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Tremendous recent progress in climate prediction on subseasonal to decadal time scales has been enabled by better observations, data assimilation, and models originating from the weather prediction and climate simulation [...]

ABSTRACT Land-atmosphere (L-A) interactions are a main driver of Earth's surface water and energy budgets; as such, they modulate near-surface climate, including clouds and precipitation, and can influence the persistence [...]

ABSTRACT A wide range of numerical weather prediction (NWP) innovations are under development in the research community that have the potential to positively impact operational models. The Developmental Testbed Center [...]

Computing resources donated by volunteers have generated the first superensemble of regional climate model results, in which the Hadley Centre Regional Model, version 3P (HadRM3P), and Hadley Centre Atmosphere Model, [...]

Complementary key elements of meteorological education are the provision of a thorough theoretical understanding of the physical laws governing atmospheric motions, and the hands-on investigation and visualization of specific weather [...]

Atmospheric scientist Ben Santer is internationally recognized for his contributions to climate science, including the historic conclusion of the Intergovernmental Panel on Climate Change report in 1995 of the 'discernible [...]

Jane C. S. Long and Dane Scott ('Vested Interests and Geoengineering Research,' Issues, Spring 2013) argue rightly that work must start now on the governance of research on climate engineering [...]

SUMMARY Models of the global warming process are obliged to include many important small-scale processes that cannot be explicitly represented, given the low spatial and temporal resolution at which the [...]

Byline: Cornelia Schwierz (1,4), Pamela Kollner-Heck (2,3), Evelyn Zenklusen Mutter (1,5), David N. Bresch (2), Pier-Luigi Vidale (1,6), Martin Wild (1), Christoph Schar (1) Abstract: Severe wind storms are one of the major natural hazards in the extratropics and inflict substantial economic damages and even casualties. Insured storm-related losses depend on (i) the frequency, nature and dynamics of storms, (ii) the vulnerability of the values at risk, (iii) the geographical distribution of these values, and (iv) the particular conditions of the risk transfer. It is thus of great importance to assess the impact of climate change on future storm losses. To this end, the current study employs--to our knowledge for the first time--a coupled approach, using output from high-resolution regional climate model scenarios for the European sector to drive an operational insurance loss model. An ensemble of coupled climate-damage scenarios is used to provide an estimate of the inherent uncertainties. Output of two state-of-the-art global climate models (HadAM3, ECHAM5) is used for present (1961--1990) and future climates (2071--2100, SRES A2 scenario). These serve as boundary data for two nested regional climate models with a sophisticated gust parametrizations (CLM, CHRM). For validation and calibration purposes, an additional simulation is undertaken with the CHRM driven by the ERA40 reanalysis. The operational insurance model (Swiss Re) uses a European-wide damage function, an average vulnerability curve for all risk types, and contains the actual value distribution of a complete European market portfolio. The coupling between climate and damage models is based on daily maxima of 10 m gust winds, and the strategy adopted consists of three main steps: (i) development and application of a pragmatic selection criterion to retrieve significant storm events, (ii) generation of a probabilistic event set using a Monte-Carlo approach in the hazard module of the insurance model, and (iii) calibration of the simulated annual expected losses with a historic loss data base. The climate models considered agree regarding an increase in the intensity of extreme storms in a band across central Europe (stretching from southern UK and northern France to Denmark, northern Germany into eastern Europe). This effect increases with event strength, and rare storms show the largest climate change sensitivity, but are also beset with the largest uncertainties. Wind gusts decrease over northern Scandinavia and Southern Europe. Highest intra-ensemble variability is simulated for Ireland, the UK, the Mediterranean, and parts of Eastern Europe. The resulting changes on European-wide losses over the 110-year period are positive for all layers and all model runs considered and amount to 44% (annual expected loss), 23% (10 years loss), 50% (30 years loss), and 104% (100 years loss). There is a disproportionate increase in losses for rare high-impact events. The changes result from increases in both severity and frequency of wind gusts. Considerable geographical variability of the expected losses exists, with Denmark and Germany experiencing the largest loss increases (116% and 114%, respectively). All countries considered except for Ireland (-22%) experience some loss increases. Some ramifications of these results for the socio-economic sector are discussed, and future avenues for research are highlighted. The technique introduced in this study and its application to realistic market portfolios offer exciting prospects for future research on the impact of climate change that is relevant for policy makers, scientists and economists. Author Affiliation: (1) Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland (2) Swiss Reinsurance Company, Zurich, Switzerland (3) Federal Office for the Environment, Berne, Switzerland (4) Seminar for Statistics, ETH Zurich, Zurich, Switzerland (5) WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland (6) NCAS-Climate, Reading University, Reading, UK Article History: Registration Date: 07/09/2009 Received Date: 12/09/2007 Accepted Date: 27/08/2009 Online Date: 25/11/2009

