-assisted cylindrical liquid jets spraying (ACLJS) system was developed to prepare high-performance catalyst-coated membranes (CCMs) for proton exchange membrane fuel cells (PEMFCs). The catalyst ink was flowed from a cylindrical orifice and was atomized by an air stream fed from a coaxial slit and sprayed directly onto the membrane, which was suctioned to a heated aluminum vacuum plate. The CCM pore architecture including size, distribution and volume can be controlled using various flow parameters, and the impact of spraying conditions on electrode structure and PEMFC performance was investigated. CCMs fabricated in the fiber-type break-upmore » regime by ACLJS achieved very high performance during PEMFC testing, with the top-performing cells having a current density greater than 1900 mA/cm 2 at 0.7 V under H 2/O 2 flows and 700 mA/cm 2 under H 2/Air at 1.5 bar(absolute) pressure and 60% gas RH, and 80°C cell temperature.« less

Effect of CO2-induced seawater acidification on growth, photosynthesis and inorganic carbon acquisition of the harmful bloom-forming marine microalga, Karenia mikimotoi.

PubMed

Hu, Shunxin; Zhou, Bin; Wang, You; Wang, Ying; Zhang, Xinxin; Zhao, Yan; Zhao, Xinyu; Tang, Xuexi

2017-01-01

Karenia mikimotoi is a widespread, toxic and non-calcifying dinoflagellate, which can release and produce ichthyotoxins and hemolytic toxins affecting the food web within the area of its bloom. Shifts in the physiological characteristics of K. mikimotoi due to CO2-induced seawater acidification could alter the occurrence, severity and impacts of harmful algal blooms (HABs). Here, we investigated the effects of elevated pCO2 on the physiology of K. mikimotoi. Using semi-continuous cultures under controlled laboratory conditions, growth, photosynthesis and inorganic carbon acquisition were determined over 4-6 week incubations at ambient (390ppmv) and elevated pCO2 levels (1000 ppmv and 2000 ppmv). pH-drift and inhibitor-experiments suggested that K. mikimotoi was capable of acquiring HCO3-, and that the utilization of HCO3- was predominantly mediated by anion-exchange proteins, but that HCO3- dehydration catalyzed by external carbonic anhydrase (CAext) only played a minor role in K. mikimotoi. Even though down-regulated CO2 concentrating mechanisms (CCMs) and enhanced gross photosynthetic O2 evolution were observed under 1000 ppmv CO2 conditions, the saved energy did not stimulate growth of K. mikimotoi under 1000 ppmv CO2, probably due to the increased dark respiration. However, significantly higher growth and photosynthesis [in terms of photosynthetic oxygen evolution, effective quantum Yield (Yield), photosynthetic efficiency (α), light saturation point (Ek) and ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) activity] were observed under 2000 ppmv CO2 conditions. Furthermore, elevated pCO2 increased the photo-inhibition rate of photosystem II (β) and non-photochemical quenching (NPQ) at high light. We suggest that the energy saved through the down-regulation of CCMs might lead to the additional light stress and photo-damage. Therefore, the response of this species to elevated CO2 conditions will be determined by more than regulation and efficiency of CCMs.

Inferring heuristic classification hierarchies from natural language input

NASA Technical Reports Server (NTRS)

Hull, Richard; Gomez, Fernando

1993-01-01

A methodology for inferring hierarchies representing heuristic knowledge about the check out, control, and monitoring sub-system (CCMS) of the space shuttle launch processing system from natural language input is explained. Our method identifies failures explicitly and implicitly described in natural language by domain experts and uses those descriptions to recommend classifications for inclusion in the experts' heuristic hierarchies.

Central Control and Automation Systems: A Management and Procurement Handbook. Second Edition.

ERIC Educational Resources Information Center

Association of Physical Plant Administrators of Universities and Colleges, Washington, DC.

It is presumed that those reading this book presently own and operate a central control and monitoring system (CCMS) or plan to implement one in the near future. One objective of this workbook is to present useful knowledge from case histories of members of the Association of Physical Plant Administrators of Universities and Colleges as well as…

Uncertainties in modelling Mt. Pinatubo eruption with 2-D AER model and CCM SOCOL

NASA Astrophysics Data System (ADS)

Kenzelmann, P.; Weisenstein, D.; Peter, T.; Luo, B. P.; Rozanov, E.; Fueglistaler, S.; Thomason, L. W.

2009-04-01

Large volcanic eruptions may introduce a strong forcing on climate. They challenge the skills of climate models. In addition to the short time attenuation of solar light by ashes the formation of stratospheric sulphate aerosols, due to volcanic sulphur dioxide injection into the lower stratosphere, may lead to a significant enhancement of the global albedo. The sulphate aerosols have a residence time of about 2 years. As a consequence of the enhanced sulphate aerosol concentration both the stratospheric chemistry and dynamics are strongly affected. Due to absorption of longwave and near infrared radiation the temperature in the lower stratosphere increases. So far chemistry climate models overestimate this warming [Eyring et al. 2006]. We present an extensive validation of extinction measurements and model runs of the eruption of Mt. Pinatubo in 1991. Even if Mt. Pinatubo eruption has been the best quantified volcanic eruption of this magnitude, the measurements show considerable uncertainties. For instance the total amount of sulphur emitted to the stratosphere ranges from 5-12 Mt sulphur [e.g. Guo et al. 2004, McCormick, 1992]. The largest uncertainties are in the specification of the main aerosol cloud. SAGE II, for instance, could not measure the peak of the aerosol extinction for about 1.5 years, because optical termination was reached. The gap-filling of the SAGE II [Thomason and Peter, 2006] using lidar measurements underestimates the total extinctions in the tropics for the first half year after the eruption by 30% compared to AVHRR [Rusell et. al 1992]. The same applies to the optical dataset described by Stenchikov et al. [1998]. We compare these extinction data derived from measurements with extinctions derived from AER 2D aerosol model calculations [Weisenstein et al., 2007]. Full microphysical calculations with injections of 14, 17, 20 and 26 Mt SO2 in the lower stratosphere were performed. The optical aerosol properties derived from SAGE II measurements and from AER model calculation serve as input for the 3D chemistry climate model (CCM) SOCOL [Schraner et al., 2008]. The heating rates, calculated with SOCOL, are compared with a reference radiative transfer model LibRadtran [Mayer and Kylling, 2005]. This comparison suggests that SOCOL underestimates the net heating rate by 10-20%. In stark contrast, the temperature increase in the lower stratosphere due to absorption of longwave and near infrared radiation is overestimated by all SOCOL scenarios. This lets us conclude that SOCOL, and similarly other state-of-the-art CCMs, misrepresent processes required to model the effect of volcanic eruptions on the lower stratosphere and tropopause region. Possible reasons for model deficiencies could be too coarse vertical resolution or missing dynamical feedbacks near the tropopause and in the lower stratosphere. Another important feature is the warming of the tropical troposphere, which is present in the model simulation but was not observed with comparable amplitude in reality. The heating of the lower stratosphere in the models leads to an increase of stratospheric water vapour and influences the radiative and chemical properties of the stratosphere. Eyring, V. et al (2006), Assessment of temperature, trace species, and ozone in chemistry-climate model simulations of the recent past, Journal of Geophysical Research-Atmospheres, 111, D22,308. Guo, S., G. J. S. Bluth, W. I. Rose, I. M. Watson, and A. J. Prata (2004), Re-evaluation of SO2 release of the 15 June 1991 Pinatubo eruption using ultraviolet and infrared satellite sensors, Geochemistry Geophysics Geosystems, 5. Mayer, B., and A. Kylling (2005), Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys, 5, 1855-1877. McCormick, M. P. (1992), Initial assessment of the stratospheric and climatic impact of the 1991 Mount- Pinatubo eruption - prologue, Geophysical Research Letters, 19 (2), 149-149. Russell, P. B. et al. (1996), Global to microscale evolution of the Pinatubo volcanic aerosol derived from diverse measurements and analyses, Journal of Geophysical Research-Atmospheres, 101 (D13), 18,745- 18,763. Schraner, M. et al (2008), Technical note: Chemistry-climate model SOCOL: version 2.0 with improved transport and chemistry/microphysics schemes, Atmos. Chem. Phys., 8, 5957-5974. Stenchikov, G. L. et al (1998), Radiative forcing from the 1991 Mount Pinatubo volcanic eruption, Journal Of Geophysical Research-Atmospheres, 103 (D12), 13,837-13,857. Thomason, L., and T. Peter (2006), (eds.), SPARC Report No. 4, World Climate Research Programme WCRP- 124, WMO/TD No. 1295. Weisenstein, D. K. et al., Global 2-D intercomparison of sectional and modal aerosol modules, Atmos. Chem. Phys, 7 (9), 2339-2355.

Modelling the chemistry and transport of bromoform within a sea breeze driven convective system during the SHIVA Campaign

NASA Astrophysics Data System (ADS)

Hamer, P. D.; Marécal, V.; Hossaini, R.; Pirre, M.; Warwick, N.; Chipperfield, M.; Samah, A. A.; Harris, N.; Robinson, A.; Quack, B.; Engel, A.; Krüger, K.; Atlas, E.; Subramaniam, K.; Oram, D.; Leedham, E.; Mills, G.; Pfeilsticker, K.; Sala, S.; Keber, T.; Bönisch, H.; Peng, L. K.; Nadzir, M. S. M.; Lim, P. T.; Mujahid, A.; Anton, A.; Schlager, H.; Catoire, V.; Krysztofiak, G.; Fühlbrügge, S.; Dorf, M.; Sturges, W. T.

2013-08-01

We carry out a case study of the transport and chemistry of bromoform and its product gases (PGs) in a sea breeze driven convective episode on 19 November 2011 along the North West coast of Borneo during the "Stratospheric ozone: Halogen Impacts in a Varying Atmosphere" (SHIVA) campaign. We use ground based, ship, aircraft and balloon sonde observations made during the campaign, and a 3-D regional online transport and chemistry model capable of resolving clouds and convection explicitly that includes detailed bromine chemistry. The model simulates the temperature, wind speed, wind direction fairly well for the most part, and adequately captures the convection location, timing, and intensity. The simulated transport of bromoform from the boundary layer up to 12 km compares well to aircraft observations to support our conclusions. The model makes several predictions regarding bromine transport from the boundary layer to the level of convective detrainment (11 to 12 km). First, the majority of bromine undergoes this transport as bromoform. Second, insoluble organic bromine carbonyl species are transported to between 11 and 12 km, but only form a small proportion of the transported bromine. Third, soluble bromine species, which include bromine organic peroxides, hydrobromic acid (HBr), and hypobromous acid (HOBr), are washed out efficiently within the core of the convective column. Fourth, insoluble inorganic bromine species (principally Br2) are not washed out of the convective column, but are also not transported to the altitude of detrainment in large quantities. We expect that Br2 will make a larger relative contribution to the total vertical transport of bromine atoms in scenarios with higher CHBr3 mixing ratios in the boundary layer, which have been observed in other regions. Finally, given the highly detailed description of the chemistry, transport and washout of bromine compounds within our simulations, we make a series of recommendations about the physical and chemical processes that should be represented in 3-D chemical transport models (CTMs) and chemistry climate models (CCMs), which are the primary theoretical means of estimating the contribution made by CHBr3 and other very short-lived substances (VSLS) to the stratospheric bromine budget.

The Revenge of K-12: How Common Core and the New SAT Lower College Standards in the U.S. White Paper No. 122

ERIC Educational Resources Information Center

Phelps, Richard P.; Milgram, R. James

2014-01-01

It is now clear that the original promise to anchor K-12 education to higher education and backmap the Common Core Mathematics Standards (CCMS) from the upper grades down to the primary grades was empty rhetoric. Higher education has scarcely been involved at all, with the exception of the institutions that agreed to place high school students who…

Cross-Continuum Tool Is Associated with Reduced Utilization and Cost for Frequent High-Need Users

PubMed Central

Hardin, Lauran; Kilian, Adam; Muller, Leslie; Callison, Kevin; Olgren, Michael

2017-01-01

Introduction High-need, high-cost (HNHC) patients can over-use acute care services, a pattern of behavior associated with many poor outcomes that disproportionately contributes to increased U.S. healthcare cost. Our objective was to reduce healthcare cost and improve outcomes by optimizing the system of care. We targeted HNHC patients and identified root causes of frequent healthcare utilization. We developed a cross-continuum intervention process and a succinct tool called a Complex Care Map (CCM)© that addresses fragmentation in the system and links providers to a comprehensive individualized analysis of the patient story and causes for frequent access to health services. Methods Using a pre-/post-test design in which each subject served as his/her own historical control, this quality improvement project focused on determining if the interdisciplinary intervention called CCM© had an impact on healthcare utilization and costs for HNHC patients. We conducted the analysis between November 2012 and December 2015 at Mercy Health Saint Mary’s, a Midwestern urban hospital with greater than 80,000 annual emergency department (ED) visits. All referred patients with three or more hospital visits (ED or inpatient [IP]) in the 12 months prior to initiation of a CCM© (n=339) were included in the study. Individualized CCMs© were created and made available in the electronic medical record (EMR) to all healthcare providers. We compared utilization, cost, social, and healthcare access variables from the EMR and cost-accounting system for 12 months before and after CCMs© implementation. We used both descriptive and limited inferential statistics. Results ED mean visits decreased 43% (p<0.001), inpatient mean admissions decreased 44% (p<0.001), outpatient mean visits decreased 17% (p<0.001), computed tomography mean scans decreased 62% (p<0.001), and OBS/IP length of stay mean days decreased 41% (p<0.001). Gross charges decreased 45% (p<0.001), direct expenses decreased 47% (p<0.001), contribution margin improved by 11% (p=0.002), and operating margin improved by 73% (p<0.001). Patients with housing increased 14% (p<0.001), those with primary care increased 15% (p<0.001), and those with insurance increased 16% (p<0.001). Conclusion Individualized CCMs© for a select group of patients are associated with decreased healthcare system overutilization and cost of care. PMID:28210351

Cross-Continuum Tool Is Associated with Reduced Utilization and Cost for Frequent High-Need Users.

PubMed

Hardin, Lauran; Kilian, Adam; Muller, Leslie; Callison, Kevin; Olgren, Michael

2017-02-01

High-need, high-cost (HNHC) patients can over-use acute care services, a pattern of behavior associated with many poor outcomes that disproportionately contributes to increased U.S. healthcare cost. Our objective was to reduce healthcare cost and improve outcomes by optimizing the system of care. We targeted HNHC patients and identified root causes of frequent healthcare utilization. We developed a cross-continuum intervention process and a succinct tool called a Complex Care Map (CCM)© that addresses fragmentation in the system and links providers to a comprehensive individualized analysis of the patient story and causes for frequent access to health services. Using a pre-/post-test design in which each subject served as his/her own historical control, this quality improvement project focused on determining if the interdisciplinary intervention called CCM© had an impact on healthcare utilization and costs for HNHC patients. We conducted the analysis between November 2012 and December 2015 at Mercy Health Saint Mary's, a Midwestern urban hospital with greater than 80,000 annual emergency department (ED) visits. All referred patients with three or more hospital visits (ED or inpatient [IP]) in the 12 months prior to initiation of a CCM© (n=339) were included in the study. Individualized CCMs© were created and made available in the electronic medical record (EMR) to all healthcare providers. We compared utilization, cost, social, and healthcare access variables from the EMR and cost-accounting system for 12 months before and after CCMs© implementation. We used both descriptive and limited inferential statistics. ED mean visits decreased 43% (p<0.001), inpatient mean admissions decreased 44% (p<0.001), outpatient mean visits decreased 17% (p<0.001), computed tomography mean scans decreased 62% (p<0.001), and OBS/IP length of stay mean days decreased 41% (p<0.001). Gross charges decreased 45% (p<0.001), direct expenses decreased 47% (p<0.001), contribution margin improved by 11% (p=0.002), and operating margin improved by 73% (p<0.001). Patients with housing increased 14% (p<0.001), those with primary care increased 15% (p<0.001), and those with insurance increased 16% (p<0.001). Individualized CCMs© for a select group of patients are associated with decreased healthcare system overutilization and cost of care.

Proceedings: NATO CCMS (Committee on the Challenges of Modern Society) Seminar Blue Book 159, Preservation of Flora and Fauna in Military Training Areas Held in Soesterberg, Netherlands on 28-30 November 1984

DTIC Science & Technology

1987-01-01

or C, where military training is intense. A lot of species (Stonechat, Tree pipit, Wheatear, Linnet, Yellowhammer, Woodiar’, Willow warb-er, Great tit...no: P•a ’fully legible lajury to game tarough firing or vohic•a traffii seldon happona e.gnd biars no relaticn to that whiet ta.os place outioide of

Tracing Growth of Teachers' Classroom Interactions with Representations of Functions in the Connected Classroom

NASA Astrophysics Data System (ADS)

Morton, Brian Lee

The purpose of this study is to create an empirically based theoretic model of change of the use and treatment of representations of functions with the use of Connected Classroom Technology (CCT) using data previously collected for the Classroom Connectivity in Promoting Mathematics and Science Achievement (CCMS) project. Qualitative analysis of videotapes of three algebra teachers' instruction focused on different categories thought to influence teaching representations with technology: representations, discourse, technology, and decisions. Models for rating teachers low, medium, or high for each of these categories were created using a priori codes and grounded methodology. A cross case analysis was conducted after the completion of the case studies by comparing and contrasting the three cases. Data revealed that teachers' decisions shifted to incorporate the difference in student ideas/representations made visible by the CCT into their instruction and ultimately altered their orientation to mathematics teaching. The shift in orientation seemed to lead to the teachers' growth with regards to representations, discourse, and technology.

Stratospheric changes caused by geoengineering applications: potential repercussions and uncertainties

NASA Astrophysics Data System (ADS)

Kenzelmann, P.; Weisenstein, D.; Peter, T.; Luo, B. P.; Rozanov, E.; Fueglistaler, S.; Thomason, L. W.

2009-04-01

Anthropogenic greenhouse gas emissions tend to warm the global climate, calling for significant rapid emission reductions. As potential support measures various ideas for geoengineering are currently being discussed. The assessment of the possible manifold and as yet substantially unexplored repercussions of implementing geoengineering ideas to ameliorate climate change poses enormous challenges not least in the realm of aerosol-cloud-climate interactions. Sulphur aerosols cool the Earth's surface by reflecting short wave radiation. By increasing the amount of sulphur aerosols in the stratosphere, for example by sulphur dioxide injections, part of the anthropogenic climate warming might be compensated due to enhanced albedo. However, we are only at the beginning of understanding possible side effects. One such effect that such aerosol might have is the warming of the tropical tropopause and consequently the increase of the amount of stratospheric water vapour. Using the 2D AER Aerosol Model we calculated the aerosol distributions for yearly injections of 1, 2, 5 and 10 Mt sulphur into the lower tropical stratosphere. The results serve as input for the 3D chemistry-climate model SOCOL, which allows calculating the aerosol effect on stratospheric temperatures and chemistry. In the injection region the continuously formed sulphuric acid condensates rapidly on sulphate aerosol, which eventually grow to such extent that they sediment down to the tropical tropopause region. The growth of the aerosol particles depends on non-linear processes: the more sulphur is emitted the faster the particles grow. As a consequence for the scenario with continuous sulphur injection of totally 10 Mt per year, only 6 Mt sulphur are in the stratosphere if equilibrium is reached. According to our model calculations this amount of sulphate aerosols leads to a net surface forcing of -3.4 W/m2, which is less then expected radiative forcing by doubling of carbon dioxide concentration. Hence, larger injections might be required than previously assumed. Rasch et al. (2008) showed that smaller particles would be advantageous in terms of cooling the surface. However, with a continuous injection of sulphur dioxide into to lower tropical stratosphere aerosol size distributions with mode radii larger than 0.5 microns are likely to form. An additional complication is that the sedimenting particles tend to heat the tropical tropopause region and as a consequence the entry mixing ratio of water vapour increases. For the extreme scenario of 10 Mt/year injection SOCOL predicts an enhancement of the water vapour entry mixing ratio by more than 1 ppmv. This is predicted to have a significant impact on the radiative forcing and the total ozone, because of enhanced heterogeneous reactions and because the increased water vapour intensifies the hydrogen and chlorine catalysed ozone destruction cycles. The intense warming of the lower stratosphere further intensifies the catalytic ozone destruction cycles. Furthermore, the stratospheric circulation is predicted to change due to the strong heating of the lower stratosphere. As a consequence of the intensified meridional temperature gradient the polar vortices are strengthened with enhanced formation of polar stratospheric clouds and ozone depletion. The ozone loss due to changed stratospheric dynamic is four times larger than the ozone loss caused by the increase of aerosol surface for heterogeneous reactions, which would postpone the recovery of the ozone hole even more as already pointed out by Tilmes et al. [2008]. At the same time the uncertainties involved in the different modelling steps are tremendous. Model validation, by comparing model runs of the 1991 Mt. Pinatubo eruption with observations, reveals that the temperature increase in the lower stratosphere and the tropopause region is probably overestimated by SOCOL. Other CCMs show similar behaviour. This lets us conclude that with the present modelling tools we are not capable to reliably predict the changes in stratospheric climate following geoengineering applications. Rasch, P. J. et al. (2008), Exploring the geoengineering of climate using stratospheric sulfate aerosols: The role of particle size, Geophysical Research Letters, 35 (2), L02,809. Tilmes, S. et al. (2008), The sensitivity of polar ozone depletion to proposed geoengineering schemes, Science, 320 (5880), 1201-1204.

Multicomponent Calibration and Quantitation Methods.

DTIC Science & Technology

1984-11-01

liquid chromatographic peaks (45). Applying rank annihilation to LC /UV data requires that the elution profiles of each individual component in the...measurements, e.g. LC /UV, CC/MS, or GC/FTIR analyses. The response of a single component can be described as M = a x y’ (45) 0 where M contains the...in the second dimension. For example, a GC/MS peak consisting - 3 of 50 mass spectra each composed of 20 distinct m/e ratios would result in a matrix

Fabrication Method for Laboratory-Scale High-Performance Membrane Electrode Assemblies for Fuel Cells.

PubMed

Sassin, Megan B; Garsany, Yannick; Gould, Benjamin D; Swider-Lyons, Karen E

2017-01-03

Custom catalyst-coated membranes (CCMs) and membrane electrode assemblies (MEAs) are necessary for the evaluation of advanced electrocatalysts, gas diffusion media (GDM), ionomers, polymer electrolyte membranes (PEMs), and electrode structures designed for use in next-generation fuel cells, electrolyzers, or flow batteries. This Feature provides a reliable and reproducible fabrication protocol for laboratory scale (10 cm 2 ) fuel cells based on ultrasonic spray deposition of a standard Pt/carbon electrocatalyst directly onto a perfluorosulfonic acid PEM.

On the cradle of CCM research: discovery, development, and challenges ahead.

PubMed

Kaplan, Aaron

2017-06-01

Herein, 40 years after its discovery, I briefly and critically survey the development of ideas that propelled research on CO2-concentrating mechanisms (CCMs; a term proposed by Dean Price) of phytoplankton, mainly focusing on cyanobacteria. This is not a comprehensive review on CCM research, but a personal view on the past developments and challenges that lie ahead. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Safety Profile of Cough and Cold Medication Use in Pediatrics.

PubMed

Green, Jody L; Wang, George Sam; Reynolds, Kate M; Banner, William; Bond, G Randall; Kauffman, Ralph E; Palmer, Robert B; Paul, Ian M; Dart, Richard C

2017-06-01

The safety of cough and cold medication (CCM) use in children has been questioned. We describe the safety profile of CCMs in children <12 years of age from a multisystem surveillance program. Cases with adverse events (AEs) after ingestion of at least 1 index CCM ingredient (brompheniramine, chlorpheniramine, dextromethorphan, diphenhydramine, doxylamine, guaifenesin, phenylephrine, and pseudoephedrine) in children <12 years of age were collected from 5 data sources. An expert panel determined relatedness, dose, intent, and risk factors. Case characteristics and AEs are described. Of the 4202 cases reviewed, 3251 (77.4%) were determined to be at least potentially related to a CCM, with accidental unsupervised ingestions (67.1%) and medication errors (13.0%) the most common exposure types. Liquid (67.3%), pediatric (75.5%), and single-ingredient (77.5%) formulations were most commonly involved. AEs occurring in >20% of all cases included tachycardia, somnolence, hallucinations, ataxia, mydriasis, and agitation. Twenty cases (0.6%) resulted in death; most were in children <2 years of age (70.0%) and none involved a therapeutic dose. The overall reported AE rate was 0.573 cases per 1 million units (ie, tablets, gelatin capsules, or liquid equivalent) sold (95% confidence interval, 0.553-0.593) or 1 case per 1.75 million units. The rate of AEs associated with CCMs in children was low. Fatalities occurred even less frequently. No fatality involved a therapeutic dose. Accidental unsupervised ingestions were the most common exposure types and single-ingredient, pediatric liquid formulations were the most commonly reported products. These characteristics present an opportunity for targeted prevention efforts. Copyright © 2017 by the American Academy of Pediatrics.

Effects of seven antifouling compounds on photosynthesis and inorganic carbon use in sugar kelp Saccharina latissima (Linnaeus).

PubMed

Johansson, Per; Eriksson, Karl Martin; Axelsson, Lennart; Blanck, Hans

2012-10-01

Macroalgae depend on carbon-concentrating mechanisms (CCMs) to maintain a high photosynthetic activity under conditions of low carbon dioxide (CO(2)) availability. Because such conditions are prevalent in marine environments, CCMs are important for upholding the macroalgal primary productivity in coastal zones. This study evaluated the effects of seven antifouling compounds-chlorothalonil, DCOIT, dichlofluanid, diuron, irgarol, tolylfluanid, and zinc pyrithione (ZnTP)-on the photosynthesis and CCM of sugar kelp (Saccharina latissima (L.)). Concentration-response curves of these toxicants were established using inhibition of carbon incorporation, whereas their effects over time and their inhibition of the CCM were studied using inhibition of O(2) evolution. We demonstrate that exposure to all compounds except ZnTP (< 1000 nM) resulted in toxicity to photosynthesis of S. latissima. However, carbon incorporation and O(2) evolution differed in their ability to detect toxicity from some of the compounds. Diuron, irgarol, DCOIT, tolylfluanid, and, to some extent, dichlofluanid inhibited carbon incorporation. Chlorothalonil did not inhibit carbon incorporation but clearly inhibited oxygen (O(2)) evolution. Photosynthesis showed only little recovery during the 2-h postexposure period. Inhibition of photosynthesis even increased after the end of exposure to chlorothalonil and tolylfluanid. Through changes in pH of the medium, toxic effects on the CCM could be studied isolated from photosynthesis effects. The CCM of S. latissima was inhibited by chlorothalonil, DCOIT, dichlofluanid, and tolylfluanid. Such inhibition of the CCM, or the absence thereof, deepens the understanding the mechanism of action of the studied compounds.

Spatial clustering and meteorological drivers of summer ozone in Europe

NASA Astrophysics Data System (ADS)

Carro-Calvo, Leopoldo; Ordóñez, Carlos; García-Herrera, Ricardo; Schnell, Jordan L.

2017-04-01

We present a regionalization of summer near-surface ozone (O3) in Europe. For this purpose we apply a K-means algorithm on a gridded MDA8 O3 (maximum daily average 8-h ozone) dataset covering a European domain [15° W - 30° E, 35°-70° N] at 1° x 1° horizontal resolution for the 1998-2012 period. This dataset was compiled by merging observations from the European Monitoring and Evaluation Programme (EMEP) and the European Environment Agency's air quality database (AirBase). The K-means method allows identifying sets of different regions where the O3 concentrations present coherent spatiotemporal patterns and are thus expected to be driven by similar meteorological factors. After some testing, 9 regions were selected: the British Isles, North-Central Europe, Northern Scandinavia, the Baltic countries, the Iberian Peninsula, Western Europe, South-Central Europe, Eastern Europe and the Balkans. For each region we examine the synoptic situations associated with elevated ozone extremes (days exceeding the 95th percentile of the summer MDA8 O3 distribution). Our analyses reveal that there are basically two different kinds of regions in Europe: (a) those in the centre and south of the continent where ozone extremes are associated with elevated temperature within the same region and (b) those in northern Europe where ozone extremes are driven by southerly advection of air masses from warmer, more polluted areas. Even when the observed patterns were initially identified only for days registering high O3 extremes, all summer days can be projected on such patterns to identify the main modes of meteorological variability of O3. We have found that such modes are partly responsible for the day-to-day variability in the O3 concentrations and can explain a relatively large fraction (from 44 to 88 %, depending on the region) of the interannual variability of summer mean MDA8 O3 during the period of analysis. On the other hand, some major teleconnection patterns have been tested but do not seem to exert a large impact on the variability of surface O3 over most regions. The identification of these independent regions where surface ozone presents a coherent behaviour and responds similarly to specific meteorological modes of variability has multiple applications. For instance, the performance of chemical transport models (CTMs) and chemistry-climate models (CCMs) can be separately assessed over such regions to identify areas where they present large biases that need to be corrected. Our results can also be used to test the models' sensitivity to the day-to-day changing meteorology and to climate change over specific regions.

Untreated clinical course of cerebral cavernous malformations: a prospective, population-based cohort study.

