Search

Intravitreal (IVT) injection remains the preferred administration route of pharmacological agents intended for the treatment of back of the eye diseases such as diabetic macular edema (DME) and neovascular age-related macular degeneration (nvAMD). The procedure enables drugs to be delivered locally at high concentrations whilst limiting whole body exposure and associated risk of systemic adverse events. Nevertheless, intravitreally-delivered drugs do enter the general circulation and achieving an accurate understanding of systemic exposure is pivotal for the evaluation and development of drugs administered in the eye. We report here the full pharmacokinetic properties of THR-687, a pan RGD integrin antagonist currently in clinical development for the treatment of DME, in both rabbit and minipig. Pharmacokinetic characterization included description of vitreal elimination, of systemic pharmacokinetics, and of systemic exposure following IVT administration. For the latter, we present a novel pharmacokinetic model that assumes clear partition between the vitreous humor compartment itself where the drug is administered and the central systemic compartment. We also propose an analytical solution to the system of differential equations that represent the pharmacokinetic model, thereby allowing data analysis with standard nonlinear regression analysis. The model accurately describes circulating levels of THR-687 following IVT administration in relevant animal models, and we suggest that this approach is relevant to a range of drugs and analysis of subsequent systemic exposure.

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.

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

This repository contains the data and scripts required to reproduce the results of the paper "Characterizing Convection Schemes Using Their Responses to Imposed Tendency Perturbations" submitted to the Journal of Advances inModeling Earth Systems (JAMES). Brief description of project An SCM intercomparison project based on the Linear Response Function framework of Kuang (2010), where we examine the temperature and moisture responses to small convective tendency perturbations. Participating Single-Column Models (SCMs): WRF, LMDZ, CNRM, UM, SCAM Convection schemes tested: WRF Kain-Fritsch WRF New-Tiedtke WRF New-Simplified-Arakawa-Schubert WRF Betts-Miller-Janjic WRF Zhang-McFarlane CNRM PCMT scheme UM Simplified-Betts-Miller UM Mass-Flux scheme (Gregory & Rowntree) SCAM Zhang-McFarlane LMDZ modified-Emanuel scheme + Cold pool formulation Repository structure /data directory: contains the data from the SCMs, in csv format /data/[model_name]/ directories: contain the data of the individual models. There are four sub-directories (five for WRF) for each model: REF directory: contains data for RCE mean state, including Temperature (T) and Relative Humidity (RH) matrix_X_raw directory: contains raw data for the T and q responses to dT/dt and dq/dt perturbations matrix_M_inv directory: contains the post-processed (normalized and standardized) M-1 matrix data response_profiles directory: contains the post-processed response profiles (vertical column) for perturbation at two levels (850 and 650 hPa) (for WRF only) pbl_mp_sensitivity directory: data for WRF PBL and MP sensitivity tests. There are three sub-directories in this folder: mean_states directory: contains data for the RCE mean state sensitivity to PBL and MP schemes response_profiles directory: contains data for the sensitivity of T and q responses to PBL and MP schemes response_profiles_non_idealized directory: contains data to compare sensitivity of T and q responses to PBL and MP schemes between idealized and non-idealized setups /scripts directory: contains the python scripts to post-process and plot the figures in the paper /scripts/plot_inidividual_matrix/ directory: contains scripts to plot the M-1 matrix and the 2-levels response profiles for individual SCMs plot_matrix.py : script to post-process and plot the matrix and response profiles for selected SCM using the raw data in the matrix_X_raw folders. Option available to save the (post-processed) outputs as csv files in the matrix_M_inv and response_profiles folders of the selected SCM /scripts/plot_figures/ directory: contains scripts to plot the Figures in the paper plot_rce_mean_states_all_models.py : script to plot Figure 1 (RCE mean state T and RH of all SCMs) plot_anomaly_profiles.py : script to plot Figure 2 - 3 (vertical profiles of T and q responses for selected SCMs) plot_matrix_all_models.py : script to plot Figures 4 - 7 (M-1 matrices of all SCMs) plot_rh_q_correlation_matrix.py : script to plot Figure 8 (correlation matrix of RH vs. q') plot_pbl_mp_sensitivity_mean_states.py : script to plot Figures 9 - 10 (sensitivity of RCE mean state to PBL and MP schemes) plot_pbl_mp_sensitivity_responses.py : script to plot Figures 11 - 12 (sensitivity of T and q responses to PBL and MP schemes) plot_pbl_mp_sensitivity_responses_non_idealized.py : script to plot Figures A1 - A2 (sensitivity of T and q responses to PBL and MP schemes comparison between idealized and non-idealized setups) &nbsp

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.

