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Introduction. The article analyzes the methods of assessing the fire hazard level of operated buildings (structures). The indicator "the proportion of people injured in fires from the total number of people injured in fires" is proposed to assess the fire hazard level of operated buildings (structures).Problem Statement. To assess the fire hazard level of operated buildings (structures), various indicators are used that do not take into account the number of people present at the facility during the fire, but are dependent on this value. The calculation of the indicator "the proportion of people injured in fires from the total number of people injured in fires" as an additional one allows us to estimate the magnitude of fire hazard factors at facilities without taking into account the number of people who were at the protection facility during the fire.Theoretical Part. As the basic information in the study, the statistics of fires and their consequences for 2017-2020 at operated facilities grouped by functional fire hazard classes were used. The indicators used in the calculation are the number of fires, the number of people killed and the number of injured people.Conclusions. The indicator "the proportion of people injured in fires from the total number of people injured in fires" evaluates the probability of survival of people caught in the zone of exposure to fire hazards that lead to injury or death of a person, and characterizes the magnitude of fire hazard factors. Large values of this indicator may indicate a low level of fire hazard — the damage to health does not lead to the death of victims. The calculations of this and other indicators for objects of protection by functional fire hazard classes based on statistical data on fires and their social consequences are given. The high level of fire hazard in single-family residential buildings, agricultural buildings and cultural and leisure institutions is shown.

The Canadian Earth System Model version 5 (CanESM5) developed at Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis (CCCma) is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A 40-member ensemble of CanESM5 retrospective decadal forecasts (or hindcasts) is integrated for 10 years from realistic initial states once a year during 1961 to the present using prescribed external forcing. The results are part of CCCma's contribution to the Decadal Climate Prediction Project (DCPP) component of CMIP6. This paper evaluates CanESM5 large ensemble decadal hindcasts against observational benchmarks and against historical climate simulations initialized from pre-industrial control run states. The focus is on the evaluation of the potential predictability and actual skill of annual and multi-year averages of key oceanic and atmospheric fields at regional and global scales. The impact of initialization on prediction skill is quantified from the hindcasts decomposition into uninitialized and initialized components. The dependence of potential and actual skill on ensemble size is examined. CanESM5 decadal hindcasts skillfully predict upper-ocean states and surface climate with a significant impact from initialization that depend on climate variable, forecast range, and geographic location. Deficiencies in the skill of North Atlantic surface climate are identified and potential causes discussed. The inclusion of biogeochemical modules in CanESM5 enables the prediction of carbon cycle variables which are shown to be potentially skillful on decadal timescales, with a strong long-lasting impact from initialization on skill in the ocean and a moderate short-lived impact on land.

Introduction. The effectiveness of the actions of the fire protection units depends on their readiness for action, that is, on the state of the forces and means of the station and the ability to perform the main task within the existing tactical capabilities.Problem Statement. When developing mathematical models of the operational activities of the fire department, its effectiveness and the assessment of the readiness of fire protection units to extinguish fires, it is important to study the dependence of the death of people in fires on the duration of the fire. This study is devoted to this task.Theoretical Part. The distribution of fires in the Russian Federation for 2016-2020 by their duration and the dependence of deaths and injuries of people in fires, as well as their ratio to the duration of fires in the Russian Federation for 2016-2020, is studied. It is shown that the average duration of the fire during the studied period was 26 minutes, the median value of the distribution was 18 minutes. The dependences of deaths and injuries of people in fires on the duration of fires are approximated by a logarithmic function. When the duration of the fire is up to 26 minutes. the ratio of the number of people injured in fires to those killed decreases exponentially, but remains stable in the rest of the time range.Conclusions. It is concluded that it is necessary to increase the readiness of the fire department for actions to extinguish fires and rescue people, which includes the development of an information and analytical model of the operational activities of the fire department.

The Canadian Earth System Model version 5 (CanESM5) is a global model developed to simulate historical climate change and variability, to make centennial-scale projections of future climate, and to produce initialized seasonal and decadal predictions. This paper describes the model components and their coupling, as well as various aspects of model development, including tuning, optimization, and a reproducibility strategy. We also document the stability of the model using a long control simulation, quantify the model's ability to reproduce large-scale features of the historical climate, and evaluate the response of the model to external forcing. CanESM5 is comprised of three-dimensional atmosphere (T63 spectral resolution equivalent roughly to 2.8∘) and ocean (nominally 1∘) general circulation models, a sea-ice model, a land surface scheme, and explicit land and ocean carbon cycle models. The model features relatively coarse resolution and high throughput, which facilitates the production of large ensembles. CanESM5 has a notably higher equilibrium climate sensitivity (5.6 K) than its predecessor, CanESM2 (3.7 K), which we briefly discuss, along with simulated changes over the historical period. CanESM5 simulations contribute to the Coupled Model Intercomparison Project phase 6 (CMIP6) and will be employed for climate science and service applications in Canada.

