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The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s Air Resources Laboratory. In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include a new vertical interpolation algorithm for WRF-derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian time scale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show that the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical direction and is in better agreement with observations when compared with the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared with backward-in-time equivalents and were found to be statistically comparable to one another. [ABSTRACT FROM AUTHOR]

An integrated technique combining global climate model (GCM) simulation results and a statistical time series forecasting model [the autoregressive integrated moving average (ARIMA) model] was developed to bring together the climate change signal from GCMs to city-level historical observations as an approach to obtain location-specific temperature and precipitation projections. This approach assumes that regional temperature and precipitation time series reflect a combination of an underlying climate change signal series and a regional-deviation-from-the-signal series. An ensemble of GCMs is used to describe and provide the climate change signal, and the ARIMA model is used to model and project the regional deviation. Qualitative and quantitative assessments were conducted for evaluating the projection performance of the hybrid GCM-ARIMA (G-ARIMA) model. The results indicate that the G-ARIMA model can provide projected city-specific daily temperature and precipitation series comparable to historical observations and can have improved projection accuracy for several assessed annual indices compared to a commonly used downscaled projection product. The G-ARIMA model is subject to some limitations and uncertainties from the GCM-provided climate change signal. A notable feature of the G-ARIMA model is the efficiency with which projections can be updated when new observations become available, thus facilitating updating of regional temperature and precipitations projections. Given the increasing need for and use of location-specific climate projections in practical engineering applications, the G-ARIMA model is an option for regional temperature and precipitation projection for such applications. [ABSTRACT FROM AUTHOR]

A high-resolution (4 km) regional climate simulation conducted with the Weather Research and Forecasting Model is used to investigate potential impacts of global warming on skiing conditions in the interior western United States (IWUS). Recent-past and near-future climate conditions are compared. The past climate period is from November 1981 to October 2011. The future climate applies to a 30-yr period centered on 2050. A pseudo–global warming approach is used, with the driver reanalysis dataset perturbed by the CMIP5 ensemble mean model guidance. Using the 30-yr retrospective simulation, a vertical adjustment technique is used to determine meteorological parameters in the complex terrain where ski areas are located. For snow water equivalent (SWE), Snowpack Telemetry sites close to ski areas are used to validate the technique and apply a correction to SWE in ski areas. The vulnerability to climate change is assessed for 71 ski areas in the IWUS considering SWE, artificially produced snow, temperature, and rain; 20 of the ski areas will tend to have fewer than 100 days per season with sufficient natural and artificial snow for skiing. These ski areas are located at either low elevations or low latitudes, making these areas the most vulnerable to climate change. Throughout the snow season, natural SWE decreases significantly at the low elevations and low latitudes. At higher elevations, changes in SWE are predicted to not be significant in the midseason. In mid-February, SWE decreases by 11.8% at the top elevations of ski areas and decreases by 25.8% at the base elevations. [ABSTRACT FROM AUTHOR]

Over the past several decades, many efforts have been devoted to increasing the simulation performance of climate models, but significant biases remain that hinder the performance of coupled systems. Hence, bias correction is regarded not only as a useful tool for improving climate simulations but also as an important step before data assimilation, which depends on the hypothesis of unbiasedness. In this study, using sea temperature climatological data, a new ensemble‐based approach is proposed for correcting the biases of the sea temperature in CAS‐ESM‐C. Through analyzing the results of the proposed bias correction method with various intensities and time windows, its performance in suppressing the simulation biases of ocean fields is evaluated. The simulation biases of atmospheric variables are also reduced via air‐sea interactions, which will improve the ocean simulation performance. Additional benefits can be realized by applying the bias correction method. For example, a superior simulation of climate variabilities in a coupled model, such as ENSO (El Niño‐Southern Oscillation), is realized due to the improvement of climatological fields. The ability to assimilate various ocean observations is also significantly improved with a better background mean state. Plain Language Summary: Coupled model is the usual tool for climate prediction and research, but there are still many unignorable simulation biases, especially global sea temperature bias has a greater impact on simulation and prediction under air‐sea interactions. So, we proposed a new ensemble‐based correcting approach for the systematic sea temperature bias and applied it to a climate system model, which use sea temperature climatology data as a "weak" observation to correct the model bias. Through comparing the results of different test designs, the reasonable bias approach could be based on the improvement of model biases, then provide more accurate climate mean states for ENSO simulations and background fields for ocean data assimilation. Key Points: A new ensemble‐based approach is proposed for correcting the biases of sea temperature in CAS‐ESM‐C using climatological dataThe simulation biases of atmospheric and oceanic fields are all significantly reduced through air‐sea interactionsThe benefits of improving the simulation of climate variations and the ability to assimilate various ocean observations are explored [ABSTRACT FROM AUTHOR]

Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk, the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This paper proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes. [ABSTRACT FROM AUTHOR]

Delta systems formed by the deposition of sediments at the mouths of large catchments are vulnerable to sea level rise and other climate change impacts. Deltas often have some of the highest population densities in the world and the Mahanadi Delta in India is one of these, with a population of 39 million. The Mahanadi River is a major river in East Central India and flows through Chattisgarh and Orissa states before discharging into the Bay of Bengal. This study uses an Integrated Catchment Model (INCA) to simulate flow dynamics and water quality (nitrogen and phosphorus) and to analyze the impacts of climate change and socio-economic drivers in the Mahanadi River system. Future flows affected by large population growth, effluent discharge increases and changes in irrigation water demand from changing land uses are assessed under shared socio-economic pathways (SSPs). Model results indicate a significant increase in monsoon flows under the future climates at 2050s (2041–2060) and 2090s (2079–2098) which greatly enhances flood potential. The water availability under low flow conditions will be worsened because of increased water demand from population growth and increased irrigation in the future. Decreased concentrations of nitrogen and phosphorus are expected due to increased flow hence dilution. Socio-economic scenarios have a significant impact on water quality but less impact on the river flow. For example, higher population growth, increased sewage treatment discharges, land use change and enhanced atmospheric deposition would result in the deterioration of water quality, while the upgrade of the sewage treatment works lead to improved water quality. In summary, socio-economic scenarios would change future water quality of the Mahanadi River and alter nutrient fluxes transported into the delta region. This study has serious implications for people's livelihoods in the deltaic area and could impact coastal and Bay of Bengal water ecology. [ABSTRACT FROM AUTHOR]

Field and laboratory measurements using continuous flow diffusion chambers (CFDCs) have been used to construct parameterizations of the number of ice nucleating particles (INPs) in mixed-phase and completely glaciated clouds in weather and climate models. Because of flow nonidealities, CFDC measurements are subject to systematic low biases. Here, the authors investigate the effects of this undercounting bias on simulated cloud forcing in a global climate model. The authors assess the influence of measurement variability by constructing a stochastic parameterization framework to endogenize measurement uncertainty. The authors find that simulated anthropogenic longwave ice-bearing cloud forcing in a global climate model can vary up to 0.8 W m-2 and can change sign from positive to negative within the experimentally constrained bias range. Considering the variability in the undercounting bias, in a range consistent with recent experiments, leads to a larger negative cloud forcing than that when the variability is ignored and only a constant bias is assumed. [ABSTRACT FROM AUTHOR]

Copyright of Berichte zur Wissenschafts-Geschichte is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Berichte zur Wissenschafts-Geschichte is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Computer simulations of climate change is introduced at the topic of the June 2017 issue of the science history periodical "Berichte zur Wissenschafts-Geschichte".

The article presents an interview with Paul N. Edwards and Oliver Geden on climate change and global warming. Topics of conversation include the environmental and political impacts of the potential decision by U.S. president Donald Trump to withdraw from the Paris Accord climate agreement, Peter Galison's "trading zone" model of climate change simulation, and replacement of the Cold War theory of containment.