PubMed

Al-Shahi Salman, Rustam; Hall, Julie M; Horne, Margaret A; Moultrie, Fiona; Josephson, Colin B; Bhattacharya, Jo J; Counsell, Carl E; Murray, Gordon D; Papanastassiou, Vakis; Ritchie, Vaughn; Roberts, Richard C; Sellar, Robin J; Warlow, Charles P

2012-03-01

Cerebral cavernous malformations (CCMs) are prone to bleeding but the risk of intracranial haemorrhage and focal neurological deficits, and the factors that might predict their occurrence, are unclear. We aimed to quantify these risks and investigate whether they are affected by sex and CCM location. We undertook a population-based study using multiple overlapping sources of case ascertainment (including a Scotland-wide collaboration of neurologists, neurosurgeons, stroke physicians, radiologists, and pathologists, as well as searches of registers of hospital discharges and death certificates) to identify definite CCM diagnoses first made in Scottish residents between 1999 and 2003, which study neuroradiologists independently validated. We used multiple sources of prospective follow-up both to identify outcome events (which were assessed by use of brain imaging, by investigators masked to potential predictive factors) and to assess adults' dependence. The primary outcome was a composite of intracranial haemorrhage or focal neurological deficits (not including epileptic seizure) that were definitely or possibly related to CCM. 139 adults had at least one definite CCM and 134 were alive at initial presentation. During 1177 person-years of follow-up (completeness 97%), for intracranial haemorrhage alone the 5-year risk of a first haemorrhage was lower than the risk of recurrent haemorrhage (2·4%, 95% CI 0·0-5·7 vs 29·5%, 4·1-55·0; p<0·0001). For the primary outcome, the 5-year risk of a first event was lower than the risk of recurrence (9·3%, 3·1-15·4 vs 42·4%, 26·8-58·0; p<0·0001). The annual risk of recurrence of the primary outcome declined from 19·8% (95% CI 6·1-33·4) in year 1 to 5·0% (0·0-14·8) in year 5 and was higher for women than men (p=0·01) but not for adults with brainstem CCMs versus CCMs in other locations (p=0·17). The risk of recurrent intracranial haemorrhage or focal neurological deficit from a CCM is greater than the risk of a first event, is greater for women than for men, and declines over 5 years. This information can be used in clinical practice, but further work is needed to quantify risks precisely in the long term and to understand why women are at greater risk of recurrence than men. UK Medical Research Council, Chief Scientist Office of the Scottish Government, and UK Stroke Association. Copyright © 2012 Elsevier Ltd. All rights reserved.

A Climatology of Polar Stratospheric Cloud Types by MIPAS-Envisat

NASA Astrophysics Data System (ADS)

Spang, Reinhold; Hoffmann, Lars; Griessbach, Sabine; Orr, Andrew; Höpfner, Michael; Müller, Rolf

2015-04-01

For Chemistry Climate Models (CCM) it is still a challenging task to properly represent the evolution of the polar vortices over the entire winter season. The models usually do not include comprehensive microphysical modules to evolve the formation of different types of polar stratospheric clouds (PSC) over the winter. Consequently, predictions on the development and recovery of the future ozone hole have relatively large uncertainties. A climatological record of hemispheric measurement of PSC types could help to better validate and improve the PSC schemes in CCMs. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument onboard the ESA Envisat satellite operated from July 2002 to April 2012. The infra-red limb emission measurements compile a unique dataset of day and night measurements of polar stratospheric clouds up to the poles. From the spectral measurements in the 4.15-14.6 microns range it is possible to select a number of atmospheric window regions and spectral signatures to classify PSC cloud types like nitric acid hydrates, sulfuric ternary solution droplets, and ice particles. The cloud detection sensitivity is similar to space borne lidars, but MIPAS adds complementary information due to its different measurement technique (limb instead of nadir) and wavelength region. Here we will describe a new classification method for PSCs based on the combination of multiple brightness temperature differences (BTD) and colour ratios. Probability density functions (PDF) of the MIPAS measurements in conjunction with a database of radiative transfer model calculations of realistic PSC particle size distributions enable the definition of regions attributed to specific or mixed types clouds. Applying a naive bias classifier for independent criteria to all defined classes in four 2D PDF distributions, it is possible to assign the most likely PSC type to any measured cloud spectrum. Statistical Monte Carlo test have been applied to quantify uncertainties and the sensitivity to a priori information of the approach. The processing of the complete MIPAS data set of almost 10 years of PSC observations with a first version of the new classification approach is completed. Results for various northern and southern hemisphere winters will be presented. The temporal evolution of the PSC types with respect to the temporal development of the meteorological conditions of the polar vortex as well as comparison with space and ground based lidar measurements will be investigated.

Strong signatures of high-latitude blocks and subtropical ridges in winter PM10 over Europe

NASA Astrophysics Data System (ADS)

Ordonez, C.; Garrido-Perez, J. M.; Garcia-Herrera, R.

2017-12-01

Atmospheric blocking is associated with persistent, slow-moving high pressure systems that interrupt the eastward progress of extratropical storm systems at middle and high latitudes. Subtropical ridges are low latitude structures manifested as bands of positive geopotential height anomalies extending from sub-tropical latitudes towards extra-tropical regions. We have quantified the impact of blocks and ridges on daily PM10 (particulate matter ≤ 10 µm) observations obtained from the European Environment Agency's air quality database (AirBase) for the winter period of 2000-2010. For this purpose, the response of the PM10 concentrations to the location of blocks and ridges with centres in two main longitudinal sectors (Atlantic, ATL, 30˚-0˚ W; European, EUR, 0˚-30˚ E) is examined. EUR blocking is associated with a collapse of the boundary layer as well as reduced wind speeds and precipitation occurrence, yielding large positive anomalies which average 12 µg m-3 over the whole continent. Conversely, the enhanced zonal flow around 50˚-60˚ N and the increased occurrence of precipitation over northern-central Europe on days with ATL ridges favour the ventilation of the boundary layer and the impact of washout processes, reducing PM10 concentrations on average by around 8 µg m-3. The presence of EUR blocks is also concurrent with an increased probability of exceeding the European air quality target (50 µg m-3 for 24-h averaged PM10) and the local 90th percentiles for this pollutant at many sites, while the opposite effect is found for ridges. In addition, the effect of synoptic persistence on the PM10 concentrations is particularly strong for EUR blocks. Finally, we have found that the effect of both synoptic patterns can partly control the interannual variability of winter mean PM10 at many sites of north-western and central Europe, with coefficients of determination (R2) exceeding 0.80 for southern Germany. These results indicate that the response of the particulate matter (PM) concentrations to large-scale circulation patterns is stronger than previously reported for Europe and other mid-latitude regions. These findings can be exploited in the future to evaluate the modelled response of air quality to circulation changes within chemical transport models (CTMs) and chemistry-climate models (CCMs).

Preliminary evidences of CCM operation and its down regulation in relation to increasing CO2 levels in natural phytoplankton assemblages from the coastal waters of Bay of Bengal

NASA Astrophysics Data System (ADS)

Biswas, Haimanti; Rahman Shaik, Aziz Ur; Bandyopadhyay, Debasmita

2014-05-01

Bay of Bengal (BoB), a low productive part of the North Indian Ocean, often possesses low CO2 levels in its surface water and diatoms dominate the phytoplankton communities. Virtually no studies are available from this area reporting how this diatom dominated phytoplankton community would respond any increase in dissolved CO2 levels either naturally or anthopogenically. In most of the marine phytoplankton, the inefficiency of the sole carbon fixing enzyme Rubisco necessitates the need of concentrating dissolved inorganic carbon (DIC) (mostly as HCO3) inside the cell in excess of the ambient water concentrations in order to maintain high rate of photosynthesis under low CO2 levels through an energy consuming carbon concentration mechanisms (CCMs). The ubiquitous enzyme carbonic anhydrase (CA) plays a vital role in CCMs by converting HCO3- to CO2 and usually utilizes the trace metal zinc (Zn) as a cofactor. However, it is evident in many marine phytoplankton species that with increasing external CO2 levels, CCMs can be down-regulated leading to energetic savings which can be reallocated to growth; although exceptions occur. Hence, in order to predict their responses to the projected changes, it is imperative to understand their carbon metabolism patterns. We have conducted a series of incubation experiments in microcosms with natural phytoplankton communities from the coastal waters of BoB under different CO2 levels. Our results revealed that the rate of net photosynthetic oxygen evolution and biomass build-up increased in response to increasing CO2 levels. The depletion in δ13CPOM values were more in the high CO2 treatments relative to the low CO2 treated cells (control), indicating that dissolved CO2 uptake was higher when CO2 levels were increased. When additional Zn was added to the low CO2 treated cells, net photosynthetic oxygen evolution rate was increased significantly than that of the untreated control. It is likely that upon the supply of Zn under low CO2 levels, CA activity was enhanced and accelerated DIC transport and photosynthetic rate. Moreover, δ13CPOM values of low CO2 samples (both Zn treated and untreated) were almost identical, though the rate of photosynthesis was higher in response to Zn addition. This could be because of the fact that under low CO2 levels, DIC was possibly transported as HCO3- and an active HCO3- transport can contribute to low discrimination of 13C compared to diffusive CO2uptake leading to unaltered values of δ13CPOM. Furthermore, under low CO2 treatments, the need of nitrogen resource can be higher to maintain an active CCM (to build-up required proteins, Rubisco and CCM components) and our results showed higher values of δ15NPOMunder low CO2 levels relative to the high CO2treatments suggesting higher nitrogen utilization efficiency in the former case. These observations strengthen the possibility of operating an active CCM under low CO2 levels. HPLC pigment analysis revealed the occurrences of diatoxanthin (DT) [indicator of non-photo-chemical quenching (NPQ)] and high values of photoprotective carotenoid to light harvesting carotenoid ratios (PPC/LHC) in the low CO2 treated cells indicating light stress. This is likely that, when CO2, the only substrate for Rubisco, is low, absorbed light energy within the cell can be surplus leading to photo-damage and to protect the cell from potential damage, DT was produced by energy dissipation via NPQ and PPC were synthesized in excess of LHC. Conversely, in Zn and high CO2 treated cells, the absence of DT and reduced values of PPC/LHC indirectly indicates reduced light stress which was possibly because of enhanced supply of Rubisco substrate either via active bicarbonate transport or diffusive CO2 supply. Thus, we infer that the diatom dominated phytoplankton communities from the study area perform CCMs under low CO2 conditions and the same can be down regulated upon the increasing levels of CO2 and the community may benefit from the increasing CO2 levels followed by increased rate of carbon fixation. These can have large biogeochemical significance.

Micro-computed tomography in murine models of cerebral cavernous malformations as a paradigm for brain disease.

PubMed

Girard, Romuald; Zeineddine, Hussein A; Orsbon, Courtney; Tan, Huan; Moore, Thomas; Hobson, Nick; Shenkar, Robert; Lightle, Rhonda; Shi, Changbin; Fam, Maged D; Cao, Ying; Shen, Le; Neander, April I; Rorrer, Autumn; Gallione, Carol; Tang, Alan T; Kahn, Mark L; Marchuk, Douglas A; Luo, Zhe-Xi; Awad, Issam A

2016-09-15

Cerebral cavernous malformations (CCMs) are hemorrhagic brain lesions, where murine models allow major mechanistic discoveries, ushering genetic manipulations and preclinical assessment of therapies. Histology for lesion counting and morphometry is essential yet tedious and time consuming. We herein describe the application and validations of X-ray micro-computed tomography (micro-CT), a non-destructive technique allowing three-dimensional CCM lesion count and volumetric measurements, in transgenic murine brains. We hereby describe a new contrast soaking technique not previously applied to murine models of CCM disease. Volumetric segmentation and image processing paradigm allowed for histologic correlations and quantitative validations not previously reported with the micro-CT technique in brain vascular disease. Twenty-two hyper-dense areas on micro-CT images, identified as CCM lesions, were matched by histology. The inter-rater reliability analysis showed strong consistency in the CCM lesion identification and staging (K=0.89, p<0.0001) between the two techniques. Micro-CT revealed a 29% greater CCM lesion detection efficiency, and 80% improved time efficiency. Serial integrated lesional area by histology showed a strong positive correlation with micro-CT estimated volume (r(2)=0.84, p<0.0001). Micro-CT allows high throughput assessment of lesion count and volume in pre-clinical murine models of CCM. This approach complements histology with improved accuracy and efficiency, and can be applied for lesion burden assessment in other brain diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

John, David E.; Wang, Zhaohui A.; Liu, Xuewu

River plumes deliver large quantities of nutrients to oligotrophic oceans, often resulting in significant CO 2 drawdown. To determine the relationship between expression of the major gene in carbon fixation (large subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase, RuBisCO) and CO 2 dynamics, we evaluated rbcL mRNA abundance using novel quantitative PCR assays, phytoplankton cell analyses, photophysiological parameters, and pCO 2 in and around the Mississippi River plume (MRP) in the Gulf of Mexico. Lower salinity (30–32) stations were dominated by rbcL mRNA concentrations from heterokonts, such as diatoms and pelagophytes, which were at least an order of magnitude greater than haptophytes, alpha-Synechococcusmore » or high-light Prochlorococcus. However, rbcL transcript abundances were similar among these groups at oligotrophic stations (salinity 34–36). Diatom cell counts and heterokont rbcL RNA showed a strong negative correlation to seawater pCO 2. While Prochlorococcus cells did not exhibit a large difference between low and high pCO 2 water, Prochlorococcus rbcL RNA concentrations had a strong positive correlation to pCO 2, suggesting a very low level of RuBisCO RNA transcription among Prochlorococcus in the plume waters, possibly due to their relatively poor carbon concentrating mechanisms (CCMs). These results provide molecular evidence that diatom/pelagophyte productivity is largely responsible for the large CO 2 drawdown occurring in the MRP, based on the co-occurrence of elevated RuBisCO gene transcript concentrations from this group and reduced seawater pCO 2 levels. This may partly be due to efficient CCMs that enable heterokont eukaryotes such as diatoms to continue fixing CO 2 in the face of strong CO 2 drawdown. Finally, our work represents the first attempt to relate in situ microbial gene expression to contemporaneous CO 2 flux measurements in the ocean.« less

CO2 induced growth response in a diatom dominated phytoplankton community from SW Bay of Bengal coastal water

NASA Astrophysics Data System (ADS)

Biswas, Haimanti; Shaik, Aziz Ur Rahman; Bandyopadhyay, Debasmita; Chowdhury, Neha

2017-11-01

The ongoing increase in surface seawater CO2 level could potentially impact phytoplankton primary production in coastal waters; however, CO2 sensitivity studies on tropical coastal phytoplankton assemblages are rare. The present study investigated the interactive impacts of variable CO2 level, light and zinc (Zn) addition on the diatom dominated phytoplankton assemblages from the western coastal Bay of Bengal. Increased CO2 supply enhanced particulate organic matter (POC) production; a concomitant depletion in δ13CPOM values at elevated CO2 suggested increased CO2 diffusive influx inside the cell. Trace amount of Zn added under low CO2 level accelerated growth probably by accelerating Zn-Carbonic Anhydrase activity which helps in converting bicarbonate ion to CO2. Almost identical values of δ13CPOM in the low CO2 treated cells grown with and without Zn indicated a low discrimination between 13C and 12C probably due to bicarbonate uptake. These evidences collectively indicated the existence of the carbon concentration mechanisms (CCMs) at low CO2. A minimum growth rate was observed at low CO2 and light limited condition indicating light dependence of CCMs activity. Upon the increase of light and CO2 level, growth response was maximum. The cells grown in the low CO2 levels showed higher light stress (higher values of both diatoxanthin index and the ratio of photo-protective to light-harvesting pigments) that was alleviated by both increasing CO2 supply and Zn addition (probably by efficient light energy utilization in presence of adequate CO2). This is likely that the diatom dominated phytoplankton communities benefited from the increasing CO2 supply and thus may enhance primary production in response to any further increase in coastal water CO2 levels and can have large biogeochemical consequences in the study area.

Contralateral posterior interhemispheric approach to deep medial parietooccipital vascular malformations: surgical technique and results.

PubMed

Burkhardt, Jan-Karl; Winkler, Ethan A; Lawton, Michael T

2017-07-21

OBJECTIVE Deep medial parietooccipital arteriovenous malformations (AVMs) and cerebral cavernous malformations (CCMs) are traditionally resected through an ipsilateral posterior interhemispheric approach (IPIA), which creates a deep, perpendicular perspective with limited access to the lateral margins of the lesion. The contralateral posterior interhemispheric approach (CPIA) flips the positioning, with the midline positioned horizontally for retraction due to gravity, but with the AVM on the upper side and the approach from the contralateral, lower side. The aim of this paper was to analyze whether the perpendicular angle of attack that is used in IPIA would convert to a parallel angle of attack with the CPIA, with less retraction, improved working angles, and no significant increase in risk. METHODS A retrospective review of pre- and postoperative clinical and radiographic data was performed in 8 patients who underwent a CPIA. RESULTS Three AVMs and 5 CCMs were resected using the CPIA, with an average nidus size of 2.3 cm and CCM diameter of 1.7 cm. All lesions were resected completely, as confirmed on postoperative catheter angiography or MRI. All patients had good neurological outcomes, with either stable or improved modified Rankin Scale scores at last follow-up. CONCLUSIONS The CPIA is a safe alternative approach to the IPIA for deep medial parietooccipital vascular malformations that extend 2 cm or more off the midline. Contralaterality and retraction due to gravity optimize the interhemispheric corridor, the surgical trajectory to the lesion, and the visualization of the lateral margin, without resection or retraction of adjacent normal cortex. Although the falx is a physical barrier to accessing the lesion, it stabilizes the ipsilateral hemisphere while gravity delivers the dissected lesion through the transfalcine window. Patient positioning, CSF drainage, venous preservation, and meticulous dissection of the deep margins are critical to the safety of this approach.

Clinical course of untreated cerebral cavernous malformations: a meta-analysis of individual patient data.

PubMed

Horne, Margaret A; Flemming, Kelly D; Su, I-Chang; Stapf, Christian; Jeon, Jin Pyeong; Li, Da; Maxwell, Susanne S; White, Philip; Christianson, Teresa J; Agid, Ronit; Cho, Won-Sang; Oh, Chang Wan; Wu, Zhen; Zhang, Jun-Ting; Kim, Jeong Eun; Ter Brugge, Karel; Willinsky, Robert; Brown, Robert D; Murray, Gordon D; Al-Shahi Salman, Rustam

2016-02-01

Cerebral cavernous malformations (CCMs) can cause symptomatic intracranial haemorrhage (ICH), but the estimated risks are imprecise and predictors remain uncertain. We aimed to obtain precise estimates and predictors of the risk of ICH during untreated follow-up in an individual patient data meta-analysis. We invited investigators of published cohorts of people aged at least 16 years, identified by a systematic review of Ovid MEDLINE and Embase from inception to April 30, 2015, to provide individual patient data on clinical course from CCM diagnosis until first CCM treatment or last available follow-up. We used survival analysis to estimate the 5-year risk of symptomatic ICH due to CCMs (primary outcome), multivariable Cox regression to identify baseline predictors of outcome, and random-effects models to pool estimates in a meta-analysis. Among 1620 people in seven cohorts from six studies, 204 experienced ICH during 5197 person-years of follow-up (Kaplan-Meier estimated 5-year risk 15·8%, 95% CI 13·7-17·9). The primary outcome of ICH within 5 years of CCM diagnosis was associated with clinical presentation with ICH or new focal neurological deficit (FND) without brain imaging evidence of recent haemorrhage versus other modes of presentation (hazard ratio 5·6, 95% CI 3·2-9·7) and with brainstem CCM location versus other locations (4·4, 2·3-8·6), but age, sex, and CCM multiplicity did not add independent prognostic information. The 5-year estimated risk of ICH during untreated follow-up was 3·8% (95% CI 2·1-5·5) for 718 people with non-brainstem CCM presenting without ICH or FND, 8·0% (0·1-15·9) for 80 people with brainstem CCM presenting without ICH or FND, 18·4% (13·3-23·5) for 327 people with non-brainstem CCM presenting with ICH or FND, and 30·8% (26·3-35·2) for 495 people with brainstem CCM presenting with ICH or FND. Mode of clinical presentation and CCM location are independently associated with ICH within 5 years of CCM diagnosis. These findings can inform decisions about CCM treatment. UK Medical Research Council, Chief Scientist Office of the Scottish Government, and UK Stroke Association. Copyright © 2016 Horne et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.

Ocean acidification modulates expression of genes and physiological performance of a marine diatom

NASA Astrophysics Data System (ADS)

Li, Y.; Zhuang, S.; Wu, Y.; Ren, H.; Cheng, F.; Lin, X.; Wang, K.; Beardall, J.; Gao, K.

2015-09-01

Ocean Acidification (OA) is known to affect various aspects of the physiological performance of diatoms, but there is little information on the underlining molecular mechanisms involved. Here, we show that in the model diatom Phaeodactylum tricornutum expression of the genes related to light harvesting, carbon acquisition and carboxylation, nitrite assimilation and ATP synthesis are modulated by OA. Growth and photosynthetic carbon fixation were enhanced by elevated CO2 (1000 μatm) under both constant indoor and fluctuating outdoor light regimes. The genetic expression of nitrite reductase (NiR) was up-regulated by OA regardless of light levels and/or regimes. The transcriptional expression of fucoxanthin chlorophyll a/c protein (lhcf type (FCP)) and mitochondrial ATP synthase (mtATP synthase) genes were also enhanced by OA, but only under high light intensity. OA treatment decreased the expression of β-carbonic anhydrase (β-CA) along with down-regulation of CO2 concentrating mechanisms (CCMs). Additionally, the genes for these proteins (NiR, FCP, mtATP synthase, β-CA) showed diel expressions either under constant indoor light or fluctuating sunlight. Thus, OA enhanced photosynthetic and growth rates by stimulating nitrogen assimilation and indirectly by down-regulating the energy-costly inorganic carbon acquisition process.

Bifurcating effects of hope and support in short- and long-term health outcomes among primary care patients without mental illness.

PubMed

Katerndahl, David

2014-08-01

Adverse life events and stressors can lead to symptoms, increased alcohol intake, and impaired functional status, while hope and social support can mitigate such adverse effects. Yet, there is reason to question such simple, linear relationships among healthy adults. The purpose of this study was to assess whether short-term or long-term changes in alcohol intake, psychological symptoms or functional status were better modelled as cusp catastrophic or linear processes among primary care patients without mental illness. This secondary analysis of a study on the stability of psychological symptoms among primary care patients without mental disorders included 38 subjects who completed baseline, and 2-month and/or 6-month assessments of psychological symptoms, functional status and stressors as well as hope and social support. The analyses modelled short-term and long-term changes in alcohol intake, psychological symptoms and functional status using cusp catastrophe (CCM) and linear modelling. Overall, four of the nine 2-month analyses found CCM superior to linear models; however, only one 6-month analysis (alcohol intake) found that CCM was superior. The 2-month cusp phenomena included both symptomatology and functional status. The asymmetry variables were often not significant in the CCM models; in fact, only distress was significant at all. While hope was a significant bifurcation variable at both the 2-month and 6-month levels, social support was a significant bifurcation variable for three of the four 2-month CCMs. In conclusion, while 6-month outcomes were rarely explained through CCM, half of 2-month outcomes were. Hope and support demonstrated bifurcation effects. © 2013 John Wiley & Sons, Ltd.

[Total analysis of organic rubber additives].

PubMed

He, Wen-Xuan; Robert, Shanks; You, Ye-Ming

2010-03-01

In the present paper, after middle pressure chromatograph separation using both positive phase and reversed-phase conditions, the organic additives in ethylene-propylene rubber were identified by infrared spectrometer. At the same time, by using solid phase extraction column to maintain the main component-fuel oil in organic additves to avoid its interfering with minor compounds, other organic additves were separated and analysed by GC/Ms. In addition, the remaining active compound such as benzoyl peroxide was identified by CC/Ms, through analyzing acetone extract directly. Using the above mentioned techniques, soften agents (fuel oil, plant oil and phthalte), curing agent (benzoylperoxide), vulcanizing accelerators (2-mercaptobenzothiazole, ethyl thiuram and butyl thiuram), and antiagers (2, 6-Di-tert-butyl-4-methyl phenol and styrenated phenol) in ethylene-propylene rubber were identified. Although the technique was established in ethylene-propylene rubber system, it can be used in other rubber system.

Assessing effects of variation in global climate data sets on spatial predictions from climate envelope models

USGS Publications Warehouse

Romañach, Stephanie; Watling, James I.; Fletcher, Robert J.; Speroterra, Carolina; Bucklin, David N.; Brandt, Laura A.; Pearlstine, Leonard G.; Escribano, Yesenia; Mazzotti, Frank J.

2014-01-01

Climate change poses new challenges for natural resource managers. Predictive modeling of species–environment relationships using climate envelope models can enhance our understanding of climate change effects on biodiversity, assist in assessment of invasion risk by exotic organisms, and inform life-history understanding of individual species. While increasing interest has focused on the role of uncertainty in future conditions on model predictions, models also may be sensitive to the initial conditions on which they are trained. Although climate envelope models are usually trained using data on contemporary climate, we lack systematic comparisons of model performance and predictions across alternative climate data sets available for model training. Here, we seek to fill that gap by comparing variability in predictions between two contemporary climate data sets to variability in spatial predictions among three alternative projections of future climate. Overall, correlations between monthly temperature and precipitation variables were very high for both contemporary and future data. Model performance varied across algorithms, but not between two alternative contemporary climate data sets. Spatial predictions varied more among alternative general-circulation models describing future climate conditions than between contemporary climate data sets. However, we did find that climate envelope models with low Cohen's kappa scores made more discrepant spatial predictions between climate data sets for the contemporary period than did models with high Cohen's kappa scores. We suggest conservation planners evaluate multiple performance metrics and be aware of the importance of differences in initial conditions for spatial predictions from climate envelope models.

Impacts of weighting climate models for hydro-meteorological climate change studies

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe; Caya, Daniel

2017-06-01

Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961-1990) and future (2061-2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable.

The Monash University Interactive Simple Climate Model

NASA Astrophysics Data System (ADS)

Dommenget, D.

2013-12-01

The Monash university interactive simple climate model is a web-based interface that allows students and the general public to explore the physical simulation of the climate system with a real global climate model. It is based on the Globally Resolved Energy Balance (GREB) model, which is a climate model published by Dommenget and Floeter [2011] in the international peer review science journal Climate Dynamics. The model simulates most of the main physical processes in the climate system in a very simplistic way and therefore allows very fast and simple climate model simulations on a normal PC computer. Despite its simplicity the model simulates the climate response to external forcings, such as doubling of the CO2 concentrations very realistically (similar to state of the art climate models). The Monash simple climate model web-interface allows you to study the results of more than a 2000 different model experiments in an interactive way and it allows you to study a number of tutorials on the interactions of physical processes in the climate system and solve some puzzles. By switching OFF/ON physical processes you can deconstruct the climate and learn how all the different processes interact to generate the observed climate and how the processes interact to generate the IPCC predicted climate change for anthropogenic CO2 increase. The presentation will illustrate how this web-base tool works and what are the possibilities in teaching students with this tool are.

The Art and Science of Climate Model Tuning

DOE PAGES

Hourdin, Frederic; Mauritsen, Thorsten; Gettelman, Andrew; ...

2017-03-31

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling withmore » its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. Here, we discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.« less

The Art and Science of Climate Model Tuning

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hourdin, Frederic; Mauritsen, Thorsten; Gettelman, Andrew

The process of parameter estimation targeting a chosen set of observations is an essential aspect of numerical modeling. This process is usually named tuning in the climate modeling community. In climate models, the variety and complexity of physical processes involved, and their interplay through a wide range of spatial and temporal scales, must be summarized in a series of approximate submodels. Most submodels depend on uncertain parameters. Tuning consists of adjusting the values of these parameters to bring the solution as a whole into line with aspects of the observed climate. Tuning is an essential aspect of climate modeling withmore » its own scientific issues, which is probably not advertised enough outside the community of model developers. Optimization of climate models raises important questions about whether tuning methods a priori constrain the model results in unintended ways that would affect our confidence in climate projections. Here, we present the definition and rationale behind model tuning, review specific methodological aspects, and survey the diversity of tuning approaches used in current climate models. We also discuss the challenges and opportunities in applying so-called objective methods in climate model tuning. Here, we discuss how tuning methodologies may affect fundamental results of climate models, such as climate sensitivity. The article concludes with a series of recommendations to make the process of climate model tuning more transparent.« less

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, E.; Sokolov, A. P.; Schlosser, C. A.; Scott, J. R.; Gao, X.

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an earth system model of intermediate complexity, with a two-dimensional zonal-mean atmosphere, to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three dimensional atmospheric model; and a statistical downscaling, where a pattern scaling algorithm uses climate-change patterns from 17 climate models. This framework allows for key sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections; climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate); natural variability; and structural uncertainty. Results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also nd that dierent initial conditions lead to dierences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider all sources of uncertainty when modeling climate impacts over Northern Eurasia.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.

Selection of climate change scenario data for impact modelling.