A refined full-order method is presented for estimating the extreme wind load effects of rigid structures with given mean recurrence intervals (MRIs) by combining the distributions of annual maximum wind speed and extreme load coefficients. This refined method is capable of dealing with any type of asymptotic extreme value distribution. With this full-order method, the predictions of wind load effects by using distributions of annual maximum wind velocity pressure and wind speed are compared that provide information on the sensitivity of predictions to the upper tail of wind speed distribution. The efficacy of the first-order method is examined. The influences of the type of distributions and the variations of annual maximum wind speed and extreme load coefficient on the predictions are quantified. Finally, the first- and full-order methods are extended to wind load effects of dynamically sensitive structures which facilitate a comprehensive probabilistic analysis as compared to the Monte Carlo simulation schemes used in literature. It is pointed out that 78% fractile extreme load coefficient can be used for defining the characteristic load effects of both rigid and dynamically sensitive structures. The wind load factor is insensitive to the variation of extreme load coefficient. It can be approximately estimated through the wind speed factor and the growth rate of extreme wind load effect with increasing wind speed. The result concerning the wind load factor justifies the advantage of specifying design wind speeds with various MRIs in reducing the uncertainties of design wind loading. DOI: 10.1061/(ASCE)EM.1943-7889.0000118 CE Database subject headings: Wind loads; Uncertainty principles; Reliability; Probability; Aerodynamics. Author keywords: Wind; Wind load; Wind load effect; Uncertainty; Reliability analysis; Probabilistic analysis; Wind load factor; Rigid structures; Dynamically sensitive structures.