The aim of this paper is to explore, detect, describe and explain the path of Serbian press in France during the 20th century. The main hypothesis is that the press in the Serbian language was the paving stone from the cultural diplomacy to the information on the Serbian diaspora. The paper would explain how Serbian journals in French language and Serbian journals in Serbian language which were lanched in France became the paths of presentation of the Serbian people through mechanisms that would be recognized as elements of cultural diplomacy. Key issue is the role which Serbian journals played in the general circulation of men and ideas, the cultural transfers that they facilitated, as well as the hybrid identities they fostered. On the basis of contents of the journals and the reading public, as well as the intellectuals engaged in creation of the journals it would be shed light on the Serbian press in France during the 20th century. L’objectif de cet article est d’explorer, de détecter, de décrire et d’expliquer le chemin de la presse serbe en France au cours du xxe siècle. L’hypothèse principale est que la presse en langue serbe a été le pavé de la diplomatie culturelle vers l’information sur la diaspora serbe. L’article explique comment les revues serbes en langue française et les revues serbes en langue serbe, lancées en France, sont devenues les voies pour présenter le peuple serbe à travers des mécanismes qui seraient reconnus comme éléments de la diplomatie culturelle. La question clé est le rôle que les journaux serbes ont joué dans la circulation générale des hommes et des idées, les transferts culturels, qu’elles ont facilités, ainsi que les identités hybrides qu’elles ont favorisées. À partir du contenu des journaux et leur public, ainsi que les intellectuels engagés dans leur création, on fera la lumière sur la presse serbe en France au cours du xxe siècle. Циљ овог рада јесте да истражи, открије, опише и објасни пут српске штампе у Француској током 20. вијека. Главна хипотеза је да је штампа на српском језику стаза од културне дипломатије до информација о српској дијаспори. Рад би објаснио како су српски часописи на француском језику и српски часописи на српском језику ,публиковани у Француској, постали начин презентације српског народа током 20. века кроз механизме који се могу препознати као елементи културне дипломатије. Кључно питање јесте улога коју је српска штампа играла у општој циркулацији људи и идеја, културних трансфера, које су обликовали и хибридни идентитети које су они подстакли. На основу садржаја часописа и читалачке публике, као и интелектуалаца који су били укључени у стварање часописа расветлиће се постојање српске штампе у Француској током 20. века.

The simulated data set described in this paper was created by an ensemble of nine different crop models: HERMES (HE), Simplace (L5), SiriusQuality (SQ), MONICA (MO), Sirius2014 (S2), FASSET (FA), 4M (4M), SSM (SS), DSSAT-CSM IXIM (IX). Simulations were performed for grain maize (six models) and winter wheat (eight models) under diverse conditions over agriculturally relevant areas in the EU-27 at a 25 x 25 km spatial resolution. Simulations were drawn from combinations of three representative concentration pathways and climate outputs from five general circulation models for time periods 2040-2069 and 2070-2099. Historical climate data was the basis for simulation years 1980-2010 and considered as a baseline. Simulation results from 1980-2010 and 2040-2069 were used to analyze crop responses to changing climatic variables and their diverging sensitivities to these variables. This data paper describes the creation, motivation and format of the simulation results to enable others to use the data set.

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

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.

In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.

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.

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.

