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The complexity and isomerization of communication networks have put forth new requirements for cryptographic schemes to ensure the operation of network security protocols. Robust cryptographic schemes have been gradually favored. The robust initialization vector (RIV) instead of the synthetic initialization vector (SIV) was first introduced to support strong security and robust authenticated encryption. This paper first introduces RIV to GCM-SIV1, proposes a robust variant, GCM-RIV1, and proves that it ensures birthday-bound subtle AE (SAE) security and nonce-misuse resistance. Then, to support beyond-birthday-bound (BBB) security with graceful degradation, we introduce another, stronger security variant, GCM-RIV2, and prove that it allows gracefully degrading BBB SAE security in the faulty nonce setting. Finally, the performance of GCM-RIV1 and GCM-RIV2 is discussed and compared. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world. [ABSTRACT FROM AUTHOR]

The global climate is changing. Predicting the impacts this will have on buildings is the first step in the process of finding suitable building adaptation measures. Future climate adaptation of buildings and infrastructure is a growing field of research, relying on both socio-economical and meteorological research for input values to the simulation models. Models producing hourly future weather data rely on global climate models which are based on emission scenarios made from assumptions of future political, social, and economic developments. Accounting for the uncertainties from these underlying models as much as possible, and communicating the uncertainties in the results, is obviously paramount for reliable conclusions from the building simulation models. This paper is a scoping review, investigating how 132 studies treat and communicate the string of uncertainties from underlying models connected to future weather file generation in the scientific literature on building adaptation research. The findings suggest that climate-model-induced uncertainties are often under-communicated, due to either insufficient analysis or neglect. The studies that included the most comprehensive analyses of the uncertainties frequently concluded that treatment of these is important for the reliability of the results, and neglecting this could lead to misleading conclusions. [ABSTRACT FROM AUTHOR]

Investigating the role of complex dynamical components of a global climate model (GCM) in improving near-surface wind speed (WS) simulation is vital for the climate community in building reliable future WS projections. The relative skill of GCMs in representing WS at diverse climate variable scales (daily, monthly, seasonal, and annual) over land and ocean areas of the South Asian domain is not clear yet. With this in mind, this paper evaluated the skill of 28 Coupled Model Intercomparison Project phase five GCMs in reproducing the WS using a devised relative score approach. It is recommended to use the mean ensemble of MPI-ESM-MR, CSIRO-Mk3.6.0, and GFDL-ESM2G GCMs for understanding future changes in wind–wave climate and offshore wind energy potential. The inter-comparison of GCMs shows that the GCM with high or low atmospheric resolution does not necessarily exhibit the best or worst performance, respectively, whereas the dynamic components in the model configuration play the major role, especially the atmosphere component relative to other dynamical components. The strengthening of annual and seasonal mean WS is observed over coastal plains of the United Republic of Tanzania, Oman, eastern Thailand, eastern Gulf of Thailand and Sumatra, and weakening over the central northern equatorial region of the Indian Ocean in the 21st century for RCP4.5 and RCP8.5 emission scenarios. [ABSTRACT FROM AUTHOR]

The Global Climate Model (GCM) run at a coarse spatial resolution cannot be directly used for climate impact studies. Downscaling is required to extract the sub-grid and local scale information. This paper investigates if the artificial neural network (ANN) is better than the widely-used regression-based statistical downscaling model (SDSM) for downscaling climate for a site in Colombo, Sri Lanka. Based on seasonal and annual model biases and the root mean squared error (RMSE), the ANN performed better than the SDSM for precipitation. This paper proposes a novel methodology for improving climate predictions by combining SDSM with neural networks. This method will allow a user to apply SDSM with a neural network model for higher skills in downscaling. The study uses the Canadian Earth System Model (CanESM2) of the IPCC Fifth Assessment Report, reanalysis from the National Center for Environmental Prediction (NCEP), and the Asian Precipitation Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) project data as the observation. SDSM and the focused time-delayed neural network (TDNN) models are used for the downscaling. The projected annual increase for Representative Concentration Pathway (RCP) is 8.5; the average temperature is 2.83 °C (SDSM) and 3.03 °C (TDNN), and rainfall is 33% (SDSM) and 63% (TDNN) for 2080's. [ABSTRACT FROM AUTHOR]

Communication performance in the color-independent visual-multiple input multiple output (visual-MIMO) technique is deteriorated by light emitting array (LEA) detection and tracking errors in the received image because the image sensor included in the camera must be used as the receiver in the visual-MIMO system. In this paper, in order to improve detection reliability, we first set up the color-space-based region of interest (ROI) in which an LEA is likely to be placed, and then use the Harris corner detection method. Next, we use Kalman filtering for robust tracking by predicting the most probable location of the LEA when the relative position between the camera and the LEA varies. In the last step of our proposed method, the perspective projection is used to correct the distorted image, which can improve the symbol decision accuracy. Finally, through numerical simulation, we show the possibility of robust detection and tracking of the LEA, which results in a symbol error rate (SER) performance improvement. [ABSTRACT FROM AUTHOR]

A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [ABSTRACT FROM AUTHOR]