It has been widely predicted that Southeast Asia is among the regions facing the most severe climate change impacts. Despite this forecast, little research has been published on the potential impacts of climate change on soil erosion in this region. This study focused on the impact of climate change on spatial and temporal patterns of soil erosion in the Laos-Vietnam transnational Upper Ca RiverWatershed. The Soil andWater Assessment Tool (SWAT) coupled with downscaled global climate models (GCMs) was employed for simulation. Soil erosion in the watershed was mostly found as "hill-slope erosion", which occurred seriously in the upstream area where topography is dominated by numerous steep hills with sparse vegetation cover. However, under the impact of climate change, it is very likely that soil erosion rate in the downstream area will increase at a higher rate than in its upstream area due to a greater increase in precipitation. Seasonally, soil erosion is predicted to increase significantly in the warmer and wetter climate of the wet season, when higher erosive power of an increased amount and intensity of rainfall is accompanied by higher sediment transport capacity. The results of this study provide useful information for decision makers to plan where and when soil conservation practice should be focused. [ABSTRACT FROM AUTHOR]

Integrated urban drainage modelling combines different aspects of the urban water system into a common framework. With increasing pressures of a changing climate, urban growth and economic constraints, the need for wider spread integration is necessary in the interest of a sustainable future. Greater complexity results in greater computational burden but modelling packages will, likewise, need to be flexible enough to allow incorporation of new algorithms. With advancements in modern information technology, a parallel implementation of such a modelling toolkit is mandatory while still leaving its users the flexibility of extensions. The design and implementation of the integrated modelling framework CityDrain3 shows that it is possible to write research code that is high-performance and extensible by many research projects. Three use case scenarios are presented to showcase the application of CityDrain3. The performance advantage of parallelization (up to 40 times compared to its predecessor) and the scalability of the framework are also demonstrated. [ABSTRACT FROM AUTHOR]

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness. [ABSTRACT FROM AUTHOR]

The goal of the Palaeoclimate Modelling Intercomparison Project (PMIP) is to understand the response of the climate system to changes in different climate forcings and to feedbacks. Through comparison with observations of the environmental impacts of these climate changes, or with climate reconstructions based on physical, chemical or biological records, PMIP also addresses the issue of how well state-of-the-art models simulate climate changes. Palaeoclimate states are radically different from those of the recent past documented by the instrumental record and thus provide an out-of-sample test of the models used for future climate projections and a way to assess whether they have the correct sensitivity to forcings and feedbacks. Five distinctly different periods have been selected as focus for the core palaeoclimate experiments that are designed to contribute to the objectives of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). This manuscript describes the motivation for the choice of these periods and the design of the numerical experiments, with a focus upon their novel features compared to the experiments performed in previous phases of PMIP and CMIP as well as the benefits of common analyses of the models across multiple climate states. It also describes the information needed to document each experiment and the model outputs required for analysis and benchmarking. [ABSTRACT FROM AUTHOR]

This paper reports the implementation of high-performance computing using the K supercomputer in Kobe, Japan, for large-scale/high-resolution flood simulation. Supercomputer K was developed in 2012 by RIKEN and Fujitsu and ranked first in the list of Top 500 supercomputer sites in 2011 during its development stage. A two-dimensional inundation simulation model developed based on a shallow water equation using an existing numerical scheme was parallelized with the K supercomputer. Osaka and other cities along the Yodo River were chosen as application sites and the area discretized by 12824442 (= 3453 × 3714) nodes with a resolution of 10 m. The computational time for the five-hour flood simulation was measured by changing the number of 8-core CPUs of the K supercomputer. As a result, computational time was decreased to 9.3 min by using 128 × 64 = 8192 8-core CPUs. The computational time was 1423.7 min for one 8-core CPU. Thus, the simulation speed increased by a factor of 153.2 with the use of the K supercomputer. [ABSTRACT FROM AUTHOR]

In this text we propose to analyze the epistemic possibilities of the ideal types for use their signification in the practice of numerical climate modeling; we will argue that the scientific concepts and particularly the models and all more the climatic models should have the epistemic status of ideal types. The plan is translate the methodological topic of ideal type into the numerical climate modeling topic to put forward a proposal that disempowers the universalistic pretentions of the cognitive coinages of contemporary science and particularly about the climatic modeling, so that laying down a scientific self-control mechanism synthesizing subjective explanations and objective validation processes becomes feasible. From these reflections, we consider it would be appropriate that the modelers and their institutions assume the climatic modeling keeping a space of scientific self-control allowing epistemological apertures instead of nomological conclusive impossible tasks about global warming. [ABSTRACT FROM AUTHOR]