The current paper describes the Computational Fluid-Dynamics (CFD) modelling of Heavy Liquid Metal (HLM) flows in a pool configuration and in particular how this is approached within the Horizon 2020 SESAME project. SESAME’s work package structure, based on a systematic approach of redundancy and diversification, is explained along with its motivation. The main achievements obtained and the main lessons learned during the project are illustrated. The paper focuses on the strong coupling between the experimental activities and CFD simulations performed within the SESAME project. Two different HLM fluids are investigated: pure lead and Lead-Bismuth Eutectic. The objective is to make CFD a valid instrument used during the design of safe and innovative Gen-IV nuclear plants. Some effort has also been devoted to Proper Orthogonal Decomposition with Galerkin projection modelling (POD-Galerkin), a reduced order model suited for Uncertainty Quantification that operates by post-processing CFD results. Assessment of Uncertainty highly improves the reliability of CFD simulations. Dedicated experimental campaigns on heavily instrumented facilities have been conceived with the specific objective to build a series of datasets suited for the calibration and validation of the CFD modelling. In pool configuration, the attention is focused on the balance between conductive and convective heat transfer phenomena, on transient test-cases representative of incidental scenarios and on the possible occurrence of solidification phenomena. Four test sections have been selected to generate the datasets: (i) the CIRCE facility from ENEA, (ii) the TALL-3D pool test section from KTH, (iii) the TALL-3D Solidification Test Section (STS) from KTH and (iv) the SESAME Stand facility from CVR. While CIRCE and TALL-3D were existing facilities, the STS and SESAME Stand facility have been conceived, built and operated within the project, heavily relying on the use of CFD support. Care has been taken to ensure that almost all tasks were performed by at least two partners. Specific examples are given on how this strategy has allowed to uncover flaws and overcome pitfalls. Furthermore, an overview of the performed work and the achieved results is presented, as well as remaining or new uncovered issues. Finally, the paper is concluded with a description of one of the main goals of the SESAME project: the construction of the Gen-IV ALFRED CFD model and an investigation of its general circulation.

The accuracy of the climate impact models depends on the representative power of the climate model used in the analysis. Many problems in these impact models can be eliminated by the selection of the appropriate climate models as an input. The main purpose of this research is to find a methodology to select the more representative climate models. In recent years, the quantity and quality of climate models have significantly improved. In several projects, many different Global Climate Model (GCM)–Regional Climate Model (GCM) combinations have been carried out. One of these recent comprehensive projects is CORDEX. The monthly temperature outputs of various GCM-RCM combinations over Europe between the years 1976 and 2005 were used in this paper for the determination of the more representative combinations. E-OBS is used as the observation data set in this research. Using the proposed method, a clear difference between the more, moderately, and less representative model combinations could be obtained. To model the future temperature climatology of Europe, the best choice of the analysis method is using MPI as GCM and either REMO or CCLM5 as RCM over the whole domain. According to our analysis, the best representative climate model combination can be performed by running MPI-CCLM5 combination over the EURO-CORDEX domain.

Dimethyl sulfide (DMS) is a naturally occurring aerosol precursor gas which plays an important role in the global sulfur budget, aerosol formation and climate. While DMS is produced predominantly by phytoplankton, recent observational literature has suggested that corals and their symbionts produce a comparable amount of DMS, which is unaccounted for in models. It has further been hypothesised that the coral reef source of DMS may modulate regional climate. This hypothesis presents a particular concern given the current threat to coral reefs under anthropogenic climate change. In this paper, a global climate model with online chemistry and aerosol is used to explore the influence of coral-reef-derived DMS on atmospheric composition and climate. A simple representation of coral-reef-derived DMS is developed and added to a common DMS surface water climatology, resulting in an additional flux of 0.3 Tg yr−1 S, or 1.7 % of the global sulfur flux from DMS. By comparing the differences between both nudged and free-running ensemble simulations with and without coral-reef-derived DMS, the influence of coral-reef-derived DMS on regional climate is quantified. In the Maritime Continent–Australian region, where the highest density of coral reefs exists, a small decrease in nucleation- and Aitken-mode aerosol number concentration and mass is found when coral reef DMS emissions are removed from the system. However, these small responses are found to have no robust effect on regional climate via direct and indirect aerosol effects. This work emphasises the complexities of the aerosol–climate system, and the limitations of current modelling capabilities are highlighted, in particular surrounding convective responses to changes in aerosol. In conclusion, we find no robust evidence that coral-reef-derived DMS influences global and regional climate.

The prediction of internal solitary waves (ISWs) propagation is a difficult problem in the field of oceanography due to the complexity of its generative mechanism and the lack of in-situ data. In this paper, the simulation data is used to establish a prediction model of ISWs propagation in the southern Andaman Sea by deep learning. The global tidal model TPXO7.2 is used to obtain the semi-diurnal tidal velocity data at the generation source of ISWs. The generation and propagation characteristics of ISWs are obtained by the Massachusetts Institute of Technology General Circulation Model (MITgcm). The generative location, amplitude, and propagation characteristics of the ISWs are calculated based on MITgcm. The database is set up by the extracted data from MITgcm. Then, the prediction model of ISWs propagation is established using long and short-term memory (LSTM) networks. Finally, the accuracy of the model is verified by the ISWs Moderate Resolution Imaging Spectroradiometer (MODIS) images of the southern Andaman Sea and in-situ data. The results demonstrate that, compared with the MODIS images, the average relative error of time predicted by the LSTM model is 17.81%, and the average absolute error is 0.95 h. The average relative error of longitude predicted by the LSTM model is 0.10%, and the average absolute error is 0.10°. The average of the amplitudes predicted by the LSTM model is 56.99 m. The in-situ data of the amplitude of ISWs is 60 m in the previous research. The two amplitudes are relatively close.