PubMed

Sloth Madsen, M; Maule, C Fox; MacKellar, N; Olesen, J E; Christensen, J Hesselbjerg

2012-01-01

Impact models investigating climate change effects on food safety often need detailed climate data. The aim of this study was to select climate change projection data for selected crop phenology and mycotoxin impact models. Using the ENSEMBLES database of climate model output, this study illustrates how the projected climate change signal of important variables as temperature, precipitation and relative humidity depends on the choice of the climate model. Using climate change projections from at least two different climate models is recommended to account for model uncertainty. To make the climate projections suitable for impact analysis at the local scale a weather generator approach was adopted. As the weather generator did not treat all the necessary variables, an ad-hoc statistical method was developed to synthesise realistic values of missing variables. The method is presented in this paper, applied to relative humidity, but it could be adopted to other variables if needed.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Potential climate-induced runoff changes and associated uncertainty in four Pacific Northwest estuaries

USGS Publications Warehouse

Steele, Madeline O.; Chang, Heejun; Reusser, Deborah A.; Brown, Cheryl A.; Jung, Il-Won

2012-01-01

As part of a larger investigation into potential effects of climate change on estuarine habitats in the Pacific Northwest, we estimated changes in freshwater inputs into four estuaries: Coquille River estuary, South Slough of Coos Bay, and Yaquina Bay in Oregon, and Willapa Bay in Washington. We used the U.S. Geological Survey's Precipitation Runoff Modeling System (PRMS) to model watershed hydrological processes under current and future climatic conditions. This model allowed us to explore possible shifts in coastal hydrologic regimes at a range of spatial scales. All modeled watersheds are located in rainfall-dominated coastal areas with relatively insignificant base flow inputs, and their areas vary from 74.3 to 2,747.6 square kilometers. The watersheds also vary in mean elevation, ranging from 147 meters in the Willapa to 1,179 meters in the Coquille. The latitudes of watershed centroids range from 43.037 degrees north latitude in the Coquille River estuary to 46.629 degrees north latitude in Willapa Bay. We calibrated model parameters using historical climate grid data downscaled to one-sixteenth of a degree by the Climate Impacts Group, and historical runoff from sub-watersheds or neighboring watersheds. Nash Sutcliffe efficiency values for daily flows in calibration sub-watersheds ranged from 0.71 to 0.89. After calibration, we forced the PRMS models with four North American Regional Climate Change Assessment Program climate models: Canadian Regional Climate Model-(National Center for Atmospheric Research) Community Climate System Model version 3, Canadian Regional Climate Model-Canadian Global Climate Model version 3, Hadley Regional Model version 3-Hadley Centre Climate Model version 3, and Regional Climate Model-Canadian Global Climate Model version 3. These are global climate models (GCMs) downscaled with regional climate models that are embedded within the GCMs, and all use the A2 carbon emission scenario developed by the Intergovernmental Panel on Climate Change. With these climate-forcing outputs, we derived the mean change in flow from the period encompassing the 1980s (1971-1995) to the period encompassing the 2050s (2041-2065). Specifically, we calculated percent change in mean monthly flow rate, coefficient of variation, top 5 percent of flow, and 7-day low flow. The trends with the most agreement among climate models and among watersheds were increases in autumn mean monthly flows, especially in October and November, decreases in summer monthly mean flow, and increases in the top 5 percent of flow. We also estimated variance in PRMS outputs owing to parameter uncertainty and the selection of climate model using Latin hypercube sampling. This analysis showed that PRMS low-flow simulations are more uncertain than medium or high flow simulations, and that variation among climate models was a larger source of uncertainty than the hydrological model parameters. These results improve our understanding of how climate change may affect the saltwater-freshwater balance in Pacific Northwest estuaries, with implications for their sensitive ecosystems.

Designing ecological climate change impact assessments to reflect key climatic drivers

USGS Publications Warehouse

Sofaer, Helen R.; Barsugli, Joseph J.; Jarnevich, Catherine S.; Abatzoglou, John T.; Talbert, Marian; Miller, Brian W.; Morisette, Jeffrey T.

2017-01-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive – such as means or extremes – can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the ‘model space’ approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling.

Designing ecological climate change impact assessments to reflect key climatic drivers.

PubMed

Sofaer, Helen R; Barsugli, Joseph J; Jarnevich, Catherine S; Abatzoglou, John T; Talbert, Marian K; Miller, Brian W; Morisette, Jeffrey T

2017-07-01

Identifying the climatic drivers of an ecological system is a key step in assessing its vulnerability to climate change. The climatic dimensions to which a species or system is most sensitive - such as means or extremes - can guide methodological decisions for projections of ecological impacts and vulnerabilities. However, scientific workflows for combining climate projections with ecological models have received little explicit attention. We review Global Climate Model (GCM) performance along different dimensions of change and compare frameworks for integrating GCM output into ecological models. In systems sensitive to climatological means, it is straightforward to base ecological impact assessments on mean projected changes from several GCMs. Ecological systems sensitive to climatic extremes may benefit from what we term the 'model space' approach: a comparison of ecological projections based on simulated climate from historical and future time periods. This approach leverages the experimental framework used in climate modeling, in which historical climate simulations serve as controls for future projections. Moreover, it can capture projected changes in the intensity and frequency of climatic extremes, rather than assuming that future means will determine future extremes. Given the recent emphasis on the ecological impacts of climatic extremes, the strategies we describe will be applicable across species and systems. We also highlight practical considerations for the selection of climate models and data products, emphasizing that the spatial resolution of the climate change signal is generally coarser than the grid cell size of downscaled climate model output. Our review illustrates how an understanding of how climate model outputs are derived and downscaled can improve the selection and application of climatic data used in ecological modeling. © 2017 John Wiley & Sons Ltd.

The Effects of Climate Model Similarity on Local, Risk-Based Adaptation Planning

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Brown, C. M.

2014-12-01

The climate science community has recently proposed techniques to develop probabilistic projections of climate change from ensemble climate model output. These methods provide a means to incorporate the formal concept of risk, i.e., the product of impact and probability, into long-term planning assessments for local systems under climate change. However, approaches for pdf development often assume that different climate models provide independent information for the estimation of probabilities, despite model similarities that stem from a common genealogy. Here we utilize an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to develop probabilistic climate information, with and without an accounting of inter-model correlations, and use it to estimate climate-related risks to a local water utility in Colorado, U.S. We show that the tail risk of extreme climate changes in both mean precipitation and temperature is underestimated if model correlations are ignored. When coupled with impact models of the hydrology and infrastructure of the water utility, the underestimation of extreme climate changes substantially alters the quantification of risk for water supply shortages by mid-century. We argue that progress in climate change adaptation for local systems requires the recognition that there is less information in multi-model climate ensembles than previously thought. Importantly, adaptation decisions cannot be limited to the spread in one generation of climate models.

Integrated approaches to climate-crop modelling: needs and challenges.

PubMed

Betts, Richard A

2005-11-29

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

An evaluation of 20th century climate for the Southeastern United States as simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models

USGS Publications Warehouse

David E. Rupp,

2016-05-05

The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.

Beyond a Climate-Centric View of Plant Distribution: Edaphic Variables Add Value to Distribution Models

PubMed Central

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change. PMID:24658097

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models.

PubMed

Beauregard, Frieda; de Blois, Sylvie

2014-01-01

Both climatic and edaphic conditions determine plant distribution, however many species distribution models do not include edaphic variables especially over large geographical extent. Using an exceptional database of vegetation plots (n = 4839) covering an extent of ∼55,000 km2, we tested whether the inclusion of fine scale edaphic variables would improve model predictions of plant distribution compared to models using only climate predictors. We also tested how well these edaphic variables could predict distribution on their own, to evaluate the assumption that at large extents, distribution is governed largely by climate. We also hypothesized that the relative contribution of edaphic and climatic data would vary among species depending on their growth forms and biogeographical attributes within the study area. We modelled 128 native plant species from diverse taxa using four statistical model types and three sets of abiotic predictors: climate, edaphic, and edaphic-climate. Model predictive accuracy and variable importance were compared among these models and for species' characteristics describing growth form, range boundaries within the study area, and prevalence. For many species both the climate-only and edaphic-only models performed well, however the edaphic-climate models generally performed best. The three sets of predictors differed in the spatial information provided about habitat suitability, with climate models able to distinguish range edges, but edaphic models able to better distinguish within-range variation. Model predictive accuracy was generally lower for species without a range boundary within the study area and for common species, but these effects were buffered by including both edaphic and climatic predictors. The relative importance of edaphic and climatic variables varied with growth forms, with trees being more related to climate whereas lower growth forms were more related to edaphic conditions. Our study identifies the potential for non-climate aspects of the environment to pose a constraint to range expansion under climate change.

Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

2012-01-01

Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

Atmospheric, Climatic, and Environmental Research

NASA Technical Reports Server (NTRS)

Broecker, Wallace S.; Gornitz, Vivien M.

1994-01-01

The climate and atmospheric modeling project involves analysis of basic climate processes, with special emphasis on studies of the atmospheric CO2 and H2O source/sink budgets and studies of the climatic role Of CO2, trace gases and aerosols. These studies are carried out, based in part on use of simplified climate models and climate process models developed at GISS. The principal models currently employed are a variable resolution 3-D general circulation model (GCM), and an associated "tracer" model which simulates the advection of trace constituents using the winds generated by the GCM.

Assessing NARCCAP climate model effects using spatial confidence regions.

PubMed

French, Joshua P; McGinnis, Seth; Schwartzman, Armin

2017-01-01

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference.

Decomposing the uncertainty in climate impact projections of Dynamic Vegetation Models: a test with the forest models LANDCLIM and FORCLIM

NASA Astrophysics Data System (ADS)

Cailleret, Maxime; Snell, Rebecca; von Waldow, Harald; Kotlarski, Sven; Bugmann, Harald

2015-04-01

Different levels of uncertainty should be considered in climate impact projections by Dynamic Vegetation Models (DVMs), particularly when it comes to managing climate risks. Such information is useful to detect the key processes and uncertainties in the climate model - impact model chain and may be used to support recommendations for future improvements in the simulation of both climate and biological systems. In addition, determining which uncertainty source is dominant is an important aspect to recognize the limitations of climate impact projections by a multi-model ensemble mean approach. However, to date, few studies have clarified how each uncertainty source (baseline climate data, greenhouse gas emission scenario, climate model, and DVM) affects the projection of ecosystem properties. Focusing on one greenhouse gas emission scenario, we assessed the uncertainty in the projections of a forest landscape model (LANDCLIM) and a stand-scale forest gap model (FORCLIM) that is caused by linking climate data with an impact model. LANDCLIM was used to assess the uncertainty in future landscape properties of the Visp valley in Switzerland that is due to (i) the use of different 'baseline' climate data (gridded data vs. data from weather stations), and (ii) differences in climate projections among 10 GCM-RCM chains. This latter point was also considered for the projections of future forest properties by FORCLIM at several sites along an environmental gradient in Switzerland (14 GCM-RCM chains), for which we also quantified the uncertainty caused by (iii) the model chain specific statistical properties of the climate time-series, and (iv) the stochasticity of the demographic processes included in the model, e.g., the annual number of saplings that establish, or tree mortality. Using methods of variance decomposition analysis, we found that (i) The use of different baseline climate data strongly impacts the prediction of forest properties at the lowest and highest, but not so much at medium elevations. (ii) Considering climate change, the variability that is due to the GCM-RCM chains is much greater than the variability induced by the uncertainty in the initial climatic conditions. (iii) The uncertainties caused by the intrinsic stochasticity in the DVMs and by the random generation of the climate time-series are negligible. Overall, our results indicate that DVMs are quite sensitive to the climate data, highlighting particularly (1) the limitations of using one single multi-model average climate change scenario in climate impact studies and (2) the need to better consider the uncertainty in climate model outputs for projecting future vegetation changes.

What’s Needed from Climate Modeling to Advance Actionable Science for Water Utilities?

NASA Astrophysics Data System (ADS)

Barsugli, J. J.; Anderson, C. J.; Smith, J. B.; Vogel, J. M.

2009-12-01

“…perfect information on climate change is neither available today nor likely to be available in the future, but … over time, as the threats climate change poses to our systems grow more real, predicting those effects with greater certainty is non-discretionary. We’re not yet at a level at which climate change projections can drive climate change adaptation.” (Testimony of WUCA Staff Chair David Behar to the House Committee on Science and Technology, May 5, 2009) To respond to this challenge, the Water Utility Climate Alliance (WUCA) has sponsored a white paper titled “Options for Improving Climate Modeling to Assist Water Utility Planning for Climate Change. ” This report concerns how investments in the science of climate change, and in particular climate modeling and downscaling, can best be directed to help make climate projections more actionable. The meaning of “model improvement” can be very different depending on whether one is talking to a climate model developer or to a water manager trying to incorporate climate projections in to planning. We first surveyed the WUCA members on present and potential uses of climate model projections and on climate inputs to their various system models. Based on those surveys and on subsequent discussions, we identified four dimensions along which improvement in modeling would make the science more “actionable”: improved model agreement on change in key parameters; narrowing the range of model projections; providing projections at spatial and temporal scales that match water utilities system models; providing projections that water utility planning horizons. With these goals in mind we developed four options for improving global-scale climate modeling and three options for improving downscaling that will be discussed. However, there does not seem to be a single investment - the proverbial “magic bullet” -- which will substantially reduce the range of model projections at the scales at which utility planning is conducted. In the near term we feel strongly that water utilities and climate scientists should work together to leverage the upcoming Coupled Model Intercomparison Project, Phase 5 (CMIP5; a coordinated set climate model experiments that will be used to support the upcoming IPCC Fifth Assessment) to better benefit water utilities. In the longer term, even with model and downscaling improvements, it is very likely that substantial uncertainty about future climate change at the desired spatial and temporal scales will remain. Nonetheless, there is no doubt the climate is changing, and the challenge is to work with what we have, or what we can reasonably expect to have in the coming years to make the best decisions we can.

Intercomparison of the capabilities of simplified climate models to project the effects of aviation CO2 on climate

NASA Astrophysics Data System (ADS)

Khodayari, Arezoo; Wuebbles, Donald J.; Olsen, Seth C.; Fuglestvedt, Jan S.; Berntsen, Terje; Lund, Marianne T.; Waitz, Ian; Wolfe, Philip; Forster, Piers M.; Meinshausen, Malte; Lee, David S.; Lim, Ling L.

2013-08-01

This study evaluates the capabilities of the carbon cycle and energy balance treatments relative to the effect of aviation CO2 emissions on climate in several existing simplified climate models (SCMs) that are either being used or could be used for evaluating the effects of aviation on climate. Since these models are used in policy-related analyses, it is important that the capabilities of such models represent the state of understanding of the science. We compare the Aviation Environmental Portfolio Management Tool (APMT) Impacts climate model, two models used at the Center for International Climate and Environmental Research-Oslo (CICERO-1 and CICERO-2), the Integrated Science Assessment Model (ISAM) model as described in Jain et al. (1994), the simple Linear Climate response model (LinClim) and the Model for the Assessment of Greenhouse-gas Induced Climate Change version 6 (MAGICC6). In this paper we select scenarios to illustrate the behavior of the carbon cycle and energy balance models in these SCMs. This study is not intended to determine the absolute and likely range of the expected climate response in these models but to highlight specific features in model representations of the carbon cycle and energy balance models that need to be carefully considered in studies of aviation effects on climate. These results suggest that carbon cycle models that use linear impulse-response-functions (IRF) in combination with separate equations describing air-sea and air-biosphere exchange of CO2 can account for the dominant nonlinearities in the climate system that would otherwise not have been captured with an IRF alone, and hence, produce a close representation of more complex carbon cycle models. Moreover, results suggest that an energy balance model with a 2-box ocean sub-model and IRF tuned to reproduce the response of coupled Earth system models produces a close representation of the globally-averaged temperature response of more complex energy balance models.

Pollen-proxies say cooler, climate models say warmer: resolving conflicting views of the Holocene climate of the Mediterranean region

NASA Astrophysics Data System (ADS)

Russo, E.; Mauri, A.; Davis, B. A. S.; Cubasch, U.

2017-12-01

The evolution of the Mediterranean region's climate during the Holocene has been the subject of long-standing debate within the paleoclimate community. Conflicting hypotheses have emerged from the analysis of different climate reconstructions based on proxy records and climate models outputs.In particular, pollen-based reconstructions of cooler summer temperatures during the Holocene have been criticized based on a hypothesis that the Mediterranean vegetation is mainly limited by effective precipitation and not summer temperature. This criticism is important because climate models show warmer summer temperatures during the Holocene over the Mediterranean region, in direct contradiction of the pollen-based evidence. Here we investigate this problem using a high resolution model simulation of the climate of the Mediterranean region during the mid-to-late Holocene, which we compare against pollen-based reconstructions using two different approaches.In the first, we compare the simulated climate from the model directly with the climate derived from the pollen data. In the second, we compare the simulated vegetation from the model directly with the vegetation from the pollen data.Results show that the climate model is unable to simulate neither the climate nor the vegetation shown by the pollen-data. The pollen data indicates an expansion in cool temperate vegetation in the mid-Holocene while the model suggests an expansion in warm arid vegetation. This suggests that the data-model discrepancy is more likely the result of bias in climate models, and not bias in the pollen-climate calibration transfer-function.

How does the sensitivity of climate affect stratospheric solar radiation management?

NASA Astrophysics Data System (ADS)

Ricke, K.; Rowlands, D. J.; Ingram, W.; Keith, D.; Morgan, M. G.

2011-12-01

If implementation of proposals to engineer the climate through solar radiation management (SRM) ever occurs, it is likely to be contingent upon climate sensitivity. Despite this, no modeling studies have examined how the effectiveness of SRM forcings differs between the typical Atmosphere-Ocean General Circulation Models (AOGCMs) with climate sensitivities close to the Coupled Model Intercomparison Project (CMIP) mean and ones with high climate sensitivities. Here, we use a perturbed physics ensemble modeling experiment to examine variations in the response of climate to SRM under different climate sensitivities. When SRM is used as a substitute for mitigation its ability to maintain the current climate state gets worse with increased climate sensitivity and with increased concentrations of greenhouse gases. However, our results also demonstrate that the potential of SRM to slow climate change, even at the regional level, grows with climate sensitivity. On average, SRM reduces regional rates of temperature change by more than 90 percent and rates of precipitation change by more than 50 percent in these higher sensitivity model configurations. To investigate how SRM might behave in models with high climate sensitivity that are also consistent with recent observed climate change we perform a "perturbed physics" ensemble (PPE) modelling experiment with the climateprediction.net (cpdn) version of the HadCM3L AOGCM. Like other perturbed physics climate modelling experiments, we simulate past and future climate scenarios using a wide range of model parameter combinations that both reproduce past climate within a specified level of accuracy and simulate future climates with a wide range of climate sensitivities. We chose 43 members ("model versions") from a subset of the 1,550 from the British Broadcasting Corporation (BBC) climateprediction.net project that have data that allow restarts. We use our results to explore how much assessments of SRM that use best-estimate models, and so near-median climate sensitivity, may be ignoring important contingencies associated with implementing SRM in reality. A primary motivation for studying SRM via the injection of aerosols in the stratosphere is to evaluate its potential effectiveness as "insurance" in the case of higher-than-expected climate response to global warming. We find that this is precisely when SRM appears to be least effective in returning regional climates to their baseline states and reducing regional rates of precipitation change. On the other hand, given the very high regional temperature anomalies associated with rising greenhouse gas concentrations in high sensitivity models, it is also where SRM is most effective in reducing rates of change relative to a no SRM alternative.

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

Integrated approaches to climate–crop modelling: needs and challenges

PubMed Central

A. Betts, Richard

2005-01-01

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate–vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (O3) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate–chemistry–crop–hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models. PMID:16433093

Documenting Climate Models and Simulations: the ES-DOC Ecosystem in Support of CMIP

NASA Astrophysics Data System (ADS)

Pascoe, C. L.; Guilyardi, E.

2017-12-01

The results of climate models are of increasing and widespread importance. No longer is climate model output of sole interest to climate scientists and researchers in the climate change impacts and adaptation fields. Now non-specialists such as government officials, policy-makers, and the general public, all have an increasing need to access climate model output and understand its implications. For this host of users, accurate and complete metadata (i.e., information about how and why the data were produced) is required to document the climate modeling results. Here we describe the ES-DOC community-govern project to collect and make available documentation of climate models and their simulations for the internationally coordinated modeling activity CMIP6 (Coupled Model Intercomparison Project, Phase 6). An overview of the underlying standards, key properties and features, the evolution from CMIP5, the underlying tools and workflows as well as what modelling groups should expect and how they should engage with the documentation of their contribution to CMIP6 is also presented.

Assessing NARCCAP climate model effects using spatial confidence regions

PubMed Central

French, Joshua P.; McGinnis, Seth; Schwartzman, Armin

2017-01-01

We assess similarities and differences between model effects for the North American Regional Climate Change Assessment Program (NARCCAP) climate models using varying classes of linear regression models. Specifically, we consider how the average temperature effect differs for the various global and regional climate model combinations, including assessment of possible interaction between the effects of global and regional climate models. We use both pointwise and simultaneous inference procedures to identify regions where global and regional climate model effects differ. We also show conclusively that results from pointwise inference are misleading, and that accounting for multiple comparisons is important for making proper inference. PMID:28936474

Linking models of human behaviour and climate alters projected climate change

DOE PAGES

Beckage, Brian; Gross, Louis J.; Lacasse, Katherine; ...

2018-01-01

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with themore » largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Lastly, our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.« less

Linking models of human behaviour and climate alters projected climate change

NASA Astrophysics Data System (ADS)

Beckage, Brian; Gross, Louis J.; Lacasse, Katherine; Carr, Eric; Metcalf, Sara S.; Winter, Jonathan M.; Howe, Peter D.; Fefferman, Nina; Franck, Travis; Zia, Asim; Kinzig, Ann; Hoffman, Forrest M.

2018-01-01

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4-6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with the largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.

Linking models of human behaviour and climate alters projected climate change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beckage, Brian; Gross, Louis J.; Lacasse, Katherine

Although not considered in climate models, perceived risk stemming from extreme climate events may induce behavioural changes that alter greenhouse gas emissions. Here, we link the C-ROADS climate model to a social model of behavioural change to examine how interactions between perceived risk and emissions behaviour influence projected climate change. Our coupled climate and social model resulted in a global temperature change ranging from 3.4–6.2 °C by 2100 compared with 4.9 °C for the C-ROADS model alone, and led to behavioural uncertainty that was of a similar magnitude to physical uncertainty (2.8 °C versus 3.5 °C). Model components with themore » largest influence on temperature were the functional form of response to extreme events, interaction of perceived behavioural control with perceived social norms, and behaviours leading to sustained emissions reductions. Lastly, our results suggest that policies emphasizing the appropriate attribution of extreme events to climate change and infrastructural mitigation may reduce climate change the most.« less

Determing Credibility of Regional Simulations of Future Climate

NASA Astrophysics Data System (ADS)

Mearns, L. O.

2009-12-01

Climate models have been evaluated or validated ever since they were first developed. Establishing that a climate model can reproduce (some) aspects of the current climate of the earth on various spatial and temporal scales has long been a standard procedure for providing confidence in the model's ability to simulate future climate. However, direct links between the successes and failures of models in reproducing the current climate with regard to what future climates the models simulate has been largely lacking. This is to say that the model evaluation process has been largely divorced from the projections of future climate that the models produce. This is evidenced in the separation in the Intergovernmental Panel on Climate Change (IPCC) WG1 report of the chapter on evaluation of models from the chapter on future climate projections. There has also been the assumption of 'one model, one vote, that is, that each model projection is given equal weight in any multi-model ensemble presentation of the projections of future climate. There have been various attempts at determing measures of credibility that would avoid the 'ultrademocratic' assumption of the IPCC. Simple distinctions between models were made by research such as in Giorgi and Mearns (2002), Tebaldi et al., (2005), and Greene et al., (2006). But the metrics used were rather simplistic. More ambitous means of discriminating among the quality of model simulations have been made through the production of complex multivariate metrics, but insufficent work has been produced to verify that the metrics successfully discriminate in meaningful ways. Indeed it has been suggested that we really don't know what a model must successfully model to establish confidence in its regional-scale projections (Gleckler et al., 2008). Perhaps a more process oriented regional expert judgment approach is needed to understand which errors in climate models really matter for the model's response to future forcing. Such an approach is being attempted in the North American Climate Change Assessment Program (NARCCAP) whereby multiple global models are used to drive multiple regional models for the current period and the mid-21st century over the continent. Progress in this endeavor will be reported.

Climate data induced uncertainty in model-based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Zhendong; Ahlström, Anders; Smith, Benjamin; Ardö, Jonas; Eklundh, Lars; Fensholt, Rasmus; Lehsten, Veiko

2017-06-01

Model-based estimations of historical fluxes and pools of the terrestrial biosphere differ substantially. These differences arise not only from differences between models but also from differences in the environmental and climatic data used as input to the models. Here we investigate the role of uncertainties in historical climate data by performing simulations of terrestrial gross primary productivity (GPP) using a process-based dynamic vegetation model (LPJ-GUESS) forced by six different climate datasets. We find that the climate induced uncertainty, defined as the range among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 11 Pg C yr-1 globally (9% of mean GPP). We also assessed a hypothetical maximum climate data induced uncertainty by combining climate variables from different datasets, which resulted in significantly larger uncertainties of 41 Pg C yr-1 globally or 32% of mean GPP. The uncertainty is partitioned into components associated to the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (climate data range) and the apparent sensitivity of the modeled GPP to the driver (apparent model sensitivity). We find that LPJ-GUESS overestimates GPP compared to empirically based GPP data product in all land cover classes except for tropical forests. Tropical forests emerge as a disproportionate source of uncertainty in GPP estimation both in the simulations and empirical data products. The tropical forest uncertainty is most strongly associated with shortwave radiation and precipitation forcing, of which climate data range contributes higher to overall uncertainty than apparent model sensitivity to forcing. Globally, precipitation dominates the climate induced uncertainty over nearly half of the vegetated land area, which is mainly due to climate data range and less so due to the apparent model sensitivity. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than apparent model sensitivity to forcing. Our study highlights the need to better constrain tropical climate, and demonstrates that uncertainty caused by climatic forcing data must be considered when comparing and evaluating carbon cycle model results and empirical datasets.

The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and Pilot Studies

NASA Technical Reports Server (NTRS)

Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P.; Antle, J. M.; Nelson, G. C.; Porter, C.; Janssen, S.;

A transient stochastic weather generator incorporating climate model uncertainty

NASA Astrophysics Data System (ADS)

Glenis, Vassilis; Pinamonti, Valentina; Hall, Jim W.; Kilsby, Chris G.

2015-11-01

Stochastic weather generators (WGs), which provide long synthetic time series of weather variables such as rainfall and potential evapotranspiration (PET), have found widespread use in water resources modelling. When conditioned upon the changes in climatic statistics (change factors, CFs) predicted by climate models, WGs provide a useful tool for climate impacts assessment and adaption planning. The latest climate modelling exercises have involved large numbers of global and regional climate models integrations, designed to explore the implications of uncertainties in the climate model formulation and parameter settings: so called 'perturbed physics ensembles' (PPEs). In this paper we show how these climate model uncertainties can be propagated through to impact studies by testing multiple vectors of CFs, each vector derived from a different sample from a PPE. We combine this with a new methodology to parameterise the projected time-evolution of CFs. We demonstrate how, when conditioned upon these time-dependent CFs, an existing, well validated and widely used WG can be used to generate non-stationary simulations of future climate that are consistent with probabilistic outputs from the Met Office Hadley Centre's Perturbed Physics Ensemble. The WG enables extensive sampling of natural variability and climate model uncertainty, providing the basis for development of robust water resources management strategies in the context of a non-stationary climate.

[Lake eutrophication modeling in considering climatic factors change: a review].

PubMed

Su, Jie-Qiong; Wang, Xuan; Yang, Zhi-Feng

2012-11-01

Climatic factors are considered as the key factors affecting the trophic status and its process in most lakes. Under the background of global climate change, to incorporate the variations of climatic factors into lake eutrophication models could provide solid technical support for the analysis of the trophic evolution trend of lake and the decision-making of lake environment management. This paper analyzed the effects of climatic factors such as air temperature, precipitation, sunlight, and atmosphere on lake eutrophication, and summarized the research results about the lake eutrophication modeling in considering in considering climatic factors change, including the modeling based on statistical analysis, ecological dynamic analysis, system analysis, and intelligent algorithm. The prospective approaches to improve the accuracy of lake eutrophication modeling with the consideration of climatic factors change were put forward, including 1) to strengthen the analysis of the mechanisms related to the effects of climatic factors change on lake trophic status, 2) to identify the appropriate simulation models to generate several scenarios under proper temporal and spatial scales and resolutions, and 3) to integrate the climatic factors change simulation, hydrodynamic model, ecological simulation, and intelligent algorithm into a general modeling system to achieve an accurate prediction of lake eutrophication under climatic change.

Uncertainty and the Social Cost of Methane Using Bayesian Constrained Climate Models

NASA Astrophysics Data System (ADS)

Errickson, F. C.; Anthoff, D.; Keller, K.

2016-12-01

Social cost estimates of greenhouse gases are important for the design of sound climate policies and are also plagued by uncertainty. One major source of uncertainty stems from the simplified representation of the climate system used in the integrated assessment models that provide these social cost estimates. We explore how uncertainty over the social cost of methane varies with the way physical processes and feedbacks in the methane cycle are modeled by (i) coupling three different methane models to a simple climate model, (ii) using MCMC to perform a Bayesian calibration of the three coupled climate models that simulates direct sampling from the joint posterior probability density function (pdf) of model parameters, and (iii) producing probabilistic climate projections that are then used to calculate the Social Cost of Methane (SCM) with the DICE and FUND integrated assessment models. We find that including a temperature feedback in the methane cycle acts as an additional constraint during the calibration process and results in a correlation between the tropospheric lifetime of methane and several climate model parameters. This correlation is not seen in the models lacking this feedback. Several of the estimated marginal pdfs of the model parameters also exhibit different distributional shapes and expected values depending on the methane model used. As a result, probabilistic projections of the climate system out to the year 2300 exhibit different levels of uncertainty and magnitudes of warming for each of the three models under an RCP8.5 scenario. We find these differences in climate projections result in differences in the distributions and expected values for our estimates of the SCM. We also examine uncertainty about the SCM by performing a Monte Carlo analysis using a distribution for the climate sensitivity while holding all other climate model parameters constant. Our SCM estimates using the Bayesian calibration are lower and exhibit less uncertainty about extremely high values in the right tail of the distribution compared to the Monte Carlo approach. This finding has important climate policy implications and suggests previous work that accounts for climate model uncertainty by only varying the climate sensitivity parameter may overestimate the SCM.

The Swedish Regional Climate Modelling Programme, SWECLIM: a review.

PubMed

Rummukainen, Markku; Bergström, Sten; Persson, Gunn; Rodhe, Johan; Tjernström, Michael

2004-06-01

The Swedish Regional Climate Modelling Programme, SWECLIM, was a 6.5-year national research network for regional climate modeling, regional climate change projections and hydrological impact assessment and information to a wide range of stakeholders. Most of the program activities focussed on the regional climate system of Northern Europe. This led to the establishment of an advanced, coupled atmosphere-ocean-hydrology regional climate model system, a suite of regional climate change projections and progress on relevant data and process studies. These were, in turn, used for information and educational purposes, as a starting point for impact analyses on different societal sectors and provided contributions also to international climate research.

Signal to noise quantification of regional climate projections

NASA Astrophysics Data System (ADS)

Li, S.; Rupp, D. E.; Mote, P.