Due to the spatial-temporal inadequacy of large-scale general circulation models (GCMs), linking large-scale GCM data with small-scale local climatic data has found great interest. In this paper, in order to downscale minimum and maximum temperatures and precipitation predictands, the performance of three statistical downscaling techniques including Long Ashton Research Station-Weather Generator (LARS-WG), statistical downscaling model (SDSM), and artificial neural network (ANN) was compared based on Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5) in northwest Iran. For this purpose, a nonparametric test named Mann-Whitney test, Spearman correlation coefficient, and the root mean square error (RMSE) were utilized to assess the efficiency of downscaling models. To scrutinize the climate change impacts, periods of 1961–1990 and 1991–2005 were considered as the baseline and verification periods, respectively. The findings revealed the superior performance of the ANN model for minimum and maximum temperatures, while for precipitation predictand, the SDSM represented the best performance among the models. Simulation results for future temperature indicated an ascending trend as 0.1–1.3 °C, 0.3–1.7 °C, and 0.5–2.1 °C for LARS-WG, SDSM, and ANN techniques, respectively. On the other hand, simulation outputs for the precipitation indicated a descending trend of 10–30% in future precipitation of the region according to downscaling models under Representative Concentration Pathway 8.5 (RCP8.5) pessimistic scenario of Hadley Center Coupled Model version 3 (HadCM3) GCM model. [ABSTRACT FROM AUTHOR]

Many general circulation models (GCMs) assume some heterogeneity of water amounts in their grid boxes and use probability density functions to parameterize cloud fractions CF and amounts of condensed water qc. Most GCM cloud schemes calculate the CF as the volume of the grid box that contains clouds (CFvol), whereas radiative fluxes primarily depend on the CF by surface (CFsurf), that is, the surface of the grid box covered by clouds when looking from above. This discrepancy matters as previous findings suggest that CFsurf is typically greater than CFvol by about 30%. In this paper we modify the single column model version of the LMDz GCM cloud scheme by introducing the vertical subgrid‐scale heterogeneity of water content. This allows to distinctly compute the two fractions, CFvol and CFsurf, as well as the amount of condensed water qc. This study is one of the first to take into account such vertical subgrid‐scale heterogeneity in a GCM cloud scheme. Three large eddy simulation cases of cumuliform boundary layer clouds are used to test and calibrate two different parameterizations. These new developments increase cloud cover by about 10% for the oceanic cases RICO and Barbados Oceanographic Meteorological Experiment and by up to 50% for the continental case ARM. The change in condensed water reduces the liquid water path by 10–20% and therefore the cloud opacity by 5–50%. These results show the potential of the new scheme to reduce the too few, too bright bias by increasing low‐level CF and decreasing cloud reflectance. Key Points: Most cloud schemes assume vertically homogeneous cloud fractions in GCM grid boxesThis approximation may induce an underestimation of cloud cover and overestimation of reflectanceWe parameterize 3‐D subgrid‐scale heterogeneities for low‐level clouds and reduce these biases [ABSTRACT FROM AUTHOR]

Network on Chip (NoC) is an emerging solution to the existing scalability problems with System on Chip (SoC). However, it is exposed to security threats like extraction of secret information from IP cores. In this paper we present an Authenticated Encryption (AE)-based security framework for NoC based systems. The security framework resides in Network Interface (NI) of every IP core allowing secure communication among such IP cores. The secure cores can communicate using permanent keys whereas temporary session keys are used for communication between secure and non-secure cores. A traffic limiting counter is used to prevent bandwidth denial and access rights table avoids unauthorized memory accesses. We simulated and implemented our framework using Verilog/VHDL modules on top of NoCem emulator. The results showed tolerable area overhead and did not affect the network performance apart from some initial latency. [ABSTRACT FROM AUTHOR]

Abstract: A decadal climate prediction system named as IAP‐DecPreS was constructed in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, based on a fully coupled model FGOALS‐s2 and a newly developed initialization scheme, referred to as EnOI‐IAU. In this paper, we introduce the design of the EnOI‐IAU scheme, assess the accuracies of initialization integrations using the EnOI‐IAU and preliminarily evaluate hindcast skill of the IAP‐DecPreS. The EnOI‐IAU scheme integrates two conventional assimilation approaches, ensemble optimal interpolation (EnOI) and incremental analysis update (IAU). The EnOI and IAU were applied to calculate analysis increments and incorporate them into the model, respectively. Three continuous initialization (INIT) runs were conducted for the period of 1950–2015, in which observational sea surface temperature (SST) from the HadISST1.1 and subsurface ocean temperature profiles from the EN4.1.1 data set were assimilated. Then nine‐member 10 year long hindcast runs initiated from the INIT runs were conducted for each year in the period of 1960–2005. The accuracies of the INIT runs are evaluated from the following three aspects: upper 700 m ocean temperature, temporal evolution of SST anomalies, and dominant interdecadal variability modes, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). Finally, preliminary evaluation of the ensemble mean of the hindcast runs suggests that the IAP‐DecPreS has skill in the prediction of the PDO‐related SST anomalies in the midlatitude North Pacific and AMO‐related SST anomalies in the tropical North Atlantic. [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

