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Interpretation of changes of global temperature is important for our understanding of the climate system and for our confidence in projections for the future. Massive efforts have been devoted to improve the accuracy of reproducing the global temperature by the available climate models, but the hindcasts are still inaccurate. Notwithstanding the need to further advance climate models, one may consider data-driven approaches, providing practically useful results in a simpler and faster way. Without assuming any prior knowledge about physics and without imposing a model structure that encapsulates the existing knowledge about the underlying processes, we hindcast global temperature by automatically identified evolutionary computation models. We use 60 years of records of global temperature and climate drivers, with training and testing periods being 1950-1999 and 2000-2009, respectively. This paper demonstrates that the global temperature observed in the past is mimicked with reasonably good accuracy. Evolutionary computation holds promise for modeling the global climate system, which looks hopelessly complex in classical perspective. [ABSTRACT FROM AUTHOR]

A geophysical investigation of a hot spring system located in Rabulu, Fiji, was carried out from October 2014 to March 2015. The investigation covered a survey area of 6075 m2. Self-potential (SP), ground temperature and soil carbon dioxide (CO2) concentrations were measured and investigated for their distribution characteristics and inter-linkages. Results indicated obvious anomalous zone at the hot spring discharge site. The SP profile analysis highlighted thermal water upwelling zones and elevation-driven subsurface groundwater pathways. Measurement of subsurface temperatures up to 1 m depths revealed increasing temperatures, indicating potentially high thermal gradients in the area. Surface soil CO2 distributions also agreed with SP and ground temperature results. The overall result of the study demonstrated that synchronised measurements of SP, ground temperature and soil CO2 can be instrumental in identifying anomalous zones near the hot spring sites. Other parameters such as spring water temperature, discharge rate and energy flux estimates from the spring were calculated and analysed. The high-dense multi-parameter data coverage allowed interpretation of geothermal features at a scale never conducted in Fiji before. The near-surface investigations reported in this study corroborate previously suggested steady geothermal activity in the region, deserving further detailed investigation. [ABSTRACT FROM AUTHOR]

We investigate the predictability of monthly temperature and precipitation by applying automatic univariate time series forecasting methods to a sample of 985 40-year-long monthly temperature and 1552 40-year-long monthly precipitation time series. The methods include a naïve one based on the monthly values of the last year, as well as the random walk (with drift), AutoRegressive Fractionally Integrated Moving Average (ARFIMA), exponential smoothing state-space model with Box-Cox transformation, ARMA errors, Trend and Seasonal components (BATS), simple exponential smoothing, Theta and Prophet methods. Prophet is a recently introduced model inspired by the nature of time series forecasted at Facebook and has not been applied to hydrometeorological time series before, while the use of random walk, BATS, simple exponential smoothing and Theta is rare in hydrology. The methods are tested in performing multi-step ahead forecasts for the last 48 months of the data. We further investigate how different choices of handling the seasonality and non-normality affect the performance of the models. The results indicate that: (a) all the examined methods apart from the naïve and random walk ones are accurate enough to be used in long-term applications; (b) monthly temperature and precipitation can be forecasted to a level of accuracy which can barely be improved using other methods; (c) the externally applied classical seasonal decomposition results mostly in better forecasts compared to the automatic seasonal decomposition used by the BATS and Prophet methods; and (d) Prophet is competitive, especially when it is combined with externally applied classical seasonal decomposition. [ABSTRACT FROM AUTHOR]