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It is well known that United States paper currency in general circulation is contaminated with trace amounts of illicit substances such as cocaine, heroin and marijuana. As is the case with cocaine, differentiating "background levels" of the various cannabinoid constituents of Cannabis sativa L., namely, Delta(9)-tetrahydrocannabinol (THC), cannabinol (CBN), and cannabidiol (CBD) contaminating currency found in the general circulation from currency associated with illegal drug activity is imperative if a legal nexus is to be established with the latter. We analyzed 165 randomly collected paper currency notes from 12 U.S. cities (N = 125) and 4 foreign countries (N = 40) for THC, CBD, CBN, 11-nor-9-carboxy-Delta(9)-tetrahydrocannabinol, and 11-hydroxy-Delta(9)-tetrahydrocannabinol. Uncirculated US 1 dollar notes were added as negative controls. Drug residues were washed from individual bills, extracted using a liquid-liquid extraction protocol, derivatized, and quantitated by gas chromatography-mass spectrometry by selected ion monitoring. For the US 1 dollar currency, THC was present in 1.6% (2 notes), CBN 10.31% (13 notes), CBD 1.6% (2 notes). The following concentrations were determined: 0.085 microg/bill and 0.146 microg/bill for THC; 0.014-0.774 microg/bill (mean 0.166 microg/bill) for CBN; and 0.032 microg/bill and 0.086 microg/bill for CBD. For the foreign currency (Colombia, Qatar, India, and New Zealand), THC and CBN were present in 22.5% (9 notes). The following concentration ranges were determined: THC 0.026-0.065 microg/bill (mean 0.049 microg/bill), CBN 0.061-0.197 microg/bill (mean 0.115 microg/bill). All of the positive THC and CBN were found in the New Zealand polypropylene notes. This study demonstrated that marijuana (cannabinoids) may contaminate both paper and plastic currency.

It is known that US paper currency in the general circulation is contaminated with cocaine. Several mechanisms have been offered to explain this finding, including contamination due to handling during drug deals and the use of rolled up bills for snorting. Drug is then transferred from one contaminated bill to others during counting in financial institutions. The possibility of contamination of currency with other drugs has not been reported. In this study, the author reports the analysis of 10 randomly collected US$ 1 bills from five cities, for cocaine, heroin, 6-acetylmorphine (6-AM), morphine, codeine, methamphetamine, amphetamine and phencyclidine (PCP). Bills were immersed in acetonitrile for 2h prior to extraction and GC-MS analysis. Results showed that 92% of the bills were positive for cocaine with a mean amount of 28.75+/-139.07 microg per bill, a median of 1.37 microg per bill, and a range of 0.01-922.72 microg per bill. Heroin was detected in seven bills in amounts ranging from 0.03 to 168.50 microg per bill: 6-AM and morphine were detected in three bills; methamphetamine and amphetamine in three and one bills, respectively, and PCP was detected in two bills in amounts of 0.78 and 1.87 microg per bill. Codeine was not detected in any of the US$ 1 bills analyzed. This study demonstrated that although paper currency was most often contaminated with cocaine, other drugs of abuse may be detected in bills.

The presence of cocaine on US paper currency has been known for a long time. Banknotes become contaminated during the exchange, storage, and abuse of cocaine. The analysis of cocaine on various denominations of US banknotes in the general circulation can provide law enforcement circles and forensic epidemiologists objective and timely information on epidemiology of illicit drug use and on how to differentiate money contaminated in the general circulation from banknotes used in drug transaction. A simple, nondestructive, and accurate capillary gas chromatographic method has been developed for the determination of cocaine on various denominations of US banknotes in this study. The method comprises a fast ultrasonic extraction using water as a solvent followed by a SPE cleanup process with a C(18) cartridge and capillary GC separation, identification, and quantification. This nondestructive analytical method has been successfully applied to determine the cocaine contamination in US paper currency of all denominations. Standard calibration curve was linear over the concentration range from the LOQ (2.00 ng/mL) to 100 microg/mL and the RSD less than 2.0%. Cocaine was detected in 67% of the circulated banknotes collected in Southeastern Massachusetts in amounts ranging from approximately 2 ng to 49.4 microg per note. On average, $5, 10, 20, and 50 denominations contain higher amounts of cocaine than $1 and 100 denominations of US banknotes.

A study of the quantities of cocaine on banknotes in general circulation was conducted to investigate regional variations across England and Wales. No meaningful support was found for the proposition that there is regionalvariation in the quantities of cocaine in banknotes in general circulation in England and Wales.

A procedure is described for the determination of the distribution of the contamination of banknotes with controlled drugs using tandem mass spectrometry. The method is illustrated using diacetylmorphine, which is the major active component of heroin. A series of banknotes is introduced into the mass spectrometer and the intensities of two product ions (m/z 328 and 268) derived from the precursor protonated molecule (m/z 370) are recorded. A banknote is considered contaminated if it shows a significant peak for both product ions, and if the ratio of intensities of these two peaks falls within accepted limits. The distribution of diacetylmorphine on sterling banknotes taken from general circulation within the UK can be modelled by an arcsin (square root) transformation of the data or by a log transformation of the data with a higher proportion of contamination. The two models were found to be in close agreement, predicting an upper limit (at 99.9% confidence) of contamination on banknotes from general circulation between 9 and 10%. The percentage contamination in a case study was calculated and compared to the background distribution using the two models proposed. This comparison revealed that the contamination present in the case study was inconsistent with that present on banknotes in general circulation.

Rats were used to study the fate of a radioactive antibiotic placed on injured pulpal tissue. Radioactivity found in the general circulation within 10 minutes after application was shown to be due to protein-bound, native, tritiated antibiotic.

In statistical downscaling technique, regional or local information are derived by determining a statistical model which relates large-scale climate variables or predictors generated by Global Climate Models (GCMs) to regional and local variables or predictands. In this paper, the results of GCMs were statistically downscaled to produce future climate projections of mean temperature in the post-monsoon season (October and November), for the time periods 2021-2050 and 2071-2100 for Bangladesh. The future climate projections are based on the three emission scenarios RCP2.6, RCP4.5 and RCP8.5 provided by the fifth Coupled Model Intercomparison Project (CMIP5). This paper established a method to analyze GCMs for use in statistical downscaling and utilized fifteen GCMs. The GCMs were assessed based upon their performance in simulated past climate in Bangladesh and adjoining areas. Downscaling was undertaken by linking large scale climate variables, taken from the ERA-Interim (resolution 79 km) reanalysis temperature, a gridded data set incorporating observations and climate models, to local scale observations. Overall, all fifteen GCMs, via statistical downscaling, show that mean temperature of the post-monsoon season in Bangladesh will increase under future climate scenarios. Comparing the ensemble of future projections with the reference period (1981- 2010), the mean post-monsoon temperature in Bangladesh is projected for RCP8.5 showing warming by 0.310C in near future and 1.790C in far future. On the other hand, estimated warming is 0.390C in near future and 1.140C is far future for RCP4.5. Low emission scenarios RCP2.6, near future temperature is nearly same the far future temperature. Journal of Engineering Science 11(2), 2020, 27-35