Byline: John E. Kutzbach (1), W. F. Ruddiman (2), S. J. Vavrus (1), G. Philippon (1) Abstract: We present further steps in our analysis of the early anthropogenic hypothesis (Ruddiman, Clim Change 61:261--293, 2003) that increased levels of greenhouse gases in the current interglacial, compared to lower levels in previous interglacials, were initiated by early agricultural activities, and that these increases caused a warming of climate long before the industrial era ([proportional to]1750). These steps include updating observations of greenhouse gas and climate trends from earlier interglacials, reviewing recent estimates of greenhouse gas emissions from early agriculture, and describing a simulation by a climate model with a dynamic ocean forced by the low levels of greenhouse gases typical of previous interglacials in order to gauge the magnitude of the climate change for an inferred (natural) low greenhouse gas level relative to a high present day level. We conduct two time slice (equilibrium) simulations using present day orbital forcing and two levels of greenhouse gas forcing: the estimated low (natural) levels of previous interglacials, and the high levels of the present (control). By comparing the former to the latter, we estimate how much colder the climate would be without the combined greenhouse gas forcing of the early agriculture era (inferred from differences between this interglacial and previous interglacials) and the industrial era (the period since [proportional to]1750). With the low greenhouse gas levels, the global average surface temperature is 2.7 K lower than present day--ranging from [proportional to]2 K lower in the tropics to 4--8 K lower in polar regions. These changes are large, and larger than those reported in a pre-industrial ([proportional to]1750) simulation with this model, because the imposed low greenhouse gas levels (CH.sub.4 = 450 ppb, CO.sub.2 = 240 ppm) are lower than both pre-industrial (CH.sub.4 = 760 ppb, CO.sub.2 = 280 ppm) and modern control (CH.sub.4 = 1,714 ppb, CO.sub.2 = 355 ppm) values. The area of year-round snowcover is larger, as found in our previous simulations and some other modeling studies, indicating that a state of incipient glaciation would exist given the current configuration of earth's orbit (reduced insolation in northern hemisphere summer) and the imposed low levels of greenhouse gases. We include comparisons of these snowcover maps with known locations of earlier glacial inception and with locations of twentieth century glaciers and ice caps. In two earlier studies, we used climate models consisting of atmosphere, land surface, and a shallow mixed-layer ocean (Ruddiman et al., Quat Sci Rev 25:1--10, 2005 Vavrus et al., Quat Sci Rev 27:1410--1425, 2008). Here, we replaced the mixed-layer ocean with a complete dynamic ocean. While the simulated climate of the atmosphere and the surface with this improved model configuration is similar to our earlier results (Vavrus et al., Quat Sci Rev 27:1410--1425, 2008), the added information from the full dynamical ocean is of particular interest. The global and vertically-averaged ocean temperature is 1.25 K lower, the area of sea ice is larger, and there is less upwelling in the Southern Ocean. From these results, we infer that natural ocean feedbacks could have amplified the greenhouse gas changes initiated by early agriculture and possibly account for an additional increment of CO.sub.2 increase beyond that attributed directly to early agricultural, as proposed by Ruddiman (Rev Geophys 45:RG4001, 2007). However, a full test of the early anthropogenic hypothesis will require additional observations and simulations with models that include ocean and land carbon cycles and other refinements elaborated herein. Author Affiliation: (1) Center for Climatic Research, Nelson Institute for Environmental Studies, University of Wisconsin-Madison, 1225 W. Dayton Street, Madison, WI, 53706, USA (2) Department of Environmental Sciences, University of Virginia, Charlottesville, VA, 22904, USA Article History: Registration Date: 11/08/2009 Received Date: 04/08/2008 Accepted Date: 28/07/2009 Online Date: 03/09/2009

This article discusses the interplay between computational experiments and scientific advancement in dynamical meteorology and climate dynamics. In doing so, the emphasis is on the dual role of computations in prediction and experimentation, permitting the development of physical insight and confidence in the mechanistic insight through verification. Modern climate dynamics has steadily evolved because of the ready access to computational power that has developed over the past quarter century. The landscape for state-of-the-art computational climate science is changing rapidly, however, with the drive toward greater complexity in climate models in order to more fully represent the interactions among components, the need for higher-resolution atmospheric and oceanic models to fully capture critical aspects of the variability in these components, and the advent of petascale and (eventually) exascale computing facilities. Finally, the manner in which the combination of these changes will likely alter the planning and execution of grand-challenge computational experiments and what this might mean in terms of collaborative climate science is discussed. DOI: 10.1175/2009BAMS2712.1

Byline: Maria Fernanda Cabre (1), S. A. Solman (2), M. N. Nunez (2) Abstract: Dynamical downscaling of global climate simulations is the most adequate tool to generate regional projections of climate change. This technique involves at least a present climate simulation and a simulation of a future scenario, usually at the end of the twenty first century. However, regional projections for a variety of scenarios and periods, the 2020s or the 2050s, are often required by the impact community. The pattern scaling technique is used to estimate information on climate change for periods and scenarios not simulated by the regional model. We based our study on regional simulations performed over southern South America for present climate conditions and two emission scenarios at the end of the twenty first century. We used the pattern scaling technique to estimate mean seasonal changes of temperature and precipitation for the 2020s and the 2050s. The validity of the scalability assumptions underlying the pattern scaling technique for estimating near future regional climate change scenarios over southern South America is assessed. The results show that the pattern scaling works well for estimating mean temperature changes for which the regional changes are linearly related to the global mean temperature changes. For precipitation changes, the validity of the scalability assumption is weaker. The errors of estimating precipitation changes are comparable to those inherent to the regional model and to the projected changes themselves. Author Affiliation: (1) Centro de Investigaciones del Mar y la Atmosfera (CIMA--CONICET/UBA), Ciudad Universitaria Pabellon II, Piso 2 (1428), Buenos Aires, Argentina (2) Centro de Investigaciones del Mar y la Atmosfera (CIMA--CONICET/UBA), Dto. Cs. Atmosfera y los Oceanos (DCAO-FCEyN. UBA), Ciudad Universitaria Pabellon II, Piso 2 (1428), Buenos Aires, Argentina Article History: Registration Date: 17/09/2009 Received Date: 23/06/2008 Accepted Date: 03/08/2009 Online Date: 13/01/2010