2016-12-01

One of the biggest challenges in interpreting climate model outputs for impacts studies and adaptation planning is understanding the sources of disagreement among models (which is often used imperfectly as a stand-in for system uncertainty). Internal variability is a primary source of uncertainty in climate projections, especially for precipitation, for which models disagree about even the sign of changes in large areas like the continental US. Taking advantage of a large initial-condition ensemble of regional climate simulations, this study quantifies the magnitude of changes forced by increasing greenhouse gas concentrations relative to internal variability. Results come from a large initial-condition ensemble of regional climate model simulations generated by weather@home, a citizen science computing platform, where the western United States climate was simulated for the recent past (1985-2014) and future (2030-2059) using a 25-km horizontal resolution regional climate model (HadRM3P) nested in global atmospheric model (HadAM3P). We quantify grid point level signal-to-noise not just in temperature and precipitation responses, but also the energy and moisture flux terms that are related to temperature and precipitation responses, to provide important insights regarding uncertainty in climate change projections at local and regional scales. These results will aid modelers in determining appropriate ensemble sizes for different climate variables and help users of climate model output with interpreting climate model projections.

Modelling impacts of climate change on arable crop diseases: progress, challenges and applications.

PubMed

Newbery, Fay; Qi, Aiming; Fitt, Bruce Dl

2016-08-01

Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Climate suitability for European ticks: assessing species distribution models against null models and projection under AR5 climate.

PubMed

Williams, Hefin Wyn; Cross, Dónall Eoin; Crump, Heather Louise; Drost, Cornelis Jan; Thomas, Christopher James

2015-08-28

There is increasing evidence that the geographic distribution of tick species is changing. Whilst correlative Species Distribution Models (SDMs) have been used to predict areas that are potentially suitable for ticks, models have often been assessed without due consideration for spatial patterns in the data that may inflate the influence of predictor variables on species distributions. This study used null models to rigorously evaluate the role of climate and the potential for climate change to affect future climate suitability for eight European tick species, including several important disease vectors. We undertook a comparative assessment of the performance of Maxent and Mahalanobis Distance SDMs based on observed data against those of null models based on null species distributions or null climate data. This enabled the identification of species whose distributions demonstrate a significant association with climate variables. Latest generation (AR5) climate projections were subsequently used to project future climate suitability under four Representative Concentration Pathways (RCPs). Seven out of eight tick species exhibited strong climatic signals within their observed distributions. Future projections intimate varying degrees of northward shift in climate suitability for these tick species, with the greatest shifts forecasted under the most extreme RCPs. Despite the high performance measure obtained for the observed model of Hyalomma lusitanicum, it did not perform significantly better than null models; this may result from the effects of non-climatic factors on its distribution. By comparing observed SDMs with null models, our results allow confidence that we have identified climate signals in tick distributions that are not simply a consequence of spatial patterns in the data. Observed climate-driven SDMs for seven out of eight species performed significantly better than null models, demonstrating the vulnerability of these tick species to the effects of climate change in the future.

Multi-model projections of Indian summer monsoon climate changes under A1B scenario

NASA Astrophysics Data System (ADS)

Niu, X.; Wang, S.; Tang, J.

2016-12-01

As part of the Regional Climate Model Intercomparison Project for Asia, the projections of Indian summer monsoon climate changes are constructed using three global climate models (GCMs) and seven regional climate models (RCMs) during 2041-2060 based on the Intergovernmental Panel on Climate Change A1B emission scenario. For the control climate of 1981-2000, most nested RCMs show advantage over the driving GCM of European Centre/Hamburg Fifth Generation (ECHAM5) in the temporal-spatial distributions of temperature and precipitation over Indian Peninsula. Following the driving GCM of ECHAM5, most nested RCMs produce advanced monsoon onset in the control climate. For future climate widespread summer warming is projected over Indian Peninsula by all climate models, with the Multi-RCMs ensemble mean (MME) temperature increasing of 1°C to 2.5°C and the maximum warming center located in northern Indian Peninsula. While for the precipitation, a large inter-model spread is projected by RCMs, with wetter condition in MME projections and significant increase over southern India. Driven by the same GCM, most RCMs project advanced monsoon onset while delayed onset is found in two Regional Climate Model (RegCM3) projections, indicating uncertainty can be expected in the Indian Summer Monsoon onset. All climate models except Conformal-Cubic Atmospheric Model with equal resolution (referred as CCAMP) and two RegCM3 models project stronger summer monsoon during 2041-2060. The disagreement in precipitation projections by RCMs indicates that the surface climate change on regional scale is not only dominated by the large-scale forcing which is provided by driving GCM but also sensitive to RCM' internal physics.

How will climate novelty influence ecological forecasts? Using the Quaternary to assess future reliability.

PubMed

Fitzpatrick, Matthew C; Blois, Jessica L; Williams, John W; Nieto-Lugilde, Diego; Maguire, Kaitlin C; Lorenz, David J

2018-03-23

Future climates are projected to be highly novel relative to recent climates. Climate novelty challenges models that correlate ecological patterns to climate variables and then use these relationships to forecast ecological responses to future climate change. Here, we quantify the magnitude and ecological significance of future climate novelty by comparing it to novel climates over the past 21,000 years in North America. We then use relationships between model performance and climate novelty derived from the fossil pollen record from eastern North America to estimate the expected decrease in predictive skill of ecological forecasting models as future climate novelty increases. We show that, in the high emissions scenario (RCP 8.5) and by late 21st century, future climate novelty is similar to or higher than peak levels of climate novelty over the last 21,000 years. The accuracy of ecological forecasting models is projected to decline steadily over the coming decades in response to increasing climate novelty, although models that incorporate co-occurrences among species may retain somewhat higher predictive skill. In addition to quantifying future climate novelty in the context of late Quaternary climate change, this work underscores the challenges of making reliable forecasts to an increasingly novel future, while highlighting the need to assess potential avenues for improvement, such as increased reliance on geological analogs for future novel climates and improving existing models by pooling data through time and incorporating assemblage-level information. © 2018 John Wiley & Sons Ltd.

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

Generating High Resolution Climate Scenarios Through Regional Climate Modelling Over Southern Africa

NASA Astrophysics Data System (ADS)

Ndhlovu, G. Z.; Woyessa, Y. E.; Vijayaraghavan, S.

2017-12-01

limate change has impacted the global environment and the Continent of Africa, especially Southern Africa, regarded as one of the most vulnerable regions in Africa, has not been spared from these impacts. Global Climate Models (GCMs) with coarse horizontal resolutions of 150-300 km do not provide sufficient details at the local basin scale due to mismatch between the size of river basins and the grid cell of the GCM. This makes it difficult to apply the outputs of GCMs directly to impact studies such as hydrological modelling. This necessitates the use of regional climate modelling at high resolutions that provide detailed information at regional and local scales to study both climate change and its impacts. To this end, an experiment was set up and conducted with PRECIS, a regional climate model, to generate climate scenarios at a high resolution of 25km for the local region in Zambezi River basin of Southern Africa. The major input data used included lateral and surface boundary conditions based on the GCMs. The data is processed, analysed and compared with CORDEX climate change project data generated for Africa. This paper, highlights the major differences of the climate scenarios generated by PRECIS Model and CORDEX Project for Africa and further gives recommendations for further research on generation of climate scenarios. The climatic variables such as precipitation and temperatures have been analysed for flood and droughts in the region. The paper also describes the setting up and running of an experiment using a high-resolution PRECIS model. In addition, a description has been made in running the model and generating the output variables on a sub basin scale. Regional climate modelling which provides information on climate change impact may lead to enhanced understanding of adaptive water resources management. Understanding the regional climate modelling results on sub basin scale is the first step in analysing complex hydrological processes and a basis for designing of adaptation and mitigation strategies in the region. Key words: Climate change, regional climate modelling, hydrological processes, extremes, scenarios [1] Corresponding author: Email:gndhlovu@cut.ac.za Tel:+27 (0) 51 507 3072

A Regional Climate Model Evaluation System based on contemporary Satellite and other Observations for Assessing Regional Climate Model Fidelity

NASA Astrophysics Data System (ADS)

Waliser, D. E.; Kim, J.; Mattman, C.; Goodale, C.; Hart, A.; Zimdars, P.; Lean, P.

2011-12-01

Evaluation of climate models against observations is an essential part of assessing the impact of climate variations and change on regionally important sectors and improving climate models. Regional climate models (RCMs) are of a particular concern. RCMs provide fine-scale climate needed by the assessment community via downscaling global climate model projections such as those contributing to the Coupled Model Intercomparison Project (CMIP) that form one aspect of the quantitative basis of the IPCC Assessment Reports. The lack of reliable fine-resolution observational data and formal tools and metrics has represented a challenge in evaluating RCMs. Recent satellite observations are particularly useful as they provide a wealth of information and constraints on many different processes within the climate system. Due to their large volume and the difficulties associated with accessing and using contemporary observations, however, these datasets have been generally underutilized in model evaluation studies. Recognizing this problem, NASA JPL and UCLA have developed the Regional Climate Model Evaluation System (RCMES) to help make satellite observations, in conjunction with in-situ and reanalysis datasets, more readily accessible to the regional modeling community. The system includes a central database (Regional Climate Model Evaluation Database: RCMED) to store multiple datasets in a common format and codes for calculating and plotting statistical metrics to assess model performance (Regional Climate Model Evaluation Tool: RCMET). This allows the time taken to compare model data with satellite observations to be reduced from weeks to days. RCMES is a component of the recent ExArch project, an international effort for facilitating the archive and access of massive amounts data for users using cloud-based infrastructure, in this case as applied to the study of climate and climate change. This presentation will describe RCMES and demonstrate its utility using examples from RCMs applied to the southwest US as well as to Africa based on output from the CORDEX activity. Application of RCMES to the evaluation of multi-RCM hindcast for CORDEX-Africa will be presented in a companion paper in A41.

On the Hydrologic Adjustment of Climate-Model Projections: The Potential Pitfall of Potential Evapotranspiration

USGS Publications Warehouse

Milly, Paul C.D.; Dunne, Krista A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median -11%) caused by the hydrologic model’s apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen–Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors’ findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climate-change impacts on water.

The effects of CO2 on phytoplankton community structure in the Amazon River Plume

NASA Astrophysics Data System (ADS)

Chen, T. L.; Goes, J. I.; Gomes, H. R.; McKee, K. T.

2013-12-01

The Amazon River Plume results from an enormous discharge of freshwater and organic matter into the Atlantic Ocean. It is a unique environment with a natural pCO2 gradient in the surface waters of the plume that range from 130-950 μatm. The response of coastal marine phytoplankton to increased anthropogenic CO2 emission is still unknown, hence the Amazon River Plume gradient can serve as a natural laboratory to examine the potential influence of atmospheric CO2 increases and ocean acidification on phytoplankton community composition. A two pronged study was undertaken: the first in which shipboard samples from a 2010 cruise to the Amazon River Plume were analyzed to examine the distribution of 3 major phytoplankton groups (diatoms, diatom-diazotroph associations [DDAs], and the diazotroph Trichodesmium spp.) with respect to the natural pCO2 gradient; the second in which the growth response of Thalassiosira weisflogii, a representative diatom species, was examined under experimentally manipulated CO2 conditions. Cruise data analysis showed that diatoms were found with higher cell counts around 150 μatm; DDAs seemed to dominate waters within the narrow range of 350-400 μatm CO2; and the diazotroph Trichodesmium spp. grew in a wide range of pCO2 conditions, but with higher cell counts at upwards of 500 μatm. Phytoplankton group distributions along the CO2 gradient may be due to differences in their carbon-concentrating mechanism (CCMs) efficiencies. The CO2 manipulation apparatus was assembled such that the cells were grown under three different CO2 environments. Differential growth of T. weisflogii was observed at 150, 400, and 800 ppm CO2 treatment. T. weisflogii grew at all three CO2 concentrations, reflecting diatoms' physiological flexibility and efficient CCMs. Absorption spectra analysis of pigments and Fast Repetition Rate Fluorometer analysis indicate potential changes in photosynthetic machinery with different CO2 treatments. Future CO2 manipulation experiments on representative DDA and diazotroph species will be undertaken to compare the growth responses of the 3 major phytoplankton groups to changes in CO2. Additionally, analysis on fatty acid compositions with different CO2 treatments will be done to assess potential changes in nutritive value for higher trophic levels. Underway pCO2 measurements with overlaid cell counts from the 2010 cruise data CO2 manipulation experiment data- growth curve (in vivo chlorophyll a fluorescence) for the 3 CO2 treatments

The Future of Planetary Climate Modeling and Weather Prediction

NASA Technical Reports Server (NTRS)

Del Genio, A. D.; Domagal-Goldman, S. D.; Kiang, N. Y.; Kopparapu, R. K.; Schmidt, G. A.; Sohl, L. E.

2017-01-01

Modeling of planetary climate and weather has followed the development of tools for studying Earth, with lags of a few years. Early Earth climate studies were performed with 1-dimensionalradiative-convective models, which were soon fol-lowed by similar models for the climates of Mars and Venus and eventually by similar models for exoplan-ets. 3-dimensional general circulation models (GCMs) became common in Earth science soon after and within several years were applied to the meteorology of Mars, but it was several decades before a GCM was used to simulate extrasolar planets. Recent trends in Earth weather and and climate modeling serve as a useful guide to how modeling of Solar System and exoplanet weather and climate will evolve in the coming decade.

Impact of Future Climate on Radial Growth of Four Major Boreal Tree Species in the Eastern Canadian Boreal Forest

PubMed Central

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C.; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010–2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere. PMID:23468879

Impact of future climate on radial growth of four major boreal tree species in the Eastern Canadian boreal forest.

PubMed

Huang, Jian-Guo; Bergeron, Yves; Berninger, Frank; Zhai, Lihong; Tardif, Jacques C; Denneler, Bernhard

2013-01-01

Immediate phenotypic variation and the lagged effect of evolutionary adaptation to climate change appear to be two key processes in tree responses to climate warming. This study examines these components in two types of growth models for predicting the 2010-2099 diameter growth change of four major boreal species Betula papyrifera, Pinus banksiana, Picea mariana, and Populus tremuloides along a broad latitudinal gradient in eastern Canada under future climate projections. Climate-growth response models for 34 stands over nine latitudes were calibrated and cross-validated. An adaptive response model (A-model), in which the climate-growth relationship varies over time, and a fixed response model (F-model), in which the relationship is constant over time, were constructed to predict future growth. For the former, we examined how future growth of stands in northern latitudes could be forecasted using growth-climate equations derived from stands currently growing in southern latitudes assuming that current climate in southern locations provide an analogue for future conditions in the north. For the latter, we tested if future growth of stands would be maximally predicted using the growth-climate equation obtained from the given local stand assuming a lagged response to climate due to genetic constraints. Both models predicted a large growth increase in northern stands due to more benign temperatures, whereas there was a minimal growth change in southern stands due to potentially warm-temperature induced drought-stress. The A-model demonstrates a changing environment whereas the F-model highlights a constant growth response to future warming. As time elapses we can predict a gradual transition between a response to climate associated with the current conditions (F-model) to a more adapted response to future climate (A-model). Our modeling approach provides a template to predict tree growth response to climate warming at mid-high latitudes of the Northern Hemisphere.

Significance of hydrological model choice and land use changes when doing climate change impact assessment

NASA Astrophysics Data System (ADS)

Bjørnholt Karlsson, Ida; Obel Sonnenborg, Torben; Refsgaard, Jens Christian; Høgh Jensen, Karsten

2014-05-01

Uncertainty in impact studies arises both from Global Climate Models (GCM), emission projections, statistical downscaling, Regional Climate Models (RCM), hydrological models and calibration techniques (Refsgaard et al. 2013). Some of these uncertainties have been evaluated several times in the literature; however few studies have investigated the effect of hydrological model choice on the assessment results (Boorman & Sefton 1997; Jiang et al. 2007; Bastola et al. 2011). These studies have found that model choice results in large differences, up to 70%, in the predicted discharge changes depending on the climate input. The objective of the study is to investigate the impact of climate change on hydrology of the Odense catchment, Denmark both in response to (a) different climate projections (GCM-RCM combinations); (b) different hydrological models and (c) different land use scenarios. This includes: 1. Separation of the climate model signal; the hydrological model signal and the land use signal 2. How do the different hydrological components react under different climate and land use conditions for the different models 3. What land use scenario seems to provide the best adaptation for the challenges of the different future climate change scenarios from a hydrological perspective? Four climate models from the ENSEMBLES project (Hewitt & Griggs 2004): ECHAM5 - HIRHAM5, ECHAM5 - RCA3, ARPEGE - RM5.1 and HadCM3 - HadRM3 are used, assessing the climate change impact in three periods: 1991-2010 (present), 2041-2060 (near future) and 2081-2100 (far future). The four climate models are used in combination with three hydrological models with different conceptual layout: NAM, SWAT and MIKE SHE. Bastola, S., C. Murphy and J. Sweeney (2011). "The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments." Advances in Water Resources 34: 562-576. Boorman, D. B. and C. E. M. Sefton (1997). "Recognising the uncertainty in the quantification of the effects of climate change on hydrological response." Climate Change 35: 415-434. Hewitt, C. D. and D. J. Griggs (2004). "Ensembles-based predictions of climate changes and their impacts." Eos, Transactions American Geophysical Union 85: 1-566. Jiang, T., Y. D. Chen, C. Xu, X. Chen, X. Chen and V. P. Singh (2007). "Comparison of hydrological impacts of climate change simulated by six hydrological models in the Dongjiang Basin, South China." Journal of hydrology 336: 316-333. Refsgaard, J. C., K. Arnbjerg-Nielsen, M. Drews, K. Halsnæs, E. Jeppesen, H. Madsen, A. Markandya, J. E. Olesen, J. R. Porter and J. H. Christensen (2013). "The role of uncertainty in climate change adaptation strategies - A Danish water management example." Mitigation and Adaptation Strategies for Global Change 18: 337-359.

Multi-model approach to assess the impact of climate change on runoff

NASA Astrophysics Data System (ADS)

Dams, J.; Nossent, J.; Senbeta, T. B.; Willems, P.; Batelaan, O.

2015-10-01

The assessment of climate change impacts on hydrology is subject to uncertainties related to the climate change scenarios, stochastic uncertainties of the hydrological model and structural uncertainties of the hydrological model. This paper focuses on the contribution of structural uncertainty of hydrological models to the overall uncertainty of the climate change impact assessment. To quantify the structural uncertainty of hydrological models, four physically based hydrological models (SWAT, PRMS and a semi- and fully distributed version of the WetSpa model) are set up for a catchment in Belgium. Each model is calibrated using four different objective functions. Three climate change scenarios with a high, mean and low hydrological impact are statistically perturbed from a large ensemble of climate change scenarios and are used to force the hydrological models. This methodology allows assessing and comparing the uncertainty introduced by the climate change scenarios with the uncertainty introduced by the hydrological model structure. Results show that the hydrological model structure introduces a large uncertainty on both the average monthly discharge and the extreme peak and low flow predictions under the climate change scenarios. For the low impact climate change scenario, the uncertainty range of the mean monthly runoff is comparable to the range of these runoff values in the reference period. However, for the mean and high impact scenarios, this range is significantly larger. The uncertainty introduced by the climate change scenarios is larger than the uncertainty due to the hydrological model structure for the low and mean hydrological impact scenarios, but the reverse is true for the high impact climate change scenario. The mean and high impact scenarios project increasing peak discharges, while the low impact scenario projects increasing peak discharges only for peak events with return periods larger than 1.6 years. All models suggest for all scenarios a decrease of the lowest flows, except for the SWAT model with the mean hydrological impact climate change scenario. The results of this study indicate that besides the uncertainty introduced by the climate change scenarios also the hydrological model structure uncertainty should be taken into account in the assessment of climate change impacts on hydrology. To make it more straightforward and transparent to include model structural uncertainty in hydrological impact studies, there is a need for hydrological modelling tools that allow flexible structures and methods to validate model structures in their ability to assess impacts under unobserved future climatic conditions.

Enabling the use of climate model data in the Dutch climate effect community

NASA Astrophysics Data System (ADS)

Som de Cerff, Wim; Plieger, Maarten

2010-05-01

Within the climate effect community the usage of climate model data is emerging. Where mostly climate time series and weather generators were used, there is a shift to incorporate climate model data into climate effect models. The use of climate model data within the climate effect models is difficult, due to missing metadata, resolution and projection issues, data formats and availability of the parameters of interest. Often the climate effect modelers are not aware of available climate model data or are not aware of how they can use it. Together with seven other partners (CERFACS, CNR-IPSL, SMHI, INHGA, CMCC, WUR, MF-CNRM), KNMI is involved in the FP7 IS ENES (http://www.enes.org) project work package 10/JRA5 ‘Bridging Climate Research Data and the Needs of the Impact Community. The aims of this work package are to enhance the use of Climate Research Data and to enhance the interaction with climate effect/impact communities. Phase one is to define use cases together with the Dutch climate effect community, which describe the intended use of climate model data in climate effect models. We defined four use cases: 1) FEWS hydrological Framework (Deltares) 2) METAPHOR, a plants and species dispersion model (Wageningen University) 3) Natuurplanner, an Ecological model suite (Wageningen University) 4) Land use models (Free University/JRC). Also the other partners in JRA5 have defined use cases, which are representative for the climate effect and impact communities in their country. Goal is to find commonalities between all defined use cases. The common functionality will be implemented as e-tools and incorporated in the IS-ENES data portal. Common issues relate to e.g., need for high resolution: downscaling from GCM to local scale (also involves interpolation); parameter selection; finding extremes; averaging methods. At the conference we will describe the FEWS case in more detail: Delft FEWS is an open shell system (in development since 1995) for performing hydrological predictions and the handling of time series data. The most important capabilities of FEWS are importing of meteorological and hydrological data and organizing the workflows of the different models which can be used within FEWS, like the Netherlands Hydrological Instrumentarium (NHI). Besides predictions, the system is currently being used for hydrological climate effects studies. Currently regionally downscaled data are used, but using model data will be the next step. This coupling of climate model data to FEWS will open a wider rage of climate impact and effect research, but it is a difficult task to accomplish. Issues to be dealt with are: regridding, downscaling, format conversion, extraction of required data and addition of descriptive metadata, including quality and uncertainty parameters. Finding an appropriate solution involves several iterations: first, the use case was defined, then we just provided a single data file containing some data of interest provided via FTP, next this data was offered through OGC services. Currently we are working on providing larger datasets and improving on the parameters and metadata. We will present the results (e-tools/data) and experiences gained on implementing the described use cases. Note that we are currently using experimental data, as the official climate model runs are not available yet.

A personal perspective on modelling the climate system.

PubMed

Palmer, T N

2016-04-01

Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1 km global climate system model to run on the first exascale supercomputers in the early 2020s.

Modeling erosion under future climates with the WEPP model

Treesearch

Timothy Bayley; William Elliot; Mark A. Nearing; D. Phillp Guertin; Thomas Johnson; David Goodrich; Dennis Flanagan

2010-01-01

The Water Erosion Prediction Project Climate Assessment Tool (WEPPCAT) was developed to be an easy-to-use, web-based erosion model that allows users to adjust climate inputs for user-specified climate scenarios. WEPPCAT allows the user to modify monthly mean climate parameters, including maximum and minimum temperatures, number of wet days, precipitation, and...

Quantifying uncertainty in climate change science through empirical information theory.

PubMed

Majda, Andrew J; Gershgorin, Boris

2010-08-24

Quantifying the uncertainty for the present climate and the predictions of climate change in the suite of imperfect Atmosphere Ocean Science (AOS) computer models is a central issue in climate change science. Here, a systematic approach to these issues with firm mathematical underpinning is developed through empirical information theory. An information metric to quantify AOS model errors in the climate is proposed here which incorporates both coarse-grained mean model errors as well as covariance ratios in a transformation invariant fashion. The subtle behavior of model errors with this information metric is quantified in an instructive statistically exactly solvable test model with direct relevance to climate change science including the prototype behavior of tracer gases such as CO(2). Formulas for identifying the most sensitive climate change directions using statistics of the present climate or an AOS model approximation are developed here; these formulas just involve finding the eigenvector associated with the largest eigenvalue of a quadratic form computed through suitable unperturbed climate statistics. These climate change concepts are illustrated on a statistically exactly solvable one-dimensional stochastic model with relevance for low frequency variability of the atmosphere. Viable algorithms for implementation of these concepts are discussed throughout the paper.

SEEPLUS: A SIMPLE ONLINE CLIMATE MODEL

NASA Astrophysics Data System (ADS)

Tsutsui, Junichi

A web application for a simple climate model - SEEPLUS (a Simple climate model to Examine Emission Pathways Leading to Updated Scenarios) - has been developed. SEEPLUS consists of carbon-cycle and climate-change modules, through which it provides the information infrastructure required to perform climate-change experiments, even on a millennial-timescale. The main objective of this application is to share the latest scientific knowledge acquired from climate modeling studies among the different stakeholders involved in climate-change issues. Both the carbon-cycle and climate-change modules employ impulse response functions (IRFs) for their key processes, thereby enabling the model to integrate the outcome from an ensemble of complex climate models. The current IRF parameters and forcing manipulation are basically consistent with, or within an uncertainty range of, the understanding of certain key aspects such as the equivalent climate sensitivity and ocean CO2 uptake data documented in representative literature. The carbon-cycle module enables inverse calculation to determine the emission pathway required in order to attain a given concentration pathway, thereby providing a flexible way to compare the module with more advanced modeling studies. The module also enables analytical evaluation of its equilibrium states, thereby facilitating the long-term planning of global warming mitigation.

Predicting ecological responses in a changing ocean: the effects of future climate uncertainty.

PubMed

Freer, Jennifer J; Partridge, Julian C; Tarling, Geraint A; Collins, Martin A; Genner, Martin J

2018-01-01

Predicting how species will respond to climate change is a growing field in marine ecology, yet knowledge of how to incorporate the uncertainty from future climate data into these predictions remains a significant challenge. To help overcome it, this review separates climate uncertainty into its three components (scenario uncertainty, model uncertainty, and internal model variability) and identifies four criteria that constitute a thorough interpretation of an ecological response to climate change in relation to these parts (awareness, access, incorporation, communication). Through a literature review, the extent to which the marine ecology community has addressed these criteria in their predictions was assessed. Despite a high awareness of climate uncertainty, articles favoured the most severe emission scenario, and only a subset of climate models were used as input into ecological analyses. In the case of sea surface temperature, these models can have projections unrepresentative against a larger ensemble mean. Moreover, 91% of studies failed to incorporate the internal variability of a climate model into results. We explored the influence that the choice of emission scenario, climate model, and model realisation can have when predicting the future distribution of the pelagic fish, Electrona antarctica . Future distributions were highly influenced by the choice of climate model, and in some cases, internal variability was important in determining the direction and severity of the distribution change. Increased clarity and availability of processed climate data would facilitate more comprehensive explorations of climate uncertainty, and increase in the quality and standard of marine prediction studies.

Assessment of the Impact of Climate Change on the Water Balances and Flooding Conditions of Peninsular Malaysia watersheds by a Coupled Numerical Climate Model - Watershed Hydrology Model

NASA Astrophysics Data System (ADS)

Ercan, A.; Kavvas, M. L.; Ishida, K.; Chen, Z. Q.; Amin, M. Z. M.; Shaaban, A. J.

2017-12-01

Impacts of climate change on the hydrologic processes under future climate change conditions were assessed over various watersheds of Peninsular Malaysia by means of a coupled regional climate and physically-based hydrology model that utilized an ensemble of future climate change projections. An ensemble of 15 different future climate realizations from coarse resolution global climate models' (GCMs) projections for the 21st century were dynamically downscaled to 6 km resolution over Peninsular Malaysia by a regional numerical climate model, which was then coupled with the watershed hydrology model WEHY through the atmospheric boundary layer over the selected watersheds of Peninsular Malaysia. Hydrologic simulations were carried out at hourly increments and at hillslope-scale in order to assess the impacts of climate change on the water balances and flooding conditions at the selected watersheds during the 21st century. The coupled regional climate and hydrology model was simulated for a duration of 90 years for each of the 15 realizations. It is demonstrated that the increase in mean monthly flows due to the impact of expected climate change during 2040-2100 is statistically significant at the selected watersheds. Furthermore, the flood frequency analyses for the selected watersheds indicate an overall increasing trend in the second half of the 21st century.

The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB V1.0) Contribution to CMIP6

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Teichmann, Claas; Arnell, Nigel W.; Carter, Timothy R.; Ebi, Kristie L.; Frieler, Katja; Goodess, Clare M.; Hewitson, Bruce; Horton, Radley; Kovats, R. Sari;

The Vulnerability, Impacts, Adaptation and Climate Services Advisory Board (VIACS AB v1.0) contribution to CMIP6

NASA Astrophysics Data System (ADS)

Ruane, Alex C.; Teichmann, Claas; Arnell, Nigel W.; Carter, Timothy R.; Ebi, Kristie L.; Frieler, Katja; Goodess, Clare M.; Hewitson, Bruce; Horton, Radley; Sari Kovats, R.; Lotze, Heike K.; Mearns, Linda O.; Navarra, Antonio; Ojima, Dennis S.; Riahi, Keywan; Rosenzweig, Cynthia; Themessl, Matthias; Vincent, Katharine

2016-09-01

This paper describes the motivation for the creation of the Vulnerability, Impacts, Adaptation and Climate Services (VIACS) Advisory Board for the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), its initial activities, and its plans to serve as a bridge between climate change applications experts and climate modelers. The climate change application community comprises researchers and other specialists who use climate information (alongside socioeconomic and other environmental information) to analyze vulnerability, impacts, and adaptation of natural systems and society in relation to past, ongoing, and projected future climate change. Much of this activity is directed toward the co-development of information needed by decision-makers for managing projected risks. CMIP6 provides a unique opportunity to facilitate a two-way dialog between climate modelers and VIACS experts who are looking to apply CMIP6 results for a wide array of research and climate services objectives. The VIACS Advisory Board convenes leaders of major impact sectors, international programs, and climate services to solicit community feedback that increases the applications relevance of the CMIP6-Endorsed Model Intercomparison Projects (MIPs). As an illustration of its potential, the VIACS community provided CMIP6 leadership with a list of prioritized climate model variables and MIP experiments of the greatest interest to the climate model applications community, indicating the applicability and societal relevance of climate model simulation outputs. The VIACS Advisory Board also recommended an impacts version of Obs4MIPs and indicated user needs for the gridding and processing of model output.