To ensure water use and water resource security along "the Belt and Road", the runoff and hydrological droughts and floods under future climate change conditions in the upper Heihe River Basin were projected in this study, based on the observed meteorological and runoff data from 1987 to 2014, and data from 10 GCMs from 1987 to 2014 and from 2026 to 2100, using the SWAT model, the Standardized Runoff Index, run length theory, and the entropy-weighted TOPSIS method. Both the multi-GCM ensemble (MME) and the optimal model were used to assess future hydrological drought and flood responses to climate change. The results showed that (1) the future multi-year average runoff from the MME was projected to be close to the historical period under the SSP245 scenario and to increase by 2.3% under the SSP585 scenario, and those from the optimal model CMCC-ESM2 were projected to decrease under both scenarios; (2) both the MME and the optimal model showed that drought duration and flood intensity in the future were projected to decrease, while drought intensity, drought peak, flood duration, and flood peak were projected to increase under both scenarios in their multi-year average levels; (3) drought duration was projected to decrease most after 2080, and drought intensity, flood duration, and flood peak were projected to increase most after 2080, according to the MME. The MME and the optimal model reached a consensus on the sign of hydrological extreme characteristic responses to climate change, but showed differences in the magnitude of trends. [ABSTRACT FROM AUTHOR]

The volumetric change in unsaturated loess during loading causes serious damage to the foundation and structure, accompanied by changes in hydraulic conditions. Therefore, quantifying the change in the load effect of loess under hydraulic coupling is of great significance for revealing the mechanism of hydraulic interaction. This study conducts isotropic compression and undrained shear tests on unsaturated compacted loess, simultaneously introducing the strength parameter η to enhance the Glasgow coupled model (GCM). The objective is to elucidate the hydraulic and mechanical coupling mechanism, where saturation increases under mechanical effects lead to strength degradation. The results show that saturation increases under mechanical effects improve the compressibility of the sample, and saturation has a direct impact on the stress–strain relationship. The increase in water content and confining pressure increases the trend of the critical state stress ratio M decreasing, and the strain softening trend increases. The compression of volume during shear tests increases the saturation, changes the hydraulic characteristics of loess, and affects the deformation and strength of loess. The modified GCM improves the applicability and prediction accuracy of unsaturated loess under the same initial state. The research results are of great significance for revealing the hydraulic and mechanical behavior of loess. [ABSTRACT FROM AUTHOR]

1. Plant–soil interactions play a central role in the biogeochemical carbon (C), nitrogen (N) and hydrological cycles. In the context of global environmental change, they are important both in modulating the impact of climate change and in regulating the feedback of greenhouse gas emissions (CO2, CH4 and N2O) to the climate system. 2. Dynamic global vegetation models (DGVMs) represent the most advanced tools available to predict the impacts of global change on terrestrial ecosystem functions and to examine their feedbacks to climate change. The accurate representation of plant–soil interactions in these models is crucial to improving predictions of the effects of climate change on a global scale. 3. In this paper, we describe the general structure of DGVMs that use plant functional types (PFTs) classifications as a means to integrate plant–soil interactions and illustrate how models have been developed to improve the simulation of: (a) soil carbon dynamics, (b) nitrogen cycling, (c) drought impacts and (d) vegetation dynamics. For each of these, we discuss some recent advances and identify knowledge gaps. 4. We identify three ongoing challenges, requiring collaboration between the global modelling community and process ecologists. First, the need for a critical evaluation of the representation of plant–soil processes in global models; second, the need to supply and integrate knowledge into global models; third, the testing of global model simulations against large-scale multifactor experiments and data from observatory gradients. 5. Synthesis. This paper reviews how plant–soil interactions are represented in DGVMs that use PFTs and illustrates some model developments. We also identify areas of ecological understanding and experimentation needed to reduce uncertainty in future carbon coupled climate change predictions. [ABSTRACT FROM AUTHOR]

Planetary‐scale mountain waves have been observed at the cloud top of Venus and throughout the cloud deck. As they propagate from the surface to the cloud layers, multiple observations and numerical simulations have shown that they grow in size and do not break. However, the fate of mountain waves in the transition region and thermosphere, above the super‐rotating atmosphere, has only been addressed with two‐dimensional models. We conduct for the first time a simulation of mountain waves with a state‐of‐the‐art Venus climate model that includes the thermosphere. We find that mountain waves can propagate up to at least 150 km altitude, well above the transition region. They affect the circulation of the transition region, by reducing winds speeds, and the subsolar‐to‐antisolar circulation. Plain Language Summary: The planet Venus rotates very slowly, with a rotation period of 243 days, while its cloud cover rotates westwards much faster, with a period of 4 days at 70 km altitude, a phenomenon known as superrotation, which is not fully explained yet. Higher up, in the thermosphere, the air flows from the dayside to the nightside, rather than westwards. The thermosphere is difficult to explore by remote sounding due to the low air density, and thus its connection to the surface and the superrotating layer is poorly understood. In 2015, the Venus‐orbiting spacecraft Akatsuki detected a massive standing wave generated at the surface and reaching the superrotating layer, despite the fast zonal winds. Here, we show that this wave also reaches the thermosphere. Key Points: The first simulation of mountain waves with a 3D ground‐to‐thermosphere General Circulation Model is performedMountain waves reach the thermosphere of Venus and change the SS‐AS winds by up to 80 m/s, locally in the afternoonThe vertical wind field above the cloud deck is dominated by the effect of mountain waves [ABSTRACT FROM AUTHOR]

