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The article: Atmospheric blocking events in the North Atlantic: trends and links to climate anomalies and teleconnections, written by Hussein Wazneh, Philippe Gachon, René Laprise, Anne de Vernal, Bruno Tremblay was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 5 January 2021 without open access.

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010–2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min–Max]: 16,785 [9,902–74,382] vs 21,289 [11,796–70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Background: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. Methods: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. Results: An average of 73.3 weekly malaria cases per site (range: 0–597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment–malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). Conclusion: The influence of vector control interventions on the malaria–environment relationship need to be considered at a local scale in order to efficiently guide control programs.

Studies have estimated the impact of the environment on malaria incidence although few have explored the differential impact due to malaria control interventions. Therefore, the objective of the study was to evaluate the effect of indoor residual spraying (IRS) on the relationship between malaria and environment (i.e. rainfall, temperatures, humidity, and vegetation) using data from a dynamic cohort of children from three sub-counties in Uganda. Environmental variables were extracted from remote sensing sources and averaged over different time periods. General linear mixed models were constructed for each sub-counties based on a log-binomial distribution. The influence of IRS was analysed by comparing marginal effects of environment in models adjusted and unadjusted for IRS. Great regional variability in the shape (linear and non-linear), direction, and magnitude of environmental associations with malaria risk were observed between sub-counties. IRS was significantly associated with malaria risk reduction (risk ratios vary from RR = 0.03, CI 95% [0.03–0.08] to RR = 0.35, CI95% [0.28–0.42]). Model adjustment for this intervention changed the magnitude and/or direction of environment-malaria associations, suggesting an interaction effect. This study evaluated the potential influence of IRS in the malaria-environment association and highlighted the necessity to control for interventions when they are performed to properly estimate the environmental influence on malaria. Local models are more informative to guide intervention program compared to national models.

This dataset contains: 1. The output of an ensemble of simulations of a polar low that developed over the Norwegian Sea on 25 March 2019. 2. The manually-obtained tracks of the polar low. 1. Simulation output The simulations of the polar low were conducted with the convection-permitting Canadian Regional Climate Model version 6 (CRCM6/GEM4) driven by the reanalysis ERA5. The model has a grid mesh of 0.0225° and a vertical grid with 62 levels. The size of the model domain is 1004 x 1004 grid points, excluding the sponge zone, and the model top is at 2 hPa. The model was initialised every six hours from 23 March at 0000 UTC to 24 March at 1800 UTC, and the end date of the simulations was 26 March at 0600 UTC. This dataset contains the hourly output of these eight simulations. The 2D fields include several variables at screen level (temperature, dewpoint temperature, relative humidity, horizontal wind, wind gust) as well as sea level pressure,1 h accumulated precipitation, and surface sensible and latent heat fluxes. The 3D fields are geopotential height, temperature, relative humidity, horizontal wind, and the vertical velocity in pressure coordinate, and they are provided on 22 pressure levels from 1,000 to 10 hPa. 2. Polar low tracks This dataset contains the different tracks of the polar low that have been obtained using the observations, the reanalysis ERA5 and the output of the eight simulations. The data provided are the time, latitude and longitude of the track points, as well as the sea level pressure minimum. For the track obtained using observations, the dataset also includes the distance between the track point and the closest surface station (which is the one that provides the value of the sea level pressure minimum). Only the sea level pressure observations from stations within 25 km from the PL centre are included.

Polar lows (PLs), which are intense maritime polar mesoscale cyclones, are associated with severe weather conditions. Due to their small size and rapid development, PL forecasting remains a challenge. Convection-permitting models are adequate to forecast PLs since, compared to coarser models, they provide a better representation of convection as well as surface and near-surface processes. A PL that formed over the Norwegian Sea on 25 March 2019 was simulated using the convection-permitting Canadian Regional Climate Model version 6 (CRCM6/GEM4, using a grid mesh of 2.5 km) driven by the reanalysis ERA5. The objectives of this study were to quantify the impact of the initial conditions on the simulation of the PL, and to assess the skill of the CRCM6/GEM4 at reproducing the PL. The results show that the skill of the CRCM6/GEM4 at reproducing the PL strongly depends on the initial conditions. Although in all simulations the synoptic environment is favourable for PL development, with a strong low-level temperature gradient and an upper-level through, only the low-level atmospheric fields of three of the simulations lead to PL development through baroclinic instability. The two simulations that best captured the PL represent a PL deeper than the observed one, and they show higher temperature mean bias compared to the other simulations, indicating that the ocean surface fluxes may be too strong. In general, ERA5 has more skill than the simulations at reproducing the observed PL, but the CRCM6/GEM4 simulation with initialisation time closer to the genesis time of the PL reproduces quite well small scale features as low-level baroclinic instability during the PL development phase.