Additional warming caused by climate change will negative impacts on agricultural sectors. The problem is expected to be most harsh in regions were lacking in adaptive capacity in terms of information needed to make adaptive decisions to combat climate change impacts. Many strides have been made to understand and quantify climate impact on sensitive regions to climate change. However, for many food-insecurity hot-spot areas, the comprehensive data for impact assessment are rarely available. This paper outlined a research framework for providing knowledge for policy-makers, local authorities, and agricultural organizations to make informative climate change adaptive decisions for such areas. Several models are compared and discussed to show their advantage and disadvantage for appropriate model applications. We propose a framework to facilitate future impact assessment research, especially for regions where relevant data are lacking. This paper provides a research method of linking different models and combines the useful information acquired from models to make climate policies and adaption schemes cost-effectively with limited local data and farm information.

Global climate models (GCMs) are developed to simulate past climate and produce projections of climate in future. Their roles in ascertaining regional issues and possible solutions in water resources planning/management are appreciated across the world. However, there is substantial uncertainty in the future projections of GCM(s) for practical and regional implementation which has attracted criticism by the water resources planners. The present paper aims at reviewing the selection of GCMs and focusing on performance indicators, ranking of GCMs and ensembling of GCMs and covering different geographical regions. In addition, this paper also proposes future research directions.

Cold extremes are anticipated to warm at a faster rate than both hot extremes and average temperatures for much of the Northern Hemisphere. Anomalously warm cold extremes can affect numerous sectors, including human health, tourism and various ecosystems that are sensitive to cold temperatures. Using a selection of global climate models, this paper explores the accelerated warming of seasonal cold extremes relative to seasonal mean temperatures in the Northern Hemisphere extratropics. The potential driving physical mechanisms are investigated by assessing conditions on or prior to the day when the cold extreme occurs to understand how the different environmental fields are related. During winter, North America, Europe and much of Eurasia show amplified warming of cold extremes projected for the late 21st century, compared to the mid-20th century. This is shown to be largely driven by reductions in cold air temperature advection, suggested as a likely consequence of Arctic amplification. In spring and autumn, cold extremes are expected to warm faster than average temperatures for most of the Northern Hemisphere mid-latitudes to high latitudes, particularly Alaska, northern Canada and northern Eurasia. In the shoulder seasons, projected decreases in snow cover and associated reductions in surface albedo are suggested as the largest contributor affecting the accelerated rates of warming in cold extremes. The key findings of this study improve our understanding of the environmental conditions that contribute to the accelerated warming of cold extremes relative to mean temperatures. This study was supported by the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (grant CE170100023). Markus G. Donat received funding from the ARC (grant DE150100456) and the Spanish Ministry for the Economy, Industry and Competitiveness Ramón y Cajal 2017 grant reference RYC-2017-22964. We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Table 1 of this paper) for producing and making their model output available.

NGIMS data sets used in this paper are available on the Planetary Data System (http://pds-atmospheres.nmsu.edu/data_ and_services/atmospheres_data/MAVEN/ngims.html) as well as EUV data sets (https://pds-ppi.igpp.ucla.edu/search/view/?f=yes&id=pds://PPI/maven.euv.modelled). Data cubes containing M-GITM simulations used in this paper can be found on the University of Michigan Deep Blue repository (http://doi.org/10.7302/85qt-e980)., Using a new observational technique, the NGIMS (Neutral Gas and Ion Mass Spectrometer) instrument on the MAVEN (Mars Atmosphere and Volatile Evolution) spacecraft has the unique capability to measure horizontal thermospheric winds. Measured along the orbit track from periapsis (∼150 km) to ∼200 km, these are the first in situ observations of thermospheric winds at Mars. Significantly, this also means that simulated winds from a global circulation model can be compared to in situ observations from this part of the Martian atmosphere for the first time. In this study, observations from five NGIMS neutral wind campaigns have been compared to simulations from the Mars Global Ionosphere-Thermosphere Model (M-GITM), a ground to exosphere 3-D general circulation model. By comparing NGIMS neutral wind observations to model simulations, the processes driving the winds and their variations in the upper atmosphere are examined. These comparisons show that for certain observational periods, the M-GITM simulated winds can generally replicate the magnitude and/or direction of the NGIMS wind observations, while in others, significant differences occur. In general, wind observations from NGIMS campaigns with large orbit-to-orbit variability are not well replicated by M-GITM, while campaigns with higher observed wind speeds are better captured by the model. Additionally, using these data-model comparisons, the relative role of normal solar forcing and corresponding differential heating in driving thermospheric winds at Mars is found to be variable, likely acting as the primary driver under some conditions and secondary to other physical processes under others.©2019. American Geophysical Union. All Rights Reserved., Funding support for this research was provided by the MAVEN project, Grant NNH10CC04C. This work was also supported by NASA Headquarters under the NASA Earth and Space Science Fellowship Program - Grant 80NSSC18K1238. Funding for F. Gonzalez-Galindo and M. A. Lopez-Valverde was provided by the Spanish National Research Council under intramural project CSIC 201450E022

Dynamical downscaling (DD) consists in using archives of Coupled Global Climate Models (CGCM) simulations as the atmospheric and sea-surface boundary conditions (BC) to drive nested, Regional Climate Model (RCM) simulations. Biases in the CGCM-generated driving BC, however, can have detrimental impacts on RCM performance. It is well documented for the historical period that CGCM-simulated sea-surface temperatures (SST) suffer substantial biases, especially important near coastal regions. Assuming that these SST biases are time-invariant, they could in principle be subtracted from century-long CGCM projections before being used to drive RCMs. This paper investigates the performance of a 3-step DD approach as follows. The CGCM-simulated sea-surface temperatures (SST) are first empirically corrected by subtracting their systematic biases; the corrected SST are then used as ocean surface BC for an atmosphere-only GCM (AGCM) simulation; finally this AGCM simulation provides the atmospheric lateral BC to drive an RCM simulation. This is what we refer to as the 3-step approach CGCM–AGCM–RCM of DD, which can be compared to the traditional 2-step approach CGCM–RCM consisting of driving an RCM simulation directly by CGCM-generated BC. In this paper we compare the results obtained with the two approaches, for present and future climates under RCP8.5, using the fifth-generation Canadian Regional Climate Model (CRCM5) with a grid mesh of 0.22° over the North American CORDEX domain, driven by two CMIP5 models: the Canadian Earth System Model of the Canadian Centre for Climate modelling and analysis (CanESM2) and the Earth System Model of the Max-Planck-Institut fur Meteorologie (MPI-ESM-MR). The results show that, in current climate, the seasonal-mean 2-m temperature fields simulated with the 3-step DD have generally smaller biases with respect to the observations than those simulated with the 2-step DD; in fact the performance of the 3-step DD simulations often approaches that of the reanalyses-driven simulation. For the seasonal-mean precipitation field, however, the differences between the two DD methods are not conclusive. Differences between the projected climate changes with the two DD methods vary substantially depending upon the variable being considered. Differences are particularly important for temperature: over the bulk of the North American continent, the 3-step DD projects more warming in winter and less in summer. This result highlights the nonlinearities of the climate system, and constitutes an additional measure of uncertainty with DD.