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services.

The isotopic compositions of oxygen (δ18O) and hydrogen (δ2H) are widely used to locate the geo- graphical origin of biological remains or manufactured products. In this paper, we analyse the distributions of δ18O and δ2H in tap waters sampled across France, and in precipitation interpolated with the Online Isotopes in Precipitation Calculator and modelled with the isotope-enabled ECHAM6-wiso model. Our aim is to provide isoscapes usable in archaeology and forensics and evaluate whether the modelled data could be surrogates for measured ones. The δ18O and δ2H in the 396 tap waters sampled varied spatially within a range of 10‰ and 77‰, respectively. Their consistent distributions followed rules summarized by the effects of altitude and dis- tance from the coast. Their variations along the year were small. Therefore, the database provides a solid ref- erence for δ18O and δ2H of the water supply system at the regional scale. The areas with the most uncommon oxygen and hydrogen isotopic compositions (Atlantic coast south of Brittany and the highest elevations in the Alps) are the most accurately traceable areas in provenancing studies. The isotopic compositions of modelled precipitation have the same spatial distributions but different absolute values from those of tap waters. There- fore, our results favour the use of statistical isoscapes rather than general circulation model-based isoscapes in provenancing studies.

Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event $>\sim $ 51% of extreme events; 2- $\sigma :>\sim 98\%$ ). Our primary objective is to contrast the magnitude (∘C) and frequency of occurrence of extreme temperature events between the twentieth and twenty-first century. We project that temperatures will increase substantially in the twenty-first century. Extreme Ta events will experience the largest increases by magnitude, and extreme Ts events will experience the largest increases by proportion. On average, 2-σ extreme Ts events will increase by 3.4 ∘C in 2030–2065 and by 5.3 ∘C in 2065–2100. Increases in extreme Ts events will often exceed + 10 ∘C at 0–20 cm by 2065–2100, and at 0–100 cm will often exceed 5.0 standard deviations above 1980–2015 values. 2-σ extreme Ts events will increase from 0.9 events per decade in 1980–2015 to 23 events in 2030–2065 and 38 events in 2065–2100. By 2065–2100, the majority of months will experience extreme events that co-occur at 0–100 cm, which did not occur in 1980–2015. These projections illustrate the non-analog temperature increases that ecosystems will experience in the twenty-first century as a result of climate change.

Climate downscaling is a way to provide finer resolution data at local scales, which has been widely used in meteorological research. The two main approaches for climate downscaling are dynamical and statistical. The traditional dynamical downscaling methods are quite time- and resource-consuming based on general circulation models (GCMs). Recently, more and more researchers construct a statistical deep learning model for climate downscaling motivated by the single-image superresolution (SISR) process in computer vision (CV). This is an approach that uses historical climate observations to learn a low-resolution to high-resolution mapping and produces great enhancements in terms of efficiency and effectiveness. Therefore, it has provided an appreciable new insight and successful downscaling solution to multiple climate phenomena. However, most existing models only make a simple analogy between climate downscaling and SISR and ignore the underlying dynamical mechanisms, which leads to the overaveraged downscaling results lacking crucial physical details. In this paper, we incorporate the a priori meteorological knowledge into a deep learning formalization for climate downscaling. More specifically, we consider the multiscale spatial correlations and the chaos in multiple climate events. Depending on two characteristics, we build up a two-stage deep learning model containing a stepwise reconstruction process and ensemble inference, which is named climate downscaling network (CDN). It can extract more local/remote spatial dependencies and provide more comprehensive captures of extreme conditions. We evaluate our model based on two datasets: climate science dataset (CSD) and benchmark image dataset (BID). The results demonstrate that our model shows the effectiveness and superiority in downscaling daily precipitation data from 2.5 degrees to 0.5 degrees over Asia and Europe. In addition, our model exhibits better performance than the other traditional approaches and state-of-the-art deep learning models.