Polar lows (PLs) are maritime mesoscale cyclones associated with severe weather. They develop during marine cold air outbreaks near coastlines and the sea ice edge. Unfortunately, our knowledge about the mechanisms leading to PL development is still incomplete. This study aims to provide a detailed analysis of the development mechanisms of a PL that formed over the Norwegian Sea on 25 March 2019 using the output of a simulation with the sixth version of the Canadian Regional Climate Model (CRCM6/GEM4), a convection-permitting model. First, the life cycle of the PL is described and the vertical wind shear environment is analysed. Then, the horizontal wind divergence and the baroclinic conversion term are computed, and a surface pressure tendency equation is developed. In addition, the roles of atmospheric static stability, latent heat release, and surface heat and moisture fluxes are explored. The results show that the PL developed in a forward-shear environment and that moist baroclinic instability played a major role in its genesis and intensification. Baroclinic instability was initially only present at low levels of the atmosphere, but later extended upward until it reached the mid-troposphere. Whereas the latent heat of condensation and the surface heat fluxes also contributed to the development of the PL, convective available potential energy and barotropic conversion do not seem to have played a major role in its intensification. In conclusion, this study shows that a convection-permitting model simulation is a powerful tool to study the details of the structure of PLs, as well as their development mechanisms.

Abstract The contraction of species range is one of the most significant symptoms of biodiversity loss worldwide. While anthropogenic activities and habitat alteration are major threats for several species, climate change should also be considered. For species at risk, differentiating the effects of human disturbances and climate change on past and current range transformations is an important step towards improved conservation strategies. We paired historical range maps with global atmospheric reanalyses from different sources to assess the potential effects of recent climate change on the observed northward contraction of the range of boreal populations of woodland caribou ( Rangifer tarandus caribou ) in Quebec (Canada) since 1850. We quantified these effects by highlighting the discrepancies between different southern limits of the caribou's range (used as references) observed in the past and reconstitutions obtained through the hindcasting of the climate conditions within which caribou are currently found. Hindcasted southern limits moved ~105 km north over time under all reanalysis datasets, a trend drastically different from the ~620 km reported for observed southern limits since 1850. The differences in latitudinal shift through time between the observed and hindcasted southern limits of distribution suggest that caribou range recession should have been only 17% of what has been observed since 1850 if recent climate change had been the only disturbance driver. This relatively limited impact of climate reinforces the scientific consensus stating that caribou range recession in Quebec is mainly caused by anthropogenic drivers (i.e. logging, development of the road network, agriculture, urbanization) that have modified the structure and composition of the forest over the past 160 years, paving the way for habitat‐mediated apparent competition and overharvesting. Our results also call for a reconsideration of past ranges in models aiming at projecting future distributions, especially for endangered species.

Extratropical Cyclone (EC) characteristics depend on a combination of large-scale factors and regional processes. However, the latter are considered to be poorly represented in global climate models (GCMs), partly because their resolution is too coarse. This paper describes a framework using possibilities given by regional climate models (RCMs) to gain insight into storm activity during winter over North America (NA). Recent past climate period (1981–2005) is considered to assess EC activity over NA using the NCEP regional reanalysis (NARR) as a reference, along with the European reanalysis ERA-Interim (ERAI) and two CMIP5 GCMs used to drive the Canadian Regional Climate Model—version 5 (CRCM5) and the corresponding regional-scale simulations. While ERAI and GCM simulations show basic agreement with NARR in terms of climatological storm track patterns, detailed bias analyses show that, on the one hand, ERAI presents statistically significant positive biases in terms of EC genesis and therefore occurrence while capturing their intensity fairly well. On the other hand, GCMs present large negative intensity biases in the overall NA domain and particularly over NA eastern coast. In addition, storm occurrence over the northwestern topographic regions is highly overestimated. When the CRCM5 is driven by ERAI, no significant skill deterioration arises and, more importantly, all storm characteristics near areas with marked relief and over regions with large water masses are significantly improved with respect to ERAI. Conversely, in GCM-driven simulations, the added value contributed by CRCM5 is less prominent and systematic, except over western NA areas with high topography and over the Western Atlantic coastlines where the most frequent and intense ECs are located. Despite this significant added-value on seasonal-mean characteristics, a caveat is raised on the RCM ability to handle storm temporal ‘seriality’, as a measure of their temporal variability at a given location. In fact, the driving models induce some significant footprints on the RCM skill to reproduce the intra-seasonal pattern of storm activity.

The West African monsoon intraseasonal variability has huge socio-economic impacts on local populations but understanding and predicting it still remains a challenge for the weather prediction and climate scientific community. This paper analyses an ensemble of simulations from six regional climate models (RCMs) taking part in the coordinated regional downscaling experiment, the ECMWF ERA-Interim reanalysis (ERAI) and three satellite-based and observationally-constrained daily precipitation datasets, to assess the performance of the RCMs with regard to the intraseasonal variability. A joint analysis of seasonal-mean precipitation and the total column water vapor (also called precipitable water—PW) suggests the existence of important links at different timescales between these two variables over the Sahel and highlights the relevance of using PW to follow the monsoon seasonal cycle. RCMs that fail to represent the seasonal-mean position and amplitude of the meridional gradient of PW show the largest discrepancies with respect to seasonal-mean observed precipitation. For both ERAI and RCMs, spectral decompositions of daily PW as well as rainfall show an overestimation of low-frequency activity (at timescales longer than 10 days) at the expense of the synoptic (timescales shorter than 10 days) activity. Consequently, the effects of the African Easterly Waves and the associated mesoscale convective systems are substantially underestimated, especially over continental regions. Finally, the study investigates the skill of the models with respect to hydro-climatic indices related to the occurrence, intensity and frequency of precipitation events at the intraseasonal scale. Although most of these indices are generally better reproduced with RCMs than reanalysis products, this study indicates that RCMs still need to be improved (especially with respect to their subgrid-scale parameterization schemes) to be able to reproduce the intraseasonal variance spectrum adequately.