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Abstract Background The virtual conference format has become an essential tool for professional development of researchers around the world since the outbreak of the COVID-19 pandemic. This study aims to identify empirical evidence of the benefits and challenges of virtual conferences by investigating participants’ experiences with them. Methods The study participants were delegates to the 40th annual meeting of the Korean Society of Nephrology, which was held virtually in September, 2020. A questionnaire was developed and implemented among the conference attendees. The 44-item questionnaire included five sub-scales related to participant perceptions of the virtual conference, which were (a) convenience and accessibility, (b) planning and organization, (c) technology use, (d) social exchanges, and (e) overall satisfaction, their preferences of conference formats, and their views of future projections for a virtual conference. Results A total of 279 delegates completed and returned the questionnaires (18.8% response rate). Participants varied in gender, age, profession, work location, and prior experience with conferences. On a four-point Likert scale (1 = “strongly disagree” and 4 = “strongly agree”), participants showed positive perceptions of the virtual conference in general, where the total mean (M) was 3.03 and less positive perceptions on social exchanges (M = 2.72). Participant perceptions of the virtual conference differed across age groups, professions, and prior experience with conferences (p < .05). Approximately half of the participants (n = 139) preferred the virtual format, and 33% (n = 92) preferred the conventional format. Participant preferences for the virtual format were somewhat evenly distributed between asynchronous (32.9%) and synchronous (29.1%) modes. Participants predicted a virtual conference would continue to be a popular delivery format after the end of the COVID-19. Conclusions Although participants had positive perceptions of the virtual conference, more support needs to be offered to those who may be less comfortable with using technology or with online interactions, and there is a need for improvement in supporting social exchange among attendees. Also, it is suggested that a blend of asynchronous and synchronous delivery methods should be considered to meet the varied needs of attendees.

Climate change is a growing challenge to socio-economic development and sustainable environmental management worldwide. Developing countries with low adaptive capacity and high vulnerability to the phenomena are affected severely. This study assessed the climate change situation in a developing country, its effects on the water sector and adaptive responses to improve climate change resilience using Kenya as a case study. Findings showed that Kenya is experiencing temperature and rainfall rises currently, and future projections showed an even worse situation. Climate variability and change however differed based on time and space. Highlighted effects on the water sector included fluctuations in its quantities and deterioration of its quality. Adaptive responses such as infrastructural modifications of water body environments, forecasting using models to predict climate change uncertainties and disseminating early warnings are discussed. Their success relies on strong policy and institutions to steer their implementation in Kenya.

Abstract Fewer perfect prognosis (PP) based statistical downscaling were applied to future projections produced by global circulation models (GCM), when compared with the method of model output statistics (MOS). This study is a trial to use a multiple variable based PP downscaling for summer daily precipitation at many sites in China and to compare with the MOS. For the PP method (denoted as ‘OGB-PP’), predictors for each site are screened from surface-level variables in ERA-Interim reanalysis by an optimal grid-box method, then the biases in predictors are corrected and fitted to generalized linear models to downscale daily precipitation. The historical and the future simulations under the medium emission scenario (often represented as ‘RCP4.5’), produced by three GCMs (CanESM2, HadGEM2-ES and GFDL-ESM2G) in the coupled model intercomparison project phase five (CMIP5) were used as the downscaling bases. The bias correction based MOS downscaling (denoted as ‘BC-MOS’) were used to compare with the OGB-PP. The OGB-PP generally produced the climatological mean of summer precipitation across China, based on both ERAI and CMIP5 historical simulations. The downscaled spatial patterns of long-term changes are diverse, depending on the different GCMs, different predictor-bias corrections, and the choices on selecting PP and MOS. The annual variations downscaled by OGB-PP have small differences among the choices of different predictor-bias corrections, but have large difference to that downscaled by BC-MOS. The future changes downscaled from each GCM are sensitive to the bias corrections on predictors. The overall change patterns in some OGB-PP results on future projections produced similar trends as those projected by other multiple-model downscaling in CMIP5, while the result of the BC-MOS on the same GCMs did not, implying that PP methods may be promising. OGB-PP produced more significant increasing/decreasing trends and larger spatial variability of trends than the BC-MOS methods did. The reason maybe that in OGB-PP the independent precipitation modeling mechanism and the freely selected grid-box predictors can give rise to more diverse outputs over different sites than that from BC-MOS, which can contribute additional local variability.

Abstract Quantifying the risk of global warming exceeding critical targets such as 2.0 ◦ C requires reliable projections of uncertainty as well as best estimates of Global Mean Surface Temperature (GMST). However, uncertainty bands on GMST projections are often calculated heuristically and have several potential shortcomings. In particular, the uncertainty bands shown in IPCC plume projections of GMST are based on the distribution of GMST anomalies from climate model runs and so are strongly determined by model characteristics with little influence from observations of the real-world. Physically motivated time-series approaches are proposed based on fitting energy balance models (EBMs) to climate model outputs and observations in order to constrain future projections. It is shown that EBMs fitted to one forcing scenario will not produce reliable projections when different forcing scenarios are applied. The errors in the EBM projections can be interpreted as arising due to a discrepancy in the effective forcing felt by the model. A simple time-series approach to correcting the projections is proposed based on learning the evolution of the forcing discrepancy so that it can be projected into the future. This approach gives reliable projections of GMST when tested in a perfect model setting. When applied to observations this leads to projected warming of 2.2 ◦ C (1.7 ◦ C to 2.9 ◦ C) in 2100 compared to pre-industrial conditions, 0.4 ◦ C lower than a comparable IPCC anomaly estimate. The probability of staying below the critical 2.0 ◦ C warming target in 2100 more than doubles to 0.28 compared to only 0.11 from a comparably IPCC estimate.