The African Monsoon Multidisciplinary Analyses-Model Intercomparison Project (AMMA-MIP) was developed within the framework of the AMMA project. It is a relatively light intercomparison and evaluation exercise of both global and regional atmospheric models, focused on the study of the seasonal and intraseasonal variations of the climate and rainfall over the Sahel. Taking advantage of the relative zonal symmetry of the West African climate, one major target of the exercise is the documentation of a meridional cross section made of zonally averaged (10[degrees]W-10[degrees]E) outputs. This paper presents the motivations and design of the exercise, and it discusses preliminary results and further extensions of the project. DOI: 10.1175/2009BAMS2791.1

The systematic development of reduced low-dimensional stochastic climate models from observations or comprehensive high-dimensional climate models is an important topic for atmospheric low-frequency variability, climate sensitivity, and improved extended range forecasting. Here techniques from applied mathematics are utilized to systematically derive normal forms for reduced stochastic climate models for low-frequency variables. The use of a few Empirical Orthogonal Functions (EOFs) (also known as Principal Component Analysis, Karhunen-Loeve and Proper Orthogonal Decomposition) depending on observational data to span the low-frequency subspace requires the assessment of dyad interactions besides the more familiar triads in the interaction between the low- and high-frequency subspaces of the dynamics. It is shown below that the dyad and multiplicative triad interactions combine with the climatological linear operator interactions to simultaneously produce both strong nonlinear dissipation and Correlated Additive and Multiplicative (CAM) stochastic noise. For a single low-frequency variable the dyad interactions and climatological linear operator alone produce a normal form with CAM noise from advection of the large scales by the small scales and simultaneously strong cubic damping. These normal forms should prove useful for developing systematic strategies for the estimation of stochastic models from climate data. As an illustrative example the one-dimensional normal form is applied below to low-frequency patterns such as the North Atlantic Oscillation (NAO) in a climate model. The results here also illustrate the short comings of a recent linear scalar CAM noise model proposed elsewhere for low-frequency variability. low-frequency teleconnection patterns | nonlinearity | correlated noise

Byline: Christel Prudhomme (1), Helen Davies (1) Abstract: The first part of this paper demonstrated the existence of bias in GCM-derived precipitation series, downscaled using either a statistical technique (here the Statistical Downscaling Model) or dynamical method (here high resolution Regional Climate Model HadRM3) propagating to river flow estimated by a lumped hydrological model. This paper uses the same models and methods for a future time horizon (2080s) and analyses how significant these projected changes are compared to baseline natural variability in four British catchments. The UKCIP02 scenarios, which are widely used in the UK for climate change impact, are also considered. Results show that GCMs are the largest source of uncertainty in future flows. Uncertainties from downscaling techniques and emission scenarios are of similar magnitude, and generally smaller than GCM uncertainty. For catchments where hydrological modelling uncertainty is smaller than GCM variability for baseline flow, this uncertainty can be ignored for future projections, but might be significant otherwise. Predicted changes are not always significant compared to baseline variability, less than 50% of projections suggesting a significant change in monthly flow. Insignificant changes could occur due to climate variability alone and thus cannot be attributed to climate change, but are often ignored in climate change studies and could lead to misleading conclusions. Existing systematic bias in reproducing current climate does impact future projections and must, therefore, be considered when interpreting results. Changes in river flow variability, important for water management planning, can be easily assessed from simple resampling techniques applied to both baseline and future time horizons. Assessing future climate and its potential implication for river flows is a key challenge facing water resource planners. This two-part paper demonstrates that uncertainty due to hydrological and climate modelling must and can be accounted for to provide sound, scientifically-based advice to decision makers. Author Affiliation: (1) Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK Article History: Registration Date: 17/06/2008 Received Date: 01/06/2006 Accepted Date: 13/06/2008 Online Date: 17/09/2008