Shortwave forcing and feedbacks in Last Glacial Maximum and Mid-Holocene PMIP3 simulations.

PubMed

Braconnot, Pascale; Kageyama, Masa

2015-11-13

Simulations of the climates of the Last Glacial Maximum (LGM), 21 000 years ago, and of the Mid-Holocene (MH), 6000 years ago, allow an analysis of climate feedbacks in climate states that are radically different from today. The analyses of cloud and surface albedo feedbacks show that the shortwave cloud feedback is a major driver of differences between model results. Similar behaviours appear when comparing the LGM and MH simulated changes, highlighting the fingerprint of model physics. Even though the different feedbacks show similarities between the different climate periods, the fact that their relative strength differs from one climate to the other prevents a direct comparison of past and future climate sensitivity. The land-surface feedback also shows large disparities among models even though they all produce positive sea-ice and snow feedbacks. Models have very different sensitivities when considering the vegetation feedback. This feedback has a regional pattern that differs significantly between models and depends on their level of complexity and model biases. Analyses of the MH climate in two versions of the IPSL model provide further indication on the possibilities to assess the role of model biases and model physics on simulated climate changes using past climates for which observations can be used to assess the model results. © 2015 The Author(s).

Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models.

PubMed

Ramírez-Albores, Jorge E; Bustamante, Ramiro O; Badano, Ernesto I

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of naturally established individuals because this improves the accuracy of predictions about their distribution ranges.

Improved Predictions of the Geographic Distribution of Invasive Plants Using Climatic Niche Models

PubMed Central

Ramírez-Albores, Jorge E.; Bustamante, Ramiro O.

2016-01-01

Climatic niche models for invasive plants are usually constructed with occurrence records taken from literature and collections. Because these data neither discriminate among life-cycle stages of plants (adult or juvenile) nor the origin of individuals (naturally established or man-planted), the resulting models may mispredict the distribution ranges of these species. We propose that more accurate predictions could be obtained by modelling climatic niches with data of naturally established individuals, particularly with occurrence records of juvenile plants because this would restrict the predictions of models to those sites where climatic conditions allow the recruitment of the species. To test this proposal, we focused on the Peruvian peppertree (Schinus molle), a South American species that has largely invaded Mexico. Three climatic niche models were constructed for this species using high-resolution dataset gathered in the field. The first model included all occurrence records, irrespective of the life-cycle stage or origin of peppertrees (generalized niche model). The second model only included occurrence records of naturally established mature individuals (adult niche model), while the third model was constructed with occurrence records of naturally established juvenile plants (regeneration niche model). When models were compared, the generalized climatic niche model predicted the presence of peppertrees in sites located farther beyond the climatic thresholds that naturally established individuals can tolerate, suggesting that human activities influence the distribution of this invasive species. The adult and regeneration climatic niche models concurred in their predictions about the distribution of peppertrees, suggesting that naturally established adult trees only occur in sites where climatic conditions allow the recruitment of juvenile stages. These results support the proposal that climatic niches of invasive plants should be modelled with data of naturally established individuals because this improves the accuracy of predictions about their distribution ranges. PMID:27195983

Assessment of the Effect of Climate Change on Grain Yields in China

NASA Astrophysics Data System (ADS)

Chou, J.

2006-12-01

The paper elaborates the social background and research background; makes clear what the key scientific issues need to be resolved and where the difficulties are. In the research area of parasailing the grain yield change caused by climate change, massive works have been done both in the domestic and in the foreign. It is our upcoming work to evaluate how our countrywide climate change information provided by this pattern influence our economic and social development; and how to make related policies and countermeasures. the main idea in this paper is that the grain yield change is by no means the linear composition of social economy function effect and the climatic change function effect. This paper identifies the economic evaluation object, proposes one new concept - climate change output. The grain yields change affected by the social factors and the climatic change working together. Climate change influences the grain yields by the non ¨C linear function from both climate change and social factor changes, not only by climate change itself. Therefore, in my paper, the appraisal object is defined as: The social factors change based on actual social changing situations; under the two kinds of climate change situation, the invariable climate change situation and variable climate change situation; the difference of grain yield outputs is called " climate change output ", In order to solve this problem, we propose a method to analyze and imitate on the historical materials. Giving the condition that the climate is invariable, the social economic factor changes cause the grain yield change. However, this grain yield change is a tentative quantity index, not an actual quantity number. So we use the existing historical materials to exam the climate change output, based on the characteristic that social factor changes greater in year than in age, but the climate factor changes greater in age than in year. The paper proposes and establishes one economy - climate model (C-D-C model) to appraise the grain yield change caused by the climatic change. Also the preliminary test on this model has been done. In selection of the appraisal methods, we take the C-D production function model, which has been proved more mature in the economic research, as our fundamental model. Then, we introduce climate index (arid index) to the C-D model to develop one new model. This new model utilizes the climatic change factor in the economical model to appraise how the climatic change influence the grain yield change. The new way of appraise should have the better application prospect. The economy - climate model (The C-D-C model) has been applied on the eight Chinese regions that we divide; it has been proved satisfactory in its feasibility, rationality and the application prospect. So we can provide the theoretical fundamentals for policy-making under the more complex and uncertain climate change. Therefore, we open a new possible channel for the global climate change research moving toward the actual social, economic life.

Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations. Chapter 2

NASA Technical Reports Server (NTRS)

Nolte, Christopher; Otte, Tanya; Pinder, Robert; Bowden, J.; Herwehe, J.; Faluvegi, Gregory; Shindell, Drew

2013-01-01

Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

Climate impacts on palm oil yields in the Nigerian Niger Delta

NASA Astrophysics Data System (ADS)

Okoro, Stanley U.; Schickhoff, Udo; Boehner, Juergen; Schneider, Uwe A.; Huth, Neil

2016-04-01

Palm oil production has increased in recent decades and is estimated to increase further. The optimal role of palm oil production, however, is controversial because of resource conflicts with alternative land uses. Local conditions and climate change affect resource competition and the desirability of palm oil production. Based on this, crop yield simulations using different climate model output under different climate scenarios could be important tool in addressing the problem of uncertainty quantification among different climate model outputs. Previous studies on this region have focused mostly on single experimental fields, not considering variations in Agro-Ecological Zones, climatic conditions, varieties and management practices and, in most cases not extending to various IPCC climate scenarios and were mostly based on single climate model output. Furthermore, the uncertainty quantification of the climate- impact model has rarely been investigated on this region. To this end we use the biophysical simulation model APSIM (Agricultural Production Systems Simulator) to simulate the regional climate impact on oil palm yield over the Nigerian Niger Delta. We also examine whether the use of crop yield model output ensemble reduces the uncertainty rather than the use of climate model output ensemble. The results could serve as a baseline for policy makers in this region in understanding the interaction between potentials of energy crop production of the region as well as its food security and other negative feedbacks that could be associated with bioenergy from oil palm. Keywords: Climate Change, Climate impacts, Land use and Crop yields.

Modeling climatic effects of anthropogenic CO2 emissions: Unknowns and uncertainties

NASA Astrophysics Data System (ADS)

Soon, W.; Baliunas, S.; Idso, S.; Kondratyev, K. Ya.; Posmentier, E. S.

2001-12-01

A likelihood of disastrous global environmental consequences has been surmised as a result of projected increases in anthropogenic greenhouse gas emissions. These estimates are based on computer climate modeling, a branch of science still in its infancy despite recent, substantial strides in knowledge. Because the expected anthropogenic climate forcings are relatively small compared to other background and forcing factors (internal and external), the credibility of the modeled global and regional responses rests on the validity of the models. We focus on this important question of climate model validation. Specifically, we review common deficiencies in general circulation model calculations of atmospheric temperature, surface temperature, precipitation and their spatial and temporal variability. These deficiencies arise from complex problems associated with parameterization of multiply-interacting climate components, forcings and feedbacks, involving especially clouds and oceans. We also review examples of expected climatic impacts from anthropogenic CO2 forcing. Given the host of uncertainties and unknowns in the difficult but important task of climate modeling, the unique attribution of observed current climate change to increased atmospheric CO2 concentration, including the relatively well-observed latest 20 years, is not possible. We further conclude that the incautious use of GCMs to make future climate projections from incomplete or unknown forcing scenarios is antithetical to the intrinsically heuristic value of models. Such uncritical application of climate models has led to the commonly-held but erroneous impression that modeling has proven or substantiated the hypothesis that CO2 added to the air has caused or will cause significant global warming. An assessment of the positive skills of GCMs and their use in suggesting a discernible human influence on global climate can be found in the joint World Meteorological Organisation and United Nations Environmental Programme's Intergovernmental Panel on Climate Change, IPCC, reports (1990, 1995 and 2001). Our review highlights only the enormous scientific difficulties facing the calculation of climatic effects of added atmospheric CO2 in a GCM. The purpose of such a limited review of the deficiencies of climate model physics and the use of GCMs is to illuminate areas for improvement. Our review does not disprove a significant anthropogenic influence on global climate.

Uncertainty Quantification in Climate Modeling and Projection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qian, Yun; Jackson, Charles; Giorgi, Filippo

The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change informationmore » for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluation of uncertainties in climate projections, such as the effects of uncertain initial and boundary conditions, uncertain physics, and limitations of observational records. Progress in quantitatively estimating uncertainties in hydrologic, land surface, and atmospheric models at both regional and global scales was also reviewed. The application of Uncertainty Quantification (UQ) concepts to coupled climate system models is still in its infancy. The Coupled Model Intercomparison Project (CMIP) multi-model ensemble currently represents the primary data for assessing reliability and uncertainties of climate change information. An alternative approach is to generate similar ensembles by perturbing parameters within a single-model framework. One of workshop’s objectives was to give participants a deeper understanding of these approaches within a Bayesian statistical framework. However, there remain significant challenges still to be resolved before UQ can be applied in a convincing way to climate models and their projections.« less

A dynamic, climate-driven model of Rift Valley fever.

PubMed

Leedale, Joseph; Jones, Anne E; Caminade, Cyril; Morse, Andrew P

2016-03-31

Outbreaks of Rift Valley fever (RVF) in eastern Africa have previously occurred following specific rainfall dynamics and flooding events that appear to support the emergence of large numbers of mosquito vectors. As such, transmission of the virus is considered to be sensitive to environmental conditions and therefore changes in climate can impact the spatiotemporal dynamics of epizootic vulnerability. Epidemiological information describing the methods and parameters of RVF transmission and its dependence on climatic factors are used to develop a new spatio-temporal mathematical model that simulates these dynamics and can predict the impact of changes in climate. The Liverpool RVF (LRVF) model is a new dynamic, process-based model driven by climate data that provides a predictive output of geographical changes in RVF outbreak susceptibility as a result of the climate and local livestock immunity. This description of the multi-disciplinary process of model development is accessible to mathematicians, epidemiological modellers and climate scientists, uniting dynamic mathematical modelling, empirical parameterisation and state-of-the-art climate information.

The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle-climate simulations

NASA Astrophysics Data System (ADS)

Strassmann, Kuno M.; Joos, Fortunat

2018-05-01

The Bern Simple Climate Model (BernSCM) is a free open-source re-implementation of a reduced-form carbon cycle-climate model which has been used widely in previous scientific work and IPCC assessments. BernSCM represents the carbon cycle and climate system with a small set of equations for the heat and carbon budget, the parametrization of major nonlinearities, and the substitution of complex component systems with impulse response functions (IRFs). The IRF approach allows cost-efficient yet accurate substitution of detailed parent models of climate system components with near-linear behavior. Illustrative simulations of scenarios from previous multimodel studies show that BernSCM is broadly representative of the range of the climate-carbon cycle response simulated by more complex and detailed models. Model code (in Fortran) was written from scratch with transparency and extensibility in mind, and is provided open source. BernSCM makes scientifically sound carbon cycle-climate modeling available for many applications. Supporting up to decadal time steps with high accuracy, it is suitable for studies with high computational load and for coupling with integrated assessment models (IAMs), for example. Further applications include climate risk assessment in a business, public, or educational context and the estimation of CO2 and climate benefits of emission mitigation options.

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.

2013-12-01

Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.

The effects of climate downscaling technique and observational data set on modeled ecological responses

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe; Anne M. K. Stoner

2016-01-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training...

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

Evaluation of the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Rex, Markus

2016-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Including atmospheric ozone chemistry into climate simulations is usually done by prescribing a climatological ozone field, by including a fast linear ozone scheme into the model or by using a climate model with complex interactive chemistry. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. Although interactive chemistry provides a realistic representation of atmospheric chemistry such model simulations are computationally very expensive and hence not suitable for ensemble simulations or simulations with multiple climate change scenarios. A new approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has recently been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. Here, we show first results of EMAC-SWIFT simulations and validate these against EMAC simulations using the complex interactive chemistry scheme MECCA, and against observations.

The Milankovitch theory and climate sensitivity. I - Equilibrium climate model solutions for the present surface conditions. II - Interaction between the Northern Hemisphere ice sheets and the climate system

NASA Technical Reports Server (NTRS)

Neeman, Binyamin U.; Ohring, George; Joseph, Joachim H.

1988-01-01

A seasonal climate model was developed to test the climate sensitivity and, in particular, the Milankovitch (1941) theory. Four climate model versions were implemented to investigate the range of uncertainty in the parameterizations of three basic feedback mechanisms: the ice albedo-temperature, the outgoing long-wave radiation-temperature, and the eddy transport-meridional temperature gradient. It was found that the differences between the simulation of the present climate by the four versions were generally small, especially for annually averaged results. The climate model was also used to study the effect of growing/shrinking of a continental ice sheet, bedrock sinking/uplifting, and sea level changes on the climate system, taking also into account the feedback effects on the climate of the building of the ice caps.

Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.

Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

NASA Astrophysics Data System (ADS)

Raghavan, Srivatsan V.; Liu, Jiandong; Nguyen, Ngoc Son; Vu, Minh Tue; Liong, Shie-Yui

2018-05-01

We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models' performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Variation in Estimated Ozone-Related Health Impacts of Climate Change due to Modeling Choices and Assumptions

PubMed Central

Post, Ellen S.; Grambsch, Anne; Weaver, Chris; Morefield, Philip; Leung, Lai-Yung; Nolte, Christopher G.; Adams, Peter; Liang, Xin-Zhong; Zhu, Jin-Hong; Mahoney, Hardee

2012-01-01

Background: Future climate change may cause air quality degradation via climate-induced changes in meteorology, atmospheric chemistry, and emissions into the air. Few studies have explicitly modeled the potential relationships between climate change, air quality, and human health, and fewer still have investigated the sensitivity of estimates to the underlying modeling choices. Objectives: Our goal was to assess the sensitivity of estimated ozone-related human health impacts of climate change to key modeling choices. Methods: Our analysis included seven modeling systems in which a climate change model is linked to an air quality model, five population projections, and multiple concentration–response functions. Using the U.S. Environmental Protection Agency’s (EPA’s) Environmental Benefits Mapping and Analysis Program (BenMAP), we estimated future ozone (O3)-related health effects in the United States attributable to simulated climate change between the years 2000 and approximately 2050, given each combination of modeling choices. Health effects and concentration–response functions were chosen to match those used in the U.S. EPA’s 2008 Regulatory Impact Analysis of the National Ambient Air Quality Standards for O3. Results: Different combinations of methodological choices produced a range of estimates of national O3-related mortality from roughly 600 deaths avoided as a result of climate change to 2,500 deaths attributable to climate change (although the large majority produced increases in mortality). The choice of the climate change and the air quality model reflected the greatest source of uncertainty, with the other modeling choices having lesser but still substantial effects. Conclusions: Our results highlight the need to use an ensemble approach, instead of relying on any one set of modeling choices, to assess the potential risks associated with O3-related human health effects resulting from climate change. PMID:22796531

The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

NASA Astrophysics Data System (ADS)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data shown in the database construction and by the model reinforces the need for high-resolution datasets and stresses the importance of understanding and incorporating climate change scenarios into earth system models.

Using historical and projected future climate model simulations as drivers of agricultural and biological models (Invited)

NASA Astrophysics Data System (ADS)

Stefanova, L. B.

2013-12-01

Climate model evaluation is frequently performed as a first step in analyzing climate change simulations. Atmospheric scientists are accustomed to evaluating climate models through the assessment of model climatology and biases, the models' representation of large-scale modes of variability (such as ENSO, PDO, AMO, etc) and the relationship between these modes and local variability (e.g. the connection between ENSO and the wintertime precipitation in the Southeast US). While these provide valuable information about the fidelity of historical and projected climate model simulations from an atmospheric scientist's point of view, the application of climate model data to fields such as agriculture, ecology and biology may require additional analyses focused on the particular application's requirements and sensitivities. Typically, historical climate simulations are used to determine a mapping between the model and observed climate, either through a simple (additive for temperature or multiplicative for precipitation) or a more sophisticated (such as quantile matching) bias correction on a monthly or seasonal time scale. Plants, animals and humans however are not directly affected by monthly or seasonal means. To assess the impact of projected climate change on living organisms and related industries (e.g. agriculture, forestry, conservation, utilities, etc.), derivative measures such as the heating degree-days (HDD), cooling degree-days (CDD), growing degree-days (GDD), accumulated chill hours (ACH), wet season onset (WSO) and duration (WSD), among others, are frequently useful. We will present a comparison of the projected changes in such derivative measures calculated by applying: (a) the traditional temperature/precipitation bias correction described above versus (b) a bias correction based on the mapping between the historical model and observed derivative measures themselves. In addition, we will present and discuss examples of various application-based climate model evaluations, such as: (a) agricultural crop yield estimates and (b) species population viability estimates modeled using observed climate data vs. historical climate simulations.

Agricultural climate impacts assessment for economic modeling and decision support

NASA Astrophysics Data System (ADS)

Thomson, A. M.; Izaurralde, R. C.; Beach, R.; Zhang, X.; Zhao, K.; Monier, E.

2013-12-01

A range of approaches can be used in the application of climate change projections to agricultural impacts assessment. Climate projections can be used directly to drive crop models, which in turn can be used to provide inputs for agricultural economic or integrated assessment models. These model applications, and the transfer of information between models, must be guided by the state of the science. But the methodology must also account for the specific needs of stakeholders and the intended use of model results beyond pure scientific inquiry, including meeting the requirements of agencies responsible for designing and assessing policies, programs, and regulations. Here we present methodology and results of two climate impacts studies that applied climate model projections from CMIP3 and from the EPA Climate Impacts and Risk Analysis (CIRA) project in a crop model (EPIC - Environmental Policy Indicator Climate) in order to generate estimates of changes in crop productivity for use in an agricultural economic model for the United States (FASOM - Forest and Agricultural Sector Optimization Model). The FASOM model is a forward-looking dynamic model of the US forest and agricultural sector used to assess market responses to changing productivity of alternative land uses. The first study, focused on climate change impacts on the UDSA crop insurance program, was designed to use available daily climate projections from the CMIP3 archive. The decision to focus on daily data for this application limited the climate model and time period selection significantly; however for the intended purpose of assessing impacts on crop insurance payments, consideration of extreme event frequency was critical for assessing periodic crop failures. In a second, coordinated impacts study designed to assess the relative difference in climate impacts under a no-mitigation policy and different future climate mitigation scenarios, the stakeholder specifically requested an assessment of a mitigation level of 3.7 W/m2, as well as consideration of different levels of climate sensitivity (2, 3, 4.5 and 6oC) and different initial conditions for addressing uncertainty. Since the CMIP 3 and CMIP5 protocols did not include this mitigation level or consider alternative levels of climate sensitivity, additional climate projections were required. These two cases will be discussed to illustrate some of the trade-offs made in development of methodologies for climate impact assessments that are intended for a specific user or audience, and oriented towards addressing a specific topic of interest and providing useable results. This involvement of stakeholders from the design phase of climate impacts methodology serves to both define the appropriate method for the question at hand and also to engage and inform the stakeholders of the myriad options and uncertainties associated with different methodology choices. This type of engagement should benefit decision making in the long run through greater stakeholder understanding of the science of future climate model projections, scenarios, the climate impacts sector models and the types of outputs that can be generated by each along with the respective uncertainties at each step of the climate impacts assessment process.

Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments.

PubMed

Tao, Fulu; Rötter, Reimund P; Palosuo, Taru; Gregorio Hernández Díaz-Ambrona, Carlos; Mínguez, M Inés; Semenov, Mikhail A; Kersebaum, Kurt Christian; Nendel, Claas; Specka, Xenia; Hoffmann, Holger; Ewert, Frank; Dambreville, Anaelle; Martre, Pierre; Rodríguez, Lucía; Ruiz-Ramos, Margarita; Gaiser, Thomas; Höhn, Jukka G; Salo, Tapio; Ferrise, Roberto; Bindi, Marco; Cammarano, Davide; Schulman, Alan H

2018-03-01

Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. © 2017 John Wiley & Sons Ltd.

Simulations of the future precipitation climate of the Central Andes using a coupled regional climate model

NASA Astrophysics Data System (ADS)

Nicholls, S.; Mohr, K. I.

2014-12-01

The meridional extent and complex orography of the South American continent contributes to a wide diversity of climate regimes ranging from hyper-arid deserts to tropical rainforests to sub-polar highland regions. Global climate models, although capable of resolving synoptic-scale South American climate features, are inadequate for fully-resolving the strong gradients between climate regimes and the complex orography which define the Tropical Andes given their low spatial and temporal resolution. Recent computational advances now make practical regional climate modeling with prognostic mesoscale atmosphere-ocean coupled models, such as the Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) modeling system, to climate research. Previous work has shown COAWST to reasonably simulate the both the entire 2003-2004 wet season (Dec-Feb) as validated against both satellite and model analysis data. More recently, COAWST simulations have also been shown to sensibly reproduce the entire annual cycle of rainfall (Oct 2003 - Oct 2004) with historical climate model input. Using future global climate model input for COAWST, the present work involves year-long cycle spanning October to October for the years 2031, 2059, and 2087 assuming the most likely regional climate pathway (RCP): RCP 6.0. COAWST output is used to investigate how global climate change impacts the spatial distribution, precipitation rates, and diurnal cycle of precipitation patterns in the Central Andes vary in these yearly "snapshots". Initial results show little change to precipitation coverage or its diurnal cycle, however precipitation amounts did tend drier over the Brazilian Plateau and wetter over the Western Amazon and Central Andes. These results suggest potential adjustments to large-scale climate features (such as the Bolivian High).

Smart climate ensemble exploring approaches: the example of climate impacts on air pollution in Europe.

NASA Astrophysics Data System (ADS)

Lemaire, Vincent; Colette, Augustin; Menut, Laurent

2016-04-01

Because of its sensitivity to weather patterns, climate change will have an impact on air pollution so that, in the future, a climate penalty could jeopardize the expected efficiency of air pollution mitigation measures. A common method to assess the impact of climate on air quality consists in implementing chemistry-transport models forced by climate projections. However, at present, such impact assessment lack multi-model ensemble approaches to address uncertainties because of the substantial computing cost. Therefore, as a preliminary step towards exploring large climate ensembles with air quality models, we developed an ensemble exploration technique in order to point out the climate models that should be investigated in priority. By using a training dataset from a deterministic projection of climate and air quality over Europe, we identified the main meteorological drivers of air quality for 8 regions in Europe and developed statistical models that could be used to estimate future air pollutant concentrations. Applying this statistical model to the whole EuroCordex ensemble of climate projection, we find a climate penalty for six subregions out of eight (Eastern Europe, France, Iberian Peninsula, Mid Europe and Northern Italy). On the contrary, a climate benefit for PM2.5 was identified for three regions (Eastern Europe, Mid Europe and Northern Italy). The uncertainty of this statistical model challenges limits however the confidence we can attribute to associated quantitative projections. This technique allows however selecting a subset of relevant regional climate model members that should be used in priority for future deterministic projections to propose an adequate coverage of uncertainties. We are thereby proposing a smart ensemble exploration strategy that can also be used for other impacts studies beyond air quality.

Selecting climate change scenarios for regional hydrologic impact studies based on climate extremes indices

NASA Astrophysics Data System (ADS)

Seo, Seung Beom; Kim, Young-Oh; Kim, Youngil; Eum, Hyung-Il

2018-04-01

When selecting a subset of climate change scenarios (GCM models), the priority is to ensure that the subset reflects the comprehensive range of possible model results for all variables concerned. Though many studies have attempted to improve the scenario selection, there is a lack of studies that discuss methods to ensure that the results from a subset of climate models contain the same range of uncertainty in hydrologic variables as when all models are considered. We applied the Katsavounidis-Kuo-Zhang (KKZ) algorithm to select a subset of climate change scenarios and demonstrated its ability to reduce the number of GCM models in an ensemble, while the ranges of multiple climate extremes indices were preserved. First, we analyzed the role of 27 ETCCDI climate extremes indices for scenario selection and selected the representative climate extreme indices. Before the selection of a subset, we excluded a few deficient GCM models that could not represent the observed climate regime. Subsequently, we discovered that a subset of GCM models selected by the KKZ algorithm with the representative climate extreme indices could not capture the full potential range of changes in hydrologic extremes (e.g., 3-day peak flow and 7-day low flow) in some regional case studies. However, the application of the KKZ algorithm with a different set of climate indices, which are correlated to the hydrologic extremes, enabled the overcoming of this limitation. Key climate indices, dependent on the hydrologic extremes to be projected, must therefore be determined prior to the selection of a subset of GCM models.

Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty.

PubMed

Leedale, Joseph; Tompkins, Adrian M; Caminade, Cyril; Jones, Anne E; Nikulin, Grigory; Morse, Andrew P

2016-03-31

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

The epistemology of climate models and some of its implications for climate science and the philosophy of science

NASA Astrophysics Data System (ADS)

Katzav, Joel

2014-05-01

I bring out the limitations of four important views of what the target of useful climate model assessment is. Three of these views are drawn from philosophy. They include the views of Elisabeth Lloyd and Wendy Parker, and an application of Bayesian confirmation theory. The fourth view I criticise is based on the actual practice of climate model assessment. In bringing out the limitations of these four views, I argue that an approach to climate model assessment that neither demands too much of such assessment nor threatens to be unreliable will, in typical cases, have to aim at something other than the confirmation of claims about how the climate system actually is. This means, I suggest, that the Intergovernmental Panel on Climate Change's (IPCC's) focus on establishing confidence in climate model explanations and predictions is misguided. So too, it means that standard epistemologies of science with pretensions to generality, e.g., Bayesian epistemologies, fail to illuminate the assessment of climate models. I go on to outline a view that neither demands too much nor threatens to be unreliable, a view according to which useful climate model assessment typically aims to show that certain climatic scenarios are real possibilities and, when the scenarios are determined to be real possibilities, partially to determine how remote they are.

Evaluation of the multi-model CORDEX-Africa hindcast using RCMES

NASA Astrophysics Data System (ADS)

Kim, J.; Waliser, D. E.; Lean, P.; Mattmann, C. A.; Goodale, C. E.; Hart, A.; Zimdars, P.; Hewitson, B.; Jones, C.

2011-12-01

Recent global climate change studies have concluded with a high confidence level that the observed increasing trend in the global-mean surface air temperatures since mid-20th century is triggered by the emission of anthropogenic greenhouse gases (GHGs). The increase in the global-mean temperature due to anthropogenic emissions is nearly monotonic and may alter the climatological norms resulting in a new climate normal. In the presence of anthropogenic climate change, assessing regional impacts of the altered climate state and developing the plans for mitigating any adverse impacts are an important concern. Assessing future climate state and its impact remains a difficult task largely because of the uncertainties in future emissions and model errors. Uncertainties in climate projections propagates into impact assessment models and result in uncertainties in the impact assessments. In order to facilitate the evaluation of model data, a fundamental step for assessing model errors, the JPL Regional Climate Model Evaluation System (RCMES: Lean et al. 2010; Hart et al. 2011) has been developed through a joint effort of the investigators from UCLA and JPL. RCMES is also a regional climate component of a larger worldwide ExArch project. We will present the evaluation of the surface temperatures and precipitation from multiple RCMs participating in the African component of the Coordinated Regional Climate Downscaling Experiment (CORDEX) that has organized a suite of regional climate projection experiments in which multiple RCMs and GCMs are incorporated. As a part of the project, CORDEX organized a 20-year regional climate hindcast study in order to quantify and understand the uncertainties originating from model errors. Investigators from JPL, UCLA, and the CORDEX-Africa team collaborate to analyze the RCM hindcast data using RCMES. The analysis is focused on measuring the closeness between individual regional climate model outputs as well as their ensembles and observed data. The model evaluation is quantified in terms of widely used metrics. Details on the conceptual outline and architecture of RCMES is presented in two companion papers "The Regional climate model Evaluation System (RCMES) based on contemporary satellite and other observations for assessing regional climate model fidelity" and "A Reusable Framework for Regional Climate Model Evaluation" in GC07 and IN30, respectively.