Atmospheric radiation physical process plays an important role in climate simulations. As a radiative transfer scheme, the rapid radiative transfer model for general circulation models (RRTMG) is widely used in weather forecasting and climate simulation systems. However, its expensive computational overhead poses a severe challenge to system performance. Therefore, improving the radiative transfer model’s computational performance has significant scientific research and practical value. Numerous radiative transfer models have benefited from a widely used and powerful GPU. Nevertheless, few of them have exploited CPU/GPU cluster resources within heterogeneous high-performance computing platforms. In this paper, we endeavor to demonstrate an approach that runs a large-scale, computationally intensive, longwave radiative transfer model on a GPU cluster. First, a CUDA-based acceleration algorithm of the RRTMG longwave radiation scheme (RRTMG_LW) on multiple GPUs is proposed. Then, a heterogeneous, hybrid programming paradigm (MPI+CUDA) is presented and utilized with the RRTMG_LW on a GPU cluster. After implementing the algorithm in CUDA Fortran, a multi-GPU version of the RRTMG_LW, namely GPUs-RRTMG_LW, was developed. The experimental results demonstrate that the multi-GPU acceleration algorithm is valid, scalable, and highly efficient when compared to a single GPU or CPU. Running the GPUs-RRTMG_LW on a K20 cluster achieved a $$77.78 \times$$ speedup when compared to a single Intel Xeon E5-2680 CPU core.

Regional climate models (RCMs) are used to dynamically downscale global climate models (GCMs) to provide high-resolution projections of future climate change to better inform policy and decision making at the regional scale. However, biases from GCMs are transferred to RCMs and this can limit the usefulness of the regional climate projections. This paper investigates the influence of bias correcting 4 GCMs from the Coupled Model Intercomparison Project 3, for regional climate simulations over the CORDEX-Australasia domain using Weather Research and Forecasting. The GCM outputs are bias corrected against ERA-Interim reanalysis as a surrogate truth. Results show that over decadal time scales bias correction removes large systematic precipitation and temperature biases. However, bias correction also introduced biases where there were none, introduced biases of the opposite sign, or enhanced existing biases in other regions in some instances. The dynamical mechanisms driving the changes in the biases are explored.

The single-column model (SCM) functionality of the Energy Exascale Earth System Model version 1 (E3SMv1) is described in this paper. The E3SM SCM was adopted from the SCM used in the Community Atmosphere Model (CAM) but has evolved significantly since then. We describe changes made to the aerosol specification in the SCM, idealizations, and developments made so that the SCM uses the same dynamical core as the full general circulation model (GCM) component. Based on these changes, we describe and demonstrate the seamless capability to “replay” a GCM column using the SCM. We give an overview of the E3SM case library and briefly describe which cases may serve as useful proxies for replicating and investigate some long-standing biases in the full GCM runs while demonstrating that the E3SM SCM is an efficient tool for both model development and evaluation.

The ALADIN System is a numerical weather prediction system (NWP) developed by the international ALADIN consortium for operational weather forecasting and research purposes. It is based on a code that is shared with the global model IFS of the ECMWF and the ARPEGE model of Météo-France. Today, this system can be used to provide a multitude of high-resolution limited-area model (LAM) configurations. A few configurations are thoroughly validated and prepared to be used for the operational weather forecasting in the 16 Partner Institutes of this consortium. These configurations are called the ALADIN Canonical Model Configurations (CMCs). There are currently three CMCs: the ALADIN baseline-CMC, the AROME CMC and the ALARO CMC. Other configurations are possible for research, such as process studies and climate simulations. The purpose of this paper is (i) to define the ALADIN System in relation to the global counterparts IFS and ARPEGE, (ii) to explain the notion of the CMCs and to document their most recent versions, and (iii) to illustrate the process of the validation and the porting of these configurations to the operational forecast suites of the Partner Institutes of the ALADIN consortium. This paper is restricted to the forecast model only; data assimilation techniques and postprocessing techniques are part of the ALADIN System but they are not discussed here.

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.