Byline: Barry H. Lynn (1), Richard Healy (2), Leonard M. Druyan (3,4) Abstract: This study used 'factor separation' to quantify the sensitivity of simulated present and future surface temperatures and precipitation to alternative regional climate model physics components. The method enables a quantitative isolation of the effects of using each physical component as well as the combined effect of two or more components. Simulation results are presented from eight versions of the Mesoscale Modeling System Version 5 (MM5), one-way nested within one version of the Goddard Institute for Space Studies Atmosphere-Ocean Global Climate Model (GISS AOGCM). The MM5 simulations were made at 108 km grid spacing over the continental United States for five summers in the 1990s and 2050s. Results show that the choice of cumulus convection parameterization is the most important 'factor' in the simulation of contemporary surface summer temperatures and precipitation over both the western and eastern USA. The choice of boundary layer scheme and radiation package also increases the range of model simulation results. Moreover, the alternative configurations give quite different results for surface temperature and precipitation in the 2050s. For example, simulated 2050s surface temperatures by the scheme with the coolest 1990s surface temperatures are comparable to 1990s temperatures produced by other schemes. The study analyzes the spatial distribution of 1990s to 2050s projected changes in the surface temperature for the eight MM5 versions. The predicted surface temperature change at a given grid point, averaged over all eight model configurations, is generally about twice the standard deviation of the eight predicted changes, indicating relative consensus among the different model projections. Factor separation analysis indicates that the choice of cumulus parameterization is the most important modeling factor amongst the three tested contributing to the computed 1990s to 2050s surface temperature change, although enhanced warming over many areas is also attributable to synergistic effects of changing all three model components. Simulated ensemble mean precipitation changes, however, are very small and generally smaller than the inter-model standard deviations. The MM5 versions therefore offer little consensus regarding 1990s to 2050s changes in precipitation rates. Author Affiliation: (1) Department of Earth Sciences, Hebrew University of Jerusalem and Weather It Is, Ltd., Efrat, Israel (2) Woods Hole Oceanographic Institute, Woods Hole, MA, 02543, USA (3) Columbia University, Center for Climate Systems Research, New York, NY, 10025, USA (4) NASA/Goddard Institute for Space Studies, 2880 Broadway, New York, NY, 10025, USA Article History: Registration Date: 22/08/2008 Received Date: 25/09/2006 Accepted Date: 01/08/2008 Online Date: 17/09/2008

Byline: A. L. Kay (1), H. N. Davies (1), V. A. Bell (1), R. G. Jones (2) Abstract: This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions Global Climate Model (GCM) structure downscaling from GCMs (including Regional Climate Model structure) hydrological model structure hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology. Author Affiliation: (1) Centre for Ecology and Hydrology, Maclean Building, Crowmarsh Gifford, Wallingford, Oxfordshire, OX10 8BB, UK (2) Met Office Hadley Centre (Reading Unit), University of Reading, Meteorology Building, Reading, RG6 6BB, UK Article History: Registration Date: 19/06/2008 Received Date: 31/10/2006 Accepted Date: 17/04/2008 Online Date: 16/09/2008