Evaluating climate models: Should we use weather or climate observations?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oglesby, Robert J; Erickson III, David J

2009-12-01

Calling the numerical models that we use for simulations of climate change 'climate models' is a bit of a misnomer. These 'general circulation models' (GCMs, AKA global climate models) and their cousins the 'regional climate models' (RCMs) are actually physically-based weather simulators. That is, these models simulate, either globally or locally, daily weather patterns in response to some change in forcing or boundary condition. These simulated weather patterns are then aggregated into climate statistics, very much as we aggregate observations into 'real climate statistics'. Traditionally, the output of GCMs has been evaluated using climate statistics, as opposed to their abilitymore » to simulate realistic daily weather observations. At the coarse global scale this may be a reasonable approach, however, as RCM's downscale to increasingly higher resolutions, the conjunction between weather and climate becomes more problematic. We present results from a series of present-day climate simulations using the WRF ARW for domains that cover North America, much of Latin America, and South Asia. The basic domains are at a 12 km resolution, but several inner domains at 4 km have also been simulated. These include regions of complex topography in Mexico, Colombia, Peru, and Sri Lanka, as well as a region of low topography and fairly homogeneous land surface type (the U.S. Great Plains). Model evaluations are performed using standard climate analyses (e.g., reanalyses; NCDC data) but also using time series of daily station observations. Preliminary results suggest little difference in the assessment of long-term mean quantities, but the variability on seasonal and interannual timescales is better described. Furthermore, the value-added by using daily weather observations as an evaluation tool increases with the model resolution.« less

Climate Change Effects on Agriculture: Economic Responses to Biophysical Shocks

NASA Technical Reports Server (NTRS)

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlik, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(sup 2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Climate change effects on agriculture: Economic responses to biophysical shocks

PubMed Central

Nelson, Gerald C.; Valin, Hugo; Sands, Ronald D.; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d’Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-01-01

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change’s representative concentration pathway with end-of-century radiative forcing of 8.5 W/m2. The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change. PMID:24344285

Climate change effects on agriculture: economic responses to biophysical shocks.

PubMed

Nelson, Gerald C; Valin, Hugo; Sands, Ronald D; Havlík, Petr; Ahammad, Helal; Deryng, Delphine; Elliott, Joshua; Fujimori, Shinichiro; Hasegawa, Tomoko; Heyhoe, Edwina; Kyle, Page; Von Lampe, Martin; Lotze-Campen, Hermann; Mason d'Croz, Daniel; van Meijl, Hans; van der Mensbrugghe, Dominique; Müller, Christoph; Popp, Alexander; Robertson, Richard; Robinson, Sherman; Schmid, Erwin; Schmitz, Christoph; Tabeau, Andrzej; Willenbockel, Dirk

2014-03-04

Agricultural production is sensitive to weather and thus directly affected by climate change. Plausible estimates of these climate change impacts require combined use of climate, crop, and economic models. Results from previous studies vary substantially due to differences in models, scenarios, and data. This paper is part of a collective effort to systematically integrate these three types of models. We focus on the economic component of the assessment, investigating how nine global economic models of agriculture represent endogenous responses to seven standardized climate change scenarios produced by two climate and five crop models. These responses include adjustments in yields, area, consumption, and international trade. We apply biophysical shocks derived from the Intergovernmental Panel on Climate Change's representative concentration pathway with end-of-century radiative forcing of 8.5 W/m(2). The mean biophysical yield effect with no incremental CO2 fertilization is a 17% reduction globally by 2050 relative to a scenario with unchanging climate. Endogenous economic responses reduce yield loss to 11%, increase area of major crops by 11%, and reduce consumption by 3%. Agricultural production, cropland area, trade, and prices show the greatest degree of variability in response to climate change, and consumption the lowest. The sources of these differences include model structure and specification; in particular, model assumptions about ease of land use conversion, intensification, and trade. This study identifies where models disagree on the relative responses to climate shocks and highlights research activities needed to improve the representation of agricultural adaptation responses to climate change.

Hydrological modeling as an evaluation tool of EURO-CORDEX climate projections and bias correction methods

NASA Astrophysics Data System (ADS)

Hakala, Kirsti; Addor, Nans; Seibert, Jan

2017-04-01

Streamflow stemming from Switzerland's mountainous landscape will be influenced by climate change, which will pose significant challenges to the water management and policy sector. In climate change impact research, the determination of future streamflow is impeded by different sources of uncertainty, which propagate through the model chain. In this research, we explicitly considered the following sources of uncertainty: (1) climate models, (2) downscaling of the climate projections to the catchment scale, (3) bias correction method and (4) parameterization of the hydrological model. We utilize climate projections at the 0.11 degree 12.5 km resolution from the EURO-CORDEX project, which are the most recent climate projections for the European domain. EURO-CORDEX is comprised of regional climate model (RCM) simulations, which have been downscaled from global climate models (GCMs) from the CMIP5 archive, using both dynamical and statistical techniques. Uncertainties are explored by applying a modeling chain involving 14 GCM-RCMs to ten Swiss catchments. We utilize the rainfall-runoff model HBV Light, which has been widely used in operational hydrological forecasting. The Lindström measure, a combination of model efficiency and volume error, was used as an objective function to calibrate HBV Light. Ten best sets of parameters are then achieved by calibrating using the genetic algorithm and Powell optimization (GAP) method. The GAP optimization method is based on the evolution of parameter sets, which works by selecting and recombining high performing parameter sets with each other. Once HBV is calibrated, we then perform a quantitative comparison of the influence of biases inherited from climate model simulations to the biases stemming from the hydrological model. The evaluation is conducted over two time periods: i) 1980-2009 to characterize the simulation realism under the current climate and ii) 2070-2099 to identify the magnitude of the projected change of streamflow under the climate scenarios RCP4.5 and RCP8.5. We utilize two techniques for correcting biases in the climate model output: quantile mapping and a new method, frequency bias correction. The FBC method matches the frequencies between observed and GCM-RCM data. In this way, it can be used to correct for all time scales, which is a known limitation of quantile mapping. A novel approach for the evaluation of the climate simulations and bias correction methods was then applied. Streamflow can be thought of as the "great integrator" of uncertainties. The ability, or the lack thereof, to correctly simulate streamflow is a way to assess the realism of the bias-corrected climate simulations. Long-term monthly mean as well as high and low flow metrics are used to evaluate the realism of the simulations under current climate and to gauge the impacts of climate change on streamflow. Preliminary results show that under present climate, calibration of the hydrological model comprises of a much smaller band of uncertainty in the modeling chain as compared to the bias correction of the GCM-RCMs. Therefore, for future time periods, we expect the bias correction of climate model data to have a greater influence on projected changes in streamflow than the calibration of the hydrological model.

Regional climate projection of the Maritime Continent using the MIT Regional Climate Model

NASA Astrophysics Data System (ADS)

IM, E. S.; Eltahir, E. A. B.

2014-12-01

Given that warming of the climate system is unequivocal (IPCC AR5), accurate assessment of future climate is essential to understand the impact of climate change due to global warming. Modelling the climate change of the Maritime Continent is particularly challenge, showing a high degree of uncertainty. Compared to other regions, model agreement of future projections in response to anthropogenic emission forcings is much less. Furthermore, the spatial and temporal behaviors of climate projections seem to vary significantly due to a complex geographical condition and a wide range of scale interactions. For the fine-scale climate information (27 km) suitable for representing the complexity of climate change over the Maritime Continent, dynamical downscaling is performed using the MIT regional climate model (MRCM) during two thirty-year period for reference (1970-1999) and future (2070-2099) climate. Initial and boundary conditions are provided by Community Earth System Model (CESM) simulations under the emission scenarios projected by MIT Integrated Global System Model (IGSM). Changes in mean climate as well as the frequency and intensity of extreme climate events are investigated at various temporal and spatial scales. Our analysis is primarily centered on the different behavior of changes in convective and large-scale precipitation over land vs. ocean during dry vs. wet season. In addition, we attempt to find the added value to downscaled results over the Maritime Continent through the comparison between MRCM and CESM projection. Acknowledgements.This research was supported by the National Research Foundation Singapore through the Singapore MIT Alliance for Research and Technology's Center for Environmental Sensing and Modeling interdisciplinary research program.

Application of Climate Assessment Tool (CAT) to estimate climate variability impacts on nutrient loading from local watersheds

Treesearch

Ying Ouyang; Prem B. Parajuli; Gary Feng; Theodor D. Leininger; Yongshan Wan; Padmanava Dash

2018-01-01

A vast amount of future climate scenario datasets, created by climate models such as general circulation models (GCMs), have been used in conjunction with watershed models to project future climate variability impact on hydrological processes and water quality. However, these low spatial-temporal resolution datasets are often difficult to downscale spatially and...

Scale and modeling issues in water resources planning

USGS Publications Warehouse

Lins, H.F.; Wolock, D.M.; McCabe, G.J.

1997-01-01

Resource planners and managers interested in utilizing climate model output as part of their operational activities immediately confront the dilemma of scale discordance. Their functional responsibilities cover relatively small geographical areas and necessarily require data of relatively high spatial resolution. Climate models cover a large geographical, i.e. global, domain and produce data at comparatively low spatial resolution. Although the scale differences between model output and planning input are large, several techniques have been developed for disaggregating climate model output to a scale appropriate for use in water resource planning and management applications. With techniques in hand to reduce the limitations imposed by scale discordance, water resource professionals must now confront a more fundamental constraint on the use of climate models-the inability to produce accurate representations and forecasts of regional climate. Given the current capabilities of climate models, and the likelihood that the uncertainty associated with long-term climate model forecasts will remain high for some years to come, the water resources planning community may find it impractical to utilize such forecasts operationally.

Modeling lakes and reservoirs in the climate system

USGS Publications Warehouse

MacKay, M.D.; Neale, P.J.; Arp, C.D.; De Senerpont Domis, L. N.; Fang, X.; Gal, G.; Jo, K.D.; Kirillin, G.; Lenters, J.D.; Litchman, E.; MacIntyre, S.; Marsh, P.; Melack, J.; Mooij, W.M.; Peeters, F.; Quesada, A.; Schladow, S.G.; Schmid, M.; Spence, C.; Stokes, S.L.

2009-01-01

Modeling studies examining the effect of lakes on regional and global climate, as well as studies on the influence of climate variability and change on aquatic ecosystems, are surveyed. Fully coupled atmosphere-land surface-lake climate models that could be used for both of these types of study simultaneously do not presently exist, though there are many applications that would benefit from such models. It is argued here that current understanding of physical and biogeochemical processes in freshwater systems is sufficient to begin to construct such models, and a path forward is proposed. The largest impediment to fully representing lakes in the climate system lies in the handling of lakes that are too small to be explicitly resolved by the climate model, and that make up the majority of the lake-covered area at the resolutions currently used by global and regional climate models. Ongoing development within the hydrological sciences community and continual improvements in model resolution should help ameliorate this issue.

Documenting Climate Models and Their Simulations

DOE PAGES

Guilyardi, Eric; Balaji, V.; Lawrence, Bryan; ...

2013-05-01

The results of climate models are of increasing and widespread importance. No longer is climate model output of sole interest to climate scientists and researchers in the climate change impacts and adaptation fields. Now nonspecialists such as government officials, policy makers, and the general public all have an increasing need to access climate model output and understand its implications. For this host of users, accurate and complete metadata (i.e., information about how and why the data were produced) is required to document the climate modeling results. We describe a pilot community initiative to collect and make available documentation of climatemore » models and their simulations. In an initial application, a metadata repository is being established to provide information of this kind for a major internationally coordinated modeling activity known as CMIP5 (Coupled Model Intercomparison Project, Phase 5). We expected that for a wide range of stakeholders, this and similar community-managed metadata repositories will spur development of analysis tools that facilitate discovery and exploitation of Earth system simulations.« less

Efficient design based on perturbed parameter ensembles to identify plausible and diverse variants of a model for climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.; Sexton, D.; Murphy, J.

2017-12-01

We present exploratory work towards developing an efficient strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as basis to select model variants for a PPE suitable for future projections: a) acceptable model performance at two different timescales, and b) maintaining diversity in model response to climate change. We demonstrate that there is a strong relationship between model errors at weather and climate timescales for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of historical climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to identification of plausible parts of parameter space from which model variants can be selected for projection studies.

Climate and atmospheric modeling studies. Climate applications of Earth and planetary observations. Chemistry of Earth and environment

NASA Technical Reports Server (NTRS)

1990-01-01

The research conducted during the past year in the climate and atmospheric modeling programs concentrated on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Principal models are a one-dimensional radiative-convective model, a three-dimensional global climate model, and an upper ocean model. Principal applications have been the study of the impact of CO2, aerosols and the solar 'constant' on climate. Progress was made in the 3-D model development towards physically realistic treatment of these processes. In particular, a map of soil classifications on 1 degree x 1 degree resolution has been digitized, and soil properties have been assigned to each soil type. Using this information about soil properties, a method was developed to simulate the hydraulic behavior of soils of the world. This improved treatment of soil hydrology, together with the seasonally varying vegetation cover, will provide a more realistic study of the role of the terrestrial biota in climate change. A new version of the climate model was created which follows the isotopes of water and sources of water (or colored water) throughout the planet. Each isotope or colored water source is a fraction of the climate model's water. It participates in condensation and surface evaporation at different fractionation rates and is transported by the dynamics. A major benefit of this project has been to improve the programming techniques and physical simulation of the water vapor budget of the climate model.

On the hydrologic adjustment of climate-model projections: The potential pitfall of potential evapotranspiration

USGS Publications Warehouse

Milly, P.C.D.; Dunne, K.A.

2011-01-01

Hydrologic models often are applied to adjust projections of hydroclimatic change that come from climate models. Such adjustment includes climate-bias correction, spatial refinement ("downscaling"), and consideration of the roles of hydrologic processes that were neglected in the climate model. Described herein is a quantitative analysis of the effects of hydrologic adjustment on the projections of runoff change associated with projected twenty-first-century climate change. In a case study including three climate models and 10 river basins in the contiguous United States, the authors find that relative (i.e., fractional or percentage) runoff change computed with hydrologic adjustment more often than not was less positive (or, equivalently, more negative) than what was projected by the climate models. The dominant contributor to this decrease in runoff was a ubiquitous change in runoff (median 211%) caused by the hydrologic model's apparent amplification of the climate-model-implied growth in potential evapotranspiration. Analysis suggests that the hydrologic model, on the basis of the empirical, temperature-based modified Jensen-Haise formula, calculates a change in potential evapotranspiration that is typically 3 times the change implied by the climate models, which explicitly track surface energy budgets. In comparison with the amplification of potential evapotranspiration, central tendencies of other contributions from hydrologic adjustment (spatial refinement, climate-bias adjustment, and process refinement) were relatively small. The authors' findings highlight the need for caution when projecting changes in potential evapotranspiration for use in hydrologic models or drought indices to evaluate climatechange impacts on water. Copyright ?? 2011, Paper 15-001; 35,952 words, 3 Figures, 0 Animations, 1 Tables.

New Perspectives on the Role of Internal Variability in Regional Climate Change and Climate Model Evaluation

NASA Astrophysics Data System (ADS)

Deser, C.

2017-12-01

Natural climate variability occurs over a wide range of time and space scales as a result of processes intrinsic to the atmosphere, the ocean, and their coupled interactions. Such internally generated climate fluctuations pose significant challenges for the identification of externally forced climate signals such as those driven by volcanic eruptions or anthropogenic increases in greenhouse gases. This challenge is exacerbated for regional climate responses evaluated from short (< 50 years) data records. The limited duration of the observations also places strong constraints on how well the spatial and temporal characteristics of natural climate variability are known, especially on multi-decadal time scales. The observational constraints, in turn, pose challenges for evaluation of climate models, including their representation of internal variability and assessing the accuracy of their responses to natural and anthropogenic radiative forcings. A promising new approach to climate model assessment is the advent of large (10-100 member) "initial-condition" ensembles of climate change simulations with individual models. Such ensembles allow for accurate determination, and straightforward separation, of externally forced climate signals and internal climate variability on regional scales. The range of climate trajectories in a given model ensemble results from the fact that each simulation represents a particular sequence of internal variability superimposed upon a common forced response. This makes clear that nature's single realization is only one of many that could have unfolded. This perspective leads to a rethinking of approaches to climate model evaluation that incorporate observational uncertainty due to limited sampling of internal variability. Illustrative examples across a range of well-known climate phenomena including ENSO, volcanic eruptions, and anthropogenic climate change will be discussed.

Using Weather Data and Climate Model Output in Economic Analyses of Climate Change

DOE Office of Scientific and Technical Information (OSTI.GOV)

Auffhammer, M.; Hsiang, S. M.; Schlenker, W.

2013-06-28

Economists are increasingly using weather data and climate model output in analyses of the economic impacts of climate change. This article introduces a set of weather data sets and climate models that are frequently used, discusses the most common mistakes economists make in using these products, and identifies ways to avoid these pitfalls. We first provide an introduction to weather data, including a summary of the types of datasets available, and then discuss five common pitfalls that empirical researchers should be aware of when using historical weather data as explanatory variables in econometric applications. We then provide a brief overviewmore » of climate models and discuss two common and significant errors often made by economists when climate model output is used to simulate the future impacts of climate change on an economic outcome of interest.« less

Identification of reliable gridded reference data for statistical downscaling methods in Alberta

NASA Astrophysics Data System (ADS)

Eum, H. I.; Gupta, A.

2017-12-01

Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.

Weather Forecaster Understanding of Climate Models

NASA Astrophysics Data System (ADS)

Bol, A.; Kiehl, J. T.; Abshire, W. E.

2013-12-01

Weather forecasters, particularly those in broadcasting, are the primary conduit to the public for information on climate and climate change. However, many weather forecasters remain skeptical of model-based climate projections. To address this issue, The COMET Program developed an hour-long online lesson of how climate models work, targeting an audience of weather forecasters. The module draws on forecasters' pre-existing knowledge of weather, climate, and numerical weather prediction (NWP) models. In order to measure learning outcomes, quizzes were given before and after the lesson. Preliminary results show large learning gains. For all people that took both pre and post-tests (n=238), scores improved from 48% to 80%. Similar pre/post improvement occurred for National Weather Service employees (51% to 87%, n=22 ) and college faculty (50% to 90%, n=7). We believe these results indicate a fundamental misunderstanding among many weather forecasters of (1) the difference between weather and climate models, (2) how researchers use climate models, and (3) how they interpret model results. The quiz results indicate that efforts to educate the public about climate change need to include weather forecasters, a vital link between the research community and the general public.

A personal perspective on modelling the climate system

PubMed Central

Palmer, T. N.

2016-01-01

Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1 km global climate system model to run on the first exascale supercomputers in the early 2020s. PMID:27274686

Sources and Impacts of Modeled and Observed Low-Frequency Climate Variability

NASA Astrophysics Data System (ADS)

Parsons, Luke Alexander

Here we analyze climate variability using instrumental, paleoclimate (proxy), and the latest climate model data to understand more about the sources and impacts of low-frequency climate variability. Understanding the drivers of climate variability at interannual to century timescales is important for studies of climate change, including analyses of detection and attribution of climate change impacts. Additionally, correctly modeling the sources and impacts of variability is key to the simulation of abrupt change (Alley et al., 2003) and extended drought (Seager et al., 2005; Pelletier and Turcotte, 1997; Ault et al., 2014). In Appendix A, we employ an Earth system model (GFDL-ESM2M) simulation to study the impacts of a weakening of the Atlantic meridional overturning circulation (AMOC) on the climate of the American Tropics. The AMOC drives some degree of local and global internal low-frequency climate variability (Manabe and Stouffer, 1995; Thornalley et al., 2009) and helps control the position of the tropical rainfall belt (Zhang and Delworth, 2005). We find that a major weakening of the AMOC can cause large-scale temperature, precipitation, and carbon storage changes in Central and South America. Our results suggest that possible future changes in AMOC strength alone will not be sufficient to drive a large-scale dieback of the Amazonian forest, but this key natural ecosystem is sensitive to dry-season length and timing of rainfall (Parsons et al., 2014). In Appendix B, we compare a paleoclimate record of precipitation variability in the Peruvian Amazon to climate model precipitation variability. The paleoclimate (Lake Limon) record indicates that precipitation variability in western Amazonia is 'red' (i.e., increasing variability with timescale). By contrast, most state-of-the-art climate models indicate precipitation variability in this region is nearly 'white' (i.e., equally variability across timescales). This paleo-model disagreement in the overall structure of the variance spectrum has important consequences for the probability of multi-year drought. Our lake record suggests there is a significant background threat of multi-year, and even decade-length, drought in western Amazonia, whereas climate model simulations indicate most droughts likely last no longer than one to three years. These findings suggest climate models may underestimate the future risk of extended drought in this important region. In Appendix C, we expand our analysis of climate variability beyond South America. We use observations, well-constrained tropical paleoclimate, and Earth system model data to examine the overall shape of the climate spectrum across interannual to century frequencies. We find a general agreement among observations and models that temperature variability increases with timescale across most of the globe outside the tropics. However, as compared to paleoclimate records, climate models generate too little low-frequency variability in the tropics (e.g., Laepple and Huybers, 2014). When we compare the shape of the simulated climate spectrum to the spectrum of a simple autoregressive process, we find much of the modeled surface temperature variability in the tropics could be explained by ocean smoothing of weather noise. Importantly, modeled precipitation tends to be similar to white noise across much of the globe. By contrast, paleoclimate records of various types from around the globe indicate that both temperature and precipitation variability should experience much more low-frequency variability than a simple autoregressive or white-noise process. In summary, state-of-the-art climate models generate some degree of dynamically driven low-frequency climate variability, especially at high latitudes. However, the latest climate models, observations, and paleoclimate data provide us with drastically different pictures of the background climate system and its associated risks. This research has important consequences for improving how we simulate climate extremes as we enter a warmer (and often drier) world in the coming centuries; if climate models underestimate low-frequency variability, we will underestimate the risk of future abrupt change and extreme events, such as megadroughts.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

PubMed

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

Deriving user-informed climate information from climate model ensemble results

NASA Astrophysics Data System (ADS)

Huebener, Heike; Hoffmann, Peter; Keuler, Klaus; Pfeifer, Susanne; Ramthun, Hans; Spekat, Arne; Steger, Christian; Warrach-Sagi, Kirsten

2017-07-01

Communication between providers and users of climate model simulation results still needs to be improved. In the German regional climate modeling project ReKliEs-De a midterm user workshop was conducted to allow the intended users of the project results to assess the preliminary results and to streamline the final project results to their needs. The user feedback highlighted, in particular, the still considerable gap between climate research output and user-tailored input for climate impact research. Two major requests from the user community addressed the selection of sub-ensembles and some condensed, easy to understand information on the strengths and weaknesses of the climate models involved in the project.

Extra-Tropical Cyclones at Climate Scales: Comparing Models to Observations

NASA Astrophysics Data System (ADS)

Tselioudis, G.; Bauer, M.; Rossow, W.

2009-04-01

Climate is often defined as the accumulation of weather, and weather is not the concern of climate models. Justification for this latter sentiment has long been hidden behind coarse model resolutions and blunt validation tools based on climatological maps. The spatial-temporal resolutions of today's climate models and observations are converging onto meteorological scales, however, which means that with the correct tools we can test the largely unproven assumption that climate model weather is correct enough that its accumulation results in a robust climate simulation. Towards this effort we introduce a new tool for extracting detailed cyclone statistics from observations and climate model output. These include the usual cyclone characteristics (centers, tracks), but also adaptive cyclone-centric composites. We have created a novel dataset, the MAP Climatology of Mid-latitude Storminess (MCMS), which provides a detailed 6 hourly assessment of the areas under the influence of mid-latitude cyclones, using a search algorithm that delimits the boundaries of each system from the outer-most closed SLP contour. Using this we then extract composites of cloud, radiation, and precipitation properties from sources such as ISCCP and GPCP to create a large comparative dataset for climate model validation. A demonstration of the potential usefulness of these tools in process-based climate model evaluation studies will be shown.

Understanding Climate Uncertainty with an Ocean Focus

NASA Astrophysics Data System (ADS)

Tokmakian, R. T.

2009-12-01

Uncertainty in climate simulations arises from various aspects of the end-to-end process of modeling the Earth’s climate. First, there is uncertainty from the structure of the climate model components (e.g. ocean/ice/atmosphere). Even the most complex models are deficient, not only in the complexity of the processes they represent, but in which processes are included in a particular model. Next, uncertainties arise from the inherent error in the initial and boundary conditions of a simulation. Initial conditions are the state of the weather or climate at the beginning of the simulation and other such things, and typically come from observations. Finally, there is the uncertainty associated with the values of parameters in the model. These parameters may represent physical constants or effects, such as ocean mixing, or non-physical aspects of modeling and computation. The uncertainty in these input parameters propagates through the non-linear model to give uncertainty in the outputs. The models in 2020 will no doubt be better than today’s models, but they will still be imperfect, and development of uncertainty analysis technology is a critical aspect of understanding model realism and prediction capability. Smith [2002] and Cox and Stephenson [2007] discuss the need for methods to quantify the uncertainties within complicated systems so that limitations or weaknesses of the climate model can be understood. In making climate predictions, we need to have available both the most reliable model or simulation and a methods to quantify the reliability of a simulation. If quantitative uncertainty questions of the internal model dynamics are to be answered with complex simulations such as AOGCMs, then the only known path forward is based on model ensembles that characterize behavior with alternative parameter settings [e.g. Rougier, 2007]. The relevance and feasibility of using "Statistical Analysis of Computer Code Output" (SACCO) methods for examining uncertainty in ocean circulation due to parameter specification will be described and early results using the ocean/ice components of the CCSM climate model in a designed experiment framework will be shown. Cox, P. and D. Stephenson, Climate Change: A Changing Climate for Prediction, 2007, Science 317 (5835), 207, DOI: 10.1126/science.1145956. Rougier, J. C., 2007: Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, Climatic Change, 81, 247-264. Smith L., 2002, What might we learn from climate forecasts? Proc. Nat’l Academy of Sciences, Vol. 99, suppl. 1, 2487-2492 doi:10.1073/pnas.012580599.

Changing precipitation in western Europe, climate change or natural variability?

NASA Astrophysics Data System (ADS)

Aalbers, Emma; Lenderink, Geert; van Meijgaard, Erik; van den Hurk, Bart

2017-04-01

Multi-model RCM-GCM ensembles provide high resolution climate projections, valuable for among others climate impact assessment studies. While the application of multiple models (both GCMs and RCMs) provides a certain robustness with respect to model uncertainty, the interpretation of differences between ensemble members - the combined result of model uncertainty and natural variability of the climate system - is not straightforward. Natural variability is intrinsic to the climate system, and a potentially large source of uncertainty in climate change projections, especially for projections on the local to regional scale. To quantify the natural variability and get a robust estimate of the forced climate change response (given a certain model and forcing scenario), large ensembles of climate model simulations of the same model provide essential information. While for global climate models (GCMs) a number of such large single model ensembles exists and have been analyzed, for regional climate models (RCMs) the number and size of single model ensembles is limited, and the predictability of the forced climate response at the local to regional scale is still rather uncertain. We present a regional downscaling of a 16-member single model ensemble over western Europe and the Alps at a resolution of 0.11 degrees (˜12km), similar to the highest resolution EURO-CORDEX simulations. This 16-member ensemble was generated by the GCM EC-EARTH, which was downscaled with the RCM RACMO for the period 1951-2100. This single model ensemble has been investigated in terms of the ensemble mean response (our estimate of the forced climate response), as well as the difference between the ensemble members, which measures natural variability. We focus on the response in seasonal mean and extreme precipitation (seasonal maxima and extremes with a return period up to 20 years) for the near to far future. For most precipitation indices we can reliably determine the climate change signal, given the applied model chain and forcing scenario. However, the analysis also shows how limited the information in single ensemble members is on the local scale forced climate response, even for high levels of global warming when the forced response has emerged from natural variability. Analysis and application of multi-model ensembles like EURO-CORDEX should go hand-in-hand with single model ensembles, like the one presented here, to be able to correctly interpret the fine-scale information in terms of a forced signal and random noise due to natural variability.

Extreme Events: low and high total ozone over Arosa, Switzerland

NASA Astrophysics Data System (ADS)

Rieder, H. E.; Staehelin, J.; Maeder, J. A.; Ribatet, M.; Stübi, R.; Weihs, P.; Holawe, F.; Peter, T.; Davison, A. C.

2009-04-01

The frequency distribution of days with extreme low (termed ELOs) and high (termed EHOs) total ozone is analyzed for the world's longest total ozone record (Arosa, Switzerland - for details see Staehelin et al.,1998a,b), with new tools from extreme value theory (e.g. Coles, 2001; Ribatet, 2007). A heavy-tail focused approach is used through the fitting of the Generalized Pareto Distribution (GPD) to the Arosa time series. Asymptotic arguments (Pickands, 1975) justify the use of the GPD for modeling exceedances over a high (or below a low) enough threshold (Coles, 2001). The analysis shows that the GPD is appropriate for modeling the frequency distribution in total ozone above or below a mathematically well-defined threshold. While previous studies focused on so termed ozone mini-holes and mini-highs (e.g. Bojkov and Balis, 2001, Koch et al., 2005), this study is the first to present a mathematical description of extreme events in low and high total ozone for a northern mid-latitudes site (Rieder et al., 2009). The results show (a) an increase in days with extreme low (ELOs) and (b) a decrease in days with extreme high total ozone (EHOs) during the last decades, (c) that the general trend in total ozone is strongly determined by these extreme events and (d) that fitting the GPD is an appropriate method for the estimation of the frequency distribution of so-called ozone mini-holes. Furthermore, this concept allows one to separate the effect of Arctic ozone depletion from that of in situ mid-latitude ozone loss. As shown by this study, ELOs and EHOs have a strong influence on mean values in total ozone and the "extremes concept" could be further used also for validation of Chemistry-Climate-Models (CCMs) within the scientific community. References: Bojkov, R. D., and Balis, D.S.: Characteristics of episodes with extremely low ozone values in the northern middle latitudes 1975-2000, Ann. Geophys., 19, 797-807, 2001. Coles, S.: An Introduction to Statistical Modeling of Extreme Values, Springer Series in Statistics, ISBN:1852334592, Springer, Berlin, 2001. Koch, G., H. Wernli, C. Schwierz, J. Staehelin, and T. Peter (2005), A composite study on the structure and formation of ozone miniholes and minihighs over central Europe, Geophys. Res. Lett., 32, L12810, doi:10.1029/2004GL022062. Pickands, J.: Statistical-Inference using extreme order Statistics, Ann. Stat., 3, 1, 119-131, 1975. Ribatet, M.: POT: Modelling peaks over a threshold, R News, 7, 34-36, 2007. Rieder, H.E., Staehelin, J., Maeder, J.A., Ribatet, M., Stübi, R., Weihs, P., Holawe, F., Peter, T., and Davison, A.C.: From ozone mini holes and mini highs towards extreme value theory: New insights from extreme events and non stationarity, submitted to J. Geophys. Res., 2009. Staehelin, J., Kegel, R., and Harris, N. R.: Trend analysis of the homogenized total ozone series of Arosa (Switzerland), 1929-1996, J. Geophys. Res., 103(D7), 8389-8400, doi:10.1029/97JD03650, 1998a. Staehelin, J., Renaud, A., Bader, J., McPeters, R., Viatte, P., Hoegger, B., Bugnion, V., Giroud, M., and Schill, H.: Total ozone series at Arosa (Switzerland): Homogenization and data comparison, J. Geophys. Res., 103(D5), 5827-5842, doi:10.1029/97JD02402, 1998b.