High-resolution downscaling is vital to project climate extremes and their future changes by resolving fine topography reasonably well, which is a key to represent local climatology and impacts of weather extremes. A direct dynamical downscaling with a regional climate model (RCM) embedded within an atmosphere-ocean coupled general circulation model (AOGCM) is commonly used but is subject to systematic biases in their present-day simulations of AOGCM, which may cause unexpected effects on future projections and lead to difficult interpretation of climate change. In a high-resolution atmospheric general circulation model (AGCM)-RCM system, the present-day climate in AGCM is forced by observed sea surface temperature (SST) and sea-ice distribution. Then, the future climate is calculated with the "future" boundary conditions (SST and sea-ice), which are created by adding their future changes projected by AOGCM to the observed present-day values, besides the future radiative forcing. This system is one of methods to minimize the effects of such biases. A Meteorological Research Institute AGCM with 20-km grids is successfully applied to project future changes in weather extremes such as tropical cyclones and rain systems that cause heavy rainfall and strong winds. Regional downscaling with 5-km mesh RCM is then performed over certain area to investigate local extreme rainfall events and their future changes. In this paper, we review various downscaling methods and try to rationalize a use of high-resolution AGCM-RCM system. [ABSTRACT FROM AUTHOR]

This work shows a 3-year climatology of the horizontal components of the solar diurnal tide, obtained from wind measurements made by a multistatic specular meteor radar (SIMONe) located in Jicamarca, Peru (12 ∘ S, 77 ∘ W). Our observations show that the meridional component is more intense than the zonal component, and that it exhibits its maxima shifted with respect to the equinox times (i.e., the largest peak occurs in August–September, and the second one in April–May). The zonal component only shows a clear maximum in August–September. This observational climatology is compared to a climatology obtained with the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). Average comparisons indicate that the model amplitudes are 50% smaller than the observed ones. The WACCM-X results are also used in combination with observed altitude profiles of the tidal phases to understand the relative contributions of migrating and non-migrating components. Based on this, we infer that the migrating diurnal tide (DW1) dominates in general, but that from June until September (November until July) the DE3 (DW2) may have a significant contribution to the zonal (meridional) component. Finally, applying wavelet analysis to the complex amplitude of the total diurnal tide, modulating periods between 5 and 80 days are observed in the SIMONe measurements and the WACCM-X model. These modulations might be associated to planetary waves and intraseasonal oscillations in the lower tropical atmosphere. [ABSTRACT FROM AUTHOR]

The paper presents our solution for a message oriented communication mechanism, employing Google Cloud Messaging (GCM) on the client-side, and Java Message Service (JMS) on the server-side, in order to leverage JMS functionality to Android-based trading application. Our ongoing research has been focused upon conceiving a way to expose the trading services offered by our academic trading system ASETS to a mobile trading application based on Android platform. ASETS trading platform is a distributed SOA implementation, with an original API based on JMS. In order to design and implement an Android based client, able to inter-communicate with the server-side components of ASETS, in a manner consistent with publisher/subscriber JMS communication model, there was particularly necessary to have object imbedded messages, produced by various ASETS services, pushed to the client application. While point-to-point communication model could be resolved on the client-side by employing synchronous HTTP socket connections over TCP/IP, the asynchronously generated messages from the server-side had to reach the client application in a push manner. [ABSTRACT FROM AUTHOR]

This paper explores the evolution of idealized tropical cyclones in the Community Atmosphere Model CAM 5 with the spectral element (SE) dynamical core at grid spacings of 111, 55 and 28 km. Over 10 simulation days the storms become increasingly intense and compact with increasing resolution. The experiments reveal unrealistically strong tropical cyclone intensities of minimum surface pressures ranging from 845 to 865 hPa and absolute maximum wind speeds greater than 100 m s−1 at the highest resolution, especially when small physics time steps are used. This unphysical behavior is related to the manner in which the physics time step is applied in CAM 5. The analysis indicates that the behavior of the physics parameterizations, namely the partitioning between convective and large-scale precipitation, at small time steps contributes to this intensity. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The level of interest in Galois Counter Mode (GCM) Authenticated Encryption rose significantly within the last few years. GCM is interesting because it is the only authenticated encryption standard that can be implemented in a fully pipelined or parallelized way and it is the most appropriate for encrypting packetized data. McGrew and Viega [CHECK END OF SENTENCE] described (but did not detail) how GHASH can be implemented with more than one multiplier operating in parallel. This paper details how that can be done and shows that, when N multipliers are used, and the multipliers use the approach of multiplying polynomials then applying a modular reduction, a single modular reduction can be used instead on N separate operations. This optimization can be used even when there is a single multiplier, which makes this implementation strategy have a broader appeal. Recently Intel has introduced new ISA instructions into the next generation CPU core, namely: AES family and PCLMULQDQ operating in XMM registers domain. In this paper, we discuss the example implementation of proposed GHASH modifications using above instructions. [ABSTRACT FROM AUTHOR]