Changes in winter low flow (WLF) in cold-region rivers affect engineering design, water quality, and river ecosystem health. This paper aims to quantify to what extent WLF will change under climate warming and to propose a practical forecasting model for WLF predictions. The new concept of cumulative watershed temperature (CWT) is used as a convenient lumped surrogate for complicated hydrological processes that influence WLF. Statistical analyses of century-long flow data from the Fraser River in British Columbia, Canada show a strong dependence of WLF on CWT. In winter season, time series of CWT and WLF are shown to each divide into a falling and a rising limb at the data point of their lowest values, being −358 °C and 827 m3/s (historical averages), respectively. The falling and rising limbs describe the cumulative freezing and thawing effects of air temperatures fluctuating around the freezing point on WLF changes. The correlation between CWT and WLF (time series) data points on the rising limbs forms the forecasting model, expressing WLF as a function of CWT in terms of their z-scores. The model takes input of Global Circulation Model (GCM) projections of temperature increases by the end of 21st century under different Representative Concentration Pathways (RCPs), and predicts changes of Fraser River WLF from the historical average. RCP4.5 causes a 22.6% increase (ensemble average from six GCMs). RCP8.5 increases WLF by 20% (for some future years) to 44% (for others). The methods reported in this paper can be adapted to other cold regions.

Different downscaling approaches have been developed for linking daily climate projections at coarse-grid global or regional scales given by Global Climate Models (GCMs) or Regional Climate Models (RCMs) and observed extreme rainfalls at finer time scales for a given location or over an urban catchment for various impact and adaptation studies. In general, these downscaling methods can be grouped into two main categories: dynamical downscaling (DD) and statistical downscaling (SD) techniques. Furthermore, it has been widely known that DD methods could provide reasonable description of the climate conditions for large regional scales, but they could not accurately capture observed characteristics of hydrologic processes such as precipitation at a local or station scale; while SD procedures have been found to be able to describe accurately the observed properties of the local hydrologic processes. Therefore, the overall objective of the present paper is to provide an overview of some recent advances and existing shortcomings in these downscaling approaches for modeling of extreme precipitation processes in a changing climate. In particular, the main focus of this paper is a review of recently developed statistical downscaling methods for linking GCM climate predictors to the observed daily and sub-daily rainfall time series at a given location or at many different sites concurrently over a given urban catchment. Illustrative applications using NCEP re-analysis dataset, climate simulations from different GCMs, and rainfall data available from raingage networks located in Canada are presented to indicate the feasibility and limitations of the proposed statistical downscaling methods for climate change impact and adaptation studies.

This paper builds on recent work in the critical geographical literature concerned with advancing a more nuanced engagement with climate change science and linked knowledges. The main aim of this paper is to provide insight into the character of Soviet climate science, and reflect on the contribution of Soviet scientists to the international debate concerning anthropogenic climate change and associated forecasting as it developed from the late 1950s through to the first report of the IPCC in 1990. Such a focus is significant for a number of reasons. First, Soviet contributions are given short shrift in general reviews concerning the development of the basic science underpinning anthropogenic climate change, emerging as a subdued ‘other’ despite their relative importance during this period at the international level. Second, the Soviet contingent also played an influential role in the formation of the IPCC as well as the development of associated debates concerning the establishment of future climate change scenarios. Third, the early IPCC process resulted in the relative marginalisation of Soviet scientific input framed by debates over the most effective way to determine future climate change scenarios. The paper examines the significance of Soviet science for the evolving climate change debate on the international stage, and the related involvement of a handful of Soviet scientists in the activities of international bodies such as the World Meteorological Organisation (WMO). It also examines the role of Soviet scientists in the consolidation of a natural science conceptualisation of anthropogenic climate change during the late 1980s. It is shown how the Soviet contingent came to place an emphasis on the use of palaeoclimatic analogues in order to predict future climates, albeit whilst recognising the value of the computer modelling approach favoured by many Western climatologists. Nevertheless, this preference for an analogue approach and resultant debates surfaced strongly during the early work of the IPCC. The robust advancement of General Circulation Models (GCMs) as the prime forecasting technique within Working Group I resulted in the effective side-lining of the Soviet contingent during the process of finalising the first IPCC report in 1990.

Os efeitos das mudancas climaticas na erosividade possivelmente afetarao os processos erosivos no seculo 21, tornando emergente a discussao dessa tematica. Sendo assim, os objetivos desta revisao de literatura sao: tracar as relacoes entre a variacao de perda de solos e erosividade diante de mudancas climaticas, apresentando produtos cartograficos das decadas passadas e para todo o seculo 21; apresentar um panorama geral de pesquisas (publicadas entre 2016-2020); e demonstrar quais sao os desafios e proximos passos na pesquisa sobre esta tematica. Para tanto, a pesquisa de artigos foi realizada nas plataformas do Portal de Periodicos da CAPES, Google Scholar e Science Direct , totalizando 33 artigos. Foram consideradas pesquisas de natureza conceitual, com revisoes de literatura sobre os temas e de natureza procedimental com calculos do fator R e estimativas de perda de solos. Os trabalhos consultados mostram que a erosividade estimada e a taxa de perda de solos devem aumentar e diminuir em funcao das futuras mudancas climaticas. Embora, em escala global, haja estimativas do aumento de erosividade para a area tropical do planeta, atingindo majoritariamente paises em desenvolvimento como o Brasil. Isso demonstra a emergencia da elaboracao de estudos para o pais, em escala regional, de bioma e de bacias hidrograficas. Nesse sentido, ressaltamos a importância da abordagem sistemica, que pode ser proporcionada pela Geografia nos estudos desta tematica, visto que os desafios se relacionam a dificuldade de integrar diversos fatores ambientais e obter dados de campo nas modelagens preditivas de perda de solo. Overview of Changes in Rainfall and Erosion Patterns in the Face of Climate Change: Literature Review A B S T R A C T The effects of climate change on erosivity are likely to affect erosion processes in the 21st century, which makes the discussion emerging. Thus, the goals of this literature review are: to trace the relationship between the variation in soil loss and erosivity in the face of climate change, presenting cartographic products from the past decades and for the entire 21st century; present an overview of research (published between 2016-2021); demonstrate challenges and next steps in research on this topic. Therefore, the search for papers was carried out on the platforms of the CAPES Journal Portal, Google Scholar and Science Direct, totaling 78 papers. Conceptual researches was considered, with literature reviews on the themes and procedural researches with calculations of the R factor and soil loss estimate. The consulted papers show that the estimated erosivity and the rate of soil loss are expected to increase and decrease as a result of future climate changes. Although, on a global scale, there are estimates of the increase in erosivity for the tropical area of the planet, mainly affecting developing countries like Brazil. This demonstrates the emergence of studies for the country, on a regional scale, of biomes and hydrographic basins. In this sense, we emphasize the importance of the systemic approach, which can be provided by Geography in the studies of this theme, since the challenges are related to the difficulty of integrating various environmental factors and obtaining field data in predictive models of soil loss. Keywords: erosivity, extreme precipitation events, global climate models