Climate data induced uncertainty in model based estimations of terrestrial primary productivity

NASA Astrophysics Data System (ADS)

Wu, Z.; Ahlström, A.; Smith, B.; Ardö, J.; Eklundh, L.; Fensholt, R.; Lehsten, V.

2016-12-01

Models used to project global vegetation and carbon cycle differ in their estimates of historical fluxes and pools. These differences arise not only from differences between models but also from differences in the environmental and climatic data that forces the models. Here we investigate the role of uncertainties in historical climate data, encapsulated by a set of six historical climate datasets. We focus on terrestrial gross primary productivity (GPP) and analyze the results from a dynamic process-based vegetation model (LPJ-GUESS) forced by six different climate datasets and two empirical datasets of GPP (derived from flux towers and remote sensing). We find that the climate induced uncertainty, defined as the difference among historical simulations in GPP when forcing the model with the different climate datasets, can be as high as 33 Pg C yr-1 globally (19% of mean GPP). The uncertainty is partitioned into the three main climatic drivers, temperature, precipitation, and shortwave radiation. Additionally, we illustrate how the uncertainty due to a given climate driver depends both on the magnitude of the forcing data uncertainty (the data range) and the sensitivity of the modeled GPP to the driver (the ecosystem sensitivity). The analysis is performed globally and stratified into five land cover classes. We find that the dynamic vegetation model overestimates GPP, compared to empirically based GPP data over most areas, except for the tropical region. Both the simulations and empirical estimates agree that the tropical region is a disproportionate source of uncertainty in GPP estimation. This is mainly caused by uncertainties in shortwave radiation forcing, of which climate data range contributes slightly higher uncertainty than ecosystem sensitivity to shortwave radiation. We also find that precipitation dominated the climate induced uncertainty over nearly half of terrestrial vegetated surfaces, which is mainly due to large ecosystem sensitivity to precipitation. Overall, climate data ranges are found to contribute more to the climate induced uncertainty than ecosystem sensitivity. Our study highlights the need to better constrain tropical climate and demonstrate that uncertainty caused by climatic forcing data must be considered when comparing and evaluating model results and empirical datasets.

Quantifying Key Climate Parameter Uncertainties Using an Earth System Model with a Dynamic 3D Ocean

NASA Astrophysics Data System (ADS)

Olson, R.; Sriver, R. L.; Goes, M. P.; Urban, N.; Matthews, D.; Haran, M.; Keller, K.

2011-12-01

Climate projections hinge critically on uncertain climate model parameters such as climate sensitivity, vertical ocean diffusivity and anthropogenic sulfate aerosol forcings. Climate sensitivity is defined as the equilibrium global mean temperature response to a doubling of atmospheric CO2 concentrations. Vertical ocean diffusivity parameterizes sub-grid scale ocean vertical mixing processes. These parameters are typically estimated using Intermediate Complexity Earth System Models (EMICs) that lack a full 3D representation of the oceans, thereby neglecting the effects of mixing on ocean dynamics and meridional overturning. We improve on these studies by employing an EMIC with a dynamic 3D ocean model to estimate these parameters. We carry out historical climate simulations with the University of Victoria Earth System Climate Model (UVic ESCM) varying parameters that affect climate sensitivity, vertical ocean mixing, and effects of anthropogenic sulfate aerosols. We use a Bayesian approach whereby the likelihood of each parameter combination depends on how well the model simulates surface air temperature and upper ocean heat content. We use a Gaussian process emulator to interpolate the model output to an arbitrary parameter setting. We use Markov Chain Monte Carlo method to estimate the posterior probability distribution function (pdf) of these parameters. We explore the sensitivity of the results to prior assumptions about the parameters. In addition, we estimate the relative skill of different observations to constrain the parameters. We quantify the uncertainty in parameter estimates stemming from climate variability, model and observational errors. We explore the sensitivity of key decision-relevant climate projections to these parameters. We find that climate sensitivity and vertical ocean diffusivity estimates are consistent with previously published results. The climate sensitivity pdf is strongly affected by the prior assumptions, and by the scaling parameter for the aerosols. The estimation method is computationally fast and can be used with more complex models where climate sensitivity is diagnosed rather than prescribed. The parameter estimates can be used to create probabilistic climate projections using the UVic ESCM model in future studies.

Climate Change Impact Study with CMIP5 and Comparison with CMIP3

NASA Astrophysics Data System (ADS)

Wang, J.; Yin, H.; Reyes, E.; Chung, F. I.

2016-12-01

One of significant uncertainties in climate change impact study is the selection of climate model projection including the choosing of greenhouse gas emission scenarios. With the new generation of climate model projection, CMIP5, coming into use, CCTAG selected 11 climate models and two RCPs (rcp4.5 and rcp8.5) for California. Previous DWR climate change study was based on 6 CMIP3 climate models and two emission scenarios (SRES A2 and B1) which were selected by CAT. It is an unanswered question that how the selection of these climate model projections and emission scenarios affect the assessment of climate change impact on future water supply of California CVP/SWP project. This work will run the water planning model CalSim in DWR with 44 CMIP5 and 12 CMIP3 climate model projections to investigate the sensitivity of climate model impact study on future water supply in the CVP/SWP region to the section of climate model projection. It was found that in 2060 CMIP5 projects the wetting trend in Northern California while CMIP3 projects the drying trend in the entire California on the average. And CMIP5 projects about half-degree more warming than CMIP3. As a result, Sacramento River rim inflow increases by 8% for CMIP5 and reduces by 3% for CMIP3. In spite of this difference in rim inflow, north of Delta carryover storage will be reduced both under CMIP5 (14%) and under CMIP3 (23%) in 2060. And south Delta export will be reduced both for CMIP5 (8%) and for CMIP3 (15%). Thus, The CC impact uncertainty caused by the selection of climate model projection (CMIP3 vs CMIP5) is about 7% in terms of Delta export and about 9% in terms of north of Delta carryover storage. This uncertainty is more than the one caused by the selection of sea level rise in that the climate change impact uncertainty caused by the selection of sea level rise (Zero vs 1.5ft SLR) is about 5% in terms of Delta export and about 4-5% in terms of North of Delta carryover storage.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

Climate Sensitivity Controls Uncertainty in Future Terrestrial Carbon Sink

NASA Astrophysics Data System (ADS)

Schurgers, Guy; Ahlström, Anders; Arneth, Almut; Pugh, Thomas A. M.; Smith, Benjamin

2018-05-01

For the 21st century, carbon cycle models typically project an increase of terrestrial carbon with increasing atmospheric CO2 and a decrease with the accompanying climate change. However, these estimates are poorly constrained, primarily because they typically rely on a limited number of emission and climate scenarios. Here we explore a wide range of combinations of CO2 rise and climate change and assess their likelihood with the climate change responses obtained from climate models. Our results demonstrate that the terrestrial carbon uptake depends critically on the climate sensitivity of individual climate models, representing a large uncertainty of model estimates. In our simulations, the terrestrial biosphere is unlikely to become a strong source of carbon with any likely combination of CO2 and climate change in the absence of land use change, but the fraction of the emissions taken up by the terrestrial biosphere will decrease drastically with higher emissions.

Future Warming Patterns Linked to Today's Climate Variability.

PubMed

Dai, Aiguo

2016-01-11

The reliability of model projections of greenhouse gas (GHG)-induced future climate change is often assessed based on models' ability to simulate the current climate, but there has been little evidence that connects the two. In fact, this practice has been questioned because the GHG-induced future climate change may involve additional physical processes that are not important for the current climate. Here I show that the spatial patterns of the GHG-induced future warming in the 21(st) century is highly correlated with the patterns of the year-to-year variations of surface air temperature for today's climate, with areas of larger variations during 1950-1979 having more GHG-induced warming in the 21(st) century in all climate models. Such a relationship also exists in other climate fields such as atmospheric water vapor, and it is evident in observed temperatures from 1950-2010. The results suggest that many physical processes may work similarly in producing the year-to-year climate variations in the current climate and the GHG-induced long-term changes in the 21(st) century in models and in the real world. They support the notion that models that simulate present-day climate variability better are likely to make more reliable predictions of future climate change.

Bringing a Realistic Global Climate Modeling Experience to a Broader Audience

NASA Astrophysics Data System (ADS)

Sohl, L. E.; Chandler, M. A.; Zhou, J.

2010-12-01

EdGCM, the Educational Global Climate Model, was developed with the goal of helping students learn about climate change and climate modeling by giving them the ability to run a genuine NASA global climate model (GCM) on a desktop computer. Since EdGCM was first publicly released in January 2005, tens of thousands of users on seven continents have downloaded the software. EdGCM has been utilized by climate science educators from middle school through graduate school levels, and on occasion even by researchers who otherwise do not have ready access to climate model at national labs in the U.S. and elsewhere. The EdGCM software is designed to walk users through the same process a climate scientist would use in designing and running simulations, and analyzing and visualizing GCM output. Although the current interface design gives users a clear view of some of the complexities involved in using a climate model, it can be daunting for users whose main focus is on climate science rather than modeling per se. As part of the work funded by NASA’s Global Climate Change Education (GCCE) program, we will begin modifications to the user interface that will improve the accessibility of EdGCM to a wider array of users, especially at the middle school and high school levels, by: 1) Developing an automated approach (a “wizard”) to simplify the user experience in setting up new climate simulations; 2) Produce a catalog of “rediscovery experiments” that allow users to reproduce published climate model results, and in some cases compare model projections to real world data; and 3) Enhance distance learning and online learning opportunities through the development of a web-based interface. The prototypes for these modifications will then be presented to educators belonging to an EdGCM Users Group for feedback, so that we can further refine the EdGCM software, and thus deliver the tools and materials educators want and need across a wider range of learning environments.

Climate suitability and human influences combined explain the range expansion of an invasive horticultural plant

Treesearch

Carolyn M. Beans; Francis F. Kilkenny; Laura F. Galloway

2012-01-01

Ecological niche models are commonly used to identify regions at risk of species invasions. Relying on climate alone may limit a model's success when additional variables contribute to invasion. While a climate-based model may predict the future spread of an invasive plant, we hypothesized that a model that combined climate with human influences would most...

A model of formative assessment practice in secondary science classrooms using an audience response system

NASA Astrophysics Data System (ADS)

Shirley, Melissa L.

Formative assessment involves the probing of students' ideas to determine their level of understanding during the instructional sequence. Often conceptualized as a cycle, formative assessment consists of the teacher posing an instructional task to students, collecting data about student understanding, and engaging in follow-up strategies such as clarifying student understanding and adjusting instruction to meet learning needs. Despite having been shown to increase student achievement in a variety of classroom settings, formative assessment remains a relative weak area of teacher practice. Methods that enhance formative assessment strategies may therefore have a positive effect on student achievement. Audience response systems comprise a broad category of technologies that support richer classroom interaction and have the potential to facilitate formative assessment. Results from a large national research study, Classroom Connectivity in Promoting Mathematics and Science Achievement (CCMS), show that students in algebra classrooms where the teacher has implemented a type of audience response system experience significantly higher achievement gains compared to a control group. This suggests a role for audience response systems in promoting rich formative assessment. The importance of incorporating formative assessment strategies into regular classroom practice is widely recognized. However, it remains challenging to identify whether rich formative assessment is occurring during a particular class session. This dissertation uses teacher interviews and classroom observations to develop a fine-grained model of formative assessment in secondary science classrooms employing a type of audience response system. This model can be used by researchers and practitioners to characterize components of formative assessment practice in classrooms. A major component of formative assessment practice is the collection and aggregation of evidence of student learning. This dissertation proposes the use of the assessment episode to characterize extended cycles of teacher-student interactions. Further, the model presented here provides a new methodology to describe the teacher's use of questioning and subsequent classroom discourse to uncover student learning. Additional components of this model of formative assessment focus on the recognition of student learning by the teacher and the resultant changes in instructional practice to enhance student understanding.

Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

NASA Astrophysics Data System (ADS)

Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

2015-04-01

At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

NASA Astrophysics Data System (ADS)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Model uncertainties do not affect observed patterns of species richness in the Amazon.

PubMed

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo; Loyola, Rafael

2017-01-01

Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale-patterns of species richness and species vulnerability to climate change-are affected by the inputs used to model and project species distribution. We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species.

Model uncertainties do not affect observed patterns of species richness in the Amazon

PubMed Central

Sales, Lilian Patrícia; Neves, Olívia Viana; De Marco, Paulo

2017-01-01

Background Climate change is arguably a major threat to biodiversity conservation and there are several methods to assess its impacts on species potential distribution. Yet the extent to which different approaches on species distribution modeling affect species richness patterns at biogeographical scale is however unaddressed in literature. In this paper, we verified if the expected responses to climate change in biogeographical scale—patterns of species richness and species vulnerability to climate change—are affected by the inputs used to model and project species distribution. Methods We modeled the distribution of 288 vertebrate species (amphibians, birds and mammals), all endemic to the Amazon basin, using different combinations of the following inputs known to affect the outcome of species distribution models (SDMs): 1) biological data type, 2) modeling methods, 3) greenhouse gas emission scenarios and 4) climate forecasts. We calculated uncertainty with a hierarchical ANOVA in which those different inputs were considered factors. Results The greatest source of variation was the modeling method. Model performance interacted with data type and modeling method. Absolute values of variation on suitable climate area were not equal among predictions, but some biological patterns were still consistent. All models predicted losses on the area that is climatically suitable for species, especially for amphibians and primates. All models also indicated a current East-western gradient on endemic species richness, from the Andes foot downstream the Amazon river. Again, all models predicted future movements of species upwards the Andes mountains and overall species richness losses. Conclusions From a methodological perspective, our work highlights that SDMs are a useful tool for assessing impacts of climate change on biodiversity. Uncertainty exists but biological patterns are still evident at large spatial scales. As modeling methods are the greatest source of variation, choosing the appropriate statistics according to the study objective is also essential for estimating the impacts of climate change on species distribution. Yet from a conservation perspective, we show that Amazon endemic fauna is potentially vulnerable to climate change, due to expected reductions on suitable climate area. Climate-driven faunal movements are predicted towards the Andes mountains, which might work as climate refugia for migrating species. PMID:29023503

Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236

Advancing Climate Change and Impacts Science Through Climate Informatics

NASA Astrophysics Data System (ADS)

Lenhardt, W.; Pouchard, L. C.; King, A. W.; Branstetter, M. L.; Kao, S.; Wang, D.

2010-12-01

This poster will outline the work to date on developing a climate informatics capability at Oak Ridge National Laboratory (ORNL). The central proposition of this effort is that the application of informatics and information science to the domain of climate change science is an essential means to bridge the realm of high performance computing (HPC) and domain science. The goal is to facilitate knowledge capture and the creation of new scientific insights. For example, a climate informatics capability will help with the understanding and use of model results in domain sciences that were not originally in the scope. From there, HPC can also benefit from feedback as the new approaches may lead to better parameterization in the models. In this poster we will summarize the challenges associated with climate change science that can benefit from the systematic application of informatics and we will highlight our work to date in creating the climate informatics capability to address these types of challenges. We have identified three areas that are particularly challenging in the context of climate change science: 1) integrating model and observational data across different spatial and temporal scales, 2) model linkages, i.e. climate models linked to other models such as hydrologic models, and 3) model diagnostics. Each of these has a methodological component and an informatics component. Our project under way at ORNL seeks to develop new approaches and tools in the context of linking climate change and water issues. We are basing our work on the following four use cases: 1) Evaluation/test of CCSM4 biases in hydrology (precipitation, soil water, runoff, river discharge) over the Rio Grande Basin. User: climate modeler. 2) Investigation of projected changes in hydrology of Rio Grande Basin using the VIC (Variable Infiltration Capacity Macroscale) Hydrologic Model. User: watershed hydrologist/modeler. 3) Impact of climate change on agricultural productivity of the Rio Grande Basin. User: climate impact scientist, agricultural economist. 4) Renegotiation of the 1944 “Treaty for the Utilization of Waters of the Colorado and Tijuana Rivers and of the Rio Grande”. User: A US State Department analyst or their counterpart in Mexico.

Assessment of the Impacts of Climate Change on Stream Discharge and Water Quality in an Arid, Urbanized Watershed

NASA Astrophysics Data System (ADS)

Ranatunga, T.; Tong, S.; Yang, J.

2011-12-01

Hydrologic and water quality models can provide a general framework to conceptualize and investigate the relationships between climate and water resources. Under a hot and dry climate, highly urbanized watersheds are more vulnerable to changes in climate, such as excess heat and drought. In this study, a comprehensive watershed model, Hydrological Simulation Program FORTRAN (HSPF), is used to assess the impacts of future climate change on the stream discharge and water quality in Las Vegas Wash in Nevada, the only surface water body that drains from the Las Vegas Valley (an area with rapid population growth and urbanization) to Lake Mead. In this presentation, the process of model building, calibration and validation, the generation of climate change scenarios, and the assessment of future climate change effects on stream hydrology and quality are demonstrated. The hydrologic and water quality model is developed based on the data from current national databases and existing major land use categories of the watershed. The model is calibrated for stream discharge, nutrients (nitrogen and phosphorus) and sediment yield. The climate change scenarios are derived from the outputs of the Global Climate Models (GCM) and Regional Climate Models (RCM) simulations, and from the recent assessment reports from the Intergovernmental Panel on Climate Change (IPCC). The Climate Assessment Tool from US EPA's BASINS is used to assess the effects of likely future climate scenarios on the water quantity and quality in Las Vegas Wash. Also the presentation discusses the consequences of these hydrologic changes, including the deficit supplies of clean water during peak seasons of water demand, increased eutrophication potentials, wetland deterioration, and impacts on wild life habitats.

Centennial-scale Holocene climate variations amplified by Antarctic Ice Sheet discharge

NASA Astrophysics Data System (ADS)

Bakker, Pepijn; Clark, Peter U.; Golledge, Nicholas R.; Schmittner, Andreas; Weber, Michael E.

2017-01-01

Proxy-based indicators of past climate change show that current global climate models systematically underestimate Holocene-epoch climate variability on centennial to multi-millennial timescales, with the mismatch increasing for longer periods. Proposed explanations for the discrepancy include ocean-atmosphere coupling that is too weak in models, insufficient energy cascades from smaller to larger spatial and temporal scales, or that global climate models do not consider slow climate feedbacks related to the carbon cycle or interactions between ice sheets and climate. Such interactions, however, are known to have strongly affected centennial- to orbital-scale climate variability during past glaciations, and are likely to be important in future climate change. Here we show that fluctuations in Antarctic Ice Sheet discharge caused by relatively small changes in subsurface ocean temperature can amplify multi-centennial climate variability regionally and globally, suggesting that a dynamic Antarctic Ice Sheet may have driven climate fluctuations during the Holocene. We analysed high-temporal-resolution records of iceberg-rafted debris derived from the Antarctic Ice Sheet, and performed both high-spatial-resolution ice-sheet modelling of the Antarctic Ice Sheet and multi-millennial global climate model simulations. Ice-sheet responses to decadal-scale ocean forcing appear to be less important, possibly indicating that the future response of the Antarctic Ice Sheet will be governed more by long-term anthropogenic warming combined with multi-centennial natural variability than by annual or decadal climate oscillations.

Use of NARCCAP data to characterize regional climate uncertainty in the impact of global climate change on large river fish population: Missouri River sturgeon example

NASA Astrophysics Data System (ADS)

Anderson, C. J.; Wildhaber, M. L.; Wikle, C. K.; Moran, E. H.; Franz, K. J.; Dey, R.

2012-12-01

Climate change operates over a broad range of spatial and temporal scales. Understanding the effects of change on ecosystems requires accounting for the propagation of information and uncertainty across these scales. For example, to understand potential climate change effects on fish populations in riverine ecosystems, climate conditions predicted by course-resolution atmosphere-ocean global climate models must first be translated to the regional climate scale. In turn, this regional information is used to force watershed models, which are used to force river condition models, which impact the population response. A critical challenge in such a multiscale modeling environment is to quantify sources of uncertainty given the highly nonlinear nature of interactions between climate variables and the individual organism. We use a hierarchical modeling approach for accommodating uncertainty in multiscale ecological impact studies. This framework allows for uncertainty due to system models, model parameter settings, and stochastic parameterizations. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. We use NARCCAP data to determine confidence the capability of climate models to simulate relevant processes and to quantify regional climate variability within the context of the hierarchical model of uncertainty quantification. By confidence, we mean the ability of the regional climate model to replicate observed mechanisms. We use the NCEP-driven simulations for this analysis. This provides a base from which regional change can be categorized as either a modification of previously observed mechanisms or emergence of new processes. The management implications for these categories of change are significantly different in that procedures to address impacts from existing processes may already be known and need adjustment; whereas, an emergent processes may require new management strategies. The results from hierarchical analysis of uncertainty are used to study the relative change in weights of the endangered Missouri River pallid sturgeon (Scaphirhynchus albus) under a 21st century climate scenario.

Results from the BRACE 1.5 study: Climate change impacts of 1.5 C and 2 C warming

NASA Astrophysics Data System (ADS)

O'Neill, B. C.; Anderson, B.; Monaghan, A. J.; Ren, X.; Sanderson, B.; Tebaldi, C.

2017-12-01

In 2015, 195 countries negotiated the Paris Agreement on climate change, which set long-term goals of limiting global mean warming to well below 2 C and possibly 1.5 C. This event stimulated substantial scientific interest in climate outcomes and impacts on society associated with those levels of warming. Recently, the first set of global climate model simulations explicitly designed to meet those targets were undertaken with the Community Earth System Model (CESM) for use by the research community (Sanderson et al, accepted). The BRACE 1.5 project models societal impacts from these climate outcomes, combined with assumptions about future socioeconomic conditions according to the Shared Socioeconomic Pathways. These analyses build on a recently completed study of the Benefits of Reduced Anthropogenic Climate changE (BRACE), published as a set of 20 papers in Climatic Change, which examined the difference in impacts between two higher scenarios resulting in about 2.5 C and 3.7 C warming by late this century. BRACE 1.5 consists of a set of six papers to be submitted to a special collection in Environmental Research Letters that takes a similar approach but focuses on impacts at 1.5 and 2 C warming. We ask whether impacts differ substantially between the two climate scenarios, accounting for uncertainty in climate outcomes through the use of initial condition ensembles of CESM simulations, and in societal conditions by using alternative SSP-based development pathways. Impact assessment focuses on the health and agricultural sectors; modeling approaches include the use of a global mutli-region CGE model for economic analysis, both a process-based and an empirical crop model, a model of spatial population change, a model of climatic suitability for the aedes aegypti mosquito, and an epidemiological model of heat-related mortality. A methodological analysis also evaluates the use of climate model emulation techniques for providing climate information sufficient to support impact assessment in low warming scenarios.

Using climate models to estimate the quality of global observational data sets.

PubMed

Massonnet, François; Bellprat, Omar; Guemas, Virginie; Doblas-Reyes, Francisco J

2016-10-28

Observational estimates of the climate system are essential to monitoring and understanding ongoing climate change and to assessing the quality of climate models used to produce near- and long-term climate information. This study poses the dual and unconventional question: Can climate models be used to assess the quality of observational references? We show that this question not only rests on solid theoretical grounds but also offers insightful applications in practice. By comparing four observational products of sea surface temperature with a large multimodel climate forecast ensemble, we find compelling evidence that models systematically score better against the most recent, advanced, but also most independent product. These results call for generalized procedures of model-observation comparison and provide guidance for a more objective observational data set selection. Copyright © 2016, American Association for the Advancement of Science.

EdGCM: Research Tools for Training the Climate Change Generation

NASA Astrophysics Data System (ADS)

Chandler, M. A.; Sohl, L. E.; Zhou, J.; Sieber, R.

2011-12-01

Climate scientists employ complex computer simulations of the Earth's physical systems to prepare climate change forecasts, study the physical mechanisms of climate, and to test scientific hypotheses and computer parameterizations. The Intergovernmental Panel on Climate Change 4th Assessment Report (2007) demonstrates unequivocally that policy makers rely heavily on such Global Climate Models (GCMs) to assess the impacts of potential economic and emissions scenarios. However, true climate modeling capabilities are not disseminated to the majority of world governments or U.S. researchers - let alone to the educators who will be training the students who are about to be presented with a world full of climate change stakeholders. The goal is not entirely quixotic; in fact, by the mid-1990's prominent climate scientists were predicting with certainty that schools and politicians would "soon" be running GCMs on laptops [Randall, 1996]. For a variety of reasons this goal was never achieved (nor even really attempted). However, around the same time NASA and the National Science Foundation supported a small pilot project at Columbia University to show the potential of putting sophisticated computer climate models - not just "demos" or "toy models" - into the hands of non-specialists. The Educational Global Climate Modeling Project (EdGCM) gave users access to a real global climate model and provided them with the opportunity to experience the details of climate model setup, model operation, post-processing and scientific visualization. EdGCM was designed for use in both research and education - it is a full-blown research GCM, but the ultimate goal is to develop a capability to embed these crucial technologies across disciplines, networks, platforms, and even across academia and industry. With this capability in place we can begin training the skilled workforce that is necessary to deal with the multitude of climate impacts that will occur over the coming decades. To further increase the educational potential of climate models, the EdGCM project has also created "EZgcm". Through a joint venture of NASA, Columbia University and McGill University EZgcm moves the focus toward a greater use of Web 1.0 and Web 2.0-based technologies. It shifts the educational objectives towards a greater emphasis on teaching students how science is conducted and what role science plays in assessing climate change. That is, students learn about the steps of the scientific process as conveyed by climate modeling research: constructing a hypothesis, designing an experiment, running a computer model, using scientific visualization to support analysis, communicating the results of that analysis, and role playing the scientific peer review process. This is in stark contrast to what they learn from the political debate over climate change, which they often confuse with a scientific debate.

Why inputs matter: Selection of climatic variables for species distribution modelling in the Himalayan region

NASA Astrophysics Data System (ADS)

Bobrowski, Maria; Schickhoff, Udo

2017-04-01

Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to provide first time analysis of the potential distribution of Betula utilis in the subalpine and alpine belts of the Himalayan region using species distribution modelling. Using Generalized Linear Models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. Furthermore we evaluate the prediction ability of climate data derived from different statistical methods. GLMs were created using least correlated bioclimatic variables derived from two different climate models: 1) interpolated climate data (i.e. Worldclim, Hijmans et al., 2005) and 2) quasi-mechanistical statistical downscaling (i.e. Chelsa; Karger et al., 2016). Model accuracy was evaluated by the ability to predict the potential species distribution range. We found that models based on variables of Chelsa climate data had higher predictive power, whereas models using Worldclim climate data consistently overpredicted the potential suitable habitat for Betula utilis. Although climatic variables of Worldclim are widely used in modelling species distribution, our results suggest to treat them with caution when remote regions like the Himalayan mountains are in focus. Unmindful usage of climatic variables for species distribution models potentially cause misleading projections and may lead to wrong implications and recommendations for nature conservation. References: Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. & Jarvis, A. (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25, 1965-1978. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N., Linder, H.P. & Kessler, M. (2016) Climatologies at high resolution for the earth land surface areas. arXiv:1607.00217 [physics].

From Past to future: the Paleoclimate Modelling Intercomparison Project's contribution to CMIP6

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Harrison, Sandy; Haywood, Alan; Jungclaus, Johann; Otto-Bliesner, Bette; Abe-Ouchi, Ayako

2016-04-01

Since the 1990s, PMIP has developed with the following objectives: 1/to evaluate the ability of climate models used for climate prediction in simulating well-documented past climates outside the range of present and recent climate variability; 2/to understand the mechanisms of these climate changes, in particular the role of the different climate feedbacks. To achieve these goals, PMIP has actively fostered paleo-data syntheses, multi-model analyses, including analyses of relationships between model results from past and future simulations, and model-data comparisons. For CMIP6, PMIP will focus on five past periods: - the Last Millennium (850 CE - present), to analyse natural climate variability on multidecadal or longer time-scales - the mid-Holocene, 6000 years ago, to compare model runs with paleodata for a period of warmer climate in the Northern Hemisphere, with an enhanced hydrological cycle - the Last Glacial Maximum, 21000 years ago, to evaluate the ability of climate models to represent a cold climate extreme and examine whether paleoinformation about this period can help and constrain climate sensitivity - the Last InterGlacial (~127,000 year ago), which provides a benchmark for a period of high sea-level stand - the mid-Pliocene warm period (~3.2 million years ago), which allows for the evaluation of the model's long-term response to a CO2 level analogous to the modern one. This poster will present the rationale of these "PMIP4-CMIP6" experiments. Participants are invited to come and discuss about the experimental set-up and the model output to be distributed via CMIP6. For more information and discussion of the PMIP4-CMIP6 experimental design, please visit: https://wiki.lsce.ipsl.fr/pmip3/doku.php/pmip3:cmip6:design:index

Detection and Attribution of Simulated Climatic Extreme Events and Impacts: High Sensitivity to Bias Correction

NASA Astrophysics Data System (ADS)

Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Kirsten, T.; Mahecha, M. D.

