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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]

Global warming acts to prolong thermal summers and shorten winters. In this work, future changes in the lengths and timing of four thermal seasons in northern Europe, with threshold temperatures 0 and 10°C, are derived from bias‐adjusted output data from 23 CMIP5 global climate models. Three future periods and two Representative Concentration Pathway (RCP) scenarios are discussed. The focus is on the period 2040–2069 under RCP4.5, which approximately corresponds to a 2°C global warming relative to the preindustrial era. By the period 2040–2069, the average length of the thermal summer increases by nearly 30 days relative to 1971–2000, and the thermal winter shortens by 30–60 days. The timing of the thermal springs advances while autumns delay. Within the model ensemble, there is a high linear correlation between the modelled annual‐mean temperature increase and shifts in the thermal seasons. Thermal summers lengthen by about 10 days and winters shorten by 10–24 days per 1°C of local warming. In the mid‐21st century, about two‐thirds of all summers (winters) are projected to be very long (very short) according to the baseline‐period standards, with an anomaly greater than 20 days relative to the late‐20th century temporal mean. The proportion of years without a thermal winter increases remarkably in the Baltic countries and southern Scandinavian peninsula. Implications of the changing thermal seasons on nature and human society are discussed in a literature review. [ABSTRACT FROM AUTHOR]

The Red Sea trough (RST) is a low‐pressure trough extending from south towards the Levant. Unlike previous synoptic classifications covering all systems that affect the region, our algorithm focuses on the RST alone. It uses sea‐level pressure (SLP) and relative geostrophic vorticity for identifying the existence of an RST and classifying it to one of three types, according to the location of the trough axis with respect to 35°E longitude. The following conditions were imposed to assure the existence of an RST: (a) north to south SLP drop across the Levant, (b) average positive sea‐level relative vorticity over the region of interest, (c) existence of a distinct and continuous trough axis from the south towards the region of interest and (d) absence of any pronounced closed cyclone near the Levant. The algorithm was applied on the NCEP/NCAR reanalysis, 2.5° × 2.5° resolution and the ERA‐Interim, 2.5° × 2.5° and 0.75° × 0.75° resolutions. An evaluation of the algorithm against subjective identification, based on the NCEP reanalysis, showed an agreement of 93% for RST identification and 79% for correct classification. The use of fine resolution data may insert noise that reduce the identification rate of RSTs but improves the axis locating. The autumn is the main season of RST, with a maximum in November and a consistent decrease towards the July minimum. The annual frequency varies among the data sources between 17.6 and 24.6%. The trough axis is shown to have a diurnal oscillation; towards the eastern coast of the Mediterranean at nighttime and eastward, inland, at noontime. No consistent long‐term trend was found for the period 1979–2016, during which the global warming was persistent. This automated algorithm is flexible in the sense that it is not confined to any predetermined spatial resolution and is applicable to operational forecast model as well as to climate model outputs. [ABSTRACT FROM AUTHOR]

Simulations of the regional climate model REMO at 25 and 50 km (0.44° and 0.22°, respectively) grid spacing are evaluated over Central Africa for the period 1998-2010. The model's ability to simulate various aspects of the observed climate is assessed with a main focus on precipitation characteristics. The results show that the REMO model has a good skill to reproduce the main regional features of the mean climate fields as well as seasonal and annual cycles, although some biases still exist. The model captures the variability in precipitation anomalies between different events associated with El Niño/Southern Oscillation. Generally, the findings remain largely insensitive to the refinement of the horizontal resolution, especially when considering the regional scale. This points the fact that the model dynamical and physical setup may have to be adjusted when setting a focus on the Central African region, in order to better represent the climate mechanisms. It is further found that REMO is capable of capturing the basic features of climatic extremes and accurately represents the shape of the frequency distribution of daily precipitation data. Daily characteristics of precipitation are simulated much better by REMO at 25 km. [ABSTRACT FROM AUTHOR]