The issue of climate change is approached from the description of the “greenhouse effect” and its implications for the earth-atmosphere-ocean climate system. The paper briefly summarizes the historical data record and discusses the use of numerical models of the general circulation of the coupled atmosphere-ocean system for studying climate change. Based upon the results of the scientific assessment undertaken by the WMO/ UNEP sponsored Intergovernmental Panel on Climate Change, the paper synthesizes our current knowledge of the predicted warmer earth, summarizes our best understanding of the impact of this global climate change on selected regions, and discusses the possible impact on the frequency and strength of weather systems. The paper concludes with an outlook for future observational systems and research efforts needed to reduce the uncertainties in our understanding of climate change and its impacts.

This paper reviews the climate change impact on rainfall as well as extreme events occurrences. The global extreme weather contributes to the uncertainties of the climate trend and water scarcity problems to the whole world. Thus, numerical models such as General Circulation Models (GCMs) have been developed to simulate the response of the global climate system to the expected increment of the greenhouse gases concentrations. However, the GCM cannot be directly applied to climate change impact studies, hence downscaling is needed. A large number of downscaling methods are available but there is no universal method exists at present that performs best for all conditions, depending on the application and this remains a subject of investigation. Therefore, this paper compares the performances among statistical and dynamical downscaling models that have been applied by different researchers in various purposes. It can be concluded that the statistical downscaling has been widely used and able to provide reliable climate projected results especially for Malaysia’s climate variables. This review is very significant especially to the policy maker in deciding the reliable climatic methods for the long term planning and management of water resources. Besides, the reliable projected rainfall will be very beneficial in estimating water availability and water resource policy.

This paper describes a simple comparison of the distributions of land cover features identified from volunteered data contributed by different social groups – in this case comparing two groups of Geo-Wiki campaigns. Understanding the impacts on analyses of citizen science data contributed by different groups is critical to ensure robust scientific outputs and to fully realise the potential benefits to formal scientific research. It is well known that different people, with different backgrounds and subject to different cultural factors, hold varying landscape conceptualisations. This paper analyses volunteered geographical information on land cover to generate land cover maps. It uses a geographically weighted approach to generate land cover mappings. The mappings generated by different groups (in this case a from a specific unnamed country) are compared and the results show how the predicted land cover distributions vary, with large differences in some classes (e.g. Barren land, Shrubland, Wetland) and little difference in others (e.g. Tree cover). This suggests that for some landscape features cultural and national differences matter when it comes to using crowdsourced data in formal scientific analyses and highlights the potential problems of not considering contributor backgrounds in citizen science. This is important because such data re now routinely being used to develop global land cover data, to generate uncertainty estimates of existing global land cover products and to generate global forest inventories. These in turn are being suggested as suitable inputs to such things as global climate models. A number of critical research directions arising from these findings are discussed.

The present paper describes the Variability of the American Monsoon Systems (VAMOS) Ocean–Cloud–Atmosphere–Land Study (VOCALS), an international research program focused on the improved understanding and modeling of the southeastern Pacific (SEP) climate system on diurnal to interannual time scales. In the framework of the SEP climate, VOCALS has two fundamental objectives: 1) improved simulations by coupled atmosphere–ocean general circulation models (CGCMs), with an emphasis on reducing systematic errors in the region; and 2) improved estimates of the indirect effects of aerosols on low clouds and climate, with an emphasis on the more precise quantification of those effects. VOCALS major scientific activities are outlined, and selected achievements are highlighted. Activities described include monitoring in the region, a large international field campaign (the VOCALS Regional Experiment), and two model assessments. The program has already produced significant advances in the understanding of major issues in the SEP: the coastal circulation and the diurnal cycle, the ocean heat budget, factors controlling precipitation and formation of pockets of open cells in stratocumulus decks, aerosol impacts on clouds, and estimation of the first aerosol indirect effect. The paper concludes with a brief presentation on VOCALS contributions to community capacity building before a summary of scientific findings and remaining questions.

In order to describe changes in ice volume in the cryosphere, a differential equation mathematical model is used in this paper. The dominating effects of freezing and thawing in the cryosphere enable simplification of the heat transport equations. This results in a mathematical model that can be solved exactly and is useful in investigating other climatic components, which in turn may be similarly analyzed for possible Global Circulation Model (GCM) validation. Data forms representing temperature and ice volume during the Pleistocene are available and can be directly compared with the exact solution of the simplified differential equation used in this paper. The model parameters may then be adjusted to approximate the effects of climate change; the adjusted model then run in reverse time, to develop an alternative history of ice volume of the cryosphere to be compared with the actual data interpretations previously published in the literature. In this fashion, an assessment may be made as to possible climate impacts in the cryosphere.

[1] In the paper ‘‘Single-layer axisymmetric model for a Hadley circulation with parameterized eddy momentum forcing’’ by Adam H. Sobel and Tapio Schneider (Journal of Advances in Modeling Earth Systems, 1, 10, doi:10.3894/ JAMES.2009.1.10., 2009) [hereinafter referred to as SS09], we presented an axisymmetric single-layer model for Hadley circulations with parameterized eddy momentum fluxes. Results from this model were compared to results from the idealized general circulation model (GCM) of Schneider and Bordoni [2008]. Our axisymmetric model, as implemented in most of the paper, contained two errors. We correct these here.