Byline: Michael Vellinga (1), Richard A. Wood (1) Abstract: We discuss climate impacts of a hypothetical shutdown of the thermohaline circulation ('THC') in the 2050s, using the climate model HadCM3. Previous studies have generally focussed on the effects on pre-industrial climate. Here we take into account increased greenhouse gas concentrations according to an IS92a emissions scenario. THC shutdown causes cooling of the Northern Hemisphere of -1.7EC, locally stronger. Over western Europe cooling is strong enough for a return to pre-industrial conditions and a significant increase in the occurrence of frost and snow cover. Global warming restricts the increase in sea ice cover after THC shutdown. This lessens the amount of cooling over NW Europe, but increases it over North America, compared to pre-industrial shutdown. This reflects a non-linearity in the local temperature response to THC shutdown. Precipitation change after THC shutdown is generally opposite to that caused by global warming, except in western and southern Europe, where summer drying is enhanced, and in Central America and southeast Asia, where precipitation is also further reduced. Local rise in sea level after THC shutdown can be large along Atlantic coasts (pm 25,cm), which would add to the rise caused by global warming. Potentially rapid THC shutdown adds to the range of uncertainty of projected future climate change. Author Affiliation: (1) Met Office, Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, United Kingdom Article History: Registration Date: 23/05/2006 Received Date: 04/01/2005 Accepted Date: 28/04/2006 Online Date: 13/01/2007

Byline: Alessandra Giannini (1), Michela Biasutti (2), Isaac M. Held (3), Adam H. Sobel (4) Abstract: We describe the global climate system context in which to interpret African environmental change to support planning and implementation of policymaking action at national, regional and continental scales, and to inform the debate between proponents of mitigation v. adaptation strategies in the face of climate change. We review recent advances and current challenges in African climate research and exploit our physical understanding of variability and trends to shape our outlook on future climate change. We classify the various mechanisms that have been proposed as relevant for understanding variations in African rainfall, emphasizing a 'tropospheric stabilization' mechanism that is of importance on interannual time scales as well as for the future response to warming oceans. Two patterns stand out in our analysis of twentieth century rainfall variability: a drying of the monsoon regions, related to warming of the tropical oceans, and variability related to the El Nino--Southern Oscillation. The latest generation of climate models partly captures this recent continent-wide drying trend, attributing it to the combination of anthropogenic emissions of aerosols and greenhouse gases, the relative contribution of which is difficult to quantify with the existing model archive. The same climate models fail to reach a robust agreement regarding the twenty-first century outlook for African rainfall, in a future with increasing greenhouse gases and decreasing aerosol loadings. Such uncertainty underscores current limitations in our understanding of the global climate system that it is necessary to overcome if science is to support Africa in meeting its development goals. Author Affiliation: (1) International Research Institute for Climate and Society, Earth Institute at Columbia University, 61 Rt 9W, Palisades, NY, 10964, USA (2) Lamont--Doherty Earth Observatory, Earth Institute at Columbia University, Palisades, NY, 10964, USA (3) Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, NJ, 08542, USA (4) Departments of Applied Physics and Applied Math, and Earth and Environmental Sciences, Columbia University, New York, NY, 10025, USA Article History: Registration Date: 16/01/2008 Received Date: 07/09/2006 Accepted Date: 15/01/2008 Online Date: 14/03/2008

Byline: Levi D. Brekke (1), Michael D. Dettinger (2), Edwin P. Maurer (3), Michael Anderson (4) Abstract: Ensembles of historical climate simulations and climate projections from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect on breadth and central tendency, which were found to be more influenced by 'completeness' of the original ensemble in terms of models and emissions pathways. Author Affiliation: (1) Technical Service Center 86-68520, U.S. Bureau of Reclamation, Denver, CO, 80225-0007, USA (2) U.S. Geological Survey and Scripps Institution of Oceanography, La Jolla, CA, 92093-0224, USA (3) Civil Engineering Department, Santa Clara University, Santa Clara, CA, 95053-0563, USA (4) Division of Flood Management, California Department of Water Resources, Sacramento, CA, 95821-9000, USA Article History: Registration Date: 12/12/2007 Received Date: 01/12/2006 Accepted Date: 06/11/2007 Online Date: 28/02/2008

Author(s): Stephen Meyers (corresponding author) [1] Buried in deep vaults of sedimentary strata lies a rich history of our planet and, to some degree, a blueprint for its future. These [...]

Uncertainty in estimates of the effects of aerosols on climate stems from poor knowledge of the past, pristine atmosphere--so getting a better understanding of these effects might not be as [...]

Innovative measurements of ocean turbulence show that mixing of cold water from below makes the surface of the equatorial Pacific much colder in September than in March. See Letter p.64 [...]