The eastern Pacific (EP) pattern is a recently detected atmospheric teleconnection pattern that frequently occurs during late winter. Through analysis of daily ERA-Interim data and outgoing longwave radiation data for the period of 1979-2011, it is shown here that the formation of the EP is preceded by an anomalous tropical convection dipole, with one extremum located over the eastern Indian Ocean-Maritime Continent and the other over the central Pacific. This is followed by the excitation of two quasi-stationary Rossby wave trains. Departing from the subtropics, north of the region of anomalous convection, one Rossby wave train propagates eastward along the East Asian jet from southern China toward the eastern Pacific. The second wave train propagates northward from east of Japan toward eastern Siberia and then turns southeastward to the Gulf of Alaska. Both wave trains are associated with wave activity flux convergence where the EP pattern develops. The results from an examination of the E vector suggest that the EP undergoes further growth with the aid of positive feedback from high-frequency transient eddies. The frequency of occurrence of the dipole convection anomaly increases significantly from early to late winter, a finding that suggests that it is the seasonal change in the convection anomaly that accounts for the EP being more dominant in late winter. [ABSTRACT FROM AUTHOR]

This study presents a diagnosis of a multiresolution approach using the Model for Prediction Across Scales-Atmosphere (MPAS-A) for simulating regional climate. Four Atmospheric Model Intercomparison Project (AMIP) experiments were conducted for 1999-2009. In the first two experiments, MPAS-A was configured using global quasi-uniform grids at 120- and 30-km grid spacing. In the other two experiments, MPAS-A was configured using variable-resolution (VR) mesh with local refinement at 30 km over North America and South America and embedded in a quasi-uniform domain at 120 km elsewhere. Precipitation and related fields in the four simulations are examined to determine how well the VRs reproduce the features simulated by the globally high-resolution model in the refined domain. In previous analyses of idealized aquaplanet simulations, characteristics of the global high-resolution simulation in moist processes developed only near the boundary of the refined region. In contrast, AMIP simulations with VR grids can reproduce high-resolution characteristics across the refined domain, particularly in South America. This finding indicates the importance of finely resolved lower boundary forcings such as topography and surface heterogeneity for regional climate and demonstrates the ability of the MPAS-A VR to replicate the large-scale moisture transport as simulated in the quasi-uniform high-resolution model. Upscale effects from the high-resolution regions on a large-scale circulation outside the refined domain are observed, but the effects are mainly limited to northeastern Asia during the warm season. Together, the results support the multiresolution approach as a computationally efficient and physically consistent method for modeling regional climate. [ABSTRACT FROM AUTHOR]

An estimate of the net direction of climate interactions in different geographical regions is made by constructing a directed climate network from a regular latitude-longitude grid of nodes, using a directionality index (DI) based on conditional mutual information (CMI). Two datasets of surface air temperature anomalies--one monthly averaged and another daily averaged--are analyzed and compared. The network links are interpreted in terms of known atmospheric tropical and extra-tropical variability patterns. Specific and relevant geographical regions are selected, the net direction of propagation of the atmospheric patterns is analyzed, and the direction of the inferred links is validated by recovering some well-known climate variability structures. These patterns are found to be acting at various time-scales, such as atmospheric waves in the extratropics or longer range events in the tropics. This analysis demonstrates the capability of the DI measure to infer the net direction of climate interactions and may contribute to improve the present understanding of climate phenomena and climate predictability. The work presented here also stands out as an application of advanced tools to the analysis of empirical, real-world data. [ABSTRACT FROM AUTHOR]

The North Atlantic Oscillation (NAO) is the major source of variability in winter atmospheric circulation in the Northern Hemisphere, with large impacts on temperature, precipitation and storm tracks, and therefore also on strategic sectors such as insurance, renewable energy production, crop yields and water management. Recent developments in dynamical methods offer promise to improve seasonal NAO predictions, but assessing potential predictability on multi-annual timescales requires documentation of past low-frequency variability in the NAO. A recent bi-proxy NAO reconstruction spanning the past millennium suggested that long-lasting positive NAO conditions were established during medieval times, explaining the particularly warm conditions in Europe during this period; however, these conclusions are debated. Here, we present a yearly NAO reconstruction for the past millennium, based on an initial selection of 48 annually resolved proxy records distributed around the Atlantic Ocean and built through an ensemble of multivariate regressions. We validate the approach in six past-millennium climate simulations, and show that our reconstruction outperforms the bi-proxy index. The final reconstruction shows no persistent positive NAO during the medieval period, but suggests that positive phases were dominant during the thirteenth and fourteenth centuries. The reconstruction also reveals that a positive NAO emerges two years after strong volcanic eruptions, consistent with results obtained from models and satellite observations for the Mt Pinatubo eruption in the Philippines. [ABSTRACT FROM AUTHOR]