Reducing the uncertainties surrounding the impacts of anthropogenic climate change requires vetting general circulation models (GCMs) against long records of past natural climate variability. This is particularly challenging in the tropical Pacific Ocean, where short, sparse instrumental data preclude GCM validation on multidecadal to centennial time scales. This two-part paper demonstrates the application of two statistical methodologies to a network of accurately dated tropical climate records to reconstruct sea surface temperature (SST) variability in the Niño-3.4 region over the past millennium. While Part I described the methods and established their validity and limitations, this paper presents several reconstructions of Niño-3.4, analyzes their sensitivity to procedural choices and input data, and compares them to climate forcing time series and previously published tropical Pacific SST reconstructions. The reconstructions herein show remarkably similar behavior at decadal to multidecadal scales, but diverge markedly on centennial scales. The amplitude of centennial variability in each reconstruction scales with the magnitude of the A.D. 1860–1995 trend in the target dataset’s Niño-3.4 index, with Extended Reconstructed SST, version 3 (ERSSTv3) > the Second Hadley Centre SST dataset (HadSST2) > Kaplan SST; these discrepancies constitute a major source of uncertainty in reconstructing preinstrumental Niño-3.4 SST. Despite inevitable variance losses, the reconstructed multidecadal variability exceeds that simulated by a state-of-the-art GCM (forced and unforced) over the past millennium, while reconstructed centennial variability is incompatible with constant boundary conditions. Wavelet coherence analysis reveals a robust antiphasing between solar forcing and Niño-3.4 SST on bicentennial time scales, but not on shorter time scales. Implications for GCM representations of the tropical Pacific climate response to radiative forcing are then discussed.

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

With prevailing changes in climate and increasing population, small drinking water systems in the climate-vulnerable parts of the world have already exhibited and left traces of prominent unpredictability of water availability, in terms of both water quantity and water quality. Dimensions of climate change, such as large variability in precipitation pattern, rise in temperature and associated increase in evaporation rates, as well as their consequences, are surely going to affect the unsophisticated, small drinking water systems which are serving mass populations in South Asia and many other parts of the world. This research paper aims to analyze the possible extents of vulnerability of some selected small drinking water systems currently being operated in the coastal areas of Bangladesh as a result of the predicted changes in climatic parameters such as temperature, precipitation and evaporation along with sea level rise and extreme events such as cyclones. However, to examine possible future climate change scenarios, four Global Climate Models have been applied in developing projections of different climatic parameters for Bangladesh. Based on the projections of climate models and associated key vulnerabilities being assessed, this paper features the evaluation of potential technological resilience of specific small drinking water systems from the Bangladesh perspective.

The 1883 eruption of Krakatau was a seminal event in the study of several atmospheric phenomena. This paper reviews the work that led to the first determination of the wind in the tropical stratosphere and provides some biographical background on two quite remarkable amateur scientists whose observations and analyses in the aftermath of the eruption contributed greatly to our knowledge of the stratospheric circulation. This paper will trace the path from their work in the 1880s to the mid-twentieth century when it provided the background to the surprising discovery of the stratospheric Quasibiennial Oscillation. RESUME [Traduit par la redaction] L’eruption du Krakatoa, en 1883, a ete un evenement fecond pour l’etude de plusieurs phenomenes atmospheriques. Le present article examine le travail ayant mene a la premiere determination du vent dans la stratosphere tropicale et fournit des renseignements biographiques sur deux scientifiques amateurs fort remarquables dont les observations et les analyses suite ...

Numerical weather prediction (NWP) has proven to be computationally challenging due to its inherent multiscale nature. Currently, the highest resolution NWP models use a horizontal resolution of about 10km. In order to increase the resolution of NWP models highly scalable atmospheric models are needed. The Non-hydrostatic Unified Model of the Atmosphere (NUMA), developed by the authors at the Naval Postgraduate School, was designed to achieve this purpose. NUMA is used by the Naval Research Laboratory, Monterey as the engine inside its next generation weather prediction system NEPTUNE. NUMA solves the fully compressible Navier-Stokes equations by means of high-order Galerkin methods (both spectral element as well as discontinuous Galerkin methods can be used). Mesh generation is done using the p4est library. NUMA is capable of running middle and upper atmosphere simulations since it does not make use of the shallow-atmosphere approximation. This paper presents the performance analysis and optimization of the spectral element version of NUMA. The performance at different optimization stages is analyzed using a theoretical performance model as well as measurements via hardware counters. Machine independent optimization is compared to machine specific optimization using BG/Q vector intrinsics. By using vector intrinsics the main computations reach 1.2 PFlops on the entire machine Mira (12% of the theoretical peak performance). The paper also presents scalability studies for two idealized test cases that are relevant for NWP applications. The atmospheric model NUMA delivers an excellent strong scaling efficiency of 99% on the entire supercomputer Mira using a mesh with 1.8 billion grid points. This allows to run a global forecast of a baroclinic wave test case at 3km uniform horizontal resolution and double precision within the time frame required for operational weather prediction., Comment: 33 pages, 12 figures, submitted to the International Journal of High-Performance Computing Applications

Since 2006 the scientific community in California, in cooperation with resource managers, has been conducting periodic statewide studies about the potential impacts of climate change on natural and managed systems. This Special Issue is a compilation of revised papers that originate from the most recent assessment that concluded in 2009. As with the 2006 studies that influenced the passage of California’s landmark Global Warming Solutions Act (AB32), these papers have informed policy formulation at the state level, helping bring climate adaptation as a complementary measure to mitigation. We provide here a brief introduction to the papers included in this Special Issue focusing on how they are coordinated and support each other. We describe the common set of downscaled climate and sea-level rise scenarios used in this assessment that came from six different global climate models (GCMs) run under two greenhouse gas emissions scenarios: B1 (low emissions) and A2 (a medium-high emissions). Recommendations for future state assessments, some of which are being implemented in an on-going new assessment that will be completed in 2012, are offered.

To date, a number of studies have focused on the influence of sea surface temperature (SST) on global and regional rainfall variability, with the majority of these focusing on certain ocean basins — eg, the Pacific, North Atlantic and Indian Ocean. In contrast, relatively less work has been done on the influence of the central South Atlantic, particularly in relation to rainfall over southern Africa. Previous work by the authors, using reanalysis data and general circulation model (GCM) experiments, has suggested that cold SST anomalies in the central southern Atlantic Ocean are linked to an increase in rainfall extremes across southern Africa. In this paper we present results from idealized regional climate model (RCM) experiments forced with both positive and negative SST anomalies in the southern Atlantic Ocean. These experiments reveal an unexpected response of rainfall over southern Africa. In particular, it was found that SST anomalies of opposite sign can cause similar rainfall responses in the model experiments, with isolated increases in rainfall over central southern Africa as well as a large region of drying over the Mozambique Channel. The purpose of this paper is to highlight this finding and explore explanations for the behaviour of the climate model. It is suggested that the observed changes in rainfall might result from the redistribution of energy (associated with upper-level changes to Rossby waves) or, of more concern, model error, and therefore the paper concludes that the results of idealized regional climate models forced with SST anomalies should be viewed cautiously.

