Search

Fire activity in 288 areas (2.5° N × 10° E) in the Arctic region (50°–70° N, 0°–360° E) was analyzed using about 4.4 million satellite hotspot (HS) data from 2002 to 2021. A total of 21 high fire density areas from eastern Europe to western Canada were selected, and their fire–weather conditions during each active fire period were analyzed using about 1820 various weather maps at the upper and the lower air level. Analysis results showed that the active fires in the Arctic region occurred under the fire–weather conditions associated with the northward movement of cut-off high (COH) and warm air masses detached from the south caused by large westerly meandering (LWM). LWM is a sign of the beginning of an active fire period. Very active fires on HS peak days occurred several days after the start of the northward movement of COHs and under mainly high-pressure conditions in the upper air and strong wind conditions in the lower air. The time lag of these several days suggests that we may be prepared for very active fires. The fire–weather analysis approach described in this paper has shown that future large-scale fire outbreaks are predictable. [ABSTRACT FROM AUTHOR]

The paper is concerned with thermal disturbances in continuous permafrost due to open pit mining and tailings impoundment in the cold regions of Northern Canada. Numerical simulations were conducted to investigate issues of thermal regime changes and permafrost degradation in both the short term and the long term in connection with the proposed Kiggavik project in Nunavut, Canada. The results of numerical simulations indicate that no open talik would form below the East Zone and Center Zone pits during the estimated mining and milling operation period of 14 years, although a thin thawed zone would develop surrounding the open pits. For the Main Zone pit where the excavation would break through permafrost, the open talik remains following an extended operation period of 25 years with a 5 to 30 m thawed zone along the pit side walls. In the long term, with a plausible climate change scenario of 5 °C increase in the mean annual ground surface temperature during the next 100 years, the permafrost surrounding the in-pit tailings management facilities (TMFs) would reduce greatly in about 500 years. However, an approximately 40 m thick permafrost layer would remain on the top of the TMFs, which is impervious and would prevent any tailings pore water from migrating upward to reach the ground surface. [ABSTRACT FROM AUTHOR]

Storm surges and waves are key climate-driven parameters affecting the design and operation of ports and other infrastructure on the coast. Reliable predictions of future storm surges and waves are not yet available for the west coast of Canada, and this data gap hinders effective climate risk assessment, planning and adaptation. This paper presents numerical simulations of storm surges and waves in British Columbia coastal waters under a future climate (Representative Concentration Pathway) scenario (RCP8.5). The numerical models were first forced by wind and surface pressure fields from the ERA-5 global reanalysis, and calibrated and validated using historical wave and water level records. The models were then driven by atmospheric data from four regional climate models (RCMs) to investigate potential changes in the frequency and magnitude of storm surges and extreme waves over the 21st century. The model outputs were analyzed to determine the potential impacts of climate change on storm surges and wave effects at key ports and transportation assets in western Canada. The study is the first of its kind to utilize unstructured, computational models to simulate storm surges and waves for the entire western Canada coastal region, while maintaining the high spatial resolution in coastal sub-basins needed to capture local dynamic responses. [ABSTRACT FROM AUTHOR]

The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 ("normal"), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada's climate change. In 2025, the climate level of Canada will become "a little bad" based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change. [ABSTRACT FROM AUTHOR]

Through an empirical investigation of the historical relationship between the destination climate and tourist arrivals in the Caribbean, this study presents the first revealed preference evaluation of a climate index informed by tourists' stated climatic preferences for coastal-beach tourism (i.e., a sun-sand-surf or 3S travel market). The goal of this multi-organization collaboration was to examine the potential application of a newly designed climate index—the Holiday Climate Index (HCI):Beach—for three Caribbean destinations (Antigua and Barbuda, Barbados, Saint Lucia). This paper provides an overview of the evolution of climate indices, including the development of the (HCI):Beach. To test the validity of climate indices for a beach travel market, daily climate ratings based on outputs from the Tourism Climate Index and the HCI were correlated with monthly arrivals data from Canada (a key source market) at an island destination scale. The results underscore the strength of the new index, with each destination scoring consistently higher using the HCI:Beach, including a stronger relationship (R2) between index scores and tourist arrivals. These findings demonstrate the value of combining stated and revealed preference methodologies to predict tourism demand and highlight opportunities for future research. [ABSTRACT FROM AUTHOR]