Changes to runoff due to climate change may influence management of nutrient loading to the sea. Assuming unchanged river nutrient concentrations, we evaluate the effects of changing runoff on commitments to nutrient reductions under the Baltic Sea Action Plan. For several countries, climate projections point to large variability in load changes in relation to reduction targets. These changes either increase loads, making the target more difficult to reach, or decrease them, leading instead to a full achievement of the target. The impact of variability in climate projections varies with the size of the reduction target and is larger for countries with more limited commitments. In the end, a number of focused actions are needed to manage the effects of climate change on nutrient loads: reducing uncertainty in climate projections, deciding on frameworks to identify best performing models with respect to land surface hydrology, and increasing efforts at sustained monitoring of water flow changes. [ABSTRACT FROM AUTHOR]

To examine the use of fresh-air cooling to reduce energy consumption in container data centers (CDCs) regardless of location, a prototype fresh-air-cooled CDC was constructed in a Tokyo suburb, and a year-long operating test was conducted. Fresh-air cooling of information technology (IT) equipment does not require energy for air conditioning, and can be operated at reduced cost and energy usage. However, the characteristics of fresh-air change throughout the year, which would make it difficult to maintain inside temperature and humidity of a server room at the set points. Also, some geographic locations may be unsuitable for CDCs cooled directly by fresh-air. In addition to fresh-air cooling, CDCs at these locations have been equipped with supplemental air-conditioning units such as air conditioners to control the internal temperature and humidity. To reduce energy usage, it is desirable to use energy conservation methods such as evaporative cooling and waste heat from IT equipment as supplemental air-conditioning because they can be more feasible in compact facilities than conventional air conditioners. Although data centers (DCs) that supplement fresh-air cooling with these air-conditioning methods are currently in operation, it is not clear whether these methods can be applied in any location regardless of the climatic conditions. Depending on the application, in particular, it is necessary to set up CDCs in urban suburbs, because large access latency for users may be required. To examine these issues, a fresh-air-cooled CDC was set up and evaluated for one year in a Tokyo suburb, under different climate conditions of existing fresh-air-cooled DCs. The results confirmed that evaporative cooling and the use of waste heat from IT equipment were sufficient to support direct fresh-air cooling even when the characteristics of fresh-air were outside the range of acceptable server settings. Moreover, this CDC realized an annual energy savings of 20.8% compared with the centers that use conventional air conditioning. Furthermore, the power usage effectiveness of this CDC was found to be 1.058 from the energy usage and was compared with that of existing direct fresh-air-cooled DCs which is located in the urban suburbs. The CDC near Tokyo exhibited similar energy savings to other DCs. [ABSTRACT FROM AUTHOR]

A recent palaeo-reconstruction of the strength of the Iceland--Scotland overflow during the last 600 years suggests that its low-frequency variability exhibits strong similarity with palaeo-reconstructions of the Atlantic Multidecadal Oscillation (AMO). The underlying mechanism of the similar variation remains unclear, however, based on palaeo-reconstructions alone. In this study we use simulations of the last millennium driven by external forcing reconstructions with three coupled climate models in order to investigate possible mechanisms underlying the similar variation of Iceland--Scotland overflow strength and AMO index. Similar variation of the two time series is also largely found in the model simulations. Our analysis indicates that the basin-wide AMO index in the externally forced simulations is dominated by the low-latitude sea surface temperature (SST) variability and is not predominantly driven by variations in the strength of the Atlantic meridional overturning circulation (MOC). This result suggests that a large-scale link through the strength of the MOC is not sufficient to explain the (simulated) similar variation of Iceland--Scotland overflow strength and AMO index. Rather, a more local link through the influence of the Nordic seas surface state and density structure, which are positively correlated with the AMO index, on the pressure gradient across the Iceland-- Scotland ridge is responsible for the (simulated) similar variation. In the model simulation showing a weaker correlation between the Iceland--Scotland overflow strength and the AMO index, the wind stress in the Nordic seas also influences the overflow strength. Our study demonstrates that palaeo-climate simulations provide a useful tool to understand mechanisms and large-scale connections associated with the relatively sparse palaeo-observations. [ABSTRACT FROM AUTHOR]