The thermal regime of the middle atmosphere is determined to a great extent by the balance between the incoming solar and outgoing infrared radiation. To account for these processes in numerical models of the middle atmosphere, parameterizations that are capable of quickly and accurately calculating infrared cooling and solar heating rates are required. These parameterizations should include the breakdown of local thermodynamic equilibrium (LTE) conditions and allow for feedbacks by ensuring that dependencies on all input parameters are accounted for. This paper discusses the major mechanisms responsible for maintaining the radiative energy budget of the middle atmosphere and presents a brief review of approaches and numerical schemes currently available for use in general circulation models. The main focus of the paper is on the approaches and schemes designed for non-LTE treatment.

This paper outlines the use of geographical information systems for predicting the possible implications of future climate change (as suggested by General Circulation Models) on the archaeological resource of selected parts of two major British river systems, the Trent and Yorkshire Ouse. The effects of future climatic change on the historic environment are likely to come from both natural 'system' responses, such as increased bank erosion and valley floor sedimentation, as well as the human adaptive responses selected to manage the impacts of climate change. The models described in this paper allow predictions to be made and quantified about how selected natural and anthropogenic responses may impact upon the archaeological resource. Such approaches, which are relatively rapid to implement and low cost, provide an important management tool for guiding policy makers and heritage managers in the face of changing climates.

This paper demonstrates that the widely prophesied doubling of atmospheric carbon dioxide levels from natural, pre-industrial values will enhance the so-called ‘greenhouse effect’ but will amount to less than 1°C of global warming. It also points out that such a scenario is unlikely to arise given our limited reserves of fossil fuels—certainly not before the end of this century. Furthermore, the paper argues that general circulation models are as yet insufficiently accurate for civil engineers to rely on their predictions in any forward-planning decisions—the omission of solar wind effects being a potentially significant shortcoming. It concludes that the only certainty is that the world's fossil fuel resources are finite and should be used prudently and with proper respect to the environment.

Due to global warming, the general circulation, underlying surfaces characteristics, and geophysical and meteorological elements all show evident secular trends. This paper points out that when calculating the correlation of two variables containing their own obvious secular trends, the interannual correlation characteristics between the two variables may be distorted (overestimated or underestimated). Numerical experiments in this paper show that if two variables have opposite secular trends, the correlation coefficient between the two variables is reduced (the positive correlation is underestimated, or the negative correlation is overestimated); and if the two variables have the same sign of secular trends, the correlation coefficient between the two variables is increased (the positive correlation is overestimated, or the negative correlation is underestimated). Numerical experiments also suggest that the effect of secular trends on the interannual correlation of the two variables is interchangeable, that is to say, as long as the values of the two trends are not changed, the two variables interchange their positions, and the effect of the secular trends on the interannual correlation coefficient of the two variables remains the same. If the two variables have the same-(opposite-) sign trends, the effect of secular trends on the interannual correlation coefficient is more (less) distinctive. A meteorological example is given.

The paper presents a realized hydrodynamic three-dimensional global climatic model, which comprises the model blocks of atmospheric general circulation, thermohaline large-scale circulation of the ocean, and sea ice evolution. Before rather strongly aggregated heat-moisturebalance model of the atmosphere for temperature and humidity of a surface layer was used as a model of the atmosphere. The atmospheric general circulation model is significantly more complicated and allows us to describe processes in the atmosphere more adequately. Functioning of a coupled climatic model is considered in conditions of the seasonal cycle of solar radiation.The paper considers a procedure for coupled calculation of the ocean model and atmospheric general circulation model. Synchronization of a number of parameters in both models is necessary for their joint action. In this regard a procedure of two-dimensional interpolation of data defined on the grids of the ocean model and atmosphere model and back is developed. A feature of this task is discrepancy of grid nodes and continental configurations in models. Coupled model-based long-term calculations for more than 400 years have shown its stable work. Calculation results and comparison with observation data are under discussion.The paper shows distribution of mean global atmosphere temperature versus time in stable conditions to demonstrate that there is inter-annual variability of atmosphere temperature at the steady state of a climate system. It presents distribution of temperature difference of the ocean surface from the observations and from the model of the ocean thermohaline circulation for January. Noticeable deviations of temperature are observed near Antarctica. Apparently, it is because of inaccurate calculation of the sea ice distribution in model. The geographical distribution of the ocean surface temperature for January with coupled calculation shows, in general, a zonal uniform structure of isolines with noticeable deviations from zonality near continents that will be in compliance with observation data. The calculated field of January surface atmosphere temperatures close to the underlying surface possesses strong variability over continents.

Fluctuations of wet and dry years have long been investigated in the climatology and hydrology literature. In this, the second of two papers investigating runs of consecutive dry years, the magnitude, also known as the intensity, and severity (length × magnitude) of dry runs are investigated. In the first paper the length of dry runs was investigated. Periods of consecutive dry years are associated with drought and the attendant physical and economic stresses that are placed on society. Run magnitudes of consecutive years equal to or below the median were analysed for 3863 precipitation and 1236 runoff stations from around the world. For both annual precipitation and runoff, run magnitude was found to be predominately related to interannual variability and to a lesser extent skewness. Run magnitude of annual runoff was observed to be greater than that for annual precipitation, due to annual runoff having a higher coefficient of variation than annual precipitation. Continental differences in run magnitude of annual runoff were observed and were consistent with continental differences in interannual variability reported previously. Annual run severity was also investigated and found to be independent of run length and strongly related to run magnitude. These findings differ from previously published work; this difference is primarily due to the methodology of comparing run metrics between stations (used in this paper) rather than at a station (previous research). The relationships between run magnitude, severity and interannual variability highlight the importance of adequately reproducing interannual variability within global climate models for future modelling of drought scenarios, as well as having economic implications for drought relief and management policy-making. Copyright © 2005 Royal Meteorological Society.

The parameterization schemes used to represent clouds in general circulation models have significantly evolved in their complexity over the last 10 yr. This increases the demand for a thorough evaluation of their performance. Several techniques ranging from the evaluation of the model climate to single column modeling have been proposed for that purpose. This paper aims to provide a strategy for an improved, more coherent use of these techniques. An overview of the different techniques is given using examples from the evaluation of the global model of the European Centre for Medium-Range Weather Forecasts. Advantages and disadvantages of the individual methods are highlighted. The paper closes by proposing a strategy to join the different techniques into a coherent procedure of cloud parameterization evaluation.