This paper establishes a comprehensive assessment model to measure the regional impact of climate change on Taiwan's water resources. Working from future rainfall data simulated by Japan's high-resolution GCM model JMA/MRI TL959L60 in a SRES-A1B scenario, we first apply climate change to an assessment model of renewable water resources to estimate the volume of renewable water resources on a regional basis. We then conduct a water resources system simulation based on estimates of future water needs, regional reservoir effective capacity and renewable water resource volume. This paper uses three water resource assessment indicators: the annual water utilization ratio indicator, the water shortage indicator and the extreme event occurrence indicator. Through fuzzy comprehensive assessment, we divide the evaluation set into five levels: very good (L1), good (L2), fair (L3), poor (L4) and very poor (L5). Results indicate that, given the effects of future climate change (2080 - 2099) and the increase in water demand, future water resources conditions in northern and eastern Taiwan will not be significantly different from historical levels (1979 - 1998) and will maintain a "good" level (L2), while the conditions in southern Taiwan will visibly deteriorate from its historical "fair" level (L3) to "poor" (L4); and the future conditions for central Taiwan will be "poor" (L4). The initiation of adaptation options for water management in southern and central Taiwan would be needed by increasing reservoir capacity and reducing overall water use. [ABSTRACT FROM AUTHOR]

The hydrological impact of forest removal on the southeast Tibetan Plateau during the second half of the last century is investigated in this study using an atmospheric general circulation model. The effects of deforestation are investigated by examining the differences between the forest replacement and control experiments. Model results demonstrate that deforestation of the southeast Tibetan Plateau would influence the local and the remote climate as well. It would lead to decreased transpiration and increased summer precipitation in the deforested area and a wetter and warmer climate on the Tibetan Plateau in summer. This may produce more runoff into the rivers originating from the Tibetan Plateau and worsen flooding disasters in the downstream areas. The numerical experiments also show that deforestation would remotely impact Asian climate, and even global climate, although the statistical significance is small. A strong drought is found at middle and lower reaches of the Yellow River, where livelihoods and economics have suffered from recent droughts. Ecosystem research on the Tibetan Plateau is a relatively new topic and needs further interdisciplinary investigation. [ABSTRACT FROM AUTHOR]

The Western Ghats (WG) in South India is a biological hotspot with a cluster of small river basins and heterogeneous climate and vegetation patterns, and it is categorized under the water stress region by Central Water Commission (CWC). This study aims to evaluate the effects of climate change and land use/land cover (LULC) transformations on water balance components and irrigation water demand (IWD) across different regions of WG for a future period (2020–2050). The variable infiltration capacity model has been calibrated separately for the upper, middle, and lower regions of WG. Further, climate projections from the CMIP6 experiment (SSP2 45/SSP5 85) have been used for future projections of water balance components. The land use change shows an increase in built-up (5.79%) and a decrease in cultivable land (1.24%) by the end of 2030 from 1995. The combined impact due to climate and LULC change shows that the future rainfall/runoff increases in the lower regions of the basin by 100/36.5 mm/year through SSP 4.5. However, the summer months show an increasing water requirement in the future for the Ghats and Nilgiri regions of the basin. The present regional-based study will be useful for future agriculture water management practices in the region for sustainable development and the study can be extended to other similar regions. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

Background: Psoriasis is a common autoimmune dermatologic condition which has a pronounced negative impact on patient quality of life and disease burden. Currently, there are a number of treatments available for psoriasis, with differences in efficacy, mechanism of action, mode of administration, adverse effects, and tolerability. However, a reliable, validated patient-reported instrument to address patient expectations and of psoriasis treatment has not been developed. This project was undertaken with the aim of developing a fit-for-purpose self-reported instrument to inform patient expectations and preferences of psoriasis treatments. Methods: Two studies, both utilizing qualitative and quantitative methods, were conducted in patients within the entire spectrum of psoriasis severity. In Study 1, a group concept mapping (GCM) exercise was conducted with dermatologists and moderate-to-severe psoriasis patients to identify concepts important in the treatment of psoriasis. In Study 2, a preliminary Treatment Acceptability Questionnaire (TAQ) was developed using GCM-derived concepts from Studies 1 and 2, followed by cognitive debriefing (CD) telephone interviews of the preliminary TAQ. In Study 2, another GCM exercise was conducted with mild and newly diagnosed psoriasis patients. Psychometric analyses were performed on the TAQ to evaluate validity and reliability. Results: The Study 1 GCM exercise generated 43 concepts from moderate-to-severe psoriasis patients (n = 20) and dermatologists (n = 10). In Study 2, 37 GCM concepts were generated from mild and newly diagnosed psoriasis patients (n = 20). From the 2 GCM exercises, 28 concepts were selected to form the preliminary TAQ; CD interviews indicated strong understanding and relevance of TAQ items among patients with disease ranging from mild to severe. The final TAQ consisted of 20 items; psychometric analysis demonstrated strong validity and reliability of the TAQ. Conclusions: The TAQ is a novel psychometrically validated patient-reported instrument to inform healthcare providers of patients' expectations of and preferences for treatment of their psoriasis and can help in shared decision making between patients and physicians. [ABSTRACT FROM AUTHOR]