Most present-generation climate models simulate an increase in global-mean surface temperature (GMST) since 1998, whereas observations suggest a warming hiatus. It is unclear to what extent this mismatch is caused by incorrect model forcing, by incorrect model response to forcing or by random factors. Here we analyse simulations and observations of GMST from 1900 to 2012, and show that the distribution of simulated 15-year trends shows no systematic bias against the observations. Using a multiple regression approach that is physically motivated by surface energy balance, we isolate the impact of radiative forcing, climate feedback and ocean heat uptake on GMST-with the regression residual interpreted as internal variability-and assess all possible 15- and 62-year trends. The differences between simulated and observed trends are dominated by random internal variability over the shorter timescale and by variations in the radiative forcings used to drive models over the longer timescale. For either trend length, spread in simulated climate feedback leaves no traceable imprint on GMST trends or, consequently, on the difference between simulations and observations. The claim that climate models systematically overestimate the response to radiative forcing from increasing greenhouse gas concentrations therefore seems to be unfounded. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating annual and decadal temperature in the Mekong River Basin from 1950 to 2005. By use of Bayesian multi-model averaging method, the future projection of temperature variation under different scenarios are also analyzed. The results show, the performances of climate model are more accurate in space than time, the model can catch the warming characteristics in the Mekong river Basin, but the accuracy of simulation is not good enough. Bayesian multi-model averaging method can improve the annual and decadal temperature simulation when compared to a single result. The projected temperature in Mekong River will increase by 0.88 °C/100 year, 2.15 °C/100 year and 4.96 °C/100 year for the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, over the twenty-first century. The findings will be beneficial for local people and policy-maker to formulate regional strategies against the potential menaces of warming scenarios. [ABSTRACT FROM AUTHOR]

As the world's climate warms, the phenologies of interacting organisms in seasonally cold environments may advance at differing rates, leading to alterations in phenological synchrony that can have important ecological consequences. For temperate and boreal species, the timing of early spring development plays a key role in plant-herbivore interactions and can influence insect performance, outbreak dynamics, and plant damage. We used a field-based, meso-scale free-air forest warming experiment (B4WarmED) to examine the effects of elevated temperature on the phenology and performance of forest tent caterpillar ( Malacosoma disstria) in relation to the phenology of two host trees, aspen ( Populus tremuloides) and birch ( Betula papyrifera). Results of our 2-year study demonstrated that spring phenology advanced for both insects and trees, with experimentally manipulated increases in temperature of 1.7 and 3.4 °C. However, tree phenology advanced more than insect phenology, resulting in altered phenological synchrony. Specifically, we observed a decrease in the time interval between herbivore egg hatch and budbreak of aspen in both years and birch in one year. Moreover, warming decreased larval development time from egg hatch to pupation, but did not affect pupal mass. Larvae developed more quickly on aspen than birch, but pupal mass was not affected by host species. Our study reveals that warming-induced phenological shifts can alter the timing of ecological interactions across trophic levels. These findings illustrate one mechanism by which climate warming could mediate insect herbivore outbreaks, and also highlights the importance of climate change effects on trophic interactions. [ABSTRACT FROM AUTHOR]

Copyright of Revista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturales is the property of Academia Colombiana de Ciencias Exactas, Fisicas y Naturales and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates. This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology. [ABSTRACT FROM AUTHOR]

Dynamic vegetation models are important tools in ecological research, but not all processes of vegetation dynamics are captured adequately. Tree mortality is often modeled as a function of growth efficiency and maximum age. However, empirical studies have shown for different species that slow-growing trees may become older than fast-growing trees, implying a correlation of mortality with growth rate and size rather than age. We used the ecosystem model LPJ-GUESS to compare the standard age-dependent mortality with two size-dependent mortality approaches. We found that all mortality approaches, when calibrated, yield a realistic pattern of growing stock and Plant Functional Type (PFT) distribution at five study sites in Switzerland. However, only the size-dependent approaches match a third pattern, i.e. the observed negative relationship between growth rate and longevity. As a consequence, trees are simulated to get older at higher than at lower altitudes/latitudes. In contrast, maximum tree ages do not change along these climatic gradients when the standard age-dependent mortality is used. As tree age and size determine forest structure, our more realistic mortality assumptions improved forest biomass estimation, but indicate a potential decline of carbon storage under climate change. We conclude that tree mortality should be modeled as a function of size rather than age. [Copyright &y& Elsevier]