Summary The field of environmental statistics is one of rapid growth at the moment. Environmental decisionmaking is prevalent in much of the world, and politicians and other decision makers are requesting new tools for understanding the state of the environment. In this paper, three case studies involving water pollution, air pollution, and climate change assessment are presented, together with brief descriptions some other areas of environmental statistics. A discussion of future directions of the field concludes the paper. Resume Le domaine des statistiques environnementales est en developpement tres rapide. Dans la plupart des pays, on a tres souvent besoin de prendre des decisions relatives a l'environnement. Ainsi, politiciens et autres decideurs demandent de nouveaux instruments pour comprendre l'etat de l'environnement. Dans cet article, on presente trois etudes de cas concernant la pollution de l'eau et de l'air et le changement de climat, accompagies par des descriptions courtes d'autres domaines relatifs aux statistiques environnementales. On conclut par une discussion sur des directions futures dans le domaine.

Lagrangian stochastic (LS) modeling is a common technique in atmospheric boundary layer modeling but is relatively new in coastal oceanography. This paper presents some fundamental aspects of LS modeling as they pertain to oceanography. The theory behind LS modeling is reviewed and an introduction to the substantial atmospheric literature on the subject is provided. One of the most important properties of an LS model is that it maintains an initially uniform distribution of particles uniform for all time—the well-mixed condition (WMC). Turbulent data for use in an oceanic LS model (LSM) are typically output at discrete positions by a general circulation model. Tests for the WMC are devised, and it is shown that for inhomogeneous turbulence the data output by an oceanic general circulation model is such that the WMC cannot be demonstrated. It is hypothesized that this is due to data resolution problems. To test this hypothesis analytical turbulence data are constructed and output at various resolutions to show that the WMC can only be demonstrated if the resolution is high enough (the required resolution depending on the inhomogeneity of the turbulence data). The output of an LSM represents one trial of possible ensemble and this paper seeks to learn the ensemble average properties of the dispersion. This relates to the number of particles or trials that are performed. Methods for determining the number of particles required to have statistical certainty in one’s results are demonstrated, and two possible errors that can occur when using too few particles are shown.

The paper is based on recent climate simulations provided by the leading world climate centres and available through the Data Distribution Centre of the Intergovernmental Panel on Climate Change. Seven models have been considered. Three experiments: control, and two integrations for different greenhouse gases emission scenarios have been analysed. Assessments have been made for the European window defined as 5–40° E, 40–60° N. The paper presents some selected results for the air surface temperature and precipitation. This is not a study on evaluation of models, detailed intercomparisons between models can be found in other works (e.g., Barnett, 1999; Boer and Lambert, 2000; Lambert and Boer, 2001), as well as on Internet (e.g., a web-site of the Program for Climate Model Diagnosis and Intercomparison1). The intention of the authors was to show examples of analyses made specifically for the Central European region. For the reason of technical limitations of the paper only some results can be presented.

Climate change leads to a more frequent occurrence of hot days (HDs) and hot nights (HNs). The consecutive occurrence of HDs and HNs (COHs) is often used as a measure of the persistence of an extremely hot spell. Nonetheless, the combined effect of air temperature and relative humidity on the changing COHs has never been studied. In this paper, we use an ensemble of global climate models and multiple thermal indices to robustly examine the combined effect of air temperature and relative humidity on COHs globally on an hourly basis. Our findings reveal that COHs show an increasing trend in the future and a strong latitudinal gradient increasing from high latitudes to the equator. Compared to COHs based on air temperature, the frequency of COHs based on perceived temperature is amplified by the combined effects of high temperature and humidity for both boreal and austral summers. To investigate the underlying mechanisms, we examine two different diurnal temperature ranges (DTRs), derived from air temperature and perceived temperature, for their corresponding types of COHs. Both DTRs are projected to increase in the future relative to the historical period from 1980 to 2004, but the DTR changes derived from perceived temperature are consistently larger than those derived from air temperature. Due to the nonlinearity in thermal indices, the perceived temperature in HDs and HNs rising faster than air temperature leads to a larger increase in perceived COHs. The COHs are further amplified by the increasing number of HNs and HDs that occur consecutively under wet conditions.

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between Global Climate Models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) datasets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to approximate within-GCM uncertainty of monthly precipitation and temperature projections and assess its impact on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. To-date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2014) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), temperature (MAT) and runoff (MAR), the standard deviation of annual precipitation (SDP) and runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 world-wide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainty from the 17 catchments and 5 GCMs for 2015–2044 (A1B) were: MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould–Dincer Gamma procedure was applied to each annual runoff time-series for hypothetical reservoir capacities of 1× MAR and 3× MAR and the average uncertainty in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were: 25.1% (1× MAR) and 11.9% (3× MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1× MAR or 3× MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable – these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between Global Climate Models (GCMs) and within a GCM. Uncertainty between GCM projections of future climate can be assessed through analysis of runs of a given scenario from a wide range of GCMs. Within GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The objective of this, the first of two complementary papers, is to reduce between-GCM uncertainty by identifying and removing poorly performing GCMs prior to the analysis presented in the second paper. Here we assess how well 46 runs from 22 Coupled Model Intercomparison Project phase 3 (CMIP3) GCMs are able to reproduce observed precipitation and temperature climatological statistics. The performance of each GCM in reproducing these statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the CRU 3.10 gridded dataset and re-sampled to the resolution of each GCM for comparison. Observed and GCM based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen climate type were compared. The main metrics for assessing GCM performance were the Nash–Sutcliffe efficiency index and RMSE between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following five models as the better performing models for the next phase of our analysis in assessing the uncertainty in runoff estimated from GCM projections of precipitation and temperature: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCM (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute).