In this study, a simple polynomial bias correction method is developed to correct the bias in the forecasted streamflow (runoff) derived from a global circulation model (GCM). First, a set of polynomial correction factors was derived comparing observed and GCM-derived runoff for a hindcast period (1961–2000) for each of the 11 selected GCMs. The correction factors are used to correct the GCM-derived streamflow for projected periods (2046–2065 and 2081–2100) for the Intergovernmental Panel on Climate Change scenarios A2 and B1 (CMIP3) for the Murray-Hotham catchment of Western Australia. The assumption is that the correction factors derived for each GCM for the observed period (1961–2000) are valid for the projected periods. Results show the method reduces biases considerably for the projected runoff at a catchment scale. The method developed here uses CMIP3 data but it may be applicable to any GCM data, such as CMIP5/CMIP6. [ABSTRACT FROM AUTHOR]

Standard quantile mapping (QM) performs well, as a bias adjustment method, in removing historical climate biases, but it can significantly alter a global climate model (GCM) signal. Methods that do incorporate GCM changes commonly consider mean changes only. Quantile delta mapping (QDM) is an exception, as it explicitly preserves relative changes in the quantiles, but it might present biases in preserving GCMs changes in standard deviation. In this work we propose the unbiased quantile mapping (UQM) method, which by construction preserves GCM changes of the mean and the standard deviation. Synthetic experiments and four Chilean locations are used to compare the performance of UQM against QDM, QM, detrended quantile mapping, and scale distribution mapping. All the methods outperform QM, but a tradeoff exists between preserving the GCM relative changes in the quantiles (QDM is recommended in this case), or changes in the GCM moments (UQM is recommended in this case). [ABSTRACT FROM AUTHOR]

Predictions of biodiversity trajectories under climate change are crucial in order to act effectively in maintaining the diversity of species. In many ecological applications, future predictions are made under various global warming scenarios, as described by a range of different climate models. We propose a clustering methodology to synthesize and interpret the outputs of these various predictions.We propose an interpretable and flexible two‐step methodology to measure the similarity between predicted species range maps and to cluster the future scenario predictions utilizing a spectral clustering technique. We implement and provide code for this method.We find that clustering based on predicted species range maps is mainly driven by the amount of warming rather than climate model or future scenario. We contrast this with clustering based only on predicted climate variables, which is driven primarily by climate models, that is, scenarios of the same climate model are clustered together, even when the amount of warming input to the models is varied.The differences between species‐based and climate‐based clusterings illustrate that it is crucial to incorporate ecological information to understand the relevant differences between climate models. Our findings can be used to better synthesize forecasts of biodiversity change under the wide spectrum of results that emerge when considering potential future scenarios. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of extreme events, especially drought and wet events, spanning over the past years, evaluating their trends over time. An investigation of future projections under various scenarios such as SSP-126, SS-245, and SSP-585 for the near (2023–2048), mid (2049–2074), and far future (2075–2100) using the bias-corrected Coupled Model Intercomparisons Project 6 (CMIP6) multi-model ensemble method was also performed. The Standard Precipitation Index (SPI), a simple yet incredibly sensitive tool for measuring changes in drought, is utilized in this study, providing a valuable assessment of drought conditions across multiple timescales. The historical analysis shows that there is a significant increase in drought frequency in subdivisions such as East MP, Chhattisgarh, East UP, East Rajasthan, Tamil Nadu, and Rayalaseema over the past decades. Our findings from a meticulous examination of historical rainfall trends spanning from 1951 to 2022 show a noticeable decline in rainfall across various regions such as Uttar Pradesh, Chhattisgarh, Marathwada, and north-eastern states, with a concurrent increase in rainfall over areas such as Gujarat, adjoining regions of West MP and East Rajasthan, and South Interior Karnataka. The future projection portrays an unpredictable pattern of extreme events, including droughts and wet events, with indications that wet frequency is set to increase under extreme SSP scenarios, particularly over time, while highlighting the susceptibility of the northwest and south peninsula regions to a higher incidence of drought events in the near future. Analyzing the causes of the increase in drought frequency is crucial to mitigate its worst impacts, and recent experiences of drought consequences can help in effective planning and decision-making, requiring appropriate mitigation strategies in the vulnerable subdivisions. [ABSTRACT FROM AUTHOR]

The establishment of Global Compact for Migration (GCM) was considered as the first important step for the international community to comprehensively address the issue of global migration. However, there are certain objectives within the compact that is considered as vague and unfavourable by several states. Previous studies have explored the advantages and disadvantages of GCM but did not address the specific argumentation that creates disagreements between states. This paper uses the theoretical approach from realist perspective to investigate how certain stipulations within the compact are in contrary to certain actors’ interests, specifically the interests of key state actors in global migration governance. Using qualitative methodology, the analysis began by exploring stances of notable states that opposes the compact. The findings discovered that the GCM failed to recognise the core problem of migration itself which is the management of regular and irregular migrants. The result of the study indicates that states’ rejection towards the Global Compact for Migration are mainly driven by the incompatibility of the compact’s goals and objectives with their national interest, particularly concerning sovereignty and national security.