This paper presents regime diagrams illustrating the parametric dependence of dynamical balance in a superrotating atmosphere produced in a quasi-axisymmetric idealized system with strong horizontal diffusion studied previously by the present authors. In this system, the superrotation is maintained by the Gierasch mechanism, which possibly explains the four-day circulation in the atmosphere of Venus. Our previous paper developed a theoretical model of this system to estimate the superrotation strength and showed that the parametric dependence of the superrotation strength can be consolidated into three non-dimensional external parameters. The present study analyzes the theoretical model to determine boundaries of the regimes based on the dynamical balance and plots theoretical regime diagrams, which are important to understand the non-linear dynamical system and are useful to clearly describe the parametric dependence. Further, a parametric limit of the theoretical model is also estimated and included in the diagrams. The parametric limit shows both a lower limit for the horizontal diffusion and an upper limit of the superrotation strength in the Gierasch mechanism. The regime diagram demonstrates that the superrotation in the cyclostrophic balance is realized when the horizontal Ekman number is in a certain range whose width is mainly controlled by the vertical Ekman number. Numerical solutions covering a vast region in the parameter space are obtained by time-integrations of the primitive equations, and the dynamical regimes in the numerical solutions are compared with the theoretical regime diagrams. The theoretical regime diagrams agree well with the numerical results in most regions, confirming the validity of the theoretical model. Multiple equilibrium solutions are obtained when the horizontal Ekman number is lower than the theoretical limit. Moreover, they show that the Gierasch mechanism can maintain the superrotation even with the horizontal diffusion weaker than the predicted lower limit, but cannot generate superrotation from a motionless state.

The sea surface temperature (SST) seasonal cycle in the eastern equatorial Pacific (EEP) plays an important role in the El Nino-Southern Oscillation (ENSO) phenomenon. However, the reasonable simulation of SST seasonal cycle in the EEP is still a challenge for climate models. In this paper, we evaluated the performance of 17 CMIP6 climate models in simulating the seasonal cycle in the EEP and compared them with 43 CMIP5 climate models. In general, only CESM2 and SAM0-UNICON are able to successfully capture the annual mean SST characteristics, and the results showed that CMIP6 models have no fundamental improvement in the model annual mean bias. For the seasonal cycle, 14 out of 17 climate models are able to represent the major characteristics of the observed SST annual evolution. In spring, 12 models capture the 1–2 months leading the eastern equatorial Pacific region 1 (EP1; 5°S–5°N, 110°–85°W) against the eastern equatorial Pacific region 2 (EP2; 5°S–5°N, 140°–110°W). In autumn, only two models, GISS-E2-G and SAM0-UNICON, correctly show that the EP1 and EP2 SSTs vary in phase. For the CMIP6 MME SST simulation in EP1, both the cold bias along the equator in the warm phase and the warm bias in the cold phase lead to a weaker annual SST cycle in the CGCMs, which is similar to the CMIP5 results. However, both the seasonal cold bias and warm bias are considerably decreased for CMIP6, which leads the annual SST cycle to more closely reflect the observation. For the CMIP6 MME SST simulation in EP2, the amplitude is similar to the observed value due to the quasi-constant cold bias throughout the year, although the cold bias is clearly improved after August compared with CMIP5 models. Overall, although SAM0-UNICON successfully captured the seasonal cycle characteristics in the EEP and the improvement from CMIP5 to CMIP6 in simulating EEP SST is clear, the fundamental climate models simulated biases still exist.

A critical stage in the development of our ability to model and project climate change occurred in the late 1950s–early 1960s when the first primitive-equation atmospheric general circulation models (AGCMs) were created. A rather idiosyncratic project to develop an AGCM was conducted virtually alone by Cecil E. Leith starting near the end of the 1950s. The Leith atmospheric model (LAM) appears to have been the first primitive-equation AGCM with a hydrological cycle and the first with a vertical resolution extending above the tropopause. It was certainly the first AGCM with a diurnal cycle, the first with prognostic clouds, and the first to be used as the basis for computer animations of the results. The LAM project was abandoned in approximately 1965, and it left almost no trace in the journal literature. Remarkably, the recent internet posting of a half-century-old computer animation of LAM-simulated fields represents the first significant “publication” of results from this model. This paper summarizes what is known about the history of the LAM based on the limited published articles and reports as well as transcripts of interviews with Leith and others conducted in the 1990s and later.

Understanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind-climate, and other wind-dependent geophysical climatic variables. Understanding the skill of atmosphere-only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large-scale spatial patterns and temporal variability of historical surface winds. We show that inter-model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large-scale spatial patterns of historical near-surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter-annual and inter-seasonal variability particularly within tropical-cyclone-affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon-cycle dynamics has insignificant effect on simulated surface winds (at decadal time-scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter-model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models.