An Atmospheric radiative transfer model calculates radiative transfer of electromagnetic radiation through a planetary atmosphere. Both shortwave radiance and longwave radiance parameterizations in an atmospheric model calculate radiation fluxes and heating rates in the earth-atmospheric system. One radiative transfer model is the rapid radiative transfer model (RRTM), which calculates of longwave and shortwave atmospheric radiative fluxes and heating rates. Longwave broadband radiative transfer code for general circulation model (GCM) applications, RRTMG, is based on the single-column reference code, RRTM. The RRTMG is a validated, correlated k-distribution band model for the calculation of longwave and shortwave atmospheric radiative fluxes and heating rates. The focus of this paper is on the RRTMG long wave (RRTMG_LW) model. In order to improve computational efficiency, RRTMG_LW incorporates several modifications compared to RRTM. In RRTM_LW there are 16 g points in each of the spectral bands for a total of 256 g points. In RRTMG_LW, the number of g points in each spectral band varies from 2 to 16 depending on the absorption in each band. RRTMG_LW employs a computationally efficient correlated-k method for radiative transfer calculations. It contains 16 spectral bands with various number of quadrature points (g points) in each of the bands. In total, there are 140 g points. The radiative effects of all significant atmospheric gases are included in RRTMG_LW. Active gas absorbers include H 2 O, O 3 , CO 2 , CH 4 , N 2 O, O 2 and four types of halocarbons: CFC-11, CFC-12, CFC-22, and CCL4. RRTMG_LW also treats the absorption and scattering from liquid and ice clouds and aerosols. For cloudysky radiative transfer, a maximum-random cloud overlapping scheme is used. Small scale cloud variability, such as cloud fraction and the vertical overlap of clouds can be represented using a statistical technique in RRTMG_LW. Due to its accuracy, RRTMG_LW has been implemented operationally in many weather forecast and climate models. RRTMG_LW is in operational use in ECMWF weather forecast system, the NCEP global forecast system, the ECHAM5 climate model, Community Earth System Model (CESM) and the weather and forecasting (WRF) model. RRTMG_LW has also been evaluated for use in GFDL climate model. In this paper, we examine the feasibility of using graphics processing units (GPUs) to accelerate the RRTMG_LW as used by the WRF. GPUs can provide a substantial improvement in RRTMG speed by supporting the parallel computation of large numbers of independent radiative calculations. Furthermore, using commodity GPUs for accelerating RRTMG_LW allows getting a much higher computational performance at lower price point than traditional CPUs. Furthermore, power and cooling costs are significantly reduced by using GPUs. A GPU-compatible version of RRTMG was implemented and thorough testing was performed to ensure that the original level of accuracy is retained. Our results show that GPUs can provide significant speedup over conventional CPUs. In particular, Nvidia’s GTX 680 GPU card can provide a speedup of 69x for the compared to its single-threaded Fortran counterpart running on Intel Xeon E5-2603 CPU.

This tutorial paper discusses the problem of parameterizing unresolved gravity waves in general circulation models (GCMs) of the middle atmosphere. For readers who are unfamiliar with middle atmosphere dynamics a review of the basic dynamics of both the large-scale circulation and internal gravity waves is presented. A fairly detailed and physically-based description is given of several gravity wave drag (GWD) schemes that are currently employed in middle atmosphere GCMs. These include the parameterizations of McFarlane (1987) , Medvedev and Klaassen (1995) , and Hines, 1997a , Hines, 1997b , which are used in the Canadian Middle Atmosphere Model, as well as the parameterization of Fritts and Lu (1993) , which is used in the TIME-GCM. Results from a mechanistic model and the two above mentioned GCMs are presented and discussed. This paper is not intended as a review of all GWD parameterizations nor is it meant as a quantitative comparison of the schemes that have been chosen.

This paper compiles indices for the El Nino—Southern Oscillation (ENSO) and seven unforced decadal-to-multidecadal climate modes (Interdecadal Pacific Oscillation (IPO), Tripole Pacific Index (TPI), Pacific Decadal Oscillation focused over North Pacific (PDO) and South Pacific (SPDO), North Atlantic Multidecadal Oscillation (AMO), South Atlantic Multidecadal Oscillation (SAMO), and Indian Ocean basin (IOB)) in a 100-member ensemble of the Max Planck Institute Earth System Model (MPI-ESM1.1) and a 35-member ensemble of Community Earth System Model (CESM1). Comparison among various detrending approaches indicates that the best approach to remove the unforced component is by subtracting the ensemble average temperature at each grid point from the original model output. Similar characteristics are investigated in the indices of both MPI-ESM1.1 and CESM1 ensemble models. The results further indicate no statistically significant lead-lag correlations between the unforced multidecadal climate modes originating from North Pacific (e.g., PDO) and North Atlantic (e.g., AMO), suggesting that a high correlation found in previous observational studies may be due to biased detrending approaches.

In this paper I present two ways in which climate modelers use general circulation models for exploratory purposes. The complexity of Earth’s climate system makes it difficult to predict precisely how lower-order climate dynamics will interact over time to drive higher-order dynamics. The same issues arise for complex models built to simulate climate behavior like the Community Earth Systems Model (CESM). I argue that as a result of system complexity, climate modelers use general circulation models to perform model dynamic exploration (MDE) and climate dynamic exploration (CDE). MDE and CDE help climate modelers to better understand the dynamic structure of the general circulation model system and the actual climate system, respectively.

Anthropogenic climate change is expected to cause an increase in fire danger over south-eastern Australia during the 21st century, primarily driven by increased surface temperature. Studies of future fire weather in Victoria, Australia, have so far mostly utilised direct output from general circulation models, which have inadequate resolution for resolving the dynamics of local fire danger and are prone to substantial biases that may affect the seasonality of dry fuels. In this paper, we assess the ability of the Multivariate Adaptive Constructed Analogs (MACA) method to downscale output from general circulation models over Victoria, and replicate statistical attributes of fire danger indices. We find that climatological descriptors of meteorological variables of wind, temperature and humidity are captured extremely well, and fields on extreme fire days are well captured. We find that the method works very well for statistically downscaling fire weather elements over Victoria and provides a vehicle to assess the regional variation of fire weather projections over Victoria.

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations.

Policy makers, responsible for the economic well-being of regions and states, have sought a variety of tools for predicting global change. One set of tools for simulating climate change, at a global scale, are the general circulation models (GCMs). Current versions of GCMs are limited to global studies, which are spatially crude and have output that is of minimal use to decision-makers accustomed to day-to-day, synoptic-scale changes in weather events. This paper discusses the use of a mesoscale model to “downscale” GCM output to the regional and local scales desired by policy makers. It presents a “nested” dynamical modeling approach. The paper also addresses several issues associated with the dynamical linking of GCMs and mesoscale models. Issues having to do with the choice of an atmospheric system, a variable as the best indicator of climate change, or having to do with validation of mesoscale simulations, which were based on GCM output, are considered.

The societal need for reliable climate predictions and a proper assessment of their uncertainties is pressing. Uncertainties arise not only from initial conditions and forcing scenarios, but also from model formulation. Here, we identify and document three broad classes of problems, each representing what we regard to be an outstanding challenge in the area of mathematics applied to the climate system. First, there is the problem of the development and evaluation of simple physically based models of the global climate. Second, there is the problem of the development and evaluation of the components of complex models such as general circulation models. Third, there is the problem of the development and evaluation of appropriate statistical frameworks. We discuss these problems in turn, emphasizing the recent progress made by the papers presented in this Theme Issue. Many pressing challenges in climate science require closer collaboration between climate scientists, mathematicians and statisticians. We hope the papers contained in this Theme Issue will act as inspiration for such collaborations and for setting future research directions.