The disparate communities of climate modeling and remote sensing are finding economic, political, and societal benefit from the direct comparisons of climate model outputs to satellite observations, using these comparisons to help tune models and to provide ground truth in understanding the Earth's climate processes. In the context of the Intergovernmental Panel on Climate Change (IPCC) and its upcoming 5th Assessment Report (AR5), the authors have been working with principals in both communities to build a software infrastructure that enables these comparisons. This infrastructure must overcome several software engineering challenges, including bridging heterogeneous data file formats and metadata formats, transforming swath-based remotely sensed data into globally gridded datasets, and navigating and aggregating information from the largely distributed ecosystem of organizations that house these climate model outputs and satellite data. The authors' focus in this article is on the description of software tools and services that meet these stringent challenges, and on informing the broader communities of climate modelers, remote sensing experts, and software engineers on the lessons learned from their experience so that future systems can benefit and improve upon their existing results. [ABSTRACT FROM PUBLISHER]

The article discusses the ozone databases formulated to evaluate and restrain simulations of global climate. It mentions that calculation of the monthly mean ozone became possible through collaboration of global ozonesonde network with the ozone measurements from eight satellites. It mentions that the gap-free database for global climate model simulations is reliable in determining ozone boundary.

The likely effects of climate change on the water resources of the eastern Mediterranean and Middle East region are investigated using a high-resolution regional climate model (PRECIS) by comparing precipitation simulations of 2040-2069 and 2070-2099 with 1961-1990. The simulations show about a 10% decline in precipitation across the region by both the middle and the end of the century, with considerable variation between countries and international river basins. Results suggest that per capita water resources will not change particularly significantly in southeastern Europe, where they are relatively plentiful and population growth is minimal. However, in much of the Middle East, climate change coupled with population growth is likely to reduce per capita water resources considerably. This will inevitably result in major social, economic, and environmental change in the region. Countries where the required adaptation is likely to be particularly challenging include Turkey and Syria because of the large agricultural workforces, Iraq because of the magnitude of the change and its downstream location, and Jordan because of its meager per capita water resources coupled with limited options for desalination. If the internal water footprint of the region declines in line with precipitation but the total water footprint of the region increases in line with population, then by midcentury, as much as half the total water needs of the region may need to be provided through desalination and imported in the form of virtual water. [ABSTRACT FROM AUTHOR]

The article discusses research into the use of stochastic methods in scientific research, focusing on efforts to apply the techniques to climate modeling. Topics include the limitations of traditional climate models in forecasting future outcomes, the role of computer modeling in analysis of climate change and its potential impacts, and examples of stochastic simulations and means of testing their performance against the climate record.

A response from the author of the article regarding the impact of climate changes such as natural disasters and the use of computer models in assessing the impact, is presented.

The article discusses a study by J. Marotzke and colleagues which examines the use of climate simulation and short-term trends to determine the natural and force climate variations. Topics cited include the occurrence of fluctuations in the rate of climate warming and global surface temperature, estimation of sensitivity of climate to forced changes, intrinsic ocean and atmospheric circulation as the source of free variations, and environmental effect of greenhouse gas concentration.

A letter to the editor is presented in response to the article by J. C. Fyfe and colleagues which discusses the observation and simulation of global warming trends over the past 20 years using the 37 climate models in phase five of the Coupled Model Intercomparison Project (CMIP5).

A letter to the editor is presented in response to the article by Spencer Weart in the September 2015 issue regarding the aspects of climate change prediction through computer models and processes.

The article discusses the agreement between U.S. President Barack Obama and Chinese President Xi Jinping on domestic climate policies that seek to stabilize greenhouse gas emissions by 2030 and speculates on whether China's meeting the emissions targets is possible. Topics include computer simulation modeling of emissions stabilization, the Chinese government's financing renewable energies, and potential effects of emissions reductions on Chinese economic growth.