Based on a single-model 100-member ensemble of climate change simulations performed with the MPI-ESM, this paper investigates how the probability density function (PDF) of atmospheric general circulation (AGC)—a marginal distribution of the full climate PDF—responds to increasing $${\hbox {CO}}_2$$ concentration. The investigation provides a first assessment of $${\hbox {CO}}_2$$-induced changes in AGC-PDF derived from ensemble statistics (as oppose to time statistics in usual practice) that arise solely from internal variability consistent with the imposed $${\hbox {CO}}_2$$-forcing. By focusing on the first two moments of the AGC-PDF related to the Hadley circulation and the subtropical jets, we find that the major $${\hbox {CO}}_2$$-induced changes in the PDF are related to structure changes in the mean circulation and in the circulation variability. As $${\hbox {CO}}_2$$ concentration increases, different components of the AGC can co-evolve with changing forcing. In particular, the mean Hadley cell co-evolves with the mean subtropical jets. The co-evolution is characterized by the concurrent Hadley cell widening and poleward shifting of the subtropical jets (particularly strong in January), by a strengthening of both the Hadley cell and the subtropical jets in October, and by a weakening of the Hadley cell and a strengthening of the northern and southern jets in January. The changes in circulation variability are more subtle, and reveal only large and spatially coherent changes in places, where the variability is already strong without increasing $${\hbox {CO}}_2$$ concentration, such as at the northern and southern flanks of the summer southern subtropical jet, and at the northern flank of the southern polar night jet. There are some significant cross-variabilities between different components of the AGC. Overall, the $${\hbox {CO}}_2$$-induced AGC-changes can be described by linear trends in the first and second moments, with the trends in the means being much stronger than those in the variances and co-variances.

The surface wind field are formed by the interaction of general circulation mechanisms with the local physical, geographical and climatic characteristics of the region. The success of the implementation of the different weather models is based on the representativeness of the initial information. The paper presents the results of comparing meteorological data, such as the average monthly speed and direction of the surface wind, from the NCEP / NCAR reanalysis archive with data of surface weather observations for the period from 2015 to 2019. Considering the difficulty of obtaining data, the absence of continuous observations at many meteorological stations in Libya, it was decided to analyze the most complete series of observations for the last period and compare it with the model data of reanalysis. Meteorological stations are located in different geographic regions of Libya (Derna, Zuara, Efren, Misurata, Godames, Jagbub, El-Kufra and Ghat). Based on the monthly average values of the surface wind velocity components for past five years, was done a study of the intrannual structure of the wind field over the territory of Libya and maps of the wind field for this period were constructed. The obtained results of comparison of the NCEP / NCAR reanalysis data with Surface weather observations showed a relatively good agreement between the observed and calculated wind. Noted that the wind, according to NCEP data, is some weaker than observed at the stations. The main reasons for this are the complexity and diversity of the topography around the stations, as well as the representativeness between gridded data and meteorological measurements. Analysis of the distribution of the average monthly wind speed over the territory of Libya indicates a tendency for an increase in the surface wind speed from the southwest to the northeast, both as according to the NCEP / NCAR reanalysis data, where the average wind speed doesn’t exceed 4.7 m/s, and according to actual observations 6.7 m/s.

A recent development in climate research is the recognition that space activity such as solar flares, gamma ray bursts, supernovas etc. can have a significant impact on the Earth. The existing methods of modelling space activity do not model these phenomena explicitly. Instead, their effect on ionization rates is formulated and then built into existing climate models. In this paper, based on the authors’ earlier work, several analytical approaches for modelling a solar flare hitting the Earth are presented. These methods can also be used for modelling gamma-ray bursts or cosmic rays striking our planet. In addition, a method of calculating the total amount of radiation received during a solar flare is proposed. The future use of the proposed modelling approaches within comprehensive global climate models allows for explicit modelling of space activity and provides a new pathway for studying the possible effects of this activity on the Earth.