Horizontal diffusion and resolution are important factors to generate and maintain the super-rotation in the general circulation model (GCM) because waves which transport angular momentum are sensitive to them. Here, we investigated how the super-rotation depends on the magnitude of horizontal hyper diffusion using a Venus atmospheric GCM with medium and high resolutions. In both the runs, we found a parameter range where the structure of fully developed super-rotation is almost independent of the magnitude of horizontal diffusion. Spectral analysis shows that unrealistically strong super-rotation is developed when medium-scale disturbances are dissipated by stronger horizontal diffusion. On the other hand, artificially weak super-rotation is also realized because spurious small-scale disturbances are accumulated when the horizontal diffusion is too weak. [ABSTRACT FROM AUTHOR]

Changes in the natural climate is a major concern for food security across the world, including Bangladesh. This paper presents results from an analysis on quantitative assessment of changes in rainfall and potential evapotranspiration (PET) in the northwest region of Bangladesh, which is a major agricultural hub in the country. The study was conducted using results from 28 global climate models (GCMs), based on IPCC's 5th assessment report (AR5) for two emission scenarios. Projections were made over the period of 2045 to 2075 for 16 administrative districts in the study area, and the changes were estimated at annual, seasonal and monthly time scale. More projections result in an increase in rainfall than decrease, while almost all projections show an increase in PET. Although annual rainfall is generally projected to increase, some projections show a decrease in some months, especially in December and January. Across the region, the average change projected by the 28 GCMs for the moderate emission was an increase of 235 mm (12.4%) and 44 mm (3.4%) for rainfall and PET, respectively. Increases in rainfall and PET are slightly higher (0.6% and 0.2%, respectively) under high emission scenarios. Increases in both rainfall and PET were projected for two major cropping seasons, Kharif (May-Oct) and Rabi (Nov-Apr). Projections of rainfall show increase in the range of 160 to 250 mm (with an average of 200 mm) during the Kharif season. Although an increase is projected in the Rabi season, the amount is very small (~10mm). It is important to note that rainfall increases mostly in the Kharif season, but PET increases for both Kharif and Rabi seasons. Contrary to rainfall, increase in PET is higher during Rabi season. This information is crucial for better adaptation under increased water demand for agricultural and domestic use. [ABSTRACT FROM AUTHOR]

The complexity and isomerization of communication networks have put forth new requirements for cryptographic schemes to ensure the operation of network security protocols. Robust cryptographic schemes have been gradually favored. The robust initialization vector (RIV) instead of the synthetic initialization vector (SIV) was first introduced to support strong security and robust authenticated encryption. This paper first introduces RIV to GCM-SIV1, proposes a robust variant, GCM-RIV1, and proves that it ensures birthday-bound subtle AE (SAE) security and nonce-misuse resistance. Then, to support beyond-birthday-bound (BBB) security with graceful degradation, we introduce another, stronger security variant, GCM-RIV2, and prove that it allows gracefully degrading BBB SAE security in the faulty nonce setting. Finally, the performance of GCM-RIV1 and GCM-RIV2 is discussed and compared.

A developing country like Ethiopia suffers immensely from the effects of climate change due to its limited economic capability to build irrigation projects to combat climate change impacts on crop production. This study evaluated the impact of climate change on rainfed maize production in the southern part of Ethiopia. The AquaCrop model was employed to assess potential rainfed maize production in the study area with and without climate change. The latest version of the stochastic weather generator model LARS-WG was used to simulate local-scale level climate variables based on low-resolution GCM outputs. The expected monthly percentage change of rainfall during two-time horizons (2040 and 2060) ranged from -23.18 to 20.23 % (2031-2050) and -14.8 to 36.66% (2051-2070). Moreover, the monthly mean of the minimum and maximum temperature were estimated to increase in the range of 1.296 to 2.192 oC and 0.98 to 1.84 oC for the first time horizon (2031-2050) and from 1.86 to 3.4 oC and 1.56 to 3.18 oC in the second time horizon (2051-2070), respectively. Maize yields were expected to increase with a range of 4.13 to 7 % and 6.36 to 9.32% for the respective time horizons in the study area provided that all other parameters were kept the same. [ABSTRACT FROM AUTHOR]

We describe the model performance of a new global coupled climate model configuration, CM4‐MG2. Beginning with the Geophysical Fluid Dynamics Laboratory's fourth‐generation physical climate model (CM4.0), we incorporate a two‐moment Morrison‐Gettelman bulk stratiform microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature‐dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere‐ocean‐land‐sea ice simulations, following Coupled Model Intercomparison Project Phase 6 protocols. CM4‐MG2 generally captures CM4.0's baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward "double" Intertropical Convergence Zones south of the equator, and a stronger Madden‐Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger‐than‐observed El Nin∼ $\tilde{\mathrm{n}}$o‐Southern Oscillation. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4‐MG2. Plain Language Summary: A sophisticated cloud microphysical scheme, along with a mineral dust and temperature‐dependent ice nucleation scheme, have been implemented in a new configuration of the Geophysical Fluid Dynamics Laboratory's most recent climate model (CM4.0). This microphysical scheme predicts both mass and number concentrations of cloud drops, ice crystals, rain, and snow, and treats aerosol‐cloud interactions more consistently. The ice nucleation on mineral dust aerosol in large‐scale clouds is represented more realistically. Centennial‐scale global coupled atmosphere‐ocean‐land‐sea ice simulations from this new configuration compare favorably with observations—with improved subtropical stratocumulus clouds and better tropical intraseasonal variability (i.e., the 30‐ to 90‐day Madden‐Julian Oscillation). The new configuration also reduces the magnitude of future global warming in response to anthropogenic emissions. Key Points: The performance of two‐moment microphysics with prognostic precipitation and an ice nucleation scheme in CM4.0: CM4‐MG2, is documentedThe overall performance of CM4‐MG2 is comparable to or better than CM4.0, based on a set of Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima and historical simulationsNotable improvements include enhanced coastal stratocumulus and a stronger Madden‐Julian Oscillation. CM4‐MG2 has a lower climate sensitivity than CM4.0 [ABSTRACT FROM AUTHOR]

