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SUMMARYScrub typhus is emerging and re-emerging in many areas: climate change may affect its spread. To explore the effects of meteorological factors on scrub typhus, monthly cases of scrub typhus from January 2006 to December 2012 in the Laiwu district of temperate northern China were analysed. We examined the correlations between scrub typhus and meteorological factors (and their delayed effects). We built a time-series adjusted negative binomial model to reflect the relationships between climate variables and scrub typhus cases. The key determinants of scrub typhus transmission were temperature, relative humidity and precipitation. Each 1°C increase in monthly average temperature in the previous 3 months, each 1% increase in monthly relative humidity in the previous 2 months and each 1 mm increase in monthly precipitation in the previous 3 months induced 15·4%, 12·6% and 0·7% increases in the monthly number of cases, respectively. In conclusion, scrub typhus is affected by climate change in temperate regions.

SUMMARYAssociations between monthly counts of all laboratory-confirmed cases of salmonellosis in Arkhangelsk, northern Russia, from 1992 to 2008 and climatic variables with lags 0–2 were studied by three different models. We observed a linear association between the number of cases of salmonellosis and mean monthly temperature with a lag of 1 month across the whole range of temperatures. An increase of 1 °C was associated with a 2·04% [95% confidence interval (CI) 0·25–3·84], 1·84% (95% CI 0·06–3·63) and 2·32% (95% CI 0·38–4·27) increase in different models. Only one of the three models suggested an increase in the number of cases, by 0·24% (95% CI 0·02–0·46) with an increase in precipitation by 1 mm in the same month. Higher temperatures were associated with higher monthly counts of salmonellosis while the association with precipitation was less certain. The results may have implications for the future patterns of enteric infections in northern areas related to climate change.

This study, using the panel data modeling approach, investigates the relationships between climatic variables and southern pine beetle (SPB) (Dendroctonus frontalis Zimmermann) infestations and assesses the impact of global climate change on SPB infestation risk and damage. The panel data model alleviates possible collinearity among climatic variables, accounts for the effect of omitted or unobserved variables, and incorporates natural and human adaptation, thus representing a more robust approach to analyzing climate change impacts. SPB outbreaks in Louisiana and Texas appeared to move together; infestations in Alabama, Arkansas, Georgia, Florida, Mississippi, South Carolina, North Carolina, and Tennessee were highly correlated; and Virginia demonstrated its unique temporal pattern of SPB outbreaks. Salvage harvest was found to be helpful in lessening future infestation risk. Warmer winters and springs would positively contribute to SPB outbreaks with spring temperature showing a more severe and persistent impact than winter temperature; increases in fall temperature would ease SPB outbreaks; and summer temperature would have a mixed impact on SPB infestations. Compared to temperature, precipitation would have a much smaller impact on SPB infestations. While increases in the previous winter, spring, and fall precipitation would enhance SPB outbreak risk in the current year, a wetter summer would reduce infestations 3 years later. Global climate change induced by doubling atmospheric CO2 concentration would intensify SPB infestation risk by 2.5–5 times. If the changes in the area and productivity of southern pine forests due to climate change are accounted for, SPB would cause even more severe damage, 4–7.5 times higher than the current value of trees killed annually. # 2003 Elsevier B.V. All rights reserved.

Sea surface salinity presents one of the most important chemical elements in the water. Climatic variables, included in new view of salinity distribution at a global scale, were used in this research. For the purpose of this research newly updated climate parameters for the period until 2100 were used along with (CMIP5) climatological model. The new distribution of surface salinity may show water desalination and energy potential. This map can be useful in the determination of new littoral areas or for fishermen's routes. These data are presented in geo-tiff raster extension with the resolution of 0.1. This map could be updated with climatological parameters with obtained medium climate change effects. Some places in the world sea have low, some have high salinity. Salinity increases in accordance with the increase of precipitation and decreases with the decrease of it. The paper presents following maps; salinity world map when there is no climate change; the moderate one, if the temperature increases for 2.0 degrees C until 2100, and high if the increase of temperature was between 2.0 degrees C and 5.0 degrees C. The three scenarios were taken to show updated maps of world salinity in comparison with climate change effects.

Investigating the impact of climate variables on net primary productivity is crucial to evaluate the ecosystem health and the status of forest type response to climate change. The objective of this paper is (1) to estimate spatio-temporal patterns of net primary productivity (NPP) during 2001 to 2010 in a tropical deciduous forest based on the input variable dataset (i.e.meteorological and biophysical) derived from the remote sensing and other sources and (2) to investigate the effects of climate variables on NPP during 2001 to 2010. The study was carried out in Katerniaghat Wildlife Sanctuary that forms a part of a tropical forest and is situated in Uttar Pradesh, India, along the Indo-Nepal border. Mean annual NPP was observed to be highest during 2007 with a value of 878 g C m−2 year−1 and 781.25 g C m−2 year−1 for sal and teak respectively. A decline in mean NPP during 2002–2003, 2005 and 2008–2010 could be attributed to drought, increased temperature and vapour pressure deficit (VPD). The time lag correlation analysis revealed precipitation as the major variables affecting NPP, whereas combination of temperature and VPD showed dominant effect on NPP as revealed by generalized linear modelling. The carbon gain in NPP in sal forest was observed to be marginal higher than that of teak plantation throughout the study period. The decrease in NPP was observed during 2010, pertaining to increased VPD. Contribution of different climatic variables through some link process was revealed in statistical analysis and clearly indicated the co-dominance of all the variables in explaining NPP.

Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach.

The climate change is the major issue of the development planning in recent world. This paper focuses on the people’s understanding and experience about the climate change, its impact on tourism dependent livelihoods of the mountain community. Participatory survey design, interview, community consultation, FGD, were conducted in the field. The literature review and hydrological and meteorological data from secondary source were collected for analysis. The people of research area feel that the fluctuation of climatic variables and extremes is occurring now and they are familiar with that change in climatic variables and associated disaster. Their experience on the warming and precipitation coincides with authorized meteorological data which depicts that the mean annual temperature is increasing and average annual precipitation is decreasing. People from High mountain region said that the impact of climate change in tourism based livelihood was experienced. All types of livelihood assets are affected by climate change in mountain region. The change in frequency and intensity of climatic variables and climate change induced hazards was observed and that retard in tourism business and tourism activities in local area.

This paper examines farm household-level impacts of climate change by examining the relationship between climatic variables and Vietnamese agricultural output. The Ricardian technique is applied with panel data which accounts for both adaptation strategies and household characteristics. This study proposes a two-stage Hsiao model to correct for collinearity between climatic variables and individual effects. The results show that in the dry season, increases in temperatures are beneficial to all farms in the warmer southern regions, while increases in precipitation will damage only irrigated farms in the Central and South regions. The impact of higher temperature in the wet season is similar, except that it will negatively affect net revenue of irrigated farms in the long run. More rainfall in the wet season will increase net revenue in the North region only. Finally, this study combines the estimated results with future climate scenarios to predict how future changes in climate will affect farmers on aggregate.

This study addressed yam production in response to climate change in Cross River State using a co-integration model approach. The specific objectives of this paper are to analyze the trend in yam production, annual precipitation, and annual temperature, and to analyze the impact of climate variables on yam production. Time-series data from 1996 to 2017 was used. Based on the analysis, which constituted a linear trend analysis, co-integration test, and error correction model, the study came up with robust findings. The linear trend analysis for yam production revealed a steady increase in output between the years 2005 and 2016. The result of the rainfall trend analysis showed the presence of rainfall variability and irregularity. The trend line for temperature showed an overall downward trend for the period under study. However, the Error Correction Model result showed that temperature was statistically significant and negatively impacted yam production. The study recommends that policymakers should take appropriate steps to encourage the development of pest- and disease-tolerant yam varieties because an increase in temperature leads to the proliferation of insects, pests, and diseases.

Variations in temperature and precipitation have direct impacts on the physical, chemical and biological characteristics of the shallow lakes. This paper examines the possible linkages between climate variables and the water levels of shallow interconnected Lakes Mogan and Eymir, located 20 km south of Ankara in Central Anatolia. The variations in the lakes’ water levels during 1996–2015 are studied and the impacts of climate variables on the lake levels are assessed to address the long-term consequences. The nonparametric Mann–Kendall test was used to quantify trends in the climate variables and the lakes’ level fluctuations between the observation periods 1998–2007 and 2008–2014. Statistical analyses results showed that precipitation and temperature have crucial influence on the variations in the lakes’ levels. The projected increase in temperature and decrease in precipitation over the next century may produce substantial decreases in lake levels, with consequent drying of both lakes.

Nantong is chosen as the object of study in this paper. According to the meteorological observational data for the period from 1951 to 2010 in Nantong, the changes of climate variables on the time series for nearly 60 years are analyzed through estimation of linear tendency and sliding average. It is found that: in the past 60 years, the average temperature is rising at the rate of 0.297°C / 10a, where the warming trend in spring is the most significant, and that in summer is the least. The trends of the min-mean and max-mean temperature are on the rise. The rapidly increasing changes of the extreme minimum and maximum temperature are observed. Over the past 60 years, the tendency of precipitation is generally on the rise with the small rate. The increasing trend of precipitation in summer is the most pronounced, followed by winter. Precipitation in spring and autumn is slightly decreasing. The contribution of the increase in precipitation mainly derives from the addition in summer and winter.

This paper presents the method to develop response surface diagrams (RSD) suitable to evaluate the impacts of climate change on potential crop production and crop area. The diagrams depict the response of different agricultural crops to average long-term changes in ambient temperature and precipitation on a country basis. They take into account the spatial and seasonal variability of climate, and differences in the climate response of important crops. RSDs for Germany and the Democratic Republic of Congo illustrate that countries and crop types differ greatly in their sensitivity to unit changes in long-term average climate. In comparing the area-weighted RSDs for Germany and Democratic Republic of Congo, it was found that the potential production in Germany of a weighted aggregation of crops is mainly sensitive to changes in temperature, whereas the potential crop production in the Democratic Republic of Congo mainly responds to changes in precipitation (over the specified ranges of climate variables). The RSDs can provide a visual overview of these varying sensitivities, and are a convenient and simple-to-understand method to summarize crop responses to climate change in a particular country.

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.