Results from a series of five surveys among five groups of international climate scientists about their evaluation of elements of climate models and of climate change are presented. The first survey was done in 1996, the latest in 2015/16. Thus, our snapshots of the opinions of climate scientists cover 20 years. The results describe a strong increase in agreement concerning issues of manifestation of climate change, i.e., that the warming is real and not influenced by changing measuring and reporting practices, and concerning attribution of this ongoing climate change to ongoing anthropogenic causes. On the other hand, the evaluation of the climate models has changed little in the past 20 years. There are still significant reservations with the models ability to incorporate clouds and to describe rainfall. Obviously the growing conviction of ongoing man-made climate change is based on a variety of explanations, with modelling not being the predominant line of evidence. We suggest that it may be the repeated assessments by the IPCC, based on paleoclimatic evidence and stringent statistical analysis of the instrumental record which have led to the growing consensus of the warming and its causation. We stress that the presented results concern the opinion of climate scientists with a rather broad background. Our results do not assess if the opinions of the surveyed scientists are “valid” or “right”, but they recognize the character of science being a social process.
Global climate change is anticipated to have consequences on water resources and the envi-ronment both at global and local/regional levels. Efforts towards proper management of future water resources and resolving potential water-related conflicts require the formulation of appropriate techniques to downscale the output of global climate models (GCM) to local conditions for hydrologic prediction. The paper presents an integrated framework for modeling the impact of climate change on river runoff that combines methodology for downscaling climate change scenarios for a basin scale with a hydrological model to estimate the impact of climate change on a river runoff. The modeling framework uses long-term observations of meteorological and hydrological variables together with a climate change scenario to provide a projection of future flows for the specified time horizon. The framework is based on a spatial weather generator and a distributed rainfall-runoff model. Such a configuration enables a reflection of the uncertainty of future conditions by running multiple realizations of future conditions, and also take into account the spatial variability of hydrological properties in the catchment by maintaining the physical details at a given grid size. The performance of the framework was presented for the Kaczawa basin that is one of the main left bank tributaries of the Odra River – the second biggest river in Poland. The results show simulated changes of the future river flow regime caused by climatic changes for two time horizons: 2040 and 2080.
After years of scientific and public discussion concerning a temperature rise resultant from human activity and the on-going industrialisation, the global warming has become an evident fact. The effect of the temperature rise and the climate change will surely alter epidemiological aspects of some infectious diseases. In this review, we try to analyse various data concerning the impact of global warming on the spread of infectious diseases caused by bacterial, viral and protozoan agents. Certainly, it is extremely important for veterinarians and public health that some diseases have altered in their epidemiological aspects and distribution. Some new diseases may emerge; others, previously endemic, may migrate to new geographical regions. The review is focused on pathogens important to both humans and livestock, such as malaria, dengue, bluetongue, West Nile virus, tick-born diseases and infectious diarrhoeas. There are still few scientific papers on the subject, because of numerous difficulties involved in conducting such studies, such as their long time of duration, multiple factors involved in such predictions, and complicated mathematical models containing climate and epidemiological data.