The assessment of heat waves’ number, intensity and duration change during summer (June-August) in the Russia territory for the “historical period” (1980-1999) and for the middle of the 21st century (2046-2055) was made according to the data of the global climate model (INMCM4) of the Institute of Numerical Mathematics, Russian Academy of Sciences. The definition of the extreme events’ criteria depends on the thresholds’ choice. In this paper, heat waves’ thresholds are considered as the values of the 95th percentile of maximum daily temperature distribution. An increase in the number of days with maximum temperatures exceeding the threshold values for the south of the European Russia and Western Siberia, Yakutia, the north of Chukotka, and Primorsky krai in the middle of the 21st century according to the “soft” scenario of RCP4.5 was revealed. Maximum changes are expected in Taimyr and Yamal. In the rest of the territory, the number of such days will be reduced. For the “hard” scenario of RCP8.5, more contrast changes in the similar locations are expected. Under the “hard” scenario, by the middle of the 21st century the maximum temperature in the heat wave will exceed 40°C in the south of the country.

Introduction:Changes in climate have impacts on natural and human systems on all continents and across the oceans. Most countries, including the UAE, are expected to experience a huge impact of climate change, due to the undergoing rapid growth and huge urban developments.Materials & Methods:Representative Concentration Pathways, or RCPs, represent the latest generation of scenarios that are used as potential inputs into climate models to show imposed greenhouse-gas concentration pathways during the 21stcentury. Four emission scenarios have been used for climate research; namely RCP 2.6, RCP 4.5 and RCP 6 and RCP 8.5. RCP 4.5 and RCP 8.5 are used. The aims of this study are to assess different RCPs and their appropriateness to predict temperatures and rainfall and to study the effect of climate change on three different cities in the UAE.Results & Conclusion:The results show a strong correlation between the present TmaxvsTmax 2020, Tmax 2040, Tmax 2060, Tmax 2080 and Tmax 2095 for both RCP4.5 and RCP8.5. This means that maximum temperatures are going to increase in the coming years based on the predictions according to the different scenarios using MarksimGCMR.Precipitation projections shows greater variation than temperature. In this paper the amount of increase in temperatures and precipitation change is shown for the end of the current century.

In the paper dynamical-stochastically method of the non-convective cloudiness parameterization in the general circulation model is formulated. This algorithms is evaluated on the basis of general circulation model with given see surface temperature of oceans. The results of calculations were compared with observational data and the results simulations with sophisticated couple GCM full filled in frame of CMIP5 program. These results showed the perspectives of suggested dynamical-stochastically approach.

Climate classifications based on temperature and precipitation measurements are increasingly being used for environmental and climate change studies. Using three classification methods (Koppen, Extended Koppen, and Holdridge) and one observational dataset for present climate (CRU, Climate Research Unit), we show that GCMs have bridged the gap that led to the emergence of RCMs thirty years ago, as GCMs can now provide global climate classifications whose accuracy and precision are comparable to those of regional outputs of the RCMs. Projections of high-resolution GCMs for future climates under the assumptions of three Representative Concentration Pathways (RCP26, RCP45 and RCP85) can therefore be used as a primary source for climate change and global warming studies at high resolution. This paper provides comprehensive, model-derived climate classifications for the entire planet, using RCMs and two GCMs for present and future climate-change scenarios, and discusses how well the models actually represent the climates of the world when compared with reference, ground validation data. It turns out that both GCMs and RCMs appear still limited to provide practical estimates of the world climates even for present climate conditions. The modeling of precipitation remains the Achilles' heel of models and thus of multidimensional indices, which are very sensitive to this variable. The conclusion is that model outputs at regional scale need to be taken with extreme caution without venturing into informing policies presenting potentially large societal impacts. Nonetheless, the role of models as privileged tools to advance our scientific knowledge of the Earth's system remains undisputed.

The article considers an approach to evaluating the change in surface, subsurface and groundwater flow on a large river catchment exemplified by the Oka River basin. The study is based on the synthesis of a physical-mathematical model of runoff formation and atmosphere–ocean general circulation models. The paper presents the results of calibration and verification of a hydrological model over a period of history, as well as the assessment of reproduction accuracy of meteorological and hydrological characteristics according to the data of global climate models and observation data. Based on an ensemble of atmosphere–ocean general circulation models, the changes in meteorological (air temperature, precipitation, air humidity deficit) and hydrological (surface runoff, soil moisture content, groundwater flow) characteristics by the middle and the end of the 21st century have been calculated, under the scenarios RCP 2.6 and RCP 6.0 with regard to the historical period.

Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

This paper describes an implementation of the Combined Hybrid-Parallel Prediction (CHyPP) approach of Wikner et al. (2020) on a low-resolution atmospheric global circulation model (AGCM). The CHyPP...

This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be ∼6× the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.