2015-12-01

Understanding, quantifying and attributing the impacts of climatic extreme events and variability is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit pronounced biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. We assess how biases and their correction affect the quantification and attribution of simulated extremes and variability in i) climatological variables and ii) impacts on ecosystem functioning as simulated by a terrestrial biosphere model. Our study demonstrates that assessments of simulated climatic extreme events and impacts in the terrestrial biosphere are highly sensitive to bias correction schemes with major implications for the detection and attribution of these events. We introduce a novel ensemble-based resampling scheme based on a large regional climate model ensemble generated by the distributed weather@home setup[1], which fully preserves the physical consistency and multivariate correlation structure of the model output. We use extreme value statistics to show that this procedure considerably improves the representation of climatic extremes and variability. Subsequently, biosphere-atmosphere carbon fluxes are simulated using a terrestrial ecosystem model (LPJ-GSI) to further demonstrate the sensitivity of ecosystem impacts to the methodology of bias correcting climate model output. We find that uncertainties arising from bias correction schemes are comparable in magnitude to model structural and parameter uncertainties. The present study consists of a first attempt to alleviate climate model biases in a physically consistent way and demonstrates that this yields improved simulations of climate extremes and associated impacts. [1] http://www.climateprediction.net/weatherathome/

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Kubar, T. L.; Li, J.; Zhang, J.; Wang, W.

2015-12-01

Both the National Research Council Decadal Survey and the latest Intergovernmental Panel on Climate Change Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with the synergistic use of global satellite observations in order to improve our weather and climate simulation and prediction capabilities. The abundance of satellite observations for fundamental climate parameters and the availability of coordinated model outputs from CMIP5 for the same parameters offer a great opportunity to understand and diagnose model biases in climate models. In addition, the Obs4MIPs efforts have created several key global observational datasets that are readily usable for model evaluations. However, a model diagnostic evaluation process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. In response, we have developed a novel methodology to diagnose model biases in contemporary climate models and implementing the methodology as a web-service based, cloud-enabled, provenance-supported climate-model evaluation system. The evaluation system is named Climate Model Diagnostic Analyzer (CMDA), which is the product of the research and technology development investments of several current and past NASA ROSES programs. The current technologies and infrastructure of CMDA are designed and selected to address several technical challenges that the Earth science modeling and model analysis community faces in evaluating and diagnosing climate models. In particular, we have three key technology components: (1) diagnostic analysis methodology; (2) web-service based, cloud-enabled technology; (3) provenance-supported technology. The diagnostic analysis methodology includes random forest feature importance ranking, conditional probability distribution function, conditional sampling, and time-lagged correlation map. We have implemented the new methodology as web services and incorporated the system into the Cloud. We have also developed a provenance management system for CMDA where CMDA service semantics modeling, service search and recommendation, and service execution history management are designed and implemented.

A commentary on the Atlantic meridional overturning circulation stability in climate models

NASA Astrophysics Data System (ADS)

Gent, Peter R.

2018-02-01

The stability of the Atlantic meridional overturning circulation (AMOC) in ocean models depends quite strongly on the model formulation, especially the vertical mixing, and whether it is coupled to an atmosphere model. A hysteresis loop in AMOC strength with respect to freshwater forcing has been found in several intermediate complexity climate models and in one fully coupled climate model that has very coarse resolution. Over 40% of modern climate models are in a bistable AMOC state according to the very frequently used simple stability criterion which is based solely on the sign of the AMOC freshwater transport across 33° S. In a recent freshwater hosing experiment in a climate model with an eddy-permitting ocean component, the change in the gyre freshwater transport across 33° S is larger than the AMOC freshwater transport change. This casts very strong doubt on the usefulness of this simple AMOC stability criterion. If a climate model uses large surface flux adjustments, then these adjustments can interfere with the atmosphere-ocean feedbacks, and strongly change the AMOC stability properties. AMOC can be shut off for many hundreds of years in modern fully coupled climate models if the hosing or carbon dioxide forcing is strong enough. However, in one climate model the AMOC recovers after between 1000 and 1400 years. Recent 1% increasing carbon dioxide runs and RCP8.5 future scenario runs have shown that the AMOC reduction is smaller using an eddy-resolving ocean component than in the comparable standard 1° ocean climate models.

Climate and atmospheric modeling studies

NASA Technical Reports Server (NTRS)

1992-01-01

The climate and atmosphere modeling research programs have concentrated on the development of appropriate atmospheric and upper ocean models, and preliminary applications of these models. Principal models are a one-dimensional radiative-convective model, a three-dimensional global model, and an upper ocean model. Principal applications were the study of the impact of CO2, aerosols, and the solar 'constant' on climate.

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE PAGES

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

2016-01-01

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eum, Hyung-Il; Gachon, Philippe; Laprise, René

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affectedmore » by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.« less

Regional Climate Change across North America in 2030 Projected from RCP6.0

NASA Astrophysics Data System (ADS)

Otte, T.; Nolte, C. G.; Faluvegi, G.; Shindell, D. T.

2012-12-01

Projecting climate change scenarios to local scales is important for understanding and mitigating the effects of climate change on society and the environment. Many of the general circulation models (GCMs) that are participating in the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture local changes in temperature and precipitation extremes. We seek to project the GCM's large-scale climate change signal to the local scale using a regional climate model (RCM) by applying dynamical downscaling techniques. The RCM will be used to better understand the local changes of temperature and precipitation extremes that may result from a changing climate. In this research, downscaling techniques that we developed with historical data are now applied to GCM fields. Results from downscaling NASA/GISS ModelE2 simulations of the IPCC AR5 Representative Concentration Pathway (RCP) scenario 6.0 will be shown. The Weather Research and Forecasting (WRF) model has been used as the RCM to downscale decadal time slices for ca. 2000 and ca. 2030 over North America and illustrate potential changes in regional climate that are projected by ModelE2 and WRF under RCP6.0. The analysis focuses on regional climate fields that most strongly influence the interactions between climate change and air quality. In particular, an analysis of extreme temperature and precipitation events will be presented.

Impacts of Considering Climate Variability on Investment Decisions in Ethiopia

NASA Astrophysics Data System (ADS)

Strzepek, K.; Block, P.; Rosegrant, M.; Diao, X.

2005-12-01

In Ethiopia, climate extremes, inducing droughts or floods, are not unusual. Monitoring the effects of these extremes, and climate variability in general, is critical for economic prediction and assessment of the country's future welfare. The focus of this study involves adding climate variability to a deterministic, mean climate-driven agro-economic model, in an attempt to understand its effects and degree of influence on general economic prediction indicators for Ethiopia. Four simulations are examined, including a baseline simulation and three investment strategies: simulations of irrigation investment, roads investment, and a combination investment of both irrigation and roads. The deterministic model is transformed into a stochastic model by dynamically adding year-to-year climate variability through climate-yield factors. Nine sets of actual, historic, variable climate data are individually assembled and implemented into the 12-year stochastic model simulation, producing an ensemble of economic prediction indicators. This ensemble allows for a probabilistic approach to planning and policy making, allowing decision makers to consider risk. The economic indicators from the deterministic and stochastic approaches, including rates of return to investments, are significantly different. The predictions of the deterministic model appreciably overestimate the future welfare of Ethiopia; the predictions of the stochastic model, utilizing actual climate data, tend to give a better semblance of what may be expected. Inclusion of climate variability is vital for proper analysis of the predictor values from this agro-economic model.

Interactive Correlation Analysis and Visualization of Climate Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Kwan-Liu

The relationship between our ability to analyze and extract insights from visualization of climate model output and the capability of the available resources to make those visualizations has reached a crisis point. The large volume of data currently produced by climate models is overwhelming the current, decades-old visualization workflow. The traditional methods for visualizing climate output also have not kept pace with changes in the types of grids used, the number of variables involved, and the number of different simulations performed with a climate model or the feature-richness of high-resolution simulations. This project has developed new and faster methods formore » visualization in order to get the most knowledge out of the new generation of high-resolution climate models. While traditional climate images will continue to be useful, there is need for new approaches to visualization and analysis of climate data if we are to gain all the insights available in ultra-large data sets produced by high-resolution model output and ensemble integrations of climate models such as those produced for the Coupled Model Intercomparison Project. Towards that end, we have developed new visualization techniques for performing correlation analysis. We have also introduced highly scalable, parallel rendering methods for visualizing large-scale 3D data. This project was done jointly with climate scientists and visualization researchers at Argonne National Laboratory and NCAR.« less

The CESM Large Ensemble Project: Inspiring New Ideas and Understanding

NASA Astrophysics Data System (ADS)

Kay, J. E.; Deser, C.

2016-12-01

While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920-2100) 40+ times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 2000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Examples of scientists and stakeholders that are using the CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change will be highlighted the presentation.

Hierarchical stochastic modeling of large river ecosystems and fish growth across spatio-temporal scales and climate models: the Missouri River endangered pallid sturgeon example

USGS Publications Warehouse

Wildhaber, Mark L.; Wikle, Christopher K.; Moran, Edward H.; Anderson, Christopher J.; Franz, Kristie J.; Dey, Rima

2017-01-01

We present a hierarchical series of spatially decreasing and temporally increasing models to evaluate the uncertainty in the atmosphere – ocean global climate model (AOGCM) and the regional climate model (RCM) relative to the uncertainty in the somatic growth of the endangered pallid sturgeon (Scaphirhynchus albus). For effects on fish populations of riverine ecosystems, cli- mate output simulated by coarse-resolution AOGCMs and RCMs must be downscaled to basins to river hydrology to population response. One needs to transfer the information from these climate simulations down to the individual scale in a way that minimizes extrapolation and can account for spatio-temporal variability in the intervening stages. The goal is a framework to determine whether, given uncertainties in the climate models and the biological response, meaningful inference can still be made. The non-linear downscaling of climate information to the river scale requires that one realistically account for spatial and temporal variability across scale. Our down- scaling procedure includes the use of fixed/calibrated hydrological flow and temperature models coupled with a stochastically parameterized sturgeon bioenergetics model. We show that, although there is a large amount of uncertainty associated with both the climate model output and the fish growth process, one can establish significant differences in fish growth distributions between models, and between future and current climates for a given model.

Characterization and Quantification of Uncertainty in the NARCCAP Regional Climate Model Ensemble and Application to Impacts on Water Systems

NASA Astrophysics Data System (ADS)

Mearns, L. O.; Sain, S. R.; McGinnis, S. A.; Steinschneider, S.; Brown, C. M.

2015-12-01

In this talk we present the development of a joint Bayesian Probabilistic Model for the climate change results of the North American Regional Climate Change Assessment Program (NARCCAP) that uses a unique prior in the model formulation. We use the climate change results (joint distribution of seasonal temperature and precipitation changes (future vs. current)) from the global climate models (GCMs) that provided boundary conditions for the six different regional climate models used in the program as informative priors for the bivariate Bayesian Model. The two variables involved are seasonal temperature and precipitation over sub-regions (i.e., Bukovsky Regions) of the full NARCCAP domain. The basic approach to the joint Bayesian hierarchical model follows the approach of Tebaldi and Sansó (2009). We compare model results using informative (i.e., GCM information) as well as uninformative priors. We apply these results to the Water Evaluation and Planning System (WEAP) model for the Colorado Springs Utility in Colorado. We investigate the layout of the joint pdfs in the context of the water model sensitivities to ranges of temperature and precipitation results to determine the likelihoods of future climate conditions that cannot be accommodated by possible adaptation options. Comparisons may also be made with joint pdfs formed from the CMIP5 collection of global climate models and empirically downscaled to the region of interest.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality

PubMed Central

Hondula, David M.; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-01-01

Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634 PMID:28885979

Climate Model Diagnostic and Evaluation: With a Focus on Satellite Observations

NASA Technical Reports Server (NTRS)

Waliser, Duane

2011-01-01

Each year, we host a summer school that brings together the next generation of climate scientists - about 30 graduate students and postdocs from around the world - to engage with premier climate scientists from the Jet Propulsion Laboratory and elsewhere. Our yearly summer school focuses on topics on the leading edge of climate science research. Our inaugural summer school, held in 2011, was on the topic of "Using Satellite Observations to Advance Climate Models," and enabled students to explore how satellite observations can be used to evaluate and improve climate models. Speakers included climate experts from both NASA and the National Oceanic and Atmospheric Administration (NOAA), who provided updates on climate model diagnostics and evaluation and remote sensing of the planet. Details of the next summer school will be posted here in due course.

Climate change unlikely to increase malaria burden in West Africa

NASA Astrophysics Data System (ADS)

Yamana, Teresa K.; Bomblies, Arne; Eltahir, Elfatih A. B.

2016-11-01

The impact of climate change on malaria transmission has been hotly debated. Recent conclusions have been drawn using relatively simple biological models and statistical approaches, with inconsistent predictions. Consequently, the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) echoes this uncertainty, with no clear guidance for the impacts of climate change on malaria transmission, yet recognizing a strong association between local climate and malaria. Here, we present results from a decade-long study involving field observations and a sophisticated model simulating village-scale transmission. We drive the malaria model using select climate models that correctly reproduce historical West African climate, and project reduced malaria burden in a western sub-region and insignificant impact in an eastern sub-region. Projected impacts of climate change on malaria transmission in this region are not of serious concern.

Modeling the impact of climate change in Germany with biosphere models for long-term safety assessment of nuclear waste repositories.

PubMed

Staudt, C; Semiochkina, N; Kaiser, J C; Pröhl, G

2013-01-01

Biosphere models are used to evaluate the exposure of populations to radionuclides from a deep geological repository. Since the time frame for assessments of long-time disposal safety is 1 million years, potential future climate changes need to be accounted for. Potential future climate conditions were defined for northern Germany according to model results from the BIOCLIM project. Nine present day reference climate regions were defined to cover those future climate conditions. A biosphere model was developed according to the BIOMASS methodology of the IAEA and model parameters were adjusted to the conditions at the reference climate regions. The model includes exposure pathways common to those reference climate regions in a stylized biosphere and relevant to the exposure of a hypothetical self-sustaining population at the site of potential radionuclide contamination from a deep geological repository. The end points of the model are Biosphere Dose Conversion factors (BDCF) for a range of radionuclides and scenarios normalized for a constant radionuclide concentration in near-surface groundwater. Model results suggest an increased exposure of in dry climate regions with a high impact of drinking water consumption rates and the amount of irrigation water used for agriculture. Copyright © 2012 Elsevier Ltd. All rights reserved.

Quantitative Decision Support Requires Quantitative User Guidance

NASA Astrophysics Data System (ADS)

Smith, L. A.

2009-12-01

Is it conceivable that models run on 2007 computer hardware could provide robust and credible probabilistic information for decision support and user guidance at the ZIP code level for sub-daily meteorological events in 2060? In 2090? Retrospectively, how informative would output from today’s models have proven in 2003? or the 1930’s? Consultancies in the United Kingdom, including the Met Office, are offering services to “future-proof” their customers from climate change. How is a US or European based user or policy maker to determine the extent to which exciting new Bayesian methods are relevant here? or when a commercial supplier is vastly overselling the insights of today’s climate science? How are policy makers and academic economists to make the closely related decisions facing them? How can we communicate deep uncertainty in the future at small length-scales without undermining the firm foundation established by climate science regarding global trends? Three distinct aspects of the communication of the uses of climate model output targeting users and policy makers, as well as other specialist adaptation scientists, are discussed. First, a brief scientific evaluation of the length and time scales at which climate model output is likely to become uninformative is provided, including a note on the applicability the latest Bayesian methodology to current state-of-the-art general circulation models output. Second, a critical evaluation of the language often employed in communication of climate model output, a language which accurately states that models are “better”, have “improved” and now “include” and “simulate” relevant meteorological processed, without clearly identifying where the current information is thought to be uninformative and misleads, both for the current climate and as a function of the state of the (each) climate simulation. And thirdly, a general approach for evaluating the relevance of quantitative climate model output for a given problem is presented. Based on climate science, meteorology, and the details of the question in hand, this approach identifies necessary (never sufficient) conditions required for the rational use of climate model output in quantitative decision support tools. Inasmuch as climate forecasting is a problem of extrapolation, there will always be harsh limits on our ability to establish where a model is fit for purpose, this does not, however, limit us from identifying model noise as such, and thereby avoiding some cases of the misapplication and over interpretation of model output. It is suggested that failure to clearly communicate the limits of today’s climate model in providing quantitative decision relevant climate information to today’s users of climate information, would risk the credibility of tomorrow’s climate science and science based policy more generally.

Short-term climate change impacts on Mara basin hydrology

NASA Astrophysics Data System (ADS)

Demaria, E. M.; Roy, T.; Valdés, J. B.; Lyon, B.; Valdés-Pineda, R.; Serrat-Capdevila, A.; Durcik, M.; Gupta, H.

2017-12-01

The predictability of climate diminishes significantly at shorter time scales (e.g. decadal). Both natural variability as well as sampling variability of climate can obscure or enhance climate change signals in these shorter scales. Therefore, in order to assess the impacts of climate change on basin hydrology, it is important to design climate projections with exhaustive climate scenarios. In this study, we first create seasonal climate scenarios by combining (1) synthetic precipitation projections generated from a Vector Auto-Regressive (VAR) model using the University of East Anglia Climate Research Unit (UEA-CRU) data with (2) seasonal trends calculated from 31 models in the Coupled Model Intercomparison Project Phase 5 (CMIP). The seasonal climate projections are then disaggregated to daily level using the Agricultural Modern-Era Retrospective Analysis for Research and Applications (AgMERRA) data. The daily climate data are then bias-corrected and used as forcings to the land-surface model, Variable Infiltration Capacity (VIC), to generate different hydrological projections for the Mara River basin in East Africa, which are then evaluated to study the hydrologic changes in the basin in the next three decades (2020-2050).

Detection of greenhouse-gas-induced climatic change. Progress report, July 1, 1994--July 31, 1995

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jones, P.D.; Wigley, T.M.L.

1995-07-21

The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics.more » To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.« less

Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions.

PubMed

Mosedale, Jonathan R; Wilson, Robert J; Maclean, Ilya M D

2015-01-01

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions.

Climate Change and Crop Exposure to Adverse Weather: Changes to Frost Risk and Grapevine Flowering Conditions

PubMed Central

Mosedale, Jonathan R.; Wilson, Robert J.; Maclean, Ilya M. D.

2015-01-01

The cultivation of grapevines in the UK and many other cool climate regions is expected to benefit from the higher growing season temperatures predicted under future climate scenarios. Yet the effects of climate change on the risk of adverse weather conditions or events at key stages of crop development are not always captured by aggregated measures of seasonal or yearly climates, or by downscaling techniques that assume climate variability will remain unchanged under future scenarios. Using fine resolution projections of future climate scenarios for south-west England and grapevine phenology models we explore how risks to cool-climate vineyard harvests vary under future climate conditions. Results indicate that the risk of adverse conditions during flowering declines under all future climate scenarios. In contrast, the risk of late spring frosts increases under many future climate projections due to advancement in the timing of budbreak. Estimates of frost risk, however, were highly sensitive to the choice of phenology model, and future frost exposure declined when budbreak was calculated using models that included a winter chill requirement for dormancy break. The lack of robust phenological models is a major source of uncertainty concerning the impacts of future climate change on the development of cool-climate viticulture in historically marginal climatic regions. PMID:26496127

Multi-objective optimization for evaluation of simulation fidelity for precipitation, cloudiness and insolation in regional climate models

NASA Astrophysics Data System (ADS)

Lee, H.

2016-12-01

Precipitation is one of the most important climate variables that are taken into account in studying regional climate. Nevertheless, how precipitation will respond to a changing climate and even its mean state in the current climate are not well represented in regional climate models (RCMs). Hence, comprehensive and mathematically rigorous methodologies to evaluate precipitation and related variables in multiple RCMs are required. The main objective of the current study is to evaluate the joint variability of climate variables related to model performance in simulating precipitation and condense multiple evaluation metrics into a single summary score. We use multi-objective optimization, a mathematical process that provides a set of optimal tradeoff solutions based on a range of evaluation metrics, to characterize the joint representation of precipitation, cloudiness and insolation in RCMs participating in the North American Regional Climate Change Assessment Program (NARCCAP) and Coordinated Regional Climate Downscaling Experiment-North America (CORDEX-NA). We also leverage ground observations, NASA satellite data and the Regional Climate Model Evaluation System (RCMES). Overall, the quantitative comparison of joint probability density functions between the three variables indicates that performance of each model differs markedly between sub-regions and also shows strong seasonal dependence. Because of the large variability across the models, it is important to evaluate models systematically and make future projections using only models showing relatively good performance. Our results indicate that the optimized multi-model ensemble always shows better performance than the arithmetic ensemble mean and may guide reliable future projections.

A fresh look at the Last Glacial Maximum using Paleoclimate Data Assimilation

NASA Astrophysics Data System (ADS)

Malevich, S. B.; Tierney, J. E.; Hakim, G. J.; Tardif, R.

2017-12-01

Quantifying climate conditions during the Last Glacial Maximum ( 21ka) can help us to understand climate responses to forcing and climate states that are poorly represented in the instrumental record. Paleoclimate proxies may be used to estimate these climate conditions, but proxies are sparsely distributed and possess uncertainties from environmental and biogeochemical processes. Alternatively, climate model simulations provide a full-field view, but may predict unrealistic climate states or states not faithful to proxy records. Here, we use data assimilation - combining climate proxy records with a theoretical understanding from climate models - to produce field reconstructions of the LGM that leverage the information from both data and models. To date, data assimilation has mainly been used to produce reconstructions of climate fields through the last millennium. We expand this approach in order to produce a climate fields for the Last Glacial Maximum using an ensemble Kalman filter assimilation. Ensemble samples were formed from output from multiple models including CCSM3, CESM2.1, and HadCM3. These model simulations are combined with marine sediment proxies for upper ocean temperature (TEX86, UK'37, Mg/Ca and δ18O of foraminifera), utilizing forward models based on a newly developed suite of Bayesian proxy system models. We also incorporate age model and radiocarbon reservoir uncertainty into our reconstructions using Bayesian age modeling software. The resulting fields show familiar patterns based on comparison with previous proxy-based reconstructions, but additionally reveal novel patterns of large-scale shifts in ocean-atmosphere dynamics, as the surface temperature data inform upon atmospheric circulation and precipitation patterns.

Probabilistic Climate Scenario Information for Risk Assessment

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Takayabu, I.

2014-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in Japan, we compared the physics ensemble experiments using the 60km global atmospheric model of the Meteorological Research Institute (MRI-AGCM) with multi-model ensemble experiments with global atmospheric-ocean coupled models (CMIP3) of SRES A1b scenario experiments. The MRI-AGCM shows relatively good skills particularly in tropics for temperature and geopotential height. Variability in surface air temperature of physical ensemble experiments with MRI-AGCM was within the range of one standard deviation of the CMIP3 model in the Asia region. On the other hand, the variability of precipitation was relatively well represented compared with the variation of the CMIP3 models. Models which show the similar reproducibility in the present climate shows different future climate change. We couldn't find clear relationships between present climate and future climate change in temperature and precipitation. We develop a new method to produce probabilistic information of climate change scenarios by weighting model ensemble experiments based on a regression model (Krishnamurti et al., Science, 1999). The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. The prototype of probabilistic information in Japan represents the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Acknowledgments: This study was supported by the SOUSEI Program, funded by Ministry of Education, Culture, Sports, Science and Technology, Government of Japan.

Ocean Modeling and Visualization on Massively Parallel Computer

NASA Technical Reports Server (NTRS)

Chao, Yi; Li, P. Peggy; Wang, Ping; Katz, Daniel S.; Cheng, Benny N.

1997-01-01

Climate modeling is one of the grand challenges of computational science, and ocean modeling plays an important role in both understanding the current climatic conditions and predicting future climate change.

Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change

PubMed Central

Porfirio, Luciana L.; Harris, Rebecca M. B.; Lefroy, Edward C.; Hugh, Sonia; Gould, Susan F.; Lee, Greg; Bindoff, Nathaniel L.; Mackey, Brendan

2014-01-01

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models. PMID:25420020

Modelling climate impact on floods under future emission scenarios using an ensemble of climate model projections

NASA Astrophysics Data System (ADS)

Wetterhall, F.; Cloke, H. L.; He, Y.; Freer, J.; Pappenberger, F.

2012-04-01

Evidence provided by modelled assessments of climate change impact on flooding is fundamental to water resource and flood risk decision making. Impact models usually rely on climate projections from Global and Regional Climate Models, and there is no doubt that these provide a useful assessment of future climate change. However, cascading ensembles of climate projections into impact models is not straightforward because of problems of coarse resolution in Global and Regional Climate Models (GCM/RCM) and the deficiencies in modelling high-intensity precipitation events. Thus decisions must be made on how to appropriately pre-process the meteorological variables from GCM/RCMs, such as selection of downscaling methods and application of Model Output Statistics (MOS). In this paper a grand ensemble of projections from several GCM/RCM are used to drive a hydrological model and analyse the resulting future flood projections for the Upper Severn, UK. The impact and implications of applying MOS techniques to precipitation as well as hydrological model parameter uncertainty is taken into account. The resultant grand ensemble of future river discharge projections from the RCM/GCM-hydrological model chain is evaluated against a response surface technique combined with a perturbed physics experiment creating a probabilisic ensemble climate model outputs. The ensemble distribution of results show that future risk of flooding in the Upper Severn increases compared to present conditions, however, the study highlights that the uncertainties are large and that strong assumptions were made in using Model Output Statistics to produce the estimates of future discharge. The importance of analysing on a seasonal basis rather than just annual is highlighted. The inability of the RCMs (and GCMs) to produce realistic precipitation patterns, even in present conditions, is a major caveat of local climate impact studies on flooding, and this should be a focus for future development.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Ishizaki, N. N.

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Desert dust and anthropogenic aerosol interactions in the Community Climate System Model coupled-carbon-climate model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mahowald, Natalie; Rothenberg, D.; Lindsay, Keith

2011-02-01

Coupled-carbon-climate simulations are an essential tool for predicting the impact of human activity onto the climate and biogeochemistry. Here we incorporate prognostic desert dust and anthropogenic aerosols into the CCSM3.1 coupled carbon-climate model and explore the resulting interactions with climate and biogeochemical dynamics through a series of transient anthropogenic simulations (20th and 21st centuries) and sensitivity studies. The inclusion of prognostic aerosols into this model has a small net global cooling effect on climate but does not significantly impact the globally averaged carbon cycle; we argue that this is likely to be because the CCSM3.1 model has a small climatemore » feedback onto the carbon cycle. We propose a mechanism for including desert dust and anthropogenic aerosols into a simple carbon-climate feedback analysis to explain the results of our and previous studies. Inclusion of aerosols has statistically significant impacts on regional climate and biogeochemistry, in particular through the effects on the ocean nitrogen cycle and primary productivity of altered iron inputs from desert dust deposition.« less

The Radiative Forcing Model Intercomparison Project (RFMIP): Assessment and characterization of forcing to enable feedback studies

NASA Astrophysics Data System (ADS)

Pincus, R.; Stevens, B. B.; Forster, P.; Collins, W.; Ramaswamy, V.

2014-12-01

The Radiative Forcing Model Intercomparison Project (RFMIP): Assessment and characterization of forcing to enable feedback studies An enormous amount of attention has been paid to the diversity of responses in the CMIP and other multi-model ensembles. This diversity is normally interpreted as a distribution in climate sensitivity driven by some distribution of feedback mechanisms. Identification of these feedbacks relies on precise identification of the forcing to which each model is subject, including distinguishing true error from model diversity. The Radiative Forcing Model Intercomparison Project (RFMIP) aims to disentangle the role of forcing from model sensitivity as determinants of varying climate model response by carefully characterizing the radiative forcing to which such models are subject and by coordinating experiments in which it is specified. RFMIP consists of four activities: 1) An assessment of accuracy in flux and forcing calculations for greenhouse gases under past, present, and future climates, using off-line radiative transfer calculations in specified atmospheres with climate model parameterizations and reference models 2) Characterization and assessment of model-specific historical forcing by anthropogenic aerosols, based on coordinated diagnostic output from climate models and off-line radiative transfer calculations with reference models 3) Characterization of model-specific effective radiative forcing, including contributions of model climatology and rapid adjustments, using coordinated climate model integrations and off-line radiative transfer calculations with a single fast model 4) Assessment of climate model response to precisely-characterized radiative forcing over the historical record, including efforts to infer true historical forcing from patterns of response, by direct specification of non-greenhouse-gas forcing in a series of coordinated climate model integrations This talk discusses the rationale for RFMIP, provides an overview of the four activities, and presents preliminary motivating results.

CMIP5-downscaled projections for the NW European Shelf Seas: initial results and insights into uncertainties

NASA Astrophysics Data System (ADS)

Tinker, Jonathan; Palmer, Matthew; Lowe, Jason; Howard, Tom

2017-04-01

The North Sea, and wider Northwest European Shelf seas (NWS) are economically, environmentally, and culturally important for a number of European countries. They are protected by European legislation, often with specific reference to the potential impacts of climate change. Coastal climate change projections are an important source of information for effective management of European Shelf Seas. For example, potential changes in the marine environment are a key component of the climate change risk assessments (CCRAs) carried out under the UK Climate Change Act We use the NEMO shelf seas model combined with CMIP5 climate model and EURO-CORDEX regional atmospheric model data to generate new simulations of the NWS. Building on previous work using a climate model perturbed physics ensemble and the POLCOMS, this new model setup is used to provide first indication of the uncertainties associated with: (i) the driving climate model; (ii) the atmospheric downscaling model (iii) the shelf seas downscaling model; (iv) the choice of climate change scenario. Our analysis considers a range of physical marine impacts and the drivers of coastal variability and change, including sea level and the propagation of open ocean signals onto the shelf. The simulations are being carried out as part of the UK Climate Projections 2018 (UKCP18) and will feed into the following UK CCRA.

Pronounced differences between observed and CMIP5-simulated multidecadal climate variability in the twentieth century

NASA Astrophysics Data System (ADS)

Kravtsov, Sergey

2017-06-01

Identification and dynamical attribution of multidecadal climate undulations to either variations in external forcings or to internal sources is one of the most important topics of modern climate science, especially in conjunction with the issue of human-induced global warming. Here we utilize ensembles of twentieth century climate simulations to isolate the forced signal and residual internal variability in a network of observed and modeled climate indices. The observed internal variability so estimated exhibits a pronounced multidecadal mode with a distinctive spatiotemporal signature, which is altogether absent in model simulations. This single mode explains a major fraction of model-data differences over the entire climate index network considered; it may reflect either biases in the models' forced response or models' lack of requisite internal dynamics, or a combination of both.