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ABSTRACT The aim of this paper is to study the model's simulated frequency, track, intensity and location of cyclonic storms (CSs) and severe cyclonic storms (SCSs) in the Bay of Bengal (BoB), India. For the purpose, the PRECIS (Providing REgional Climates for Impacts Studies) a regional climate modelling system, of UK Met Office, is used. This model is integrated for the period 1961-1990 (baseline) and the future time period 2071-2100 (High emission scenario, A2). To run the model, the initial and lateral boundary conditions are supplied by UK Met Office. The analysis of frequency, track, intensity and location are carried out for May, June, September and October for the period 1961-1990 and 2071-2100. To evaluate the model's performance in simulating storms frequency during 1961-1990, chi square test is carried out with observed storms for the same period. The model's simulated frequency of storms is an overestimation of observations although the frequency of model's simulated storms during 2071-2100 is less than that of during 1961-1990. In general, model's simulated storms are found moving in the northwest direction from their initial location in all months during 1961-1990 and 2071-2100. In model's simulations, the drop in central pressure is relatively more in the months of May, June, September and October during 2071-2100 compared with that during 1961-1990. During 2071-2100, more intense storms may be possible in the months of May and June compared with that of September and October. It is observed that the model is able to simulate the initial locations of storms during 1961-1990 and 2071-2100 close to observations for the months of June and September, especially. [ABSTRACT FROM AUTHOR]

Despite repeated calls for greater collaboration between physical and human geographers, the unique interdisciplinary potential of geography remains largely underutilized. Yet geographers are well positioned to take a leading role in the interdisciplinary turn in climate‐related research. This paper explores the possibilities for physical and human geographers to collaborate within and beyond the discipline, specifically on the topic of climate modeling. We first examine geographical research critically examining the production and circulation of climate knowledge. Drawing on insights from a recent literature called "Critical Physical Geography," we then outline how geographers might engage in collaborative and interdisciplinary work in order to promote more democratic practices of producing climate knowledge, enrich understandings of climate change, and more effectively serve goals of social and environmental justice. We argue that both the discipline of geography and the field of climate research stand to gain enormously from geographers' efforts to talk across the divide between social and natural science within and beyond the boundaries of geography. [ABSTRACT FROM AUTHOR]

Many problems in climate science require extracting forced signals from a background of internal climate variability. We demonstrate that artificial neural networks (ANNs) are a useful addition to the climate science "toolbox" for this purpose. Specifically, forced patterns are detected by an ANN trained on climate model simulations under historical and future climate scenarios. By identifying spatial patterns that serve as indicators of change in surface temperature and precipitation, the ANN can determine the approximate year from which the simulations came without first explicitly separating the forced signal from the noise of both internal climate variability and model uncertainty. Thus, the ANN indicator patterns are complex, nonlinear combinations of signal and noise and are identified from the 1960s onward in simulated and observed surface temperature maps. This approach suggests that viewing climate patterns through an artificial intelligence (AI) lens has the power to uncover new insights into climate variability and change. Plain Language Summary: Many problems in climate science require the identification of signals amidst a sea of climate "noise" and across a variety of models which can disagree with one another. Here, we demonstrate that machine learning techniques, specifically artificial neural networks, can help identify forced patterns of temperature and precipitation within climate model simulations as well as the observations. In fact, the neural network is able to identify patterns of forced change of surface temperature as early as the 1960s in climate model simulations. The results shown here are strongly suggestive of the potential power of machine learning for climate research. Key Points: Neural networks can identify forced patterns of surface temperature and precipitation amidst climate noise and model disagreementThese "indicator patterns" of forced change are present in the observationsViewing climate patterns through an AI lens has the power to uncover new insights into climate variability and change [ABSTRACT FROM AUTHOR]