Background: Understanding of the impacts of climatic variability on human health remains poor despite a possibly increasing burden of vector-borne diseases under global warming. Numerous socioeconomic variables make such studies challenging during the modern period while studies of climate-disease relationships in historical times are constrained by a lack of long datasets. Previous studies have identified the occurrence of malaria vectors, and their dependence on climate variables, during historical times in northern Europe. Yet, malaria in Sweden in relation to climate variables is understudied and relationships have never been rigorously statistically established. This study seeks to examine the relationship between malaria and climate fluctuations, and to characterise the spatio-temporal variations at parish level during severe malaria years in Sweden 1749-1859.

Methods: Symptom-based annual malaria case/death data were obtained from nationwide parish records and military hospital records in Stockholm. Pearson (r(p)) and Spearman's rank (r(s)) correlation analyses were conducted to evaluate inter-annual relationship between malaria data and long meteorological series. The climate response to larger malaria events was further explored by Superposed Epoch Analysis, and through Geographic Information Systems analysis to map spatial variations of malaria deaths.

Results: The number of malaria deaths showed the most significant positive relationship with warm-season temperature of the preceding year. The strongest correlation was found between malaria deaths and the mean temperature of the preceding June-August (r(s) = 0.57, p < 0.01) during the 1756-1820 period. Only non-linear patterns can be found in response to precipitation variations. Most malaria hot-spots, during severe malaria years, concentrated in areas around big inland lakes and southern-most Sweden.

Conclusions: Unusually warm and/or dry summers appear to have contributed to malaria epidemics due to both indoor winter transmission and the evidenced long incubation and relapse time of P. vivax, but the results also highlight the difficulties in modelling climate-malaria associations. The inter-annual spatial variation of malaria hot-spots further shows that malaria outbreaks were more pronounced in the southern-most region of Sweden in the first half of the nineteenth century compared to the second half of the eighteenth century.

Glacial extent and mass balance are sensitive climate proxies providing solid information on past climatic conditions. However, series of annual mass-balance measurements of more than 60 years are scarce. To our knowledge, this is the first time the latewood density data (MXD) of the Swiss stone pine (Pinus cembra L.) have been used to reconstruct the summer mass balance (Bs) of an Alpine glacier. The MXD-based B(s) well correlates with a B(s) reconstruction based on the May to September temperature. Winter precipitation has been used as an independent proxy to infer the winter mass balance and to obtain an annual mass-balance (Bn) estimate dating back to the glaciological year 1811/12. The reconstructed MXD/precipitation-based B(n) well correlates with the data both of the Careser and of other Alpine glaciers measured by the glaciological method. A number of critical issues should be considered in both proxies, including non-linear response of glacial mass balance to temperature, bedrock topography, ice thinning and fragmentation, MXD acquisition and standardization methods, and finally the 'divergence problem' responsible for the recently reduced sensitivity of the dendrochronological data. Nevertheless, our results highlight the possibility of performing MXD-based dendroglaciological reconstructions using this stable and reliable proxy.

Tree-ring based temperature reconstructions have successfully inferred the past inter-annual to millennium scales summer temperature variability. A clear relationship between annual and summer temperatures can provide insights into the variability of past annual mean temperature from the reconstructed summer temperature. However, how similar are summer and annual temperatures is to a large extent still unknown. This study aims at investigating the relationship between annual and summer temperatures at different timescales in central Sweden during the last millennium. The temperature variability in central Sweden can represent large parts of Scandinavia which has been a key region for dendroclimatological research. The observed annual and summer temperatures during 1901-2005 were firstly decomposed into different frequency bands using ensemble empirical mode decomposition (EEMD) method, and then the scale dependent relationship was quantified using Pearson correlation coefficients. The relationship between the observed annual and summer temperatures determined by the instrumental data was subsequently used to evaluate 7 climate models. The model with the best performance was used to infer the relationship for the last millennium. The results show that the relationship between the observed annual and summer temperatures becomes stronger as the timescale increases, except for the 4-16 years timescales at which it does not show any relationship. The summer temperature variability at short timescales (2-4 years) shows much higher variance than the annual variability, while the annual temperature variability at long timescales (>32 years) has a much higher variance than the summer one. During the last millennium, the simulated summer temperature also shows higher variance at the short timescales (2-4 years) and lower variance at the long timescales (>1024 years) than those of the annual temperature. The relationship between the two temperatures is generally close at the long timescales, and weak at the short timescales. Overall the summer temperature variability cannot well reflect the annual mean temperature variability for the study region during both the 20th century and the last millennium. Furthermore, all the climate models examined overestimate the annual mean temperature variance at the 2-4 years timescales, which indicates that the overestimate could be one of reasons why the volcanic eruption induced cooling is larger in climate models than in proxy data.