The authors have conducted the factor modeling using data on fire danger, on functional and resource status of fire protection and indicators of social and economic development of rural settlements of the Russian Federation subjects. The factor analysis has revealed a low level of protection from fires in small rural settlements. It is also shown that an increase in the arrival time of the first fire-fighting unit to the place of fire increasesthe mortality risk in a fire, and that with the increase in the average distance to the fire department, the fire risk magnitude is increasing. The obtained results will make it possible to developmeasures to reduce fire danger risks in rural settlements of the Russian Federation.

The half a degree additional warming, prognosis and projected impacts (HAPPI) experimental protocol provides a multi-model database to compare the effects of stabilizing anthropogenic global warming of 1.5 °C over preindustrial levels to 2.0 °C over these levels. The HAPPI experiment is based upon large ensembles of global atmospheric models forced by sea surface temperature and sea ice concentrations plausible for these stabilization levels. This paper examines changes in extremes of high temperatures averaged over three consecutive days. Changes in this measure of extreme temperature are also compared to changes in hot season temperatures. We find that over land this measure of extreme high temperature increases from about 0.5 to 1.5 °C over present-day values in the 1.5 °C stabilization scenario, depending on location and model. We further find an additional 0.25 to 1.0 °C increase in extreme high temperatures over land in the 2.0 °C stabilization scenario. Results from the HAPPI models are consistent with similar results from the one available fully coupled climate model. However, a complicating factor in interpreting extreme temperature changes across the HAPPI models is their diversity of aerosol forcing changes.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change 1-3 . Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain 4 . This leads to low confidence in regional projections, especially for precipitation, over the coming decades 5,6 . The chaotic nature of the climate system 7-9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models 10 , and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

Rapid adjustments are responses to forcing agents that cause a perturbation to the top of atmosphere energy budget but are uncoupled to changes in surface warming. Different mechanisms are responsible for these adjustments for a variety of climate drivers. These remain to be quantified in detail. It is shown that rapid adjustments reduce the effective radiative forcing (ERF) of black carbon by half of the instantaneous forcing, but for CO 2 forcing, rapid adjustments increase ERF. Competing tropospheric adjustments for CO 2 forcing are individually significant but sum to zero, such that the ERF equals the stratospherically adjusted radiative forcing, but this is not true for other forcing agents. Additional experiments of increase in the solar constant and increase in CH 4 are used to show that a key factor of the rapid adjustment for an individual climate driver is changes in temperature in the upper troposphere and lower stratosphere.

Globally, latent heating associated with a change in precipitation is balanced by changes to atmospheric radiative cooling and sensible heat fluxes. Both components can be altered by climate forcing mechanisms and through climate feedbacks, but the impacts of climate forcing and feedbacks on sensible heat fluxes have received much less attention. Here we show, using a range of climate modelling results, that changes in sensible heat are the dominant contributor to the present global-mean precipitation change since preindustrial time, because the radiative impact of forcings and feedbacks approximately compensate. The model results show a dissimilar influence on sensible heat and precipitation from various drivers of climate change. Due to its strong atmospheric absorption, black carbon is found to influence the sensible heat very differently compared to other aerosols and greenhouse gases. Our results indicate that this is likely caused by differences in the impact on the lower tropospheric stability.

As the global temperature increases with changing climate, precipitation rates and patterns are affected through a wide range of physical mechanisms. The globally averaged intensity of extreme precipitation also changes more rapidly than the globally averaged precipitation rate. While some aspects of the regional variation in precipitation predicted by climate models appear robust, there is still a large degree of inter-model differences unaccounted for. Individual drivers of climate change initially alter the energy budget of the atmosphere leading to distinct rapid adjustments involving changes in precipitation. Differences in how these rapid adjustment processes manifest themselves within models are likely to explain a large fraction of the present model spread and needs better quantifications to improve precipitation predictions. Here, we introduce the Precipitation Driver and Response Model Intercomparison Project (PDRMIP), where a set of idealized experiments designed to understand the role of different climate forcing mechanisms were performed by a large set of climate models. PDRMIP focuses on understanding how precipitation changes relating to rapid adjustments and slower responses to climate forcings are represented across models. Initial results show that rapid adjustments account for large regional differences in hydrological sensitivity across multiple drivers. The PDRMIP results are expected to dramatically improve our understanding of the causes of the present diversity in future climate projections.