Climate change has been affecting agricultural water resources dynamics spatially and temporally. This article presents analysis results of climate change impact on agricultural water availability in Cimanuk Watershed, Indonesia. STREAM was utilized to model agricultural water availability through FAO MOSAICC web application. Climate spatial data time-series were generated using 3 Global Climate Model (GCM), i.e.,: CanESM2, CNRM-CM5, and MPI-ESM-MR following two climate change scenarios of RCP4.5 and 8.5. Model inputs were split into three periods of 1981–2010 (historical), 2010–2039 (near-future), and 2040–2069 (far-future). Historical data model validation showed the efficiency coefficient of the observed and simulated discharge data ratio was 0.68. The results showed a decreasing volumetric water availability from all generated climate data and scenarios, identified by comparing the discharge normal distribution of the historical and future data periods. Whereas, trend analysis of RCP4.5 scenario showed increasing maximum discharge of Cimanuk river using CanESM2 and MPI-ESM-MR GCM's data, with a Mann–Kendall coefficient of 3.23 and 3.57. These results indicate a different agricultural water balance status within the watershed area, particularly a "very critical" water balance in Indramayu and Majalengka, "critical" in Garut, and "close to critical" in Sumedang Regency. [ABSTRACT FROM AUTHOR]

Impacts of temperature assimilation on Venusian thermal tides are investigated by the observing system simulation experiments assuming Akatsuki Longwave Infrared Camera (LIR) observations. Synthetic temperature data are prepared by a Venusian general circulation model (VGCM) to test if LIR temperature data resolves a discrepancy in the structure of thermal tides between observations and the VGCM. They are assimilated at 70 km altitude with several combinations of frequency and horizontal region. The result shows that the three-dimensional structure of thermal tides is significantly improved not only in temperature but also in horizontal wind, even if observations are available only at a limited frequency of 6-hourly or on the dayside. The zonal–mean zonal wind and temperature fields are also modified at 60–80 km altitudes globally through the vertical momentum transport of thermal tides. It would be promising to assimilate Akatsuki LIR observations to produce realistic objective analysis of the Venus atmosphere. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world.

The Great Miami (GM) and Little Miami (LM) watersheds in Ohio are included in the 51 major river systems in the United States for long-term assessment of conditions of water quality under the USGS National Water-Quality Assessment (NAWQA) Program. Nitrogen (N) and phosphorus (P) loadings have decreased over the years in the GM and LM basins, however, they still remain among the highest concentrations detected in the nation. The latest nutrient loading analysis from Ohio Environmental Protection Agency (OEPA) showed slightly above period of record averages for N and P. Significant amounts of mobilized agricultural N and P from fertilizers in watersheds are transported to the Ohio River and other coastal marine systems such as the Gulf of Mexico, leading to increased growth of harmful algal blooms. In the GM and LM watersheds, changes in flood frequency and intensity are projected to occur in the future as heavy precipitation events are likely to increase as a result of climate change. As climate plays a role for nutrient transformation and transport, studies have shown that N and P inputs to surface waters from agriculture and other sources are projected to continue to increase over the next several decades. This paper reviewed the means of how the GM and LM watersheds are ecologically affected by the N and P nutrient pollution and how climate change impacts the quantity of N and P loadings that go into the river systems. Without better understanding of the nutrient loading processes and their association with various agricultural practices and interactions with climate, there could be additional threats to water quality in both the GM and LM watersheds in decades to come. This literature review has made references to many pertinent new publications that have become available in recent years, as well as to the classic literature. [ABSTRACT FROM AUTHOR]

The global climate is changing. Predicting the impacts this will have on buildings is the first step in the process of finding suitable building adaptation measures. Future climate adaptation of buildings and infrastructure is a growing field of research, relying on both socio-economical and meteorological research for input values to the simulation models. Models producing hourly future weather data rely on global climate models which are based on emission scenarios made from assumptions of future political, social, and economic developments. Accounting for the uncertainties from these underlying models as much as possible, and communicating the uncertainties in the results, is obviously paramount for reliable conclusions from the building simulation models. This paper is a scoping review, investigating how 132 studies treat and communicate the string of uncertainties from underlying models connected to future weather file generation in the scientific literature on building adaptation research. The findings suggest that climate-model-induced uncertainties are often under-communicated, due to either insufficient analysis or neglect. The studies that included the most comprehensive analyses of the uncertainties frequently concluded that treatment of these is important for the reliability of the results, and neglecting this could lead to misleading conclusions.